computeShufflingDistributions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load translation voxel simulation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  272\n",
      "Sequences not available for # genes in Ecocyc DB:  153\n",
      "Inconsistency in codons:  0\n",
      "3767\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  133\n",
      "CPU times: user 48.9 s, sys: 3.14 s, total: 52 s\n",
      "Wall time: 49.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "\n",
    "from analysis_utils import *\n",
    "from joblib import Parallel, delayed\n",
    "import multiprocessing  \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#Growth rate = 3.0\n",
    "#Computes the translation latencies from our model with simulations containing\n",
    "#1 to 14 cognate ternary complexes in the voxel (14 to 42 not included because\n",
    "#the probability of voxels with that number of cognates is ~0)\n",
    "path = \"./data/\"\n",
    "ensmbl_latency_dict = dict()\n",
    "rxndiff=dict()\n",
    "scalingFactor = 200\n",
    "NR_scaledrates = {'k1r':718*200/scalingFactor,'k2f':1475*200/scalingFactor,'k2r_nr':1120*200/scalingFactor,'k3_nr':6*200/scalingFactor,'k4':209*200/scalingFactor}\n",
    "\n",
    "\n",
    "data = \"191003_2210/\"\n",
    "for i in range(1,7):\n",
    "    sim_i = computeTransportRxnTimes(path+data,100*(i-1),100*(i-1)+100,cogtRNANum=i, ribosomeNum = 7,scaling=scalingFactor,NR_scaling = NR_scaledrates)\n",
    "    ensmbl_latency_dict[i] = (CellLatencies(sim_i))\n",
    "\n",
    "data = \"191105_0908/\"\n",
    "for i in range(7,14):\n",
    "    sim_i = computeTransportRxnTimes(path+data, 100*(i-1-6),100*(i-1-6)+100,cogtRNANum=i, ribosomeNum = 7,scaling=scalingFactor,NR_scaling = NR_scaledrates)\n",
    "    ensmbl_latency_dict[i] = (CellLatencies(sim_i))\n",
    "    \n",
    "#Experimentally obtained codon usage frequencies and tRNA abundances at growth rate = 2.5 dbl/hr\n",
    "gr25_Codon = np.array([2.36, 1.26, 45.55, 34.17, 16.97, 57.86, 19.27, 33.74, 14.98, 22.31, 43.18, 7.67, 24.11, 24.87, 39.49, 11.81, 0.03, 0.63, 2.19, 9.31, 17.22, 55.01, 5.61, 29.21, 21.67, 0.52, 15.79, 43.86, 4.17, 2.61, 20.64, 26.7, 7.03, 0.19, 2.76, 3.81, 6.72, 16.52, 4.27, 2.73, 7.92, 23.25, 2.51, 1.98, 16.33, 11.68, 0.62, 0.67, 43.82, 20.59, 27.28, 7.01, 6.78, 14.21, 60.75, 0.82, 3.86, 4.09, 28.82, 5.18, 4.38, 1.09])\n",
    "gr25_pCodon = gr25_Codon/np.sum(gr25_Codon)\n",
    "gr25_ptRNA = [0.0602409638554217, 0.010542168674698798, 0.07379518072289158, 0.00602409638554217, 0.010542168674698798, 0.00602409638554217, 0.021084337349397596, 0.043674698795180725, 0.021084337349397596, 0.01204819277108434, 0.01807228915662651, 0.08433734939759038, 0.03162650602409639, 0.07228915662650603, 0.01204819277108434, 0.07078313253012049, 0.06325301204819278, 0.01656626506024097, 0.009036144578313254, 0.027108433734939763, 0.010542168674698798, 0.03012048192771085, 0.013554216867469882, 0.015060240963855425, 0.0075301204819277125, 0.010542168674698798, 0.0075301204819277125, 0.003012048192771085, 0.021084337349397596, 0.004518072289156627, 0.01656626506024097, 0.01204819277108434, 0.0015060240963855425, 0.009036144578313254, 0.01656626506024097, 0.019578313253012052, 0.015060240963855425, 0.027108434, 0.05873493975903616, 0.019578313]\n",
    "gr25_ptRNA = gr25_ptRNA/np.sum(gr25_ptRNA)\n",
    "\n",
    "n_cores = 16\n",
    "\n",
    "##Compute the codon distribution and elongation latency of each gene weighted by transcript number\n",
    "codon_elongt = compute_codon_elongt(gr25_ptRNA, len(gr25_pCodon), ensmbl_latency_dict)\n",
    "gene_map, gene_latency = compute_gene_elongt(codon_elongt)\n",
    "pCodon_transcriptome, transcriptome_elongt = compute_transcript_distributions(gene_map,gene_latency)\n",
    "pCodon_transcriptome = np.array(pCodon_transcriptome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uniform Random tRNA, WT Codon usage, 100 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.3 s, sys: 421 ms, total: 13.7 s\n",
      "Wall time: 24.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "\n",
    "for i in range(100):\n",
    "    ptRNA_uniform = np.random.uniform(min(gr25_ptRNA),max(gr25_ptRNA),len(gr25_ptRNA))\n",
    "    ptRNA_list.append(ptRNA_uniform)\n",
    "    inputs.append([ptRNA_uniform,pCodon_transcriptome,ensmbl_latency_dict])\n",
    "elongt_list = Parallel(n_jobs=n_cores,backend='loky')(delayed(computeElongationLatency_multithread)(i) for i in inputs)\n",
    "elongt_list = [elongt[0][0] for elongt in elongt_list]\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_100sample_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_100sample_ptRNA',ptRNA_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uniform Random tRNA, WT Codon usage, 10k samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 27min 39s, sys: 11.3 s, total: 27min 51s\n",
      "Wall time: 44min 6s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "\n",
    "for i in range(10000):\n",
    "    ptRNA_uniform = np.random.uniform(min(gr25_ptRNA),max(gr25_ptRNA),len(gr25_ptRNA))\n",
    "    ptRNA_list.append(ptRNA_uniform)\n",
    "    inputs.append([ptRNA_uniform,pCodon_transcriptome,ensmbl_latency_dict])\n",
    "elongt_list = Parallel(n_jobs=n_cores,backend='loky')(delayed(computeElongationLatency_multithread)(i) for i in inputs)\n",
    "elongt_list = [elongt[0][0] for elongt in elongt_list]\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_10ksample_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_10ksample_ptRNA',ptRNA_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WT tRNA, Uniform Random Codon usage, 100 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "elongt_list = list()\n",
    "pCodon_list = list()\n",
    "inputs = list()\n",
    "\n",
    "syn_codon_list = [[0,1,2,3],[4,5],[6,7],[8,9,10,11],[12,13,14,15],[16,17,46,47,48,49],[18,19,42,43,44,45],\n",
    "              [20,21],[22,23],[24],[25,26,27],[28,29,30,31],[32],[33],[34,35],[36,37],[38,39,54,55,56,57],[40,41],\n",
    "              [50,51],[52,53],[58,59,60,61]]\n",
    "        \n",
    "for i in range(100):\n",
    "    pCodon_uniform = np.zeros(len(gr25_pCodon))\n",
    "    for syn_codons in syn_codon_list:\n",
    "        pCodon_uniform[syn_codons] = np.random.uniform(min(gr25_Codon),max(gr25_Codon),len(syn_codons))\n",
    "        pCodon_uniform[syn_codons] = sum(pCodon_transcriptome[syn_codons])/sum(pCodon_uniform[syn_codons])*pCodon_uniform[syn_codons]\n",
    "    pCodon_list.append(pCodon_uniform)\n",
    "    inputs.append([gr25_ptRNA, pCodon_uniform,ensmbl_latency_dict])\n",
    "\n",
    "elongt_list = Parallel(n_jobs=n_cores,backend='loky')(delayed(computeElongationLatency_multithread)(i) for i in inputs)\n",
    "elongt_list = [elongt[0][0] for elongt in elongt_list]\n",
    "\n",
    "np.save('./data/WTtRNA_UniformRandomCodonUsage_100sample_elongt',elongt_list)\n",
    "np.save('./data/WTtRNA_UniformRandomCodonUsage_100sample_pCodon',pCodon_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WT tRNA, Uniform Random Codon usage, 10k samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "elongt_list = list()\n",
    "pCodon_list = list()\n",
    "inputs = list()\n",
    "\n",
    "syn_codon_list = [[0,1,2,3],[4,5],[6,7],[8,9,10,11],[12,13,14,15],[16,17,46,47,48,49],[18,19,42,43,44,45],\n",
    "              [20,21],[22,23],[24],[25,26,27],[28,29,30,31],[32],[33],[34,35],[36,37],[38,39,54,55,56,57],[40,41],\n",
    "              [50,51],[52,53],[58,59,60,61]]\n",
    "        \n",
    "for i in range(10000):\n",
    "    pCodon_uniform = np.zeros(len(pCodon_transcriptome))\n",
    "    for syn_codons in syn_codon_list:\n",
    "        pCodon_uniform[syn_codons] = np.random.uniform(min(gr25_Codon),max(gr25_Codon),len(syn_codons))\n",
    "        pCodon_uniform[syn_codons] = sum(pCodon_transcriptome[syn_codons])/sum(pCodon_uniform[syn_codons])*pCodon_uniform[syn_codons]\n",
    "    pCodon_list.append(pCodon_uniform)\n",
    "    inputs.append([gr25_ptRNA, pCodon_uniform,ensmbl_latency_dict])\n",
    "\n",
    "elongt_list = Parallel(n_jobs=n_cores,backend='loky')(delayed(computeElongationLatency_multithread)(i) for i in inputs)\n",
    "elongt_list = [elongt[0][0] for elongt in elongt_list]\n",
    "\n",
    "np.save('./data/WTtRNA_UniformRandomCodonUsage_10ksample_elongt',elongt_list)\n",
    "np.save('./data/WTtRNA_UniformRandomCodonUsage_10ksample_pCodon',pCodon_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: faster tRNA distributions (Codon usage fixed, WT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Generation:  0  elongt:  204.7468593260982 tRNA list:  [0.04097175 0.04220117 0.07840056 0.08061873 0.00812508 0.0272181\n",
      " 0.07286863 0.00698734 0.02825523 0.03007864 0.08252737 0.03908705\n",
      " 0.04897578 0.06540679 0.00962458 0.06419479 0.0415891  0.05713749\n",
      " 0.05548289 0.04655925 0.06072482 0.03401584 0.01396239 0.0833985\n",
      " 0.05674674 0.02705444 0.0649879  0.01736798 0.01844237 0.02587218\n",
      " 0.07615736 0.01687463 0.02271396 0.04719114 0.03459146 0.0032517\n",
      " 0.00883137 0.02900779 0.07583317 0.05462228]\n",
      "Generation:  100  elongt:  180.88479053429342 tRNA list:  [0.05989368 0.02482716 0.03544915 0.00608565 0.00519211 0.00447962\n",
      " 0.04700594 0.06214881 0.01257315 0.007848   0.03816644 0.06068883\n",
      " 0.05778737 0.02478957 0.04262527 0.04176344 0.05293268 0.00223158\n",
      " 0.04477778 0.00559238 0.00317709 0.03541964 0.00744968 0.02952959\n",
      " 0.01462341 0.00341406 0.0369241  0.00362517 0.05880865 0.00478224\n",
      " 0.00172154 0.01242412 0.01561655 0.00451502 0.00939781 0.0082725\n",
      " 0.0028095  0.04866447 0.01118551 0.05078074]\n",
      "Generation:  200  elongt:  176.57675015884232 tRNA list:  [0.0558065  0.01214387 0.0699047  0.01545506 0.00851934 0.00165927\n",
      " 0.04296718 0.0586484  0.01186499 0.0075174  0.03866139 0.07109598\n",
      " 0.05502271 0.0039777  0.01154081 0.04986296 0.04995135 0.00210589\n",
      " 0.03539064 0.00680658 0.00299814 0.06533284 0.00703009 0.02786639\n",
      " 0.01568898 0.00322177 0.01383441 0.00342099 0.05549637 0.00451288\n",
      " 0.00162457 0.01080954 0.05073149 0.00426072 0.00886849 0.00780657\n",
      " 0.00265126 0.04592353 0.01055551 0.04846273]\n",
      "Generation:  300  elongt:  171.13302400478437 tRNA list:  [0.06440588 0.01339835 0.08523592 0.01086791 0.00540582 0.00183067\n",
      " 0.00260109 0.07066087 0.00693436 0.00829395 0.04272297 0.07304682\n",
      " 0.08135001 0.00605867 0.01281634 0.05501387 0.00521362 0.00232343\n",
      " 0.04662083 0.00582256 0.00330786 0.04955972 0.00775631 0.03074503\n",
      " 0.01626515 0.00292856 0.01734121 0.00389828 0.03681456 0.0070214\n",
      " 0.01291339 0.01192618 0.05597212 0.00470086 0.01660101 0.00861299\n",
      " 0.00292514 0.0506675  0.0059498  0.05346899]\n",
      "Generation:  400  elongt:  170.8145638726164 tRNA list:  [0.05764163 0.01136368 0.04317578 0.01004578 0.00499688 0.00169219\n",
      " 0.03235847 0.06788852 0.0064098  0.00970238 0.04989861 0.05983303\n",
      " 0.05611434 0.00501021 0.01184682 0.06280601 0.00889573 0.00214767\n",
      " 0.04309409 0.01268489 0.00305763 0.04740298 0.00716956 0.03630314\n",
      " 0.0178399  0.00328569 0.01632543 0.00430898 0.0452669  0.00545853\n",
      " 0.00201349 0.011024   0.05173798 0.00434525 0.02642459 0.00676355\n",
      " 0.00333581 0.06982139 0.01076493 0.06974377]\n",
      "Generation:  500  elongt:  169.21081294244752 tRNA list:  [0.0580731  0.01086542 0.07821545 0.00517182 0.00878265 0.00397213\n",
      " 0.04972111 0.05510794 0.0141539  0.00840791 0.06019791 0.06844545\n",
      " 0.05653439 0.00561825 0.01182979 0.04237506 0.00156803 0.00251215\n",
      " 0.08061913 0.0075488  0.00268315 0.05883625 0.00742859 0.02618416\n",
      " 0.01634983 0.00365953 0.01800152 0.00361491 0.04725385 0.00512606\n",
      " 0.00171666 0.01209005 0.03811043 0.00542278 0.02740923 0.00802104\n",
      " 0.00316272 0.00821705 0.00470213 0.07228965]\n",
      "Generation:  600  elongt:  167.6115958868775 tRNA list:  [0.07438274 0.0131106  0.01221344 0.00610749 0.00764479 0.00493952\n",
      " 0.04185959 0.05890053 0.00743404 0.00786745 0.01914275 0.06649212\n",
      " 0.08320904 0.00911472 0.0130813  0.05897804 0.01611895 0.00246068\n",
      " 0.06810792 0.00910866 0.00350326 0.07099395 0.00747642 0.01095614\n",
      " 0.01445594 0.00323562 0.02172129 0.00338254 0.07096688 0.0047994\n",
      " 0.00172772 0.02367849 0.04851601 0.00531169 0.02065204 0.00784013\n",
      " 0.00288099 0.009915   0.00512336 0.08258876]\n",
      "Generation:  700  elongt:  167.6115958868775 tRNA list:  [0.07438274 0.0131106  0.01221344 0.00610749 0.00764479 0.00493952\n",
      " 0.04185959 0.05890053 0.00743404 0.00786745 0.01914275 0.06649212\n",
      " 0.08320904 0.00911472 0.0130813  0.05897804 0.01611895 0.00246068\n",
      " 0.06810792 0.00910866 0.00350326 0.07099395 0.00747642 0.01095614\n",
      " 0.01445594 0.00323562 0.02172129 0.00338254 0.07096688 0.0047994\n",
      " 0.00172772 0.02367849 0.04851601 0.00531169 0.02065204 0.00784013\n",
      " 0.00288099 0.009915   0.00512336 0.08258876]\n",
      "Generation:  800  elongt:  167.6115958868775 tRNA list:  [0.07438274 0.0131106  0.01221344 0.00610749 0.00764479 0.00493952\n",
      " 0.04185959 0.05890053 0.00743404 0.00786745 0.01914275 0.06649212\n",
      " 0.08320904 0.00911472 0.0130813  0.05897804 0.01611895 0.00246068\n",
      " 0.06810792 0.00910866 0.00350326 0.07099395 0.00747642 0.01095614\n",
      " 0.01445594 0.00323562 0.02172129 0.00338254 0.07096688 0.0047994\n",
      " 0.00172772 0.02367849 0.04851601 0.00531169 0.02065204 0.00784013\n",
      " 0.00288099 0.009915   0.00512336 0.08258876]\n",
      "Generation:  900  elongt:  167.6115958868775 tRNA list:  [0.07438274 0.0131106  0.01221344 0.00610749 0.00764479 0.00493952\n",
      " 0.04185959 0.05890053 0.00743404 0.00786745 0.01914275 0.06649212\n",
      " 0.08320904 0.00911472 0.0130813  0.05897804 0.01611895 0.00246068\n",
      " 0.06810792 0.00910866 0.00350326 0.07099395 0.00747642 0.01095614\n",
      " 0.01445594 0.00323562 0.02172129 0.00338254 0.07096688 0.0047994\n",
      " 0.00172772 0.02367849 0.04851601 0.00531169 0.02065204 0.00784013\n",
      " 0.00288099 0.009915   0.00512336 0.08258876]\n",
      "Generation:  1000  elongt:  166.3934243002533 tRNA list:  [0.05953253 0.01348994 0.04784347 0.00722711 0.0104679  0.00418983\n",
      " 0.04660248 0.06557423 0.01326613 0.00791813 0.04191962 0.06448662\n",
      " 0.07075666 0.00669099 0.01109589 0.07780855 0.00189708 0.00265015\n",
      " 0.05253262 0.00830918 0.00302977 0.07116721 0.00786028 0.02761924\n",
      " 0.01609387 0.00325576 0.03315935 0.00382497 0.07505882 0.00567922\n",
      " 0.00181642 0.01287974 0.04172102 0.005611   0.00991578 0.0022028\n",
      " 0.00288802 0.00180729 0.00657076 0.05357959]\n",
      "Generation:  1100  elongt:  166.24887833357997 tRNA list:  [0.0564221  0.00318401 0.07963187 0.00662784 0.00265377 0.00420027\n",
      " 0.02239144 0.06263529 0.00241608 0.00756326 0.02917656 0.06948668\n",
      " 0.07205866 0.00420702 0.01195623 0.04566719 0.00168585 0.00212562\n",
      " 0.07990266 0.00793678 0.00289398 0.06412846 0.01271078 0.02812746\n",
      " 0.01430196 0.00289326 0.03179004 0.0033991  0.08076327 0.00544469\n",
      " 0.00163979 0.01156226 0.04877852 0.00535953 0.03419081 0.00211183\n",
      " 0.00275859 0.0214971  0.00458261 0.04913681]\n",
      "Generation:  1200  elongt:  166.24887833357997 tRNA list:  [0.0564221  0.00318401 0.07963187 0.00662784 0.00265377 0.00420027\n",
      " 0.02239144 0.06263529 0.00241608 0.00756326 0.02917656 0.06948668\n",
      " 0.07205866 0.00420702 0.01195623 0.04566719 0.00168585 0.00212562\n",
      " 0.07990266 0.00793678 0.00289398 0.06412846 0.01271078 0.02812746\n",
      " 0.01430196 0.00289326 0.03179004 0.0033991  0.08076327 0.00544469\n",
      " 0.00163979 0.01156226 0.04877852 0.00535953 0.03419081 0.00211183\n",
      " 0.00275859 0.0214971  0.00458261 0.04913681]\n",
      "Generation:  1300  elongt:  166.24887833357997 tRNA list:  [0.0564221  0.00318401 0.07963187 0.00662784 0.00265377 0.00420027\n",
      " 0.02239144 0.06263529 0.00241608 0.00756326 0.02917656 0.06948668\n",
      " 0.07205866 0.00420702 0.01195623 0.04566719 0.00168585 0.00212562\n",
      " 0.07990266 0.00793678 0.00289398 0.06412846 0.01271078 0.02812746\n",
      " 0.01430196 0.00289326 0.03179004 0.0033991  0.08076327 0.00544469\n",
      " 0.00163979 0.01156226 0.04877852 0.00535953 0.03419081 0.00211183\n",
      " 0.00275859 0.0214971  0.00458261 0.04913681]\n",
      "Generation:  1400  elongt:  165.8609138024717 tRNA list:  [0.06436814 0.00282246 0.07009103 0.00587523 0.00235243 0.00415728\n",
      " 0.01984884 0.05552292 0.00214173 0.00670443 0.03558199 0.06159632\n",
      " 0.06387624 0.00593169 0.01059858 0.06569216 0.00162512 0.00197282\n",
      " 0.06581809 0.00703554 0.00256536 0.07388619 0.00665544 0.02630534\n",
      " 0.01316775 0.00525092 0.04458113 0.00324669 0.07176966 0.00706809\n",
      " 0.00152207 0.01073112 0.04122494 0.00475094 0.06636701 0.00196003\n",
      " 0.00244534 0.00841558 0.00406224 0.05041111]\n",
      "Generation:  1500  elongt:  165.63843072163098 tRNA list:  [0.05660617 0.0030275  0.0454917  0.00687185 0.00764374 0.00398387\n",
      " 0.0443117  0.04100576 0.0023357  0.00752891 0.03985903 0.06131674\n",
      " 0.08177427 0.00636209 0.00759754 0.0737208  0.00180382 0.00251988\n",
      " 0.07597499 0.00790074 0.00288084 0.06766894 0.0074739  0.02674483\n",
      " 0.05179254 0.00309572 0.06607992 0.00290714 0.07136925 0.00517706\n",
      " 0.00170146 0.01099391 0.02492491 0.00533519 0.01201543 0.00209452\n",
      " 0.00276317 0.00171845 0.00468016 0.05094585]\n",
      "Generation:  1600  elongt:  165.63843072163098 tRNA list:  [0.05660617 0.0030275  0.0454917  0.00687185 0.00764374 0.00398387\n",
      " 0.0443117  0.04100576 0.0023357  0.00752891 0.03985903 0.06131674\n",
      " 0.08177427 0.00636209 0.00759754 0.0737208  0.00180382 0.00251988\n",
      " 0.07597499 0.00790074 0.00288084 0.06766894 0.0074739  0.02674483\n",
      " 0.05179254 0.00309572 0.06607992 0.00290714 0.07136925 0.00517706\n",
      " 0.00170146 0.01099391 0.02492491 0.00533519 0.01201543 0.00209452\n",
      " 0.00276317 0.00171845 0.00468016 0.05094585]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  1700  elongt:  165.63843072163098 tRNA list:  [0.05660617 0.0030275  0.0454917  0.00687185 0.00764374 0.00398387\n",
      " 0.0443117  0.04100576 0.0023357  0.00752891 0.03985903 0.06131674\n",
      " 0.08177427 0.00636209 0.00759754 0.0737208  0.00180382 0.00251988\n",
      " 0.07597499 0.00790074 0.00288084 0.06766894 0.0074739  0.02674483\n",
      " 0.05179254 0.00309572 0.06607992 0.00290714 0.07136925 0.00517706\n",
      " 0.00170146 0.01099391 0.02492491 0.00533519 0.01201543 0.00209452\n",
      " 0.00276317 0.00171845 0.00468016 0.05094585]\n",
      "Generation:  1800  elongt:  165.63843072163098 tRNA list:  [0.05660617 0.0030275  0.0454917  0.00687185 0.00764374 0.00398387\n",
      " 0.0443117  0.04100576 0.0023357  0.00752891 0.03985903 0.06131674\n",
      " 0.08177427 0.00636209 0.00759754 0.0737208  0.00180382 0.00251988\n",
      " 0.07597499 0.00790074 0.00288084 0.06766894 0.0074739  0.02674483\n",
      " 0.05179254 0.00309572 0.06607992 0.00290714 0.07136925 0.00517706\n",
      " 0.00170146 0.01099391 0.02492491 0.00533519 0.01201543 0.00209452\n",
      " 0.00276317 0.00171845 0.00468016 0.05094585]\n",
      "Generation:  1900  elongt:  165.15794053939456 tRNA list:  [0.06888665 0.00302059 0.04182429 0.00524326 0.00251756 0.00444912\n",
      " 0.02124219 0.05942052 0.00229208 0.00717507 0.03807977 0.06298968\n",
      " 0.06836023 0.00634808 0.01134257 0.07030362 0.0017392  0.00211131\n",
      " 0.07043839 0.01217978 0.00274545 0.07907286 0.00712264 0.02815193\n",
      " 0.04210858 0.00561952 0.04493193 0.00255265 0.06787478 0.00756425\n",
      " 0.00170478 0.01148442 0.04411885 0.00508445 0.01788073 0.00209762\n",
      " 0.002617   0.00900634 0.0043474  0.05394987]\n",
      "CPU times: user 3min 49s, sys: 2.36 s, total: 3min 51s\n",
      "Wall time: 55min 42s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/UniformRandomtRNA_WTCodonUsage_100sample_elongt.npy')\n",
    "ptRNA_list = np.load('./data/UniformRandomtRNA_WTCodonUsage_100sample_ptRNA.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_transcriptome,elongt_list,ensmbl_latency_dict,min(gr25_ptRNA),max(gr25_ptRNA),objective='fast')\n",
    "    fullelongt_list.append(min(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', min(elongt_list),'tRNA list: ', ptRNA_list[np.argmin(elongt_list)])\n",
    "\n",
    "\n",
    "        \n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_2ksample_fasttRNA_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_2ksample_fasttRNA_ptRNA',ptRNA_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_2ksample_fasttRNA_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: slower tRNA distributions (Codon usage fixed, WT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Generation:  0  elongt:  226.298128454574 tRNA list:  [0.02156449 0.03724623 0.02868449 0.04893407 0.03991538 0.02185416\n",
      " 0.03275673 0.00162713 0.03502709 0.00222362 0.02257815 0.02279118\n",
      " 0.03190735 0.01036395 0.03380584 0.00721043 0.03768863 0.04231782\n",
      " 0.01240704 0.01533812 0.04752905 0.03113864 0.01728459 0.0395583\n",
      " 0.0038256  0.02425963 0.0027816  0.04630539 0.01799604 0.02127398\n",
      " 0.0230036  0.00985721 0.00341733 0.04003672 0.04580028 0.03860111\n",
      " 0.04067715 0.00238529 0.01898319 0.01904339]\n",
      "Generation:  100  elongt:  245.57746865552343 tRNA list:  [0.00698945 0.06783574 0.0056026  0.02750386 0.03665203 0.0224422\n",
      " 0.0027198  0.00808285 0.0246369  0.0530289  0.01969177 0.00243739\n",
      " 0.00850085 0.0588725  0.02403253 0.00156008 0.07674853 0.03979749\n",
      " 0.00877295 0.05637553 0.03052934 0.01368538 0.01149256 0.0119375\n",
      " 0.00510698 0.01858461 0.00384237 0.04640251 0.0119656  0.05740312\n",
      " 0.01863849 0.00625745 0.00156008 0.0666853  0.07774423 0.01358545\n",
      " 0.00522307 0.01508022 0.01664638 0.01534541]\n",
      "Generation:  200  elongt:  248.77795292822722 tRNA list:  [0.00505247 0.0233997  0.00789391 0.02975524 0.06429165 0.01582661\n",
      " 0.00178239 0.00528458 0.01903874 0.08162034 0.00924388 0.00152207\n",
      " 0.00362018 0.02954888 0.00216605 0.00152207 0.06260239 0.05667834\n",
      " 0.00373878 0.06039692 0.04561103 0.00460566 0.00612113 0.00609249\n",
      " 0.00260643 0.02159722 0.01733503 0.0223793  0.00450989 0.0418519\n",
      " 0.05677625 0.0395081  0.00152207 0.02766739 0.06603701 0.05579384\n",
      " 0.03568101 0.01450984 0.03646337 0.00834585]\n",
      "Generation:  300  elongt:  249.5327330860751 tRNA list:  [0.00472753 0.0177695  0.00748553 0.06004839 0.05452337 0.05504185\n",
      " 0.00152207 0.00453227 0.01332442 0.04127407 0.00842068 0.00152207\n",
      " 0.00338736 0.04103409 0.00159267 0.00152207 0.05309076 0.02852104\n",
      " 0.00349578 0.04618925 0.06802015 0.00721011 0.01449455 0.00844712\n",
      " 0.02325417 0.03039725 0.00152207 0.069538   0.00459804 0.04431222\n",
      " 0.06982977 0.00853018 0.00152207 0.01319028 0.05031979 0.04524241\n",
      " 0.02312166 0.01281706 0.04755008 0.00705826]\n",
      "Generation:  400  elongt:  250.16733486988372 tRNA list:  [0.00391135 0.06822861 0.0068621  0.04337812 0.02824835 0.04911353\n",
      " 0.00179361 0.00835087 0.00618467 0.02482229 0.00608298 0.00152207\n",
      " 0.00410238 0.01555955 0.00152207 0.00152207 0.04548173 0.04926991\n",
      " 0.00311927 0.0299488  0.05986858 0.00302173 0.00844203 0.00527113\n",
      " 0.01805888 0.03518465 0.00152207 0.0625235  0.0041028  0.05075464\n",
      " 0.03695573 0.05110818 0.00152207 0.07168428 0.02724368 0.071868\n",
      " 0.03032514 0.00883807 0.04873235 0.00394813]\n",
      "Generation:  500  elongt:  250.82646929363588 tRNA list:  [0.00361372 0.05419776 0.00565583 0.06687302 0.02618103 0.02431143\n",
      " 0.00152207 0.00362737 0.00898225 0.04194925 0.00409095 0.00152207\n",
      " 0.00341984 0.05945469 0.00152207 0.00553996 0.06068937 0.07031961\n",
      " 0.00260029 0.024966   0.02952669 0.00509399 0.0093512  0.00411605\n",
      " 0.00792065 0.0600055  0.00152207 0.02358243 0.00388353 0.05132642\n",
      " 0.05112193 0.06179808 0.00152207 0.05975764 0.04821522 0.03968137\n",
      " 0.01411814 0.00685854 0.04516592 0.00439399]\n",
      "Generation:  600  elongt:  251.2615212019446 tRNA list:  [0.00401062 0.02859207 0.00547316 0.0553295  0.02867232 0.05073749\n",
      " 0.00152207 0.00359079 0.00889166 0.04655661 0.0040554  0.00152207\n",
      " 0.00379544 0.05885508 0.00152207 0.00614842 0.06007731 0.06055055\n",
      " 0.00288589 0.02471421 0.01614851 0.00504262 0.01092175 0.00408028\n",
      " 0.00784077 0.066596   0.00152207 0.05928671 0.00384437 0.07826331\n",
      " 0.05060635 0.06126094 0.00152207 0.05915497 0.01620872 0.03928117\n",
      " 0.01397576 0.00678937 0.03579574 0.0043558 ]\n",
      "Generation:  700  elongt:  251.2615212019446 tRNA list:  [0.00401062 0.02859207 0.00547316 0.0553295  0.02867232 0.05073749\n",
      " 0.00152207 0.00359079 0.00889166 0.04655661 0.0040554  0.00152207\n",
      " 0.00379544 0.05885508 0.00152207 0.00614842 0.06007731 0.06055055\n",
      " 0.00288589 0.02471421 0.01614851 0.00504262 0.01092175 0.00408028\n",
      " 0.00784077 0.066596   0.00152207 0.05928671 0.00384437 0.07826331\n",
      " 0.05060635 0.06126094 0.00152207 0.05915497 0.01620872 0.03928117\n",
      " 0.01397576 0.00678937 0.03579574 0.0043558 ]\n",
      "Generation:  800  elongt:  251.3704534261574 tRNA list:  [0.00284056 0.07484307 0.00510239 0.04396588 0.04285841 0.01911933\n",
      " 0.00549362 0.00287142 0.00784635 0.05623918 0.00427624 0.00234932\n",
      " 0.0030852  0.05863362 0.00152207 0.00152207 0.00919892 0.02925018\n",
      " 0.00238235 0.04604286 0.05476346 0.00221694 0.00855428 0.00388213\n",
      " 0.02060876 0.0652781  0.00152207 0.05212752 0.0024412  0.05451714\n",
      " 0.04818892 0.05479932 0.00152207 0.03337035 0.03770701 0.0608505\n",
      " 0.01036769 0.00542921 0.05856773 0.00384256]\n",
      "Generation:  900  elongt:  251.3704534261574 tRNA list:  [0.00284056 0.07484307 0.00510239 0.04396588 0.04285841 0.01911933\n",
      " 0.00549362 0.00287142 0.00784635 0.05623918 0.00427624 0.00234932\n",
      " 0.0030852  0.05863362 0.00152207 0.00152207 0.00919892 0.02925018\n",
      " 0.00238235 0.04604286 0.05476346 0.00221694 0.00855428 0.00388213\n",
      " 0.02060876 0.0652781  0.00152207 0.05212752 0.0024412  0.05451714\n",
      " 0.04818892 0.05479932 0.00152207 0.03337035 0.03770701 0.0608505\n",
      " 0.01036769 0.00542921 0.05856773 0.00384256]\n",
      "Generation:  1000  elongt:  251.67087123198195 tRNA list:  [0.00225589 0.06154459 0.00415005 0.03491647 0.05433988 0.03702048\n",
      " 0.00479829 0.00271122 0.00706008 0.07084575 0.00339607 0.0021139\n",
      " 0.00245018 0.05562568 0.00152207 0.00152207 0.03496338 0.02631906\n",
      " 0.00189199 0.0549195  0.05864101 0.00256055 0.00846817 0.00319233\n",
      " 0.03432776 0.06462099 0.00152207 0.00152207 0.00193874 0.07410949\n",
      " 0.07603912 0.049887   0.00152207 0.02526538 0.02248348 0.04576528\n",
      " 0.00932876 0.00431173 0.04707574 0.00305165]\n",
      "Generation:  1100  elongt:  251.67087123198195 tRNA list:  [0.00225589 0.06154459 0.00415005 0.03491647 0.05433988 0.03702048\n",
      " 0.00479829 0.00271122 0.00706008 0.07084575 0.00339607 0.0021139\n",
      " 0.00245018 0.05562568 0.00152207 0.00152207 0.03496338 0.02631906\n",
      " 0.00189199 0.0549195  0.05864101 0.00256055 0.00846817 0.00319233\n",
      " 0.03432776 0.06462099 0.00152207 0.00152207 0.00193874 0.07410949\n",
      " 0.07603912 0.049887   0.00152207 0.02526538 0.02248348 0.04576528\n",
      " 0.00932876 0.00431173 0.04707574 0.00305165]\n",
      "Generation:  1200  elongt:  251.67087123198195 tRNA list:  [0.00225589 0.06154459 0.00415005 0.03491647 0.05433988 0.03702048\n",
      " 0.00479829 0.00271122 0.00706008 0.07084575 0.00339607 0.0021139\n",
      " 0.00245018 0.05562568 0.00152207 0.00152207 0.03496338 0.02631906\n",
      " 0.00189199 0.0549195  0.05864101 0.00256055 0.00846817 0.00319233\n",
      " 0.03432776 0.06462099 0.00152207 0.00152207 0.00193874 0.07410949\n",
      " 0.07603912 0.049887   0.00152207 0.02526538 0.02248348 0.04576528\n",
      " 0.00932876 0.00431173 0.04707574 0.00305165]\n",
      "Generation:  1300  elongt:  251.67087123198195 tRNA list:  [0.00225589 0.06154459 0.00415005 0.03491647 0.05433988 0.03702048\n",
      " 0.00479829 0.00271122 0.00706008 0.07084575 0.00339607 0.0021139\n",
      " 0.00245018 0.05562568 0.00152207 0.00152207 0.03496338 0.02631906\n",
      " 0.00189199 0.0549195  0.05864101 0.00256055 0.00846817 0.00319233\n",
      " 0.03432776 0.06462099 0.00152207 0.00152207 0.00193874 0.07410949\n",
      " 0.07603912 0.049887   0.00152207 0.02526538 0.02248348 0.04576528\n",
      " 0.00932876 0.00431173 0.04707574 0.00305165]\n",
      "Generation:  1400  elongt:  251.67087123198195 tRNA list:  [0.00225589 0.06154459 0.00415005 0.03491647 0.05433988 0.03702048\n",
      " 0.00479829 0.00271122 0.00706008 0.07084575 0.00339607 0.0021139\n",
      " 0.00245018 0.05562568 0.00152207 0.00152207 0.03496338 0.02631906\n",
      " 0.00189199 0.0549195  0.05864101 0.00256055 0.00846817 0.00319233\n",
      " 0.03432776 0.06462099 0.00152207 0.00152207 0.00193874 0.07410949\n",
      " 0.07603912 0.049887   0.00152207 0.02526538 0.02248348 0.04576528\n",
      " 0.00932876 0.00431173 0.04707574 0.00305165]\n",
      "Generation:  1500  elongt:  251.729104594112 tRNA list:  [0.00198218 0.0669308  0.00484264 0.03696045 0.04162703 0.0792509\n",
      " 0.00427781 0.00277171 0.00659613 0.0704264  0.00359487 0.00198748\n",
      " 0.00343686 0.03634753 0.00152207 0.00164596 0.01422372 0.05604697\n",
      " 0.00200275 0.03870648 0.06760464 0.00224271 0.00719126 0.00326356\n",
      " 0.04054587 0.06340257 0.00152207 0.02139678 0.00205223 0.04583049\n",
      " 0.04051059 0.03820006 0.00152207 0.06928826 0.01840677 0.02654946\n",
      " 0.00910275 0.00456413 0.05839273 0.00323029]\n",
      "Generation:  1600  elongt:  251.77284852362627 tRNA list:  [0.00203721 0.06878896 0.00497709 0.05789606 0.03912638 0.0489026\n",
      " 0.00425739 0.00213629 0.00677925 0.06896014 0.00327047 0.00217754\n",
      " 0.00218185 0.02454855 0.00155629 0.00155629 0.04526955 0.02489798\n",
      " 0.00205835 0.03057274 0.05640608 0.00187121 0.00600004 0.00272296\n",
      " 0.03606794 0.06238929 0.00155629 0.05429596 0.0022627  0.04324681\n",
      " 0.02502289 0.04383013 0.00155629 0.08165194 0.0078429  0.08150289\n",
      " 0.00854234 0.00415227 0.03443289 0.0026952 ]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  1700  elongt:  251.78814240687254 tRNA list:  [0.00259199 0.06301384 0.00418609 0.06178806 0.02981495 0.01786811\n",
      " 0.00508694 0.0025147  0.00715974 0.07519083 0.00360036 0.00214374\n",
      " 0.00218041 0.04131784 0.00157457 0.00157457 0.07628336 0.07179573\n",
      " 0.00217387 0.02325427 0.00727642 0.00245911 0.00720225 0.00390497\n",
      " 0.01085952 0.06142089 0.00157457 0.03543579 0.00222758 0.06519374\n",
      " 0.05859555 0.05126672 0.00159834 0.00305364 0.06683296 0.03256376\n",
      " 0.009404   0.0045711  0.07560819 0.00383692]\n",
      "Generation:  1800  elongt:  251.8189724921221 tRNA list:  [0.00190365 0.07345248 0.00478004 0.0578975  0.04679273 0.08356264\n",
      " 0.00156668 0.00293127 0.00651086 0.01893095 0.00419678 0.00249886\n",
      " 0.00272655 0.03808278 0.0017356  0.0017356  0.01366021 0.03111206\n",
      " 0.0019234  0.04352035 0.05239339 0.00286648 0.00709829 0.00455186\n",
      " 0.03463998 0.07159564 0.00165583 0.06209074 0.0025966  0.04572837\n",
      " 0.03998689 0.04097693 0.0017356  0.07807031 0.00208746 0.05409964\n",
      " 0.01023485 0.00532833 0.03910979 0.00363204]\n",
      "Generation:  1900  elongt:  252.12992683096394 tRNA list:  [0.00167688 0.06374264 0.00208038 0.02931246 0.0683419  0.06476209\n",
      " 0.00152207 0.00214498 0.00710134 0.05121879 0.00357099 0.00168137\n",
      " 0.00205308 0.05794865 0.00519727 0.00152207 0.01224237 0.03096537\n",
      " 0.00152207 0.0306972  0.0375974  0.00208676 0.00612217 0.00326905\n",
      " 0.03690759 0.06416451 0.00152207 0.05506274 0.00220941 0.03890974\n",
      " 0.03118901 0.04093568 0.00152207 0.06262686 0.01635884 0.07290048\n",
      " 0.00632635 0.00530952 0.07346954 0.00220623]\n",
      "CPU times: user 3min 49s, sys: 2.51 s, total: 3min 51s\n",
      "Wall time: 55min 36s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/UniformRandomtRNA_WTCodonUsage_100sample_elongt.npy')\n",
    "ptRNA_list = np.load('./data/UniformRandomtRNA_WTCodonUsage_100sample_ptRNA.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_transcriptome,elongt_list,ensmbl_latency_dict,min(gr25_ptRNA),max(gr25_ptRNA),objective='slow')\n",
    "    fullelongt_list.append(max(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', max(elongt_list),'tRNA list: ', ptRNA_list[np.argmax(elongt_list)])\n",
    "\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_2ksample_slowtRNA_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_2ksample_slowtRNA_ptRNA',ptRNA_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_2ksample_slowtRNA_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: faster codon usage distributions (tRNA abundances fixed, WT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Generation:  0  elongt:  191.20403884990284\n",
      "Generation:  100  elongt:  183.8317274791367\n",
      "Generation:  200  elongt:  183.8317274791367\n",
      "Generation:  300  elongt:  183.79480130218053\n",
      "Generation:  400  elongt:  183.00058094871963\n",
      "Generation:  500  elongt:  183.00058094871963\n",
      "Generation:  600  elongt:  182.9682133905058\n",
      "Generation:  700  elongt:  181.89405407246474\n",
      "Generation:  800  elongt:  181.89405407246474\n",
      "Generation:  900  elongt:  181.89405407246474\n",
      "Generation:  1000  elongt:  181.89405407246474\n",
      "Generation:  1100  elongt:  181.89405407246474\n",
      "Generation:  1200  elongt:  181.89405407246474\n",
      "Generation:  1300  elongt:  181.88442465158676\n",
      "Generation:  1400  elongt:  181.59291600231413\n",
      "Generation:  1500  elongt:  181.59291600231413\n",
      "Generation:  1600  elongt:  181.46748086212898\n",
      "Generation:  1700  elongt:  181.38451951397764\n",
      "Generation:  1800  elongt:  181.38451951397764\n",
      "Generation:  1900  elongt:  181.38451951397764\n",
      "CPU times: user 29min 39s, sys: 4.97 s, total: 29min 44s\n",
      "Wall time: 1h 40min 44s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/WTtRNA_UniformRandomCodonUsage_100sample_elongt.npy')\n",
    "pCodon_list = np.load('./data/WTtRNA_UniformRandomCodonUsage_100sample_pCodon.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, pCodon_list, elongt_list = run_ga_CodonSweep(gr25_ptRNA,pCodon_list,elongt_list,ensmbl_latency_dict,min(gr25_ptRNA),max(gr25_ptRNA),objective='fast')\n",
    "    fullelongt_list.append(min(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', min(elongt_list))\n",
    "        \n",
    "np.save('./data/WTtRNA_UniformRandomCodonUsage_2ksample_fastCodonUsage_elongt',elongt_list)\n",
    "np.save('./data/WTtRNA_UniformRandomCodonUsage_2ksample_fastCodonUsage_pCodon',pCodon_list)\n",
    "np.save('./data/WTtRNA_UniformRandomCodonUsage_2ksample_fastCodonUsage_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: slower codon usage distributions (tRNA abundances fixed, WT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Generation:  0  elongt:  197.15079674970096\n",
      "Generation:  100  elongt:  204.67877264653683\n",
      "Generation:  200  elongt:  207.69370503330927\n",
      "Generation:  300  elongt:  208.5881553822576\n",
      "Generation:  400  elongt:  209.1548761281126\n",
      "Generation:  500  elongt:  209.9507496043587\n",
      "Generation:  600  elongt:  210.76652294650046\n",
      "Generation:  700  elongt:  207.24318243566364\n",
      "Generation:  800  elongt:  208.94475841032855\n",
      "Generation:  900  elongt:  211.12963582348067\n",
      "Generation:  1000  elongt:  210.52570746321175\n",
      "Generation:  1100  elongt:  211.24092953215046\n",
      "Generation:  1200  elongt:  210.48032627973268\n",
      "Generation:  1300  elongt:  210.47866205263128\n",
      "Generation:  1400  elongt:  209.784447529888\n",
      "Generation:  1500  elongt:  208.99184589196045\n",
      "Generation:  1600  elongt:  210.6692604960466\n",
      "Generation:  1700  elongt:  212.53593727342954\n",
      "Generation:  1800  elongt:  209.31250488275492\n",
      "Generation:  1900  elongt:  210.6189482662206\n",
      "CPU times: user 28min 55s, sys: 4.53 s, total: 29min\n",
      "Wall time: 1h 37min 45s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/WTtRNA_UniformRandomCodonUsage_100sample_elongt.npy')\n",
    "pCodon_list = np.load('./data/WTtRNA_UniformRandomCodonUsage_100sample_pCodon.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, pCodon_list, elongt_list = run_ga_CodonSweep(gr25_ptRNA,pCodon_list,elongt_list,ensmbl_latency_dict,min(gr25_ptRNA),max(gr25_ptRNA),objective='slow')\n",
    "    fullelongt_list.append(max(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', min(elongt_list))\n",
    "        \n",
    "np.save('./data/WTtRNA_UniformRandomCodonUsage_2ksample_slowCodonUsage_elongt',elongt_list)\n",
    "np.save('./data/WTtRNA_UniformRandomCodonUsage_2ksample_slowCodonUsage_pCodon',pCodon_list)\n",
    "np.save('./data/WTtRNA_UniformRandomCodonUsage_2ksample_slowCodonUsage_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Minimal code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  272\n",
      "Sequences not available for # genes in Ecocyc DB:  153\n",
      "Inconsistency in codons:  0\n",
      "3767\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  133\n"
     ]
    }
   ],
   "source": [
    "##Compute the codon distribution and elongation latency of each gene weighted by transcript number\n",
    "codon_elongt = compute_codon_elongt(gr25_ptRNA, len(gr25_pCodon), ensmbl_latency_dict)\n",
    "gene_map, gene_latency = compute_gene_elongt(codon_elongt, red20=True)\n",
    "pCodon_transcriptome, transcriptome_elongt = compute_transcript_distributions(gene_map,gene_latency)\n",
    "pCodon_transcriptome = np.array(pCodon_transcriptome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uniform Random tRNA, minimal code, 100 samples\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12 s, sys: 52.5 ms, total: 12.1 s\n",
      "Wall time: 18 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "\n",
    "for i in range(100):\n",
    "    ptRNA_uniform = np.random.uniform(min(gr25_ptRNA),max(gr25_ptRNA),len(gr25_ptRNA))\n",
    "    ptRNA_list.append(ptRNA_uniform)\n",
    "    inputs.append([ptRNA_uniform,pCodon_transcriptome,ensmbl_latency_dict])\n",
    "elongt_list = Parallel(n_jobs=n_cores,backend='loky')(delayed(computeElongationLatency_multithread)(i) for i in inputs)\n",
    "elongt_list = [elongt[0][0] for elongt in elongt_list]\n",
    "np.save('./data/UniformRandomtRNA_minimalcode_100sample_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_minimalcode_100sample_ptRNA',ptRNA_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: faster tRNA distribution with full tRNA, minimal code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Generation:  0  elongt:  206.14060944009728\n",
      "Generation:  100  elongt:  184.47065249040617\n",
      "Generation:  200  elongt:  172.76141694247272\n",
      "Generation:  300  elongt:  171.97699467004063\n",
      "Generation:  400  elongt:  170.77511429725126\n",
      "Generation:  500  elongt:  167.5584628728589\n",
      "Generation:  600  elongt:  166.85355551029303\n",
      "Generation:  700  elongt:  166.85355551029303\n",
      "Generation:  800  elongt:  166.60856577493433\n",
      "Generation:  900  elongt:  166.60856577493433\n",
      "Generation:  1000  elongt:  166.60856577493433\n",
      "Generation:  1100  elongt:  166.60856577493433\n",
      "Generation:  1200  elongt:  164.92418059150185\n",
      "Generation:  1300  elongt:  164.54101061324639\n",
      "Generation:  1400  elongt:  164.49475712293437\n",
      "Generation:  1500  elongt:  164.49475712293437\n",
      "Generation:  1600  elongt:  164.49475712293437\n",
      "Generation:  1700  elongt:  164.49475712293437\n",
      "Generation:  1800  elongt:  164.49475712293437\n",
      "Generation:  1900  elongt:  164.49475712293437\n",
      "CPU times: user 23min 37s, sys: 4.04 s, total: 23min 41s\n",
      "Wall time: 1h 18min 47s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/UniformRandomtRNA_minimalcode_100sample_elongt.npy')\n",
    "ptRNA_list = np.load('./data/UniformRandomtRNA_minimalcode_100sample_ptRNA.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_transcriptome,elongt_list,ensmbl_latency_dict,min(gr25_ptRNA),max(gr25_ptRNA),objective='fast')\n",
    "    fullelongt_list.append(min(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', min(elongt_list))\n",
    "        \n",
    "np.save('./data/UniformRandomtRNA_minimalcode_2ksample_fasttRNA_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_minimalcode_2ksample_fasttRNA_ptRNA',ptRNA_list)\n",
    "np.save('./data/UniformRandomtRNA_minimalcode_2ksample_fasttRNA_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: slower tRNA distribution with full tRNA, minimal code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Generation:  0 elongt:  229.79502262616816\n",
      "Generation:  100 elongt:  243.30179107950988\n",
      "Generation:  200 elongt:  248.55241118882523\n",
      "Generation:  300 elongt:  249.73461591316013\n",
      "Generation:  400 elongt:  250.8934690614592\n",
      "Generation:  500 elongt:  251.18101657269682\n",
      "Generation:  600 elongt:  251.7319741362925\n",
      "Generation:  700 elongt:  251.83228975842928\n",
      "Generation:  800 elongt:  251.83228975842928\n",
      "Generation:  900 elongt:  251.87044065674507\n",
      "Generation:  1000 elongt:  251.9466119058292\n",
      "Generation:  1100 elongt:  252.14476281671844\n",
      "Generation:  1200 elongt:  252.14476281671844\n",
      "Generation:  1300 elongt:  252.14476281671844\n",
      "Generation:  1400 elongt:  252.14476281671844\n",
      "Generation:  1500 elongt:  252.23480612657724\n",
      "Generation:  1600 elongt:  252.24424399888665\n",
      "Generation:  1700 elongt:  252.25445399941663\n",
      "Generation:  1800 elongt:  252.25445399941663\n",
      "Generation:  1900 elongt:  252.25445399941663\n",
      "CPU times: user 23min 39s, sys: 4.2 s, total: 23min 43s\n",
      "Wall time: 1h 18min 49s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/UniformRandomtRNA_minimalcode_100sample_elongt.npy')\n",
    "ptRNA_list = np.load('./data/UniformRandomtRNA_minimalcode_100sample_ptRNA.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_transcriptome,elongt_list,ensmbl_latency_dict,min(gr25_ptRNA),max(gr25_ptRNA),objective='slow')\n",
    "    fullelongt_list.append(max(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i,'elongt: ', max(elongt_list))\n",
    "np.save('./data/UniformRandomtRNA_minimalcode_2ksample_slowtRNA_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_minimalcode_2ksample_slowtRNA_ptRNA',ptRNA_list)\n",
    "np.save('./data/UniformRandomtRNA_minimalcode_2ksample_slowtRNA_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## minimal uniform random tRNA, minimal code, 100 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  272\n",
      "Sequences not available for # genes in Ecocyc DB:  153\n",
      "Inconsistency in codons:  0\n",
      "3767\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  133\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/akshay/opt/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:61: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n",
      "  return bound(*args, **kwds)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 26s, sys: 604 ms, total: 2min 26s\n",
      "Wall time: 2min 32s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "##Compute the codon distribution and elongation latency of each gene weighted by transcript number\n",
    "codon_elongt = compute_codon_elongt(gr25_ptRNA, len(gr25_pCodon), ensmbl_latency_dict)\n",
    "gene_map, gene_latency = compute_gene_elongt(codon_elongt, red20=True)\n",
    "pCodon_transcriptome, transcriptome_elongt = compute_transcript_distributions(gene_map,gene_latency)\n",
    "pCodon_transcriptome = np.array(pCodon_transcriptome)\n",
    "\n",
    "ptRNA_red20 = pd.read_excel('./data/tables/tRNAValues_RED20.xlsx',header=None)\n",
    "ptRNA_red20 = np.array(ptRNA_red20[6]/sum(ptRNA_red20[6]))\n",
    "tRNA_indices = np.argwhere(ptRNA_red20 == 0)\n",
    "tRNA_indices = [indices[0] for indices in tRNA_indices]\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "\n",
    "for i in range(100):\n",
    "    ptRNA_uniform = np.random.uniform(min(ptRNA_red20),max(ptRNA_red20),len(ptRNA_red20))\n",
    "    ptRNA_uniform[tRNA_indices] = 0\n",
    "    ptRNA_list.append(ptRNA_uniform)\n",
    "    inputs.append([ptRNA_uniform,pCodon_transcriptome,ensmbl_latency_dict])\n",
    "elongt_list = Parallel(n_jobs=n_cores,backend='loky')(delayed(computeElongationLatency_multithread)(i) for i in inputs)\n",
    "elongt_list = [elongt[0][0] for elongt in elongt_list]\n",
    "np.save('./data/minimaltRNA_minimalcode_100sample_elongt',elongt_list)\n",
    "np.save('./data/minimaltRNA_minimalcode_100sample_ptRNA',ptRNA_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: faster tRNA distribution with minimal tRNA, minimal code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/akshay/opt/anaconda3/lib/python3.8/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
      "  warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  0  elongt:  168.09758757389093\n",
      "[0.094584   0.         0.07732972 0.         0.         0.\n",
      " 0.09288514 0.00731383 0.05229929 0.         0.01686421 0.12535952\n",
      " 0.01398833 0.         0.05215043 0.10667536 0.         0.\n",
      " 0.05866316 0.         0.         0.11496429 0.05205781 0.00121284\n",
      " 0.06831448 0.         0.         0.         0.08328057 0.\n",
      " 0.         0.         0.09280249 0.         0.         0.\n",
      " 0.11712083 0.12272277 0.         0.09683255]\n",
      "Generation:  100  elongt:  159.35541810875426\n",
      "[0.1116884  0.         0.06227591 0.         0.         0.\n",
      " 0.04887724 0.03701001 0.01312146 0.         0.01836093 0.06510311\n",
      " 0.05769327 0.         0.01661511 0.08410263 0.         0.\n",
      " 0.11905434 0.         0.         0.0403119  0.05249154 0.01653483\n",
      " 0.01836093 0.         0.         0.         0.06233005 0.\n",
      " 0.         0.         0.01551234 0.         0.         0.\n",
      " 0.01965386 0.04941978 0.         0.09148236]\n",
      "Generation:  200  elongt:  158.69193124156322\n",
      "[0.09502171 0.         0.03589482 0.         0.         0.\n",
      " 0.02529005 0.04121552 0.01221374 0.         0.03099802 0.05195894\n",
      " 0.07606238 0.         0.01221374 0.05042878 0.         0.\n",
      " 0.08668335 0.         0.         0.05839289 0.05319465 0.01378323\n",
      " 0.01221374 0.         0.         0.         0.0213359  0.\n",
      " 0.         0.         0.06043242 0.         0.         0.\n",
      " 0.10694472 0.08337564 0.         0.07234575]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m~/Documents/tRNAShuffle/analysis_utils.py\u001b[0m in \u001b[0;36mrun_ga_tRNA\u001b[0;34m(tRNA_list, codon_list, elong_list, ensmbl_latency_dict, minRange, maxRange, objective, ptRNA_red20)\u001b[0m\n\u001b[1;32m    599\u001b[0m     \u001b[0;31m#### Compute elong_t of the recombined children, multithreaded\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m     \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrecombined_children\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcodon_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mensmbl_latency_dict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcouples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m     \u001b[0mparallel_elong\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_cores\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'loky'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomputeElongationLatency_multithread\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    602\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mitems\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparallel_elong\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m         \u001b[0mrecombined_children_elongt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1054\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1055\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1056\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1057\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1058\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    933\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    934\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 935\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    936\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    937\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    437\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "##Compute the codon distribution and elongation latency of each gene weighted by transcript number\n",
    "codon_elongt = compute_codon_elongt(gr25_ptRNA, len(gr25_pCodon), ensmbl_latency_dict)\n",
    "gene_map, gene_latency = compute_gene_elongt(codon_elongt, red20=True)\n",
    "pCodon_transcriptome, transcriptome_elongt = compute_transcript_distributions(gene_map,gene_latency)\n",
    "pCodon_transcriptome = np.array(pCodon_transcriptome)\n",
    "\n",
    "ptRNA_red20 = pd.read_excel('./data/tables/tRNAValues_RED20.xlsx',header=None)\n",
    "ptRNA_red20 = np.array(ptRNA_red20[6]/sum(ptRNA_red20[6]))\n",
    "tRNA_indices = np.argwhere(ptRNA_red20 == 0)\n",
    "tRNA_indices = [indices[0] for indices in tRNA_indices]\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/minimaltRNA_minimalcode_100sample_elongt.npy')\n",
    "ptRNA_list = np.load('./data/minimaltRNA_minimalcode_100sample_ptRNA.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_transcriptome,elongt_list,ensmbl_latency_dict,min(ptRNA_red20[np.nonzero(ptRNA_red20)]),max(ptRNA_red20),objective='fast',ptRNA_red20=ptRNA_red20)\n",
    "    fullelongt_list.append(min(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', min(elongt_list))\n",
    "        print(ptRNA_list[0])\n",
    "        \n",
    "np.save('./data/minimaltRNA_minimalcode_2ksample_fasttRNA_elongt',elongt_list)\n",
    "np.save('./data/minimaltRNA_minimalcode_2ksample_fasttRNA_ptRNA',ptRNA_list)\n",
    "np.save('./data/minimaltRNA_minimalcode_2ksample_fasttRNA_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: slower tRNA distribution with minimal tRNA, minimal code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/akshay/opt/anaconda3/lib/python3.8/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
      "  warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  0 elongt:  200.61332867794016\n",
      "[0.094584   0.         0.07732972 0.         0.         0.\n",
      " 0.09288514 0.00731383 0.05229929 0.         0.01686421 0.12535952\n",
      " 0.01398833 0.         0.05215043 0.10667536 0.         0.\n",
      " 0.05866316 0.         0.         0.11496429 0.05205781 0.00121284\n",
      " 0.06831448 0.         0.         0.         0.08328057 0.\n",
      " 0.         0.         0.09280249 0.         0.         0.\n",
      " 0.11712083 0.12272277 0.         0.09683255]\n",
      "Generation:  100 elongt:  211.36661478591012\n",
      "[0.01221374 0.         0.02760015 0.         0.         0.\n",
      " 0.02099455 0.03402786 0.11692453 0.         0.07170514 0.01233625\n",
      " 0.01251068 0.         0.11061682 0.03994041 0.         0.\n",
      " 0.01357687 0.         0.         0.0313768  0.04008047 0.07912909\n",
      " 0.07405006 0.         0.         0.         0.08182311 0.\n",
      " 0.         0.         0.03405611 0.         0.         0.\n",
      " 0.11190137 0.03066772 0.         0.04446828]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m~/Documents/tRNAShuffle/analysis_utils.py\u001b[0m in \u001b[0;36mrun_ga_tRNA\u001b[0;34m(tRNA_list, codon_list, elong_list, ensmbl_latency_dict, minRange, maxRange, objective, ptRNA_red20)\u001b[0m\n\u001b[1;32m    599\u001b[0m     \u001b[0;31m#### Compute elong_t of the recombined children, multithreaded\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m     \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrecombined_children\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcodon_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mensmbl_latency_dict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcouples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m     \u001b[0mparallel_elong\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_cores\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'loky'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomputeElongationLatency_multithread\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    602\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mitems\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparallel_elong\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m         \u001b[0mrecombined_children_elongt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1054\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1055\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1056\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1057\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1058\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    933\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    934\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 935\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    936\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    937\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    437\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "Compute the codon distribution and elongation latency of each gene weighted by transcript number\n",
    "codon_elongt = compute_codon_elongt(gr25_ptRNA, len(gr25_pCodon), ensmbl_latency_dict)\n",
    "gene_map, gene_latency = compute_gene_elongt(codon_elongt, red20=True)\n",
    "pCodon_transcriptome, transcriptome_elongt = compute_transcript_distributions(gene_map,gene_latency)\n",
    "pCodon_transcriptome = np.array(pCodon_transcriptome)\n",
    "\n",
    "ptRNA_red20 = pd.read_excel('./data/tables/tRNAValues_RED20.xlsx',header=None)\n",
    "ptRNA_red20 = np.array(ptRNA_red20[6]/sum(ptRNA_red20[6]))\n",
    "tRNA_indices = np.argwhere(ptRNA_red20 == 0)\n",
    "tRNA_indices = [indices[0] for indices in tRNA_indices]\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/minimaltRNA_minimalcode_100sample_elongt.npy')\n",
    "ptRNA_list = np.load('./data/minimaltRNA_minimalcode_100sample_ptRNA.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_transcriptome,elongt_list,ensmbl_latency_dict,min(ptRNA_red20[np.nonzero(ptRNA_red20)]),max(ptRNA_red20),objective='slow',ptRNA_red20=ptRNA_red20)\n",
    "    fullelongt_list.append(max(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i,'elongt: ', max(elongt_list))\n",
    "        print(ptRNA_list[0])\n",
    "\n",
    "np.save('./data/minimaltRNA_minimalcode_2ksample_slowtRNA_elongt',elongt_list)\n",
    "np.save('./data/minimaltRNA_minimalcode_2ksample_slowtRNA_ptRNA',ptRNA_list)\n",
    "np.save('./data/minimaltRNA_minimalcode_2ksample_slowtRNA_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## minimal uniform random tRNA, minimal code GFP, 100 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/akshay/opt/anaconda3/lib/python3.8/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
      "  warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "181.7079011198748\n",
      "CPU times: user 1.81 s, sys: 72.4 ms, total: 1.88 s\n",
      "Wall time: 14.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "gene_map, pCodon_GFP = singlegene_to_genemap('./data/tables/sfGFP_RED20.csv')\n",
    "\n",
    "ptRNA_red20 = pd.read_excel('./data/tables/tRNAValues_RED20.xlsx',header=None)\n",
    "ptRNA_red20 = np.array(ptRNA_red20[6]/sum(ptRNA_red20[6]))\n",
    "tRNA_indices = np.argwhere(ptRNA_red20 == 0)\n",
    "tRNA_indices = [indices[0] for indices in tRNA_indices]\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "\n",
    "for i in range(100):\n",
    "    ptRNA_uniform = np.random.uniform(min(ptRNA_red20[np.nonzero(ptRNA_red20)]),max(ptRNA_red20),len(ptRNA_red20))\n",
    "    ptRNA_uniform[tRNA_indices] = 0\n",
    "    ptRNA_list.append(ptRNA_uniform)\n",
    "    inputs.append([ptRNA_uniform,pCodon_GFP,ensmbl_latency_dict])\n",
    "    \n",
    "elongt_list = Parallel(n_jobs=n_cores,backend='loky')(delayed(computeElongationLatency_multithread)(i) for i in inputs)\n",
    "elongt_list = [elongt[0][0] for elongt in elongt_list]\n",
    "np.save('./data/minimaltRNA_minimalcode-sfGFP_100sample_elongt',elongt_list)\n",
    "np.save('./data/minimaltRNA_minimalcode-sfGFP_100sample_ptRNA',ptRNA_list)\n",
    "print(np.average(elongt_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.12824427480759398)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(ptRNA_red20),max(ptRNA_red20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ptRNA_red20' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/ht/q05lxg2n6vq4wbnbcfkt9kmc0000gn/T/ipykernel_15494/378968926.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mgene_map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpCodon_GFP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msinglegene_to_genemap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/akshay/Documents/tRNAShuffle/data/tables/sfGFP_RED20.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mminRange\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mptRNA_red20\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mptRNA_red20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mmaxRange\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mptRNA_red20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ptRNA_red20' is not defined"
     ]
    }
   ],
   "source": [
    "gene_map, pCodon_GFP = singlegene_to_genemap('./data/tables/sfGFP_RED20.csv')\n",
    "\n",
    "minRange = min(ptRNA_red20[np.nonzero(ptRNA_red20)])\n",
    "maxRange = max(ptRNA_red20)\n",
    "\n",
    "a = ptRNA_list[np.argmin(elongt_list)]\n",
    "print(a,sum(a))\n",
    "print(computeElongationLatency(a,pCodon_GFP,ensmbl_latency_dict))\n",
    "a = np.where((a>0)&(a<minRange), minRange, a)\n",
    "a = np.where(a>maxRange, maxRange, a)\n",
    "a_minmaxindices = np.where((a==minRange) | (a==maxRange))\n",
    "a_indices = np.where((a>minRange) & (a<maxRange))\n",
    "a[a_indices] = (a[a_indices]/sum(a[a_indices]))*(1-sum(a[a_minmaxindices]))\n",
    "print(a,sum(a))\n",
    "print(computeElongationLatency(a,pCodon_GFP,ensmbl_latency_dict))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: faster tRNA distribution with minimal tRNA, GFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/akshay/opt/anaconda3/lib/python3.8/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
      "  warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  0  elongt:  171.75080283438695 434.57787740681766 tRNA list:  [0.03367461 0.         0.05628633 0.         0.         0.\n",
      " 0.03022968 0.05726129 0.02890398 0.         0.02221581 0.03221715\n",
      " 0.06743309 0.         0.01811383 0.0896722  0.         0.\n",
      " 0.07110743 0.         0.         0.07108271 0.09471045 0.07212746\n",
      " 0.01329183 0.         0.         0.         0.05827319 0.\n",
      " 0.         0.         0.07233203 0.         0.         0.\n",
      " 0.01744351 0.0536624  0.         0.03996101]\n",
      "Generation:  100  elongt:  159.89768777287156 163.40984370652868 tRNA list:  [0.02690399 0.         0.05173235 0.         0.         0.\n",
      " 0.02328147 0.05739122 0.01221374 0.         0.01221374 0.05777824\n",
      " 0.12068634 0.         0.03182723 0.0608456  0.         0.\n",
      " 0.10107867 0.         0.         0.1005877  0.02983742 0.04378283\n",
      " 0.01235338 0.         0.         0.         0.05185915 0.\n",
      " 0.         0.         0.09850267 0.         0.         0.\n",
      " 0.01402693 0.03247801 0.         0.06061932]\n",
      "Generation:  200  elongt:  159.08845458556402 161.12549710305504 tRNA list:  [0.02710609 0.         0.05733675 0.         0.         0.\n",
      " 0.07256527 0.07414435 0.01225163 0.         0.01225163 0.06403762\n",
      " 0.07926377 0.         0.0309586  0.05049386 0.         0.\n",
      " 0.09832002 0.         0.         0.11649651 0.0290231  0.04258791\n",
      " 0.01277971 0.         0.         0.         0.02587555 0.\n",
      " 0.         0.         0.07982774 0.         0.         0.\n",
      " 0.01277971 0.03028619 0.         0.07161401]\n",
      "Generation:  300  elongt:  158.46144527985052 160.47272027667216 tRNA list:  [0.02114329 0.         0.04822865 0.         0.         0.\n",
      " 0.05165746 0.07833734 0.01471766 0.         0.01492572 0.07164285\n",
      " 0.08223129 0.         0.03112873 0.06018012 0.         0.\n",
      " 0.05816006 0.         0.         0.09837568 0.01542643 0.05143078\n",
      " 0.01549107 0.         0.         0.         0.03759432 0.\n",
      " 0.         0.         0.1090215  0.         0.         0.\n",
      " 0.01409517 0.04250797 0.         0.08370391]\n",
      "Generation:  400  elongt:  158.14019903389294 160.08633052731992 tRNA list:  [0.02521531 0.         0.05333725 0.         0.         0.\n",
      " 0.06750351 0.06897244 0.01221374 0.         0.01221374 0.0595707\n",
      " 0.07373476 0.         0.026764   0.04697168 0.         0.\n",
      " 0.09146175 0.         0.         0.10837034 0.02509073 0.03961721\n",
      " 0.01221374 0.         0.         0.         0.05355151 0.\n",
      " 0.         0.         0.07425939 0.         0.         0.\n",
      " 0.01221374 0.02817359 0.         0.10855088]\n",
      "Generation:  500  elongt:  158.14019903389294 159.9437392574158 tRNA list:  [0.02521531 0.         0.05333725 0.         0.         0.\n",
      " 0.06750351 0.06897244 0.01221374 0.         0.01221374 0.0595707\n",
      " 0.07373476 0.         0.026764   0.04697168 0.         0.\n",
      " 0.09146175 0.         0.         0.10837034 0.02509073 0.03961721\n",
      " 0.01221374 0.         0.         0.         0.05355151 0.\n",
      " 0.         0.         0.07425939 0.         0.         0.\n",
      " 0.01221374 0.02817359 0.         0.10855088]\n",
      "Generation:  600  elongt:  157.25124738792113 159.87778056110028 tRNA list:  [0.02082177 0.         0.01315237 0.         0.         0.\n",
      " 0.06859178 0.07714609 0.01223296 0.         0.01469875 0.06445072\n",
      " 0.08098082 0.         0.02913762 0.04962274 0.         0.\n",
      " 0.11049596 0.         0.         0.0968797  0.03348047 0.05064868\n",
      " 0.01223296 0.         0.         0.         0.05855248 0.\n",
      " 0.         0.         0.0846697  0.         0.         0.\n",
      " 0.01223296 0.0275404  0.         0.08243105]\n",
      "Generation:  700  elongt:  157.25124738792113 159.52245765649275 tRNA list:  [0.02082177 0.         0.01315237 0.         0.         0.\n",
      " 0.06859178 0.07714609 0.01223296 0.         0.01469875 0.06445072\n",
      " 0.08098082 0.         0.02913762 0.04962274 0.         0.\n",
      " 0.11049596 0.         0.         0.0968797  0.03348047 0.05064868\n",
      " 0.01223296 0.         0.         0.         0.05855248 0.\n",
      " 0.         0.         0.0846697  0.         0.         0.\n",
      " 0.01223296 0.0275404  0.         0.08243105]\n",
      "Generation:  800  elongt:  157.25124738792113 159.60267824475687 tRNA list:  [0.02082177 0.         0.01315237 0.         0.         0.\n",
      " 0.06859178 0.07714609 0.01223296 0.         0.01469875 0.06445072\n",
      " 0.08098082 0.         0.02913762 0.04962274 0.         0.\n",
      " 0.11049596 0.         0.         0.0968797  0.03348047 0.05064868\n",
      " 0.01223296 0.         0.         0.         0.05855248 0.\n",
      " 0.         0.         0.0846697  0.         0.         0.\n",
      " 0.01223296 0.0275404  0.         0.08243105]\n",
      "Generation:  900  elongt:  157.25124738792113 159.66732889292737 tRNA list:  [0.02082177 0.         0.01315237 0.         0.         0.\n",
      " 0.06859178 0.07714609 0.01223296 0.         0.01469875 0.06445072\n",
      " 0.08098082 0.         0.02913762 0.04962274 0.         0.\n",
      " 0.11049596 0.         0.         0.0968797  0.03348047 0.05064868\n",
      " 0.01223296 0.         0.         0.         0.05855248 0.\n",
      " 0.         0.         0.0846697  0.         0.         0.\n",
      " 0.01223296 0.0275404  0.         0.08243105]\n",
      "Generation:  1000  elongt:  157.25124738792113 159.63881320120413 tRNA list:  [0.02082177 0.         0.01315237 0.         0.         0.\n",
      " 0.06859178 0.07714609 0.01223296 0.         0.01469875 0.06445072\n",
      " 0.08098082 0.         0.02913762 0.04962274 0.         0.\n",
      " 0.11049596 0.         0.         0.0968797  0.03348047 0.05064868\n",
      " 0.01223296 0.         0.         0.         0.05855248 0.\n",
      " 0.         0.         0.0846697  0.         0.         0.\n",
      " 0.01223296 0.0275404  0.         0.08243105]\n",
      "Generation:  1100  elongt:  157.25124738792113 159.45317335772714 tRNA list:  [0.02082177 0.         0.01315237 0.         0.         0.\n",
      " 0.06859178 0.07714609 0.01223296 0.         0.01469875 0.06445072\n",
      " 0.08098082 0.         0.02913762 0.04962274 0.         0.\n",
      " 0.11049596 0.         0.         0.0968797  0.03348047 0.05064868\n",
      " 0.01223296 0.         0.         0.         0.05855248 0.\n",
      " 0.         0.         0.0846697  0.         0.         0.\n",
      " 0.01223296 0.0275404  0.         0.08243105]\n",
      "Generation:  1200  elongt:  157.25124738792113 159.25786605474505 tRNA list:  [0.02082177 0.         0.01315237 0.         0.         0.\n",
      " 0.06859178 0.07714609 0.01223296 0.         0.01469875 0.06445072\n",
      " 0.08098082 0.         0.02913762 0.04962274 0.         0.\n",
      " 0.11049596 0.         0.         0.0968797  0.03348047 0.05064868\n",
      " 0.01223296 0.         0.         0.         0.05855248 0.\n",
      " 0.         0.         0.0846697  0.         0.         0.\n",
      " 0.01223296 0.0275404  0.         0.08243105]\n",
      "Generation:  1300  elongt:  157.25124738792113 159.1667642504565 tRNA list:  [0.02082177 0.         0.01315237 0.         0.         0.\n",
      " 0.06859178 0.07714609 0.01223296 0.         0.01469875 0.06445072\n",
      " 0.08098082 0.         0.02913762 0.04962274 0.         0.\n",
      " 0.11049596 0.         0.         0.0968797  0.03348047 0.05064868\n",
      " 0.01223296 0.         0.         0.         0.05855248 0.\n",
      " 0.         0.         0.0846697  0.         0.         0.\n",
      " 0.01223296 0.0275404  0.         0.08243105]\n",
      "Generation:  1400  elongt:  157.19556326201047 159.21481442673524 tRNA list:  [0.0190201  0.         0.04156696 0.         0.         0.\n",
      " 0.04819245 0.08909173 0.01373045 0.         0.0225166  0.06683728\n",
      " 0.07671548 0.         0.04067388 0.05614344 0.         0.\n",
      " 0.11481016 0.         0.         0.09177696 0.01439167 0.04798097\n",
      " 0.01445198 0.         0.         0.         0.03206805 0.\n",
      " 0.         0.         0.07992985 0.         0.         0.\n",
      " 0.01235599 0.03965668 0.         0.07808933]\n",
      "Generation:  1500  elongt:  157.19556326201047 159.0837918733724 tRNA list:  [0.0190201  0.         0.04156696 0.         0.         0.\n",
      " 0.04819245 0.08909173 0.01373045 0.         0.0225166  0.06683728\n",
      " 0.07671548 0.         0.04067388 0.05614344 0.         0.\n",
      " 0.11481016 0.         0.         0.09177696 0.01439167 0.04798097\n",
      " 0.01445198 0.         0.         0.         0.03206805 0.\n",
      " 0.         0.         0.07992985 0.         0.         0.\n",
      " 0.01235599 0.03965668 0.         0.07808933]\n",
      "Generation:  1600  elongt:  157.19556326201047 159.1599259286987 tRNA list:  [0.0190201  0.         0.04156696 0.         0.         0.\n",
      " 0.04819245 0.08909173 0.01373045 0.         0.0225166  0.06683728\n",
      " 0.07671548 0.         0.04067388 0.05614344 0.         0.\n",
      " 0.11481016 0.         0.         0.09177696 0.01439167 0.04798097\n",
      " 0.01445198 0.         0.         0.         0.03206805 0.\n",
      " 0.         0.         0.07992985 0.         0.         0.\n",
      " 0.01235599 0.03965668 0.         0.07808933]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  1700  elongt:  157.19556326201047 159.15672749388773 tRNA list:  [0.0190201  0.         0.04156696 0.         0.         0.\n",
      " 0.04819245 0.08909173 0.01373045 0.         0.0225166  0.06683728\n",
      " 0.07671548 0.         0.04067388 0.05614344 0.         0.\n",
      " 0.11481016 0.         0.         0.09177696 0.01439167 0.04798097\n",
      " 0.01445198 0.         0.         0.         0.03206805 0.\n",
      " 0.         0.         0.07992985 0.         0.         0.\n",
      " 0.01235599 0.03965668 0.         0.07808933]\n",
      "Generation:  1800  elongt:  157.19556326201047 158.90407288679646 tRNA list:  [0.0190201  0.         0.04156696 0.         0.         0.\n",
      " 0.04819245 0.08909173 0.01373045 0.         0.0225166  0.06683728\n",
      " 0.07671548 0.         0.04067388 0.05614344 0.         0.\n",
      " 0.11481016 0.         0.         0.09177696 0.01439167 0.04798097\n",
      " 0.01445198 0.         0.         0.         0.03206805 0.\n",
      " 0.         0.         0.07992985 0.         0.         0.\n",
      " 0.01235599 0.03965668 0.         0.07808933]\n",
      "Generation:  1900  elongt:  157.19556326201047 158.8131908231008 tRNA list:  [0.0190201  0.         0.04156696 0.         0.         0.\n",
      " 0.04819245 0.08909173 0.01373045 0.         0.0225166  0.06683728\n",
      " 0.07671548 0.         0.04067388 0.05614344 0.         0.\n",
      " 0.11481016 0.         0.         0.09177696 0.01439167 0.04798097\n",
      " 0.01445198 0.         0.         0.         0.03206805 0.\n",
      " 0.         0.         0.07992985 0.         0.         0.\n",
      " 0.01235599 0.03965668 0.         0.07808933]\n",
      "CPU times: user 4min 16s, sys: 4.7 s, total: 4min 20s\n",
      "Wall time: 1h 25min 9s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "gene_map, pCodon_GFP = singlegene_to_genemap('./data/tables/sfGFP_RED20.csv')\n",
    "ptRNA_red20 = pd.read_excel('./data/tables/tRNAValues_RED20.xlsx',header=None)\n",
    "ptRNA_red20 = np.array(ptRNA_red20[6]/sum(ptRNA_red20[6]))\n",
    "tRNA_indices = np.argwhere(ptRNA_red20 == 0)\n",
    "tRNA_indices = [indices[0] for indices in tRNA_indices]\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/minimaltRNA_minimalcode-sfGFP_100sample_elongt.npy')\n",
    "ptRNA_list = np.load('./data/minimaltRNA_minimalcode-sfGFP_100sample_ptRNA.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_GFP,elongt_list,ensmbl_latency_dict,min(ptRNA_red20[np.nonzero(ptRNA_red20)]),max(ptRNA_red20),objective='fast',ptRNA_red20=ptRNA_red20)\n",
    "    fullelongt_list.append(min(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', min(elongt_list),np.average(elongt_list),'tRNA list: ',ptRNA_list[np.argmin(elongt_list)])\n",
    "        \n",
    "np.save('./data/UniformRandomtRNA_minimalcode-sfGFP_2ksample_fasttRNA_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_minimalcode-sfGFP_2ksample_fasttRNA_ptRNA',ptRNA_list)\n",
    "np.save('./data/UniformRandomtRNA_minimalcode-sfGFP_2ksample_fasttRNA_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: slower tRNA distribution with minimal tRNA, GFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/akshay/opt/anaconda3/lib/python3.8/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
      "  warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  0  elongt:  193.14207697602257 tRNA list:  [0.0337419  0.         0.10416162 0.         0.         0.\n",
      " 0.08197971 0.01281265 0.07610775 0.         0.11226017 0.08626951\n",
      " 0.03989334 0.         0.08062111 0.09833604 0.         0.\n",
      " 0.06056075 0.         0.         0.02810242 0.08690896 0.04011888\n",
      " 0.08613279 0.         0.         0.         0.03471656 0.\n",
      " 0.         0.         0.07326589 0.         0.         0.\n",
      " 0.11683489 0.05830633 0.         0.02794481]\n",
      "Generation:  100  elongt:  212.77472739971392 tRNA list:  [0.04033359 0.         0.0363369  0.         0.         0.\n",
      " 0.01449324 0.02086474 0.06319654 0.         0.12207832 0.02154472\n",
      " 0.02743971 0.         0.02158262 0.01665795 0.         0.\n",
      " 0.0317577  0.         0.         0.01852204 0.12824427 0.02756128\n",
      " 0.12062871 0.         0.         0.         0.01774003 0.\n",
      " 0.         0.         0.01456085 0.         0.         0.\n",
      " 0.10267459 0.12843    0.         0.02535219]\n",
      "Generation:  200  elongt:  217.92488749165895 tRNA list:  [0.11905324 0.         0.1054508  0.         0.         0.\n",
      " 0.01221374 0.0180734  0.10303178 0.         0.0488167  0.01393108\n",
      " 0.02488783 0.         0.01690035 0.01339266 0.         0.\n",
      " 0.02522432 0.         0.         0.01471158 0.09378433 0.01245858\n",
      " 0.11400211 0.         0.         0.         0.0138914  0.\n",
      " 0.         0.         0.01221374 0.         0.         0.\n",
      " 0.105935   0.11403851 0.         0.01798884]\n",
      "Generation:  300  elongt:  218.2160980205512 tRNA list:  [0.11414983 0.         0.01732993 0.         0.         0.\n",
      " 0.01232285 0.01730616 0.1102926  0.         0.11600333 0.01491282\n",
      " 0.0163509  0.         0.01826206 0.0123381  0.         0.\n",
      " 0.02700192 0.         0.         0.01574833 0.10039347 0.01333656\n",
      " 0.10751308 0.         0.         0.         0.05190045 0.\n",
      " 0.         0.         0.01242567 0.         0.         0.\n",
      " 0.10751308 0.09767369 0.         0.01722518]\n",
      "Generation:  400  elongt:  220.17810954336306 tRNA list:  [0.10075142 0.         0.0191372  0.         0.         0.\n",
      " 0.01391231 0.01345632 0.12179455 0.         0.1005987  0.01646802\n",
      " 0.01805606 0.         0.02321356 0.01830195 0.         0.\n",
      " 0.03402996 0.         0.         0.0180365  0.12072104 0.01257349\n",
      " 0.12072104 0.         0.         0.         0.05185061 0.\n",
      " 0.         0.         0.01345632 0.         0.         0.\n",
      " 0.12072104 0.04317838 0.         0.01902152]\n",
      "Generation:  500  elongt:  220.3466320934869 tRNA list:  [0.11233041 0.         0.01904216 0.         0.         0.\n",
      " 0.01321299 0.01879527 0.12118967 0.         0.11053046 0.01638623\n",
      " 0.01796639 0.         0.0186731  0.01821105 0.         0.\n",
      " 0.01463406 0.         0.         0.01794692 0.12012149 0.01384321\n",
      " 0.12012149 0.         0.         0.         0.0515931  0.\n",
      " 0.         0.         0.01338949 0.         0.         0.\n",
      " 0.12012149 0.04296394 0.         0.01892705]\n",
      "Generation:  600  elongt:  220.75763960676434 tRNA list:  [0.016433   0.         0.11884546 0.         0.         0.\n",
      " 0.0129659  0.01353737 0.11490682 0.         0.1147564  0.01656721\n",
      " 0.01816482 0.         0.02335338 0.01341168 0.         0.\n",
      " 0.02070555 0.         0.         0.01568771 0.10751382 0.01264923\n",
      " 0.1165896  0.         0.         0.         0.02608162 0.\n",
      " 0.         0.         0.01221374 0.         0.         0.\n",
      " 0.11775997 0.09012919 0.         0.01772755]\n",
      "Generation:  700  elongt:  220.75763960676434 tRNA list:  [0.016433   0.         0.11884546 0.         0.         0.\n",
      " 0.0129659  0.01353737 0.11490682 0.         0.1147564  0.01656721\n",
      " 0.01816482 0.         0.02335338 0.01341168 0.         0.\n",
      " 0.02070555 0.         0.         0.01568771 0.10751382 0.01264923\n",
      " 0.1165896  0.         0.         0.         0.02608162 0.\n",
      " 0.         0.         0.01221374 0.         0.         0.\n",
      " 0.11775997 0.09012919 0.         0.01772755]\n",
      "Generation:  800  elongt:  221.46501790467119 tRNA list:  [0.11235269 0.         0.02970105 0.         0.         0.\n",
      " 0.01352074 0.014816   0.12824427 0.         0.11218238 0.01636248\n",
      " 0.02013517 0.         0.02113981 0.01486646 0.         0.\n",
      " 0.02129494 0.         0.         0.01738937 0.11917593 0.0140213\n",
      " 0.12824427 0.         0.         0.         0.02586939 0.\n",
      " 0.         0.         0.01487191 0.         0.         0.\n",
      " 0.12105736 0.03689308 0.         0.01786139]\n",
      "Generation:  900  elongt:  221.50710546970095 tRNA list:  [0.11290313 0.         0.02984656 0.         0.         0.\n",
      " 0.01358698 0.01488859 0.12824427 0.         0.11273199 0.01644265\n",
      " 0.02023382 0.         0.02124337 0.01613923 0.         0.\n",
      " 0.02139927 0.         0.         0.01747457 0.1197598  0.01408999\n",
      " 0.12824427 0.         0.         0.         0.0185343  0.\n",
      " 0.         0.         0.01494478 0.         0.         0.\n",
      " 0.12476165 0.03658187 0.         0.0179489 ]\n",
      "Generation:  1000  elongt:  222.40294128510192 tRNA list:  [0.0238637  0.         0.10586285 0.         0.         0.\n",
      " 0.01359273 0.01389441 0.12798529 0.         0.12798529 0.01782753\n",
      " 0.01580583 0.         0.01886422 0.01563488 0.         0.\n",
      " 0.02320162 0.         0.         0.0155431  0.12008757 0.01259557\n",
      " 0.12798529 0.         0.         0.         0.02782921 0.\n",
      " 0.         0.         0.01733064 0.         0.         0.\n",
      " 0.12601904 0.03255938 0.         0.01553185]\n",
      "Generation:  1100  elongt:  222.40294128510192 tRNA list:  [0.0238637  0.         0.10586285 0.         0.         0.\n",
      " 0.01359273 0.01389441 0.12798529 0.         0.12798529 0.01782753\n",
      " 0.01580583 0.         0.01886422 0.01563488 0.         0.\n",
      " 0.02320162 0.         0.         0.0155431  0.12008757 0.01259557\n",
      " 0.12798529 0.         0.         0.         0.02782921 0.\n",
      " 0.         0.         0.01733064 0.         0.         0.\n",
      " 0.12601904 0.03255938 0.         0.01553185]\n",
      "Generation:  1200  elongt:  222.40294128510192 tRNA list:  [0.0238637  0.         0.10586285 0.         0.         0.\n",
      " 0.01359273 0.01389441 0.12798529 0.         0.12798529 0.01782753\n",
      " 0.01580583 0.         0.01886422 0.01563488 0.         0.\n",
      " 0.02320162 0.         0.         0.0155431  0.12008757 0.01259557\n",
      " 0.12798529 0.         0.         0.         0.02782921 0.\n",
      " 0.         0.         0.01733064 0.         0.         0.\n",
      " 0.12601904 0.03255938 0.         0.01553185]\n",
      "Generation:  1300  elongt:  222.40294128510192 tRNA list:  [0.0238637  0.         0.10586285 0.         0.         0.\n",
      " 0.01359273 0.01389441 0.12798529 0.         0.12798529 0.01782753\n",
      " 0.01580583 0.         0.01886422 0.01563488 0.         0.\n",
      " 0.02320162 0.         0.         0.0155431  0.12008757 0.01259557\n",
      " 0.12798529 0.         0.         0.         0.02782921 0.\n",
      " 0.         0.         0.01733064 0.         0.         0.\n",
      " 0.12601904 0.03255938 0.         0.01553185]\n",
      "Generation:  1400  elongt:  222.40294128510192 tRNA list:  [0.0238637  0.         0.10586285 0.         0.         0.\n",
      " 0.01359273 0.01389441 0.12798529 0.         0.12798529 0.01782753\n",
      " 0.01580583 0.         0.01886422 0.01563488 0.         0.\n",
      " 0.02320162 0.         0.         0.0155431  0.12008757 0.01259557\n",
      " 0.12798529 0.         0.         0.         0.02782921 0.\n",
      " 0.         0.         0.01733064 0.         0.         0.\n",
      " 0.12601904 0.03255938 0.         0.01553185]\n",
      "Generation:  1500  elongt:  222.40294128510192 tRNA list:  [0.0238637  0.         0.10586285 0.         0.         0.\n",
      " 0.01359273 0.01389441 0.12798529 0.         0.12798529 0.01782753\n",
      " 0.01580583 0.         0.01886422 0.01563488 0.         0.\n",
      " 0.02320162 0.         0.         0.0155431  0.12008757 0.01259557\n",
      " 0.12798529 0.         0.         0.         0.02782921 0.\n",
      " 0.         0.         0.01733064 0.         0.         0.\n",
      " 0.12601904 0.03255938 0.         0.01553185]\n",
      "Generation:  1600  elongt:  222.40294128510192 tRNA list:  [0.0238637  0.         0.10586285 0.         0.         0.\n",
      " 0.01359273 0.01389441 0.12798529 0.         0.12798529 0.01782753\n",
      " 0.01580583 0.         0.01886422 0.01563488 0.         0.\n",
      " 0.02320162 0.         0.         0.0155431  0.12008757 0.01259557\n",
      " 0.12798529 0.         0.         0.         0.02782921 0.\n",
      " 0.         0.         0.01733064 0.         0.         0.\n",
      " 0.12601904 0.03255938 0.         0.01553185]\n",
      "Generation:  1700  elongt:  222.40294128510192 tRNA list:  [0.0238637  0.         0.10586285 0.         0.         0.\n",
      " 0.01359273 0.01389441 0.12798529 0.         0.12798529 0.01782753\n",
      " 0.01580583 0.         0.01886422 0.01563488 0.         0.\n",
      " 0.02320162 0.         0.         0.0155431  0.12008757 0.01259557\n",
      " 0.12798529 0.         0.         0.         0.02782921 0.\n",
      " 0.         0.         0.01733064 0.         0.         0.\n",
      " 0.12601904 0.03255938 0.         0.01553185]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  1800  elongt:  222.80357527453845 tRNA list:  [0.12587895 0.         0.02040233 0.         0.         0.\n",
      " 0.012931   0.01430288 0.12653416 0.         0.10853045 0.01750406\n",
      " 0.01929032 0.         0.02444435 0.01560992 0.         0.\n",
      " 0.02103921 0.         0.         0.02237707 0.12679654 0.01324014\n",
      " 0.12510368 0.         0.         0.         0.01829437 0.\n",
      " 0.         0.         0.01482501 0.         0.         0.\n",
      " 0.12653416 0.03103682 0.         0.01532456]\n",
      "Generation:  1900  elongt:  222.80357527453845 tRNA list:  [0.12587895 0.         0.02040233 0.         0.         0.\n",
      " 0.012931   0.01430288 0.12653416 0.         0.10853045 0.01750406\n",
      " 0.01929032 0.         0.02444435 0.01560992 0.         0.\n",
      " 0.02103921 0.         0.         0.02237707 0.12679654 0.01324014\n",
      " 0.12510368 0.         0.         0.         0.01829437 0.\n",
      " 0.         0.         0.01482501 0.         0.         0.\n",
      " 0.12653416 0.03103682 0.         0.01532456]\n",
      "CPU times: user 3min 37s, sys: 3.09 s, total: 3min 40s\n",
      "Wall time: 53min 40s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "gene_map, pCodon_GFP = singlegene_to_genemap('./data/tables/sfGFP_RED20.csv')\n",
    "ptRNA_red20 = pd.read_excel('./data/tables/tRNAValues_RED20.xlsx',header=None)\n",
    "ptRNA_red20 = np.array(ptRNA_red20[6]/sum(ptRNA_red20[6]))\n",
    "tRNA_indices = np.argwhere(ptRNA_red20 == 0)\n",
    "tRNA_indices = [indices[0] for indices in tRNA_indices]\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/minimaltRNA_minimalcode-sfGFP_100sample_elongt.npy')\n",
    "ptRNA_list = np.load('./data/minimaltRNA_minimalcode-sfGFP_100sample_ptRNA.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_GFP,elongt_list,ensmbl_latency_dict,min(ptRNA_red20[np.nonzero(ptRNA_red20)]),max(ptRNA_red20),objective='slow',ptRNA_red20=ptRNA_red20)\n",
    "    fullelongt_list.append(max(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', max(elongt_list),'tRNA list: ',ptRNA_list[np.argmax(elongt_list)])\n",
    "        \n",
    "np.save('./data/UniformRandomtRNA_minimalcode-sfGFP_2ksample_slowtRNA_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_minimalcode-sfGFP_2ksample_slowtRNA_ptRNA',ptRNA_list)\n",
    "np.save('./data/UniformRandomtRNA_minimalcode-sfGFP_2ksample_slowtRNA_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.012213740457866095\n",
      "0.12824427480759398\n"
     ]
    }
   ],
   "source": [
    "print(min(ptRNA_red20[np.nonzero(ptRNA_red20)]))\n",
    "print(max(ptRNA_red20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## minimal uniform random tRNA, minimal code mRFP, 100 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "184.3356414849228\n",
      "CPU times: user 12.4 s, sys: 354 ms, total: 12.7 s\n",
      "Wall time: 22 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "gene_map, pCodon_GFP = singlegene_to_genemap('./data/tables/mRFP_RED20.csv')\n",
    "\n",
    "ptRNA_red20 = pd.read_excel('./data/tables/tRNAValues_RED20.xlsx',header=None)\n",
    "ptRNA_red20 = ptRNA_red20[6]/sum(ptRNA_red20[6])\n",
    "tRNA_indices = np.argwhere(ptRNA_red20 == 0)\n",
    "tRNA_indices = [indices[0] for indices in tRNA_indices]\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "\n",
    "for i in range(100):\n",
    "    ptRNA_uniform = np.random.uniform(min(ptRNA_red20),max(ptRNA_red20),len(ptRNA_red20))\n",
    "    ptRNA_uniform[tRNA_indices] = 0\n",
    "    ptRNA_list.append(ptRNA_uniform)\n",
    "    inputs.append([ptRNA_uniform,pCodon_GFP,ensmbl_latency_dict])\n",
    "elongt_list = Parallel(n_jobs=n_cores,backend='loky')(delayed(computeElongationLatency_multithread)(i) for i in inputs)\n",
    "elongt_list = [elongt[0][0] for elongt in elongt_list]\n",
    "np.save('./data/minimaltRNA_minimalcode-mRFP_100sample_elongt',elongt_list)\n",
    "np.save('./data/minimaltRNA_minimalcode-mRFP_100sample_ptRNA',ptRNA_list)\n",
    "print(np.average(elongt_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: faster tRNA distribution with minimal tRNA, mRFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Generation:  0  elongt:  168.74550105211244\n",
      "Generation:  100  elongt:  160.17600220335808\n",
      "Generation:  200  elongt:  156.65755391893552\n",
      "Generation:  300  elongt:  155.83525207164303\n",
      "Generation:  400  elongt:  155.79226660354004\n",
      "Generation:  500  elongt:  155.31931077776082\n",
      "Generation:  600  elongt:  154.47035931724673\n",
      "Generation:  700  elongt:  154.47035931724673\n",
      "Generation:  800  elongt:  154.34752486159513\n",
      "Generation:  900  elongt:  154.34752486159513\n",
      "Generation:  1000  elongt:  154.34752486159513\n",
      "Generation:  1100  elongt:  153.24877589258833\n",
      "Generation:  1200  elongt:  153.24877589258833\n",
      "Generation:  1300  elongt:  153.24877589258833\n",
      "Generation:  1400  elongt:  153.24877589258833\n",
      "Generation:  1500  elongt:  153.24877589258833\n",
      "Generation:  1600  elongt:  153.24877589258833\n",
      "Generation:  1700  elongt:  153.24877589258833\n",
      "Generation:  1800  elongt:  153.24877589258833\n",
      "Generation:  1900  elongt:  153.24877589258833\n",
      "CPU times: user 25min 43s, sys: 4.44 s, total: 25min 47s\n",
      "Wall time: 1h 25min 32s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/minimaltRNA_minimalcode-mRFP_100sample_elongt.npy')\n",
    "ptRNA_list = np.load('./data/minimaltRNA_minimalcode-mRFP_100sample_ptRNA.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_GFP,elongt_list,ensmbl_latency_dict,min(ptRNA_red20),max(ptRNA_red20),objective='fast',ptRNA_red20=ptRNA_red20)\n",
    "    fullelongt_list.append(min(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', min(elongt_list))\n",
    "        \n",
    "np.save('./data/UniformRandomtRNA_minimalcode-mRFP_2ksample_fasttRNA_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_minimalcode-mRFP_2ksample_fasttRNA_ptRNA',ptRNA_list)\n",
    "np.save('./data/UniformRandomtRNA_minimalcode-mRFP_2ksample_fasttRNA_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: slower tRNA distribution with minimal tRNA, mRFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Generation:  0  elongt:  175.1247041405708\n",
      "Generation:  100  elongt:  211.03438110718938\n",
      "Generation:  200  elongt:  222.29216190704705\n",
      "Generation:  300  elongt:  229.20293389041973\n",
      "Generation:  400  elongt:  222.00841180441049\n",
      "Generation:  500  elongt:  222.42975185073902\n",
      "Generation:  600  elongt:  235.79605392618652\n",
      "Generation:  700  elongt:  232.7473907291243\n",
      "Generation:  800  elongt:  235.0525272570431\n",
      "Generation:  900  elongt:  236.13680000893967\n",
      "Generation:  1000  elongt:  238.68686851934845\n",
      "Generation:  1100  elongt:  233.47878212946296\n",
      "Generation:  1200  elongt:  233.2166650611879\n",
      "Generation:  1300  elongt:  238.0814716424852\n",
      "Generation:  1400  elongt:  235.79969613071896\n",
      "Generation:  1500  elongt:  205.83571058026317\n",
      "Generation:  1600  elongt:  235.51616418980637\n",
      "Generation:  1700  elongt:  240.72484231501417\n",
      "Generation:  1800  elongt:  240.78492448637914\n",
      "Generation:  1900  elongt:  238.60569905800006\n",
      "CPU times: user 26min 19s, sys: 5.38 s, total: 26min 25s\n",
      "Wall time: 1h 27min 36s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/minimaltRNA_minimalcode-mRFP_100sample_elongt.npy')\n",
    "ptRNA_list = np.load('./data/minimaltRNA_minimalcode-mRFP_100sample_ptRNA.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_GFP,elongt_list,ensmbl_latency_dict,min(ptRNA_red20),max(ptRNA_red20),objective='slow',ptRNA_red20=ptRNA_red20)\n",
    "    fullelongt_list.append(min(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', min(elongt_list))\n",
    "        \n",
    "np.save('./data/UniformRandomtRNA_minimalcode-mRFP_2ksample_slowtRNA_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_minimalcode-mRFP_2ksample_slowtRNA_ptRNA',ptRNA_list)\n",
    "np.save('./data/UniformRandomtRNA_minimalcode-mRFP_2ksample_slowtRNA_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08523592085132133"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "gr25_Codon = np.array([2.36, 1.26, 45.55, 34.17, 16.97, 57.86, 19.27, 33.74, 14.98, 22.31, 43.18, 7.67, 24.11, 24.87, 39.49, 11.81, 0.03, 0.63, 2.19, 9.31, 17.22, 55.01, 5.61, 29.21, 21.67, 0.52, 15.79, 43.86, 4.17, 2.61, 20.64, 26.7, 7.03, 0.19, 2.76, 3.81, 6.72, 16.52, 4.27, 2.73, 7.92, 23.25, 2.51, 1.98, 16.33, 11.68, 0.62, 0.67, 43.82, 20.59, 27.28, 7.01, 6.78, 14.21, 60.75, 0.82, 3.86, 4.09, 28.82, 5.18, 4.38, 1.09])\n",
    "gr25_pCodon = gr25_Codon/np.sum(gr25_Codon)\n",
    "gr25_ptRNA = [0.0602409638554217, 0.010542168674698798, 0.07379518072289158, 0.00602409638554217, 0.010542168674698798, 0.00602409638554217, 0.021084337349397596, 0.043674698795180725, 0.021084337349397596, 0.01204819277108434, 0.01807228915662651, 0.08433734939759038, 0.03162650602409639, 0.07228915662650603, 0.01204819277108434, 0.07078313253012049, 0.06325301204819278, 0.01656626506024097, 0.009036144578313254, 0.027108433734939763, 0.010542168674698798, 0.03012048192771085, 0.013554216867469882, 0.015060240963855425, 0.0075301204819277125, 0.010542168674698798, 0.0075301204819277125, 0.003012048192771085, 0.021084337349397596, 0.004518072289156627, 0.01656626506024097, 0.01204819277108434, 0.0015060240963855425, 0.009036144578313254, 0.01656626506024097, 0.019578313253012052, 0.015060240963855425, 0.027108434, 0.05873493975903616, 0.019578313]\n",
    "gr25_ptRNA = gr25_ptRNA/np.sum(gr25_ptRNA)\n",
    "\n",
    "max(gr25_ptRNA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stratified high analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "high\n",
      "Unique transcripts without a Gene identifier:  0\n",
      "Sequences not available for # genes in Ecocyc DB:  1\n",
      "Inconsistency in codons:  0\n",
      "419\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3135\n"
     ]
    }
   ],
   "source": [
    "##Compute the codon distribution and elongation latency of each gene weighted by transcript number\n",
    "codon_elongt = compute_codon_elongt(gr25_ptRNA, len(gr25_pCodon), ensmbl_latency_dict)\n",
    "gene_map, gene_latency = compute_gene_elongt(codon_elongt,stratify='high')\n",
    "pCodon_transcriptome, transcriptome_elongt = compute_transcript_distributions(gene_map,gene_latency)\n",
    "pCodon_transcriptome = np.array(pCodon_transcriptome)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uniform Random tRNA, WT Codon usage, Stratify high, 100 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.02 s, sys: 119 ms, total: 1.14 s\n",
      "Wall time: 5.27 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "\n",
    "for i in range(100):\n",
    "    ptRNA_uniform = np.random.uniform(min(gr25_ptRNA),max(gr25_ptRNA),len(gr25_ptRNA))\n",
    "    ptRNA_list.append(ptRNA_uniform)\n",
    "    inputs.append([ptRNA_uniform,pCodon_transcriptome,ensmbl_latency_dict])\n",
    "elongt_list = Parallel(n_jobs=n_cores,backend='loky')(delayed(computeElongationLatency_multithread)(i) for i in inputs)\n",
    "elongt_list = [elongt[0][0] for elongt in elongt_list]\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifyhigh_100sample_elongt2',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifyhigh_100sample_ptRNA2',ptRNA_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: faster tRNA distributions (Codon usage fixed, WT, stratify high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Generation:  0  elongt:  199.9037713899387 tRNA list:  [0.07048057 0.00810124 0.0708845  0.04052682 0.01879943 0.04622592\n",
      " 0.03639752 0.07123616 0.03365665 0.08446451 0.02645896 0.06893499\n",
      " 0.0043163  0.08501174 0.04892115 0.07454627 0.05838645 0.03267077\n",
      " 0.0269581  0.03428346 0.0314576  0.07401035 0.04732401 0.05245348\n",
      " 0.03653849 0.0025605  0.01924943 0.03019563 0.00161054 0.0057083\n",
      " 0.00441681 0.07017546 0.07708379 0.01479219 0.0197264  0.00488213\n",
      " 0.0139003  0.02128221 0.00572162 0.07019199]\n",
      "Generation:  100  elongt:  186.75780992125803 tRNA list:  [0.06593568 0.04720341 0.04436855 0.00683762 0.00663098 0.01933121\n",
      " 0.03250036 0.04993901 0.02178414 0.00640306 0.01017111 0.0514152\n",
      " 0.00615683 0.05322931 0.00318596 0.05991788 0.00575576 0.00846727\n",
      " 0.06320007 0.0053006  0.00937814 0.02570879 0.01320197 0.04910308\n",
      " 0.0113881  0.00320918 0.00765257 0.02510654 0.00210618 0.01082277\n",
      " 0.00559036 0.00587524 0.05177996 0.00513192 0.04498997 0.03533445\n",
      " 0.00981554 0.01464825 0.06477666 0.03664631]\n",
      "Generation:  200  elongt:  181.8046838082766 tRNA list:  [0.07074674 0.00805761 0.05673375 0.00678198 0.00482818 0.02403193\n",
      " 0.02924744 0.04811928 0.01089757 0.00635095 0.02277995 0.03191643\n",
      " 0.00641574 0.07592589 0.00352054 0.04552381 0.03405305 0.00677905\n",
      " 0.07203205 0.00438867 0.00858906 0.06007248 0.01252493 0.07175961\n",
      " 0.00964689 0.0024383  0.01041518 0.00857634 0.00168836 0.00948906\n",
      " 0.01190157 0.00581779 0.04508465 0.00425795 0.04228245 0.0184742\n",
      " 0.00860596 0.01096619 0.060435   0.02784341]\n",
      "Generation:  300  elongt:  180.27488747867997 tRNA list:  [0.06398222 0.01876588 0.06988068 0.00492249 0.00436653 0.0118467\n",
      " 0.01926161 0.0745411  0.00985559 0.00314126 0.01142078 0.07911113\n",
      " 0.0081101  0.07582769 0.00336273 0.04231437 0.03792244 0.00754934\n",
      " 0.05039097 0.00407918 0.0066229  0.05583605 0.01336522 0.02868679\n",
      " 0.00908471 0.0136901  0.02622439 0.0284643  0.00675181 0.00858176\n",
      " 0.00271966 0.00374648 0.03727705 0.0052195  0.03297232 0.01621882\n",
      " 0.00912877 0.00237713 0.06706492 0.02531455]\n",
      "Generation:  400  elongt:  179.20412991900713 tRNA list:  [0.06222313 0.01475743 0.05452384 0.00478715 0.00424647 0.01114982\n",
      " 0.01873204 0.08030285 0.00958463 0.0030549  0.05149314 0.0566214\n",
      " 0.0054323  0.07240316 0.00309638 0.04375055 0.05482985 0.01074356\n",
      " 0.04900555 0.00396702 0.00644081 0.07372805 0.01023686 0.0337235\n",
      " 0.00883494 0.00266543 0.04279037 0.00787249 0.00152207 0.00834582\n",
      " 0.00433478 0.00364347 0.03625217 0.0039899  0.03206579 0.01577291\n",
      " 0.0080642  0.00231177 0.05808091 0.02461856]\n",
      "Generation:  500  elongt:  179.1733699417057 tRNA list:  [0.07865544 0.01550159 0.05727324 0.01987642 0.00487409 0.01171206\n",
      " 0.04033964 0.05592442 0.02976472 0.00430017 0.00282081 0.08194534\n",
      " 0.00647675 0.07664789 0.00355401 0.0459567  0.00604455 0.00667261\n",
      " 0.05486308 0.00431977 0.00780453 0.06064373 0.01186448 0.00592676\n",
      " 0.00989097 0.00279984 0.04790501 0.0074199  0.00170442 0.00492247\n",
      " 0.01081449 0.00587311 0.03907331 0.00419109 0.03446047 0.01864987\n",
      " 0.00847084 0.03808634 0.06100968 0.01096538]\n",
      "Generation:  600  elongt:  178.62225015264028 tRNA list:  [0.0653207  0.00799678 0.06471118 0.01778788 0.00445787 0.00915787\n",
      " 0.059871   0.0424877  0.00914276 0.00324957 0.02103281 0.07494771\n",
      " 0.00592367 0.07010263 0.00325052 0.04203228 0.00614196 0.00625912\n",
      " 0.07148824 0.00347267 0.00793031 0.07260235 0.00974757 0.00542065\n",
      " 0.01035171 0.00225129 0.02947565 0.00678629 0.00152207 0.00678257\n",
      " 0.01598102 0.00577386 0.04474429 0.01708164 0.06097484 0.01508088\n",
      " 0.00702523 0.01012512 0.05579983 0.02570792]\n",
      "Generation:  700  elongt:  177.958203402506 tRNA list:  [0.08523592 0.00701931 0.06682643 0.00590805 0.00460359 0.00945722\n",
      " 0.06182804 0.06220977 0.00944161 0.00335579 0.01984453 0.07739757\n",
      " 0.0061173  0.07239411 0.00335677 0.04340621 0.02082339 0.00646371\n",
      " 0.06868139 0.00358618 0.00818953 0.05727814 0.01006619 0.01872187\n",
      " 0.01069008 0.00232488 0.03043914 0.00700812 0.00155642 0.00904767\n",
      " 0.0165034  0.00537955 0.03920262 0.0046758  0.02298309 0.01609361\n",
      " 0.00725487 0.01045609 0.05762379 0.02654825]\n",
      "Generation:  800  elongt:  175.61430694843128 tRNA list:  [0.08206052 0.00675781 0.06433686 0.00568795 0.00443208 0.00994328\n",
      " 0.05952468 0.07765896 0.00908987 0.00336249 0.01580149 0.07451418\n",
      " 0.00588941 0.06969712 0.00321541 0.0693407  0.02004763 0.00622291\n",
      " 0.06612271 0.00345258 0.00788443 0.060222   0.01008628 0.00639431\n",
      " 0.01071142 0.00223827 0.02930515 0.00674703 0.00152207 0.00871061\n",
      " 0.01588858 0.00517914 0.03774215 0.00450161 0.02212687 0.01549405\n",
      " 0.00698459 0.01006655 0.05547706 0.02555921]\n",
      "Generation:  900  elongt:  175.61430694843128 tRNA list:  [0.08206052 0.00675781 0.06433686 0.00568795 0.00443208 0.00994328\n",
      " 0.05952468 0.07765896 0.00908987 0.00336249 0.01580149 0.07451418\n",
      " 0.00588941 0.06969712 0.00321541 0.0693407  0.02004763 0.00622291\n",
      " 0.06612271 0.00345258 0.00788443 0.060222   0.01008628 0.00639431\n",
      " 0.01071142 0.00223827 0.02930515 0.00674703 0.00152207 0.00871061\n",
      " 0.01588858 0.00517914 0.03774215 0.00450161 0.02212687 0.01549405\n",
      " 0.00698459 0.01006655 0.05547706 0.02555921]\n",
      "Generation:  1000  elongt:  175.61430694843128 tRNA list:  [0.08206052 0.00675781 0.06433686 0.00568795 0.00443208 0.00994328\n",
      " 0.05952468 0.07765896 0.00908987 0.00336249 0.01580149 0.07451418\n",
      " 0.00588941 0.06969712 0.00321541 0.0693407  0.02004763 0.00622291\n",
      " 0.06612271 0.00345258 0.00788443 0.060222   0.01008628 0.00639431\n",
      " 0.01071142 0.00223827 0.02930515 0.00674703 0.00152207 0.00871061\n",
      " 0.01588858 0.00517914 0.03774215 0.00450161 0.02212687 0.01549405\n",
      " 0.00698459 0.01006655 0.05547706 0.02555921]\n",
      "Generation:  1100  elongt:  175.61430694843128 tRNA list:  [0.08206052 0.00675781 0.06433686 0.00568795 0.00443208 0.00994328\n",
      " 0.05952468 0.07765896 0.00908987 0.00336249 0.01580149 0.07451418\n",
      " 0.00588941 0.06969712 0.00321541 0.0693407  0.02004763 0.00622291\n",
      " 0.06612271 0.00345258 0.00788443 0.060222   0.01008628 0.00639431\n",
      " 0.01071142 0.00223827 0.02930515 0.00674703 0.00152207 0.00871061\n",
      " 0.01588858 0.00517914 0.03774215 0.00450161 0.02212687 0.01549405\n",
      " 0.00698459 0.01006655 0.05547706 0.02555921]\n",
      "Generation:  1200  elongt:  175.61430694843128 tRNA list:  [0.08206052 0.00675781 0.06433686 0.00568795 0.00443208 0.00994328\n",
      " 0.05952468 0.07765896 0.00908987 0.00336249 0.01580149 0.07451418\n",
      " 0.00588941 0.06969712 0.00321541 0.0693407  0.02004763 0.00622291\n",
      " 0.06612271 0.00345258 0.00788443 0.060222   0.01008628 0.00639431\n",
      " 0.01071142 0.00223827 0.02930515 0.00674703 0.00152207 0.00871061\n",
      " 0.01588858 0.00517914 0.03774215 0.00450161 0.02212687 0.01549405\n",
      " 0.00698459 0.01006655 0.05547706 0.02555921]\n",
      "Generation:  1300  elongt:  175.61430694843128 tRNA list:  [0.08206052 0.00675781 0.06433686 0.00568795 0.00443208 0.00994328\n",
      " 0.05952468 0.07765896 0.00908987 0.00336249 0.01580149 0.07451418\n",
      " 0.00588941 0.06969712 0.00321541 0.0693407  0.02004763 0.00622291\n",
      " 0.06612271 0.00345258 0.00788443 0.060222   0.01008628 0.00639431\n",
      " 0.01071142 0.00223827 0.02930515 0.00674703 0.00152207 0.00871061\n",
      " 0.01588858 0.00517914 0.03774215 0.00450161 0.02212687 0.01549405\n",
      " 0.00698459 0.01006655 0.05547706 0.02555921]\n",
      "Generation:  1400  elongt:  175.61430694843128 tRNA list:  [0.08206052 0.00675781 0.06433686 0.00568795 0.00443208 0.00994328\n",
      " 0.05952468 0.07765896 0.00908987 0.00336249 0.01580149 0.07451418\n",
      " 0.00588941 0.06969712 0.00321541 0.0693407  0.02004763 0.00622291\n",
      " 0.06612271 0.00345258 0.00788443 0.060222   0.01008628 0.00639431\n",
      " 0.01071142 0.00223827 0.02930515 0.00674703 0.00152207 0.00871061\n",
      " 0.01588858 0.00517914 0.03774215 0.00450161 0.02212687 0.01549405\n",
      " 0.00698459 0.01006655 0.05547706 0.02555921]\n",
      "Generation:  1500  elongt:  175.61430694843128 tRNA list:  [0.08206052 0.00675781 0.06433686 0.00568795 0.00443208 0.00994328\n",
      " 0.05952468 0.07765896 0.00908987 0.00336249 0.01580149 0.07451418\n",
      " 0.00588941 0.06969712 0.00321541 0.0693407  0.02004763 0.00622291\n",
      " 0.06612271 0.00345258 0.00788443 0.060222   0.01008628 0.00639431\n",
      " 0.01071142 0.00223827 0.02930515 0.00674703 0.00152207 0.00871061\n",
      " 0.01588858 0.00517914 0.03774215 0.00450161 0.02212687 0.01549405\n",
      " 0.00698459 0.01006655 0.05547706 0.02555921]\n",
      "Generation:  1600  elongt:  175.61430694843128 tRNA list:  [0.08206052 0.00675781 0.06433686 0.00568795 0.00443208 0.00994328\n",
      " 0.05952468 0.07765896 0.00908987 0.00336249 0.01580149 0.07451418\n",
      " 0.00588941 0.06969712 0.00321541 0.0693407  0.02004763 0.00622291\n",
      " 0.06612271 0.00345258 0.00788443 0.060222   0.01008628 0.00639431\n",
      " 0.01071142 0.00223827 0.02930515 0.00674703 0.00152207 0.00871061\n",
      " 0.01588858 0.00517914 0.03774215 0.00450161 0.02212687 0.01549405\n",
      " 0.00698459 0.01006655 0.05547706 0.02555921]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  1700  elongt:  175.61430694843128 tRNA list:  [0.08206052 0.00675781 0.06433686 0.00568795 0.00443208 0.00994328\n",
      " 0.05952468 0.07765896 0.00908987 0.00336249 0.01580149 0.07451418\n",
      " 0.00588941 0.06969712 0.00321541 0.0693407  0.02004763 0.00622291\n",
      " 0.06612271 0.00345258 0.00788443 0.060222   0.01008628 0.00639431\n",
      " 0.01071142 0.00223827 0.02930515 0.00674703 0.00152207 0.00871061\n",
      " 0.01588858 0.00517914 0.03774215 0.00450161 0.02212687 0.01549405\n",
      " 0.00698459 0.01006655 0.05547706 0.02555921]\n",
      "Generation:  1800  elongt:  175.61430694843128 tRNA list:  [0.08206052 0.00675781 0.06433686 0.00568795 0.00443208 0.00994328\n",
      " 0.05952468 0.07765896 0.00908987 0.00336249 0.01580149 0.07451418\n",
      " 0.00588941 0.06969712 0.00321541 0.0693407  0.02004763 0.00622291\n",
      " 0.06612271 0.00345258 0.00788443 0.060222   0.01008628 0.00639431\n",
      " 0.01071142 0.00223827 0.02930515 0.00674703 0.00152207 0.00871061\n",
      " 0.01588858 0.00517914 0.03774215 0.00450161 0.02212687 0.01549405\n",
      " 0.00698459 0.01006655 0.05547706 0.02555921]\n",
      "Generation:  1900  elongt:  175.61430694843128 tRNA list:  [0.08206052 0.00675781 0.06433686 0.00568795 0.00443208 0.00994328\n",
      " 0.05952468 0.07765896 0.00908987 0.00336249 0.01580149 0.07451418\n",
      " 0.00588941 0.06969712 0.00321541 0.0693407  0.02004763 0.00622291\n",
      " 0.06612271 0.00345258 0.00788443 0.060222   0.01008628 0.00639431\n",
      " 0.01071142 0.00223827 0.02930515 0.00674703 0.00152207 0.00871061\n",
      " 0.01588858 0.00517914 0.03774215 0.00450161 0.02212687 0.01549405\n",
      " 0.00698459 0.01006655 0.05547706 0.02555921]\n",
      "CPU times: user 2min, sys: 3.22 s, total: 2min 3s\n",
      "Wall time: 17min 11s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifyhigh_100sample_elongt2.npy')\n",
    "ptRNA_list = np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifyhigh_100sample_ptRNA2.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_transcriptome,elongt_list,ensmbl_latency_dict,min(gr25_ptRNA),max(gr25_ptRNA),objective='fast')\n",
    "    fullelongt_list.append(min(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', min(elongt_list),'tRNA list: ', ptRNA_list[np.argmin(elongt_list)])\n",
    "        \n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifyhigh_2ksample_fasttRNA_elongt2',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifyhigh_2ksample_fasttRNA_ptRNA2',ptRNA_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifyhigh_2ksample_fasttRNA_fullelongt2',fullelongt_list)\n",
    "#181.67441493531658\n",
    "#182.98327531311577"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stratified low analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "low\n",
      "Unique transcripts without a Gene identifier:  87\n",
      "Sequences not available for # genes in Ecocyc DB:  15\n",
      "Inconsistency in codons:  0\n",
      "319\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3552\n"
     ]
    }
   ],
   "source": [
    "##Compute the codon distribution and elongation latency of each gene weighted by transcript number\n",
    "codon_elongt = compute_codon_elongt(gr25_ptRNA, len(gr25_pCodon), ensmbl_latency_dict)\n",
    "gene_map, gene_latency = compute_gene_elongt(codon_elongt,stratify='low')\n",
    "pCodon_transcriptome, transcriptome_elongt = compute_transcript_distributions(gene_map,gene_latency)\n",
    "pCodon_transcriptome = np.array(pCodon_transcriptome)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uniform Random tRNA, WT Codon usage, Stratify low, 100 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 972 ms, sys: 107 ms, total: 1.08 s\n",
      "Wall time: 5.18 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "\n",
    "for i in range(100):\n",
    "    ptRNA_uniform = np.random.uniform(min(gr25_ptRNA),max(gr25_ptRNA),len(gr25_ptRNA))\n",
    "    ptRNA_list.append(ptRNA_uniform)\n",
    "    inputs.append([ptRNA_uniform,pCodon_transcriptome,ensmbl_latency_dict])\n",
    "elongt_list = Parallel(n_jobs=n_cores,backend='loky')(delayed(computeElongationLatency_multithread)(i) for i in inputs)\n",
    "elongt_list = [elongt[0][0] for elongt in elongt_list]\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifylow_100sample_elongt2',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifylow_100sample_ptRNA2',ptRNA_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: faster tRNA distributions (Codon usage fixed, WT, stratify low)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Generation:  0  elongt:  205.02217986591748 tRNA list:  [0.05739365 0.02452347 0.05411782 0.0410329  0.06325301 0.06396481\n",
      " 0.05145258 0.08381041 0.05253797 0.02733521 0.04495403 0.02110141\n",
      " 0.07295808 0.08267254 0.01551077 0.03220597 0.01933165 0.03860729\n",
      " 0.07530392 0.04397849 0.02767561 0.08132883 0.02805681 0.04836318\n",
      " 0.03523914 0.04839127 0.03847096 0.011078   0.0150149  0.04141308\n",
      " 0.0735473  0.03765651 0.04584569 0.0170185  0.05367809 0.05410025\n",
      " 0.05104488 0.07819832 0.06618586 0.04712858]\n",
      "Generation:  100  elongt:  192.72839451553105 tRNA list:  [0.06229472 0.01033428 0.03865035 0.05416503 0.00446923 0.00495768\n",
      " 0.05587404 0.06382311 0.00398067 0.00200928 0.00916225 0.07600789\n",
      " 0.01780029 0.03183878 0.00550863 0.00449426 0.00324904 0.01385048\n",
      " 0.0603513  0.00735626 0.00152962 0.02921757 0.00362565 0.01818448\n",
      " 0.01821738 0.02036695 0.03658897 0.00770462 0.00555446 0.01361861\n",
      " 0.04563591 0.01208479 0.04456169 0.00842448 0.03221553 0.03050091\n",
      " 0.05557348 0.02179187 0.04734247 0.01708297]\n",
      "Generation:  200  elongt:  186.55446686951922 tRNA list:  [0.02442853 0.0080463  0.05994585 0.01336846 0.00471938 0.00788451\n",
      " 0.05979355 0.0733893  0.00347443 0.00570592 0.0092599  0.07120658\n",
      " 0.01621771 0.07279068 0.00556733 0.00454215 0.00744746 0.01399809\n",
      " 0.05687607 0.00774061 0.00157802 0.06254184 0.00366429 0.01827203\n",
      " 0.01348442 0.00200905 0.01346397 0.00381069 0.00578789 0.01376375\n",
      " 0.07103111 0.01054791 0.07316333 0.00670249 0.01715514 0.02831975\n",
      " 0.02332116 0.0410877  0.04663195 0.0172607 ]\n",
      "Generation:  300  elongt:  183.9710079831706 tRNA list:  [0.0413629  0.00720887 0.06161085 0.01498248 0.00398753 0.00670585\n",
      " 0.06040715 0.08108017 0.00732284 0.00451269 0.00865999 0.0637956\n",
      " 0.00156359 0.08523592 0.01927334 0.00373726 0.00667235 0.01254121\n",
      " 0.05095658 0.00693499 0.00156359 0.05603267 0.0034269  0.01069714\n",
      " 0.01460226 0.0016975  0.07450363 0.00341409 0.00188875 0.01088547\n",
      " 0.0636384  0.00945012 0.05936222 0.00600491 0.01536969 0.01872565\n",
      " 0.02089396 0.02322472 0.04060192 0.01546425]\n",
      "Generation:  400  elongt:  182.5400698464053 tRNA list:  [0.03831095 0.00667696 0.06791162 0.00927558 0.00391623 0.00560596\n",
      " 0.05464238 0.06089972 0.00294701 0.00417973 0.00702874 0.06947426\n",
      " 0.01307869 0.08047027 0.00461987 0.03106713 0.00618003 0.01161586\n",
      " 0.04510273 0.00642329 0.00152207 0.05189831 0.00317405 0.02842099\n",
      " 0.01352483 0.00166715 0.0690064  0.00316218 0.00174939 0.01008229\n",
      " 0.05894286 0.00875284 0.04605161 0.00556184 0.01423564 0.01519838\n",
      " 0.0193523  0.0413832  0.06256345 0.01432323]\n",
      "Generation:  500  elongt:  182.5400698464053 tRNA list:  [0.03831095 0.00667696 0.06791162 0.00927558 0.00391623 0.00560596\n",
      " 0.05464238 0.06089972 0.00294701 0.00417973 0.00702874 0.06947426\n",
      " 0.01307869 0.08047027 0.00461987 0.03106713 0.00618003 0.01161586\n",
      " 0.04510273 0.00642329 0.00152207 0.05189831 0.00317405 0.02842099\n",
      " 0.01352483 0.00166715 0.0690064  0.00316218 0.00174939 0.01008229\n",
      " 0.05894286 0.00875284 0.04605161 0.00556184 0.01423564 0.01519838\n",
      " 0.0193523  0.0413832  0.06256345 0.01432323]\n",
      "Generation:  600  elongt:  182.5400698464053 tRNA list:  [0.03831095 0.00667696 0.06791162 0.00927558 0.00391623 0.00560596\n",
      " 0.05464238 0.06089972 0.00294701 0.00417973 0.00702874 0.06947426\n",
      " 0.01307869 0.08047027 0.00461987 0.03106713 0.00618003 0.01161586\n",
      " 0.04510273 0.00642329 0.00152207 0.05189831 0.00317405 0.02842099\n",
      " 0.01352483 0.00166715 0.0690064  0.00316218 0.00174939 0.01008229\n",
      " 0.05894286 0.00875284 0.04605161 0.00556184 0.01423564 0.01519838\n",
      " 0.0193523  0.0413832  0.06256345 0.01432323]\n",
      "Generation:  700  elongt:  182.5400698464053 tRNA list:  [0.03831095 0.00667696 0.06791162 0.00927558 0.00391623 0.00560596\n",
      " 0.05464238 0.06089972 0.00294701 0.00417973 0.00702874 0.06947426\n",
      " 0.01307869 0.08047027 0.00461987 0.03106713 0.00618003 0.01161586\n",
      " 0.04510273 0.00642329 0.00152207 0.05189831 0.00317405 0.02842099\n",
      " 0.01352483 0.00166715 0.0690064  0.00316218 0.00174939 0.01008229\n",
      " 0.05894286 0.00875284 0.04605161 0.00556184 0.01423564 0.01519838\n",
      " 0.0193523  0.0413832  0.06256345 0.01432323]\n",
      "Generation:  800  elongt:  182.52086641299152 tRNA list:  [0.04605386 0.00622548 0.06111242 0.01049327 0.00418333 0.00664592\n",
      " 0.07033168 0.0650534  0.00240684 0.00971742 0.00726835 0.0600207\n",
      " 0.0114778  0.07491616 0.00417004 0.00447316 0.00557828 0.01048483\n",
      " 0.06888602 0.0098417  0.00152207 0.05834139 0.00234511 0.05210234\n",
      " 0.01026322 0.00152207 0.05918263 0.00274091 0.00162864 0.00915978\n",
      " 0.05487458 0.00152207 0.05209726 0.00502029 0.04431423 0.00464653\n",
      " 0.04114852 0.00225136 0.03713993 0.01883639]\n",
      "Generation:  900  elongt:  182.52086641299152 tRNA list:  [0.04605386 0.00622548 0.06111242 0.01049327 0.00418333 0.00664592\n",
      " 0.07033168 0.0650534  0.00240684 0.00971742 0.00726835 0.0600207\n",
      " 0.0114778  0.07491616 0.00417004 0.00447316 0.00557828 0.01048483\n",
      " 0.06888602 0.0098417  0.00152207 0.05834139 0.00234511 0.05210234\n",
      " 0.01026322 0.00152207 0.05918263 0.00274091 0.00162864 0.00915978\n",
      " 0.05487458 0.00152207 0.05209726 0.00502029 0.04431423 0.00464653\n",
      " 0.04114852 0.00225136 0.03713993 0.01883639]\n",
      "Generation:  1000  elongt:  182.52086641299152 tRNA list:  [0.04605386 0.00622548 0.06111242 0.01049327 0.00418333 0.00664592\n",
      " 0.07033168 0.0650534  0.00240684 0.00971742 0.00726835 0.0600207\n",
      " 0.0114778  0.07491616 0.00417004 0.00447316 0.00557828 0.01048483\n",
      " 0.06888602 0.0098417  0.00152207 0.05834139 0.00234511 0.05210234\n",
      " 0.01026322 0.00152207 0.05918263 0.00274091 0.00162864 0.00915978\n",
      " 0.05487458 0.00152207 0.05209726 0.00502029 0.04431423 0.00464653\n",
      " 0.04114852 0.00225136 0.03713993 0.01883639]\n",
      "Generation:  1100  elongt:  181.51444627728566 tRNA list:  [0.06324979 0.00713161 0.06215368 0.00952478 0.00358419 0.0055714\n",
      " 0.07713459 0.05904922 0.00269715 0.00382534 0.00278407 0.05042231\n",
      " 0.02261554 0.07170667 0.00316675 0.00377479 0.00613904 0.01277206\n",
      " 0.0625281  0.00893335 0.0015307  0.05297592 0.00212867 0.0199065\n",
      " 0.00968236 0.0015307  0.04199912 0.00266919 0.00378506 0.00222324\n",
      " 0.05394535 0.0120964  0.05032049 0.00434018 0.04542332 0.00394602\n",
      " 0.00309537 0.04564316 0.03821988 0.06577393]\n",
      "Generation:  1200  elongt:  180.20115759154515 tRNA list:  [0.04116604 0.00487301 0.05955201 0.00912609 0.00454037 0.00601343\n",
      " 0.07390584 0.0626484  0.00258425 0.00352413 0.00266754 0.0561836\n",
      " 0.00548756 0.07628348 0.00376935 0.06243696 0.00588207 0.01223744\n",
      " 0.05106787 0.00855941 0.00152207 0.06945627 0.00203956 0.00868825\n",
      " 0.00927706 0.00152207 0.04353404 0.00314364 0.00362662 0.00213017\n",
      " 0.05168726 0.00795924 0.04821414 0.00511191 0.02976953 0.00378085\n",
      " 0.01629817 0.04373259 0.03297697 0.06302072]\n",
      "Generation:  1300  elongt:  180.20115759154515 tRNA list:  [0.04116604 0.00487301 0.05955201 0.00912609 0.00454037 0.00601343\n",
      " 0.07390584 0.0626484  0.00258425 0.00352413 0.00266754 0.0561836\n",
      " 0.00548756 0.07628348 0.00376935 0.06243696 0.00588207 0.01223744\n",
      " 0.05106787 0.00855941 0.00152207 0.06945627 0.00203956 0.00868825\n",
      " 0.00927706 0.00152207 0.04353404 0.00314364 0.00362662 0.00213017\n",
      " 0.05168726 0.00795924 0.04821414 0.00511191 0.02976953 0.00378085\n",
      " 0.01629817 0.04373259 0.03297697 0.06302072]\n",
      "Generation:  1400  elongt:  180.20115759154515 tRNA list:  [0.04116604 0.00487301 0.05955201 0.00912609 0.00454037 0.00601343\n",
      " 0.07390584 0.0626484  0.00258425 0.00352413 0.00266754 0.0561836\n",
      " 0.00548756 0.07628348 0.00376935 0.06243696 0.00588207 0.01223744\n",
      " 0.05106787 0.00855941 0.00152207 0.06945627 0.00203956 0.00868825\n",
      " 0.00927706 0.00152207 0.04353404 0.00314364 0.00362662 0.00213017\n",
      " 0.05168726 0.00795924 0.04821414 0.00511191 0.02976953 0.00378085\n",
      " 0.01629817 0.04373259 0.03297697 0.06302072]\n",
      "Generation:  1500  elongt:  180.20115759154515 tRNA list:  [0.04116604 0.00487301 0.05955201 0.00912609 0.00454037 0.00601343\n",
      " 0.07390584 0.0626484  0.00258425 0.00352413 0.00266754 0.0561836\n",
      " 0.00548756 0.07628348 0.00376935 0.06243696 0.00588207 0.01223744\n",
      " 0.05106787 0.00855941 0.00152207 0.06945627 0.00203956 0.00868825\n",
      " 0.00927706 0.00152207 0.04353404 0.00314364 0.00362662 0.00213017\n",
      " 0.05168726 0.00795924 0.04821414 0.00511191 0.02976953 0.00378085\n",
      " 0.01629817 0.04373259 0.03297697 0.06302072]\n",
      "Generation:  1600  elongt:  180.20115759154515 tRNA list:  [0.04116604 0.00487301 0.05955201 0.00912609 0.00454037 0.00601343\n",
      " 0.07390584 0.0626484  0.00258425 0.00352413 0.00266754 0.0561836\n",
      " 0.00548756 0.07628348 0.00376935 0.06243696 0.00588207 0.01223744\n",
      " 0.05106787 0.00855941 0.00152207 0.06945627 0.00203956 0.00868825\n",
      " 0.00927706 0.00152207 0.04353404 0.00314364 0.00362662 0.00213017\n",
      " 0.05168726 0.00795924 0.04821414 0.00511191 0.02976953 0.00378085\n",
      " 0.01629817 0.04373259 0.03297697 0.06302072]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  1700  elongt:  180.20115759154515 tRNA list:  [0.04116604 0.00487301 0.05955201 0.00912609 0.00454037 0.00601343\n",
      " 0.07390584 0.0626484  0.00258425 0.00352413 0.00266754 0.0561836\n",
      " 0.00548756 0.07628348 0.00376935 0.06243696 0.00588207 0.01223744\n",
      " 0.05106787 0.00855941 0.00152207 0.06945627 0.00203956 0.00868825\n",
      " 0.00927706 0.00152207 0.04353404 0.00314364 0.00362662 0.00213017\n",
      " 0.05168726 0.00795924 0.04821414 0.00511191 0.02976953 0.00378085\n",
      " 0.01629817 0.04373259 0.03297697 0.06302072]\n",
      "Generation:  1800  elongt:  180.20115759154515 tRNA list:  [0.04116604 0.00487301 0.05955201 0.00912609 0.00454037 0.00601343\n",
      " 0.07390584 0.0626484  0.00258425 0.00352413 0.00266754 0.0561836\n",
      " 0.00548756 0.07628348 0.00376935 0.06243696 0.00588207 0.01223744\n",
      " 0.05106787 0.00855941 0.00152207 0.06945627 0.00203956 0.00868825\n",
      " 0.00927706 0.00152207 0.04353404 0.00314364 0.00362662 0.00213017\n",
      " 0.05168726 0.00795924 0.04821414 0.00511191 0.02976953 0.00378085\n",
      " 0.01629817 0.04373259 0.03297697 0.06302072]\n",
      "Generation:  1900  elongt:  180.20115759154515 tRNA list:  [0.04116604 0.00487301 0.05955201 0.00912609 0.00454037 0.00601343\n",
      " 0.07390584 0.0626484  0.00258425 0.00352413 0.00266754 0.0561836\n",
      " 0.00548756 0.07628348 0.00376935 0.06243696 0.00588207 0.01223744\n",
      " 0.05106787 0.00855941 0.00152207 0.06945627 0.00203956 0.00868825\n",
      " 0.00927706 0.00152207 0.04353404 0.00314364 0.00362662 0.00213017\n",
      " 0.05168726 0.00795924 0.04821414 0.00511191 0.02976953 0.00378085\n",
      " 0.01629817 0.04373259 0.03297697 0.06302072]\n",
      "CPU times: user 1min 48s, sys: 2.42 s, total: 1min 50s\n",
      "Wall time: 17min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifylow_100sample_elongt2.npy')\n",
    "ptRNA_list = np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifylow_100sample_ptRNA2.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_transcriptome,elongt_list,ensmbl_latency_dict,min(gr25_ptRNA),max(gr25_ptRNA),objective='fast')\n",
    "    fullelongt_list.append(min(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', min(elongt_list),'tRNA list: ', ptRNA_list[np.argmin(elongt_list)])\n",
    "        \n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifylow_2ksample_fasttRNA_elongt2',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifylow_2ksample_fasttRNA_ptRNA2',ptRNA_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifylow_2ksample_fasttRNA_fullelongt2',fullelongt_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stratified middle analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "middle\n",
      "Unique transcripts without a Gene identifier:  185\n",
      "Sequences not available for # genes in Ecocyc DB:  138\n",
      "Inconsistency in codons:  0\n",
      "3035\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  550\n"
     ]
    }
   ],
   "source": [
    "##Compute the codon distribution and elongation latency of each gene weighted by transcript number\n",
    "codon_elongt = compute_codon_elongt(gr25_ptRNA, len(gr25_pCodon), ensmbl_latency_dict)\n",
    "gene_map, gene_latency = compute_gene_elongt(codon_elongt,stratify='middle')\n",
    "pCodon_transcriptome, transcriptome_elongt = compute_transcript_distributions(gene_map,gene_latency)\n",
    "pCodon_transcriptome = np.array(pCodon_transcriptome)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uniform Random tRNA, WT Codon usage, Stratify middle, 100 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 992 ms, sys: 120 ms, total: 1.11 s\n",
      "Wall time: 4.92 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "\n",
    "for i in range(100):\n",
    "    ptRNA_uniform = np.random.uniform(min(gr25_ptRNA),max(gr25_ptRNA),len(gr25_ptRNA))\n",
    "    ptRNA_list.append(ptRNA_uniform)\n",
    "    inputs.append([ptRNA_uniform,pCodon_transcriptome,ensmbl_latency_dict])\n",
    "elongt_list = Parallel(n_jobs=n_cores,backend='loky')(delayed(computeElongationLatency_multithread)(i) for i in inputs)\n",
    "elongt_list = [elongt[0][0] for elongt in elongt_list]\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifymiddle_100sample_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifymiddle_100sample_ptRNA',ptRNA_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: faster tRNA distributions (Codon usage fixed, WT, stratify middle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Generation:  0  elongt:  205.3754195271518 tRNA list:  [0.02252387 0.04710881 0.04466926 0.00487615 0.01892844 0.00373341\n",
      " 0.03994096 0.04051519 0.03860483 0.00378887 0.00259674 0.03993372\n",
      " 0.0332039  0.03485956 0.04172088 0.03948993 0.0457827  0.0090039\n",
      " 0.01835594 0.01508113 0.02964443 0.0322906  0.00503252 0.02785924\n",
      " 0.02036634 0.01447032 0.02791339 0.01494941 0.00968979 0.03523283\n",
      " 0.01435726 0.00771904 0.02694867 0.03606587 0.03100204 0.03865371\n",
      " 0.01183661 0.00729494 0.03036608 0.03358871]\n",
      "Generation:  100  elongt:  194.01558690722592 tRNA list:  [0.07455976 0.03542896 0.07320964 0.00995677 0.01023491 0.00882236\n",
      " 0.06242049 0.05031413 0.01079745 0.0020487  0.01351142 0.04283526\n",
      " 0.00301156 0.0447745  0.02251528 0.05661246 0.0186842  0.03494189\n",
      " 0.02783951 0.01361468 0.03064923 0.04194094 0.0199993  0.05756731\n",
      " 0.01285794 0.00941409 0.01758992 0.00808338 0.00838646 0.00228525\n",
      " 0.00976225 0.01476069 0.01457158 0.00761609 0.00910374 0.02491972\n",
      " 0.00545812 0.0036589  0.03685719 0.048384  ]\n",
      "Generation:  200  elongt:  190.5440113011135 tRNA list:  [0.06223671 0.06319175 0.04521853 0.0033993  0.00859419 0.00568944\n",
      " 0.06417054 0.06558761 0.00378326 0.00152207 0.04824351 0.04701967\n",
      " 0.00961072 0.04790364 0.01791357 0.06565386 0.00453881 0.00897779\n",
      " 0.03395373 0.0088921  0.02001781 0.02503738 0.04188412 0.03632644\n",
      " 0.00214055 0.0097306  0.07358575 0.00548425 0.00390336 0.00569005\n",
      " 0.00696828 0.0037979  0.01862981 0.00655655 0.02142512 0.03252932\n",
      " 0.00396284 0.00835897 0.03121916 0.02665092]\n",
      "Generation:  300  elongt:  189.93887712967646 tRNA list:  [0.05164576 0.03057156 0.04625007 0.00278201 0.01063372 0.00489458\n",
      " 0.02684765 0.05558862 0.00297963 0.00765082 0.00417836 0.03782648\n",
      " 0.01087218 0.0433303  0.0223721  0.0625103  0.01445729 0.0107054\n",
      " 0.06166734 0.00840108 0.01868344 0.08347027 0.0025326  0.05965173\n",
      " 0.00355076 0.01169949 0.04965479 0.00523118 0.00426338 0.0053775\n",
      " 0.0067871  0.0082097  0.02107507 0.00730942 0.01030398 0.0083332\n",
      " 0.01637759 0.05913749 0.05363015 0.04855591]\n",
      "Generation:  400  elongt:  189.2487400474151 tRNA list:  [0.06021252 0.05661013 0.0410514  0.00307701 0.01018498 0.00530766\n",
      " 0.07684473 0.06361999 0.0035294  0.00152939 0.02758723 0.06441988\n",
      " 0.01018055 0.07248381 0.01975433 0.05519595 0.01276564 0.02689142\n",
      " 0.05906503 0.00969574 0.02118681 0.00886612 0.00224793 0.0607456\n",
      " 0.00199691 0.03581101 0.04384467 0.0048984  0.00399217 0.00515057\n",
      " 0.00370855 0.00908024 0.00182747 0.00593492 0.0083682  0.00780309\n",
      " 0.00367866 0.00601295 0.05015914 0.03467982]\n",
      "Generation:  500  elongt:  185.63363451379908 tRNA list:  [0.06978386 0.01780315 0.07351822 0.00283405 0.00901514 0.00538358\n",
      " 0.05048371 0.06171911 0.01751825 0.00155039 0.02131926 0.08051607\n",
      " 0.01059385 0.0763974  0.02012184 0.08051607 0.03287799 0.00983652\n",
      " 0.05228085 0.01008937 0.00783874 0.00903107 0.00228008 0.0168926\n",
      " 0.00195174 0.02323687 0.04838366 0.00478761 0.00415424 0.00535967\n",
      " 0.00460291 0.01425584 0.01904627 0.00617586 0.00770747 0.00718698\n",
      " 0.00373127 0.00553819 0.06172509 0.04195516]\n",
      "Generation:  600  elongt:  184.53768432019356 tRNA list:  [0.05735302 0.01809054 0.07552399 0.00312649 0.00990923 0.00477387\n",
      " 0.03085477 0.06059867 0.00327274 0.00162727 0.02481276 0.08179683\n",
      " 0.00915668 0.05141511 0.02650048 0.072703   0.00842777 0.00434786\n",
      " 0.05906069 0.00872063 0.02061319 0.0545971  0.00202185 0.05463636\n",
      " 0.00199677 0.00751451 0.06074016 0.00476578 0.00359067 0.00463257\n",
      " 0.00376819 0.00381663 0.01886468 0.00533804 0.00814164 0.00701833\n",
      " 0.00636395 0.00585016 0.05771098 0.05594603]\n",
      "Generation:  700  elongt:  184.53768432019356 tRNA list:  [0.05735302 0.01809054 0.07552399 0.00312649 0.00990923 0.00477387\n",
      " 0.03085477 0.06059867 0.00327274 0.00162727 0.02481276 0.08179683\n",
      " 0.00915668 0.05141511 0.02650048 0.072703   0.00842777 0.00434786\n",
      " 0.05906069 0.00872063 0.02061319 0.0545971  0.00202185 0.05463636\n",
      " 0.00199677 0.00751451 0.06074016 0.00476578 0.00359067 0.00463257\n",
      " 0.00376819 0.00381663 0.01886468 0.00533804 0.00814164 0.00701833\n",
      " 0.00636395 0.00585016 0.05771098 0.05594603]\n",
      "Generation:  800  elongt:  184.53768432019356 tRNA list:  [0.05735302 0.01809054 0.07552399 0.00312649 0.00990923 0.00477387\n",
      " 0.03085477 0.06059867 0.00327274 0.00162727 0.02481276 0.08179683\n",
      " 0.00915668 0.05141511 0.02650048 0.072703   0.00842777 0.00434786\n",
      " 0.05906069 0.00872063 0.02061319 0.0545971  0.00202185 0.05463636\n",
      " 0.00199677 0.00751451 0.06074016 0.00476578 0.00359067 0.00463257\n",
      " 0.00376819 0.00381663 0.01886468 0.00533804 0.00814164 0.00701833\n",
      " 0.00636395 0.00585016 0.05771098 0.05594603]\n",
      "Generation:  900  elongt:  183.68492602445076 tRNA list:  [0.05423904 0.01492918 0.06395422 0.00258013 0.00755982 0.00393962\n",
      " 0.0423341  0.06537906 0.00295681 0.00152207 0.02375197 0.07257699\n",
      " 0.00227021 0.07352189 0.02052933 0.07400527 0.02085004 0.0087778\n",
      " 0.0579917  0.00787879 0.01669798 0.04505613 0.00196527 0.04689317\n",
      " 0.00337189 0.00372935 0.06314853 0.00363585 0.00365499 0.00382302\n",
      " 0.01293354 0.00340395 0.01704358 0.00510983 0.0193899  0.00672436\n",
      " 0.00312563 0.00566769 0.06730068 0.04577662]\n",
      "Generation:  1000  elongt:  183.68492602445076 tRNA list:  [0.05423904 0.01492918 0.06395422 0.00258013 0.00755982 0.00393962\n",
      " 0.0423341  0.06537906 0.00295681 0.00152207 0.02375197 0.07257699\n",
      " 0.00227021 0.07352189 0.02052933 0.07400527 0.02085004 0.0087778\n",
      " 0.0579917  0.00787879 0.01669798 0.04505613 0.00196527 0.04689317\n",
      " 0.00337189 0.00372935 0.06314853 0.00363585 0.00365499 0.00382302\n",
      " 0.01293354 0.00340395 0.01704358 0.00510983 0.0193899  0.00672436\n",
      " 0.00312563 0.00566769 0.06730068 0.04577662]\n",
      "Generation:  1100  elongt:  183.68492602445076 tRNA list:  [0.05423904 0.01492918 0.06395422 0.00258013 0.00755982 0.00393962\n",
      " 0.0423341  0.06537906 0.00295681 0.00152207 0.02375197 0.07257699\n",
      " 0.00227021 0.07352189 0.02052933 0.07400527 0.02085004 0.0087778\n",
      " 0.0579917  0.00787879 0.01669798 0.04505613 0.00196527 0.04689317\n",
      " 0.00337189 0.00372935 0.06314853 0.00363585 0.00365499 0.00382302\n",
      " 0.01293354 0.00340395 0.01704358 0.00510983 0.0193899  0.00672436\n",
      " 0.00312563 0.00566769 0.06730068 0.04577662]\n",
      "Generation:  1200  elongt:  183.68492602445076 tRNA list:  [0.05423904 0.01492918 0.06395422 0.00258013 0.00755982 0.00393962\n",
      " 0.0423341  0.06537906 0.00295681 0.00152207 0.02375197 0.07257699\n",
      " 0.00227021 0.07352189 0.02052933 0.07400527 0.02085004 0.0087778\n",
      " 0.0579917  0.00787879 0.01669798 0.04505613 0.00196527 0.04689317\n",
      " 0.00337189 0.00372935 0.06314853 0.00363585 0.00365499 0.00382302\n",
      " 0.01293354 0.00340395 0.01704358 0.00510983 0.0193899  0.00672436\n",
      " 0.00312563 0.00566769 0.06730068 0.04577662]\n",
      "Generation:  1300  elongt:  183.47354917246565 tRNA list:  [0.06583396 0.01477709 0.07702935 0.00255385 0.0019416  0.00389949\n",
      " 0.04190283 0.06471302 0.00277563 0.00152207 0.02351    0.06681497\n",
      " 0.00790421 0.07277289 0.02032019 0.07325135 0.02063763 0.00868838\n",
      " 0.05243144 0.00310594 0.01652787 0.04459713 0.00215102 0.04641545\n",
      " 0.00179475 0.0166834  0.0457737  0.00359881 0.00902913 0.00462889\n",
      " 0.01280179 0.00336927 0.01540943 0.00484188 0.01919237 0.00665585\n",
      " 0.00309378 0.00512427 0.06661506 0.04531027]\n",
      "Generation:  1400  elongt:  183.47354917246565 tRNA list:  [0.06583396 0.01477709 0.07702935 0.00255385 0.0019416  0.00389949\n",
      " 0.04190283 0.06471302 0.00277563 0.00152207 0.02351    0.06681497\n",
      " 0.00790421 0.07277289 0.02032019 0.07325135 0.02063763 0.00868838\n",
      " 0.05243144 0.00310594 0.01652787 0.04459713 0.00215102 0.04641545\n",
      " 0.00179475 0.0166834  0.0457737  0.00359881 0.00902913 0.00462889\n",
      " 0.01280179 0.00336927 0.01540943 0.00484188 0.01919237 0.00665585\n",
      " 0.00309378 0.00512427 0.06661506 0.04531027]\n",
      "Generation:  1500  elongt:  183.47354917246565 tRNA list:  [0.06583396 0.01477709 0.07702935 0.00255385 0.0019416  0.00389949\n",
      " 0.04190283 0.06471302 0.00277563 0.00152207 0.02351    0.06681497\n",
      " 0.00790421 0.07277289 0.02032019 0.07325135 0.02063763 0.00868838\n",
      " 0.05243144 0.00310594 0.01652787 0.04459713 0.00215102 0.04641545\n",
      " 0.00179475 0.0166834  0.0457737  0.00359881 0.00902913 0.00462889\n",
      " 0.01280179 0.00336927 0.01540943 0.00484188 0.01919237 0.00665585\n",
      " 0.00309378 0.00512427 0.06661506 0.04531027]\n",
      "Generation:  1600  elongt:  183.47354917246565 tRNA list:  [0.06583396 0.01477709 0.07702935 0.00255385 0.0019416  0.00389949\n",
      " 0.04190283 0.06471302 0.00277563 0.00152207 0.02351    0.06681497\n",
      " 0.00790421 0.07277289 0.02032019 0.07325135 0.02063763 0.00868838\n",
      " 0.05243144 0.00310594 0.01652787 0.04459713 0.00215102 0.04641545\n",
      " 0.00179475 0.0166834  0.0457737  0.00359881 0.00902913 0.00462889\n",
      " 0.01280179 0.00336927 0.01540943 0.00484188 0.01919237 0.00665585\n",
      " 0.00309378 0.00512427 0.06661506 0.04531027]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  1700  elongt:  183.47354917246565 tRNA list:  [0.06583396 0.01477709 0.07702935 0.00255385 0.0019416  0.00389949\n",
      " 0.04190283 0.06471302 0.00277563 0.00152207 0.02351    0.06681497\n",
      " 0.00790421 0.07277289 0.02032019 0.07325135 0.02063763 0.00868838\n",
      " 0.05243144 0.00310594 0.01652787 0.04459713 0.00215102 0.04641545\n",
      " 0.00179475 0.0166834  0.0457737  0.00359881 0.00902913 0.00462889\n",
      " 0.01280179 0.00336927 0.01540943 0.00484188 0.01919237 0.00665585\n",
      " 0.00309378 0.00512427 0.06661506 0.04531027]\n",
      "Generation:  1800  elongt:  183.1331048154038 tRNA list:  [0.07239488 0.0041974  0.07119533 0.00292975 0.0080727  0.0041129\n",
      " 0.0733338  0.07465469 0.00315385 0.00166446 0.02341311 0.07788409\n",
      " 0.00164793 0.08284207 0.02344192 0.06591361 0.00858288 0.0055972\n",
      " 0.07986374 0.00821771 0.00171869 0.02106108 0.00221083 0.01539289\n",
      " 0.01473594 0.0102677  0.05167095 0.00415168 0.00415025 0.00436541\n",
      " 0.01454958 0.00382926 0.02329484 0.01104749 0.00744879 0.00780666\n",
      " 0.00356907 0.0053798  0.0572263  0.04300878]\n",
      "Generation:  1900  elongt:  183.0315347914841 tRNA list:  [0.08330298 0.02262602 0.05247852 0.00284563 0.00508024 0.00418213\n",
      " 0.05935428 0.06940346 0.00295236 0.00160213 0.00311816 0.0748714\n",
      " 0.00830723 0.0812795  0.02289815 0.0593226  0.00821651 0.00578944\n",
      " 0.08077764 0.00786692 0.00166396 0.03773531 0.00232117 0.07492502\n",
      " 0.01851097 0.00407667 0.03987621 0.00405538 0.00243544 0.00417906\n",
      " 0.0092015  0.00372096 0.01760802 0.00558571 0.00723811 0.00721633\n",
      " 0.00254467 0.00544505 0.05458759 0.04079757]\n",
      "CPU times: user 1min 50s, sys: 5.47 s, total: 1min 56s\n",
      "Wall time: 32min 1s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifymiddle_100sample_elongt.npy')\n",
    "ptRNA_list = np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifymiddle_100sample_ptRNA.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_transcriptome,elongt_list,ensmbl_latency_dict,min(gr25_ptRNA),max(gr25_ptRNA),objective='fast')\n",
    "    fullelongt_list.append(min(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', min(elongt_list),'tRNA list: ', ptRNA_list[np.argmin(elongt_list)])\n",
    "        \n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifymiddle_2ksample_fasttRNA_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifymiddle_2ksample_fasttRNA_ptRNA',ptRNA_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifymiddle_2ksample_fasttRNA_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stratified highest analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  0\n",
      "Sequences not available for # genes in Ecocyc DB:  0\n",
      "Inconsistency in codons:  0\n",
      "1\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3553\n"
     ]
    }
   ],
   "source": [
    "##Compute the codon distribution and elongation latency of each gene weighted by transcript number\n",
    "codon_elongt = compute_codon_elongt(gr25_ptRNA, len(gr25_pCodon), ensmbl_latency_dict)\n",
    "gene_map, gene_latency = compute_gene_elongt(codon_elongt,stratify='highest')\n",
    "pCodon_transcriptome, transcriptome_elongt = compute_transcript_distributions(gene_map,gene_latency)\n",
    "pCodon_transcriptome = np.array(pCodon_transcriptome)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uniform Random tRNA, WT Codon usage, Stratify highest, 100 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.02 s, sys: 116 ms, total: 1.13 s\n",
      "Wall time: 4.96 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "elongt_list = list()\n",
    "ptRNA_list = list()\n",
    "inputs = list()\n",
    "\n",
    "for i in range(100):\n",
    "    ptRNA_uniform = np.random.uniform(min(gr25_ptRNA),max(gr25_ptRNA),len(gr25_ptRNA))\n",
    "    ptRNA_list.append(ptRNA_uniform)\n",
    "    inputs.append([ptRNA_uniform,pCodon_transcriptome,ensmbl_latency_dict])\n",
    "elongt_list = Parallel(n_jobs=n_cores,backend='loky')(delayed(computeElongationLatency_multithread)(i) for i in inputs)\n",
    "elongt_list = [elongt[0][0] for elongt in elongt_list]\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_100sample_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_100sample_ptRNA',ptRNA_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic algorithm: faster tRNA distributions (Codon usage fixed, WT, stratify fastest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Generation:  0  elongt:  200.8479691584339 tRNA list:  [0.06533194 0.01021503 0.04558136 0.04064659 0.01063363 0.02416097\n",
      " 0.0541582  0.08449833 0.06131693 0.00766698 0.03732016 0.07882316\n",
      " 0.00881026 0.08344595 0.07381428 0.02091468 0.03642972 0.06425687\n",
      " 0.05564017 0.04036498 0.07460494 0.02017291 0.06869501 0.07061022\n",
      " 0.01502535 0.02198949 0.07569388 0.01776585 0.00162098 0.01170162\n",
      " 0.04320962 0.05502687 0.04550927 0.04011707 0.0531998  0.08120076\n",
      " 0.02187229 0.0127736  0.04858387 0.0731898 ]\n",
      "Generation:  100  elongt:  176.61027935870848 tRNA list:  [0.07253325 0.00389356 0.03342516 0.00544211 0.03430507 0.02325614\n",
      " 0.05690058 0.05271788 0.00910503 0.02959502 0.01938204 0.01466569\n",
      " 0.00531966 0.06703732 0.01104767 0.06690533 0.00184519 0.00326017\n",
      " 0.03514793 0.00771885 0.01982972 0.03253043 0.02603628 0.06135471\n",
      " 0.00288682 0.00707298 0.01167688 0.00492644 0.01037277 0.00157513\n",
      " 0.00243007 0.0219357  0.03987281 0.02416707 0.03737572 0.00620004\n",
      " 0.01370883 0.03929983 0.03870886 0.04453525]\n",
      "Generation:  200  elongt:  171.70462360963197 tRNA list:  [0.07050236 0.00405968 0.01088957 0.00657186 0.00298511 0.01702234\n",
      " 0.06170267 0.07588851 0.00800942 0.03085769 0.02020897 0.00507909\n",
      " 0.00541167 0.07062925 0.00292493 0.06975986 0.04024836 0.00341092\n",
      " 0.01372268 0.00897207 0.01611489 0.06787096 0.00284168 0.07564745\n",
      " 0.00359263 0.00486476 0.01128435 0.01283444 0.01081532 0.00158701\n",
      " 0.01540218 0.01359289 0.0129434  0.02519816 0.02412997 0.00623286\n",
      " 0.01434274 0.05838973 0.05183443 0.04162315]\n",
      "Generation:  300  elongt:  169.86289670762733 tRNA list:  [0.07067935 0.00429851 0.01241991 0.00663538 0.00311541 0.01941455\n",
      " 0.06393318 0.08346979 0.00835904 0.00587034 0.00599612 0.00446814\n",
      " 0.0056479  0.08055502 0.00295321 0.0767117  0.04204649 0.00344389\n",
      " 0.01432169 0.0092964  0.00529472 0.04293311 0.0248946  0.07637862\n",
      " 0.00341993 0.00504062 0.01287018 0.00770556 0.00583446 0.00164277\n",
      " 0.00279339 0.01408426 0.03485534 0.01546837 0.03711942 0.00645817\n",
      " 0.02806966 0.06050048 0.06760108 0.02339925]\n",
      "Generation:  400  elongt:  169.14283037821338 tRNA list:  [0.06967872 0.00388932 0.01076235 0.00369393 0.00304992 0.01682348\n",
      " 0.06779543 0.08523592 0.00818334 0.00609473 0.03895816 0.02232657\n",
      " 0.00534845 0.06980413 0.00289076 0.07385634 0.01925984 0.0033494\n",
      " 0.01066863 0.0115789  0.0050503  0.06156419 0.00292218 0.0747637\n",
      " 0.00355066 0.00480793 0.01235917 0.0126845  0.0079379  0.00204149\n",
      " 0.00242058 0.01343409 0.01270994 0.00431017 0.04940375 0.01367477\n",
      " 0.01417518 0.05242824 0.05122888 0.06528405]\n",
      "Generation:  500  elongt:  168.0933203656842 tRNA list:  [0.07631698 0.00397914 0.02047223 0.00804102 0.00311676 0.0177731\n",
      " 0.08449509 0.08378379 0.00732598 0.00711392 0.00980506 0.02397039\n",
      " 0.01978868 0.08011823 0.00916383 0.06800879 0.01778081 0.00445586\n",
      " 0.06062519 0.01103821 0.00533537 0.01258502 0.00336006 0.07898382\n",
      " 0.00703535 0.00506673 0.01178204 0.01450787 0.00818462 0.00194616\n",
      " 0.00255722 0.01461877 0.02301027 0.00700312 0.04684398 0.01724067\n",
      " 0.00623542 0.05294869 0.02551028 0.03807145]\n",
      "Generation:  600  elongt:  167.51549529817885 tRNA list:  [0.08190028 0.00444121 0.01379749 0.00380627 0.00181958 0.01694279\n",
      " 0.08122372 0.08110193 0.00791855 0.00641344 0.01059815 0.06025489\n",
      " 0.00538638 0.08233801 0.00376578 0.00506673 0.01984558 0.00342241\n",
      " 0.06344334 0.01193105 0.00576693 0.08145776 0.00351626 0.03810457\n",
      " 0.00373633 0.00543741 0.00814987 0.01457664 0.00747144 0.00210357\n",
      " 0.00276406 0.01580122 0.01465204 0.02691238 0.04782827 0.00557486\n",
      " 0.01427571 0.05811123 0.037191   0.04115088]\n",
      "Generation:  700  elongt:  165.7583287039614 tRNA list:  [0.06489114 0.00373309 0.0108148  0.00319939 0.00301537 0.00321522\n",
      " 0.07927032 0.07186034 0.0060338  0.0058499  0.00807562 0.00504422\n",
      " 0.00465372 0.08523592 0.00263275 0.04715244 0.04065639 0.00287673\n",
      " 0.05508074 0.01002872 0.01600425 0.06109099 0.00276681 0.04768203\n",
      " 0.00660032 0.01348485 0.00454836 0.0116266  0.0074361  0.0016223\n",
      " 0.00232335 0.01328182 0.05882222 0.02050679 0.03932794 0.01419971\n",
      " 0.00950579 0.05123186 0.07209677 0.03252052]\n",
      "Generation:  800  elongt:  164.4598000114116 tRNA list:  [0.0770652  0.00496446 0.00971835 0.00326983 0.00308176 0.00329916\n",
      " 0.08101559 0.08523592 0.00598117 0.00777952 0.00800517 0.07588172\n",
      " 0.0273719  0.08444145 0.00298069 0.00566369 0.01606354 0.00343445\n",
      " 0.05651866 0.01024952 0.00644638 0.03788005 0.00313247 0.06807126\n",
      " 0.00674564 0.00168529 0.00450869 0.01682967 0.00739212 0.00700378\n",
      " 0.002384   0.00924114 0.01409991 0.02321192 0.01985127 0.01407585\n",
      " 0.00907507 0.07629251 0.06681473 0.03323651]\n",
      "Generation:  900  elongt:  164.4598000114116 tRNA list:  [0.0770652  0.00496446 0.00971835 0.00326983 0.00308176 0.00329916\n",
      " 0.08101559 0.08523592 0.00598117 0.00777952 0.00800517 0.07588172\n",
      " 0.0273719  0.08444145 0.00298069 0.00566369 0.01606354 0.00343445\n",
      " 0.05651866 0.01024952 0.00644638 0.03788005 0.00313247 0.06807126\n",
      " 0.00674564 0.00168529 0.00450869 0.01682967 0.00739212 0.00700378\n",
      " 0.002384   0.00924114 0.01409991 0.02321192 0.01985127 0.01407585\n",
      " 0.00907507 0.07629251 0.06681473 0.03323651]\n",
      "Generation:  1000  elongt:  163.8214899798814 tRNA list:  [0.08235055 0.00453968 0.0091871  0.00277523 0.00282934 0.00301687\n",
      " 0.08505906 0.08262333 0.00706063 0.00800097 0.01055665 0.00597755\n",
      " 0.00550848 0.08523592 0.00311698 0.05735037 0.01518543 0.0034983\n",
      " 0.05812753 0.00869917 0.0058948  0.05952185 0.00277545 0.04557891\n",
      " 0.00708231 0.00826981 0.00463703 0.01510476 0.00675962 0.00640451\n",
      " 0.00245186 0.01342141 0.01582536 0.00534518 0.04406667 0.0168078\n",
      " 0.01891024 0.07846427 0.08523592 0.01674311]\n",
      "Generation:  1100  elongt:  163.8214899798814 tRNA list:  [0.08235055 0.00453968 0.0091871  0.00277523 0.00282934 0.00301687\n",
      " 0.08505906 0.08262333 0.00706063 0.00800097 0.01055665 0.00597755\n",
      " 0.00550848 0.08523592 0.00311698 0.05735037 0.01518543 0.0034983\n",
      " 0.05812753 0.00869917 0.0058948  0.05952185 0.00277545 0.04557891\n",
      " 0.00708231 0.00826981 0.00463703 0.01510476 0.00675962 0.00640451\n",
      " 0.00245186 0.01342141 0.01582536 0.00534518 0.04406667 0.0168078\n",
      " 0.01891024 0.07846427 0.08523592 0.01674311]\n",
      "Generation:  1200  elongt:  163.8214899798814 tRNA list:  [0.08235055 0.00453968 0.0091871  0.00277523 0.00282934 0.00301687\n",
      " 0.08505906 0.08262333 0.00706063 0.00800097 0.01055665 0.00597755\n",
      " 0.00550848 0.08523592 0.00311698 0.05735037 0.01518543 0.0034983\n",
      " 0.05812753 0.00869917 0.0058948  0.05952185 0.00277545 0.04557891\n",
      " 0.00708231 0.00826981 0.00463703 0.01510476 0.00675962 0.00640451\n",
      " 0.00245186 0.01342141 0.01582536 0.00534518 0.04406667 0.0168078\n",
      " 0.01891024 0.07846427 0.08523592 0.01674311]\n",
      "Generation:  1300  elongt:  163.8214899798814 tRNA list:  [0.08235055 0.00453968 0.0091871  0.00277523 0.00282934 0.00301687\n",
      " 0.08505906 0.08262333 0.00706063 0.00800097 0.01055665 0.00597755\n",
      " 0.00550848 0.08523592 0.00311698 0.05735037 0.01518543 0.0034983\n",
      " 0.05812753 0.00869917 0.0058948  0.05952185 0.00277545 0.04557891\n",
      " 0.00708231 0.00826981 0.00463703 0.01510476 0.00675962 0.00640451\n",
      " 0.00245186 0.01342141 0.01582536 0.00534518 0.04406667 0.0168078\n",
      " 0.01891024 0.07846427 0.08523592 0.01674311]\n",
      "Generation:  1400  elongt:  162.71428469850076 tRNA list:  [0.08206257 0.00414204 0.00484458 0.00287327 0.00478962 0.00275261\n",
      " 0.07513728 0.07538617 0.00703594 0.00671008 0.00853641 0.06545035\n",
      " 0.00746809 0.08269352 0.00330518 0.0097945  0.0138553  0.00354351\n",
      " 0.04092881 0.00900648 0.00537846 0.0543082  0.00261354 0.04616799\n",
      " 0.00429104 0.00507114 0.00469696 0.00533411 0.00788267 0.00584352\n",
      " 0.00205627 0.01280908 0.05596698 0.0193666  0.04607284 0.01972131\n",
      " 0.00757168 0.08523592 0.05574609 0.04354929]\n",
      "Generation:  1500  elongt:  161.8932194267041 tRNA list:  [0.08088572 0.00532297 0.00917371 0.00328306 0.00355354 0.00890304\n",
      " 0.08523592 0.08523592 0.0064131  0.00834132 0.00858327 0.08217277\n",
      " 0.0043546  0.07679413 0.00319594 0.00607269 0.01722357 0.0041019\n",
      " 0.05385432 0.01020014 0.0069119  0.04061555 0.00335868 0.07363719\n",
      " 0.00729577 0.00180699 0.00483428 0.01804502 0.00792594 0.0015431\n",
      " 0.00255616 0.0128794  0.03579539 0.00601138 0.03513247 0.01509234\n",
      " 0.00973043 0.08180197 0.02406223 0.0480622 ]\n",
      "Generation:  1600  elongt:  161.8932194267041 tRNA list:  [0.08088572 0.00532297 0.00917371 0.00328306 0.00355354 0.00890304\n",
      " 0.08523592 0.08523592 0.0064131  0.00834132 0.00858327 0.08217277\n",
      " 0.0043546  0.07679413 0.00319594 0.00607269 0.01722357 0.0041019\n",
      " 0.05385432 0.01020014 0.0069119  0.04061555 0.00335868 0.07363719\n",
      " 0.00729577 0.00180699 0.00483428 0.01804502 0.00792594 0.0015431\n",
      " 0.00255616 0.0128794  0.03579539 0.00601138 0.03513247 0.01509234\n",
      " 0.00973043 0.08180197 0.02406223 0.0480622 ]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation:  1700  elongt:  161.8932194267041 tRNA list:  [0.08088572 0.00532297 0.00917371 0.00328306 0.00355354 0.00890304\n",
      " 0.08523592 0.08523592 0.0064131  0.00834132 0.00858327 0.08217277\n",
      " 0.0043546  0.07679413 0.00319594 0.00607269 0.01722357 0.0041019\n",
      " 0.05385432 0.01020014 0.0069119  0.04061555 0.00335868 0.07363719\n",
      " 0.00729577 0.00180699 0.00483428 0.01804502 0.00792594 0.0015431\n",
      " 0.00255616 0.0128794  0.03579539 0.00601138 0.03513247 0.01509234\n",
      " 0.00973043 0.08180197 0.02406223 0.0480622 ]\n",
      "Generation:  1800  elongt:  161.8932194267041 tRNA list:  [0.08088572 0.00532297 0.00917371 0.00328306 0.00355354 0.00890304\n",
      " 0.08523592 0.08523592 0.0064131  0.00834132 0.00858327 0.08217277\n",
      " 0.0043546  0.07679413 0.00319594 0.00607269 0.01722357 0.0041019\n",
      " 0.05385432 0.01020014 0.0069119  0.04061555 0.00335868 0.07363719\n",
      " 0.00729577 0.00180699 0.00483428 0.01804502 0.00792594 0.0015431\n",
      " 0.00255616 0.0128794  0.03579539 0.00601138 0.03513247 0.01509234\n",
      " 0.00973043 0.08180197 0.02406223 0.0480622 ]\n",
      "Generation:  1900  elongt:  161.8932194267041 tRNA list:  [0.08088572 0.00532297 0.00917371 0.00328306 0.00355354 0.00890304\n",
      " 0.08523592 0.08523592 0.0064131  0.00834132 0.00858327 0.08217277\n",
      " 0.0043546  0.07679413 0.00319594 0.00607269 0.01722357 0.0041019\n",
      " 0.05385432 0.01020014 0.0069119  0.04061555 0.00335868 0.07363719\n",
      " 0.00729577 0.00180699 0.00483428 0.01804502 0.00792594 0.0015431\n",
      " 0.00255616 0.0128794  0.03579539 0.00601138 0.03513247 0.01509234\n",
      " 0.00973043 0.08180197 0.02406223 0.0480622 ]\n",
      "CPU times: user 1min 48s, sys: 3.68 s, total: 1min 52s\n",
      "Wall time: 15min 47s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from analysis_utils import *\n",
    "\n",
    "### Codon array fixed\n",
    "elongt_list = np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_100sample_elongt.npy')\n",
    "ptRNA_list = np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_100sample_ptRNA.npy')\n",
    "fullelongt_list = list()\n",
    "\n",
    "for i in range(2000):\n",
    "    fitness, ptRNA_list, elongt_list = run_ga_tRNA(ptRNA_list,pCodon_transcriptome,elongt_list,ensmbl_latency_dict,min(gr25_ptRNA),max(gr25_ptRNA),objective='fast')\n",
    "    fullelongt_list.append(min(elongt_list))\n",
    "    if i%100 == 0:\n",
    "        print('Generation: ',i, ' elongt: ', min(elongt_list),'tRNA list: ', ptRNA_list[np.argmin(elongt_list)])\n",
    "        \n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_2ksample_fasttRNA_elongt',elongt_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_2ksample_fasttRNA_ptRNA',ptRNA_list)\n",
    "np.save('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_2ksample_fasttRNA_fullelongt',fullelongt_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}