minimal_code_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "wicked-reward",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tskit\n",
    "import msprime\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "handmade-mambo",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import sleepy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "growing-puppy",
   "metadata": {},
   "source": [
    "# sleepy function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "comprehensive-copper",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0msleepy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_simulations\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnum_generations\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100000000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mN\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mm\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mb\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mgc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5e-05\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mL\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0md\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'0.5'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mselection_position\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'5000'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mselection_activation_generation\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mstop_after_mrca\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0moutput_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'run'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mgenerations_post_fixation_threshold\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0madd_mutations_after_fixation\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0moutput_directory\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'./'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_parallel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcontinue_from\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mprint_cmd\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mslurm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mslurm_start\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mslurm_divide\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mslurm_limit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Python-wrapper for sleepy simulator bash script generation. Also, capable of\n",
       "directly running simulations locally if print_cmd is false and slurm is false.\n",
       "\n",
       "Arg types: \n",
       "    * **n_simulations** *(int)* - Number of simulations if script is directly to be run locally.\n",
       "    * **num_generations** *(int)* - Maximum number of generations (multiplied by gc).\n",
       "    * **N** *(int)* - Diploid population size N=500 -> 1000 Haplotypes.\n",
       "    * **m** *(int)* - Number of generations from which seeds can resusicate.\n",
       "    * **b** *(float)* - Germination rate.\n",
       "    * **gc** *(int)* - Garbage collection interval.\n",
       "    * **r** *(int)* - Recombination rate.\n",
       "    * **L** *(int)* - Mapping length.\n",
       "    * **s** *(str)* - Selection coefficient.\n",
       "    * **d** *(str)* - Dominance coefficient.\n",
       "    * **selection_position** *(str)* - Selection position.\n",
       "    * **selection_activation_generation** *(int)* - Generation from which to start introducing selective alleles.\n",
       "    * **stop_after_mrca** *(bool)* - Stop simulation after MRCA is reached.\n",
       "    * **output_name** *(str)* - Output name.\n",
       "    * **generations_post_fixation_threshold** *(int)* - Number of generations to add after fixation event.\n",
       "    * **add_mutations_after_fixation** *(bool)* - Adding mutation after first fixation event, necessary for recovery.\n",
       "    * **output_directory** *(int)* - Output directory.\n",
       "    * **n_parallel** *(int)* - Number of parallel simulation when running locally.\n",
       "    * **continue_from** *(int)* - Continue simulations from run n if running locally.\n",
       "    * **print_cmd** *(bool)* - Number of simulations if script is directly to be run locally.\n",
       "    * **slurm** *(bool)* - Create part of slurm script for easy parallelization.\n",
       "    * **slurm_start** *(int)* - Start slurm simulatons from.\n",
       "    * **slurm_divide** *(int)* - Divid slurm_limit by.\n",
       "    * **slurm_limit** *(int)* - Number of slurm simulations.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/projects/sleepy-analysis/utils.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?sleepy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "detailed-graduate",
   "metadata": {},
   "source": [
    "# Example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "iraqi-california",
   "metadata": {},
   "source": [
    "## no selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "frequent-aerospace",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.46 ms, sys: 16.1 ms, total: 19.5 ms\n",
      "Wall time: 44.7 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "sleepy(\n",
    "    N=500,\n",
    "    b=1.0,\n",
    "    s=0,\n",
    "    stop_after_mrca=True,\n",
    "    output_directory=\"./germination_rate_b1/\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "permanent-dining",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.26 ms, sys: 19.8 ms, total: 21 ms\n",
      "Wall time: 2min 6s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "sleepy(\n",
    "    N=500,\n",
    "    b=0.5,\n",
    "    s=0,\n",
    "    stop_after_mrca=True,\n",
    "    output_directory=\"./germination_rate_b05/\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "optional-antibody",
   "metadata": {},
   "source": [
    "## selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "hidden-portland",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.96 ms, sys: 20.5 ms, total: 26.5 ms\n",
      "Wall time: 3min 59s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "sleepy(\n",
    "    N=500,\n",
    "    b=1.0,\n",
    "    s=1,\n",
    "    stop_after_mrca=False,\n",
    "    output_directory=\"./germination_rate_b1_selection/\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "outer-digest",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 14.3 ms, sys: 5.13 ms, total: 19.4 ms\n",
      "Wall time: 4min 44s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "sleepy(\n",
    "    N=500,\n",
    "    b=0.5,\n",
    "    s=1,\n",
    "    stop_after_mrca=False,\n",
    "    output_directory=\"./germination_rate_b05_selection/\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "consecutive-montgomery",
   "metadata": {},
   "source": [
    "## plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "otherwise-nicholas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "              <style>\n",
       "                .tskit-table thead tr th {text-align: left;padding: 0.5em 0.5em;}\n",
       "                .tskit-table tbody tr td {padding: 0.5em 0.5em;}\n",
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       "                details {display: inline-block;}\n",
       "                summary {cursor: pointer; outline: 0; display: list-item;}\n",
       "              </style>\n",
       "              <div class=\"tskit-table-set\">\n",
       "                <div class=\"tskit-table-set-table\">\n",
       "                  <table class=\"tskit-table\">\n",
       "                    <thead>\n",
       "                      <tr>\n",
       "                        <th style=\"padding:0;line-height:21px;\">\n",
       "                          <img style=\"height: 32px;display: inline-block;padding: 3px 5px 3px 0;\" src=\"https://raw.githubusercontent.com/tskit-dev/administrative/main/tskit_logo.svg\"/>\n",
       "                          <a target=\"_blank\" href=\"https://tskit.readthedocs.io/en/latest/python-api.html#the-treesequence-class\"> Tree Sequence </a>\n",
       "                        </th>\n",
       "                      </tr>\n",
       "                    </thead>\n",
       "                    <tbody>\n",
       "                      <tr><td>Trees</td><td>5726</td></tr>\n",
       "                      <tr><td>Sequence Length</td><td>10000.0</td></tr>\n",
       "                      <tr><td>Sample Nodes</td><td>1000</td></tr>\n",
       "                      <tr><td>Total Size</td><td>879.9 KiB</td></tr>\n",
       "                      <tr>\n",
       "                        <td>Metadata</td><td style=\"text-align: left;\">No Metadata</td></tr>\n",
       "                    </tbody>\n",
       "                  </table>\n",
       "                </div>\n",
       "                <div class=\"tskit-table-set-table\">\n",
       "                  <table class=\"tskit-table\">\n",
       "                    <thead>\n",
       "                      <tr>\n",
       "                        <th style=\"line-height:21px;\">Table</th>\n",
       "                        <th>Rows</th>\n",
       "                        <th>Size</th>\n",
       "                        <th>Has Metadata</th>\n",
       "                      </tr>\n",
       "                    </thead>\n",
       "                    <tbody>\n",
       "                    \n",
       "                  <tr>\n",
       "                    <td>Edges</td>\n",
       "                      <td>21564</td>\n",
       "                      <td>589.6 KiB</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Individuals</td>\n",
       "                      <td>0</td>\n",
       "                      <td>12 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Migrations</td>\n",
       "                      <td>0</td>\n",
       "                      <td>4 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Mutations</td>\n",
       "                      <td>1</td>\n",
       "                      <td>37 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Nodes</td>\n",
       "                      <td>5191</td>\n",
       "                      <td>121.7 KiB</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Populations</td>\n",
       "                      <td>0</td>\n",
       "                      <td>4 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Provenances</td>\n",
       "                      <td>0</td>\n",
       "                      <td>8 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Sites</td>\n",
       "                      <td>1</td>\n",
       "                      <td>25 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                    </tbody>\n",
       "                  </table>\n",
       "                </div>\n",
       "              </div>\n",
       "            </div>\n",
       "            "
      ],
      "text/plain": [
       "<tskit.trees.TreeSequence at 0x7f9c2bcabc70>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_b1 = tskit.load(\"./germination_rate_b1_selection/run_0.trees\"); ts_b1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "mobile-commodity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "              <style>\n",
       "                .tskit-table thead tr th {text-align: left;padding: 0.5em 0.5em;}\n",
       "                .tskit-table tbody tr td {padding: 0.5em 0.5em;}\n",
       "                .tskit-table tbody tr td:first-of-type {text-align: left;}\n",
       "                .tskit-details-label {vertical-align: top; padding-right:5px;}\n",
       "                .tskit-table-set {display: inline-flex;flex-wrap: wrap;margin: -12px 0 0 -12px;width: calc(100% + 12px);}\n",
       "                .tskit-table-set-table {margin: 12px 0 0 12px;}\n",
       "                details {display: inline-block;}\n",
       "                summary {cursor: pointer; outline: 0; display: list-item;}\n",
       "              </style>\n",
       "              <div class=\"tskit-table-set\">\n",
       "                <div class=\"tskit-table-set-table\">\n",
       "                  <table class=\"tskit-table\">\n",
       "                    <thead>\n",
       "                      <tr>\n",
       "                        <th style=\"padding:0;line-height:21px;\">\n",
       "                          <img style=\"height: 32px;display: inline-block;padding: 3px 5px 3px 0;\" src=\"https://raw.githubusercontent.com/tskit-dev/administrative/main/tskit_logo.svg\"/>\n",
       "                          <a target=\"_blank\" href=\"https://tskit.readthedocs.io/en/latest/python-api.html#the-treesequence-class\"> Tree Sequence </a>\n",
       "                        </th>\n",
       "                      </tr>\n",
       "                    </thead>\n",
       "                    <tbody>\n",
       "                      <tr><td>Trees</td><td>7390</td></tr>\n",
       "                      <tr><td>Sequence Length</td><td>10000.0</td></tr>\n",
       "                      <tr><td>Sample Nodes</td><td>1000</td></tr>\n",
       "                      <tr><td>Total Size</td><td>1.1 MiB</td></tr>\n",
       "                      <tr>\n",
       "                        <td>Metadata</td><td style=\"text-align: left;\">No Metadata</td></tr>\n",
       "                    </tbody>\n",
       "                  </table>\n",
       "                </div>\n",
       "                <div class=\"tskit-table-set-table\">\n",
       "                  <table class=\"tskit-table\">\n",
       "                    <thead>\n",
       "                      <tr>\n",
       "                        <th style=\"line-height:21px;\">Table</th>\n",
       "                        <th>Rows</th>\n",
       "                        <th>Size</th>\n",
       "                        <th>Has Metadata</th>\n",
       "                      </tr>\n",
       "                    </thead>\n",
       "                    <tbody>\n",
       "                    \n",
       "                  <tr>\n",
       "                    <td>Edges</td>\n",
       "                      <td>27437</td>\n",
       "                      <td>750.2 KiB</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Individuals</td>\n",
       "                      <td>0</td>\n",
       "                      <td>12 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Migrations</td>\n",
       "                      <td>0</td>\n",
       "                      <td>4 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Mutations</td>\n",
       "                      <td>1</td>\n",
       "                      <td>37 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Nodes</td>\n",
       "                      <td>6115</td>\n",
       "                      <td>143.3 KiB</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Populations</td>\n",
       "                      <td>0</td>\n",
       "                      <td>4 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Provenances</td>\n",
       "                      <td>0</td>\n",
       "                      <td>8 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                  <tr>\n",
       "                    <td>Sites</td>\n",
       "                      <td>1</td>\n",
       "                      <td>25 Bytes</td>\n",
       "                      <td style=\"text-align: center;\">\n",
       "                        \n",
       "                      </td>\n",
       "                    </tr>\n",
       "                \n",
       "                    </tbody>\n",
       "                  </table>\n",
       "                </div>\n",
       "              </div>\n",
       "            </div>\n",
       "            "
      ],
      "text/plain": [
       "<tskit.trees.TreeSequence at 0x7f9c2c031df0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_b05 = tskit.load(\"./germination_rate_b05_selection/run_0.trees\"); ts_b05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "exterior-meditation",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_diversity_along_genome(ts):\n",
    "    ts = msprime.sim_mutations(ts, rate=5e-5, discrete_genome=False, keep=False)\n",
    "    windows = np.linspace(0, ts.sequence_length, 50) # window size 200\n",
    "    diversity = ts.diversity(windows=windows, span_normalise=True, mode=\"site\").tolist()\n",
    "    return diversity\n",
    "\n",
    "windows = np.linspace(0, 10_000, 50) # window size 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "moral-reverse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'b = 0.5')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"ticks\")\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 6), sharey=True)\n",
    "\n",
    "sns.lineplot(x=windows[:-1], y=calculate_diversity_along_genome(ts_b1), ax=ax[0])\n",
    "ax[0].set(xlabel=\"mapping length\", ylabel=\"genetic diversity [tajima's pi]\")\n",
    "ax[0].set_title('b = 1.0')\n",
    "\n",
    "sns.lineplot(x=windows[:-1], y=calculate_diversity_along_genome(ts_b05), ax=ax[1])\n",
    "ax[1].set(xlabel=\"mapping length\", ylabel=\"genetic diversity [tajima's pi]\")\n",
    "ax[1].set_title('b = 0.5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "piano-reporter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for j in 0 100 ; do \n",
      "        sleepy  --num_generations 100000000 --N 500 --m 100 --b 1 --gc 50 --r 5e-05 --L 10000 --selection_coefficient 1 --dominance_coefficient 0.5 --selection_position 5000 --selection_activation_generation 100000 --stop_after_mrca False --debug_print False --output_name run_$(($i+$j)) --output_directory ./ --generations_post_fixation_threshold 0 --add_mutations_after_fixation True \n",
      "        done\n"
     ]
    }
   ],
   "source": [
    "sleepy(print_cmd=True, slurm=True, slurm_start=0, slurm_limit=200, slurm_divide=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efficient-affiliation",
   "metadata": {},
   "source": [
    "## slurm header and bash script from above"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "potential-volume",
   "metadata": {},
   "source": [
    "```console\n",
    "\n",
    "#!/bin/bash\n",
    "\n",
    "#SBATCH --job-name=array  \n",
    "#SBATCH --output=./log/array_%A_%a.out  \n",
    "#SBATCH --error=./log/array_%A_%a.err  \n",
    "#SBATCH --array=0-99  \n",
    "#SBATCH --clusters=serial  \n",
    "#SBATCH --partition=serial_std  \n",
    "#SBATCH --get-user-env  \n",
    "#SBATCH --time=72:00:00  \n",
    "#SBATCH --ntasks=1  \n",
    "#SBATCH --mem=5000mb  \n",
    "\n",
    "conda activate sleepy\n",
    "\n",
    "i=$SLURM_ARRAY_TASK_ID\n",
    "\n",
    "for j in 0 100 ; do   \n",
    "    sleepy  --num_generations 100000000 --N 500 --m 100 --b 1 --gc 50 --r 5e-05 --L 10000 --selection_coefficient 1 --dominance_coefficient 0.5 --selection_position 5000 --selection_activation_generation 100000 --stop_after_mrca False --debug_print False --output_name run_$(($i+$j)) --output_directory ./ --generations_post_fixation_threshold 0 --add_mutations_after_fixation True      \n",
    "        done     \n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "honest-classification",
   "metadata": {},
   "source": [
    "# print script which rans locally:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "unlike-legislation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "            n_simulations=1\n",
      "            n_prog=10\n",
      "            n_prog_count=0\n",
      "\n",
      "            i=0\n",
      "            while [[ $i -lt $n_simulations ]]; do\n",
      "\n",
      "            sleepy --num_generations 100000000 --N 500 --m 100 --b 1 --gc 50 --r 5e-05 --L 10000 --selection_coefficient 1 --dominance_coefficient 0.5 --selection_position 5000 --selection_activation_generation 100000 --stop_after_mrca False --debug_print False --output_name run_$i --output_directory ./ --generations_post_fixation_threshold 0 --add_mutations_after_fixation True  &\n",
      "                 (( n_prog_count+=1 ))  \n",
      "                 [[ $((n_prog_count%n_prog)) -eq 0 ]] && wait\n",
      "                 (( i+=1 ))\n",
      "            done\n",
      "            \n"
     ]
    }
   ],
   "source": [
    "sleepy(print_cmd=True, slurm=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
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