fig_S1A_mixture.ipynb

{
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   "id": "hollow-vampire",
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     "end_time": "2021-04-01T22:18:53.062018Z",
     "start_time": "2021-04-01T22:18:52.720952Z"
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   "source": [
    "# Standard imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Load figure style\n",
    "plt.style.use('fig_style.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "valuable-canada",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-01T22:18:53.077978Z",
     "start_time": "2021-04-01T22:18:53.063376Z"
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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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">OH</th>\n",
       "      <th>mean</th>\n",
       "      <td>0.263368</td>\n",
       "      <td>3.029235</td>\n",
       "      <td>36.898937</td>\n",
       "      <td>43.733341</td>\n",
       "      <td>14.094712</td>\n",
       "      <td>1.296028</td>\n",
       "      <td>0.684379</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.020203</td>\n",
       "      <td>0.185416</td>\n",
       "      <td>1.951370</td>\n",
       "      <td>0.512872</td>\n",
       "      <td>1.526490</td>\n",
       "      <td>0.571514</td>\n",
       "      <td>0.021010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">PPP</th>\n",
       "      <th>mean</th>\n",
       "      <td>0.047600</td>\n",
       "      <td>0.366913</td>\n",
       "      <td>3.152471</td>\n",
       "      <td>46.679295</td>\n",
       "      <td>41.586507</td>\n",
       "      <td>7.779855</td>\n",
       "      <td>0.387359</td>\n",
       "    </tr>\n",
       "    <tr>\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>std</th>\n",
       "      <td>0.007826</td>\n",
       "      <td>0.032022</td>\n",
       "      <td>0.249122</td>\n",
       "      <td>0.112469</td>\n",
       "      <td>0.343352</td>\n",
       "      <td>0.049401</td>\n",
       "      <td>0.015927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">mixture</th>\n",
       "      <th>mean</th>\n",
       "      <td>0.318222</td>\n",
       "      <td>2.571846</td>\n",
       "      <td>37.441155</td>\n",
       "      <td>42.513111</td>\n",
       "      <td>14.797994</td>\n",
       "      <td>1.948896</td>\n",
       "      <td>0.408776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep1</th>\n",
       "      <td>0.277853</td>\n",
       "      <td>2.453139</td>\n",
       "      <td>37.185241</td>\n",
       "      <td>42.677836</td>\n",
       "      <td>15.016152</td>\n",
       "      <td>1.977627</td>\n",
       "      <td>0.412153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>0.313381</td>\n",
       "      <td>2.655502</td>\n",
       "      <td>35.627318</td>\n",
       "      <td>42.484272</td>\n",
       "      <td>16.213136</td>\n",
       "      <td>2.272071</td>\n",
       "      <td>0.434319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>0.363431</td>\n",
       "      <td>2.606897</td>\n",
       "      <td>39.510905</td>\n",
       "      <td>42.377225</td>\n",
       "      <td>13.164695</td>\n",
       "      <td>1.596991</td>\n",
       "      <td>0.379856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.042994</td>\n",
       "      <td>0.105637</td>\n",
       "      <td>1.954400</td>\n",
       "      <td>0.152366</td>\n",
       "      <td>1.535885</td>\n",
       "      <td>0.338456</td>\n",
       "      <td>0.027388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                      p4        p5         p6         p7         p8        p9  \\\n",
       "end     sample                                                                  \n",
       "OH      mean    0.263368  3.029235  36.898937  43.733341  14.094712  1.296028   \n",
       "        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",
       "        std     0.020203  0.185416   1.951370   0.512872   1.526490  0.571514   \n",
       "PPP     mean    0.047600  0.366913   3.152471  46.679295  41.586507  7.779855   \n",
       "        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",
       "        std     0.007826  0.032022   0.249122   0.112469   0.343352  0.049401   \n",
       "mixture mean    0.318222  2.571846  37.441155  42.513111  14.797994  1.948896   \n",
       "        rep1    0.277853  2.453139  37.185241  42.677836  15.016152  1.977627   \n",
       "        rep2    0.313381  2.655502  35.627318  42.484272  16.213136  2.272071   \n",
       "        rep3    0.363431  2.606897  39.510905  42.377225  13.164695  1.596991   \n",
       "        std     0.042994  0.105637   1.954400   0.152366   1.535885  0.338456   \n",
       "\n",
       "                     p10  \n",
       "end     sample            \n",
       "OH      mean    0.684379  \n",
       "        rep1    0.697808  \n",
       "        rep2    0.695162  \n",
       "        rep3    0.660167  \n",
       "        std     0.021010  \n",
       "PPP     mean    0.387359  \n",
       "        rep1    0.375971  \n",
       "        rep2    0.380547  \n",
       "        rep3    0.405560  \n",
       "        std     0.015927  \n",
       "mixture mean    0.408776  \n",
       "        rep1    0.412153  \n",
       "        rep2    0.434319  \n",
       "        rep3    0.379856  \n",
       "        std     0.027388  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pct_df = pd.read_csv('csv_results/mixture_profiles.csv', index_col=[0,1])\n",
    "pct_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "banned-popularity",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-01T22:18:53.089601Z",
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   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>-4</th>\n",
       "      <th>-3</th>\n",
       "      <th>-2</th>\n",
       "      <th>-1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rep1</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.305001</td>\n",
       "      <td>21.338729</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.356270</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep2</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.209520</td>\n",
       "      <td>25.424816</td>\n",
       "      <td>1.141728e-21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.365664</td>\n",
       "      <td>3.634946e-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rep3</th>\n",
       "      <td>5.205406e-22</td>\n",
       "      <td>0.123904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.730058</td>\n",
       "      <td>15.807355</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.338682</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.735135e-22</td>\n",
       "      <td>0.041301</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.748193</td>\n",
       "      <td>20.856967</td>\n",
       "      <td>3.805761e-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.353539</td>\n",
       "      <td>1.211649e-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.005343e-22</td>\n",
       "      <td>0.071536</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.775717</td>\n",
       "      <td>4.826796</td>\n",
       "      <td>6.591772e-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013696</td>\n",
       "      <td>2.098637e-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                -4        -3   -2         -1          0             1    2  \\\n",
       "rep1  0.000000e+00  0.000000  0.0  78.305001  21.338729  0.000000e+00  0.0   \n",
       "rep2  0.000000e+00  0.000000  0.0  74.209520  25.424816  1.141728e-21  0.0   \n",
       "rep3  5.205406e-22  0.123904  0.0  83.730058  15.807355  0.000000e+00  0.0   \n",
       "mean  1.735135e-22  0.041301  0.0  78.748193  20.856967  3.805761e-22  0.0   \n",
       "std   3.005343e-22  0.071536  0.0   4.775717   4.826796  6.591772e-22  0.0   \n",
       "\n",
       "             3             4  \n",
       "rep1  0.356270  0.000000e+00  \n",
       "rep2  0.365664  3.634946e-23  \n",
       "rep3  0.338682  0.000000e+00  \n",
       "mean  0.353539  1.211649e-23  \n",
       "std   0.013696  2.098637e-23  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.read_csv('csv_results/mixture_coefficients.csv', index_col=[0])\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "radical-chair",
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     "data": {
      "image/png": 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bbDyG8Orlr/LxSx9vfL/HLj3Y6eKdfHijQBLz1zJhwXoANGE144d0bdvwXaJ3R/3f5qd+uHal6Q2RVi6ezapH5mx83XvQSY1yUJXiZkkys+JsSBoLPGNm90o6BDjAzC5NeX80MDq+3KcoQbkOxcxU6hjS8frhSi1d/ShmT2IVkLzdYy9gZeqbZjYdmJ7rRiQ9ZWb75lqOl1d+5eU7tjJT0PqRy98u1797R9x2e427OcU8cP0YcHCcPgRYUsRtO1fuvH64slS0RsLMngHWSloEbDCzJ4q1befKndcPV66Kep2EmZ1bhM3kPGTl5ZVtefmOrawUuH7k8rfL9e/eEbfdXuPeRNEOXDvnnGt/PHeTc865tLyRcM45l1bFNRKSzpO0OA/l9JO0QtJ8SffnKbZTJT0Uy9w+x7IOi+XMl/S2pGNyLK+HpLtiebdL2jzH8rpIuknSPEmT21jGdpKekbRWUpc4z1NX5Cj+fy/Lcp3TJC2U9KSkc9qwzdGSlsTHyVmue7ikF7Ot123dV5rb77JY92uSHpW0WNKUbNaN6+8W118k6XeSsr62J1/fgUkV1UjEL7Y981jkA2ZWY2aH5lpQbBSGmNnBscw3cynPzO6N5dQAbxCu0s3FYcDjsbwn4utcjAD+YmZDge6S9mhDGR8QTgtdAo1TVwBdJQ3IMcYOR9LuQPc2rHqjmX2dcGX499qw/v1mtj9wIHB+lusuAbLaf3LcVxrtd1laDhxkZoOBrSX1z3L9l8zsgBg3QFbXOxTgO7CyGgngTGBmHssbGlv08/JQ1jCgc+xJXCupcx7KRNJOwAozW5NjUa8APeN0H+BfOZa3E/BcnF4KHJBtAWa21sw+TJnVXOoKl50fAb/OdiUzq4uTXYEX2rD+63GyPj6yWfdDM1uX5SbbvK80s99lzMzeMbPkfQfqgA1Zrl+X8nId8I8sQ8j3d2DlNBKSNgNqzOzhPBX5NrAzMBQ4JP4Cy0U10NXMDgY+Bo7OsbykbwFz81DO34GBkp4n/Hp5NMfyXgKGxOmhhIYnV32A1XF6VZ7K7DAk7Qq8R5OrubNYfxxhP3k6hzDOBm7PYf1M9aGE+0r8vqgys7+2Yd2jJNUSvjMy/rFWgO9AoLLuJ/Ed4MZ8FRZ/uawDkHQnsBsNv4zbYhWwIE4/TJbdyBZ8k9BQ5Oo04M9mdqWknwKnADfkUN6fgYMlPQS8DqzIPcSWU1e4QNI2wE1NZr9D+NIcB+ya7bpmdqKZXSppErBI0m/NbJMvsJbWl/Q1YDhwTLbbThdvC0q2r0jqC1wHHN+W9c3sDuAOSdcCR5L5j8C8fgcmVVIjsQuwp6Szga9KGmNm17a1MElbmtm/48tBQJvLih4FzorTewKv5VheslKtb66ytqU4wlgswPtA71wKM7MNwBgASdOB+3KKLniMMB7+R0Lqihl5KLPimNk7QE3T+ZLuI/zN+gKfkfSAmS3IcN3N4w+n9YSecLPDPy2svz1wNXBU3DcyXreNSrKvxAPds4Cfxs+T7fqbpwytrQY+yWL1vH4HJlXMcJOZXWhmw8zsMOD5PPxxDpT0tKRHgTfN7PEc41sKfCJpPjAAuDnH+CAMWeWr634jcHyMbyQwO5fCJG0fz5R6GHi0LQfqJW0m6UHCQcv7gM3w1BVtllI//hOY07SBaMXFcd94BPhDG46BjSMMn9wa94uMD55L2jfuB7tJelBSqznoc0lz0nS/iz2gTH2bUL8nx8+Z7XGzwyQtkLSA8PfK+MzKAnwHAn7FtXPOuRZUTE/COedc/nkj4ZxzLi1vJJxzzqXljYRzzrm0vJFwzjmXljcSGZBUk21CtDxv/zBJR2S5zvUxyVhOiQRbKP8MSa9JmpUybztJD8cEZYcUYruufMVTPoty7ZWkbST9LIPlaiQtj7HdkDJvVJ7iOEzSEZL6SPpWyvy8nH5aDryRaAdiMr+7UudJau1/t4uZDU69PiGDdbJxB/CNJvMuAi4BDgX+K4/bcq6RmCPp5xku/vuYDPPUAsSRrJt9SMl8YGZj8r2tUvFGInP7Sbon/lLuGy+4eUghffItkjpL+qKkxxTSY48FkPTdmCRwUcxMuZGkhKQ58QKh38R5e8aLaR5PKWNULKdfLPtmYJSkmXHZeakNQOz17C7pzvir6Q5JdwDDJF0o6ZH4OXaIyy+TNCs+n6SQMvxpSZ9L98cws/fZNFFbf8KFc2uAf0vqtemarhJI6hR7qwsk3ZPy1iSFdOJnxuUuTtmf94rz5ku6uslyR8V9brpimutYn+6P6/9Xk+33S/ZiFdKP/5+kpZKay158Uqx/J8XXT9Ak1YWkGZJ+E+vzpXHeDrGePCLpwjjvB3F78yTtnaybwGjgG/GzVaV8hkPUkCL9kHSfv6yZmT9aeRBSBTwQp08g/GIW0D3Ou4zwq/pMYFScJ+CzhF/cIqRCuK1JuQngZ3F6KiFzZXcaLnKcF1+PAr4L9AOeBzoTrj5+KLmtZmJenBL7vXF6G+C+OD0YmBqnVwDdCJlanyH8eDgZ+Ekrf5d+wKyU1wtTpmcBO5T6f+ePwjwIqeD/O053is/zgb2AzZP7AtAjPn8RmN3Cco8QshBvD/w9zvsD8Pk4PQf4XMr2N+57wN+AreO6c5vEuUWsKz0JqTqq0nyeGcDIOH1nLOtXwIFx3r3AdoQroJP1Xk3qZmpdSNa/xYTcUb0IP6Ca/fzl/Kik3E2F9mx8XkpoEHoC0xXG/KsJ2TH/BCQkzSZ8Sf6LcGn/vAzL/SIhX8vVknoQcrFs3WT5v1jIfbMh9iRmAcslXWJmn6bZxjPxuR8NSQqfAsbH6ZfNbK2kt4AXzOzTOP3l1ELi59oe+LGFNCNNpW7fE/BVtp2JmYKb7He1ZlYnKTnvO5JGEvYNa2G5DWb2EfCRpPfjvF2A3yvcd6cPYd/7ZzOxvGdm7wJI6pP6hjWkD6mTtBD4EiETbnOSdXEZsCPwBRrqztI4bzwwVdJ6wtBqa8zMVsfYUnNWNf38ZcuHmzK3R8rzK4T7Q/zNzIYAtxB+VdSZ2U+A04FLCUn8nrSGmwM1HcNvrtzvA5NiuS/HclN9CqBwP4o5ZnYKUEXIF5NOckd8PWV7+8btQePKmzrdaNtmNjJ+lqVptvOcpIGSegK9kpXDVaSXCD3fpse6mub5OYfQmz2LxvtT0+U6KdwdcTtCDzy5jZNi3dkHeDJNLGn32eSQZ6wvAwh1IJ1k3dgtLvdK3C6EX/6vA0vNbBShNzAqZd06Qg+/qU6SesU4Ut9vN/mQvCeRuTpJ9xKGZY4lDAP9TNK+hLTEfweOkvRDoAeh6/leHN9fSLj5yMPAxCblflkhnfYbZvaYpN7AdZL+Ssi4mc6WhHTCnQm9j2WtfQAzeyeOpT4ayz4t84/fmKQjCcNuX5B0i5kdC0wmpBfvTkMvxVWmO4Bvxn17DSEFeHOeABbGR0smx2WW0pBW/mfAbxXutlZHqHfZJhY8XtJowg+lOWb2VgvLDlG4NesCM/unwm13Z0rqSkij/2bsve9IGCo6HdgvrvsO0FfheOHolDIn0HDzo3FZxl4WPMFfCUlKEMYuc731qHPtmqQuZlYfh2+nm1lWp3znYfszgMvM7OVibrc98J6Ec64cHCfp+4RjfT8qdTCugfcknHPOpeUHrp1zzqXljYRzzrm0vJFwzjmXljcSzjnn0vJGwjnnXFr/D/ECGr+QeUcXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 396x136.8 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot mean profile only\n",
    "min_p5_pos = 4\n",
    "max_p5_pos = 10\n",
    "p5_pos = np.arange(min_p5_pos, max_p5_pos+1)\n",
    "max_shift_left = 4\n",
    "max_shift_right = 4\n",
    "shifts = np.array(range(-max_shift_left, max_shift_right+1))\n",
    "M = pct_df.shape[1]\n",
    "N = coeff_df.shape[1]\n",
    "width = 0.4\n",
    "\n",
    "errorbar_kwargs = {'capsize':2,\n",
    "                   'color':'k',\n",
    "                   'elinewidth':1,\n",
    "                   'capthick':1,\n",
    "                   'linewidth':0}\n",
    "\n",
    "# Plot mixture coefficients    \n",
    "fig, axs = plt.subplots(1,2,figsize=[5.5,1.9])\n",
    "\n",
    "# Extract values to plot\n",
    "ppp_hist = pct_df.loc[('PPP','mean'),:]\n",
    "oh_hist = pct_df.loc[('OH','mean'),:]\n",
    "mix_hist = pct_df.loc[('mixture','mean'),:]\n",
    "mix_coeffs = coeff_df.loc['mean',:]\n",
    "\n",
    "ppp_dhist = pct_df.loc[('PPP','std'),:]\n",
    "oh_dhist = pct_df.loc[('OH','std'),:]\n",
    "mix_dhist = pct_df.loc[('mixture','std'),:]\n",
    "mix_dcoeffs = coeff_df.loc['std',:]\n",
    "\n",
    "# Plot percentages vs. 5' positions\n",
    "ax = axs[0]\n",
    "left_pos = p5_pos-width/2\n",
    "right_pos = p5_pos+width/2\n",
    "ax.bar(left_pos, oh_hist, width, label='OH', color='C1')\n",
    "ax.bar(right_pos, mix_hist, width, label='mixture', color='C2')\n",
    "ax.errorbar(x=left_pos, y=oh_hist, yerr=oh_dhist, \n",
    "            **errorbar_kwargs)\n",
    "ax.errorbar(x=right_pos, y=mix_hist, yerr=mix_dhist, \n",
    "            **errorbar_kwargs)\n",
    "ax.set_xticks(p5_pos)\n",
    "ax.set_ylim([0,50])\n",
    "ax.set_yticks([0, 25, 50])\n",
    "ax.set_xlabel('base pairs from -10')\n",
    "ax.set_ylabel(f'%5\\'-ends')\n",
    "ax.legend(loc='upper left', framealpha=0)\n",
    "\n",
    "# Plot mixture coefficients\n",
    "ax = axs[1]\n",
    "ax.bar(shifts, mix_coeffs, .8)\n",
    "ax.errorbar(x=shifts, y=mix_coeffs, yerr=mix_dcoeffs, \n",
    "                **errorbar_kwargs)\n",
    "ax.set_xlabel('change in 5\\' position')\n",
    "ax.set_ylabel(f'% of mixture')\n",
    "ax.set_xticks(shifts)\n",
    "ax.set_ylim([0, 100])\n",
    "ax.set_yticks([0, 50, 100])\n",
    "\n",
    "fig.tight_layout(w_pad=2, h_pad=2)\n",
    "fig.savefig(f'figures/fig_S1A_mixture.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "senior-brief",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
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