23.07.13 EDC37 autofluorescence.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change directory to where the data is stored, define groups, and read area\n",
    "os.chdir('C:/Users/Bryant Kane/OneDrive - University of North Carolina at Chapel Hill/Data/Raw Data/23.07.13 EDC37 autofluorescence/CSVs')\n",
    "\n",
    "files = os.listdir()\n",
    "files\n",
    "\n",
    "group1 = '10 uM L-ala'\n",
    "group2 = '10 uM AcAc'\n",
    "group3 = '10 uM BHB'\n",
    "group4 = 'Blank'\n",
    "\n",
    "\n",
    "start_row = 3\n",
    "end_row = 228\n",
    "end_read = end_row - start_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\681207735.py:14: FutureWarning: Passing 'suffixes' which cause duplicate columns {'2_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_group = df_group.merge(data, how='outer', left_index=True, right_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\681207735.py:14: FutureWarning: Passing 'suffixes' which cause duplicate columns {'2_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_group = df_group.merge(data, how='outer', left_index=True, right_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\681207735.py:14: FutureWarning: Passing 'suffixes' which cause duplicate columns {'2_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_group = df_group.merge(data, how='outer', left_index=True, right_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\681207735.py:14: FutureWarning: Passing 'suffixes' which cause duplicate columns {'2_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_group = df_group.merge(data, how='outer', left_index=True, right_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\681207735.py:14: FutureWarning: Passing 'suffixes' which cause duplicate columns {'2_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_group = df_group.merge(data, how='outer', left_index=True, right_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\681207735.py:14: FutureWarning: Passing 'suffixes' which cause duplicate columns {'2_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_group = df_group.merge(data, how='outer', left_index=True, right_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\681207735.py:14: FutureWarning: Passing 'suffixes' which cause duplicate columns {'2_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_group = df_group.merge(data, how='outer', left_index=True, right_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\681207735.py:14: FutureWarning: Passing 'suffixes' which cause duplicate columns {'2_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_group = df_group.merge(data, how='outer', left_index=True, right_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\681207735.py:14: FutureWarning: Passing 'suffixes' which cause duplicate columns {'2_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_group = df_group.merge(data, how='outer', left_index=True, right_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\681207735.py:14: FutureWarning: Passing 'suffixes' which cause duplicate columns {'2_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_group = df_group.merge(data, how='outer', left_index=True, right_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\681207735.py:14: FutureWarning: Passing 'suffixes' which cause duplicate columns {'2_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_group = df_group.merge(data, how='outer', left_index=True, right_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\681207735.py:14: FutureWarning: Passing 'suffixes' which cause duplicate columns {'2_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_group = df_group.merge(data, how='outer', left_index=True, right_index=True)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fluorescence Units 1</th>\n",
       "      <th>Fluorescence Units 2</th>\n",
       "      <th>Fluorescence Units 3</th>\n",
       "      <th>Fluorescence Units 4</th>\n",
       "      <th>Fluorescence Units 5</th>\n",
       "      <th>Fluorescence Units 6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emission Wavelength</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>359.8</th>\n",
       "      <td>1289.4824</td>\n",
       "      <td>1299.4030</td>\n",
       "      <td>1299.9530</td>\n",
       "      <td>1314.3285</td>\n",
       "      <td>1295.3635</td>\n",
       "      <td>1309.2618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359.6</th>\n",
       "      <td>1297.3389</td>\n",
       "      <td>1308.0729</td>\n",
       "      <td>1307.4655</td>\n",
       "      <td>1322.2787</td>\n",
       "      <td>1303.0408</td>\n",
       "      <td>1317.5709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359.4</th>\n",
       "      <td>1305.2064</td>\n",
       "      <td>1316.9619</td>\n",
       "      <td>1315.1654</td>\n",
       "      <td>1331.3329</td>\n",
       "      <td>1311.5682</td>\n",
       "      <td>1325.7778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359.2</th>\n",
       "      <td>1313.1184</td>\n",
       "      <td>1325.7931</td>\n",
       "      <td>1324.3258</td>\n",
       "      <td>1341.0656</td>\n",
       "      <td>1320.5831</td>\n",
       "      <td>1333.9652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359.0</th>\n",
       "      <td>1321.8712</td>\n",
       "      <td>1334.9197</td>\n",
       "      <td>1333.1858</td>\n",
       "      <td>1350.4667</td>\n",
       "      <td>1328.9824</td>\n",
       "      <td>1342.4459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.8</th>\n",
       "      <td>1965.5704</td>\n",
       "      <td>1978.6626</td>\n",
       "      <td>2008.2128</td>\n",
       "      <td>2027.7307</td>\n",
       "      <td>1993.5530</td>\n",
       "      <td>2020.9957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.6</th>\n",
       "      <td>1958.5354</td>\n",
       "      <td>1972.3091</td>\n",
       "      <td>2001.4403</td>\n",
       "      <td>2021.4875</td>\n",
       "      <td>1987.4109</td>\n",
       "      <td>2015.1624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.4</th>\n",
       "      <td>1951.0967</td>\n",
       "      <td>1966.0538</td>\n",
       "      <td>1993.8951</td>\n",
       "      <td>2015.1602</td>\n",
       "      <td>1981.4077</td>\n",
       "      <td>2007.9814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.2</th>\n",
       "      <td>1943.9104</td>\n",
       "      <td>1959.3969</td>\n",
       "      <td>1986.5726</td>\n",
       "      <td>2008.2694</td>\n",
       "      <td>1975.2524</td>\n",
       "      <td>2000.9133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.0</th>\n",
       "      <td>1937.2159</td>\n",
       "      <td>1951.9514</td>\n",
       "      <td>1979.5822</td>\n",
       "      <td>2001.3655</td>\n",
       "      <td>1969.1552</td>\n",
       "      <td>1994.5643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>225 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Fluorescence Units 1  Fluorescence Units 2  \\\n",
       "Emission Wavelength                                               \n",
       "359.8                           1289.4824             1299.4030   \n",
       "359.6                           1297.3389             1308.0729   \n",
       "359.4                           1305.2064             1316.9619   \n",
       "359.2                           1313.1184             1325.7931   \n",
       "359.0                           1321.8712             1334.9197   \n",
       "...                                   ...                   ...   \n",
       "315.8                           1965.5704             1978.6626   \n",
       "315.6                           1958.5354             1972.3091   \n",
       "315.4                           1951.0967             1966.0538   \n",
       "315.2                           1943.9104             1959.3969   \n",
       "315.0                           1937.2159             1951.9514   \n",
       "\n",
       "                     Fluorescence Units 3  Fluorescence Units 4  \\\n",
       "Emission Wavelength                                               \n",
       "359.8                           1299.9530             1314.3285   \n",
       "359.6                           1307.4655             1322.2787   \n",
       "359.4                           1315.1654             1331.3329   \n",
       "359.2                           1324.3258             1341.0656   \n",
       "359.0                           1333.1858             1350.4667   \n",
       "...                                   ...                   ...   \n",
       "315.8                           2008.2128             2027.7307   \n",
       "315.6                           2001.4403             2021.4875   \n",
       "315.4                           1993.8951             2015.1602   \n",
       "315.2                           1986.5726             2008.2694   \n",
       "315.0                           1979.5822             2001.3655   \n",
       "\n",
       "                     Fluorescence Units 5  Fluorescence Units 6  \n",
       "Emission Wavelength                                              \n",
       "359.8                           1295.3635             1309.2618  \n",
       "359.6                           1303.0408             1317.5709  \n",
       "359.4                           1311.5682             1325.7778  \n",
       "359.2                           1320.5831             1333.9652  \n",
       "359.0                           1328.9824             1342.4459  \n",
       "...                                   ...                   ...  \n",
       "315.8                           1993.5530             2020.9957  \n",
       "315.6                           1987.4109             2015.1624  \n",
       "315.4                           1981.4077             2007.9814  \n",
       "315.2                           1975.2524             2000.9133  \n",
       "315.0                           1969.1552             1994.5643  \n",
       "\n",
       "[225 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "f_name_list = os.listdir()\n",
    "\n",
    "def process_group_data(group_name, f_name_list, start_row, end_row):\n",
    "    df_group = pd.DataFrame()\n",
    "\n",
    "    count = 1\n",
    "    for f_name in f_name_list:\n",
    "        if group_name in f_name:\n",
    "            data = pd.read_csv(f_name, skiprows=start_row, nrows=end_row - start_row, header=None)\n",
    "            data.rename(columns={0: \"Emission Wavelength\", 1: \"Fluorescence Units {}\".format(count)}, inplace=True)\n",
    "            data.set_index(\"Emission Wavelength\", inplace=True)\n",
    "            df_group = df_group.merge(data, how='outer', left_index=True, right_index=True)\n",
    "            count += 1\n",
    "    \n",
    "    # Remove the \"average\" and \"sd\" columns if they exist\n",
    "    df_group.drop(columns=[\"average\", \"sd\"], errors=\"ignore\", inplace=True)\n",
    "    \n",
    "    # Remove NaN columns\n",
    "    df_group.dropna(axis=1, how='all', inplace=True)\n",
    "    \n",
    "    return df_group\n",
    "\n",
    "# Define the group names and call the function for each group\n",
    "group_names = [group1, group2, group3, group4]\n",
    "dataframes = []\n",
    "\n",
    "for group_name in group_names:\n",
    "    df_group = process_group_data(group_name, f_name_list, start_row, end_row)\n",
    "    dataframes.append(df_group)\n",
    "\n",
    "# The resulting dataframes will be stored in the list 'dataframes'.\n",
    "# Access each dataframe by using dataframes[0], dataframes[1], and so on.\n",
    "# For example, df_1 = dataframes[0], df_2 = dataframes[1], and so on.\n",
    "\n",
    "dataframes[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average 1</th>\n",
       "      <th>Average 2</th>\n",
       "      <th>Average 3</th>\n",
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       "    <tr>\n",
       "      <th>Emission Wavelength</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>359.8</th>\n",
       "      <td>1294.44270</td>\n",
       "      <td>1307.14075</td>\n",
       "      <td>1302.31265</td>\n",
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       "    <tr>\n",
       "      <th>359.6</th>\n",
       "      <td>1302.70590</td>\n",
       "      <td>1314.87210</td>\n",
       "      <td>1310.30585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359.4</th>\n",
       "      <td>1311.08415</td>\n",
       "      <td>1323.24915</td>\n",
       "      <td>1318.67300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359.2</th>\n",
       "      <td>1319.45575</td>\n",
       "      <td>1332.69570</td>\n",
       "      <td>1327.27415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359.0</th>\n",
       "      <td>1328.39545</td>\n",
       "      <td>1341.82625</td>\n",
       "      <td>1335.71415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.8</th>\n",
       "      <td>1972.11650</td>\n",
       "      <td>2017.97175</td>\n",
       "      <td>2007.27435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.6</th>\n",
       "      <td>1965.42225</td>\n",
       "      <td>2011.46390</td>\n",
       "      <td>2001.28665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.4</th>\n",
       "      <td>1958.57525</td>\n",
       "      <td>2004.52765</td>\n",
       "      <td>1994.69455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.2</th>\n",
       "      <td>1951.65365</td>\n",
       "      <td>1997.42100</td>\n",
       "      <td>1988.08285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.0</th>\n",
       "      <td>1944.58365</td>\n",
       "      <td>1990.47385</td>\n",
       "      <td>1981.85975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>225 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Average 1   Average 2   Average 3\n",
       "Emission Wavelength                                    \n",
       "359.8                1294.44270  1307.14075  1302.31265\n",
       "359.6                1302.70590  1314.87210  1310.30585\n",
       "359.4                1311.08415  1323.24915  1318.67300\n",
       "359.2                1319.45575  1332.69570  1327.27415\n",
       "359.0                1328.39545  1341.82625  1335.71415\n",
       "...                         ...         ...         ...\n",
       "315.8                1972.11650  2017.97175  2007.27435\n",
       "315.6                1965.42225  2011.46390  2001.28665\n",
       "315.4                1958.57525  2004.52765  1994.69455\n",
       "315.2                1951.65365  1997.42100  1988.08285\n",
       "315.0                1944.58365  1990.47385  1981.85975\n",
       "\n",
       "[225 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def average_pairs_of_columns(df_group):\n",
    "    df_new = pd.DataFrame()\n",
    "\n",
    "    col_count = 1\n",
    "    for i in range(0, len(df_group.columns), 2):\n",
    "        col1 = df_group.columns[i]\n",
    "        col2 = df_group.columns[i+1]\n",
    "        averaged_column = (df_group[col1] + df_group[col2]) / 2\n",
    "        df_new[f'Average {col_count}'] = averaged_column\n",
    "        col_count += 1\n",
    "\n",
    "    return df_new\n",
    "\n",
    "# Create a list to store the new dataframes\n",
    "averaged_dataframes = []\n",
    "\n",
    "# Iterate over the dataframes for each group and calculate averaged dataframes\n",
    "for df_group in dataframes:\n",
    "    df_averaged = average_pairs_of_columns(df_group)\n",
    "    averaged_dataframes.append(df_averaged)\n",
    "\n",
    "# The resulting dataframes with averaged values will be stored in the list 'averaged_dataframes'.\n",
    "# Access each dataframe by using averaged_dataframes[0], averaged_dataframes[1], and so on.\n",
    "# For example, df_1_averaged = averaged_dataframes[0], df_2_averaged\n",
    "\n",
    "averaged_dataframes[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>average</th>\n",
       "      <th>sd</th>\n",
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       "    <tr>\n",
       "      <th>Emission Wavelength</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>359.8</th>\n",
       "      <td>1301.298700</td>\n",
       "      <td>6.409461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359.6</th>\n",
       "      <td>1309.294617</td>\n",
       "      <td>6.145816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359.4</th>\n",
       "      <td>1317.668767</td>\n",
       "      <td>6.144361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359.2</th>\n",
       "      <td>1326.475200</td>\n",
       "      <td>6.656036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359.0</th>\n",
       "      <td>1335.311950</td>\n",
       "      <td>6.724427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.8</th>\n",
       "      <td>1999.120867</td>\n",
       "      <td>23.990320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.6</th>\n",
       "      <td>1992.724267</td>\n",
       "      <td>24.185619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.4</th>\n",
       "      <td>1985.932483</td>\n",
       "      <td>24.196821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.2</th>\n",
       "      <td>1979.052500</td>\n",
       "      <td>24.183114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315.0</th>\n",
       "      <td>1972.305750</td>\n",
       "      <td>24.391326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>225 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         average         sd\n",
       "Emission Wavelength                        \n",
       "359.8                1301.298700   6.409461\n",
       "359.6                1309.294617   6.145816\n",
       "359.4                1317.668767   6.144361\n",
       "359.2                1326.475200   6.656036\n",
       "359.0                1335.311950   6.724427\n",
       "...                          ...        ...\n",
       "315.8                1999.120867  23.990320\n",
       "315.6                1992.724267  24.185619\n",
       "315.4                1985.932483  24.196821\n",
       "315.2                1979.052500  24.183114\n",
       "315.0                1972.305750  24.391326\n",
       "\n",
       "[225 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calc_average_and_sd(df):\n",
    "    # Calculate row-wise average and standard deviation\n",
    "    df_new = pd.DataFrame()\n",
    "    df_new['average'] = df.mean(axis=1)\n",
    "    df_new['sd'] = df.std(axis=1)\n",
    "    return df_new\n",
    "\n",
    "# Create a list to store the new dataframes\n",
    "average_sd_dataframes = []\n",
    "\n",
    "# Iterate over the averaged_dataframes and calculate average and sd dataframes\n",
    "for df_averaged in averaged_dataframes:\n",
    "    df_avg_sd = calc_average_and_sd(df_averaged)\n",
    "    average_sd_dataframes.append(df_avg_sd)\n",
    "\n",
    "# The resulting dataframes with average and sd values will be stored in the list 'average_sd_dataframes'.\n",
    "# Access each dataframe by using average_sd_dataframes[0], average_sd_dataframes[1], and so on.\n",
    "\n",
    "average_sd_dataframes[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make a scatter plot of the average fluorescence units for each wavelength\n",
    "#import seaborn as sns\n",
    "#cmap = sns.color_palette('deep')\n",
    "\n",
    "import pickle\n",
    "\n",
    "# Load the colormap object from the saved file\n",
    "load_path = 'C:/Users/Bryant Kane/OneDrive - University of North Carolina at Chapel Hill/Data/Python Templates/Colormaps/custom_blend_cmap.pkl'\n",
    "with open(load_path, 'rb') as f:\n",
    "    cmap_blended = pickle.load(f)\n",
    "\n",
    "#make axis labels larger font\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "plt.xlabel('Emission Wavelength (nm)', fontweight='bold', fontsize=12)\n",
    "plt.ylabel('Fluorescence Intensity (AU)', fontweight='bold', fontsize=12)\n",
    "#emission wavelength (nm) in increments of 10\n",
    "plt.xlim(315, 345)\n",
    "plt.ylim(2300, 2500)\n",
    "plt.xticks(np.arange(315, 350, 10))\n",
    "plt.yticks(np.arange(2300, 2500, 50))\n",
    "\n",
    "#bold the axis labels\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "#smooth line through the points, color with tab20, make the lines thicker\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(average_sd_dataframes[0].index, average_sd_dataframes[0]['average'], color='purple', linewidth=3, linestyle=(0, (1, 0.5)), label='Group 1')\n",
    "plt.plot(average_sd_dataframes[1].index, average_sd_dataframes[1]['average'], color=cmap_blended(0.8), linewidth=3, linestyle=(0, (1, 0.5)), label='Group 2')\n",
    "plt.plot(average_sd_dataframes[2].index, average_sd_dataframes[2]['average'], color=cmap_blended(0.2), linewidth=3, linestyle=(0, (1, 0.5)), label='Group 3')\n",
    "plt.plot(average_sd_dataframes[3].index, average_sd_dataframes[3]['average'], color='darkgrey', linewidth=3, linestyle=(0, (1, 0.5)), label='Blank')\n",
    "\n",
    "plt.tick_params(axis='x', which='minor', length=3, width=1.5, direction='out', top=False, bottom=True)\n",
    "plt.tick_params(axis='x', which='major', length=7, width=1.5, direction='out', top=False, bottom=True)\n",
    "plt.tick_params(axis='y', which='major', length=7, width=1.5, direction='out', left=True, right=False)\n",
    "plt.tick_params(axis='y', which='minor', length=3, width=1.5, direction='out', left=True, right=False)\n",
    "\n",
    "plt.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.spines['top'].set_linewidth(1.5)\n",
    "ax.spines['bottom'].set_linewidth(1.5)\n",
    "ax.spines['left'].set_linewidth(1.5)\n",
    "ax.spines['right'].set_linewidth(1.5)\n",
    "\n",
    "ax.spines['top'].set_color('black')\n",
    "ax.spines['bottom'].set_color('black')\n",
    "ax.spines['left'].set_color('black')\n",
    "ax.spines['right'].set_color('black')\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [4, 3]\n",
    "\n",
    "group11 = \"10 μM L-ala\"\n",
    "group21 = \"10 μM AcAc\"\n",
    "group31 = \"10 μM BHB\"\n",
    "group41 = \"Blank\"\n",
    "\n",
    "plt.legend([group11, group21, group31, group41], fontsize=10.5, loc='upper right', frameon=False)\n",
    "\n",
    "plt.savefig('23.07.13 EDC37 Scatter.png', dpi=1200, bbox_inches='tight') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'df_1': {'max_average': 2486.599233333333,\n",
       "  'corresponding_sd': 8.251524646138627},\n",
       " 'df_2': {'max_average': 2464.0584375, 'corresponding_sd': 24.631474537583653},\n",
       " 'df_3': {'max_average': 2376.45905, 'corresponding_sd': 50.19855394054352},\n",
       " 'df_4': {'max_average': 2470.1694899999998,\n",
       "  'corresponding_sd': 34.0506777641871}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize a dictionary to store the maximum average value and corresponding standard deviation for each group\n",
    "max_values_dict = {}\n",
    "\n",
    "# Loop over each dataframe in the 'average_sd_dataframes' list\n",
    "for i, df in enumerate(average_sd_dataframes):\n",
    "    # Find the row with the maximum average value\n",
    "    max_row = df[df['average'] == df['average'].max()]\n",
    "    \n",
    "    # Get the maximum average value (this is the first and only entry in the 'average' column of 'max_row')\n",
    "    max_average = max_row['average'].values[0]\n",
    "    \n",
    "    # Get the corresponding standard deviation (this is the first and only entry in the 'sd' column of 'max_row')\n",
    "    corresponding_sd = max_row['sd'].values[0]\n",
    "    \n",
    "    # Store these values in the dictionary\n",
    "    max_values_dict[f'df_{i+1}'] = {'max_average': max_average, 'corresponding_sd': corresponding_sd}\n",
    "\n",
    "# Now 'max_values_dict' contains the maximum average value and corresponding standard deviation for each group\n",
    "max_values_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\1914042303.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res_all_df = res_all_df.append({'comparison': f'{group_names[i]} vs {group_names[j]}', 'p-value': ttest_res.pvalue}, ignore_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\1914042303.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res_all_df = res_all_df.append({'comparison': f'{group_names[i]} vs {group_names[j]}', 'p-value': ttest_res.pvalue}, ignore_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\1914042303.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res_all_df = res_all_df.append({'comparison': f'{group_names[i]} vs {group_names[j]}', 'p-value': ttest_res.pvalue}, ignore_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\1914042303.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res_all_df = res_all_df.append({'comparison': f'{group_names[i]} vs {group_names[j]}', 'p-value': ttest_res.pvalue}, ignore_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\1914042303.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res_all_df = res_all_df.append({'comparison': f'{group_names[i]} vs {group_names[j]}', 'p-value': ttest_res.pvalue}, ignore_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\1914042303.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res_all_df = res_all_df.append({'comparison': f'{group_names[i]} vs {group_names[j]}', 'p-value': ttest_res.pvalue}, ignore_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\1914042303.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res_all_df = res_all_df.append({'comparison': f'{group_names[i]} vs {group_names[j]}', 'p-value': ttest_res.pvalue}, ignore_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\1914042303.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res_all_df = res_all_df.append({'comparison': f'{group_names[i]} vs {group_names[j]}', 'p-value': ttest_res.pvalue}, ignore_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\1914042303.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res_all_df = res_all_df.append({'comparison': f'{group_names[i]} vs {group_names[j]}', 'p-value': ttest_res.pvalue}, ignore_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\1914042303.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res_all_df = res_all_df.append({'comparison': f'{group_names[i]} vs {group_names[j]}', 'p-value': ttest_res.pvalue}, ignore_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\1914042303.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res_all_df = res_all_df.append({'comparison': f'{group_names[i]} vs {group_names[j]}', 'p-value': ttest_res.pvalue}, ignore_index=True)\n",
      "C:\\Users\\Bryant Kane\\AppData\\Local\\Temp\\ipykernel_24172\\1914042303.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  res_all_df = res_all_df.append({'comparison': f'{group_names[i]} vs {group_names[j]}', 'p-value': ttest_res.pvalue}, ignore_index=True)\n"
     ]
    },
    {
     "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>comparison</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10 uM L-ala vs 10 uM AcAc</td>\n",
       "      <td>0.195898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10 uM L-ala vs 10 uM BHB</td>\n",
       "      <td>0.014357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10 uM L-ala vs Blank</td>\n",
       "      <td>0.455518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10 uM AcAc vs 10 uM L-ala</td>\n",
       "      <td>0.195898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10 uM AcAc vs 10 uM BHB</td>\n",
       "      <td>0.020241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10 uM AcAc vs Blank</td>\n",
       "      <td>0.772945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10 uM BHB vs 10 uM L-ala</td>\n",
       "      <td>0.014357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10 uM BHB vs 10 uM AcAc</td>\n",
       "      <td>0.020241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10 uM BHB vs Blank</td>\n",
       "      <td>0.012309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Blank vs 10 uM L-ala</td>\n",
       "      <td>0.455518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Blank vs 10 uM AcAc</td>\n",
       "      <td>0.772945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Blank vs 10 uM BHB</td>\n",
       "      <td>0.012309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   comparison   p-value\n",
       "0   10 uM L-ala vs 10 uM AcAc  0.195898\n",
       "1    10 uM L-ala vs 10 uM BHB  0.014357\n",
       "2        10 uM L-ala vs Blank  0.455518\n",
       "3   10 uM AcAc vs 10 uM L-ala  0.195898\n",
       "4     10 uM AcAc vs 10 uM BHB  0.020241\n",
       "5         10 uM AcAc vs Blank  0.772945\n",
       "6    10 uM BHB vs 10 uM L-ala  0.014357\n",
       "7     10 uM BHB vs 10 uM AcAc  0.020241\n",
       "8          10 uM BHB vs Blank  0.012309\n",
       "9        Blank vs 10 uM L-ala  0.455518\n",
       "10        Blank vs 10 uM AcAc  0.772945\n",
       "11         Blank vs 10 uM BHB  0.012309"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "\n",
    "# Initialize a dataframe to store all p-values\n",
    "res_all_df = pd.DataFrame(columns=['comparison', 'p-value'])\n",
    "\n",
    "# Identify the emission wavelength corresponding to the maximum average for each group\n",
    "max_wavelengths = [df['average'].idxmax() for df in average_sd_dataframes]\n",
    "\n",
    "# Define the group names\n",
    "group_names = [group1, group2, group3, group4]\n",
    "\n",
    "# Loop over each pair of dataframes in the 'averaged_dataframes' list\n",
    "for i, df1 in enumerate(averaged_dataframes):\n",
    "    for j, df2 in enumerate(averaged_dataframes):\n",
    "        if i != j:\n",
    "            # Get the values at the max wavelength for each dataframe\n",
    "            values1 = df1.loc[max_wavelengths[i]]\n",
    "            values2 = df2.loc[max_wavelengths[j]]\n",
    "\n",
    "            # Perform t-test\n",
    "            ttest_res = ttest_ind(values1, values2)\n",
    "\n",
    "            # Store the comparison and p-value in the dataframe\n",
    "            res_all_df = res_all_df.append({'comparison': f'{group_names[i]} vs {group_names[j]}', 'p-value': ttest_res.pvalue}, ignore_index=True)\n",
    "\n",
    "# Now 'res_all_df' is a dataframe that contains all comparisons and their p-values\n",
    "res_all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "comparison: 10 uM L-ala vs Blank, p-value: 0.45551787861052095, label: n.s.\n",
      "comparison: 10 uM AcAc vs Blank, p-value: 0.7729450891310623, label: n.s.\n",
      "comparison: 10 uM BHB vs Blank, p-value: 0.012308963386323738, label: *\n",
      "comparison: Blank vs 10 uM L-ala, p-value: 0.45551787861052095, label: n.s.\n",
      "comparison: Blank vs 10 uM AcAc, p-value: 0.7729450891310623, label: n.s.\n",
      "comparison: Blank vs 10 uM BHB, p-value: 0.012308963386323738, label: *\n"
     ]
    },
    {
     "data": {
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9ir59+0IgELS6OY+Bltc42tLifV56errc3CZNqbS0FG5ubgBqntXR09Nrls+tZW5uzmv+mOZWe4XR0HmiK4yWje/vhEK9pAYNGoSYmBgANQlEIBA0uK2VlRUyMzP5xNwitLQf4JYWb6309HR06dwFpWWlqg6lWegJ9XAr7ZZaJw2g5f49kRdrkl5SoaGhcHFxgUwm45JFfXlGQ0MDM2fO5BszIZy8vDyUlpViHMbBHOZN/nmVqEQ4wgEAn+EzaEO7yT+zVh7ycLjsMPLy8tQ+YRACKJgwevTogWvXrqGkpATvvvsuLC0tcezYMW69QCCAlpYWHBwcYGZm1mTBkteHOcxhi6afE74CFdxrG9hABzpN/pmEtFQKN3p3794dABAdHQ1dXV288847TRYUIYQQ9aNQwvjll18gEokwatQo5OTkAKi519yQ8ePHKyc6QgghakOhhDFhwgTY2dlh1KhRmDBhwgsbvQUCASUMQghphRS+JfV8I/eLOlY1YmgqQgghLYBCCUMmk9X7mpCW6tk//6tCFVeWjWxo/fOVaPPP/wgh/6NWc3oT0lwSkYgYxMiV7cRO7rU73DEQNFoBIc9rVMI4d+4cCgsL4enpiYSEBHz22WfIzc3FtGnTEBIS8sI2DkLUQS/0Qmd0bnA9XV0QUhfvhHH06FF8+OGHGDt2LDw9PeHj44O0tDQAwJo1a9C2bVt8+eWXSg+UEGWiW06E8Md7Tu81a9ZAJpPB2toa165dQ1paGpydnbFy5UowxrB9+/amiJMQQoiK8U4Yt27dgr6+PkJDQ3Hu3DkAwKRJkxAQEABjY2Pcu3dP2TESQghRA7wTRnV1NXR1daGpqYno6GgIBAL069cP1dXVqKyshJYWtaMTQkhrxPvXvV27dkhOTsbixYvx22+/wdzcHN27d8esWbMgkUjQs2fPJgiz5WvOIbuBmmG7a127do2G7SaEvDLeCePzzz+Hr68vVq9eDcYYpk6dCg0NDWzbtg0CgQBz5sxpijhbtPT0dHTp0kXuR7w51c5j0Jz09PRw65b6D9tNCFEc74Tx1VdfQSAQ4OTJk3B2dsa3334LLS0t9OrVC5MnT8a4ceOaIs4WLS8vD6Wlpfj6668hFoub5TMrKioQGBgIAAgKCoKOTvONwpqRkYGwsDAatpuQVqZRDQ5ffvllna6zcXFxSgmoNROLxWjfvn2zfFZZWRn32tHREUKhsFk+lxDSejUqYRQWFuL8+fN49uxZvUOF+Pj4vHJghBBC1AvvhHHq1CmMHz8eEomk3vUCgYASBiGEtEK8E4afnx9KSkq49zo6OjQUCCEq0Jw976jXHQEakTDu3LkDgUCAyMhIjBs3Dpqamk0RFyHkBdLT09G1cxdIy5q/550qet2JhHpITaNed6rGO2F07twZDx8+xIcfftgU8RBCFJCXlwdpWSlCXXqjY5umHxOrrLoa3rE1o/sedHOHsBn/oXj32TPMvpJAve7UAO+EsXLlSowZMwbbt2/H9OnTmyImQoiCOrZpg+7GJk3+OdKq/80b8qaRMUQ0osNrifd/9YiICIjFYsyYMQP+/v4wNzeXGw5EIBDg5s2bSg2SEEKI6vFOGAcPHuReFxUVoaioSG49NYATQkjrxDth1D49TAgh5PVCCYMQQohCeA9vDgCMMRw7dgyzZs2Cl5cXACAqKkplg+sRQghperwTRklJCdzd3TFu3DiEhYXh6NGjAIBp06ahV69eyMnJ4VVfRkYGvL29YWpqCrFYjLlz58qNgwQAxcXFEIvFiIiIkCs3NjaGQCCQW2ofKiwrK8PUqVNhbGwMGxsbrFu3ju+hEkIIeQ7vW1JLlixBbGwsunTpgvT0dJSWlkIqlaK8vBy3bt3CwoULsXPnToXqYozB29sbJiYmuHDhAgoKCjBlyhRoampi7dq13Hb+/v7IzMyU2zcjIwPFxcX4+++/IRKJuHJ9fX0AwIIFC5CYmIizZ8/i4cOHmDRpEhwcHODt7c33kAkhhKARCePQoUPQ1tZGbGwsnJycUFpaCpFIhOvXr6NTp044deqUwnWlpaUhPj4e2dnZsLKyAgAEBwdj/vz5XMKIjY3FmTNnYG1tLbdvamoqbGxs6h39VSKRYMeOHTh58iRcXFzg4uKCmzdvIiwsjBIGIYQ0Eu9bUk+ePIGhoSFMTU3lyh0cHKCrq1unm+2LWFtb49SpU1yyqFVcXAwAKC8vx/Tp07Fp0ybo6urKbZOSkoJOnTrVW+/169dRWVmJd999lytzc3PDn3/+We/oukBNknnZQgghiti/fz+GDh2q6jCUjnfCaN++PQoKCnDy5EmuTCqVIiAgACUlJXjjjTcUrsvY2BjDhg3j3stkMoSFhWHw4MEAap4qd3Z2rvfEp6amQiqVwsPDAzY2NhgxYgRu374NAMjKyoK5ubncpEFWVlYoKytDfn5+vbEYGBi8cPl3UiOEkH+7ffs2li1bhoqKCpiZmeHIkSPYtm2bqsNSGt63pObNm4fp06dj1KhRXFmbf8ayEQgEmDlzZqOD8fPzw5UrV5CQkICUlBRs2bIFN27cqHfbW7duoaCgACtXroShoSHWrFmDwYMHIyUlBVKptM4VSe378vLyRsdHCCEvIpFIkJKSgh9//BGFhYXIzMyEp6enqsNSGt4JY+rUqcjPz0dwcDCkUilX3qZNG/j7+2PGjBmNCsTf3x+hoaE4cOAAunXrBjc3NwQHBzf4L/tTp06hsrISBgYGAIC9e/fCzs4OUVFREAqFdRJD7fvnG8if9/yQ7fWRSCR0lUEIeSFnZ2esX78e/fv3h1gsxoQJE17pH9HqplEjiPn5+WHmzJmIi4tDfn4+bGxs4OLiAkNDw0YF4evri82bN2PPnj3w8vLCw4cPcenSJVy/fh3z5s0DUHPba8aMGThw4ABOnjwJXV1duasIoVCIdu3aISMjA/369UNeXh6qqqq4ca6ys7Ohp6cHY2PjemOo7V1FCCGvIiwsDGPGjMFnn32GDz74AP/5z39gZGSk6rCUgnfCGDRoECwtLREZGSnXtlBdXQ1XV1eYmprixIkTCtcXFBSELVu2IDIykuvBJBaLcefOHbntPDw8MGvWLHz88cdgjKFjx45YunQpJk+eDKDmCuDOnTvo0qULevbsCW1tbcTHx3Nj98fGxqJ3797Q0GjUs4qEEKKQ1atXo6KiAjo6Orh9+3ad2+Mt2UsTBmMMsbGxYIwBAM6dOwcLCwucP39ebrunT5/ixo0bvAYfTE1NxfLly7Fw4UK4ubkhOzubW9exY0f5QLW0YGlpCbFYDAAYOXIkAgMD4ejoCAsLCyxduhRt27bFiBEjoKmpiUmTJmHGjBkIDw9HRkYGvv/+e4SHhyscGyGENFZth5vWlCwABRKGQCDADz/8gMOHD3NleXl5GDhwYL3b29nZKfzhx44dQ3V1NVasWIEVK1bIratNUA357rvvoK2tjf/85z8oLi7GoEGDcOLECW4GwPXr12PmzJkYOHAgjIyMEBQUhHHjxikcGyGEEHkK3Z9Zu3YtdHR0wBjjriAYY3UWIyMjLFmyROEPDwgIqLee+pLFgwcPuNtPQE2bxbp165CZmQmJRIKoqCi5ZCUSibBr1y6UlJQgIyMDs2fPVjguQgghdSnUhuHo6IjMzEyUl5fD1tYWtra2SEpK4tYLBAJoaWnBxMSE5sMghJBWSuFGbxOTmmkg79+/Dy0tLepiSgghrxnevaQcHByQlpaGDRs24NmzZ/UOtfHtt98qJThCiGrllpUit6wMZdXVXFlKcRGE/7QVWgqFsBTqqSo80sx4J4yff/4ZU6ZMeWGjNCUMQlqHvQ/uY0NaqlyZd2wM9/qbzl0xp8ubzR0WURHeCSM4OBgymQwaGhqwtbWFSCSidgtCWqmPHdvhPWubBtdbCoXNGA1RNd4JIzMzEwKBABcvXsQ777zTFDGRV1BYWIjCwkJUVFRwZQ8ePOD6hZuYmHDtUYS8jKVQj245EQ7vhNGzZ0+kpKRQslBTf/zxBw4ePChX9vw87N7e3vjwww+bOyxCSCvAO2GEhoZi8ODBWLp0KRYsWNDo8aNI0xgyZAjefvvtBtfT1QUhpLF4J4yvv/4aBgYGWLlyJVauXAlNTU1ugD+g5pkMmmxIdeiWEyGkqfBOGImJiXLvq6qqUFVVxb2nBnBCCGmdeCcMGsCPEEJeT7wTxqRJk5oiDkIIIWpOoYTx3Xff8arUz8+vUcEQQghRXwoljICAAF5tE5QwCCGvk6ysLGRlZTW43sbGBjY2DT8A2VIolDDs7e2pMZsQQhqwdetWBAUFNbg+MDAQy5Yta76AmohCCePBgwdNHAYhhLRcX3zxBT744AOUlpbKTQutp1fzlHxruLoAGtHoTQghRF7tLafnn0Hr2bMn9PX1VRiV8ik04x4hhBBCCYMQQohCKGEQQghRCCUMQgghCmlUwmCM4dixY5g1axa8vLwAAFFRUSgtLVVqcIQQQtQH715SJSUlGDFiBC5evAjGGPd8xrRp02Bubo6zZ8/CyspK6YESQghRLd5XGEuWLEFsbCw6d+4MkUgEAJBKpSgvL8etW7ewcOFCpQdJCCFE9XgnjEOHDkFbWxuxsbHcvAsikQjXr1+HlpYWTp06pfQgCSGEqB7vhPHkyRMYGhrC1NRUrtzBwQG6urooKipSVmyEEELUCO+E0b59exQUFODkyZNcmVQqRUBAAEpKSvDGG28oNUBCCCHqgXej97x58zB9+nSMGjWKK2vTpg2Amtn2Zs6cqbzoCCGEqA3eVxhTp07F6tWrIRQKwRjjFgMDAyxfvhwzZsxoijgJIYSoWKMGH/Tz88PMmTMRFxeH/Px82NjYwMXFBYaGhsqOjxBCiJpo1IN7eXl52L9/P4YOHYqJEyeiXbt2WLt2LbKzs5UdHyGEEDXBO2HcuXMHTk5O8PX1RWVlJQDg6tWrCAkJQa9evXD37l2lB0kIIUT1eCeMRYsWISsrCzY2NsjLywMAmJiYoH379sjKysLSpUuVHiQhhBDV450wLly4AB0dHSQmJnKzSLm7uyM+Ph7a2tqIiYlRepCEEEJUj3fCKC4uhq6uLszNzeXKzczMoK2tjcLCQl71ZWRkwNvbG6amphCLxZg7dy7KysrqfKZYLEZERIRc+f79+9GhQweIRCJ4enpyVzxAzQCJAQEBsLCwgKmpKfz8/CCTyfgdLCGEEA7vhNG5c2eUlJQgICCAe6o7JycH33zzDSQSCTp37qxwXYwxeHt7QyqV4sKFC4iMjERUVFSd21r+/v7IzMyUK7t8+TKmTp2KwMBAxMfHo7CwEJMnT+bWr1+/Hvv27cORI0dw6NAh7N27F+vXr+d7uIQQQv7BO2HMmjULjDGsXbuWu6qwtbXFpk2bIBAIMHv2bIXrSktLQ3x8PMLDw9GtWzf0798fwcHB2LdvH7dNbGwszpw5A2tra7l9w8LCMH78ePj4+KBHjx7YvXs3Tpw4gfv37wMANmzYgODgYLi5uWHgwIFYs2YNwsLC+B4uIYSQf/BOGFOmTEFISAj34F51dTUYYxAKhQgJCZH7V/7LWFtb49SpU3WGQy8uLgYAlJeXY/r06di0aRN0dXXltomPj8eAAQO493Z2drC3t0d8fDwyMzPx6NEjufVubm54+PAhsrKy6o1FIpG8dCGEkNdZox7cW7hwIb788kvExcWhoKAAZmZmcHV1hZGREa96jI2NMWzYMO69TCZDWFgYBg8eDABYuXIlnJ2dMXTo0Dr7ZmVlwdbWVq7MysoKjx8/5pLC8+trk9Ljx4+5xvrnGRgY8IqdEEJeN41KGABgZGSE4cOHKzMW+Pn54cqVK0hISEBKSgq2bNmCGzdu1LutVCqtc9Whq6uL8vJySKVS7v3z64CaqxZCCCH88U4YjDHs2LEDJ06cwLNnz+r0PBIIBDhz5gzvQPz9/REaGooDBw6gW7ducHNzQ3BwcIOz9wmFwjo//uXl5RCJRBAKhdz7518D4CZ9+reSkpIXxieRSGgmQULIa413wvD398e6desA1CSPf6udspUPX19fbN68GXv27IGXlxcePnyIS5cu4fr165g3bx6AmiuKGTNm4MCBAzh58iTEYnGdoUiys7NhY2MDsVjMvXd0dOReA6j3dhQA6Ovr846bEEJeJ7wTxq5du8AYg52dHdzd3SESiRqVJGoFBQVhy5YtiIyMhLe3NwBALBbjzp07ctt5eHhg1qxZ+PjjjwEArq6uiI2N5RrZHz16hEePHsHV1RW2trawt7dHbGwslzBiY2Nhb2/fYMIghBDyYrwTRmlpKXR1dXH16tU6s+7xlZqaiuXLl2PhwoVwc3OTu2Lo2LGjfKBaWrC0tOSuHmbOnAkPDw/07dsXvXv3xjfffINRo0ahXbt23Hp/f3+0bdsWABAQEMBdrRBCCOGPd8IYPnw4jh8/Dj09vVf+8GPHjqG6uhorVqzAihUr5NbVd7vreX379sXWrVvx7bffoqCgAEOHDsX27du59QsWLEBubi48PT2hpaWFqVOnYs6cOa8cMyGEvK54J4yVK1ciKSkJY8eOxVdffQVzc3NoaclX06dPH4XqCggIQEBAgELbPnjwoE7Z5MmTG3zuQ1NTE+vXr6enuwkhREl4J4zaoT8ePHiAP/74o856gUCAqqqqV4+MEEKIWmlUt9pXWU8IIaRl4p0wasdqIoQQ8nrhnTAcHBzk3j99+pTm8iaEkNdAo+b0vnHjBjw9PWFsbMx1rZ08eXK9bRqEEEJaB94JIykpCX379sWxY8fw9OlTMMbAGMMvv/yCESNGUNIghJBWinfCWLx4McrKyuDv78/NuldRUYERI0agqqoKQUFBSg+SEEKI6vFOGJcuXYKhoSFWrlzJjQCrq6uLAwcOwNDQENevX1d6kIQQQlSPd8JgjKGyshLV1dVy5QUFBZBIJHUe4iOEENI68E4YAwYMQGlpKaZMmcLNO7F582YMHjwYMpkM/fr1U3qQhBBCVI/35cDatWtx6dIl7N27lyv7+uuvwRiDgYFBnTGhCCGEtA68rzDefPNNJCQkYOLEibCysoKWlhbs7Ozw6aefIjExEU5OTk0RJyGEEBVrVINDx44dsWfPHu69TCaDhkajHukghBDSQjTqV37v3r1wd3fn3iclJcHR0RG7du1SWmCEEELUC+8rjMjISHz66acQCAQoKCiAqakpbt68ifT0dEyZMgUikQgffvhhU8RKCCG8paenIy8vr1k+q7S0lHt97do1pcwbxIe5uTns7e2brH7eCeP777+HQCDAzJkzuecwRo4ciW+++QYbNmzA999/TwmDEKIW0tPT0aVLV5SWSpv9s93c3Jr9M/X0RLh1K7XJkgbvhJGWlgZDQ0OEhYVxZRYWFvh//+//ISIiArdu3VJqgIQQ0lh5eXkoLZXCrX8YjIzeaPLPq6oqw+lTYwAAw4Yfg5aWsMk/s1Zx8R3EXvgaeXl56pMwtLS0IJFIkJ+fDzMzM648KysLz549Q5s2bZQaICGEvCojozdgZtajyT+nsvJ/VzKmpt2hrS1q8s9sTrwThru7O6KiouDu7o5p06bB3NwcGRkZ2LFjBxhjco3hhBBCWo9GzekdHR2NlJQUzJs3jytnjMHQ0BCrVq1SaoCEEELUQ6Mf3Pvoo49gYWEBTU1NWFpaYuLEibh8+TK6du3aFHESQghRsUY9uNepUyfs379f2bEQQghRY41+PFsikXCvDx48iB9//BEZGRlKCYoQQoj64Z0wCgsLMWjQIPj5+QEAZs2ahY8++gi+vr5wcXFBWlqa0oMkhBCierwTxqJFi3Du3DkkJyejqKgI27Ztg4aGBqytrfHkyRMsW7asCcIkhBCiarwTxokTJ6ChoYHQ0FBER0ejoqICX331FZKTk6GlpYVLly41RZyEEEJUjHfCyM3Nhbm5OZydnRETEwOBQAB3d3eYmJjAxMQET548aYo4CSGEqBjvhGFmZoaioiIUFxfj5MmTEAgEGDBgAG7duoW8vDxYW1s3RZyEEEJUjHfCePfdd1FRUQGxWIy7d+/Czc0Nurq6cHV1BVAzECEhhJDWh3fCWLlyJdq2bQupVAoDAwOsWbMGBgYGAGqez1i6dKnSgySEEKJ6vB/c69ixI+7cuYPk5GR06NABxsbGAIAdO3Zg2LBhNPggIYS0Uo16cE9XVxc9e/bEvXv38OuvvwIARo0aRcmCEEJasUYljO3bt8PW1ha9e/fGuHHjAACurq70DAYhhLRijZqi9YsvvpArq6ioQHJyMv766y+Ym5vj66+/VlqAhBBC1APvK4zvvvsOAoEAx48fh1gsBgBoa2tj3bp1YIzhxx9/5FVfRkYGvL29YWpqCrFYjLlz56KsrAwAcPr0aTg5OUFPTw9OTk44efKk3L5OTk4QCARyS3JyMoCa4dYDAgJgYWEBU1NT+Pn5QSaT8T1cQggh/+B9hZGamgozMzO8//77XJlAIMA333yD4OBgPHjwQOG6GGPw9vaGiYkJLly4gIKCAkyZMgWampr44osv4OnpiZCQEIwZMwZHjx7F2LFjkZaWBkdHR1RXV+P27duIiYlBp06duDrNzc0BAOvXr8e+fftw5MgRVFZW4pNPPoGlpSXmz5/P95AJIYSgEVcYhoaGKC4uRkFBgVx5YmIiCgsLYWJionBdaWlpiI+PR3h4OLp164b+/fsjODgY+/btw+PHj/H5559jzpw5aN++PebOnQt9fX1cvnwZAHD//n1UVFSgT58+sLa25hYtrZocuGHDBgQHB8PNzQ0DBw7EmjVr5OYhJ4QQwg/vhDFmzBhUVlaif//+KCwsBAB89NFH8PDwgEAgwKhRoxSuy9raGqdOnYKVlZVceXFxMTw8PBAaGgoAqKysxE8//YTy8nL06dMHAJCSkgI7OzsIhXUnWc/MzMSjR48wYMAArszNzQ0PHz5EVlZWvbFIJJKXLoQQ8jrjfUvqu+++Q2JiIq5du8aV/fe//wVQMxtfSEiIwnUZGxtj2LBh3HuZTIawsDAMHjyYK7t79y66dOmC6upqrF69Go6OjgBqbo3p6Ohg1KhRSExMROfOnbF27Vr06dOHSwq2trZcPbVJ6fHjx7CxsakTS+3Dh4QQQurHO2EYGxsjPj4eu3fvxpkzZ5Cfnw8bGxu4u7vj008/5W4JNYafnx+uXLmChIQErszCwgIJCQmIi4vD3Llz0bFjR3h5eeHWrVsoLCzEtGnTEBwcjO3bt2Pw4MFISUmBVCoFUPO8SK3a1+Xl5Y2OjxBCXme8f90HDhyIHj16YNmyZZg6darSAvH390doaCgOHDiA7t27c+VGRkZwdnaGs7MzUlJSsHHjRnh5eWH79u2QSqUwNDQEAPz444+4ePEidu/ejffeew9ATXKovWVVmyhEIlG9n19SUvLC+CQSSZ1bZ4QQ8jrh3Ybx559/YteuXdyQIMrg6+uLdevWYc+ePfDy8gIA3Lx5ExcuXJDb7s0330ReXh4AQEtLi0sWQE1PrS5duiAjI4Pr7pudnc2tr31d3+0oANDX13/pQgghrzPeCWP48OGQSqVcb6VXFRQUhC1btiAyMhITJkzgyqOiojB9+nQwxriypKQkdO3aFUDNlU5QUBC3TiaT4caNG+jSpQtsbW1hb2+P2NhYbn1sbCzs7e0bTBiEEEJejPctKVtbW2hoaODdd99Fx44dYWNjAz09PQgEAgDgHupTRGpqKpYvX46FCxfCzc1N7orgk08+wapVqxAQEIBp06bht99+w549exAXFwcAGD16NIKDg+Hs7IzOnTtjw4YNKCoqwuTJkwEAM2fOhL+/P9q2bQsACAgIwLx58/geLiGEkH/wThjPP8l9584d3LlzR259beJQxLFjx1BdXY0VK1ZgxYoVcusYYzh9+jRmz56NjRs3wtHREf/973/h4uICAJgzZw7Kysrg6+uLnJwcvPPOO/jjjz+4ARAXLFiA3NxceHp6QktLC1OnTsWcOXP4Hi4hhJB/8E4YPj4+vJLCiwQEBCAgIKDB9a6uroiPj693nUAgwKJFi7Bo0aJ612tqamL9+vVYv369UmIlhJDXHe+EERER0QRhEEIIUXeNfmji2rVrOHLkCHJycmBjYwMvLy+57rCEEEJal0YljAULFtS51bN8+XIsWrQIwcHBSgmMEEKIeuHdrTYiIoIbytzOzg79+/eHnZ0dZDIZQkJCsHfv3qaIkxBCiIrxThgbN26EQCBASEgIHjx4gHPnzuHBgwdYvnw5GGPcgIGEEEJaF94JIzU1Ffr6+li4cKFc+eLFi6Gvr4+bN28qLThCCCHqg3fCMDAwQFlZGTe0ea38/HyUlZVxz0EQQghpXXgnDHd3d1RXV+ODDz5ATEwM0tPTERMTgzFjxkAmk8Hd3b0p4iSEEKJivHtJBQYG4vjx47h06RIGDRrElTPGoKuriyVLlig1QEIIIeqB9xVG9+7dcfbsWfTo0QOMMW558803cerUKfTo0aMp4iSEEKJijXoOw9XVFVevXsWDBw+Qk5MDa2trODg4KDs2QgghaqTx0+MBcHR05KZMJYQQ0rrxThiampovXC8QCFBVVdXogAghhKgn3gnj+QmNGrOeEEJIy8Q7Yezfv1/ufVVVFYqLi7F//348evQI+/btU1pwhBDSEkilOSgtzUFVVRlXVlCQDC0tIQBAT88KIpGVqsJTGt4J46OPPqq3fMqUKXBwcMDu3bvx7rvvvnJghBDSUty+vRs3rq+TKzt9agz3uofTPPTsOb+5w1K6V2r0fp5QKIRQKMR///tfbN68WVnVEkKI2uvU6VPY2Q1tcL2eXsu/ugAakTC+++67OmXl5eW4ePEiHj16BGNjY2XERQghLYZI1DpuOb0M74QREBBQ7xSttY3dH3744atHRQghRO3wThj29vZ1EoaGhgZMTEwwZMgQBAYGKi04Qggh6oN3wnjw4EEThEEIIUTd8R5LihBCyOtJoSuMN998U+EKBQIBTaJECCGtkEIJ49atWwpXWF+DOCGEkJZPoYRBDdmEEEIoYRBCCFGIQo3eP/zwA8LDw5s6FkIIIWpMoYQxe/ZsLFu2TK5sypQpWLBgQVPERAghRA0p3K3238OWR0RE4MCBA0oPiBBCiHqi5zAIIYQohBIGIYQQhVDCIIQQohCFx5IqKirCl19++dIygUCATZs2KSc6QgghakPhhCGRSLB161a5spKSErkyxhglDEIIaaUUShgDBgygIT8IIeQ1p1Abxrlz5xAdHa3wwkdGRga8vb1hamoKsViMuXPnoqysZiL106dPw8nJCXp6enBycsLJkyfl9v3jjz/QvXt3iEQiDBo0CPfu3ZNbHxoaCrFYjDZt2mDq1KmQSqW8YiOEEPI/Km30ZozB29sbUqkUFy5cQGRkJKKiorB06VLcvXsXnp6emDx5Mm7evIlJkyZh7Nix3Hwc6enpGDt2LD777DMkJCTAwsICY8eO5Z4XOXToEJYtW4atW7fi7NmziI+Ph5+fnwqPlhBCWjbeEygpU1paGuLj45GdnQ0rq5r5cIODgzF//nyMHDkSn3/+OebMmQMAmDt3LlasWIHLly/D0dERO3bsQK9evTBv3jwAQHh4OKytrRETEwMPDw9s2LABs2fPxqhRowAAW7duxdChQ/Hdd99BJBLViUUikbww1pKSEoW3/bfS0lIAQEVFBXf11JpVVFQAqDnuxp6rSlSiAhVKj02dVKISwKudp7LqakirqpQemzopq64G8GrnqaqqDJWVrfsOQ1VVzW8Ln/P0/Hb/fji7XkyFCgsL2alTp+TK9u3bx/T19eXKKioq2I4dO5hIJGL3799njDH23nvvsaVLl8pt5+7uzlauXMmqqqqYUChkZ86c4dZVVlYyTU1NdunSpXpjAUALLbTQ8touOTk5L/3NVukVhrGxMYYNG8a9l8lkCAsLw+DBg7myu3fvokuXLqiursbq1avh6OgIAMjKyoKtra1cfVZWVnj8+DGKiopQVlYmt15LSwtmZmZ4/Phx0x4UIYS0UipNGP/m5+eHK1euICEhgSuzsLBAQkIC4uLiMHfuXHTs2BFeXl6QSqXQ1dWV219XVxfl5eVc43ZD6+vz/C2n+shkMuTl5UFfXx8ikUjte41JJBLuNl9OTg709fVVHJF6ovOkGDpPimlp54kxxv1empubv3R7tUkY/v7+CA0NxYEDB9C9e3eu3MjICM7OznB2dkZKSgo2btwILy8vCIXCOj/+5eXlMDY2hlAo5N7/e3197RcAFPoP26ZNG76HpRb09fXV/g9XHdB5UgydJ8W0lPNkYGCg8LZqMTSIr68v1q1bhz179sDLywsAcPPmTVy4cEFuuzfffBN5eXkAALFYjOzsbLn12dnZsLGxgZmZGYRCodz6qqoq5Ofnw8bGpomPhhBCWieVJ4ygoCBs2bIFkZGRmDBhAlceFRWF6dOny7XcJyUloWvXrgAAV1dXxMbGcuukUimuXr0KV1dXaGhooHfv3nLr4+LioK2tDScnp2Y4KkIIaYVe3pep6aSkpDBNTU22ZMkSlpWVJbc8evSIGRoaMj8/P3b79m0WFhbGdHR0WFJSEmOMsfv37zOhUMhWrVrFkpOT2fjx41mPHj2YTCZjjDG2f/9+ZmhoyI4cOcIuX77MunXrxnx9fVV5uM2qpKSE6/1QUlKi6nDUFp0nxdB5UkxrP08qTRirVq1qsIsXY4zFxcWxd955h+np6bGuXbuyY8eOye1/4sQJ1qlTJ6anp8cGDx7M7t27V6d+S0tLZmRkxKZMmcJKS0ub7dhUrbX/4SoLnSfF0HlSTGs/TwLGFHlag7Q0EomEa8wqKSlpEY1vqkDnSTF0nhTT2s+TytswCCGEtAyUMAghhCiEbkkRQghRCF1hEEIIUQglDEIIIQqhhEEIIUQhlDAIIYQohBIGIYQQhVDCUDMeHh4QCARyi7GxMQYNGoSYmBhuO0dHR0yePFnpnz958mRuzpGWwsPDAx4eHk1St0AgwLJly5qkblX6+OOPIRAIsG7duleq5/fff4dAIMBbb72lpMjUh6LfxWXLlr1wuoN//31Onjy5Tr2GhoZwdXXF4cOHm/KQXhklDDXk7OyMuLg4xMXFITY2FhEREdDR0cGwYcNw8+ZNVYdHWrji4mIcOXIEb731FrZt26bY1JwN2LlzJ9566y0kJyfj4sWLSoxSPTTVd9Ha2pqr9+LFi9i3bx86dOgAb29v/P7770o8AuVSm/kwyP/U/mvjee+99x4sLCwQERGBtWvXqigy0hrs378fALBhwwYMGjQIZ8+elZvlUlFFRUU4evQotmzZglWrVmHLli3o16+fssNVqab6Lurq6tapd+TIkbh06RK2bduG9957r9ExNyW6wmghRCIRhEJhg5e+Dx48gI+PD2xtbaGtrQ1LS0v4+PggPz+f28bR0RGBgYFYsGABrKysoKenh+HDh+POnTsNfu7Vq1dhbGyMESNGNDhbYUulyDn7txs3bmDcuHGwsLCAtrY2xGIxZs2ahdLS0maM/NXs3LkTgwcPxsCBA9GxY0ds3bq1zja7d++Gi4sLRCIR7O3tsXDhQlRUVMhts2/fPlRWVmL48OH45JNPcPDgQRQUFNSpKy0tDePGjYOpqSlMTEwwatQopKamNtnxNbWXfRcbSyAQwMjISK1n86SEoYYYY6iqqkJVVRUqKyuRnZ2NhQsXory8HFOmTKmzvVQqhYeHB1JTU7Fp0yb89ttv+Oabb7B//34sXrxYbtsNGzYgNTUVERER2LFjBxITE+Hj41NvHKmpqRg2bBjeeecdHDlypM6Uty0Zn3NWKysrC/3794dEIkFERAROnjyJCRMmYOPGjdiwYUMzH0Hj3Lx5EwkJCZg0aRIAYNKkSTh69ChycnK4bTZt2gQfHx+8/fbbOHLkCBYuXIgffvgBvr6+cnXt3LkTw4cPh5WVFXx8fFBRUYGIiAi5bTIyMuDq6orbt29j8+bN2LNnD3JycjB48OB6k4u64fNdrN3u30tDt/yerzc/Px8//PADkpOTMXPmzOY4tMZR3UC5pD7u7u4NDvm+cuVKbjsHBwc2adIkxhhjV69eZW5ubuzvv/+Wq2v06NGsc+fOcvs4OjqyqqoqriwoKIgBYHl5eYwxxiZNmsQcHBzY33//zWxtbdmQIUPUflh4d3d35u7uzmsfRc8ZABYYGMgYY+z06dNswIAB7OnTp3L7vPXWW2zYsGGNir25zZ07l5mZmbHy8nLGGGPp6elMQ0ODhYSEMMYYq66uZpaWlmzs2LFy+61du5a5uLiwiooKxhhjN27cYADYwYMHuW2GDBkid+4YY2zevHlMT0+PZWVlcWWPHj1i9vb27Pjx401yjMqi6HcxMDCwwe1ql+f/PidNmtTgdjNnzmTV1dUqOFrFUBuGGnJxceFuEzDGUFhYiJMnT2Lx4sWQSCRYsWKF3PY9e/bEhQsXIJPJcOfOHdy5cwcpKSlITU1FVVWV3La9e/eGpqYm975t27YAaoZlNjMzAwA8e/YMQ4YMQVZWFs6fP8/Nkd4SMcZQXV0tV6apqcnrnNUaOnQohg4disrKSqSkpODu3bv466+/kJuby507dVZZWYk9e/Zg7NixkEqlkEqlaNOmDdzc3LB9+3YEBATg9u3byM3Nxbhx4+T2nT9/PubPn8+937lzJ4yNjdG/f38UFRUBALy9vTFjxgxER0dj4MCBAIDY2Fj07dsX1tbW3L5t27bFw4cPm/6AlYDPdzEhIaHeOr744os6ZTY2Nvj111+590+fPsX58+exevVqPH36FHv27FHykSgHJQw11KZNG/Tq1UuubOjQoSgpKcGaNWswa9asOvusX78eK1euRH5+PqysrNCrVy/o6+tzX+ZaIpFI7r2GRs1dSZlMxpUVFBSga9euKCwshJ+fHw4dOqSkI2t+MTEx3I9XrejoaHh4eCh8zmrJZDIsWrQImzZtQklJCezs7NCnTx/o6em9Uk+j5vJ///d/yM3NxU8//YSffvqpzvrTp0/D0NAQAGBpadlgPbWJp6ioCFZWVnXWb9myhTvn+fn5aNeunZKOoPnx+S7+e7vn6/g3HR2dOtsPGjQI2traWLJkCebOnQsXFxclHIFyURtGC9KrVy9UVVXh/v37cuX79u3DvHnz4O/vjydPniA7Oxv/93//h06dOjXqc8zMzHDq1CmsWrUKhw8fxtGjR5UQvWq8/fbbSEhIkFvefvvtRp2z1atXY/369fjhhx9QVFSE9PR0HDx4EBYWFs14RI0XHh6O9u3bIzo6Wm45e/YsjIyMsGXLFhgbGwMAnjx5Irdvfn4+fv/9d0gkEkRFRSEvLw/btm2rU9f48eNx5MgR5ObmAgCMjY3r1AUAZ86cqfN33JI09F1URr0AXtgRRZUoYbQgly9fhqamJtq3by9XHhsbC2NjYyxYsADm5uYAamb7io2NlbtyUJSBgQEMDAzw+eefw9XVFV999RWKi4uVcgzNrfZfiM8vbdq0adQ5i42NRbdu3fDZZ5/ByMgIQE2j7l9//dWo89ycsrOzuUb62gfJapeBAwfiww8/xPHjx2FsbAxzc3NERUXJ7f/zzz9jxIgRqKiowM6dO9G2bVtMmzatTl2zZs1CZWUldu7cCQDo378/4uPjkZeXx9WVm5uL4cOH4/jx4816DpSpoe+iMuoFgDfeeEOp9SoL3ZJSQ0+fPkV8fDz3vry8HL/++it27tyJzz//vM6/aPv06YPNmzdj3rx5GD16NDIzM7F27VpkZ2fDxMSk0XFoaGhg69atePvtt+Hn51dv90t18fjxY4SGhtYp7969O4YMGVKnvDHnrE+fPli+fDlWr16Nvn374u7du1i5ciXKy8shkUiUfUhK9fPPP6OqqgoTJ06sd72Pjw927NiB7du3IygoCF999RUsLS3xwQcfIC0tDYGBgfj6669RVlaGU6dOYfbs2fV2/+zXrx86dOiAbdu2wd/fH3PmzMGuXbswbNgwLFq0CDo6OlixYgXs7Ozw8ccfN/VhvzK+30VFlZeXy9VbVVWFc+fOYfny5Rg6dKha3o4CQL2k1E19PTOEQiHr1q0bCwkJ4XqpPN9LSiaTsW+//Za1bduWCYVC1qFDBzZr1iy2bds2BoClpKTU2adWeHg4A8Du37/PGPtfL6nnzZ8/nwkEAhYTE9OUh95oL+rNMnXq1Hr3UfSc4bleUmVlZeyrr75i1tbWTE9Pj3Xu3JkFBgayoKAgpqurywoLC5vpiPnr0qUL69atW4PrZTIZa9euHROLxayqqopFRESwbt26MR0dHda+fXu2YsUKVllZyVavXs0AsISEhAbrWrZsGQPATp48yRhjLDU1lY0aNYoZGBgwU1NT5uXlxf29qTNFv4u1vaReVM/Leknp6OiwN954gy1YsIA9e/asqQ+t0WjGPUIIIQqhNgxCCCEKoYRBCCFEIZQwCCGEKIQSBiGEEIVQwiCEEKIQShiEEEIUQgmDEEKIQihhEEIIUQglDEIIIQqhhEEIIUQhlDAIIYQo5P8Dju9/Zk07dBoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "cmap = sns.color_palette('deep')\n",
    "\n",
    "# Show plot with caps on the error bars, with black outlines around the bars\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "plt.ylabel('Fluorescence Intensity (AU)', fontweight='bold', fontsize=12)\n",
    "\n",
    "# Define group names in the desired order\n",
    "group_names = ['Blank', 'L-ala', 'AcAc', 'BHB']\n",
    "colors = ['darkgrey', 'purple', cmap_blended(0.8), cmap_blended(0.2)]\n",
    "\n",
    "df_names = ['df_4', 'df_1', 'df_2', 'df_3']  # corresponding to 'Blank', 'L-ala', 'AcAc', 'BHB'\n",
    "max_values = [max_values_dict[df_name]['max_average'] for df_name in df_names]\n",
    "sd_values = [max_values_dict[df_name]['corresponding_sd'] for df_name in df_names]\n",
    "\n",
    "for group, max_val, sd_val, color in zip(group_names, max_values, sd_values, colors):\n",
    "    plt.bar(group, max_val, yerr=sd_val, capsize=3, edgecolor='black', color=color)\n",
    "\n",
    "plt.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "plt.tick_params(axis='x', which='minor', length=3, width=1.5, direction='out', top=False, bottom=True)\n",
    "plt.tick_params(axis='x', which='major', length=7, width=1.5, direction='out', top=False, bottom=True)\n",
    "plt.tick_params(axis='y', which='major', length=7, width=1.5, direction='out', left=True, right=False)\n",
    "plt.tick_params(axis='y', which='minor', length=3, width=1.5, direction='out', left=True, right=False)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.spines['top'].set_linewidth(1.5)\n",
    "ax.spines['bottom'].set_linewidth(1.5)\n",
    "ax.spines['left'].set_linewidth(1.5)\n",
    "ax.spines['right'].set_linewidth(1.5)\n",
    "\n",
    "ax.spines['top'].set_color('black')\n",
    "ax.spines['bottom'].set_color('black')\n",
    "ax.spines['left'].set_color('black')\n",
    "ax.spines['right'].set_color('black')\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [4, 3]\n",
    "\n",
    "#optimize the y axis range\n",
    "plt.ylim(2300, 2550)\n",
    "plt.yticks(np.arange(2300, 2550, 50), fontsize=10)\n",
    "plt.xticks(fontsize=12)\n",
    "\n",
    "plt.xticks(group_names, ['Blank', 'L-ala', 'AcAc', 'BHB'])\n",
    "\n",
    "# Get the rows for comparisons with 'Blank'\n",
    "rows = res_all_df[res_all_df['comparison'].str.contains('Blank')]\n",
    "\n",
    "# Assign labels based on p-values\n",
    "pval_label = []\n",
    "for i, row in rows.iterrows():\n",
    "    pvalue = row['p-value']\n",
    "    comparison = row['comparison']\n",
    "    if pvalue < 0.001:\n",
    "        label = '***'\n",
    "    elif pvalue < 0.01:\n",
    "        label = '**'\n",
    "    elif pvalue < 0.05:\n",
    "        label = '*'\n",
    "    else:\n",
    "        label = 'n.s.'\n",
    "    pval_label.append(label)\n",
    "    print(f'comparison: {comparison}, p-value: {pvalue}, label: {label}')  # print the comparison, p-value, and its corresponding label\n",
    "\n",
    "# Add the p-value labels to the plot, where the asterisk is centered, right on top of the error bar\n",
    "for group, max_val, sd_val, label in zip(group_names[1:], max_values[1:], sd_values[1:], pval_label):  # Skip 'Blank'\n",
    "    plt.text(group, max_val + sd_val + 10, label, ha='center', va='bottom', fontsize=12)\n",
    "\n",
    "\n",
    "\n",
    "# plt.show()\n",
    "plt.savefig('23.07.13 EDC37 Bar.png', dpi=1200, bbox_inches='tight') \n"
   ]
  }
 ],
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   "language": "python",
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