3_bbmodel_concordance_v3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compared to v1 and v2, v3 models tree-growth including cell survival d, such that each cell reaching generation k has a chance (1-d) to die (independently of other cells) before dividing to the next generation or becoming quiescent.\n",
    "## To speed up this procedure, we pre-compute the probabilities of the families from which observed families are recovered. The pre-computation occurs on a lattice of possible rho values (e.g., 0, 0.001, 0.002, ..., 1). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/giulio/Desktop/curie/cross analysis/Generational_multiplex_analysis_cloned/\n"
     ]
    }
   ],
   "source": [
    "import itertools as itt\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import scipy.optimize as spo\n",
    "import scipy.special as spsp\n",
    "import scipy.stats as sps\n",
    "import seaborn as sns\n",
    "import time\n",
    "\n",
    "from matplotlib.ticker import FixedLocator\n",
    "\n",
    "mpl.rcParams['axes.titlesize'] = 'xx-large'\n",
    "mpl.rcParams['axes.labelsize'] = 'xx-large'\n",
    "mpl.rcParams['xtick.labelsize'] = 'x-large'\n",
    "mpl.rcParams['ytick.labelsize'] = 'x-large'\n",
    "mpl.rcParams['xtick.direction'] = 'out'\n",
    "mpl.rcParams['ytick.direction'] = 'out'\n",
    "mpl.rcParams['legend.frameon'] = True\n",
    "mpl.rcParams['legend.framealpha'] = 0.5\n",
    "mpl.rcParams['legend.fontsize'] = 'large'\n",
    "\n",
    "path_proj = os.getcwd()+ '/'\n",
    "path_sc = path_proj + 'csv/Single_cell/'\n",
    "path_sort = path_proj + 'csv/Sort/'\n",
    "path_plot = path_proj + 'figures/'\n",
    "print(path_proj)\n",
    "\n",
    "#LOAD THE PROCESSED DATA\n",
    "df = pd.read_csv(path_sc+'Pooled_data.csv', sep=';', decimal=',')\n",
    "\n",
    "#GLOBAL VARIABLES FOR ITERATION\n",
    "conds = ['P', 'P+ILs']\n",
    "or_cells = ['SLAM-HSC', 'ST-HSC', 'MPP']\n",
    "\n",
    "#SET A RANDOM SEED TO REPLICATE THE BOOTSTRAP CONFIDENCE INTEVALS\n",
    "#BASED ON RANDOM SAMPLING WITH REPLACEMENT\n",
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#IMPORTANT: SAMPLING PROBABILITY AND SURVIVAL PROBABILITY MUST BE SPECIFIED FROM THE USER! \n",
    "r_sample = 0.71\n",
    "r_surv = 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOTE: A FAMILY VECTOR IS THE VECTOR OF CELL COUNTS PER GENERATION (ONE GENERATION PER VECTOR ENTRY);\n",
    "\n",
    "#BELOW ARE THE FUNCTIONS AND CLASSES TO DEFINE FULL AND SAMPLED FAMILY VECTORS\n",
    "\n",
    "#GIVEN A FAMILY VECTOR, POPULATE THE NEXT GENERATION WITH ALL POSSIBLE COUNTS THAT ARE PHYSICALLY POSSIBLE\n",
    "#(HERE IT IS ASSUMED THAT EACH DIVIDING CELLS PRODUCES TWO CELLS IN THE NEXT GENERATION AND THAT CELLS CAN DIE BEFORE DIVIDING OR BECOMING QUIESCENT)\n",
    "def fam_generator(max_gen, remove_empty=False):\n",
    "    fam_vec = np.vstack((np.zeros(1), np.ones(1)))\n",
    "    if max_gen > 0:\n",
    "        for i in range(1,max_gen+1):            \n",
    "            next_gen_counts = np.arange(0,2**i+1)\n",
    "            next_fam_vec = np.repeat(fam_vec, len(next_gen_counts), axis=0)\n",
    "            next_gen_counts = np.tile(next_gen_counts, len(fam_vec))[None,:].T\n",
    "            next_fam_vec = np.hstack((next_fam_vec,next_gen_counts))\n",
    "            mult = 2.**-np.arange(0,next_fam_vec.shape[1])\n",
    "            is_fam_possible = (next_fam_vec * mult).sum(axis=1)<=1\n",
    "            fam_vec = next_fam_vec[is_fam_possible]\n",
    "    if remove_empty:\n",
    "        fam_vec = fam_vec[fam_vec.sum(axis=1)>0]\n",
    "    return fam_vec\n",
    "\n",
    "\n",
    "#fam_generator(6)[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BELOW ARE THE FUNCTIONS TO CALCLUATE THE DISTRIBUTION AND CONFIDENCE INTERVALS\n",
    "#OF DATA IN A GIVEN SUPPORT (SIMILARLY AS IN NOTEBOOK 2)\n",
    "\n",
    "#TRANSFORM THE PANDAS DATAFRAME OF A FAMILY INTO A FAMILY VECTOR\n",
    "def df_fam_to_vec(dff, max_g):\n",
    "    res = np.zeros(max_g+1)\n",
    "    gens, counts = np.unique(dff.Generation, return_counts=True)\n",
    "    res[gens] = counts\n",
    "    return res\n",
    "\n",
    "def epmf_on_support(data, support, bool_norm=True):\n",
    "    classes, counts = np.unique(data, return_counts=True)\n",
    "    counts_sum = counts.sum()\n",
    "    if counts_sum == 0:\n",
    "        return np.zeros(len(support))\n",
    "    elif bool_norm:\n",
    "        return np.hstack([counts[classes==cl] if any(classes==cl) else np.zeros(1) for cl in support]\n",
    "                        )/counts_sum\n",
    "    else:\n",
    "        return np.hstack([counts[classes==cl] if any(classes==cl) else np.zeros(1) for cl in support])\n",
    "    \n",
    "def stat_bootCI(func, args, data, n_iter=250000, bool_trim01=True):\n",
    "    stat = func(data, *args)\n",
    "    boot_stat = np.array([func(data[boot_index], *args)\n",
    "                          for boot_index in np.random.choice(len(data), size=(n_iter,len(data)))])\n",
    "    boot_per = np.percentile(boot_stat, q=[97.5, 2.5], axis=0)\n",
    "    boot_ci = 2.*stat - boot_per\n",
    "    if bool_trim01:\n",
    "        boot_ci[0,boot_ci[0,:]<0.] = np.zeros((boot_ci[0,:]<0.).sum())\n",
    "        boot_ci[1,boot_ci[1,:]>1.] = np.ones((boot_ci[1,:]>1.).sum())\n",
    "    return stat, boot_ci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PROBABILITY THAT A BETA-BINOMIAL DISTRIBUTION, WITH PARAMETERS n, a, b, EQUALS k\n",
    "def bb_dist(k, n, a, b):\n",
    "    if a == 0.:\n",
    "        if k==0.:\n",
    "            return 1.\n",
    "        else:\n",
    "            return 0.\n",
    "    elif b == 0.:\n",
    "        if k == n:\n",
    "            return 1.\n",
    "        else:\n",
    "            return 0.\n",
    "    else:\n",
    "        return spsp.binom(n,k) * spsp.beta(k+a,n-k+b) / spsp.beta(a,b)\n",
    "    \n",
    "#PROBABILITY THAT A BINOMIAL DISTRIBUTION, WITH PARAMETERS n, R, EQUALS k    \n",
    "def bino_dist(k, n, r):\n",
    "    if r == 0.:\n",
    "        if k==0.:\n",
    "            return 1.\n",
    "        else:\n",
    "            return 0.\n",
    "    elif r == 1.:\n",
    "        if k == n:\n",
    "            return 1.\n",
    "        else:\n",
    "            return 0.\n",
    "    else:\n",
    "        return spsp.binom(n, k) * r**(k) * (1.-r)**(n-k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#BETA-BINOMIAL (BB) MODEL FOR CELL DIVISION,\n",
    "#WHERE A FAMILY DEVELOPS FROM A 1 CELL IN GENERATION 0 ITERATIVELY AS FOLLOWS:\n",
    "#THE NUMBER OF CELLS THAT DIVIDE FROM GENERATION i TO i+1 FOLLOWS A BETA-BINOMIAL DISTRIBUTION\n",
    "#PARAMETRISED SO THAT ONE CELL DIVIDES WITH PROBABILITY p, AND THE CELLS IN THE SAME GENERATION\n",
    "#DECIDE TO DIVIDE WITH (INTRACLASS) CORRELATION rho.\n",
    "#IN ACCORDANCE WITH EXPERIMENTAL DATA, THE CELLS FROM FULL FAMILY VECTOR GROWN UNDER THE BB MODEL\n",
    "#ARE THEN SAMPLED (INDEPENDENTLY AND WITH EQUAL DISTRIBUTION) WITH PROBABILITY r.    \n",
    "    \n",
    "#RECURSIVE FUNCTIONS TO CALCULATE THE PROBABILITY OF A FAMILY VECTOR UNDER THE BB MODEL (WITH DEATH) FOR 1>rho>0, =0, =1\n",
    "    \n",
    "def rec_model_rho(m, alpha, beta, u_start, u_end, r_surv):\n",
    "    if len(m) == 1:\n",
    "        return bino_dist(m[0], u_end, r_surv) \n",
    "    gen_mult = [\n",
    "        bino_dist(u, u_end, r_surv) * bb_dist(m[0], u, beta[0], alpha[0])\n",
    "    for u in np.arange(u_start, u_end+1)]\n",
    "    return sum([gm * rec_model_rho(m[1:], alpha[1:], beta[1:], u_start=m[1], u_end=2*(u-m[0]), r_surv=r_surv)\n",
    "        for gm,u in zip(gen_mult, np.arange(u_start, u_end+1))\n",
    "        if gm > 0.])\n",
    "\n",
    "def rec_model_rho0(m, p, u_start, u_end, r_surv):\n",
    "    if len(m) == 1:\n",
    "        return bino_dist(m[0], u_end, r_surv) \n",
    "    gen_mult = [\n",
    "        bino_dist(u, u_end, r_surv) * bino_dist(m[0], u, 1-p[0])\n",
    "    for u in np.arange(u_start, u_end+1)]\n",
    "    return sum([gm * rec_model_rho0(m[1:], p[1:], u_start=m[1], u_end=2*(u-m[0]), r_surv=r_surv)\n",
    "        for gm,u in zip(gen_mult, np.arange(u_start, u_end+1))\n",
    "        if gm > 0.])\n",
    "\n",
    "def rec_model_rho1(m, p, u_start, u_end, r_surv):\n",
    "    #print(m[0], p[0], u_start, u_end, r_surv)\n",
    "    if len(m) == 1:\n",
    "        return bino_dist(m[0], u_end, r_surv) * (1-p[0])\n",
    "    gen_mult = [\n",
    "        bino_dist(u, u_end, r_surv) * p[0]\n",
    "    for u in np.arange(u_start, u_end+1)]\n",
    "    return sum([gm * rec_model_rho1(m[1:], p[1:], u_start=m[1], u_end=2*(u-m[0]), r_surv=r_surv)\n",
    "        for gm,u in zip(gen_mult, np.arange(u_start, u_end+1))\n",
    "        if gm > 0.])\n",
    "\n",
    "#PROBABILITY OF OBSERVING THE FAMILY VECTOR m UNDER THE BB MODEL WITH PARAMETERS p, rho, r_surv\n",
    "\n",
    "def pmf_m(m, p, r_surv, rho):\n",
    "    if rho == 0.:\n",
    "        res = rec_model_rho0(m=m, p=p, u_start=0, u_end=1, r_surv=r_surv)\n",
    "    elif rho == 1.:\n",
    "        where_cells = m.nonzero()[0]\n",
    "        #if len(where_cells) <= 1:\n",
    "        #    res = rec_model_rho1(m, p=p, u_start=0, u_end=1, r_surv=r_surv)\n",
    "        if len(where_cells) == 0:\n",
    "            #res = rec_model_rho1(m, p=p, u_start=0, u_end=1, r_surv=r_surv)\n",
    "            res = sum([\n",
    "                rec_model_rho1(m[:gen+1], p=p[:gen+1], u_start=0, u_end=1, r_surv=r_surv)\n",
    "                for gen in range(len(m))])\n",
    "        elif len(where_cells) == 1:\n",
    "            reached_gen = where_cells[0]\n",
    "            res = rec_model_rho1(m[:reached_gen+1], p=p[:reached_gen+1], u_start=0, u_end=1, r_surv=r_surv)\n",
    "        else:\n",
    "            res = 0.\n",
    "    else:\n",
    "        alpha = np.multiply(p, (1.-rho)/rho)\n",
    "        beta = np.multiply(np.subtract(1., p), (1.-rho)/rho)\n",
    "        res = rec_model_rho(m=m, alpha=alpha, beta=beta, u_start=0, u_end=1, r_surv=r_surv)\n",
    "    return res\n",
    "\n",
    "    \n",
    "#PROBABILITY OF OBSERVING THE SAMPLED FAMILY VECTOR v,\n",
    "#SAMPLING EACH CELL FROM THE FAMILY VECTOR m WITH SUCCESS RATE r\n",
    "#CONDITIONED ON HAVING OBSERVED AT LEST 1 CELL\n",
    "def pmf_v_given_m(v, m, r):\n",
    "    if sum(v) == 0:\n",
    "        return 0.\n",
    "    else:\n",
    "        return np.prod([bino_dist(v[i], m[i], r)\n",
    "                        for i in range(len(v))]) / (1.-((1.-r)**sum(m)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CLASS FOR FULL FAMILY VECTORS\n",
    "class Family_vector(object):\n",
    "    #Initialize family vector array and range\n",
    "    def __init__(self, fv):\n",
    "        self.fv = fv\n",
    "        if (fv == 0).all():\n",
    "            self.range = -np.inf\n",
    "        else:\n",
    "            self.range = np.arange(len(fv))[fv>0].max() - np.arange(len(fv))[fv>0].min()\n",
    "\n",
    "        self.sv_lst = []\n",
    "        self.sv_prob_lst = []\n",
    "\n",
    "        self.fv_rhos = np.array([])\n",
    "        self.fv_prob = {}\n",
    "    \n",
    "    #Method to select all family vectors from a list that can be sampled from fv and their sampling probability\n",
    "    def select_sub_family_vector(self, fv_ary, r):\n",
    "        sub_fams = fv_ary[(fv_ary<=self.fv).all(axis=1)]\n",
    "        if (self.fv == 0).all():#force the subfamilies to be the empty family (only possibility)\n",
    "            self.sv_lst = np.array([Family_vector(self.fv)])\n",
    "            self.sv_prob_lst = np.array([1.])        \n",
    "        else:\n",
    "            self.sv_lst = np.array([Family_vector(k) for k in sub_fams])\n",
    "            self.sv_prob_lst = np.array([pmf_v_given_m(k, self.fv, r) for k in sub_fams])        \n",
    "    \n",
    "    #Method to calculate the probability of fv under the bb model for a list of rho values\n",
    "    def generate_fv_prob(self, p, r_surv, rhos):\n",
    "        self.fv_rhos = rhos\n",
    "        self.fv_prob = {rho: pmf_m(self.fv, p, r_surv, rho) for rho in rhos}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PROBABILITY OF OBSERVING FRAY/RANGE EQUAL TO k UNDER THE BB MODEL rho, p, r_surv\n",
    "#GENERATING FAMILY VECTORS IN lst_Fam_vec, THEN SAMPLED WITH SUCCESS RATE r \n",
    "#Fray probability must be conditioned on the event that the bb model produce a non-empty family\n",
    "def fray_prob(k, rho, lst_Fam_vec):\n",
    "    #Probability of the model returning non-empty family (for conditioning)\n",
    "    cond_prob = 1. - [Fv.fv_prob[rho] for Fv in lst_Fam_vec if (Fv.fv==0).all()][0]\n",
    "    return sum([\n",
    "        #Fv.fv_prob[Fv.fv_prob[:,0]==rho, 1][0]\n",
    "        Fv.fv_prob[rho] / cond_prob#[0]\n",
    "        * sum([Fv.sv_prob_lst[i] for i,sv in enumerate(Fv.sv_lst) if sv.range==k])\n",
    "        for Fv in lst_Fam_vec])\n",
    "    \n",
    "\n",
    "#NEGATIVE LOG-LIKELIHOOD OF FRAY/RANGE DATA\n",
    "def func_alogb(a,b):\n",
    "    if a == 0.:\n",
    "        return 0.\n",
    "    else:\n",
    "        if b <=0.:\n",
    "            return -np.inf\n",
    "        else:\n",
    "            return a * np.log(b)\n",
    "\n",
    "def log_like_data(rho, lst_Fam_vec, fray_freq):\n",
    "    fray_pb = [fray_prob(k, rho, lst_Fam_vec)\n",
    "               if fray_freq[k] != 0\n",
    "               else 0.\n",
    "               for k in range(len(fray_freq))]\n",
    "    return sum([func_alogb(fray_freq[k], fray_pb[k])\n",
    "                for k in range(len(fray_freq))])\n",
    "\n",
    "\n",
    "def fray_pmf(rho, lst_Fam_vec, fray_support):\n",
    "    return [fray_prob(k, rho, lst_Fam_vec) for k in fray_support]\n",
    "    \n",
    "def log_like_fray(fray_pb, fray_freq):\n",
    "    return sum([func_alogb(fray_freq[k], fray_pb[k])\n",
    "                for k in range(len(fray_freq))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.0 0.9999999999999999\n",
      "0.2\n",
      "0.2 0.9999999999999989\n",
      "0.4\n",
      "0.4 0.9999999999999999\n",
      "0.6000000000000001\n",
      "0.6000000000000001 1.0000000000000009\n",
      "0.8\n",
      "0.8 0.9999999999999998\n",
      "1.0\n",
      "1.0 1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([], shape=(0, 6), dtype=float64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###TESTING CELL\n",
    "rhos = np.arange(0, 1.1, 0.2)\n",
    "p_div = np.array([0.95492958, 0.80648968, 0.67, 0.417703  , 0.12434326, 0.        ])\n",
    "#p_div = np.array([0.95492958, 0.80648968, 0.417703  , 0.12434326, 0.        ])\n",
    "max_g = len(p_div)-1\n",
    "ary_fam_vec = fam_generator(max_g, remove_empty=False)\n",
    "lst_Fam_vec = [Family_vector(fv) for fv in ary_fam_vec]\n",
    "\n",
    "#Calculate probabilities for sampled families and generation from bb model \n",
    "for Fv in lst_Fam_vec:\n",
    "    Fv.generate_fv_prob(p=p_div, r_surv=r_surv, rhos=rhos)\n",
    "    Fv.select_sub_family_vector(ary_fam_vec, r=r_sample)\n",
    "\n",
    "#Check the sum of generated fams probability is 1\n",
    "for rho in rhos:\n",
    "    print(rho)\n",
    "    res = 0\n",
    "    for Fv in lst_Fam_vec:\n",
    "        try:\n",
    "            res += Fv.fv_prob[rho]\n",
    "        except:\n",
    "            print(Fv.fv, Fv.fv_prob)\n",
    "    print(rho, res)\n",
    "#Check the sum of sampled subfamilies probability is 1 for every family\n",
    "sampleprob_sum = [sum(Fv.sv_prob_lst) for Fv in lst_Fam_vec]\n",
    "ary_fam_vec[~np.isclose(sampleprob_sum,1.)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "\n",
      " SLAM-HSC P 73\n",
      "[0.81881095 0.65578179 0.22202695 0.00791557 1.         0.33333333\n",
      " 0.        ]\n",
      "Family vectors generation time: 0.3427090000000135\n",
      "Probabilities calculation time: 371.45818899999995\n",
      "MLE rho time: 560.6954780000001\n",
      "Boot ci fray epmf time: 4.69936400000006\n",
      "Boot ci rho time: 0.02371499999981097\n",
      "LR test pval= 1.1935144265712356e-12\n",
      "Best-fit rho: 0.82 in 95% CI (0.69,0.952)\n",
      "6\n",
      "\n",
      " SLAM-HSC P+ILs 81\n",
      "[0.92310003 0.66677514 0.48462665 0.10574018 0.00952381 1.\n",
      " 0.        ]\n",
      "Family vectors generation time: 4.6368539999998575\n",
      "Probabilities calculation time: 410.2533639999997\n",
      "MLE rho time: 561.512718\n",
      "Boot ci fray epmf time: 4.7373829999996815\n",
      "Boot ci rho time: 0.023673000000144384\n",
      "LR test pval= 3.0646635099752058e-24\n",
      "Best-fit rho: 0.83 in 95% CI (0.735,0.9)\n",
      "4\n",
      "\n",
      " ST-HSC P 77\n",
      "[0.95806029 0.7373461  0.11688312 0.01587302 0.        ]\n",
      "Family vectors generation time: 2.065241999999671\n",
      "Probabilities calculation time: 0.8354349999999613\n",
      "MLE rho time: 0.2883569999999054\n",
      "Boot ci fray epmf time: 4.222762999999759\n",
      "Boot ci rho time: 0.022840000000087457\n",
      "LR test pval= 9.877345598230145e-12\n",
      "Best-fit rho: 0.8 in 95% CI (0.69,0.961)\n",
      "6\n",
      "\n",
      " ST-HSC P+ILs 63\n",
      "[0.96949476 0.79547689 0.39678616 0.22741433 0.12328767 0.11111111\n",
      " 0.        ]\n",
      "Family vectors generation time: 0.6380009999998038\n",
      "Probabilities calculation time: 467.30839799999967\n",
      "MLE rho time: 562.6109850000003\n",
      "Boot ci fray epmf time: 4.543609000000288\n",
      "Boot ci rho time: 0.023881000000073982\n",
      "LR test pval= 9.117547812859166e-35\n",
      "Best-fit rho: 0.96 in 95% CI (0.92,1.01)\n",
      "5\n",
      "\n",
      " MPP P 48\n",
      "[0.79946285 0.28331467 0.24110672 0.01639344 1.         0.        ]\n",
      "Family vectors generation time: 2.1611089999996693\n",
      "Probabilities calculation time: 10.34083499999997\n",
      "MLE rho time: 6.43649699999969\n",
      "Boot ci fray epmf time: 4.264589999999771\n",
      "Boot ci rho time: 0.022414000000026135\n",
      "LR test pval= 0.003228045561576793\n",
      "Best-fit rho: 0.64 in 95% CI (0.4,0.841)\n",
      "6\n",
      "\n",
      " MPP P+ILs 43\n",
      "[0.94165907 0.69022265 0.3941094  0.37366548 0.23809524 0.04\n",
      " 0.        ]\n",
      "Family vectors generation time: 0.6508450000001176\n",
      "Probabilities calculation time: 469.14696000000004\n",
      "MLE rho time: 562.5798930000001\n",
      "Boot ci fray epmf time: 4.612241000000722\n",
      "Boot ci rho time: 0.02506600000015169\n",
      "LR test pval= 3.421520649214604e-20\n",
      "Best-fit rho: 0.96 in 95% CI (0.92,1.055)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nT58q9Np8aMuWLcjMzET79u3xww8/QF9fX3Lus88+k6yIt337diQlJRW53sPDA0OHDpUM5zI0NMS3334LIH+4pLTwWZxRo0ZJekpMTU0hEAgKDY387rvvCg2Jq169OiZNmgQgP+xJCyCmpqbw9fUtNORp+PDhktfvzp07kuNbt25FRkYGPv/8c3z77beSkG5gYID/+7//k9rbGBcXh3379kFbWxtr164tFEjNzMywfPly1K5dG2FhYXJPik9NTcWCBQtgYGCAhQsXynUNkL8gQ+PGjQHk92o+ePAAaWlp0NLSQq1atYpdMnrOnDn47LPPJF/b29tjxIgRAPL3NJLXp59+it27dxdaavqff/7ByZMnsXDhQnTp0gUeHh64du2a3PdUlnXr1sHJyanYP7L2EhIIBFi5ciXGjRsnGW6Xk5ODe/fu4bfffsPo0aPRpk0bLF26tMjPfWpqquTv0nqXylpOTg5iYmKwc+dOyc/T559/rvBeS0RE9C8OsSOVEYvFhf77XwkJCWjXrp3M6wMDA2FtbY2QkBC8ffsW1atXLzI3pUCPHj1w9+5dXL58WfJGsEDB3Jb/srOzA4BCb4ACAwMB5A+dkRbsqlWrhtatWyMwMBCXL18uMhzw008/lVqfv78/srKyJG/GPpSVlSUZ4vfu3Tup18ujYEjj4MGDpZ7v0qULateujVevXuHGjRtF5iNJ+wTa3t5e8neRSFRkCFJxpL0WzZo1w+3bt5GRkSF1mF9BqMvLy0NmZmahkAcALVu2lDpfxM7ODi9evCj0vbxw4QIASBb++JBQKMTAgQOLTGy/fPkysrOz0aRJE6mLKOjq6qJLly7YuXMnLl++jC5dukh55oUtWbIE8fHxmDt3LqytrT/aHgAePHgAT09PJCcnw9PTE8OHD4eFhQUeP36MFStW4MCBA7h16xb27Nkj9edUWl2Ojo4AoPBKc40aNcKff/6J27dv4+zZs7h27VqhxRju3buHkSNHYvz48Zg2bZpC9y4NWXspfSg2NlbmQhY6Ojrw8fHByJEjcebMGVy6dAlBQUGSn6HU1FRs374dJ0+exO7duyXfuw9//nJzc5X0bKQLCgqCk5NTsW3atGmDxYsXl2kdRESVHQMSqUzBp6uJiYlSz2traxfZTFIsFhf5hLugVyU9PR2DBg2Seq/k5GQA+T0P/yVtEQbg3zc6H77JefbsGQAgODhY5mNFR0fLfCwLCwup1wD5b8gSEhJw+/ZtREZGIjo6Gs+ePcOjR48kwUhWmPwYkUiEhIQEACh2GF7Dhg3x6tUrREVFFTknbYL9h4tRKPpmsLjXQk9PD2FhYXjw4AFevHiBFy9e4PHjx4VeU2k9SLIWASj4XhZck5WVJfk+yXqDKe11Kvj+P3/+XOb3v+B1lvb9/6+//voLhw4dwqeffophw4Z9tH2BRYsWITk5GQMHDsScOXMkx11cXLBlyxYMHToUoaGh2Lhxo2SIZgEDAwOpQdbAwABAfo+sogQCAZo3by6ZS5OYmIjr16/j7NmzOHPmDHJzc7Fx40Y0a9ZMZUO95NlL6b+LnUhjbm6OgQMHYuDAgcjNzUVYWBguX76MgIAAvHz5Eq9fv8bUqVPxxx9/AMjv2dPR0UFWVpbUnlhlkrZRbJUqVWBsbAx7e3u0b99e5ia2REQkPwYkUpmC8fkRERFFVuIC8gPUh/NsgPyVmP4bmgo+0U1PT5csWiCLtGFg0nptPnZ9fHy8ZKllWT4calPgv6vbFUhOTsbSpUtx9OhRZGdnS46bmpqibdu2ePTokeQNfUl8uIJV1apVZbYreJMsbcWrj71OioY3Wa/FrVu3sGTJEslSy0D+G/C6deviq6++wp9//inznvLW+OEb14Ln/F/SXqeC7//bt29L9LP2obS0NMybNw/a2tr48ccfZS4n/V/x8fGSDwmkLY1fpUoVjBs3DhMmTMCpU6eKBCRpK/kpm7m5Ob788kt8+eWXePToEcaMGYOEhATs37+/VAFpypQpkgD6oQ4dOnx0mwBlKFi5sXHjxvDy8sLPP/+MXbt24e+//8bDhw8lC1TY2tri8ePHePLkiVz3TUlJQXJyssILexS3USwRESkPAxKpTLt27bB69Wq8efMGISEhJV7JqWCYVceOHbFp0yZllijzsb777juMHj1aKfcUi8UYP348QkJCYG5ujqFDh6Jx48ZwdHSUDBEaOHBgqQLShyFAJBLJXO2u4E29rNBQ1h4/foxRo0YhKysLzZs3R+/eveHk5AQHBwcYGhpKVlsrrQ+fX1pamtTXQ1pILPj+DxkyRLLBZ0ndv39fso9QwTLt0syZMwdz5sxB3759sXTpUrx69UpSi6whZLa2tgDyw1ROTk6pF0GR5ueff0ZgYCAGDRqEkSNHymzXoEEDjBkzBkuWLMHz589L9ZgfvmYfKtjrRxkiIyMxdepUpKam4syZMzJfO21tbcyaNQvHjh1DUlISoqKiJAGpQ4cOePz4sWQ1u485efIk5s+fD3t7exw7dkzq8FIiIlIfBiRSmUaNGsHFxQX379/Hhg0bsHXr1hLdp+DNYEREhMw20dHR+Oeff2BjY/PRJZSLU/BGrLjHevDgAQQCAWxsbOSakxMaGoqQkBBUqVIF+/btk/pmr2BVrJIyMjKChYUFEhIS8ODBA6lvrMViMR48eABAuW84FbFr1y5kZWWhdevW2Lp1a5E3iqV9HQoYGRnB0tIScXFxCA8Pl7oEsrRNTeX5WXv27BnS09NRp04dmJiYFFvDf3tDP/To0SOkp6fD1tYW5ubmkscu+JnKyMhAamqq1HBXEKL09PTKJBwB+cMUnz9/jvPnzxcbkABIFs0o7aIF8i58URqmpqaSBVZCQkKKHaKmra0NExMTJCUlFXpuXbt2xZYtWxAWFoagoKBi7yEWiyXbEzg6OjIcERGVQ1zFjlTq//7v/6Cjo4OrV6/C19f3o8O0Tp48WeRY8+bNYWBggBcvXshcLev777/HgAEDsHTp0lLV27FjRwDAmTNnpM6dSk1NhaenJ/r06SO1VmkKPhE3NDSUGkz++usvyUTynJycQucKViqTZ3hbwapl/x22WODcuXOIi4uDtrY23Nzc5Kpd2QpeCycnJ6lvFAvmeQClnwBfsM+Rv7+/1PPSVjxs3749hEIhgoKCpIaknJwcTJw4Ed988w22b99e7OM7Oztj7969Mv8ULBLi5eWFvXv3SoaQ2dvbo3r16hCLxTJXZQwICAAAtGjRotgaSqNgEY+goCDJ4iWynDp1CgDk3lBYnczMzCQrYa5cuVLmct4A8PDhQzx//hxGRkaFVj1s3Lgx2rdvDwBYuHAh3rx5I/MeO3bswP379yEQCIrdHJiIiNSHAYlUqkGDBpg/fz60tLTw66+/YsyYMbh582ahN/zZ2dm4fPkyhg8fLtnTpmbNmpJP0g0NDeHp6QkAmDFjRqGQlJGRgcWLF+PGjRuSJbNLw83NDS1atEBKSgq8vLwKDRmKi4vDxIkTkZycDAsLi2KHTX2ooGfg7du32LNnj+R4Xl4ezp49i+nTp0uO/ffNWsFQsYIeg+KMHj0aenp6uHLlCn766adCK+JdvnxZ8toOHz680DLZqlTwWpw4caLQa5ucnIzFixfj2LFjkmMlWUzgQwWvx6lTp/Drr79KAldWVhaWLFmCoKCgItfUqVMHX331FXJzczF+/PhC86SSk5Mxc+ZMREVFwcDAQOYiDqUlFArh5eUFAFi1ahWOHDkiWXwiJycHa9euxdGjRyEUCst0Xk7z5s3xxRdfAACmTp2KdevWFQkCsbGxmD17Ns6ePYtatWrJXEGxvPHx8YGenh5CQ0MxaNAg3Lx5s9CiIDk5OThz5gzGjh0LsViMCRMmFOkt/vHHH1G9enU8ffoUAwcOxOnTpwvd4+3bt1i2bJnkQ5tx48ZJlm0nIqLyhUPsSOU8PDxgZ2eHGTNm4OrVq7h69SqMjIxQu3ZtiMViREdHIz09HUB+IBgwYAC8vb0LvSGZNGkSIiIicOrUKYwcORJWVlYwNTXF8+fPJfNqFi5cCBcXl1LXu2LFCowePRr37t1Dt27d4OjoCKFQiIiICGRnZ8PQ0BBbtmyRuty0NI0aNULnzp0RGBiIhQsXYtOmTahevTpevXqFxMRE6Ovro0mTJrh7926RhSGcnJwQEhKC6dOnw97eHtOmTZO5NLqDgwN++eUXzJgxA7t27cKhQ4fg4OCAxMRESc9N9+7dVboU83+NHDkSR48eRXx8PHr06AE7OzsIBAJERUUhKysLDRo0wOvXr/H27VvEx8cXuxLex9jY2GDRokWYPXs2fH19sWvXLlhZWeH58+dISUmRDP/8b0/W/Pnz8erVK9y6dQv9+vWDra0tDAwMEBkZiXfv3kFbWxtr1qyRuaKeMgwbNgxPnjzBgQMH8N1332HZsmWoUaMGoqKikJ6eDi0tLSxYsACurq5lVgOQv+9YlSpVJBsMr1+/HjY2NjAxMcHbt2/x4sULAPnBd926dWrZF6gkGjVqhPXr12P27Nm4f/8+hg8fDlNTU8m/SS9fvoRIJIJQKMS4ceOkzkesVasWdu7ciUmTJiEyMhJTpkxB1apVUadOHeTm5iIiIgI5OTmSwKvO3zsiIioeAxKpRfPmzXH69GkcP34cgYGBePDgASIiIqClpSXZW6hNmzb46quvpM7rqFKlClavXo2TJ0/ijz/+QFhYGOLi4mBiYoLWrVvD09OzxItA/JelpSUOHjyI3bt34+TJk5JgVKNGDbRr1w7jxo2Tez+bAr6+vti9ezcCAgLw4sULJCUloWbNmvj8888xatQovHz5EmPGjMGlS5cwd+5cyYpnixcvxoIFC3Dv3j1ERUXh+fPnxe4d1bVrVwQEBGDr1q24du0aHj16BBMTE7Rv3x79+/dH165dS/XalJaNjQ2OHDmCNWvWIDg4GFFRUdDT00ODBg3w5ZdfYvDgwZg9ezaOHz+OCxculHrzy969e8Pa2hqbNm3CnTt38PjxYzg6OmLkyJEQCASSnoQPGRoaYvv27fD398eRI0fw+PFjREdHo1q1aujSpQvGjBlTZP8rZRMIBPjxxx/RoUMH7N27F/fv38eTJ09gamoKd3d3jBo1SikfBnyMjo4OVqxYgUGDBuH48eMIDg5GfHw8Xr16BRMTE7i5uaFbt2745ptvZK5aWF61a9cOp06dwh9//IErV67g2bNniIiIgFAoRI0aNfDVV1/Bw8Oj2J9BBwcH/Pnnnzhy5AjOnj2Lhw8f4unTpxAKhbC2toabmxsGDx5c5j8vRERUOgJxSTdaISKqRPbs2YOFCxeiTZs2H51PRERERJVXhZuDFBcXB1dXV/j5+Uk9HxAQgD59+uDTTz/FZ599hiVLlkhdvhcALl68iAEDBqBp06Zo3bo15s6dW+zkWiKquGbPno2+ffvKXBntypUrACBZupmIiIg0U4UKSGlpaZg8ebLMDRk3bdqEWbNmIS8vD0OHDkWDBg3g5+eH0aNHF5nsfuzYMXh5eeHNmzcYNGgQWrVqhcOHD2PgwIFISUlRxdMhIhWqV68eHjx4gBUrVhTaYyorKwubN2/G+fPnoaOjg6+//lqNVRIREZG6VZg5SDExMZg8eXKhVaT+e37NmjVo2rQpdu3aBW1tbQD5cz1+/fVXHDhwAEOHDgWQH7QWLVoEGxsbBAQESCb/t23bFt9//z02bNiAWbNmqeaJEZFKDBo0CEeOHEF4eDi6du2KunXrQl9fHy9fvkRKSgq0tbXxv//9Dw4ODuoulYiIiNSoQvQg+fn54auvvsKjR4/QqlUrqW0OHDiAnJwceHl5ScIRAIwfPx6GhoY4ePCg5Njx48eRnJwMT0/PQiujffPNN7Czs4O/v3+p91whovLFwMAABw4cwPz589GoUSMkJyfjyZMnMDIyQt++fbF//372HhEREVHF6EHauXMnrKyssHDhQkRFReHGjRtF2ty6dQsAiuxgrquri08//RRXr16V7EJf0Fba5pgtW7bE/v378eTJE640RFTJ6OnpYciQIRgyZIi6SyEiIqJyqkL0IC1cuBABAQFo1qyZzDYvXrxA9erVUbVq1SLnrKysAACRkZEAgJcvXwLIX2L4vwqWay5oS0REREREmqNC9CC1b9/+o23evn0rcy8aIyMjAJAs7pCUlAQdHR2pG3sWDLmTtRAEkD+p+8NFH8RiMbKzs2FmZgaBQPDRWomIiIiIqHyqEAFJHjk5OdDR0ZF6ruB4Zmamwm2l2bRpE9atW1fk+O3btwvNaSIiIiIiooql0gQkPT09ZGdnSz1X0Nujr6+vcFtpvLy8MHLkSMnXIpEIHTp0KFHdRERERERUflSagGRsbIzU1FSp5wqOFwy1MzY2RmZmJrKysor0JBUMrStoK42Ojo7MHigiIiIiIqq4KsQiDfKwtbXFmzdvkJGRUeRcTEwMhEIh6tatK2kLoNBmkQUKjtnZ2ZVdsUREREREVC5VmoDk6uqKvLw8BAcHFzqemZmJO3fuwNHRUTI/yNXVFcC/S4N/6ObNmzAyMuJmkUREREREGqjSBKSePXtCS0sL69atK7TC3MaNGyESiTBgwADJsS5duqBq1ar47bff8PbtW8nxP/74A1FRUfDw8IBQWGleGiIiIiIiklOlmYPk4OCAUaNGYcuWLejTpw/c3d3x9OlTXLx4Ec2aNUP//v0lbU1NTTFz5kz873//Q58+fdC9e3fExcXh5MmTsLW1hZeXlxqfCRERERERqUulCUgA4OPjg1q1amHPnj3YuXMnLCws4OnpCW9v7yKLKgwaNAgmJib47bffsHv3bpiYmKBPnz6YNm0aTE1N1fMEiIiIiIhIrQRisVis7iIqOpFIBFdXV+6DRERERERUwVWqHiRNExsbi9jYWABAUlISzMzMAAC1atVCrVq11FkaERERlVBubi7y8vLUXQZRpSEUCqGlpSV3ewakCmzTpk1YuHBhkeMLFizA//73P9UXRERERCX27t07iEQimZvZE1HJaWtrw9DQEPr6+h9ty4BUgXl5eaFXr154+PAhhg4dit9//x0NGzZk7xEREVEF8+7dOyQlJUFXVxdmZmbQ0tKCQCBQd1lEFZ5YLEZubi7S09ORlJQEAB8NSQxIFdh/h9I1bNgQzZo1U2NFREREVBIikQi6urowNzdnMCIqA3p6ekhMTIRIJPpoQOJmP0RERERqlJubi+zsbBgYGDAcEZURgUAAAwMDZGdnIzc3t9i2DEhEREREalSwIIMik8iJSHEFv2MfWwSFAYmIiIioHGDvEVHZkvd3jAGJiIiIiIjoPQYkIiIiIiKi97iKHX1Ubp4YWsKSd/uX9noiIiLSDGvXrsW6desKHRMIBNDT00ONGjXg5uYGT09PODg4lPgxcnNzsXfvXvTr1w8GBgalLZkqIQYk+igtoQCzDv6NiPg0ha+1r1EVP3s0KoOqiIiINEhuLlCeF3FQcn2dO3dGw4YNAeRPqBeJRHj06BEOHDiAP//8E76+vujYsWOJ7u3j44OTJ0+iV69eSquXKhcGJJJLRHwaHsamqrsMIiIizaSlBQwZAjx8qO5KimrYENi9W6m37NKlC/r161fk+KVLlzBp0iRMmzYNAQEBqFu3rsL3fvPmjTJKpEqMAYmIiIioInj4EAgNVXcVatWhQwd8++23WL58OX799Vf8/PPP6i6JKiEu0kBEREREFcbQoUOhq6uLM2fOICcnBwCQnZ2NHTt2oH///nB1dYWLiwvc3d0xf/58JCYmSq51cnJCUFAQAKBFixYYNmyY5FxMTAwWLFiALl26oFGjRmjatCn69euHvXv3qvYJktqxB4mIiIiIKgx9fX04OzsjNDQUDx8+RKNGjeDj44PTp0/D1dUV/fv3R1ZWFq5evYr9+/cjLCwMhw4dAgB4e3vj8OHDiImJwdixY2Fvbw8AiI6OxjfffIN3797h888/R61atRAXF4fTp0/jf//7H3JzczF06FB1Pm1SIQYkIiIiIqpQLC0tAQAJCQm4c+cOTp8+ja+++grLly+XtMnJyUHfvn1x//59REZGws7ODpMnT0ZQUBBiYmIwbtw4GBsbAwA2b96MpKQkbN++HW3atJHcY+jQofDw8MCxY8cYkDQIAxIRERERVSg6OjoAAJFIBGdnZyxduhTNmzcv1KZKlSpwdXXF48eP8ebNG9jZ2cm8X69evdCkSZNC4QgAGjduDD09PS7soGEYkIiIiIioQklLy996xMDAADVr1kTfvn2Rk5ODsLAwREZG4sWLF3j48CGuXbsGIH+p8OI0b94czZs3x9u3b/Hw4UO8ePECkZGRuHPnDjIzM5Gbm1vmz4nKDwYkIiIiIqpQYmJiAAA2NjYAgH379mH9+vWIj48HABgbG6NJkyZwcHDA3bt3IRaLi71fcnIylixZgmPHjiE7OxsCgQBWVlZo1aoVHjx4ULZPhsodBiQiIiIiqjDevn2Lp0+fwtjYGI6Ojjh58iQWLFgAJycnLFiwAJ988glq1aoFAFiwYAHu3r370XvOnDkTly5dwsCBA9G7d2/Ur18fhoaGAICjR4+W6fOh8ocBiYiIiIgqjAMHDiAnJwfdu3eHlpYWjh07BgBYsWIF6tWrV6htREQEABTbg5SSkoJLly7BxcUFCxcuLHQuOjoamZmZH+2BosqF+yARERERUYVw/fp1rF+/HgYGBvDy8gIA6OrqAgD++eefQm0DAgIkex4V7JcEANra2gDy904q+FooFCIlJQVZWVmSdhkZGfjxxx8LtSXNwB4kIiIiIipXzp07J5lnlJeXB5FIhAcPHiA4OBh6enpYtWoVrKysAOSvQHf8+HF4e3vjyy+/hKGhIf7++28EBQWhWrVqePPmDd6+fSu5d8ES4XPnzkXbtm0xfPhwfP755zh9+jQ8PDzQtm1bpKen48KFC/jnn39gYmKC1NRU5OXlQShk34ImYEAiIiIiqggaNlR3BdKVQV2BgYEIDAyUfK2vrw8rKysMHToUI0aMQJ06dSTnOnbsiFWrVmHLli04evQo9PT0YGNjg/nz56Np06bo27cvLl26hJ49ewIAxo8fj2fPnuGvv/5CVFQUhg8fjsWLF6NmzZo4d+4cfv/9d1hYWKBRo0YYN24cjh07hh07duDmzZto3bq10p8rlT8CMQdVlppIJIKrqytu374tmdCnSiEhIZLHb9asWZk8hsf6G3gYm6rwdQ1rGeHgpFZlUBEREVHlkJ2djYSEBFhYWEiGfxWRmwtoaam2MEWU9/qIIOfvGjgHqeIo7fr7XL+fiIio4irv4aO810ekAA6xqyi0tIAhQ4CHD4ueS0/P/+/gwYCBQdHz7dohd/Vq8J8uIiIiIqLiMSBVJA8fAqGhss+Hh0s/3qABtIRCjFpzGeExbwEAIzrVw7huDbH59EPsOP9E5i0//9Qa/xtUNsP2iIiIiIjKGwYkDRIe8xZ3IxPxXb/GGNetIRbtC8Ey/3vFXjOqS30VVUdEREREpH6cg6RhvuvXGPMHNpMrHA1zd8TIzk4qqoyIiIiISP0YkDTIiE71FApH673a4uL9VyqqjoiIiIhI/RiQNIi8w+oKwtG2c+H4/eJTFVVHRERERKR+DEgaZPPphwqFo2lbb4C7ZBERERGRJmFA0iDFrVYHMBwRERERETEgEQCGIyIiIiIigAGJwHBERERERFSAAUnDMRwREREREf2LAUmDMRwRERERERXGgKShGI6IiIiIiIpiQNJAioSjxrbmqiuMiIiIiEjNGJA0jCLhqFtTa3j3cFZdcURERETlUE5ODvz8/NCjRw80btwYnTt3xvr165GdnS3X9UlJSViwYAHat28PFxcXdOrUCcuWLcO7d+8UruWHH37A+PHjJV+vXbsWTk5ORf40a9YMffr0wZYtW+Sus7Tu3buHq1evytXW398fXbt2hYuLC9q0aYOoqCjMnj0bTk5OePjwYbH3XL16NQYMGIC8vDyl1l+AAUmDfNncRqFwtGeGO/5+nqi6AomIiEiq3DJ6I6gs5b2+0lq0aBGWLFkCU1NTDB8+HJaWllizZg18fHw+em1aWhoGDx6Mffv2wc7ODsOGDUONGjWwdetWjBw5Ejk5OXLXcePGDRw+fBgzZ84scq5z587w9vaGt7c3JkyYgH79+gEAli9fjqlTp8r9GCV18eJFDBgwAE+fPv1o22fPnuH7779HUlISBg8ejF69eqF27dro0qULvL29Ub169WLvOWbMGLx8+RK///57mTyXKmVyVyqXZn/9qULh6ExoNI7cfI7fJluorkgiIiIqQksoxKg1l+FYywizv/4UR25GYcWRv4v9/3lrJ0ssHtYcN8LjMW/3beQUE2IaWpti1ZjWiHydAp9tN5GeJftNu011Q6wd1xqijBxM2XwdFiZ62Dbls9I8vXItJCQE+/fvR7du3eDr6wuBQACxWIzZs2cjICAAFy5cgLu7u8zr9+/fj4iICAwfPhzff/89AEAsFmPmzJk4evQojh49ir59+360jpycHMyfPx89e/aEg4NDkfNdunSRhKICYrEYEyZMwLlz53D9+nW0bt1awWcvv8TERLl7dB4+fIi8vDwMHjwY06ZNkxzv0qULunTp8tF7GhoaYty4cVi1ahW6d+8OCwvlvldlD1IFFgsgBEBBJ+TD91/Hymh/5GaUQuFo+KpLyM3j6g1ERETlQUE42nYuHMNXX8KdiETcjZT+p6apAf5vWHOcDo1G7/87i9vP/pHZtopQiBWjW+H+80R0XXAS18PjZbZNy8jBytFueJOaic4/nMCFv18hPOatul+aMrV7924AgLe3NwQCAQBAIBBg+vTpEAgEOHjwYLHX//333wCAr7/+WnJMIBDAw8MDAHDnzh256jh9+jSeP3+OYcOGyV27QCCQhKZbt27JfV1Zy8rKAgCYmZmV+B7ffPMNxGIxdu3apayyJBiQKrBNAFwBDH3/9dD3X2+S0f5jnzT9Nxxl51bu7nIiIqKKpCQjQT72/3NXh+r4c15XPHiRhL6Lz0KUIbvnyLGWMU4s6IaU9Gz0WHga8cmKz59RVHBwMIYNG4ZmzZrB2dkZ7u7u+PXXX8ts7omsGszMzFC/fv1Cxy0tLWFra/vR4GFqagoAePXqVaHjcXFxAABzc/kWxNq+fTvs7e3h4uIiZ+X5tLS0AAA6OjqFjotEIixfvhxdunSBi4sL2rdvjwULFuDNmzeF2uXk5GDdunX46quv8Omnn6Jly5YYPXo0rl+/Lmkze/ZszJkzBwCwZMkSODk5ITo6Wmo9nTp1KtJ27dq1kvsUzEH62D0NDQ3RsWNH7Nu3D+np6Qq9Jh/DgFSBeQG4/f7PuQ/+7iWjPcMRERFRxVWSkSAVORytWrUKQ4cORUpKCgYOHAgPDw+IRCL4+vpiw4YNZf74QH5Px+vXr1GnTh2p562srJCSkoLERNlztr/++mtoa2tjyZIluH37Nt69e4ebN29i+fLlMDIyKtSzJMuLFy/w999/o127dgrVLxaLcfjwYWhpaRUaupaamopBgwZhy5YtsLa2xvDhw9G0aVMcOHAAHh4eiI+Pl7T98ccfsXbtWpiammLIkCH44osvcPfuXYwePRo3b94EkD80rnPnzgCAdu3awdvbG8bGxlJrGj58eJG2LVu2LNJOnnu2a9cOycnJci8MIS/OQarAar3/U1oMR0REROWfMkeClPdw5Ofnh40bN2LMmDGYMWOGZGjbgAED0LdvX/j5+WHChAkQCot+1u/n54fU1FS5H6thw4aFwsOH3r59CwAwMjKSer7geGpqqsyeIBcXF2zfvh3Tp0/H4MGDJcdr166NvXv3wtra+qM1FgSR4nqPzp07h5iYGAD5wSgtLQ1BQUF48uQJ5s2bB0dHR0nblStX4vHjx5g/fz6GDBkiOR4YGIiJEyfi//7v/+Dr6wuRSIQDBw6gRYsWhYayeXh44JtvvsHu3bvh5uaGLl26ICUlBYGBgWjfvj08PT1l1unp6QljY+OPtpXnngWvR1BQELp27SrzMRXFgKThGI6IiIgqBk0JR3FxcfD19UXTpk3h4+MjCUcA4OzsDHt7e0RERCA2NhZWVlZFrt+5c6ckKMijb9++MgNSwQpz/x2eVqDgeGZmpsz7v3nzBitXrkRCQgLc3d1hZ2eH+/fvIygoCPPnz8emTZtk9rYUePDgAQAUCjn/FRgYiMDAwCLHTUxMkJKSgtzcXGhpaSEnJwcBAQGoV69eoXAE5K+E16xZM5w9exYikQh5eXkQi8WIjY1FQkKCZDGERo0a4dy5c6hZs2axdZc1e3t7CIVC3L9/X6n3ZUDSYAxHREREFV9lCkcAcPz4caSnp8PT01NqD5G+vj6Af+fW/Nf58+eVVouenh4AyNxHqGCxgYKapPHx8UFISAhWrVqFHj16SI77+flhyZIlmDdvHnx9fYuto2BeUHGLGixZsqTQKnbp6emIiIjAmjVrsHLlSkRFRWHJkiWIjIxEeno6cnNzJXN/PpSZmYnc3FyEh4fD1dUVPXr0wPHjx+Hu7o6mTZvis88+g7u7e7FhTVV0dHRgaGiIpKQkpd6XAUlDMRwRERFVfOUhHJkb6pXqOfxXYGAghEIhOnToIPV8fHw8DAwMUKNGDaU+rjSGhoYQCoUQiURSzxcM5ZM1BO/169e4fv06WrRoUSgcAflDzQ4ePIgzZ85AJBLB0NBQZh0Fj18Q2ORhYGAAFxcXrFu3Dl26dIG/vz/Gjh2LlJQUAEBERATWrVsn8/rk5GQAwM8//wwXFxf4+/sjKCgIQUFBWL58OVxcXPDTTz+hYcOGctdUFvT19SW1KgsDkgZS5B9TO0vpv/BERESkXuUhHNUw0ceaccrbWycnJwf37t2DmZmZ1F6Ze/fuSYaqSetdApQ7B0lHRwe1a9eWuSJbdHQ0zM3NJSvV/VdsbP7mK/b29lLPOzg44OnTp4iLiys2IJmYmADID0ryrnpXQEdHB02bNsWpU6cQHh4OOzs7AEDv3r2xbNmyj16vra2NUaNGYdSoUXj16hX++usvnDp1ClevXoWXlxcCAwOhra2tUE3KlJqaKnl9lIUBScMo+o+pT59GKqyOiIiI5FFewtGJBd1gqKe8t5NPnz5FVlYWsrOzkZeXVyQEbd++HQDQv39/mfdQ5hwkAHB1dcWRI0cQGRkpCRdA/lypqKioYjeJrV69OgAgKipK6vnnz59DIBCgWrVqxdZYMPcnKSlJ5op6xSnoNTIyMoKdnR10dHQQFhYGsVhcaI4XkB8w09PTMWjQIIhEIhw8eBBNmzaFu7s7ateuDQ8PD3h4eGDEiBG4ceMGoqOjYWdnV+Q+yvCxe2ZmZiI9PR1OTk5KfVwu861BWjtZKvyP6as3aSqskIiIiD6mPIUjYwNtTN58XWY7RRVMtk9JScGVK1cKndu9ezdOnDiB9u3bo1OnTjLvcf78eYSHh8v9Z+nSpcXW1KdPHwD5y44X7L8kFouxcuVKAPkr68liY2ODTz75BEFBQTh37lyhcwcPHsSjR4/Qrl07mT1QBerVqwcAePLkSbHtpLl79y6CgoJgYmKC5s2bQ1dXFz169MDTp08lgbPAzZs3sWzZMhw6dAgmJibQ09PDli1b4OvrK5lvBeTPvUpISICOjo4kvFWpkh+UZc3XKomP3fPx48cAgAYNGijtMQH2IGmUxe931FbkH9Od559gw0TF1twnIiKislHewlGPhadRVYk9SGFhYQAAd3d3TJkyBT179oS5uTmCg4MREhICFxcXrFixQmmPJ482bdqgR48eOHHiBAYMGAA3NzeEhoYiODgY3bp1Q8eOHSVtU1JSsGPHDhgZGUmWpV68eDGGDRuGyZMnS1axCw8Px5UrV2BhYYEFCxZ8tIYOHTpAIBDg9u3b+Oabb6S2+XCZbwDIzc3F06dPcfHiReTm5mLu3LmSOUyzZs1CaGgofv75ZwQGBqJx48aIi4vDmTNnUKVKFSxevBhCoRAWFhYYMWIEtm/fjp49e6JDhw4QCoW4cuUKnj17hokTJ0qGBlpaWgIA9u7di+TkZAwbNkxyrKQ+ds+QkBAAQNu2bUv1OP/FgKRBboTHK/yPaXdXGxVWSERERLJ4tLXHxO4NcSM8HssP/w3nOqYy2za0NsWqMa0R+ToF83ffhkMt2ctI21Q3xNpxrSHKyMHM7UGoZa6PWubSV2UzN9TDmnGtYahXBZM3X0dVvSpwspJdh6LCwsKgra2N1atXY/369QgICEBSUhKsra0xdepUjBo1Crq6ukp7PHktW7YMjo6OOHz4MHbs2IHatWtjypQpGDt2bKFhYCkpKVi3bh2srKwkAalBgwY4dOgQ1q9fj7/++guXLl1CtWrVMGDAAHh7e8u12ESNGjXQqFEjXL9+XerQQ6DoMt/a2towNzeHu7s7hg0bVmgzVnNzcxw4cACbNm3C2bNnsWvXLpibm6NTp06YOHFioR6ZmTNnom7dujh48CAOHz6M3NxcODo6YunSpejbt6+kXYsWLTBkyBAcOXIEu3fvRps2bUodkD52z7/++gvGxsb47LPPSvU4/yUQi4tbVZ/kIRKJ4Orqitu3bxc7wa7UmjUDQkMVv27QIGDPHnSYcwy3n/0js5m0T5o82tph+7cd4LH+Bh7Gyj/hsUDDWkY4OKmV4jUTERFpiOzsbMkeM7Imu+fm5UFLxqIE5YEy6svNzUWzZs3g4OAAf39/JVVWeRw/fhzTp0/Htm3blN5jUhHFxcXB3d0d48aNw9SpU+W6Rp7fNYBzkDRKTp5yuuGJiIhItcpzOAKUU9/Tp0+RkZEBZ2dnJVRU+XTv3h22trY4cOCAukspF/z9/aGrq4sRI0Yo/d7l+7eNVILhiIiIiNStYP6RuvfVKa+EQiHmzp2LM2fO4NGjR+ouR61SUlLg5+eHSZMmFbt5bkkxIGk4hiMiIiIqDwoC0ieffKLmSsqvDh06oG/fvipfqKK82bJlC+rUqYORI0eWyf25SIMGYzgiIiKi8mLevHmYN2+eusso9xYvXqzuEtTOx8enTO/PHiQNxXBERERERFQUA5IGUiQcWZpKX+aTiIiIiKgyYkDSMIpuGjerX2MVVkdEREREpF4MSBqkobWpwjtqp2flqrBCIiIiIiL1YkDSIKvGtFYoHKWkZ+MX/3sqrJCIiIiISL0YkDRI5OsUhcJRj4WnkZyepcIKiYiIiIjUiwFJg/hsu6lQOIpPfqfC6oiIiIiI1I8BSYOkZzEcEREREREVhwGJGI6IiIiIiN5jQNJwDEdERERERP9iQNJgDEdERERERIUxIGkohiMiIiIioqIYkDSQIuHIxEBHhZUREREREakXA5KGUSQc1TDRx8x+jVVYHREREVH5k5OTAz8/P/To0QONGzdG586dsX79emRnZ8t1fWJiIhYsWIB27dqhSZMm6N27N/bs2YO8vLwyrpxKggFJg9hUN1QoHJ1Y0A0GOloqrJCIiIikyc0Tq7uEYpX3+kpr0aJFWLJkCUxNTTF8+HBYWlpizZo18PHx+ei1b968gYeHB/bt24fatWtj4MCBMDY2xsKFC+Hj4wOxuHK/dhVRFXUXQKqzdlxrvEnNlDscGRto42f/e1g6oqUKqyQiIqL/0hIKMOvg34iIT1N3KUXY16iKnz0aqbuMMhMSEoL9+/ejW7du8PX1hUAggFgsxuzZsxEQEIALFy7A3d1d5vW//PILoqOjMWzYMHz//fcQCAQAgGXLlmHr1q1o3749+vXrp6qnQ3KoVAHJycnpo2127twJNzc3AMDBgwfxww8/SG3XpEkTHDhwQKn1qZsoI0ehcNRj4Wk0ta9WqsesbqiDvDwxhEJBia4vzbVERESVSUR8Gh7Gpqq7DI2ze/duAIC3t7ck3AgEAkyfPh1HjhzBwYMHZQaknJwcnD59GqampvDx8ZFcDwDffvst9u3bBz8/PwakcqZSBSRvb2+px9+8eYO9e/eiWrVqsLe3lxwPDw8HAIwdOxa6urqFrqlZs2bZFaomUzZfVygcPY1NKXVAMtKrAqFQAN9LkYhOzlDoWmsTPXzbwa5Uj09EREQVV3BwMHx9fREWFoaMjAxYWlrCw8MD48ePh1CompkiwcHBMDMzQ/369Qsdt7S0hK2tLW7duiXz2sTERKSnp8PFxQX6+vqFzunq6sLW1hYPHjyASCSCoaFhmdRPiqtUAWny5MlSj0+YMAECgQC//PILLCwsJMfDw8NhamqKGTNmqKpEtUoUyQ4o0sKRMkUnZyDyDZcSJyIiIvmsWrUKmzZtgpOTEwYOHIi0tDScOHECvr6+EIvFmDRpUpnXkJWVhdevX6NJkyZSz1tZWSEyMhKJiYkwNzcvcl5HR0dyH2lEIhHEYjFevXpVJICR+lSqgCTNn3/+ifPnz6N///5o27ZtoXOPHz/mDyPKPhwRERERKcLPzw8bN27EmDFjMGPGDMnQtAEDBqBv377w8/PDhAkTpPYi+fn5ITVV/qGIDRs2RJcuXaSee/v2LQDAyMhI6vmC46mpqVIDkqmpKaytrfHw4UO8fPkSNjY2knNPnjzBy5cvJddT+VGpA1JmZiZWrVoFIyMjTJ8+vdC5169f4+3bt3LNW6rMGI6IiIioPImLi4Ovry+aNm1aZN6Os7Mz7O3tERERgdjYWFhZWRW5fufOnYiJiZH78fr27SszIOXk5AD4tyfovwqOZ2Zmyrz/qFGjsGjRIkycOBH/+9//0KBBAzx8+BDz5s2Dnp4e0tPTuZJdOVOpA9KePXvw6tUrTJs2DWZmZoXOFcw/ys7OxsSJExEaGoqMjAw0a9YM3377LRo3rvz7/zAcERERUXlz/PhxpKenw9PTU2oPUcFcHi0t6VuRnD9/Xmm16OnpAYDM/Y4Khs79d37RhwYPHoyoqCjs2rULgwcPlhz/6quv0LJlS+zbt6/Y60n1Km1Ays3Nxc6dO1G1atVCP4wFCgLSvn370K5dO/Tr1w/Pnz/H+fPncfPmTWzYsAHt27dXddkqw3BERERE5VFgYCCEQiE6dOgg9Xx8fDwMDAxQo0aNMq/F0NAQQqEQIpFI6vmCoXGyhuAB+Sveff/99/jmm29w/fp1iMViNG/eHI0aNcKUKVMAANWrV1d+8VRilTYgnT9/Hq9evcLIkSNhbGxc5HxeXh6srKwwdepU9OrVS3I8KCgInp6emDNnDgIDA4usbgfkf1rw4WQ7Wb805ZUi4UhPmxvFEhERkWrk5OTg3r17MDMzk9qrcu/ePSQkJMDd3V3mKnbKnIOko6OD2rVrIzo6Wur56OhomJubw9TU9KOP4+TkVGRqx/3792FkZARLS0u566WyV2kDUkBAAACgf//+Us+PHz8e48ePL3K8ZcuW+OqrrxAQEICgoCCpvUibNm3CunXrlFqvqigSjgz1qmB6bxcVVkdERESa7OnTp8jKykJ2djby8vKKhKDt27cDkP3+DlDuHCQAcHV1xZEjRxAZGQk7u3+3H4mLi0NUVFSxm8QCwPTp03Hr1i1cvHix0LDABw8eICYmBl988YXctZJqVMqAlJmZiWvXrqF+/fqF9j2Sl7OzMwICAmR+WuDl5YWRI0dKvhaJRDK7gcsTc0M9bJ38mdzh6PDcz1G7WlUVVkhERESa7P79+wCAlJQUXLlypdD7q927d+PEiRNo3749OnXqJPMeypyDBAB9+vTBkSNHsGrVKqxevRpCoRBisRgrV64EkL+yXnHs7e1x/PhxHDt2DL179wYAvHv3Dj/99BOA/P04qXyplAEpKCgI6enp6Natm8w2YWFhSE9PR4sWLYqcK1iJRNrwOiC/u1XWaibl2ZpxraGrLZQ7HDnXMcOKgL8xf2AzFVZJREREmiosLAwA4O7ujilTpqBnz54wNzdHcHAwQkJC4OLighUrVqi0pjZt2qBHjx44ceIEBgwYADc3N4SGhiI4OBjdunVDx44dJW1TUlKwY8cOGBkZwdPTEwDg6ekJf39/fP/99/jrr79QrVo1nD17Fi9fvsSUKVPg4sLROuVNpQxId+/eBZDfJSrLpEmTEBcXh7/++qvIuvW3b98GgEr3A2uoVwWfzz8pdzjq9eMZ2NeUPemQiIiIVMe+Rvkc1aHMusLCwqCtrY3Vq1dj/fr1CAgIQFJSEqytrTF16lSMGjVK5gfYZWnZsmVwdHTE4cOHsWPHDtSuXRtTpkzB2LFjCy1DnpKSgnXr1sHKykoSkAwNDbF3714sX74c169fR1paGurXr4/vvvsOXbt2VflzoY+rlAHpwYMHAIBPPvlEZpsvvvgC27dvx6pVq7Bo0SLJD/fJkydx8eJFtGjRotJtIjt583WFwtHtZ/8wIBEREZUDuXli/OzRSN1lyJSbJ4aWUPDxhsXdIzcX4eHhqF+/PvT09ODj4wMfHx8lVVg62tramDRpEiZNmlRsO2tra8lKyR+ytLTEL7/8UlblkZJVyoD08uVL6OnpSV29rsDEiRNx+fJlHDhwAOHh4XB1dUVkZCQuXrwICwsLLFmyRIUVq8bLf2SvtictHBEREVH5UNrwUdaUUd/Tp0+RkZEBZ2dnJVREVHLS10es4JKSkopdjx4AjI2NsW/fPowYMQIJCQnYtWsX7t+/j2+++Qb+/v6wsbFRUbXqx3BERERE6lYw/6hhw4ZqroQ0XaXsQbp69apc7YyNjTF37lzMnTu3jCsqvxiOiIiIqDwoCEjFTZEgUoVK2YNE8mE4IiIiovJi3rx5CA8Px6effqruUkjDMSBpKIYjIiIiIqKiGJA0kCLhqLxPCiUiIiIiUiYGJA2jSDjS1hJiYndOlCQiIiIizcGApEEMdBQLRzundUCjuuYy2xARERERVTYMSBpkxSg3hcJR16bWWHfigQorJCIiIiJSr0q5zDdJZ1fTGD0XnZY7HA1efgHGBtoqrJCIiIiISL3Yg6RBpv12XaFwdDo0WoXVERERERGpHwOSBnkY/VbmOYYjIiIiIiIGJALDERERERFRAQYkDcdwRERERET0LwYkDcZwRERERERUGAOShmI4IiIiIiIqigFJAykSjgQCFRZGRERERKRm3AdJwygajoZ2dFRhdURERETlT05ODn7//XccOHAA0dHRsLCwQL9+/TBu3Dhoa398z8jVq1djw4YNUs/16NEDq1atUnbJVAoMSBqkilCxcLRqdCt0dKmtwgqJiIhImrw8MYTC8juso7zXV1qLFi3C/v374erqik6dOiEkJARr1qxBeHg41qxZ89HrHz16BB0dHYwbN67IuXr16pVFyVQKDEga5MchrnBzqiF3OBrVxQnbA8Mx+vMGKqySiIiI/ksoFMD3UiSikzPUXUoR1iZ6+LaDnbrLKDMhISHYv38/unXrBl9fXwgEAojFYsyePRsBAQG4cOEC3N3di73H48eP4ejoiMmTJ6uoaioNBiQN0sqpBgYpEI4mbfoLGVm5DEhERETlQHRyBiLfvFN3GRpn9+7dAABvb28I3k/OFggEmD59Oo4cOYKDBw8WG5BEIhFiYmLQsmVLldRLpcdFGjTI3F3BCoWjXReeqrA6IiIiosKCg4MxbNgwNGvWDM7OznB3d8evv/6KvLw8ldZgZmaG+vXrFzpuaWkJW1tb3Lp1q9jrHz16BABwcnIqsxpJuRiQNMj18DiZ5xiOiIiIqDxZtWoVhg4dipSUFAwcOBAeHh4QiUTw9fWVueCBsmVlZeH169eoU6eO1PNWVlZISUlBYmKizHuEh4cDABITEzFy5Ei0aNECLVq0wJQpUxAREVEmdVPpcIgdMRwRERFRueLn54eNGzdizJgxmDFjhmRo24ABA9C3b1/4+flhwoQJEAqLftbv5+eH1NRUuR+rYcOG6NKli9Rzb9++BQAYGRlJPV9wPDU1Febm5lLbFASkbdu2oVOnTvDw8EB4eDhOnz6Na9euYdeuXWjYsKHc9VLZY0DScAxHREREVJ7ExcXB19cXTZs2hY+PjyQcAYCzszPs7e0RERGB2NhYWFlZFbl+586diImJkfvx+vbtKzMg5eTkAAB0dHSkni84npmZKfP+WlpasLKywpIlS+Dm5iY5/ueff2LmzJmYO3cuDh8+LHe9VPYYkDQYwxERERGVN8ePH0d6ejo8PT2l9hDp6+sDyA8e0pw/f15ptejp6QEAsrOzpZ7PysoqVJM0CxYskHq8V69eOHDgAG7duoWIiAjY29uXslpSljKbg/Tu3TuIRKKyuj2VEsMRERERlUeBgYEQCoXo0KGD1PPx8fEwMDBAjRo1yrwWQ0NDCIVCme9pC4byyRqC9zHOzs4AgOho2YtokeqVqgcpNzcXx48fh66uLrp16wYgfynDOXPm4Ny5cwCAZs2aYdGiRXBwcCh9taQUDEdERERUHuXk5ODevXswMzOT2itz7949JCQkwN3dXWrvEqDcOUg6OjqoXbu2zAATHR0Nc3NzmJqaSj2fk5ODBw8eQCwWo0mTJkXOZ2Tk72ulq6srd71U9kockDIyMuDp6Ym7d++ia9eukoC0cOFCnD17VtLu9u3bGDp0KI4dO4Zq1aqVvmIqFUXDUc8W0ldtISIiIlK2p0+fIisrC9nZ2cjLyysSgrZv3w4A6N+/v8x7KHMOEgC4urriyJEjiIyMhJ3dvxvixsXFISoqqtg9kPLy8jB48GAYGBjg+vXrhYYFisVihIaGokqVKlykoZwp8RC73bt3486dOzA1NUWrVq0AAG/evMGJEycgEAiwePFiXLhwAV9++SWSkpLw22+/Ka1oKhlFw9F3/Rrj6zaVd2dsIiIiKl/u378PAEhJScGVK1cKndu9ezdOnDiB9u3bo1OnTjLvcf78eYSHh8v9Z+nSpcXW1KdPHwD5y44X7L8kFouxcuVKAPkr68mio6MDd3d3JCcnY/PmzYXObdu2DY8fP0bPnj1hbGxcbA2kWiXuQTp9+jSEQiG2b9+OBg0aAMj/gczNzcUnn3yCfv36AQAWLVqECxcu4NKlS5g1a5ZyqqYS8endCL3dbOUOR/MHNsOha5EMSURERKQSYWFhAAB3d3dMmTIFPXv2hLm5OYKDgxESEgIXFxesWLFCpTW1adMGPXr0wIkTJzBgwAC4ubkhNDQUwcHB6NatGzp27Chpm5KSgh07dsDIyAienp4AgFmzZiE0NBSrV69GUFAQGjRogPv37yMoKAiOjo6YPXu2Sp8PfVyJA1JkZCRsbW0l4QgALl++DIFAUOgHpWrVqqhTpw6eP39eqkKp9BQNR4v2hSAyLpUBiYiIqBywNtFTdwlSKbOusLAwaGtrY/Xq1Vi/fj0CAgKQlJQEa2trTJ06FaNGjVLLfJ1ly5bB0dERhw8fxo4dO1C7dm1MmTIFY8eOLbQMeUpKCtatWwcrKytJQLK2tsahQ4fg6+uLy5cv49atW6hRowZGjRqFiRMnlniBByo7JQ5I7969KzR5Ljc3Fzdu3AAAtG7dulDbgnGkpF5LD91RKBwt878Hj7YMR0REROqWlyfGtx3K7/+T8/LEEAoFH29YjNzcXISHh6N+/frQ09ODj48PfHx8lFRh6Whra2PSpEmYNGlSse2sra0lG8N+yNLSEosXLy6r8kjJSjwHydLSEtHR0cjNzQUAhISEIDU1FVWrVsWnn34qaRcfH4/nz5+jZs2apS6WSud48Mtiz/83HBEREVH5UNrwUdaUUd/Tp0+RkZEhWfqaSF1KHJBat26NlJQU/PLLL3j06BFWrFgBgUCATp06oUqV/I6phIQEzJgxA7m5uWjTpo3SiiblYzgiIiIidSqYf8QV3UjdShyQvLy8YGRkhB07dqBv3764c+cOdHV1MX78eADArVu30LFjRwQFBcHU1BRjx45VWtGkXAxHREREpG4FAemTTz5RcyWk6UockGxsbLB37158/vnnsLW1RYcOHbBz507Y29sDAGrWrInc3Fy4ubnh4MGDqF27ttKKJuVhOCIiIqLyYN68eQgPDy80VYNIHUq8SAMAODg4YM2aNVLPWVtb4/Lly6hRo0ZpHoLKEMMREREREVFhJe5B+hiBQMBwVI4xHBERERERFVXqgJSeno5du3Zh7Nix6NKlC1q1agUASExMxKxZs/Dw4cNSF0nKpUg4audsqaKqiIiIiIjUr1RD7MLCwjB58mTExsZCLBYDgGSzrJcvX+LIkSM4fvw4Fi1ahH79+pW+Wio1RcLRMHdHjOzspKLKiIiIiIjUr8Q9SAkJCRg7dixevXqFxo0bY86cOXBwcJCcr169Otzc3JCTk4Pvv/8eISEhSimYSm5Ep3oKhaP1Xm1x8f4rFVVHRERERKR+JQ5ImzdvRmJiIoYMGYL9+/djxIgRMDExkZy3srLCjh07MHz4cIjFYvj5+SmjXiqFcd0aKhSOtp0Lx+8Xn6qoOiIiIiIi9StxQLp48SIMDAwwc+bMYtv5+PjA0NCQPUjlwObTDxUKR9O23sD7kZNERERERBqhxAEpLi4O9vb20NPTK7adrq4u6tati7dv35b0oUhJdpx/Uux5hiMiIiIi0nQlDkj6+vqIj4+Xq21SUhIMDQ1L+lCkAgxHRERERESlCEguLi5ISEjAzZs3i213/fp1vHr1Cp988klJH4rKGMMREREREVG+EgekQYMGQSwW47vvvsOtW7ektrl+/TpmzJgBgUCAb775psRFUtlhOCIiIiIi+leJ90Hq0qULPDw8cPDgQQwfPhzVqlVDeno6AGD8+PGIjIzEixcvIBaL0b17d3Tv3l1pRZNyMBwRERERERVWqo1if/zxR9jb22PTpk34559/JMcvXrwIADAwMICnpycmTZpUqiJJ+RiOiIiIiIiKKlVAAoCRI0diyJAhuH37Np49ewaRSAR9fX3Y2tqiefPmqFq1qjLqJCVSJBw1tjVXXWFERERE5VBOTg5+//13HDhwANHR0bCwsEC/fv0wbtw4aGtrl/n1pFqlDkgAoKOjg9atW6N169bKuB2VIUXCUbem1vDu4ay64oiIiEiqPLEYQoFA3WXIVN7rK61FixZh//79cHV1RadOnRASEoI1a9YgPDwca9asKfPrSbWUEpCoYviyuQ1mf/2p3OFozwx3/P08Ec0cLVRXJBERERUhFAhw7EEc3qRlqbuUIqpV1UFPZ0t1l1FmQkJCsH//fnTr1g2+vr4QCAQQi8WYPXs2AgICcOHCBbi7u5fZ9aR6pQpI9+7dw/r163Hnzh2kpaUhNzdXZluBQIAHDx6U5uGolBQNR2dCo3Hk5nP8NpkBiYiISN3epGUhTlT+AlJlt3v3bgCAt7c3BO97yQQCAaZPn44jR47g4MGDxQac0l5PqlfigHT//n0MGzYMWVlZEMsxw1+eNlS2jtyMUigcDV91CX1a1VVdgUREREQfCA4Ohq+vL8LCwpCRkQFLS0t4eHhg/PjxEApLvFuNwjWYmZmhfv36hY5bWlrC1tZW5nY3yrqeVK/EAWnDhg3IzMyEo6Mjxo0bBzs7O+jp6SmzNlKyFUf+VigcZefmqa44IiIiog+sWrUKmzZtgpOTEwYOHIi0tDScOHECvr6+EIvFKlklOSsrC69fv0aTJk2knreyskJkZCQSExNhbl50YavSXk/qUeKAFBwcDF1dXfj5+aF69erKrInKCMMRERERVQR+fn7YuHEjxowZgxkzZkiGpg0YMAB9+/aFn58fJkyYILUXyc/PD6mpqXI/VsOGDdGlSxep596+fQsAMDIyknq+4HhqaqrUgFPa60k9ShyQMjIy4ODgwHBUCTAcERERUXkRFxcHX19fNG3aFD4+PpJwBADOzs6wt7dHREQEYmNjYWVlVeT6nTt3IiYmRu7H69u3r8yAlJOTAyB/xWZpCo5nZmaWyfWkHiUOSHXq1EF8fLwyayE1YDgiIiKi8uT48eNIT0+Hp6en1B4ifX19AICWlpbU68+fP6+0Wgqmj2RnZ0s9n5WVVagmZV9P6lHi2W29evXCP//8g1OnTimzHlIhhiMiIiIqbwIDAyEUCtGhQwep5+Pj42FgYIAaNWqUeS2GhoYQCoUQiURSzxcM5ZM1hK6015N6lLgHadSoUbh58ybmzp2LmJgYfPbZZ7C0tCx2N2Cm4/KD4YiIiIjKm5ycHNy7dw9mZmZS3zfeu3cPCQkJcHd3l7mKnTLnIOno6KB27dqIjo6Wej46Ohrm5uYwNTUtk+tJPUockPr06YPc3Fykp6dj+fLlWL58ebHtuQ9S+aFIOLKz5CcaREREpBpPnz5FVlYWsrOzkZeXVyQEbd++HQDQv39/mfdQ5hwkAHB1dcWRI0cQGRkJOzs7yfG4uDhERUV9dA+j0l5PqlfigPTkyRPJ37kPUsWhSDhydagOnz6NVFgdERERabL79+8DAFJSUnDlypVCw+x2796NEydOoH379ujUqZPMeyhzDhKQ3ylw5MgRrFq1CqtXr4ZQKIRYLMbKlSsB5K+sV5bXk+qVOCAFBgYqsw5SgdZOlvi/Yc3lDkd/zuuKV2/SUM/KVHVFEhERkcYKCwsDALi7u2PKlCno2bMnzM3NERwcjJCQELi4uGDFihUqralNmzbo0aMHTpw4gQEDBsDNzQ2hoaEIDg5Gt27d0LFjR0nblJQU7NixA0ZGRvD09FT4eiofShyQpC2rSOXb4mHNcVqBcPTgRRJ2nn+CDRPbqbBKIiIikqZaVelLRaubMusKCwuDtrY2Vq9ejfXr1yMgIABJSUmwtrbG1KlTMWrUKOjq6irt8eS1bNkyODo64vDhw9ixYwdq166NKVOmYOzYsYWWIU9JScG6detgZWUlCUiKXE/lQ4kD0ofEYjHCwsIQFRWFtLQ0GBgYoG7duvjkk09kLsFIqncjPF6hcNR38Vl0d7VRYYVEREQkTZ5YjJ7OluouQ6Y8sRjCUr7Rz83NRXh4OOrXrw89PT34+PjAx8dHSRWWjra2NiZNmoRJkyYV287a2hrh4eElvp7Kh1IHpEOHDmHNmjVS90QyNTXFt99+i4EDB5b2YUgJ5u2+rVA4EmXkqLA6IiIikqW04aOsKaO+p0+fIiMjA87OzkqoiKjkShWQli9fjq1bt0IsFkNHRwf29vYwMDBAamoqIiMjkZSUhIULF+L58+eYNWuWsmqmEsrJYzgiIiKi8qlg/lHDhg3VXAlpuhIHpOvXr+O3336Djo4OfHx8MGDAAMluwQDw7t077N+/HytXroSfnx/c3d3RsmVLpRRNylVew5GpfpVSd9kro8ufiIiIyl5BQPrkk0/UXAlpuhIHpJ07d0IgEODHH39E7969i5zX19eHp6cnTE1NMXv2bOzZs4cBqRwqr+EIAKrqVIFQIMCxB3F4k5al8PXVquqU6/HaRERE9K958+Zh3rx56i6DqOQB6c6dO7CwsJAajj7Up08frFixAnfu3CnpQ1EZKc/h6ENv0rIQJ1I8IBERERERKUr48SbSpaamombNmnK1rVWrFt68eVPSh6IyUFHCERERERGRKpU4IJmbm+P58+fIK2biP5C/ZOPz589hZmZW0ociJVMkHFma6quwMiIiIiIi9SpxQGrRogVSUlKwdevWYttt3boVycnJaNGiRUkfipRIkXDkWMsYs/o1VmF1RERERETqVeI5SKNHj8apU6ewatUqxMbGYtCgQahXr57k/OPHj7F3717s378fWlpaGDlypFIKppJraG2KFaNbyR2OTizohvSsXLDvj4iIiIg0RYkDkrOzM+bOnYuffvoJe/fuxd69e1GlShUYGBggPT0dOTn5b74FAgHmzp0LFxcXpRVNJbNqTGvcf54odzhKSc/GxpMPsXpsaxVWSUREpJnEYrG6SyCq1OT9HSvxEDsAGDJkCPz8/NCyZUtoaWkhOzsbycnJyM7OhlAohJubG/z8/DBkyJDSPAwpSeTrFIXCUY+Fp5GcztXjiIiIypJQmP92LDc3V82VEFVuBb9jBb9zspS4B6mAm5sb3NzckJ6ejpcvXyItLQ0GBgaoU6cODAwMSnt7UiKfbTcVCkfxye9UWB0REZFm0tLSgra2NtLT06GnpwcBNzgnUjqxWIz09HRoa2tDS0ur2LalDkgFDAwM4OTkpKzbURlIz2I4IiIiKo8MDQ2RlJSExMREGBgYQEtLi0GJSAnEYjFyc3ORnp6OzMxMuVbWlisgXbp0qdTFAUCHDh2Uch9SLoYjIiIi9dLXz99WQyQSISkpSc3VEFU+2traMDMzk/yuFUeugOTl5VXqTzEEAgEePHhQqnvIa/Xq1diwYYPUcz169MCqVaskXwcEBMDPzw9RUVEwNjZG9+7dMWXKFFStWlUltaobwxEREVH5oK+vD319feTm5n50n0kikp9QKPzosLoPyRWQateuXeKC1OHRo0fQ0dHBuHHjipz7cCnyTZs2YeXKlXBycsLQoUPx+PFj+Pn54e7du9i5cyd0dHRUWbbKMRwRERGVP1paWgq9mSMi5ZIrIJ0/f76s61Cqx48fw9HREZMnT5bZJiYmBmvWrEHTpk2xa9cuaGtrAwB8fX3x66+/4sCBAxg6dKiqSlY5hiMiIiIioqJKtcx3gezs7CLHnj17ppYxtCKRCDExMR9dMOLAgQPIycmBl5eXJBwBwPjx42FoaIiDBw+Wdalqo0g4MjGo3L1oREREREQfKlVASktLw7x589C2bVukp6cXOrdy5Uq0b98eP/zwA0QiUamKVMSjR48A4KMB6datWwCAli1bFjquq6uLTz/9FI8ePUJqamrZFKlGioSjGib6mNmvsQqrIyIiIiJSrxIHJJFIhEGDBuHgwYNISUnBixcvCp3Py8tDTk4ODh06BE9PT2RlqWbD0fDwcABAYmIiRo4ciRYtWqBFixaYMmUKIiIiJO1evHiB6tWrS12MwcrKCgAQGRmpkppVxaa6oULh6MSCbjDQ4RhoIiIiItIcJQ5IGzduxOPHj1GvXj388ccfaNCgQaHzGzZswPHjx9GgQQOEhYVh+/btpS5WHgUBadu2bTA0NISHhwcaN26M06dPo3///nj48CEA4O3btzAyMpJ6j4Ljsnq+srKyIBKJCv2pCNaOa61QODI20MbP/vdUWCERERERkXqVeKPYs2fPQkdHB5s3b0atWrWktnFwcMDatWvxxRdf4NixY/Dy8ipxofLS0tKClZUVlixZAjc3N8nxP//8EzNnzsTcuXNx+PBh5OTkyFylruB4Zmam1PObNm3CunXrlF98GRNl5CgUjnosPI2m9tVUWCERERERkXqVOCC9evUKjo6OMsNRAWtra9jZ2eH58+clfSiFLFiwQOrxXr164cCBA7h16xYiIiKgp6cndXEJAJLhgLI2kvLy8sLIkSMlX4tEogqxCe6UzdcVCkdPY1MYkIiIiIhIo5R4iJ2RkRHS0tLkapubm1su9hRydnYGAERHR8PY2FjmIgwFx2UNwdPR0YGhoWGhPxVBoihD5jlp4YiIiIiISNOUOCA5Ojri5cuXCAkJKbZdWFgYIiIiUL9+/ZI+lNxycnJw79493L17V+r5jIz8gKCrqwtbW1u8efNGcuxDMTExEAqFqFu3bpnWW14wHBERERER5StxQPrmm28gFosxZcoUXL16VWqbmzdvwtvbGwKBAF9//XWJi5RXXl4eBg8ejLFjxyI3N7fQObFYjNDQUFSpUgUNGzaEq6sr8vLyEBwcXKhdZmYm7ty5A0dHxwrTM1QaDEdERERERP8q8RykXr164dSpUzh//jzGjh0LCwsL1KtXDwYGBkhPT8ezZ88QFxcHsViMjh07ol+/fsqsWyodHR24u7vjzJkz2Lx5MyZMmCA5t23bNjx+/Bh9+vSBsbExevbsKVlsoWXLlpIhgBs3boRIJMKAAQPKvF51YzgiIiIiIiqsxAEJAHx9fbFx40Zs374d8fHxiI+PL3ReX18fQ4YMwbfffguBQFCqQuU1a9YshIaGYvXq1QgKCkKDBg1w//59BAUFwdHREbNnzwaQv8LeqFGjsGXLFvTp0wfu7u54+vQpLl68iGbNmqF///4qqVddGI6IiIiIiIoqVUDS1tbG5MmT4eXlheDgYLx48QJv376Fnp4e7Ozs4OrqqvJhatbW1jh06BB8fX1x+fJl3Lp1CzVq1MCoUaMwceLEQgsv+Pj4oFatWtizZw927twJCwsLeHp6wtvbu1wsKlFWFAlHetrcKJaIiIiINEepAlIBHR0dtGnTBm3atFHG7UrN0tISixcv/mg7gUCAIUOGYMiQISqoqnxQJBwZ6lXB9N4uKqyOiIiIiEi9lBKQqGIwN9TD1smfyR2ODs/9HLWrVVVhhURERERE6lXiVeyo4lkzrrVC4ci5jhlWBPytwgqJiIiIiNSLPUgaxFCvCj6ff1LucNTrxzOwryl9s1wiIiIiosqIPUgaZPLm6wqFo9vP/lFhdURERERE6seApEFe/iOSeY7hiIiIiIhIzoC0aNEi/Pbbb2VdC6kJwxERERERUT65AtLx48dx6tSpQsc6d+6MadOmlUlRpDoMR0RERERE/5IrIGVmZuLNmzcQi8WSYzExMYiPjy+zwqjsMRwRERERERUm1yp2tra2CA8Px/Dhw9GsWTNoa2sDAGJjY7Fu3Tq5H8zb27tkVZLSMRwRERERERUlV0AaN24cpk+fjlu3biE4OBgAIBAIEBsbi/Xr13/0erFYDIFAwIBUTigSjrSEAhVWRkRERESkXnIFpB49eqBGjRo4deoUEhMTkZOTgzNnzsDMzAwtWrQo6xpJiRQJR9paQkzs3lCF1RERERERqZfcG8U2b94czZs3l3zdoEED2NvbY82aNWVSGCmfgY5i4WjntA5oVNdchRUSEREREalXifdB8vb2Rr9+/ZRZC5WxFaPcFApHXZtaY92JByqskIiIiIhIveTuQfqvD+cTicVihIWFISoqCmlpaTAwMEDdunXxySefQEtLSymFUunZ1TRGz0Wn5Q5Hg5dfgLGBtgorJCIiIiJSrxIHpAKHDh3CmjVrpC75bWpqim+//RYDBw4s7cOQEkz77bpC4eh0aDQ82tqpsEIiIiIiIvUqVUD65ZdfsG3bNojFYujo6MDe3h4GBgZITU1FZGQkkpKSsHDhQjx//hyzZs1SVs1UQg+j38o8Jy0cERERERFpmhIHpOvXr2Pr1q3Q0dGBj48PBgwYAD09Pcn5d+/eYf/+/Vi5ciX8/Pzg7u6Oli1bKqVoUi6GIyIiIiKifCVepGHnzp0QCAT48ccfMWLEiELhCAD09fXh6emJRYsWQSwWY8+ePaUulpSP4YiIiIiI6F8lDkh37tyBhYUFevfuXWy7Pn36wMLCAnfu3CnpQ1EZYTgiIiIiIiqsxAEpNTUVNWvWlKttrVq18ObNm5I+FJUBhiMiIiIioqJKHJDMzc3x/Plz5OXlFdsuNzcXz58/h5mZWUkfipRMkXAkEKiwMCIiIiIiNStxQGrRogVSUlKwdevWYttt3boVycnJaNGiRUkfipRI0XA0tKOjCqsjIiIiIlKvEq9iN3r0aJw6dQqrVq1CbGwsBg0ahHr16knOP378GHv37sX+/fuhpaWFkSNHKqVgKrkqQsXC0arRrdDRpbYKKyQiIiIiUq8SByRnZ2fMnTsXP/30E/bu3Yu9e/eiSpUqMDAwQHp6OnJycgAAAoEAc+fOhYuLi9KKppL5cYgr3JxqyB2ORnVxwvbAcIz+vIEKqyQiIiIiUp8SD7EDgCFDhsDPzw8tW7aElpYWsrOzkZycjOzsbAiFQri5ucHPzw9DhgxRVr1UCq0UDEeTNv2Fqw/iVFghEREREZF6lbgHqYCbmxvc3NyQnp6Oly9fIi0tDQYGBqhTpw4MDAyUUSMpydxdwQqFo10XnsKjrZ0KKyQiIiIiUq9SB6QCBgYGcHJyUtbtqAxcD5fdGyQtHBERERERaZpSDbGjyoHhiIiIiIgoHwOShmM4IiIiIiL6FwOSBmM4IiIiIiIqjAFJQzEcEREREREVxYCkgRiOiIiIiIikY0DSMIqGo54t6qioMiIiIiIi9WNA0iCKhqPv+jXG1224DxKpTp5YrNbriYiIiJS2DxIAREREYOfOnfj7778hEolgZmaGli1bYsSIEahWrZoyH4pKwKd3I/R2s5U7HM0f2AyHrkUyJJHKCAUCHHsQhzdpWQpfW62qDno6W5ZBVURERKRJlBaQrl+/jvHjxyMzM1Ny7Pnz57hz5w78/f2xe/du1K1bV1kPRyWgaDhatC8EkXGpDEikUm/SshAnUjwgERERESmD0obY/e9//4OpqSk2bdqEmzdvIiwsDBcvXoSXlxf++ecfLF68WFkPRSW09NAdhcLRMv97KqqMiIiIiKh8kCsgZWUV/2lufHw8nj9/jgkTJqBDhw4wMTGBlpYWatasiWnTpsHOzg5BQUFKKZhK7njwy2LPMxwRERERkaaTKyB17doVBw8eRF5entTzenp6APKH1P2XSCRCYmKipA2VTwxHRERERERyzkGqXbs25s2bh99++w2TJ09Gz549C503NjZGy5YtsWPHDrx58wZNmjSBjo4O4uPjcfToUaSkpGDo0KFl8gSo9BiOiIiIiIjyyRWQ9uzZg0uXLmH16tWYMWMGNm/ejG+//RadO3eWtFm+fDlmzpyJP//8E0ePHgUAiN8vudulSxfMnDmzDMqn0mI4IiIiIiL6l9yr2HXo0AEdOnTAiRMnsGbNGnh7e6Nx48aYOnUqWrdujRo1amDHjh148OABwsLC8ObNG5iamqJZs2aoX79+WT4HKiGGIyIiIiKiwhRe5rtHjx744osvcOjQIWzYsAGjRo1Cy5YtMW3aNHz66adwdnaGs7NzWdRKSsRwRERERERUVImW+RYKhfDw8MCpU6cwe/ZsPH36FIMGDcL48eMRHh6u7BpJyRQJR+248SYRERERaZBS7YOko6ODESNG4Ny5c5gyZQpu376Nvn37Yvr06YiKilJSiaRMioSjYe6OGNnZSUWVERERERGpn9xD7EQiEfz9/RESEoLU1FSYmJigefPm6Nu3L/T19TFhwgQMGTIEmzdvxu7du3HmzBn07t0b3t7eqFWrVlk+B5LTiE71MK5bQ7nD0Xqvtrh4/xU6NbZSUYVEREREROolV0CKjY3F0KFD8erVK8nKdABw8uRJ7NixA7t27UKNGjVgbGyMGTNmwNPTE7/++isOHjyIo0ePon///pgwYQKqVatWZk+EPk7RcLTtXDiuPYpjQCIiIiIijSHXELslS5YgJiYGvXr1wt69e3Hq1Cns3r0bvXv3xvPnz7F06dJC7atXr4758+fj1KlT6N69O/bs2YPPP/+8TJ4AyW/z6YcKhaNpW2/ggzxMRERERFTpydWD9Ndff8HGxgY///yz5JitrS1cXV1x+/ZtXLp0Sep1VlZW+PnnnzFu3Dj4+voqp2IqsR3nnxR7nuGIiIiIiDSd3Is0ZGVlIS8vr9CxnJwcZGZmQigs/jYODg5Ys2ZNySoklWA4IiIiIiKSswepXbt2OHPmDMaPH4++ffvC1NQUSUlJOHToEBISEtCzZ8+yrpPKEMMREREREVE+uQLSDz/8gJiYGFy+fBlXrlyRHBeLxWjQoAHmzJlTZgVS2WI4IiIiIiL6l1wBycLCAn/88QeuXLmCO3fuICEhAUZGRmjatCk6duyIKlXkXi2cyhGGIyIiIiKiwhRKNu3bt0f79u3LqhZSIYYjIiIiIqKi5F6kgSoPRcJRY1tz1RVGRERERKRmDEgaRpFw1K2pNbx7OKuuOCIiIiIiNWNA0iBfNrdRKBztmeGOv58nqq5AIiIiIiI1Y0DSILO//lShcHQmNBq/nnyougKJiIiIiNSMAUmDHLkZpVA4Gr7qEnLzuHoDEREREWkOBiQNsuLI3wqFo+zcPNUVR0RERERUDjAgaRCGIyIiIiKi4jEgEcMREREREdF7DEgajuGIiIiIiOhfDEgajOGIiIiIiKgwBiQNxXBERERERFQUA5IGUiQc2VkaqbAyIiIiIiL1YkDSMIqEI1eH6vDp00iF1RERERERqRcDkgZp7WSpUDj6c15XvHqTpsIKiYiIiIjUiwFJgywe1lyhcPTgRRJWHrmvwgqJiIiIiNSLAUmD3AiPVygc9V18FhnZuSqskIiIiIhIvRiQNMi83bcVCkeijBwVVkdEREREpH4MSBokJ4/hiIiIiIioOAxIxHBERERERPQeA5KGYzgiIiIiIvoXA5IGYzgiIiIiIiqMAUlDMRwRERERERVVRd0FKFtCQgLWrl2LS5cu4c2bNzAxMUHr1q3x7bffwsbGRtLu4MGD+OGHH6Teo0mTJjhw4ICqSlY5RcKRpam+CisjIiIiIlKvShWQEhIS4OHhgdjYWLRt2xY9evRAZGQkjh07hitXrmD//v2wtbUFAISHhwMAxo4dC11d3UL3qVmzpqpLVxlFwpFjLWPM6tdYhdUREREREalXpQpIa9euRWxsLGbPno2RI0dKjh85cgTfffcdli5dio0bNwLID0impqaYMWOGuspVuYbWplgxupXc4ejEgm5Iz8qFmQprJCIiIiJSp0o1B+ncuXMwNzfHiBEjCh3v3bs36tSpg6tXryLv/V5Ajx8/Rv369dVRptqsGtNaoXCUkp6NX/zvqbBCIiIiIiL1qjQ9SLm5ufDy8kKVKlUgFBbNfTo6OsjOzkZOTg4SExPx9u1bODk5qaFS9Yl8naJQOOqx8DQ6uFTe4YZERERERP9VaQKSlpZWkZ6jAs+ePUNERATq1KkDHR0dyfyj7OxsTJw4EaGhocjIyECzZs3w7bffonHjyjnvxmfbTYXCUXzyOxVWR0RERESkfpVqiJ00eXl5+PHHH5GXl4f+/fsD+HeBhn379iEzMxP9+vVD27Ztcf36dQwePBhXrlwp9p5ZWVkQiUSF/lQE6VkMR0RERERExak0PUjSiMVizJ8/H9evX4eLi4ukhykvLw9WVlaYOnUqevXqJWkfFBQET09PzJkzB4GBgUVWtyuwadMmrFu3TiXPQRUYjoiIiIiI8lXaHqScnBzMnTsXBw8ehI2NDX799Vfo6OgAAMaPH4/z588XCkcA0LJlS3z11VdISEhAUFCQzHt7eXnh9u3bkj+XLl0q0+dSlhiOiIiIiIj+VSkD0rt37zBx4kT4+/vD1tYWO3fuhKWlpVzXOjs7AwCio6NlttHR0YGhoWGhPxURwxERERERUWGVLiAlJydjxIgRuHTpEpydnbFnzx7Url27UJuwsDDcunVL6vWZmZkAIHN4XWXBcEREREREVFSlmoOUmZkJLy8v3L17Fy1btsSGDRuk9u5MmjQJcXFx+Ouvv2Bubl7o3O3btwEALi4uKqlZHRQJRyYGOiqsjIiIiIhIvSpVD9LKlSsRGhqKpk2bYsuWLTKHvn3xxRfIy8vDqlWrIBaLJcdPnjyJixcvokWLFpV2E1lFwlENE33M7Fc5lzwnIiIiIpKm0vQgJSQkYPfu3QAAe3t7bNmyRWq7cePGYeLEibh8+TIOHDiA8PBwuLq6IjIyEhcvXoSFhQWWLFmiytJVxqa6IVaOdpM7HJ1Y0A0GOloqrJCIiIiISL0qTUC6e/cusrOzAQCHDh2S2W7EiBEwNjbGvn37sG7dOpw9exa7du2CqakpvvnmG0yZMgU1atRQVdkqtXZca7xJzZQ7HBkbaONn/3tYOqKlCqskIiIiIlKfShOQunTpItkAVh7GxsaYO3cu5s6dW4ZVlS+ijByFwlGPhafR1L6aCiskIiIiIlKvSjUHiYo3ZfN1hcLR09gUFVZHRERERKR+DEgaJFGUIfMcwxEREREREQMSgeGIiIiIiKgAA5KGYzgiIiIiIvoXA5IGYzgiIiIiIiqMAUlDMRwRERERERXFgKSBFAlHetrcKJaIiIiINAcDkoZRJBwZ6lXB9N4uKqyOiIiIiEi9GJA0iLmhnkLh6PDcz1G7WlUVVkiVQV6eWN0lEBEREZVYFXUXQKqzZlxr6GoL5Q5HznXMsCLgb8wf2EyFVVJFJxQK4HspEtHJsvfdkqaplTEGu1qVUVVERERE8mFA0iCGelXw+fyTcoejXj+egX1NIxVWSJVFdHIGIt+8U+gaKxO9MqqGiIiISH4cYqdBJm++rlA4uv3sHxVWR0RERESkfgxIGuTlPyKZ5xiOiIiIiIgYkAgMR0REREREBRiQNBzDERERERHRvxiQNBjDERERERFRYQxIGorhiIiIiIioKAYkDaRIONISClRYGRERERGRejEgaRhFwpG2lhATuzdUYXVEREREROrFgKRBDHQUC0c7p3VAo7rmKqyQiIiIiEi9GJA0yIpRbgqFo65NrbHuxAMVVkhEREREpF5V1F0AqY5dTWP0XHRa7nA0ePkFGBtoq7BCIiIiIiL1Yg+SBpn223WFwtHp0GgVVkdEREREpH4MSBrkYfRbmecYjoiIiIiIGJAIDEdERERERAUYkDQcwxERERER0b8YkDQYwxERERERUWEMSBqK4YiIiIiIqCgGJA2kSDgSCFRYGBERERGRmjEgaRhFw9HQjo4qrI6IiIiISL0YkDRIFaFi4WjV6Fbo6FJbhRUSEREREakXA5IG+XGIq0LhaFQXJ2wPDFdhhURERERE6sWApEFaOdVQKBxN2vQXrj6IU2GFRERERETqxYCkQebuClYoHO268FSF1RERERERqR8Dkga5Hi67N4jhiIiIiIgIqKLuAkj9GI4qhtjYWMTGxgIAkpKSYGZmBgCoVasWatWqpc7SiIiIiCoN9iBpOIajimPTpk1wdXWFq6srunTpIvn7pk2b1F0aERERUaXBHiQNxnBUsXh5eaFXr154+PAhhg4dit9//x0NGzZk7xERERGREjEgaSiGo4rnv0PpGjZsiGbNmqmxIiIiIqLKh0PsNBDDERERERGRdAxIGkbRcNSzRR0VVUZEREREpH4MSBpE0XD0Xb/G+LqNnYqqIyIiIiJSPwYkDeLTu5FC4Wj+wGY4dC1SRdUREREREakfA5IG6e1mq1A4WrQvBMduvVBRdURERERE6seApEGWHrqjUDha5n9PRZUREREREZUPDEga5Hjwy2LPMxwRERERkaZjQCIADEdERERERAADEoHhiIiIiIioAAOShmM4IiIiIiL6FwOSBmM4IiIiIiIqjAFJQzEcEREREREVxYCkgRQJR+2cLVVUFRERERGR+jEgaRhFwtEwd0eM7OykospImXLz8tRyLREREVFFV0XdBZDqjOhUD+O6NZQ7HK33aouL91+hU2MrFVVIyqIlFCLk2T+Y9tsN5BQTeBpam2LVmNaIfJ0Cn203YWNhiG1TPlNhpURERETlCwOSBlE0HG07F45rj+IYkCqoab/dwO1n/8g87+pQHStGt8L954nou/gsRBk5SM/KUWGFREREROUPh9hpkM2nHyoUjqZtvQGxWEXFUWG5uaW+RXE9R64O1fHnvK548CJJEo6IiIiIiD1IGmXH+SfFnmc4Kke0tIAhQ4CHD4ueS0/P/+/gwYCBQdHz3bsD//d/Mm/NcEREREQkGwMSAWA4KpcePgRCQ2WfDw+XfrxBA5mXMBwRERERFY9D7IjhSEMwHBERERF9HAOShmM40gwMR0RERETyYUDSYAxHmkGRcGRT3VCFlRERERGVPwxIGorhSDMoEo4caxlj7bjWKqyOiIiIqPxhQNJAioSjxrbmqiuMlKqhtalC4ejEgm4cekcqlVfKT2ZKez0REZE0XMVOwygSjro1tYZ3D2fVFUdKtWpM60KbwMpSEI5S0rMxc3sQjs7rqsIqSZMJBQIcexCHN2lZCl9braoOejpblkFVRESk6RiQNMiXzW0w++tP5Q5He2a44+/niWjmaKG6IklpIl+nKBSOeiw8jVrm+iqskAh4k5aFOJHiAYmIiKiscIidBlE0HJ0JjcavJ6VsVEoVgs+2mwqFo/jkdyqsjoiIiKh8YkDSIEduRikUjoavuoTcPI7xr6jSsxiOiIiIiBTFgKRBVhz5W6FwlJ2bp7riSGUYjoiIiIhkY0DSIAxH9LFwZGmqz15DIiIi0mhcpIEYjjSEPD1HJgY60BIKMOvg34iIT1P4MdrVr4ZvP6+njHKJiIiI1IIBScMxHGkGRYfVRcSn4WFsqsKPY1fdoKQlEhEREZULHGKnwRiONIMi4cjEQEeFlRERERGVPwxIGorhSDMoEo5qmOhjZr/GKqyOiIiIqPzhEDsNpEg4srM0UmFlpEw21Q2xcrSb3OHoxIJuMNDRUmGFRFQgNjYWsbGxAICkpCSYmZkBAGrVqoVatWqpszQiIo3DHiQNo0g4cnWoDp8+jVRYHSnT2nGtFQpHxgba+Nn/ngorJKICmzZtgqurK1xdXdGlSxfJ3zdt2qTu0oiINA4DkgZp7WSpUDj6c15XvHqj+EpmVD6IMnIUCkc9Fp5G3FvuiUSKyeOy8Erh5eWF27dv4/fffwcA/P7777h9+za8vLzUXBkRkebR+CF2OTk5+P3333HgwAFER0fDwsIC/fr1w7hx46Ctra3u8pRq8bDmOK1AOHrwIgk7zz/BhontVFglKcuUzdcVCkdPY1PQ1L6aCiukykAoFMD3UiSikzMUuq6plTEGu1qVUVUVz3+H0jVs2BDNmjVTY0VERJpL4wPSokWLsH//fri6uqJTp04ICQnBmjVrEB4ejjVr1qi7PKW6ER6vUDjqu/gsurvaqLBCUqZEkew3rNLCEVFJRSdnIPKNYr2PViZ6ZVQNERFR6Wh0QAoJCcH+/fvRrVs3+Pr6QiAQQCwWY/bs2QgICMCFCxfg7u6u7jKVZt7u2wqFI1FGjgqro4+Jff/n4fuvC/5b6/0feTEcEREREcmm0XOQdu/eDQDw9vaGQCAAAAgEAkyfPh0CgQAHDx5UZ3lKl5PHcFSRbQLgCmDo+6+Hvv9akSncDEdE5UBurnqvJyKiYml0D1JwcDDMzMxQv379QsctLS1ha2uLW7duqaky1WI4qhi8APR6//ckAGbv/y5v7xHDEVE5oaUFDBkCPHxY9Fx6ev5/Bw8GDAyKnm/YEHj/4R4REZUNjQ1IWVlZeP36NZo0aSL1vJWVFSIjI5GYmAhzc3MVV6c6DEcVh6JD6T7EcFS55eblQUtYsgEBuXliaAkFSq6IPurhQyA0VPb58HDV1UJERIVobEB6+/YtAMDISPpGqAXHU1NTK21AYjjSDIqEIz1tbhRbEWkJhRi37gqGdnREK6camLsrGNfD42S2FwgAn96N0NvNVnVFEhERVRAaG5BycvLDgI6OjtTzBcczMzOLnMvKykJWVpbk69TUVACASCRSdpmF1a8PiEuw54i1NSASwcFCF3nZ+UM2nKxMsGR4SzyMeI0ffg+GXXUdAEVfi9rGWhCJRLAxFiA3U/EfFwt9MUQiEWro5CK7qmK1mwizIRKJUFWQDVNhtsKPXVUgKPvvSVlSwvfbRMcMy0a2hJ4gG99u+Av6WjloZC1l2A4AA50qmNLDgd/vcuLevXt49OhRkeMNGjRA48aNixwf3qEOnK0MMPO3Kwh6Ei/zvgIB4P2lMzp/Uh0HLz/Al83rYuvlSMQlF/237mOcaxuhj6sVv98l8Z/f79fZ2XidnY3HmZkQvnyJOzY2SNfVRU1tbdT8cMuJ+vWBCvq888RiCAUl760s7fXFqVq1qmQuMhGRQCwuyTuwii8xMRGtW7dG+/bt8dtvvxU5P3XqVJw8eRLnzp2DjU3hpa7Xrl2LdevWqapUIiIiKkO3b9+GoaGhussgonJCY3uQDA0NIRQKZX4CWdArJG0InpeXF0aOHCn5Oi8vD8nJyTA1NdWoT6BEIhE6dOiAS5cu8X8sGoDfb83C77dm0fTvd9WqVdVdAhGVIxobkHR0dFC7dm1ER0dLPR8dHQ1zc3OYmppKvfa/Q/OMjY3LoswKwdDQUCP/h6qp+P3WLPx+axZ+v4mINHwfJFdXVyQkJCAyMrLQ8bi4OERFRclc4Y6IiIiIiConjQ5Iffr0AQCsWrUKee83URWLxVi5ciUAYMCAAeoqjYiIiIiI1EBjh9gBQJs2bdCjRw+cOHECAwYMgJubG0JDQxEcHIxu3bqhY8eO6i6xXNPR0YG3t7fMlQCpcuH3W7Pw+61Z+P0mIvqXxq5iVyA7OxubN2/G4cOHERcXh9q1a6NXr14YO3Ys/0dBRERERKRhND4gERERERERFdDoOUhEREREREQfYkAiIiIiIiJ6jwGJFJaTkwM/Pz/06NEDjRs3RufOnbF+/XpkZ2eruzQqQ3FxcXB1dYWfn5+6S6EylJCQgPnz56NDhw5wcXFB27ZtMWPGDLx8+VLdpVEZSEpKwk8//YQuXbqgcePG6NGjB7Zs2YKcnBx1l0ZEpDYMSKSwRYsWYcmSJTA1NcXw4cNhaWmJNWvWwMfHR92lURlJS0vD5MmTIRKJ1F0KlaGEhAR4eHhg//79cHBwwLBhw9CoUSMcO3YM33zzDaKiotRdIimRSCTC4MGDsWvXLjg6OmLIkCEwMjLC8uXL4e3tDU5RJiJNpdHLfJPiQkJCsH//fnTr1g2+vr4QCAQQi8WYPXs2AgICcOHCBbi7u6u7TFKimJgYTJ48GWFhYeouhcrY2rVrERsbi9mzZ2PkyJGS40eOHMF3332HpUuXYuPGjWqskJRp8+bNiIiIwPfff4/hw4dLjvv4+ODYsWO4dOkSt7sgIo3EHiRSyO7duwEA3t7eEAgEAACBQIDp06dDIBDg4MGD6iyPlMzPzw9fffUVHj16hFatWqm7HCpj586dg7m5OUaMGFHoeO/evVGnTh1cvXpVsqk2VXwxMTGoVasWBg8eXOh4jx49AAChoaHqKIuISO3Yg0QKCQ4OhpmZGerXr1/ouKWlJWxtbXHr1i01VUZlYefOnbCyssLChQsRFRWFGzduqLskKiO5ubnw8vJClSpVIBQW/exMR0cH2dnZyMnJ4R5xlcSKFSukHo+IiAAAVK9eXZXlEBGVGwxIJLesrCy8fv0aTZo0kXreysoKkZGRSExMhLm5uYqro7KwcOFCtGnTBlpaWpx/UslpaWkV6Tkq8OzZM0RERKBOnToMR5WUWCxGYmIiTp06hbVr10o2TSci0kQMSCS3t2/fAgCMjIykni84npqayoBUSbRv317dJZCa5eXl4ccff0ReXh769++v7nKojPj6+mLDhg0A8nuOtm7dChMTEzVXRUSkHpyDRHIrWPZV1ifIBcczMzNVVhMRlR2xWIz58+fj+vXrcHFxkdnDRBWfjY0Nxo4di88//xyJiYkYMmQIF2YhIo3FHiSSm56eHgDI3O8oKysLAKCvr6+ymoiobOTk5GDevHnw9/eHjY0Nfv31Vw6vq8S+/vpryd8vXLiACRMmYNasWTh69KhkQR4iIk3BHiSSm6GhIYRCocy9cFJTUwHIHoJHRBXDu3fvMHHiRPj7+8PW1hY7d+6EpaWlussiFXF3d0fr1q3x5MkTvHjxQt3lEBGpHAMSyU1HRwe1a9dGdHS01PPR0dEwNzeHqampagsjIqVJTk7GiBEjcOnSJTg7O2PPnj2oXbu2ussiJcvJycG1a9fw119/ST1f8D1PSkpSZVlEROUCAxIpxNXVFQkJCYiMjCx0PC4uDlFRUTJXuCOi8i8zMxNeXl64e/cuWrZsiV27dqFatWrqLovKyPjx4zFjxgzk5uYWOffo0SMIBAJYW1uroTIiIvViQCKF9OnTBwCwatUqyYaRYrEYK1euBAAMGDBAXaURUSmtXLkSoaGhaNq0KbZs2QJDQ0N1l0RlpEqVKpIFGbZu3Vro3J49e3D//n107NiReyERkUbiIg2kkDZt2qBHjx44ceIEBgwYADc3N4SGhiI4OBjdunVDx44d1V0iEZVAQkICdu/eDQCwt7fHli1bpLYbN24cdHV1VVkalZHvvvsOwcHBWLFiBW7evIn69evj4cOHuH79OqytrbFw4UJ1l0hEpBYMSKSwZcuWwdHREYcPH8aOHTtQu3ZtTJkyBWPHjuVqR0QV1N27dyUrVB46dEhmuxEjRjAgVRKWlpb4448/sGbNGly4cAE3btxAjRo1MGLECEyYMAFmZmbqLpGISC0EYrFYrO4iiIiIiIiIygPOQSIiIiIiInqPAYmIiIiIiOg9BiQiIiIiIqL3GJCIiIiIiIjeY0AiIiIiIiJ6jwGJiIiIiIjoPQYkIiIiIiKi9xiQiIiIiIiI3qui7gKIiGSJjo5G586di22jp6eH6tWro1GjRhg9ejQaNWqkouqIiIioMmJAIqIKwcXFBTo6OoWOicViJCUl4cWLF4iOjsbp06exYsUK9OjRQ01VEhERUUUnEIvFYnUXQUQkzYc9SIGBgbC2tpba7uXLl5g2bRr+/vtvGBgY4OLFizAxMVFlqURERFRJcA4SEVV4NjY2WLVqFapUqYL09HQcP35c3SURERFRBcWARESVgo2NDezs7AAAERERaq6GiIiIKirOQSKiSkMgEADIn5v0oZcvX2Lnzp24ceMGYmJikJWVBVNTUzRp0gTDhg1Dq1atCrWfPXs2Dh8+DF9fX9ja2mL9+vW4desW0tLSYGNjg169emHUqFFF5kQBwP3797F582aEhoYiOTkZDg4OGDp0KNzc3NC5c2dYWVnh/Pnzha7Jy8tDQEAADh06hPDwcGRlZcHa2hpdu3bF6NGjYWRkpORXioiIiGRhQCKiSiEiIgJPnjwBgEIr2V29ehWTJk1CRkYGjIyMUKdOHWRmZuLly5c4d+4cAgMDsXz5cvTs2bPIPW/cuIEZM2YAAOzs7KCvr49nz55h1apVuHv3LjZs2FCo/YkTJ/Ddd98hO/v/27u3kKjWPo7jX5UpNW0XKBI1pYhkFgkVQkZI0RkGySKKKNIuCqO8KrRAGCGFsAsL7EQIGiWkWSnV1TSYGShFBzWckNHR0iYirSxdpu6LdHYy0+F9tb23m9/navyvZ635M3c/n8MaYObMmcTExOB0Ojl69CirV6/22bdhGBw8eBC73Q7A7Nmz+eOPP3jx4gVnzpyhqqqKoqIizGbzRPxMIiIi8hNaYicik97z5885dOgQw8PDzJkzh40bNwJfw0dWVhZ9fX3s2bOH2tparl+/zu3bt7Hb7SQmJjI8PExhYaHP5165coUVK1Zgt9uprKzk7t27HDt2DACbzcbTp089Yzs7O8nKymJgYID09HRqamooLy+npqaGlJQUr1mjUSdOnMButxMdHU1FRQU2m42Kigru3bvHunXraG9vJyMjg6GhoQn+1URERMQXzSCJyKSQkZHhtaTNMAzcbjdutxuAuXPncvbsWaZOnQp8Xe726dMnIiIiOHLkCAEBAZ57w8LCOHDgALW1tTidToaGhvD3H/s/oxkzZlBQUEBgYKCntnv3bkpKSnC5XDx+/JjFixcDcPHiRfr6+li7di0ZGRme8cHBwRw/fpwXL17w7NmzMc9//fo1paWlmEwmTp8+TXR0tOfazJkzyc/PZ8OGDTQ2NmKz2VizZs14fkIRERH5BQpIIjIpNDQ0+KybTCbWr19PUlISFotlTIhasmQJDx8+pK+vb0w4GhUUFAR83QPU39/v+XtUQkLCmHA0KioqCpfLxcePHz21u3fvArBt2zav8f7+/mzfvt0rIFVXVzMwMEB8fPyYcDRq6tSprFmzhuLiYqqrqxWQRERE/gYKSCIyKXz7HiTDMLh//z65ubm4XC56e3tZvXq1z0MTAAIDA2lsbKSpqQmXy4XL5cLhcOB0Oj1jfC1hi4iI+O7zvr3HMAw6OjoAmD9/vs974uLivGotLS0AtLW1sWPHDp/3vXnzBmBMryIiIvL7KCCJyKQzZcoUVq1aRVxcHFu2bKGmpoZ9+/ZRXFzsNeNTX19PXl4ejY2Nnpqfnx/z5s3DYrFw8+bN736PyWT6YR+jp+W9e/fOUwsODvY5dtq0aV610Rmo7u5uHj169MPv+na2SkRERH4fBSQRmbQiIiI4ceIEaWlpPHnyhLy8PKxWq+e6w+EgLS0NwzBYtmwZycnJzJ8/n+joaEJCQnA6nT8MSL/q21DU29vr81ju3t5er9rokr6dO3eSnZ097j5ERERk/HSKnYhMaomJiZ59P6WlpTx48MBzraSkBMMwWL58OcXFxWzbto34+HhCQkIA6OrqmpAeQkNDPcvxmpubfY5xOBxetcjISODHL7ZtaWnh2bNn9PT0jL9RERER+SkFJBGZ9A4fPkxYWBgAVqsVwzAAePnyJfB1X5CvQxrKyso8nwcHB8fVw+h7jq5du+bzenl5uVdt5cqV+Pv7U1dX5zMkffnyhfT0dLZu3UpRUdG4+hMREZFfo4AkIpNeaGgoR44cAb4eZnDhwgXgrxmaW7du0dbW5hnf09NDbm4uVVVVnlp/f/+4eti7dy+BgYHcuXOHwsJCT+AyDIO8vDzq6uq87pk7dy4Wi4XBwUH2798/Zp9UT08Phw8fprW1leDg4O8e4iAiIiITS3uQROQ/ITk5mbKyMurq6jh37hwWi4XU1FQqKytxu91s2rSJqKgo/Pz8aG1txTAMYmNj6erqoru7G7fbTXh4+P/9/WazmZycHDIzMykoKKCkpITZs2fT1tbG+/fvWbRoEQ0NDV4zWdnZ2bx69Yr6+npSUlKIjIwkODgYp9PJ58+fMZlMnDp16rsn6omIiMjE0gySiPxnZGdnYzKZ6O/vx2q1YjabuXHjBps3b2bWrFm0trbS2dlJbGwsWVlZXL16lRUrVgB/vcdoPJKTk7l06RJJSUkMDg7icDgwm83k5+eTmpoK4HXKXkhICEVFReTk5LB06VLevn2Lw+Fg+vTpWCwWysrKWLly5bh7ExERkV/jNzx6Tq2IiPw2ly9fxmq1kpiYqP1EIiIi/2KaQRIRmQCZmZls3rwZm83m8/q9e/cAWLBgwd/ZloiIiPyPFJBERCZATEwMTU1NnDx5ko6ODk/dMAzOnz+PzWZjypQpbNmy5R/sUkRERH5GS+xERCbAp0+f2L59O83NzQQEBDBv3jyCgoJob2/n/fv3mEwmrFarApKIiMi/nAKSiMgE6evro7y8nJs3b9Le3s6HDx8IDw8nISGBXbt2sXDhwn+6RREREfkJBSQREREREZER2oMkIiIiIiIyQgFJRERERERkhAKSiIiIiIjICAUkERERERGREQpIIiIiIiIiIxSQRERERERERiggiYiIiIiIjFBAEhERERERGaGAJCIiIiIiMuJPUtuDTT4q5UkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df[df.Culture_time=='48h']#Use 48h data only\n",
    "boot_iter = 250000\n",
    "boot_n_rho = 100\n",
    "rhos = np.arange(0,1.001,0.01)\n",
    "\n",
    "for n_oc,oc in enumerate(or_cells):\n",
    "    for n_cnd,cnd in enumerate(conds):\n",
    "        #if n_oc!=1 or n_cnd!=0:\n",
    "        #    continue\n",
    "        df_temp = df[(df.Original_cell==oc)&(df.Culture_condition==cnd)]\n",
    "        \n",
    "        #SET MAX GENERATION AND ALL SAMPLED FAMILY VECTORS FROM THE DATA \n",
    "        max_g = df_temp.Generation.max()\n",
    "        uni_fams = np.unique(df_temp.Family)\n",
    "        print(max_g)\n",
    "        print('\\n', oc, cnd, len(uni_fams))\n",
    "        fam_vec = np.array([df_fam_to_vec(df_temp[df_temp.Family==fam], max_g)\n",
    "                            for fam in uni_fams])\n",
    "        \n",
    "        #ESTIMATE p FOR THE BB MODEL, THE PROBABILITY FOR A CELL TO DIVIDE TO NEXT GENERATION \n",
    "        cc_pop = fam_vec.sum(axis=0)*(2.** -np.arange(max_g+1))\n",
    "        p_div = np.array([cc_pop[i+1:].sum()/cc_pop[i:].sum() for i in np.arange(len(cc_pop)-1)] + [0.])\n",
    "        print(p_div)\n",
    "        #CALCULATE THE OBSERVED FRAY/RANGE, AND ITS DISTRIBUTION, FROM THE DATA\n",
    "        fray_data = np.array([df_temp[df_temp.Family==fam].Generation.max()\n",
    "                              - df_temp[df_temp.Family==fam].Generation.min()\n",
    "                              for fam in uni_fams])\n",
    "        fray_support = np.arange(max_g)\n",
    "        fray_freq = epmf_on_support(fray_data, fray_support, bool_norm=False)\n",
    "        \n",
    "        #Generate all family vectors up to max observed depth d\n",
    "        start = time.process_time()\n",
    "        ary_fam_vec = fam_generator(max_g, remove_empty=False)\n",
    "        lst_Fam_vec = [Family_vector(fv) for fv in ary_fam_vec]\n",
    "        end = time.process_time()\n",
    "        print('Family vectors generation time:', end-start)\n",
    "        \n",
    "        #Calculate probabilities for sampled families and generation from bb model \n",
    "        start = time.process_time()\n",
    "        for Fv in lst_Fam_vec:\n",
    "            Fv.generate_fv_prob(p=p_div, r_surv=r_surv, rhos=rhos)\n",
    "            Fv.select_sub_family_vector(ary_fam_vec, r=r_sample)\n",
    "        end = time.process_time()\n",
    "        print('Probabilities calculation time:', end-start)\n",
    "        \n",
    "        #MLE rho estimation\n",
    "        start = time.process_time()\n",
    "        rho_fray_pmf = np.array([fray_pmf(rho, lst_Fam_vec, fray_support) for rho in rhos])\n",
    "        log_like_data_rho = np.array([log_like_fray(fray_pb, fray_freq) for fray_pb in rho_fray_pmf])\n",
    "        #log_like_data_rho = np.array([log_like_data(rho, lst_Fam_vec, fray_freq) for rho in rhos])\n",
    "        est_rho = rhos[np.argmax(log_like_data_rho)]\n",
    "        end = time.process_time()\n",
    "        print('MLE rho time:', end-start)\n",
    "        \n",
    "        #UNCOMMENT TO CALCULATE BOOTSTRAP CIs\n",
    "        \n",
    "        #BOOTSTRAP EPMF OF FRAY DISTRIBUTION\n",
    "        start = time.process_time()\n",
    "        fray_epmf, boot_ci = stat_bootCI(func=epmf_on_support, args=(fray_support,), data=fray_data)\n",
    "        end = time.process_time()\n",
    "        print('Boot ci fray epmf time:', end-start)\n",
    "        \n",
    "        #BOOTSTRAP RHO\n",
    "        start = time.process_time()\n",
    "        boot_rho = []\n",
    "        for boot_data in np.random.choice(fray_data, size=(boot_n_rho,len(fray_data))):\n",
    "            boot_freq = epmf_on_support(boot_data, fray_support, bool_norm=False)\n",
    "            boot_log_like_data_rho = np.array([log_like_fray(fray_pb, boot_freq) for fray_pb in rho_fray_pmf])\n",
    "            #boot_log_like_data_rho = np.array([log_like_data(rho, lst_Fam_vec, boot_freq) for rho in rhos])\n",
    "            boot_est_rho = rhos[np.argmax(boot_log_like_data_rho)]\n",
    "            boot_rho.append(boot_est_rho)\n",
    "        end = time.process_time()\n",
    "        print('Boot ci rho time:', end-start)\n",
    "        \n",
    "        #Fray distribution under the BB model for other values of rho\n",
    "        rho_lst = np.array([est_rho, 0.9, 0.5, 0.0])\n",
    "        #fray_pmf = {str(rho):[fray_prob(gen, rho, lst_Fam_vec) \n",
    "        #                      for gen in fray_support] for rho in rho_lst}\n",
    "        dct_rho_fray_pmf = {str(rho):fray_pmf(rho, lst_Fam_vec, fray_support) for rho in rho_lst}\n",
    "        #LIKELIHOOD-RATIO TEST (EXTRA)\n",
    "        neg_log_like_H0 = -log_like_data(0., lst_Fam_vec, fray_freq)\n",
    "        neg_log_like_H1 = -log_like_data(est_rho, lst_Fam_vec, fray_freq)\n",
    "        like_ratio_stat = 2. * (neg_log_like_H0 - neg_log_like_H1)\n",
    "        pval = sps.chi2.sf(like_ratio_stat, df=1)\n",
    "        print('LR test pval=', pval)\n",
    "        \n",
    "        #SAVE VARIABLES THAT TAKE A LONG TIME TO CALCULATE\n",
    "        with open('./pickled_data/AAFray_ci_'+oc+cnd, 'wb') as fp:\n",
    "            pickle.dump((fray_epmf,boot_ci,est_rho,boot_rho,dct_rho_fray_pmf,pval), fp)\n",
    "        #END COMMENT HERE\n",
    "        \n",
    "        with open('./pickled_data/AAFray_ci_'+oc+cnd, 'rb') as fp:\n",
    "            fray_epmf,boot_ci,est_rho,boot_rho,dct_rho_fray_pmf,pval = pickle.load(fp)\n",
    "\n",
    "        #PRINT RHO CI\n",
    "        boot_per_rho = np.percentile(boot_rho, q=[97.5, 2.5], axis=0)\n",
    "        boot_ci_rho = 2.*est_rho - boot_per_rho\n",
    "        print('Best-fit rho: {} in 95% CI ({},{})'.format(round(est_rho,3),round(boot_ci_rho[0],3),round(boot_ci_rho[1],3)))            \n",
    "        \n",
    "        #PLOTTING CODE\n",
    "        rho_lst = np.array([est_rho, 0.9, 0.5, 0.0])###THIS IS ADDED FOR WHEN THE PART ABOVE IS COMMENTED\n",
    "        width = 0.5/len(rho_lst)\n",
    "        t_cols = sns.color_palette('Blues', n_colors=6)[::-1]\n",
    "        color_dct = {str(est_rho):t_cols[0], '0.9':t_cols[1],\n",
    "                     '0.5':t_cols[2], '0.0':t_cols[3]}\n",
    "        shift_dct = {str(rho_lst[k]):-0.30 + (k+1)*0.125 for k in range(len(rho_lst))}    \n",
    "        sns.set_style(\"ticks\")\n",
    "        fig, ax = plt.subplots()\n",
    "        ax.bar(x=fray_support-0.30, height=fray_epmf, width=width, align='edge',\n",
    "           color='Red', label='Data')\n",
    "        for rho in rho_lst:\n",
    "            if rho == est_rho:\n",
    "                ax.bar(x=fray_support+shift_dct[str(rho)], height=dct_rho_fray_pmf[str(rho)],\n",
    "                       width=width, align='edge', color=color_dct[str(rho)], hatch='///',\n",
    "                       label=r'$\\rho = $'+str(round(rho, 3))+' (Best fit)')\n",
    "            else:\n",
    "                ax.bar(x=fray_support+shift_dct[str(rho)], height=dct_rho_fray_pmf[str(rho)],\n",
    "                       width=width, align='edge', color=color_dct[str(rho)],\n",
    "                       label=r'$\\rho = $'+str(round(rho, 3)))     \n",
    "        xx_fray = np.add(np.arange(len(fray_epmf)), (width/2.)-0.30)\n",
    "        for k in range(len(fray_epmf)):\n",
    "            ax.plot([xx_fray[k]]*2, [boot_ci[0,k], boot_ci[1,k]],\n",
    "                     color='Black', ls='-', lw=1., mfc='Black', mec='Black',\n",
    "                     marker='_', mew=1, ms=5.)\n",
    "        sns.despine(fig=fig)\n",
    "        ax.set_xlabel('Range', fontsize='xx-large')\n",
    "        ax.set_ylabel('% of clones', fontsize='xx-large')\n",
    "        ax.set_xticks(np.arange(max_g))#len(fray_epmf)))\n",
    "        ax.set_xticklabels(np.arange(max_g), fontsize='x-large')# np.arange(len(fray_epmf))\n",
    "        ax.set_yticks(np.arange(0., 1.1, 0.25))\n",
    "        ax.set_yticklabels(('0', '25', '50', '75', '100'), fontsize='x-large')\n",
    "        if oc == 'SLAM-HSC' and cnd == 'P':\n",
    "            ax.set_xlim(-0.5, 2.5)\n",
    "        elif oc == 'SLAM-HSC' and cnd == 'P+ILs':\n",
    "            ax.set_xlim(-0.5, 3.5)\n",
    "        elif oc == 'ST-HSC' and cnd == 'P':\n",
    "            ax.set_xlim(-0.5, 3.5)\n",
    "        elif oc == 'ST-HSC' and cnd == 'P+ILs':\n",
    "            ax.set_xlim(-0.5, 4.5)\n",
    "        elif oc == 'MPP' and cnd == 'P':\n",
    "            ax.set_xlim(-0.5, 2.5)\n",
    "        elif oc == 'MPP' and cnd == 'P+ILs':\n",
    "            ax.set_xlim(-0.5, 4.5)\n",
    "        #ax.set_xlim(-0.5, fray_data.max()+0.5)\n",
    "        ax.set_ylim(bottom=-0.0, top=1.0)\n",
    "        ax.legend(fontsize='x-large', loc='upper left', ncol=1, bbox_to_anchor= (1.05, 1.))\n",
    "        ax.set_title('Generation range 48h '+oc+' '+cnd, fontdict = {'fontsize':'xx-large'})\n",
    "        fig.savefig(path_plot+'/AAFray fit 48h '+oc+' '+cnd+'.pdf', bbox_inches='tight')"
   ]
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