Flaship_Paper_Figure2_SupplemetaryFigure1_ExtendedDataFigure1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "#mpl.style.use('seaborn-white')\n",
    "import multiprocess as mp\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cooltools\n",
    "import cooler\n",
    "#import bbi\n",
    "import bioframe\n",
    "import math\n",
    "\n",
    "import glob, os\n",
    "import os.path\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import decomposition\n",
    "from sklearn import linear_model, datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 2 - Compartments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 2a - Heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_path=\"/Figures/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename1=\"4DNFIF1J6GC5_H1-hESC_ChIA_PET_PolII.mcool\"\n",
    "filename2=\"4DNFIK3276U7_H1-hESC_ChIA_PET_CTCF.mcool\"\n",
    "filename3=\"4DNFICOGAKW2_H1-hESC_PLAC-Seq.mcool\"\n",
    "filename4=\"4DNFI9GMP2J8_H1-hESC_MicroC.mcool\"\n",
    "filename5=\"4DNFI82R42AD_H1-hESC_FA_DSG_DpnII_Hi-C.mcool\"\n",
    "filename6=\"4DNFIV3PDS5F_4DNFIIY1TXUZ_H1-hESC_DNA_clusters_R1_R2_2-100.mcool\"\n",
    "filename7=\"H1hESC.GAM.NPMI.normalized.pairwise.curated.matrices.at100Kb.mcool\"\n",
    "\n",
    "path=\"./\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hg38 = bioframe.fetch_chromsizes('hg38')\n",
    "chromsizes = bioframe.fetch_chromsizes('hg38')\n",
    "chromosomes = list(chromsizes.index)\n",
    "\n",
    "print(filename1,filename2,filename3,filename4,filename5,filename6,filename7)\n",
    "\n",
    "conditions = [ 'ChIA_PET_PolII' ,\n",
    "    'ChIA_PET_CTCF' ,\n",
    "    'PLAC-Seq' ,\n",
    "    'MicroC' ,\n",
    "    'FA_DSG_DpnII_Hi-C' ,\n",
    "    'SPRITE',\n",
    "    'GAM']\n",
    "\n",
    "\n",
    "cool = [filename1,filename2,filename3,filename4,filename5,filename6,filename7]\n",
    "\n",
    "\n",
    "cooler_paths = {    \n",
    "    'ChIA_PET_PolII' : path+filename1+'::/resolutions/100000',\n",
    "    'ChIA_PET_CTCF' : path+filename2+'::/resolutions/100000',\n",
    "    'PLAC-Seq' : path+filename3+'::/resolutions/100000',\n",
    "    'MicroC' : path+filename4+'::/resolutions/100000',\n",
    "    'FA_DSG_DpnII_Hi-C' : path+filename5+'::/resolutions/100000',\n",
    "    'SPRITE' : path+filename6+'::/resolutions/100000',\n",
    "    'GAM' : path+filename7+'::/resolutions/100000'\n",
    "}\n",
    "\n",
    "\n",
    "long_names = {\n",
    "    'ChIA_PET_PolII' : 'ChIA-PET PolII',\n",
    "    'ChIA_PET_CTCF' : 'ChIA-PET CTCF',\n",
    "    'PLAC-Seq' : 'PLAC-Seq',\n",
    "    'MicroC' : 'MicroC',\n",
    "    'FA_DSG_DpnII_Hi-C' : 'Hi-C',\n",
    "    'SPRITE' : 'SPRITE',\n",
    "    'GAM' : 'GAM'}\n",
    "\n",
    "clrs = {\n",
    "    cond: cooler.Cooler(cooler_paths[cond]) for cond in conditions\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(20,20))\n",
    "gs = gridspec.GridSpec(nrows=3,ncols=3)\n",
    "\n",
    "\n",
    "for k,cond in enumerate(np.sort(conditions)):\n",
    "    print(cond)\n",
    "    ax = plt.subplot(gs[k])\n",
    "    x,y=clrs[cond].extent(\"chr19\")\n",
    "    #print(x)\n",
    "    #print(y)\n",
    "    x1=x+300\n",
    "    y1=y\n",
    "    if cond=='GAM':\n",
    "        mat = clrs[cond].matrix(balance=False, sparse=True)[x1:y1, x1:y1]\n",
    "        print(mat.shape)\n",
    "        arr = mat.toarray()\n",
    "        arr_tmp=mat.toarray()\n",
    "        arr_tmp=arr_tmp[arr_tmp>0]\n",
    "        vmin=np.percentile(arr_tmp, 10)\n",
    "        vmax=np.percentile(arr_tmp, 90)\n",
    "        print(vmin)\n",
    "        print(vmax)\n",
    "        im =ax.matshow(arr, cmap='YlOrRd',vmin=vmin,vmax=vmax)\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_yticklabels([])\n",
    "        ax.set_title(long_names[cond], fontdict={'fontsize': 30, 'fontweight': 'medium'})\n",
    "        plt.colorbar(im)\n",
    "    else:\n",
    "        mat = clrs[cond].matrix(balance=True, sparse=True)[x1:y1, x1:y1]\n",
    "        arr = mat.toarray()       \n",
    "        arr_tmp=mat.toarray()  \n",
    "        arr_tmp=arr_tmp[arr_tmp>0]\n",
    "        vmin=np.percentile(np.log10(arr_tmp), 10)\n",
    "        vmax=np.percentile(np.log10(arr_tmp), 90)\n",
    "        print(vmin)\n",
    "        print(vmax)\n",
    "        im =ax.matshow(np.log10(arr), cmap='YlOrRd',vmin=vmin,vmax=vmax)\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_yticklabels([])\n",
    "        ax.set_title(long_names[cond], fontdict={'fontsize': 30, 'fontweight': 'medium'})\n",
    "        plt.colorbar(im)\n",
    "    #l=l+1\n",
    "plt.savefig('H1-hESC_chr19_100kb_30000000-58700000.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 2b -  Compartment Score correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4DNFI9FVHJZQ_HFFc6_MicroC.cis.100000.vecs.tsv\n",
      "4DNFI9REIU8H_HFFc6_PLAC-Seq.cis.100000.vecs.tsv\n",
      "4DNFIAVXXO55_HFFc6_FA_DSG_DpnII_Hi-C.cis.100000.vecs.tsv\n",
      "4DNFIELOAD41_HFFc6_ChIA_PET_CTCF.cis.100000.vecs.tsv\n",
      "4DNFIOOSHTQV_HFFc6_ChIA_PET_PolII.cis.100000.vecs.tsv\n",
      "4DNFIP9HGF9M_HFFc6_SPRITE.100000.cis.vecs.tsv\n",
      "4DNFI9GMP2J8_H1-hESC_MicroC.cis.100000.vecs.tsv\n",
      "4DNFI82R42AD_H1-hESC_FA_DSG_DpnII_Hi-C.cis.100000.vecs.tsv\n",
      "4DNFICOGAKW2_H1-hESC_PLAC-Seq.cis.100000.vecs.tsv\n",
      "4DNFIF1J6GC5_H1-hESC_ChIA_PET_PolII.cis.100000.vecs.tsv\n",
      "4DNFIK3276U7_H1-hESC_ChIA_PET_CTCF.cis.100000.vecs.tsv\n",
      "GAM_H1_new.ALL.AB.at100Kb.final.chosen_formatted.tsv\n",
      "4DNFITX6WCRT_H1-hESC_DNA.cis.100000.vecs.tsv\n",
      "(25668, 14)\n"
     ]
    }
   ],
   "source": [
    "names = [\"HFFc6 MicroC\",\n",
    "\"HFFc6 PLAC-Seq\",\n",
    "\"HFFc6 Hi-C\",\n",
    "\"HFFc6 ChIA-PET CTCF\",\n",
    "\"HFFc6 ChIA-PET PolII\",\n",
    "\"HFFc6 SPRITE\",      \n",
    "\"H1-hESC MicroC\",\n",
    "\"H1-hESC Hi-C\",\n",
    "\"H1-hESC PLAC-Seq\",\n",
    "\"H1-hESC ChIA-PET PolII\",\n",
    "\"H1-hESC ChIA-PET CTCF\",\n",
    "\"Hi-hESC GAM\",\n",
    "\"H1-hESC SPRITE\"]\n",
    "\n",
    "files=[\n",
    "\"4DNFI9FVHJZQ_HFFc6_MicroC.cis.100000.vecs.tsv\",\n",
    "\"4DNFI9REIU8H_HFFc6_PLAC-Seq.cis.100000.vecs.tsv\",\n",
    "\"4DNFIAVXXO55_HFFc6_FA_DSG_DpnII_Hi-C.cis.100000.vecs.tsv\",\n",
    "\"4DNFIELOAD41_HFFc6_ChIA_PET_CTCF.cis.100000.vecs.tsv\",\n",
    "\"4DNFIOOSHTQV_HFFc6_ChIA_PET_PolII.cis.100000.vecs.tsv\",\n",
    "\"4DNFIP9HGF9M_HFFc6_SPRITE.100000.cis.vecs.tsv\",     \n",
    "\"4DNFI9GMP2J8_H1-hESC_MicroC.cis.100000.vecs.tsv\",\n",
    "\"4DNFI82R42AD_H1-hESC_FA_DSG_DpnII_Hi-C.cis.100000.vecs.tsv\",\n",
    "\"4DNFICOGAKW2_H1-hESC_PLAC-Seq.cis.100000.vecs.tsv\",\n",
    "\"4DNFIF1J6GC5_H1-hESC_ChIA_PET_PolII.cis.100000.vecs.tsv\",\n",
    "\"4DNFIK3276U7_H1-hESC_ChIA_PET_CTCF.cis.100000.vecs.tsv\",\n",
    "\"GAM_H1_new.ALL.AB.at100Kb.final.chosen_formatted.tsv\",\n",
    "\"4DNFITX6WCRT_H1-hESC_DNA.cis.100000.vecs.tsv\"]\n",
    "       \n",
    "\n",
    "\n",
    "i=0\n",
    "E1_data=pd.DataFrame()\n",
    "file_list=[]\n",
    "os.chdir(\"compartments/\")\n",
    "for file_name in files:\n",
    "    if file_name == \"4DNFI9FVHJZQ_HFFc6_MicroC.cis.100000.vecs.tsv\":\n",
    "        print(file_name)\n",
    "        file=pd.read_csv(file_name,sep=\"\\t\")\n",
    "        \n",
    "        E1_data=file\n",
    "        E1_data=E1_data[E1_data.loc[:,\"chrom\"]!=\"chrY\"]\n",
    "        E1_data=E1_data[E1_data.loc[:,\"chrom\"]!=\"chrX\"]\n",
    "        E1_data[\"combined\"]=E1_data[\"chrom\"]+E1_data[\"start\"].astype(str)+E1_data[\"end\"].astype(str)\n",
    "        E1_data=E1_data[[\"combined\",\"E1\"]]\n",
    "    else:\n",
    "        print(file_name)\n",
    "        file=pd.read_csv(file_name,sep=\"\\t\")\n",
    "        file=file[file.loc[:,\"chrom\"]!=\"chrY\"]\n",
    "        file=file[file.loc[:,\"chrom\"]!=\"chrX\"]\n",
    "        file[\"combined\"]=file[\"chrom\"]+file[\"start\"].astype(str)+file[\"end\"].astype(str)\n",
    "        file=file[[\"combined\",\"E1\"]]\n",
    "\n",
    "        E1_data=pd.merge(E1_data,file,on=\"combined\")\n",
    "\n",
    "#E1_data.columns=names\n",
    "\n",
    "#E1_data=pd.concat([E1_data,file[\"chrom\"]],axis=1)\n",
    "#E1_data=pd.concat([E1_data,file[\"start\"]],axis=1)\n",
    "#E1_data=pd.concat([E1_data,file[\"end\"]],axis=1)\n",
    "print(E1_data.shape)\n",
    "\n",
    "#E1_data_new_chr1=E1_data_new[E1_data_new.loc[:,\"chrom\"]=='chr1']\n",
    "\n",
    "\n",
    "E1_data_new=E1_data.iloc[:,1:]\n",
    "\n",
    "\n",
    "E1_data_new.columns=names\n",
    "#print(E1_data_new.head)\n",
    "\n",
    "\n",
    "#for col in E1_data_new:\n",
    "#    E1_data_new=E1_data_new[E1_data_new.loc[:,col].notnull()]\n",
    "#print(E1_data_new.shape)  \n",
    "\n",
    "\n",
    "#E1_data_new.to_csv(out_path+\"Compartment_Scores_all_methods.csv\")\n",
    "#print(E1_data.head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24258, 13)\n"
     ]
    }
   ],
   "source": [
    "for col in E1_data_new:\n",
    "    E1_data_new=E1_data_new[E1_data_new.loc[:,col].notnull()]\n",
    "print(E1_data_new.shape)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_corr=E1_data_new.corr(method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_corr.to_csv(out_path+\"H1-hESC_compartment_correlations.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np; np.random.seed(0)\n",
    "import seaborn as sns;\n",
    "\n",
    "\n",
    "fig, (ax) = plt.subplots(1, 1)\n",
    "sns.set(font_scale=1.3)\n",
    "\n",
    "ax = sns.clustermap(all_corr,cmap='Reds',annot=False,vmin=0, vmax=1,annot_kws={'size': 12})\n",
    "\n",
    "\n",
    "plt.savefig(out_path+\"Figure_2b_Comp_correlations_100kb.pdf\",bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 2c - Compartment strength quantifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The functions below were added to saddle.py script in cooltools \n",
    "# Here AA and BB compartments quantified seperately\n",
    "\n",
    "def saddle_strength_middle(S, C):\n",
    "    \"\"\"\n",
    "    Parameters\n",
    "    ----------\n",
    "    S, C : 2D arrays, square, same shape\n",
    "        Saddle sums and counts, respectively\n",
    "    Returns\n",
    "    -------\n",
    "    1D array\n",
    "    Ratios of cumulative corner interaction scores, where the saddle data is\n",
    "    grouped over the AA+BB corners and AB+BA corners with increasing extent.\n",
    "    \"\"\"\n",
    "    m, n = S.shape\n",
    "    if m != n:\n",
    "        raise ValueError(\"`saddledata` should be square.\")\n",
    "\n",
    "    ratios = np.zeros(n)\n",
    "    for k in range(1, n):\n",
    "        intra_sum = S[k:n-k, k:n-k].sum()\n",
    "        intra_count = C[k:n-k, k:n-k].sum()\n",
    "        intra = intra_sum / intra_count\n",
    "\n",
    "        # NO NEED to modify the inter - part \n",
    "        # as it is symmetrical and it's still\n",
    "        # a fair average , when \n",
    "\n",
    "        inter_sum = S[0:k, n-k:n].sum() + S[n-k:n, 0:k].sum()\n",
    "        inter_count = C[0:k, n-k:n].sum() + C[n-k:n, 0:k].sum()\n",
    "        inter = inter_sum / inter_count\n",
    "\n",
    "        ratios[k] = intra / inter\n",
    "    return ratios\n",
    "\n",
    "\n",
    "\n",
    "def saddle_strength_lowerright(S, C):\n",
    "    \"\"\"\n",
    "    Parameters\n",
    "    ----------\n",
    "    S, C : 2D arrays, square, same shape\n",
    "        Saddle sums and counts, respectively\n",
    "    Returns\n",
    "    -------\n",
    "    1D array\n",
    "    Ratios of cumulative corner interaction scores, where the saddle data is\n",
    "    grouped over the AA+BB corners and AB+BA corners with increasing extent.\n",
    "    \"\"\"\n",
    "    print(S.shape)\n",
    "\n",
    "    S_mod = S[~np.all(S == 0, axis=0)]\n",
    "    S_mod = S_mod[:S_mod.shape[0],:S_mod.shape[0]]\n",
    "\n",
    "    print(S_mod.shape)\n",
    "    S=S_mod\n",
    "\n",
    "    m, n = S.shape\n",
    "    if m != n:\n",
    "        raise ValueError(\"`saddledata` should be square.\")\n",
    "\n",
    "    ratios = np.zeros(n)\n",
    "    for k in range(1, n):\n",
    "        # lower right  - aka AA\n",
    "\n",
    "        intra_sum = S[n-k:n, n-k:n].sum()\n",
    "        intra_count = C[n-k:n, n-k:n].sum()\n",
    "        intra = intra_sum / intra_count\n",
    "\n",
    "        inter_sum = S[0:k, n-k:n].sum() + S[n-k:n, 0:k].sum()\n",
    "        inter_count = C[0:k, n-k:n].sum() + C[n-k:n, 0:k].sum()\n",
    "        inter = inter_sum / inter_count\n",
    "\n",
    "        ratios[k] = intra / inter\n",
    "    return ratios\n",
    "\n",
    "\n",
    "\n",
    "def saddle_strength_upperleft(S, C):\n",
    "    \"\"\"\n",
    "    Parameters\n",
    "    ----------\n",
    "    S, C : 2D arrays, square, same shape\n",
    "        Saddle sums and counts, respectively\n",
    "    Returns\n",
    "    -------\n",
    "    1D array\n",
    "    Ratios of cumulative corner interaction scores, where the saddle data is\n",
    "    grouped over the AA+BB corners and AB+BA corners with increasing extent.\n",
    "    \"\"\"\n",
    "    m, n = S.shape\n",
    "    if m != n:\n",
    "        raise ValueError(\"`saddledata` should be square.\")\n",
    "\n",
    "    ratios = np.zeros(n)\n",
    "    for k in range(1, n):\n",
    "        # upper left - aka BB\n",
    "        intra_sum = S[0:k, 0:k].sum()\n",
    "        intra_count = C[0:k, 0:k].sum()\n",
    "        intra = intra_sum / intra_count\n",
    "\n",
    "\n",
    "        inter_sum = S[0:k, n-k:n].sum() + S[n-k:n, 0:k].sum()\n",
    "        inter_count = C[0:k, n-k:n].sum() + C[n-k:n, 0:k].sum()\n",
    "        inter = inter_sum / inter_count\n",
    "\n",
    "        ratios[k] = intra / inter\n",
    "    return ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The code below were added to compute_saddle function in compute_saddle.py script in cooltools \n",
    "if strength:\n",
    "    ratios = saddle.saddle_strength(S, C)\n",
    "    ratios = ratios[1:-1]  # drop outlier bins\n",
    "    to_save[\"saddle_strength\"] = ratios\n",
    "    ratios_lowright=saddle.saddle_strength_lowerright(S, C)\n",
    "    ratios_upleft=saddle.saddle_strength_upperleft(S, C)\n",
    "    ratios_mid=saddle.saddle_strength_middle(S, C)\n",
    "    ratios_all=[ratios_upleft[10],ratios_mid[10],ratios_lowright[10]]\n",
    "    ratios_all_dict[\"ratios\"]=ratios_all\n",
    "\n",
    "# Save data\n",
    "np.savez(out_prefix + \".saddledump_corners\",  # .npz auto-added\n",
    "    **ratios_all_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4DNFI82R42AD_H1-hESC_FA_DSG_DpnII_Hi-C_quantiles.saddledump_corners.npz\n",
      "[2.4651427359502684, 1.8203798893059846, 3.1942461845231693]\n",
      "4DNFI9GMP2J8_H1-hESC_MicroC_quantiles.saddledump_corners.npz\n",
      "[2.1188227939999105, 1.6404796973761204, 2.733246937281261]\n",
      "4DNFICOGAKW2_H1-hESC_PLAC-Seq_quantiles.saddledump_corners.npz\n",
      "[2.534833506968519, 1.8739944470968048, 3.0440612567332717]\n",
      "4DNFIF1J6GC5_H1-hESC_ChIA_PET_PolII_quantiles.saddledump_corners.npz\n",
      "[2.8024984605658405, 1.834832486063365, 3.5252391501496696]\n",
      "4DNFIK3276U7_H1-hESC_ChIA_PET_CTCF_quantiles.saddledump_corners.npz\n",
      "[2.297427224107463, 1.7011362854583791, 2.9654461378697112]\n",
      "4DNFIV3PDS5F_4DNFIIY1TXUZ_H1-hESC_DNA_clusters_R1_R2_2-100_quantiles.saddledump_corners.npz\n",
      "[3.7451834419147443, 2.1160561690711654, 4.1167376657599775]\n",
      "H1hESC.GAM.NPMI.normalized.pairwise.curated.matrices.at100Kb_new.saddledump.npz\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name_list=['Hi-C', 'MicroC', 'PLAC-Seq','ChIA PET PolII','ChIA PET CTCF' ,'SPRITE 2-100',\"GAM\"]\n",
    "\n",
    "c=['dodgerblue','g','orange','cyan','pink','r','k']\n",
    "\n",
    "cell_line=\"ESC\"\n",
    "i=0\n",
    "names_list=[]\n",
    "BB_list=[]\n",
    "AA_list=[]\n",
    "#name_list=[]\n",
    "os.chdir(\"/npz_files\")\n",
    "plt.clf()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "\n",
    "\n",
    "for file in sorted(glob.glob(\"*\"+cell_line+\"*.npz\")):\n",
    "    print(file)\n",
    "    file_name = np.load(file)\n",
    "    name=file.split('.')[0]\n",
    "    name=name.split('1-')[1]\n",
    "    strength=list(file_name[\"ratios\"])\n",
    "    print(strength)\n",
    "    BB_tmp=strength[0]\n",
    "    AA_tmp=strength[2]\n",
    "    BB_list.append(BB_tmp)\n",
    "    AA_list.append(AA_tmp)\n",
    "for i in range(len(c)):\n",
    "    ax.scatter(AA_list[i],BB_list[i],color=c[i], s=90, marker='o',label=name_list[i])\n",
    "ax.set_xlabel(\"Compartment Strength, AA\",fontsize=15)\n",
    "ax.set_ylabel(\"Compartment Strength, BB\",fontsize=15)\n",
    "ax.set_aspect('equal', 'box')\n",
    "#ax.axis('equal')\n",
    "plt.setp(ax.get_xticklabels(), fontsize=10, horizontalalignment=\"right\")\n",
    "plt.setp(ax.get_yticklabels(), fontsize=10, horizontalalignment=\"right\")\n",
    "ax.set_xlim([0,12])\n",
    "ax.set_ylim([0,12])\n",
    "ax.legend()\n",
    "BB_list\n",
    "plt.savefig(out_path+\"Figure_2c_H1-hESC.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cell_line=\"HFFc6\"\n",
    "i=0\n",
    "names_list=[]\n",
    "BB_list=[]\n",
    "AA_list=[]\n",
    "name_list=[]\n",
    "os.chdir(\"/npz_files\")\n",
    "for file in sorted(glob.glob(\"*\"+cell_line+\"*.npz\")):\n",
    "\tprint(file)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "4DNFI9FVHJZQ_HFFc6_MicroC_quantiles.saddledump_corners.npz\n",
      "4DNFI9FVHJZQ_HFFc6_MicroC\n",
      "[3.6651209608412567, 1.8949718873885697, 2.9537934139192332]\n",
      "4DNFI9REIU8H_HFFc6_PLAC-Seq_quantiles.saddledump_corners.npz\n",
      "4DNFI9REIU8H_HFFc6_PLAC-Seq\n",
      "[5.16803646355291, 2.828615312484335, 6.050740833886499]\n",
      "4DNFIAVXXO55_HFFc6_FA_DSG_DpnII_Hi-C_quantiles.saddledump_corners.npz\n",
      "4DNFIAVXXO55_HFFc6_FA_DSG_DpnII_Hi-C\n",
      "[9.214975682803948, 3.733997998375283, 8.154035215984816]\n",
      "4DNFIELOAD41_HFFc6_ChIA_PET_CTCF_quantiles.saddledump_corners.npz\n",
      "4DNFIELOAD41_HFFc6_ChIA_PET_CTCF\n",
      "[6.408928028374868, 2.8146476145593766, 5.603225584885729]\n",
      "4DNFIOOSHTQV_HFFc6_ChIA_PET_PolII_quantiles.saddledump_corners.npz\n",
      "4DNFIOOSHTQV_HFFc6_ChIA_PET_PolII\n",
      "[3.3430090894768143, 1.709620972820173, 2.661247512130866]\n",
      "4DNFIRXON7Z2_HFFc6_DNA_clusters_R1_R2_2-100_quantiles.saddledump_corners.npz\n",
      "4DNFIRXON7Z2_HFFc6_DNA_clusters_R1_R2_2-100\n",
      "[9.626664841869466, 3.9679132920839306, 5.0035251650669075]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name_list=[ 'MicroC', 'PLAC-Seq','Hi-C','ChIA-PET CTCF', 'ChIA-PET PolII','SPRITE 2-100']\n",
    "\n",
    "c=['g','orange','dodgerblue','pink','cyan','r']\n",
    "\n",
    "\n",
    "cell_line=\"HFFc6\"\n",
    "print(len(name_list))\n",
    "i=0\n",
    "names_list=[]\n",
    "BB_list=[]\n",
    "AA_list=[]\n",
    "#name_list=[]\n",
    "os.chdir(\"npz_files\")\n",
    "plt.clf()\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "for file in sorted(glob.glob(\"*\"+cell_line+\"*.npz\")):\n",
    "\tprint(file)\n",
    "\tfile_name = np.load(file)\n",
    "\tname=file.split('.')[0]\n",
    "\tname=name.split('_q')[0]\n",
    "\tstrength=list(file_name[\"ratios\"])\n",
    "\tprint(strength)\n",
    "\tBB_tmp=strength[0]\n",
    "\tAA_tmp=strength[2]\n",
    "\tBB_list.append(BB_tmp)\n",
    "\tAA_list.append(AA_tmp)\n",
    "m=[\"o\",\"o\",\"o\",\"o\",\"v\",\"v\",\"v\",\"v\",\"s\",\"s\",\"s\",\"s\"]\n",
    "for i in range(len(c)):\n",
    "    ax.scatter(AA_list[i],BB_list[i],color=c[i], s=90, marker='o',label=name_list[i])\n",
    "ax.set_xlabel(\"Compartment Strength, AA\",fontsize=15)\n",
    "ax.set_ylabel(\"Compartment Strength, BB\",fontsize=15)\n",
    "ax.set_aspect('equal', 'box')\n",
    "plt.setp(ax.get_xticklabels(), fontsize=10, horizontalalignment=\"right\")\n",
    "plt.setp(ax.get_yticklabels(), fontsize=10, horizontalalignment=\"right\")\n",
    "ax.set_xlim([0,12])\n",
    "ax.set_ylim([0,12])\n",
    "ax.legend()\n",
    "plt.savefig(out_path+\"Figure_2c_HFFc6.pdf\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 2d - Insulation Score correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "ins_files=[\n",
    "\"4DNFI9FVHJZQ_HFFc6_MicroC.25kb.cis.bed\",\n",
    "\"4DNFI9GMP2J8_H1-hESC_MicroC.25kb.cis.bed\",\n",
    "\"4DNFI9REIU8H_HFFc6_PLAC-Seq.25kb.cis.bed\",\n",
    "\"4DNFI82R42AD_H1-hESC_FA_DSG_DpnII_Hi-C.25kb.cis.bed\",\n",
    "\"4DNFIAVXXO55_HFFc6_FA_DSG_DpnII_Hi-C.25kb.cis.bed\",\n",
    "\"4DNFICOGAKW2_H1-hESC_PLAC-Seq.25kb.cis.bed\",\n",
    "\"4DNFIELOAD41_HFFc6_ChIA_PET_CTCF.25kb.cis.bed\",\n",
    "\"4DNFIF1J6GC5_H1-hESC_ChIA_PET_PolII.25kb.cis.bed\",\n",
    "\"4DNFIK3276U7_H1-hESC_ChIA_PET_CTCF.25kb.cis.bed\",\n",
    "\"4DNFIOOSHTQV_HFFc6_ChIA_PET_PolII.25kb.cis.bed\",\n",
    "\"4DNFIRXON7Z2_HFFc6_DNA_clusters_R1_R2_2-100.25kb.cis.bed\",\n",
    "\"4DNFIV3PDS5F_4DNFIIY1TXUZ_H1-hESC_DNA_clusters_R1_R2_2-100.25kb.cis.bed\",\n",
    "\"GAM_H1_new.IS.scores.at25Kb_formatted.bed\"]\n",
    "\n",
    "\n",
    "names = [\n",
    "\"HFFc6 MicroC\",\n",
    "\"H1-hESC MicroC\",\n",
    "\"HFFc6 PLAC-Seq\",\n",
    "\"H1-hESC Hi-C\",\n",
    "\"HFFc6 Hi-C\",\n",
    "\"H1-hESC PLAC-Seq\",\n",
    "\"HFFc6 ChIA-PET CTCF\",\n",
    "\"H1-hESC ChIA-PET PolII\",\n",
    "\"H1-hESC ChIA-PET CTCF\",\n",
    "\"HFFc6 ChIA-PET_PolII\",\n",
    "\"HFFc6 SPRITE\",\n",
    "\"H1-hESC SPRITE\",\n",
    "\"H1-hESC GAM\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4DNFI9FVHJZQ_HFFc6_MicroC.25kb.cis.bed\n",
      "4DNFI9GMP2J8_H1-hESC_MicroC.25kb.cis.bed\n",
      "4DNFI9REIU8H_HFFc6_PLAC-Seq.25kb.cis.bed\n",
      "4DNFI82R42AD_H1-hESC_FA_DSG_DpnII_Hi-C.25kb.cis.bed\n",
      "4DNFIAVXXO55_HFFc6_FA_DSG_DpnII_Hi-C.25kb.cis.bed\n",
      "4DNFICOGAKW2_H1-hESC_PLAC-Seq.25kb.cis.bed\n",
      "4DNFIELOAD41_HFFc6_ChIA_PET_CTCF.25kb.cis.bed\n",
      "4DNFIF1J6GC5_H1-hESC_ChIA_PET_PolII.25kb.cis.bed\n",
      "4DNFIK3276U7_H1-hESC_ChIA_PET_CTCF.25kb.cis.bed\n",
      "4DNFIOOSHTQV_HFFc6_ChIA_PET_PolII.25kb.cis.bed\n",
      "4DNFIRXON7Z2_HFFc6_DNA_clusters_R1_R2_2-100.25kb.cis.bed\n",
      "4DNFIV3PDS5F_4DNFIIY1TXUZ_H1-hESC_DNA_clusters_R1_R2_2-100.25kb.cis.bed\n",
      "GAM_H1_new.IS.scores.at25Kb_formatted.bed\n",
      "   HFFc6 MicroC  H1-hESC MicroC  HFFc6 PLAC-Seq  H1-hESC Hi-C  HFFc6 Hi-C  \\\n",
      "0           NaN             NaN             NaN           NaN         NaN   \n",
      "1           NaN             NaN             NaN           NaN         NaN   \n",
      "2           NaN             NaN             NaN           NaN         NaN   \n",
      "3           NaN             NaN             NaN           NaN         NaN   \n",
      "4           NaN             NaN             NaN           NaN         NaN   \n",
      "\n",
      "   H1-hESC PLAC-Seq  HFFc6 ChIA-PET CTCF  H1-hESC ChIA-PET PolII  \\\n",
      "0               NaN                  NaN                     NaN   \n",
      "1               NaN                  NaN                     NaN   \n",
      "2               NaN                  NaN                     NaN   \n",
      "3               NaN                  NaN                     NaN   \n",
      "4               NaN                  NaN                     NaN   \n",
      "\n",
      "   H1-hESC ChIA-PET CTCF  HFFc6 ChIA-PET_PolII  HFFc6 SPRITE  H1-hESC SPRITE  \\\n",
      "0                    NaN                   NaN           NaN             NaN   \n",
      "1                    NaN                   NaN           NaN             NaN   \n",
      "2                    NaN                   NaN           NaN             NaN   \n",
      "3                    NaN                   NaN           NaN             NaN   \n",
      "4                    NaN                   NaN           NaN             NaN   \n",
      "\n",
      "   H1-hESC GAM  \n",
      "0          NaN  \n",
      "1          NaN  \n",
      "2          NaN  \n",
      "3          NaN  \n",
      "4          NaN  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i=0\n",
    "ins_path=\"insulation/\"\n",
    "ins_data=pd.DataFrame()\n",
    "file_list=[]\n",
    "for file_name in ins_files:\n",
    "    if file_name == \"4DNFI9FVHJZQ_HFFc6_MicroC.25kb.cis.bed\":\n",
    "        print(file_name)\n",
    "        file=pd.read_csv(ins_path+file_name,sep=\"\\t\")\n",
    "        \n",
    "        ins_data=file\n",
    "        ins_data=ins_data[ins_data.loc[:,\"chrom\"]!=\"chrY\"]\n",
    "        ins_data=ins_data[ins_data.loc[:,\"chrom\"]!=\"chrX\"]\n",
    "        ins_data[\"combined\"]=ins_data[\"chrom\"]+ins_data[\"start\"].astype(str)+ins_data[\"end\"].astype(str)\n",
    "        ins_data=ins_data[[\"combined\",\"log2_insulation_score_100000\"]]\n",
    "    else:\n",
    "        print(file_name)\n",
    "        file=pd.read_csv(ins_path+file_name,sep=\"\\t\")\n",
    "        file=file[file.loc[:,\"chrom\"]!=\"chrY\"]\n",
    "        file=file[file.loc[:,\"chrom\"]!=\"chrX\"]\n",
    "        file[\"combined\"]=file[\"chrom\"]+file[\"start\"].astype(str)+file[\"end\"].astype(str)\n",
    "        file=file[[\"combined\",\"log2_insulation_score_100000\"]]\n",
    "\n",
    "        ins_data=pd.merge(ins_data,file,on=\"combined\")\n",
    "\n",
    "ins_data=ins_data.iloc[:,1:]        \n",
    "ins_data.columns=names\n",
    "print(ins_data.head(5))\n",
    "\n",
    "ins_corr=ins_data.corr(method='pearson')\n",
    "\n",
    "\n",
    "import numpy as np; np.random.seed(0)\n",
    "import seaborn as sns;\n",
    "fig, (ax) = plt.subplots(1, 1)\n",
    "sns.set(font_scale=1.3)\n",
    "ax = sns.clustermap(ins_corr,cmap='Reds',annot=False,vmin=0, vmax=1,annot_kws={'size': 12})\n",
    "plt.savefig(out_path+\"Figure_2d_insulation_corr_25kb.pdf\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 2e - Insulation Pileup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Needs to run the script below in high performance computing cluster or aws\n",
    "python Figure_2e_insulation_pileup_H1-hESC_HFFc6.py\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 2g - Compute enrichment of 1D datasets in all 3D methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bin 1D datasets into 50kb bins \n",
    "\n",
    "%% bash\n",
    "\n",
    "bash Fig2g_H1-hESC_HFFc6_bin_bed_files.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take E/L ratio for Early and Late replication\n",
    "\n",
    "## H1-hESC\n",
    "\n",
    "E_file=\"H1-hESC_E_L_counts_4DNFI4BSJRMF.binned.50kb.bed\"\n",
    "L_file=\"H1-hESC_E_L_counts_4DNFIISI1ZA8.binned.50kb.bed\"\n",
    "\n",
    "\n",
    "E_data=pd.read_csv(E_file,sep=\"\\t\")\n",
    "L_data=pd.read_csv(L_file,sep=\"\\t\")\n",
    "\n",
    "E_data.iloc[:,3] = E_data.iloc[:,3].fillna(0)\n",
    "L_data.iloc[:,3] = L_data.iloc[:,3].fillna(0)\n",
    "\n",
    "E_data.iloc[:,3]=pd.to_numeric(E_data.iloc[:,3],errors='coerce')\n",
    "\n",
    "L_data.iloc[:,3]=pd.to_numeric(L_data.iloc[:,3],errors='coerce')\n",
    "\n",
    "E_L_divison=[E_data.iloc[i,3]/L_data.iloc[i,3] for i in range(E_data.shape[0])]\n",
    "\n",
    "E_L_data=pd.concat([E_data.iloc[:,[0,1,2]],pd.DataFrame(E_L_divison)],axis=1)\n",
    "print(E_L_data.head(5))\n",
    "E_L_data.columns=[\"chrom\",\"start\",\"end\",\"E1\"]\n",
    "print(E_L_data.head(5))\n",
    "\n",
    "E_L_data.to_csv(\"H1-hESC_E_L_counts_4DNFI4BSJRMF_4DNFIISI1ZA8.binned.50kb.bed\",index= None,sep=\"\\t\")\n",
    "\n",
    "\n",
    "## HFFc6\n",
    "\n",
    "E_file=\"HFFc6_E_L_counts_4DNFI7EOU166.binned.50kb.bed\"\n",
    "L_file=\"HFFc6_E_L_counts_4DNFI9FTS684.binned.50kb.bed\"\n",
    "\n",
    "\n",
    "\n",
    "E_data=pd.read_csv(E_file,sep=\"\\t\")\n",
    "L_data=pd.read_csv(L_file,sep=\"\\t\")\n",
    "\n",
    "E_data.iloc[:,3] = E_data.iloc[:,3].fillna(0)\n",
    "L_data.iloc[:,3] = L_data.iloc[:,3].fillna(0)\n",
    "\n",
    "E_data.iloc[:,3]=pd.to_numeric(E_data.iloc[:,3],errors='coerce')\n",
    "\n",
    "L_data.iloc[:,3]=pd.to_numeric(L_data.iloc[:,3],errors='coerce')\n",
    "\n",
    "E_L_divison=[E_data.iloc[i,3]/L_data.iloc[i,3] for i in range(E_data.shape[0])]\n",
    "\n",
    "E_L_data=pd.concat([E_data.iloc[:,[0,1,2]],pd.DataFrame(E_L_divison)],axis=1)\n",
    "print(E_L_data.head(5))\n",
    "E_L_data.columns=[\"chrom\",\"start\",\"end\",\"E1\"]\n",
    "E_L_data.to_csv(\"HFFc6_E_L_counts_4DNFI7EOU166_4DNFI9FTS684.binned.50kb.bed\",index= None,sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the min and max for 1D data to specify --range parameter in saddle plot\n",
    "\n",
    "path='oneD/'\n",
    "comp_bed=[\n",
    "\"H1-hESC_DamID-seq_normalized_counts_4DNFIXNBG8L1_2.binned.bed\",\n",
    "\"H1-hESC_E_L_counts_4DNFI4BSJRMF_4DNFIISI1ZA8.binned.50kb.bed\",\n",
    "\"H1-hESC_TSA-seq_normalized_counts_4DNFI625PP2A_2_binned.bed\",\n",
    "\"H1-hESC_TSA-seq_normalized_counts_4DNFICLC27JC_2_binned.bed\",\n",
    "\"H1-hESC_TSA-seq_normalized_counts_4DNFIFNIK4HD_2_binned.bed\",\n",
    "\"H1-hESC_TSA-seq_normalized_counts_4DNFILHYV7Q7_2_binned.bed\",\n",
    "\"H1-hESC_TSA-seq_normalized_counts_4DNFILYDJU8T_2_binned.bed\"]\n",
    "for file in comp_bed: \n",
    "    data=pd.read_csv(path+file,sep=\"\\t\")\n",
    "    print(file)\n",
    "    \n",
    "    data=data[data['E1'].notnull()]\n",
    "    val=data[\"E1\"]\n",
    "    \n",
    "    print(np.nanmin(val[val != -np.inf]))\n",
    "    print(np.nanmax(val[val != np.inf]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the min and max for 1D data to specify --range parameter in saddle plot\n",
    "\n",
    "path='oneD/'\n",
    "comp_bed=[\n",
    "\"HFFc6_DamID-seq_normalized_counts_4DNFI7724Y7Q_2.binned.50kb.bed\",\n",
    "\"HFFc6_E_L_counts_4DNFI7EOU166_4DNFI9FTS684.binned.50kb.bed\",\n",
    "\"HFFc6_TSA-seq_normalized_counts_4DNFI6FTPH5V_2.binned.50kb.bed\",\n",
    "\"HFFc6_TSA-seq_normalized_counts_4DNFI8CJWFUT_2.binned.50kb.bed\",\n",
    "\"HFFc6_TSA-seq_normalized_counts_4DNFIEWPL92Y_2.binned.50kb.bed\",\n",
    "\"HFFc6_TSA-seq_normalized_counts_4DNFIHYF6H13_2.binned.50kb.bed\",\n",
    "\"HFFc6_TSA-seq_normalized_counts_4DNFIMTKDNJW_2.binned.50kb.bed\"]\n",
    "\n",
    "for file in comp_bed: \n",
    "    data=pd.read_csv(path+file,sep=\"\\t\")#,dtype={\"E1\": np.float64})#,header=None)\n",
    "    print(file)\n",
    "    filename=file.split('.')[0]\n",
    "    data.columns=['chrom', 'start', 'end', 'E1']\n",
    "    print(data.head(4))\n",
    "    data.to_csv(filename+'_50kb.bed',sep=\"\\t\")\n",
    "    \n",
    "    data=data[data['E1'].notnull()]\n",
    "    data=data[data['E1']!=\"NAN\"]\n",
    "    val=data[\"E1\"]\n",
    "    \n",
    "    print(np.nanmax(val[val != np.inf]))\n",
    "    print(np.nanmin(val[val != -np.inf]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute saddle plots using 1D data for sorting\n",
    "\n",
    "%% bash\n",
    "\n",
    "bash Fig2g_H1-hESC_HFFc6_saddle_plots.sh\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot saddle plot quantifications\n",
    "# Used 2nd and 98th percentile for all files \n",
    "# H1-hESC\n",
    "\n",
    "name_list=[\n",
    "           'FA_DSG_DpnII_Hi-C',\n",
    "           'MicroC',\n",
    "           'PLAC-Seq',\n",
    "           'ChIA_PET_PolII',\n",
    "           'ChIA_PET_CTCF',\n",
    "            '2-100',\n",
    "            'GAM',]\n",
    "\n",
    "name_list_final=[\n",
    "           'Hi-C',\n",
    "           'Micro-C',\n",
    "           'PLAC-Seq',\n",
    "           'ChIA-PET PolII',\n",
    "           'ChIA-PET CTCF',\n",
    "           'SPRITE',\n",
    "            'GAM']\n",
    "\n",
    "\n",
    "filenames=[ 'DamID-seq',\n",
    "    'H1-hESC_E_L_counts',\n",
    "    '4DNFI625PP2A',\n",
    "     '4DNFICLC27JC',  \n",
    "    \"4DNFIFNIK4HD\",\n",
    "    '4DNFILYDJU8T',\n",
    "    \n",
    "    ]\n",
    "\n",
    "filenames_tmp=[ 'DamID-seq',\n",
    "    'E/L_RT',\n",
    "    'SON',\n",
    "    'POLR1E', \n",
    "    'NIFK'   ,        \n",
    "    'LMNB1']\n",
    "\n",
    "filenames_final=[ 'DamID-seq',\n",
    "    'E/L RT',\n",
    "    'SON (TSA-Seq)',\n",
    "    'POLR1E (TSA-Seq)',\n",
    "    'NIFK (TSA-Seq)' ,     \n",
    "    'LMNB1 (TSA-Seq)',\n",
    "    ]\n",
    "\n",
    "c=['dodgerblue','g','orange','cyan','pink','r','k',]\n",
    "\n",
    "total_list_temp=[]\n",
    "for k in range(7):\n",
    "    cell_line=\"ESC\"\n",
    "    #i=0\n",
    "    #filename=[]\n",
    "    BB_list=[]\n",
    "    AA_list=[]\n",
    "    #name_list=[]\n",
    "    os.chdir(\"/Users/betulakgoloksuz/Desktop/gam_new/oneD/npz_July2024_final_normalized_mod/\")\n",
    "    for file in sorted(glob.glob(\"*\"+cell_line+\"*\"+name_list[k]+\"*saddledump_corners.npz\")):\n",
    "        if \"4DNFILHYV7Q7\" in file:\n",
    "            continue;\n",
    "\n",
    "\n",
    "        else:\n",
    "            print(file)\n",
    "            file_name = np.load(file)\n",
    "            name=file.split('.')[0]\n",
    "           # name=name.split('_q')[0]\n",
    "            strength=list(file_name[\"ratios\"])\n",
    "            print(strength)\n",
    "            BB_tmp=strength[0]\n",
    "            AA_tmp=strength[2]\n",
    "            print(AA_tmp)\n",
    "            BB_list.append(BB_tmp)\n",
    "            AA_list.append(AA_tmp)\n",
    "\n",
    "    print(AA_list)\n",
    "    total_list_temp.append(AA_list)\n",
    "\n",
    "sns.set(font_scale=2)\n",
    "total_list=pd.DataFrame()\n",
    "total_list=pd.DataFrame(total_list_temp)\n",
    "total_list=total_list.fillna(0)\n",
    "total_list.replace([np.inf, -np.inf], 0, inplace=True)\n",
    "\n",
    "print(total_list)\n",
    "\n",
    "total_list.columns=filenames_final\n",
    "total_list.index=name_list_final\n",
    "\n",
    "total_list=total_list.transpose()\n",
    "ax = sns.clustermap(np.round(total_list,2),vmin=0,vmax=9,cmap=\"Oranges\",annot=True,row_cluster=False,col_cluster=False,fmt='g',annot_kws={\"size\": 15})\n",
    "\n",
    "plt.setp(ax.ax_heatmap.get_yticklabels(), rotation=0)  # For y axis\n",
    "plt.setp(ax.ax_heatmap.get_xticklabels(), rotation=90) # For x axis\n",
    "plt.savefig(out_path+\"Figure2g_H1-hESC_August2024.pdf\",bbox_inches='tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot saddle plot quantifications\n",
    "# Used 2nd and 98th percentile for all files \n",
    "# HFFc6\n",
    "\n",
    "name_list=[\n",
    "           'FA_DSG_DpnII_Hi-C',\n",
    "           'MicroC',\n",
    "           'PLAC-Seq',\n",
    "           'ChIA_PET_PolII',\n",
    "           'ChIA_PET_CTCF',\n",
    "            '2-100']\n",
    "\n",
    "name_list_final=[\n",
    "           'Hi-C',\n",
    "           'Micro-C',\n",
    "           'PLAC-Seq',\n",
    "           'ChIA-PET PolII',\n",
    "           'ChIA-PET CTCF',\n",
    "           'SPRITE']\n",
    "\n",
    "\n",
    "gs = plt.GridSpec(nrows=1, ncols=1)  \n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "\n",
    "filenames=[ 'DamID-seq',\n",
    "    'HFFc6_E_L_counts',\n",
    "    '4DNFI6FTPH5V',\n",
    "    '4DNFIHYF6H13',\n",
    "    '4DNFIMTKDNJW',     \n",
    "    '4DNFIEWPL92Y',\n",
    "    '4DNFI8CJWFUT',\n",
    "    ]\n",
    "\n",
    "filenames_tmp=[ 'DamID-seq',\n",
    "    'E/L_RT',\n",
    "    'SON',\n",
    "    'POLR1E',\n",
    "    'NIFK' ,     \n",
    "    'LMNB1',\n",
    "    'CENPB',\n",
    "    ]\n",
    "\n",
    "filenames_final=[ 'DamID-seq',\n",
    "    'E/L RT',\n",
    "    'SON (TSA-Seq)',\n",
    "    'POLR1E (TSA-Seq)',\n",
    "    'NIFK (TSA-Seq)' ,     \n",
    "    'LMNB1 (TSA-Seq)',\n",
    "    ]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "c=['dodgerblue','g','orange','cyan','pink','r','k',]\n",
    "\n",
    "total_list_temp=[]\n",
    "for k in range(6):\n",
    "    cell_line=\"HFFc6\"\n",
    "    #i=0\n",
    "    #filename=[]\n",
    "    BB_list=[]\n",
    "    AA_list=[]\n",
    "    #name_list=[]\n",
    "    os.chdir(\"/Users/betulakgoloksuz/Desktop/gam_new/oneD/npz_July2024_final_normalized_mod/\")\n",
    "    for file in sorted(glob.glob(\"*\"+cell_line+\"*\"+name_list[k]+\"*.saddledump_corners.npz\")):\n",
    "        if \"4DNFI8CJWFUT\" in file:\n",
    "            continue;\n",
    "        else:\n",
    "            print(file)\n",
    "            file_name = np.load(file)\n",
    "            name=file.split('.')[0]\n",
    "           # name=name.split('_q')[0]\n",
    "            strength=list(file_name[\"ratios\"])\n",
    "            print(strength)\n",
    "            BB_tmp=strength[0]\n",
    "            AA_tmp=strength[2]\n",
    "            BB_list.append(BB_tmp)\n",
    "            AA_list.append(AA_tmp)\n",
    "            print(AA_list)\n",
    "    total_list_temp.append(AA_list)\n",
    "\n",
    "sns.set(font_scale=2)\n",
    "total_list=pd.DataFrame()\n",
    "total_list=pd.DataFrame(total_list_temp)\n",
    "total_list=total_list.fillna(0)\n",
    "print(total_list)\n",
    "total_list.columns=filenames_final\n",
    "total_list.index=name_list_final\n",
    "\n",
    "total_list=total_list.transpose()\n",
    "\n",
    "\n",
    "ax = sns.clustermap(np.round(total_list,2),vmin=0,vmax=4,cmap=\"Oranges\",annot=True,row_cluster=False,col_cluster=False,fmt='g',annot_kws={\"size\": 15})\n",
    "plt.setp(ax.ax_heatmap.get_yticklabels(), rotation=0)  # For y axis\n",
    "plt.setp(ax.ax_heatmap.get_xticklabels(), rotation=90) # For x axis\n",
    "\n",
    "plt.savefig(out_path+\"Figure2g_HFFc6_August2024.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplemental Figure 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplemental Figure 1c - HicRep Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file=pd.read_csv(\"hicrep_matrix_for_fig.txt\",sep=\"\\t\")\n",
    "pivoted = file.pivot('file1', 'file2')\n",
    "pivoted = np.triu(pivoted)\n",
    "pivoted = pivoted + pivoted.T - np.diag(np.diag(pivoted))\n",
    "#pivoted.columns=[]\n",
    "data_new=pd.DataFrame(pivoted)\n",
    "\n",
    "names = [\"H1-hESC Hi-C\",\n",
    "         \"HFFc6 MicroC\",\n",
    "         \"H1-hESC MicroC\",\n",
    "        \"HFFc6 PLAC-Seq\",      \n",
    "        \"HFFc6 Hi-C\",\n",
    "        \"H1-hESC PLAC-Seq\",      \n",
    "        \"HFFc6 ChIA-PET CTCF\",\n",
    "        \"H1-hESC ChIA-PET PolII\",\n",
    "        \"H1-hESC ChIA-PET CTCF\",       \n",
    "        \"HFFc6 ChIA-PET PolII\",\n",
    "        \"HFFc6 SPRITE\",      \n",
    "        \"H1-hESC SPRITE\",\n",
    "        \"Hi-hESC GAM\"]\n",
    "\n",
    "data_new.index=names\n",
    "data_new.columns=names\n",
    "\n",
    "import numpy as np; np.random.seed(0)\n",
    "import seaborn as sns;\n",
    "fig, (ax) = plt.subplots(1, 1)\n",
    "sns.set(font_scale=1.3Supplemental)\n",
    "ax = sns.clustermap(data_new,cmap='Reds',annot=False,vmin=0, vmax=1,annot_kws={'size': 12})\n",
    "plt.savefig(out_path+\"Supplementary_Figure_2c_HicRep_50kb.pdf\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extended Data Figure 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extended Data Figure 1c, 1d - Compartment strength quantifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a mcool file for every cluster in SPRITE\n",
    "# Compute saddle plot for every cluster in SPRIRE data\n",
    "# Finally plot the scatter plot scores for every cluster\n",
    "\n",
    "c=['m','g','orange','b','r','pink','dodgerblue','k']\n",
    "\n",
    "name_list=[\"All\",\"1001-10000\",\"101-1000\",\"11-100\",\"2-10\",\"2-100\"]\n",
    "\n",
    "\n",
    "cell_line=\"ESC\"\n",
    "i=0\n",
    "names_list=[]\n",
    "BB_list=[]\n",
    "AA_list=[]\n",
    "#name_list=[]\n",
    "plt.clf()\n",
    "fig, (ax) = plt.subplots(1, 1)\n",
    "os.chdir(\"sprite\")\n",
    "for file in sorted(glob.glob(\"*\"+cell_line+\"*.ref.quantiles.saddledump_corners.npz\")):\n",
    "\tprint(file)\n",
    "\tfile_name = np.load(file)\n",
    "\tname=file.split('.')[0]\n",
    "\tname=name.split('1-')[1]\n",
    "\tstrength=list(file_name[\"ratios\"])\n",
    "\tprint(strength)\n",
    "\tBB_tmp=strength[0]\n",
    "\tAA_tmp=strength[2]\n",
    "\tBB_list.append(BB_tmp)\n",
    "\tAA_list.append(AA_tmp)\n",
    "for i in range(len(name_list)):\n",
    "\tax.scatter(AA_list[i],BB_list[i],color=c[i], s=70, marker='o')\n",
    "\tax.annotate(name_list[i], (AA_list[i],BB_list[i]),fontsize=10,ha='left',color=c[i])\n",
    "\n",
    "ax.set_xlabel(\"AA\",fontsize=15)\n",
    "ax.set_ylabel(\"BB\",fontsize=15)\n",
    "ax.set_aspect('equal', 'box')\n",
    "plt.setp(ax.get_xticklabels(), fontsize=10, horizontalalignment=\"right\")\n",
    "plt.setp(ax.get_yticklabels(), fontsize=10, horizontalalignment=\"right\")\n",
    "ax.set_xlim([1,6])\n",
    "ax.set_ylim([1,6])\n",
    "BB_list\n",
    "plt.savefig(\"H1-hESC_cis_250kb_AA_BB_all_clusters.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=['m','g','orange','b','r','pink','dodgerblue','k','cyan']\n",
    "\n",
    "name_list=[\"All\",\"1001-10000\",\"101-1000\",\"11-100\",\"2-10\",\"2-100\",\"2-2\"]\n",
    "\n",
    "cell_line=\"HFFc6\"\n",
    "print(len(name_list))\n",
    "i=0\n",
    "names_list=[]\n",
    "BB_list=[]\n",
    "AA_list=[]\n",
    "#name_list=[]\n",
    "plt.clf()\n",
    "fig, (ax) = plt.subplots(1, 1)\n",
    "os.chdir(\"/Users/betulakgoloksuz/Desktop/dekkerlab/Projects/flagship_paper/sprite\")\n",
    "for file in sorted(glob.glob(\"*\"+cell_line+\"*.ref.quantiles.saddledump_corners.npz\")):\n",
    "\tprint(file)\n",
    "\tfile_name = np.load(file)\n",
    "\tname=file.split('.')[0]\n",
    "\tname=name.split('_q')[0]\n",
    "\tstrength=list(file_name[\"ratios\"])\n",
    "\tprint(strength)\n",
    "\tBB_tmp=strength[0]\n",
    "\tAA_tmp=strength[2]\n",
    "\tBB_list.append(BB_tmp)\n",
    "\tAA_list.append(AA_tmp)\n",
    "m=[\"o\",\"o\",\"o\",\"o\",\"v\",\"v\",\"v\",\"v\",\"s\",\"s\",\"s\",\"s\"]\n",
    "#plt.clf()\n",
    "for i in range(len(names)):\n",
    "\tax.scatter(AA_list[i],BB_list[i],color=c[i], s=70, marker='o')\n",
    "\tax.annotate(name_list[i], (AA_list[i],BB_list[i]),fontsize=10,ha='left',color=c[i])\n",
    "\n",
    "ax.set_xlabel(\"AA\",fontsize=15)\n",
    "ax.set_ylabel(\"BB\",fontsize=15)\n",
    "ax.set_aspect('equal', 'box')\n",
    "plt.setp(ax.get_xticklabels(), fontsize=10, horizontalalignment=\"right\")\n",
    "plt.setp(ax.get_yticklabels(), fontsize=10, horizontalalignment=\"right\")\n",
    "ax.set_xlim([1,10])\n",
    "ax.set_ylim([1,10])\n",
    "plt.savefig(\"HFFc6_AA_BB_all_clusters.pdf\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extended Data Figure 1e - Compartment size cumulative plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4DNFI9GMP2J8_H1-hESC_MicroC.cis.100000.vecs.tsv\n",
      "(30894, 9)\n",
      "398\n",
      "4DNFI82R42AD_H1-hESC_FA_DSG_DpnII_Hi-C.cis.100000.vecs.tsv\n",
      "(30894, 9)\n",
      "401\n",
      "4DNFICOGAKW2_H1-hESC_PLAC-Seq.cis.100000.vecs.tsv\n",
      "(30894, 9)\n",
      "369\n",
      "4DNFIF1J6GC5_H1-hESC_ChIA_PET_PolII.cis.100000.vecs.tsv\n",
      "(30894, 9)\n",
      "684\n",
      "4DNFIK3276U7_H1-hESC_ChIA_PET_CTCF.cis.100000.vecs.tsv\n",
      "(30894, 9)\n",
      "427\n",
      "4DNFITX6WCRT_H1-hESC_DNA.cis.100000.vecs.tsv\n",
      "(30894, 9)\n",
      "444\n",
      "GAM_H1_new.ALL.AB.at100Kb.final.chosen_formatted.tsv\n",
      "(25668, 8)\n",
      "1204\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from random import sample\n",
    "\n",
    "os.chdir(\"compartments/\")\n",
    "\n",
    "\n",
    "gs = GridSpec(nrows=1, ncols=1)\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "names = [      \n",
    "\"MicroC\",\n",
    "\"Hi-C\",\n",
    "\"PLAC-Seq\",\n",
    "\"ChIA-PET PolII\",\n",
    "\"ChIA-PET CTCF\",\n",
    "\"SPRITE\",\n",
    "\"GAM\"]\n",
    "\n",
    "files=[    \n",
    "\"4DNFI9GMP2J8_H1-hESC_MicroC.cis.100000.vecs.tsv\",\n",
    "\"4DNFI82R42AD_H1-hESC_FA_DSG_DpnII_Hi-C.cis.100000.vecs.tsv\",\n",
    "\"4DNFICOGAKW2_H1-hESC_PLAC-Seq.cis.100000.vecs.tsv\",\n",
    "\"4DNFIF1J6GC5_H1-hESC_ChIA_PET_PolII.cis.100000.vecs.tsv\",\n",
    "\"4DNFIK3276U7_H1-hESC_ChIA_PET_CTCF.cis.100000.vecs.tsv\",\n",
    "\"4DNFITX6WCRT_H1-hESC_DNA.cis.100000.vecs.tsv\",\n",
    "\"GAM_H1_new.ALL.AB.at100Kb.final.chosen_formatted.tsv\"]\n",
    "  \n",
    " \n",
    "    \n",
    "\n",
    "name_list=['Hi-C', 'MicroC', 'PLAC-Seq','ChIA PET PolII','ChIA PET CTCF' ,'SPRITE 2-100',\"GAM\"]\n",
    "\n",
    "c=['g','dodgerblue','orange','cyan','pink','r','k']\n",
    "\n",
    "\n",
    "\n",
    "k=0\n",
    "ax = plt.subplot(gs[0])\n",
    "for file in files:\n",
    "    print(file)\n",
    "    data = pd.read_csv(file,sep=\"\\t\")\n",
    "    print(data.shape)\n",
    "    d=data\n",
    "    A_start=[]\n",
    "    A_end=[]\n",
    "    B_start=[]\n",
    "    B_end=[]\n",
    "    A_size=[]\n",
    "    B_size=[]\n",
    "    E1=d.columns.get_loc(\"E1\")\n",
    "    for i in range(len(d[\"E1\"])-1):\n",
    "        if (d.iloc[i,E1]>0) & (d.iloc[i+1,E1]>0):\n",
    "            A_val=d.iloc[i,E1]\n",
    "            A_start.append(d.iloc[i,1])\n",
    "            A_end.append(d.iloc[i,2])\n",
    "        if (d.iloc[i,E1]>0) & (d.iloc[i+1,E1]<0):\n",
    "            B_start=[]\n",
    "            B_end=[]\n",
    "            if(len(A_end))>0:\n",
    "                A_size.append(max(A_end)-min(A_start))\n",
    "            else:\n",
    "                A_size.append(d.iloc[i,2]-d.iloc[i,1])\n",
    "        if (d.iloc[i,E1]<0) & (d.iloc[i+1,E1]<0):\n",
    "            B_val=d.iloc[i,E1]\n",
    "            B_start.append(d.iloc[i,1])\n",
    "            B_end.append(d.iloc[i,2])\n",
    "        if (d.iloc[i,E1]<0) & (d.iloc[i+1,E1]>0):\n",
    "            A_start=[]\n",
    "            A_end=[]\n",
    "            if(len(B_end))>0:\n",
    "                B_size.append(max(B_end)-min(B_start))\n",
    "            else:\n",
    "                B_size.append(d.iloc[i,2]-d.iloc[i,1] )\n",
    "    bins=100\n",
    "    A_size=[x for x in A_size if 100000 < x <= 7000000]\n",
    "    print(len(A_size))\n",
    "    A_size_subset=sample(A_size,360)\n",
    "    n, bins, patches = ax.hist(A_size,bins=bins,density=True, histtype='step',color=c[k],\n",
    "                           cumulative=True, label=names[k],linewidth=1.8)\n",
    "    ax.grid(True)\n",
    "    ax.legend(loc='right')\n",
    "    ax.set_xlabel('Compartment size')\n",
    "    ax.set_ylabel('Density')\n",
    "    #plt.xscale('log')\n",
    "    k=k+1\n",
    "plt.savefig(out_path+\"Supp_Figure_2d_H1-hESC_comp_size_A.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4DNFI9GMP2J8_H1-hESC_MicroC.cis.100000.vecs.tsv\n",
      "(30894, 9)\n",
      "694\n",
      "431\n",
      "4DNFI82R42AD_H1-hESC_FA_DSG_DpnII_Hi-C.cis.100000.vecs.tsv\n",
      "(30894, 9)\n",
      "695\n",
      "462\n",
      "4DNFICOGAKW2_H1-hESC_PLAC-Seq.cis.100000.vecs.tsv\n",
      "(30894, 9)\n",
      "803\n",
      "352\n",
      "4DNFIF1J6GC5_H1-hESC_ChIA_PET_PolII.cis.100000.vecs.tsv\n",
      "(30894, 9)\n",
      "2029\n",
      "782\n",
      "4DNFIK3276U7_H1-hESC_ChIA_PET_CTCF.cis.100000.vecs.tsv\n",
      "(30894, 9)\n",
      "909\n",
      "464\n",
      "4DNFITX6WCRT_H1-hESC_DNA.cis.100000.vecs.tsv\n",
      "(30894, 9)\n",
      "1328\n",
      "526\n",
      "GAM_H1_new.ALL.AB.at100Kb.final.chosen_formatted.tsv\n",
      "(25668, 8)\n",
      "3224\n",
      "1096\n"
     ]
    },
    {
     "data": {
      "image/png": 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LVoCszunOquz2vPLD3wgXSmyc4VjLgGLTtaG2e1yueA1XrhViZsam0vsqeKcnc6mROzBgIyLyImc2D5xXq5DlgSnQquz2DBdKEKkulpVio7ysEn9cEf0QflNDu8dNzpjpcKenKQ+bcupjcjSNqooBGxGRFzm1ecD+wJNbVWV92iWjP9CmiUvnys3Qxp2eVNswYCNyo7I7QCtbQ8P0HFSe7M0D0e6fyrOXnsMRW9Ofro6uAcqvOEDkTQzYiNxIToF2pufwPXKmLV3l6Vxp5Rmv5AAA1OE6S5vc9By2pj8vGa9Pazo4tyZUHGBqDvIkBmxEbmbeAaq0NTTkOjnTlq7ydK608tThOviFhaHRqzNcuoet6U8505o1oeIAU3OQJzFgI5KJ1QhqB1ujab5cIN3ZtWhyk9cCwK4mppxorqoJ69C4mYA8hQEbkUyc7vR9cqY2L6oFAECssXT9YXWOgrmqugqty01eCwAqiJAkCVMyZjp1D8D31qHZy6VG5AkM2IicwIS3vk3O1GasUUS0KGFvzlUP9sx51VloPTJQXhUB/1/yIUquBbJKWYcmF3OpkbcxYCOiWsVXpzZtcUdpKN3psxBKSnd67mlqGmGMOGG/2gAAqFQByMU1n5/WlIvTn+RNDNiIyOe4umvTV8s5mac/zwgqiNf772h0rXwgZkvONRX8/UzHZJWYdnqKkvx1abnSNeSK1xAo73AiqgIGbETkc1zdtanEtWhymKc/BZ3O0uZoulMoKbHU3KyMCBUuFvvjstEPj/91o6U9IkDCxrsdTwlPvr52bTZukfEpiKgqGLAR2WBrRyh3gCpLTZralEMdFo5O7/2n0lQx5UfULAXSE5pVes3en5kKye/pW4A97WvPsyyLudTIVzBgI7LB1o5Q7gAlJSs/oib6qSH6Of8jXm61AaBm7PRUGy4AAIyaOLvncoMBeRsDNqJKcEco+RpHI2py2EpoW5masNPTqImDqI1Bzi0ZXuwZkWMM2KjWYQJcUhJH+dQEYwmM+fnwDw4BDv2ICBv1aQFYja7JTX6bXaiCVp2HyRmzLG3mYK2m7vzkTk/yVQzYqNZhAlxSEof51CTAPzgE/sHBNt/ONxRAgmkjRe61a5iZsQl/5b6CImMItOp8u/f2F0SoVdbTn74waiZr3ZlahQij9QYTrkMjX8aAjWolTneSktjLpxZx4iQAmKY6o0OhL7fpYHLGTJtTmFp1PlLiZ8ERU+1O3xpNk1vDszyuQyNfxoCNiBRFTo41X8+nVlbZ0TVbudMcpeYAKtbkNO/+rKnTmoDjqc1oG8EtkS9jwEY1Cten+T45OdaUmE9NVm3PKzkAAHW4ztJWNp+ardxpru729EVMsUFUudrxU4BqDa5Pqxl8McearNqe4Tr4hYWh0aszKj3GHTs9fZXcqU5ObVJtxICNahyuTyNvqWwtWoWpzuvr0sorO7pmKx+aIAgQy+0SzTg/AkYp3DINCgCXClWIDPTOCKTcUTJbzMEad3ESVcSAjXxa+SlQTndSdZA13WlndE1OmSjAevpTbj40oxSOImMoQlEaoEUGSogI8E7A5uqGAIAjZ0T2MGAjn1Z+CpTTnVQdZE13Oqjt6cpUZ/nNBNHRoRVKU/X+LBihkLCnr3KmkDlKRuR+DNjI53EK1HeZd4QKAMQI05SeUneA2ku9QURU3RiwEZHXmHeElq3i6O0doI5SbzjiSmoOW+vVfK1OJxFVLwZsRORVMaKE82oVshSyK9TW9Kej6c6yXEnNYWu9mi9UHCAiz2HARookJ58awE0G3iInua0cNXX607xercLI2Xnbx9f0+p1EVHWCtztAZIt5M4Ej3GTgHeapzKry9vRndTOPnDnC0TQicoQjbKRY3EygbG5Nbhtdc4MVjpwRkTswYCOiWkNuPjX/4GBL0XVnycm3Jsdju4Ogv2Y9iikIgCgGW7UpLUkuy0YRVQ8GbERUa8jNp+av1boceLlS+9NWcJZdaFqxEhUo2jrFQmlJcpn8lqh6MGAjolpFzoaCiBMnIQIeq+mpv6aqMFIWFSgiIkDCxruvWtpMiXOVsZvWjElyiTyDARspAktMka+rai61yEDlVCuQWw+U059EnsOAjRSBJaaUy1YKD6Wm4yirqglwnVWTcqnJrQfK6U8iz2HARorBXaHKZE7hUTZA84V0HFVNgOuKmrQjlFOdRMrCgI2qnZwkuJwCVTa3pvBwA7m7PcuvV7OUjXKwA7Q6d3ra4s2dnkTkGxiwUbUrP91pC6dAyRlyd3uWH02zVTbKFld2eh76dxQMYih6f1aadsMXdnoSkW9gwEYewelOcreyo2e2Cq5blBlNMwdrVd39aWuDwTXjZBSLISi7Ys3WTk8iIlcwYCMin1edI2e22NpgIKgEBKgLsKdvlS9PRFQBAzaiWkxOEXdP7wh1Zn1aWe4YOZPLPP0ZWiZgMxjN69CUs9ZPDlYrIPINDNiIajFbO0DL8/SOUFfXp7mDralOW2xNf/rqOjRWKyDyDQzYiGo5pe0ABeRVI6gOtqY6balp059M4UGkfAzYiGoJJSbANU9/nhFUEK/3ozqT28ohJ5da6U5QZQW6ZbFaAVHNwoCNqJZQYgJc8/SnoNNZ2uRMd5bfFequvGm2UnPY4gt501itgKhmYcBGVIsodfqz03v/QVaW47VjZuV3hbpr96dBDEWR0Xptmi2+sl6NU51ENQcDNqoSVjEgb6nKrtDKNheI0mRo1fnY09dxdQIiIk9iwEZVwioG1U9O6g05vL1ezRPk7vK8cv2Y8HKF2QWVABWUFaxZrUVTqxBhlPd3yLVpRDULAzaqMlYxqF5yUm/I4e31ap4gd5dnuCYUoZpQTOo02qpdiZsJ5K5FK49r04hqFgZsRD5AiWvPHHE1AW5VydnlaS7Knv6ZdbtSNxOY16JFR4dC78RaPyKqORiwESmIElNvuMqbCXAd0V9T2QzOfGUzARHVPgzYiBREiak3qsJbCXDliAyUsKevb41aElHtxYCNSGF8YfqzuqY7y+dXq4y78q4REfkKBmxE5DR3THdaBWcnBUSIItQlRgCA0UEwVlneNV9PfMtC7ERUGQZsROSSqk53lk9+C5gCNdHPDzlNbnDpmr6e+JaF2ImoMgzYSDZzklxBECCKIgAmxa0NbE1/umt3pzn5rTt3P/p64ltWJyAiWxiwkWzmJLkxMaX/tc+kuDWfrelPb+3uLMtWklxRmgxBJXipR0RE1YcBGzklKioKx44dc6ruI/k+Je72tJUkV4mVCoiI3IEBGxH5rPJJcpVYqYCIyB0YsBGRositB5pxfgSMUrjVjlBf2v1pC3eEElFluNiDiBTFPNXpiFEKR5HRej+o0nd/OsIdoURUGY6wEXmIrbJT5Xm6DJW36n06IqceaO/PghEK36lWwN2fRFQVHGEj8hBz2Sl7PF2GyrwD1B4l7AglIqrtOMJG5EFKLDulxB2gRERkzeMBW3FxMebMmYOdO3cCAB555BGMGTMGglBxsO/ixYuYMWMGvvnmGwQFBeGBBx7A6NGjoVazhqC7mZPi2sMkuSRHVeuB+np5KSKi6uDxgG3RokU4cOAAVq9ejYKCAkyYMAFhYWEYOnRohWNHjRqFiIgIbN68GRcvXsT48eMRGhpq81iqGnNSXHsBGZPkkhy2Sk7ZUlk9UF8vL8V6oERUHTwasBUVFWHTpk148803kZiYCAAYO3YsFi5ciCFDhkClKl3fc+XKFXz//ff45JNP0LRpUzRt2hT33XcfDh06xICtmkRFRSEtbb+3u0E1gLnklKt8ubwU64ESUXXwaMB2/PhxFBYWIikpydKWlJSErKwsnDt3Dg0aNLC0BwQEICgoCFu2bEGLFi2g1+uRnp6OO++805NdJnJJd10QLgEQI0qn9Ty9A5Tcy9lcatwRSkTu5NGA7eLFiwgJCUFwcOkvsejoaMt7ZQM2rVaL6dOnY/r06fjoo49gNBpx6623YtSoUZ7sMpFLsgQVMgGUHVPx9A5QT7C1Xk3OdKiZOUmuIAgQRRGAcuuB2ho5s4WjaURUHTwasBUWFkKr1Vq1aTQaAIDBYKhw/KlTp9C5c2c899xzyM7OxqxZs/DGG29gypQpTt03MjLE8UEAoqMdrZqpucybPuQ+g9r8rOQQAMQBOK8uF3ioASjo2Z25nmbE5b/PkyJQYgQ0/qVtGgFqjZ+saxaUFCDXkA9dQJjle1BQCVCpVMr7HlOrgMA4qPued3wogOhq6obinouC8VnJ5+5nJfd3ikf/jlRVuWduFc51D48GbAEBARUCM/PrgIAAq/bDhw9jw4YN+PrrrxESYgq4tFothg4diueee86p3YqXLuVDdDCyER0dWqsLmptHN+Q8g9r+rOQQI4IhqAVFPSdbSXLNSXHL99PZnZ76Zk0qvinjs4uiiDBNCFY9MNfSB/PuUCU9OwCIMJp+hui92C/+25OPz0q+6nhWcn+nePLvKOp6GJBdhXtWd38FQVXpIJNHA7a4uDjk5eWhsLAQgYGBAICsrCwAQGys9TTDzz//jAYNGliCNQBo1aoVjEYjzp8/z/QS5DVyKxbEeag/gMyKBVdyAADqcJ2lrbKkuFXd6enruNOTiJTGoz9pW7ZsicDAQBw9ehTJyckAgCNHjiAmJgbx8fFWx8bGxuLcuXO4du2aZfTt5MmTAID69et7sttEVswVC+xtIIgRJcSqPbfL0VyxwF4JKXW4Dn5hYWj06gyrdt3psxBOnLRqs4ycVWGnpxzmnGsd/wOIomlkTQn51bjTk4iUxuNToqmpqZgxYwbmzp2LoqIiLFy4EE8//TQAICcnB2q1GqGhoejevTsWLlyI8ePHY9SoUcjJycG0adPw0EMPISIiwpPdJqpATsWC6OhQZHmoP4DrFQtsjaZ5auTMnHOtLKXkV+NOTyJSEo/PZYwfPx5FRUUYPHgwtFotUlNTMWjQIADAiBEjEB8fj7lz5yIkJATr1q3DnDlzMGDAAAQFBaF3794YM2aMp7tMtYgSC7R7gidG0yqjVefju8FhyMqq/pJdzqbmICJSCo8HbFqtFjNnzsTMmTMrvLdhwwar1zfccANWrVrlqa4RyZ7u9GR6Dlnr02xMh1a1RFRVmNN1OOJqCg+5gVd5asMFAIBRY3+FIac/iUhpat5qYaIqUlqBdlnr02xsHvDmxoE8Qx5yDfkI09hPqSOoBKjg/Fo/uTnRyjNq4iBqY5BzS4bT9yQi8iYGbEQ+wNX1ad6c6gzThGB28lS7xzgq8G4P15gRUW3CgI2IFINrzIiIbFNe/RciqrXMU52OcI0ZEdU2HGGrBfr16wu9PtvuMdnZ2UxGrAD2qhHYU9WanlXx2O4g6K9Zr0PLM7wCwP6U5weBHbAr8CIEFYDPTJUEWDidiMg2Bmy1gF6f7TAgi4qKQkQEAzZ3+Wv6NJzJz3NYEq08Z6oRlOXNXGr6ayqXkt1Gqi4iUnUBl8vUhODIGRGRbQzYaomoqCikpe33djdqDTk7O22prBqBHN7cYBAZKGFP39KdtZMzZgGA3U0HEekSJMRCTPkDiA71ao1OIiKlY8BGtZatJLnuTIrrr9Oh8fzFVb6OnHxq1TH9aWuq0xYllJIiIqrpuOmAai1zktyyPJ0UVw7zdKc91TH9aZ7qdEQppaSIiGoyjrBRraa0JLmV8dZ0Z/mpTiIi8g6OsBEREREpHEfYiJwkt7anoNM5fW1vpeewtV5N7tq08nVDp15bh3lSPgSoEJq+odLzmPyWiEg+jrAROcm8A9QedVg4NC4EbLbWq3kiPYet9Wpy16aZ64aahUkF0OEqVA6KujOFBxGRfBxhoxrJ1g7Q8qqyI1RObc/o6FBkuZCqwhfXq5WtGxqavgESQmFIOQG9OztIRFSLMWCjGsm8A9ReQKbEHaHuJDctB8DUHERESseAjWosX9kBWl2cqUDA1BxERMrGgI2oDLkbCpytYOAtTMtBRFQzMGAjKkNOSSk5tT3Lk1OtAPBcwXZ30R1OxvzCUwBg2RHK3Z9ERO7HgI2oHDkbCpxlqzi7LZ4q2O4uQlEmwlCAXARb2rj7k4jI/XznNwORj/NmcfbqlItgjA98wW6hdyIiqhoGbEQ1QFUS39qiO5yMq/lnIEmi3eNUKICIIJfuQURE8jFxLlENUJXEt7YIRZkIlfIgwv75uQhGvioEoZpQl+5DRETycISNyM0sGwxOCogQTSNUnthM4O4dobkIxssypjoDAUxy212JiMgWBmw1TL9+faHXZ1u1ZWdnIyoqyks9qn0s5aU0pQPYvraZgIiIlIW/QWoYvT67QoAWFRWFiAgGbJ4k+qmhvjURehdKUxEREZXHgK0GioqKQlrafm93g4iIiNyEARvVWraqGiitioHceqBV3REqFGVatQmGiwB3fxIRKQZ3iVKtZa5qUJYrVQyqk63dn7ZUdUeoKUArJWpikasKruQMIiLyNI6wkc/prgtClmA/iMkUVIgRHQcw1VHVwN08UQ9U1MRCn3LCqm1mxsxqvScREcnHgI18TpagchiQxYgSomUEbM6o7nqg1ZH8tvxUpy2C4SJyVSGYXC5AyzXkI0wT4tK9iYjIvRiwkU+KESX8rK/eUafynK0H6mzIZp7+LBuguWOq01EhdlETiyvFYoUALUzDhLhERErBgI1qJFsbCspzZYOBM/VAo526som7pz9tTXXaMjNjJsIA1gMlIlIoBmzkc+aPHQ9dzhWcsjPlabySAwBQh+sqPUZpGwzcLb+4AJIkVpjqtIXTn0REysaAjRTN1khZk+vBGOwFY+E6+IWFodGrM6qvczJ5IjWHLZIkOqwFasbpTyIiZWPARopmTr1RdupSX6cOcnTh6D3lNe91zAm21qbZUpX1apURoOI0JxFRDcCAjbxGTnqOdwQVoNNhyLurLW3mHaK9PbzpoCqqOzVH0VcJCBGty2CFoQC5YC41IqKagIlzyWvM6TmcVR0pO3xdiJiHMFgHhLkIRr7AaU4iopqAI2zkVY7Sc5g3Fng6hYcvykUwinv+a9Wm9VJfiIjIvRiwkUfYmv6UW43AG2wlyXU1IW5VzPl2CUbnLkOYZD9g5fQnEVHNxoCNPMJWdQIlT23aSpJrTohrT9kdoYIAiGJwlXZ/5hnyECLlIwxX7QZk5ulPjqgREdVMDNjIY7xRnaAqnEmSa+buagWAaaenpIlDsYMEuAzWiIhqLgZsRG5m3hEaHR2KrCzfCVCJiEi5GLD5kH79+kKvz7Z7THZ2NqKiojzUo6qprvJRRERENQ3TevgQvT4b2dn2A7aoqChERPhGwGZOimtPTS8fRUREJAdH2HxMVFQU0tL2e7sbbqMOC0fThUu83Q0iIiJFY8BGpBBzvl2CPIN1tYJcQ76XekNERErCKVEihcgz5FUI0MI0IVCp+M+UiKi24wgb1Sq2EuLa4o0kuQAwX9yCeKN1gCZI+RCZFJeIqFZjwEZVJqeIu1KqGthKiGuLnCS51SFMKoBguApRE1vaF00sRG2Mx/tCRETKwYCNqsxWFYPylFTVwJWEuJ4kamKhd5Akl4iIahcGbOQWvlbFgIiIyJcwYCOPsJUkt7YkxbW1+9MW7gglIqLKcPsZeYStJLm1JSmurd2ftnBHKBERVYYjbOQxNS1J7mO7g6C/Zr3Zonzhd7MwTQhmJ091eM2Q9A1u6x8REdUcDNioxrKVwsOd6Tr011QVArTIQAkRAcrYXEFERDUHAzaqsWyl8HB3uo7IQAl7+nKzBRERVS8GbFSjKT2FBxERkRwM2IjcqOyOUEEQIIoicg35mC9uQYSM9WmC4aJV0lwiIiKAu0SJ3KqyeqDhKIRguOjwfFY1ICIiWzjCRnbZKjslABAjSmtbKqXslFKYd4RGR4ciK8s02haSvgEiglnBgEiBCgsLkJ+fA6PRcZ3hmigz0zQb4E4LFiwAAFy48JfN9+s++5Td96tDZngUAEB04Z7Lkkz/f+FC1fqgVvshJESHwEDn60MzYCO75JSdWvfSOETkXMEpO8e4O0munCLu3irgTkS+o7CwAHl5l6HTRcPfXwOVyn5d5JrIz09ASYl7A7bc3EIAQFxcI5vv5xebJvhC4hq49b72+F01zX6UBIU4fW6O3vT7LS7C9e8PSZJQXGxATk4WADgdtDFgI4fKl52Kjg5FVpnXp/Q5pqS4dgIydyfJlVPE3VsF3InId+Tn50Cni4ZGo/V2V6iGU6lU0Gi00OmiceVKNgM28g5vJMXlDlAiqiqjsQT+/hpvd4NqEX9/jUvT79x0QEREtVptnAYl73H1+40BGxEREZHCcUqUqBxbNUJtqaxuKBH5rrK5FD0pVBOKSZ1Ge/y+5Ds4wkZUjrlGqCOsG0pU89jKpVjdcg35TgeJyclJSE5OwunTJyu8d/z4r0hOTsKLLw4FAPz773kkJyfhr7/OuqO7NhUU5OPtt5ehf/++6N69C15+eQy2bPkIV69erbZ71jYcYaNazdZomnnkzNUaoVOvrTNVNVCrEGE0BXSsYEDkO8y5FD1lcsZMl87z8/PD11/vQ5Mm1puv9u3ba7VOKiYmFv/97x7odHWq1M/K5OXlYdiwZxEaGooxY15G/foNcODA1/jwww8wceIYLFq0DH7csV9lHGGjWs3WaFpVR87CpIIKVQ1YwYCI3K1duw7Yvz+9Qnt6+v+hVavWltdqtRqRkVFQq6snL+WqVcsAAEuWrECnTregXr14JCa2w8iRY/DDD8eQnr63Wu5b2zDkJQtbVQ3mjB2PqHJJcc8IKohlXrs7Ka6nuTqaZmuti3kqRdTEQt33PPRZnl8LQ0S1Q7dud2DRonnIzLyImBjTCP6pUydx9WoBunVLwW+//QrANCX6yCP344MPPkGjRjcgN/cKli5dhIyMryEIAlJSbseoUeMREBCA1NT70L17T3z55edQq/2wfv2HKCgowPLlS/Ddd4dRUlKCLl26YdSocQgLC4PBYMAXX+zBsGEjoNUGWPWvbt16eOutVWjatLnHn01NxBE2sjBXNSgrKucKdDk5ds9zd1Lc8nSnzyLixEmrP0KJsdruJ1dldUNVKv6zIqLqV7duPTRt2hz79++ztKWn70W3brfb/Tk0efJ4/PXXGSxc+BYWLVqGn3/+CcuXv2l5f+fO/2LOnIWYNWse/P39MXr0MOj1eixatAwLFizFmTOnMHOmacr4/Pn/4erVAtx4480275WY2A4hIc5XFqCKOMJGVspXNTglSkC5pLhla2R6gq2qBkqpYmBrrUtI+gYv9YaIaptu3W7H11+n4+GH+wEwTYe++OJoHDnyrc3jT58+hR9+OIYNGz5C48ZNAAATJ07BL7/8YjmmR49eaNGiJQAgI+NrnD//P7z11mrUqWNaAzdt2iw88cQjOHnyTxQWmjYVMCirft7/jUckA6saEBFV1K3bHVi37l3k5eXhypUcZGVlol27DpUGbGfPnkFAQIAlWAOAtm3bo1WrtpbX9erFW77+668zqFu3niVYA4AbbmiM0NAwnD17GgkJpsAuLy/XzZ+MymPARjWO3DxqAHOpEZFva9asOeLi6uLgwf24dCkbycnd7O7I9Pd3/Gtfqy2tq1pZjVVRNEIURcTH10dYWDh+++1Xm9OiCxbMQfPmLfDAAw/J+DRkDxfbUI0jN48awFxqROT7unW7AwcO7MfXX+/D7bffaffYBg0a4dq1a/j7778sbRkZX+Pxx1MhSRV/FjZqdAP+/fc8Ll/WW9pOnz6FgoICNGzYCGq1Gj179sKWLZtRVHTN6txz5/7B9u1bERgYVMVPSABH2MiLdKfPQihxXAC3/Po1OaqSR42IardcQ77LudFcvV+YxvU1YN263Y4xY0ZArVajY8fOdo+94YbG6NTpVsydOxOjR49DSUkJli9fio4dO9uscZmU1Ak33NAY06e/guHDR6GoyIBFi+aiVas2aNHiRgDAM88MxeHDBzFq1AsYNOg51KsXj2+/PYyPPtqEDh064c47e7r82agUAzbyGlubCWxRygYD3eFkCEWZltfzr6f0CC23yYBJcol8V6gm1OP3DNOEVOm+N9/cGkFBgWjfviP8/f0dHj916nQsXjwfw4cPhVarQY8ed2HYsJE2jxUEAXPmLMSSJfPxwguD4efnj27dbsfw4aMtAZ5Op8PKle9izZp3MG/eLOj1etSpUwfJyd0wcuTYasv/Vtt4/7cg1Wq+tJlAKMqUFYyZk+TyRxSR7/GVep4ZGUcsXwuCgP/+93Or94cNG2H5um7delbH16kTgRkz5lhe+/kJKCkRAQCffLK9wr1iY+MwZ85Cu/2JiIjEuHETLa9PnPgdABAQEFDZKeQkBmxEThA1sdCnnABQWk6mshI20R7rFRER1XQM2GoJW1UMyssUVIgRuQCfiIhIabhLtJawVcWgvBhRQjQDNiIiIsXhCJtC9OvXF3p9tt1jsrOzERUV5fI9ylcxICIiIt/AETaF0OuzkZ1tP2CLiopCRITrARsRERH5Jo6wKUhUVBTS0vZ7uxtERESkMAzYyOKv6dNQkmtdD86YewXqsHAv9cg75ny7BKNzlyFMsp4+VqEAuQi27A6tarJLIiIiuTglShYlubkw5l6xalOHhcMvLMxLPfKOPEMeQqR8hME6YMtFMHJVwZbXVU12SUREJBdH2MiKOiwcTRcu8XY3vE6ACpImDsXXc66ZBQKY7Z0uEZEHPLY7CPpr8moRu1NEgISNd1/1+H3Jd3CEjYiI6Dr9NRUuFXo2YLtUqHIpSExNvQ9bt35Sof2vv84iOTkJ//57Hi++OBQrV77l8FpffZWGF14YjF69UnDvvT0wceIY/PnnCYfnkedwhI08wlahd1eKurvbnG+XIO96TVCzXEO+l3pDREoQGShhT1/PpUDq/Vmw44NcNHv2fPj52a8vunLlW/j00y0YOvQFTJw4FUVF1/Dhh+9j+PAhWL58NZo3b1Ft/SP5OMJGHmEu9F6WEoq65xnyKgRoYZoQqFT8p0FEvi8sLBxBQUGVvv/LLz9j/fr38Prrb6Bv31Q0bNgIzZu3wCuvzEDTps3w9tvLPdhbsocjbDWQrTJUSig7pdRC72GakAr1QEPSN3ipN0RE7vPii0Nx882trYrBl7Vr1zbceOPNaN8+yapdpVJh0qSpADy/no9sY8BWA5nLUJUN0Fh2ioiIyvvzzxO4+eZWNt9r2PAGz3aG7PJ4wFZcXIw5c+Zg586dAIBHHnkEY8aMgSBUnIIqKCjAnDlz8Pnnn0OtVuOee+7Byy+/DI1G4+lu+xyWoSIiqvmWLl2E5cvftGqTJPn/cZ6Xl4eQEOaT9AUeD9gWLVqEAwcOYPXq1SgoKMCECRMQFhaGoUOHVjh24sSJOHPmDNasWYPCwkKMGzcO4eHhGDlypKe7TZWwtZnAFiVsMCAiqmmefPJZ9OjRy6rt/Pn/YcyYFysc27NnV8vXsbF18f77H0Gn0yEvL7fCsaQ8Hg3YioqKsGnTJrz55ptITEwEAIwdOxYLFy7EkCFDoFKVzpWfOnUKX3zxBf773/+iZcuWAIAXXngBX331lSe7TA6YNxM4CsaUsMEAAHSHkyEUZVpez7++QzS03Jo1wXARoibWo30jInKWTqdD/foNrNqMRqPNY9eu3Wj52u/6z+OWLW/Cr7/+ZPP4/fv34csvP8fUqTMtx5P3ePRv4Pjx4ygsLERSUunixqSkJGRlZeHcuXNo0KD0m+7QoUNo0qSJJVgDgP79+6N///6e7DLJoNTNBLYIRZmygjFREwtRG+OhXhERVb/ygR0A9O7dB1u2bMaxY0esNh4YjUZs3LgBAQEBDNYUwqN/CxcvXkRISAiCg0tzzkRHR1veKxuw/f3332jYsCE2btyIdevWobi4GH369MGoUaPg728/pwxVZKtOaHm+WDfUVlbyS4UqRAZWvoZD1MRCf72CgbkuaPldokRUe10qVFVrbjRb97P3M6s6tWx5Ex59tD8mTRqLYcNGICmpM65cuYL331+LM2dOYcWKd73SL6rIowFbYWEhtFqtVZt5A4HBYLBqLygowNGjR1FcXIx58+ZBr9dj+vTpKCkpwcSJE526b2SkvAWV0dHeqwtp3nThjj6Yt2+UvdaZ/DwYc6/AX6er/DydDhqdTlYfLMecdF+/XZFjAC4VAtFlfrZGBwNRQSrbfVKbgjvze+587rZ483vK1/BZycPnJJ+cZ5WZKcDPr3TTW2SA5PFEFlGBEiICJKt+yCUIQoXz1Nd/zqnVAlQqFQRBZffaL700Hjfc0BifffYpVqxYCq02AG3aJOKdd95D48ZNnO5TWY4+kyuf2WWqqtzTWIVzKxIEwel/yx4N2AICAioEZubXAQEBVu1qtRpFRUVYsmQJwq4XH7969SqmTJmCCRMm2NxVWplLl/IhOkhpER0diqysPLvHVCdRFAHALX0QI0zRS1aZXaKiKEEdFo7G8xc7PN9RH8o+q4jr/dZ76dmJYjAiA4FdD1TcEZuVVfH4CKPp+8DcX3c+9/K8/T3lS/is5OFzkk/usxJFESUlouX1B16s5ylj/5aVTz7Zfv080aq9fv1GyMg4AgB4661VNo8py89PwH33PYT77nvIRp8qP08OR+dX9frO8JOqfk939VcURZvfn4KgqnSQyaMBW1xcHPLy8lBYWIjAwEAAQNb136qxsdZrimJiYhAdHW0J1gCgSZMmuHbtGvR6PaKiojzXcfJJ5TcYANxMQEREvsmjAVvLli0RGBiIo0ePIjk5GQBw5MgRxMTEID4+3urY9u3bY+XKlbh06RIiIyMBAH/++SdCQkKgszOtR2SuDzq/8BTCUIBclF2LEoTcYhEzr69dyzXkI0zDHERERKRsHp8STU1NxYwZMzB37lwUFRVh4cKFePrppwEAOTk5UKvVCA0NxS233IIbb7wRY8eOxaRJk6DX67Fo0SL079+fO1bIrrL1QXMRjPGBL1R6bJgmBKEargkiIiJl83jkM378eBQVFWHw4MHQarVITU3FoEGDAAAjRoxAfHw85s6dC0EQsGrVKsycORP9+/eHVqvFww8/jNGjR3u6y+SDwjQhCDWaAjHuACUiIl/n8YBNq9Vi5syZmDlzZoX3NmywTl4aGRmJJUuWeKhnRERERMrEuUXySeYNBXuCTPu0I9JLdwGbqxcIuMoNBkREVCN4MAEKkfuYKxbYw2oFRERUU3CEjXyWqIlFb/05AMCeXqV52Fi9gIiIahoGbERERNfZyt/oCaI2Bjm3ZHj8vuQ7GLARERFdZ15u4cn1r46Wd1QmNfU+XLjwr+W1Wq1GZGQUevTohaFDX8C6de/im28OYfXq9+xeRxRFPPBAb0iShE8/3WkzdVZ2djbWr38XBw7sx+XLl1G3bl3cc8/9ePTRxxym2vrss0+wdesn+OefvxEQEIjExHZ4/L4HcUODRi597tqKARvJpjt9FkJJCXBSsJSkEkqMEP3Ubr+XraLuZZk3G3izaDIR1UyiJhb6lBMeu19EeoLL5w4bNgK9e98DADAajfj99+OYOXMagoKCZF/jyJHvIEkSrl27hsOHDyI5uZvV+//+ex7Dhg1CQkJLTJs2E9HRMfjtt1+wdOkinDp1ElOnzqj02p9++jHee+8/GDNmAhISWiI/Pw+bNr2PcdOn4O033kRIgwauffBaiAEbySaUlEAoMQKa0r0qop8aYjUkMtZfU8kKxiKvF00mIqqNgoODERlZWqoxJiYWd93VG/v27UXXrimyrvH557uQmNgOJSXF2LVre4WAbcGCuWjU6AbMnbvQUse7Xr14hIeH46WXXsTDDz+Km25qZfPaO3b8F488MgC3336npW3KlNeQ+tA92HdwP55ObOvkJ669GLCRU0Q/NdS3Jnqk2HtkoIQ9fSsWdQdK03iU3WxARESmqVF/f3m/3ouKirBv3//hueeGQ6PRYMGCObh8+TLq1KkDAMjKysS33x7CG28ssQRrZh073oI331yJJk2aVXp9QRDwww/H8Oij/aHVBgAA/Pz8MH/aLIQEl5YNPHBgP1avXo5//vkH9evXx8CBz6Bnz96W9z/4YB0++mgTioqu4eGH++HHH79Hjx534cEHU2U/F1/nVFqPf//91/FBRERE5HFGoxFHj36Hzz/fheRkeaNrBw7sx9WrBejaNQXJySmQJAlpabst7588+SckScJNN91s8/wOHToiICCg0us/+uhj+Oabg3jggbvx6quTsW3bZ7h48QLqxsYhNMRUjebUqZN49dVJeOSRAdiwYTMee+xJzJ8/B4cOmTZhbN36CdatW4NRo8ZhxYr/4PjxX/Hbb7/IfSw1hlMjbN27d0eHDh1w//33o1evXggPD6+uftV4/fr1hV6fbXmdnZ2NqKgoO2eQLeZC72WxoDsR1RZLly7C8uVvAgAMBgPUajV69uyNAQMGYsOGtQ7PT0vbjZtuuhnR0aaclYmJ7bBz53Y8+uhjAIC8PNPP1+Bg136m3nVXb0REROCjjzZi//50fPXVF1CpVOienIJRg4cBADZuXI+7774P9977AAAgPr4+/vrrLD78cCNuvTUZ27Z9hoceegTdu/cAAEydOgMPP3yfS/3xZU4FbO+99x527tyJhQsXYubMmUhOTsb999+P7t27Q6vVVlcfayS9PtsqSIuKikJEBAM2Z5kLvZcN0FjQnYhqiyeffBY9evQCAPj7+yMyMsrhrk2z3NxcHD58EEOHDrO0paR0x+LFb+CPP35HixYtodPpAJgCN/M0qS0//vg9xo0baXndufOtePLJZwEASUmdkJTUCUVFRfjpp++RlvY5du/egbDQUIyd/CrOnj2D06dPYs+eHZbzjUYjdDrT/f766yyeeOIZy3t16kSgQS3crOBUwNa5c2d07twZ06ZNQ0ZGBnbs2IFp06bhlVdeQc+ePXHffffhtttug0pV+e4+KhUVFYW0tP3e7obX2doR6szuzzBNCJPkElGtpNPpUL++a8HL//3flyguLsbKlcvw9tvLAQCSZPq5u2vXNrRo0RItWrSEIAg4fvxX3HZbcoVrvPLKBNx1Vx907nwL1q7daGm/cOE89PpLWLBgLp5//kWEhIRAq9WiY8db0LHjLdCIEo799AMAU3D2yCMDcP/9fa2ubV4zFxAQYOmXmZ+fv0uf2Ze5VJrKz88Pt99+OxYsWIC3334bLVu2xNatWzFo0CCkpKRg9erVMBgM7u4r1VDmHaFlcfcnEVH1Skvbg5tuaoUNGz7E2rUfYO3aD/Deextxyy23IS3tcxgMBoSH63DLLV2wadMGiNfTOZl9880h7Nu3F3Xq1IFWG4D69RtY/oSFhcPPzx/bt3+G/fv3Vbh3UFAgwsNMy6oaNboB58+fszr/4MEM7NjxXwBA48ZNrdasXb16Ff/883d1PRbFcmmX6E8//YRdu3Zhz549uHjxIhITEzFt2jR07doVGRkZWLRoEX755RcsXbrU3f2lGsrejlAiIk8SDBerlBvNlftVV6LegoJ8HD580KotODgY0dGx+PHH7zFp0jQ0bdoMJSWlwVi/fo/hpZdeREbG1+jevQdGjHgJw4YNwsSJY/HEE08hIiIS339/FCtWLMV99z2I1q0Tbd47LCwMqan9sGjRG8jNvYJbb+2CkhIjfvrpe2zdvQMTR4wFAAwYMBDPPfc03n//Pdx++5347bdf8Pbbb2HkSNP7jz/+JF555WUkJLRAy5Y3Yc2a1bh6tfb9vnAqYFu8eDF27dqFc+fOoWHDhkhNTcX999+Phg0bWo4ZMGAALly4gPXr17u9s1Q72SoV4+lM5ERUO4jaGM/fUxNbbff966+zVmvLAKBZswT06HEXgoOD0b17zwrnJCV1RsOGjbBz5zZ0794DDRo0xNtvr8Hatasxbdok5OXlol69eDzzzGA89NCjdu8/fPho1K1bD7t2bcd//vM2JElC8+YtMHHEWHRq1wEA0LLljZgxYy7WrFmFd99dhaioaDz33HA8+ODDAIBbb03GiBFjsGrVcuTl5eGRR/ojNjbOTU/IdzgVsG3evBl9+vTB/fffj7Zt21Z6XFJSklUQR541f+x46HKu4JRYOqVozL0CdZhv7uq1VSrG8gOuxIsdI6Iax5fqeX7yyXa77w8a9BwGDXqu0vefeOJpm+0qlQobN26xaqtfvwGmTp3pdB8FQUBqan+kpva3as//5x+r1ykpdyAl5Y5Kr/Pggw9bAjgA+OKL3ZUeW1M5FbAtWrQIbdq0QUhIxe29ubm5yMjIQJ8+fdC1a1e3dZCcp8u5Al1ODlAmQFOHhcMvLMx7naqiSkvFZDj/A4SIiMjXOBWwDRo0CJs3b0abNm0qvPf9999j4sSJ6NOnj9s6R67L0elwy/zFso611Ah1oLrqhhIREZF9DgO2oUOH4rfffgNg2u47dOhQmzlerly5gmbNKi9PQcplrhHqKBgz1w31RMhWNiHu/Ov/P9nGaBqT5BIR1T6OpoNrIocB2/jx4/H5559DkiQsX74cPXr0QFyc9WI/QRAQFhbG0TUfJvqpoU+QF3BHV3NfANsJcW1hklwiIqoNHAZszZs3R/PmzQGYFiI+8sgjiI3l7jxv6a4LQpZgPzHxKriYYE9hzAlxQ9M3AACT4xIRUa3lMGA7c+YM4uPjodFocM899+Dq1as4c+ZMpcc3btzYrR0ka1mCCpmCCjFi5UllBfhGwGZO17EnyBSARqSXfibzNGho+gam8CAiolrPYcB2991346OPPkKbNm1w9913V1p2SpIkqFQqHD9+3O2dJGsxooSf9ZUnDTxlJ5hTEnO6DsB+Pp3qzFFERETkCxwGbOvXr0fTpk0tXxPJZatGaFmmkbU4dLz0P1Olg16lQah5gwGnQYmIiGQEbJ06dbL5tVlxcTHy8/NRp04d9/aMfJ65RqijIu6sG0pESiFnnXB1iBYl7M256vH7ku9wKg9bUVERli1bhiZNmqBv375IT0/H2LFjUVBQgA4dOuDNN99EZGRkdfWVfJC9GqHmNWtlR9aIiLxJzjphd8t0MUAsKMjHhg3vYd++r5CZmYmoqCjceeddGDjwGQQFBQEAXn/9NRgMRZg+fY7Naxw7dgQjRz6Pr746AK1Wa2kXRRF9+/aBJEn49NOdNtN5lfXvv+fxyCP3W7X5+fmjfv0GePzxJ3H33fda+rN79w6b1+jaNQVz5ixEaup9uHDh30rvtXTp22jfPsmqrfx1BUFASEgokpI6YdSosYiMjLLbfwDYtWs73n57GbZt+xwA8OKLQ3Hzza0xbNgIAMAbk59Dx8TS157mVMA2f/58fPrpp5g+fTokScL06dPRsGFDDB48GCtXrsScOXOwYMGC6uorERFRtXO0TtjdWkcEO31OXl4ehg17FqGhoRgz5mXUr98AZ8+ewdKli/Drrz9j0aJlDoMse44e/Q6SJOHatWs4fPggkpO7yTpv+fL/oH79+jh9+iSKi4tx6tRJvP76a6hbtx7atm0PwBSYDXvsKQBAcL16lnM1GlPA+M476yGKRgDApk3v49ixI5g/f4nluLBKyix27ZqCceMmATCtq8/MvIg5c2ZgxoypePPNlc49AAVy6m/z888/x5QpU3Dffffh+++/x/nz5zFp0iT07NkTGo0GU6ZMqa5+Ug1WNkmuGRPiEhFVbtWqZQCAJUtWQKsNAADUqxeP+Pj6GDjwUaSn78Wdd97l8vXT0vYgMbEdSkqKsWvXdtkBm06nQ2RkFC5dygYA3HZbV+zevQPp6f9nCdg0Gg0idKZlVCE2Rr7KLrEKDAyEn5+frBEyjUZjdVxUVDSeemoQXnttCnJzcxHh5wv5EyrnVO9zc3MtRd337t0LjUaD5ORkAEBQUBBKZJQ3IirPnCS3LCbEJSKyzWAw4Isv9uDhh/tZgjWzRo1uwFtvrULnzrdZ2goLCzFr1qvo2bMr7r+/FzZtet/u9YuKipCevhft2nVAly7dcPDgfly+fNnl/qrVavj7uz7aVxVqtRoqlQr+/v4AgN/++B0vvjgUPXt2Rd++fbB27TsQRdErfXOWU0+wadOm2Lt3Lxo3bowdO3bglltuQWBgIIxGIz788ENLgl0iZ5mT5BIRkX3nz/8PV68W4MYbb7b5fmJiO6vXBw9m4JlnhuC99zYhPf3/sHz5EnTs2BnNmtn+nX3gwH5cvXoVXbumwN9fg/nzZyMtbTceffQxp/ppMBjw8ccf4syZ0xg3brJT51aVJEn455+/sGHDWnTo0BGBgYH4+8RxvDBuNB548GFMmDAZZ86cxhtvvA6NRoPHH3/Ko/1zhVMB24gRIzBq1Ci899578Pf3x8KFCwEAvXr1wqVLl7BixYpq6SSVmj92PHQ5V+zmWjPmXoG6kjl+bzEnyS2LCXGJiJyXl5cLAAgJkbdsJCGhBQYNeg4A8NhjA7F+/Rr8+ecflQZsaWm7ceONNyM62pT/MjGxHXbu3C4rYBs06AmoVCqIooiioiKEh4dj5MgxSExsazlm3769OJCxHwCgKrPh4rXXZqNLl66yPpMt+/btRc+epvNLSkpgNBqRmNgOEyeaBgM+27kdDRs0wIgRLwEAGja8AXq9HqtXr6h5Adsdd9yBHTt24JdffkHr1q3RoEEDAKZArn379pbXVH10OVegy8kB7ARk6rBw+IWFea5TMpiT5JYN0CwJcTmTTkQkW3i4DkBp4OZIvXr1rV6HhISgqKjI5rG5ubk4fPggBg9+3tKWktIdixe/gT/++B0tWrTE/Pmz8cUXuy3vL1iwFDExpp/ts2cvQL168Th79jQ0Gg06drylQsL9zp1vxeBHTMFfUN26lnY569Ts6dz5VowcORaAaSpUp6uDwMBAy/tn//4LN7e80eqc1q0TkZeXa1lzp2ROTyo3bNjQso7N7IEHHnBbh8ixHJ0Ot8xf7O1uOE3UxEKfcqLiG9eT5BIRkWPx8fURFhaO33771ea06IIFc9C8eQs88MBDAAC12tZydduzNP/3f1+iuLgYq1Ytx+rVplkzSTIdu2vXNrRo0RKDBz+PAQMGWs6Jjo6GXq8HAMTGxqF+/Qa4etW0y9ZWdaTAwEDUizMFaiH13TfQExgYiPp2rqfRaCq0mXej+sI6NqcCNqPRiI8//hjp6ekoLCy0+QFZDYGIiKj6qNVq9OzZC1u2bMa9995vtfHg9OmT2L59K6ZMme7StdPS9uCmm1ph4sRXrNpXrFiKtLTPMXz4aNSpE4E6dSKq9Bm84YYGDXHoyLdWbT///BNCQkJ84vM4FbDNmTMH77//Ptq2bYu4uDgIgm9vkSUiIiovU1C5lButKvdzNlHvM88MxeHDBzFq1AsYNOg51KsXj99/P47ly5egQ4dOuPPOnk7348KFC/jxx+8xadI0NGnSzOq9fv0ew0svvYiMjK/RvXsPp69dlsFggD7HtOu0KKh0ylIQhGoNnB6+/0F8tHUL3nprMR54oC/OnDmNNWtW4cEHU6uUs85TnOrh9u3bMWbMGAwdOrS6+kNEROQ10R6scGAWI0pO31en02HlynexZs07mDdvFvR6PWJiYnHPPffj8cefglqtdrofX365B8HBwejevWKwl5TUGQ0bNsLOnduqHLDt35+O/fvTK7SHh4dj586vqnRte2KiorFo1jwsfXc1Pv30I0RERKJfv8fxxBNPV9s93UklmSenZUhKSsJbb72FW2+9tTr75HaXLuVDdPCPITo6FFlZeXaPcSfzTpa0tP1OnXd4vGl3izvXsEWcOAkA0Cc0c3CkidxnlbwZkCAhJX4W5hea1kKMD3yhwnHmJLk1La2Hp7+nfBmflTx8TvLJfVYXLvyFuLhGHuiRcvn5CSgpce8arhMnfgcAJCS0tPl+/j//AABCPLhZ0e+qKd9nSZDzSdl/15tiiJYR7qkzW9n3nSCoEBlpu39OjbDddddd2Llzp88FbL5CTtHhVXAy27EXSZAgSo5/CDBJLhERkX1OBWwtWrTAsmXLcPLkSbRp0wYBAQEVjhkzZozbOlfbyCk6LMB3AjYAEFQCZidPRWj6BgCocaNoREREnuBUwLZu3TqEhoYiMzMTX375ZYX3VSoVA7YqclR02F7CXG96bHcQ9NesRweLjIBWnV/JGURERCSXUwHb3r17q6sf5KPMFQw2CiqIgUDZGV0xSIIKEiLSVaxqQEREVAUu7WPV6/U4duwYsrKy0KtXL+j1ejRu3NilXSnk28wVDIA4CCogIqB0BDDPYF7wG1pa1YCIiIic5lTAJkkSFixYgPXr16O4uBgqlQpt2rTBokWLkJWVhTVr1iAqqmqlJah66U6fhVBiXQtKKDFC9HM92BY1seitPwcA2NOrdDp38vUKBly3RkREVDVOrV9fvnw5Nm7ciFdffRX79u2zlKsYN24crl69igULFlRLJ8l9hJISCCVGqzbRTw3RB5IGEhER1VZO/Zb+6KOPMHbsWKSmpsJoLP2lf+ONN2L06NGYM2eO2ztI7if6qWXnXCMiIiLvc2qE7cqVK2jUyHaCQZ1Oh4KCync3EhEREZFrnBphu/nmm7F582Z07drV0qZSmbYF7ty5EzfeeKN7e0dERORBttb5eoLo54ecJjc4dU5BQT42bHgP+/Z9hczMTERFReHOO+/CwIHPICgoCADw+uuvwWAowvTptmfAjh07gpEjn8dXXx2AVqst7Y8oom/fPpAkCZ9+utNhrc1//z2PRx6536rNz88f9es3wOOPP4m7777X0p/du3fYvEbXrimYM2chUlPvw4UL/1Z6r6VL30b79kkV2iVJwn//uwXbt/8Xf/99FkFBwejQoSMGD34e9erFAwBuuet2u5/j44+3oW7degCAr75Kw5Ytm3Hq1J9Qqf3RrGUbjBr2PJo3T6j0M5f/LO7kVMA2fvx4PP3007jvvvvQpUsXqFQqfPrpp1i8eDEOHTqENWvWuLVzREREnmRe51uVjVjO39Po+KBy8vLyMGzYswgNDcWYMS+jfv0GOHv2DJYuXYRff/0ZixYtq1JB86NHv4MkSbh27RoOHz6I5ORuss5bvvw/qF+/Pk6fPoni4mKcOnUSr7/+GurWrYe2bdsDMAUzwx57CgAQXK+e5VyNxhQwvvPOeoii6Zls2vQ+jh07gvnzl1iOCwsLt3nv6dNfwU8//YDnnhuOVq3aICcnB+++uwovvDAYq1atRXxoCHZ+uAUlgcEAgDffXIjiYgPGjZtkuYZOVwcAsGrVcmzdugVDh76AiROn4vfMQnyx9QMMHz4Ey5evRvPmLSp85rLMn8WdnPrbbNeuHT766COsXLkS27dvhyAI2LFjB9q1a4cPPvgAiYmJbu8gERGRJ3l6na+5nrMzVq1aBgBYsmQFtFpT1aF69eIRH18fAwc+ivT0vbjzzrtc7lNa2h4kJrZDSUkxdu3aLjtg0+l0iIyMwqVL2QCA227rit27dyA9/f8sAZtGo0HE9cAoJLJiZok6depYvg4MDISfnx8ibRxX1ldffYH09L1Yt+5DNGxoWroVH18fc+YswMCBj2LDhrWY+MIIREZEWmqJarVaqFSocO1ffvkZ77//Ht58c6VlJO9qiIRBL03H0leG4O23l2PhwqUVPnN1cypgO336NH777Te0atUKrVq1QnR0NNq3b48GHizeSsqUbyiABAmTM2ZZ2sxF3YmIyH0MBgO++GIPhg0bYQnWzBo1ugFvvbUKTZs2t7QVFhZi1qxXkZ6+F4GBQRgwYCAGDHii0usXFRUhPX0vnnvuRWg0GixYMAeXL1+2CqScoVar4e9fvZkIdu7chq5db7cEa2ZarRavvz4fYWFhsq+1a9c2tGx5U4VpV5VKhUmTpgJwTwF4Z8l6gn/88QdeffVV/Pjjj5ZUHmYqlQodO3bEq6++iqZNm1ZLJ0n5bBV6Z1F3IiL3O3/+f7h6tQA33nizzfcTE9tZvT54MAPPPDME7723Cenp/4fly5egY8fOaNasuc3zDxzYj6tXr6Jr1xT4+2swf/5spKXtxqOPPuZUPw0GAz7++EOcOXMa48ZNdupcZ/355wk89tiTNt8zrznDVXmlEv/88wRuusn2s23Y8AZXuucWDgO2U6dOYcCAAYiLi8OsWbNw6623IjIyEgCQmZmJb7/9FmvXrsWAAQPw8ccfV7qLlGo+c6F3IiKqPnl5uQCAkBB5MxgJCS0waNBzAIDHHhuI9evX4M8//6g0YEtL240bb7wZ0dGm6jSJie2wc+d2WQHboEFPQKVSQRRFFBUVITw8HCNHjkFiYlvLMfv27cWBjP0AAFWZeoavvTYbXbp0LX9JWfLycmU/D8fXykNwsPxrmT+zWWBgELZt+9wtfSnLYcC2bNkyNG7cGB988AECAqyHXhs0aIAGDRrgvvvuw+OPP44VK1Zg3rx5bu8kKYO5bijUKkQYTSOtrBFKRORZ4eE6AKWBmyP16lkviA8JCUFRUZHNY3Nzc3H48EEMHvy8pS0lpTsWL34Df/zxO1q0aIn582fjiy92W95fsGApYmJMvwdmz16AevXicfbsaWg0GnTseItVMAMAnTvfisGPmIK/oLp1Le1VWQcWHq6T/Twc0el0yMvLc3zgdebPbFb+87qLw4Dtu+++w6RJkyoEa2VpNBo88cQTWL58uVs7R8piqRsaGGdpY41QIiLPio+vj7CwcPz22682p0UXLJiD5s1b4IEHHgIAqNW2Uq5KNtqA//u/L1FcXIxVq5Zj9eoVpiOvL4XatWsbWrRoicGDn8eAAQMt50RHR0Ov1wMAYmPjUL9+A1y9asrLait4CQwMRL04U6AWUt89a+BvvPEm/Pbbrzbf27r1E5w6dQovDxsu61otW96EX375yeZ7+/fvw5dffo6pU2da2syfubo5DNhycnIQG+t4BCU+Ph4XL150S6dqq/ljx0OXcwWnRNv/kADAmHsF6kq2NFeHx3YHQX/N9A9uT5AKQBz6XDoPUbRer1ZkBLRqeesDiIjIdWq1Gj179sKWLZtx7733W208OH36JLZv34opU6a7dO20tD246aZWmDjxFav2FSuWIi3tcwwfPhp16kSgTp2IKn0Gd+vd+x5Mn/4K/v77rNU6s8LCQnz44Qdo165i3rbKr9UHW7ZsxrFjR6w2HohGIzZu3ICAgIAqpUxxlcM7lpSUQKPROLyQv78/DAaDWzpVW+lyrkCXkwPYCcjUYeHws7HbRW6yR2fzC+mvqXCpUIXIwMqDSMAUrGmEPADyd+IQEZFrnnlmKA4fPohRo17AoEHPoV69ePz++3EsX74EHTp0wp139nT6mhcuXMCPP36PSZOmoUkT67Qm/fo9hpdeehEZGV+je/ceVeq7wWCAPucyAKAoKNDSLgiCy4Hg7bffic8/341Ro17AsGEjcfPNrXDx4gX85z9vw2AwWNbwydGy5U1ITe2PSZPGYtiwEUhK6ozT/+Rg1yfv4cyZU1ix4l2X+lhVrPitMDk6HW6Zv9jp8+Qme3Sl0HtkoIQ9fQsQkW4K2r57AsjKsi5DVprOg5sOiMi3CSVGl3KjVeV+zibq1el0WLnyXaxZ8w7mzZsFvV6PmJhY3HPP/Xj88aegVjuf+PfLL/cgODgY3btXDPaSkjqjYcNG2LlzW5UDtv3707F/f3qF9vDwcOzc+ZXL1505cy42bXof69evwb///g9hYeFISuqEV1+dhaioKNm7RAFg1KixaNToBmzb9imWL18KP00Amt3YBitXrkHjxk1c7mNVqKTyeTrKadmyJbp06QKdTmf3Qjk5OTh48CCOHz/uzv65xaVL+RDtTDMCQHR0KLKy5C8yrKqePU07YdLS9lvaDo9/CQBcCtjMP1zcneyx92emjNCmgM20NVqd+m+FZzU5wzSfz12ipTz9PeXL+Kzk4XOST+6zunDhL8TFlWY38KXSVO7i5yegpER0fKATTpz4HQCQkNDS5vv5//wDAAjxYB5Xv+sBmzlxrjN+15tiiJYR7tlQUP77zkwQVIiMtN0/h0MtHTt2hMFgQGZmpsMOJCXJnyOu7bJVKogAWkcEW9pWAbC1NJSIiDzDW0ETkSMOA7YNGzZ4oh+1jghALLd7RgADNiIiIqqIa9i8SJAk/KgvXQtmb3coERER1V4c0CEiIiJSOAZsRERERArHKVGyKJsk10xODjYiIiKqXhxhIwtzktyyIgMlRAQwYCMiIvImjrCRFXOSXCIiIlIOjrARERERKRxH2Ai6w8kQijKvF3eHpQRVeYLhIkRNrCe7RkTkUX9Nn4aS3FyP39cvLAyNXp3h1DklJSXYuHE9du3agYsX/0VISCg6dboFQ4YMQ1xcXezatR2zZ5cWgVepVAgICEBCQksMGzYSrVq1BgA8+OA9uHDhX6vjgoKCcOONN2PUqHGWUky7dm3H228vw7Ztn1e4dnktWrTEyy+/gtTU+6yuXdZzA5/FwOdecOoz12YM2AhCUSYEw0UAcXaPEzWxELUxcL5CHRGRbyjJzYUx9wrUYeEeu6cx94pL561evRz796dj9OhxaNToBly6dAlr1qzCiy8OxfvvfwwA0OnqYN26TZZzCgrysXr1SowfPwoff7wNISGmMkjDho1A7973AAAkCfjf/85h6dKFmDRpLDZu3AJBsJ6Qu/POnujc+VbL6wce6I1XX52F9u1NFY/++uus5b2y17b04/x5BAUGufS5aysGbNWgX7++0Ouz7R+UnQ1ER3umQzKImlj01p8DAOzpVbqGbc63S5BnKFOPrwQQ/jsRomhddy7XkI8wjfP12YiIlEYdFo6mC5d47H6nxo526bwdO7ZhzJgJuOWW2wAAdevWw8yZc3HPPT1w6FAGAEAQBERGRlnOiYyMwksvjccDD/TGsWNH0K3b7QCA4OBgq+OioqIwatRYvPDCYJw6dRLNmydY3VurDYBWG2DVFhoaZrnGpUulvwPLXxsAtFcLXfrMtRnXsFUDvT4b2dkOArboaEUFbJXJM+Qh15Dv8LgwTQhCNaEe6BEREQGmQuFHjnyLkjLF6oODQ7B+/War0a/y1GrTWI2/v/0xG39//+vHc15FCTjCVk2ioqKQlra/0vctRd/1yt+RGaYJwezkqZbX0dGhyMrKs3MGERFVt379HseqVctx6FAGOne+De3bJ6Fz59vQsGGjSs/JycnBW28tgk6nQ6tWiZUel52dhdWrV6Bx4yZo1OiGaug9OYsBGxERkQ8aOPAZxMc3wNatn+Dzz3dh167tUKvVeOSRARg+fBQA4PJlPXr27AoAEEURRUVFaNCgIV57bTZCQ0tnRZYuXYTly98EABiNIlQqoGPHzpg/f2mVR9jKXttMEiV8sOI/4EIa+RiwERER+aju3Xuge/ceuHq1AEePfofdu3fiww/fR2xsHIKDgxEeHo63314LwLT7MzQ0FGE2NlQ8+eSz6NGjF65du4ZNm9bj559/wtChwxEXZ38zmhzma5d19d9/ERgQWOVr1yZcw0ZERORjTp78E4sXv2F5HRQUjK5db8fs2fPRtWsKvvvuMABAENSoX78B6tdvgPj4+jaDNQDQ6XSoX78BmjVrjilTpiM2Ng4TJoxGfr7jNcyOmK9d9k+9uLoVdp6SfXxaREREPkYUjdiy5SN8//3RCu8FBQVDp6vj8rUFQcDEiVNx+bIeb7/9VlW6SW7EKVEfpTt9FkKZnUFCiRGiH3fyEBHVBgkJLdGt2x2YNm0SnnvuBbRrl4SCgnx8881hfP31PqxY8Q7+/POEy9ePj6+PgQOfwZo1q3H//X2RkNDS5WsVFBRYpfkAgIKcy9D4a7iGzQkM2HyUUFJiFaSJfmqIfvzrJCKqKmPuFZdzo7l6P1cS9b722uv44IN1+PDDjVi8eD4EQY3WrdtgyZIVaN68RZUCNgB47LEnsXv3Dixa9AZWrnzX5eusXPkWVq6sOFKX3OlWzF3EETy5+Bveh4l+augTmnm7G0RENYZfWJjH76kOC3fpvhqNBs88MwTPPDPE5vt9+tyHPn3uc3idrVt3oqRErNCu0WiwefNWWdfLyDhis/2TT7bbbM//5x+H/SJrDNjIIt9QAAkSJmfMsrSxggER1SbO1vMk8hRuOiALCRJEyfq/sljBgIiIyPs4wkZWBJVgVdWAiIiIvI8jbEREREQKx4CNiIiISOEYsBEREREpHNew1QKP7Q6C/poKAPBBYAdEqi5ava9SXcQlKQ5FxhBo1VUvQ0JERETuxRG2WkB/TYVLhaaALVJ1EZGqC1bvX5LicEmKhVadD42Q540uEhERkR0cYaslIgMl7OlbgIh0CRJigZQ/rN8HkJIx8/or7hIlIiJSEgZsRERE1/Xr1xd6fbbjA90sIiIKmzd/5vR5BoMBmzdvxJdf7sH//ncOarUaLVrciAEDnsCttyZbHXvo0AGMHz8K/fs/gRdfHG313rvvrsLate/g9tu7Y9asNyrcZ/z4UTh06ACWLn0b7dsnOd1PqjoGbF4yf+x46HKu4JQoWdpcrSdHRETuoddnIzs7G1FRUR67Z3a2awFiUVERRo0ahry8XDz77HO46aabUVhYiH37vsKkSeMwbdosdO/ew3J8Wtoe1K/fAF98sRvPP/8i/MrVn/bz88M33xxGUVERtFqtpb2gIB9Hj9ouPUWew4DNS3Q5V6DLyQHKBGiu1pMjIiL3iYqKQlrafo/dr2fPri6d9/777+HcuX/wwQcfIzxcZ2lv0qQpJEnCO++ssARshYWF2L9/H8aOnYg5c2bg8OEDSE5Osbpe06bNcf78/3DkyLfo0qW0TwcOZKBFixb4+eefXOonuYfHA7bi4mLMmTMHO3fuBAA88sgjGDNmDATB/v6HwYMHIzQ0FIsXL/ZENz0iR6fDLfNrzuchIiLPEEUR27Z9hv79H7cK1sz6938cDz30iOX1/v3pMBgMSE5OQatWbbBz5/YKAZtarUaXLl2xf/8+q4AtPf0rpKR0Z8DmZR7fJbpo0SIcOHAAq1evxuLFi7F161b85z//sXvOZ599hv37PfdfO0REREp2/vz/cOlSNtq27WDz/eDgENSpE2F5nZa2G+3bJyEkJATdut2OQ4cycPny5Qrndet2Bw4c2A9RNNWVLiq6hm+//Qbdut1RPR+EZPNowFZUVIRNmzZh8uTJSExMxG233YaxY8di/fr1kCTJ5jlZWVlYuHAh2rRp48muEhERKVZOjinYCg8vXVbzv/+dQ8+eXa3+/P33X7h8+TK+/fYwUlJMQVdKSneUlJTgiy92Vbhu5863oLDwqmU07fDhQ2jWrDkiIz23po9s82jAdvz4cRQWFiIpqXSHSVJSErKysnDu3Dmb50yfPh39+/dH06ZNPdVNIiIiRQu7vt45Ly/X0hYbG4e1azdi7dqNeOONJSgsLIQkSdi7Nw2SJKFr19sBAHFxddGy5U3YtWt7hetqtQHo1OlW7N+/DwCQnr4Xt9/evZo/Dcnh0YDt4sWLCAkJQXBwsKUtOjra8l55u3btwpkzZzB06FCP9ZGIiEjp6tWrD51Oh59//tHS5ufnh/r1G6B+/QaIi6traU9L2wNRFPHQQ/cgJaUzUlI6448/juPUqZP4/ffjFa6dknIHMjLSUVJSgoMHM5CSwoBNCTy66aCwsNBqqzAAaDQaAKZcMmVdvnwZs2fPxrJlyyzHuCoyMkTWcdHRoVW6j5l5A4Wc67l8z5Py72HezxEdHQqoVZWe55F+1zJ8TvLxWcnD5ySfnGeVmSnAz6/s2IXpZ6R1W3Vz/p5+fho88MBD2Lx5I+69936rqVEAuHQpC4Dp5/8vv/yEYcNeRHJyN8v7hYVXMWzYEOzZsx2tWt0MQVBBpTL1oVu3FMyZMwNbt36MBg0aoH79eBQVFQEA1Oryz8tRP+0f69HnrKrKPY1VOLciQRCc/rfs0YAtICCgQmBmfh0QEGDVPmvWLPTu3Rtt27at8n0vXcqHKNpeI2cWHR2KrCz3lGUyL9aUcz1X7xlx/R56GefnXgMkSBjy2SzMLzQNn4//bELF4wz5CNOEOOyTO59VTcbnJB+flTx8TvLJfVaiKKKkRCzTYvpdYd1W3Vy751NPDcaPP/6AZ58diGefHYqbb24Ng6EI+/btxaZN76NRoxuQlvYFAgOD0LfvowgKCrI6//bb78Tnn+/ByJFjIIoSJMnUh8DAYLRr1wGrV7+NgQOfQUlJ6TMyGss/L/scHevJ5+wnVf2e7uqvKIo2vz8FQVXpIJNHA7a4uDjk5eWhsLAQgYGBAEybCgAgNjbW6tgdO3YgICAAW7ZsAVAa2LVr1w7ff/+9B3vt+yRIECXH32RhmhCEavhf70RUu2VnZ7ucG83V+7mSqFej0WDJkhXYuvUTfPzxh1i4cB4kSUSTJs3w/PPDce+9D2LQoCfQo0evCsEaADz00CNIS9uD9PT/q/Bet2534LvvvuH6NQXxaMDWsmVLBAYG4ujRo0hONpXMOHLkCGJiYhAfH2917BdffGH1esGCBTAYDJg8ebLH+luTCCoBs5OnIjR9AwBgdjLrhRIRlRcR4fndkFFRUS7f18/PD6mp/ZGa2t/m+++//3Gl57ZunYiMjCPw8xNQUiJi0KDnLO/17ZuKvn1TLa+1Wi0yMljtwJs8PiWampqKGTNmYO7cuSgqKsLChQvx9NNPAwBycnKgVqsRGhqKRo0aWZ0bHBwMPz+/Cu1ERETu4ko9TyJP8Hilg/Hjx6OoqAiDBw+GVqtFamoqBg0aBAAYMWIE4uPjMXfuXE93i4iIiEixPB6wabVazJw5EzNnzqzw3oYNGyo9j0EcERER1VYs/l4NslUqiABaRwRXeswqeKEuGBEREfkkxgzVQAQgqlR2jxHAh09ERETycIStmgiShB/1BZW+f8pBXjgiIvIMSZKgcvAf2UTuUlntdEc4yENERLWWWu2H4mKD4wOJ3KS42AC12vnxMgZsRERUa4WE6JCTkwWDocjlkQ8iOSRJgsFQhJycLISE6Jw+n1OiNYTucDKEokzkFKlQfrZ1fzgAlYSIdEAwXISoibV5DSKi2iYw0LQ57MqVbBiNJV7ujXcIgmApqeguhYVXAAAXLvxl8/2i/MsAgPwLnitNJRhM9VBFjdbBkRUVXl/hdKGKg7FqtR9CQ+tYvu+cwYCthhCKMk3BmBQHUQKEsssxVBJUkACoIGpiIWpjvNVNIiLFCQwMdukXaE1RHTVqBw58AgCQlrbf5vuHFy4BANwyf7Fb72tP1M8HAQDZrW9z+tyHN5tGQg70895aRwZsNYioiUVv/TkAwJ6+pRseXskw5bxjOSoiIiLfxDVsRERERArHgI2IiIhI4RiwERERESkc17ApjO70WQgljncqCSVGiH5qD/SIiIiIvI0jbAojlJRAKDE6PE70U0P0Y7xNRERUG/A3vgKJfmroE5p5uxtERESkEAzYaph8QwEkSJicMcvSlmvIR5gmxIu9IiIioqrglGgNI0GCKFlnjg7ThCBUE+qlHhEREVFVcYStBhJUApPkEhER1SAcYSMiIiJSOAZsRERERArHgI2IiIhI4biGzUfpDidDKMq0vBYMFyFqYr3YIyIiIqouHGHzUUJRJgTDRctrURMLURvjxR4RERFRdeEImw8TNbHQp5ywbvxL8k5niIiIqNpwhI2IiIhI4RiwERERESkcp0SrQVhuLgRRwqmxoys9xph7BeqwcM91ioiIiHwWR9iqgSBKEMqVhypPHRYOv7AwD/WIiIiIfBlH2KqJqBLQdOESb3eDiIiIagCOsBEREREpHEfYvEh3+iyEkhKrNqHECNFP7aUeERERkRJxhM2LhJISCCVGqzbRTw3Rj3E0ERERlWJk4GWinxr6hGbe7gYREREpGEfYiIiIiBSOARsRERGRwnFK1Ac8tjsI+msqq7Y9QabXvT8LtmovMgJadb7H+kZERETVjwGbwugOJ0MoyrRq2yioIAYCQpmYLVJ1AZekuArna9X50Ah5AJiUl4iIqKZgwKYwQlEmBMNFiJpY63YVEBEgWV5LiIVOG409vQqsjpucMev6V1Oru6tERETkIQzYFEjUxEKfcsLy2jztWT44IyIiotqBAZsPyDcUQIJUZvSscrmGfIRpQjzQKyIiIvIU7hL1ARIkiA6KyZuFaUIQqgmt5h4RERGRJ3GEzUcIKgGzk7kujYiIqDbiCBsRERGRwjFgIyIiIlI4BmxERERECseAjYiIiEjhGLARERERKRwDNiIiIiKFY8BGREREpHAM2IiIiIgUjgEbERERkcIxYCMiIiJSOAZsRERERArHgI2IiIhI4RiwERERESkcAzYiIiIihWPARkRERKRwft7uQG0mGC4BkhER6X3KtF2EqIn1Yq+IiIhIaTjC5k2SEYBo1SRqYiFqY7zTHyIiIlIkjrB5nQB9yglvd4KIiIgUjCNsRERERArHETaFeWx3EPTXVFZtRUZAq873Uo+IiIjI2zjCpjD6aypcKrQO2LTqfGiEPC/1iIiIiLyNI2wKFBkoYU/fAsvryRmzrn811TsdIiIiIq/iCBsRERGRwjFgIyIiIlI4BmxERERECseAjYiIiEjhGLARERERKRx3iSpMvqEAEqQyO0OBXEM+wjQhXuwVEREReRNH2BRGggRRsq4vGqYJQagm1Es9IiIiIm/jCJsCCSoBs5OZc42IiIhMGLBVAwESACDixEkHB9YBxMse6BERERH5MgZsVdSvX1/o9dlWbTkGA3QajeOTxcuAmFM9HSMiIqIagwFbFen12cjOzkZUVJSlTafRQKfRQJ/QzO65Eel9rn/Vx+5xREREVLsxYHODqKgopKXtt7w+NfpFL/aGiIiIahruEiUiIiJSOAZsRERERArHgI2IiIhI4RiwERERESkcNx1UUbZKBRFA64hgS9tWr/WGiIiIaiKOsFWRCEBUqbzdDSIiIqrBOMLmBoIk4Ud9geX1GcmLnSEiIqIahyNsRERERArHgI2IiIhI4Tgl6iGP7Q6C/pr1Wrc9QabXvT8r3bBQZAS06nyP9o2IiIiUjSNsHqK/psKlQsebE7TqfGiEPA/0iIiIiHwFR9g8KDJQwp6+pZsTItJNuxP29Cptm5wx6/pXUz3ZNSIiIlIwjrARERERKRwDNiIiIiKFY8BGREREpHAM2IiIiIgUjpsOqoUIAIhIT7C0mFN4mDcaAIBguAhRE+vZrhEREZHP8fgIW3FxMWbMmIHOnTujc+fOWLBgAURRtHnsyZMnMWjQICQlJSElJQXz5s1DUVGRh3tcfURNLERtjLe7QURERArn8RG2RYsW4cCBA1i9ejUKCgowYcIEhIWFYejQoVbHFRQUYMiQIejYsSM2b96MrKwsTJkyBUajEZMnT/Z0t12iTzlh+dqcHLdsCg8iIiIiOTw6wlZUVIRNmzZh8uTJSExMxG233YaxY8di/fr1kCTriumHDh1CXl4eZs2ahaZNm+KWW27BqFGjsG3bNk92mYiIiMjrPBqwHT9+HIWFhUhKSrK0JSUlISsrC+fOnbM6tnXr1li+fDk0Go2lTaVSIT8/v0JwR0RERFSTeXRK9OLFiwgJCUFwcGntzOjoaMt7DRo0sLTHxsYiNrZ0Qb7RaMSGDRvQuXNnqFSOSzwRERER1RQeDdgKCwuh1Wqt2swjaAaDwe65M2fOxO+//46PPvrI6ftGRobIOi46OtTpa+N68Fj23DM2ricI8u4hXD/Qpb54kNL7pxR8TvLxWcnD5yQfn5V87n5Wcn+XefTvSFWVe+ZW4Vz38GjAFhAQUCEwM78OCAiweY7RaMT06dOxZcsWvPnmm2jZsqXT9710KR+iaH8aNTo6FFlZLhRdvz49a+vcsm2iGHy9zf6mA/OOWZf64iEuP6tahs9JPj4refic5OOzkq86npXc32We/DuKuh4GZFfhntXdX0FQVTrI5NGALS4uDnl5eSgsLERgYCAAICsrCwCspj/NiouLMW7cOOzduxdLly7FnXfe6cnuulW+oQASpDLF3W3LNeQjTCNvRJCIiIhqB49uOmjZsiUCAwNx9OhRS9uRI0cQExOD+Pj4CsdPmzYN6enpWLVqlU8HawAgQYIo2c43V1aYJgShGg7jExERUSmPT4mmpqZixowZmDt3LoqKirBw4UI8/fTTAICcnByo1WqEhoYiPT0dn376KaZNm4bmzZtbRuKA0o0KvkZQCZidPNXb3SAiIiIf4/HEuePHj0dRUREGDx4MrVaL1NRUDBo0CAAwYsQIxMfHY+7cudi9ezcAYMaMGZgxY4bVNX766acKmxeIiIiIaiqPB2xarRYzZ87EzJkzK7y3YcMGy9dz587F3LlzPdk1l4Tl5kIQJZwaO9rSZiy4Cv/gIO91ioiIiGoUj9cSrWkEUYJQbm2af3AQNEGBXuoRERER1TQeH2GriUSVgKYLl1heR/18EACQ7aX+EBERUc3CETYiIiIihWPARkRERKRwDNiIiIiIFI4BGxEREZHCMWAjIiIiUjgGbEREREQKx4CNiIiISOEYsBEREREpHAM2IiIiIoVjwEZERESkcAzYiIiIiBSOARsRERGRwjFgIyIiIlI4P293oCaSrv/p/Vmwpa3ICGjV+V7rExEREfkujrBVA8nyP6W06nxohDxvdIeIiIh8HEfYqosK2NO3wPJycsas619N9U5/iIiIyGdxhI2IiIhI4RiwERERESkcAzYiIiIihWPARkRERKRwDNiIiIiIFI67RKvR5IyZlq9zDfkI04R4sTdERETkqzjC5iFhmhCEakK93Q0iIiLyQRxhq0azk5lzjYiIiKqOI2xERERECscRtioSrtegijhxsrRNqANRvOytLhEREVENwxG2aiCKlyEac7zdDSIiIqohOMLmJvqEZpavxc/7AAAE9PFWd4iIiKgG4QgbERERkcIxYCMiIiJSOAZsRERERArHgI2IiIhI4RiwERERESkcAzYiIiIihWPARkRERKRwDNiIiIiIFI6Jc90kIj3B8rVKdRFZUqwXe0NEREQ1CUfYqkGWFItLYrS3u0FEREQ1BEfY3ESfcsLyddfNpoLwB7zVGSIiIqpROMJGREREpHAM2IiIiIgUjgEbERERkcIxYCMiIiJSOAZsRERERArHgI2IiIhI4RiwERERESkc87C5Se/Pgi1fFxkBrTrfi70hIiKimoQjbNVAq86HRsjzdjeIiIiohuAIm5vs6Vtg+XpyxqzrX031TmeIiIioRuEIGxEREZHCMWAjIiIiUjgGbEREREQKx4CNiIiISOEYsBEREREpHAM2IiIiIoVjWg83mZwx0/J1riEfYZoQL/aGiIiIahKOsFWDME0IQjWh3u4GERER1RAcYXOT2clMkktERETVgyNsRERERArHgI2IiIhI4RiwERERESkcAzYiIiIihWPARkRERKRwDNiIiIiIFI4BGxEREZHCMWAjIiIiUjgGbEREREQKx4CNiIiISOEYsBEREREpHAM2IiIiIoVjwEZERESkcAzYiIiIiBSOARsRERGRwjFgIyIiIlI4BmxERERECseAjYiIiEjhGLARERERKRwDNiIiIiKFY8BGREREpHAM2IiIiIgUjgEbERERkcIxYCMiIiJSOAZsRERERArHgI2IiIhI4RiwERERESkcAzYiIiIihWPARkRERKRwDNiIiIiIFI4BGxEREZHCeTxgKy4uxowZM9C5c2d07twZCxYsgCiKNo/Ny8vD2LFj0aFDB3Tt2hVr1671cG+JiIiIvM/P0zdctGgRDhw4gNWrV6OgoAATJkxAWFgYhg4dWuHYKVOmIDMzEx988AHOnj2LSZMmISYmBvfcc4+nu01ERETkNR4N2IqKirBp0ya8+eabSExMBACMHTsWCxcuxJAhQ6BSqSzH/u9//8MXX3yBHTt2oFmzZmjZsiVOnjyJdevWMWAjIiKiWsWjU6LHjx9HYWEhkpKSLG1JSUnIysrCuXPnrI794YcfoNPp0KxZM6tjf/31VxQXF3usz0RERETe5tERtosXLyIkJATBwcGWtujoaMt7DRo0sDo2JibG6vzo6GiUlJQgOzsbdevWlX1fQVA5PsiJ48qqFx/v8rm+rLZ9XlfxOcnHZyUPn5N8fFbyuftZ1atXz+51A6Miq+W+dgUEuHzP+FC4fK4z7F3fowFbYWEhtFqtVZtGowEAGAwGl491pE6dYMcHAYiMDHHqugDwRUaG0+fUBK48q9qIz0k+Pit5+Jzk47OSz93PateuXXbf77FgnlvvJ8ttKQCASBdO/b+n3NsVV3h0SjQgIKBCsGV+HXA98nXlWCIiIqKazKMBW1xcHPLy8lBYWGhpy8rKAgDExsZWONb8nllmZib8/f1Rp06d6u8sERERkUJ4NGBr2bIlAgMDcfToUUvbkSNHEBMTg/jra8HM2rZti0uXLuHMmTOWtqNHj6JVq1aWqVEiIiKi2sDjU6KpqamYMWMGjh07hkOHDmHhwoV46inT5HBOTg7y8vIAAPHx8bjjjjvw8ssv47fffsPnn3+Od99913IsERERUW2hkiRJ8uQNi4qKMGvWLOzcuRNarRapqakYM2YMVCoVBg4ciPj4eMydOxeAKYCbNm0a0tPTER4ejkGDBjFgIyIiolrH4wEbERERETmHxd+JiIiIFI4BGxEREZHCMWAjIiIiUrhaH7AVFxdjxowZ6Ny5Mzp37owFCxZAFEVvd0uxioqKcM899+Drr7/2dlcU68KFCxg5ciQ6d+6MLl26YPLkycjNzfV2txTpn3/+wXPPPYd27dohOTkZ8+fPR0lJibe7pWjTpk3Do48+6u1uKNbevXvRokULqz/33nuvt7ulOCUlJZg/fz5uu+02JCUlYezYsfw5ZcOnn35a4fvJ/Oe7777zaF88WppKiRYtWoQDBw5g9erVKCgowIQJExAWFoahQ4d6u2uKU1hYiJdeegknT570dlcUSxRFDB8+HDqdDuvWrYPBYMBrr72GSZMmYfny5d7unqJIkoTnn38eTZs2xZYtW5CdnY3x48cjMDAQL774ore7p0jffPMNPvroI7Rp08bbXVGskydP4tZbb8X8+fMtbX5+tf5XXQULFizAzp07sWTJEgQFBWHSpEl4/fXXMW+eF0pGKVifPn3QtWtXq7ZJkyYhLy8P7dq182hfavV3cVFRETZt2oQ333wTiYmJAICxY8di4cKFGDJkCFQqFg42++WXX/Dyyy/D39/f211RtD/++AO//PILMjIyEB0dDQCYMmUKHn/8ceTn5yMkhLUNzbKystC8eXO8+uqrqFOnDpo0aYLevXvj22+/9XbXFKmwsBBTp05F+/btOQppx8mTJ9G8eXPLvz+qKC8vD++//z6WLl2KTp06AQDGjRuHhQsXQpIk/u4rIyAgwKoc5pdffolvvvkGu3bt8vh/CNTqKdHjx4+jsLAQSUlJlrakpCRkZWXh3LlzXuyZ8hw6dAgpKSn48MMPvd0VRatbty7eeecdq18WKpUKkiQhPz/fiz1TnpiYGCxZssRSau7333/HV199hdtuu83LPVOmJUuWoEOHDrj11lu93RVFO3nyJBo3buztbijakSNH4O/vj27dulnaUlJSsG3bNgZrdpinkZ955hk0aNDA4/ev1SNsFy9eREhICIKDgy1t5l+0Fy9e9MpfiFINGTLE213wCTqdzuqHIAC89957aNy4MeLi4rzUK+W7//778ccff6BVq1Z48sknvd0dxfnhhx+wc+dO7NixAxs2bPB2dxRLkiScOXMG3333HTZs2IBr166ha9euGD9+PEJDQ73dPcX4+++/ER8fjy+//BIrVqxATk4Obr/9dkyYMIGzAHZ8/vnnyMzMxODBg71y/1o9wlZYWAitVmvVZq5TajAYvNElqmFWr16NtLQ0TJkyxdtdUbS5c+di7dq1yM/Px5gxY7zdHUUxGAyYPHkyJk+eDJ1O5+3uKNr58+dx9epVqFQqLFy4ENOnT8eRI0f4PVVOQUEBzp8/j3feeQdTpkzBG2+8gWPHjmHSpEne7pqibdy4EQ899BDCwsK8cv9aPcIWEBBQITAzvy47Z03kiuXLl2Pp0qWYNm1ahUWrZO2mm24CAMyaNQtPPPEEzp07h/r163u5V8qwfPlyNGrUCH369PF2VxQvPj4e33zzDcLDwy1Te3Xq1EFqair++ecfzppc5+fnh4KCArzxxhto2rQpAGDmzJno378/srKyuP7PhosXL+LIkSN4+eWXvdaHWh2wxcXFIS8vD4WFhQgMDARgWggNALGxsd7sGvm42bNnY/369XjttdcwYMAAb3dHkbKzs3H06FH06tXL0ta8eXMAwOXLlxmwXbd9+3ZkZWVZdqQVFxfDaDSiXbt22LlzJ+rVq+flHipL+VHIZs2aAeAyl7JiYmIgCAKaNGliaTN/ff78eQZsNnz99deoW7euV3do1+op0ZYtWyIwMBBHjx61tB05cgQxMTGIj4/3Ys/Ily1btgzvv/8+5s6dy2DNjnPnzmHkyJH4559/LG2//vor/Pz8uGi8jA0bNmDHjh3YunUrtm7div79+6NFixbYunUrYmJivN09RUlPT0fHjh2tNvj89ttvEAQBN9xwg/c6pjDt2rWDKIr47bffLG1//vknBEHg775K/PDDD1YbFL2hVgdsAQEBSE1NxYwZM3Ds2DEcOnQICxcuxFNPPeXtrpGP+uOPP7B8+XIMGjQIXbp0QVZWluWP0Wj0dvcUpU2bNkhMTMSkSZNw4sQJHDp0CK+++iqefPJJLnwuIz4+Ho0aNbL8CQ8Ph0ajQaNGjZhfrJz27dsjMDAQEydOxKlTp/DNN99gypQpSE1NRVRUlLe7pxiNGjXCXXfdhcmTJ+Onn37CTz/9hOnTp6NXr158TpX4888/LTMA3lLr/7WPHz8eRUVFGDx4MLRaLVJTUzFo0CBvd4t81BdffAFRFLF69WqsXr3a6r1du3ZZ1osQIAgCli1bhtdffx2PP/44/Pz80LdvX7z00kve7hr5qNDQULz77ruYO3cuHn30Ufj7++Pee+/FhAkTvN01xZk3bx7mzp2LQYMGQZIk9OrVC5MnT/Z2txQrOzsb4eHhXu2DSpIkyas9ICIiIiK7avWUKBEREZEvYMBGREREpHAM2IiIiIgUjgEbERERkcIxYCMiIiJSOAZsRER2KGkjvZL6QkQmRUVFuOeee/D111/LPic7OxsjR45Eu3btkJycjGXLljk8hwEbEVXZ9u3b8cQTT6Bjx47o1KkTBg4ciPT0dG93q0oMBgOmT5+OgwcPersruHLlCkaPHo0zZ864fI3u3btjwYIFbuwVERUWFmLUqFE4efKk7HNEUcTzzz+Py5cv46OPPsL06dOxdu1abNmyxe55DNiIyGWSJGHChAmYOnUqEhMTsXDhQsybNw8xMTEYOnQoPvzwQ2930WWZmZnYuHGjIipU/P7779i9e3eVRtiWLVuGxx9/3I29IqrdfvnlF6SmpuLChQtOnbd//378+eefWLx4MZo3b44777wTAwcOxA8//GD3vFpf6YCIXLd582Zs374da9euxS233GJpv+OOO6DRaDB79mzcddddiIiI8GIvCQBuuukmb3eBqEY5dOgQUlJSMHLkSCQmJlq9d+rUKbz++us4evQoIiMjcf/992P48OHw9/fH4cOH0bFjR6syYKNHj3Z4P46wEZHL1q1bhx49elgFa2Yvvvgi+vfvj4KCAkvbrl270LdvXyQmJuLOO+/EO++8YzVqZC5q/vzzzyMxMRHdu3fHrl27cOLECfTr1w+JiYno168fTp06ZXXOxx9/jEGDBqFNmza4++67sXv3bqu+HDt2DM888wzat2+P1q1b4/7778dXX31leX/ixIkYNWoURowYgcTERAwaNAh33nknAGDIkCGYOHGiy/2TJAmrV69G9+7d0bp1azz00EP45ptvLO9/8803aNGiBY4dO4bU1FS0bt0avXr1wpdffml5/8knnwQA9OnTB2+99ZbNv4vMzEyMGDECnTt3Rrt27fDMM8/g+PHjlvfLTol2794dLVq0qPBn4MCBluO///57DBgwAG3atEHXrl3x1ltvQRRFm/cmqo2GDBmCCRMmICAgwKrdXO4yISEBW7duxezZs7Fnzx4sXrwYAPD3338jPj4ey5YtQ/fu3dGrVy+8++67jkfQJSIiF1y4cEFKSEiQNm/eLOv4DRs2SC1atJBmz54t7d+/X3rrrbekm2++WZo3b57lmISEBKl9+/bSm2++KR08eFB66qmnpLZt20q9evWSNm7cKO3du1fq0qWL9NRTT1md06FDB2nixIlSenq6NGHCBKlFixbSwYMHJUmSpH/++Udq3bq1NG7cOOngwYPSl19+KfXv319q27atdOXKFUmSJOnll1+WbrrpJmnixIlSRkaG9M0330hffPGFlJCQIK1du1b666+/XO7fwoULpVatWklvv/22lJ6eLo0ZM0Zq1aqV9Msvv0iSJEmHDx+WEhISpDvuuEPasGGDdODAAenpp5+WWrduLV2+fFnKy8uT3n//fSkhIUHauXOn9O+//9p8vk8++aTUt29fae/evdLXX38t9evXT0pOTpaKi4slSZKkO+64Q5o/f74kSZL066+/St9//73lz6pVq6SEhATpk08+kSRJko4fPy61bt1aev7556V9+/ZJ69atkxITE6W5c+fK+rsmqm0SEhKk9PR0SZIk6eOPP5b69Olj9f7+/fulVq1aSSUlJdJTTz0ldejQQRo7dqz0888/S9u2bZM6dOggrV+/3u49GLARkUt+/PFHKSEhQfr6668dHltSUiJ16tRJmjJlilX7smXLpJtvvlm6dOmSJEmmH3ojRoywvP/NN99ICQkJ0vLlyy1tK1askNq3b295nZCQID3xxBNW13344YelwYMHS5IkSfv27ZOeffZZyWg0Wt7/9ddfpYSEBOnQoUOSJJUGbIWFhZZj/vnnH6sfwq707/Lly1KrVq2kVatWWfXvsccek4YNGyZJUmnAtnHjRsv7p0+flhISEqQ9e/ZYHXPy5Mnyj9bCHBSanTlzRlqwYIElKC0bsJWVmZkpdenSRZo4caKlbeTIkVKfPn2kkpISS9tHH30k3XzzzVJ2dnalfSCqrcr+rJg7d6504403Sm3btrX8SUxMlBISEqS//vpLevbZZ6WuXbta/mNKkiTp7bfflnr16mX3HlzDRkQuUavVACBrmuz06dPIyclB7969rdr79OmDpUuX4scff8Qdd9wBAGjTpo3l/cjISADAzTffbGnT6XTIz8+vcJ2yunfvjnfffRcAkJKSgpSUFFy7dg2nT5/GmTNncOjQIQBAcXGx5Zy4uLgKUxu2ONO/H3/8EQaDAV27dkVJSYnlmOTkZPznP/+xum7ZNTBxcXEATDvQ5EpKSsJbb72FEydOoHv37khJScHYsWPtnmM0GvHSSy+hTp06ePXVVy3t3333He69915IkmTpd9euXVFcXIxjx46hZ8+esvtFVNuUlJSgQ4cOmDVrVoX34uLiEBMTA4PBAD+/0hCsSZMmOH/+vN3rMmAjIpfUrVsXAPDvv/9WesyFCxcQFxeHK1euAIDVIlugNOApG4AFBwdXuI6jQCo6OtrqdUREBPLz8yGKIkRRxOzZs/HRRx9BFEU0btwYLVu2BGCd18zcF0ec6V9OTg4A4MEHH7T5ftmATKvVWr4WBNPyYmfWjC1evBjLli3Drl27sGPHDmi1WvTr1w+TJk2yXK+8hQsX4tdff8WWLVusPkNOTg7WrVuHdevWVTgnMzNTdp+IaqOmTZtiz549qFu3LjQaDQDg8OHD2LhxI+bPn4/27dtj3759MBgMlvf//PNPNGjQwO51GbARkUsiIiLQsmVLHDx4EP3796/w/v/+9z90794d06ZNQ6dOnQCYkkWWZX4dHh5epb6YA0KzS5cuQafTQRAErFixAv/973+xdOlS3HbbbQgICMCpU6ewY8eOKt1TjrCwMADAu+++a/Mzmn9Yu4NOp8Mrr7yCKVOm4Oeff8aWLVuwfv16JCYm4t57761w/Jdffok1a9Zg0aJFaNKkidV7oaGhuPfee20GmvXq1XNbn4lqovvvvx/Lly/HxIkTMWzYMOTk5GDKlClITEyEVqtFnz59sGLFCkyaNAnDhw/HqVOnsGbNGowZM8budblLlIhc9sQTTyAtLQ3fffddhffeeust+Pv7o0ePHmjSpAl0Oh327Nljdczu3buhVqutphldUT5J71dffYWkpCQAwA8//ID27duje/fullGkAwcOALBfOcA85VsVbdq0gZ+fH65cuYLWrVtb/hw4cACbNm2SfQ9Hx+Xl5aF79+5IS0uDSqVCmzZt8NprryEgIMBmjqi///4bEydOxIABAypMJwNAu3btcPbsWas++/n5YcmSJbh06ZK8D09USwUFBeHdd9/F5cuXkZqaipEjR6JLly6WKdLg4GCsW7cOV65cwYMPPoiZM2fi+eefx2OPPWb3uhxhIyKXPfzww/jqq68wZMgQPPnkk+jcuTMKCgrw2WefYe/evZg1axZiY2MBAC+88ALmzJmD4OBgdOvWDT/88ANWrlyJgQMHQqfTVakfaWlpmDdvHm677TZs27YNJ06cwGuvvQYAaNWqFdasWYPNmzfjhhtuwHfffYfVq1cDAK5evVrpNUNDQwGYklzWr1+/wiiUHJGRkejfvz9ee+01ZGdno0WLFvj222+xcuVKjBo1SvZ1zH356quvoNVqUb9+/QrvN27cGLNnz0ZhYSFiYmKwa9cuGI1G3H777VbHGgwGjBgxAhEREejbty9+/PFHq8C1bdu2eP755/H4449j0qRJ6NOnD65cuYLFixcjMDAQjRs3dvo5ENV0f/zxh9XrhIQErF27ttLjGzZsWGEdqyMM2IjIZYIgYNmyZXj//fexdetWfPDBB/Dz80OLFi2wZs0adOnSxXLsU089Ba1Wi7Vr1+KDDz5A3bp18dJLL+HZZ5+tcj+GDRuG7777Dh988AGaNWuGd955xzJqN3ToUFy8eBGLFi2C0WhEkyZN8Oabb2L27Nn48ccfcffdd9u8ZkhICJ599ll88MEH+Pvvv7Fq1SqX+jZ58mTUqVMH69atQ1ZWFurVq4eXX34ZTz/9tOxrNG/eHPfccw+WLl2KCxcuYNq0aRWOeeONNzBv3jzMmzcPubm5aN68OVasWIFmzZpZHZeZmYnff/8dAPDII49UuM4ff/yBtm3b4t1338WSJUswfPhwBAcHIzk5GePHj4e/v79zD4CI3EIl2ZsTICJSuBYtWuC1117DgAEDvN0VIqJqwzVsRERERArHgI2IiIhI4TglSkRERKRwHGEjIiIiUjgGbEREREQKx4CNiIiISOEYsBEREREpHAM2IiIiIoVjwEZERESkcP8Pb3L8spmMvcIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs = GridSpec(nrows=1, ncols=1)\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "\n",
    "names = [      \n",
    "\"MicroC\",\n",
    "\"Hi-C\",\n",
    "\"PLAC-Seq\",\n",
    "\"ChIA-PET PolII\",\n",
    "\"ChIA-PET CTCF\",\n",
    "\"SPRITE\",\n",
    "\"GAM\"]\n",
    "\n",
    "files=[    \n",
    "\"4DNFI9GMP2J8_H1-hESC_MicroC.cis.100000.vecs.tsv\",\n",
    "\"4DNFI82R42AD_H1-hESC_FA_DSG_DpnII_Hi-C.cis.100000.vecs.tsv\",\n",
    "\"4DNFICOGAKW2_H1-hESC_PLAC-Seq.cis.100000.vecs.tsv\",\n",
    "\"4DNFIF1J6GC5_H1-hESC_ChIA_PET_PolII.cis.100000.vecs.tsv\",\n",
    "\"4DNFIK3276U7_H1-hESC_ChIA_PET_CTCF.cis.100000.vecs.tsv\",\n",
    "\"4DNFITX6WCRT_H1-hESC_DNA.cis.100000.vecs.tsv\",\n",
    "\"GAM_H1_new.ALL.AB.at100Kb.final.chosen_formatted.tsv\"]\n",
    "  \n",
    "    \n",
    "\n",
    "\n",
    "c=['g','dodgerblue','orange','cyan','pink','r','k']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "k=0\n",
    "ax = plt.subplot(gs[0])\n",
    "for file in files:\n",
    "    print(file)\n",
    "    data = pd.read_csv(file,sep=\"\\t\")\n",
    "    print(data.shape)\n",
    "    d=data\n",
    "    A_start=[]\n",
    "    A_end=[]\n",
    "    B_start=[]\n",
    "    B_end=[]\n",
    "    A_size=[]\n",
    "    B_size=[]\n",
    "    E1=d.columns.get_loc(\"E1\")\n",
    "    for i in range(len(d[\"E1\"])-1):\n",
    "        if (d.iloc[i,E1]>0) & (d.iloc[i+1,E1]>0):\n",
    "            A_val=d.iloc[i,E1]\n",
    "            A_start.append(d.iloc[i,1])\n",
    "            A_end.append(d.iloc[i,2])\n",
    "        if (d.iloc[i,E1]>0) & (d.iloc[i+1,E1]<0):\n",
    "            B_start=[]\n",
    "            B_end=[]\n",
    "            if(len(A_end))>0:\n",
    "                A_size.append(max(A_end)-min(A_start))\n",
    "            else:\n",
    "                A_size.append(d.iloc[i,2]-d.iloc[i,1])\n",
    "        if (d.iloc[i,E1]<0) & (d.iloc[i+1,E1]<0):\n",
    "            B_val=d.iloc[i,E1]\n",
    "            B_start.append(d.iloc[i,1])\n",
    "            B_end.append(d.iloc[i,2])\n",
    "            #print(\"-\")         \n",
    "        if (d.iloc[i,E1]<0) & (d.iloc[i+1,E1]>0):\n",
    "            A_start=[]\n",
    "            A_end=[]\n",
    "            if(len(B_end))>0:\n",
    "                B_size.append(max(B_end)-min(B_start))\n",
    "            else:\n",
    "                B_size.append(d.iloc[i,2]-d.iloc[i,1] )\n",
    "    bins=100\n",
    "    print(len(B_size))\n",
    "    B_size=[x for x in B_size if 100000 < x <= 7000000]\n",
    "    print(len(B_size))\n",
    "    B_size_subset=sample(B_size,350)\n",
    "    n, bins, patches = ax.hist(B_size_subset,bins=bins,density=True, histtype='step',color=c[k],\n",
    "                           cumulative=True, label=names[k],linewidth=1.8)\n",
    "    ax.grid(True)\n",
    "    ax.legend(loc='right')\n",
    "    ax.set_xlabel('Compartment size')\n",
    "    ax.set_ylabel('Density')\n",
    "    #plt.xscale('log')\n",
    "    k=k+1\n",
    "plt.savefig(out_path+\"Supp_Figure_2d_H1-hESC_comp_size_B.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4DNFI9FVHJZQ_HFFc6_MicroC.cis.100000.vecs.tsv\n",
      "(30895, 8)\n",
      "814\n",
      "4DNFI9REIU8H_HFFc6_PLAC-Seq.cis.100000.vecs.tsv\n",
      "(30895, 8)\n",
      "719\n",
      "4DNFIAVXXO55_HFFc6_FA_DSG_DpnII_Hi-C.cis.100000.vecs.tsv\n",
      "(30895, 8)\n",
      "811\n",
      "4DNFIELOAD41_HFFc6_ChIA_PET_CTCF.cis.100000.vecs.tsv\n",
      "(30895, 8)\n",
      "931\n",
      "4DNFIOOSHTQV_HFFc6_ChIA_PET_PolII.cis.100000.vecs.tsv\n",
      "(30895, 8)\n",
      "943\n",
      "4DNFIP9HGF9M_HFFc6_SPRITE.100000.cis.vecs.tsv\n",
      "(30894, 8)\n",
      "469\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "os.chdir(\"compartments/\")\n",
    "\n",
    "\n",
    "gs = GridSpec(nrows=1, ncols=1)\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "\n",
    "names = [\"MicroC\",\n",
    "\"PLAC-Seq\",\n",
    "\"Hi-C\",\n",
    "\"ChIA-PET CTCF\",\n",
    "\"ChIA-PET PolII\",\n",
    "\"SPRITE\"]\n",
    "\n",
    "files=[\n",
    "\"4DNFI9FVHJZQ_HFFc6_MicroC.cis.100000.vecs.tsv\",\n",
    "\"4DNFI9REIU8H_HFFc6_PLAC-Seq.cis.100000.vecs.tsv\",\n",
    "\"4DNFIAVXXO55_HFFc6_FA_DSG_DpnII_Hi-C.cis.100000.vecs.tsv\",\n",
    "\"4DNFIELOAD41_HFFc6_ChIA_PET_CTCF.cis.100000.vecs.tsv\",\n",
    "\"4DNFIOOSHTQV_HFFc6_ChIA_PET_PolII.cis.100000.vecs.tsv\",\n",
    "\"4DNFIP9HGF9M_HFFc6_SPRITE.100000.cis.vecs.tsv\"]\n",
    "c=['g','orange','dodgerblue','pink','cyan','r']\n",
    "\n",
    "\n",
    "k=0\n",
    "ax = plt.subplot(gs[0])\n",
    "for file in files:\n",
    "    print(file)\n",
    "    data = pd.read_csv(file,sep=\"\\t\")\n",
    "    print(data.shape)\n",
    "    d=data\n",
    "    A_start=[]\n",
    "    A_end=[]\n",
    "    B_start=[]\n",
    "    B_end=[]\n",
    "    A_size=[]\n",
    "    B_size=[]\n",
    "    E1=d.columns.get_loc(\"E1\")\n",
    "    for i in range(len(d[\"E1\"])-1):\n",
    "        if (d.iloc[i,E1]>0) & (d.iloc[i+1,E1]>0):\n",
    "            A_val=d.iloc[i,E1]\n",
    "            A_start.append(d.iloc[i,1])\n",
    "            A_end.append(d.iloc[i,2])\n",
    "        if (d.iloc[i,E1]>0) & (d.iloc[i+1,E1]<0):\n",
    "            B_start=[]\n",
    "            B_end=[]\n",
    "            if(len(A_end))>0:\n",
    "                A_size.append(max(A_end)-min(A_start))\n",
    "            else:\n",
    "                A_size.append(d.iloc[i,2]-d.iloc[i,1])\n",
    "        if (d.iloc[i,E1]<0) & (d.iloc[i+1,E1]<0):\n",
    "            B_val=d.iloc[i,E1]\n",
    "            B_start.append(d.iloc[i,1])\n",
    "            B_end.append(d.iloc[i,2])\n",
    "        if (d.iloc[i,E1]<0) & (d.iloc[i+1,E1]>0):\n",
    "            A_start=[]\n",
    "            A_end=[]\n",
    "            if(len(B_end))>0:\n",
    "                B_size.append(max(B_end)-min(B_start))\n",
    "            else:\n",
    "                B_size.append(d.iloc[i,2]-d.iloc[i,1] )\n",
    "    bins=100\n",
    "    A_size=[x for x in A_size if 100000 < x <= 7000000]\n",
    "    print(len(A_size))\n",
    "    A_size_subset=sample(A_size,460)\n",
    "    n, bins, patches = ax.hist(A_size,bins=bins,density=True, histtype='step',color=c[k],\n",
    "                           cumulative=True, label=names[k],linewidth=1.8)\n",
    "    ax.grid(True)\n",
    "    ax.legend(loc='right')\n",
    "    ax.set_xlabel('Compartment size')\n",
    "    ax.set_ylabel('Density')\n",
    "    k=k+1\n",
    "plt.savefig(out_path+\"Supp_Figure_2e_HFFc6_comp_size_A.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4DNFI9FVHJZQ_HFFc6_MicroC.cis.100000.vecs.tsv\n",
      "(30895, 8)\n",
      "1227\n",
      "880\n",
      "4DNFI9REIU8H_HFFc6_PLAC-Seq.cis.100000.vecs.tsv\n",
      "(30895, 8)\n",
      "1259\n",
      "863\n",
      "4DNFIAVXXO55_HFFc6_FA_DSG_DpnII_Hi-C.cis.100000.vecs.tsv\n",
      "(30895, 8)\n",
      "1224\n",
      "883\n",
      "4DNFIELOAD41_HFFc6_ChIA_PET_CTCF.cis.100000.vecs.tsv\n",
      "(30895, 8)\n",
      "1478\n",
      "985\n",
      "4DNFIOOSHTQV_HFFc6_ChIA_PET_PolII.cis.100000.vecs.tsv\n",
      "(30895, 8)\n",
      "1794\n",
      "997\n",
      "4DNFIP9HGF9M_HFFc6_SPRITE.100000.cis.vecs.tsv\n",
      "(30894, 8)\n",
      "729\n",
      "476\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from random import sample\n",
    "\n",
    "\n",
    "os.chdir(\"compartments/\")\n",
    "\n",
    "\n",
    "gs = GridSpec(nrows=1, ncols=1)\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "\n",
    "names = [\"MicroC\",\n",
    "\"PLAC-Seq\",\n",
    "\"Hi-C\",\n",
    "\"ChIA-PET CTCF\",\n",
    "\"ChIA-PET PolII\",\n",
    "\"SPRITE\"]\n",
    "\n",
    "files=[\n",
    "\"4DNFI9FVHJZQ_HFFc6_MicroC.cis.100000.vecs.tsv\",\n",
    "\"4DNFI9REIU8H_HFFc6_PLAC-Seq.cis.100000.vecs.tsv\",\n",
    "\"4DNFIAVXXO55_HFFc6_FA_DSG_DpnII_Hi-C.cis.100000.vecs.tsv\",\n",
    "\"4DNFIELOAD41_HFFc6_ChIA_PET_CTCF.cis.100000.vecs.tsv\",\n",
    "\"4DNFIOOSHTQV_HFFc6_ChIA_PET_PolII.cis.100000.vecs.tsv\",\n",
    "\"4DNFIP9HGF9M_HFFc6_SPRITE.100000.cis.vecs.tsv\"]\n",
    "\n",
    "c=['g','orange','dodgerblue','pink','cyan','r']\n",
    "\n",
    "\n",
    "\n",
    "k=0\n",
    "ax = plt.subplot(gs[0])\n",
    "for file in files:\n",
    "    print(file)\n",
    "    data = pd.read_csv(file,sep=\"\\t\")\n",
    "    print(data.shape)\n",
    "    d=data\n",
    "    A_start=[]\n",
    "    A_end=[]\n",
    "    B_start=[]\n",
    "    B_end=[]\n",
    "    A_size=[]\n",
    "    B_size=[]\n",
    "    E1=d.columns.get_loc(\"E1\")\n",
    "    for i in range(len(d[\"E1\"])-1):\n",
    "        if (d.iloc[i,E1]>0) & (d.iloc[i+1,E1]>0):\n",
    "            A_val=d.iloc[i,E1]\n",
    "            A_start.append(d.iloc[i,1])\n",
    "            A_end.append(d.iloc[i,2])\n",
    "        if (d.iloc[i,E1]>0) & (d.iloc[i+1,E1]<0):\n",
    "            B_start=[]\n",
    "            B_end=[]\n",
    "            if(len(A_end))>0:\n",
    "                A_size.append(max(A_end)-min(A_start))\n",
    "            else:\n",
    "                A_size.append(d.iloc[i,2]-d.iloc[i,1])\n",
    "        if (d.iloc[i,E1]<0) & (d.iloc[i+1,E1]<0):\n",
    "            B_val=d.iloc[i,E1]\n",
    "            B_start.append(d.iloc[i,1])\n",
    "            B_end.append(d.iloc[i,2])\n",
    "        if (d.iloc[i,E1]<0) & (d.iloc[i+1,E1]>0):\n",
    "            A_start=[]\n",
    "            A_end=[]\n",
    "            if(len(B_end))>0:\n",
    "                B_size.append(max(B_end)-min(B_start))\n",
    "            else:\n",
    "                B_size.append(d.iloc[i,2]-d.iloc[i,1] )\n",
    "    bins=100\n",
    "    print(len(B_size))\n",
    "    B_size=[x for x in B_size if 100000 < x <= 7000000]\n",
    "    print(len(B_size))\n",
    "    B_size_subset=sample(B_size,470)\n",
    "    n, bins, patches = ax.hist(B_size_subset,bins=bins,density=True, histtype='step',color=c[k],\n",
    "                           cumulative=True, label=names[k],linewidth=1.8)\n",
    "    ax.grid(True)\n",
    "    ax.legend(loc='right')\n",
    "    ax.set_xlabel('Compartment size')\n",
    "    ax.set_ylabel('Density')\n",
    "    #plt.xscale('log')\n",
    "    k=k+1\n",
    "plt.savefig(out_path+\"Supp_Figure_2e_HFFc6_comp_size_B.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}