S. cerevisiae pathway occupancy, Extended Data Fig 8 b and e.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5bd0df5c",
   "metadata": {},
   "source": [
    "# *S. cerevisiae* pathway occupancy, Extended Data Fig 8 b and e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f82b44da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import scipy as scipy\n",
    "import math \n",
    "from tqdm.auto import tqdm\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eab329ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# don't print warning messages\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "461958f2",
   "metadata": {},
   "source": [
    "### Inital data wranggling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b840d37",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in proteomics datasets \n",
    "yeast_proteomics = pd.read_csv('./data/2024_01_05_yeast_proteomics_full.csv')\n",
    "\n",
    "#read in gene inclusion list for fermentation and respiration\n",
    "yeast_inclusion = pd.read_csv('./data/yeast_inclusion.csv')\n",
    "yeast_inclusion = yeast_inclusion[['EC number', 'pathway', 'Gene name', 'meta_pathway', 'ORF', 'MW']].drop_duplicates()\n",
    "\n",
    "#read in proteomics datasets \n",
    "yeast_meta = pd.read_csv('./data/2024_01_05_yeast_metadata.csv')\n",
    "yeast_meta.rename(columns = {'column_title':'variable'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0bde1f0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding membrance protein category\n",
    "ec_numbers = [\n",
    "    'glucose transport', \n",
    "    'pyruvate transport', \n",
    "    'phosphate carrier protein', \n",
    "    'etc', \n",
    "    'ATP/ADP carrier protein', \n",
    "    'mito_shuttle']\n",
    "\n",
    "yeast_inclusion.loc[yeast_inclusion['EC number'].isin(ec_numbers), 'protein category'] = 'membrane'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f3ab8d86",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating column list from column names without ORF\n",
    "column_list = np.array([col for col in yeast_proteomics.columns if col != 'ORF'])\n",
    "\n",
    "#reshaping df with melt\n",
    "yeast_proteomics_melt = pd.melt(yeast_proteomics, id_vars=['ORF'],\n",
    "                                 value_vars=column_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f03aa853",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merging with meta data\n",
    "yeast_proteomics_melt = pd.merge(yeast_proteomics_melt, yeast_meta, on='variable', how='left')\n",
    "\n",
    "#subsetting df\n",
    "yeast_inclusion = yeast_inclusion[['Gene name', 'pathway', 'ORF', 'meta_pathway', 'protein category', 'EC number', 'MW']]\n",
    "\n",
    "#merging with inclusion list\n",
    "total_inclusion = pd.merge(yeast_inclusion, yeast_proteomics_melt, on='ORF', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bceb67ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop datases that are not mass spec or do not meet the extraction criteria \n",
    "total_inclusion = total_inclusion.loc[total_inclusion['quant'] == 'mass_spec']\n",
    "\n",
    "total_inclusion = total_inclusion.loc[~(total_inclusion['author'].str.contains('Lu'))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc0cf082",
   "metadata": {},
   "source": [
    "### ETC Correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c21be31",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subsetting the etc inclusion list\n",
    "total_inclusion_etc = total_inclusion.loc[((total_inclusion['pathway'].str.contains('etc')) | (total_inclusion['pathway'].str.contains('atp')))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a88cb9f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fcd7811f050>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x2592 with 70 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot histogram of all proteins in subset list\n",
    "sns.set_theme(style=\"white\")\n",
    "sns.displot(\n",
    "    total_inclusion_etc, x=\"value\", col=\"Gene name\",\n",
    "    height=2, facet_kws=dict(margin_titles=True),\n",
    "    col_wrap=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ac665bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the fraction of 'value' <= 0.00001 for each Gene name\n",
    "fractions = total_inclusion_etc.groupby('Gene name')['value'].apply(lambda x: (x <= 0.00001).mean())\n",
    "\n",
    "#create a df from the fractions\n",
    "df_zero = fractions.reset_index(name='Fraction Zero')\n",
    "\n",
    "#determine which genes meet the criteria (less than 20 percent 0 occupancy)\n",
    "geneCorrect = fractions[fractions >= 0.2].index.tolist()\n",
    "geneTruth = fractions[fractions < 0.2].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3d9d603",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fcd98c02c50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot histogram of fraction zeros\n",
    "sns.set_theme(style=\"white\")\n",
    "sns.displot(\n",
    "    df_zero, x=\"Fraction Zero\", bins = 20)\n",
    "plt.axvline(0.2, color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "36b5150a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#filter the dataset for genes in geneTruth\n",
    "filtered_dataset = total_inclusion_etc[total_inclusion_etc['Gene name'].isin(geneTruth)]\n",
    "\n",
    "#calculate correction_value as value / MW for each gene within each condition\n",
    "result = (\n",
    "    filtered_dataset\n",
    "    .assign(correction_value=lambda x: x['value'] / x['MW'])\n",
    "    .groupby(['variable', 'Gene name'])\n",
    "    .agg(correction_value=('correction_value', 'first'))  #first occurance\n",
    "    .reset_index()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3756e690",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop duplicates\n",
    "ec_number = total_inclusion_etc[['Gene name', 'EC number']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b7c63959",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge the correction values with EC numbers\n",
    "result = pd.merge(ec_number, result, on='Gene name', how = 'right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49cb9936",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the mean correction_value for each combination of 'variable' and 'EC number'\n",
    "mean_correction_values = (\n",
    "    result.groupby(['variable', 'EC number'])['correction_value']\n",
    "    .mean()\n",
    "    .reset_index(name='mean_correction_value')\n",
    ")\n",
    "\n",
    "#merge the mean_correction_values back with the original DataFrame to update the 'correction_value'\n",
    "df_correction = pd.merge(result, mean_correction_values, on=['variable', 'EC number'])\n",
    "\n",
    "#update 'correction_value' with the mean values\n",
    "df_correction['correction_value'] = df_correction['mean_correction_value']\n",
    "\n",
    "#drop the temporary mean_correction_value column and any duplicates\n",
    "df_correction.drop(columns=['mean_correction_value'], inplace=True)\n",
    "df_correction.drop_duplicates(inplace=True)\n",
    "df_correction = df_correction[['variable', 'correction_value', 'EC number']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "73a71e34",
   "metadata": {},
   "outputs": [],
   "source": [
    "#correct occupancy of genes\n",
    "genes_to_correct = (\n",
    "    total_inclusion_etc.loc[total_inclusion_etc['Gene name'].isin(geneCorrect)]\n",
    "    .merge(df_correction, on=['variable', 'EC number'], how='left')\n",
    "    .assign(corrected_value=lambda x: x['correction_value'] * x['MW'])\n",
    "    .drop_duplicates()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "953b439b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fcdc894aa90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1008 with 28 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot correction results\n",
    "sns.set_theme(style=\"white\")\n",
    "sns.displot(genes_to_correct, x=\"corrected_value\", col=\"Gene name\",\n",
    "    height=2, facet_kws=dict(margin_titles=True),\n",
    "    col_wrap=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "17486863",
   "metadata": {},
   "outputs": [],
   "source": [
    "#iterate over each row in genes_to_correct to apply corrections\n",
    "for index, row in genes_to_correct.iterrows():\n",
    "    orf = row['ORF']\n",
    "    variable = row['variable']\n",
    "    corrected_value = row['corrected_value']\n",
    "    \n",
    "    #check if the variable (column) exists in yeast_proteomics before assignment\n",
    "    if variable in yeast_proteomics.columns:\n",
    "        yeast_proteomics.loc[yeast_proteomics['ORF'] == orf, variable] = corrected_value\n",
    "\n",
    "#assuming 'Unnamed: 0' is a column that needs to be dropped\n",
    "if 'Unnamed: 0' in yeast_proteomics.columns:\n",
    "    yeast_proteomics.drop(columns='Unnamed: 0', inplace=True)\n",
    "\n",
    "#if ORF is not already set as the index\n",
    "if yeast_proteomics.index.name != 'ORF':\n",
    "    yeast_proteomics.set_index('ORF', inplace=True)\n",
    "\n",
    "#normalize the df to total sum of 1 by dividing each value by the sum of its column\n",
    "yeast_proteomics = yeast_proteomics.div(yeast_proteomics.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7160777",
   "metadata": {},
   "source": [
    "## Pathway Occupancy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c1cd67",
   "metadata": {},
   "source": [
    "### Fermentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "852c58ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in gene inclusion list for fermentation and respiration\n",
    "yeast_inclusion_ferm = yeast_inclusion.loc[yeast_inclusion['meta_pathway']== 'fermentation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9bd94372",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge the proteomics data with the inclusion list \n",
    "yeast_proteomics_ferm_inclusion_final = pd.merge(yeast_inclusion_ferm, yeast_proteomics, on = 'ORF', how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "69dbe2d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#melt df\n",
    "column_list = list(yeast_proteomics.columns)\n",
    "yeast_proteomics_ferm_melt = pd.melt(yeast_proteomics_ferm_inclusion_final, id_vars=['ORF'],\n",
    "                                 value_vars=column_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "418ec0c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge with meta data\n",
    "yeast_proteomics_ferm_melt = pd.merge(yeast_proteomics_ferm_melt, \n",
    "                                      yeast_meta[['variable', 'author', 'quant', 'media', 'growth_rate', 'carbon_source', 'carbon_source_cat', 'method']].drop_duplicates(), \n",
    "                                      on='variable', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3b22ca6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use only the fermentable carbon sources from batch culture for fermentation specific activity\n",
    "yeast_proteomics_ferm_melt_sa = yeast_proteomics_ferm_melt.loc[ (yeast_proteomics_ferm_melt['carbon_source_cat'] == 'fermentable') \n",
    "                               & (yeast_proteomics_ferm_melt['method'] == 'batch')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ddac703d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#only use datasets that quantify using mass spec\n",
    "yeast_proteomics_ferm_melt_sa = yeast_proteomics_ferm_melt_sa.loc[yeast_proteomics_ferm_melt_sa['quant'] == 'mass_spec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "47cf8bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove papers that do not fit quant or extraction the criteria\n",
    "additional_drop = np.array(['LAW', 'Lu', 'DGD', 'NAG', 'LU', 'LEE2', 'PIC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3d055a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop above datasets\n",
    "for author in additional_drop:\n",
    "    yeast_proteomics_ferm_melt_sa = yeast_proteomics_ferm_melt_sa.loc[~(yeast_proteomics_ferm_melt_sa['variable'].str.contains(author))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aeab759b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#make a copy that will not get biomass corrected for comparision\n",
    "yeast_proteomics_ferm_no_biomass_correction = yeast_proteomics_ferm_melt_sa.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e08429aa",
   "metadata": {},
   "source": [
    "### Biomass Correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ae425fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in the fraction to biomass spreadsheet\n",
    "yeast_biomass = pd.read_csv('./data/2022_11_03_yeast_biomass.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b41cc365",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fraction of carbon dedicated to producing biomass\n",
    "yeast_biomass_ferm_mean = yeast_biomass.loc[yeast_biomass['Condition'] == 'Fermentation']['Fraction '].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "216dbe80",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge with inclusion list\n",
    "yeast_proteomics_ferm_melt_sa = pd.merge(yeast_proteomics_ferm_melt_sa, yeast_inclusion[['pathway', 'Gene name', 'ORF']], how='left', on='ORF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4f422976",
   "metadata": {},
   "outputs": [],
   "source": [
    "#biomass correction applies to only the enzymes in glycolysis\n",
    "yeast_proteomics_ferm_melt_sa.loc[((yeast_proteomics_ferm_melt_sa['media'] == 'minimal') & \n",
    "                                  (yeast_proteomics_ferm_melt_sa['pathway'] == 'glycolysis')), \n",
    "                                  'value'] = yeast_proteomics_ferm_melt_sa['value'] * (1-yeast_biomass_ferm_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a63bf360",
   "metadata": {},
   "source": [
    "#### Fermentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "384c4128",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes for final sum\n",
    "yeast_proteomics_ferm_melt_sa.drop_duplicates(inplace = True)\n",
    "yeast_proteomics_ferm_inclusion_final_gene_sum = yeast_proteomics_ferm_melt_sa.groupby('variable').sum()\n",
    "yeast_proteomics_ferm_inclusion_final_gene_sum.reset_index(inplace= True)\n",
    "yeast_proteomics_ferm_inclusion_final_gene_sum['estimate'] = 'fermentation'\n",
    "yeast_proteomics_ferm_inclusion_final_gene_sum['organism'] = 'yeast'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "44f4c259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value    0.091283\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean occupancy\n",
    "yeast_proteomics_ferm_inclusion_final_gene_sum.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "84187b50",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save df\n",
    "yeast_proteomics_ferm_inclusion_final_gene_sum.to_csv('./data/2024_02_25_yeast_specific_activity_fermentation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "36b9f3f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate occupancy for df that was not biomass corrected\n",
    "yeast_proteomics_ferm_no_biomass_correction.drop_duplicates(inplace = True)\n",
    "yeast_proteomics_ferm_inclusion_final_gene_sum_no_correction = yeast_proteomics_ferm_no_biomass_correction.groupby('variable').sum()\n",
    "yeast_proteomics_ferm_inclusion_final_gene_sum_no_correction.reset_index(inplace= True)\n",
    "yeast_proteomics_ferm_inclusion_final_gene_sum_no_correction['estimate'] = 'fermentation'\n",
    "yeast_proteomics_ferm_inclusion_final_gene_sum_no_correction['organism'] = 'yeast'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9d2286c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value    0.093893\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean occupancy\n",
    "yeast_proteomics_ferm_inclusion_final_gene_sum_no_correction.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9cebba9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Percent decrease = 2.78%'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate mean values\n",
    "mean_initial = yeast_proteomics_ferm_inclusion_final_gene_sum_no_correction['value'].mean()\n",
    "mean_final = yeast_proteomics_ferm_inclusion_final_gene_sum['value'].mean()\n",
    "\n",
    "#calculate percent decrease\n",
    "percent_decrease = (mean_initial - mean_final) / mean_initial * 100\n",
    "\n",
    "#create the message string\n",
    "message = \"Percent decrease = {:.2f}%\".format(percent_decrease)\n",
    "message"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed97d9f3",
   "metadata": {},
   "source": [
    "### Respiration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ef65719",
   "metadata": {},
   "source": [
    "#### Full mitochondria list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6d8cd7d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load data\n",
    "gene_orf = pd.read_csv('./data/gene_orfs.csv', header=None, names=['ORF', 'Gene name'])\n",
    "bartolomeo_2020 = pd.read_csv('./data/bartolomeo_2020.csv')\n",
    "\n",
    "#filter for non-null Gene ontology (cellular component) and contains 'mito'\n",
    "bartolomeo_2020_mito = bartolomeo_2020.dropna(subset=['Gene ontology (cellular component)'])\n",
    "bartolomeo_2020_mito = bartolomeo_2020_mito[bartolomeo_2020_mito['Gene ontology (cellular component)'].str.contains('mito') & bartolomeo_2020_mito['Gene name'].notnull()]\n",
    "\n",
    "# Merge with gene_orf to get ORF values\n",
    "bartolomeo_2020_mito = bartolomeo_2020_mito.merge(gene_orf, on='Gene name', how='left')\n",
    "\n",
    "# Select only 'ORF' and 'Gene name' columns\n",
    "mito_list = bartolomeo_2020_mito[['ORF', 'Gene name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a34d82f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert the 'ORF' column of 'yeast_inclusion_ferm' to a set for faster lookup\n",
    "ferm_set = set(yeast_inclusion_ferm['ORF'])\n",
    "\n",
    "#use boolean indexing to filter out rows in 'mito_list' where 'ORF' values are in 'ferm_set'\n",
    "mito_list = mito_list[~mito_list['ORF'].isin(ferm_set)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0e8da1aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mito list to csv\n",
    "mito_list.to_csv('./data/yeast_full_mito_list.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dc2f015c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge mito list with proteomics df\n",
    "resp_proteomics_full_mito = pd.merge(mito_list, yeast_proteomics, on = 'ORF', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d2cee401",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge \n",
    "resp_proteomics_full_mito = pd.merge(resp_proteomics_full_mito, yeast_inclusion[['ORF', 'pathway']], on='ORF', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a8abf3c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "resp_proteomics_full_mito_melt = pd.melt(resp_proteomics_full_mito, id_vars=['ORF', 'pathway'],\n",
    "                                 value_vars=column_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ec2bb887",
   "metadata": {},
   "outputs": [],
   "source": [
    "resp_proteomics_full_mito_melt = pd.merge(resp_proteomics_full_mito_melt, yeast_meta, on='variable', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ef2007c",
   "metadata": {},
   "source": [
    "#### Partial correlated list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3b4fe5ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#define columns to drop in advance (this part remains the same)\n",
    "yeast_columns_to_drop = ['pathway', 'meta_pathway', 'ORF', 'protein category', 'MW', 'EC number']\n",
    "\n",
    "#combine filtering and merging into one line\n",
    "yeast_proteomics_resp = pd.merge(yeast_inclusion[yeast_inclusion['meta_pathway'] == 'respiration'], yeast_proteomics, on='ORF', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3eb580e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate pearson correlation of mito proteins with core genes\n",
    "def geneCoexpression(proteomics_dataset, core_list, merger, columns_to_drop):\n",
    "    \n",
    "    core_list = core_list.drop(columns = columns_to_drop)\n",
    "    core_list = core_list.set_index('Gene name')\n",
    "    core_list_t = core_list.transpose()\n",
    "    core_list_t[\"Core_Sum\"] = core_list_t.sum(axis=1)\n",
    "    core_list_t = core_list_t[[\"Core_Sum\"]]\n",
    "    core_list_sum = core_list_t.transpose()\n",
    "    core_list_sum.reset_index(inplace = True)\n",
    "    core_list_sum = core_list_sum.set_index('Gene name')\n",
    "    proteomics_dataset = proteomics_dataset.set_index('ORF')\n",
    "    yeast_proteomics_with_core = pd.concat([core_list_sum, proteomics_dataset])\n",
    "    yeast_proteomics_with_core_t = yeast_proteomics_with_core.transpose()\n",
    "    column_list = yeast_proteomics_with_core_t.columns\n",
    "    yeast_proteomics_with_core_t = yeast_proteomics_with_core_t.loc[:,~yeast_proteomics_with_core_t.columns.duplicated()]\n",
    "    \n",
    "    gene_name = []\n",
    "    r_value = []\n",
    "    p_value = []\n",
    "\n",
    "    #Calculates the Pearson correlation and p value for each gene with the core sum \n",
    "    for gene in column_list: \n",
    "\n",
    "        x = yeast_proteomics_with_core_t['Core_Sum'].values\n",
    "        y = yeast_proteomics_with_core_t[gene].values\n",
    "        \n",
    "        #print(x)\n",
    "\n",
    "        bad = ~np.logical_or(np.isnan(x), np.isnan(y))\n",
    "\n",
    "        test1 = np.compress(bad, x)  \n",
    "        test2 = np.compress(bad, y)  \n",
    "\n",
    "        #pearson correlation\n",
    "        r, p = scipy.stats.pearsonr(test1, test2)\n",
    "        #print(p)\n",
    "        #print(r)\n",
    "    \n",
    "        gene_name.append(gene)\n",
    "        r_value.append(r)\n",
    "        p_value.append(p)\n",
    "    \n",
    "    d = {'ORF': gene_name, 'R value': r_value, 'P value': p_value}\n",
    "    df = pd.DataFrame(data=d)\n",
    "    \n",
    "    df = df.sort_values('P value')\n",
    "    df = df.reset_index(drop = True)\n",
    "    \n",
    "    #bonferroni correction\n",
    "    df['Bon'] = df['P value'] * len(df) \n",
    "    #benjamini hochberg correction\n",
    "    df['BH'] = df['P value'] * len(df) / (df.index + 1)\n",
    "    \n",
    "    \n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "01baf854",
   "metadata": {},
   "outputs": [],
   "source": [
    "#renaming the df\n",
    "proteomics_dataset_temp = yeast_proteomics.copy().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "50cdbf93",
   "metadata": {},
   "outputs": [],
   "source": [
    "#running function \n",
    "yeast_mito_geneCoexpress = geneCoexpression(proteomics_dataset_temp, yeast_proteomics_resp, \"ORF\", yeast_columns_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0611997f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function output to csv\n",
    "yeast_mito_geneCoexpress.to_csv('./data/yeast_mito_geneCoexpress.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "977b75c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "yeast_inclusion_resp = yeast_inclusion.loc[yeast_inclusion['meta_pathway'] == 'respiration']\n",
    "\n",
    "#merge 'mito_list' and 'test' dataframes and immediately filter rows based on conditions\n",
    "df_mito_sig = pd.merge(mito_list, yeast_mito_geneCoexpress, on='ORF', how='inner') \\\n",
    "                .query('`R value` >= 0 and `BH` < 0.05')\n",
    "\n",
    "#assuming yeast_inclusion_resp is already prepared and doesn't require the dropped columns\n",
    "df_resp = yeast_inclusion_resp.copy()\n",
    "\n",
    "resp_selected_columns = df_resp[['ORF', 'Gene name']]\n",
    "mito_sig_selected_columns = df_mito_sig[['ORF', 'Gene name']]\n",
    "\n",
    "#concat the filtered mito dataframe with the resp dataframe and remove duplicates\n",
    "partial_mito_inclusion_list = pd.concat([resp_selected_columns, mito_sig_selected_columns]).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2df7c707",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge partial list with proteomics df\n",
    "resp_proteomics_partial_mito = pd.merge(partial_mito_inclusion_list, yeast_proteomics, on = 'ORF', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c8ef4302",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge partial list with inclusion\n",
    "resp_proteomics_partial_mito = pd.merge(resp_proteomics_partial_mito, yeast_inclusion[['ORF', 'pathway']], on='ORF', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ece321dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#melt df\n",
    "resp_proteomics_partial_mito_melt = pd.melt(resp_proteomics_partial_mito, id_vars=['ORF', 'pathway'],\n",
    "                                 value_vars=column_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "676470c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge with meta data\n",
    "resp_proteomics_partial_mito_melt = pd.merge(resp_proteomics_partial_mito_melt, yeast_meta, on='variable', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb5eea58",
   "metadata": {},
   "source": [
    "#### Core Respiration list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "99a02e65",
   "metadata": {},
   "outputs": [],
   "source": [
    "#melt df\n",
    "resp_proteomics_core_melt = pd.melt(yeast_proteomics_resp, id_vars=['ORF', 'pathway'],\n",
    "                                 value_vars=column_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d7b6db80",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge with meta data\n",
    "resp_proteomics_core_melt = pd.merge(resp_proteomics_core_melt, yeast_meta, on='variable', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7dab3005",
   "metadata": {},
   "outputs": [],
   "source": [
    "#make copy of df without biomass correction\n",
    "resp_proteomics_core_melt_no_biomass_correction = resp_proteomics_core_melt.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad7c422",
   "metadata": {},
   "source": [
    "### Biomass Correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7ea6e715",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in the fraction to biomass data\n",
    "yeast_biomass = pd.read_csv('./data/2022_11_03_yeast_biomass.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7d306383",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fraction of carbon dedicated to producing biomass\n",
    "yeast_biomass_resp_mean = yeast_biomass.loc[yeast_biomass['Condition'] == 'Respiration']['Fraction '].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "070486c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#list of the three dfs\n",
    "datasets = [resp_proteomics_core_melt, resp_proteomics_partial_mito_melt, resp_proteomics_full_mito_melt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "26389843",
   "metadata": {},
   "outputs": [],
   "source": [
    "#biomass correction for all three dfs\n",
    "\n",
    "#biomass correction only for tca enzymes\n",
    "condition = (lambda df: (df['media'] == 'minimal') & (df['pathway'] == 'tca'))\n",
    "operation = (lambda df: df.loc[condition(df), 'value'] * (1 - yeast_biomass_resp_mean))\n",
    "\n",
    "#iterate over each DataFrame and apply the operation\n",
    "for dataset in datasets:\n",
    "    dataset.loc[condition(dataset), 'value'] = operation(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92837f20",
   "metadata": {},
   "source": [
    "#### Full mito quant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c129a5cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use only the nonfermentable carbon sources from batch culture for fermentation specific activity\n",
    "resp_proteomics_full_mito_sa = resp_proteomics_full_mito_melt.loc[ (resp_proteomics_full_mito_melt['carbon_source_cat'] == 'nonfermentable') \n",
    "                               & (resp_proteomics_full_mito_melt['method'] == 'batch')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ebe60b36",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop duplicates\n",
    "resp_proteomics_full_mito_sa = resp_proteomics_full_mito_sa.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0b24ef54",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes\n",
    "resp_proteomics_full_mito_sa_sum = resp_proteomics_full_mito_sa.groupby('variable').sum().reset_index().assign(estimate = 'respiration', organism = 'yeast')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9946fe9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2166295200132669"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean occupancy\n",
    "resp_proteomics_full_mito_sa_sum['value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "193cc745",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df to csv\n",
    "resp_proteomics_full_mito_sa_sum.to_csv('./data/2024_02_25_yeast_specific_activity_resp_full_mito.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0980a8a1",
   "metadata": {},
   "source": [
    "#### Partial mito quant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9a883eee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use only the nonfermentable carbon sources from batch culture for fermentation specific activity\n",
    "resp_proteomics_partial_mito_sa = resp_proteomics_partial_mito_melt.loc[ (resp_proteomics_partial_mito_melt['carbon_source_cat'] == 'nonfermentable') \n",
    "                               & (resp_proteomics_partial_mito_melt['method'] == 'batch')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "754be6a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop duplicates\n",
    "resp_proteomics_partial_mito_sa = resp_proteomics_partial_mito_sa.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "93b766b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes\n",
    "resp_proteomics_partial_mito_sa_sum = resp_proteomics_partial_mito_sa.groupby('variable').sum().reset_index().assign(estimate = 'respiration', organism = 'yeast')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f0109d6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1490351620636183"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean occupancy\n",
    "resp_proteomics_partial_mito_sa_sum['value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "bc931bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df to csv\n",
    "resp_proteomics_partial_mito_sa_sum.to_csv('./data/2024_02_25_yeast_specific_activity_resp_partial_mito.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2008584",
   "metadata": {},
   "source": [
    "#### Core mito quant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7150eec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use only the nonfermentable carbon sources from batch culture for fermentation specific activity\n",
    "resp_proteomics_core_melt_sa = resp_proteomics_core_melt.loc[ (resp_proteomics_core_melt['carbon_source_cat'] == 'nonfermentable') \n",
    "                               & (resp_proteomics_core_melt['method'] == 'batch')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c93d752b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop duplicates\n",
    "resp_proteomics_core_melt_sa = resp_proteomics_core_melt_sa.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "24d12afa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes\n",
    "resp_proteomics_core_melt_sa_sum = resp_proteomics_core_melt_sa.groupby('variable').sum().reset_index().assign(estimate = 'respiration', organism = 'yeast')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "009c80ae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11676296447919755"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean occupancy\n",
    "resp_proteomics_core_melt_sa_sum['value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "177a2d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df to csv\n",
    "resp_proteomics_core_melt_sa_sum.to_csv('./data/2024_02_25_yeast_specific_activity_resp_core.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d1dad777",
   "metadata": {},
   "outputs": [],
   "source": [
    "#uncorrected biomass data\n",
    "#use only the nonfermentable carbon sources from batch culture for fermentation specific activity\n",
    "resp_proteomics_core_melt_no_biomass_correction_sa = resp_proteomics_core_melt_no_biomass_correction.loc[ (resp_proteomics_core_melt_no_biomass_correction['carbon_source_cat'] == 'nonfermentable') \n",
    "                               & (resp_proteomics_core_melt_no_biomass_correction['method'] == 'batch')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c7fc93a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop duplicates\n",
    "resp_proteomics_core_melt_no_biomass_correction_sa = resp_proteomics_core_melt_no_biomass_correction_sa.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a78c500e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes\n",
    "resp_proteomics_core_melt_no_biomass_sum = resp_proteomics_core_melt_no_biomass_correction_sa.groupby('variable').sum().reset_index().assign(estimate = 'respiration', organism = 'yeast')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a4eb8d0d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11916916883498596"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean occupancy\n",
    "resp_proteomics_core_melt_no_biomass_sum['value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "266d0287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Percent decrease = 2.02%'"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate mean values\n",
    "mean_initial = resp_proteomics_core_melt_no_biomass_sum['value'].mean()\n",
    "mean_final = resp_proteomics_core_melt_sa_sum['value'].mean()\n",
    "\n",
    "#calculate percent decrease\n",
    "percent_decrease = (mean_initial - mean_final) / mean_initial * 100\n",
    "\n",
    "#print percent decrease\n",
    "message = \"Percent decrease = {:.2f}%\".format(percent_decrease)\n",
    "message"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ab8f4aa",
   "metadata": {},
   "source": [
    "### ATP-producing space"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6a58cad",
   "metadata": {},
   "source": [
    "#### Ferm space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e107f394",
   "metadata": {},
   "outputs": [],
   "source": [
    "#filter out datasets that do not meet the criteria\n",
    "yeast_proteomics_ferm_melt_atp_space = yeast_proteomics_ferm_melt.loc[yeast_proteomics_ferm_melt['quant'] == 'mass_spec']\n",
    "\n",
    "yeast_proteomics_ferm_melt_atp_space = yeast_proteomics_ferm_melt_atp_space[~yeast_proteomics_ferm_melt_atp_space['variable'].isin(['Lu_2007_minimal_1', 'ho_2018_LU_1', 'Lu_2007_rich_1'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "588bc92a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge with inclusion list\n",
    "yeast_proteomics_ferm_melt_atp_space = pd.merge(yeast_proteomics_ferm_melt_atp_space, yeast_inclusion[['ORF', 'pathway']], on='ORF', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "fa28b8dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#biomass correction \n",
    "yeast_proteomics_ferm_melt_atp_space.loc[((yeast_proteomics_ferm_melt_atp_space['media'] == 'minimal') & \n",
    "                                  (yeast_proteomics_ferm_melt_atp_space['pathway'] == 'glycolysis')), \n",
    "                                  'value'] = yeast_proteomics_ferm_melt_atp_space['value'] * (1-yeast_biomass_ferm_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3702ac49",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop duplicates\n",
    "yeast_proteomics_ferm_melt_atp_space.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3131c4fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes\n",
    "yeast_proteomics_ferm_atp_space_sum = yeast_proteomics_ferm_melt_atp_space.groupby('variable').sum().reset_index().assign(estimate = 'ferm', organism = 'yeast')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "361a335e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10253128643135465"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean occupancy\n",
    "yeast_proteomics_ferm_atp_space_sum['value'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79af18c2",
   "metadata": {},
   "source": [
    "#### Respiration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b550e1c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#list of datasets\n",
    "datasets = [\n",
    "    resp_proteomics_core_melt,\n",
    "    resp_proteomics_partial_mito_melt,\n",
    "    resp_proteomics_full_mito_melt]\n",
    "\n",
    "#specified condition and operation\n",
    "condition = lambda df: (df['media'] == 'minimal') & (df['pathway'] == 'tca')\n",
    "operation = lambda df: df['value'] * (1 - yeast_biomass_resp_mean)\n",
    "\n",
    "#list to hold the processed datasets\n",
    "processed_datasets = []\n",
    "\n",
    "#drops datasets that do not meet the criteria\n",
    "for dataset in datasets:\n",
    "    # Drop datasets that are not mass spec or do not meet the extraction criteria\n",
    "    filtered_dataset = dataset.loc[dataset['quant'] == 'mass_spec']\n",
    "    filtered_dataset = filtered_dataset[~filtered_dataset['variable'].isin(['Lu_2007_minimal_1', 'ho_2018_LU_1', 'Lu_2007_rich_1'])]\n",
    "    filtered_dataset = filtered_dataset.drop_duplicates()\n",
    "    #filtered_dataset = pd.merge(filtered_dataset, yeast_inclusion[['ORF', 'pathway']], on='ORF', how='left')\n",
    "    #print(filtered_dataset)\n",
    "    \n",
    "    #apply the condition and operation for adjusting for biomass\n",
    "    filtered_dataset.loc[condition(filtered_dataset), 'value'] = operation(filtered_dataset)\n",
    "    \n",
    "    #append the processed dataset to the list\n",
    "    processed_datasets.append(filtered_dataset)\n",
    "\n",
    "#replacing the processed datasets back to their respective variables\n",
    "resp_proteomics_core_mito_melt_atp_space, resp_proteomics_partial_mito_melt_atp_space,resp_proteomics_full_mito_melt_atp_space  = processed_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526b8985",
   "metadata": {},
   "source": [
    "#### ATP-producing occupancy sum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2686319",
   "metadata": {},
   "source": [
    "#### Core"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b1f9bfa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#append ferm and resp\n",
    "atp_space_core = yeast_proteomics_ferm_melt_atp_space.append(resp_proteomics_core_mito_melt_atp_space, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "5134ffa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes\n",
    "atp_space_core_sum = atp_space_core.groupby('variable').sum().reset_index().assign(estimate = 'total', organism = 'yeast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0e798f8b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16757971719697087"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean occupancy\n",
    "atp_space_core_sum['value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e37eb0e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save df to csv\n",
    "atp_space_core_sum.to_csv('./data/2024_02_25_yeast_atp_total_core.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "698cb72c",
   "metadata": {},
   "source": [
    "#### Partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "6fc3e898",
   "metadata": {},
   "outputs": [],
   "source": [
    "#append ferm and resp\n",
    "atp_space_partial = yeast_proteomics_ferm_melt_atp_space.append(resp_proteomics_partial_mito_melt_atp_space, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c0d0211d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes\n",
    "atp_space_partial_sum = atp_space_partial.groupby('variable').sum().reset_index().assign(estimate = 'total', organism = 'yeast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e665aff4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.188555312319481"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean occupancy\n",
    "atp_space_partial_sum['value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "67f1a011",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save df to csv\n",
    "atp_space_partial_sum.to_csv('./data/2024_02_25_yeast_atp_total_partial_mito.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec0ad78b",
   "metadata": {},
   "source": [
    "#### Mito"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0991fa60",
   "metadata": {},
   "outputs": [],
   "source": [
    "#append ferm and resp\n",
    "atp_space_full = yeast_proteomics_ferm_melt_atp_space.append(resp_proteomics_full_mito_melt_atp_space, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "a6302788",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes\n",
    "atp_space_full_sum = atp_space_full.groupby('variable').sum().reset_index().assign(estimate = 'total', organism = 'yeast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "043fd87d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2587029120622031"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean occupancy\n",
    "atp_space_full_sum['value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "124fd030",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save df to csv\n",
    "atp_space_full_sum.to_csv('./data/2024_02_25_yeast_atp_total_full_mito.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4799e84",
   "metadata": {},
   "source": [
    "### Elsemann Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5cd926e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in elesemann data\n",
    "elsemann_2022 = pd.read_excel('./data/2023_10_31_elsemann_2022_proteomics.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6908bfa",
   "metadata": {},
   "source": [
    "## *S. cerevisiae* translation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3bf2211c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#path to excel\n",
    "file_path = './data/2023_10_31_elsemann_2022_proteomics.xlsx'\n",
    "\n",
    "#read all sheet (the result is a dictionary where each key is a sheet name)\n",
    "#and each value is a df containing the data from that sheet\n",
    "all_sheets_dict = pd.read_excel(file_path, sheet_name=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b24d7ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "for sheet_name, df in all_sheets_dict.items():\n",
    "    #melt the df with 'Majority protein IDs' as id_vars\n",
    "    melted_df = pd.melt(df, id_vars=['Majority protein IDs'], var_name='Variable', value_name='Value')\n",
    "    \n",
    "    melted_df['Value'] = melted_df['Value'].fillna(0)\n",
    "    \n",
    "    #update the dictionary with the melted df\n",
    "    all_sheets_dict[sheet_name] = melted_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6e454f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "#initialize an empty list to store each modified DataFrame\n",
    "df_list = []\n",
    "\n",
    "#iterate through each sheet name and DataFrame in the dictionary\n",
    "for condition, df in all_sheets_dict.items():\n",
    "    #add a new column 'condition' to the df\n",
    "    df['Condition'] = condition\n",
    "    \n",
    "    #append the modified df to the list\n",
    "    df_list.append(df)\n",
    "\n",
    "#concat all dfs into one\n",
    "combined_df = pd.concat(df_list, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a2091b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_df[['Substrate', 'Growth_Rate']] = combined_df['Condition'].str.split('_', n=1, expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "bda6bdc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(combined_df)\n",
    "df2 = pd.DataFrame(gene_orf)\n",
    "\n",
    "# Create a mapping from ORFs to gene names\n",
    "orf_to_gene_map = df2.set_index('ORF')['Gene name'].to_dict()\n",
    "\n",
    "# Function to extract and map ORF to gene name\n",
    "def map_orf_to_gene(majority_protein_id):\n",
    "    for orf in orf_to_gene_map.keys():\n",
    "        if orf in majority_protein_id:\n",
    "            return orf  # Return ORF if found\n",
    "    return None  # Return None if no ORF found\n",
    "\n",
    "# Vectorized application of the mapping function\n",
    "df1['ORF'] = df1['Majority protein IDs'].apply(map_orf_to_gene)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "568ea6cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge df1 with df_gene_info on the 'ORF' column\n",
    "elsemann_atp_total = pd.merge(yeast_inclusion, df1, on='ORF', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0b4d2d57",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop duplicates\n",
    "elsemann_atp_total.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "480bbd07",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes\n",
    "elsemann_atp_total_sum = elsemann_atp_total.groupby('Variable').sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "838ecca9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split string to obtain growth rate and repicates number\n",
    "elsemann_atp_total_sum[['condition', 'growth_rate', 'rep']] = elsemann_atp_total_sum['Variable'].str.extract(r'([^_]+)_([0-9.]+)_([0-9]+)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "4a8e5c08",
   "metadata": {},
   "outputs": [],
   "source": [
    "#growth rate to numeric\n",
    "elsemann_atp_total_sum['growth_rate'] = pd.to_numeric(elsemann_atp_total_sum['growth_rate'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "12b5be10",
   "metadata": {},
   "outputs": [],
   "source": [
    "#expport atp sum to csv\n",
    "elsemann_atp_total_sum.to_csv('./data/elsemann_atp_total_sum.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "345a61f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subset condition\n",
    "elsemann_atp_total_glucose = elsemann_atp_total_sum.loc[elsemann_atp_total_sum['condition'] == 'glucose']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "156156c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot atp-producing occupancy vs. growth rate\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ax.patch.set_facecolor('white')\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "sns.scatterplot(elsemann_atp_total_glucose['growth_rate']/100, elsemann_atp_total_glucose['Value'], color = 'black', alpha=.9, linewidth = 0, s=200, zorder = 3)\n",
    "\n",
    "ax.set_ylim(-0.01, 0.32)\n",
    "ax.set_xlim(.13, 0.43)\n",
    "\n",
    "#axis format\n",
    "ax.set_xlabel(\"Growth Rate \" + r'($h^{-1}$)', fontsize=23)\n",
    "ax.set_ylabel(r\"$\\phi_{\\mathrm{Total}}^{\\mathrm{ATP}}$\", fontsize=23)\n",
    "ax.tick_params(axis='both', which='major', labelsize=20, length=0)\n",
    "\n",
    "#save figure\n",
    "fig.savefig('./Figures/yeast_total_atp_growth.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "d271aa00",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in translation inclusion list\n",
    "yeast_translation_inclusion = pd.read_csv('./data/2024_01_27_scerevisiae_translation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "233a0e76",
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename columns\n",
    "yeast_translation_inclusion.rename(columns = {'\tGene/Complex': 'Gene name', 'Systematic Name/Complex Accession': 'ORF'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "d2bfbdb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge df1 with df_gene_info on the 'ORF' column\n",
    "elsemann_translation = pd.merge(yeast_translation_inclusion, df1, on='ORF', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "a4878658",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes\n",
    "elsemann_translation_sum = elsemann_translation.groupby('Variable').sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "18218b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split string to obtain growth rate and repicates number\n",
    "elsemann_translation_sum[['condition', 'growth_rate', 'rep']] = elsemann_translation_sum['Variable'].str.extract(r'([^_]+)_([0-9.]+)_([0-9]+)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "8365f944",
   "metadata": {},
   "outputs": [],
   "source": [
    "#growth rate to numeric\n",
    "elsemann_translation_sum['growth_rate'] = pd.to_numeric(elsemann_translation_sum['growth_rate'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "e6ee5a5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subset condition\n",
    "elsemann_translation_glucose = elsemann_translation_sum.loc[elsemann_translation_sum['condition'] == 'glucose']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "0b946722",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eMfjFThCEBuvpph9QqVSwsbHhR83IKIsLG8CtWTs/++wzFBYWYuDAgfrlZWVlKC4uRmhoaJPbFxcXIyYmBhUVFXjhhRfw+uuv6//B3K68vBwRERGQyWT4/PPPDcpqamqgUCjQs2dPeHl5meW4iIia014TbI0cOVIfLgRBMBo0dGUSiUT//1CO1yBjLO4xCgBMmzYNALBp0ybU19cDuHXBb9y4EYIgGLxNcqf6+nosWrQIFRUViI6OxrJly4wGDQDo378//P39UVBQgH379umXC4KADRs2oKKiAlFRUY1uT0RkTu05wZZuvqKmgoaObh1BEPg2ChllkT0bo0ePRnh4OLKyshAREYGQkBCcOXMGcrkcYWFhBj0bKSkpAKCfavzrr79Gbm4u7Ozs4OTkpC+/Xe/evREVFQXg1jdYZs+ejaVLl+LgwYPw8vKCXC5Hbm4uRo4ciXnz5ol/wETU7d05wVZTb4aYY4ItY2+jmIJvo5AxFhk2AGDdunUYNGgQMjMz8dFHH8HT0xMLFizA3LlzDXoadK+l6sKGLq2r1Wq8//77Rvft5+enDxtDhw5FRkYGkpOTcfz4cXz77bfw8vLS19XYK7hEROakmzDL1DdDkpKS2hQ2lEqlftCnqaqrq/k2ChklEZrrH6MW06V6dicSkTnk5OTgySefNPlmLpVK4ezsjH379rW6l2HhwoVISUlp9hHK7SQSCRYsWMCZRLuh5u57Fjlmg4ioOzl69Ki+R8MUKpUKtbW1OHLkSKvr1H1NuyX4uys1hmGDiKiTUyqVLX48oVKp2vRIo6WPUHRqampaXSd1XRY7ZoOIqLswNqnWnW+J3P76KXBrsq3KyspW11laWtri3g2JRILffvut1XVS18WwQUTUyd1+w2/sVVTd8jtDR2v169dPvx9TAoeu3rvuuqvNdVPXw8coRESdnLu7O2xsbFBfX2/SnBf19fWwsbGBu7t7q+v08/ODjY2NSeFFt46NjQ18fX1bXSd1XQwbRESdnIuLC6ysWva/aysrqzZ9p2Ts2LH6D0xKJBJYWVnpQ4Xuj5WVlX45ADg7O3O6cjKKYYOIqJMbO3YsamtrW7RNbW1tm278I0eOxIgRIwwCy51h43ZSqRRBQUGc0IuMYtggIurkFApFq15DbWxac1MlJCTA2dkZrq6ujX4R28bGBq6urnB2dsbChQvbVB91XQwbRESdXEZGRosHfUokEuzZs6dN9U6ZMgUrVqyAg4MDevXqBXd3dzg7O8PJyQnOzs7o2bMnevXqBQcHB6xcudIsH4CjrolvoxARdXJKpbJVb4aYY+rwuLg4+Pj46L80e+cnGvz9/bFw4UIGDWoSwwYRURvk5OTg6NGjUCqVcHFxwbhx48w+bsHFxQUATAoct4+naMsA0dtNmTIFU6ZMgVwux5EjR6BSqSCVShEaGoqgoCCz1EFdG8MGEVEr7N+/H0lJSThx4gRu3rypn+PCwcEBISEhSEhIMNtv+zNnzkRWVhaA/4WJOwOHsccss2bNMkv9OsHBwRwASq3CD7GJgB9iI+raUlNT8dprr+HmzZuNruPg4ID169dj/vz5ZqmzZ8+e+PPPP01e383NDdeuXTNL3UTN4YfYiIjMaP/+/Xj11VebDBoAcPPmTSQkJGD//v1mqTcuLq5F65sr5BCZA8MGEVELzJkzp8F3Shqj1WoxZ84cs9SbmJiI6dOnm7TujBkzkJiYaJZ6icyBYYOIyEQ5OTkoKytr0TZlZWVme6S6Z88erFy5Eq6urkbL3dzcsHLlSuzevdss9RGZC8dsiIBjNoi6pkceeQSHDh1q1XZfffWVWdvy8ccfY8+ePfo3QyIiIvDss8+atQ4iUzV33+PbKEREJjpz5kyrtjt9+rSZWwJER0cjOjra7PslEgMfoxARmaiurq5dtyPqKhg2iIhM1NhYCbG2I+oqGDaIiEzU2scWzz//vHkbQmRhGDaIiEx0zz33tGq7gQMHmrklRJaFYYOIyESbNm1q1+2IugqGDSIiE/3666/tuh1RV8GwQURERKJi2CAiMlH//v1btd3dd99t5pYQWRaGDSIiE7366qut2m7RokVmbgmRZWHYICIy0XPPPQd7e/sWbePg4MCZPqnbY9ggImqBpUuXtmj91157TaSWEFkOhg0iohZITEzEzJkzTVp31qxZ/NQ7ERg2iIhabM+ePVi1ahXc3d2Nlvfs2ROrVq3ip96J/n/8xLwI+Il5otbJycnB0aNHoVQq4eLignHjxun/PXVW/NQ7ET8xT0QWYP/+/UhKSsJPP/0EtVqN+vp6WFlZwc7ODsOGDUNCQgKmTJnS0c00ip96J2oewwYRdajU1FS8+eabUKlU0Gg0AABBECCRSAAAx44dw5kzZ5CYmIj58+d3ZFOJqJU4ZoOIOsz+/fuxcuVKVFZWQq1WQxAE6J7s6v5brVajsrISK1aswP79+zu4xUTUGgwbRNRh3njjDSiVSjQ3dEwQBCiVSrzxxhvt1DIiMieGDSLqEDk5OTh//nyzQUNHEAScP3+eA6+JLBDDBhF1iPT0dGi12hZto9VqsWPHDpFaRERiYdggog7xww8/tGq748ePm7klRCQ2hg0i6hCVlZXtuh0RdRyGDSLqEI3NvinWdkTUcRg2iKhDjBo1qlXbPfjgg2ZuCRGJjWGDiDpEYGBgq7YbMWKEmVtCRGJj2CCiDlFeXg4bm5ZNYmxjY4OrV6+K1CIiEgvDBhF1CN3H1nTTkjdHIpHAxcUFKpVK5JYRkbkxbBBRh3BxcYGDg4NJgUMXNBwcHCCVStuphURkLgwbRNQhxo4dCwBwdnaGm5sb7OzsIJFIDP7ovvzq5uYGZ2dnAMC4ceM6stlE1Ar86isRdYiRI0diyJAhUCgUcHBwgIODA9Rqtf6DbBKJBPb29rC1tdVv4+/vj+Dg4A5sNRG1Bns2iKjDJCQkGPxsZ2cHqVQKFxcXSKVSg6ABAAsXLmzH1hGRuTBsEFGHmTJlClasWGHSuitXrsSUKVNEbhERicFiw4ZWq8X27dsRHh6OgIAATJw4EWlpadBoNCZtn5ubi1deeQUhISEYOnQoJk2ahPXr1+PGjRsN1q2pqUFSUhImT56MgIAAhIeHIz093eSvVRJR4+Li4rB161YMGTLEaLm/vz+2bt2K+fPnt3PLiMhcJIKF3jFXr16NXbt2ISgoCCNGjMDp06dx6tQphIWFITk5ucltjx8/jjlz5gAAwsLC0LdvX+Tk5ODcuXO4//77kZ6eDnt7ewBAXV0dXn75ZRw5cgTjxo2DTCbD0aNHkZ+fj5iYGLz++usN9q97psxPYZM5fPTRR8jIyNC/Kjpr1ixER0d3dLNEIZfLceTIEahUKkilUoSGhiIoKKijm0VEzWj2vidYoFOnTgkymUyIj48X6uvrBUEQhPr6emHp0qWCTCYTvvnmmya3f/TRR4UhQ4YIP/30k35ZfX298MYbbwgymUzYtm2bfvm+ffsEmUwmvPfee/plarVaiI6OFnx9fYW8vLwG+w8KChKCgoLaepjUza1atUpwd3cXrKysGvxxd3cXVq1a1dFNJCISBKH5+55FPkZJT08HcKv7Vfd+vkQiwaJFiyCRSLBnz55Gt/3ll19w6dIlTJw4EQEBAfrlEolE30179OhRg7psbGwwb948/TJbW1skJCRAEARkZGSY9diIAGDWrFlYu3Yt/vzzT6Plf/75J9auXYunn366nVtGRNRyFhk25HI53N3dIZPJDJZ7eHjAx8cHOTk5jW4rlUqxZMkSzJgxo0GZnZ0dAOjHbajVapw7dw5+fn5wdXU1WDcgIACOjo5N1kXUGqtXr8ann35q0rqffPIJVq9eLXKLiIjaxuLChlqtRmlpKby9vY2We3l54fr166ioqDBa3q9fP8ydO9foxECHDh0CAAwaNAgAcOXKFWi1WqN1WVtbo1+/figqKmrlkRAZl5qaavCzIAgN/jS1PhFRZ2NxYaOqqgrAramOjdEtVyqVLdpveXm5fmBpRESEyXXV1NRAq9W2qC6ixnz00Uf6RyeCIKC+vt5o2NAtB249Uvn44487stlERE2yuLChu7HrHnncSbe8trbW5H0qlUrExsaivLwcs2fP1o/lEKMuoqboxgAZ68G40+3rNDVOiYioo1lc2HBwcACARufTUKvVAABHR0eT9ldRUYHnnnsO58+fx/jx47Fs2TJ9me7116bqkkgkJtdF1BylUmlS0NDRrcsvoRJRZ2ZxYUMqlcLKyqrR/7nqHp809ujjdpcvX0ZERATOnz+PCRMmIDk5GTY2//tcjG5QaFN1OTk5wcrK4k4jdVIuLi4tnixOEAR+CZWIOjWLu0va2dnB09MTJSUlRstLSkrg7u4ONze3Jvdz4cIFREZG4vLly3jqqaeQkpLS4HGJl5cXbG1tjdZVV1eH0tJSDBw4sNXHQnSnESNGtGo7fpyMiDoziwsbABAUFISrV6+isLDQYHlZWRmKi4sxfPjwJrcvLi5GTEwMrl27hhdeeAHvvvuuQY+Gjo2NDYYNGwaFQtGgd+Ps2bOoqalBYGBgm4+HSKdHjx6t2o49G0TUmVlk2Jg2bRoAYNOmTaivrwdwqyt548aNEARB/zaJMfX19Vi0aBEqKioQHR2NZcuW6ScGa6wutVqNlJQU/TKNRoPNmzcDuDX5EpG55OXltWq7goICM7eEiMh8Gv46bwFGjx6N8PBwZGVlISIiAiEhIThz5gzkcjnCwsIQGhqqX1cXEuLj4wEAX3/9NXJzc2FnZwcnJyeDEKHTu3dvREVFAQCmT5+OTz75BNu3b0dBQQH8/f1x7Ngx5OXlISYmBr6+vuIfMHUbpaWlsLKy0odoU1hZWeG3334TsVVERG1jkWEDANatW4dBgwYhMzMTH330ETw9PbFgwQLMnTvXoKdCN+GRLmzoZvxUq9V4//33je7bz89PHzasra3xwQcfICUlBV9++SVOnToFb29vrF69Wr8OWY6cnBwcPXpU/1GzcePGdarxDv369QMAkwOHbnDyXXfdJWq7iIjawuSvvgqCgMzMTHzxxRf4/fffG8wtIZFI8PXXX4vSSEvDr752Pvv370dSUhIUCkWDsiFDhiAhIQFTpkzpgJYZ2rBhA1asWGEwUdydoUMikRgEahsbG7zzzjtYvHhxu7WTiOh2zd33TO7ZSE1NRVpaGu677z4MHjy40YmuiDqb1NRUvPPOO42WKxQKxMbGYuXKlfqP8XWUsWPHwtnZ2eADbM29Wu3s7Gx0+n0ios7C5LCRmZmJ6OhorFixQsz2EJnV/v37DYKGRqOBWq1GfX09rKysYGdnB1tbWwDA2rVrMWDAgA7t4Rg5ciRGjBiBnJwckybqkkqlCAoK6lSPgoiI7mTy2yiVlZUYP368mG0hMrukpCQAt6aUv3btGioqKqBSqXDjxg2oVCpUVFTg2rVr+seCuvU7UkJCApydneHq6mr0lWzg1qMTV1dXODs7Y+HChe3cQiKiljE5bDzwwAO4cOGCmG0hMqucnBwoFApUV1ejqqqq0Q/mabVaVFVVobq6GgqFosPH2kyZMgUrVqyAg4MDevXqBXd3dzg7O8PJyQnOzs7o2bMnevXqBQcHB6xcubJTjDUhImqKyWFj6dKlyMzMREZGBq5evQq1Wt3gD1FncvToUdTW1ho8jmjqc+0qlQq1tbU4cuRIRzTXQFxcHLZu3YohQ4bAzs4OUqkULi4ukEqlsLW1hb+/P7Zu3drhY0yIiExh8piNxx9/HACwatUqo+USicToSH+ijqJUKvVBo6mPmwmCoH/DQ6VSdZqPmk2ZMgVTpkyBXC7HkSNHoFKpIJVKERoaiqCgoI5uHhGRyUwOG/Pnz29ypk2izkb36MTUz7UDtx6pVFZWtkfzTBYcHMwBoERk0UwOG7pJsYgshbFHJc2tT0RE5meR30YhMoW7u3u7bkdERMa1aLryP/74Ax9++CFOnjyJqqoquLm5ISQkBDExMejbt69YbSRqlcrKyhb3VgiC0OkeoxARWTqTezaKioowbdo0pKenQyqVIjAwEFKpFDt27MBTTz2FoqIiEZtJ1HIcY0RE1DmY3LPxt7/9DVKpFLt378bdd9+tX37lyhXExMRg/fr1+o+eEXUG1dXVrdqupqbGzC0hIureTO7ZOHnyJBYuXGgQNADAy8sL8fHxOHHihNkbR9QWpaWlLe7dkEgk/Fw7EZGZmRw2NBoNnJycjJY5OztDo9GYrVFE5tCvX78GX0htim5dfq6diMi8TA4bfn5+2LlzZ4MBd4IgYMeOHfDz8zN744jaws/PDzY2NiYFDt06NjY28PX1bacWEhF1DyaP2YiLi8NLL72ERx99FI8++ih69+6N8vJyHDhwACUlJdi6dauY7SRqsds/164LE3eG5TtDCD/XTkRkfiaHjTFjxuDvf/87Nm3ahK1bt+qneB46dCi2bt2Khx56SMx2ErWYsc+1N9XDwc+1ExGJo0XzbIwZMwZjxoxBTU0Nrl+/jh49esDR0VGsthG1WUJCAmJjY2FtbY3q6mqjX361sbGBs7MzHBwc+Ll2IiIRtGoGUUdHR3h4eDBoUKfHz7UTEXW8Jns2Bg8ejF27diEgIAB+fn5NdkHzq6/UWcXFxcHHxwdJSUlQKBSws7MzKPf398fChQsZNIiIRNJk2Jg/fz48PDz0/80ZGclS8XPtREQdRyLwU5dmpxtgKJfLO7glRERE4mvuvmfymI2JEyciLy/PaFlBQQEmTpzYiuYRERFRV2dy2Lhy5QrUarXRstraWk7xTEREREa16m2UO+Xm5qJHjx7m2BURERF1MU0OEN22bRu2bdsG4NbbJrGxsbCxMdxErVZDqVQiPDxcvFYSERGRxWoybHh7e+Phhx8GAGRmZmLYsGHo2bOnwTq2trYYNGgQZs2aJV4riYiIyGI1GTYmTZqESZMm6X9+5ZVX0L9/f9EbRURERF2HydOVv/vuu2K2g4iIiLqoFn0bRaPR4NixY7h06RJu3rxpUCaRSDB//nyzNo6IiIgsn8lho7y8HM888wyKi4sNPtV9+6yiDBtERER0J5NffV2/fj2kUim+/fZbCIKA3bt349ChQ3jppZcwYMAAHD58WMx2EhERkYUyOWycPHkSMTEx6NOnz60NrazQv39/JCQkYNKkSVi7dq1ojSQiIiLLZXLYuHbtGjw8PGBtbQ1HR0colUp92UMPPYQTJ06I0kAiIiKybCaHjT59+qCqqgoA4OnpiVOnTunLLl261GCyLyIiIiKgBQNEg4OD8eOPP2LSpEl48sknkZycjNLSUtja2mLv3r2YPHmymO0kIiIiC2Vy2HjllVfwxx9/AABiYmJQWVmJrKws3Lx5ExMnTsTKlStFayQRERFZLomge4eVzCY4OBgAIJfLO7glRERE4mvuvmeWr74SERERNYZhg4iIiETV5JgNPz8/gxlCmyKRSKBQKMzSKCIiIuo6mgwb8+fPNzlsEBERERnTZNiIj49vr3YQERFRF2Xyq69arRb19fWws7PTL/v2229RUFCAUaNG4f777xelgURERGTZTA4bixcvhoODA/76178CANLT0/H222/f2omNDbZs2YKHH35YnFYSERGRxTL5bZSffvoJY8eO1f+8fft2TJ8+HXK5HI888gi2bt0qSgOJiIjIspkcNioqKuDh4QEA+PXXX/Hrr7/iueeeg1QqxfTp01FQUCBaI4mIiMhymRw2pFKp/kNsJ06cgKurK3x9fQEA1tbWUKvVojSQiIiILJvJYzYCAwOxdetWWFtbY/v27QgNDdWXFRcX63s9iIiIiG5ncs/Ga6+9hqqqKrz88suora01eC02KysLgYGBojSQiIiILJvJPRs+Pj44ePAgKisr4e7ublC2cuVK9OnTp8E2Ys5AqtVqsWPHDuzevRslJSXo06cPpk+fjtjYWNja2pq8HwDIzs7GvHnzsHfvXgwePLhB+ZIlS/D5558b3Xbu3LlYsmRJi+ojIiLqTkwOGzp3Bg0A+rEbdxJzBtLExETs2rULQUFBmDBhAk6fPo3k5GTk5+cjOTnZ5P1cvHgRy5cvb3Kd/Px89O7dG5GRkQ3KgoKCWtx2IiKi7sTksCEIAjIzM/HFF1/g999/R21trUG5RCLB119/bbBMrBlIT58+jV27diEsLAybN2+GRCKBIAhYtmwZ9u7di+zsbIwfP77Z/Rw/fhwJCQmorKxsdB2NRoPCwkKEhoZyRlUiIqJWMDlspKamIi0tDffddx8GDx5sMJNoe0tPTwcAxMXF6XtOJBIJFi1ahM8++wx79uxpMmzcvHkTb7/9Nj799FP06NED/v7+OH/+vNF1L168CI1G02jvDRERETXN5LCRmZmJ6OhorFixotWVaTQaHDt2DJcuXcLNmzcNyiQSCebPn2/SfuRyOdzd3SGTyQyWe3h4wMfHBzk5OU1uX15ejoyMDIwfPx5vvfUWNm3a1GjYyM/PB9D4oyIiIiJqmslho7Ky0qRHE40pLy/HM888g+LiYv1jDwAGYzpMCRtqtRqlpaUYNmyY0XIvLy8UFhaioqICPXv2NLqOq6srdu7cadJ4C13YKCoqQmRkJPLz8+Hg4IDQ0FAkJCTwlV8iIqJmmPzq6wMPPIALFy60uqL169dDKpXi22+/hSAI2L17Nw4dOoSXXnoJAwYMwOHDh03aj25iMRcXF6PluuVKpbLRfbi4uJg8sFMXNtLS0nD33XcjIiICPj4++PTTTzFr1iyUlpaatB8iIqLuyuSejaVLlyIhIQE9evTAuHHj4Orq2mCdpsZxnDx5EosXL9a/ImtlZYX+/fsjISEBWq0Wa9euRVpaWrPt0Gq1TdalW37nANbWcnBwgI+PD1JTU3Hffffpl2/ZsgVJSUlYs2YNUlNTzVIXERFRV2Ry2Hj88ccBAKtWrTJa3tw8GdeuXYOHhwesra3h6Oho0PPw0EMP4T//+Y9J7XBwcABwa/yHMbpp0x0dHU3aX3MaC0AvvfQSMjIykJ2djerqajg7O5ulPiIioq7G5LDR1jkz+vTpo38E4unpiVOnTmHUqFEAgEuXLsHGxrSmSKVSWFlZQaVSGS3XhZjGHrOYi5WVFfz8/FBSUoLS0lLce++9otZHRERkqUwOG22dYyI4OBg//vgjJk2ahCeffBLJyckoLS2Fra0t9u7di8mTJ5u0Hzs7O3h6eqKkpMRoeUlJCdzd3eHm5tam9gJATU2NfkCon59fg3LdGzX29vZtrouIiKiravEMoq31yiuv4I8//gAAxMTEoLKyEllZWbh58yYmTpyIlStXmryvoKAgfPbZZygsLMTAgQP1y8vKylBcXGzwkbi2KC8vR0REBGQyWYPpymtqaqBQKNCzZ094eXmZpT4iIqKuqEVhoy3zZHh7e8Pb2/tWpTY2eP311/H666+3osnAtGnT8Nlnn2HTpk1ISkqClZUVBEHAxo0bIQgCIiIiWrXfO/Xv318/4de+ffswdepUALdmU92wYQMqKipEnZKdiIioKzA5bJhrngxzGD16NMLDw5GVlYWIiAiEhITgzJkzkMvlCAsLM+jZSElJAdD6x0CJiYmYPXs2li5dioMHD8LLywtyuRy5ubkYOXIk5s2bZ45DIiIi6rJMnmejrfNkTJw4EXl5eUbLCgoKMHHixBY1fN26dViwYAEqKyvx0Ucfoby8HAsWLMD69esNAlBqamqbXk0dOnQoMjIyEBYWhpycHKSnp0OlUmHBggXYtm1bh07bTkREZAkkgq6LohkTJkzA4sWL8eijj8Lf3x8ZGRkYOnQogFtBpLCwsMl5Mvz8/LB7924EBAQ0KDt37hyefvrpNk0a1pkEBwcDuDWtOhERUVfX3H3P5J6N5ubJOHHiRKsbmZubix49erR6eyIiIuq8TB6z0Zp5MrZt24Zt27YBuDW2IzY2tsF6arUaSqUS4eHhrT0GIiIi6sRMDhutmSfD29sbDz/8MIBbX40dNmxYg4+j2draYtCgQZg1a1YbD4WIiIg6I5PDRmvmyZg0aRImTZpksI/+/fubodlERERkKUweIGoOf/zxBz788EOcPHkSVVVVcHNzQ0hICGJiYtC3b9/2aoboOECUiIi6E7MMEK2trcXy5ctx+vTpVjekqKgI06ZNQ3p6OqRSKQIDAyGVSrFjxw489dRTKCoqavW+iYiIqPMy6TGKvb09Dhw4gGnTprW6or/97W+QSqXYvXs37r77bv3yK1euICYmBuvXr+en2omIiLogk199HT58OM6dO9fqik6ePImFCxcaBA0A8PLyQnx8fJtenSUiIqLOy+QBoq+//joWLFgAR0dHhIaGok+fPg3WaWo2TY1GAycnJ6Nlzs7O0Gg0pjaFiIiILIjJA0R1n1hv7KNjEokECoWi0e0jIyPh4uKCrVu3GuxDEATMmTMH1dXV+M9//tOStndaHCBKRETdSXP3PZN7Ntr6ddO4uDi89NJLePTRR/Hoo4+id+/eKC8vx4EDB1BSUoKtW7e2et9ERETUebXrq6///e9/sWnTJigUCgiCAIlEgqFDhyIhIQEPPfRQezVDdOzZICKi7qRNPRsTJ05EWlqa/hFKW40ZMwZjxoxBTU0Nrl+/jh49esDR0dEs+yYiIqLOqcm3Ua5cuQK1Wt3qnTf2WXlHR0d4eHgwaBAREXUDJr/62hptDStERERk+UQNG0RERETNvo3yl7/8BVKptNkdSSQSfPTRR2ZpFBEREXUdzYYNjUaD2traVlfAsEJERNS9NRs21q5di4CAgFZX0NawQkRERJbN5Em9WqutYYWIiIgsGweIEhERkagYNoiIiEhUTT5GMTYhFxEREVFLiDpmg2GFiIiI2hw2fv/9d6SkpODIkSOoqKiAvb09vL298cADD2Dq1KkcHEpERNTNtSlsXL58GVFRUaioqIDu47E3b95EQUEBfv75Z6Snp+PBBx9EYmIi+vfvb5YGExERkWVpU9hYv349rl27hv79+2Pu3LkYMmQI6uvrUVhYiOzsbBw7dgw//PADIiIisG3bNrN9PZaIiIgsh0TQdUk0Y/HixfD19YWvry/8/Pzg4eGBkJAQqNVqHDp0CL17926wjVKpxDvvvIPMzEx4enpi3759Js0maumCg4MBAHK5vINbQkREJL7m7nsm92x88cUXyMrK0v/co0cP/Pnnn+jXrx/kcjmGDBkCb29vg21cXFzw7rvv4u6770ZKSgq2bduGBQsWtOY4iIiIyEKZ3LPx73//GwUFBcjPz0dBQQFUKhUkEonBOs7OzvDz88OQIUP0f+69914AwNixY+Hq6moQWLoq9mwQEVF3YraejaioKIOff/31V0yZMgVSqRR+fn64cOECKioqIJfLIZfL9UHEzs4O9913H7RaLX799VcoFArce++9sLe3b+0xERERkQVp9QDR/v37Y8CAASgrK8OWLVtgZ2eHsrIyKBQK/Z8LFy7gt99+Q25uLoBbX3adMWMGrKys4O3tDZlMBj8/P7z88stmOyAiIiLqXEx+jGLM+++/j6SkJDz33HNYvny50XX+/PNPnD9/Hm+88QZ+//13eHt749dff0V9ff2tBkgkuHDhQmub0CnxMQoREXUnZnuMYsxzzz2HzMxMfPzxx7h8+TLeeust9O3b12AdV1dXeHt749q1a3Bzc8NXX32F2tpa/PzzzygoKEBBQUFbmkBERESdXJvChqOjI7Zs2YIXXnhBP6/GAw88gHHjxmHAgAGQSCRQKBRIT0+HWq3GI488AgCwt7fH0KFDMXToULMcBBEREXVebXqMolNeXo41a9bgq6++giAIDd5SEQQB/fr1w65du+Dh4dHW6jo9PkYhIqLuRNTHKDq9e/dGUlISioqKkJWVhZycHBQVFeHGjRvo2bMnxo4di9jYWPTq1csc1REREZEFMetXX318fPDKK6+Yc5dERERk4aw6ugFERETUtTFsEBERkagYNoiIiEhUDBtEREQkKoYNIiIiEhXDBhEREYmKYYOIiIhExbBBREREomLYICIiIlExbBAREZGoGDaIiIhIVAwbREREJCqLDRtarRbbt29HeHg4AgICMHHiRKSlpUGj0bR4X9nZ2fD19cWFCxeMltfU1CApKQmTJ09GQEAAwsPDkZ6eDkEQ2noYREREXZ7Fho3ExES8++67cHNzQ3R0NDw8PJCcnIzFixe3aD8XL17E8uXLGy2vq6vDwoULsWXLFgwcOBDR0dGwsbFBYmIi1q1b19bDICIi6vIsMmycPn0au3btQlhYGNLT07FkyRKkp6dj2rRp+Oqrr5CdnW3Sfo4fP45nnnkGlZWVja6TlZWFI0eOICYmBlu3bsWSJUvwySef4MEHH8Q///lP5Ofnm+uwiIiIuiSLDBvp6ekAgLi4OEgkEgCARCLBokWLIJFIsGfPnia3v3nzJlauXIkXXngBgiDA39+/ybpsbGwwb948/TJbW1skJCRAEARkZGSY4YiIiIi6LosMG3K5HO7u7pDJZAbLPTw84OPjg5ycnCa3Ly8vR0ZGBsaNG4d9+/Y12I+OWq3GuXPn4OfnB1dXV4OygIAAODo6NlsXERFRd2fT0Q1oKbVajdLSUgwbNsxouZeXFwoLC1FRUYGePXsaXcfV1RU7d+5EUFBQk3VduXIFWq0W3t7eDcqsra3Rr18/FBUVtfgYiIiIuhOL69moqqoCALi4uBgt1y1XKpWN7sPFxaXZoGFqXTU1NdBqtc3ui4iIqLuyuLChu7Hb2dkZLdctr62ttai6iIiIuiqLCxsODg4A0Oh8Gmq1GgDg6OjY5rrs7e2brUsikZilLiIioq7K4sKGVCqFlZUVVCqV0XLd45PGHn20hG5QaFN1OTk5wcrK4k4jERFRu7G4u6SdnR08PT1RUlJitLykpATu7u5wc3Nrc11eXl6wtbU1WlddXR1KS0sxcODANtdDRETUlVlc2ACAoKAgXL16FYWFhQbLy8rKUFxcjOHDh5ulHhsbGwwbNgwKhaJB78bZs2dRU1ODwMBAs9RFRETUVVlk2Jg2bRoAYNOmTaivrwcACIKAjRs3QhAEREREmLUutVqNlJQU/TKNRoPNmzcDAGbNmmW2uoiIiLoii5tnAwBGjx6N8PBwZGVlISIiAiEhIThz5gzkcjnCwsIQGhqqX1cXEuLj41tV1/Tp0/HJJ59g+/btKCgogL+/P44dO4a8vDzExMTA19fXHIdERETUZVlk2ACAdevWYdCgQcjMzMRHH30ET09PLFiwAHPnztVPYQ4AqampAFofNqytrfHBBx8gJSUFX375JU6dOgVvb2+sXr0aUVFRZjkWIiKirkwi8DvpZhccHAzg1rTqREREXV1z9z2LHLNBREREloNhg4iIiETFsEFERESiYtggIiIiUTFsEBERkagYNoiIiEhUDBtEREQkKoYNIiIiEhXDBhEREYmKYYOIiIhExbBBREREomLYICIiIlExbBAREZGoGDaIiIhIVAwbREREJCqGDSIiIhIVwwYRERGJimGDiIiIRMWwQURERKJi2CAiIiJRMWwQERGRqBg2iIiISFQMG0RERCQqhg0iIiISFcMGERERiYphg4iIiETFsEFERESiYtggIiIiUTFsEBERkagYNoiIiEhUDBtEREQkKoYNIiIiEhXDBhEREYmKYYOIiIhExbBBREREomLYICIiIlExbBAREZGoGDaIiIhIVAwbREREJCqGDSIiIhIVwwYRERGJimGDiIiIRMWwQURERKJi2CAiIiJRMWwQERGRqBg2iIiISFQMG0RERCQqhg0iIiISFcMGERERiYphg4iIiERl09ENaC2tVosdO3Zg9+7dKCkpQZ8+fTB9+nTExsbC1ta22e2rqqqQnJyMb7/9FteuXcO9996LOXPmIDw8vMG6S5Ysweeff250P3PnzsWSJUvafDxERERdlcWGjcTEROzatQtBQUGYMGECTp8+jeTkZOTn5yM5ObnJbW/cuIGYmBgoFAo89thjuOuuu3Dw4EG8+uqrqKiowLPPPmuwfn5+Pnr37o3IyMgG+woKCjLrcREREXU1Fhk2Tp8+jV27diEsLAybN2+GRCKBIAhYtmwZ9u7di+zsbIwfP77R7T/++GOcP38eq1evxjPPPAMAeOWVVxAZGYn169fjscceQ69evQAAGo0GhYWFCA0NRXx8fLscHxERUVdikWM20tPTAQBxcXGQSCQAAIlEgkWLFkEikWDPnj1Nbr9z584GPRVSqRTz5s1DTU2NwSOTixcvQqPRwNfXV4QjISIi6vosMmzI5XK4u7tDJpMZLPfw8ICPjw9ycnIa3fby5csoKytDUFAQrK2tDcpCQkIAwGD7/Px8AGDYICIiaiWLCxtqtRqlpaXw9vY2Wu7l5YXr16+joqLCaPnly5cBwOj2ffr0gb29PYqKivTLdGGjqKgIkZGRCAwMxKhRo7B8+XKUlZW18WiIiIi6PosLG1VVVQAAFxcXo+W65Uqlssnte/ToYbRcKpUabKsLG2lpabj77rsREREBHx8ffPrpp5g1axZKS0tbcxhERETdhsUNENVqtQAAOzs7o+W65bW1ta3evqamRv+zg4MDfHx8kJqaivvuu0+/fMuWLUhKSsKaNWuQmpra8gMhIiLqJiwubDg4OAC49ZaIMWq1GgDg6OhotNze3t5gPWPbOzk56X9OS0szut5LL72EjIwMZGdno7q6Gs7OzqYdABERUTdjcY9RpFIprKysoFKpjJbrHoE09pjF1dUVABrdXqVSQSqVNtsOKysr+Pn5QavV8lEKERFREyyuZ8POzg6enp4oKSkxWl5SUgJ3d3e4ubkZLffx8dGvd6c//vgDtbW1GDhwIACgpqYG+fn5cHBwgJ+fX4P1b968CeB/vSVERETUkMX1bAC3Zu28evUqCgsLDZaXlZWhuLgYw4cPb3RbT09PeHp64tSpU6ivrzcoO3nyJAAgMDAQAFBeXo6IiAi89tprDfZTU1MDhUKBnj17wsvLq41HRERE1HVZZNiYNm0aAGDTpk36wCAIAjZu3AhBEBAREdHk9lOnTkVpaSl27NihX6ZSqfD+++/DwcEBTz75JACgf//+8Pf3R0FBAfbt26dfVxAEbNiwARUVFYiKitJPLEZEREQNSQRBEDq6Ea3x6quvIisrCwEBAQgJCcGZM2cgl8sNpjAHgJSUFAAwmGpcpVJhxowZKCoqwiOPPIL+/fvj4MGD+PXXX7Fq1SqDb6Pk5uZi9uzZqKmpwaRJk+Dl5QW5XI7c3FyMHDkS27Zta/BmS3BwMIBbk48RERF1dc3d9yw2bGg0GmzduhWZmZkoKyuDp6cnpk6dirlz5xrc/HUzf+rmy9ApLy/Hxo0bkZ2djZqaGtxzzz148cUX8fjjjzeo6+LFi0hOTsbx48dRXV0NLy8vo3XpMGwQEVF30mXDRmfGsEFERN1Jc/c9ixyzQURERJaDYYOIiIhExbBBREREomLYICIiIlExbBAREZGoGDaIiIhIVAwbREREJCqGDSIiIhIVwwYRERGJimGDiIiIRMWwQURERKJi2CAiIiJRMWwQERGRqBg2iIiISFQMG0RERCQqhg0iIiISFcMGERERiYphg4iIiETFsEFERESiYtggIiIiUTFsEBERkagYNoiIiEhUDBtEREQkKoYNIiIiEhXDBhEREYmKYYOIiIhExbBBREREomLYICIiIlExbBAREZGoGDaIiIhIVAwbREREJCqGDSIiIhIVwwYRERGJimGDiIiIRMWwQURERKJi2CAiIiJRMWwQERGRqBg2iIiISFQMG0RERCQqhg0iIiISFcMGERERiYphg4iIiETFsEFERESiYtggIiIiUTFsEBERkagYNoiIiEhUDBtEREQkKoYNIiIiEhXDBhEREYnKYsOGVqvF9u3bER4ejoCAAEycOBFpaWnQaDQmbV9VVYXExERMmDABw4YNw/Tp05GVlWV03ZqaGiQlJWHy5MkICAhAeHg40tPTIQiCOQ+JiIioS7LYsJGYmIh3330Xbm5uiI6OhoeHB5KTk7F48eJmt71x4wZiYmKwc+dODBs2DM888wyuX7+OV199FTt27DBYt66uDgsXLsSWLVswcOBAREdHw8bGBomJiVi3bp1Yh0dERNR1CBbo1KlTgkwmE+Lj44X6+npBEAShvr5eWLp0qSCTyYRvvvmmye23bNkiyGQyYceOHfplSqVSePzxx4Vhw4YJ5eXl+uX79u0TZDKZ8N577+mXqdVqITo6WvD19RXy8vIa7D8oKEgICgpq62ESERFZhObuexbZs5Geng4AiIuLg0QiAQBIJBIsWrQIEokEe/bsaXL7nTt3onfv3oiMjNQvk0qlmDdvHmpqavD5558b1GVjY4N58+bpl9na2iIhIQGCICAjI8Och0ZERNTlWGTYkMvlcHd3h0wmM1ju4eEBHx8f5OTkNLrt5cuXUVZWhqCgIFhbWxuUhYSEAIB+e7VajXPnzsHPzw+urq4G6wYEBMDR0bHJuoiIiMgCw4ZarUZpaSm8vb2Nlnt5eeH69euoqKgwWn758mUAMLp9nz59YG9vj6KiIgDAlStXoNVqja5rbW2Nfv366dclIiIi4ywubFRVVQEAXFxcjJbrliuVyia379Gjh9FyqVSq39aUumpqaqDVak1pOhERUbdkcWFDd2O3s7MzWq5bXltb2+rtddu2tS4iIiKywLDh4OAAAI3Op6FWqwEAjo6ORsvt7e0N1jO2vZOTk8G6TdUlkUgarYuIiIgsMGxIpVJYWVlBpVIZLdc9Amns0YduoGdj26tUKkilUpPWVSqVcHJygpWVxZ1GIiKidmNxd0k7Ozt4enqipKTEaHlJSQnc3d3h5uZmtNzHx0e/3p3++OMP1NbWYuDAgQBuDTa1tbU1um5dXR1KS0v16xIREZFxFhc2ACAoKAhXr15FYWGhwfKysjIUFxdj+PDhjW7r6ekJT09PnDp1CvX19QZlJ0+eBAAEBgYCAGxsbDBs2DAoFIoGvRtnz55FTU2Nfl0iIiIyziLDxrRp0wAAmzZt0gcGQRCwceNGCIKAiIiIJrefOnUqSktLDaYmV6lUeP/99+Hg4IAnn3zSoC61Wo2UlBT9Mo1Gg82bNwMAZs2aZa7DIiIi6pJsOroBrTF69GiEh4cjKysLERERCAkJwZkzZyCXyxEWFobQ0FD9urqQEB8fr182d+5cHDhwAGvXrkVOTg769++PgwcP4tdff8WqVavQs2dP/brTp0/HJ598gu3bt6OgoAD+/v44duwY8vLyEBMTA19f33Y7biIiIkskEQTL/HSpRqPB1q1bkZmZibKyMnh6emLq1KmYO3euwauqujCQn59vsH15eTk2btyI7Oxs1NTU4J577sGLL76Ixx9/vEFdKpUKKSkp+PLLL1FVVQVvb29ERUUhKirK6ODQ4OBgALdmOiUiIurqmrvvWWzY6MwYNoiIqDtp7r5nkWM2iIiIyHIwbBAREZGoGDaIiIhIVBb5Nkpnp1KpIAiC/hkWERFRV6ZUKiGRSBotZ8+GCKysrJo86URERF2JRCJp8tMdfBuFiIiIRMWeDSIiIhIVwwYRERGJimGDiIiIRMWw0YlotVps374d4eHhCAgIwMSJE5GWlgaNRtPifWVnZ8PX1xcXLlwwWr5kyRL4+voa/bN+/fq2Hoqo2nqecnNz8corryAkJARDhw7FpEmTsH79ety4caPBujU1NUhKSsLkyZMREBCA8PBwpKenw1KGOrXnuerO19TPP/+MuLg4jBkzBoGBgYiKisLBgweNrtvdr6mWnKvufE3drr6+Hk8//XSj3+KyhGuKA0Q7kdWrV2PXrl0ICgrCiBEjcPr0aZw6dQphYWFITk42eT8XL17EM888g8rKSuzduxeDBw9usM4TTzyBiooKREZGNigLCgrC6NGj23QsYmrLeTp+/DjmzJkDAAgLC0Pfvn2Rk5ODc+fO4f7770d6ejrs7e0BAHV1dXj55Zdx5MgRjBs3DjKZDEePHkV+fj5iYmLw+uuvi36sbdVe5wrovtdUXl4eIiMjIQgCwsPDIZVKcfjwYVy5cgWvvfaa/hwCvKZacq6A7ntN3Wnbtm3461//CqDhd74s5poSqFM4deqUIJPJhPj4eKG+vl4QBEGor68Xli5dKshkMuGbb74xaT8//PCDEBISIshkMkEmkwkKhaLBOmq1WvD39xfmz59v1mNoD209T48++qgwZMgQ4aefftIvq6+vF9544w1BJpMJ27Zt0y/ft2+fIJPJhPfee0+/TK1WC9HR0YKvr6+Ql5dn5qMzr/Y8V935moqIiBD8/f2Fc+fO6ZepVCph8uTJwv333y9UVFTol3f3a6ol56o7X1O3Ky4uFgICAvT/T7+TpVxTfIzSSaSnpwMA4uLi9HN0SCQSLFq0CBKJBHv27Gly+5s3b2LlypV44YUXIAgC/P39G1334sWL0Gg0jXbJdWZtOU+//PILLl26hIkTJyIgIEC/XCKRYP78+QCAo0ePGtRlY2ODefPm6ZfZ2toiISEBgiAgIyPDrMdmbu15rrrrNaVSqXDjxg2EhoZi6NCh+uXOzs4YP348amtrDR5ldudrqqXnqrteU7cTBAErV65E37594ePj02hdlnBNcQbRTkIul8Pd3R0ymcxguYeHB3x8fJCTk9Pk9uXl5cjIyMD48ePx1ltvYdOmTTh//rzRdXXdcJb4j7gt50kqlWLJkiUNtgUAOzs7ANCPRVCr1Th37hz8/Pzg6upqsG5AQAAcHR2b/TvpaO11roDufU3t27fPaNmlS5cAAL169QLAa6ol5wrovtfU7f7973/j5MmT2L59O957770G5ZZ0TbFnoxNQq9UoLS2Ft7e30XIvLy9cv34dFRUVje7D1dUVO3fuxPvvvw8PD48m69P9Iy4qKkJkZCQCAwMxatQoLF++HGVlZa0/EJG19Tz169cPc+fOxbhx4xqUHTp0CAAwaNAgAMCVK1eg1WqN1mVtbY1+/fqhqKiolUcivvY8V0D3vabuVFdXh+LiYqxZswZHjx7F+PHj9TfL7n5N3ampcwXwmvr999+xfv16zJw5E6NGjTK6jiVdUwwbnUBVVRUAwMXFxWi5brlSqWx0Hy4uLggKCjKpPt0/4rS0NNx9992IiIiAj48PPv30U8yaNQulpaUtaH37Mcd5Mqa8vFw/YCsiIsLkumpqaqDValtUV3tpz3MF8JrSmT17Nh555BH861//wogRI7Bx48YW1dWdrqmmzhXAa2r16tVwcnJqcoCnJV1TfIzSCeguBF339J10y2tra81Sn4ODA3x8fJCamor77rtPv3zLli1ISkrCmjVrkJqaapa6zEmM86RUKhEbG4vy8nLMnj1bPz6hJXXZ2HS+f0btea4AXlM6gYGBGDZsGM6cOYPTp0/jueeewz/+8Q+4ubnxmrpDU+cK6N7X1N69e3H06FEkJyejR48eZqmro6+pzndFd0MODg4A0Oj712q1GgDg6OholvrS0tKMLn/ppZeQkZGB7OxsVFdXw9nZ2Sz1mYu5z1NFRQXmzJmD8+fPY/z48Vi2bJm+TPdKZ1N1SSQSs/2dmFt7niuA15TOa6+9pv/vdevW4cMPP8TmzZvx5ptv8pq6Q1PnCui+11R5eTneffddTJ48GWFhYU3WZUnXFB+jdAJSqRRWVlZQqVRGy3XdbY11lZmLlZUV/Pz8oNVqO2UXpTnP0+XLlxEREYHz589jwoQJSE5ONkj+usFWTdXl5OTU5FcOO1J7nqumdKdr6k4JCQlwdHTE4cOHAfCaasqd56opXf2aSkxMRF1dHVavXt1sXZZ0TbFnoxOws7ODp6cnSkpKjJaXlJTA3d1d373YFjU1NcjPz4eDgwP8/PwalN+8eRMADCZr6izMdZ4uXLiAF198EdeuXcNTTz2FNWvWNLh5enl5wdbW1mhddXV1KC0txb333tvqYxFbe56r7nxNVVVV4fTp0/D09Gxw7HZ2dujTp4/+htjdr6mWnKvufE199dVXAICHH37YaLmvry+8vLzwzTffWNQ11fFxhwDcmg3v6tWrKCwsNFheVlaG4uJiDB8+3Cz1lJeXIyIiwqALU6empgYKhQI9e/aEl5eXWeozt7aep+LiYsTExODatWt44YUX8O677xr9Ld3GxgbDhg2DQqFo8FvD2bNnUVNTg8DAwDYfj5ja61x152vq4sWLePnll412+SuVSvz222/6NwW6+zXVknPVna+puLg4o3969+6tL4+OjgZgWdcUw0YnMW3aNADApk2bUF9fD+DWhC4bN26EIAgGI//bon///vD390dBQYHBO++CIGDDhg2oqKhAVFSUfiKazqYt56m+vh6LFi1CRUUFoqOjsWzZsiaPc9q0aVCr1UhJSdEv02g02Lx5MwBg1qxZZjgi8bTXuerO19Tw4cPh6emJw4cPQy6X65drtVq89dZb0Gq1mDFjhkFd3fWaasm56s7XVHx8vNE/urARHx+P559/3qAuS7im+G2UTuTVV19FVlYWAgICEBISgjNnzkAulyMsLAybN2/W/8PSXVTx8fGN7mvZsmXIzMw0+m2U3NxczJ49GzU1NZg0aRK8vLwgl8uRm5uLkSNHYtu2bY2Obu4MWnueDh48iPj4eNjZ2SEmJsbob+m9e/dGVFQUgFvdkM888wzOnDmD0aNHw9/fH8eOHUNeXl7n+uZAE9rrXHXXawoAvv/+e8TGxgIAHnvsMbi7u+P777/Hzz//jNDQUKSlpenPX3e+poCWnavufE0Z8+STTyIvL8/ot1Es4Zpi2OhENBoNtm7diszMTJSVlcHT0xNTp07F3LlzDf5R6Sa+ufOiu11TYQO41aWZnJyM48ePo7q6Gl5eXkbr6oxae57Wrl2Ljz/+uMl9+/n54bPPPtP/rFKpkJKSgi+//BJVVVXw9vZGVFQUoqKiOsWgq+a057nqjteUTm5uLlJTUyGXy1FbWwsfHx/MmDEDs2fPhrW1tcG63fWa0mnJuerO19SdGgsbgGVcUwwbREREJKrOEXmIiIioy2LYICIiIlExbBAREZGoGDaIiIhIVAwbREREJCqGDSIiIhIVwwYRERGJimGDiIiIRMWvvhIREUpLS/HBBx8gNzcXFy5cwM2bN5GVldVpvhpKlo09G0REhOLiYmRlZcHFxQUjR47s6OZQF8OeDSIiwsiRI/H9998DAD799FMcO3asg1tEXQl7NoiIOrHx48fD19cXeXl5otbTWT7YRV0Try4iapWu/A3HznJsf/75J3777TfY2trinnvu6ejmELUawwZRO/n555+xceNGzJw5Ew8//DCGDh2KBx98ELNmzcLGjRtRUlLS0U00WXZ2Nl588cUGy1NSUuDr64vExMQ213HixAn4+vo2+mfIkCEYOXIkZsyYgffffx81NTVtrhNo/NjEUlZWhlGjRiE1NbVB2YULFwAA99xzT6f6pPo333wDf39/nDp1qqObQhaCYzaIRKZSqfDmm29i//79AICePXtiyJAhcHFxgVKpxLlz53D27Fl88MEHSEhIQGxsbAe3uGkKhQLz5s2Dl5dXu9Tn5OSEiRMnNlh+48YNXL58Gbm5ucjNzcXhw4fx8ccfw9HRsdV1tfexCYKApUuXQiqVGv1714WNwYMHt2i/Bw4cwMKFC5td74EHHsC//vWvFu0bACZMmICxY8di8eLF2L9/P6RSaYv3Qd0LwwaRiGpra/Hiiy/ixx9/hJeXF1auXIkJEyZAIpHo16mrq8O+ffuQmJiIDRs2oG/fvpg2bVrHNboZdXV17Vqfu7s71q9f32j5f//7X7zyyis4e/YsduzYgblz57a6rvY+toyMDBw/fhwpKSlGey50YcPPz69F+x0+fDg2btzY7Ho9e/Zs0X5vt2zZMoSHh+Nvf/sb3nrrrVbvh7oHhg0iEaWmpuqDxu7du9G7d+8G61hbW+Opp56CVCpFXFwckpKS8MQTT8Da2roDWmx5xowZg2eeeQbbtm3D4cOH2xQ22lN1dTWSkpJwzz33YPLkyUbXuT1sXL58Gdu2bcPRo0dRXl6Ou+66CzNnzkRMTEyDa6Vfv354/PHHRW3/gAED8Nhjj2HPnj149tlncd9994laH1k2jtkgEklNTQ3S09MBAG+++abRoHG7yZMnY8aMGYiIiNCPP9CNgcjIyMC6deswcuRIBAYGNhhTkJ2djTlz5iAkJARDhw5FaGgoli9fjkuXLhmsN3PmTPj6+uLIkSMN6n/kkUfg6+uL//u//2tQFhcXB19fX4wZMwYzZ84EAFy5cgW+vr6YMGGC0eM5c+YMYmNjMXLkSAwfPhxPPfUUdu3a1eQ5aK27774bAFBVVdWgTKPRYPfu3Xj++ecxatQoDB06FEFBQZg1axb++c9/QqPRALj1m7opx/bjjz9iwYIFGD16NIYOHYpx48ZhxYoVuHz5covavGfPHpSXlyMiIsKgp0tHrVbr//4uXbqEJ554Al988QXc3d3h6uqKoqIirF+/Hh9++GGL6jWnyMhI1NXV4e9//3uHtYEsA8MGkUi+/vprVFdXo0+fPhg7dqxJ27zzzjt4+eWXGzwD/+CDD7B9+3YMHz4cgwcPxsCBA/Vlb731FubNm4fvvvsO9957LyZMmAB7e3t8+umneOqpp/D111/r19XdPO+cQ6GkpATFxcUAbg3MvJ1arcZ3330HqVSK+Ph4/bE4OTnhiSeewKRJkxocx3fffYdnnnkGeXl5CAkJwaBBg6BQKLB69Wps2rTJpHPREtnZ2QAaPm7QaDSIiYnBqlWrkJubC39/f0yYMAH9+/fH2bNn8d577+H1118HAAQGBjZ7bDt27EBUVBQOHjyIfv36YcKECZBKpfjkk08wbdq0BueuKbt37wZwK+QZk5+fD61WCysrK2zYsAGvv/46fvjhB3zyySc4cuSIPnDq9mMOBw4cwIEDB5Cbmwvg1t/jgQMHcPLkSaPrjxgxAr169cKBAwegVCrN1g7qggQiEsXbb78tyGQyYdGiRa3eR3JysiCTyQSZTCYcPnxYv7yurk4QBEHYtWuXIJPJhAcffFD46aef9OX19fXC9u3bBZlMJgwbNky4fPmyIAiCcOHCBUEmkwlhYWEG9fznP/8RZDKZMHjwYOH+++8Xamtr9WX//e9/BZlMJiQkJAiCIAhnz54VZDKZMH78+Cbb+/bbbwsajUZf9s9//lPfntv335jjx483Wk99fb2gVCqFn376SVi0aJEgk8mE+++/X1AoFAbr/etf/xJkMpkwffp0QaVSGZQdPHhQ39bS0tJmjy0nJ0fw8/MTgoODhZMnTxqU6c5fSEiIUFVV1eyxXblyRZDJZMKkSZMaXWf37t369u3du7dBeWlpqf7vzFx09d3559lnn210mwULFggymUw4ePCg2dpBXQ/HbBCJ5PfffwcA9O3b12h5dnY2vvjiC6NlTzzxBMaNG6f/2cfHx6BLXzcB0wcffAAAWLVqFQICAvTlEokEzz33HM6ePYv9+/fj448/xsqVK+Hn5wcvLy8UFhbit99+g6enJ4Bbv8FaWVnh0UcfxRdffIEzZ84gJCRE304ARnswGuPl5YXly5cbjCWIjo7G5s2bcePGDRQVFUEmk5m0L90jjab4+Pjg7bffbvDWhrW1NcaPH4/nn38ezs7OBmWTJ0+Gh4cHysrK8Ntvv8HDw6PJOj744APU19dj8eLFDabzjoiIwPfff48DBw4gIyOj2Vdnjx8/DqDpt0wUCgUA4KGHHsKTTz7ZoFz3+OfO42qL/Pz8Fm8zePBgHDhwAMePH2907AkRwwaRSJp7s+Hnn3/G559/brRs8ODBBmHD2E2ptLQUxcXFsLOza/R/8lOnTsX+/fv1Nzfg1qOUf/3rX/jvf/+Lp59+GvX19Thx4gSGDBmCsWPH4osvvsDx48f1YePIkSOwtbU1+VEQcOttiDsHLVpZWcHDwwOFhYW4fv26yfu6/dVXQRBQWVmJ48ePo66uDgEBAVixYgWGDx9udNxDVFQUoqKiDJap1WoUFRXh3LlzUKvVAP53425MXV2d/hHJQw89ZHSd8ePH62+6zYWN3377DQCafMVWNzh0ypQpRssvXrwIAB0+2Ze3tzeA/x0TkTEMG0Qi6devHwDg6tWrRstjY2MbzK2wbNkyZGZmNljXzc2twbI//vgDAHDXXXfB1tbWaB39+/c3WBdoGDZyc3NRVVWFp59+Wh8wdDfWixcv4vLlyxgzZgxcXFyaOlwDPXr0MLrcxubW/3Ja8oqpsVdfCwsLMXfuXJw9exZbt25FcnJyo+egqqoKu3fvxnfffYfCwkL88ccf+hlCdQFFaGbG0KqqKty4cQNA8z08uh6tply7dg1A4+dJEAR9L4Pu7+RO58+fB9DyOTjMTTe+SHdMRMYwbBCJZMiQIQCA06dPQxAEo795m8rYdyvq6+ub3U53E7W3t9cvGzlyJFxcXPS9A9999x0AYNSoUbjrrrvg4+ODs2fP4saNG/q3VoxNqtXS9prTwIED8fe//x0zZ87EN998g9WrV+Pdd99tsN6pU6fw0ksvQalUws3NDUOHDkVYWBhkMhmCg4MRFxeHX375pdn6dOfa2toa4eHhTa5rytwVWq0WQOOhq6ioCDdu3ICbm1ujvR+6ng/dddZRdOdGd0xExjBsEInksccew9q1a3HlyhV89913GDNmjFn3rxtjUFpaCo1GY/Q3e93rmLe/dmtra4uHH34YWVlZOHfuHH744QfY2dlhxIgRAIDRo0dj586dOHXqFLKzsyGRSFocNtrDvffei2XLlmH16tX49NNPMWbMGIO5JQRBwPLly6FUKvHiiy9i8eLFDR7t/PnnnybV5ebmBltbW2g0Grz99tttmqVUtz8AqKysNFpuysyhujEdHd2zoevRMNb7RqTDV1+JROLi4oIXXngBAPDGG2+gvLy8yfWrq6tRWFho8v7vuusueHt7o7a2FocOHTK6jm6K9AcffNBguW6waXZ2Nn766ScEBgbCwcEBwK2wAQCHDh3C6dOnMXToUIPBk23poTG3iIgIjBo1CgCwZs0ag5v3tWvX9K/zxsXFNQgaeXl5+kdcut/OGzs2W1tbfRj75ptvjK6TmpqKJ554wqQ5J3TjLMrKyoyWNxc2rl+/jpKSEtjY2Jg80FYsukd0HT12hDo3hg0iEcXHx+Ohhx7C77//jieffBKff/65flCijlqtxueff44nn3wSP/74IwCgV69eJu1fNxBx7dq1+mf4Ounp6di/fz+cnJwQERFhUDZu3DjY2Nhg586duHnzpv6GDdwaI2BtbY1PPvkEWq22wRgFXShRqVQmPcoRW2JiIhwcHFBRUYG//vWv+uVSqVTf23NnGCsoKEBCQoL+Z93fSVPHpjvXa9aswQ8//GBQ9u2332Lr1q0oKCgwqachKCgIwK2Jz4xp7hGJrlfjnnvuMXhE1hF0H2MLDg7u0HZQ58bHKEQisra2xt///nekpqbiH//4B5YsWYK//OUvGDx4MHr16oXKykqcP38eKpUKACCTybB8+XJ970JzIiMjoVAosGvXLsycORMjRoxA7969UVBQgEuXLsHJyQnr1q3DgAEDDLbr0aMHgoKC9ANBbw8bPXr0wJAhQ3Du3DkADcdreHp6wt7eHn/++SciIyPh7e3d5LdLxObt7Y358+djw4YNyMzMxNSpUzF69Gg4ODjg2WefxT//+U8sXboU//73v9G3b1/8/vvvOHfuHOzs7DBgwAAUFxfreziaOrZx48YhPj4eKSkpeP755+Hn5wdvb29cuXJFH/Refvllk97a8fb2hkwmQ0FBAX755RcMGjTIoLy5sNFZxmtotVqcPn0ajo6OZn9MSF0LezaIRGZra4tXX30Vhw4dwuLFi+Hn54crV67g8OHDyMvLg6enJ6KiorB9+3Z8/vnnJgcNncTERKSmpmL06NH4+eef8c0330Cj0eD//b//h7179zb6WqwuREilUtx///0GZbo2DBgwoME3L5ycnPC3v/0NPj4+UCgU+O677xode9BeYmJi9LOHvvnmm7h58yYAYOnSpVizZg38/f3xyy+/4Pvvv8fNmzcxY8YMZGZm6r+jcvDgQQDNH1tcXBw+/vhjTJo0CVevXkV2djYqKiowbtw4fPjhhwa9Jc2JjIwEAHz55ZcGy8vLy1FeXg5HR0eDmWJv11nGaxw9ehTV1dV44okn+OVXapJEaO6dLyIiMju1Wo2wsDBoNBpkZ2c3+upuZ/biiy/ixIkTOHjwoH6COCJj2LNBRNQB7OzsEBcXh6tXryIrK6ujm9Niv/zyC7777jtERkYyaFCz2LNBRNRBBEHAiy++iEuXLuHLL79s8yu17SkmJgbFxcX47LPP+AiFmsWeDSKiDiKRSPDXv/4VarUa77//fkc3x2SHDh3CiRMnsGHDBgYNMgl7NoiIiEhU7NkgIiIiUTFsEBERkagYNoiIiEhUDBtEREQkKoYNIiIiEhXDBhEREYmKYYOIiIhExbBBREREovr/ACgXOygBjSr8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot translation occupancy vs. growth rate\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ax.patch.set_facecolor('white')\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "sns.scatterplot(elsemann_translation_glucose['growth_rate']/100, elsemann_translation_glucose['Value'], color = 'black', alpha=.9, linewidth = 0, s=200, zorder = 3)\n",
    "\n",
    "ax.set_ylim(-0.01, 0.32)\n",
    "ax.set_xlim(.13, 0.43)\n",
    "\n",
    "#axis format\n",
    "ax.set_xlabel(\"Growth Rate \" + r'($h^{-1}$)', fontsize=23)\n",
    "ax.set_ylabel(r\"$\\phi_{\\mathrm{Total}}^{\\mathrm{Translation}}$\", fontsize=23)\n",
    "ax.tick_params(axis='both', which='major', labelsize=20, length=0)\n",
    "\n",
    "#save figure\n",
    "fig.savefig('./Figures/yeast_total_translation_growth.svg',format='svg', dpi=1200)"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}