1_efficiency.ipynb

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     "end_time": "2021-04-05T16:17:51.367149Z",
     "start_time": "2021-04-05T16:17:50.553042Z"
    }
   },
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "black-possession",
   "metadata": {
    "ExecuteTime": {
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N10</th>\n",
       "      <th>treatment</th>\n",
       "      <th>sample</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAAAAAAAAA</td>\n",
       "      <td>both</td>\n",
       "      <td>rep1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAAAAAAAAC</td>\n",
       "      <td>both</td>\n",
       "      <td>rep1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAAAAAAAAG</td>\n",
       "      <td>both</td>\n",
       "      <td>rep1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAAAAAAAAT</td>\n",
       "      <td>both</td>\n",
       "      <td>rep1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAAAAAAACA</td>\n",
       "      <td>both</td>\n",
       "      <td>rep1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "          N10 treatment sample  p4  p5  p6  p7  p8  p9  p10\n",
       "0  AAAAAAAAAA      both   rep1   0   0   0   0   0   0    0\n",
       "1  AAAAAAAAAC      both   rep1   0   0   0   0   0   0    0\n",
       "2  AAAAAAAAAG      both   rep1   0   0   0   0   0   0    0\n",
       "3  AAAAAAAAAT      both   rep1   0   0   0   0   0   0    0\n",
       "4  AAAAAAAACA      both   rep1   0   0   0   0   0   0    0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load Bryce's data\n",
    "raw_df = pd.read_csv('data/in_vivo_data.csv.gz', compression='gzip')\n",
    "\n",
    "# Rename columns and samples for added convenience\n",
    "raw_df.rename(columns={'DNA count':'DNA'}, inplace=True)\n",
    "raw_df['treatment'] = np.where(raw_df['treatment']=='All_end', 'all', raw_df['treatment'])\n",
    "raw_df['treatment'] = np.where(raw_df['treatment']=='background', 'P', raw_df['treatment'])\n",
    "\n",
    "#Rename treatment and replicates\n",
    "raw_df['treatment'] = raw_df['treatment'].map({'P':'none', \n",
    "                                       'PPP+P':'Rpp', \n",
    "                                       'OH+P':'PNK', \n",
    "                                       'all':'both'})\n",
    "raw_df['sample'] = raw_df['sample'].map({'KS112':'rep1', \n",
    "                                 'KS113':'rep2', \n",
    "                                 'KS114':'rep3'})\n",
    "\n",
    "# Drop DNA column\n",
    "raw_df.drop(columns=['DNA'], inplace=True)\n",
    "\n",
    "# Preview dataframe\n",
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "structured-sample",
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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>treatment</th>\n",
       "      <th>sample</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PNK</th>\n",
       "      <th>rep1</th>\n",
       "      <td>2159</td>\n",
       "      <td>18472</td>\n",
       "      <td>171298</td>\n",
       "      <td>424330</td>\n",
       "      <td>297070</td>\n",
       "      <td>42419</td>\n",
       "      <td>9740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>2203</td>\n",
       "      <td>19020</td>\n",
       "      <td>178467</td>\n",
       "      <td>408130</td>\n",
       "      <td>281619</td>\n",
       "      <td>36924</td>\n",
       "      <td>8993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>1981</td>\n",
       "      <td>17266</td>\n",
       "      <td>162142</td>\n",
       "      <td>363861</td>\n",
       "      <td>250827</td>\n",
       "      <td>34292</td>\n",
       "      <td>8845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Rpp</th>\n",
       "      <th>rep1</th>\n",
       "      <td>2284</td>\n",
       "      <td>16532</td>\n",
       "      <td>116990</td>\n",
       "      <td>1122393</td>\n",
       "      <td>1005002</td>\n",
       "      <td>175858</td>\n",
       "      <td>15351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>2479</td>\n",
       "      <td>17349</td>\n",
       "      <td>121263</td>\n",
       "      <td>1059097</td>\n",
       "      <td>934347</td>\n",
       "      <td>161024</td>\n",
       "      <td>14103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>2248</td>\n",
       "      <td>15708</td>\n",
       "      <td>110428</td>\n",
       "      <td>986685</td>\n",
       "      <td>883533</td>\n",
       "      <td>155240</td>\n",
       "      <td>14984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">both</th>\n",
       "      <th>rep1</th>\n",
       "      <td>3190</td>\n",
       "      <td>28138</td>\n",
       "      <td>256476</td>\n",
       "      <td>1301158</td>\n",
       "      <td>1055196</td>\n",
       "      <td>180330</td>\n",
       "      <td>18186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>3547</td>\n",
       "      <td>29141</td>\n",
       "      <td>265148</td>\n",
       "      <td>1217222</td>\n",
       "      <td>969366</td>\n",
       "      <td>163306</td>\n",
       "      <td>16739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>3118</td>\n",
       "      <td>26252</td>\n",
       "      <td>237161</td>\n",
       "      <td>1078663</td>\n",
       "      <td>867636</td>\n",
       "      <td>143873</td>\n",
       "      <td>15589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">none</th>\n",
       "      <th>rep1</th>\n",
       "      <td>1717</td>\n",
       "      <td>11909</td>\n",
       "      <td>71287</td>\n",
       "      <td>381157</td>\n",
       "      <td>330009</td>\n",
       "      <td>48133</td>\n",
       "      <td>10077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>1839</td>\n",
       "      <td>12044</td>\n",
       "      <td>70791</td>\n",
       "      <td>345523</td>\n",
       "      <td>296254</td>\n",
       "      <td>40609</td>\n",
       "      <td>8837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>1578</td>\n",
       "      <td>11152</td>\n",
       "      <td>66784</td>\n",
       "      <td>326953</td>\n",
       "      <td>283616</td>\n",
       "      <td>39450</td>\n",
       "      <td>8507</td>\n",
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      ],
      "text/plain": [
       "                    p4     p5      p6       p7       p8      p9    p10\n",
       "treatment sample                                                      \n",
       "PNK       rep1    2159  18472  171298   424330   297070   42419   9740\n",
       "          rep2    2203  19020  178467   408130   281619   36924   8993\n",
       "          rep3    1981  17266  162142   363861   250827   34292   8845\n",
       "Rpp       rep1    2284  16532  116990  1122393  1005002  175858  15351\n",
       "          rep2    2479  17349  121263  1059097   934347  161024  14103\n",
       "          rep3    2248  15708  110428   986685   883533  155240  14984\n",
       "both      rep1    3190  28138  256476  1301158  1055196  180330  18186\n",
       "          rep2    3547  29141  265148  1217222   969366  163306  16739\n",
       "          rep3    3118  26252  237161  1078663   867636  143873  15589\n",
       "none      rep1    1717  11909   71287   381157   330009   48133  10077\n",
       "          rep2    1839  12044   70791   345523   296254   40609   8837\n",
       "          rep3    1578  11152   66784   326953   283616   39450   8507"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum across N10 sequences\n",
    "sum_df = raw_df.groupby(['treatment','sample']).sum()\n",
    "sum_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "patent-atlantic",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:18:08.763665Z",
     "start_time": "2021-04-05T16:18:08.759539Z"
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   },
   "outputs": [],
   "source": [
    "# Name shit\n",
    "L = 7\n",
    "poss = [f'p{pos}' for pos in range(4,11)]\n",
    "reps = ['rep1', 'rep2', 'rep3']\n",
    "samples = ['none','Rpp','PNK','both']\n",
    "ends = ['PPP_v1', 'PPP_v2', 'OH_v1', 'OH_v2']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "finnish-variance",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-06T20:58:15.670203Z",
     "start_time": "2021-02-06T20:57:46.091Z"
    }
   },
   "source": [
    "First we use two different ways to naively compute the number of PPP-end and OH-end mRNAs. These four profiles are computed separately for each of the three replicates.\n",
    "\n",
    "$$\\vec{c}_{PPP,1} = \\vec{c}_{both} - \\vec{c}_{PNK},$$\n",
    "$$\\vec{c}_{PPP,2} = \\vec{c}_{Rpp} - \\vec{c}_{none},$$\n",
    "$$\\vec{c}_{OH,1} = \\vec{c}_{both} - \\vec{c}_{Rpp},$$\n",
    "$$\\vec{c}_{OH,2} = \\vec{c}_{PNK} - \\vec{c}_{none}.$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "wrong-mailing",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:18:10.799335Z",
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   "outputs": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>p4</th>\n",
       "      <th>p5</th>\n",
       "      <th>p6</th>\n",
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       "      <th>end</th>\n",
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       "    <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute naive counts profiles\n",
    "pairs = [(end, rep) for end in ends for rep in reps]\n",
    "index = pd.MultiIndex.from_tuples(pairs, names=[\"end\", \"sample\"])\n",
    "naive_ct_df = pd.DataFrame(index=index, columns=poss)\n",
    "\n",
    "for rep in reps:\n",
    "    \n",
    "    # Get df corresponding to specific replicate\n",
    "    tmp_df = sum_df.loc[pd.IndexSlice[:, rep], :]\n",
    "    tmp_df.index = samples\n",
    "    \n",
    "    # Extract count vectors\n",
    "    naive_ct_df.loc[('PPP_v1',rep),:] = sum_df.loc[('both', rep),:] - sum_df.loc[('PNK', rep),:]\n",
    "    naive_ct_df.loc[('PPP_v2',rep),:] = sum_df.loc[('Rpp', rep),:] - sum_df.loc[('none', rep),:]\n",
    "    naive_ct_df.loc[('OH_v1',rep),:] = sum_df.loc[('both', rep),:] - sum_df.loc[('Rpp', rep),:]\n",
    "    naive_ct_df.loc[('OH_v2',rep),:] = sum_df.loc[('PNK', rep),:] - sum_df.loc[('none', rep),:]\n",
    "    \n",
    "naive_ct_df.to_csv('csv_results/pct_profiles_naive.csv')\n",
    "naive_ct_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deluxe-latvia",
   "metadata": {},
   "source": [
    "To visualize these, we normalize each counts vector by the number of reads to get a probability distribution across positions $i = 4, 5, ..., 10$. \n",
    "    \n",
    "$$\\vec{p}_{xxx}(i) = \\frac{\\vec{c}_{xxx}(i)}{\\sum_j \\vec{p}_{xxx}(j)} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "muslim-sessions",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:18:12.479395Z",
     "start_time": "2021-04-05T16:18:12.146839Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot naive count profiles\n",
    "fig, axs = plt.subplots(1,2,figsize=[8,4])\n",
    "xs = list(range(4,11))\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', label=f'{end.replace(\"_\",\" \")},{rep}')\n",
    "    ax.set_ylabel('probability')\n",
    "    ax.set_xticks(xs)\n",
    "    ax.set_xticklabels(poss)\n",
    "    ax.legend()\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', label=f'{end.replace(\"_\",\" \")},{rep}')\n",
    "    ax.set_ylabel('probability')\n",
    "    ax.set_xticks(xs)\n",
    "    ax.set_xticklabels(poss)\n",
    "    ax.legend()\n",
    "    \n",
    "fig.tight_layout()\n",
    "#fig.savefig(f'results/naive_profiles.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weighted-facing",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-06T20:58:15.670203Z",
     "start_time": "2021-02-06T20:57:46.091Z"
    }
   },
   "source": [
    "The six OH profiles are messed up. They show substantial variation across replications. Moreover, four of them have probabilities well below zero (at p8 and p9). \n",
    "\n",
    "We therefore consider the possibility that each enzymatic treatment has an efficiency below 100%. Let the efficiencies of four treatments be $\\epsilon_{both}$, $\\epsilon_{PNK}$, $\\epsilon_{Rpp}$, and $\\epsilon_{none}$. Then the number of true underlying counts in the four samples become:\n",
    "\n",
    "$$\\vec{c}_{PPP,1} = \\frac{\\vec{c}_{both}}{\\epsilon_{both}} - \\frac{\\vec{c}_{PNK}}{\\epsilon_{PNK}},$$\n",
    "$$\\vec{c}_{PPP,2} = \\frac{\\vec{c}_{Rpp}}{\\epsilon_{Rpp}} - \\frac{\\vec{c}_{none}}{\\epsilon_{none}},$$\n",
    "$$\\vec{c}_{OH,1} = \\frac{\\vec{c}_{both}}{\\epsilon_{both}} - \\frac{\\vec{c}_{Rpp}}{\\epsilon_{PNK}},$$\n",
    "$$\\vec{c}_{OH,2} = \\frac{\\vec{c}_{PNK}}{\\epsilon_{Rpp}} - \\frac{\\vec{c}_{none}}{\\epsilon_{none}}.$$\n",
    "\n",
    "We can solve for the unknown efficiencies by setting $\\vec{c}_{PPP,1} = \\vec{c}_{PPP,2}$, or equivalently $\\vec{c}_{OH,1} = \\vec{c}_{OH,2}$, both of which give\n",
    "\n",
    " $$ \\frac{\\vec{c}_{both}}{\\epsilon_{both}} - \\frac{\\vec{c}_{PNK}}{\\epsilon_{PNK}} - \\frac{\\vec{c}_{Rpp}}{\\epsilon_{Rpp}} + \\frac{\\vec{c}_{none}}{\\epsilon_{none}} = 0$$\n",
    "\n",
    "This provides a system of 7 equations with 4 unknowns. We can't determine absolute overall efficiencies, but setting $\\epsilon_{none}=1$ does allow us to solve for the other three relative efficiencies. We do this by minimizing the objective function \n",
    "\n",
    "$$\\sum_i\\frac{\\vec{r}(i)^2}{\\vec{v}(i)}$$\n",
    "\n",
    "where \n",
    "\n",
    "$$\\vec{r} = \\frac{\\vec{c}_{both}}{\\epsilon_{both}} - \\frac{\\vec{c}_{PNK}}{\\epsilon_{PNK}} - \\frac{\\vec{c}_{Rpp}}{\\epsilon_{Rpp}} + \\frac{\\vec{c}_{none}}{\\epsilon_{none}}$$ \n",
    "\n",
    "is the vector of residuals and\n",
    "\n",
    "$$ \\vec{v} = \\frac{\\vec{c}_{both}}{\\epsilon_{both}^2} - \\frac{\\vec{c}_{PNK}}{\\epsilon_{PNK}^2} - \\frac{\\vec{c}_{Rpp}}{\\epsilon_{Rpp}^2} + \\frac{\\vec{c}_{none}}{\\epsilon_{none}^2}$$ \n",
    "\n",
    "is the correpsonding vector of Poisson variances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "grand-person",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:18:14.642976Z",
     "start_time": "2021-04-05T16:18:13.980127Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>none</td>\n",
       "      <td>rep2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>none</td>\n",
       "      <td>rep3</td>\n",
       "      <td>1.0</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>0.98748</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        1.0\n",
       "1    none      rep2        1.0\n",
       "2    none      rep3        1.0\n",
       "3     Rpp      rep1   0.910393\n",
       "4     Rpp      rep2   0.922886\n",
       "5     Rpp      rep3    0.98748\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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define objective function\n",
    "def objective(x, rep, min_std=1):\n",
    "    c_both = sum_df.loc[('both', rep),:]\n",
    "    c_PNK = sum_df.loc[('PNK', rep),:]\n",
    "    c_Rpp = sum_df.loc[('Rpp', rep),:]\n",
    "    c_none = sum_df.loc[('none', rep),:]\n",
    "    eps_Rpp, eps_PNK, eps_both = x\n",
    "\n",
    "    # Compute residuals\n",
    "    residuals = c_both/eps_both - c_PNK/eps_PNK - c_Rpp/eps_Rpp + c_none\n",
    "    \n",
    "    # Compute variances assuming possion flucuations in c_xxx\n",
    "    variances = c_both/eps_both**2 + c_PNK/eps_PNK**2 + c_Rpp/eps_Rpp**2 + c_none\n",
    "    \n",
    "    # Compute objective function to minimize\n",
    "    obj = np.sum((residuals**2)/variances)\n",
    "    \n",
    "    return obj\n",
    "\n",
    "# Compute efficiendies for each replicate\n",
    "\n",
    "# Iterate over repliates\n",
    "eps_df = pd.DataFrame(index=reps, columns=samples)\n",
    "eps_df.index.name = 'rep'\n",
    "profile_df = pd.DataFrame(columns=['rep', 'profile']+poss)\n",
    "for rep in reps:\n",
    "    \n",
    "    # Perform optimization, using upper and lower bounds for safety sake\n",
    "    bounds = [(.01, 100)]*3\n",
    "    result = minimize(objective, x0=[1, 1, 1], args=(rep), bounds=bounds)\n",
    "    eps_Rpp, eps_PNK, eps_both = result.x\n",
    "    eps_df.loc[rep,:] = [1., eps_Rpp, eps_PNK, eps_both]\n",
    "    \n",
    "eps_df = eps_df.reset_index().melt(id_vars='rep', value_vars=samples, )\n",
    "eps_df.columns = ['replicate','sample','efficiency']\n",
    "eps_df = eps_df[['sample','replicate','efficiency']]\n",
    "eps_df.to_csv(f'csv_results/efficiencies.csv', index=False)\n",
    "eps_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "secure-mitchell",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:18:15.337293Z",
     "start_time": "2021-04-05T16:18:15.187891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff3b4313cd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot efficiencies\n",
    "fig, ax = plt.subplots(1,1,figsize=[4,4])\n",
    "eps_df = pd.read_csv(f'csv_results/efficiencies.csv').set_index(['sample','replicate'])\n",
    "eps_df = eps_df.reset_index()\n",
    "sns.barplot(data=eps_df, y='efficiency', x='sample', hue='replicate', ax=ax)\n",
    "ax.set_ylabel('relative efficiency')\n",
    "ax.legend(loc='lower right')\n",
    "#fig.savefig(f'results/efficiencies.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "isolated-interstate",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:18:16.194605Z",
     "start_time": "2021-04-05T16:18:16.133023Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PPP_v1</th>\n",
       "      <th>rep1</th>\n",
       "      <td>642.428171</td>\n",
       "      <td>6423.95422</td>\n",
       "      <td>54677.919649</td>\n",
       "      <td>866286.870523</td>\n",
       "      <td>764832.43656</td>\n",
       "      <td>141764.369594</td>\n",
       "      <td>7063.543788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>1023.292276</td>\n",
       "      <td>7180.866816</td>\n",
       "      <td>58136.401255</td>\n",
       "      <td>814367.831253</td>\n",
       "      <td>706339.223514</td>\n",
       "      <td>133001.773077</td>\n",
       "      <td>6697.795638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>751.040662</td>\n",
       "      <td>5544.121502</td>\n",
       "      <td>41906.753451</td>\n",
       "      <td>686592.0686</td>\n",
       "      <td>607827.922147</td>\n",
       "      <td>110483.443922</td>\n",
       "      <td>5161.378991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PPP_v2</th>\n",
       "      <th>rep1</th>\n",
       "      <td>791.807983</td>\n",
       "      <td>6250.200341</td>\n",
       "      <td>57218.011365</td>\n",
       "      <td>851710.127282</td>\n",
       "      <td>773912.646565</td>\n",
       "      <td>145034.230435</td>\n",
       "      <td>6784.957684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>847.138526</td>\n",
       "      <td>6754.635453</td>\n",
       "      <td>60604.407861</td>\n",
       "      <td>802069.27695</td>\n",
       "      <td>716164.504813</td>\n",
       "      <td>133869.729336</td>\n",
       "      <td>6444.408485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>698.502057</td>\n",
       "      <td>4755.15939</td>\n",
       "      <td>45044.100145</td>\n",
       "      <td>672242.031977</td>\n",
       "      <td>611119.183151</td>\n",
       "      <td>117758.26481</td>\n",
       "      <td>6666.979902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OH_v1</th>\n",
       "      <th>rep1</th>\n",
       "      <td>992.665435</td>\n",
       "      <td>12726.210017</td>\n",
       "      <td>153013.451464</td>\n",
       "      <td>195336.681485</td>\n",
       "      <td>54304.292326</td>\n",
       "      <td>4770.293551</td>\n",
       "      <td>3099.733722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>1270.532236</td>\n",
       "      <td>13708.086478</td>\n",
       "      <td>164376.607741</td>\n",
       "      <td>210216.208785</td>\n",
       "      <td>68907.153599</td>\n",
       "      <td>7688.757426</td>\n",
       "      <td>3390.909501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>1104.521748</td>\n",
       "      <td>12559.369447</td>\n",
       "      <td>145338.989806</td>\n",
       "      <td>170460.285497</td>\n",
       "      <td>46091.619132</td>\n",
       "      <td>-1198.631133</td>\n",
       "      <td>1730.054764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OH_v2</th>\n",
       "      <th>rep1</th>\n",
       "      <td>1142.045247</td>\n",
       "      <td>12552.456138</td>\n",
       "      <td>155553.543179</td>\n",
       "      <td>180759.938244</td>\n",
       "      <td>63384.502331</td>\n",
       "      <td>8040.154393</td>\n",
       "      <td>2821.147617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>1094.378487</td>\n",
       "      <td>13281.855115</td>\n",
       "      <td>166844.614347</td>\n",
       "      <td>197917.654483</td>\n",
       "      <td>78732.434897</td>\n",
       "      <td>8556.713685</td>\n",
       "      <td>3137.522348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>1051.983142</td>\n",
       "      <td>11770.407334</td>\n",
       "      <td>148476.336499</td>\n",
       "      <td>156110.248874</td>\n",
       "      <td>49382.880136</td>\n",
       "      <td>6076.189755</td>\n",
       "      <td>3235.655674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PPP</th>\n",
       "      <th>rep1</th>\n",
       "      <td>717.118077</td>\n",
       "      <td>6337.07728</td>\n",
       "      <td>55947.965507</td>\n",
       "      <td>858998.498903</td>\n",
       "      <td>769372.541562</td>\n",
       "      <td>143399.300014</td>\n",
       "      <td>6924.250736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>935.215401</td>\n",
       "      <td>6967.751135</td>\n",
       "      <td>59370.404558</td>\n",
       "      <td>808218.554102</td>\n",
       "      <td>711251.864164</td>\n",
       "      <td>133435.751207</td>\n",
       "      <td>6571.102061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>724.77136</td>\n",
       "      <td>5149.640446</td>\n",
       "      <td>43475.426798</td>\n",
       "      <td>679417.050288</td>\n",
       "      <td>609473.552649</td>\n",
       "      <td>114120.854366</td>\n",
       "      <td>5914.179446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OH</th>\n",
       "      <th>rep1</th>\n",
       "      <td>1067.355341</td>\n",
       "      <td>12639.333078</td>\n",
       "      <td>154283.497322</td>\n",
       "      <td>188048.309864</td>\n",
       "      <td>58844.397329</td>\n",
       "      <td>6405.223972</td>\n",
       "      <td>2960.44067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>1182.455362</td>\n",
       "      <td>13494.970797</td>\n",
       "      <td>165610.611044</td>\n",
       "      <td>204066.931634</td>\n",
       "      <td>73819.794248</td>\n",
       "      <td>8122.735555</td>\n",
       "      <td>3264.215924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>1078.252445</td>\n",
       "      <td>12164.888391</td>\n",
       "      <td>146907.663153</td>\n",
       "      <td>163285.267185</td>\n",
       "      <td>47737.249634</td>\n",
       "      <td>2438.779311</td>\n",
       "      <td>2482.855219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        p4            p5             p6             p7  \\\n",
       "end    sample                                                            \n",
       "PPP_v1 rep1     642.428171    6423.95422   54677.919649  866286.870523   \n",
       "       rep2    1023.292276   7180.866816   58136.401255  814367.831253   \n",
       "       rep3     751.040662   5544.121502   41906.753451    686592.0686   \n",
       "PPP_v2 rep1     791.807983   6250.200341   57218.011365  851710.127282   \n",
       "       rep2     847.138526   6754.635453   60604.407861   802069.27695   \n",
       "       rep3     698.502057    4755.15939   45044.100145  672242.031977   \n",
       "OH_v1  rep1     992.665435  12726.210017  153013.451464  195336.681485   \n",
       "       rep2    1270.532236  13708.086478  164376.607741  210216.208785   \n",
       "       rep3    1104.521748  12559.369447  145338.989806  170460.285497   \n",
       "OH_v2  rep1    1142.045247  12552.456138  155553.543179  180759.938244   \n",
       "       rep2    1094.378487  13281.855115  166844.614347  197917.654483   \n",
       "       rep3    1051.983142  11770.407334  148476.336499  156110.248874   \n",
       "PPP    rep1     717.118077    6337.07728   55947.965507  858998.498903   \n",
       "       rep2     935.215401   6967.751135   59370.404558  808218.554102   \n",
       "       rep3      724.77136   5149.640446   43475.426798  679417.050288   \n",
       "OH     rep1    1067.355341  12639.333078  154283.497322  188048.309864   \n",
       "       rep2    1182.455362  13494.970797  165610.611044  204066.931634   \n",
       "       rep3    1078.252445  12164.888391  146907.663153  163285.267185   \n",
       "\n",
       "                          p8             p9          p10  \n",
       "end    sample                                             \n",
       "PPP_v1 rep1     764832.43656  141764.369594  7063.543788  \n",
       "       rep2    706339.223514  133001.773077  6697.795638  \n",
       "       rep3    607827.922147  110483.443922  5161.378991  \n",
       "PPP_v2 rep1    773912.646565  145034.230435  6784.957684  \n",
       "       rep2    716164.504813  133869.729336  6444.408485  \n",
       "       rep3    611119.183151   117758.26481  6666.979902  \n",
       "OH_v1  rep1     54304.292326    4770.293551  3099.733722  \n",
       "       rep2     68907.153599    7688.757426  3390.909501  \n",
       "       rep3     46091.619132   -1198.631133  1730.054764  \n",
       "OH_v2  rep1     63384.502331    8040.154393  2821.147617  \n",
       "       rep2     78732.434897    8556.713685  3137.522348  \n",
       "       rep3     49382.880136    6076.189755  3235.655674  \n",
       "PPP    rep1    769372.541562  143399.300014  6924.250736  \n",
       "       rep2    711251.864164  133435.751207  6571.102061  \n",
       "       rep3    609473.552649  114120.854366  5914.179446  \n",
       "OH     rep1     58844.397329    6405.223972   2960.44067  \n",
       "       rep2     73819.794248    8122.735555  3264.215924  \n",
       "       rep3     47737.249634    2438.779311  2482.855219  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load efficiencies\n",
    "eps_series = pd.read_csv(f'csv_results/efficiencies.csv', index_col=[0,1])['efficiency']\n",
    "eps_series\n",
    "\n",
    "# Compute adjusted counts profiles\n",
    "pairs = [(end, rep) for end in ends+['PPP','OH'] for rep in reps]\n",
    "index = pd.MultiIndex.from_tuples(pairs, names=[\"end\", \"sample\"])\n",
    "adjusted_ct_df = pd.DataFrame(index=index, columns=poss)\n",
    "\n",
    "for rep in reps:\n",
    "    \n",
    "    # Get df corresponding to specific replicate\n",
    "    tmp_df = sum_df.loc[pd.IndexSlice[:, rep], :]\n",
    "    tmp_df.index = samples\n",
    "    \n",
    "    # Extract count vectors\n",
    "    adjusted_ct_df.loc[('PPP_v1',rep),:] = sum_df.loc[('both', rep),:]/eps_series[('both', rep)] \\\n",
    "                                         - sum_df.loc[('PNK', rep),:]/eps_series[('PNK', rep)] \n",
    "    \n",
    "    adjusted_ct_df.loc[('PPP_v2',rep),:] = sum_df.loc[('Rpp', rep),:]/eps_series[('Rpp', rep)] \\\n",
    "                                         - sum_df.loc[('none', rep),:]/eps_series[('none', rep)]\n",
    "    \n",
    "    adjusted_ct_df.loc[('PPP',rep),:] = .5*(adjusted_ct_df.loc[('PPP_v1',rep),:] \n",
    "                                            + adjusted_ct_df.loc[('PPP_v2',rep),:])\n",
    "    \n",
    "    adjusted_ct_df.loc[('OH_v1',rep),:] = sum_df.loc[('both', rep),:]/eps_series[('both', rep)] \\\n",
    "                                        - sum_df.loc[('Rpp', rep),:]/eps_series[('Rpp', rep)] \n",
    "    \n",
    "    adjusted_ct_df.loc[('OH_v2',rep),:] = sum_df.loc[('PNK', rep),:]/eps_series[('PNK', rep)] \\\n",
    "                                        - sum_df.loc[('none', rep),:]/eps_series[('none', rep)] \n",
    "    \n",
    "    adjusted_ct_df.loc[('OH',rep),:] = .5*(adjusted_ct_df.loc[('OH_v1',rep),:] \n",
    "                                           + adjusted_ct_df.loc[('OH_v2',rep),:])\n",
    "    \n",
    "adjusted_ct_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "elementary-selling",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:18:16.978247Z",
     "start_time": "2021-04-05T16:18:16.946605Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "      <td>0.383536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>40.90577</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",
       "    </tr>\n",
       "    <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>42.0216</td>\n",
       "      <td>7.875011</td>\n",
       "      <td>0.368407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>0.04906</td>\n",
       "      <td>0.391175</td>\n",
       "      <td>3.50973</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>0.45718</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>36.06738</td>\n",
       "      <td>46.043548</td>\n",
       "      <td>12.80027</td>\n",
       "      <td>1.124424</td>\n",
       "      <td>0.73065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>0.27058</td>\n",
       "      <td>2.919358</td>\n",
       "      <td>35.006648</td>\n",
       "      <td>44.76893</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>45.32479</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>36.66521</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>46.64169</td>\n",
       "      <td>41.775202</td>\n",
       "      <td>7.78626</td>\n",
       "      <td>0.375971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>0.05416</td>\n",
       "      <td>0.403518</td>\n",
       "      <td>3.438273</td>\n",
       "      <td>46.805748</td>\n",
       "      <td>41.19019</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>2.98129</td>\n",
       "      <td>46.590446</td>\n",
       "      <td>41.79413</td>\n",
       "      <td>7.82574</td>\n",
       "      <td>0.40556</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>2.87395</td>\n",
       "      <td>35.26919</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   40.90577  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    42.0216  7.875011   \n",
       "       rep2     0.04906  0.391175    3.50973  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   36.06738  46.043548   12.80027  1.124424   \n",
       "       rep2     0.27058  2.919358  35.006648   44.76893  14.674889  1.637445   \n",
       "       rep3    0.293688  3.339492  38.645126   45.32479  12.255599 -0.318712   \n",
       "OH_v2  rep1    0.269189  2.958714   36.66521  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   46.64169  41.775202   7.78626   \n",
       "       rep2     0.05416  0.403518   3.438273  46.805748   41.19019  7.727563   \n",
       "       rep3    0.049701  0.353132    2.98129  46.590446   41.79413   7.82574   \n",
       "OH     rep1    0.251587  2.979228  36.366299  44.325032  13.870265  1.509781   \n",
       "       rep2    0.251821   2.87395   35.26919  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     0.45718  \n",
       "OH_v1  rep1     0.73065  \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     0.40556  \n",
       "OH     rep1    0.697808  \n",
       "       rep2    0.695162  \n",
       "       rep3    0.660167  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ns = adjusted_ct_df.sum(axis=1)\n",
    "pct_df = 100*adjusted_ct_df/Ns.values[:,np.newaxis]\n",
    "pct_df.to_csv('csv_results/pct_profiles_corrected.csv')\n",
    "pct_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "electoral-asset",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:18:19.063652Z",
     "start_time": "2021-04-05T16:18:17.701186Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,4,figsize=[16,8])\n",
    "\n",
    "# Load efficiencies, and set index in a a useful way\n",
    "eps_df = pd.read_csv(f'csv_results/efficiencies.csv').set_index(['sample','replicate'])\n",
    "\n",
    "for rep in reps:\n",
    "    \n",
    "    # Compute counts profiles   \n",
    "    c_PPP_1 = adjusted_ct_df.loc[('PPP_v1',rep),:]\n",
    "    c_PPP_2 = adjusted_ct_df.loc[('PPP_v2',rep),:]\n",
    "    c_PPP = adjusted_ct_df.loc[('PPP',rep),:]\n",
    "    c_OH_1 = adjusted_ct_df.loc[('OH_v1',rep),:]\n",
    "    c_OH_2 = adjusted_ct_df.loc[('OH_v2',rep),:]\n",
    "    c_OH = adjusted_ct_df.loc[('OH',rep),:]\n",
    "    \n",
    "    # Compute normalized profiles\n",
    "    p_PPP_1 = c_PPP_1/np.sum(c_PPP_1)\n",
    "    p_PPP_2 = c_PPP_2/np.sum(c_PPP_2)\n",
    "    p_PPP = c_PPP/np.sum(c_PPP)\n",
    "    p_OH_1 = c_OH_1/np.sum(c_OH_1)\n",
    "    p_OH_2 = c_OH_2/np.sum(c_OH_2)\n",
    "    p_OH = c_OH/np.sum(c_OH)\n",
    "    \n",
    "    for i, yscale in enumerate(['linear','log']):\n",
    "        \n",
    "        ax = axs[i,0]\n",
    "        ax.plot(p_PPP_1, '-o', label=f'v1 {rep}')\n",
    "        ax.plot(p_PPP_2, '-o', label=f'v2 {rep}')\n",
    "        ax.set_title(f'PPP v1 & v2 ({yscale} scale)')\n",
    "        ax.set_yscale(yscale)\n",
    "\n",
    "        ax = axs[i,1]\n",
    "        ax.plot(p_PPP, '-o', label=f'{rep}')\n",
    "        ax.set_title(f'PPP mean ({yscale} scale)')\n",
    "        ax.set_yscale(yscale)\n",
    "\n",
    "        ax = axs[i,2]\n",
    "        ax.plot(p_OH_1, '-o', label=f'v1 {rep}')\n",
    "        ax.plot(p_OH_2, '-o', label=f'v2 {rep}')\n",
    "        ax.set_title(f'OH v1 & v2 ({yscale} scale)')\n",
    "        ax.set_yscale(yscale)\n",
    "\n",
    "        ax = axs[i,3]\n",
    "        ax.plot(p_OH, '-o', label=f'{rep}')\n",
    "        ax.set_title(f'OH mean ({yscale} scale)')\n",
    "        ax.set_yscale(yscale)\n",
    "    \n",
    "for ax in axs.ravel():\n",
    "    ax.axhline(0, linestyle=':', color='k', zorder=-1)\n",
    "    ax.set_ylabel('probability')\n",
    "    ax.set_xticks(range(L))\n",
    "    ax.set_xticklabels(poss)\n",
    "    ax.legend(loc='upper left')\n",
    "    \n",
    "fig.tight_layout()\n",
    "#fig.savefig(f'results/corrected_profiles.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ordinary-senegal",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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