RandomAccess_Different_file_size_new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_path = './fig/' #LO's path\n",
    "# save_path = './fig/' #YC's path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions to simulate PCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sim_random_access_eff_uniFileSize(primer_specs):\n",
    "    pool_size, n_row, n_col = primer_specs\n",
    "    max_file_size = 1\n",
    "    pool = np.reshape(np.ones(pool_size), (n_row, n_col))\n",
    "    amp_efficiency = 0.9\n",
    "    cycle = 15\n",
    "    ra_eff_bfPCR_list, ra_eff_afPCR_list, ra_eff_afLig_list = [], [], []\n",
    "    for row in range(n_row):\n",
    "        for col in range(n_col):\n",
    "            pool_pcr = np.copy(pool)\n",
    "            pool_pcr[:,col] = pool[:,col]*(1+amp_efficiency)*cycle\n",
    "            pool_pcr[row,:] = pool[row,:]*(1+amp_efficiency)*cycle\n",
    "            pool_pcr[row,col] = pool[row,col]*(1+amp_efficiency)**cycle\n",
    "            dsDNA = sum(pool[row,:])-pool[row,col]+pool_pcr[row,col]\n",
    "            ra_eff_bfPCR_list.append(100.0*pool[row,col]/np.sum(pool))\n",
    "            ra_eff_afPCR_list.append(100.0*pool_pcr[row,col]/np.sum(pool_pcr))\n",
    "            ra_eff_afLig_list.append(100.0*pool_pcr[row,col]/dsDNA)\n",
    "    return (ra_eff_bfPCR_list, ra_eff_afPCR_list, ra_eff_afLig_list)\n",
    "\n",
    "def sim_random_access_eff_ranFileSize(primer_specs):\n",
    "    pool_size, n_row, n_col = primer_specs\n",
    "    max_file_size = 100\n",
    "    pool = np.random.randint(low=1, high=max_file_size, size=(n_row, n_col))\n",
    "    amp_efficiency = 0.9\n",
    "    cycle = 15\n",
    "    ra_eff_bfPCR_list, ra_eff_afPCR_list, ra_eff_afLig_list = [], [], []\n",
    "    for row in range(n_row):\n",
    "        for col in range(n_col):\n",
    "            pool_pcr = np.copy(pool)\n",
    "            pool_pcr[:,col] = pool[:,col]*(1+amp_efficiency)*cycle\n",
    "            pool_pcr[row,:] = pool[row,:]*(1+amp_efficiency)*cycle\n",
    "            pool_pcr[row,col] = pool[row,col]*(1+amp_efficiency)**cycle\n",
    "            dsDNA = sum(pool[row,:])-pool[row,col]+pool_pcr[row,col]\n",
    "            ra_eff_bfPCR_list.append(100.0*pool[row,col]/np.sum(pool))\n",
    "            ra_eff_afPCR_list.append(100.0*pool_pcr[row,col]/np.sum(pool_pcr))\n",
    "            ra_eff_afLig_list.append(100.0*pool_pcr[row,col]/dsDNA)\n",
    "    return (ra_eff_bfPCR_list, ra_eff_afPCR_list, ra_eff_afLig_list)\n",
    "\n",
    "def sim_random_access_eff_linspaceFileSize(primer_specs):\n",
    "    pool_size, n_row, n_col = primer_specs\n",
    "    max_file_size = 100\n",
    "    pool = np.reshape(np.linspace(1,max_file_size, num=pool_size), (n_row, n_col))\n",
    "    amp_efficiency = 0.9\n",
    "    cycle = 15\n",
    "    ra_eff_afPCR_list, ra_eff_bfPCR_list, ra_eff_afLig_list = [], [], []\n",
    "    for row in range(n_row):\n",
    "        for col in range(n_col):\n",
    "            pool_pcr = np.copy(pool)\n",
    "            pool_pcr[:,col] = pool[:,col]*(1+amp_efficiency)*cycle\n",
    "            pool_pcr[row,:] = pool[row,:]*(1+amp_efficiency)*cycle\n",
    "            pool_pcr[row,col] = pool[row,col]*(1+amp_efficiency)**cycle\n",
    "            dsDNA = sum(pool[row,:])-pool[row,col]+pool_pcr[row,col]\n",
    "            ra_eff_bfPCR_list.append(100.0*pool[row,col]/np.sum(pool))\n",
    "            ra_eff_afPCR_list.append(100.0*pool_pcr[row,col]/np.sum(pool_pcr))\n",
    "            ra_eff_afLig_list.append(100.0*pool_pcr[row,col]/dsDNA)\n",
    "    return (ra_eff_bfPCR_list, ra_eff_afPCR_list, ra_eff_afLig_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions to shape and plot the PCR simulation data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sim_pcr_data(sim_pcr, title, save_path, fname):\n",
    "    \"\"\" \n",
    "    Given a panda dataframe of simulated PCR data, a string that is the title of the plot,\n",
    "    a string that is the path to save the image (i.e., './dir/'), \n",
    "    and a string that is the saved file's name (i.e., 'my_fig.png'),\n",
    "    saves a box plot of the data\n",
    "    \"\"\"\n",
    "    sns.set_theme(style=\"whitegrid\")\n",
    "    ax = sns.boxplot(x=\"Number of Files\", y=\"Target File % of Pool\", hue=\"PCR\",\n",
    "                     data=sim_pcr, palette=\"Set2\")\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel('Number of Files')\n",
    "    ax.set_ylabel('Target File % of Pool')\n",
    "    filename = save_path + fname\n",
    "    plt.show()\n",
    "    plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "    plt.clf()\n",
    "    \n",
    "def make_pcr_sim_dict(ra_eff_bfPCR, ra_eff_afPCR, ra_eff_afLig, pspec, sim_pcr):\n",
    "    \"\"\"\n",
    "    Given two lists that are the PCR frequency data before and after PCR,\n",
    "    and a primer spec list (num of files, num of forward primers, num of reverse primers),\n",
    "    and a sim_pcr dictionary,\n",
    "    returns a panda that's a dictionary that can be plotted\n",
    "    \"\"\"\n",
    "    print (\"ra_eff_bfPCR_len =\" + len(ra_eff_bfPCR)) \n",
    "    for i in range(len(ra_eff_bfPCR)):\n",
    "            # fill in data for Before PCR\n",
    "            sim_pcr[\"Number of Files\"].append(pspec[0])\n",
    "            sim_pcr[\"Target File % of Pool\"].append(ra_eff_bfPCR[i])\n",
    "            sim_pcr[\"PCR\"].append('Before')\n",
    "            # fill in data for After PCR\n",
    "            sim_pcr[\"Number of Files\"].append(pspec[0])\n",
    "            sim_pcr[\"Target File % of Pool\"].append(ra_eff_afPCR[i])\n",
    "            sim_pcr[\"PCR\"].append('After')\n",
    "            # fill in data for After ligation\n",
    "            sim_pcr[\"Number of Files\"].append(pspec[0])\n",
    "            sim_pcr[\"Target File % of Pool\"].append(ra_eff_afLig[i])\n",
    "            sim_pcr[\"Lig\"].append('After')\n",
    "    return sim_pcr\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform and plot PCR simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def make_pcr_sim_dict_subfigs(ra_eff_bfPCR, ra_eff_afPCR, ra_eff_afLig, pspec, sim_pcr, method):\n",
    "    bf_col_label = method + \"-\" + \"Before PCR\"\n",
    "    af_col_label = method + '-' + 'After PCR'\n",
    "    afLig_col_label = method + \"-\" + \"After Lig\"\n",
    "    if bf_col_label not in sim_pcr.keys(): # initialize dictionary if needed\n",
    "        sim_pcr[bf_col_label] = []\n",
    "        sim_pcr[af_col_label] = []\n",
    "        sim_pcr[afLig_col_label] = []\n",
    "    for i in range(len(ra_eff_bfPCR)):\n",
    "        # only initialize this once, it's identical order for all methods\n",
    "        # NOTE- CHANGE THIS IF YOU CHANGE NUMBER OF FILES BEING ANALYZED\n",
    "        if len(sim_pcr['Number of Files']) < 11124: \n",
    "            sim_pcr['Number of Files'].append(pspec[0])\n",
    "        sim_pcr[bf_col_label].append(ra_eff_bfPCR[i]) # before PCR\n",
    "        sim_pcr[af_col_label].append(ra_eff_afPCR[i]) # after PCR\n",
    "        sim_pcr[afLig_col_label].append(ra_eff_afLig[i]) # after Ligation\n",
    "    return sim_pcr\n",
    "\n",
    "def boxplots_sim_pcr_data_subfigs(sim_pcr, save_path, fname):\n",
    "    \"\"\" \n",
    "    Given a panda dataframe of simulated PCR data, a string that is the title of the plot,\n",
    "    a string that is the path to save the image (i.e., './dir/'), \n",
    "    and a string that is the saved file's name (i.e., 'my_fig.png'),\n",
    "    saves a box plot of the data\n",
    "    \"\"\"\n",
    "    \n",
    "    sns.set_theme(style=\"whitegrid\")\n",
    "    fig, axes = plt.subplots(3, 3, sharex=True, figsize=(20, 16))\n",
    "    #fig.suptitle('Simulated File Behavior Before and After PCR')\n",
    "    ylab = 'Percent of Pool'\n",
    "    \n",
    "    sns.boxplot(ax=axes[0, 0], data=sim_pcr, x='Number of Files', y='Uniform File Size-Before PCR')\n",
    "    sns.stripplot(ax=axes[0, 0], data=sim_pcr, x=\"Number of Files\", y=\"Uniform File Size-Before PCR\", dodge=True)\n",
    "    axes[0,0].set_title('Uniform File Size (Before PCR)')\n",
    "    axes[0,0].set_ylabel(ylab)\n",
    "    axes[0,0].set_xlabel('')\n",
    "    \n",
    "    sns.boxplot(ax=axes[1, 0], data=sim_pcr, x='Number of Files', y='Uniform File Size-After PCR')\n",
    "    sns.stripplot(ax=axes[1, 0], data=sim_pcr, x=\"Number of Files\", y=\"Uniform File Size-After PCR\", dodge=True)\n",
    "    axes[1,0].set_title('Uniform File Size (After PCR)')\n",
    "    axes[1,0].set_ylabel(ylab)\n",
    "\n",
    "    sns.boxplot(ax=axes[2, 0], data=sim_pcr, x='Number of Files', y='Uniform File Size-After Lig')\n",
    "    sns.stripplot(ax=axes[2, 0], data=sim_pcr, x=\"Number of Files\", y=\"Uniform File Size-After Lig\", dodge=True)\n",
    "    axes[2,0].set_title('Uniform File Size (After ligation)')\n",
    "    axes[2,0].set_ylabel(ylab)\n",
    "    \n",
    "    sns.boxplot(ax=axes[0, 1], data=sim_pcr, x='Number of Files', y='Linearly Spaced File Sizes-Before PCR')\n",
    "    sns.stripplot(ax=axes[0, 1], data=sim_pcr, x=\"Number of Files\", y=\"Linearly Spaced File Sizes-Before PCR\", dodge=True)\n",
    "    axes[0,1].set_title('Linearly Spaced File Sizes (Before PCR)')\n",
    "    axes[0,1].set_ylabel('')\n",
    "    axes[0,1].set_xlabel('')\n",
    "\n",
    "    \n",
    "    sns.boxplot(ax=axes[1, 1], data=sim_pcr, x='Number of Files', y='Linearly Spaced File Sizes-After PCR')\n",
    "    sns.stripplot(ax=axes[1, 1], data=sim_pcr, x=\"Number of Files\", y=\"Linearly Spaced File Sizes-After PCR\", dodge=True)\n",
    "    axes[1,1].set_title('Linearly Spaced File Sizes (After PCR)')\n",
    "    axes[1,1].set_ylabel('')\n",
    "    \n",
    "    sns.boxplot(ax=axes[2, 1], data=sim_pcr, x='Number of Files', y='Linearly Spaced File Sizes-After Lig')\n",
    "    sns.stripplot(ax=axes[2, 1], data=sim_pcr, x=\"Number of Files\", y=\"Linearly Spaced File Sizes-After Lig\", dodge=True)\n",
    "    axes[2,1].set_title('Linearly Spaced File Sizes (After Ligation)')\n",
    "    axes[2,1].set_ylabel('')\n",
    "    \n",
    "    sns.boxplot(ax=axes[0, 2], data=sim_pcr, x='Number of Files', y='Randomly Generated File Size-Before PCR')\n",
    "    sns.stripplot(ax=axes[0, 2], data=sim_pcr, x=\"Number of Files\", y='Randomly Generated File Size-Before PCR', dodge=True)\n",
    "    axes[0,2].set_title('Randomly Generated File Size (Before PCR)')\n",
    "    axes[0,2].set_ylabel('')\n",
    "    axes[0,2].set_xlabel('')\n",
    "\n",
    "    sns.boxplot(ax=axes[1, 2], data=sim_pcr, x='Number of Files', y='Randomly Generated File Size-After PCR')\n",
    "    sns.stripplot(ax=axes[1, 2], data=sim_pcr, x=\"Number of Files\", y=\"Randomly Generated File Size-After PCR\", dodge=True)\n",
    "    axes[1,2].set_title('Randomly Generated File Size (After PCR)')\n",
    "    axes[1,2].set_ylabel('')\n",
    "\n",
    "    sns.boxplot(ax=axes[2, 2], data=sim_pcr, x='Number of Files', y='Randomly Generated File Size-After Lig')\n",
    "    sns.stripplot(ax=axes[2, 2], data=sim_pcr, x=\"Number of Files\", y=\"Randomly Generated File Size-After Lig\", dodge=True)\n",
    "    axes[2,2].set_title('Randomly Generated File Size (After Ligation)')\n",
    "    axes[2,2].set_ylabel('')\n",
    "    \n",
    "    filename = save_path + fname\n",
    "    plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    plt.clf()\n",
    "    \n",
    "#(pool_size, max_file_size, n_row, n_col) = primer_specs\n",
    "primer_specs_to_analyze = [[100,10,10], [1024, 32, 32], [10000, 100,100]]\n",
    "sim_pcr = {\"Number of Files\": []}\n",
    "methods = [\"Uniform File Size\", \"Randomly Generated File Size\", \"Linearly Spaced File Sizes\"]\n",
    "\n",
    "for method in methods:\n",
    "    for pspec in primer_specs_to_analyze:\n",
    "        if method == \"Uniform File Size\":\n",
    "            ra_eff_bfPCR, ra_eff_afPCR, ra_eff_afLig = sim_random_access_eff_uniFileSize(pspec)\n",
    "        elif method == \"Linearly Spaced File Sizes\":\n",
    "            ra_eff_bfPCR, ra_eff_afPCR, ra_eff_afLig = sim_random_access_eff_linspaceFileSize(pspec)\n",
    "        elif method == \"Randomly Generated File Size\":\n",
    "            ra_eff_bfPCR, ra_eff_afPCR, ra_eff_afLig = sim_random_access_eff_ranFileSize(pspec)\n",
    "        else:\n",
    "            print (\"error\")\n",
    "        sim_pcr = make_pcr_sim_dict_subfigs(ra_eff_bfPCR, ra_eff_afPCR, ra_eff_afLig, pspec, sim_pcr, method)\n",
    "        \n",
    "sim_pcr = pd.DataFrame(sim_pcr)\n",
    "\n",
    "boxplots_sim_pcr_data_subfigs(sim_pcr, save_path, 'box_pcr_sim_fig_1xto100X.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Files</th>\n",
       "      <th>Uniform File Size-Before PCR</th>\n",
       "      <th>Uniform File Size-After PCR</th>\n",
       "      <th>Uniform File Size-After Lig</th>\n",
       "      <th>Randomly Generated File Size-Before PCR</th>\n",
       "      <th>Randomly Generated File Size-After PCR</th>\n",
       "      <th>Randomly Generated File Size-After Lig</th>\n",
       "      <th>Linearly Spaced File Sizes-Before PCR</th>\n",
       "      <th>Linearly Spaced File Sizes-After PCR</th>\n",
       "      <th>Linearly Spaced File Sizes-After Lig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>1.00</td>\n",
       "      <td>96.234579</td>\n",
       "      <td>99.940751</td>\n",
       "      <td>1.195289</td>\n",
       "      <td>96.844420</td>\n",
       "      <td>99.960396</td>\n",
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       "      <td>100</td>\n",
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       "      <td>96.234579</td>\n",
       "      <td>99.940751</td>\n",
       "      <td>0.562489</td>\n",
       "      <td>93.364673</td>\n",
       "      <td>99.908482</td>\n",
       "      <td>0.039604</td>\n",
       "      <td>61.044683</td>\n",
       "      <td>99.825745</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>1.00</td>\n",
       "      <td>96.234579</td>\n",
       "      <td>99.940751</td>\n",
       "      <td>0.580067</td>\n",
       "      <td>94.265178</td>\n",
       "      <td>99.911452</td>\n",
       "      <td>0.059406</td>\n",
       "      <td>69.918426</td>\n",
       "      <td>99.885953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>1.00</td>\n",
       "      <td>96.234579</td>\n",
       "      <td>99.940751</td>\n",
       "      <td>0.826156</td>\n",
       "      <td>96.522019</td>\n",
       "      <td>99.939771</td>\n",
       "      <td>0.079208</td>\n",
       "      <td>75.398572</td>\n",
       "      <td>99.916085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>1.00</td>\n",
       "      <td>96.234579</td>\n",
       "      <td>99.940751</td>\n",
       "      <td>0.615222</td>\n",
       "      <td>94.381070</td>\n",
       "      <td>99.916883</td>\n",
       "      <td>0.099010</td>\n",
       "      <td>79.119356</td>\n",
       "      <td>99.934172</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11119</th>\n",
       "      <td>10000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>49.570821</td>\n",
       "      <td>99.352100</td>\n",
       "      <td>0.010447</td>\n",
       "      <td>50.863218</td>\n",
       "      <td>99.388999</td>\n",
       "      <td>0.019794</td>\n",
       "      <td>62.431567</td>\n",
       "      <td>99.355030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11120</th>\n",
       "      <td>10000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>49.570821</td>\n",
       "      <td>99.352100</td>\n",
       "      <td>0.005224</td>\n",
       "      <td>34.070930</td>\n",
       "      <td>98.778992</td>\n",
       "      <td>0.019796</td>\n",
       "      <td>62.433205</td>\n",
       "      <td>99.355094</td>\n",
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       "    <tr>\n",
       "      <th>11121</th>\n",
       "      <td>10000</td>\n",
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       "      <td>49.570821</td>\n",
       "      <td>99.352100</td>\n",
       "      <td>0.019488</td>\n",
       "      <td>65.931891</td>\n",
       "      <td>99.674558</td>\n",
       "      <td>0.019798</td>\n",
       "      <td>62.434842</td>\n",
       "      <td>99.355158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11122</th>\n",
       "      <td>10000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>49.570821</td>\n",
       "      <td>99.352100</td>\n",
       "      <td>0.006831</td>\n",
       "      <td>40.499244</td>\n",
       "      <td>99.065119</td>\n",
       "      <td>0.019800</td>\n",
       "      <td>62.436480</td>\n",
       "      <td>99.355222</td>\n",
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       "    <tr>\n",
       "      <th>11123</th>\n",
       "      <td>10000</td>\n",
       "      <td>0.01</td>\n",
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       "      <td>99.352100</td>\n",
       "      <td>0.014063</td>\n",
       "      <td>57.983811</td>\n",
       "      <td>99.547078</td>\n",
       "      <td>0.019802</td>\n",
       "      <td>62.438117</td>\n",
       "      <td>99.355286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11124 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number of Files  Uniform File Size-Before PCR  \\\n",
       "0                  100                          1.00   \n",
       "1                  100                          1.00   \n",
       "2                  100                          1.00   \n",
       "3                  100                          1.00   \n",
       "4                  100                          1.00   \n",
       "...                ...                           ...   \n",
       "11119            10000                          0.01   \n",
       "11120            10000                          0.01   \n",
       "11121            10000                          0.01   \n",
       "11122            10000                          0.01   \n",
       "11123            10000                          0.01   \n",
       "\n",
       "       Uniform File Size-After PCR  Uniform File Size-After Lig  \\\n",
       "0                        96.234579                    99.940751   \n",
       "1                        96.234579                    99.940751   \n",
       "2                        96.234579                    99.940751   \n",
       "3                        96.234579                    99.940751   \n",
       "4                        96.234579                    99.940751   \n",
       "...                            ...                          ...   \n",
       "11119                    49.570821                    99.352100   \n",
       "11120                    49.570821                    99.352100   \n",
       "11121                    49.570821                    99.352100   \n",
       "11122                    49.570821                    99.352100   \n",
       "11123                    49.570821                    99.352100   \n",
       "\n",
       "       Randomly Generated File Size-Before PCR  \\\n",
       "0                                     1.195289   \n",
       "1                                     0.562489   \n",
       "2                                     0.580067   \n",
       "3                                     0.826156   \n",
       "4                                     0.615222   \n",
       "...                                        ...   \n",
       "11119                                 0.010447   \n",
       "11120                                 0.005224   \n",
       "11121                                 0.019488   \n",
       "11122                                 0.006831   \n",
       "11123                                 0.014063   \n",
       "\n",
       "       Randomly Generated File Size-After PCR  \\\n",
       "0                                   96.844420   \n",
       "1                                   93.364673   \n",
       "2                                   94.265178   \n",
       "3                                   96.522019   \n",
       "4                                   94.381070   \n",
       "...                                       ...   \n",
       "11119                               50.863218   \n",
       "11120                               34.070930   \n",
       "11121                               65.931891   \n",
       "11122                               40.499244   \n",
       "11123                               57.983811   \n",
       "\n",
       "       Randomly Generated File Size-After Lig  \\\n",
       "0                                   99.960396   \n",
       "1                                   99.908482   \n",
       "2                                   99.911452   \n",
       "3                                   99.939771   \n",
       "4                                   99.916883   \n",
       "...                                       ...   \n",
       "11119                               99.388999   \n",
       "11120                               98.778992   \n",
       "11121                               99.674558   \n",
       "11122                               99.065119   \n",
       "11123                               99.547078   \n",
       "\n",
       "       Linearly Spaced File Sizes-Before PCR  \\\n",
       "0                                   0.019802   \n",
       "1                                   0.039604   \n",
       "2                                   0.059406   \n",
       "3                                   0.079208   \n",
       "4                                   0.099010   \n",
       "...                                      ...   \n",
       "11119                               0.019794   \n",
       "11120                               0.019796   \n",
       "11121                               0.019798   \n",
       "11122                               0.019800   \n",
       "11123                               0.019802   \n",
       "\n",
       "       Linearly Spaced File Sizes-After PCR  \\\n",
       "0                                 44.211346   \n",
       "1                                 61.044683   \n",
       "2                                 69.918426   \n",
       "3                                 75.398572   \n",
       "4                                 79.119356   \n",
       "...                                     ...   \n",
       "11119                             62.431567   \n",
       "11120                             62.433205   \n",
       "11121                             62.434842   \n",
       "11122                             62.436480   \n",
       "11123                             62.438117   \n",
       "\n",
       "       Linearly Spaced File Sizes-After Lig  \n",
       "0                                 99.645556  \n",
       "1                                 99.825745  \n",
       "2                                 99.885953  \n",
       "3                                 99.916085  \n",
       "4                                 99.934172  \n",
       "...                                     ...  \n",
       "11119                             99.355030  \n",
       "11120                             99.355094  \n",
       "11121                             99.355158  \n",
       "11122                             99.355222  \n",
       "11123                             99.355286  \n",
       "\n",
       "[11124 rows x 10 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_pcr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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