Metabolites_Simulation_V4.ipynb

{
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   "source": [
    "# Creation of semi-synthetic simulated data\n",
    "The method is outlined in Wieder, C., Lai, R.P.J. & Ebbels, T.M.D. Single sample pathway analysis in metabolomics: performance evaluation and application. BMC Bioinformatics 23, 481 (2022). https://doi.org/10.1186/s12859-022-05005-1\n",
    "\n",
    "The code is contained in ```simulate_pathway_signals.py```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Peusdo-code \n",
    "\n",
    "Simulation \n",
    "- Look at different effect sizes\n",
    "- Look at different pathway pairs\n",
    "\n",
    "Step 1:\n",
    "- Create null simulation (effect size = 0) \n",
    "    Shuffling was done in the simulation code\n",
    "- Then bootstrap to begin on samples from the null simulation\n",
    "- Function to use: pd.sample(replace= False) [because bootstrap is sampling without replacment] can sample rows randomly; set n to as many samples as you have (aka # of metabolites); axis =0 so it does reampling from the rows\n",
    "\n",
    "\n",
    "Step 2:\n",
    "- Run pipleline of building network \n",
    "    Create pathway scores\n",
    "    Compute correlation matrix \n",
    "    Need to store correlation matrix in a list\n",
    "\n",
    "Step 3:\n",
    "- Compute mean and SD of each correlation matrix and compare to the null (so effect size x vs effect size 0); want to see the distribution of correlation (do a kernal density or histogram; overlay plots for all correlations)\n",
    "- Do the z-score for each pair of pathways - will get one Z score for every pair of pathways (will represent how much the pair of pathways is deviating from the mean correlation)\n",
    "- Compute p value from the z-score (can turn it into a p value with a function from SciPy)\n",
    "\n",
    "Step 4:\n",
    "- Change the effect size 0.5 step\n",
    "- You get as many p values you have p values; you only get the p value of the enriched pathways - want to see does the correlation become significant\n",
    "\n",
    "Step 5:\n",
    "- Scatter plot of the original effect size vs the p value for the enriched pathway\n",
    "\n",
    "Last step:\n",
    "- AFTER RUN ONCE: Write a for loop to iterate to run it multiple times (10 times to start); when you make the outter loop you need variable outside where you store the correlation dataframes from every itteration; The loop is going to be on the entire pipeline\n",
    "    Question: New null simulation everytime? - bootstrap creates variability, but if you also compute the null everytime you have a slightly different null distribution everytime; because there would be two levels of variability, need to ask Tim if we need 2 levels or variability or 1 is fine\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Packges \n",
    "import numpy as np\n",
    "from pandas import DataFrame\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import sklearn \n",
    "import sspa\n",
    "import sspa.utils\n",
    "import gseapy.plot as gp\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import statsmodels.api as sm\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import bioinfokit as bf\n",
    "import networkx as nx\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import itertools\n",
    "from simulate_pathway_signals import SimulateData\n",
    "import statistics"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading datasets and dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Reactome download...\n",
      "Reactome DB file saved to ./Reactome_Homo_sapiens_pathways_compounds_R83.gmt\n",
      "Complete!\n",
      "Number of possible pathways: 2040\n",
      "type: <class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "# Make dictionary of the pathway and all the metabolites that define that pathway using ChEBI IDs\n",
    "reactome_pathways = sspa.process_reactome(\"Homo sapiens\", download_latest=True, filepath=\".\")\n",
    "reactome_dict_metabolites = sspa.utils.pathwaydf_to_dict(reactome_pathways)\n",
    "print(\"Number of possible pathways:\", len(reactome_dict_metabolites))\n",
    "print(\"type:\",type(reactome_dict_metabolites))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "                 1372     16610     72665     27823     30915     37373  \\\n",
       "sample_id                                                                 \n",
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       "1022407     -0.739817 -0.288710 -0.782552 -0.526128  0.288048  0.550284   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "INCOV135-BL -2.228940 -0.101002 -0.679609  0.000163  0.284214  0.734004   \n",
       "INCOV136-BL -0.169748  1.599117  0.173275  0.046243 -0.470655  1.219268   \n",
       "INCOV137-BL  0.317113  0.383352 -0.981562  0.857227 -1.462299  0.594609   \n",
       "INCOV139-BL  0.856401  1.847067  1.837225  2.220553 -0.167827  0.148805   \n",
       "INCOV142-BL -0.955167  2.240840 -1.548758 -1.160095 -1.424368  1.334007   \n",
       "\n",
       "                16831    545959     17050     16359  ...    133693    133694  \\\n",
       "sample_id                                            ...                       \n",
       "1004596     -1.195697 -0.253528  0.237192 -0.502347  ...  0.057068  0.228500   \n",
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       "...               ...       ...       ...       ...  ...       ...       ...   \n",
       "INCOV135-BL -0.041038 -0.210435  0.070459 -0.374044  ... -0.525115 -0.495566   \n",
       "INCOV136-BL  0.504222  1.204577  2.016911  0.431724  ... -0.768909 -0.829643   \n",
       "INCOV137-BL  0.561018 -0.709397  0.361510 -0.550916  ... -1.380566 -1.338709   \n",
       "INCOV139-BL  0.901317  0.782439  2.592986 -0.374388  ... -1.104065 -1.005903   \n",
       "INCOV142-BL -0.021600 -0.290402  2.540530 -0.508633  ... -1.316335 -1.294325   \n",
       "\n",
       "                28036     28238     76341     89312     17861     89188  \\\n",
       "sample_id                                                                 \n",
       "1004596     -0.546769 -0.587946 -0.175542 -0.693254 -1.201150  0.403074   \n",
       "1008097     -0.053133 -0.048306 -0.920090 -1.434730 -0.475391  0.134784   \n",
       "1008631      0.099614  0.508498 -1.272854  0.448616  1.207374 -0.637957   \n",
       "1012545     -0.341433  1.612615  0.851680 -0.504009  0.084370 -0.279907   \n",
       "1022407     -0.576132 -0.567979 -1.182109 -0.260231 -1.009292 -0.095161   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "INCOV135-BL -0.182214  0.714288 -0.265526 -0.151931  1.330576 -1.105986   \n",
       "INCOV136-BL -0.247622 -1.579635  0.567450 -0.364475  1.220314 -0.755291   \n",
       "INCOV137-BL  2.431694  0.961980  0.630608 -0.262134  0.475143 -1.235598   \n",
       "INCOV139-BL  1.430243 -0.016018 -0.279534  1.291420 -1.179473  0.417561   \n",
       "INCOV142-BL -0.292315 -0.281823 -0.788803  0.266498  1.055099 -0.409015   \n",
       "\n",
       "             WHO_status           Group  \n",
       "sample_id                                \n",
       "1004596               0  Healthy Donor   \n",
       "1008097               0  Healthy Donor   \n",
       "1008631               0  Healthy Donor   \n",
       "1012545               0  Healthy Donor   \n",
       "1022407               0  Healthy Donor   \n",
       "...                 ...             ...  \n",
       "INCOV135-BL         1-2        COVID19   \n",
       "INCOV136-BL         3-4        COVID19   \n",
       "INCOV137-BL         5-7        COVID19   \n",
       "INCOV139-BL         3-4        COVID19   \n",
       "INCOV142-BL         5-7        COVID19   \n",
       "\n",
       "[263 rows x 335 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save data into variable ; name after authors; # Read CVS to important\n",
    "# python 0 means the first thing, if you give it nothing it will give an index for you\n",
    "# if it reads with pandas, it automatrically puts it into a pd.dataframe!! so you dont need to put it into a pandas data frame\n",
    "su_data = pd.read_csv('/Users/sarapatti/Documents/Code MRes DS SP 2022-2023/Exported files/su_data_metabolites', index_col=0) # Proccesed data\n",
    "su_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of metabolites from our data: 333\n"
     ]
    }
   ],
   "source": [
    "# Computing coverage - which metabolites are in the dataset\n",
    "su_data_nometa = su_data.iloc[:,:-2]\n",
    "compounds_present = su_data_nometa.columns.tolist() #metabolites from oringal data set, using ChEBI IDs; turns the column names into a list\n",
    "print(\"Number of metabolites from our data:\", len(set(compounds_present))) # 333 metabolites\n",
    "\n",
    "# All pathways in reactome\n",
    "reactome_dict_metabolites = sspa.utils.pathwaydf_to_dict(reactome_pathways)\n",
    "reactome_dict_metabolites_series = pd.Series(reactome_dict_metabolites) # Need to put into a series first\n",
    "\n",
    "pathways_present_dict = {k: v for k, v in reactome_dict_metabolites_series.items() if len([i for i in compounds_present if i in v]) >= 2} \n",
    "pathways_present = pd.Series(pathways_present_dict)\n",
    "# Make a dictionary with pathway names and metabolites\n",
    "pathway_dictionary = pathways_present.to_dict()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2291"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chebi to me keys, names ot be values\n",
    "list3 = reactome_pathways.index\n",
    "list4 = reactome_pathways[\"Pathway_name\"] \n",
    "\n",
    "# Make a dictionary with two lists \n",
    "reactome_dict = dict(zip(list3, list4)) # zip is a way of pulling two lists together (need to be same length) and put it into a tupple (pair of things)\n",
    "reactome_dict\n",
    "\n",
    "len(reactome_dict)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Enrichment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-HSA-5619102\n",
      "R-HSA-71291\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['R-HSA-5619102', 'R-HSA-71291']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enriched_names = ['SLC transporter disorders', 'Metabolism of amino acids and derivatives']\n",
    "enriched_names ## need put in pathway ID\n",
    "\n",
    "print(list(reactome_dict.keys())[list(reactome_dict.values()).index('SLC transporter disorders')])\n",
    "print(list(reactome_dict.keys())[list(reactome_dict.values()).index('Metabolism of amino acids and derivatives')])\n",
    "\n",
    "enriched = ['R-HSA-5619102', 'R-HSA-71291']\n",
    "enriched\n",
    "\n",
    "# overlap of pathways = 0.3, abs_correlation = 0.787682"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of metabolites in R-HSA-5619102: 83\n",
      "Number of metabolites in R-HSA-71291: 300\n"
     ]
    }
   ],
   "source": [
    "type(pathway_dictionary)\n",
    "print(\"Number of metabolites in R-HSA-5619102:\", len(pathway_dictionary[\"R-HSA-5619102\"]))\n",
    "print(\"Number of metabolites in R-HSA-71291:\", len(pathway_dictionary[\"R-HSA-71291\"]))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<simulate_pathway_signals.SimulateData at 0x7f79af8db970>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd = SimulateData(\n",
    "    input_data=[su_data.iloc[:,:-2]],\n",
    "    metadata=[su_data['Group']],\n",
    "    pathways=pathway_dictionary,\n",
    "    enriched_paths=enriched)\n",
    "sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating for a single one\n",
    "sim_data = sd.enrich_paths_base\n",
    "sim_data_null = sd.enrich_paths_base(effect=0)\n",
    "# this is where the simulated data is stored\n",
    "# effect side is adding the constant; its keeping all the other correlations outside of the ones chosen as enriched\n",
    "# This data matrix would replace the original COVID one \n",
    "metab_sim_null = sim_data_null[0]\n",
    "metab_sim_null = metab_sim_null.iloc[:,:-1] # remove group"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Null (Effect size = 0) - Bootstrap, Pathway Analysis, Correlation Matrix, Mean & Stdev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating bootstrap for effect size = 0 [null]\n",
    "bootstrap_resampling = metab_sim_null.sample(n=263,replace=False, axis=0) # its shuffling patient samples \n",
    "\n",
    "## Bootstrap multiple times for null \n",
    "n_replicates = 100\n",
    "bootstap_list_n = []\n",
    "for i in range(n_replicates):\n",
    "    resampling_bootstap = metab_sim_null.sample(n=263,replace=True, axis=0)\n",
    "    bootstap_list_n.append(resampling_bootstap)\n",
    "len(bootstap_list_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confirming bootstrap is different\n",
    "\n",
    "boot_1 = bootstap_list_n[5]\n",
    "boot_2 = bootstap_list_n[55]\n",
    "boot_3 = bootstap_list_n[98]\n",
    "\n",
    "boot_1_counts = pd.DataFrame(boot_1.value_counts(\"sample_id\"), columns=[\"Count\"])\n",
    "boot_1_counts=boot_1_counts.reset_index()\n",
    "\n",
    "boot_2_counts = pd.DataFrame(boot_2.value_counts(\"sample_id\"), columns=[\"Count\"])\n",
    "boot_2_counts=boot_2_counts.reset_index()\n",
    "\n",
    "boot_3_counts = pd.DataFrame(boot_3.value_counts(\"sample_id\"), columns=[\"Count\"])\n",
    "boot_3_counts=boot_3_counts.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Overlaping plots\n",
    "\n",
    "# Creating subplots\n",
    "ax= plt.subplots(figsize=(25,5))\n",
    "\n",
    "# ploting columns\n",
    "ax = sns.barplot(x = boot_1_counts[\"sample_id\"], y= boot_1_counts[\"Count\"], color= 'b', alpha = 0.2, label='sample_1')\n",
    "ax = sns.barplot(x = boot_2_counts[\"sample_id\"], y= boot_2_counts[\"Count\"], color= 'g', alpha = 0.2, label='sample_2')\n",
    "ax = sns.barplot(x = boot_3_counts[\"sample_id\"], y= boot_3_counts[\"Count\"], color= 'r', alpha = 0.2, label='sample_3')\n",
    "\n",
    "# renaming thr axes\n",
    "ax.set(xlabel=\"Individuals\", ylabel=\"Number Count\")\n",
    "plt.tick_params(\n",
    "    axis='x',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False)\n",
    "\n",
    "# Visualizing graph\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making correlation matrix for null - bootstrapping\n",
    "\n",
    "null_correlation_matrix_list = []\n",
    "null_stack_correlation_matrix_list = []\n",
    "for n in bootstap_list_n:\n",
    "    kpca_scores= sspa.sspa_kpca(n, reactome_pathways)\n",
    "    correlation_t_test, pval = stats.spearmanr(kpca_scores, axis=0, nan_policy='propagate', alternative='two-sided')\n",
    "    correlation_t_test_df = pd.DataFrame(correlation_t_test, columns = kpca_scores.columns, index=kpca_scores.columns)\n",
    "    null_correlation_matrix_list.append(correlation_t_test_df)\n",
    "    corr = correlation_t_test_df.stack().reset_index()\n",
    "    corr.columns = [\"Pathway_1\", \"Pathway_2\", \"Correlation\"]\n",
    "    corr[\"Correlation_abs\"]= abs(corr[\"Correlation\"])\n",
    "    null_stack_correlation_matrix_list.append(corr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null Correlation Matrix 100\n",
      "Null Stacked Correlation Matrix 100\n"
     ]
    }
   ],
   "source": [
    "print(\"Null Correlation Matrix\", len(null_correlation_matrix_list))\n",
    "print(\"Null Stacked Correlation Matrix\", len(null_stack_correlation_matrix_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pathway_1</th>\n",
       "      <th>Pathway_2</th>\n",
       "      <th>Correlation</th>\n",
       "      <th>Correlation_abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112310</td>\n",
       "      <td>-0.574740</td>\n",
       "      <td>0.574740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112311</td>\n",
       "      <td>-0.393313</td>\n",
       "      <td>0.393313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112315</td>\n",
       "      <td>-0.587532</td>\n",
       "      <td>0.587532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112316</td>\n",
       "      <td>-0.587525</td>\n",
       "      <td>0.587525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27220</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9735804</td>\n",
       "      <td>0.091759</td>\n",
       "      <td>0.091759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27221</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9748784</td>\n",
       "      <td>0.006645</td>\n",
       "      <td>0.006645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27222</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9749641</td>\n",
       "      <td>-0.102932</td>\n",
       "      <td>0.102932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27223</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9753281</td>\n",
       "      <td>0.096170</td>\n",
       "      <td>0.096170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27224</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27225 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Pathway_1      Pathway_2  Correlation  Correlation_abs\n",
       "0      R-HSA-110331   R-HSA-110331     1.000000         1.000000\n",
       "1      R-HSA-110331   R-HSA-112310    -0.574740         0.574740\n",
       "2      R-HSA-110331   R-HSA-112311    -0.393313         0.393313\n",
       "3      R-HSA-110331   R-HSA-112315    -0.587532         0.587532\n",
       "4      R-HSA-110331   R-HSA-112316    -0.587525         0.587525\n",
       "...             ...            ...          ...              ...\n",
       "27220  R-HSA-975634  R-HSA-9735804     0.091759         0.091759\n",
       "27221  R-HSA-975634  R-HSA-9748784     0.006645         0.006645\n",
       "27222  R-HSA-975634  R-HSA-9749641    -0.102932         0.102932\n",
       "27223  R-HSA-975634  R-HSA-9753281     0.096170         0.096170\n",
       "27224  R-HSA-975634   R-HSA-975634     1.000000         1.000000\n",
       "\n",
       "[27225 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Labels for pathways\n",
    "pathway_labels = null_stack_correlation_matrix_list[0]\n",
    "pathway_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of null matrix: 100\n",
      "Null: # of correlations and # of matrix (27225, 100)\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",
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       "    }\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>Pathway_1</th>\n",
       "      <th>Pathway_2</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.222315e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112310</td>\n",
       "      <td>0.320239</td>\n",
       "      <td>1.816498e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112311</td>\n",
       "      <td>0.375219</td>\n",
       "      <td>6.450182e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112315</td>\n",
       "      <td>0.434103</td>\n",
       "      <td>1.473007e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112316</td>\n",
       "      <td>0.434101</td>\n",
       "      <td>1.473011e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27220</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9735804</td>\n",
       "      <td>0.062191</td>\n",
       "      <td>4.347414e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27221</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9748784</td>\n",
       "      <td>0.051057</td>\n",
       "      <td>4.725560e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27222</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9749641</td>\n",
       "      <td>0.098202</td>\n",
       "      <td>5.964241e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27223</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9753281</td>\n",
       "      <td>0.053103</td>\n",
       "      <td>4.162601e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27224</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.338979e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27225 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Pathway_1      Pathway_2      Mean           Std\n",
       "0      R-HSA-110331   R-HSA-110331  1.000000  1.222315e-16\n",
       "1      R-HSA-110331   R-HSA-112310  0.320239  1.816498e-01\n",
       "2      R-HSA-110331   R-HSA-112311  0.375219  6.450182e-02\n",
       "3      R-HSA-110331   R-HSA-112315  0.434103  1.473007e-01\n",
       "4      R-HSA-110331   R-HSA-112316  0.434101  1.473011e-01\n",
       "...             ...            ...       ...           ...\n",
       "27220  R-HSA-975634  R-HSA-9735804  0.062191  4.347414e-02\n",
       "27221  R-HSA-975634  R-HSA-9748784  0.051057  4.725560e-02\n",
       "27222  R-HSA-975634  R-HSA-9749641  0.098202  5.964241e-02\n",
       "27223  R-HSA-975634  R-HSA-9753281  0.053103  4.162601e-02\n",
       "27224  R-HSA-975634   R-HSA-975634  1.000000  1.338979e-16\n",
       "\n",
       "[27225 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop pathway_1, pathway_2, and correlation from all the dataframes\n",
    "listdfs = [df.drop([\"Pathway_1\", \"Pathway_2\", \"Correlation\"], axis = 1) for df in null_stack_correlation_matrix_list]\n",
    "print(\"Number of null matrix:\", len(listdfs))\n",
    "contact = pd.concat(listdfs, axis = 1)\n",
    "print(\"Null: # of correlations and # of matrix\", contact.shape)\n",
    "\n",
    "# Calculate mean and sd\n",
    "mean_correlation = contact.mean(axis=1)\n",
    "std_correlation = contact.std(axis=1)\n",
    "mean_and_std_df = pd.DataFrame({\"Mean\": mean_correlation, \"Std\" : std_correlation})\n",
    "pathway_mean_and_std_df = pd.merge(pathway_labels[[\"Pathway_1\", \"Pathway_2\"]], mean_and_std_df, right_index= True, left_index= True)\n",
    "pathway_mean_and_std_df\n",
    "\n",
    "## Is there a good way to viziualize this?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Effect Size Varriation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Effect Index</th>\n",
       "      <th>Effect Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Effect Index  Effect Size\n",
       "0              0          0.1\n",
       "1              1          0.2\n",
       "2              2          0.3\n",
       "3              3          0.4\n",
       "4              4          0.5\n",
       "5              5          0.6\n",
       "6              6          0.7\n",
       "7              7          0.8\n",
       "8              8          0.9\n",
       "9              9          1.0\n",
       "10            10          1.1\n",
       "11            11          1.2\n",
       "12            12          1.3\n",
       "13            13          1.4\n",
       "14            14          1.5\n",
       "15            15          1.6\n",
       "16            16          1.7\n",
       "17            17          1.8\n",
       "18            18          1.9\n",
       "19            19          2.0\n",
       "20            20          2.1\n",
       "21            21          2.2\n",
       "22            22          2.3\n",
       "23            23          2.4\n",
       "24            24          2.5\n",
       "25            25          2.6\n",
       "26            26          2.7\n",
       "27            27          2.8\n",
       "28            28          2.9\n",
       "29            29          3.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Label for what the effect size is\n",
    "effect_size_labels = []\n",
    "i = 0\n",
    "while i < 3:\n",
    "    i = i+0.1\n",
    "    effect_size_labels.append(i)\n",
    "\n",
    "effect_size_labels =  [round(x,2) for x in effect_size_labels] \n",
    "effect_size_labels\n",
    "\n",
    "j = 0\n",
    "effect_size_index = []\n",
    "for j in range(30):\n",
    "    effect_size_index.append(j)\n",
    "    j = j+1\n",
    "\n",
    "effect_size_index_df = pd.DataFrame(effect_size_index, columns= [\"Effect Index\"])\n",
    "len(effect_size_index_df)\n",
    "effect_size_labels_df = pd.DataFrame(effect_size_labels, columns= [\"Effect Size\"])\n",
    "len(effect_size_labels_df)\n",
    "\n",
    "effect_size_labels_and_index = pd.merge(effect_size_index_df, effect_size_labels_df, right_index= True, left_index= True)\n",
    "effect_size_labels_and_index\n",
    "# effect_size_labels_and_index_transpose = effect_size_labels_and_index.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "# Write a loop for effect size 0 to 100 with 0.5 step\n",
    "i = 0 \n",
    "effect_size = []\n",
    "while i < 3:\n",
    "    sim_data_effect = sd.enrich_paths_base(effect=i)\n",
    "    metab_sim = sim_data_effect[0]\n",
    "    metab_sim = metab_sim.iloc[:,:-1]\n",
    "    i = i+0.1\n",
    "    effect_size.append(metab_sim)\n",
    "print(len(effect_size))\n",
    "\n",
    "## also interested between 0.1, add the tiny effect size so where youd see most difference\n",
    "# while loop only does integers\n",
    "# go between 0-2 with step size 0.1 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making correlation matrix for effect sizes - each matrix is for a different effect size\n",
    "\n",
    "correlation_matrix_list = []\n",
    "stack_correlation_matrix_list = []\n",
    "for n in effect_size:\n",
    "    kpca_scores= sspa.sspa_kpca(n, reactome_pathways)\n",
    "    correlation_t_test, pval = stats.spearmanr(kpca_scores, axis=0, nan_policy='propagate', alternative='two-sided')\n",
    "    correlation_t_test_df = pd.DataFrame(correlation_t_test, columns = kpca_scores.columns, index=kpca_scores.columns)\n",
    "    correlation_matrix_list.append(correlation_t_test_df)\n",
    "    corr = correlation_t_test_df.stack().reset_index()\n",
    "    corr.columns = [\"Pathway_1\", \"Pathway_2\", \"Correlation\"]\n",
    "    corr[\"Correlation_abs\"]= abs(corr[\"Correlation\"])\n",
    "    stack_correlation_matrix_list.append(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Correlation Matrix (one per effect size) 30\n",
      "Number Stacked Correlation Matrix (one per effect size) 30\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Correlation Matrix (one per effect size)\", len(correlation_matrix_list))\n",
    "print(\"Number Stacked Correlation Matrix (one per effect size)\", len(stack_correlation_matrix_list))\n",
    "\n",
    "# correlation_matrix_list[100]\n",
    "# stack_correlation_matrix_list[100]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Z score and signficance calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define z score : z score = (x - mean from null boostrap)/ (std from null bootstrap)\n",
    "def z_score_calc(xcritical, mean, stdev):\n",
    "    return (xcritical-mean)/stdev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Pathway_1</th>\n",
       "      <th>Pathway_2</th>\n",
       "      <th>Z-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>-1.816590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112310</td>\n",
       "      <td>1.152885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112311</td>\n",
       "      <td>2.180950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112315</td>\n",
       "      <td>0.692393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112316</td>\n",
       "      <td>0.692403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27220</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9735804</td>\n",
       "      <td>6.188060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27221</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9748784</td>\n",
       "      <td>8.854630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27222</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9749641</td>\n",
       "      <td>7.488970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27223</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9753281</td>\n",
       "      <td>2.089054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27224</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>-1.658312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27225 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Pathway_1      Pathway_2   Z-score\n",
       "0      R-HSA-110331   R-HSA-110331 -1.816590\n",
       "1      R-HSA-110331   R-HSA-112310  1.152885\n",
       "2      R-HSA-110331   R-HSA-112311  2.180950\n",
       "3      R-HSA-110331   R-HSA-112315  0.692393\n",
       "4      R-HSA-110331   R-HSA-112316  0.692403\n",
       "...             ...            ...       ...\n",
       "27220  R-HSA-975634  R-HSA-9735804  6.188060\n",
       "27221  R-HSA-975634  R-HSA-9748784  8.854630\n",
       "27222  R-HSA-975634  R-HSA-9749641  7.488970\n",
       "27223  R-HSA-975634  R-HSA-9753281  2.089054\n",
       "27224  R-HSA-975634   R-HSA-975634 -1.658312\n",
       "\n",
       "[27225 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Effect size 5 - proof of coding example\n",
    "effect_size_10 = z_score_calc(stack_correlation_matrix_list[10][\"Correlation_abs\"], pathway_mean_and_std_df[\"Mean\"], pathway_mean_and_std_df[\"Std\"])\n",
    "effect_size_10 = pd.DataFrame(effect_size_10, columns=[\"Z-score\"])\n",
    "effect_size_10 = pd.merge(pathway_mean_and_std_df[[\"Pathway_1\", \"Pathway_2\"]], effect_size_10, right_index= True, left_index= True)\n",
    "effect_size_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of matrix for Z score - one per effect size 30\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Pathway_1</th>\n",
       "      <th>Pathway_2</th>\n",
       "      <th>Z-score</th>\n",
       "      <th>Z-score_abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>-1.816590</td>\n",
       "      <td>1.816590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112310</td>\n",
       "      <td>0.164011</td>\n",
       "      <td>0.164011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112311</td>\n",
       "      <td>0.442473</td>\n",
       "      <td>0.442473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112315</td>\n",
       "      <td>0.383823</td>\n",
       "      <td>0.383823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112316</td>\n",
       "      <td>0.383834</td>\n",
       "      <td>0.383834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27220</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9735804</td>\n",
       "      <td>-0.976135</td>\n",
       "      <td>0.976135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27221</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9748784</td>\n",
       "      <td>-0.625120</td>\n",
       "      <td>0.625120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27222</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9749641</td>\n",
       "      <td>0.330618</td>\n",
       "      <td>0.330618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27223</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9753281</td>\n",
       "      <td>-1.039949</td>\n",
       "      <td>1.039949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27224</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>-1.658312</td>\n",
       "      <td>1.658312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27225 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Pathway_1      Pathway_2   Z-score  Z-score_abs\n",
       "0      R-HSA-110331   R-HSA-110331 -1.816590     1.816590\n",
       "1      R-HSA-110331   R-HSA-112310  0.164011     0.164011\n",
       "2      R-HSA-110331   R-HSA-112311  0.442473     0.442473\n",
       "3      R-HSA-110331   R-HSA-112315  0.383823     0.383823\n",
       "4      R-HSA-110331   R-HSA-112316  0.383834     0.383834\n",
       "...             ...            ...       ...          ...\n",
       "27220  R-HSA-975634  R-HSA-9735804 -0.976135     0.976135\n",
       "27221  R-HSA-975634  R-HSA-9748784 -0.625120     0.625120\n",
       "27222  R-HSA-975634  R-HSA-9749641  0.330618     0.330618\n",
       "27223  R-HSA-975634  R-HSA-9753281 -1.039949     1.039949\n",
       "27224  R-HSA-975634   R-HSA-975634 -1.658312     1.658312\n",
       "\n",
       "[27225 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate z-score for each effect size\n",
    "z_scores_effect_sizes = []\n",
    "for m in stack_correlation_matrix_list:\n",
    "    z_scores = z_score_calc(m[\"Correlation_abs\"], pathway_mean_and_std_df[\"Mean\"], pathway_mean_and_std_df[\"Std\"])\n",
    "    z_scores = pd.DataFrame(z_scores, columns=[\"Z-score\"])\n",
    "    z_scores_df = pd.merge(pathway_mean_and_std_df[[\"Pathway_1\", \"Pathway_2\"]], z_scores, right_index= True, left_index= True)\n",
    "    z_scores_df[\"Z-score_abs\"] = abs(z_scores_df[\"Z-score\"]) \n",
    "    z_scores_effect_sizes.append(z_scores_df)\n",
    "\n",
    "print(\"Number of matrix for Z score - one per effect size\", len(z_scores_effect_sizes))\n",
    "z_scores_effect_sizes[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Pathway_1</th>\n",
       "      <th>Pathway_2</th>\n",
       "      <th>p_values_one_sided</th>\n",
       "      <th>p_values_two_sided</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>0.034640</td>\n",
       "      <td>0.069280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112310</td>\n",
       "      <td>0.434861</td>\n",
       "      <td>0.869722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112311</td>\n",
       "      <td>0.329074</td>\n",
       "      <td>0.658147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112315</td>\n",
       "      <td>0.350555</td>\n",
       "      <td>0.701110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112316</td>\n",
       "      <td>0.350551</td>\n",
       "      <td>0.701102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27220</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9735804</td>\n",
       "      <td>0.164499</td>\n",
       "      <td>0.328997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27221</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9748784</td>\n",
       "      <td>0.265946</td>\n",
       "      <td>0.531892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27222</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9749641</td>\n",
       "      <td>0.370466</td>\n",
       "      <td>0.740933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27223</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9753281</td>\n",
       "      <td>0.149182</td>\n",
       "      <td>0.298364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27224</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>0.048627</td>\n",
       "      <td>0.097254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27225 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Pathway_1      Pathway_2  p_values_one_sided  p_values_two_sided\n",
       "0      R-HSA-110331   R-HSA-110331            0.034640            0.069280\n",
       "1      R-HSA-110331   R-HSA-112310            0.434861            0.869722\n",
       "2      R-HSA-110331   R-HSA-112311            0.329074            0.658147\n",
       "3      R-HSA-110331   R-HSA-112315            0.350555            0.701110\n",
       "4      R-HSA-110331   R-HSA-112316            0.350551            0.701102\n",
       "...             ...            ...                 ...                 ...\n",
       "27220  R-HSA-975634  R-HSA-9735804            0.164499            0.328997\n",
       "27221  R-HSA-975634  R-HSA-9748784            0.265946            0.531892\n",
       "27222  R-HSA-975634  R-HSA-9749641            0.370466            0.740933\n",
       "27223  R-HSA-975634  R-HSA-9753281            0.149182            0.298364\n",
       "27224  R-HSA-975634   R-HSA-975634            0.048627            0.097254\n",
       "\n",
       "[27225 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate p-value for each effect size\n",
    "p_values_effect_size = []\n",
    "for m in z_scores_effect_sizes:\n",
    "    p_values_one_sided = stats.norm.sf(m[\"Z-score_abs\"]) # one sided\n",
    "    p_values_two_sided = stats.norm.sf(m[\"Z-score_abs\"])*2 # two sided\n",
    "    p_values_df = pd.DataFrame({\"p_values_one_sided\": p_values_one_sided, \"p_values_two_sided\" : p_values_two_sided})\n",
    "    p_values_df = pd.merge(pathway_mean_and_std_df[[\"Pathway_1\", \"Pathway_2\"]], p_values_df, right_index= True, left_index= True)\n",
    "    p_values_effect_size.append(p_values_df)\n",
    "\n",
    "len(p_values_effect_size)\n",
    "p_values_effect_size[2]\n",
    "\n",
    "# Survival function - why is that what I use to get a p value from a Z score? - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of null matrix: 30\n",
      "Null: # of correlations and # of matrix (27225, 30)\n"
     ]
    }
   ],
   "source": [
    "# Drop pathway_1, pathway_2, and one_sided p value from all the dataframes \n",
    "listdfs_p = [df.drop([\"Pathway_1\", \"Pathway_2\", \"p_values_one_sided\"], axis = 1) for df in p_values_effect_size]\n",
    "print(\"Number of null matrix:\", len(listdfs_p))\n",
    "contact_p = pd.concat(listdfs_p, axis = 1)\n",
    "print(\"Null: # of correlations and # of matrix\", contact_p.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>Pathway_1</th>\n",
       "      <th>Pathway_2</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.4</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.6</th>\n",
       "      <th>0.7</th>\n",
       "      <th>0.8</th>\n",
       "      <th>...</th>\n",
       "      <th>2.1</th>\n",
       "      <th>2.2</th>\n",
       "      <th>2.3</th>\n",
       "      <th>2.4</th>\n",
       "      <th>2.5</th>\n",
       "      <th>2.6</th>\n",
       "      <th>2.7</th>\n",
       "      <th>2.8</th>\n",
       "      <th>2.9</th>\n",
       "      <th>3.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>0.069280</td>\n",
       "      <td>0.069280</td>\n",
       "      <td>0.069280</td>\n",
       "      <td>0.069280</td>\n",
       "      <td>0.069280</td>\n",
       "      <td>0.069280</td>\n",
       "      <td>0.069280</td>\n",
       "      <td>0.069280</td>\n",
       "      <td>...</td>\n",
       "      <td>6.927988e-02</td>\n",
       "      <td>6.927988e-02</td>\n",
       "      <td>6.927988e-02</td>\n",
       "      <td>6.927988e-02</td>\n",
       "      <td>6.927988e-02</td>\n",
       "      <td>6.927988e-02</td>\n",
       "      <td>6.927988e-02</td>\n",
       "      <td>6.927988e-02</td>\n",
       "      <td>6.927988e-02</td>\n",
       "      <td>6.927988e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112310</td>\n",
       "      <td>0.870563</td>\n",
       "      <td>0.966195</td>\n",
       "      <td>0.869722</td>\n",
       "      <td>0.681632</td>\n",
       "      <td>0.825299</td>\n",
       "      <td>0.777380</td>\n",
       "      <td>0.800885</td>\n",
       "      <td>0.600301</td>\n",
       "      <td>...</td>\n",
       "      <td>3.120883e-01</td>\n",
       "      <td>7.183945e-01</td>\n",
       "      <td>1.384846e-02</td>\n",
       "      <td>4.917815e-02</td>\n",
       "      <td>8.993063e-01</td>\n",
       "      <td>1.361886e-02</td>\n",
       "      <td>6.206660e-02</td>\n",
       "      <td>4.189327e-02</td>\n",
       "      <td>3.925926e-02</td>\n",
       "      <td>7.578200e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112311</td>\n",
       "      <td>0.823885</td>\n",
       "      <td>0.821848</td>\n",
       "      <td>0.658147</td>\n",
       "      <td>0.811124</td>\n",
       "      <td>0.921878</td>\n",
       "      <td>0.922002</td>\n",
       "      <td>0.683646</td>\n",
       "      <td>0.631473</td>\n",
       "      <td>...</td>\n",
       "      <td>2.634617e-02</td>\n",
       "      <td>2.101327e-07</td>\n",
       "      <td>1.036331e-06</td>\n",
       "      <td>6.817468e-06</td>\n",
       "      <td>2.702844e-01</td>\n",
       "      <td>1.456352e-06</td>\n",
       "      <td>8.116236e-02</td>\n",
       "      <td>1.732563e-06</td>\n",
       "      <td>3.122313e-09</td>\n",
       "      <td>6.298839e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112315</td>\n",
       "      <td>0.663532</td>\n",
       "      <td>0.686185</td>\n",
       "      <td>0.701110</td>\n",
       "      <td>0.739430</td>\n",
       "      <td>0.790865</td>\n",
       "      <td>0.903830</td>\n",
       "      <td>0.788116</td>\n",
       "      <td>0.241564</td>\n",
       "      <td>...</td>\n",
       "      <td>4.241999e-01</td>\n",
       "      <td>1.410320e-02</td>\n",
       "      <td>2.423718e-02</td>\n",
       "      <td>1.037083e-01</td>\n",
       "      <td>6.492392e-01</td>\n",
       "      <td>2.521513e-02</td>\n",
       "      <td>1.089174e-01</td>\n",
       "      <td>8.303941e-02</td>\n",
       "      <td>8.824117e-02</td>\n",
       "      <td>5.884437e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>R-HSA-112316</td>\n",
       "      <td>0.663525</td>\n",
       "      <td>0.686177</td>\n",
       "      <td>0.701102</td>\n",
       "      <td>0.739421</td>\n",
       "      <td>0.790856</td>\n",
       "      <td>0.903821</td>\n",
       "      <td>0.788125</td>\n",
       "      <td>0.241570</td>\n",
       "      <td>...</td>\n",
       "      <td>4.241943e-01</td>\n",
       "      <td>1.410394e-02</td>\n",
       "      <td>2.423684e-02</td>\n",
       "      <td>1.037068e-01</td>\n",
       "      <td>6.492487e-01</td>\n",
       "      <td>2.521477e-02</td>\n",
       "      <td>1.089158e-01</td>\n",
       "      <td>8.303818e-02</td>\n",
       "      <td>8.823987e-02</td>\n",
       "      <td>5.884366e-01</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27220</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9735804</td>\n",
       "      <td>0.778139</td>\n",
       "      <td>0.670472</td>\n",
       "      <td>0.328997</td>\n",
       "      <td>0.159646</td>\n",
       "      <td>0.882207</td>\n",
       "      <td>0.678998</td>\n",
       "      <td>0.767712</td>\n",
       "      <td>0.130892</td>\n",
       "      <td>...</td>\n",
       "      <td>4.304315e-06</td>\n",
       "      <td>8.139958e-13</td>\n",
       "      <td>1.562928e-37</td>\n",
       "      <td>1.180889e-48</td>\n",
       "      <td>5.361904e-01</td>\n",
       "      <td>1.207136e-46</td>\n",
       "      <td>2.191864e-15</td>\n",
       "      <td>3.978406e-46</td>\n",
       "      <td>1.156811e-19</td>\n",
       "      <td>6.179926e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27221</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9748784</td>\n",
       "      <td>0.303772</td>\n",
       "      <td>0.289406</td>\n",
       "      <td>0.531892</td>\n",
       "      <td>0.653557</td>\n",
       "      <td>0.465542</td>\n",
       "      <td>0.303952</td>\n",
       "      <td>0.026099</td>\n",
       "      <td>0.000929</td>\n",
       "      <td>...</td>\n",
       "      <td>4.513984e-01</td>\n",
       "      <td>5.273371e-10</td>\n",
       "      <td>1.754582e-45</td>\n",
       "      <td>3.542736e-46</td>\n",
       "      <td>6.746528e-11</td>\n",
       "      <td>3.915009e-36</td>\n",
       "      <td>1.258751e-38</td>\n",
       "      <td>6.608026e-31</td>\n",
       "      <td>1.531550e-47</td>\n",
       "      <td>7.285726e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27222</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9749641</td>\n",
       "      <td>0.790108</td>\n",
       "      <td>0.849449</td>\n",
       "      <td>0.740933</td>\n",
       "      <td>0.780966</td>\n",
       "      <td>0.239141</td>\n",
       "      <td>0.021189</td>\n",
       "      <td>0.007190</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>...</td>\n",
       "      <td>4.692969e-12</td>\n",
       "      <td>1.008448e-02</td>\n",
       "      <td>2.939342e-25</td>\n",
       "      <td>5.332622e-27</td>\n",
       "      <td>8.467299e-12</td>\n",
       "      <td>2.407944e-19</td>\n",
       "      <td>1.298402e-14</td>\n",
       "      <td>4.195701e-16</td>\n",
       "      <td>2.391486e-31</td>\n",
       "      <td>4.114323e-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27223</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9753281</td>\n",
       "      <td>0.450468</td>\n",
       "      <td>0.436835</td>\n",
       "      <td>0.298364</td>\n",
       "      <td>0.224488</td>\n",
       "      <td>0.234156</td>\n",
       "      <td>0.376519</td>\n",
       "      <td>0.644731</td>\n",
       "      <td>0.294414</td>\n",
       "      <td>...</td>\n",
       "      <td>2.512778e-01</td>\n",
       "      <td>3.898136e-01</td>\n",
       "      <td>6.524581e-01</td>\n",
       "      <td>7.032857e-01</td>\n",
       "      <td>7.674621e-01</td>\n",
       "      <td>4.238562e-01</td>\n",
       "      <td>6.283902e-01</td>\n",
       "      <td>3.125116e-01</td>\n",
       "      <td>2.729674e-01</td>\n",
       "      <td>6.332456e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27224</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>0.097254</td>\n",
       "      <td>0.097254</td>\n",
       "      <td>0.097254</td>\n",
       "      <td>0.097254</td>\n",
       "      <td>0.097254</td>\n",
       "      <td>0.097254</td>\n",
       "      <td>0.097254</td>\n",
       "      <td>0.097254</td>\n",
       "      <td>...</td>\n",
       "      <td>9.725443e-02</td>\n",
       "      <td>9.725443e-02</td>\n",
       "      <td>9.725443e-02</td>\n",
       "      <td>9.725443e-02</td>\n",
       "      <td>9.725443e-02</td>\n",
       "      <td>9.725443e-02</td>\n",
       "      <td>9.725443e-02</td>\n",
       "      <td>9.725443e-02</td>\n",
       "      <td>9.725443e-02</td>\n",
       "      <td>9.725443e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27225 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Pathway_1      Pathway_2       0.1       0.2       0.3       0.4  \\\n",
       "0      R-HSA-110331   R-HSA-110331  0.069280  0.069280  0.069280  0.069280   \n",
       "1      R-HSA-110331   R-HSA-112310  0.870563  0.966195  0.869722  0.681632   \n",
       "2      R-HSA-110331   R-HSA-112311  0.823885  0.821848  0.658147  0.811124   \n",
       "3      R-HSA-110331   R-HSA-112315  0.663532  0.686185  0.701110  0.739430   \n",
       "4      R-HSA-110331   R-HSA-112316  0.663525  0.686177  0.701102  0.739421   \n",
       "...             ...            ...       ...       ...       ...       ...   \n",
       "27220  R-HSA-975634  R-HSA-9735804  0.778139  0.670472  0.328997  0.159646   \n",
       "27221  R-HSA-975634  R-HSA-9748784  0.303772  0.289406  0.531892  0.653557   \n",
       "27222  R-HSA-975634  R-HSA-9749641  0.790108  0.849449  0.740933  0.780966   \n",
       "27223  R-HSA-975634  R-HSA-9753281  0.450468  0.436835  0.298364  0.224488   \n",
       "27224  R-HSA-975634   R-HSA-975634  0.097254  0.097254  0.097254  0.097254   \n",
       "\n",
       "            0.5       0.6       0.7       0.8  ...           2.1  \\\n",
       "0      0.069280  0.069280  0.069280  0.069280  ...  6.927988e-02   \n",
       "1      0.825299  0.777380  0.800885  0.600301  ...  3.120883e-01   \n",
       "2      0.921878  0.922002  0.683646  0.631473  ...  2.634617e-02   \n",
       "3      0.790865  0.903830  0.788116  0.241564  ...  4.241999e-01   \n",
       "4      0.790856  0.903821  0.788125  0.241570  ...  4.241943e-01   \n",
       "...         ...       ...       ...       ...  ...           ...   \n",
       "27220  0.882207  0.678998  0.767712  0.130892  ...  4.304315e-06   \n",
       "27221  0.465542  0.303952  0.026099  0.000929  ...  4.513984e-01   \n",
       "27222  0.239141  0.021189  0.007190  0.000036  ...  4.692969e-12   \n",
       "27223  0.234156  0.376519  0.644731  0.294414  ...  2.512778e-01   \n",
       "27224  0.097254  0.097254  0.097254  0.097254  ...  9.725443e-02   \n",
       "\n",
       "                2.2           2.3           2.4           2.5           2.6  \\\n",
       "0      6.927988e-02  6.927988e-02  6.927988e-02  6.927988e-02  6.927988e-02   \n",
       "1      7.183945e-01  1.384846e-02  4.917815e-02  8.993063e-01  1.361886e-02   \n",
       "2      2.101327e-07  1.036331e-06  6.817468e-06  2.702844e-01  1.456352e-06   \n",
       "3      1.410320e-02  2.423718e-02  1.037083e-01  6.492392e-01  2.521513e-02   \n",
       "4      1.410394e-02  2.423684e-02  1.037068e-01  6.492487e-01  2.521477e-02   \n",
       "...             ...           ...           ...           ...           ...   \n",
       "27220  8.139958e-13  1.562928e-37  1.180889e-48  5.361904e-01  1.207136e-46   \n",
       "27221  5.273371e-10  1.754582e-45  3.542736e-46  6.746528e-11  3.915009e-36   \n",
       "27222  1.008448e-02  2.939342e-25  5.332622e-27  8.467299e-12  2.407944e-19   \n",
       "27223  3.898136e-01  6.524581e-01  7.032857e-01  7.674621e-01  4.238562e-01   \n",
       "27224  9.725443e-02  9.725443e-02  9.725443e-02  9.725443e-02  9.725443e-02   \n",
       "\n",
       "                2.7           2.8           2.9           3.0  \n",
       "0      6.927988e-02  6.927988e-02  6.927988e-02  6.927988e-02  \n",
       "1      6.206660e-02  4.189327e-02  3.925926e-02  7.578200e-01  \n",
       "2      8.116236e-02  1.732563e-06  3.122313e-09  6.298839e-06  \n",
       "3      1.089174e-01  8.303941e-02  8.824117e-02  5.884437e-01  \n",
       "4      1.089158e-01  8.303818e-02  8.823987e-02  5.884366e-01  \n",
       "...             ...           ...           ...           ...  \n",
       "27220  2.191864e-15  3.978406e-46  1.156811e-19  6.179926e-01  \n",
       "27221  1.258751e-38  6.608026e-31  1.531550e-47  7.285726e-09  \n",
       "27222  1.298402e-14  4.195701e-16  2.391486e-31  4.114323e-21  \n",
       "27223  6.283902e-01  3.125116e-01  2.729674e-01  6.332456e-01  \n",
       "27224  9.725443e-02  9.725443e-02  9.725443e-02  9.725443e-02  \n",
       "\n",
       "[27225 rows x 32 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename effect_size_labels\n",
    "contact_p.set_axis(effect_size_labels_and_index[\"Effect Size\"], axis=1, inplace=True)\n",
    "contact_p_path = pd.merge(pathway_mean_and_std_df[[\"Pathway_1\", \"Pathway_2\"]],contact_p, right_index=True, left_index=True)\n",
    "contact_p_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dataframe of just enriched pathways ['R-HSA-5619102', 'R-HSA-71291']\n",
    "\n",
    "enriched_path = contact_p_path.loc[contact_p_path['Pathway_1'] == 'R-HSA-5619102']\n",
    "enriched_paths = enriched_path.loc[enriched_path['Pathway_2'] == 'R-HSA-71291']\n",
    "enriched_paths_1 = enriched_paths.iloc[:,2:]\n",
    "enriched_paths_melt = pd.melt(enriched_paths_1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualize\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.pointplot(data=enriched_paths_melt, x=\"variable\", y=\"value\", color = \"cornflowerblue\", errorbar =('ci', 100), join=False)\n",
    "plt.xlabel(\"Efect Size\")\n",
    "plt.ylabel(\"p-value\")\n",
    "plt.title(\"'R-HSA-5619102' & 'R-HSA-71291': Effect size vs. Signficance of p-value (two-sided)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.069280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.870563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.823885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.663532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.663525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108895</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.159646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108896</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.653557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108897</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.780966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108898</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.224488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108899</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.097254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108900 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       variable     value\n",
       "0           0.1  0.069280\n",
       "1           0.1  0.870563\n",
       "2           0.1  0.823885\n",
       "3           0.1  0.663532\n",
       "4           0.1  0.663525\n",
       "...         ...       ...\n",
       "108895      0.4  0.159646\n",
       "108896      0.4  0.653557\n",
       "108897      0.4  0.780966\n",
       "108898      0.4  0.224488\n",
       "108899      0.4  0.097254\n",
       "\n",
       "[108900 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melt the data\n",
    "filtered_contact_p_path = contact_p_path.iloc[:, 2:6]\n",
    "filtered_contact_p_path_melt = pd.melt(filtered_contact_p_path)\n",
    "filtered_contact_p_path_melt\n",
    "\n",
    "# melt pivots a dataframe; make a column from ; get variable and value \n",
    "# transpose flips and x and y, but melting makes it into longform data from wide form data; every column isnt a observation but an ID variable and the value variable \n",
    "# need melt for ggplots and other vizualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Effect size 0.1 to 3 (stepsize = 0.1)')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hOE6PxSYNw+Dwww9n77335te//jWvvPJKrwHQmjVr+NSnPsUnPvEJ7r777m5fD4fDHHvssaxevZoDDjgA27b7Nd6u7rrrLlpbW3d43I6CDNM0Oe2003jooYf44Q9/SGlpKQDr16/n6aef5mtf+9qAx7YjH3zwAZWVld0CUjH2SAAkxqR///vfPVbtnTFjBtXV1QM+32WXXcaDDz7I0Ucfzde+9jUOOOAAPM9j/fr1PPHEE3z961/n8MMPZ/78+SxevJjvfve7bNmyhVNPPZVAIMDq1asJh8P5HJL999+f3/72tzzwwAPsscceBIPB/IzK9q6++mo++ugjjjvuOCZNmkRzczM333wzlmVxzDHH9HiftWvXctZZZ7HHHntw4YUXdsu5mDNnDoFAgLvuuouTTz6ZE088kQsuuICJEyfS2NjIm2++ySuvvMLvf//7Xl8TwzD44Q9/yOLFi/niF7/IOeecw7vvvss3vvENTjjhhILltmeeeYbjjjuOq6++mquvvjp/+8svv1xQZkApxR/+8AfAz33qa1Zkv/32A+Duu++mtLSUYDDI9OnTqays5Dvf+Q7f+ta3OO+88zjnnHNoaGjguuuuIxgM7nAH0jXXXMOWLVs4+uijmThxIs3Nzfz1r3/lZz/7GWeddVZBUNGTXIDxwgsvFARAn//85wmFQhx11FGMHz+euro6lixZQiwWy+d59fWcbr31Vs4//3waGxv59Kc/zbhx49i6dSuvvvoqW7du5Y477igYx0MPPYRpmpxwwgn5XWAHHnggZ599NgB33nknTz31FKeccgpTpkwhmUzmZziPP/74Hp9bPB7nk5/8JKFQiCuuuIKXX3654OuzZ88mGo1y880387GPfYz58+fzn//5n0ybNo3W1lbee+89/vznP+9wB2NfM48Ddd1113HooYdy6qmn8s1vfpNkMsnVV19NVVUVX//61wuONU2TY445piBP7MMPP+Sll14C/KAWyP+MTps2rdtS+wsvvMAxxxxTtBw4MYSKnYUtxED0tQsMUD/72c/yxw5kF5hSSrW1talvf/vbaq+99lK2batYLKb2339/9bWvfa1gx4/ruuonP/mJ2m+//fLHzZs3T/35z3/OH7Nu3Tq1YMECVVpaqgA1derUXp/To48+qk4++WQ1ceJEZdu2GjdunPrkJz+pnn322fwx2+8Ce/rpp/t8HdauXZu/76uvvqrOPvtsNW7cOGVZlqqtrVWf+MQn1J133tmfl1z95je/UQcccICybVvV1taq//qv/1Ktra0Fx+TGs/1uovPPP7/XMfa2o62rm266SU2fPl0ZhtHtPj//+c/z44rFYmrhwoXq9ddf3+E5H3nkEXX88cermpoaZZqmKikpUYcddpi65ZZbuu2W6838+fO77UL71a9+pY499lhVU1OjbNtWEyZMUGeffbb617/+1e/n9Mwzz6hTTjlFVVRUKMuy1MSJE9Upp5xSUHE6twts1apV6rTTTlMlJSWqtLRUnXPOOWrLli3541auXKnOOOMMNXXqVBUIBFRlZaU65phj1COPPFIwnq7ft9zPWW9vXXevrV27Vl100UVq4sSJyrIsVV1drY488kj13e9+t1+v4VB6+eWX1XHHHafC4bCKRqPq9NNPV++9916344BuOxH7+p2y/S609957TwHqwQcfHMZnI0aKplQ/MtGEEELkPfjggyxatIgPP/ywIA9nJFx77bVcd911bN26VfJQRth3vvMd7r33Xt5///0d1osSo59sgxdCiAE688wzOfTQQ1myZEmxhyJGSHNzM7fddhs33HCDBD+7CAmAhBBigDRN42c/+xkTJkwYUDd4MXatXbuWK6+8knPPPbfYQxFDRJbAhBBCCLHbkRkgIYQQQux2JAASQgghxG5HAiAhhBBC7HYklb0HnuexadMmSktLpdiVEEIIMUYopWhtbWXChAk77CUnAVAPNm3axOTJk4s9DCGEEEIMwoYNG5g0aVKfx0gA1INcP5kNGzYQjUaLPBohhBBC9Ec8Hmfy5Mn563hfJADqQW7ZKxqNSgAkhBBCjDH9SV+RJGghhBBC7HYkABJCCCHEbkcCICGEEELsdiQHSAghhBghruuSyWSKPYwxzbbtHW5x7w8JgIQQQohhppSirq6O5ubmYg9lzNN1nenTp2Pb9k6dRwIgIYQQYpjlgp9x48YRDoelyO4g5QoVb968mSlTpuzU6ygBkBBCCDGMXNfNBz+VlZXFHs6YV11dzaZNm3AcB8uyBn0eSYIWQgghhlEu5yccDhd5JLuG3NKX67o7dR4JgIQQQogRIMteQ2OoXkcJgIQQQgix25EASAghhBjl6urqOOGEE4hEIpSVlfV622jx97//HU3TRvWuNwmAhBBCiCK64IIL0DSt29tJJ52UP+YnP/kJmzdvZs2aNbzzzju93razpk2bxk033bTD41avXs2pp57KuHHjCAaDTJs2jUWLFrFt2zYAjjzySDZv3kwsFhuScQ0H2QUmhBBCFNlJJ53EPffcU3BbIBDIf/z+++8zd+5cZs2a1edtI6G+vp7jjz+e0047jccff5yysjLWrl3LI488QkdHB+AnKtfW1o7ouAZKZoBGUEN7mrtXruWB1R/x+FtbWNfYUewhCSGEGAUCgQC1tbUFb+Xl5YA/K/Pggw9y7733omkaF1xwQY+3AbS0tPCFL3yBcePGEY1G+cQnPsGrr75a8FiPPPIIhxxyCMFgkKqqKs4880wAPv7xj/Phhx/yta99LT8L1ZPnn3+eeDzOz3/+c+bMmcP06dP5xCc+wU033cSUKVOA7ktgH//4x3uc5Vq3bl2/xz3UZAZoBH3UkuCnz63Nfx4wdf76hSOpjOxcNUshhBC7rpdeeonzzjuPaDTKzTffTCgUIp1Od7tNKcUpp5xCRUUFy5YtIxaLcdddd3HcccfxzjvvUFFRwWOPPcaZZ57JVVddxf/+7/+STqd57LHHAHjooYc48MAD+cIXvsDnP//5XsdTW1uL4zg8/PDDfPrTn+7XrqyHHnqIdDqd//zSSy/l9ddfp6ampl/jHg4SAI2gaMDkjP3H05LIsOqjZlqSDqs+ambBXuOKPTQhhBBF9Oijj1JSUlJw2//7f/+P73znO1RXVxMIBAiFQgXLStvf9tRTT/Haa69RX1+fXz678cYb+eMf/8gf/vAHvvCFL/C9732Pz3zmM1x33XX58xx44IEAVFRUYBgGpaWlfS5fHXHEEXzrW9/i3HPP5ZJLLuGwww7jE5/4BOeddx41NTU93qdrEPOTn/yEp556ihdffJFQKNSvcQ8HCYBG0PTKCNedtA8ANzz5Nr9dvZFXJAASQojd3rHHHssdd9xRcNtAZz5WrVpFW1tbt2rTiUSC999/H4A1a9b0ObvTX9/73ve4/PLLeeqpp3jhhRe48847ueGGG1ixYgX7779/r/f7y1/+wje/+U3+/Oc/s+eee/Z73MNBAqAiOXhSWT4AEkIIsXuLRCLMnDlzp87heR7jx4/n73//e7ev5bbJh0KhnXqMriorKznrrLM466yzWLJkCXPmzOHGG2/kV7/6VY/Hv/HGG3zmM5/h+9//PgsWLBjQuIeDBEBFMmdiGQDvbG2jLeVQEpBvhRBCiME7+OCDqaurwzRNpk2b1uMxBxxwAH/729+48MILe/y6bduDajFh2zYzZsygvb29x683NDRw2mmnceaZZ/K1r31twOMeDrILrEhqSgNMjAXxFLy6qaXYwxFCCFFEqVSKurq6grdcTZ3+Ov7445k3bx6nn346jz/+OOvWreP555/n29/+Ni+//DIA11xzDffffz/XXHMNb775Jq+99ho//OEP8+eYNm0aK1asYOPGjb0+/qOPPspnP/tZHn30Ud555x3efvttbrzxRpYtW8bChQt7vM+ZZ55JKBTi2muvLXiOruv2a9zDQaYdimjOxDI2ttSxemMLR02XDsFCCLG7+utf/8r48eMLbttrr7146623+n0OTdNYtmwZV111FRdddBFbt26ltraWo48+Op+c/PGPf5zf//73/Pd//zff//73iUajHH300flzXH/99Xzxi19kxowZpFIplFLdHmf27NmEw2G+/vWvs2HDBgKBALNmzeLnP/85ixcv7nFsK1asAOg2w7N27VqmTZu2w3EPB0319Ox2c/F4nFgsRktLC9FodNge5w+vbuT6J97mkMll/PIzBw/b4wghhCieZDLJ2rVrmT59OsFgsNjDGfP6ej0Hcv2WJbAimjupDIDXNsfJuF5xByOEEELsRiQAKqJpFWHKQxYpx+ONLa3FHo4QQgix25AAqIg0TeOACX6juDfrJAASQgghRooEQEU2pcyvybAxnizySIQQQojdR9EDoNtvvz2fyDR37lyeffbZXo997rnnOOqoo6isrCQUCrH33nvzk5/8pNtxDz74ILNnzyYQCDB79mwefvjh4XwKO2VCzE/g2tSSKPJIhBBCiN1HUQOgBx54gMsuu4yrrrqK1atXM3/+fE4++WTWr1/f4/GRSIQvf/nLrFixgjfffJNvf/vbfPvb3+buu+/OH7Ny5UoWLVrE4sWLefXVV1m8eDFnn302L7744kg9rQHJB0AyAySEEEKMmKJugz/88MM5+OCDC/qf7LPPPpx++uksWbKkX+c488wziUQi/O///i8AixYtIh6P85e//CV/zEknnUR5eTn3339/v845XNvgXdfj3B//gYWH782n5s0mYJm8U9/Gp3/1T8pDFs98ef6QPZYQQojRQbbBD60xvw0+nU6zatWqgn4gAAsWLOD555/v1zlWr17N888/zzHHHJO/beXKld3OeeKJJ/Z5zlQqRTweL3gbDsteeZeHXniT829+mOlf+AlL/rCC2qjf+bYpkaEj7QzL4wohhBCiUNECoG3btuG6brcqjzU1NdTV1fV530mTJhEIBDjkkEO49NJL+dznPpf/Wl1d3YDPuWTJEmKxWP5t8uTJg3hGO3bozIlc+5mPM7GilK3xDq6+/2nW1zcRDfoFuTe2yDKYEEIIMRKKngStaVrB50qpbrdt79lnn+Xll1/mzjvv5Kabbuq2tDXQc1555ZW0tLTk3zZs2DDAZ9E/teUlXHXWMbx352V8bJ8pAPzjzfVMjPk7wSQPSAghhBgZResFVlVVhWEY3WZm6uvrd9j7Y/r06QDsv//+bNmyhWuvvZZzzjkHgNra2gGfMxAIEAgEBvM0BsU0dI7dfzrPvbmef7y1nglTZ/HmllY2yQyQEEIIMSKKNgNk2zZz585l+fLlBbcvX76cI488st/nUUqRSqXyn8+bN6/bOZ944okBnXMkHLW3PwP0/JvrmZjdCbZRtsILIcQuS1pvDo2heh2L2g3+8ssvZ/HixRxyyCHMmzePu+++m/Xr13PJJZcA/tLUxo0buffeewG47bbbmDJlCnvvvTfg1wW68cYb+cpXvpI/51e/+lWOPvpofvCDH7Bw4UL+9Kc/8eSTT/Lcc8+N/BPsw2F7TsTQNT7c2kI4+12QJTAhhNj1WJYFQEdHB6FQqMijGfvS6TQAhmHs1HmKGgAtWrSIhoYGrr/+ejZv3sx+++3HsmXLmDp1KgCbN28uqAnkeR5XXnkla9euxTRNZsyYwfe//32++MUv5o858sgj+e1vf8u3v/1tvvOd7zBjxgweeOABDj/88BF/fn0pDQU4cHotr7y/mcZmf9eZJEELIcSuxzAMysrKqK+vByAcDu8w11X0zPM8tm7dSjgcxjR3LoQpah2g0Wq46gBt7/Jf/pVbH3uR8xYcxkvxALGgybNfOXrYHk8IIURxKKWoq6ujubm52EMZ83RdZ/r06di23e1rA7l+F3UGaHd35N6TufWxF1nz3gYYN5OWpENbyqEkIN8WIYTYlWiaxvjx4xk3bhyZTKbYwxnTbNtG13c+hVmutEV0ZDYR+t/r6jhsyt60JB02xZPsWV1S5JEJIYQYDoZh7HTuihgaRa8DtDubUFHKHjXleJ6i1PK/FbIVXgghhBh+EgAV2SEzJwBgKL8Nxqa4bIUXQgghhpsEQEU2vaYcADfj1zKSGSAhhBBi+EkAVGTTxpUB0N7WAUgtICGEEGIkSABUZNPH+TNAjS2tADS0p4s5HCGEEGK3IAFQkU2rKQOgrrEZgMYOCYCEEEKI4SYBUJFNqYqh6xodHf7S1zaZARJCCCGGndQBGkHtKYd3trYRsU1ClkFNaQDbNJhcGWVDY5t/TNolmXEJWlInQgghhBguEgCNoDe3tDL/5mfzn0+MBXnlG8cybVw5H25twdDAVdDQkWZiTBrmCSGEEMNFlsBG0La2JGVBg5Clo+E3P13+9tZ8HlAgO+kjy2BCCCHE8JIZoBEUtTS2bdoAgB6KoodK+O3L6zkouxVe81zAoFECICGEEGJYyQzQCBoXDXHqQVM569AZjC/xX/qn3t3GlOoyADJpP/BpkJ1gQgghxLCSGaARNLMmxsNfPRmAO596ncv+/C4dGUhqNgAdHR2Y0ZAsgQkhhBDDTGaAiuSMudNRjr/1/aWP/B1gza3tgBRDFEIIIYabBEBFUhMLM7MiCMCT7zYQsAwcWQITQgghRoQEQCMs7bhs2NbOy+9t44JDpqOUYnNrmonVlTiZDCAzQEIIIcRwkxygEbSuvo1lqz5CZT8PmwEmhnU2JRSBaAVO6yZAAiAhhBBiuMkM0AiyTR0FKMA0NAAOm1gGQAILNzcDJEtgQgghxLCSAGgE1bUmebc5wVtNHXwY9xOgDxhfBkC7q+Nk/MCnPe2SyLjFGqYQQgixy5MlsBF08NQKpleX8MamOE+8XodSimjAoipkEU+m8FwXlAeaTkN7mkll0g5DCCGEGA4yAzTCysM2R82s4rDpFbQ7HgD7jSsh7WmgaSjXAWQZTAghhBhOEgAVyb4TYrSm/WWu/atLQNPADHRWg5ZEaCGEEGLYyBLYCGpPOryzuYW3t7Sxan0DKD8RempZiGjApMkOk0qmsMMlEgAJIYQQw0gCoBG0/I3NvP1RHICqYABPKRKOS8g02KsyzEvbwvlEaFkCE0IIIYaPLIGNoKlVEd6sj/NeQxvtaQdd03A8vypQbcTGCITzW+GlH5gQQggxfCQAGkFzplRw0TEzuG3xIew/pQwADX8ZbHxJAGVYnTNAEgAJIYQQw0YCoBF29Kxx2KbBCfuOB0DXsjNAJQE83cy3w2iUJTAhhBBi2EgAVCRlERtHKbzsDFA0YGIbBo7rb42XJTAhhBBi+EgAVET7TIwB4Cl/Fqg6YuNiADIDJIQQQgwnCYCKaP6e1QBk86AZF7FxDQvw22FksrNBQgghhBhaEgAVUVVpAMfzUHTuBFNGALIzQs2JTDGHJ4QQQuyyJAAqIk3TiATN/AzQhNIgmDbK8ytESwAkhBBCDA8JgIpsUkUknwNUE7HRDDPfDkMCICGEEGJ4SABUZLMnRPMzQGVBE93o3AovAZAQQggxPCQAKrLJVREUoJRC1zSqI0HcbEd4CYCEEEKI4SG9wEZYPJnhb+/U88Rb9diGzg2n7ovjKTwFhubvBNvS5O/+ak5KACSEEEIMBwmARtDKdQ38z9/eIeOq/G1/e3craOChMNCoidhsavS/1twhAZAQQggxHGQJbATtWV2C6yn2qIzw8ZlVAPz5tU2Uhjt3go0vtcmV/2mSJTAhhBBiWMgM0AiqjAS4/aw5TIgFSTkeL29opq41xaETYrQm/Z1flSEbN9seo0WWwIQQQohhITNAI2xiWQhN0whaBiftUwPAhy0JvGwxxLKAiaf8b4vMAAkhhBDDQ2aARlBLR5o3NsVpTzl0pBzGh20MTeOt+jYOHxcFIGIb+QapLRIACSGEEMNCAqAR1NSR4bFXNxXcNndijH9+1IzjqfxW+JBtA7INXgghhBguEgCNoLKwxb4TY0QCJnUtCdY3dFBq+d3fU15uHxiUBv0AqDXlkHE9LENWKoUQQoihVPQr6+2338706dMJBoPMnTuXZ599ttdjH3roIU444QSqq6uJRqPMmzePxx9/vOCYpUuXomlat7dkMjncT2WHysI25xwxlf+YM5EF+40HoLktjQ50OG6uByplQQuV/SSedIo0WiGEEGLXVdQA6IEHHuCyyy7jqquuYvXq1cyfP5+TTz6Z9evX93j8ihUrOOGEE1i2bBmrVq3i2GOP5bTTTmP16tUFx0WjUTZv3lzwFgwGR+Ip9dvUyjCVJTaOpygPmLSm3XwidEXIwnX9hqhNiXQxhymEEELskooaAP34xz/m4osv5nOf+xz77LMPN910E5MnT+aOO+7o8fibbrqJb3zjGxx66KHMmjWLG264gVmzZvHnP/+54DhN06itrS14G200TePgqRUA1JYESLpefgaoKtwZAEkekBBCCDH0ihYApdNpVq1axYIFCwpuX7BgAc8//3y/zuF5Hq2trVRUVBTc3tbWxtSpU5k0aRKnnnpqtxmi7aVSKeLxeMHbSJgztRwNMNFwlSJXH9qfAcq2w5AASAghhBhyRQuAtm3bhuu61NTUFNxeU1NDXV1dv87xox/9iPb2ds4+++z8bXvvvTdLly7lkUce4f777ycYDHLUUUfx7rvv9nqeJUuWEIvF8m+TJ08e3JMaoGjIYsa4EgACho6ncrWALLxsaWgJgIQQQoihV/QkaE3TCj5XSnW7rSf3338/1157LQ888ADjxo3L337EEUfw2c9+lgMPPJD58+fzu9/9jj333JNbb72113NdeeWVtLS05N82bNgw+Cc0QFOrIgDYho6bDYBKAwa5dmESAAkhhBBDr2jb4KuqqjAMo9tsT319fbdZoe098MADXHzxxfz+97/n+OOP7/NYXdc59NBD+5wBCgQCBAKB/g9+CE0oCwEQMQ1SrqLEgqCh55fDJAASQgghhl7RZoBs22bu3LksX7684Pbly5dz5JFH9nq/+++/nwsuuIDf/OY3nHLKKTt8HKUUa9asYfz48Ts95uGQC4BsQyflevkZsLDlx6YSAAkhhBBDr6iFEC+//HIWL17MIYccwrx587j77rtZv349l1xyCeAvTW3cuJF7770X8IOf8847j5tvvpkjjjgiP3sUCoWIxWIAXHfddRxxxBHMmjWLeDzOLbfcwpo1a7jtttuK8yR70JF2+fXLG1AoPj9vGiVBk7akQ8bzE6E1oMS2SCMBkBBCCDEcihoALVq0iIaGBq6//no2b97Mfvvtx7Jly5g6dSoAmzdvLqgJdNddd+E4DpdeeimXXnpp/vbzzz+fpUuXAtDc3MwXvvAF6urqiMVizJkzhxUrVnDYYYeN6HPrScpxWfrih/z6pQ2U2iaeUtSUBJhQFuKdulY8pfAU6BqU2iYNjisBkBBCCDEMNJUrOSzy4vE4sViMlpYWotHokJ335r+/xwsfNGDqnUne2xJpvvaxGax4ZytJx2VSSQBL1/nbugbea0sxpSzEo5+fN2RjEEIIIXZVA7l+F30X2O7kMwdPwtAgZBscNKkMgFLL5N2GdgBMXcsXQywPWgA0J2UGSAghhBhq0gx1BNVEgyw5fX8mlYVwPcUX738FgEdfr2NubRRT0/C2C4DiSQfH8zB1iVWFEEKIoSJX1RE2uTzMu3VxfvH0O8yuCANQEjAwdA00LV8MMRbojE2lIaoQQggxtGQGaAQ99e9NvPpBI5XhACEMQrZBuW3SBCQcF1vXcbIBUIlt4LoehqHTnMhQEbaLO3ghhBBiFyIzQCMonnGpDAfwlKI94zc7HR8uzPVxs2tgtqGjlN8PrEV2ggkhhBBDSgKgEbRHTSlPbWzm1+/V88TGJpRSTCwJEjF1MtneF65S5DbmBbJ5Py2yBCaEEEIMKVkCG0H7j4/x8X3GMW9aBaUBk5899R7TS4NMLQnyXjwBgMq+aUDI1GnzoEV2ggkhhBBDSgKgEaRpGhccNjX/uRXyX/7xEZsPWhP521U2AgqZBm1pl7gEQEIIIcSQkiWwIrpg3jS2dKTRNY3qkI2bbYWRq0xZku0HJktgQgghxNCSAKiIJsRCtLt+MnSZbZJ2PZQClQ2BogFpiCqEEEIMB1kCG2FKKR5/q56bnnkfUJyxTw1kFDHbYHNHNgcoOwVUahkAsgQmhBBCDDEJgEbQa5vjXPOXN1mzsSV/24bJKWbaFiHTQMMPgHLFEHMzQLINXgghhBhasgQ2ghzXY83GFkKWzun7jwfgD69uoinl5/iETP/bkd0RT4ntzwBJDpAQQggxtGQGaATNmVTG906ZzfF7VlMVsdnWnua5DxrYmshQGbQImwYJ18vPAAXNXB0gmQESQgghhpLMAI2wcw6eRHVJAE3TuPK4PdGAD1r8LfC5nJ9cABQwJAASQgghhoMEQEW0T20pn5xdw4fZIoiltoFSKr8Epmsalq7RlnJxPK+IIxVCCCF2LbIEVgQt7QnuePR5FHDMXnvx2BtbSDoeQVPH1LTsTjCFpmmETZ2WtEtr0qFcGqIKIYQQQ0ICoBGUSGW447F/8IPfPUVDvAOA//nPMAB1HWmmRYMEsn3Bcu0wgoZGC34itARAQgghxNCQJbAR9Ptn1/CNnz9KQ7yDyqgf+Pzg139lQjTAR20pAEKGjofK1wIKGbmdYJIHJIQQQgwVCYBG0LnHHsxxB83iZ5edzYf3fod9p9awLd6Olmylrt0PgAKGjlLgZatBl0gxRCGEEGLISQA0gkzD4PEbvsiFCw4jaFvc+qUzAfjXG+9Q354G/BmgrtWgc7WApB2GEEIIMXQkACqio/efwZlH7U+ypZFtyQyeUhi6hqH5SdAAUVsaogohhBBDTQKgIjv9yP1x00lw0rSk/caolq6T2/QeyS6BSTsMIYQQYuhIAFRkxx00C4B4Qz2N2TwfS9fwsktgYTOXAyQzQEIIIcRQkQCoyGrKSzlwjwmk4s00ZAMgU9e6VIPWANkFJoQQQgwlCYBGgePmzCLT0ca2Dj8R2tY1nGwAZOoaGhIACSGEEENJAqBR4Pg5e5JJtLMluxMsYOh4qrMadNDUJQlaCCGEGEISAI0C8/fdA9vQ2NgUBzqboGbTgAibOnFJghZCCCGGjARAo0AoYPGxfacTj7eQcPydYKbWGQCFTEPqAAkhhBBDSAKgUSKXB9SU8pe6TF3PF0OMmDqtKSefGC2EEEKInSMBUBF4nuIPL77Dfc+9kS94OHfWZDIdbTQkOrfCq+wcUMQyUEBbSvKAhBBCiKEg3eBH2N/f2MD//fUK1nxYD0Aq43Lxsftz4PQJZBJtbOsSAGWyxYBKuxRDjAat4gxcCCGE2IXIDNAI+t9n3+CEG/7Amg/rsbMFDi+792n+tX4rVbEI1SGDLdmmqJau52eH8v3AZCeYEEIIMSQkABpBC+fOYEJ5hP884UDW3vw5TjpwGsmMy7m3PkZbMs0B08ezObsTzDY03Oz9ctWgpRaQEEIIMTRkCWwERcMB3vifC4lkl7HuueQkDvnWfby9uYmfP/1vDpg+gdX/agTA0LR8EnRuW7z0AxNCCCGGhswAjbBIlxyeqtIQV5x2CAB/fOldDtxjPIn2OO0Zf+5H97tgYGU/kH5gQgghxNCQAKjIFs6dCcDz725iQlU5mY424mk/0Ml9c0xd+oEJIYQQQ0kCoCKbXFnKoTNqUQpe39yMnknSkt3urmta/r2laxIACSGEEENEAqBR4IxD/FmgR1Z9wN61sXwxRD8PyE8ECpk6LQlZAhNCCCGGggRAo8DCbAD09Bsb2HtyDQ3tScBf+sq3wzB04jIDJIQQQgwJCYBGgT3Hl7PvpEoc1wM7yNYWfyu8qXfuBAuZhiyBCSGEEENEAqBR4oxD/VmgjS0p6ptagMIZoBJLl23wQgghxBCRAGiU+Pg+kwF4t76FbU1NgJ8DlOsJH7VNGjskABJCCCGGggRAo8TB02vQNNjU1A5uJl8LKKfUNmhNOfmkaCGEEEIMngRAo0RpyGb2xEoAYuFAt1pA+Y7wabfnEwghhBCi34oeAN1+++1Mnz6dYDDI3LlzefbZZ3s99qGHHuKEE06gurqaaDTKvHnzePzxx7sd9+CDDzJ79mwCgQCzZ8/m4YcfHs6nMCivvr+Bp155q2BG57AZ4wEImDrN29UCknYYQgghxNApagD0wAMPcNlll3HVVVexevVq5s+fz8knn8z69et7PH7FihWccMIJLFu2jFWrVnHsscdy2mmnsXr16vwxK1euZNGiRSxevJhXX32VxYsXc/bZZ/Piiy+O1NPaoT89t5oj/vMGFlzxY87975/RGG8H4NAZNQCkXGhKpIHOAMjOt8OQAEgIIYTYWZoqYlLJ4YcfzsEHH8wdd9yRv22fffbh9NNPZ8mSJf06x7777suiRYu4+uqrAVi0aBHxeJy//OUv+WNOOukkysvLuf/++/t1zng8TiwWo6WlhWg0OoBntGO/e/olFn/vF7iel79tYlUZT9/0f2nNwCFX3UfIMlgw9wD+Y++JJBwXR4HjKX7xxmbuOusg5k2rGNIxCSGEELuCgVy/izYDlE6nWbVqFQsWLCi4fcGCBTz//PP9OofnebS2tlJR0RkQrFy5sts5TzzxxD7PmUqliMfjBW/D4aEVq/js936O63l89oQjeO6n32TmxHFs3NbMDfc9xr6TKgkHTBIZl/pmfwxGdgbI0EBD+oEJIYQQQ6FoAdC2bdtwXZeampqC22tqaqirq+vXOX70ox/R3t7O2Wefnb+trq5uwOdcsmQJsVgs/zZ58uQBPJP+O3yfPZhWU8nFn/wYd1y+mP33mMw937wQgN/87Z80xts4eJo/9q3xViBXDFGhaRoBQ2oBCSGEEEOh6EnQWnaGIyd3sd+R+++/n2uvvZYHHniAcePG7dQ5r7zySlpaWvJvGzZsGMAz6L+J1eX847YrufbCM1n+8kc88c8NVEYrOXTvaaQzDnc/uoLDZtQC0NyeADpzgCDbDywp/cCEEEKInVW0AKiqqgrDMLrNzNTX13ebwdneAw88wMUXX8zvfvc7jj/++IKv1dbWDvicgUCAaDRa8DYclFK0tHusfL0ex1Uo4N9rG/n62WdgmQZ3/unvHDS1GoCM69KRrQWUi4EilvQDE0IIIYZC0QIg27aZO3cuy5cvL7h9+fLlHHnkkb3e7/777+eCCy7gN7/5Daecckq3r8+bN6/bOZ944ok+zzlSPqxr5V/vNwAwtaaEA2dUommg6wHOOe5o6hrj1G3xg7f2ZJp4LgDCj4BKLOkHJoQQQgwFs5gPfvnll7N48WIOOeQQ5s2bx91338369eu55JJLAH9pauPGjdx7772AH/ycd9553HzzzRxxxBH5mZ5QKEQsFgPgq1/9KkcffTQ/+MEPWLhwIX/605948sknee6554rzJLuYNK6EtXWtTKqOMHNiDE3TMHSNV97dxomHHsyvlz/DX59fQ0nQoi2ZIZ7KUBu2yTXEiFoGLQlZAhNCCCF2VlFzgBYtWsRNN93E9ddfz0EHHcSKFStYtmwZU6dOBWDz5s0FNYHuuusuHMfh0ksvZfz48fm3r371q/ljjjzySH77299yzz33cMABB7B06VIeeOABDj/88BF/ftszDZ1jDpzArEll+ZykyTWlBCwDy7Q4bJ89+ce/32Pv8f6uttxsT24JrNQ22dqWKsrYhRBCiF1JUesAjVbDWQeoJ2+sa+St9c2s3VzH/7trKQtOOJ7lr29k0cfmsmDmeJKOS0bBpvYU/9zayvL//Niwj0kIIYQYa8ZEHSDRaY8JUTQNpo+vZcaEWlIdfmXoba0dQOdOsJChE5ddYEIIIcROkwBoFAjaJpOqSwA4/pA5fLRpMwCN222Ftw2dpONJR3ghhBBiJ0kANEpMrfEDoDkz9+D99R8BsK0tFwD5x1i6hgISGekIL4QQQuwMCYBGicpYEF3XqIiWMqmijKBl0NDqL4XlZoDMbCQkxRCFEEKInSMB0Chh6DpVsSAAB8yYRokFqVQyXwxRxw+ETE2TdhhCCCHETpIAaBQZVxYC4IAZ00m0teE6TpdiiL6wpUsxRCGEEGInSQA0itSU+wHQ7KmTSXa0ARBPFdYCipiGLIEJIYQQO0kCoFEkGrEJWAZB22bPGr8YYnMiDXS2wwhbhvQDE0IIIXaSBECjiKZpjMvOAh04dQIAje3J7Nf8Y0plCUwIIYTYaRIAjTK5AOiAPaZiarCtLVcM0f96qW3Q2J4u1vCEEEKIXcKgAqC1a9cO9ThEVnU2EXp6bQ225tKYC4CyS2ARy6SuNVm08QkhhBC7gkEFQDNnzuTYY4/lvvvuI5mUi/FQCgdMApaOrutMKg11qQWU/bqpU9cir7kQQgixMwYVAL366qvMmTOHr3/969TW1vLFL36Rf/7zn0M9tt1WRdSvBzRjXBnb4v5usFz3+ICh09AhS2BCCCHEzhhUALTffvvx4x//mI0bN3LPPfdQV1fHxz72Mfbdd19+/OMfs3Xr1qEe526lrCQAwIxxlaRSKRJOZzFEW9ekIaoQQgixk3YqCdo0Tc444wx+97vf8YMf/ID333+fK664gkmTJnHeeeexefPmoRrnbqW8NBsA1VahlCKe8gMeTQNT10k4XjGHJ4QQQox5OxUAvfzyy3zpS19i/Pjx/PjHP+aKK67g/fff56mnnmLjxo0sXLhwqMa5W8kFQBMqKwjbJi3ZAEjXNAwNPE+6wQshhBA7wxzMnX784x9zzz338Pbbb/PJT36Se++9l09+8pPouh9PTZ8+nbvuuou99957SAe7uwhYBqGAQSLlMr2ilOZECoig4ecChSyDZMYlaBnFHqoQQggxJg0qALrjjju46KKLuPDCC6mtre3xmClTpvCLX/xipwa3O6uIBtm4tZ0ZVdHOYojZr5VYOi1JRwIgIYQQYpAGFQAtX76cKVOm5Gd8cpRSbNiwgSlTpmDbNueff/6QDHJ3VF4SYOPWdmZWl/Nqo18LKFcNOmwZtCQz1GSXyoQQQggxMIPKAZoxYwbbtm3rdntjYyPTp0/f6UGJzjygPWoqaGxLAF2KIZrSD0wIIYTYGYMKgJTqOQm3ra2NYDC4UwMSPn8rvKI6Wko6nW2Imp0BKrENWhKyFV4IIYQYrAEtgV1++eWAn4h79dVXEw6H819zXZcXX3yRgw46aEgHuLuyTJ2AZZDKeJRYfpyqZyOgUstkW3uqmMMTQgghxrQBBUCrV68G/Bmg1157Ddu281+zbZsDDzyQK664YmhHuBurjAbZ1NBBVSRIxvOwdH8RLGzqrG9sL/bwhBBCiDFrQAHQ008/DcCFF17IzTffTDQaHZZBCV+sJMCmhg4ml5fQlnYpD+roGgQNnfVNHcUenhBCCDFmDWoX2D333DPU4xA9iIYtACaXlxJPZSgPWmhoBAydLc2yBCaEEEIMVr8DoDPPPJOlS5cSjUY588wz+zz2oYce2umBCYhG/CXGyRUxXq5rYWosjKaBpWs0JWQXmBBCCDFY/Q6AYrFYviN5LBYbtgGJTiUhC1AELJNk2g94dMDQNTrSsgtMCCGEGKx+B0Bdl71kCWxkaJpGJGjSnnTRlJe/Tdc06QcmhBBC7IRB1QFKJBJ0dHQm4X744YfcdNNNPPHEE0M2MOGriIYAP/EZOtthBExpgyGEEEIM1qACoIULF3LvvfcC0NzczGGHHcaPfvQjFi5cyB133DGkA9zdxbJ5QKUBf7JOz0ZAEekDJoQQQgzaoAKgV155hfnz5wPwhz/8gdraWj788EPuvfdebrnlliEd4O4ulwhdFfErbOdmgMKWSdrxijQqIYQQYmwbVADU0dFBaWkpAE888QRnnnkmuq5zxBFH8OGHHw7pAHd3uRmgcaVhlFJomoYGRCxd+oEJIYQQgzSoAGjmzJn88Y9/ZMOGDTz++OMsWLAAgPr6eimOOMSCtgEoDF0nk0181oBS06BZAiAhhBBiUAYVAF199dVcccUVTJs2jcMPP5x58+YB/mzQnDlzhnSAuztN0ygJ+fk/GS+3E8xviFofTxZzaEIIIcSYNahK0J/+9Kf52Mc+xubNmznwwAPztx933HGcccYZQzY44auKhWlLtOa3vuuaRsgweHdrK0fuUVXk0QkhhBBjz6ACIIDa2lpqa2sLbjvssMN2ekCiu1widG4HmAYETI33trYVb1BCCCHEGDaoAKi9vZ3vf//7/O1vf6O+vh7PK9yN9MEHHwzJ4ISvNOT3BAua/oqlroGl62xokgBICCGEGIxBBUCf+9zneOaZZ1i8eDHjx4/Pt8gQw6M07M8AldgmCUehoWFoGg0d6SKPTAghhBibBhUA/eUvf+Gxxx7jqKOOGurxiB6EAgZK+TvBNFw0DQwNOtJusYcmhBBCjEmD2gVWXl5ORUXFUI9F9ELTNAKWP8uma/43TZN+YEIIIcSgDSoA+u///m+uvvrqgn5gYuA81f8AJtcTTNe0/JKj9AMTQgghBmdQS2A/+tGPeP/996mpqWHatGlYllXw9VdeeWVIBrerSqQcXn1nKy1taUIBk2jEZq9p5YQCvX87ykuD1DUm8p9rQFD6gQkhhBCDMqgA6PTTTx/iYew+WjvSrHqznlQ2fyeRckikHNoTGQ7brxbL7HlSrjTsB5m5fHNdg6g1qAk8IYQQYrc3qADommuuGepx7Baa4kleeasex1VEQhYHzKoi47i89m4DbYkMr76zlYP3Hoeud99Vl9sJZmga4O8EK7EGXcZJCCGE2K0NegqhubmZn//851x55ZU0NjYC/tLXxo0bh2xwuxrD0FFAWWmAw/atIRqxqYyFOHjvagxdo6ElyXsbmnu8b0nIzO4E85uh6tl2GBlXOsILIYQQAzWoAOhf//oXe+65Jz/4wQ+48cYbaW5uBuDhhx/myiuvHNC5br/9dqZPn04wGGTu3Lk8++yzvR67efNmzj33XPbaay90Xeeyyy7rdszSpUv9junbvSWTxe+bFY3YHDa7hkP2GYfdJX8nWhJgv5mVAKyvayWd6b693dB1dC3bDFXzc4DChsH6hvYRGbsQQgixKxlUAHT55ZdzwQUX8O677xIMBvO3n3zyyaxYsaLf53nggQe47LLLuOqqq1i9ejXz58/n5JNPZv369T0en0qlqK6u5qqrriroQba9aDTK5s2bC966jrOYoiUBDKP7y15TESYasXE9xYebW3u8b2m+JYYf1AVMnZfXNw7reIUQQohd0aACoJdeeokvfvGL3W6fOHEidXV1/T7Pj3/8Yy6++GI+97nPsc8++3DTTTcxefJk7rjjjh6PnzZtGjfffDPnnXcesVis1/NqmpbvVdZTz7LRSNM0pk+MAv4skON0X9qqjoWBzlpAlq7xel18JIcphBBC7BIGFQAFg0Hi8e4X3rfffpvq6up+nSOdTrNq1SoWLFhQcPuCBQt4/vnnBzOsvLa2NqZOncqkSZM49dRTWb16dZ/Hp1Ip4vF4wVsx1FSEiYRMHNdj/Zbus0DRghkgMDWN9U1Si0kIIYQYqEEFQAsXLuT6668nk8kA/uzF+vXr+eY3v8mnPvWpfp1j27ZtuK5LTU1Nwe01NTUDmkXa3t57783SpUt55JFHuP/++wkGgxx11FG8++67vd5nyZIlxGKx/NvkyZMH/fg7w58F8me21m9uRW1XKDHXFFXP5gDpusbW9sxID1MIIYQY8wYVAN14441s3bqVcePGkUgkOOaYY5g5cyalpaV873vfG9C5tm+kqpTaqeaqRxxxBJ/97Gc58MADmT9/Pr/73e/Yc889ufXWW3u9z5VXXklLS0v+bcOGDYN+/J01vjKCZeqkMi6N8VTB10pytYDwXzcDSPSQMC2EEEKIvg2qkEw0GuW5557j6aefZtWqVXiex8EHH8zxxx/f73NUVVVhGEa32Z76+vpus0I7Q9d1Dj300D5ngAKBAIFAYMgec2fousa4ijAb69uo29ZOZawzeTtgGbiei6Eb+Rkgq4eaQUIIIYTo24ADIM/zWLp0KQ899BDr1q3zl22mT6e2tnZAsze2bTN37lyWL1/OGWeckb99+fLlLFy4cKDD6pVSijVr1rD//vsP2TmH2/gqPwDa0tjBPtMr8oURNU3DNECpzkToiLTDEEIIIQZsQAGQUor/+I//YNmyZRx44IHsv//+KKV48803ueCCC3jooYf44x//2O/zXX755SxevJhDDjmEefPmcffdd7N+/XouueQSwF+a2rhxI/fee2/+PmvWrAH8ROetW7eyZs0abNtm9uzZAFx33XUcccQRzJo1i3g8zi233MKaNWu47bbbBvJUi6oiGsS2dNIZj4aWJNXlofzXKqMhtrWk81vhI1INWgghhBiwAV09ly5dyooVK/jb3/7GscceW/C1p556itNPP517772X8847r1/nW7RoEQ0NDVx//fVs3ryZ/fbbj2XLljF16lTAL3y4fU2gOXPm5D9etWoVv/nNb5g6dSrr1q0D/ArVX/jCF6irqyMWizFnzhxWrFjBYYcdNpCnWlSaplFbGWF9XSt1De0FAVB1LJwNgPxcIJkBEkIIIQZOU9tvNerDggUL+MQnPsE3v/nNHr9+ww038Mwzz/D4448P2QCLIR6PE4vFaGlpIRqNFmUMTfEk/3x9C4ahcewhkzGyy2Ab6tt46a16XE8RTzu82dTOjYsOLsoYhRBCiNFkINfvAe0C+9e//sVJJ53U69dPPvlkXn311YGcUvSirDRA0DZwXUVjS2cbj5JQZ1d4DY2wadDSkertNEIIIYTowYACoMbGxj53aNXU1NDU1LTTgxL+Mlhlmb/01dCSyN9ekq8F5BdDDBo6r34kr7kQQggxEAMKgFzXxTR7TxsyDAPHcXZ6UMJXVeZvgd/W3BkAWaZOOvsamxpYus4LH0o/MCGEEGIgBrwL7IILLui1Zk4qJUsxQ6kyFkID2hMOiZRDKOB/u3IxqKHrmLrGv7e0FW+QQgghxBg0oADo/PPP3+Ex/d0BJnbMMnVipQGaW1Nsa04wuaYUgNryEuqbkugamLpGfVu6yCMVQgghxpYBBUD33HPPcI1D9KKqLJgNgJL5AKimPEx9UxINMDSNlNPvjXxCCCGEYJC9wMTIqYz5idCNLQk8zw90uiZCG5qGKd9FIYQQYkDk0jnKxUpsLFPHcRUtbX6OVUmXrvC6BiFTiiEKIYQQAyEB0CinaVq+IWpDth5QJGjl+67pQMSWAEgIIYQYCAmAxoCKbADUFPcDIF3XSGayW+ENnYisgQkhhBADIlfOMaC81A+AmlvT+TwgTfPfmxqUWiaO5xVtfEIIIcRYIwHQGBAJmdimjqc684BqyyOAnwgdNHU+auoo5hCFEEKIMUUCoDFA0zTKo9llsFY/AJpQVQKAoUHAMFjzUXOxhieEEEKMORIAjRHlUb/6dq4xanmp/7muawQMTfqBCSGEEAMgAdAYkZsBam5N4SlFNGwDoAG2rvPeNlkCE0IIIfpLAqAxojRsYRo6rqdobU8TtI38Vnjb0IgnpQmtEEII0V8SAI0Rfh6Qv+zVFE+haRqZ7I6wgKHheNIOQwghhOgvCYDGkHweULYeUMpxATB1HUPXijYuIYQQYqyRAGgMydUDamlNoZQi7WYDIE0jZEg1aCGEEKK/JAAaQ0ojNpoGaccjkXKwsxWgDV2jxPJzgoQQQgixYxIAjSGGrhGN+Lu/mltTlJf6H+saRCyd5kSmmMMTQgghxgwJgMaYWImfB9TSlmZSZWc16BLL4J2tbcUcmhBCCDFmSAA0xpRlCyA2t6aYMT6Gl132itkm/97YUsyhCSGEEGOGBEBjTC4Aau1IUxK0ye1+j9omb26JF3FkQgghxNghAdAYE7QNApaBUhBvT+NmI6CgqbGpJVHk0QkhhBBjgwRAY4ymacS6LINlPA8A29Bpk2rQQgghRL9IADQGlZVkd4K1pUhmOmsBaZoUQxRCCCH6QwKgMagzETpNwvFnfUxdw9Tl2ymEEEL0h1wxx6BoriBixsXz3HxT1PKAVIMWQggh+kMCoDHIMHRKw/4yWDRkkav/XB2y80tiQgghhOidBEBjVG4ZrCzUuRW+LGDyXn1rEUclhBBCjA0SAI1RuYrQJbaF53UWQ1y9sbmIoxJCCCHGBgmAxqiybB8wPA03Ww06bOq8ur6hiKMSQgghxgaz2APYnSmlaGlLEW9NEQxalIQtQgGzX9vZQwET29JJZ7z8bQFDZ0NTx3AOWQghhNglSABUJE3xJB9ubKG1PV1we0VZkL2mVWKafU/OaZpGWUmA+qYEejZgMnWNREb1eT8hhBBCyBJYUTS2JPj3O1tpbU+jaxqV5SFKs1vbG5uTrHlrCx2JzA7Pk6sIbepafit8NGgP9/CFEEKIMU9mgEZYa3uaN9/383SqykPMmFKObRn5r73x3jYSSYd/vV3PwfvW5r/Wk7JsInTQ1Ek6HoYGFSFr+J+EEEIIMcbJDNAISiQdXn93K56nKCsNsNf0yoIApzRiM2d2DeGgScbxeHttI0r1vqQVLbHR8PuA5ZQHrD7vI4QQQggJgEZUIpnBcT0iIYt9Zlah692TnW3LYO8Z/tea40k+quu9ro9p6JRE/CWv3KnKAyZvfrR1WMYvhBBC7CokABpBFWUh9t9zHPvNqsY0en/pIyGLGZPLAPhwUwvtfeQD5RqjGtlE6Jht8vi/NwzdoIUQQohdkARAIyxWGsC2d9yzq6YqQkVZEKVgXR/FDXOJ0EZ2Cihs6fx7Y3xIxiqEEELsqiQAGqU0TWP6pDLA3xnW0prq8bhcIrSVDYAsXacl7fV4rBBCCCF8EgCNYuGgRW1VBIC1HzX3mNwcDpqgkS2e6H+9JBgayWEKIYQQY44EQKPc1AkxdF2jtT1NY3Oy29c1TSMS8qsZ6PizQJXh4IiOUQghhBhrJAAa5WzbYMK4EgA+qus5t6cq5s/45HaVVYYsWtqkJYYQQgjRm6IHQLfffjvTp08nGAwyd+5cnn322V6P3bx5M+eeey577bUXuq5z2WWX9Xjcgw8+yOzZswkEAsyePZuHH354mEY/MiaOK0XTIN6eJt7WPReoqswPgMwuO8He2lA3omMUQgghxpKiBkAPPPAAl112GVdddRWrV69m/vz5nHzyyaxfv77H41OpFNXV1Vx11VUceOCBPR6zcuVKFi1axOLFi3n11VdZvHgxZ599Ni+++OJwPpVhZdsG4yrCAGzc0r0uUKwkgFIqvxMsZpuseW/ziI5RCCGEGEs0VcSywYcffjgHH3wwd9xxR/62ffbZh9NPP50lS5b0ed+Pf/zjHHTQQdx0000Fty9atIh4PM5f/vKX/G0nnXQS5eXl3H///f0aVzweJxaL0dLSQjQa7f8TGkbtHWleeWMLAIfuP55goLCLye+ffodoMEDScXEV/PXfr3P3l04rxlCFEEKMcrlLv6Z1L8g7lg3k+l20GaB0Os2qVatYsGBBwe0LFizg+eefH/R5V65c2e2cJ554Yp/nTKVSxOPxgrfRJhK2KcvW/NlU330WqKE9AZDvDB/PFH11UwghRJEppfAcDzfl4CQyZNrTZNrSOO0ZnPYMmbY0mY4MbsrBc7zdqpVS0a6S27Ztw3VdampqCm6vqamhrm7w+St1dXUDPueSJUuIxWL5t8mTJw/68YfTxJpSALZs68DzCn9I29J+blCuJUZppHRExyaEEGJ0yAU9TsIPctykg5fxUK7KVUsp5Cm8jIebdPzjUw7K2/UDoaJPE2w//aaU2ukpuYGe88orr6SlpSX/tmHD6GwlUR4LErANHNdjW1PhLi/T9H9Ycy0xxkVL6Ej2XDxRCCHErkcphZt2cTr8oEe52SBGA93UMQIGRsjEDFuYEct/HzIxgia6pZOtpIKX8XA6MngZd5eeETJ3fMjwqKqqwjCMbjMz9fX13WZwBqK2tnbA5wwEAgQCgUE/5kjRNI2aqgjrN8Wp29bOuMpI/mvVsSCeUuiahgZUhgO88eFmDtlrWtHGK4QQYvgp5c/geGm388Zs0KNbBloPjbdzx4B/zcDU0W2Fcv0gCk/hplw0x8MImrtcrhAUcQbItm3mzp3L8uXLC25fvnw5Rx555KDPO2/evG7nfOKJJ3bqnKNJrjJ0S2uKRLKzSerkqhgJx2+BYWga5QGL19duKsoYhRBCjAzPyc7W5IIfDYyAgRm2MAJm78FPDzRNQzd1zJCJnu1ZqVyFm3B2yZmgos0AAVx++eUsXryYQw45hHnz5nH33Xezfv16LrnkEsBfmtq4cSP33ntv/j5r1qwBoK2tja1bt7JmzRps22b27NkAfPWrX+Xoo4/mBz/4AQsXLuRPf/oTTz75JM8999yIP7/hELBNymNBmlqS1G1rz/cLm1gZZe2mrUQsA12HUtvgxTckABJCiF2R8pSfq9NlmcuwDTRTH5I0EsM20AzND348hZNwMEO71kxQUQOgRYsW0dDQwPXXX8/mzZvZb7/9WLZsGVOnTgX8wofb1wSaM2dO/uNVq1bxm9/8hqlTp7Ju3ToAjjzySH7729/y7W9/m+985zvMmDGDBx54gMMPP3zEntdwq62K0NSSZMu29nyrjAnlpWxuXU9tSRAdDV3T2NDQXuyhCiGEGGJexsVNdS536ZaObhtDHpzoho4WMnGSjr8klnAwdqEgqKh1gEar0VgHqCvPU/zzX5vIOB77zqyiIlsJ+gt3/Z1PHTAdgPaMywMvrOGBb5xZzKEKIYQYIkr5eTkqm+6g6RpGwEAzhjebRXkKp8NPudBNHSNY1LmTPo2JOkBi8HRdozpbGbq+sXM3WGN7G05266KuwcRx42hqlVkgIYQY63JBSC740W1/R9dwBz+QDbSyQY/neHgZdwf3GBskABqjxlX6AVBDcwLH9f9D2Loi7XYmQk+qiPFvSYQWQogxLZfojMLP9QmZfo7OCC5F6aaeT4x2U+4uUSdIAqAxqiRsEwqaeJ6iocmvAh0LmyRzfx1oGlXhAK+v3VjMYQohhBgkf8nLwU06gD8TY4Ys9BGY9emJbun5XWVuauzvDJMAaIzSNC3fILU+m+w8oSxMh+NPTRoalNgmb63fUrQxCiGEGBylVL6CM/jBhxEa2Lb2QT2up3BSDulEhlRHmnRHGift4Hl+QWEj0Lk9Pr8DbYwavZlMYofGVUb4cFOc5tYUqbTL5Koo8YRbUBCxrrGt2MMUQggxAMr1/J1X2fjCCBjoljF8j5dtnZHJ9gPrJu0CGUzbwApa6Jbut85IOWiGNWZ3hckM0BgWDJhES2wAtja2M6kySiLjksmuzRq6RkZZY36aUgghdhdexsVJOPl8HzNkDmvw4zkeqfY0qfZ0PvjRDS0b7JiYtoFu+AGOk3ZJtCbxNH9sKPIzVGORBEBjXK4dRn1DBxMqojQmUmTyidAwoXY8G7c1F3GEQgghdiSf75Ot76MZGmbYGrZdXkop0okMybZUPvAxAwbB0gDB0iB22MYKWthhm2BpkECJ7S+/Kch0ZPKdt7302O0XJgHQGFdVHkbToD2RoTISZku8Lb8TTNc0ptVWs/rd9Ts4ixBCiGJR2SKDBfk+w9h/y3M9km0pnJSfXG1YBsFoADtk95pgbZh+cGRkZ6My2fvC2J0FkgBojLNMnYqYXwixoyNDQ7ydlKNQ2TygCdEIa94bnd3thRBid+e5Hk4ik99WbgRNv4fXMAQ/SikyyQzJ1pSfwKyBHbYIRGx0fcfhgKZp2GErHwTlSrCM1VkgCYB2AbmaQPUNHWTSCRyl8gURw5bJW+vrijk8IYQQ21HK77ru5vJ9dH/JSzeH57LseR6ptjSZ7JZ63dQJlQYx7YHthcoFQbqpo5TKBz5jcRZIAqBdQEUshGlopDMuM6uiOJ4inftrQoOm1nSRRyiEECIn19Ii18Fdy3ZgH44t7kopnLRDMp7CyxXNDfmzPoN9PD8IskED18sFQGNvFkgCoF2ArmtUlfuzQAdPHkfaU/lEaF3XKA2X0NAi2+GFEKLYcp3VC1paBIanqrPyFOmODOlcHy9DJ1gawByCJTZd17BDducskCL/nMYKCYB2EbndYNMqy0g7nVvhdWDPKVNY/Z4kQgshRDHlW1p4fv6NERyelhZK+cUME61J3GzfLitoEijpPcl5MEzbwLB0PG9sLoNJALSLiJbYBGwDyzCwDf//l5Ot3LnXpFrWvCuJ0EIIUQy9trQY4nwfpRRuxiXVliKd8HuHabpGoCSAFRyegoVW0MLLzgIpT+WX2cYCCYB2EV1bY1SF/B/I3Hb4slCA19dtLubwhBBit9TjFvchzvfJ5fmk2rIFDbMtKqyg6W9dH6bEavCX1Uzb6JIMPXY6xUsAtAupzi6DTSgNAYp09j+Brmtsbe4o4siEEGL3M1xb3JVSeF426OlIk4gnSXdk8rMvpm0QjAaHbdZne1bQyi+DKUeNmWRo6QW2C4mELMJBk46kQ8QySTm5itAapaES2hJJSkLBIo9SCCF2bUopvLTbmROja5jBwlmfXJCgvGwCcddk4s6DurxTKM8/3vO8Lgf5NE3DsA3MgIk+zA1Tt6fp/mMr10+7UI6HNoztO4aKBEC7mJqqEtZ+1ExpwCDheDieh6nrHDhzOq++/xFH7Tez2EMUQohdlvL8Lu65WR/N1NFtPb/13XU9lOvll6l2hqZrGJaBYeropl7UpqRW0CLVmsIwNNy0O6z9y4aKBEC7mOqKMO9vaCJiGZi6Q8pVmDpMq6nm5bfWSQAkhBDDxHO8fKIzgGZpeK5Hpq0zIOqJpvn/aJoGWvZztM6vkfu6H/Touo5maKOqC7uma2iGH+hpaChPDUtdo6EkAdAuJmAbbG6JM7EsRomlk8h4YBlEQ0Eee3FdsYcnhBC7nG5LXoDjeahEYdCjG/5MjW7o6IbmBw2jKIjZWVbIJNOeQdPATTuYQavYQ+qTBECjhFKKRNJB08AwdKydmM5sSSWZSIyIbdCS7Sxs6BpNcakILYQQQ0l5Cifp+LVHANfz8gnB4DcaNWx/mWpXCnZ6ohs6Kru1yssmQ4/m5ywBUJG1d6Sp39pOvDWJ22VN2DR1KivCVFWECQQG9m0Kh00cz8M2dCxDI+N6WIbOnpMmsbmhmfGVZUP8LIQQYveilEI5Hm72j0ylFK7nobK1d6zA8BQ5HO0M20ClXD8Z2vXQzNGbCyTb4IvE8xQfbWrh7Xe30dScwHUVuq5hZKt0Oo7Hlvo2Xn+rnk118QFtK5xeU877DX7rixLbIJHdGrnf9Cm8+MbaoX8yQgixG1HKT3TOBT+ep3BcD83QCUTsIWs3MRaZVmdNIHeUV4aWGaAicByXd99vIJFNlisvC1FdGSYSsdE0Dc9TtMSTbGtop7UtTd2WNlriKfaYWt6v2aDpNeXc8/d32Ks6Soll0JxwwIaashjPvvEKp8+fM9xPUQghdkme4+IkXTQ66/FgaATDgWHr5D6WaJqGZurggXK9Ub0MJgHQCHMcLx/8mKbOlElllMUKa/PoukZ5WYiyWJCm5iQbNjaTSGR4+71tzNyjknCo78Sy8eWlrHxnHf/nkD2xdH8ZTCmFaehs3to6nE9PCCF2SZ7n4SZdf1lH83+neiisiIUxipZ5lFJ4rkJlc5H8j/3aQf77zkJDuV1lmq5jmDqmpWNYO79sZ9gGbsLx/6B3PIxRuiVeAqAR5Loe733QGfzsOaOSYB9Z8pqmUVEeoqTE5v3s/d59bxsz9qigJBLo9X66rhE2YWt7hgmlAWIBk5TrETQNaiuqcFwX0xidP5BCCDGa5NpMeGkPPbtV3VMK3TYIDEGOT64AonL94KSgGGJBIcSCO+Xf5XpwKU/hKdWtQGLfjw3KVeC6uBmXdMIPiqyAiRWy8ikZA2WYBg4OGuClXQmABDS3JOlIZDAMnVl79B38dGVbBrNmVvH+2kba29O8/0Eje86sItTHTND4sjDr25JMKA0QtnQakxmCpsHsqZP51/sfcfCeU4fqaQkhxC5HKYXneGQSGXRNQ8/O+mimjhUw0fXBBQd+w1IPJ+PiOi6u072q807T/D+ENV1H17X8lntd17Nb73NjId/A1HU8nLSLUop00iGddPwO8mF7UJWldVOHLrNPg329hpMEQCOosiKM63qUlAT6DF56Yho6s/ao4L0PGmlrT/PeBw3sOauKgN3zt3B6TTkb40mS1SUETQMr+wM8rizGP99aJwGQEEL0wnVcMgkHlMrP+gCYYQt9ELMiuS7tmZRLJu30GPD4AYqWL3gI5AsjQv5d9oPOIomalq0nlK0ppGc/HhijYJzphIOTcckkHZyUQ7A0gNXLtabXMwYMnPZMvjUGtgRAu71x1SWDvq+u6+wxrYJ33t9GMunw3geN7D2rqsdpyuk1Zaza3E5zspzaEoOyoInj+f+ZP9zUvBPPQAghdk2e65FOZPy8FUPPz1popo4RGPhyl/JysymZgkrQmqZh2npnGwtjdNQI8sdlYtomTtol2Z7Gcz0S8RRuyCMQ7n9zVV3X81Gbm/Ew7GEc+CCNvpBM9Mk0dWZOr8SydFIph3Xrm3rcIj+9ppyN25poSKbxlCJoGv76MBAJRsZMt14hhBhunuuRak+TbE2hXA/T0NGzF3ojYPiNTAcQoHieItmeprWpg1RH2g9+NLCCJuFYkJKKEKHSIHbQT6AeDcHP9kzbIFIWxAr68yTpRIZkW3pA1w4ttysu18B1lJEAaAyybYM9plWgadAST7F5S/edXdNrytm4dRtJR9G+XS2G2VMn89b6zSM1XCGEGJU8T5Hu8AMfN+Oi6xqmYeSXlcywNaCmnrnAp62xg3QiAwp0QyNYYlNaESZUEsAcgl1WI0XTNEIlAYIl/qabTMoh0ZrqdxBk2EZ+G7znjL4/uiUAGqMiYZspk8oAsnWCkgVf36OmnG0NjSQcj3i23lBJ9ocxEgzywr8/HOkhCyHEqKA85c9oxJM4ab+YoWnqGNklL93SMUJmv3NpPNcj2ZbqDHzw20KESgNEykLYwf4vHY1GdtAkVOoHQU7a7fdMkL8M5j9vL+MO6xgHQwKgMayyIkxVZRiAdeubSKc7f8BKQwGiQZMOxyXheqRcfwtnLl2ooSXZ0ymFEGKXpZQik8yQiCdxUv4fhrqhY5kGWjZhxQiaGP2s4uy5Hom2FG1NCdLZPzR1UycUDfjLR7tQNWgrYBKKds4EpRPODu7h04zs889t8R9FJAAa4yZNiBEKWbiuYu2HjQU/YPtMrqYpkcb1FK3Zku1W9i+c8RVVuO7oi8iFEGKoKaVwUg7JeJJMNlDRdA07aGJonduszLDVr2rOruOSaPUDn9z5DFMnHA0QiQWx7F0n8OnKsk0CET+bOdWR9ne07YAR6FwGc0fZLJAEQGOcrmvsMbUcXddo78iwcXM8/7XZk6vZ1tqO4yna0i6upzB0DQ1FVSzKq+9tKuLIhRBieCmlcDIuydYU6USms1FpyMSyDVQ2L0Uz/Hyfvpa8lFJkUg7tLQnam5NksjNIhqUTjgYJx4KYu2jg05UdNPOJ0YnWFJ7bd3Jz4TLY6EqElgBoFxAImEydXAZA/db2fD7QPpOq+WhbI2nPQwFtubXu7CzQm+u2FmO4Qggx7FzHI9WWJt3edReW5c9guOSDH902MPrY5eW5HqmONG1NCRKtqXyDT9M2CMeCRGIhzN2o67umaQQjtj9TpiDRtuOk6NwyWNdSAKOBBEC7iPKyENVVESCXD+Swz+Rq6rY1kHT9tdfWbABkGf5qt8bAijEKIcRo53mKVHuaVFvn7IQZMAmVBjFMHTfhQPZCbARNjB6CF79+T4b25gRtTQlSHZl8EGWHLErKQ4SjQcxR2uJhuOV2h4Ff4ye3DNgbw/ZfJw1/+XC0kEKIu5CJ46O0t6fpSGRY+2ET+0ysYmtDAwnXxVUWGU+xtSNNddjG1KEyGqWxJUFFLFTsoQshxE7J5fl0vRgbtoEVtNA0f/nFy/4RqOmaP+vTZcnLcz0yaRcn7eRnefLnsfz2F2MhqTnXGyzXV0wp/MrTuarRGtnCi+zUczFMnWDEJtmeJtmexrSNXqtk64aOQ3Y7fNodNc1jJQDahei6xvSp5bz1zlbaOzJEWjPEQhbtaQ8nrDDRaEo62QBII+MpXnrrI048fFaxhy6EEIPmt2/orLasGzp2yE9oVkrhJh2/6SedVZ0BnIyLkw16PLdweUY3tHzQM5j2FyPBcz2cjIfjuDgZDzfj4e4gJydH08h2gDcwLR3LNjH6kQDelRU0yWQDxmR7mnA02Pvj6Rp4dHudi0kCoF1MIGAydUoZH6xron5rO2cesg+NHQky0QBBw1+zbU07lNompq5oS5LP0BdCiLEkV88nv7sou0RlZIsNKtfDSXb23tJs3S9+2JrCybjdenIZlo5p+wnSozHo8TxFJu2QSbmkU92Dtq78Yo6Fszz+7BD5mSEn4wdQvhSGoWOHTIIhq1/BkJ8PFKC9OYGT9vuc9dYzTLcMvJTrd4h3vVHx+koAtAsqi4UYV5Wmfls7iw7dj5uef4eUE6XUAsvQebOxg8Nqo1i6RtC2aWhOUFUeLvawhRCiXzqbdmbyQYwZyC13dRbec7PlPxSQcVzcZKbwRJqfzGzZpl+heRBdz4eb63qkEw6ppN+gdHuGofuBm+XP5hiG7nd/7+OPWqUUrpPtAJ/xyKQdfwbJ9Ui0pUm0pbFsg1CJjbWDBG/D9Gfbcq0yzPKej9dNHTfp+Nvh0y56SAIgMUwmjI/S1pGGDvj07Im81+riZTsb17WnybgelqFj6oo31m3j6PIpxR6yEELskOd5pDv8hqWQrecTtvMzFq7jBz5aNjByXY90l8BBzy372H4j0tE4++25HqmkQyrRPegxuizNWbbhd5AfIE3TsktfBoEQQMCfGUs5pBKZbNd6l0xjAtPSiUT77gYfCFtkUk5+Ri4Q7t75NNdeBMUOt86PFAmAdlG5fKB/v7mFGVUxPCNBa8rBNjQqgxYbWlPsURbC0jXaOlwcx8Mc4PqvEEKMFKUUbtrNt5oAPwdFtww8xyOd9JfCrC5d3DOOi+t6mAEjG/SYgwoYRkIu6EknHTLpwqDHD1RM7MDA83T6S9c1giGLYMjyZ4La0yQ7MjgZj5aGBHbQpCQa6HHpStM0AmGLZFuaVCLjt/7o4XXWTB2V8dDU6Ei9kABoFxawTcbVlLB1SzuzykO809hB2lWUB0zebOpgSjSIqWsETJ2NW9uYOj5a7CELIUQ3Ktu01M3O+vh1PDRSiQxeWxrwL+B2LvdHKZSuEcjWqyn2hbY3nutlZ116Cnp0AkELO2RijHC+jGHolESDhCM2HW1+IJROOjSlXUpiAQLB7iVUrIBJOpHBcxWpRIZgpPsskG7pOGkXTdNw0g5WoLilWORP/l3c5NoYf3zlTYD8jE/Q0NmayOQLI1q6xgcbW0ZdnxYhxO5FKZXd2eSSTjqkOtJ0xJN0xJO4jufPAuV2PqXdfBKwZRnYuVwVDayITSBi55OhR5Pc7EpLQweN9e20taTywY9p6YRLA5RXRyirihAqsUc8+OlKN3RKYkHKqsIYpo7yFK1NSdpakt2uF/4skB/0+IFQ92WuwqrQxb/eFD0Auv3225k+fTrBYJC5c+fy7LPP9nn8M888w9y5cwkGg+yxxx7ceeedBV9funRpttZB4Vsyufs2/1y1YTPrmtowdY3J0QC6plERNFkXT+J4KrtbQkmDVCHEsCoMcDKkOtIkWlO0tyRobeygtaGDtqYEHS1Jkm0pv6aPp9Cy93VdBZqGaRsEwhahUptw2MY0dDQ0NFPfYUuLkeYXVXRoa0nStLWdpvp22uPbBz12PugJl9jDtsw1WKZlUFYVJpSd1Ul2ZIg3JvC2q+ycy6sCSCUy3c4DoJujpzlqUZfAHnjgAS677DJuv/12jjrqKO666y5OPvlk3njjDaZM6Z6Uu3btWj75yU/y+c9/nvvuu49//OMffOlLX6K6uppPfepT+eOi0Shvv/12wX2Dwd7rE+zqDp4xnu8//iK3nHUsYctgfIlNfcJmayJNezpILGhi6rBuY5yqMimKKIQYPD/I8QMdz8u+z37e31YImq6h67n+7H4rBSvgb83O5fd4jufX98keYwQM9CJWZs4VIHTyu6uytXmc7jMhpqVjBy0CweHL6RlqmqYRiQYwLZ3WliSZtEtLQwexilA+Lyg3C9SRbTobCFndcoZ0y8B1HDTNr99k9pFcPdyKGgD9+Mc/5uKLL+Zzn/scADfddBOPP/44d9xxB0uWLOl2/J133smUKVO46aabANhnn314+eWXufHGGwsCIE3TqK2tHZHnMBYcOmsiN/5pJS/WxfnYhBgVIYtZ5SEeW9dI2lOkHI+AqdPanqI9kSESkhYZQojeFQQ5uQDH89/3J8jRdQ3N0ND17Jbt3HsNnLSL26Visx22CioHK6VwUy6qaz6QqeN6fiVosgm2/rEA2WrIO/WEc+8K6+jkX4fsc+/1+RoadnbXlhUYvYnY/RHI1giKNyZwHY+WxkRBEGRYut9yxPFIJzIEsy0zcjRd8wtTa5rfHLV7qtCIKVoAlE6nWbVqFd/85jcLbl+wYAHPP/98j/dZuXIlCxYsKLjtxBNP5Be/+AWZTAbL8i/cbW1tTJ06Fdd1Oeigg/jv//5v5syZMzxPZAw4dNZEHNfl9a2tjA8HmFkeYmJJgKmlQbYm0hiajW1omLrGO+sambNPTbGHLIQosnxLhS6BjtvfmRyNfHCjG13e6/5W6J7ycjzHbzqaO7dhGxi2geMo0sm0X+HYU9ldXn6icyrtkkqPnt5S4Ac7pulXVzYsHdM0xswsT3+ZlkGsMkxLQ4cfBDUkiFWGsi02/B1hHfEU6aSDHbYLAr78dnivf8HycCpaALRt2zZc16WmpvBiW1NTQ11dXY/3qaur6/F4x3HYtm0b48ePZ++992bp0qXsv//+xONxbr75Zo466iheffVVZs3queVDKpUilUrlP4/H4zv57EaX8eWlTK6K8lFjnPdjEcoCJlVhixOmlPP8phZcBdta26mOlhBvS9HakaI0HNjxiYUQu4Rcbo7reP7SkpNtqdDX9SnbU0rvMpPjfz6wPlPb9/BSgOsqEq3pggtkMGASyDbV9DxFRzLj5wSRnVHK7gzzHzb3OWj+P4NWcNfs+XM9tXJLdZ2vQ98FCIeCUiq7tJh9nw1UVZePPaWyM2HbPReNfBCq6/73zbR0zEHslDNMvTMIcj3iTQlilWE0TcOwDHRT98sT9LAjTLd0vJRb9Jmwom+D79aFdwe1AXo6vuvtRxxxBEcccUT+60cddRQHH3wwt956K7fcckuP51yyZAnXXXfdoMY/HJRSpFIuyWSGdNrNT7eapv+DGgyaBAbYlO/QmRN5q76exJQa1rUksA2NaMDk8PFR6jvSuGaAVMYhYJm8+X4Dh+0/YRifoRCimPLJyGkPN+PiON3bQuTkL/BmZ4CTqzS8Mxd7pRSZlOO3qshyHI/0do1IbdsgYBvo2cdSGuhBg1jE6nU2aSwpDGj8vCm3Sw6V22WJzcs2OR0OlmVgBwwCQZNA0OpXcGKYOtHKEC3bOnAyHq1NSUrLg/4sUMgi0ZoincwQCBUmpudKE2jGbhoAVVVVYRhGt9me+vr6brM8ObW1tT0eb5omlZWVPd5H13UOPfRQ3n333V7HcuWVV3L55ZfnP4/H40yePLm/T2XIJJMZmpuTtLQkd/hDrmka4bBFaWmA0tLADosYHjprIst+t4LEQbNJuooN8RRTYxoltsG4sM3WjgwvvP0+x+y3F8mkw5aGdmoqI0P59IQQRZRrH5FJ+c0/u+XFaNm2CqaObuoYprHDlgqDGYOTcUl1ZHAdDzN7fqUU6YyHws8xsWwdw9DRlEI5uUZeYARHb2NS6MwNyieAe/62/dzHXfOF3FzH9kEGNHouUVzX0LPLSppO58fZ75uW/4d8l/jc47quwnFclIJMxiWTcWnP1lUKhiwipfYO/9g2TYNoRYiWhgTplENHa9pPlrb9nx/P9StMB7rklmqahmYWP3AtWgBk2zZz585l+fLlnHHGGfnbly9fzsKFC3u8z7x58/jzn/9ccNsTTzzBIYccks//2Z5SijVr1rD//vv3OpZAIEAgULwln3Tapb6+jdbWzmU4XdcIBk1s28TIRsmO45HJuCSTDp6naG9P096epq6ulZISm/LyEJGI3eMP62GzJpJMpdnanmJcyMZVivXxJLURm4qQRW3EpqG6ms1NLYwvj/HuukYqYiGsXWztWojdSa7nUybpdA96NP/iZdh+vkouf2M4xuCk3XyVY6UUAdvI/27xlJ/vEy0N+rVmlEI5Hm7azY9Xt3T0HfSkGi5dZ2hc118azH2cv93r/HiwNF3D0LWCJUVD78ydMrouNQ7hzFeuvEAm7fcbSyYcXNcjmciQTGQwTZ3SWJBQ2Or1MS3bpLQsSGtzkkR7GtPOFnEMWiTb06QTGezgwFYtRkJRl8Auv/xyFi9ezCGHHMK8efO4++67Wb9+PZdccgngz8xs3LiRe++9F4BLLrmEn/70p1x++eV8/vOfZ+XKlfziF7/g/vvvz5/zuuuu44gjjmDWrFnE43FuueUW1qxZw2233VaU59gXpRSNjR1s3dqe/49eWhqgrCzYayCTu18q5dDWlqa1NUUy6X/c1pbGsnTKykKUlYUKZoUO3mMCuq7xYf029iiPkPb8vmDvNXUw2QsyPhJgv4lV/OKJ5/jMsfPQ0fj3O1s5aJ9xo+6HVgjRN+X5y0vpZKbgoqxpYAZMLNvEsIavQrJSikw6W8ww6eRnOUxTwzI7AxkrZGF2CWyUp3BTDsod2Vmf3G6uTMb1t687Xv6tp23sO5LLtckFL7mPjfzHhcHMSOQO9T5WDdPUME2bUNjOztJ5tLel6GhP4zgeTQ0dtMUNyipC2IGew4ZAyPL/QG/P0NacxKwysIJmPrHdSbtYvdy3WIo6mkWLFtHQ0MD111/P5s2b2W+//Vi2bBlTp04FYPPmzaxfvz5//PTp01m2bBlf+9rXuO2225gwYQK33HJLwRb45uZmvvCFL1BXV0csFmPOnDmsWLGCww47bMSfX19c12PTpjht2enGcNiipqaUYHDH3xJN0wgGLYJBi6qqCKmUQ1NTgpaWJJmMx9at7Wzb1k40GqS8PEQoZFESstlnUjVrN2xk3szJJB2XoKETMg0e+aCBs2ZVUxG0uOiEo1j1/npmT51EW0eaDZvjTJkQG+6XQwgxBNxsT6xMl7waIN88sxhBD4Cu4y+lZD83TB07bOfzQpRSeBkPr8uOLt020IdhvLlZsXTaJZ1ysks/O97ZlgtcDEP3Z2NyAU0uyMkFNdmAZqzSNA3LNiirCBMtC9HWmqItniSTcdm6pY1IaYBoLNjjc4yUBnDSfg2keFOCsqowVtDvFJ9OZEZdAKSpYpdiHIXi8TixWIyWlhai0aHvj5XJuKxf30w67aJpUFNTSllZcKf/o3ueIh5P0tycIJHo/AUYCpmUl4f5f79Zzi+ffIVLPnUqk0qCVIcsDE3j3ZYOmlMOZ+85jki2kFh7OgOa/1fXPjMqqSoP79TYhBDDx3X8vBqnawBhaNhBCytgDltlZM/1/KAn5ZBOuQVBhKaBHTQxDa1gRscOWQUtKjzHw83W7wG/6KExhGP2PH95J51ySWff95ZjaeY6xWd3RpnZXChjiHOhxhrX9WhpSpDo8Ks7m5ZOZVUEs4fCk57r0bS1A6UUoYhNKGLR1pQAIBILYgxzscqBXL9HVzi2G0inHT78sDnffX3SpBihISo8qOtafvkrkcjQ1JQgHk+SSDgkEnH+c/4hGI5GWzJFOmyTdj1CpsHESIB1rSleqYtzwLhSYgGTiG2RcT3SnuKtDxo4YC+DaIlsjRdiNPETitO4XXZOmbbhBxlD3AQ0t0yUq3CcTjndlof8oMfCDhroGmSSnctZhm0UdAnvqaChYRtoOzFupfxKzJl8sOOQyfS8hGXZ/q4nyzKxbB1rFPYNGy0MQ6eiKkIykaGpwd/xVV/XRkVVmOB21y+/f1ggnw9kB/3ij5mUQyqZIVzEat3bkxmgHgzXDFAq5bB+vR/82LbBlCllWMP8w+A4Hk1NCZqaEn5dDyDjeqyPJ9nansbI7sD4R12ckKlzzMQyLDyqwhYBy8JTipTroesa++81jpJwEct2CrEL8DyV3XreWVwwV3RQKTpnUbLX4vxuHl1D71JfR3mFhV50U8/2YjLydXFywcZAavLkdgd1LXyYa+3Q09XCMHU/kMhWOvZcj0yiM/do+2rO+STnVJfZKlNHDww8AHFzM1C55ax0z7M7RrYSs20b+YrMEuwMjut4NGxrz/cyK68ME+6h83trc4JUwkE3NKJlITrifq/JkvLQsOZ0yQzQKOVki4sFAgZTppTvcOv6UDBNnerqCFVVYeLxFCvWrGPmuApmlIeZUR4mkXFpSGQ4yHF5vi5OYyJNRcjmZ8uf44zDDmJiVTkh0yDjevzr7Xr2m1UtM0FCDICXTUjOpF0yaXcQSbVdlpUguxuoS+6MB66nIONBwunxDLlgqKeLfr6tQz+3YxvZZaLcDEruYua5HuntZqOsoInZZRu1cv3AJ/9Yuub38NrBBTG3Eyv3GqbTLpm0ky+EuL1coGMHDH8n7W62m7Vrqw7oUrhxCII+w9SprimhqaGDRIc/I+R5ipLSwutCJBokk2rHcxWpZKazPUbS6VYYsVhkBqgHw5kD1N6eJhAwRyT46clV9z3Jsn++yzc/tYDZVSWYXdbZN8STfNSapCpo0djaztd++jNu//J57DVhPACeUjhKMWtaBZWSEyREr5TyO4CnEv4yzPZ0XfN7JhnZejddZmxyF6uufaw818NJuXhul+DJnw7KP17XGaTcBXCw8km92fEZpoZpGT0uqylPkUkW5h+ZtoG13XKXl3b93k+5x+iS5JxbXnO7FPxzXQ8n4888ZZzek5RNU/eDMdsPeIZydicXePmVsXMVlrOvM4DqDC51rfB7qHepFD3Y8XTWD/L8ukFdXiO36wydpwpnE3v53ud2pxmGXwHaL35oEggYA14CVErR0pTI1w2KlYe6BUHppEM8m/9TEguQ7siABqUV4WGbgRvI9VsCoB4MdxJ0Ma14fR3HXf0r5uy7N8cfsDf7VkaYURYiul2xq0TG5Zl/v83tf36Cuy+/iIgZygdLrqeoqAixx5RymUYWogvX8Uh0pEl1ZAouQvlmmNkLTX+XADzXI9WRIdMliDJtg0DY3uGsRtdZgHxw1MOv+3xrhy7BV3/+X+e22jtdxqabOna26m++E7yj0DyV3wHmeB6JtNulno7qcVw9MUwd287OPtkGlj00jUU9zy/Wl84tp6UdMmkvnzYwFHLLkno2cO1aoFCDzu+V6gy8RvLqbBiaX/wwYhOO2P16XZVSxFuStMX9GnY9LYe1NiVIJR1/5tDQUJ4iGLGxh6nptgRAO2lXDoDSGZeaC35IRyrDhad/kqmlQaKWQWXIoixoYRgak0qDBfepa2qhtipGvC2DqRfmJEyZGKOiPCSBkNitORmXRFuaVJft57rutwMIhMwBJyR7XjbwSW4f+BR2Rh9pftE8v7Ci6rKU5ylF2vHIOJ0zOKahUxqxsLLjdVyPto50t1YXXW1f/C+3K8uyDL9f1RDtDHNdj2TSIZnIkEhkSKf6bqiamzXxg5ds0JjtMZabcfO2m30bypYV2xdCzG/F1/33XbflF1SGzlWAzi6V5vqEuY5HJuORyfi74lKpwiKZmgaRiE00FiSwgwKG288EVVZHChKj/V1hfq27YNjEy3johkakbHiuG5IDJHplWwaf2H86j7z0NiknQ2va3wofcTzq29N81JbkwbZ6TppWwcTSICFTp7Y8Bi5EQ5a/yyL/H17x4YZmNtXFGVdVQmVFCLOIv5yFGGmZtF/6P9Nl+ceyDUIRG2sQSb3KU6SyNVNyDEsnELZ73HI8XPKBTramSybj4jouhq4R6LJU4rgeiZSf0J2ja1AasQlla74opUhmXDKuwgqYBEK5pbXCisfD3dcrnXbpyFbPTyW7L0sWJEpnZ5lMM7tEuRO70rzc78suAQgKFLl1NP9dZ5PVzuWzoXpdNE1D7zphuF0ap1KKZNIh0ZGhrS2Fk/HyxXUDAb8mULiXStCaphErD6GUoqM9Q+O2dqprSrGyjWt1QycSDdDWkiKVcLBM3Z/5y3iYdnGvFxIA7YYWzJnJIy+9zfqPPqJkjxmU2AYdjkuJZWa3xCd5fF0jJ0ypwPM8Hnz6GWbWVnHmUYcQtCysLg3slPJ72tRtaWVTXZyyWJDKijClJQGZFRK7rEzapaMtRabLzEEgaBIqGVygopQinciQSmTyF0XDzAY+w3iR6FoBOZPd3p6rhpxvQ6FrBG2DcJdlctf1SLsKNP95h3O5QhpoqrODupbdmWYXoTBgrmJ+e1uajvYMmUzhLI9l6QRDFqGQRTBkDssfb5qm5VsZjWaaphHKvhblFSFSKZd4S5L2thSplMuWza0EAgaVVZFu295z9y+rCOM47aRTDg1b26iuLcXILvUGQhaJdr//Wy7BLZ3MFD0AkiWwHuzKS2AAGxviTP/iT1AKLjj9FKqDFrGASXXQwtQ12jIOK7e0Mrs8zF7lYTY2NPJ/b/8lug5P//gKqkvLaW5OEjD1fIfmHC/3Fw9QWmpTFgsRLQ3k/yOIsa9rYmi+sWKX3AU/t0HLLxNo+Sq5I/OX/nByMi4dremCxOZg2CIU2XFOTk+UUmSSTjZnyP9VrBtaPvAZ6mTergHOjiogG4ZGOGhidvm/q+kaVtAsKGSolMJzslWcVedxej92dw01z1MkExna29N0tKe77RILhSwiJTbhiCWz1f3guh7NTQniLcl8QFxSGqCyMtzjz7vnetRvacN1POyAQdW4kvzPSSbt0NLgJ0Tbln/f4dgSL0tgok8TK6Mcf8AMlr/6PprTQWumhBLboCXtUBEwKbFMJkVs3mrqYGLEZmJlBZ896XiWPvY4Z1x9G8/d+k32mF7Bq+9sRXmKkKkTsU10ze9ErGf/4km0Z+hoy/CRUtgBg9ISv3N9JGyh68UJiHJT+67b2RE5fxH3Dyg4vmtSaMHH23VdztddGaMX9t50TQ71t3D7yx07+2dTZ3+kLu0D8kFSlx5JhtbttS8G1/HoaE0VLJ0EQibhkkC/k5Gzn/n/5rZ0d2kZoekagbDfG2ug/z/8x1C4TmfDTtfN5Xr4Mzt9/a3bmWvjVz/WtutSblg6ZsAqeK5dm5bmd+oPQTHDgXJdj472DB3taTo60oW5LLpGOJxN7A1bo7qT/GhkGH7F51hZiKbGDlrjKdpaU3R0pKmujhDZriSKbuhUVkfYWtdKOjuLFCsLAX7D1EDQ9Nuk4M8SFntLvMwA9WBXnwECeOC5f/PZnzzIlKoYx37sY1QFLcoDJuW2QdA08JTin/WtaCiOm+wvhd340F94+fXXmVgZ5fEbv8Ye48fx+geN1Dd2ABAJGJQETDxXYRv+hayrXN6QUv70cyhsEQ7bhIIWoaA5ZLNEnuuRzridNUMyXv7C3VvdkKHSdUeNvl2g1DUxUd8ucCr8fLv7DXB3zs7wPL9mRzK7fdvpo2aNnv0eazoFY++84Hcmg+Z2BO3sb5vOYDT7WuQ+z34xe1P+Y3KzUN0+7tyFkzuff7fcx/57z/O3s2/fYsKwjfzOnc4cj1xBQ38mtLft6KahYVsGRnZZyPM6E4h7er4UjL9z7IqBb3k3DN0Pcmw/sdjKtn0AcDOFARn4wYwVKGxG2lvgo1vD07tre7mlrY72jL/jbrsEZsPQiZRYhCM2oVDvHczFwCWTGbbVt5PO/n8oKQ1QVR3ptmMs0ZGmcZt/XaioihAK+8tmbjYhWgOs7PJoOFq46WZnyS6wnbQ7BECJVIbJn/sRLR0pvnPBQupbPaqDJiHToDpoYegaadfjH3Ut7BENsm9lCR2pNNfc9xAfbtxIVWmQv/zwq8yZNYUtDR28ubaRVHaNPRaxiQRNWtuSmLqOpevYPSyX5XdLZAOj3C/nYMDMJyNapuH/R8m+datBovy/pJNJJ7+ro6+Ldk6umJyudwky8leY7mPsWiwud+HJXeC7JjKisl8fpv9VBTVH9M5kya4BiK53SabsMjOVDxy2m63KzUakssX6ur1W2a3Htm3ka8H09L3oj1yg4O8U6pyJy2+Z9gq/PtJbgbsyckt2ubyXbG2awQ7HygY+XYsYpjMe6UF0G++Nlt0tZOTr92QDHrPnXVRKKZyUi5NyCmaIzIBfwLDrhS3fsDTTJfBh+JqW5nhebseW32Q1mcx0+5mwbYNwxCYSsbEHkXw+1uSXobv9P/Kyv686j8v9Hui6ayyX3D3Yx25q6KC52a/sbNsGNbWdSc85zU0J2ltTaJpGzfjS/OxhR1uKjta037qph+BpZ0kAtJN2hwAI4NK7HuXuJ1ZxzvwDMComEbUMakI2AUOjKuj/5dSecXlhSwtzqkqZEg2ytaWVb//vQzS1NBPQXe647FzOO/FIHMdj7aYW1m1q9Xc54AdCZaU2rW0pkikXQ9MwDY2AoWOZxvaxBpANNCgMjlR2x4RS2aJnpo5p6BiaRm9XI13X8rVC8hduo3NWxv/LvfCv9675LLmPXbdLjoTXcx2V3miaHzwYhr+ko+k6GnSZKejcOtt1xsDzCnePDMf/UF3rXIba/mKRz+NSXbb/Zi+quUDU7HJRHe7O14VBaNdif507abq+Tiq/y6bLxyq3xJl9fl2DLa/z+ars9m3TKAx8HMf1v1f+A3SOLf9Plx/F3GNlbwwFTYJ2l8CHbBBr61iWmX8Nu86a0eWxco+Rf665MWidP89dA+B+vaa5Gj7pziakaH7XeNMubESqPIWXKSxiONQzPrkeXk625UY6X+3Z7fEPGl33a9YEg34hPyD/PSyckevf/6WCZbPcNvdcuY+CZfCe3+ful5+t6+XzXHCS+z2yfd2fzqCm8w+AngOdnf+lYBgatm0QCFgEgybhsIU9gLyzREeG+i2tuK5C1zXG1ZYQ7tIqSSnF1i1tZNIugYBJ5bhIfoa4aatfIToUsYlEh7azgARAO2l3CYBefOcjPnblLwjZJndc/n947u0GSm2DyqBFUNcoy+766HBcXq6Pc3hNjMqQRX1LnP++/xG2NLWAm+bsow/g1q+cQ2WshETKYe3GFjbWt5H7PxoJmUyoiqA8xbamBOnsTJGmgaXrRIIWlqn7F7Ue/mPn+h/p2/3CycktreVaAuR/pLUu77LLH/2l6xqWqWMZ3S/w+ZofuQtyfpzZWaU+foEYhk4g6FesDWSX/Xb0C6drXZHOwKlwqaUzgOoeYPl/FfrH9/T6dj2/O4hfrLqu5QMiq+v77MfDHSBtz8+F8ZeUnGyir+Nk32fcHpdBNQ0Clj/bmO9S7vmVz/VsXZr8RQ/yP1sFs4Oqs1Kv8hS2qROwjfzPg+spEimHZA81ZzRd84N6M/dez/+VbnYJonemqnD+OaUyOF3GoOkaVsD0l/W6JDYrz6/erLq+Xpq/swtD63zu3X4Otws6vM7/Lz0tEzrZZPodyj7t/EycXLmyy+d6fkbb33FWuKTb22xRT0xTp6TEpqQkQKQfxRAdx2NLXWs+N66qOkI01rmk5WRc6utaUQqiZUFKs8tduQrRuqFRXh0Z0hk7CYB20u4SACmlOOz/3s2atXX839OPIm5XgqsoMXXKgxZhUyea3e2RdD1eb2jjgKoSyoMW7ckU19//J9Zu2QZKETTgW+ccz//7zIkYhk4q7bBucysb6lrzF1Xb0plYXUJ5aYDW9hSNLUkS29XjCJg60bCNnZue7eGns2sAkpshGujzzn+c+yc7I2KbOnYPF21Nz84c0fkLJdfbrbek4NyuJ8PQsYyeL1y6oREImAQCJnbQHPTS0vbPz3P9PIlU0u/AvP1F3zD8In3BoEUgaBYsyxRczLNl9t1cQ8wub24/L1yG4bdRsLKJtrkLer54Wz8v7H4Ce/Yv42wFYSeb29V1TP1ZAtVyAa5lYOQSanLjtXTCJTZ2oO8CcD2Nz0m72ZmVwgBDM3W/AJ2rCsbr9NHioa+xG3rnEu72OWKaXpjrlGNq+NvUuwR4GdfD8Tr/T4EiaBoEbKNg91cy7dDWkSaR7Ltg4M7onBXJjoftE8h7VlAksIfl3675dV31/K31fxi2D2x7et/9uM7bcs+j6+ddH7fn2SStYGk+vzlgu+WrrkUZB/vHhev6hRBTKYdUyiGRLQjZdayGoRGNBikrCxEM9r5fSinF1vo22lr9QojlFSHKuhTHbW9L0dzo7/4al10qU0qRSjgEQgP7P9YfEgDtpN0lAAJ47OV3OH3J/YQDFs//zxdZ8sR7lAUswoZGZcgumAlyleKDlgSTS4NUhfxO8Y+9tJr/feqF/PmCpsanPnYAV3/2ZGZMqCLjuKyva2VDXVs+R0gDxlWEmTgugq1rtMSzu2sU+cTQrlyl/EAhaFISsQnYRsFUctcp5cJpa39WKP/XqefhuIpU2iGT8fLT7K7rEQqYhAOdidhKKVIZl5TjYpqGn6gdMv1aGUGre26Epzqn7bNT+LlfLtv/UjFz1Vt7uOjnlu6sbJJqLo9j+11mueebC1Icx++Knckmf/cUmNgBg2DQIhiyMIdo6cLLPXZ2tsXJeGSc3PtBXNy7XBTySz+DCHIBv9eRaeR7HpnZgEc3NDIph2RHZ8dyACvg55EMdPu55/oNHjOp7RKILcOvAr2DHkv5YLpLQO06Ho5b+H4wr4Gha0TCFqEueTHpjEtbR6agInMoYBIOmYS6BH2ep+hIZmjryPSYoN3tD4kuH6jcv4puf8OoLgfnvsfadhf57pWOO9/nAoD+Bs6ifzxP0ZEthNjamir4QyISsams7L0YolKKpsYEzdm+X7GyIBWV4fwMVOO2dpIJB9PSGVdbOqzfMwmAdtLuFAAppfjYlb/gn+9u5L9OPZzDDtyXJ/5dR8g0CGhQnZ2NKbfNfHDSmnZAg/ERf+026WS4e9mTPPfGB3TNIq4qDXHqEfuy+LhDOHyvqTS2JKlv6MDNeIQsg0Avsx1aNgG7LZEm0aUgW46u+/VJwiGLSDYgCQYMgvbAdpIppWhvTdEaTxUEDI5StCfTJPv4azcYyAZDIZNQ0CIcsrB6KICnlB8YJZN+MJR7n/vlYuSWPnoJiLq9Nl2WXnbEsvxlNn/JbWh6Jg1EQe2ZbFDkZDp34+VmlwYaJOVaERSucXZedDtzZvKpMmiahmVoWIaOqXW+zgqF0vzt9kYu4MwmiVp9LN8ppcikXDKpTEH3c03zO6Bb220Z31lKqXxgnSl4HTuXNPzX0g+UTEOjNGIT7tLGIJlyaG3P5P8QCQYMSkI24ZBV8IeHf1yatkQ6v4zdUyDT9Tlvn2jb86xFYUDTNZCRIGZ0UUrR3p6muTlJa2sqf3s4bFFdXUI43HMfr5bmBA3Z3V/RWIDKKn95y3U96je34nmKSGmAsvLQsI1dAqCdtDsFQABPvvo+J19/HwHL4M2ffoVf/PMjNjYkCBg6uvKoLQliahpl2S3ykL2we4qygB8YOZ5iWzLFX//5Msv/+S8qSkvYb8p49p1Uy76Tazlg6ngqSyPdHtvxPJIZj0TGxbQMxlWGGF9VgmnqKOXnTLS1p2nNvrV3ZPJJ1j2xTH8XmWX5S1l2rrZJbleM7u+SctOePwOQ/Q1vmDql0QDhiF3wF3AyNz2czGSniR2cXhokmqaenykK55aXbKPHoMxxvIKAKJl0SKedfB5Rfomjl7ynrnLT/EZ2iSnX6bm3++Sm5js/Llx+6O0Y/7H816/rssv/b+/Lw+Sq6rTfu99auqr3TjdNEgKETQUJAgEhqExw5JGgovEbPlG/GZTJ+BiNI8vDJjoOooMgDrgwUcYRCM8AQfxYwzcSkhAUkD0BQghk66TTW3VVV9Vdz/fHOefWvbV0uju9pNPnfZ7uqrp169apU/fWfe/v9/7eX7BdAMQH7VYd0hzwk3PQyTqsRyiv+iIEIOFu6HTLYVIzUsiShJihIGZoiJVFdSzHQ67gIG9VVhSFoSgSLRdnuiZDU2BqCsqprqop0Ex1TOaFnChyY8LoLb0/0qacmiojnSwjPraHvOXC8wli7ELBKCN3PqEtbhyfRKoEq1cWllUVCvJySMO2XfT1FTAwUAiOlbo6A21tyaoXfYOZInr2DQGgZfItTPxcLDjoZcubWhMwTdEM9aDETCNAhBCce/1/4pk33sfHP3gE/u81/xtfu+9FqD6t2PJ9H01xHQlFhqnIqNMVaMzjRwKQ0BWkTRUxTUFMU6ArMmJVDgzb9bB55168/N4uvPzebuzo7cPJR83BohOOQUsqFfyQypKE5noTbU1xtDTEoZWZrxUtF0MFB0MFB/mCw4Sl7og8fpKmioaEAY2REtfz0T9kI1ukvZdkXloe/vEPPabjYxsLiz2HiWKoTAxrmlpQ2q/rcnBi5VfA1N/EixAiftXPIxnR722/H3dSEdFwRLQQ0cq+kQ5bklCKFMiV2ofwc1ScTMVcEiGQCCreiAAgEuCz9BovvXcZMQuiUmW6LkkCkjEN6YQOUy9pIRzXx2DehsXIu6ErMJinFSXhpYsFL9RugkdxgvYTIxUBA6UqvDKRtKpI1NU2nG5VZSjMoNR3/aiYmUFWZUiaPK3duQUofD+kifP8oJo0kAhIgBJOKcqU3I80Muw4Hnp6hoLyd0kCWloSaGyMV+w72ayFfXtzAIBEUkdrG3WEHujLYyhnQ1EktLanJiQqLQjQAWKmESAAeGtXD0797q+Rtxz8+Mt/g69/8jR8+ffPIybLSLEffVOT0GLo6Kwz0JrQ0WBqqDPUgEyEQQhB3vGwI1PA63sy2PDODjz3xtvo3bcH8BxAjhKkhmQcH/vgMVh84vE4vKUxWO4TAtdzIKuAoWvQNRWSpKDoeChYHgq2C4ulIGQJUGUJqixBkSQoEvNykSSkYxpaUyZMrdSZeoARn/E6AGQJkCGVHLFHcXWsKFIQpSq/6gbYLSmr8uLamDDhCH0YUhYyqfo5a3z4KuoOhmgVFP90oz158pSIopZ0OrqmQNdphZw2AjG45/lUdOx4cG2aYiuHorKqO1MdVY8u7i9lFxz4IS0EIQQWIz7ZoTLXYVZpGCbMw1UEVoyVR5tY1JLqlkr3q6WKPJeaF4bHqDBSTfwqbFNipEeRIYnU07QCT6dbtlu6UGL3Hae67m8k0HUFJivEME0VibiO2DAd4ItFB3v25FBgDXtjMQ0dHXXQ9ahQOpez0L2HkqC6OgPNrQkQAnTvycJzfcQTOhqa4mMa83AQBOgAMRMJEAD8x5oX8Y+//L/QVBnr//UfcGxHM+5ctxUpVcWR9TG0JHQYVciOTwiGbA8520XepsJhmze9YyCEhtfzjofBooOBogPLdQHfY38+DFVGwjBQnzDREI8hpslQy9ykPZ/A9nzYrIKFX9T6xAfxPcgSrTYzNBWmrqEpYaA5pgWpO9fz8X5/Adv784G+gRfLSAAakgba6k20pGNIM4t2Tjb8EPmIlKGTkHcPoYJWm5Vd2yEfE1kqO0li6tIHQdos1HZCYeXeQbQl1Km7POIil0VmAAS+Sa4bjaS4bjSdMxJwU0xNU6BrrCxcoT5KxKedpKv94MuyBM2g3jCaPvpeVIG2p8gaNwYTBkiKDB8EjkuCz8RF7yP5FeX7kCTzqKCKmKkiHqdtGkaqXyOE9t5yLBfE9UvpKLm60YMkS5AUCbIqAyLSc1AjIDmWC8v2UGSFFJbtBpHg4SBJCGwUItWBrLrNY+lnfnzW2p4sS0gmdaTqTKRTBowyckMIQSZTxN69Ofg+NVtsa0uivt6M7F9hEpRKm2hqjsO2PPR002VNLdWbqx4IBAE6QMw0AhQcdEUXf9j4JmKqiuM6mhHTK3dM1yfYl7exd8hCV86ihMfzEFNUGMxoUGHRD12WoLMS8GpXwkE5O7gbdOmWw3FdyCCIqQpSMb2ivYbtutixrxfv7O7C9r09GCzayDseZrc24ktnn4wPzZ4FALBcD3/ethub9/ajpT6GdEyHqelQZRWqrMJxCYbKRM+6KmNOWxLzZqXQWnZgj3Z+PZ/AdjxYFv1RK1ouCjZ1tS3aHnyPBCQs4ljE7nBPIpU5+2pKqW+TTIUYEXPDcBsN/jhcMj3ZgujwXFDywL15KvUuMkt9qTJL7VTxYuLb4vsMtSmgUSUpVD5cS6MSNsrzmXhYIrRUPFxpV7Q9DBWcIMo4HHgER9dpxRnATRR9WDb9zmsRQFmm3bjjphp05Y6ZatATjBAC4hF4jgff9YePvMmsh5qI8hyU4BWjlk1JDq8W5aRnOEgSYOgqs85QSvf1kinpiM0w2bHISRbVO7oYytsVFxfxmIaGhhiaGuLBvg3QtNju3YPI52k0KJHQ0dGRiqyTHSxiXzfV/tQ3xNDYFEemv4Bc1oKiSGjrSI3rPioI0AHiUCZAnkfLpflVhm15sG236pVAwXZgGCrqUzH0WQ6+/9Sb6Mm76EwaETE0AZDQFJiKjJgqI67KgUaIQwF12A1I0X4OVNvzUXQ92B4T0AJQJAmmKgd/hlLZXsNUZbSmTKTYVYVPCLoGi3i3dwhZ24PrE3iECnWz+SIy+Tz2DQyiu38Aiizh2I5WzG5pRGMyAVmSA/GtaSg4vCWBuW11SCf0miflsYD/EBUsF4Wii4LlwrIcODatngJ4Sk2KamkIgcdO/opC21VwMz+VRUz4bSklg8B1OKLV4ek1RgyCWz/62POrLy89ZtGlkECWeyGpITdpXaUpL1mSAJ8aF/K/WnPkExrBc5jGoVpp9mgRMxTETQ1myMbf9Xzkiy6Gim5wIpBlKdDd8JJ6lbUI4WmrkewPruujUGTaNfZXtCqPP1NXEDNUJOO0rQz/DqtBCpMdEeGZUpTrvRzXD8gOv91fFFSSwEgNIzmGSoXrBt3PJvr7JYSgUHAxmCticNBCbsiOjK2hPoaWpkRQEk8IQX9/Ad3dOVqBqMro7EwjForsZDLFQADd2BRHOm2it2cIdWkThjG+PdkFATpAHAoEyPdKPjfcUt62awuFJUkKDjaH+Pj6r/+Ix19+B8mYjn//2vn4X2d9EJ5PsPrVXfjZ2ndgKAoOSxqoC4VGadSHkhNFAgxWcmyytEo5FKbZUSQp0O6o7IQ5HOiJn/oDEUIgSxIaYho662Ooj5fSVgN5B93ZIpwJaoAqSygRDaXUokNnehZDp1VoBvvTtVL/ncCd1fXhe/zkXzIcHAs834fjETiuH6QhbdeD5fjDVs5NNBRZgqEqMFhlHq2kkiuieRw+U3zLCvVEMlkZf6k6r5RmK5WC02Xh9gE8NVlezabIgKGpMLQSgebGe+CRJDVaEj9ejXqrfl6f+gi5jg+J0AuFWhHTwE3acUFADSZpOwPaO28kVgoCJVRWP4Ya+IYKHIILgGCfI9SfySv5NDksLTqSQ02WWRsKRmzMgPDQCtaD6Tt0HA8DmSJ6+vKB7gegLV7aWpNoqKemh5blYufOTNAota0tiYaQIeJAfwF9vbREvtwxejwhCNABYjoRIBpOdUMkh5n7DXMSDTe35C7E5WXTvdk8PvujVXj2zR0AgC9+9AP4/t99HEe0NcAnBOvf7cFdf34PW/bl0RzT0RLTAvdmWQI0WYpEaGQQuH6pgkpn5EiRJcigJ0mZlT5LKImXFanUMoNvM0yQ0jENHWkTKVZSSQhBT87G5r2DGLTc4L0MVQnem7vkcu1P8OMXmiP6Q0iN/GQJMDUq9pal0Z1gVFkKTviGqiBu0DJqrcZJLgxZKXP4laRQ76uRVaBxuD79gbZdH5bjwfF86gLs+SAhnQDXBsls/sNuw+F0WrkTsQxWGg8uviWQfD7f1cEFxZbjoeh41HjSoVG6inlUZZh66GqYCTf5MnU/BIW7NNtFF54TdWlWWa84vi/w8ZdE5lGxeWRHCe1TvLN8xS2YHE7i5f0871vadvkclSrm6HdcdFz0D9rI5CwMB0lCyfxRVaCG3LYVuUwXEuxX0VQaYWHPEimgj0v3K5dVrQCseT/UMDh0v3wb5T3cIstRfTvlPdMqP0v0c0wU1JCGjadFDUZ4dEMZNqJ3sIIQgnzBwb6eIfSHSuJNQ0X7rDrUp034PkFXVzbwDkqnTcyaVRdER/t684FZYmtbEsm68e0DBggCdMA4WAmQ59GuyLZFlf+27VatfOGgze7USFNQfRTCUNfzceMDz+Bf/vsZ+D69Mv3fiz6E/3PuyTh9fickSYLj+XhjTxZ/2tKNV3YMwHF81OkqVLbDc9KiyuEfWIKi58PyfFiuDx9MxwEgriqo01XEVEp4qpGEmCphbkMCRzfFA+Lj+TTVtS9bhL2fiI/DRNR015fo1R1oRIlrShyfBOPLOh525yzsHLTgg+Dk1jqc1JZEo6nTqJVCtSdJU0fSUGizV0WmJG+YaFaEALBbjxDIMiWoMVOlho8m1YbUqowKrlpZg89wOmkk7Sp4o9OwZqgilUI1lBHSFW6X4e9nzmUeSVFZN3kmbCaEOhPzv0D4Gdy6cEcQwVMVmV5J6ypMQ0FMV5luSoIMCVJoDgIyst+tji/ClgrV+tkFmjhCnc9lNldyWX+yYtFBvuBG0iphsb3AgYOTwnINHS8hD3zF1FL1ZlC9NwX97yYbrutjX+8QuvflgqxCzKREKFVnoL+/iG4mdDYMFYcfnoam0RYYPfuGkB2kBGlWR12kgep4QBCgA8TBQoBc10eR6wSKLpwaArlqREerYcA3Fjz/zi5cf8+fsOaVrcGyua31OPfEeVh4zOE46Yh2HNXeiLhByYjj+Xhjdwav7szg7b1ZDOQdSAB0RYIuRyM4PJ3l+D4cPyqALrgePJ9AlSQYqoz5jXGc2JbCEY3xgFjYno93eoewuTuHAjsBqLIEreKPEqqEQc0cDZau0lhrCpVVO/GoR/hWkfeflqsFbgeQs1xkLRd520XB8VFk/i8KG58+goiQqkjUddrgxIi5ULNu47WuKAO/G06IeNpojO0VhgMlOvTErTCRtnKA/c0CEXGIFNm2x0MCQZRNZ+7iIymh5+CVe55filAQCQCkUnRHKkVNIEeb3nK7Ah4V4u9KfEroUSMKxlvoEh6NVLhofezCZd/n4nKqHeMGih77/nkFUCkKUtbLio8zLMYP5qA0FxySVL5OKbKKqvd5VKzyfvk2+HYr3yMaVQu2U+09g88SXif6uNb2p1t0ZqrgeT669w1h775ccKFVl9TReVgavkewa1cGnkegKDIOP5zqgggh6N6bY35AMg6fUz+uhFEQoAPEVBIg1/UxlLMxNGShWHArnuftDYK8vzF+RGd/ePbNHfjlE8/j4b+8iaGiU/F8azqB5ro40gmzoleWBwVGMo10uh71yQSSTDStl43dIzTyUnR9DDkuFEXBiW1JnNnZgPZkKVz6fqaAddv7sWlfDopMiRUnPrIENMV0zEoaaEvqaE0aaIhpSBnqqHxZasHjYmASvbU96mptMd1N0aWpndHYc8gIRwoAVaZdyPdnViRLCMhQjBEk01Cp/oiJo6t1tSdBo1PeLy0U4Yk2dgKAIErEy6559Gg8OpVXAx+jz6JMvueDjKATeDjV4fo+8paLfNGF7dLUn+PyFOD4/fzFDBXpuI76hI5UXK84BoaKLjJDNjJ5G7miU1HpF6IWNZYjQk6CRWVTTk/s0dRpEHmS+f4VbcYpl61fc5lcZbvDEopyElUiKdVSq4J4TDx8n1CCzKoxHXahGdbNEUJKES9mnqgq1LrB0Ct/S1zXx959OXTvywXHZmtLAs2NcezePQjL8iBJQEdHCqmUCUJoA9V0fUyIoA82TDYB8n2CoZyN7GARxbLu6LqhIMbaKpix0fW6mijkLQf/79V3sWHzdmx8awc27diHgaHiiF+fSMQxd/bhaG9vR8zQkdIUpHQVSVYV1BLXMac+hiMaYmgOhUct18cLXRn84e1uvNYzFCyXARzVEMfJs+qwoD2N41qSiNcwvbM8H91DFrqHbPQXHPQXXORsFwXHY9EEKt6OawpUCYFjcHCUEAJFpn40Mj9JAMH5SAYCG4BI6wXXx4Dlot9yUGTeLbJECVtCk5HUVMQ06rSt1kj98feUJClClEZK6nQ1WinG/wIBNyu1D1ds8V5l5TqRiQL3uOHicJ+GZqpD4q+h6T9+H6CRKM1QobFSci4gdhw/EK8GXe6ZE7Qb6k/GPVIqKt3YfUkC4rqCuKEhaWpIxrQKHZLleMgM2RgYspAZsuGN0ahuJiFs16CUpZzUsvSTWv6cyp87NMkUt2UoWi7Vy9lecOs4HhyvtD9TnRmpIMfj5fpKm+dqqEvoSCUN1CUo4bcsFzt3DyIzSM8HqiqjY1Yd8kMOhlg1WXNzHM2sR9hEQBCgA8RkESDP85EZKGIwU4zoNAxDRSKpI5HUq/ZaORjRm81jR88g+nOFqmQoYWhoSsXRkkqgORVH3NDg+QSv7BrA+q09GMjaOKYxjg+2JpE2SuWTnk+wPVPAlr483h8oBALZOkPB3Po4ZtfHMKc+VhFJKroe3hsoYFt/HtsHCugpOMg5HlyPQGWpOFORqpbSc2gKLRf3CEHe9TBkexgsuiAADEVCXFVRb1LyxkkLJyQKqHCbVrlFyYNPCAaLNnZli9g2kMfWTBH7Ci5khV5ZmaqMOXUG5jXE0Z7QYSrUVkBTJErGAMRUGWlDhcLTAUBw1c7JUZgwHSgkidroR4lRlCQFbtbhsnc5upyvx9NUXEfku16Qnqv6/nJJBO57pKrIX1FlKmg2DvxCIfhZZOQ3EEZz0XmtX02Zmg6ScPQHUTFuST9MIuuE70SeqQzElT8VPAjx9ApdUSCcL099ha76Az1ZZJ1q24iuz+eMhMbFxd4VgmWUtGR+6PXjjaDJMLuoCHeUD++b5T5Z5fel8P3yFB7Ko1zDH2vlIuxycu2wvm28vU+J4NCIJQiCCBp9v9IYRoNgHGwskkSPZ41FjE1dgQSUjBNZetWq4TgtyxLSdQYa0yaaG+IoFBzs3J2BZVHZRl2djoSpI5Oh54ZUykC7aIVxcGKiCZDn+hgYKGAwUwwOBJU146yrM0Zl2T8d4Xk+FXLzzuhlQu6i62FTzxBe25fF1r48ZjfEUacpSKgyOlMmjmpMoKlMODdku9g5WMTOTBG7BovoL4y8xYUEgpakgeaEgbY6Ha0JAy0JvWYUKQzL9dFXsJEpuhgoOMgUHQwWXAwWHbgeYcaQNKqkK9wksvKgJ4TAJQS2R1OABc9H3vEw5NIUGvc94tJdAoK848MlBIpEfZjiqsxu6f1yUkiJUfTHmyBEnNgPvwT23ASdnOKGgvq4jsakgVRcq9BX8ZQi2DhqfZGSLFExtUaruGRFLhGXssoglJ14UHZy5q+JvH5/kBD478iU+R5yUYfJQFU/KZ8EkTjXK0XnSqXntZ6b+tNZQIYglbRekzAsSULg/cUju0FUjOYq4RNQnyKWps/laS/FakjGNTSmTLQ0xNCYMpl/GI2i0l6MNgaHbAzmrApvo3SdgbbmBDzHw17mDyTLEpoaYshlaSTINFV0dqbH/SJfEKADxEQRINf1kekvYHCwRHx0XUF9YwyJUBfyQw37IzwAoGoyLXGOaei1HPxpSw/Wv9uDVlPDSbNSOLGtLhIZ8gnBjkwRb3Rn8W7/EPoKLnUQliSYrKTZ86kD9JBDDRVtjwqtHd9He52JY5oT+GBbHT7QmgyMHcsxZLvI2h5slhpRmGA5oSmo20/uetBysS9noTdP022ZAiVHEgh0Raa6pSrpslrgV9g0AFGqWPNIyEk7tJwwgbCmlETg/K88zSYBgdg7/APO59rzfbh8uxIAQvUkvCReASDLMhSZ6pZU/mMsSTA0GQlDRdJUkahSss6jAfR+7c9uufQ74OEuLZz+CCJw43wM8e9GQuA2zb2CDtXjdTqDpzqD1KbnU/NTL+zdw5vf0vvVUpzc1LN8+USOG+BCdHpsKUwUryoSNJWX0SsBuQnMOFkBwFg9oDyPEppswUEma6Fv0MJQIarxVBUZrY0xtDXG0VQfqyhkyRcc9GWK6B0oIBsyTtRUGS0NcdhFBwUm70jENcCnc6vrCubNaxzXY0kQoAPERBGgoZyFvXt4aaCC+sZ44KZ5KCFCeIpu1fJcTngMZnLHUxZ+6LWOFa16yzseXu3O4uU9g3i1OwtJknBUSxIpXYHl+tg7WMRgsfJqRpUl1Mc1zK2P4biWJI5rTkTIFAD0FWxs3pfDtoECdmSK2Je30Vdw4PJqHh5q5gJPQkv8600NDXEVTTEdHXUGOlMmOlMmWhK1/S0yRQfdORv7hiz0FxwMWS5zN6YhCS0UKZKlkjB6pKDGgzQCpLMyXY3pfAyFujCbmgxTK5Xsj7XKrRbCAtvqJd+VESYukqVGiJRccZPJ0cJlhDU4+fH7fuikGArvRx4TH54POK4HKdSOIzjxsJJ+jZs76kqpCm0U3bUFDl74PmHRJXpc8vtcQ+a4HmzHp9YNDr3vMDPEat9++UnW1GSYIYuLREyj9w010kZiqmDZHvqzRfQOFNHdn4cdumhVFQntzQkc1ppEqsqFe9FysbdnCHt6cpHX1Sd1uKyfmSxJiJsq2tvrUFc3voaIggAdICaKAFHl+xCSSR2xQ4j4+D6JEB7HqSzXr0V4CKF6Dpv5G5VHh2RFgs6q3bqGbLy8O4PXdmfwetcghqrYAigA6gwVhioDBGiJazi5I40F7Wk0J8KCag9v9+bxZk8Ob+0bQtd+DOaqIaEpaE7oSJsaUqaKlKEibWqMfEhI6iqSugpTkwOHbF3hKaySTiNsEldwWFNZx0fB8VBgbR8kCdQ4kkVcuKWAxk7ImixRl2X1wMiMF/gjRfUkARghC8qJAcgyAtFpLcLj+bR5retSsiEz/YWmUHdofT8/+q5HrQMKjseq7Kh3kut5zPmaGz1Se4GQAXRQNcX1WcFYMTHCblotIweESGcu4NUej4XcCdRGBXFx/apEhjuJczNQr+y5A3FOlyRQP6qQWSe/jZu0x9t4X3BMJAgh6M9a2NubR3dfHsXQ724ypuGw1iQ6WhLQy1JZvk/QO1DA7u4cBtnvqwQgaaqBb1hjQwxzZzeM63gFATpAHCw+QAcrgiZ6BQfFggPbGjnh4a93mCuvbbkVJnqqJlPSY6o1PWQ8n2Bb3xA2dQ3i3d4hbOvNY3emgKLro6POwOmd9VjYWY+O0NWF5fp4ec8gXtiVwaZ9OXismqd2xZUEjxA0xjQcnjbRkTLRmqBl9R2pGNLj3MWYz00tIepojlTX95nJIrt1PXbro2B7yDseSyn5kFnUxfVITemLJFFimYpp9M+k9+tMtQrhAfK2C8v1QUCduBOmCqNGmpGjaHvIWQ5yRRdDFv3LW7RCb7hydZ8QDBRd9BZs9BZs9OQt+pcrYt9QEZmCRVtkeB4814XvuvA8F57rAr5HBbIybb6qMF8oRaa3uqqgIW6gLZVAc10MTQkT9XEDqZiBlKkjoWswVBWaXNJnjQaKLJX0Gkqp4s71PBRtB7brsBO0B9enDWR5NFJmKRJFlmFoGj3JahpMXYWuaSCQInoan/ggFUV1JKhIVFhqT5El6CzCpbEGmyUDx/LS+dKy8nWCx4gS/tK+HRVV03HyFFW4Ii9UpReO6HFCHSIv4302k2WpFPULpVw1jUX/woRWLd0/VC5uy0EIQd9gEbu6h7C3Nx8QRVmW0NGcwOz2OtRVMTbM5W1s7xpEL3OB5t5uhx+eRktjYlzHKAjQAUIQoEoQQqM8hYKLYsGpqMJRFBmGqQZ/5VU4PEpkF104VZo/aoYSkKWROlVHtu/RKJJlOUELBoBVXDkeem0XA7aHOl1BY0xDUlMQUxWosgSJ5/d5xCD44a7dTZzD8XwUbBeDRRvZooNMwUbOclCwXeRtD0XPh0MAXdOgabQkW5ZopVedoaLeVNEQ09Ac15HQ9++HUXA8DBQc9Oct7MsV0T2Yx97BAnZn8tjZn8P7fTns6s9BhoR5rQ04qrUB7fVJpAytprO2x/Q1AIGhKUgYdEyNcQ31MQ0tSR1pUw+ElGEvlyBlBYwomjFQsLE7U8D2/iHs6M+jO1tE75CNwaIDCTJSMQ31po46XUVCUxDTlJKtAEreNNx+oBaBDcP2fPQVHWQsD4O2i4zlYtD2kLFcZCwHGcvBYMGGw8iR4zhwbAfFogXPc4GgD4PP/tj9KkgaGurjJhriBjrSSbTXJ9BaF0dD3ECdqSOuaYhrlKiI6M/EgVccqmqJuCjlREYNk5roc5yMinRmbTiujz09Q9ixN4tsvqQZakybmDOrDi2hPmAcQ4wI9TAi1NQQw/FHNo/ruAQBOkAIAkTh+wRW0UEh76BYcFG+qxgmFS2bMRVq2dV9kNqyXNhFD25ZWkySpSDKoxtj63DsuT5cm2qFuA8Mh6opUHR6dcz9ZKq1ayi519YmOxXOuRi/6iifEBQcHwUWofHYeygSSqkthbZ2iI2wWsL1fPTni8gVbBRd2mLC9X0oioKEoSGmazBUhbbq4CXCrNQXiBrvjfZ7cT0fmSIVfGcKLjJFh1bHsT+Xpdh4M1vH91lFiouCZcGybLi+D9vz4Hk+LNeFqauoMwykYjGkYwYa4gbShh6kEcJEiEcbABL4LI3kM/iEYMjx2B+twOPWB1nLQdZyULAd5G3u5u0GURXXpfNLDRZdOJ4XfI+EEMiEQJFYRSAIFBDIEkFj3MCsVAJNiRhSMR1JQ0dc1xDXFCR0DUlDg6kxM0s1ZOIZvakJx/Xgeh481iDW8334YeLG9mFFkaEpClRFCSJKvK9bBFL196y6bJg5Hy7KiBDB5aL8QJwvc8+tcC8/KTIfhP0vjzhF/YUQ6mknRzyxuFaO/02X5rKEEGry6VJdEu/75zAX9cG8hWzeQt5y6O+BR0AghaJwdDs+Yfom14XrurBdelzargPP92jDa1VGc30cR3c244NHHIaW+iQyORvvd2Wxty8fjClmqJjdXofO1mQF2edEaHZ7CgnRCuPgwkwmQJ5XSm0Vi27k10qWJZgxSnoMU6vqLOxYXhDpKa+aUNRSaqu8+epIQAiB5/hwHQ+uXZk64+0XZEmiRnZlz4dD95CGSVhwt1v2o0vkkt6F8LrqiH6HPsGfIwSRMmyuNeBRJkWm2pexgouFw1GYkfqQjBWWQ8nAkO0iZ3sYKNjoyzu0uq3ooMCec1jpPySqxyo1tS1FcUYzRgklghOJAEGCKlFvIl79V8sUkm+jZJxIyQAhBKoswdTUmp3pDzY4no+842LIcpApWOjPF9GTy6NrIIe+oQKyRRt522Hd4qnFAxeEUwJEgorBwLeH70XhqkBJQtw00JRKoimVQEMihvqYiXScpv7qDA1JXUNCkwPbhXGvwDsI4PtUZ0aJJCOThHvhEMgyoEgk8LYyNQWmodELFkNDzNAQNzTETQNxU0dM16BrKgiAvO0hW3SoEavtwWYaNsel35lEWE1YyMZBBgHxCF2PNTX2fWD/dHgC5oYQ9AxksG8gA9u10FIfw8lHH46mujR278vDZRelqiKhs7UOs9vrEBtn1+dqEAToADHTCJDn+rTfWN6BVeYJoagydaKOa9DL+k2VojweHLuyagugqa1AzzPKkz7fPo30eJEu3hwqaxYJgkgUaKRkR2LGdZES50n4IQ/7zfisVcNA3sa+bAHv9+WxN2dTka9PNTSmqiDOKrfi7Ic2psqIqQpimgxDlZl3D9MtSIBHQPURvDSY0Eq6PBMTF10fWctFf8FBb97G3pyFQcsDQJDS1MBXqF5XatoEAMwokkdOXA8uI4YSIya6Em3nIUu0go4TG0WSmGFkqfptJKktDp6CIwAkwqMHCLZXbkRZ/j2UBNPsqhjRnnSQaERODhG58vujQdjOwGPfjUtolRq3NXB9wiJk9M9lt8P9WGctB5miTSNwRQeDloOC6wGghFtmRJnp8On7s8/nE+arpCisi/zILlAIIfBYtM6QCWKKjLgiI6bKSKgKdCZ0DzcI1hUFGmPDEkr2BWGtUTDx7KvxSSmqw5sWe4zMuWx+XDZf3BbCI9TvxnM9KCDQZcBgNhbBrarA1FSYmsqiYFIovTp+vwOcdAafbAwXAyOB5XosSukgZ9nIFixkC0XkLEqMC7aLglO65X5b1IrCB0ALLFRFCQoU6mMG0nEafU3HDDQmTDTVJRE3qkdudvf0YqiQx5y2BrTVNwSVYJIEzGpKYG5HCqnE+EZ9whAE6ABxqBMgrucp8qqtsmoqTZNhxnXEYlokUsNdex3bZaTHo664IciyxNJaKrRRpLbotmlPKu4KXM3tV2KOrpKE4L0j4kzU/lGZKrJzoOjJWXhzXw5b9uWwvb+A7QN5bO8vYG929JVrY0VKV9BgqEjrKtLsNqUrSLD2JRMFzyewfB+W51OTSFay7oRchGUpGmnihEoNLQ83xNVkiVbUjUHfUcuLKey95BHaR80H3Udd9jl8ArhlJ++ABLETOQEnZSVDSoV9Do19Lu4wHm70Sz8nr3IDFIzcX6p8rosune9iyJAz7/oouD415gwRsZKImXvl0CgTYQQOvg9VlqCHCIfBiLupqYjrY9NCyVKoKlKqjDTy5Sr7rsd6nEug3lr8LMmjj5Sg0/lVpFAKPfzasaT1mR7RDRE726ceZjbb722Pfj+WT9Nelk9gex4sj+1zbFvhU3sgPEeouCL0HP+wJLRfk/D+6flwfB9Fy0bRdmHZNnTiI60raI3rOKq5DvNb6tGWTkY+j+t5GBzKoSWVgKmVrEEaUgbmdqTQUl+pEzpQCAJ0gDjUCJDn+rBtGqWxbZqiKv/WdUOBGdMQi2uBnocQAteh0RfH8eDaVWzQJbAu9JTwDNf5mxveEd7Y0ifD6nMAFiFQZEpYCCn58IyG7CgyMMqTwXRA0fHQnbPQnbOwL2djX87CPiYoHiy6wW3B8eD4PmyXaW48mvrhGiNVpr5AtJxfQ9pUo7cxFfWmhnRMQ9qkwuiEroAQYN+QhT9v7cVruwewvTePTN5G0fGhyRLimoKYplIvouAkTb8/PxQhChMRTgT41Xw177kghROK1HBiEmg/UNJq8U3wE6MqSVBlQJclxBQl6MFmsJYjKluv1Oxzeu874ShXWWBrVJE2DoeRI8vn5qL05OyVR6gYIamF4P1Dyq2AXIBHMkvGnWqIvI6JXDBSxiOirk/gkHCEjUZcg4ipT+BGSC4jBGANetmnkBgZC4+Pkz5DpdEmQ6bRsJJRaGluRnIC5hHB8C0lST4zeSVwwKOIUVLuo+xxQNJ5WplH4koEOkzohtv/CZs/26e2Ha7jwoCPJkPBMc0pdNSXCJEMwPdcxI2Sd9CJRzdjVrOoAjuoMFEEyLaYmDjUd4f/KPH9i1vq867LslxqRBnu5hyOyvDt+Dxl5PFbH47jVSUXsizBiKkwTQ2GqUKWJZpqcn14jgfH9iuEyxyqrkDXFWgGbUFQPhbatZuA+KUO3rzD+LDgZCcczUHp4KtJdkJRnUOV7EwnEEKwL2dhS3cWb+/NYkd/Hl2ZIroyBezOFLB3sIhM0UHe9kL7tgxTl9EQN1Af05EyVNpsVKPptxhLp0ze90qY3xHtrWYqEkyWvlHlaNoufMII92EDEBIOR9OwUuS50DJUPyGWiF2J1PEreo9QCwOHRXBohICg4FIHc59tW5UBSaKeVLoiRVql0EgTO0GXR9JCJ/WxpPvKP9N4EcpI5A0syhHMD4lGMyIzXdazLLS9YDuhaIlfZV2+7dJ9vuWRgUf29FC/QE2RoZVFtMYyV6RsPvgYS5++9BmqCeqjUSw+H2xOwVKnjIjxPxqZotEqPmeuRwmaLhE0GyqObKRiaLov0kYh2WIGX/zESeN6XI/m/D3xiiSBAE7RRaxKtRQBDZVzvw7X8mi+n4QP1JJafyxVSKomQ9NkaBp1GpUk2hPGczwM5ivL2jkkSYKm0yaTmqZA0bjehoa7rbwTRHDKK7FqQVY4weOnBgSakTC5qzqeUFRH4joCQXYOKkiShNY6E611Js48sqXmeq7nY5BVjNmBULd0UjPUUsrEUGXEdAUyJBRdD7mii95cEf25IvrzRQzmbWTyFooOFZM6IZM7MILFQoesEogSdx8SPHbV73rhiJMUnPh8ADmXIOd6YKqZ/SJoOorSCRXgRpMIefIQKuhmjXlNRUZMo8QvrikV/dxqzDgr4QZiGP8+grxiz/J5lKYUOQpHEKpFDCLHeA2Eo3clolEietXI3/5//qRAY6RUvPnYU5/lYywnRmxldlErgYdZ+LzsT99GozWA45W2yuNEMttQqeFxKWJWPuecYCuTLI7m2iuepuYpO5cAmwfyMGQZCVVGa1yHpsh46Pm38L/O/fCkjjEMQYAmEdU8JYKdVZGgKIAGACxVyvvPBE3/3MrDPhLAYwc8JwXBMcbzuQ6B5TioqRyRqKhYUWWoqgJFoYJQwtJVVt4eNlXF34/3SeIhbr7xUo58hFVLMiApMmRBdg5JqIqMxoSBxmHahlRD3FDRmDAwu2l8Q+cAtX4ouh4KNhWKF2wPWctF10ABuweL6M5a6M87GCzQCJbt0dYHBms7oityYBA4XPqnFmwAtutjwKFicp5ylqRSFKZc/8Ob7KpyKY3B3/lAjpZaJCMsGh8O0ZgLaDUTf33o1eHEWbULu/JFBAjal/AWJjSd5cP1AMf3g6hYSUtD01ultBtLsQVzKAcNhw1+q8iBjUJ5VK/yE44e5XqygNyFHo/skrI0Gvq5KDEKuteDEVSEfqNrDJ1UeyBVflJO6IBwUUDJaV2TgVhofZelyWiUyMf7WQsxVYbSPBuEkCn7XRcEaBKhxzVKIEKXEb7HUlh+ie3zA06WJMjMkIufIkiEFLEr5vLUEikdXNUgs+aRkiKznRfgwgyfUI2O5/vwJX7oML0G04sA/ACSqh8co92ZJURSWNKkpjoEBEqQZQlxXUW83JRyGLv+TMHBe315bOsdQlemSDVZWQs9QzZylhtEonyfVcAptG2JHuiw5IjOhRMojZ+pQ/ABKooNjvmRRaS4QJkaOBJIBCDEp2lq4sNnzUF5Gpsf84SGp2kqUJIhyTJLy9P7NC2vsFv2exFEhkppwdKJPizCLQnDg0quMgFwWA/mBM2Mh6+GG09IAPRQdI42MZYqblV2cRuuEORkrkRmCBM5S/DggxAJkkSCKA6A0Gs4GYqm4nwf8InHGrn6cD0XjkPNOyXiQ5OBhE71fC1JE42pGBqTMaTjJuoTJtJxE4oiU0Lo+AHh4jYJnsfIpMc8hByftp1hkdVKhM5nKGmKZJTmQQn2bQVxdlj5hO7DrUlzSn/rp5wA3XHHHfjJT36Crq4unHDCCbj11ltx1lln1Vx/7dq1WLFiBd544w10dHTg8ssvx2WXXRZZ54EHHsC1116LrVu34sgjj8QPf/hDfOYzn5noj7JfKIoCZQQRat/34fIWEX5J+MuJkaLQSqhwIWE4XRa9pqqEFLoX2fcminiwX1NJLvnrBBVYIqojMM2Rjmk48bA0TjwsPS7bo+7i1FdpiLUCKToeO0khaFsClFJRMtORGKxBq8lu47qCpKHtt9faeIEQgsGii71ZC3tzRXqbpU1/MwVmill0kWXE0PaoC7nt+sFFFj9pBvcVCQldKRl2SuFKL1Y+D16VhVA0jCePqowTPE2JQCReeozI68JEJEzcLELgSAQWi3hExh58J3zMiHwubg2gh5oW64oCEB9F20HBsqEA0GRmnAlaxl9nUnNM2u5ERUPCRHMqjoZEbMJdqz2fwGb7YpFFR3MF2rqmL2dhXyaPXNGFJzFPKMJonMcqGbm+iV3cm4qE5AjNXScKU0qA7rvvPnzrW9/CHXfcgTPPPBO/+tWv8Ld/+7fYtGkTZs+eXbH+tm3b8KlPfQqXXnopfv/732PDhg1YtmwZWlpa8LnPfQ4AsHHjRixduhQ/+MEP8JnPfAarV6/GF77wBaxfvx6nnXbaZH/EMUGWZeixqE+C7/vwbBeeG4oWhURy4bzvgYZmAU6hpOjmeFqNk5bgcRmRGUl6S0BAoCo0RYYWo21BphskSaLVgjEN81uT+3+BwLSBIkuIGep+zQx9n2Awb+PV7T3YvKMXewbycDygPhFHTNcBkIAQ+r4z7LYmGlNaBXbaaafh5JNPxi9+8Ytg2XHHHYcLL7wQN954Y8X6V1xxBR5++GFs3rw5WHbZZZfhlVdewcaNGwEAS5cuxeDgIB577LFgnU9+8pNoaGjAvffeO6JxTacyeN+nVVvEI4GoUuJRSU5iQgQGYKFphREUmZEo7oQryIuAgICAwDhj70AOGzbvxMvb9mJXXx6eL+FLi+bjEyfOG9f3mRZVYLZt48UXX8SVV14ZWb548WI8++yzVV+zceNGLF68OLLsvPPOw8qVK+E4DjRNw8aNG/Htb3+7Yp1bb711XMd/sECWZcjG9LDxFxAQEBCYmWirT+KzC4/FZxceCwAo2u6UNwSeMgLU09MDz/PQ1tYWWd7W1oY9e/ZUfc2ePXuqru+6Lnp6etDe3l5znVrbBADLsmBZpdqoTCYDgDJJAQEBAQEBgfGHPQHb5OftkSS3plwEXZ5q2V9JXLX1y5ePdps33ngjbrjhhorlhx9+eO2BCwgICAgICByUyGazSKeHL0yYMgLU3NwMRVEqIjPd3d0VERyOWbNmVV1fVVU0NTUNu06tbQLAVVddhRUrVgSPfd9HX18fmpqaxl0LMzg4iMMPPxw7duw46PVF0xlinicHYp4nB2KeJw9iricHEzXPhBBks1l0dHTsd90pI0C6rmPBggVYs2ZNpER9zZo1WLJkSdXXLFy4EH/84x8jy5588kmccsop0DQtWGfNmjURHdCTTz6JM844o+ZYDMOAYUTN2Orr60f7kUaFVColDq5JgJjnyYGY58mBmOfJg5jrycFEzPP+Ij8cU5oCW7FiBb70pS/hlFNOwcKFC/HrX/8a27dvD3x9rrrqKuzatQu/+93vANCKr3//93/HihUrcOmll2Ljxo1YuXJlpLpr+fLlOPvss3HTTTdhyZIl+MMf/oCnnnoK69evn5LPKCAgICAgIHDwYUoJ0NKlS9Hb24vvf//76Orqwgc+8AE8+uijmDNnDgCgq6sL27dvD9Y/4ogj8Oijj+Lb3/42br/9dnR0dOC2224LPIAA4IwzzsCqVatwzTXX4Nprr8WRRx6J++67b9p4AAkICAgICAhMPKZcBL1s2TIsW7as6nN33XVXxbJFixbhr3/967DbvOiii3DRRReNx/DGHYZh4Prrr69IuQmML8Q8Tw7EPE8OxDxPHsRcTw4OhnmeUiNEAQEBAQEBAYGpgHDQExAQEBAQEJhxEARIQEBAQEBAYMZBECABAQEBAQGBGQdBgAQEBAQEBARmHAQBmgDccccdOOKII2CaJhYsWIB169YNu/7atWuxYMECmKaJefPm4Ze//OUkjXR6YzTz/OCDD+Jv/uZv0NLSglQqhYULF+KJJ56YxNFOX4x2f+bYsGEDVFXFSSedNLEDPEQw2nm2LAtXX3015syZA8MwcOSRR+I3v/nNJI12+mK083z33XfjxBNPRDweR3t7O7761a+it7d3kkY7PfHMM8/g05/+NDo6OiBJEh566KH9vmZKzoNEYFyxatUqomkaufPOO8mmTZvI8uXLSSKRIO+//37V9d99910Sj8fJ8uXLyaZNm8idd95JNE0j999//ySPfHphtPO8fPlyctNNN5G//OUv5O233yZXXXUV0TSN/PWvf53kkU8vjHaeOQYGBsi8efPI4sWLyYknnjg5g53GGMs8X3DBBeS0004ja9asIdu2bSN//vOfyYYNGyZx1NMPo53ndevWEVmWyc9+9jPy7rvvknXr1pETTjiBXHjhhZM88umFRx99lFx99dXkgQceIADI6tWrh11/qs6DggCNM0499VRy2WWXRZYde+yx5Morr6y6/uWXX06OPfbYyLKvf/3r5PTTT5+wMR4KGO08V8Pxxx9PbrjhhvEe2iGFsc7z0qVLyTXXXEOuv/56QYBGgNHO82OPPUbS6TTp7e2djOEdMhjtPP/kJz8h8+bNiyy77bbbSGdn54SN8VDDSAjQVJ0HRQpsHGHbNl588UUsXrw4snzx4sV49tlnq75m48aNFeufd955eOGFF+A4zoSNdTpjLPNcDt/3kc1m0djYOBFDPCQw1nn+7W9/i61bt+L666+f6CEeEhjLPD/88MM45ZRT8OMf/xiHHXYY5s+fj3/+539GoVCYjCFPS4xlns844wzs3LkTjz76KAgh2Lt3L+6//36cf/75kzHkGYOpOg9OuRP0oYSenh54nlfReb6tra2iQz3Hnj17qq7vui56enrQ3t4+YeOdrhjLPJfj5ptvxtDQEL7whS9MxBAPCYxlnrds2YIrr7wS69atg6qKn5eRYCzz/O6772L9+vUwTROrV69GT08Pli1bhr6+PqEDqoGxzPMZZ5yBu+++G0uXLkWxWITrurjgggvw85//fDKGPGMwVedBEQGaAEiSFHlMCKlYtr/1qy0XiGK088xx77334nvf+x7uu+8+tLa2TtTwDhmMdJ49z8Pf/d3f4YYbbsD8+fMna3iHDEazP/u+D0mScPfdd+PUU0/Fpz71Kfz0pz/FXXfdJaJA+8Fo5nnTpk345je/ieuuuw4vvvgiHn/8cWzbti1o2C0wfpiK86C4RBtHNDc3Q1GUiquJ7u7uCnbLMWvWrKrrq6qKpqamCRvrdMZY5pnjvvvuw9///d/jv//7v3HuuedO5DCnPUY7z9lsFi+88AJeeuklfOMb3wBAT9SEEKiqiieffBIf//jHJ2Xs0wlj2Z/b29tx2GGHIZ1OB8uOO+44EEKwc+dOHH300RM65umIsczzjTfeiDPPPBPf/e53AQAf+tCHkEgkcNZZZ+Ff/uVfRIR+nDBV50ERARpH6LqOBQsWYM2aNZHla9aswRlnnFH1NQsXLqxY/8knn8Qpp5wCTdMmbKzTGWOZZ4BGfr7yla/gnnvuETn8EWC085xKpfDaa6/h5ZdfDv4uu+wyHHPMMXj55Zdx2mmnTdbQpxXGsj+feeaZ2L17N3K5XLDs7bffhizL6OzsnNDxTleMZZ7z+TxkOXqaVBQFQClCIXDgmLLz4IRKrGcgeJnlypUryaZNm8i3vvUtkkgkyHvvvUcIIeTKK68kX/rSl4L1efnft7/9bbJp0yaycuVKUQY/Aox2nu+55x6iqiq5/fbbSVdXV/A3MDAwVR9hWmC081wOUQU2Mox2nrPZLOns7CQXXXQReeONN8jatWvJ0UcfTf7hH/5hqj7CtMBo5/m3v/0tUVWV3HHHHWTr1q1k/fr15JRTTiGnnnrqVH2EaYFsNkteeukl8tJLLxEA5Kc//Sl56aWXAruBg+U8KAjQBOD2228nc+bMIbquk5NPPpmsXbs2eO7LX/4yWbRoUWT9p59+mnz4wx8muq6TuXPnkl/84heTPOLpidHM86JFiwiAir8vf/nLkz/waYbR7s9hCAI0cox2njdv3kzOPfdcEovFSGdnJ1mxYgXJ5/OTPOrph9HO82233UaOP/54EovFSHt7O7n44ovJzp07J3nU0wt/+tOfhv29PVjOgxIhIo4nICAgICAgMLMgNEACAgICAgICMw6CAAkICAgICAjMOAgCJCAgICAgIDDjIAiQgICAgICAwIyDIEACAgICAgICMw6CAAkICAgICAjMOAgCJCAgICAgIDDjIAiQgIDAhON73/seTjrppEl5r//5n//BscceC9/3J+X9xoq5c+fi1ltvHXYdSZLw0EMPHdD7fOUrX8GFF14YPL7ooovw05/+9IC2KSBwKEAQIAEBgUMKl19+Oa6++uqgh9Nkkq/R4Pnnn8fXvva1SX/f6667Dj/84Q8xODg46e8tIHAwQRAgAQGBQwbPPvsstmzZgs9//vNTPZT9oqWlBfF4fNLf90Mf+hDmzp2Lu+++e9LfW0DgYIIgQAICMwjnnHMOvvGNb+Ab3/gG6uvr0dTUhGuuuaZmZ+tMJoNYLIbHH388svzBBx9EIpEIupFfccUVmD9/PuLxOObNm4drr70WjuMMO45vfetbkWUXXnghvvKVrwSPbdvG5ZdfjsMOOwyJRAKnnXYann766WE/36pVq7B48WKYpgkAuOuuu3DDDTfglVdegSRJkCQJd911F77zne/g05/+dPC6W2+9FZIk4ZFHHgmWHXPMMfjVr34FAPB9H9///vfR2dkJwzBw0kknVcxJNXzve9/D7NmzYRgGOjo68M1vfjN4rjwFtmXLFpx99tkwTRPHH398RXdsANi1axeWLl2KhoYGNDU1YcmSJXjvvfeC5z3Pw4oVK4Lv9vLLL6/63V5wwQW499579zt+AYFDGYIACQjMMPznf/4nVFXFn//8Z9x222245ZZb8B//8R9V102n0zj//PMrogX33HMPlixZgmQyCQCoq6vDXXfdhU2bNuFnP/sZ7rzzTtxyyy0HNM6vfvWr2LBhA1atWoVXX30Vn//85/HJT34SW7ZsqfmaZ555BqecckrweOnSpfjOd76DE044AV1dXejq6sLSpUtxzjnnYN26dYFOaO3atWhubsbatWsBAHv27MHbb7+NRYsWAQB+9rOf4eabb8a//du/4dVXX8V5552HCy64YNix3H///bjlllvwq1/9Clu2bMFDDz2ED37wg1XX9X0fn/3sZ6EoCp577jn88pe/xBVXXBFZJ5/P42Mf+xiSySSeeeYZrF+/HslkEp/85Cdh2zYA4Oabb8ZvfvMbrFy5EuvXr0dfXx9Wr15d8X6nnnoq/vKXv8CyrJrjFxA45DHh7VYFBAQOGixatIgcd9xxxPf9YNkVV1xBjjvuuJqvefDBB0kymSRDQ0OEEEIymQwxTZM88sgjNV/z4x//mCxYsCB4XN4VftGiRWT58uWR1yxZsiToFv3OO+8QSZLIrl27Iut84hOfIFdddVXN902n0+R3v/tdZFm1jvQDAwNElmXywgsvEN/3SVNTE7nxxhvJRz7yEUIIIffccw9pa2sL1u/o6CA//OEPI9v4yEc+QpYtW1ZzLDfffDOZP38+sW276vNz5swht9xyCyGEkCeeeIIoikJ27NgRPP/YY48RAGT16tWEEEJWrlxJjjnmmMh3Z1kWicVi5IknniCEENLe3k5+9KMfBc87jkM6OzvJkiVLIu/9yiuvEADkvffeqzl+AYFDHSICJCAww3D66adDkqTg8cKFC7FlyxZ4nod//dd/RTKZDP62b9+O888/H6qq4uGHHwYAPPDAA6irq8PixYuDbdx///346Ec/ilmzZiGZTOLaa6/F9u3bxzzGv/71ryCEYP78+ZHxrF27Flu3bq35ukKhEKS/hkM6ncZJJ52Ep59+Gq+99hpkWcbXv/51vPLKK8hms3j66aeD6M/g4CB2796NM888M7KNM888E5s3bwaAqvP2+c9/HoVCAfPmzcOll16K1atXw3XdquPZvHkzZs+ejc7OzmDZwoULI+u8+OKLeOedd1BXVxe8T2NjI4rFIrZu3YpMJoOurq7I61RVjUTEOGKxGAAaVRIQmKlQp3oAAgICBw8uu+wyfOELXwged3R0QFVVXHTRRbjnnnvwxS9+Effccw+WLl0KVaU/H8899xy++MUv4oYbbsB5552HdDqNVatW4eabb675PrIsV2hTwpoh3/ehKApefPFFKIoSWY+n3aqhubkZ/f39I/qs55xzDp5++mnouo5FixahoaEBJ5xwAjZs2ICnn366QqMUJo0AQAgJltWat7feegtr1qzBU089hWXLluEnP/kJ1q5dC03TKrZVjvL3830fCxYsqCpebmlpGdFn5ujr6xvT6wQEDiUIAiQgMMPw3HPPVTw++uijoSgKGhsb0djYWPGaiy++GIsXL8Ybb7yBP/3pT/jBD34QPLdhwwbMmTMHV199dbDs/fffH3YMLS0t6OrqCh57nofXX38dH/vYxwAAH/7wh+F5Hrq7u3HWWWeN+LN9+MMfxqZNmyLLdF2H53kV655zzjlYuXIlVFXFueeeCwBYtGgRVq1aFdH/pFIpdHR0YP369Tj77LOD1z/77LM49dRTAaDmvMViMVxwwQW44IIL8E//9E849thj8dprr+Hkk0+OrHf88cdj+/bt2L17Nzo6OgAAGzdujKxz8skn47777kNraytSqVTVz9/e3o7nnnsuGKfrunjxxRcr3u/1119HZ2cnmpubq25HQGAmQKTABARmGHbs2IEVK1bgrbfewr333ouf//znWL58+bCvWbRoEdra2nDxxRdj7ty5OP3004PnjjrqKGzfvh2rVq3C1q1bcdttt1UV3obx8Y9/HI888ggeeeQRvPnmm1i2bBkGBgaC5+fPn4+LL74Yl1xyCR588EFs27YNzz//PG666SY8+uijNbd73nnnYf369ZFlc+fOxbZt2/Dyyy+jp6cnEP6effbZyGaz+OMf/4hzzjkHACVFv//979HS0oLjjz8+2MZ3v/td3HTTTbjvvvvw1ltv4corr8TLL7887LzdddddWLlyJV5//XW8++67+K//+i/EYjHMmTOnYt1zzz0XxxxzDC655BK88sorWLduXYRQApSENjc3Y8mSJVi3bh22bduGtWvXYvny5di5cycAYPny5fjRj36E1atXV51XjnXr1kVSmAICMxJTK0ESEBCYTCxatIgsW7aMXHbZZSSVSpGGhgZy5ZVXRoS1tfDd736XACDXXXdd1eeamppIMpkkS5cuJbfccgtJp9PB8+VCZNu2yT/+4z+SxsZG0traSm688caICJqvc91115G5c+cSTdPIrFmzyGc+8xny6quv1hxjX18ficVi5M033wyWFYtF8rnPfY7U19cTAOS3v/1t8NyCBQtIS0tL8Pl7e3uJJEnkoosuimzX8zxyww03kMMOO4xomkZOPPFE8thjjw07X6tXryannXYaSaVSJJFIkNNPP5089dRTwfNhETQhhLz11lvkox/9KNF1ncyfP588/vjjERE0IYR0dXWRSy65hDQ3NxPDMMi8efPIpZdeSjKZDCGEip6XL19OUqkUqa+vJytWrCCXXHJJRARdKBRIKpUiGzduHHb8AgKHOiRCahiACAgIHHI455xzcNJJJ+23BcN0xuWXX45MJhN4+AhEcfvtt+MPf/gDnnzyyakeioDAlEKkwAQEBA4pXH311ZgzZ05V3Y8AoGkafv7zn0/1MAQEphwiAiQgMIMwEyJAAgICAiOBIEACAgICAgICMw4iBSYgICAgICAw4yAIkICAgICAgMCMgyBAAgICAgICAjMOggAJCAgICAgIzDgIAiQgICAgICAw4yAIkICAgICAgMCMgyBAAgICAgICAjMOggAJCAgICAgIzDgIAiQgICAgICAw4/D/AW9hHOWi7Ex4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at p-values\n",
    "ax = sns.kdeplot(data=contact_p, legend= True, cut = 0, palette= 'PuBu')\n",
    "ax.legend(loc='upper right',ncol=5, title=\"Effect Size\" )\n",
    "\n",
    "plt.title(\"Effect size - p-value\")\n",
    "plt.xlabel(\"p-value (two-sided)\")\n",
    "plt.title(\"Effect size 0.1 to 3 (stepsize = 0.1)\")\n",
    "\n",
    "# cut = 0 ; stops it at the min and max of the data\n",
    "# do a scatter plot - x axis as effect size and y be p value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>6.927988e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>8.705630e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.1</td>\n",
       "      <td>8.238848e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.1</td>\n",
       "      <td>6.635323e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1</td>\n",
       "      <td>6.635246e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816745</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.179926e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816746</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.285726e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816747</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.114323e-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816748</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.332456e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816749</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9.725443e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>816750 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       variable         value\n",
       "0           0.1  6.927988e-02\n",
       "1           0.1  8.705630e-01\n",
       "2           0.1  8.238848e-01\n",
       "3           0.1  6.635323e-01\n",
       "4           0.1  6.635246e-01\n",
       "...         ...           ...\n",
       "816745      3.0  6.179926e-01\n",
       "816746      3.0  7.285726e-09\n",
       "816747      3.0  4.114323e-21\n",
       "816748      3.0  6.332456e-01\n",
       "816749      3.0  9.725443e-02\n",
       "\n",
       "[816750 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melt all the data\n",
    "contact_p_path_loc = contact_p_path.iloc[:,2:] # dont need the pathway info\n",
    "\n",
    "contact_p_path_melt = pd.melt(contact_p_path_loc)\n",
    "contact_p_path_melt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualize\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.pointplot(data=contact_p_path_melt, x=\"variable\", y=\"value\", color = \"cornflowerblue\", errorbar =('ci', 100), join=False)\n",
    "plt.xlabel(\"Efect Size\")\n",
    "plt.ylabel(\"p-value\")\n",
    "plt.title(\"Effect size vs. Signficance of p-value (two-sided)\")\n",
    "plt.show()\n",
    "\n",
    "# Why can't I see the error bars?? - what kind of error bars should I use? sd,se,ci,pi?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualize\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.boxplot(data=contact_p_path_melt, x=\"variable\", y=\"value\", notch = True, color = \"cornflowerblue\")\n",
    "plt.xlabel(\"Efect Size\")\n",
    "plt.ylabel(\"p-value\")\n",
    "plt.title(\"Effect Size vs. Signficance of p-value (two-sided)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualize\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.violinplot(data=contact_p_path_melt, x=\"variable\", y=\"value\", color = \"cornflowerblue\")\n",
    "plt.xlabel(\"Efect Size\")\n",
    "plt.ylabel(\"p-value\")\n",
    "plt.title(\"Effect Size vs. Signficance of p-value (two-sided)\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questions, other code, and notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random seed - \n",
    "    # set at the beginging of the code; this will make sure code if reproducible (good practice) but need to check what happens with other random seeds\n",
    "    # Random seeds - bootstrapping, kPCA kernal, simulated data etc. \n",
    "    # give it an integret and thats what it starts the random sampling from ; says where in the sequence you stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DONT NEED THIS\n",
    "\n",
    "## Bootstrap multiple times\n",
    "\n",
    "# n_replicates = 4\n",
    "\n",
    "# bootstap_list = []\n",
    "# for i in range(n_replicates):\n",
    "#     for n in effect_size:\n",
    "#         resampling_bootstap = effect_size[0].sample(n=263,replace=True, axis=0)\n",
    "#         bootstap_list.append(resampling_bootstap)\n",
    "# len(bootstap_list)\n",
    "\n",
    "# bootstap_list_[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This gives averages across the list of 100 data frames, not each pair - ask Cecilia how i could change it for the pairs in each dataframe\n",
    "\n",
    "# stacked_mean_and_sd = []\n",
    "# for m in stack_correlation_matrix_list:\n",
    "#     mean_correlation = np.mean(m[\"Correlation_abs\"])\n",
    "#     sd_correlation = np.std(m[\"Correlation_abs\"])\n",
    "#     stacked_mean_and_sd.append({\"Mean\" : mean_correlation, \"Standard_deviation\" : sd_correlation})\n",
    "#     print(mean_correlation,sd_correlation)\n",
    "\n",
    "# print(len(stacked_mean_and_sd))\n",
    "# stacked_mean_and_sd = pd.DataFrame(stacked_mean_and_sd)\n",
    "# stacked_mean_and_sd\n",
    "\n",
    "\n",
    "## Trying to do the loop over\n",
    "\n",
    "## DID AN EASIER WAY\n",
    "\n",
    "## For one pairway\n",
    "\n",
    "# corr_values = []\n",
    "# mean_and_std_list = []\n",
    "# for m in stack_correlation_matrix_list:\n",
    "#     correlation_values = m[\"Correlation_abs\"][3]\n",
    "#     corr_values.append({\"Abs_Corr\": correlation_values})\n",
    "#     corr_values_sd_df = pd.DataFrame(corr_values)\n",
    "#     corr_calc = corr_values_sd_df[\"Abs_Corr\"]\n",
    "\n",
    "# mean_correlation = statistics.mean(corr_calc) \n",
    "# std_correlation = statistics.stdev(corr_calc) \n",
    "# mean_and_std_list.append({\"Mean\": mean_correlation, \"Std\" : std_correlation})\n",
    "# mean_and_std_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subplots as having two types of quality\n",
    "# x = pd.DataFrame(range(0,27225))\n",
    "# x.columns = [\"Number\"]\n",
    "# x\n",
    "\n",
    "# y = pathway_mean_and_std_df['Mean']\n",
    "# e = pathway_mean_and_std_df['Std']\n",
    "\n",
    "# plt.figure(figsize=(35,5))\n",
    "# plt.errorbar(x, y, e, linestyle='None', marker='^') \n",
    "# plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python_3104",
   "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": "3.10.6"
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  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "c9ca5e3136d0330de2a9294dd81319dfd613941b64e9f48a76d4b964decbcf1c"
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 },
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}