fig_S8_profiles.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "intermediate-farming",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-01T22:26:36.943232Z",
     "start_time": "2021-04-01T22:26:36.379640Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import minimize\n",
    "import time\n",
    "import pdb\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# Load figure style\n",
    "plt.style.use('fig_style_small.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "delayed-space",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-01T22:26:36.957458Z",
     "start_time": "2021-04-01T22:26:36.944862Z"
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>p4</th>\n",
       "      <th>p5</th>\n",
       "      <th>p6</th>\n",
       "      <th>p7</th>\n",
       "      <th>p8</th>\n",
       "      <th>p9</th>\n",
       "      <th>p10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end</th>\n",
       "      <th>sample</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PPP_v1</th>\n",
       "      <th>rep1</th>\n",
       "      <td>1031</td>\n",
       "      <td>9666</td>\n",
       "      <td>85178</td>\n",
       "      <td>876828</td>\n",
       "      <td>758126</td>\n",
       "      <td>137911</td>\n",
       "      <td>8446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>1344</td>\n",
       "      <td>10121</td>\n",
       "      <td>86681</td>\n",
       "      <td>809092</td>\n",
       "      <td>687747</td>\n",
       "      <td>126382</td>\n",
       "      <td>7746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>1137</td>\n",
       "      <td>8986</td>\n",
       "      <td>75019</td>\n",
       "      <td>714802</td>\n",
       "      <td>616809</td>\n",
       "      <td>109581</td>\n",
       "      <td>6744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PPP_v2</th>\n",
       "      <th>rep1</th>\n",
       "      <td>567</td>\n",
       "      <td>4623</td>\n",
       "      <td>45703</td>\n",
       "      <td>741236</td>\n",
       "      <td>674993</td>\n",
       "      <td>127725</td>\n",
       "      <td>5274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>640</td>\n",
       "      <td>5305</td>\n",
       "      <td>50472</td>\n",
       "      <td>713574</td>\n",
       "      <td>638093</td>\n",
       "      <td>120415</td>\n",
       "      <td>5266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>670</td>\n",
       "      <td>4556</td>\n",
       "      <td>43644</td>\n",
       "      <td>659732</td>\n",
       "      <td>599917</td>\n",
       "      <td>115790</td>\n",
       "      <td>6477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OH_v1</th>\n",
       "      <th>rep1</th>\n",
       "      <td>906</td>\n",
       "      <td>11606</td>\n",
       "      <td>139486</td>\n",
       "      <td>178765</td>\n",
       "      <td>50194</td>\n",
       "      <td>4472</td>\n",
       "      <td>2835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>1068</td>\n",
       "      <td>11792</td>\n",
       "      <td>143885</td>\n",
       "      <td>158125</td>\n",
       "      <td>35019</td>\n",
       "      <td>2282</td>\n",
       "      <td>2636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>870</td>\n",
       "      <td>10544</td>\n",
       "      <td>126733</td>\n",
       "      <td>91978</td>\n",
       "      <td>-15897</td>\n",
       "      <td>-11367</td>\n",
       "      <td>605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OH_v2</th>\n",
       "      <th>rep1</th>\n",
       "      <td>442</td>\n",
       "      <td>6563</td>\n",
       "      <td>100011</td>\n",
       "      <td>43173</td>\n",
       "      <td>-32939</td>\n",
       "      <td>-5714</td>\n",
       "      <td>-337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>364</td>\n",
       "      <td>6976</td>\n",
       "      <td>107676</td>\n",
       "      <td>62607</td>\n",
       "      <td>-14635</td>\n",
       "      <td>-3685</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>403</td>\n",
       "      <td>6114</td>\n",
       "      <td>95358</td>\n",
       "      <td>36908</td>\n",
       "      <td>-32789</td>\n",
       "      <td>-5158</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 p4     p5      p6      p7      p8      p9   p10\n",
       "end    sample                                                   \n",
       "PPP_v1 rep1    1031   9666   85178  876828  758126  137911  8446\n",
       "       rep2    1344  10121   86681  809092  687747  126382  7746\n",
       "       rep3    1137   8986   75019  714802  616809  109581  6744\n",
       "PPP_v2 rep1     567   4623   45703  741236  674993  127725  5274\n",
       "       rep2     640   5305   50472  713574  638093  120415  5266\n",
       "       rep3     670   4556   43644  659732  599917  115790  6477\n",
       "OH_v1  rep1     906  11606  139486  178765   50194    4472  2835\n",
       "       rep2    1068  11792  143885  158125   35019    2282  2636\n",
       "       rep3     870  10544  126733   91978  -15897  -11367   605\n",
       "OH_v2  rep1     442   6563  100011   43173  -32939   -5714  -337\n",
       "       rep2     364   6976  107676   62607  -14635   -3685   156\n",
       "       rep3     403   6114   95358   36908  -32789   -5158   338"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naive_ct_df = pd.read_csv('csv_results/pct_profiles_naive.csv', index_col=[0,1])\n",
    "naive_ct_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "typical-eligibility",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-01T22:26:36.970870Z",
     "start_time": "2021-04-01T22:26:36.959982Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>replicate</th>\n",
       "      <th>efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>none</td>\n",
       "      <td>rep1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>none</td>\n",
       "      <td>rep2</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>none</td>\n",
       "      <td>rep3</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rpp</td>\n",
       "      <td>rep1</td>\n",
       "      <td>0.910393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rpp</td>\n",
       "      <td>rep2</td>\n",
       "      <td>0.922886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rpp</td>\n",
       "      <td>rep3</td>\n",
       "      <td>0.987480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PNK</td>\n",
       "      <td>rep1</td>\n",
       "      <td>0.755147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PNK</td>\n",
       "      <td>rep2</td>\n",
       "      <td>0.751011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PNK</td>\n",
       "      <td>rep3</td>\n",
       "      <td>0.753237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>both</td>\n",
       "      <td>rep1</td>\n",
       "      <td>0.911045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>both</td>\n",
       "      <td>rep2</td>\n",
       "      <td>0.896461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>both</td>\n",
       "      <td>rep3</td>\n",
       "      <td>0.922206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sample replicate  efficiency\n",
       "0    none      rep1    1.000000\n",
       "1    none      rep2    1.000000\n",
       "2    none      rep3    1.000000\n",
       "3     Rpp      rep1    0.910393\n",
       "4     Rpp      rep2    0.922886\n",
       "5     Rpp      rep3    0.987480\n",
       "6     PNK      rep1    0.755147\n",
       "7     PNK      rep2    0.751011\n",
       "8     PNK      rep3    0.753237\n",
       "9    both      rep1    0.911045\n",
       "10   both      rep2    0.896461\n",
       "11   both      rep3    0.922206"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "efficiency_df = pd.read_csv(f'csv_results/efficiencies.csv').set_index(['sample','replicate'])\n",
    "efficiency_df = efficiency_df.reset_index()\n",
    "efficiency_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "juvenile-tablet",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-01T22:26:36.985438Z",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>p4</th>\n",
       "      <th>p5</th>\n",
       "      <th>p6</th>\n",
       "      <th>p7</th>\n",
       "      <th>p8</th>\n",
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       "      <th>end</th>\n",
       "      <th>sample</th>\n",
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       "      <th rowspan=\"3\" valign=\"top\">PPP_v1</th>\n",
       "      <th>rep1</th>\n",
       "      <td>0.034883</td>\n",
       "      <td>0.348807</td>\n",
       "      <td>2.968897</td>\n",
       "      <td>47.037566</td>\n",
       "      <td>41.528803</td>\n",
       "      <td>7.697509</td>\n",
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       "      <th>rep2</th>\n",
       "      <td>0.059261</td>\n",
       "      <td>0.415861</td>\n",
       "      <td>3.366816</td>\n",
       "      <td>47.161961</td>\n",
       "      <td>40.905770</td>\n",
       "      <td>7.702446</td>\n",
       "      <td>0.387885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>0.051502</td>\n",
       "      <td>0.380186</td>\n",
       "      <td>2.873737</td>\n",
       "      <td>47.082749</td>\n",
       "      <td>41.681533</td>\n",
       "      <td>7.576354</td>\n",
       "      <td>0.353939</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PPP_v2</th>\n",
       "      <th>rep1</th>\n",
       "      <td>0.042993</td>\n",
       "      <td>0.339371</td>\n",
       "      <td>3.106801</td>\n",
       "      <td>46.245817</td>\n",
       "      <td>42.021600</td>\n",
       "      <td>7.875011</td>\n",
       "      <td>0.368407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>0.049060</td>\n",
       "      <td>0.391175</td>\n",
       "      <td>3.509730</td>\n",
       "      <td>46.449537</td>\n",
       "      <td>41.474609</td>\n",
       "      <td>7.752681</td>\n",
       "      <td>0.373209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>0.047899</td>\n",
       "      <td>0.326079</td>\n",
       "      <td>3.088842</td>\n",
       "      <td>46.098149</td>\n",
       "      <td>41.906727</td>\n",
       "      <td>8.075124</td>\n",
       "      <td>0.457180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OH_v1</th>\n",
       "      <th>rep1</th>\n",
       "      <td>0.233985</td>\n",
       "      <td>2.999743</td>\n",
       "      <td>36.067380</td>\n",
       "      <td>46.043548</td>\n",
       "      <td>12.800270</td>\n",
       "      <td>1.124424</td>\n",
       "      <td>0.730650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>0.270580</td>\n",
       "      <td>2.919358</td>\n",
       "      <td>35.006648</td>\n",
       "      <td>44.768930</td>\n",
       "      <td>14.674889</td>\n",
       "      <td>1.637445</td>\n",
       "      <td>0.722149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>0.293688</td>\n",
       "      <td>3.339492</td>\n",
       "      <td>38.645126</td>\n",
       "      <td>45.324790</td>\n",
       "      <td>12.255599</td>\n",
       "      <td>-0.318712</td>\n",
       "      <td>0.460015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OH_v2</th>\n",
       "      <th>rep1</th>\n",
       "      <td>0.269189</td>\n",
       "      <td>2.958714</td>\n",
       "      <td>36.665210</td>\n",
       "      <td>42.606559</td>\n",
       "      <td>14.940233</td>\n",
       "      <td>1.895128</td>\n",
       "      <td>0.664967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>0.233062</td>\n",
       "      <td>2.828543</td>\n",
       "      <td>35.531727</td>\n",
       "      <td>42.149134</td>\n",
       "      <td>16.767094</td>\n",
       "      <td>1.822263</td>\n",
       "      <td>0.668176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>0.279706</td>\n",
       "      <td>3.129564</td>\n",
       "      <td>39.477499</td>\n",
       "      <td>41.507235</td>\n",
       "      <td>13.130123</td>\n",
       "      <td>1.615562</td>\n",
       "      <td>0.860309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PPP</th>\n",
       "      <th>rep1</th>\n",
       "      <td>0.038938</td>\n",
       "      <td>0.344089</td>\n",
       "      <td>3.037849</td>\n",
       "      <td>46.641690</td>\n",
       "      <td>41.775202</td>\n",
       "      <td>7.786260</td>\n",
       "      <td>0.375971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>0.054160</td>\n",
       "      <td>0.403518</td>\n",
       "      <td>3.438273</td>\n",
       "      <td>46.805748</td>\n",
       "      <td>41.190190</td>\n",
       "      <td>7.727563</td>\n",
       "      <td>0.380547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>0.049701</td>\n",
       "      <td>0.353132</td>\n",
       "      <td>2.981290</td>\n",
       "      <td>46.590446</td>\n",
       "      <td>41.794130</td>\n",
       "      <td>7.825740</td>\n",
       "      <td>0.405560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OH</th>\n",
       "      <th>rep1</th>\n",
       "      <td>0.251587</td>\n",
       "      <td>2.979228</td>\n",
       "      <td>36.366299</td>\n",
       "      <td>44.325032</td>\n",
       "      <td>13.870265</td>\n",
       "      <td>1.509781</td>\n",
       "      <td>0.697808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>0.251821</td>\n",
       "      <td>2.873950</td>\n",
       "      <td>35.269190</td>\n",
       "      <td>43.459023</td>\n",
       "      <td>15.720999</td>\n",
       "      <td>1.729855</td>\n",
       "      <td>0.695162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>0.286697</td>\n",
       "      <td>3.234526</td>\n",
       "      <td>39.061322</td>\n",
       "      <td>43.415968</td>\n",
       "      <td>12.692872</td>\n",
       "      <td>0.648448</td>\n",
       "      <td>0.660167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     p4        p5         p6         p7         p8        p9  \\\n",
       "end    sample                                                                  \n",
       "PPP_v1 rep1    0.034883  0.348807   2.968897  47.037566  41.528803  7.697509   \n",
       "       rep2    0.059261  0.415861   3.366816  47.161961  40.905770  7.702446   \n",
       "       rep3    0.051502  0.380186   2.873737  47.082749  41.681533  7.576354   \n",
       "PPP_v2 rep1    0.042993  0.339371   3.106801  46.245817  42.021600  7.875011   \n",
       "       rep2    0.049060  0.391175   3.509730  46.449537  41.474609  7.752681   \n",
       "       rep3    0.047899  0.326079   3.088842  46.098149  41.906727  8.075124   \n",
       "OH_v1  rep1    0.233985  2.999743  36.067380  46.043548  12.800270  1.124424   \n",
       "       rep2    0.270580  2.919358  35.006648  44.768930  14.674889  1.637445   \n",
       "       rep3    0.293688  3.339492  38.645126  45.324790  12.255599 -0.318712   \n",
       "OH_v2  rep1    0.269189  2.958714  36.665210  42.606559  14.940233  1.895128   \n",
       "       rep2    0.233062  2.828543  35.531727  42.149134  16.767094  1.822263   \n",
       "       rep3    0.279706  3.129564  39.477499  41.507235  13.130123  1.615562   \n",
       "PPP    rep1    0.038938  0.344089   3.037849  46.641690  41.775202  7.786260   \n",
       "       rep2    0.054160  0.403518   3.438273  46.805748  41.190190  7.727563   \n",
       "       rep3    0.049701  0.353132   2.981290  46.590446  41.794130  7.825740   \n",
       "OH     rep1    0.251587  2.979228  36.366299  44.325032  13.870265  1.509781   \n",
       "       rep2    0.251821  2.873950  35.269190  43.459023  15.720999  1.729855   \n",
       "       rep3    0.286697  3.234526  39.061322  43.415968  12.692872  0.648448   \n",
       "\n",
       "                    p10  \n",
       "end    sample            \n",
       "PPP_v1 rep1    0.383536  \n",
       "       rep2    0.387885  \n",
       "       rep3    0.353939  \n",
       "PPP_v2 rep1    0.368407  \n",
       "       rep2    0.373209  \n",
       "       rep3    0.457180  \n",
       "OH_v1  rep1    0.730650  \n",
       "       rep2    0.722149  \n",
       "       rep3    0.460015  \n",
       "OH_v2  rep1    0.664967  \n",
       "       rep2    0.668176  \n",
       "       rep3    0.860309  \n",
       "PPP    rep1    0.375971  \n",
       "       rep2    0.380547  \n",
       "       rep3    0.405560  \n",
       "OH     rep1    0.697808  \n",
       "       rep2    0.695162  \n",
       "       rep3    0.660167  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrected_ct_df = pd.read_csv('csv_results/pct_profiles_corrected.csv', index_col=[0,1])\n",
    "corrected_ct_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "apparent-baseline",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-01T22:26:37.482302Z",
     "start_time": "2021-04-01T22:26:36.986615Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 270x162 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot naive count profiles\n",
    "fig, axs = plt.subplots(1,2,figsize=[3.75,2.25], sharey=True)\n",
    "xs = list(range(4,11))\n",
    "\n",
    "reps = ['rep1','rep2','rep3']\n",
    "poss = [f'p{x}' for x in xs]\n",
    "ylim = [-.4, 1.0]\n",
    "\n",
    "ax = axs[0]\n",
    "for rep in reps:\n",
    "    ax.axhline(0, linestyle=':', color='k')\n",
    "    for end in ['PPP_v1', 'PPP_v2']:\n",
    "        cts = naive_ct_df.loc[(end, rep),:]\n",
    "        probs = cts/np.sum(cts)\n",
    "        ax.plot(xs, probs, '-o', markersize=5, markeredgewidth=0, label=f'{rep}, {end[-2:]}', alpha=.5)\n",
    "    ax.set_ylabel('probability')\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.set_xticks(xs)\n",
    "    ax.set_xticklabels(poss)\n",
    "    ax.set_title('$5^\\prime$-ppp, naive')\n",
    "    ax.set_xlabel('$5^\\prime$ end position')\n",
    "    ax.legend(loc='upper left', ncol=1)\n",
    "    \n",
    "ax = axs[1]\n",
    "for rep in reps:\n",
    "    ax.axhline(0, linestyle=':', color='k')\n",
    "    for end in ['OH_v1', 'OH_v2']:\n",
    "        cts = naive_ct_df.loc[(end, rep),:]\n",
    "        probs = cts/np.sum(cts)\n",
    "        ax.plot(xs, probs, '-o', \n",
    "                markersize=5, markeredgewidth=0, label=f'{rep}, {end[-2:]}', alpha=.5)\n",
    "    ax.set_xticks(xs)\n",
    "    ax.set_xticklabels(poss)\n",
    "    ax.set_title('$5^\\prime$-OH, naive')\n",
    "    ax.set_xlabel('$5^\\prime$ end position')\n",
    "    \n",
    "fig.tight_layout()\n",
    "fig.savefig(f'figures/fig_S8A_naive_profiles.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "diagnostic-crest",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-01T22:26:37.625966Z",
     "start_time": "2021-04-01T22:26:37.484674Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 180x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot efficiencies\n",
    "fig, ax = plt.subplots(1,1,figsize=[2.5,2.25])\n",
    "sns.barplot(data=efficiency_df, y='efficiency', x='sample', hue='replicate', ax=ax)\n",
    "ax.set_ylabel('relative efficiency')\n",
    "ax.set_xlabel('treatment')\n",
    "ax.legend(loc='lower right')\n",
    "fig.tight_layout()\n",
    "fig.savefig(f'figures/fig_S8B_efficiencies.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "printable-publication",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-01T22:26:37.966684Z",
     "start_time": "2021-04-01T22:26:37.627840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 270x162 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot naive count profiles\n",
    "fig, axs = plt.subplots(1,2,figsize=[3.75,2.25], sharey=True)\n",
    "xs = list(range(4,11))\n",
    "\n",
    "reps = ['rep1','rep2','rep3']\n",
    "poss = [f'p{x}' for x in xs]\n",
    "ylim = [-.4, 1.0]\n",
    "\n",
    "ax = axs[0]\n",
    "for rep in reps:\n",
    "    ax.axhline(0, linestyle=':', color='k')\n",
    "    for end in ['PPP_v1', 'PPP_v2']:\n",
    "        cts = corrected_ct_df.loc[(end, rep),:]\n",
    "        probs = cts/np.sum(cts)\n",
    "        ax.plot(xs, probs, '-o', markersize=5, markeredgewidth=0, label=f'{rep}, {end[-2:]}', alpha=.5)\n",
    "    ax.set_ylabel('probability')\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.set_xticks(xs)\n",
    "    ax.set_xticklabels(poss)\n",
    "    ax.set_title('$5^\\prime$-ppp, corrected')\n",
    "    ax.legend(loc='upper left', ncol=1)\n",
    "    ax.set_xlabel('$5^\\prime$ end position')\n",
    "    \n",
    "ax = axs[1]\n",
    "for rep in reps:\n",
    "    ax.axhline(0, linestyle=':', color='k')\n",
    "    for end in ['OH_v1', 'OH_v2']:\n",
    "        cts = corrected_ct_df.loc[(end, rep),:]\n",
    "        probs = cts/np.sum(cts)\n",
    "        ax.plot(xs, probs, '-o', \n",
    "                markersize=5, markeredgewidth=0, label=f'{rep}, {end[-2:]}', alpha=.5)\n",
    "    ax.set_xticks(xs)\n",
    "    ax.set_xticklabels(poss)\n",
    "    ax.set_title('$5^\\prime$-OH, corrected')\n",
    "    ax.set_xlabel('$5^\\prime$ end position')\n",
    "    \n",
    "fig.tight_layout()\n",
    "fig.savefig(f'figures/fig_S8C_corrected_profiles.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "twenty-force",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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