{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48ca0ee3",
   "metadata": {},
   "source": [
    "# How many real people did I actually reach across platforms?\n",
    "\n",
    "You ran the same campaign on two platforms. Each one tells you how many people it reached.\n",
    "Adding the two numbers together counts every person who was on both platforms twice, so the\n",
    "sum is not your reach - and nobody can tell you the overlap, because neither platform can see\n",
    "the other's audience. This notebook is about what you *can* know from the data you already\n",
    "have, what you can only estimate, and the one number you cannot have at all.\n",
    "\n",
    "The working uses a synthetic market with the answer built in: I decide who is on each\n",
    "platform and who clicks, so every reading below is scored against a truth nobody has to guess\n",
    "at. Six things are worth knowing before the working.\n",
    "\n",
    "**The people who visited your site can be counted exactly.** Your site sees which platform\n",
    "each visitor arrived from, so the number of visitors who came from both platforms is a count,\n",
    "not an estimate - and so is the number of new visitors each platform brought. That is the one\n",
    "exact answer in this notebook, and it covers visitors only, never the people who saw an ad and\n",
    "scrolled on.\n",
    "\n",
    "**The share of your visitors who came from both platforms is not the overlap of your reach.**\n",
    "It is the overlap times the click rate, which makes it under a five-hundredth of the real thing. Read\n",
    "straight off the analytics report it says the audiences barely overlap, and that is the\n",
    "report's most common lie.\n",
    "\n",
    "**The visitors do carry the overlap - but scaled by how keen they are.** The same logic that\n",
    "counts fish in a lake turns the visitors from both platforms into an estimate of the people\n",
    "on both platforms. It is exact when the people you reach on both platforms are no keener to\n",
    "click than anyone else, and it overstates the overlap when they are. Measured here: on the\n",
    "worked market, where the double-reached are keener, it understates the unique reach by\n",
    "more than a fifth.\n",
    "\n",
    "**Assuming the audiences overlap only by chance errs the other way.** With a number for the\n",
    "population both platforms draw from, the chance-overlap formula gives a unique reach that is\n",
    "too high when the audiences really do overlap more than chance - which they usually do. On\n",
    "the worked market it overstates by about a twentieth.\n",
    "\n",
    "**So the honest estimate is a range, and a single total is a guess.** The truth sits between\n",
    "the click-based figure and the chance figure, and nothing in the data says where. Two markets\n",
    "with identical platform reports and identical visitor counts are built below, and their true\n",
    "unique reach differs by more than a fifth. The instrument that would settle it - a panel that\n",
    "sees the same people's impressions across platforms - is the one thing a site does not have.\n",
    "\n",
    "**The data in this notebook is synthetic.** It is generated here with the answer built in.\n",
    "Run it on your own figures in the last section; nothing leaves your machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "919e569f",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed 20260915\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import warnings\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "SEED = 20260915\n",
    "rng = np.random.default_rng(SEED)\n",
    "\n",
    "PAPER = \"#fdfcf9\"\n",
    "INK = \"#1f2124\"\n",
    "MUTED = \"#565c66\"\n",
    "LINE = \"#e6e2da\"\n",
    "SIGNAL = \"#0f6e56\"\n",
    "FLAG = \"#b06a2c\"\n",
    "NAIVE = \"#2a78d6\"\n",
    "BAND = \"#a9d6c5\"\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"figure.facecolor\": PAPER, \"axes.facecolor\": PAPER, \"savefig.facecolor\": PAPER,\n",
    "    \"axes.edgecolor\": LINE, \"axes.labelcolor\": INK, \"text.color\": INK,\n",
    "    \"xtick.color\": MUTED, \"ytick.color\": MUTED, \"axes.grid\": True,\n",
    "    \"grid.color\": LINE, \"grid.linewidth\": 0.8, \"axes.spines.top\": False,\n",
    "    \"axes.spines.right\": False, \"font.size\": 11, \"figure.dpi\": 140,\n",
    "})\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "HERE = Path.cwd()\n",
    "FIGURES = HERE / \"figures\"\n",
    "DATA = HERE / \"data\"\n",
    "FIGURES.mkdir(exist_ok=True)\n",
    "DATA.mkdir(exist_ok=True)\n",
    "\n",
    "print(f\"seed {SEED}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c41ff029",
   "metadata": {},
   "source": [
    "## The market I build, and the truth I hide from the reports\n",
    "\n",
    "One consumer brand, one month, one campaign on two platforms - call them Meta and TikTok.\n",
    "The generator builds a market of people and gives it three properties real markets have.\n",
    "\n",
    "**Each platform reaches some of the market, and the audiences overlap more than chance.**\n",
    "Every person has a latent \"how online\" trait. People high on it are more likely to be on\n",
    "either platform, so the two audiences share more people than two random slices of the\n",
    "market would. That shared trait is what makes the chance-overlap formula optimistic.\n",
    "\n",
    "**Some reached people click through to the site, and the keen ones click more.** Each person\n",
    "has a keenness, and keenness is partly the same trait that put them on both platforms. So the\n",
    "people reached on both platforms are, on average, keener to click than people reached on one -\n",
    "and the keenest of them click on both. That is what makes the click-based estimate\n",
    "pessimistic, and it is the one thing in this notebook no report can measure.\n",
    "\n",
    "**The site sees visitors, not viewers.** A person who saw the ad and scrolled on leaves no\n",
    "trace on the site. A person who clicked lands with the platform's click tag in the URL and is\n",
    "recognised on their next visit by the site's own first-party cookie - so the site knows, for\n",
    "its visitors, which platforms each arrived from. Everything the method estimates is built\n",
    "from those visitors, plus the two reach numbers the platforms report.\n",
    "\n",
    "The two dials that matter later are the audience correlation (how much more than chance the\n",
    "audiences overlap) and the keenness spread (how unevenly the urge to click is spread, and how\n",
    "much it follows the same trait as being on both platforms). Both can be set to zero, which\n",
    "is the world the simple formulas are exact in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b82a4e33",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:46:36.138724Z",
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     "shell.execute_reply": "2026-10-24T16:46:36.748166Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "what the platforms report\n",
      "  platform A reached 714,202 people, platform B 481,237\n",
      "what the site sees\n",
      "  22,368 visitors arrived via A, 12,279 via B, 335 via both\n",
      "what nobody reports - the truth the generator knows\n",
      "  196,986 people were reached on both platforms, so the unique reach is 998,453, not 1,195,439\n",
      "  platform B added 284,251 people platform A had not reached\n"
     ]
    }
   ],
   "source": [
    "N_MARKET = 2_400_000        # people the two platforms draw from (the addressable population)\n",
    "ALPHA_A = -1.0              # sets platform A's reach share (about thirty per cent of the market)\n",
    "ALPHA_B = -1.6              # sets platform B's reach share (about twenty per cent)\n",
    "CORRELATION = 0.9           # how strongly being on either platform follows the shared trait\n",
    "KEEN_SPREAD = 0.7           # spread of keenness to click across people (log scale)\n",
    "KEEN_FOLLOWS = 0.6          # how much keenness follows the same trait as multi-platform membership\n",
    "CLICK_A = 0.025             # a reached person's chance of visiting from platform A this month\n",
    "CLICK_B = 0.020             # the same for platform B\n",
    "SPEND_B_EUR = 24_000.0      # what the month on platform B cost\n",
    "\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1.0 / (1.0 + np.exp(-x))\n",
    "\n",
    "\n",
    "def build_market(n, alpha_a, alpha_b, correlation, keen_spread, keen_follows, click_a, click_b,\n",
    "                 generator):\n",
    "    \"\"\"One market: who each platform reached, and who clicked through from each.\n",
    "\n",
    "    `trait` is the shared \"how online\" trait; membership on each platform follows it with\n",
    "    strength `correlation`, and keenness to click follows it with strength `keen_follows` and\n",
    "    has spread `keen_spread` (log scale, mean one). Clicks on the two platforms are independent\n",
    "    given a person's keenness - a person's two clicks are only linked through who they are.\n",
    "    \"\"\"\n",
    "    trait = generator.standard_normal(n)\n",
    "    noise = generator.standard_normal(n)\n",
    "    on_a = generator.random(n) < sigmoid(alpha_a + correlation * trait)\n",
    "    on_b = generator.random(n) < sigmoid(alpha_b + correlation * trait)\n",
    "    keen = np.exp(keen_spread * (keen_follows * trait + np.sqrt(1 - keen_follows ** 2) * noise)\n",
    "                  - keen_spread ** 2 / 2)\n",
    "    visit_a = on_a & (generator.random(n) < np.minimum(1.0, click_a * keen))\n",
    "    visit_b = on_b & (generator.random(n) < np.minimum(1.0, click_b * keen))\n",
    "    return pd.DataFrame({\"on_a\": on_a, \"on_b\": on_b, \"visit_a\": visit_a, \"visit_b\": visit_b,\n",
    "                         \"keen\": keen, \"trait\": trait})\n",
    "\n",
    "\n",
    "def observe(market):\n",
    "    \"\"\"What the platforms and the site report: reach per platform, visitors per platform, and the\n",
    "    visitors who arrived via both. The overlap of the audiences is NOT in here - nobody reports it.\"\"\"\n",
    "    return {\n",
    "        \"reach_a\": int(market.on_a.sum()), \"reach_b\": int(market.on_b.sum()),\n",
    "        \"visitors_a\": int(market.visit_a.sum()), \"visitors_b\": int(market.visit_b.sum()),\n",
    "        \"visitors_both\": int((market.visit_a & market.visit_b).sum()),\n",
    "    }\n",
    "\n",
    "\n",
    "def truth_of(market):\n",
    "    both = int((market.on_a & market.on_b).sum())\n",
    "    return {\"overlap\": both, \"unique\": int(market.on_a.sum() + market.on_b.sum() - both),\n",
    "            \"net_new_b\": int(market.on_b.sum() - both), \"net_new_a\": int(market.on_a.sum() - both)}\n",
    "\n",
    "\n",
    "market = build_market(N_MARKET, ALPHA_A, ALPHA_B, CORRELATION, KEEN_SPREAD, KEEN_FOLLOWS,\n",
    "                      CLICK_A, CLICK_B, rng)\n",
    "report = observe(market)\n",
    "truth = truth_of(market)\n",
    "\n",
    "print(\"what the platforms report\")\n",
    "print(f\"  platform A reached {report['reach_a']:,} people, platform B {report['reach_b']:,}\")\n",
    "print(\"what the site sees\")\n",
    "print(f\"  {report['visitors_a']:,} visitors arrived via A, {report['visitors_b']:,} via B, \"\n",
    "      f\"{report['visitors_both']:,} via both\")\n",
    "print(\"what nobody reports - the truth the generator knows\")\n",
    "print(f\"  {truth['overlap']:,} people were reached on both platforms, so the unique reach is \"\n",
    "      f\"{truth['unique']:,}, not {report['reach_a'] + report['reach_b']:,}\")\n",
    "print(f\"  platform B added {truth['net_new_b']:,} people platform A had not reached\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6062914",
   "metadata": {},
   "source": [
    "## The one exact answer: the visitors, de-duplicated\n",
    "\n",
    "The site's own data settles one question completely. Each visitor arrived from one platform\n",
    "or both, so the number of distinct people who visited from paid social is the two visitor\n",
    "counts minus the ones counted twice - inclusion-exclusion, the oldest arithmetic in counting:\n",
    "\n",
    "$$|A \\cup B| = |A| + |B| - |A \\cap B|$$\n",
    "\n",
    "and the visitors platform B brought that platform A had not are $|B| - |A \\cap B|$. These\n",
    "are counts of identified visitors, so they carry no estimate at all - with one condition that\n",
    "gets its own section later: the site has to recognise the same person on both arrivals.\n",
    "\n",
    "What this does not tell you is the reach overlap. The visitors who came from both platforms\n",
    "are a small, keen sub-sample of the people who were reached on both. Everything after this\n",
    "cell is about how far that sub-sample can be trusted to speak for the rest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "000c26bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:46:36.754872Z",
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     "shell.execute_reply": "2026-10-24T16:46:36.763845Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "distinct visitors from paid social: 34,312 (the two counts add to 34,647; 335 people were in both)\n",
      "visitors platform B brought that A had not: 11,944\n",
      "share of visitors who came from both platforms: 1.0 per cent\n",
      "share of the REACH that was on both platforms: 19.7 per cent - the visitor share is nothing like it\n"
     ]
    }
   ],
   "source": [
    "def visitor_counts(observed):\n",
    "    \"\"\"The measured layer: inclusion-exclusion over the site's own visitors.\"\"\"\n",
    "    a, b, both = observed[\"visitors_a\"], observed[\"visitors_b\"], observed[\"visitors_both\"]\n",
    "    return {\"unique_visitors\": a + b - both, \"counted_twice\": both,\n",
    "            \"net_new_visitors_b\": b - both, \"net_new_visitors_a\": a - both,\n",
    "            \"share_of_visitors_from_both\": both / (a + b - both)}\n",
    "\n",
    "\n",
    "visitors = visitor_counts(report)\n",
    "print(f\"distinct visitors from paid social: {visitors['unique_visitors']:,} \"\n",
    "      f\"(the two counts add to {report['visitors_a'] + report['visitors_b']:,}; \"\n",
    "      f\"{visitors['counted_twice']:,} people were in both)\")\n",
    "print(f\"visitors platform B brought that A had not: {visitors['net_new_visitors_b']:,}\")\n",
    "print(f\"share of visitors who came from both platforms: \"\n",
    "      f\"{visitors['share_of_visitors_from_both'] * 100:.1f} per cent\")\n",
    "print(f\"share of the REACH that was on both platforms: \"\n",
    "      f\"{truth['overlap'] / truth['unique'] * 100:.1f} per cent - the visitor share is nothing like it\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c489dec",
   "metadata": {},
   "source": [
    "### The site counts its customers, too\n",
    "\n",
    "The same identity that tells the site which platforms a visitor arrived from tells it which\n",
    "visitors bought. So the customers split the same way - arrived via A only, via B only, via\n",
    "both - and those are counts, not estimates. They are the numbers the money question below\n",
    "rests on. In this market a flat share of the visitors buy, decided by a coin the platforms\n",
    "know nothing about."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de539ce4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:46:36.772624Z",
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     "shell.execute_reply": "2026-10-24T16:46:36.862933Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "customers this month: 1,013 - 663 arrived via A only, 341 via B only, 9 via both\n",
      "customers B brought that A had not: 341 - a count, like the visitors\n"
     ]
    }
   ],
   "source": [
    "BUY_RATE = 0.03             # share of the site's visitors who buy this month\n",
    "\n",
    "\n",
    "def add_customers(market, buy_rate, generator):\n",
    "    \"\"\"Who bought: a flat share of the visitors, drawn from a stream of its own.\"\"\"\n",
    "    visited = market.visit_a | market.visit_b\n",
    "    return market.assign(buy=visited & (generator.random(len(market)) < buy_rate))\n",
    "\n",
    "\n",
    "def customer_counts(market):\n",
    "    \"\"\"The other exact count: customers, split by the platforms they arrived from.\"\"\"\n",
    "    a, b = market.buy & market.visit_a, market.buy & market.visit_b\n",
    "    return {\"customers_a_only\": int((a & ~market.visit_b).sum()), \"customers_b_only\": int((b & ~market.visit_a).sum()),\n",
    "            \"customers_both\": int((a & b).sum()), \"customers\": int(market.buy.sum())}\n",
    "\n",
    "\n",
    "market = add_customers(market, BUY_RATE, np.random.default_rng(SEED + 11))\n",
    "customers = customer_counts(market)\n",
    "print(f\"customers this month: {customers['customers']:,} - {customers['customers_a_only']:,} arrived via A only, \"\n",
    "      f\"{customers['customers_b_only']:,} via B only, {customers['customers_both']:,} via both\")\n",
    "print(f\"customers B brought that A had not: {customers['customers_b_only']:,} - a count, like the visitors\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d549219f",
   "metadata": {},
   "source": [
    "## Four ways to turn the reports into a reach, and where each one lands\n",
    "\n",
    "**The sum.** Add the two reach numbers. It counts the overlap twice, so it is a ceiling on the\n",
    "unique reach and nothing more - and it is what most dashboards silently do.\n",
    "\n",
    "**The visitor share.** \"Three per cent of our visitors came from both platforms, so three per\n",
    "cent of our reach overlaps.\" This is the most natural move and the worst one. A person on\n",
    "both platforms shows up as a visitor from both only if they clicked *both* ads - and clicking\n",
    "each is a few-per-cent event. The visitor share is the reach overlap multiplied by the click\n",
    "rates, which is why it comes out a hundred times too small and lands almost on the sum.\n",
    "\n",
    "**The chance formula.** If the two audiences were random slices of the same population, the\n",
    "expected overlap would be reach A times reach B divided by that population, and the unique\n",
    "reach follows. It needs a number nobody reports - the population both platforms draw from -\n",
    "and it assumes the audiences overlap only by chance. Real audiences overlap more than chance,\n",
    "because the same people are the ones who are online, so the formula understates the overlap\n",
    "and overstates the unique reach. It is a ceiling, and a tighter one than the sum when the\n",
    "population number is right.\n",
    "\n",
    "**The click-based estimate.** This is the capture-recapture idea from ecology. Mark some fish,\n",
    "release them, catch again: the share of marked fish in the second catch tells you how big the\n",
    "lake is. Here the two catches are the visitors from A and the visitors from B, and the\n",
    "recaptures are the visitors from both. Scaled up by the two click rates, the visitors from\n",
    "both platforms estimate the people on both platforms:\n",
    "\n",
    "$$\\hat{O} = \\frac{v_{AB} \\cdot R_A \\cdot R_B}{v_A \\cdot v_B}$$\n",
    "\n",
    "where $v_A$, $v_B$ and $v_{AB}$ are the visitors from A, from B and from both, and $R_A$,\n",
    "$R_B$ the two reported reaches. It is exact when everyone reached has the same chance of\n",
    "clicking - the ecologist's *equal catchability*. It is not exact when the people reached on\n",
    "both platforms are keener than the rest, because then the visitors from both are inflated\n",
    "relative to the people on both, the estimated overlap is too high, and the unique reach is\n",
    "too low. The direction is one-way: keen people click more, never less, so the click-based\n",
    "figure is a floor on the unique reach.\n",
    "\n",
    "So the two formulas bracket the truth from opposite sides, for opposite reasons. The worked\n",
    "market shows both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d911b85b",
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    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading                   overlap  unique reach    error\n",
      "the sum                         0     1,195,439   +19.7%\n",
      "the visitor share          13,129     1,182,310   +18.4%\n",
      "the chance formula        143,209     1,052,230    +5.4%\n",
      "the click-based estimate    419,213       776,226   -22.3%\n",
      "the truth                 196,986       998,453\n",
      "\n",
      "the click-based figure's sampling interval: 727,796 to 819,640 unique people (plus or minus 11 per cent on the overlap) - noise, not bias\n"
     ]
    }
   ],
   "source": [
    "def readings(observed, population=None, keenness_inflation=0.0):\n",
    "    \"\"\"Every reading of the reach a reader could make from the same reports.\n",
    "\n",
    "    `population` is the number the chance formula needs; None skips it. `keenness_inflation` is\n",
    "    the unobservable factor by which keen double-reached people inflate the visitors-from-both\n",
    "    count: the click-based overlap is divided by (1 + it), and zero is the equal-catchability\n",
    "    case the formula is exact in.\n",
    "    \"\"\"\n",
    "    ra, rb = observed[\"reach_a\"], observed[\"reach_b\"]\n",
    "    va, vb, vab = observed[\"visitors_a\"], observed[\"visitors_b\"], observed[\"visitors_both\"]\n",
    "    out = {\"sum\": ra + rb}\n",
    "    out[\"visitor_share\"] = ra + rb - (vab / vb) * rb if vb else float(\"nan\")\n",
    "    if population:\n",
    "        chance_overlap = ra * rb / population\n",
    "        out[\"chance_overlap\"] = chance_overlap\n",
    "        out[\"chance\"] = ra + rb - chance_overlap\n",
    "    click_overlap = vab * ra * rb / (va * vb) / (1.0 + keenness_inflation) if va and vb else float(\"nan\")\n",
    "    # an overlap larger than the smaller reach is impossible; when the click-based figure asks for\n",
    "    # one, the double-reached are clicking far more than others and the floor collapses to the\n",
    "    # larger reach - the flag says so\n",
    "    out[\"click_overlap_uncapped\"] = click_overlap\n",
    "    out[\"click_overlap_capped\"] = bool(click_overlap > min(ra, rb))\n",
    "    click_overlap = min(click_overlap, min(ra, rb))\n",
    "    out[\"click_overlap\"] = click_overlap\n",
    "    out[\"click_based\"] = ra + rb - click_overlap\n",
    "    out[\"net_new_b_click\"] = rb - click_overlap\n",
    "    out[\"net_new_a_click\"] = ra - click_overlap\n",
    "    return out\n",
    "\n",
    "\n",
    "def click_interval(observed, keenness_inflation=0.0, level=1.96):\n",
    "    \"\"\"A sampling interval for the click-based overlap: the three visitor counts are counts, so\n",
    "    their relative variance is one over each, and the overlap's log-scale error adds them up.\n",
    "    It says how noisy the figure is, not how biased - the keenness bias is a separate matter.\"\"\"\n",
    "    va, vb, vab = observed[\"visitors_a\"], observed[\"visitors_b\"], observed[\"visitors_both\"]\n",
    "    if not (va and vb and vab):\n",
    "        return None\n",
    "    se = np.sqrt(1.0 / vab + 1.0 / va + 1.0 / vb)\n",
    "    centre = readings(observed, keenness_inflation=keenness_inflation)[\"click_overlap\"]\n",
    "    ra, rb = observed[\"reach_a\"], observed[\"reach_b\"]\n",
    "    lo, hi = centre * np.exp(-level * se), centre * np.exp(level * se)\n",
    "    hi = min(hi, min(ra, rb))   # an overlap larger than the smaller reach is impossible\n",
    "    return {\"overlap_low\": lo, \"overlap_high\": hi, \"unique_low\": ra + rb - hi,\n",
    "            \"unique_high\": ra + rb - lo, \"relative_half_width\": level * se}\n",
    "\n",
    "\n",
    "RANGE_LEVEL = 1.96              # the interval's normal multiplier\n",
    "FLOOR_TARGET_HALF_WIDTH = 0.20  # the estimate is refused where the sampling half-width would exceed this\n",
    "\n",
    "\n",
    "def shared_visitor_floor(target=FLOOR_TARGET_HALF_WIDTH, level=RANGE_LEVEL):\n",
    "    \"\"\"The smallest visitors-from-both count at which the overlap's sampling half-width, with the\n",
    "    two single-platform counts taken as large, comes under `target`. Measured in its own section.\"\"\"\n",
    "    return int(np.ceil((level / target) ** 2))\n",
    "\n",
    "\n",
    "SHARED_VISITOR_FLOOR = shared_visitor_floor()\n",
    "\n",
    "\n",
    "read = readings(report, population=N_MARKET)\n",
    "interval = click_interval(report)\n",
    "print(f\"{'reading':22s} {'overlap':>10s} {'unique reach':>13s} {'error':>8s}\")\n",
    "for name, key in [(\"the sum\", \"sum\"), (\"the visitor share\", \"visitor_share\"),\n",
    "                  (\"the chance formula\", \"chance\"), (\"the click-based estimate\", \"click_based\")]:\n",
    "    overlap = report[\"reach_a\"] + report[\"reach_b\"] - read[key]\n",
    "    print(f\"{name:22s} {overlap:10,.0f} {read[key]:13,.0f} {(read[key] / truth['unique'] - 1) * 100:+7.1f}%\")\n",
    "print(f\"{'the truth':22s} {truth['overlap']:10,} {truth['unique']:13,}\")\n",
    "print(f\"\\nthe click-based figure's sampling interval: {interval['unique_low']:,.0f} to \"\n",
    "      f\"{interval['unique_high']:,.0f} unique people (plus or minus \"\n",
    "      f\"{interval['relative_half_width'] * 100:.0f} per cent on the overlap) - noise, not bias\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc400c1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:46:36.964544Z",
     "iopub.status.busy": "2026-10-24T16:46:36.962926Z",
     "iopub.status.idle": "2026-10-24T16:46:37.495285Z",
     "shell.execute_reply": "2026-10-24T16:46:37.494024Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1470x644 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10.5, 4.6))\n",
    "rows = [(\"the sum\", read[\"sum\"], NAIVE), (\"the visitor share\", read[\"visitor_share\"], NAIVE),\n",
    "        (\"the chance formula\", read[\"chance\"], FLAG), (\"the click-based estimate\", read[\"click_based\"], SIGNAL)]\n",
    "y = np.arange(len(rows))[::-1]\n",
    "for yi, (name, value, colour) in zip(y, rows):\n",
    "    ax.barh(yi, value / 1e6, color=colour, alpha=0.85, height=0.62)\n",
    "    ax.text(value / 1e6 + 0.012, yi, f\"{value / 1e6:.2f}M\", va=\"center\", color=INK, fontsize=10)\n",
    "ax.axvline(truth[\"unique\"] / 1e6, color=INK, lw=1.6, ls=\"--\")\n",
    "ax.text(truth[\"unique\"] / 1e6 + 0.012, -0.72, f\"the truth: {truth['unique'] / 1e6:.2f}M unique people\",\n",
    "        color=INK, fontsize=10, va=\"center\")\n",
    "ax.set_ylim(-1.0, len(rows) - 0.45)\n",
    "ax.set_yticks(y)\n",
    "ax.set_yticklabels([r[0] for r in rows])\n",
    "ax.set_xlabel(\"unique people reached (millions)\")\n",
    "ax.set_xlim(0, max(r[1] for r in rows) / 1e6 * 1.18)\n",
    "ax.set_title(\"Four readings of the same two reports, against the truth the generator knows\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_readings.png\", dpi=140)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "621f51dd",
   "metadata": {},
   "source": [
    "The sum overstates the reach by the overlap, which is a fifth of it. The visitor share lands\n",
    "almost on the sum, for the reason above: it is the overlap times two click rates. The chance\n",
    "formula, given the true population, lands a few per cent high - the audiences overlap more\n",
    "than chance, but not enormously more here. The click-based estimate lands more than a fifth\n",
    "low, because the people reached on both platforms in this market are keener to click than\n",
    "the rest, so the visitors from both overstate the people on both.\n",
    "\n",
    "The truth is inside the range the two formulas make. That range is the honest estimate, and\n",
    "the rest of this notebook is about how wide it is, why it cannot be narrowed from these\n",
    "reports, and what a marketer can still decide with it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54b9db90",
   "metadata": {},
   "source": [
    "## What platform B is worth, in money\n",
    "\n",
    "The decision behind all of this is whether platform B earns its budget. Three costs answer\n",
    "it, from the one that matters most to the one that matters least.\n",
    "\n",
    "**What a new customer from B cost.** B's spend over the customers who arrived via B and never\n",
    "via A. Both are counts the site made, so this is exact - it is the acquisition cost a planner\n",
    "actually compares against a margin.\n",
    "\n",
    "**What a new visitor from B cost.** The same, over the visitors B brought that A had not.\n",
    "Exact for the same reason.\n",
    "\n",
    "**What an extra person reached cost.** B's spend over the people B reached that A had not.\n",
    "This is an awareness figure, not an acquisition cost, and it needs the overlap - so it is only\n",
    "a range, and it swings with the overlap far more than the unique reach does, because the\n",
    "overlap is subtracted from a smaller number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b195335a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:46:37.504552Z",
     "iopub.status.busy": "2026-10-24T16:46:37.504009Z",
     "iopub.status.idle": "2026-10-24T16:46:37.520916Z",
     "shell.execute_reply": "2026-10-24T16:46:37.519206Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "platform B cost 24,000 euros this month and reached 481,237 people\n",
      "  exactly: 341 new customers came via B alone, so a new customer cost 70.38 euros; another 9 came via both platforms, and crediting them all to B would make it 68.57 euros\n",
      "  exactly: 11,944 new visitors from B, so a new visitor cost 2.01 euros\n",
      "  estimated: the cost of an extra person REACHED, which depends on the overlap\n",
      "  the sum (no overlap)       extra people reached   481,237   cost per extra person   5.0 cents\n",
      "  the chance formula         extra people reached   338,028   cost per extra person   7.1 cents\n",
      "  the click-based estimate   extra people reached    62,024   cost per extra person  38.7 cents\n",
      "  the truth                  extra people reached   284,251   cost per extra person   8.4 cents\n"
     ]
    }
   ],
   "source": [
    "def cost_per_net_new(spend, reach_b, overlap):\n",
    "    net_new = reach_b - overlap\n",
    "    return spend / net_new if net_new > 0 else float(\"inf\")\n",
    "\n",
    "\n",
    "money = {}\n",
    "for name, key in [(\"the sum\", \"sum\"), (\"the chance formula\", \"chance\"), (\"the click-based estimate\", \"click_based\")]:\n",
    "    overlap = report[\"reach_a\"] + report[\"reach_b\"] - read[key]\n",
    "    money[key] = {\"overlap\": overlap, \"net_new_b\": report[\"reach_b\"] - overlap,\n",
    "                  \"cost_per_net_new_cents\": cost_per_net_new(SPEND_B_EUR, report[\"reach_b\"], overlap) * 100}\n",
    "money[\"truth\"] = {\"overlap\": truth[\"overlap\"], \"net_new_b\": truth[\"net_new_b\"],\n",
    "                  \"cost_per_net_new_cents\": cost_per_net_new(SPEND_B_EUR, report[\"reach_b\"], truth[\"overlap\"]) * 100}\n",
    "# the two costs that need no estimate at all: spend over the customers, and over the visitors,\n",
    "# that B brought and A had not - exact, because every number in them is a count\n",
    "money[\"per_new_customer\"] = {\"customers_b_only\": customers[\"customers_b_only\"], \"customers_both\": customers[\"customers_both\"],\n",
    "                             \"cost_per_new_customer_eur\": SPEND_B_EUR / customers[\"customers_b_only\"],\n",
    "                             \"cost_if_shared_credited_to_b_eur\": SPEND_B_EUR / (customers[\"customers_b_only\"] + customers[\"customers_both\"])}\n",
    "money[\"per_new_visitor\"] = {\"net_new_visitors_b\": visitors[\"net_new_visitors_b\"],\n",
    "                            \"cost_per_net_new_visitor_eur\": SPEND_B_EUR / visitors[\"net_new_visitors_b\"]}\n",
    "print(f\"platform B cost {SPEND_B_EUR:,.0f} euros this month and reached {report['reach_b']:,} people\")\n",
    "print(f\"  exactly: {customers['customers_b_only']:,} new customers came via B alone, so a new customer cost \"\n",
    "      f\"{money['per_new_customer']['cost_per_new_customer_eur']:.2f} euros; another {customers['customers_both']:,} \"\n",
    "      f\"came via both platforms, and crediting them all to B would make it \"\n",
    "      f\"{money['per_new_customer']['cost_if_shared_credited_to_b_eur']:.2f} euros\")\n",
    "print(f\"  exactly: {visitors['net_new_visitors_b']:,} new visitors from B, so a new visitor cost \"\n",
    "      f\"{money['per_new_visitor']['cost_per_net_new_visitor_eur']:.2f} euros\")\n",
    "print(\"  estimated: the cost of an extra person REACHED, which depends on the overlap\")\n",
    "for key, label in [(\"sum\", \"the sum (no overlap)\"), (\"chance\", \"the chance formula\"),\n",
    "                   (\"click_based\", \"the click-based estimate\"), (\"truth\", \"the truth\")]:\n",
    "    m = money[key]\n",
    "    print(f\"  {label:26s} extra people reached {m['net_new_b']:9,.0f}   cost per extra person \"\n",
    "          f\"{m['cost_per_net_new_cents']:5.1f} cents\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e252d7",
   "metadata": {},
   "source": [
    "The two ends of the honest range put a new person on platform B at anything from a few cents\n",
    "to several tens of cents. On this market the range is wide enough that \"is B worth keeping for\n",
    "new people\" cannot be settled from the click data alone - and the population scoring below\n",
    "measures how often that is the case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4172bd2d",
   "metadata": {},
   "source": [
    "## Why no single number: two markets, one report\n",
    "\n",
    "Here is the reason the method refuses to give a total, built rather than argued. Take the\n",
    "worked market's reports - two reaches, three visitor counts - and build a second market with\n",
    "the same reports but with keenness spread evenly, so that the click-based formula is exact in\n",
    "it. That second market has a *smaller* overlap and a *smaller* unique reach. Both markets\n",
    "produce the same numbers on every dashboard the marketer can open, and their true unique\n",
    "reaches differ by more than a fifth.\n",
    "\n",
    "This is the capture-recapture literature's identifiability problem in one picture: with two\n",
    "lists, only three cells are observable, and the dependence between the lists is a fourth\n",
    "unknown the data cannot pin. Different assumptions about the people you cannot see fit the\n",
    "people you can see equally well and imply different totals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d4cdf32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:46:37.525573Z",
     "iopub.status.busy": "2026-10-24T16:46:37.524864Z",
     "iopub.status.idle": "2026-10-24T16:46:37.553195Z",
     "shell.execute_reply": "2026-10-24T16:46:37.551303Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               worked market     the twin\n",
      "reach on A                           714,202      714,202\n",
      "reach on B                           481,237      481,237\n",
      "visitors via A                        22,368       22,356\n",
      "visitors via B                        12,279       12,305\n",
      "visitors via both                        335          325\n",
      "what the dashboards cannot show\n",
      "people on both platforms             196,986      419,213\n",
      "unique people reached                998,453      776,226\n",
      "new people from B                    284,251       62,024\n",
      "\n",
      "same reports, and one market reached 29 per cent more people than the other\n"
     ]
    }
   ],
   "source": [
    "def twin_market(observed, generator):\n",
    "    \"\"\"A market with even keenness whose reports match `observed` in expectation.\n",
    "\n",
    "    The overlap it needs is exactly the click-based estimate, and its click rates are the\n",
    "    observed visitor rates, so its visitor counts come out the same - while its unique reach\n",
    "    is the click-based figure rather than the worked market's truth.\n",
    "    \"\"\"\n",
    "    ra, rb = observed[\"reach_a\"], observed[\"reach_b\"]\n",
    "    overlap = int(round(readings(observed)[\"click_overlap\"]))\n",
    "    q_a, q_b = observed[\"visitors_a\"] / ra, observed[\"visitors_b\"] / rb\n",
    "    n_a_only, n_b_only = ra - overlap, rb - overlap\n",
    "    only_a_visits = generator.binomial(n_a_only, q_a)\n",
    "    only_b_visits = generator.binomial(n_b_only, q_b)\n",
    "    both_a = generator.random(overlap) < q_a\n",
    "    both_b = generator.random(overlap) < q_b\n",
    "    twin_report = {\"reach_a\": ra, \"reach_b\": rb,\n",
    "                   \"visitors_a\": int(only_a_visits + both_a.sum()),\n",
    "                   \"visitors_b\": int(only_b_visits + both_b.sum()),\n",
    "                   \"visitors_both\": int((both_a & both_b).sum())}\n",
    "    twin_truth = {\"overlap\": overlap, \"unique\": ra + rb - overlap, \"net_new_b\": rb - overlap,\n",
    "                  \"net_new_a\": ra - overlap}\n",
    "    return twin_report, twin_truth\n",
    "\n",
    "\n",
    "twin_report, twin_truth = twin_market(report, np.random.default_rng(SEED + 1))\n",
    "print(f\"{'':28s} {'worked market':>15s} {'the twin':>12s}\")\n",
    "for label, key in [(\"reach on A\", \"reach_a\"), (\"reach on B\", \"reach_b\"), (\"visitors via A\", \"visitors_a\"),\n",
    "                   (\"visitors via B\", \"visitors_b\"), (\"visitors via both\", \"visitors_both\")]:\n",
    "    print(f\"{label:28s} {report[key]:15,} {twin_report[key]:12,}\")\n",
    "print(\"what the dashboards cannot show\")\n",
    "for label, key in [(\"people on both platforms\", \"overlap\"), (\"unique people reached\", \"unique\"),\n",
    "                   (\"new people from B\", \"net_new_b\")]:\n",
    "    print(f\"{label:28s} {truth[key]:15,} {twin_truth[key]:12,}\")\n",
    "twins_gap_pct = (truth[\"unique\"] / twin_truth[\"unique\"] - 1) * 100\n",
    "print(f\"\\nsame reports, and one market reached {twins_gap_pct:.0f} per cent more people than the other\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "25b0b59f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:46:37.560598Z",
     "iopub.status.busy": "2026-10-24T16:46:37.556584Z",
     "iopub.status.idle": "2026-10-24T16:46:38.982379Z",
     "shell.execute_reply": "2026-10-24T16:46:38.981177Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1610x616 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.4), gridspec_kw={\"width_ratios\": [1, 1.25]})\n",
    "ax = axes[0]\n",
    "labels = [\"reach A\", \"reach B\", \"visitors A\", \"visitors B\", \"visitors both\"]\n",
    "keys = [\"reach_a\", \"reach_b\", \"visitors_a\", \"visitors_b\", \"visitors_both\"]\n",
    "x = np.arange(len(labels))\n",
    "ax.bar(x - 0.19, [report[k] for k in keys], width=0.36, color=INK, alpha=0.8, label=\"worked market\")\n",
    "ax.bar(x + 0.19, [twin_report[k] for k in keys], width=0.36, color=FLAG, alpha=0.8, label=\"its twin\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels, rotation=20, ha=\"right\")\n",
    "ax.set_ylabel(\"people (log scale)\")\n",
    "ax.set_title(\"What every dashboard shows: identical\")\n",
    "ax.legend(frameon=False, loc=\"upper right\")\n",
    "ax = axes[1]\n",
    "for i, (name, t, colour) in enumerate([(\"worked market\", truth, INK), (\"its twin\", twin_truth, FLAG)]):\n",
    "    ra, rb = report[\"reach_a\"], report[\"reach_b\"]\n",
    "    only_a, both, only_b = ra - t[\"overlap\"], t[\"overlap\"], rb - t[\"overlap\"]\n",
    "    yy = 1 - i\n",
    "    ax.barh(yy, only_a / 1e6, color=colour, alpha=0.85, height=0.5)\n",
    "    ax.barh(yy, both / 1e6, left=only_a / 1e6, color=BAND, height=0.5)\n",
    "    ax.barh(yy, only_b / 1e6, left=(only_a + both) / 1e6, color=colour, alpha=0.5, height=0.5)\n",
    "    ax.text((only_a + both / 2) / 1e6, yy, f\"on both\\n{both / 1e3:.0f}k\", ha=\"center\", va=\"center\", fontsize=9, color=INK)\n",
    "    ax.text((only_a + both + only_b) / 1e6 + 0.02, yy, f\"{t['unique'] / 1e6:.2f}M unique\", va=\"center\", fontsize=10, color=INK)\n",
    "ax.set_yticks([1, 0])\n",
    "ax.set_yticklabels([\"worked market\", \"its twin\"])\n",
    "ax.set_xlim(0, (report[\"reach_a\"] + report[\"reach_b\"]) / 1e6 * 1.22)\n",
    "ax.set_xlabel(\"people reached (millions): only on A | on both | only on B\")\n",
    "ax.set_title(\"What is true: different\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_twins.png\", dpi=140)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dc87420",
   "metadata": {},
   "source": [
    "## The mechanism, dialled: how keenness moves the click-based figure\n",
    "\n",
    "The click-based estimate assumes the people reached on both platforms click no more than\n",
    "anyone else. Build markets where they click more - by spreading keenness more unevenly and\n",
    "tying it more tightly to the trait that puts people on both platforms - and watch the\n",
    "estimate fall away from the truth. The quantity on the horizontal axis is the one thing a\n",
    "report cannot show: how much the visitors-from-both count is inflated relative to what even\n",
    "keenness would give. Zero is the world the formula is exact in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "638721e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:46:38.990214Z",
     "iopub.status.busy": "2026-10-24T16:46:38.989743Z",
     "iopub.status.idle": "2026-10-24T16:46:45.607593Z",
     "shell.execute_reply": "2026-10-24T16:46:45.606609Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " keen_spread  keen_follows  inflation  click_error_pct  visitors_both\n",
      "         0.0           0.0      0.071           -1.411            105\n",
      "         0.0           0.5      0.092           -1.823            105\n",
      "         0.0           0.9     -0.093            1.841             89\n",
      "         0.3           0.0      0.085           -1.684            105\n",
      "         0.3           0.5      0.226           -4.447            143\n",
      "         0.3           0.9      0.335           -6.591            183\n",
      "         0.5           0.0      0.362           -7.101            136\n",
      "         0.5           0.5      0.578          -11.392            209\n",
      "         0.5           0.9      0.655          -12.913            271\n",
      "         0.7           0.0      0.565          -11.145            156\n",
      "         0.7           0.5      1.118          -22.001            305\n",
      "         0.7           0.9      1.446          -28.479            486\n",
      "         0.9           0.0      1.333          -26.182            230\n",
      "         0.9           0.5      1.439          -28.385            501\n",
      "         0.9           0.9      1.442          -28.408            883\n"
     ]
    }
   ],
   "source": [
    "def keenness_inflation(observed, true_overlap):\n",
    "    \"\"\"How far the visitors-from-both count sits above what even keenness would give: the\n",
    "    click-based overlap over the true one, minus one. Zero means the formula is exact.\"\"\"\n",
    "    return readings(observed)[\"click_overlap\"] / true_overlap - 1.0\n",
    "\n",
    "\n",
    "sweep_rows = []\n",
    "for spread in [0.0, 0.3, 0.5, 0.7, 0.9]:\n",
    "    for follows in [0.0, 0.5, 0.9]:\n",
    "        m = build_market(N_MARKET, ALPHA_A, ALPHA_B, CORRELATION, spread, follows, CLICK_A, CLICK_B,\n",
    "                         np.random.default_rng(SEED + 100 + int(spread * 10) * 10 + int(follows * 10)))\n",
    "        o, t = observe(m), truth_of(m)\n",
    "        r = readings(o)\n",
    "        sweep_rows.append({\"keen_spread\": spread, \"keen_follows\": follows,\n",
    "                           \"inflation\": keenness_inflation(o, t[\"overlap\"]),\n",
    "                           \"click_error_pct\": (r[\"click_based\"] / t[\"unique\"] - 1) * 100,\n",
    "                           \"visitors_both\": o[\"visitors_both\"]})\n",
    "keen_sweep = pd.DataFrame(sweep_rows)\n",
    "print(keen_sweep.round(3).to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e68b8f28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:46:45.612862Z",
     "iopub.status.busy": "2026-10-24T16:46:45.612425Z",
     "iopub.status.idle": "2026-10-24T16:46:46.223782Z",
     "shell.execute_reply": "2026-10-24T16:46:46.222860Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1330x644 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9.5, 4.6))\n",
    "ax.scatter(keen_sweep.inflation, keen_sweep.click_error_pct, s=48, color=SIGNAL, zorder=3)\n",
    "ax.axhline(0, color=INK, lw=1)\n",
    "ax.axvline(0, color=LINE, lw=1)\n",
    "grid_g = np.linspace(0, keen_sweep.inflation.max() * 1.05, 100)\n",
    "share = truth[\"overlap\"] / truth[\"unique\"]\n",
    "ax.plot(grid_g, -share * grid_g * 100, color=MUTED, lw=1, ls=\":\",\n",
    "        label=\"the worked market's overlap share times the inflation\")\n",
    "ax.set_xlabel(\"keenness inflation of the visitors-from-both count (0 = even keenness)\")\n",
    "ax.set_ylabel(\"click-based unique reach, error vs truth (%)\")\n",
    "ax.set_title(\"The click-based figure is exact at zero inflation and falls one way from it\")\n",
    "ax.legend(frameon=False, loc=\"lower left\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_keenness.png\", dpi=140)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7cb257a",
   "metadata": {},
   "source": [
    "The error is one-directional and proportional: every unit of inflation pulls the click-based\n",
    "unique reach down by roughly the overlap's share of the reach. The worked market sits at an\n",
    "inflation above one - the visitors from both are more than double what even keenness would\n",
    "produce - and no report distinguishes it from the twin at zero."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66be61dd",
   "metadata": {},
   "source": [
    "## Where the inflation comes from, and when the figure is a floor\n",
    "\n",
    "The source gives the bias of the Petersen-Lincoln estimator a closed form: the true population\n",
    "is the estimator times one plus a dependence measure, the coefficient of covariation between the\n",
    "two capture chances. Positive dependence - \"the marked rate in the sample is larger than the\n",
    "marked rate in the population\" - inflates the recapture count relative to independence.\n",
    "\n",
    "The click-based estimate here is not quite the paper's estimator, because the site cannot see the\n",
    "shared audience's own visitor counts; it divides the shared visitor count by each platform's\n",
    "click rate over its WHOLE audience. So the inflation of the overlap has two pieces: one plus the\n",
    "dependence measure among the people on both platforms, and two between-group factors - how much\n",
    "keener the people on both platforms are to click each ad than that platform's average reached\n",
    "person. The generator knows every person's click chance, so both pieces can be measured. The\n",
    "figure is a floor when the product is at least one: when the people reached on both platforms\n",
    "click at least as readily as each platform's average, and their two clicks are positively\n",
    "dependent. That is the condition the post states, rather than \"always\".\n",
    "\n",
    "And the population the clicks can speak for: the people with a real chance of ever visiting.\n",
    "The source's retreat from the total is to that observable population, and with two platforms\n",
    "in one window it is not estimable either - it needs several captures - but the generator can\n",
    "show how much smaller than the audience it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38c9e078",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:46:46.249816Z",
     "iopub.status.busy": "2026-10-24T16:46:46.236878Z",
     "iopub.status.idle": "2026-10-24T16:46:46.429125Z",
     "shell.execute_reply": "2026-10-24T16:46:46.424845Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a reached person's chance of visiting: 3.11 per cent on A, 2.59 on B; among the people on both platforms 3.78 and 3.03\n",
      "between-group factors 1.217 and 1.170; dependence among the people on both 0.567\n",
      "predicted inflation of the overlap 2.231, measured 2.128\n",
      "chance of ever visiting above 1 per cent: 195,933 of 196,986 people on both platforms; 98.4 per cent of everyone reached\n",
      "chance of ever visiting above 5 per cent: 106,778 of 196,986 people on both platforms; 41.0 per cent of everyone reached\n",
      "chance of ever visiting above 10 per cent: 33,741 of 196,986 people on both platforms; 10.2 per cent of everyone reached\n",
      "chance of ever visiting above 25 per cent: 1,709 of 196,986 people on both platforms; 0.4 per cent of everyone reached\n"
     ]
    }
   ],
   "source": [
    "def inflation_decomposition(market, click_a, click_b):\n",
    "    \"\"\"The two pieces of the click-based overlap's inflation, from the generator's own click chances.\"\"\"\n",
    "    p_a = np.minimum(1.0, click_a * market.keen.to_numpy())\n",
    "    p_b = np.minimum(1.0, click_b * market.keen.to_numpy())\n",
    "    on_a, on_b = market.on_a.to_numpy(), market.on_b.to_numpy()\n",
    "    lake = on_a & on_b\n",
    "    mean_a, mean_b = p_a[on_a].mean(), p_b[on_b].mean()          # each platform's average reached person\n",
    "    lake_a, lake_b = p_a[lake].mean(), p_b[lake].mean()         # the people on both platforms\n",
    "    gamma = (p_a[lake] * p_b[lake]).mean() / (lake_a * lake_b) - 1.0\n",
    "    k_a, k_b = lake_a / mean_a, lake_b / mean_b\n",
    "    return {\"dependence_gamma\": gamma, \"between_group_factor_a\": k_a, \"between_group_factor_b\": k_b,\n",
    "            \"predicted_inflation\": (1 + gamma) * k_a * k_b,\n",
    "            \"click_chance_reached_a_pct\": mean_a * 100, \"click_chance_reached_b_pct\": mean_b * 100,\n",
    "            \"click_chance_on_both_a_pct\": lake_a * 100, \"click_chance_on_both_b_pct\": lake_b * 100}\n",
    "\n",
    "\n",
    "def observable_population(market, click_a, click_b, alphas=(0.01, 0.05, 0.10, 0.25)):\n",
    "    \"\"\"How many people on both platforms had more than an alpha chance of ever visiting.\"\"\"\n",
    "    p_a = np.minimum(1.0, click_a * market.keen.to_numpy())\n",
    "    p_b = np.minimum(1.0, click_b * market.keen.to_numpy())\n",
    "    p_any = 1 - (1 - p_a) * (1 - p_b)\n",
    "    lake = (market.on_a & market.on_b).to_numpy()\n",
    "    reached = (market.on_a | market.on_b).to_numpy()\n",
    "    return {str(a): {\"on_both_with_chance_above\": int((lake & (p_any > a)).sum()),\n",
    "                     \"reached_with_chance_above_share_pct\": float(((reached) & (p_any > a)).sum() / reached.sum() * 100)}\n",
    "            for a in alphas}\n",
    "\n",
    "\n",
    "inflation = inflation_decomposition(market, CLICK_A, CLICK_B)\n",
    "inflation[\"measured_inflation\"] = read[\"click_overlap\"] / truth[\"overlap\"]\n",
    "print(f\"a reached person's chance of visiting: {inflation['click_chance_reached_a_pct']:.2f} per cent on A, \"\n",
    "      f\"{inflation['click_chance_reached_b_pct']:.2f} on B; among the people on both platforms \"\n",
    "      f\"{inflation['click_chance_on_both_a_pct']:.2f} and {inflation['click_chance_on_both_b_pct']:.2f}\")\n",
    "print(f\"between-group factors {inflation['between_group_factor_a']:.3f} and {inflation['between_group_factor_b']:.3f}; \"\n",
    "      f\"dependence among the people on both {inflation['dependence_gamma']:.3f}\")\n",
    "print(f\"predicted inflation of the overlap {inflation['predicted_inflation']:.3f}, measured {inflation['measured_inflation']:.3f}\")\n",
    "observable = observable_population(market, CLICK_A, CLICK_B)\n",
    "for a, row in observable.items():\n",
    "    print(f\"chance of ever visiting above {float(a) * 100:.0f} per cent: {row['on_both_with_chance_above']:,} of \"\n",
    "          f\"{truth['overlap']:,} people on both platforms; {row['reached_with_chance_above_share_pct']:.1f} per cent of everyone reached\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b7cdad",
   "metadata": {},
   "source": [
    "## What narrows the range: stratify by a segment both sides can see\n",
    "\n",
    "The source names the remedies for correlation bias, and the first is the cheapest: heterogeneity\n",
    "across groups you can observe is part of what makes the pooled samples dependent - their example\n",
    "is a population half male and half female, independent within each sex and dependent when\n",
    "pooled - so estimate within the groups and add up. Both platforms report reach by region, device\n",
    "and age band; a site sees region and device. Where a segment carries part of the keenness, the\n",
    "click-based floor computed per segment and summed sits closer to the truth than the pooled one.\n",
    "\n",
    "The market gets such a segment: three device classes, assigned from the same trait that drives\n",
    "platform membership and keenness plus noise of the trait's own spread, so the segment explains\n",
    "about half of the trait - an ordinary case, not a flattering one. The stratified figure keeps\n",
    "every guard: per-segment caps, and a refusal when the combined sampling half-width crosses the\n",
    "same twenty per cent the pooled figure refuses at. A segment with no shared visitors adds\n",
    "nothing to the overlap, which keeps the sum a floor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "562a18a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:46:46.439694Z",
     "iopub.status.busy": "2026-10-24T16:46:46.439081Z",
     "iopub.status.idle": "2026-10-24T16:46:48.208199Z",
     "shell.execute_reply": "2026-10-24T16:46:48.207247Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "segment        reach A   reach B   vis A   vis B  both  overlap est     truth\n",
      "desktop        157,485    97,533   3,373   1,678    13       35,280    24,092\n",
      "mobile web     189,056   123,236   5,104   2,702    45       76,023    43,100\n",
      "app            367,661   260,468  13,891   7,899   277      241,755   129,794\n",
      "stratified: overlap 353,058 (truth 196,986), unique reach 842,381 - -15.6 per cent against -22.3 pooled; combined half-width 11.8 per cent\n"
     ]
    }
   ],
   "source": [
    "SEGMENT_NOISE = 1.0                               # the segment explains about half of the trait, about two-fifths once cut into three classes\n",
    "SEGMENT_CUTS = (-0.5, 0.5)\n",
    "SEGMENT_NAMES = (\"desktop\", \"mobile web\", \"app\")\n",
    "\n",
    "\n",
    "def add_segment(market, generator, noise=SEGMENT_NOISE, cuts=SEGMENT_CUTS, names=SEGMENT_NAMES):\n",
    "    \"\"\"An observable segment both platforms report and the site sees, carrying part of the trait.\"\"\"\n",
    "    z = market.trait.to_numpy() + noise * generator.standard_normal(len(market))\n",
    "    return market.assign(segment=np.array(names)[np.digitize(z, cuts)])\n",
    "\n",
    "\n",
    "def segment_reports(market):\n",
    "    \"\"\"Per segment: what the platforms report and what the site sees, plus the generator's truth.\"\"\"\n",
    "    rows = []\n",
    "    for name in SEGMENT_NAMES:\n",
    "        i = (market.segment == name).to_numpy()\n",
    "        rows.append({\"segment\": name,\n",
    "                     \"reach_a\": int((market.on_a & i).sum()), \"reach_b\": int((market.on_b & i).sum()),\n",
    "                     \"visitors_a\": int((market.visit_a & i).sum()), \"visitors_b\": int((market.visit_b & i).sum()),\n",
    "                     \"visitors_both\": int((market.visit_a & market.visit_b & i).sum()),\n",
    "                     \"true_overlap\": int((market.on_a & market.on_b & i).sum())})\n",
    "    return rows\n",
    "\n",
    "\n",
    "def stratified_floor(rows, level=RANGE_LEVEL, target=FLOOR_TARGET_HALF_WIDTH):\n",
    "    \"\"\"The click-based overlap per segment, capped and summed, with the combined sampling half-width.\n",
    "    Refused when that half-width crosses the target the pooled figure refuses at.\"\"\"\n",
    "    overlap, variance, per_segment = 0.0, 0.0, []\n",
    "    for r in rows:\n",
    "        va, vb, vab, ra, rb = r[\"visitors_a\"], r[\"visitors_b\"], r[\"visitors_both\"], r[\"reach_a\"], r[\"reach_b\"]\n",
    "        raw = vab * ra * rb / (va * vb) if va and vb else 0.0\n",
    "        capped = raw > min(ra, rb)\n",
    "        o = min(raw, min(ra, rb))\n",
    "        v_log = (1 / vab + 1 / va + 1 / vb) if vab and va and vb else 0.0\n",
    "        overlap += o\n",
    "        variance += o ** 2 * v_log\n",
    "        per_segment.append({**r, \"click_overlap\": o, \"capped\": capped})\n",
    "    total_a, total_b = sum(r[\"reach_a\"] for r in rows), sum(r[\"reach_b\"] for r in rows)\n",
    "    half_width = level * np.sqrt(variance) / overlap if overlap else float(\"inf\")\n",
    "    return {\"click_overlap\": overlap, \"click_based\": total_a + total_b - overlap,\n",
    "            \"relative_half_width\": half_width, \"refused\": bool(half_width > target),\n",
    "            \"segments\": per_segment}\n",
    "\n",
    "\n",
    "market = add_segment(market, np.random.default_rng(SEED + 12))\n",
    "segments = segment_reports(market)\n",
    "strat = stratified_floor(segments)\n",
    "print(f\"{'segment':12s} {'reach A':>9s} {'reach B':>9s} {'vis A':>7s} {'vis B':>7s} {'both':>5s} {'overlap est':>12s} {'truth':>9s}\")\n",
    "for r in strat[\"segments\"]:\n",
    "    print(f\"{r['segment']:12s} {r['reach_a']:9,} {r['reach_b']:9,} {r['visitors_a']:7,} {r['visitors_b']:7,} \"\n",
    "          f\"{r['visitors_both']:5,} {r['click_overlap']:12,.0f} {r['true_overlap']:9,}\")\n",
    "print(f\"stratified: overlap {strat['click_overlap']:,.0f} (truth {truth['overlap']:,}), unique reach \"\n",
    "      f\"{strat['click_based']:,.0f} - {(strat['click_based'] / truth['unique'] - 1) * 100:+.1f} per cent against \"\n",
    "      f\"{(read['click_based'] / truth['unique'] - 1) * 100:+.1f} pooled; combined half-width \"\n",
    "      f\"{strat['relative_half_width'] * 100:.1f} per cent\" + (\" - REFUSED\" if strat[\"refused\"] else \"\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5547eb8",
   "metadata": {},
   "source": [
    "## Scored on five hundred markets, not one\n",
    "\n",
    "One market proves nothing, so the notebook builds five hundred with their own sizes, reach\n",
    "shares, audience correlations, keenness spreads and click rates, and scores every reading\n",
    "against each market's true unique reach. Three things are scored, and they were chosen\n",
    "before the run:\n",
    "\n",
    "- **the error of each reading**, as a share of the true unique reach - the sum, the visitor\n",
    "  share, the chance formula with the population known exactly (its best case) and with the\n",
    "  population guessed wrong by up to a factor of two either way (the usual case), and the\n",
    "  click-based estimate;\n",
    "- **whether the truth sits inside the range** between the click-based figure and the chance\n",
    "  figure, and inside the assumption-free range between the click-based figure and the sum;\n",
    "- **whether the decision survives the range**: the cost per new person on platform B at the\n",
    "  two ends of the range, and whether they sit within a factor of one and a half of each other.\n",
    "\n",
    "The chance formula's population guess is drawn at random per market, because a real planner\n",
    "has an audience-size estimate from each platform and a census figure, and none of them is the\n",
    "population the two audiences actually draw from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a22df1a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:46:48.226855Z",
     "iopub.status.busy": "2026-10-24T16:46:48.226178Z",
     "iopub.status.idle": "2026-10-24T17:01:21.095063Z",
     "shell.execute_reply": "2026-10-24T17:01:21.088136Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500 markets built; the method refused 207 for too few shared visitors and is scored on the 293 it ran on\n",
      "reading                     median error  typical |error|  worst tenth  within 10%\n",
      "the sum                           +25.9%            25.9%        37.6%          5%\n",
      "the visitor share                 +24.4%            24.4%        35.9%          6%\n",
      "chance, population known           +9.2%             9.2%        15.9%         54%\n",
      "chance, population guessed         +7.5%             8.3%        20.7%         59%\n",
      "the click-based estimate          -16.4%            16.4%        28.2%         30%\n",
      "\n",
      "the truth sat between the click-based figure and the chance figure on 96% of markets, and between the click-based figure and the sum on 96%, and between the click-based figure's interval low and the chance figure on 100%\n",
      "the click-based overlap exceeded the smaller platform's whole reach on 34% of the markets it ran on - the floor collapsed to the larger reach there\n",
      "the audiences overlapped 1.54 times chance on a typical market and 1.90 times on the top tenth; on the 162 markets at one and a half times chance or more the chance formula ran +14%\n",
      "that range was 26% of the truth wide on a typical market and 41% on the worst tenth\n",
      "net new people from B: the click-based figure runs -53% on a typical market, the chance figure +27%\n",
      "the cost per new person agreed within a factor of 1.5 at both ends of the range on 23% of markets\n",
      "on the 20 markets with nearly even keenness the click-based figure's median error was -0.8% and its sampling interval covered the truth on 85%\n",
      "\n",
      "stratified by the segment: ran on 274 markets (refused on 6% for a combined half-width over 20%); median error -13.4% against -16.1% pooled on the same markets; it sat closer to the truth on 89% of them, removing 1.6 points of error on a typical one\n"
     ]
    }
   ],
   "source": [
    "N_POP = 500\n",
    "DECISION_TOLERANCE = 1.5\n",
    "\n",
    "\n",
    "def population_truths(n, generator):\n",
    "    return pd.DataFrame({\n",
    "        \"n_market\": generator.integers(500_000, 3_000_000, n),\n",
    "        \"alpha_a\": generator.uniform(-2.2, -0.4, n),\n",
    "        \"alpha_b\": generator.uniform(-2.4, -0.6, n),\n",
    "        \"correlation\": generator.uniform(0.0, 2.0, n),\n",
    "        \"keen_spread\": generator.uniform(0.0, 1.0, n),\n",
    "        \"keen_follows\": generator.uniform(0.0, 0.9, n),\n",
    "        \"click_a\": generator.uniform(0.008, 0.04, n),\n",
    "        \"click_b\": generator.uniform(0.008, 0.04, n),\n",
    "        \"population_guess_factor\": np.exp(generator.uniform(np.log(0.5), np.log(2.0), n)),\n",
    "        \"spend_b\": generator.uniform(5_000, 60_000, n),\n",
    "    })\n",
    "\n",
    "\n",
    "def score_market(row, generator):\n",
    "    m = build_market(int(row.n_market), row.alpha_a, row.alpha_b, row.correlation, row.keen_spread,\n",
    "                     row.keen_follows, row.click_a, row.click_b, generator)\n",
    "    m = add_segment(m, generator)                # drawn after the market, so nothing above moves\n",
    "    o, t = observe(m), truth_of(m)\n",
    "    if o[\"visitors_a\"] == 0 or o[\"visitors_b\"] == 0:\n",
    "        return None\n",
    "    r_true_pop = readings(o, population=int(row.n_market))\n",
    "    r_guess_pop = readings(o, population=int(row.n_market) * row.population_guess_factor)\n",
    "    unique = t[\"unique\"]\n",
    "    out = {\"visitors_both\": o[\"visitors_both\"], \"reach_a\": o[\"reach_a\"], \"reach_b\": o[\"reach_b\"],\n",
    "           \"overlap_share\": t[\"overlap\"] / unique,\n",
    "           \"overlap_over_chance\": t[\"overlap\"] / r_true_pop[\"chance_overlap\"],\n",
    "           \"refused\": o[\"visitors_both\"] < SHARED_VISITOR_FLOOR,\n",
    "           \"capped\": r_true_pop[\"click_overlap_capped\"],\n",
    "           \"inflation\": keenness_inflation(o, t[\"overlap\"]) if o[\"visitors_both\"] else np.nan,\n",
    "           \"keen_spread\": row.keen_spread, \"correlation\": row.correlation}\n",
    "    for key in [\"sum\", \"visitor_share\", \"chance\", \"click_based\"]:\n",
    "        out[f\"err_{key}\"] = (r_true_pop[key] / unique - 1) * 100\n",
    "    out[\"err_chance_guessed\"] = (r_guess_pop[\"chance\"] / unique - 1) * 100\n",
    "    st = stratified_floor(segment_reports(m))\n",
    "    out[\"stratified_refused\"] = st[\"refused\"]\n",
    "    out[\"err_click_based_stratified\"] = (st[\"click_based\"] / unique - 1) * 100 if not st[\"refused\"] else np.nan\n",
    "    lo, hi = r_true_pop[\"click_based\"], r_true_pop[\"chance\"]\n",
    "    out[\"in_range_chance\"] = lo <= unique <= hi\n",
    "    out[\"in_range_sum\"] = lo <= unique <= r_true_pop[\"sum\"]\n",
    "    # The ceiling above uses the TRUE population, which no reader has. Read with the population a reader\n",
    "    # would guess (within a factor of two of the truth), the same bracket holds less often.\n",
    "    out[\"in_range_chance_guessed\"] = r_guess_pop[\"click_based\"] <= unique <= r_guess_pop[\"chance\"]\n",
    "    out[\"not_capped\"] = not r_true_pop[\"click_overlap_capped\"]\n",
    "    out[\"range_width_pct\"] = (hi - lo) / unique * 100\n",
    "    out[\"err_net_new_click\"] = (r_true_pop[\"net_new_b_click\"] / t[\"net_new_b\"] - 1) * 100 if t[\"net_new_b\"] else np.nan\n",
    "    net_new_chance = o[\"reach_b\"] - r_true_pop[\"chance_overlap\"]\n",
    "    out[\"err_net_new_chance\"] = (net_new_chance / t[\"net_new_b\"] - 1) * 100 if t[\"net_new_b\"] else np.nan\n",
    "    cost_lo = cost_per_net_new(row.spend_b, o[\"reach_b\"], r_true_pop[\"chance_overlap\"])\n",
    "    cost_hi = cost_per_net_new(row.spend_b, o[\"reach_b\"], r_true_pop[\"click_overlap\"])\n",
    "    out[\"decision_ratio\"] = cost_hi / cost_lo if np.isfinite(cost_hi) and cost_lo > 0 else np.inf\n",
    "    out[\"decidable\"] = out[\"decision_ratio\"] <= DECISION_TOLERANCE\n",
    "    iv = click_interval(o)\n",
    "    out[\"interval_half_width_pct\"] = iv[\"relative_half_width\"] * 100 if iv else np.nan\n",
    "    out[\"interval_covers_even\"] = (iv[\"unique_low\"] <= unique <= iv[\"unique_high\"]) if iv else False\n",
    "    out[\"in_range_chance_noise\"] = (iv[\"unique_low\"] <= unique <= hi) if iv else False\n",
    "    return out\n",
    "\n",
    "\n",
    "pop_truths = population_truths(N_POP, np.random.default_rng(SEED + 7))\n",
    "pop_rows = []\n",
    "for i, row in pop_truths.iterrows():\n",
    "    scored = score_market(row, np.random.default_rng(SEED + 1000 + i))\n",
    "    if scored is not None:\n",
    "        pop_rows.append(scored)\n",
    "population_all = pd.DataFrame(pop_rows)\n",
    "population = population_all[~population_all.refused].reset_index(drop=True)\n",
    "REFUSED_SHARE = float(population_all.refused.mean())\n",
    "print(f\"{len(population_all)} markets built; the method refused {int(population_all.refused.sum())} for too few shared \"\n",
    "      f\"visitors and is scored on the {len(population)} it ran on\")\n",
    "\n",
    "\n",
    "def summarise(frame):\n",
    "    q = lambda s, p: float(np.nanpercentile(s, p))\n",
    "    out = {\"markets\": int(len(frame))}\n",
    "    for key in [\"sum\", \"visitor_share\", \"chance\", \"chance_guessed\", \"click_based\"]:\n",
    "        err = frame[f\"err_{key}\"]\n",
    "        out[key] = {\"median_error_pct\": q(err, 50), \"median_abs_error_pct\": q(err.abs(), 50),\n",
    "                    \"worst_tenth_abs_error_pct\": q(err.abs(), 90),\n",
    "                    \"within_10pct_share\": float((err.abs() <= 10).mean())}\n",
    "    ran = frame[~frame.stratified_refused]\n",
    "    err_s, err_p = ran[\"err_click_based_stratified\"], ran[\"err_click_based\"]\n",
    "    out[\"click_based_stratified\"] = {\"markets\": int(len(ran)), \"median_error_pct\": q(err_s, 50),\n",
    "                                     \"median_abs_error_pct\": q(err_s.abs(), 50),\n",
    "                                     \"worst_tenth_abs_error_pct\": q(err_s.abs(), 90),\n",
    "                                     \"within_10pct_share\": float((err_s.abs() <= 10).mean()),\n",
    "                                     \"pooled_median_error_pct_same_markets\": q(err_p, 50),\n",
    "                                     \"narrowed_share\": float((err_s.abs() < err_p.abs()).mean()),\n",
    "                                     \"median_error_removed_pct_points\": q(err_p.abs() - err_s.abs(), 50),\n",
    "                                     \"refused_share\": float(frame.stratified_refused.mean())}\n",
    "    out[\"truth_in_click_to_chance_range_share\"] = float(frame.in_range_chance.mean())\n",
    "    out[\"truth_in_noise_widened_range_share\"] = float(frame.in_range_chance_noise.mean())\n",
    "    out[\"refused_share\"] = REFUSED_SHARE\n",
    "    out[\"capped_share\"] = float(frame.capped.mean())\n",
    "    strong = frame[frame.overlap_over_chance >= 1.5]\n",
    "    out[\"strong_correlation_markets\"] = int(len(strong))\n",
    "    out[\"strong_correlation_chance_median_error_pct\"] = q(strong.err_chance, 50) if len(strong) else float(\"nan\")\n",
    "    out[\"overlap_over_chance_median\"] = q(frame.overlap_over_chance, 50)\n",
    "    out[\"overlap_over_chance_worst_tenth\"] = q(frame.overlap_over_chance, 90)\n",
    "    out[\"truth_in_click_to_sum_range_share\"] = float(frame.in_range_sum.mean())\n",
    "    out[\"truth_in_range_guessed_population_share\"] = float(frame.in_range_chance_guessed.mean())\n",
    "    out[\"truth_in_range_uncapped_share\"] = float(frame[frame.not_capped].in_range_chance.mean())\n",
    "    out[\"range_width_median_pct\"] = q(frame.range_width_pct, 50)\n",
    "    out[\"range_width_worst_tenth_pct\"] = q(frame.range_width_pct, 90)\n",
    "    out[\"net_new_click_median_error_pct\"] = q(frame.err_net_new_click, 50)\n",
    "    out[\"net_new_chance_median_error_pct\"] = q(frame.err_net_new_chance, 50)\n",
    "    out[\"decidable_share\"] = float(frame.decidable.mean())\n",
    "    out[\"decision_ratio_median\"] = q(frame.decision_ratio.replace(np.inf, np.nan), 50)\n",
    "    even = frame[frame.keen_spread < 0.15]\n",
    "    out[\"even_keenness_markets\"] = int(len(even))\n",
    "    out[\"even_keenness_interval_coverage\"] = float(even.interval_covers_even.mean()) if len(even) else float(\"nan\")\n",
    "    out[\"even_keenness_click_median_error_pct\"] = q(even.err_click_based, 50) if len(even) else float(\"nan\")\n",
    "    return out\n",
    "\n",
    "\n",
    "pop_summary = summarise(population)\n",
    "print(f\"{'reading':26s} {'median error':>13s} {'typical |error|':>16s} {'worst tenth':>12s} {'within 10%':>11s}\")\n",
    "for key, label in [(\"sum\", \"the sum\"), (\"visitor_share\", \"the visitor share\"), (\"chance\", \"chance, population known\"),\n",
    "                   (\"chance_guessed\", \"chance, population guessed\"), (\"click_based\", \"the click-based estimate\")]:\n",
    "    s = pop_summary[key]\n",
    "    print(f\"{label:26s} {s['median_error_pct']:+12.1f}% {s['median_abs_error_pct']:15.1f}% \"\n",
    "          f\"{s['worst_tenth_abs_error_pct']:11.1f}% {s['within_10pct_share'] * 100:10.0f}%\")\n",
    "print(f\"\\nthe truth sat between the click-based figure and the chance figure on \"\n",
    "      f\"{pop_summary['truth_in_click_to_chance_range_share'] * 100:.0f}% of markets, and between the \"\n",
    "      f\"click-based figure and the sum on {pop_summary['truth_in_click_to_sum_range_share'] * 100:.0f}%, and \"\n",
    "      f\"between the click-based figure's interval low and the chance figure on \"\n",
    "      f\"{pop_summary['truth_in_noise_widened_range_share'] * 100:.0f}%\")\n",
    "print(f\"the click-based overlap exceeded the smaller platform's whole reach on {pop_summary['capped_share'] * 100:.0f}% of \"\n",
    "      f\"the markets it ran on - the floor collapsed to the larger reach there\")\n",
    "print(f\"the audiences overlapped {pop_summary['overlap_over_chance_median']:.2f} times chance on a typical market and \"\n",
    "      f\"{pop_summary['overlap_over_chance_worst_tenth']:.2f} times on the top tenth; on the {pop_summary['strong_correlation_markets']} \"\n",
    "      f\"markets at one and a half times chance or more the chance formula ran {pop_summary['strong_correlation_chance_median_error_pct']:+.0f}%\")\n",
    "print(f\"that range was {pop_summary['range_width_median_pct']:.0f}% of the truth wide on a typical market \"\n",
    "      f\"and {pop_summary['range_width_worst_tenth_pct']:.0f}% on the worst tenth\")\n",
    "print(f\"net new people from B: the click-based figure runs {pop_summary['net_new_click_median_error_pct']:+.0f}% \"\n",
    "      f\"on a typical market, the chance figure {pop_summary['net_new_chance_median_error_pct']:+.0f}%\")\n",
    "print(f\"the cost per new person agreed within a factor of {DECISION_TOLERANCE} at both ends of the range on \"\n",
    "      f\"{pop_summary['decidable_share'] * 100:.0f}% of markets\")\n",
    "print(f\"on the {pop_summary['even_keenness_markets']} markets with nearly even keenness the click-based figure's \"\n",
    "      f\"median error was {pop_summary['even_keenness_click_median_error_pct']:+.1f}% and its sampling interval \"\n",
    "      f\"covered the truth on {pop_summary['even_keenness_interval_coverage'] * 100:.0f}%\")\n",
    "cs = pop_summary[\"click_based_stratified\"]\n",
    "print(f\"\\nstratified by the segment: ran on {cs['markets']} markets (refused on {cs['refused_share'] * 100:.0f}% for a combined \"\n",
    "      f\"half-width over {FLOOR_TARGET_HALF_WIDTH * 100:.0f}%); median error {cs['median_error_pct']:+.1f}% against \"\n",
    "      f\"{cs['pooled_median_error_pct_same_markets']:+.1f}% pooled on the same markets; it sat closer to the truth on \"\n",
    "      f\"{cs['narrowed_share'] * 100:.0f}% of them, removing {cs['median_error_removed_pct_points']:.1f} points of error on a typical one\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3d3c9caf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T17:01:21.123620Z",
     "iopub.status.busy": "2026-10-24T17:01:21.117723Z",
     "iopub.status.idle": "2026-10-24T17:01:23.874621Z",
     "shell.execute_reply": "2026-10-24T17:01:23.867440Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1820x672 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(13, 4.8), gridspec_kw={\"width_ratios\": [1.3, 1], \"wspace\": 0.42})\n",
    "ax = axes[0]\n",
    "order = [(\"sum\", \"the sum\", NAIVE), (\"visitor_share\", \"the visitor share\", NAIVE),\n",
    "         (\"chance_guessed\", \"chance, population guessed\", FLAG), (\"chance\", \"chance, population known\", FLAG),\n",
    "         (\"click_based\", \"the click-based estimate\", SIGNAL)]\n",
    "for i, (key, label, colour) in enumerate(order):\n",
    "    vals = population[f\"err_{key}\"].clip(-60, 60)\n",
    "    jitter = np.random.default_rng(1).uniform(-0.18, 0.18, len(vals))\n",
    "    ax.scatter(vals, i + jitter, s=6, color=colour, alpha=0.45)\n",
    "    ax.plot([np.nanmedian(vals)] * 2, [i - 0.3, i + 0.3], color=INK, lw=2)\n",
    "ax.axvline(0, color=INK, lw=1)\n",
    "ax.set_yticks(range(len(order)))\n",
    "ax.set_yticklabels([o[1] for o in order])\n",
    "ax.set_xlabel(\"error in unique reach vs the truth (%), clipped at 60\")\n",
    "ax.set_title(\"Each reading's error across the markets (bar = median)\", fontsize=11)\n",
    "ax = axes[1]\n",
    "ax.scatter(population.inflation.clip(upper=4), population.err_click_based, s=8, color=SIGNAL, alpha=0.5)\n",
    "ax.axhline(0, color=INK, lw=1)\n",
    "ax.set_xlabel(\"keenness inflation of the visitors-from-both count\")\n",
    "ax.set_ylabel(\"click-based error (%)\")\n",
    "ax.set_title(\"Click-based error against keenness (flat where the cap binds)\", fontsize=11)\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_population.png\", dpi=140)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a0c94b3",
   "metadata": {},
   "source": [
    "Read across the markets: the sum is always high by the overlap; the visitor share is the sum\n",
    "in disguise; the chance formula is a few per cent high when the population is known and a\n",
    "coin toss when it is guessed; the click-based figure is low by an amount set entirely by the\n",
    "keenness, which the data cannot show. The truth sits between the click-based figure and the\n",
    "chance figure on nearly every market where both assumptions hold in the direction the\n",
    "formulas need - keen people clicking more, audiences overlapping more than chance - and\n",
    "between the click-based figure and the sum on all but about one market in twenty-five, through noise.\n",
    "\n",
    "The range is wide. On a typical market it spans a fifth of the unique reach, and the cost per\n",
    "new person on the second platform moves enough across it that the planner's decision is\n",
    "settled by the range on only a minority of markets. That is not a weakness of the method; it\n",
    "is the size of what the reports do not contain."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24568169",
   "metadata": {},
   "source": [
    "## How many shared visitors the estimate needs\n",
    "\n",
    "The click-based figure is built from three counts, and the smallest of them - the visitors\n",
    "who came from both platforms - sets its precision. A count of thirty carries a sampling error\n",
    "of about a third; a hundred, about a fifth. The notebook refuses to estimate below the count\n",
    "at which the interval's half-width crosses twenty per cent, and reports the interval with\n",
    "every estimate above it. This is a precision floor, not a bias floor: the keenness bias is\n",
    "not reduced by more visitors, only the noise is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7833eec3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T17:01:23.888588Z",
     "iopub.status.busy": "2026-10-24T17:01:23.886280Z",
     "iopub.status.idle": "2026-10-24T17:01:23.914592Z",
     "shell.execute_reply": "2026-10-24T17:01:23.910116Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " visitors_both  half_width_pct\n",
      "            10            62.0\n",
      "            20            43.8\n",
      "            30            35.8\n",
      "            50            27.7\n",
      "           100            19.6\n",
      "           200            13.9\n",
      "           400             9.8\n",
      "          1000             6.2\n",
      "\n",
      "the notebook needs at least 97 visitors from both platforms before it estimates\n"
     ]
    }
   ],
   "source": [
    "floor_rows = [{\"visitors_both\": v, \"half_width_pct\": RANGE_LEVEL * np.sqrt(1.0 / v) * 100}\n",
    "              for v in [10, 20, 30, 50, 100, 200, 400, 1000]]\n",
    "floor_table = pd.DataFrame(floor_rows)\n",
    "print(floor_table.round(1).to_string(index=False))\n",
    "print(f\"\\nthe notebook needs at least {SHARED_VISITOR_FLOOR} visitors from both platforms before it estimates\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "785b1b46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T17:01:23.925696Z",
     "iopub.status.busy": "2026-10-24T17:01:23.925303Z",
     "iopub.status.idle": "2026-10-24T17:01:25.538946Z",
     "shell.execute_reply": "2026-10-24T17:01:25.535168Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1260x588 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 4.2))\n",
    "v = np.linspace(10, 1000, 300)\n",
    "ax.plot(v, RANGE_LEVEL * np.sqrt(1 / v) * 100, color=SIGNAL, lw=2.2)\n",
    "ax.axhline(FLOOR_TARGET_HALF_WIDTH * 100, color=FLAG, lw=1.4, ls=\"--\")\n",
    "ax.axvline(SHARED_VISITOR_FLOOR, color=FLAG, lw=1.4, ls=\"--\")\n",
    "ax.text(SHARED_VISITOR_FLOOR + 12, 55, f\"floor: {SHARED_VISITOR_FLOOR} shared visitors\", color=FLAG, fontsize=10)\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_xlabel(\"visitors who came from both platforms (log scale)\")\n",
    "ax.set_ylabel(\"sampling half-width of the overlap (%)\")\n",
    "ax.set_title(\"Precision is set by the smallest count: the shared visitors\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_floor.png\", dpi=140)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db955836",
   "metadata": {},
   "source": [
    "## When the site does not recognise the same person twice\n",
    "\n",
    "Everything above assumes the site recognises a visitor on both arrivals. Its instrument for\n",
    "that is a first-party cookie, and cookies fragment: a click from a social app opens in that\n",
    "app's own browser with its own cookie jar; a person who clicks one ad on their phone and the\n",
    "other on their laptop is two visitors to the site; a cleared browser is a new person. Each\n",
    "fragment splits one person into two, and a person split across platforms is never counted as\n",
    "a visitor from both.\n",
    "\n",
    "The effect is not small and it runs against the keenness bias. If a share $f$ of each\n",
    "platform's arrivals lands in a jar the site cannot tie to the person's other arrival, the\n",
    "visitors-from-both count falls to $(1-f)^2$ of what it should be, the estimated overlap\n",
    "falls with it, and the click-based unique reach rises. Sweep it on the worked market:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3c68f07e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T17:01:25.552067Z",
     "iopub.status.busy": "2026-10-24T17:01:25.550620Z",
     "iopub.status.idle": "2026-10-24T17:01:25.583267Z",
     "shell.execute_reply": "2026-10-24T17:01:25.582296Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " fragment_share  visitors_both_counted  click_overlap  click_based  error_pct\n",
      "            0.0                  335.0       419213.0     776226.0      -22.3\n",
      "            0.1                  271.4       339562.5     855876.5      -14.3\n",
      "            0.2                  214.4       268296.3     927142.7       -7.1\n",
      "            0.3                  150.4       188184.7    1007254.3        0.9\n",
      "            0.5                   83.8       104803.2    1090635.8        9.2\n",
      "\n",
      "at a fragment share of 0.33 the two biases cancel and the click-based figure lands within 1% of the truth - right by accident\n"
     ]
    }
   ],
   "source": [
    "def fragmented(observed, fraction):\n",
    "    \"\"\"The report the site would see if a share `fraction` of each platform's arrivals landed in\n",
    "    a cookie jar that cannot be tied to the person's other arrival. The single-platform counts\n",
    "    are unchanged; only the visitors-from-both count shrinks.\"\"\"\n",
    "    out = dict(observed)\n",
    "    out[\"visitors_both\"] = observed[\"visitors_both\"] * (1.0 - fraction) ** 2\n",
    "    return out\n",
    "\n",
    "\n",
    "frag_rows = []\n",
    "for f in [0.0, 0.1, 0.2, 0.33, 0.5]:\n",
    "    r = readings(fragmented(report, f))\n",
    "    frag_rows.append({\"fragment_share\": f, \"visitors_both_counted\": fragmented(report, f)[\"visitors_both\"],\n",
    "                      \"click_overlap\": r[\"click_overlap\"], \"click_based\": r[\"click_based\"],\n",
    "                      \"error_pct\": (r[\"click_based\"] / truth[\"unique\"] - 1) * 100})\n",
    "fragmentation = pd.DataFrame(frag_rows)\n",
    "print(fragmentation.round(1).to_string(index=False))\n",
    "crossing = fragmentation.iloc[(fragmentation.error_pct.abs()).argmin()]\n",
    "print(f\"\\nat a fragment share of {crossing.fragment_share:.2f} the two biases cancel and the click-based figure \"\n",
    "      f\"lands within {abs(crossing.error_pct):.0f}% of the truth - right by accident\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2f4d76c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T17:01:25.591250Z",
     "iopub.status.busy": "2026-10-24T17:01:25.590828Z",
     "iopub.status.idle": "2026-10-24T17:01:26.543313Z",
     "shell.execute_reply": "2026-10-24T17:01:26.536281Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pzm84cFD+ZUZkVBSu37iF6zdu4djxk/h35TK5ygKurg0BSPJ4UHAwbG1s5NJ7eNzP+f/efYW8L31JVLZsGZXP08X9u+hzwCAbqe3Ro8fIzMwEgCL/2L956w5+/uV3iMViVHZxxozpP6BundpISkrCydNnsHLVWvj6+uH7kWNx4thB6Ovry6X/ac5vsgDb//r2xqAB/WFjYwNfXz+sWbcBFy9dwZ59B2BdxlplbYK8EhMTMXrcJJVvEguSmpqGYcNH4/mLlyhduhQmjB+DFs2bwtLCAsEhodizdz/+27kH27bvhIN9RZXBq7waNWyAp489EBQUjA7Zbxa2bFqHRg3zPweTf5iO0tal8dfCP9CoUQNkpGfgzdu3svmWlhYYPOg7fNWkMcrb2cK6jDXS09Ph5+ePffsP4djxk/jjz0WoW7cOateqKbfuzMxMjB0/GUFBwdDT08PECWPxTZdOMDExwZOnz7BoyTJMn/UT1Bn89PCRYwgODsHECWPRo/s3sDA3x113D/z8y2+IiYnFb3/8iW+6dsaJk6cxaeI4dO/WFRbm5rhz1x0///IbYmPj8Ovv83H86AGFdW/YuAWr/l0LkUiEfv/rg759esPeviJSUlJw/eYt/L10Bd6+e49xE6Zg/97/VL6Fmz7jJ6SmpeKvBX+gebOvoKenhzt33fHb738iKjoav8ydJ7f9rZvXwd8/QO3z5ehgj149u6NB/XooV7YMSpcujdi4WLx8+Rpbtm7Hg4ePMXHyNFw8dxLGxsYAADs7Wzx97IE16zZi7bqNsLW1wbnTx+TWq6Oj+MCqTHhEhCzAZmFhjmk/TEbr1i2gq6uLO3fcsWjxMgSHhGDMuMk4eewgKlVyUroe93v3cfjocXT7pguGDR0E+4oVEB4egdVrN+D4iVM4dfosenTrirZtW6u1X8pMmToTZuZmWLhgHtzcGiElOQWnz5zD8pWrERoahjFjJ+HEsYNKH9Y1df3GTdlDXo/u32DggH4ob2cHPT1dhIaFw9PzKU6eOiPXN2VeY8ZOgpmZKX7/9Wc0algfYjFw8dIVzF+wCBERkVjw1xKsXb1CId28+Qux/8Ah6OrqYPiwIfimSyfY2dkiLi4e5y9ewj/LV8Hj3n38OPsXrPl3uSxdZmYmfpw9F2lpaXBycsQPkyegdq2aMDc3Q3x8At68fYcrV68V6gfv/D9+w2+//oxadV0ln+f9iu7dusgto+zly5EjxxEQGIihgwfi2297waZcWQQEBmHR4mW4dfsONm7ehu7dusr9sAQAX18/DB46AgmJiahRvRrGjhmJ+vXqQl9fH2/fvsPK1Wtx+/ZdTJvxIxwc7NV+Kyx1190D4yf+gMzMTNja2mD82FFo+lUTmJubITIyCvcfPMKx4yfyPa8lub2Hjx5j+IixiIuLQ5MmbtiwdpWsfNBEfHwCZs3+BY6Ojpg9axpq1ayJmNhYHDp8FOs3bMabt+8wdfosbNuyQSFtjerV0LVLJ9SuXRNly5SBpaUloqKi4PnkKTZs3II3b99h4uRpOHn8kNz3i4mJwW/z/kRWVhbq1qmNCePHoEoVF5iamCA6OgYvX73GxUuXFZ4BAM3zBiCpVTp+whSkpKSgdOlS+HHmNDRr+hUyMzNx+co1LFm6HLN/nqvRcdy6eR0yMzMxZ+48HDt+Eg0b1sfWTetk89UtmxYtXioLsPXu2R3Dhg6Cra0NfP38sW79JqxZuwEVyiuv3Se0/J4nxGIxJk6ejqvXrsPE2BhjxoxEu7atUaaMNSIjo3DsxCmsXbcRZ86eh52dLX76ccYH2WdlFvy1BJGRUfhx5jR06dwRevp68PC4jwV/LUFwcAgm/zADZ08dRcWKFT7I/oRHRGDIsJF49eoNbGzK4b/tm+VqlRX1uVKoMlTTMlzKx9cXs3/+FU0au2HcmFFwcXFGbFws/tu5B1u3/YcHDx5h9579GDJYfv+L8v3fvXuPVf+uBQC0bNkcI78fCidHRxgaGiAyMgpPnz3H2XMXoK2tLbfNX379A7GxcShTxhpTp0yCq2sDWFpYIDk5Be+9vHHjxk2EhIapfY7zuutxD4GBQWjf7muMHTMCFStUgJ+/P9au24TzFy7i5KkzqFq1CsaN0bw2rTJ/LVqKoKBgGBsbYdoPk9GieVNYWVkiPT0d3j5+uHPXHffuP1CZXtPnu5iYGAwcNBzBISGwr1gBE8aPgZtrI5iamsDH1w+bt2zHyVNnsGDhEjja28ulF+LZqzCePvbAseOnMGfu77LPueW9VvKTlZWFseMnwz8gALq6Ohg/djS6d+sKExMTPPZ8gkWLl2LGrJ/zXcfyFf/KAmztvm6DMaNGwMHBHiEhIdi2YxcOHDyMy1eu4Ze58+RqN1atUhkWFuaIiYmFh8d9uSByUHCwbJ/S0zPkAm5S0mn5BS4L+7voi5SZkSrmH//U+du58z+xvaOz2N7RWfzwwb0irevrdh3E9o7O4pat24qjo8IV5p8+fUq2rfXr18vNe/jgnmze/Pl/KqTNSE8Rjxw1Wmzv6Cx2rlxVHBwUIDd/3769svTSaVGRYeIePXuL7R2dxfUbuomfPnmsdL+lab9q1kJh3r+r/xXbOzqLa9SqK/Z6/1Zp+uXLl4vtHZ3FtevWFyfExxTqmPn6eMn2+9atG/nun72js7iha2NxaGiQxudo8eLFYntHZ/GEiZMU5h06dFC2nUOHDinMj4oMEzdt3lK2zL59exWW+apZC9n8HTt25LuNSi5VxTt3/qewzIED+2XLvHz5XG6ev7+P2LlyVaXXkPTvzeuX4qrVa4ntHZ3FR44clpt369YN2bpVndPz58+p3L4650udv7TUJPG3ffuJ7R2dlR6DpcuWqbwm1b12f/r5Z7G9o7PYpXI18RNPxWvfx/u9uGbtemJ7R2fxsOHfK8zv+7/+su8655dflObJrt26i+0dncVjxo4r9DHIfV3XqFVX7OvjpbDMqVMnZcvs2rVT5bnMbzvS77F02TK56b/Pmye2d3QWd/mmu8b73aRpc3FEeKjCMlu3bpFd43nLwvv3PGTpT5w4rnQbt2/dFDs4uYjtHZ3F9+65y6Y/e/ZElvbFi2caX3/K/vLL17n/cufxdevWKcxPTIgVN27STGVZPmjwENlxT0qMV5ifnpYs/m7AILG9o7N44KDBhfoOaalJ4hYtW8vuQ2GhwSqXTU1JVOs6yW+eENubOnWabNq1a1fF1WpIyq7vR4xUenzU/ZOWIdLyISoyTGGZ9RvWy5a5evVKodafEB8jbtm6rSTtlcty886dO6Py+s/vryh5IzMjVTzrxx/F9o7O4spVa4hf5Sm7MzNSxffuuYsdK1WWbUNZmVPQ39Sp08T2js7ivv/rX2AeyZuX/Hy9ZdufOWuWQjrJc84Y2f7lvjYKWnfuv/zuC+o+Txw9ekT2vJX3OEv/Dh48IDvP/v4+Gh1HZd9R+pf7fpv3XOWeZ+/oLD516qTS9DVq1RXbOzqLJ06arHFeUvWn7D7t5+ctyxctW7cV+/l5K6Qr6nNlUcvQopbh0nMnLafS05IVlhk9ZqzY3tFZ3LWb4v21KN9/8+bNsms3JTlBrfMUEx0p29/z588Jeg3kfk4aMXKUOCM9RW5+RnqK+PsRI8X2js7iqtVriaOjIwTNB7Xr1hfbOzqLt27dovF+a/J89+Psn2TPQJERoUq3MX3GTLG9o7O4Tdv2csdF02evgv7yeyZU9htRk7/Dhw/J1rNnzx6F+eFhIeLGXzVT+TwVGOAnruQi+Q0zbvwEheslMyNV/Ouvv8nSP3r0QG6e9FrKe/+Q/rYaPWasuGbtemIHJxdxeFiI3DINXRsr/W1W1N9FX9qfsHVR6bOWu+p0UTryffz4Cd5mD4Qw7YdJMDMzU1imQ/uv0fQrSTPNvNVkDxw6CgCwsDDHD1MmKKQViUT4Zc6P0NLSQnp6Bo4eO5Hv/oRHRKD/wKF49NgTNuXKYd/u7ahevVqhv9fOnXsBAJMmjoW9fUWly4waORzGxkaIjY3D9Ru3Cr2Nwhg5YhhKlyqlcfpu30hqqNy5664wT9pss2aN6ujRvavCfHNzc0wYN0ZhujIVK1TAd/37Kkxv93Ub2dspO1sb9PvftwrLdGj/tWyZp0/lmxLs2bMf6ekZqFq1Mr4fPkTptitVckK3byT9GUgH9FBm6OABSs9p61YtYGFhrnT7QtHW1kaXLpK+bO7c9Shg6cLLyMjAsWMnAQDfftsLNWooXvsVKpTHqBGSfhiuXruB8IgIpesyMjLE9KlTFKaLRCL06CZ5k/b0WdFGdBoy6DulfX517NAODerXBQAcztOsuKgyMyQ1eMtYa97P1cQJyvuLk75hzMjIwMuXr+Xm7di5GwDQtk0rdO7UQel63dwa4asmjQEAJ0/mXMPSWseS/bbWeL+FYGdnqzQPGhgYoGPHdgCAp8/k84+3j6+sjFww/zfo6yvWuNPS0sLkSeMASGpHx8TEqL1P12/ckjVz+G3uTyhVykrlspr0I1ic2zt/4RJGjBqHpKRkfNO1M9b8u1zp8dHEhHGjlTY3HTZkkKxfyLzN9gtiaGiI9tlN8G7nuZ9kZOctAwMDmJio36dOUfJGenq67HO/vn3g4uKskLZ+vboq1/shHDtxCllZWdDT1cXM6VMV5otEIsyeNe2D7U9+zxPSc/Fdv/+prP3fs0c3ODjYIyMjA2fOKvYR9KE0aFAPHTu0U5hevrwdBg/6DoCkyXRSUpLCMkLy8vZB336D4OPjiypVKmPf7u1K++UsynOlkGWoJmV4Xj/Pnqm0CWTPHt0AAK9evUZ6errcvKJ8/4xMSbNUS0tLtWuPZmbmNGUtU6b47puzf5yuUPtKJBLJmtwmJycLnk+kzzHWGj4PaPJ8l5CQKLtfzPlplsp+OaUtjt57eeHFi1cK+1yUZ6+SIv3eVatWRt9veynMt7KyxPixo1WmP3rsBDIyMqCtrY05P81SWltv+rTJst/jBw8dkZsnbTLqnqemmrQpaJMmbtmtKsTwuJezjJe3D8LDJc/4btnrUKYkfxd9Khhkow/uwaNHACQ/JNq2aaVyuU4dJX28vHvvhdjYWNn0h9npW7ZornJgBDtbW1lb8AcPH6ncRmBQEP7XfzBevXoDe/uK2L/3P6V9xhTEy9sHwSEhACTVaxMTE5X+ZWRkwNFRsv5nRQw2FKSlGgM/hIWF45/lq9CrT3/Ua9gELlVrw8mlBpxcasiaOkZERCI+PkGWRiwW49HjJwCANvmcv/zObW5NmrgqvXkYGxvDykryQ7Rx44KXiYiQ729EGpBq7OaKpKQklefExVnyA+vZsxcq97F586ZKp2tpacmaleTdfmE9f/4SP//yOzp27o7adV1RqXJN2bn49bf5ACDXj4xQXr9+K+tnp0N7xR8fUtL8mJWVhUePPJUuU7duHZWdzzo4SPp1KOpx+jqf/nK+biuZ9/Tpc7m+HouqajVJZ9jXrt/Ejp275fpwUleLZsqvITMzM5SSXsORea9hSUDCza2Ryus3MTERlStLruGnz3N14OvoIGtiN2v2HIU+/z6kpl81Udm/jKrr4m72d7eytISjg73K7+7o4ABAUi49e/5S7X2SHttSpUqhuYpzIyShtnf02AlMmDQVaWlp6N+vL/5ZukiQIKCUNA/lpa2tjTatWwKQdB2hjLv7PUybMRtt2nVGzToNZeWXk0sNbNi0FQDg7e0rl6Zq1coQiURISEjAnLm/qwzg51WUvPH6TU6Z166d6vJEqH74NCE9xvXq1VUanAcAe/uKcFExqJTQVD1PJCUlwdPzKQDJj7r8zoW0I+z87rXFrZ2K61syT9JMLS0tDc9fqF+WFNbLl6/wv/6DERwcgjq1a2HPzq1KAx9Ffa4UsgzVpAzPzb5iBZVBMofs6enpGYiNy3mZX9TvX62qpOnqmzdv8c/yVYiLj1e5f1IWFhawKSd5mTDvjwV48/ZdgWkKy8W5ksp+rpwcHeCc3R2HqucsTUmfY5b+sxIPVZTh+dHk+e7Ro8dISUmBtrY26tWto/IcmpmZyp6DnuUqq4V49ioJuX8n5Vvm5HP/kZ6junVro2zZMkqXMTIyQssWkrL5wQP537puro0AAD4+vggLC5dNlwbd3FwbwbWRJIiWu8noveyAWykrK5WDFgIf5nfRp459spHazM1zapzFq3GzUiUwMAiA5M1YfqOHSjO3WCxGUHCI7O16YKCk005nFf1C5U7/6LGnQiefufXtNwjBwSGoXNkF/23bqPEbntzBj569+6mVJioquuCFikDVSGxSd+66Y8y4yWqdy4SEBNnNNS4uDgkJkqCbo4PqDuZLlbKCmZkZ4nI9NCmT3yh4Bgb6ai+TkpoqN93bxwcAsG37TmzbvjPffQCAqKgolfPyqwlkaGCodPuFsXHTVixaskyh4++8pMddSIFBQbL/88tTjo4O0NHRQUZGhso8le9xMpTk9aI+JDk5qg6CSwPkqampiIiIzHcwi8Lo2f0bbNu+E69fv8Fvv/+JRYuXoVHD+mjUqCGafdUYderULnAd+b0VN1BybBITE2UPRgsWLsGChUsK3EbuMsXIyAgTJ4zF30uX4+KlK7h46QqqVa0CN9dGcHNrhGZNmxSp/67CyO8ttGH2PUAx/0qCMVHR0bI+4AqSXx7Oy89P0hFxlSougvXnUtzbu33HHYePHodYLMaoEcMK7Gg8OTlZaZmipaWldCQ/c3OzfGvYSfNeULBi/p/3x0Js21FwOZuQKF+GOdjbo1/fPtiz7wD27juIAwePoFbNGnBt1ACNG7uhSWM3hRo4Rc0b0mcQAKjkpLrMq6TBCzehSMtlR8f8B3FxdHSQtQwoTqqeJ/wDAmUvNMZP/EGtdRUmnwrNyclB5TzHXOc7MDAYjVRX4tBYbEws+g8chri4ODR2a4SN61erLIeL+lwpZBmqSRmem3U+97/cZVFqSs46ivr9m37VGC2aN8X1G7ewavU6rN+wGfXq1YVrowbZtXgaKO1ja9bMqZgydSYePHyMjp27w9HRAY2z75vNmzVVGfRWl2M+zzCA5Dnm3XsvuXJKCNOnTsHAwcPh5eWNPn0HwNbWBo3dGsG1USO0bNFMZSBHSpPnO+lzeGZmJpo0U68v3shc51CIZ6+SEBcXJ/ttlV/FDevSpVX+TpKe/4J+60pftOS9L1evVhUmJiZISEiAu8c9fNO1M8LCwuHj44tSVlao7OKM5Owau/KjYWf3x9Yo/4Ekivt30eeANdlIbeXtcppovffy1ng9SUnJAADjAkYnNTHOmZ+Ya0QraTX+gkY3Nc5Or2o0LACIj5MUgoYGBjAwUD18eEFy1/RSV94R/4SWXwAzLi4O4yf+gPj4eNjbV8Qfv8/FyWMH4XH3Op488sDTxx44m6sT/dxNz6TnD1DjHKgxAq2WGh2JqrOMOM8oC4U9J2l5minkpk5np3m3ry4Pj/tYuOhvZGVloUljV/y7chkunD2BBx638PSx5Fz88bukA+6MXOdBKLmbxRgZqz5fIpFI9jCcqCJPaQs8GpYyRkaq82nuh3Uhm/vo6elh765tGD5sMCwszJGcnIzrN25h6bIV6NmnP9q064yz5y7ku47CXkNClCnjxozEsr8XoWrVygAkI5xu27ETY8dPhmuTlpi/YNEHeTOsWf4t/IucwpSp0mvY5AMFGoXYXnx8vOw4mVsUPIJo+07dUKuuq8Jf+07dlC5vZJh/eS3Ne+npGUhNzTnWR44elwXYOnZohw3rVuHKxTN4eC+nDBszegSAnOY/uf0xby7mzpkN+4oVkJmZiceeT7Bh01YMHzEGjZu2xOq1G+TuQUXNG8nJOfcww/zKk3zmFbfk7PussmBobkUd5V1dqp4nNMunqu+1xc0wn2vcqJjuH7mlZ2TI1m1sbJzvwDNFvc6FLEM1KcNz09ZSr8N4oe+B69euwpRJ41GmjDXS0tPh7nEPq1avw3cDh6FZy6+xe89+hXV0+6YLtm5ehwb160IkEsHb2wd79h3AlKkz0bhpS8yY+ROio2MKvW9S+T3DADl5Xuhr0LVRA+zf8x9aNG8KbW1tBAUF4/ARyYivTVu0xeixE/MN7GnyfFfUcyjEs1dJKMzvJCMVZbz6v3UlzxR5RyzX1tZGgwb1AOQEzqTBNGkArUaN6jA2NsLrN29lLcakTUelNeFUKc7fRZ8L1mQjtdWtWwdaWlrIysrC/QcP0atnd43WI73BJBZwA8k9P/ebPiMjI8THxxd4A5IWcvn9qNm0cS2GfT8ank+eYvjIMdi+ZYNGD625b5r33W/Cysqy0Ov4kM6cPY+YmFiYmpri4L5dSmsvZKgIOuX+rgWdg4LOcXEyNDREfHw8Zs+ajpHZ/Yl9jPbsk4y+07Bhffy3fbPSJhmpxfg2KPf1npSYBDMV/S2KxWLZj9MPVQNKmaSkZJVNFnL/eM79vURQr+ZQRqbqJqbm5uaY89MszJ41Hc+ev8CDB49w6/Zd3Lx1Cz4+vhg3YQpWLv9bYSh0TeXOZ/+uXKZx/1A9undFj+5dERQcjPv3H8Ld/R4uXr6C8PAIbNm6A+/fe2Hr5vWC7LOQpD80Krs4ywX8haLOS5iPbXsd2n8NExMTbP9vF5b8vRz6evoYPmywULuIpGT17qm6ujpytct275X8UO32TRcsX7ZYadr8grlaWloYOmQghg4ZCG8fXzx48BB37nrg0uWriI2Nw9JlKxAcHIL58yQvG4qaN3IHrpKTklWWecm5fih9aNIAX+4yTZn87sHq1JjMLOKLm9w/EI8e3qcwErkg1Pge+ZXduSXnc40nqbh/CEk6ku2UqTNx6fJVTP5hBlYu/1tpk++iPlcWdxla3IR4rtbX18ekieMwccJYvH7zFg8ePMLtO3dx9doNhIaGYc7c3xEXFyd7CSDVskVztGzRHBGRkbh//yE87t3HpUtX4R8QgENHjuHZi5c4fmS/RqOYJxVQrkjzvMI1KEA+qFu3NrZt2YC4uDg8ePgY9+4/wOXLV/Hm7TtcuHgZT54+w5mTR1T2nVZY0mvQyMgQzzwVR7FUx4d+9hJCYX4nJako46Xnv+DfutKgvWKZ5daoIa5duwGP7OBa7qaigKTbpgb16+H6jVu4d/8BqlapImup4lpATTYqGGuykdpMTU3QuLGkyvmp0+c0fstiZyfp3DUwMCjfB+932U0gRCIRbLM7XJakt5HMf++V73ak6aXbU8a1UQNsXP8v9PX18eDBI4wYNU6jmh0VcnXE7u/vX+j0H9qrV28AAE0au6psHqSqLwozMzNZB9XS5gjKREZGFdhUtDhJz4nfR34+Xr2WnItOHdqr7POkOPoFkcrd2XJ+ecrbx1fWLCi/PFXcvLxV16L1yq5hq6+vj9KlczrplvZNBuT/Yz8sNFzlPCltbW3UqV0Lw4cNxuaNa3D5whlUKC9pSrV67YYC06vLzMxM1kTf3z+gyOuztbFBt2+64M/5v+HmtYvo308y2Mi16zfx7HnJ9ZGkivSYBgYFFTkQoIy0z5DXr99+kLetQm3v17k/yc7d/AWLsHPXXpXL3rh6AV5vnyv83biq/M1/bGwcIiNVN+WT5j1bGxu56a+z7yddOndUmVbdMszRwR59evfE0iULcfvGJVnfnnv3HZDtW1HzRu7y672X6jKvKDX2i0paLuftwy6v/Prp1NfL7kohRfVLmtDQsMLvXC52drayYJ4Q5ZQy0vI7v6ZH6pTdAODl5aNynneu8y19ziwOXTp3xJJFf0JLSwtnz13AtBmzlTbrLupzZXGXocVNyOdqkUiEqlUqY8B3/8PqVf/g5rWLqF1bEhBet2GzyuNTulQpdOzQDnPnzMbVy2cxdcpEAMDr129w6fJVjfbFO59nGCDnOSbvc5aQ+cDMzAytW7XAzOk/4OzpY/h78QIAkvLg4OGjaq1DHdJm5klJyWr3t6nKh3r2EoKZmZlsQAKvfO4j4RERKn8nSc9/Qb91pd0FKBs4RTr4wbv3XoiMjJIF21xzDWjgmh1wc/e4L6vFZm5uhipVKue7XSoYg2xUKN8Pk4wuFB8fj6X/rFQ73ftchUTD+pLqqxkZGbh46YrKNGfOSn4IuDhXkhvtrEF2+mvXb6j8wRwcHILHnk/kllflqyaNsX7tSujp6eGu+z2MHjtRrimMOqpWrYJS2SNvnT5TciNnqUtaHTu/B69jJ04pnS4SiVCvrqQfhMtXrqlMn9+8D0E6Ou3FS1eKvWmuMrnfTGdmqu5rTXYuspSfi+TkZFy4eEllet3s7WTls438VKniIqvtmV+1e+lIV1paWqhbt+T6wbiUT5lx8dJlAECtWjXkjr91rv5kVP0o9fbxhX+AZj/Yv+3TU7IOgX+US0dGPH32nKDr1dXVxYRxOaNa5fcQqIz02GYW0IdgUTRtKsm/iYlJuHb9puDrb9LYDQAQGRmJm7fuCL7+4tze/Hlz0ae35Jr79ff52H/gcAEp1HfxsvL8lZmZiStXrwOQdMafW0H3k5CQULmOldVlZGSEUSOHA5AMuOLr5yebV5S8UaVyTpl34cJllcudv6C63C1u0mP86NFjlSM++vn559sfm7W15JlE2i+SMjduFm2Uc3Nzc9SsWR0AcPqMsOWUlLT8zi+geF3N76Hq+gaAC9n3Fj09PdTQYIT5wujR/RssmP87RCIRTpw8jVmzf1EIvhf1ubK4y9DiVpzP1ZaWFhg6eCAASfcp+b1ckBKJRBg7ZiT0smuveWk4ENXbd+/h46s8eO7t4ysLqtSrV0dunpD5IK9ePbvDPvtFkHc+gejCcm3UALq6kucFoUdLLa5nLx2dnNqJmganc/9OunBJ9T0mv/uP9Lfr48dPVL4MSU5OluVtadPQ3GrVrCGrTXj23AW8e+8FCwtzVM0VQJPWWPO4dz+nP7aGDVS+9Cf18QhSobRu1UI2yuDWbf9h3fpN+b6Vj4qKxpSpM7Frzz7ZtDp1ass6alymYsSfi5cu4+at2wAg+zEh1adXDwBATEwslq/4VyGtWCzGvPkLkZWVBV1dHfTo3rXA79WieTOsXrkMuro6uHHzNsZPnKIwlHh+RCIRhmQP/779v10F/qAIDAwqdCBPSHbZbwgfqniAP3nqDK5du6EyfY/ukuG6nz17jhMnTyvMj4uPx+o1JdsEbeCAftDR0UFoaBjmL1iU73WampomeCez5uZmsjf84eGq3y6Wz35bdUVFUHLBX0vy7f9DWq0/KjpaoxE1dXR00D07jxw8eBgvlIyqFhgYhE2bJaMDtmrZPN+BKIrbth27lI6UefbcBTx4+BgA0KuHfJ9Tdna2sof1I8dOKKTNzMzE/D8XqdxmQUEoXz/JW3Z1+skqjKFDJD8Cnj59jvUbNue7bEJCotwIUiEhofnWNvbzy6kZYFHI/ZYuHx6m3ltzTVR2ccZXX0kCKfP+WJhvHgIKHyhs2aKZ7EfFb/P+zHcgGiFGqhVyeyKRCH8tmIdu33SBWCzGT3N+xdFjJ4u8jwDw7+p1cqN5S23d/p+sGUnPvPkr+36irGZHZmYm5sz9XeV38vcPyPdeK3ed5nrZVpS8oauri65dJU2L9u4/iLdKatk9fPS42IJG6ujWtTO0tLSQlp6OJUtXKMwXi8X4a/HSfNchbbp59uwFpS8kT5w8LSszi2LYkEGS7Zy7oPR5ILeIyEil11d+pN/jxctXsprfuXn7+GLHjl1qrev+/Yc4d/6iwvSAgEDs+G83AEm/gh+ir7u+3/bCvN9+AQAcOnwUc+bOk5tf1OfK4i5Di1tRv7+Pr2++g0lJ79va2tqyLiiioqJVBrUByX1V2n9v7vKosBb+9bfCM6lYLMZfi/4GIGlmKf2tJVWUfJCUlITg4BCV+5OUlCQb3VzI5xhzc3P07CHpWmj5in9lLYxUyXsNlsSzl6VlzrrCivCMI71Pvnr1BgcOHlGYHx0dg9VrVf9O6t6tK3R0dCTPpwuUP58u/WelrCZc3t/KgOReVz/7hc26DZsASAJoubsSqF2rJgwMDPDixSvcuCEJ0roWx6gvXyAG2ajQFi6Yhzq1awEAFv/9D3r16Y/9Bw7Dy8sbMTExCAwKwu07dzF/wSK0atsRx5XUiPplzmyIRCL4+Pjif/0H48rV64iMjEJAQCDWb9iMSVNmAJCMqjJoYH+5tHXq1JYFeTZs2oqf5/yGl69eIyYmBp6eTzBm3CTZQ9S4saPVHjG0bdvWWPGPpG+My1euYdKU6YX6cTVyxDDUrFkDaWlpGDx0BOYvWITHj58gKioaUVHReP3mLQ4eOoLRYyei9dedVHYe/yF0bP81tLS0EB0dg2Hfj8Gdu+6IjIzC+/deWLpsBabN+DHfEW2+6doZ1apKhtae+eMcrF2/Ef7+AYiKisa16zfQ/7shiI2LhZmZmcp1FLcKFcpjxrQpAICdu/biu4HDcO78RQQHhyAuLg4BAYG4cvU6/vjzLzRv9bXgP6gMDQ1lo9Pt+G83Xr1+g5SUFGRkZMhdV9IHqTt3PTB1+o948eIloqMl1/LEydOwa/e+fM9FzRqSN+6pqalY9e9ahIeHy7ZR0GilUpMmjoOVpSXS0tMxaOgI7Nm7HyEhoQiPiMCJk6fRt/8gxMbGQV9fH7MLGNGwuJmbm6HfgCE4feYcIiIjERgUhI2btmLq9B8BSH5Y9M4OxOcmDbxt2boDq9esR2BQEKKjY3DnrjsGDxuJW7fvoFxZ5aOR/vzL7+j8TU+sW78JDx89RnhEBKKjY/D8+UvMX7AIh7KbV3TppLq5nCYaNWwgK/8WLVmGMeMm4fqNmwgLC0dsbCx8ff1w/sIl/DTnVzRt0Rb3HzyUpb156zaatmiLOXPn4dKlKwgICERsbCz8/Pxx4OARTJk2E4Ckj6DCPlDVqCGpuXLoyDE8fvwESUlJCte1EObPmwtTU1P4+fujS7fe2LRlG96+fYfY2FiEh4fj8eMn2Lx1O3r16Y9xao5sKKWlpYWFC+ZBW1sb3t4+6N6rL/btP4iAgEDExcXB28cXh48cw8Ah38tqcBWF0NvT0tLC0iUL0alje2RlZWHGrJ+KXIZJRjmLR78BQ3Ht+g1ERUXDx9cXfy9bgUWLlwEAmjf7Cs2bfSWXTlqGHTp8FH8uXIx3794jOjoGd909MGT4KFy+ck1lGXboyDE0b9UOC/5aghs3biEoOBixsbHw8vbBtu078fsfCwEAVatWlhulrSh5AwAmjh8LE2NjpKamYsDg4Thy9DjCw8MREhKK3Xv2Y/iIsbC1Lb4mgwUpX94OA777HwBgz979mPnjHNlzzpOnzzB+4g84e+6CrLmUMj16dIOWlhaCQ0IwcvQEPHn6DLGxsXj37j2W/bMS02bMRsUKFYq8r927dUWb1i0hFosxZepMzJr9C9zd7yEiMhIxMTHw8vLGiZOnMWXqTDRv2U72w1hdzZo2kY18OHrMRFy+cg3R0TEICg7Gnr370bffIFhZqR4VNzc7O1v8MG0WNm7aisCgIERERuL0mXPoN2AIEhISYGBgIGsSmFtAQCCcXGrAyaUGlq9cXaj9z8+A7/6HX36W3Lv27N2PednXu1RRnyuLswz9EIry/VevXo+27TrjnxX/4q67B0JDwxATE4M3b9/h39XrsCY7wNG6VQtZbZ83b9+iaYuvMW3GbJw+cw4+vr6IjY1FYGAQTp85h2HfS2qAGxgYoE3rlhp9Jzs7W1y4eBnjJkzBk6fPZHl67PgpuHBRUrNp/LjRCn1FFiUfREVFo1XbDhg7fjKOHjuBd+/eIyYmBsHBIbhy9ToGDx2JxMQkiEQiheBeUc2aMRV2draIiYlFrz79sWLVGjx//hIxMTGIiIzE8+cvsWv3Pgwc8j169PqfXNqSePaqXq2qrBbXilVrEBQcjPT0dGRkZBSqZlvXLp1Qq1YNAMCcub9h5ao18PPzR1RUNC5fuYb/9R+ExMQklb+TypUrizGjvgcAnDp9FmPGTYan5xPExMTg5avXmP3zXGzZugMA0LtXD9SqWUPpeqQ11aQVCXI3FQUkNXfr1auDzMxMhISGKl2GNMOBD6jQzExNsXvnVvz+x0IcPHQEnk+ewvPJU5XLV6taBd2/6SI3rVnTJvjzj18x97f5eP36Db4fOVYhnb19RWzeuFauPyWpP//4FdExMbh27Qb27Dsg6zw+t37/64OJ48cU6rt17NAOS5csxNTpP+Lc+YuYOv1H/LN0kVqjqOjr62Pb5vWYOHkq7tz1wJatO2QFYF5aWlrQ1i65GLeTkyMmjB+DlavWwPPJUwwYNFxuvoODPf5a+Af69B2gNL2Ojg7Wrl6B/gOGIjgkBEv+Xo4lfy+XzdfT08O/K5fi9z8WIi4uTmmnvh/CyBHDkJWVhaX/rIS7xz25Yarz0qQD24IMGTwQv/w6D489n6BzV/m3TF5vnwOQvH06efosbt++i6PHTuBonppWX7dtjbZtW2P2T3OVbqNOndqoV7cOHj32xKrV67Bq9TrZvN49u2NJdl8b+bEuXRqbN67B9yPHISo6Gj//8rvCMkZGhli96h9UKmA48eL2z9JFGPb9aEyYNFVhXtmyZbBu7Uql53L8+NG4cvUa3r33wtJ/Vso1d9fV1cHiv/7E3n0HZQ8Zeb169UbWl6EyDRrUw9QfJmnwjfI3d85s6OjoYOu2/3D+wqV8m7Dp6cl/79jYOOzesw+7c9Ukzs3E2Bgrl/+d70jEygwZNADXrt2Ar68fen0r/xLk+pXzKJ+rL52icLC3x+7/tmLM+EkIDAzCgoVLsGDhEqXLatK8q7GbK1atWIppM2YjMDAIs3/+Velygwd+V+h1f4jtaWtrY/myxcjIyMCFi5cxZepM6Orqot3XbTTaP1NTE8yeNR0/TJuJYd8r3jtdnCvhn6WKAxuMGTUCly5fxevXb7B5y3Zs3rJdbv6ggf1haWmJlavWKN1uWFg4Nm3ehk2btymdX6aMNZYvUzzvRckbNjblsHrVPxg9bhIiIiIxbcZshW0u/HMeBg35XuU6i9tPP87Au/deuHPHHQcPHcHBQ/K1IcaOGYmw0DD4BwRAW8k9tmqVyhgzegTWrN2AW7fv4FYv+WbK7b5ugzZtWqm8t6hLJBJh1YqlmDX7F5w8dQYHDh7GgYOqmzDrFvJ5QFdXFwvm/44x4ybCPyAAI0aNk5vv6OiAxX/Nx7f/G1jgumbPmo6/l63AwkV/Y2F2raGc7ehg+bLFsv4TP5RhQwchPT0dfy1eim07dkJPTxc/Zr/MKupzZXGXocWtqN/f188fq/5di1X/rlWappKTk6w2oVRycjKOHD2OI0ePK02jp6uLRQv/QLlyyl/KFaSxayOYW5hjy9YdSmtVdu3SSRZcya2o+SA9PQPnzl9Uuk1Ako9/nDlNZbBGU5aWFtj931aMHjcRr169wYqVq7FCRaBa2tdmbh/62cva2hqdO3XAyVNnsP/AIew/cEg2z821Efbs2qbWerS1tbHm3+X4bsAw+AcEYPnK1XIBej1dXaxcsRR//PmXyn7ZJk8aj5DQMBw8dATnL1zE+QuK565N65b44/dflKTO2ef8PgOSARLu3HEHIHku/BjLgk8Rg2ykEUNDQ/y1YB6+HzYYR4+fxK3bdxAYGIy4uFjo6xvA1qYc6tatgy6dOqBZs6+UjnLV73/fokH9eti8dQfu3HFHaFgY9PT04OTkgE4d2mPI4AEqf/wZGhpiy8a1OH7iFA4dPoYXL14iPiEeFhYWqFe3Dr7r3xctmjfT6Lt907Uz0tPTMWPWzzh56gz09HSx+K8/1WqfbmVliZ07tuDipSs4euwEHns+kfX1UKqUFSpXdkHTrxqja+dOcv3MlYQpk8bDuZITtm3fiZevXkMkknSc2aH91xg5YniBTToqVqyAUycOYfXaDTh//hJCQ0NhbmGOhg3qY+yYkahZozpmzZYU/CU5GuXoUd+jQ4evsXPXXty6fReBgUFITpaMUOlgb4/69euifbuvi2UknQHf/Q9GRobYu+8gXr95i4SEBIXaZTo6OtiycS02btqKo8dOwt/fH4ZGhqjk5ITevbqj3/++lb2tU2XLprX4d816XL16AwGBgRqNRlqnTm1cOHcSm7dux6XLV+Hv74+sLDFsbW3QonlTjPx+GGxyDUBSUho2qI/DB/ZgzbqNuOvugeioaFiXsUa7tm0wYfwYlSOQmZma4sC+nbLrNSQkBKZmZnBt1ABjRo9ArZo1sHffQaVp/168AFev3cCt23fw/r0XQsPCkZSUBHNzc1SvVgVdu3RGr57d1ArGF5a2tjZ++flH9OrRHbv37IP7vfsICQlBWlo6zM3M4OjkANdGDdGpQ3vUqJHzYNS5UwdYWljgxq3beOwp6dMjKioK+nr6sLeviGbNvsLQwQNlb8YLo1XL5ti0YQ2279iJ589fIjYurtg61q5RoxounD2B/QcP4/yFS3j9WjLUvJ6eLsqVK4fq1aqiVcvm6ND+a43W37FDO9SrWwdbt+3A1es3ERgYiKwsMcqUsYaLcyV06dwRLZo3Fez7CL09XV1drFqxFKPHTcK1azcwcdJUrFu7Cq1aNtdo/zp36oBy5cpiw8YtePDwMeLj42FnZ4uunTti9KjvlTajMzExxr49O7B69XqcPXcBIaEhMDUxRdVqVfBdv77o3KmDyto/w4cOgotzJdy4eRvPn79AaFgYYmJiZTWB27RuicGDvlP6tl/TvCHVvHlTnDx+CP+uXodbt+8gNjYOZcpYo1XL5pgwbkyJ9OWZm76+PrZv2YD/du7BocNH4eXtA319PVStUgWDB32HTh3bY9yEKQCUjywHANOnToZzJSfs3LUXr16/hkgkgpOTI/r17aPWvUVdhoaGWLn8b3zXvy8OHjqCe/ceIjwiAlmZmbCwtEClSk74qrEbOnXqACdHh0Kvv3WrFti7awfWrNuABw8fISkpGba2NujcqQPGjBqRbxO/3KysLHH00F6sXrMB5y9eQnBwCExNTODm1gjjx42W1dDPKzgkp6ldvWLok3TUyOFIS0vDsuWrsGHTVujp6ckCB0V9rizuMrS4afr9Z86cisaNXXHz1h28ev0GYWFhiI9PgKmpCVycndGh/df4rn9fuRf59evVxfatG3Hz5m08ePgIwSEhiIyIhI6uDuxsbdGkiRsGDxqg0TWc25yfZqFmjerYtWcf3rx5h8zMDLg4O6N/v774tk9PlSMDa5oPbG1tsG/PDty8eRse9x4gMCgI4eGSgQhsbMqhYYP6GDSwv+ABNqkKFcrj+JEDOHHyNE6dPotnz18gOjoa2to6KGNtjapVq6B5s68URoouqWevJYv+RKVKTjh3/gJ8ff0LHOVZFTtbW5w4fhBr1m7AuXMXEBwcAjNzMzRsUB+jR32POrVr4Y8//1KZXltbG4v/mo9OHdtj774DePTYE7GxsTA1MUWNGtXQp3dPdO3SKd+RpOvUqQ19fX2kpqbC1NQU1atVVVgmd+CtQYN6xfIs+yUSZWakFv+wWkT0xYmLi0PdBpKOd48e3ifrT4KosA4eOoKZP84BkFMDkIjoS9e1W2+8ePkKs2dNx8gRw0p6dz5KAQGBaNFa0gRu986taOzmWuh1rFi1BitWrkbDhvWxf89/Qu8ifQH6DxgKd497arcwIKJPG/tkI6JicfmKpF8hXV0dVKnsUsJ7Q0RE9PkICg6WdYBenc17itWt25KmttOnTinZHSEiok8Cg2xEpJH8mpPGxMTIRn5t3aql0n71iIiISLmUlBSVTf+zsrIw/89FyMrKQikrq2Lp7oAkkpKS4On5BC1bNONxJiIitbBPNiLSyMlTZ3Hg0GH0/9+3aNSwAUqXLoX4+ATcdffAv6vXwc/fH9ra2hg/bnRJ7yoREdEnJTAwCIOHjcR3/f+HFs2borydHTIy0vH8xSts3LQFd+56AADGjR1VLAP3kISRkRFev/As6d0gIqJPCINsRKSxJ0+e4cmTZ0rnaWtr488/fiu2jlSJiIg+Z8HBIVi6bAWWLluhdH7fb3tjyGDlo4ATERFRyWCQjYg00rZNKyQkJODGrdvw8/NHZGQUxGIxypYtgyaNXTFsyCC4uDiX9G4SERF9cuzsbLHwz99x9doNvHn7DhERkUhNSYGllSXq1qmN//Xto/EoskRERFR8OLooERERERERERFREXHgAyIiIiIiIiIioiJikI2IiIiIiIiIiKiIGGQjIiIiIiIiIiIqIgbZillcTCTiYiJLejeIiIiIiIiIiKgYcXTRYpaRnlbSuyCoqPBgAICVtU0J7wnRp4V5h0gzzDtEmmHeIdIM8w6RZph3JFiTjYiIiIiIiIiIqIgYZCMiIiIiIiIiIioiBtmIiIiIiIiIiIiKiEE2IiIiIiIiIiKiImKQjYiIiIiIiIiIqIgYZCMiIiIiIiIiIioiBtmIiIiIiIiIiIiKiEE2IiIiIiIiIiKiItIp6R0oLG8ffzx98RpeXr547+2HiMgoAMDKJb/B2rpUodYVGBSCh4+fwfPpS/gFBCEpKQlGRkao5FgR7du2QL06NYrjKxARERERERER0WfmkwuyHT52BvcfPRVkXQuWrEZUdAz09fRQqZI9zE1NERIWjsdPXuDxkxfo2rEtBvTrIci2iIiIiIiIiIjo8/XJBdlcnB1RobwtnBwrwsmxIn76dTFi4+I1WpdNuTLo26sLmrjVh56enmz6w8fPsGzVRpw8ewm1alZF7ZpVhdp9IiIiIiIiIiL6DH1yQbZuXdoJtq45syYqnV6/bk20at4El67ewu279xlkIyIiIiIiIiKifHHgAxUqVrAFAETHxJbwnhARERERERER0ceOQTYVQsMiAADm5mYlvCdERERERERERPSx++Sai34I8QmJuH7THQDQsF4ttdLM/WOp0um9v2kNm7KlERUeLNj+laSMjHQA+Gy+D9GHwrxDpBnmHSLNMO8QaYZ5h0gzn2PesbK2KXQa1mRTYtPWPUhITEIVFyc0alCnpHeHiIiIiIiIiIg+cqzJlsfBI6fh8cATZqYmGD9qMEQikVrp5v0yTel0aRRXkwjox+hz+z5EHwrzDpFmmHeINMO8Q6QZ5h0izTDvSDDIlsv5S9dx6NgZGBoa4Mdp42BtXUqQ9aaki/EuNA1lzbVhaqAtm+4XmY60DDEsjLVQ2iTnVITEZiAhJQtGeiLYWurKpkcnZiIyIRM62oBDaT3Z9KTULATFZAAAHK11oa0lCQxmZIrhEyGpsmlrqQMjvZyKi97hacjMAkqbasPCKGefgqLTkZQmhqmhFsqa5exTRHwGYpKykByXBTuLnPXEJWciLC4TAOBcNmefUtOz4B8l2acKVjrQ181J8y40DQBQxkwbZoY52/aPTEdqhhgWRloobZqz7dC4DMQnKx6PmKRMRMRnQlsLcLTOdTzSshAULdm2Q2ld6GhLjkdmlhje4dnHw0IHRvo5++QTkYaMTKCUiTYsjRWPh4mBFsqZ5zoeCRmIScyCno4IFUvl7FN8SiZCYxWPR1qGGH6R6UqPx/uwNIjFSo5HVDpS0xWPR1hcBuKSs2CoJ4JdruMRm5SJ8PhMaGkBTiqOh31pXegqOR42FjowVuN4BMekIzFV8XhEJmQgWsnxSEjJQkisZNuVyujKgtbpGWL4Zh+P8lY6MMh1PLzC0pAlBqzNtGGe63gERKUjJV0McyMtWCs5Hga6IpS3UnI8RIBTmZzjkZyWhUDp8SilC10dyT5lZYnhpeJ4+EakIT0TsDLRhlWu4xESk4GE1CyY6GuhnEXOPkUlZiIqIRO62oB97ryaJkZU9vXvZK0LLS3F42FnqQNDPSXHw1Qb5kaKx8PMUAtlcuXV8PgMxCYpOR7JmQiPUzweKelZCIhSPB5isRjvwyT7VM5cByYGOfskLbssjbVQSknZZawvgo1FwWVXYmoWgpWUXemZYviqKLu8wtOQlaV4PAKj05Gcpng8pGWXvq4IFXIdD2nZJRIBlcooL7sqltKFnk7OixZp2fU5lOX6OiJUKKV4PICPryyPjc5CBcuc88CynGU5ULJlee6y61Moy6V5lWW5BMtyPpcXVJbHRGWhtIkIVsjBspxludzx4HO55Hh8AWV57rJDXWwumu3G7XvYtvMg9PR0MXPKaDg6VBBs3a9D0tBzlT9uv0uWm/7DnhD0XOWP3XfkRzBdeSESPVf5Y8HJCLnpxx/Fo+cqf4zcKt/G2dM/BT1X+aPnKn8kpGbJpselZMmmPw9IlUvz/ZYg9Fzlj5OP4+Wmzzsejp6r/PHvxSi56Ttux6LnKn/8dkZ+PTfeJKHnKn/0We0vN90/KkO2bWnGkOqzWjL9xpskuenT9kmOx47b8sfj34tR6LnKH/OOh8tNP/lYcjy+3xIkN/15QKps23EpOccjITXneHj6p8ilGbk1GD1X+eP4I/njseBkBHqu8sfKC5Fy03ffkRyPH/aEyE2//S5Zto3cgmLSZdOlD1hS/1sTgJ6r/HH1lfzxmLU/FD1X+WPrzRi56WsuS47Hr0fkj8fpJwnoucofQzcGyk1/FZRzPKITM2XTU9LFsukPfeWvzTHbJcfj8IM4uel/nZJcm8vOyR+Pfe5x6LnKH5N2yV+b7l5Jsm1k5pwKhMTlXB/SG4ZU/3WS43H5RaLc9J8OhqHnKn9svi5/PDZci0bPVf745XCY3PTzzxPRc5U/Bm6QPx5vQ9Nk245IyLk20zJzjsc9b/njMf4/ybV5wEP+eCw+I7k+Fp+Rz6sHPCTHY/x/8tfHo4BM2TbSMsWy6REJOcfjbfYNQ2rghkD0XOWP88/lj8cvhyXHY8O1aLnpm6/HoOcqf/x0UP54XH4hOR791wXITX8flnNthsTlHI/MLMimu3vJX5uTdkmuj33u8sdj2TnJ9fHXKfnr4/ADyfEYs13++njom5NfUtJzjkd0Ys5xehUkX+YM3Sg5HqefJMhN//WIpOxac1m+7Np6U3I8Zu0PlZt+9ZXk2vzfGvnj4RORczyCYuSvTen0z6Esn7ZP/tr8mMvyoTtTkJCryGZZzrIcKNmy/J53zvXx0ZflYpblLMs/jrK856pP57l86M4U3PLKlJvOspxlOcDn8i+xLNcEg2wA7j3wxPrNO6GtrY0fJoxA1SrOJb1LRERERERERET0CRFlZqSKC17s4zVm0k+IjYvHyiW/adS888mzV/h7+XpkZmVh8rhhcG1YV9D9iwoPRkq6GEnapT+LaunJceGws9CStbNmtXQ2MQJYLV2daulR4cFIShMjRac0AFZLB1gtnU2MJApuLhqOCpYilClrC4BlOcCyHGATI3XK8qjwYIjFYkSLJfcdluUSLMv5XF5wc9FwlDYRwb68bc7xYFnOshx8Li+oLJf2yZasU/qzKcs1aS762QfZoqJj8OeiVQCAn2dNhJWlhWzem7deWPD3aqSlpWPsyEFo/lUjwffvc+v873P7PkQfCvMOkWaYd4g0w7xDpBnmHSLNMO9IfHIDHzx8/AxHjp+TfU5IlLRJXrpqE3R1JF+nXp0a6NW9IwAgMyMTQSFhsv9zW/zPOqSmpqGUlSWePX+FZ89fKWzP1NQEA/v1LJbvQkREREREREREn4dPLsgWH5+Ad14+CtN9/XI6ybO1KaPWuhKTJJ3kRUZF4/otD6XLlC5lxSAbERERERERERHl65NvLvqx+9yqTH5u34foQ2HeIdIM8w6RZph3iDTDvEOkGeYdCY4uSkREREREREREVEQMshERERERERERERURg2xERERERERERERFxCAbERERERERERFRETHIRkREREREREREVEQMshERERERERERERURg2xERERERERERERFxCAbfTJ+PhSGWnPeIzA6vaR3hYiIiIiIiIhIDoNs9NG455WMWnPeY82lqJLeFSIiIiIiIiKiQmGQjYiIiIiIiIiIqIgYZCMiIiIiIiIiIioinZLeASIAWHMpCmuvRAMA1l6Jlv0PAGenVYSdpa7ss1gM7LoTg73ucQiMTkdpEx30bGCK0a0soaUlUlj35ZeJ2H0nFi+CUpGaIUZFKx10r2+GQV+ZQ1vJ8sp4eCVjy41ovAlJQ0xSJkwNtOFQWhdd65ri20ZmAIDA6HR0XOqHbvVM8WfvMgrrqDXnPRo6GGDrCDvZtGGbAnHfJwUPfnPC2stROOmZgOjETDiV0cUP7UuhibMR4lMyseJCFC6/SERschaq2ujhxy6lUau8gXoHl4iIiIiIiIiKHYNs9FFo5GiIwJgMHH8Uj4YOBmjkaCibZ2ogX+Fy2blI3PdORosqxmjqYoTLLxKx5nI00jPFmNSulNyyy89HYvP1GJQx08bX1Y1hYqCFh74pWHo2Ek/8U7Csf7kC9+3660RM2BkCUwMttK5qDGtTbUQnZuJ1SBpOPo6XBdmKYvq+ELwNSUOrqkZITRfj1JMETNgZgh0j7fD7sTCkZwLta5ogKjET554mYMz2YJydVhGmBtpF3jYRERERERERFR2DbPRRaOQkCaodfxSPRo6GGNfWSuWyL4JScWhiBVibSi7f0a0s0eUfP+y+G4uxra2gqyOpnXb7XRI2X49BUxdDLOtfDkZ6kmCdWCzG/BMR2O8RhwvPE9Cuhkm++3bkQTzEYmDLcFtUsdGXmxeTlKnxd84tNikLhyZWkO3jVy5GmLEvFCO3BqGJsyEWfVsWOtqS71XVRg//nIvC4QfxGNLUQpDtExEREREREVHRsE82+uSMbmUpC7ABgKWxNlpXM0ZiqhjeEWmy6XvuxgIAfu1uLQteAYBIJMKU9lYQiYAzTxLU3q6+rmLTUgsjYWqSTfzaSm4f29cwho42EJ+ShekdS8kCbADQuZYpAOBNcKog2yYiIiIiIiKioiuWmmxxcXGIi4uHmZkpzMyK3pSOKLfqdvoK08qaSYJd8SlZsmlP/FNhqCfCkQfxStdjoCOCd3ia0nm5daxtgosvEjFgfSA61zZB40qGqG9vCEtj4ZpqVs1TQ05LSwQrY22kpIthY6ErN6+0qWS7YfHC1KIjIiIiIiIioqITJMh27/4DXLp0BXc97uHVy9fIyMz58a+jo4NqVavAza0R2rZphUYNGwixSfqCmegrVsDUyR7AICsnxoa45ExkZEFuEIW8ktPEBW6vQ00T6A4QYcetGBy4F4e97nEQiQBXR0NM71RKIUCmCRMD5d/JRF+x9py0VltGZsH7TkREREREREQfhsZBtszMTOzddxD/7dqDd+/eQyzO+cFvbGwMUxMTxMfHIzEpCU+ePsOTp8+wafM2uLg4Y9CA/vhf397Q1man7VR8jPW1IBIBN35yLPK62lQzRptqxkhMzcIj3xRcfJGAIw/iMWZ7MI5PrgAzQ21IByrNzFIMfsWnsNYZERERERER0edMoyDbufMXsfjvf+Dj4wtDQwN06dwRTRq7om7dOnCu5AQdnZzVZmRk4O2793j82BN37nrg0uWrmPvbH9iybQdmTv8B7du1FezL0KdNK7syl1AVtGpVMMDNN0nwjUiDfWk9QdZprK+FZpWN0KyyEbKygCMP4/E0IBVNXYxkI32GxWUopHsVVHCzVCIiIiIiIiL6dGkUZBs3YQocHR2waOEf6NypA4yMjFRvILu5aLWqVdC/X18kJibi1OlzWL9xM8ZNmIJ3r59qvPP0eTE3lASpQmIVg1SaGNDYHDffJGHukXCsGFBOYZCCiPgMxCVnwalM/gG4+97JqGdvAG0t+aabUYmS2ml62aOZmhhowaG0Lh75psAvMh0VS0n6UktMzcLyC5GCfCciIiIiIiIi+jhpFGRbsuhP9Oj+DbS0Cj84qbGxMfp+2wt9evfA0WMnNNk8faYcrXVRxlQbZ58mQE9bhLLm2hBBhO+amMlqiRVGs8pGGN3KEuuvRqPzMj80czGCjYUOYpMy4ReVjoe+KZjQ1qrAINtfpyIQHp+BevaGsLXQgUgEPPJNwdOAVNSuoI/69gayZYc0tcDvx8IxcH0A2tc0QZYYuPkmCTXL6wPgaKBEREREREREnyuNgmy9enYv8oa1tLQEWQ99PrS1RPjnu3L451wkzjyNR2KqpN1o17omGgXZAGDC11Zo4GCA3Xdj4e6VhLiULFgYasPOUgdjW1uhax3TAtcxoqUlLj5PwIugVNx+mwQdbRFsLXTwQwcr9HM1l6vh1qeRGTKyxNh5OxaHHsTB2kQH3eubYlQrS1z41Uuj70BEREREREREHz9RZkYqhygsRlHhwQAAK2ubEt4TYXxu34foQ2HeIdIM8w6RZph3iDTDvEOkGeYdCY1HF1VHZmYm9h84hNdv3sLWxgb9+n0LM9OCaw4RERERERERERF9SgQJsq1esx4r/12DHVs3wc2tEQBALBZj8NARcPe4D7FYDJFIhP0HD+PooX0wMTEWYrNEREREREREREQfhcKPXKDEzVt3YGVlJQuwAcCly1dw1/0eKlQoj1kzpqJJYzf4+Phiz979QmySiIiIiIiIiIjooyFIkM3Pzx/OlSrJTTt77gJEIhFWrViKUSOHY8umtShduhROnj4jxCaJiIiIiIiIiIg+GoIE2aKio1G6dCm5affuPYStrQ1q1qgOANDV1UXdOrURFBgsxCaJiIiIiIiIiIg+GoIE2UxNTRAbGyf7HBoahoDAQLg2aiC3nKGhIZKSk4XYJBERERERERER0UdDkCBbxQoVcP/+A8TGxgIAjh0/CZFIhGZNv5JbLiwsDKVKWQmxSSIiIiIiIiIioo+GIEG2b/v0RGJSErr16Isx4yZj2T8rYWJigjatW8qWSUtLw9Onz+Hk6CDEJomIiIiIiIiIiD4aOkKspHevHrh1+y5OnT6LgMBAGBkZYsH832BmZiZb5tLlq0hMSkJjN1chNklERERERERERPTRECTIpqOjg5XL/8asGVMRGRUFJ0dHmJgYyy1jX7EC1q1Zibp1awuxSSIiIiIiIiIioo+GIEE2KTs7W9jZ2SqdV716NVSvXk3IzREREREREREREX0UBOmTjYiIiIiIiIiI6EsmaE02j3sP4O7ugdCwcKSlpSldRiQSYdHCP4TcLBERERERERERUYkSJMiWmJiIseOn4PaduxCLxfkuyyAbERERERERERF9bgQJsi39ZyVu3b6D8nZ2GDigH5ycHGFibFxwQiIiIiIiIiIios+AIEG2c+cuwtLCAocO7kbpUqWEWCUREREREREREdEnQ5CBDyKjotCwYX0G2IiIiIiIiIiI6IskSJDNxqZcgX2xERERERERERERfa4ECbJ1+6YL3D3uIy4uTojVERERERERERERfVIECbKNHzsK1atVwfARY/Hu3XshVklERERERERERPTJEGTgg2Hfj0Z6RgYePfZEp649YWtjA1tbG2hpiRQXFomwa8cWITZLRERERERERET0URAkyHbX/Z7s/6ysLAQEBiIgMFDpsiKRksAbERERERERERHRJ0yQINv1K+eFWA0REREREREREdEnSZAgm52drRCrISIiIiIiIiIi+iQJMvABERERERERERHRl0yQmmxSaWlpOH3mPDzu3UNISBgAoFy5MnBt1AidO7WHnp6ekJsjIiIiIiIiIiL6KAgWZHv0yBMTp0xDSEgoxGKx3Lz9Bw7j72XL8e+KZahbt7ZQmyQiIiIiIiIiIvooCBJk8/cPwNDvRyMhIQGVnBzRo0c3VKxQXjIvIABHj57Au/deGDZiNI4fOYAK2fOIiIiIiIiIiIg+B4IE2Vav3YCEhARMnDAWkyeOg0gkkps/ZtQIrPx3LVauWoM16zZg4Z/zhNgsERERERERERHRR0GQgQ9u3roNF+dKmDJpvEKADQBEIhEmTxwH50pOuHHzthCbJCIiIiIiIiIi+mgIEmSLiIhE1apVClyuWrWqiIiIFGKTREREREREREREHw1BgmymJiYICQktcLnQ0DCYGBsLsUkiIiIiIiIiIqKPhiBBtjp1auHBw0e4feeuymVu37mLe/cfoF69OkJskoiIiIiIiIiI6KMhyMAH3w8fgitXr2PEqPHo17cPunfrgvLlJSOIBgQE4Oixk9h34JBsWSIiIiIiIiIios+JIEG2Jo3d8NPsGVi0eBl27NyNHTt3y80Xi8XQ0tLCTz/OQGM3VyE2SURERERERERE9NEQJMgGAN8PG4LGrq7YtmMn7t9/gJDQMABAubJl0KhRQwwe9B1q1qgu1OaIiIiIiIiIiIg+GoIF2QCgRo1qWLLoTyFXSURERERERERE9NETZOADIiIiIiIiIiKiLxmDbEREREREREREREWkUXPRlq07QCQS4b/tm1ChQnm0bN1B7bQikQhXL5/VZLNEREREREREREQfJY2CbAGBgQCA9IwMuc9ERERERERERERfIo2CbO/fPMv3MxERERERERER0ZeEfbIREREREREREREVEYNsRERERERERERERcQgGxERERERERERURFp1Cebc5VaGm9QJBLh7asnGqcnIiIiIiIiIiL62GgUZLOxKQeRSCT0vhAREREREREREX2SNAqy3bh6Qej9ICIiIiIiIiIi+mSxTzYiIiIiIiIiIqIiYpCNiIiIiIiIiIioiDRqLhoYFFSkjdrZ2hYpPRERERERERER0cdEoyBbi1btNR74gKOLEhERERERERHR50ajIJtro4bg4KJEREREREREREQSGgXZ9uzaJvBuEBERERERERERfbo48AEREREREREREVERMchGRERERERERERURBo1F/XwuA8AqFOnFvT19WWf1eXq2lCTzRIREREREREREX2UNAqy9R84FCKRCOfPnoCTo4Pss7revX6qyWYBAN4+/nj64jW8vHzx3tsPEZFRAICVS36DtXWpQq0rLj4BDx49hZe3H957+8LPPwiZmZno3b0T+vTsrPE+EhERERERERHRl0WjIFuvHt0AkQimpiZynz+Ew8fO4P4jzYN0ub1+8x4btuwWZF1ERERERERERPTl0ijItmTxgnw/FycXZ0dUKG8LJ8eKcHKsiJ9+XYzYuHiN1mVuboav2zSDk0NFODlUwI3b93Dq7GWB95iIiIiIiIiIiD53GgXZSlK3Lu0EW1dlZ0dUdnaUfb5994Fg6yYiIiIiIiIioi8HRxclIiIiIiIiIiIqIkFrsgUGBcHD4z5CQ8OQlpamdBmRSISJE8YKuVkiIiIiIiIiIqISJUiQLT09Hb/8+gcOHT4KsVgMsVisctnPNcg294+lSqf3/qY1bMqWRlR48Afeo+KRkZEOAJ/N9yH6UJh3iDTDvEOkGeYdIs0w7xBp5nPMO1bWNoVOI0iQbdnyVThw8DAsLMzRvVtXODjYw9jISIhVExERERERERERffQECbIdO3YS5uZmOHnsEGxsygmxyk/OvF+mKZ0ujeJqEgH9GH1u34foQ2HeIdIM8w6RZph3iDTDvEOkGeYdCUGCbDGxsWjRvOkXG2AjIiIiIiIiIvoSJael4Z6/NxJSU2AbH4e65R1hqKdX0rtVIgQJslWsUB4ZGRlCrIqIiIiIiIiIiD5yQTFRWHv9LHa5X0dMcqJsuoWhMQa4tcC4lp1gY25Zgnv44WkJsZI+vXvC3eMewsPDhVgdERERERERERF9pJ4G+qL1sl+w+uoZuQAbAMQkJ2L11TNotXQOngb6ltAelgxBgmzfDx+C5s2aYsCg4bh1+26+o4sSEREREREREdGnKSgmCn3WL0Z4Qly+y4UnxKHP+sUIjo3+QHtW8gRpLioSiTB/3q/4btAwDBk2Ejo6OrC2Lg0tkWIMTyQS4erls0JsVi1R0TH4c9EqAMDPsybCytLig22biIiIiIiIiOhzsvb62QIDbFLhCXFYe+0s5nXrX8x79XEQJMjm4+uL/gOGIjw8AmKxGOnp6QgKChZi1QoePn6GI8fPyT4nJCYBAJau2gRdHcnXqVenBnp17wgAyMzIRFBImOz/vH6Zt1T2f2SUJLp65fodeD59KZv+x1zlI4cSEREREREREX0pktPSsMv9eqHS7PK4htkde38RgyEIEmT7a9FShIWF45uunTF65HDY21eEkZGREKtWEB+fgHdePgrTff0CZP/b2pRRe33K1hUVHYOo6BgN9o6IiIiIiIiI6POSnpmBp4F+2Hf/pkIfbAWJTkqEZ4A3GjtVKaa9+3gIEmRz97gH50pOWL5ssRCry1fL5o3RsnljtZe3ti6FPdtWqZyf3zwiIiIiIiIioi9NXHISPHzewd37Ndy93+Kh/3skpaVpvr6UZAH37uMlSJAtK0uMqlU//4gkEREREREREdHnRCwWwy8qHO7eb+Hu8wbu3m/xMiRA0EEtzQwMBVvXx0yQIFutWjUQEBAoxKqIiIiIiIiIiKiYSJt+unu/gbvPG3h4v0VIXEyxbc/SyBh1yjsW2/o/JoIE2SaOG4NBQ0fg3PmL6ND+ayFWSURERERERERERRSbnAgPn3fw8H5T5KafJvoGsDAyRkB0pNppBri2/CIGPQAECrKJRCIMHvQdJkyaim+6dkaL5k1RrlxZaIm0lC7v6tpQiM0SEREREREREVE2sVgM36hwSS0177dw936DV6GBGjf9LG9ZCm4OleHm6AI3x8qoblMBYfGxaLV0DsIT4gpMX8bUHGNbdtRo258iUWZGapEb2VaqXBMikUh20kQiUb7Lv3v9tKib/GREhQcDAKysbUp4T4TxuX0fog+FeYdIM8w7RJph3iHSDPMOfWrSMzPwJMBX1peah/cbhMbHarQuLZEItezs4ebgAlfHynBzrAw7Cyulyz4N9EWf9YvzDbSVMTXHwVEzUNPOXqP9+RQJUpOtV49uQAGBNSIiIiIiIiIi0lxMUiLu+byFu4+kltpDPy8kp2ve9LORgwvcHCS11BrYV4KJvoFaaWvZ2ePqtPlYe+0sdnlcQ3RSomyepZExBri2xNiWHWFjbqnRvn2qBKnJRqp9bm9CPrfvQ/ShMO8QaYZ5h0gzzDtEmmHeoY9J7qafd70kgxS8Dg0SpOlnY8fKqGZTAdpayrv5KozktDTceHYf8akpsCtrhzrlHb+YPtjyEqQmGxERERERERERaU7Ipp/aWlqoaVtRraafRWWop4eGFSSjh37pAWoG2YiIiIiIiIiIPrCPpeknCUejINu+/QfxbZ9e0CpCtcLMzEwcPHQE/+vbR+N1EBERERERERF97MRiMXwiw2Qjfrr7vMGrkECN11fBsrRsxE83BxfBmn5S0WgUZPtpzm9Yv3ELRo/6Hl06dYSJibHaaePjE3Dy1Gls2LgF/gGBDLIRERERERER0WclLSMDTwJ9ZEE1D5+3CCtC089atvZwzRVUsy2mpp9UNBoF2Tas+xcLF/2Nn37+FfP+WIi2bVqhsZsr6tSuhUqVHGFgkFMlMTk5Ge+9vOHp+RR37rrj8pVrSEtLg6ODAzas+1ewL0JEREREREREVBJikhLhkd3sU9r0MyUjXaN1mRoYopG9sySg5uiC+hXZ9PNToVGQrW2bVmjVsjn2HziEHTv34NTpszh95pxsvp6eHoyNjJCQmIj09JyLSiwWo0oVFwwe+B2+7dML2traRf8GREREREREREQfiFgshndEWPYABZJBCl6Hat70s6JVadmon26OlVG1XHk2/fxEaTzwgba2Nvr364v+/fri4aPHuHT5Kjw87uHlq9dITk5BamoqAMDQ0ADVqlaBq2sjtG3TCvXr1RVq34mIiIiIiIiIihWbfpK6BBldtH69unLBs+TkZMTFxcPMzBSGhoZCbIKIiIiIiIiIqNhFJybgnu87Nv2kQhMkyJaXoaEhg2tERERERERE9FFj008SUrEE2YiIiIiIiIiIPjZpGRnwDPCBu88beGQ3/wxPiNNoXdpaWqhtZw/XXEE1G3NLgfeYPiUMshERERERERHRZyk6MSFn1E+ft3hUhKafZgZGaOTgLAmoOVRG/YpOMGbTT8qFQTYiIiIiIiIi+uSJxWJ4RYTKmn26+7zBm9Agjddnb2Ut60vNzaEyqpSzY9NPyheDbERERERERET0yUnNSJc0/fSWNP308HlbpKafdewcZKN+ujq4sOknFRqDbERERERERET00YtKjM9u+ilp/vnI3xupbPpJHxEG2YiIiIiIiIjoo5K76efd7Oafb8OEa/pZtZwdtNj0kwTGIBsRERERERERlai8TT/dfd4gIiFeo3XpaGlLRv3Mbvrp5lgZ5cwshN1hIiUYZCMiIiIiIiKiD0ropp+uDs6ygFr9ik4w0tMXeI+JCiZokC0lJQVPnj5HWFgY0tLSVC7Xq2d3ITdLRERERERERB8psViM9+EhcPd5Kxv5syhNPx1KlZE1+3R1dEHVsmz6SR8HwYJsq9esx/qNW5CUlKRyGbFYDJFIxCAbERERERER0WcqNSMdj/294e79Fh4+b+Dh87ZoTT/L20tqqWUH1dj0kz5WggTZtu/YhWXLV0FLSwstWzRHpUqOMDY2FmLVRERERERERPQRi0yQNv18A3eft3hchKaf5oZGcHXI7kvNwQX12PSTPiGCBNl27d4LXV1d7N65FfXr1RVilURERERERET0kRGLxXgXHpLd7FNSS+1tWLDG62PTT/qcCBJk8w8IRKOGDRhgIyIiIiIiIvqM5G366e79FpGJwjT9dHN0QVk2/aTPiCBBNjMzU1haWgixKiIiIiIiIiIqIWz6SaQ5QYJsLZo3w113D2RlZbFaJxEREREREdEnQOimn46lysDNUdLs082xMqqUsWWMgL4oggTZZkybgm49v8XvfyzAz7NnQk9PT4jVEhEREREREZFAUtLT8DjABx7eb7Kbfxat6Wed8g6SWmqOLnB1YNNPIo2CbCtXrVGY1rJFc+zavQ9Xrl5HYzdX2NraQEskUlhOJBJh4oSxmmyWiIiIiIiIiNQUkRCX0/TTW9L0My0zQ6N15W762dixMupVcIIhK9gQydEoyLZi1RqIRCKIxWKFeYGBQTh0+KjKtAyyEREREREREQlLLBbjbViwrC81D+83eBceovH62PSTqPA0CrIt/mu+0PtBRERERERERGpKSU+Tjfrp7iPpTy0qMUGjdelqa6O2XU7TTzfHyihjai7wHhN9/jQKsvXu1UPg3SAiIiIiIiIiVYRs+mlhaCypoZbd/JNNP4mEIcjAB0REREREREQkDKGbfjqVLps9OEFluDlWRuUyNmz6SVQMBAmyxcbG4vXrt7C3r4iyZcsoXSYkJBR+fv6oWrUyzMzMhNgsERERERER0SdP6Kafdco7Spp9Okj6VGPTT6IPQ5Ag25atO7B67QYcO7xfZZAtIjIS3w0ahkkTxmLSxHFCbJaIiIiIiIjokxORECdr9unu8wae/j4aN/20NDKBq4Nzdn9qlVG3vCObfhKVEEGCbFeuXoejgwNq1KimcpmaNarDwcEel65cZZCNiIiIiIiIvghisRhvwoIkATVvSS219wI1/WzsVBku1mz6SfSxECTIFhAYiIYN6he4nJOjAx4+fCzEJomIiIiIiIg+OinpaXjk751dU+0NPHzeITqJTT+JvgSCBNlSUlJhYGBQ4HIGBgZISEwUYpNEREREREREJS48PlbW7NPd+y08A7yRnpmp0brY9JPo0yZIkK1s2TJ4+ux5gcs9ffYcZcpYC7FJIiIiIiIiog8qKysLb7JH/fTIDqp5RYRqvL5K1uXg6uCSHVRzYdNPok+cIEG2r5o0xr79B7F7z35817+v0mX27N0PPz9/9P22lxCbJCIiIiIiIipWyWlpeBwgXNPPuhUc4eZQObtPNRdYs+kn0WdFkCDbqBHDcPTYCfz6+3y8ev0afXr3hKODAwDA28cHhw4fxZ69B2BgYICRI4YLsUkiIiIiIiIiQUUmJuBBgA+e37lS5KafVsYmklpq2UG1uhUcYaDLpp9EnzNBgmz29hWxfNli/DBtJnbv2Y/de/bLzReLxTAwMMCyv/+Ck6ODEJskIiIiIiIi0pjQTT+drcvB1bEy3LKbf7qUsYFIJBJwj4noYydIkA0A2n3dBmdOHsWmLdtw6/ZdBAUFAwBsbW3QrGkTDB86GBUrVhBqc0RERERERERqS05LwyN/L0nTT5+38PB+i5hkzQbm09PWQZ0KDmz6SURyBAuyAUCFCuXx+69zhFwlERERERERUaGFxcdm96X2Fu7eb/Ak0IdNP4moWAkaZCMiIiIiIiL60LKysvA6LEjS9DM7qOYdGabx+tj0k4g0wSAbERERERERfVKS09Lw0O893H0kAbV7Pu+K1PSzbgVHuDm6oLqVNeqXt4eLvYvAe0xEXwJBg2ynTp/F2XMX4O3tg4SERIjFYoVlRCIRrl4+K+RmiYiIiIiI6DOWt+mnZ4APMrI0b/op60vNsTLqlneQNf2MCg8WcreJ6AsjSJAtMzMTY8dPxuUr15QG1gBJcE3VPCIiIiIiIiJA+KafLmVsJP2pOVaGm2NlOFuXY9NPIioWggTZtu/YhUuXr6Jli+aY+8uP+Hf1Ohw9dhIvnz2Er68fjh4/iS1bd2D4sMGYPnWyEJskIiIiIiKiz0BSWioe+XkJ1vSzXkUnWV9qrg4uKGViKvAeExEpJ0iQ7fjJUzA1NcWKf5bA1NQEIpEWAEBXVxfOzpUwfepkNGpYH9+PHAcX50ro3q2rEJslIiIiIiKiT0xoXIyk2afPG8monwG+Gjf9LGVsml1LTbHpJxHRhyZIkM3rvTfq168LU1MTAJBVvc3MzIS2tjYAoGWL5qhdqyb+27mHQTYiIiIiIqIvQFZWFl6FBkqaffpI+lTzYdNPIvpMCdMnW1YWzM3NZZ8NDQ0AAHFx8bC0tJBNr1ixAi5fuSbEJomIiIiIiOgjk5SWiod+XtmDFLzBPd93iE1O0mhd+jq6klE/2fSTiD4RggTZypSxRnh4uOyzna0tAODV69do0thNNj0gIBA6OtpCbJKIiIiIiIhKWEhcjCyg5uH9Fk8Ci9b0083RBa7ZI3/WreAIfR1dgfeYiKj4CBJkq1qlMh499pR9btzYFWKxGCtWrkGtmjVhYmKMo8dO4tFjT7g2aijEJomIiIiIiOgDEr7ppy3cHLObfjq4oBKbfhLRJ06QIFurVi1w/sIl3Lp9F02/aozatWqiVcsWuHrtOhq4fgVjY2PExcVDJBJh0sSxQmySiIiIiIiIipHQTT/rVXDM7kvNBY3s2fSTiD4/ggTZenTrinp166B0qVKyaatW/I1FS5bh7LkLiI2JReXKzpgwboxc81EiIiIiIiL6OAjZ9LO0iSncHCrDNbumWp3yDmz6SUSfPVFmRqq4pHficxYVHgwAsLK2KeE9Ecbn9n2IPhTmHSLNMO8QaYZ5hwqSlZWFVyGBsmaf7t5v4BsVXnBCFT6Xpp/MO0SaYd6REKQmGxEREREREX28ElNTJE0/s4Nq93zeIS5FmKafrg4usDJm008iIkGDbJmZmbhy9Roeez5FdFQ06tSpjb7f9gIAhIaGISo6Gi7OlaCjw9geERERERFRcQmOjc5u+vkWHj5v8DTQj00/iYiKmWDRrgcPH+GHabMQFBQMsVgMkUiE9IwMWZDtwcNHmDRlOlav+gcd2n8t1GbpCzJj5k84dOQYrl85j/Ll7Up6d0rM8pWrsXLVGuzeuRWN3VxLeneIiIiIqIRJm37e9X4tafrp8wZ+UREar69yWVu4OVSWNf90Kl32k2z6SUT0oQkSZPPy8saw4aORmpaKwQO/Q8OG9TFx8jS5Zdq2aQ0DA32cO3+RQTZS6q67B74bOAyTJo7DlEnjS3p3ZAICAtGidXv07tkdSxYv+Oy2R0RERESflsTUFDzwe59dS61oTT8NdHRRr6IT3BwkAbVGDs5s+klEpCFBgmyrVq9DUnIyNq5fjdatWgCAQpBNX18P1atXw/PnL4TYJBERERER0Rchb9PPJ4G+yMzK0mhd1iZmcHPMbvrpIGn6qcfufIiIBCFIaXrnrjtq16opC7CpYlOuHN68eSfEJomIiIiIiD47mVlZeBUSIAuqCdX0s7FTZbg5VIZj6TJs+klEVEwECbLFRMfAtVHDApdLTU1FWlqaEJukz4y0nzEAWLlqjex/AAp9sInFYmzbvhM7d+9FgH8ASluXxrd9emHi+DHQ0tJSWPeFi5exbcdOPH/+EikpKXCwr4jevXpg+LDB0NbWzne/Dh46gpk/zgEAHDpyDIeOHJPNk/aJlruPtICAQGzfsQvvvbxRp3Yt7Nm1Ld8+1KTrX/zXfPTp3VOt7eV27PhJbNi4BV7ePjAzM0WXTh0xc8YPMDAwyPd7EREREdHHIXfTT3fvN7jn+w7xKckarStv009XBxdYGpsIvMdERKSKIEE2Kysr+Pn7F7icl5c3ypYtI8Qm6TPT2K0RAgO649CRY3BzbQQ3t0ayeWZm8n1CLFz0Nzw87qN165Zo0awpzl+8hBUrVyM9PR3Tp06WW3bx3/9g3fpNKFe2LDq0/xqmpia4d/8BFi76G489n2D1qn/y3a/q1api6JCB2LZ9J6pVrYJ27drK5pW3kx98YcPGrbjr7oGv27ZGs2ZfFRjAK+r2dvy3G9dv3MTXbdugSRM3XL9+E9t27ERUdDSWL1tc6G0TERERUfELiomCu48koObh/RZPg9j0k4jocyFICdywYX2cPnMOjx55ol69OkqXuX7jJt57eaNf3z5CbJI+M9IaWoeOHIObW6N8Bz54/vwlTp88gjJlrAEAE8aPQZt2nbFjxy5MmjAWenp6AIAbN29j3fpNaNG8Kdb8uxxGRkYAJDXhfvn1D+zesw9nzp5Hp47tVW6revVqGG5mhm3bd6J6tar57pfHvXs4fHAPqlapXOjvr8n2bt2+i+NHDsDJyREAkDJ1Mrp0642Tp85g9qzpDGgTERERlbDMrCy8DPaXBdXcvd/CP1rzpp9VytrJRvxk008ioo+PIEG2kSOG4ey5Cxg1ZgJ++/VntG3TSjYvMzMTV65ew8+//A5dXV0MGzpIiE3SF2zC+DGyABsAWFlZol3b1jh05Bi8vH1kQa7/du4GAPw5/zdZgA0ARCIRZs74AXv27seJk6fzDbIVRr//fVukAFthDR0yUBZgAwADAwN807UzVq5ag2fPnjPIRkRERPSBJaSm4IHve0lAzect7hex6Wf9ik6SgJpjZTSyd2bTTyKij5wgQbZaNWvg919/xq+//4nJP8yAjrY2RCIRTp46gxMnTyM9PR1aWlpYMP83ODtXEmKT9AWrWbO6wrRy5coCAOLi4mXTHj1+AiMjQxw4eETpegwMDODl5S3YftWpXUuwdamjlpLjYCM9DvHxCvOIiIiISFiBMVHZzT4lQbVnQX4aN/0sY2oO1+y+1NwcXVDbjk0/iYg+NYKV2v379UX16tWwdt0m3L59B+lJSUhNTYW+vj5atmiOiePHoG7d2kJtjr5gpiaKb/C0sx9AsrIyZdNiY2ORkZEhN4hCXknJmr1ZVKZ06VKCrUsdJsqOQ3Y/cJmZmQrziIiIiEhzQjf9rFrODm4OlWVBNYdSbPpJRPSpEyTI9vLVa2hpaaFO7VpYt2YFxGIxoqKikSXOgpWlpUYdwBMVlYmJMUQiER543Pog21P1UKSVPT0jQzHwFR+fUKz7RERERPSlSE5Lw+MAb8SnJMPUwBB1yzvCMLuvXk0UV9PPxo6V0cjBBRZGxhrvGxERfZwECbJ17dYbjRo2wN7d2wFIgg2lSlkJsWr6gmhpSYKxWQLVwqpTpzauXbsBbx9fODrYa75f2loAoHHVf3NzcwBAaGiowrwXL14Kvj0iIiKiL0lQTBTWXj+LXe7XEZOcKJtuYWiMAW4tMK5lJ9iYWxa4Hjb9JCKiohKkpDc3N4ONTTkhVkVfMAsLSTAqODhEkPUNHTwA167dwKzZc7B+zSpYWlrIzQ8PD0dsbFyB/QSam5lBJBJpvF+1a9UEABw+ehw9e3SDlpYkiPbw0WMcO3FK8O0RERERfSmeBvqiz/rFCE+IU5gXk5yI1VfPYP/9Wzg4eiZq2eW8dM3MysKLYH9Zs093nzcIiI7UeD/Y9JOIiACBgmw1a9TA+/deQqyKvmCVnBxRtmwZnDh1Bnp6eihXrixEIhEGDx4AM1PTQq9P2hfgqtXr0PrrTmjRvCns7GwRExMDX18/3Lv/EFN/mFhgkM3Y2Bi1a9WEx737mDr9RzjYV4SWlhZ69ugGOzvbAvejXr06aNCgHu7ccUfvb7+Da6OGCAwKwsWLl9GmdSucv3BR0O0RERERfQmCYqJUBthyC0+IQ+/1i/FXz4F4GxYMd29J08+E1BSNtmuoq5cz6qeDC5t+EhGRjCBBtlEjh2Po8FE4kl1Th0gT2traWPvvCixasgwnTp5GQqKkun+P7t9oFGQDgB+mTESjRg2xfcdO3Lnjjrj4OFhYWKBC+fKYPHEcunfrqtZ6lv79F+b/uQiXr1xDfHw8xGIxGjasr3bQa8Paf/HnQkn612/eolrVKti4fjVCw8IUgmxCbI+IiIjoc7f2+tkCA2xSEQlxGPGf6sGw8lPW1Byu2TXU3Bwqo3Z5e+hqs+knEREpEmVmpIqLuhIPj/s4fvI09u47gBbNm6Hd161hZ2cLA30Dpcu7ujYs6iY/GVHhwQAAK2ubEt4TYXxu34foQ2HeIdIM8w6RZj73vJOcloYav0+S64NNCCKRCFXL2smafbo5Voa9lTWbfn5BPve8Q1RcmHckBHkF03/gUIhEIojFYly7fgPXb9zMd/l3r58KsVkiIiIiIvoCPfL3EiTAZqirhwYVK8HN0QWujpXh6uAMc0M2/SQiIs0IEmTr1aMb8IHe7nj7+OPpi9fw8vLFe28/RERGAQBWLvkN1talNFrn0+evcOL0JXj5+CEjPQO2NmXRptVXaNuqKd9aERERERF9BOJSknH19TNcePkYp54+0GgdFoZGaO5SA26OLmjsWAW17Cqy6ScREQlGkDvKksULhFiNWg4fO4P7j4SrCXfxyk1s2bEfIpEINapVhqGBPp4+f43N2/fhzTtvjBs5SLBtERERERGResRiMV6HBuHCy8e4+NITd7zeICMrs0jr3DX8BzSpVFWgPSQiIpL3yb22cXF2RIXytnByrAgnx4r46dfFiI2L12hdoWER2LbzILS0tPDzzAmoVsUZABAVHYvfF/yDG7c8UKdWNTRt/OX0IUdEREREVFKS0lJx891LnH/xGBdeesI/OkKwdVsaGaNuBSfB1kdERJTXJxdk69alnWDrOnP+KjIzM9G+bXNZgA0ArCzN0f/b7lixZgtOnL7EIBsRERERUTHxjgjFhZeeuPDSEzffvURqRnqxbGeAa0sY6ukVy7qJiIiATzDIJqSHjyXNThu71leY17B+Lejq6sLXLwARkVEoXcrqQ+8eEREREdFnJzUjHXe8XksCay8e4114SKHSu5SxwdfV6qBBRSfMPrIT4QlxBaYpY2qOsS07arrLREREavlig2xJSckIj5AMmuBoX0Fhvo6ODsrblYO3jz98/QIZZCMiIiIi0lBAdCQuvvLExZeeuPbmORLTUtVOq6+ji2bO1dC+Wl18Xa02HEuXlc1zKWOLPusX5xtoK2NqjoOjZsDG3LJI34GIiKggX2yQLTx7VFJjI0MYGOgrXaaUpQW8ffxlI5jmZ+4fS5VO7/1Na9iULY2o8GDNd/YjkpFdff9z+T5EHwrzDpFmmHeINFPSeScjKxOPAv1w9f0rXHv/Cq/CCrcfduaWaFWpKlpVqorG9pVgqJvdzFOcJfed7PT0cHToRGy9dwMHPO8hNiVZNs/CwAh96jTEsEbNUVZPj+UIqaWk8w7Rp+pzzDtW1jaFTvPFBtlSUyRvz/T1lQfYAEA/O/iWnKL+mzYiIiIioi9RZGICrr1/hater3DT+y3icgW8CqKjpYWGFRxlgbVKpcpAJBKplbasqTl+bNMVU5p3wLOQAMSnpsBU3wA1y5WHga6upl+HiIio0L7YIJvQ5v0yTen0qPBghIVHYOzEGQWuo2ePbpg0cZzsc1xcHLr37KvW9q9cOiv3ef6CRbh06UqB6SaMH4PevXrIPr948RLjJ/6gcvnMTMmw6RUqVsCuHVvk5o0ZNxmvX78ucJt/LfgDbm6NZJ/PX7iEhX8tKTCdq2sjLFr4h9y0b7r3QUJCQoFpd+7YAjs7W9nnrdv+w47/dhWYrlfP7pg4Yazsc0xMDHr27ldgOkDxnPzx51+4fPlqgekmTRyHnj26yT4/f/4SEyapPidSduXtsHP7ZrlpY8ZNwuvXbwpMu2jhfLi65gzwce78Rfy16O8C07m5ueKvBfPkpnXt1huJiYkFpt353xbY2eack81bt2Pnzj0FpuvdqwcmjB8j+xwdHYNefQo+JyItLVy+cFpu2rw/FuLK1WsFpp08aTx6dP9G9vnZ8xeYOGlqgekqVKiAHds2Ash5o/PjL3/i7dt3BaZd9NefcG3UQPb57LkLWLRYeY3Z3Jo0ccOC+b/LTevyTS8kJSUVmHbXzq2wtcl5W7Npyzbs2rW3wHR9evfE+HGjZZ+joqLR+9v+BabT0tbGpfOn5Kb9Pm8Brl67XmDayZMmoEf3rrLPT54+w+Qp0wtMV7FiBWzfulFu2sjR4/Hu3fsC0y5ZvAANG+T04Xn6zDks+fufAtN91aQx/pz/m9y0zt/0RHJSwT8+9+zajnLlcppFbdy0Fbv37CswXZ8+vTB+7CjZ58jIKPTp+12B6XR0dHHh3Am5ab/N+xPXrt0oMO2UyRPQvVvOOfF88hRTfij43mdvXxHbtmyQmzZi1Di8f+8FIOe+o62trZD27yUL0aB+Pdlntc/JV03w5x+/yk3r2KUHUlNSCky7d/cOlC1bRvZ5w8Yt2LN3f4Hp+vbtjbGjR8o+h0dEoO//BhaYTldXF+fPyp+TX3+fj+vXbxaYduoPk/BN186yz48fP8EP02YWmM7BwR5bN6+Xm/b9yHHw8vIqMO2yvxehXr06ss+nTp/F30uXF5iuadOvMH/eXLlpHTt3R2pqwS869+35D2XKWMs+r9+wGXv3HSgw3f/69sGY0SNkn8PDw9G336AC0+np6eHcmeNy0375dR5u3rxdYNppUyeja5dOss/qnhNHR0ds2bRWbtrwEWPh7e2tMo0076xcvhR169aWTT956gyWLltR4DabNfsKf/wuf046dOqGtLQ02WcxJP2rJaelISktFakZGQCAhKaVITbICW7pvwuBnm+kwjZ0tLRgqKcHIz19GOrqoXvlFhjdNeeeHhYWjv/1L/ic6Ovr4+zpYwAAW9uKAIA5c+dh4a2Cz8n0aVPQpXNOX22PHnli6vRZBaZzcnLC5o1r5KYN+340fHx8C0z7z9LFcufkxMnTWPbPygLTtWjRDL//OkduWvuO3yA9veCBIvbv2wnr0qVln9eu34j9+w8VmK5/v74YNXK47HNoaBj6fTe4wHT6BgY4e+qo3LSff/kdt2/fKTDtjOk/oHOnDrLPDx4+wvQZswtMV6mSEzZtkD8nQ4ePgq+vX4Fpl/+zBHVq1wIgeWY7e/4SNm7ZWWC6li2b47e5P8tNa9fhG1mNnvwc3L8bpXJ1SbR67QYcPHi4wHTf9f8fRo4YJvscEhKK/gOGFJjO0MgQp08ckZv285zfcPvO3QLT5j0n9x88xIyZPxWYztm5EjauXy03bciwkfDz8y8w7Yrlf6N2rZqyz0ePncSKlf8WmK5Vyxb4da78vrVt3wVZ2WVifg4d2AMrq5xm5avXrMfBQ0fySSExYEA/jBg+VPY5KDgYAwYOU50gm5GREU6dkD/nP835FXfuuBeYdtbMaejYIWdQSI97DzDrx5/zSSHh4uKMDevkj+PgoSPh71/wOVm1chlq1qgu+3z02AmsWJlzflU9s7Vu1RJzf5HPw23adYY4K6vAbR4+uBeWlhayz/+uXodDh48WmG7gwP74flhOvggMCsLAQcPzSSFhbGyMk8cLLhvz88UG2aS11PJ7eJPWdjNU0ZxUXZmZmfBVoyCJjo6R+5yVlaVWOmUiIiLVShsfLx+gSktLVyudto7ipRMSGqJW2uQ8P2KSkpLUSufg6KAwzT8gEHFxBXd2m57nRhcTG6vhORF/gHMSL/c5NS1VrXQiLS2FacEh6p2TlFT5c5KYmKhWOkdHR4Vp/gGBCt9BmcwM+RtdbIx65yQqOlrus7r5REvJ8YmIVO+cxOU9J6nqnROl+SQkVK20eX/sq3tOKlVyUpjm7x+ABDUCn5qek+iYGPn1ZKlX5ikLmqh7TvIG19U9Jzo6ijUa1D0nKXlqNScmqld2OTtXUpjm7x+AxMSCA5+ZeR4I1S27YvKckyw1z4muruI1Gx4eoeY5kb/GUlPT1Eqnp2Skv+BgNcuuPOckIUG9fFK5sovCNH//ACQnFxz4zMjMkPus9jmJjpX7nJWpXtmlp6QWjvrnRLN8oqyWf3BIsEb3k4SEBLXSVakSoTDNzz8AKWoEPvPmk+iYGI3ySaaa50TZ8QkP16zsSklNUSudgaGhwjRN7/Fqn5NwxaCYn39Avs/OslJdLJabLkrLgHaSYjoxgCQkQ1oaKtxP1HyGNjAwUJgWEaFZPlH3nBgaKTknapZdeY+huuckPFxJPvHzR5oaQbasTPkfszHRapZdsfJlV0ZmhnrHR8k1q/45kb+fpKSoV3YZGRkpTFP/nKTJfU5Q8x6v9Jz4+yE9PUPJ0vKysvLc49Utu/KcE3XzibGx4vEJCw9XK23eZxZ1z4mJiYnCtKCg4s0nEZFKyi4/f4V7hTKZWZrdT2Jj8pyTDPXOiYmxscK0sDB1z0me564U9couU1NThWlBwerd4/Oek7j4+CKdkyw1gmx5l4mKji7Wc6Ls+BSW4EG2l69ew9PzCaKiY1DZpRK+btsGgKTgSktLg6mpYkYrCdbZbw0Sk5KRkpKqtF+2yOwAS1EHPdDW1oZ9RcXBFfLKHaEFJEEBddIpU7p0KbXS5j0fenq6+aaTFk62toptk8uVLYfYirEK0/MyzPMgZGRkpNa+lrG2VphWobwdEhLMC0yrm+eHtYW5uYbnRPQBzol8xtbX01crnbJzYlOuHOJiCw5CGujLnxNjY2P1zkkZ5edEnZps2jryARZzC/XOiZWlfKfF6uYTZUHI0qXUOydmec+JvprnxEZJPilXVq0gpL6BgOekQnm1arJpek4sLSzk16OlXpmnpSTIpu45yfvQpvY5sS2nMK1cubJq1YjNe58wNi5C2VWhvFo12fIGItUtuyzynBMtNc+JsiCktXVpNc+J/IOivr6eWulsbBTPiY1NOdk1m19NtrznxMREvXxSOldtDqkKFcqrVZNNR1v+0Untc2Ipf6/S0lav7NJVEmRT/5xolk+UnpNyNmpds3nvJyYmJhqfk4oVyqtVky3vtWFpYaFRPtFW85woCwxbW2tWdhnoG6h3TsopOyflkJJPYFiadzQ9J9bWpQAAYrEYz4P8cOGlJ8TG+sjSlgTH8pWrmaeVsQkqOTojKkEMQz19aOfTBFThfqLmM7SywGfp0prlE/XPiZLnLptyal2zefdX/XOiJJ9UrKBWTTYtbfnnIAtLNcsuc/myS0dbR71zoiTwqf45kb+fGBgUoeyyKSdX+1IVfX35fG2i5j1e6TmpUFGtmmxaWnnu8eqWXXnOibr5RFlguIy1tVpp8wbo1D0nuWvjS9nallPr+GiaT0qXKqUwrWLFCmrVZNPW0ux+Ym6R55zoqHdOlAWGy5RR95zkee4yUK/sUnpObGyQmVFwYDjvOTEzNZXbpqpnNlXnRJ2abHkrSlhZWhbrOcl7XDUhysxILfA+qQ5vH1/MnPUTHj1+IpvWq2d3LP5rPgBg/4HD+GnOr9iyaS1aNG8mxCYBAGMm/YTYuHisXPKb7GFAXZOm/4rwiCjMnT0Z1ao4y83LyMjA8LEzkZ6ejlVLf9c40CZtIqZJh3kfo8/t+xB9KMw7RJph3iHSTFHyTnxKMq6/fY4LLz1x4aUngmOjC06US93yjvi6Wh20q1YH9Ss6QVvJiy6ijxXvO0SaYd6REKQmm7RtfkREJNq2aQXXRg2xME9/Tl06d8Dc3/7AuXMXBQ2yFUX9urVw7uI13PV4qBBku//wKdLT02FfsXyRa7IREREREX2sxGIx3oWH4PyLx7j40hO3vV4hXY1aH1KmBoZoXaUm2leri7ZVa6OsmUXx7SwREdFHTJAg26rV6xAREYn58+aifz9JR/15g2zGxsaoXq0qHnl6CrFJtUVFx+DPRasAAD/PmgirXM3/OrVviYtXbuLS1dto7FpfFmiLio7FngOSzlO/6dz2g+4vEREREVFxS05Lw833L3Exu7aaT2RYodJXK1ce7bJrq7k6ukBX+4vt6pmIiEhGkLvh1WvXUaWKiyzApkr58na4pcaIMvl5+PgZjhw/J/uckN0J49JVm6Cb3cl4vTo10Ku7ZJSgzIxMBIWEyf7PrWwZawwd2AdbduzH/EWrULN6ZRjo6+Ppi9dITk5B868aoWnjhiAiIiIi+tT5RobJmoDeePsCKWr0iyRlpKeHFi41ZIG18paKfVERERF96QQJskWER6Be3ToFLpeZmYkkNTrNzU98fALeefkoTPf1C5D9b2tTRu31fd26GcqWKY3jpy/i3XtfZGRmwNamLNq0bIqvWzct0r4SEREREZWUtMwMub7V3oQGFSq9U+myaFetLtpVr4OvnKrAQFdx0AciIiLKIUiQzdTMDCGhoQUu5+vrh1JF7N+sZfPGaNm8sdrLW1uXwp5tq/JdplaNqqhVo2qR9ouIiIiIqKQFx0bj4ktPnPZ0x03vt0hMK3i0Syk9bR00da4mq61WyVpxtEYiIiJSTZAgW906tXDjxi28e/cezs6VlC7j6fkEL1+9RrdvugixSSIiIiKiL15mVhbu+b6T9a32NNC3UOntLEqhXbU6aF+9Dpo7V4exvkEx7SkREdHnT5Ag25DBA3H5yjWMGT8ZSxcvQJ06teXmv3z1GtNm/gQtLS0MGTRAiE0SEREREX2RIhLicPnVU1x46YnLr58iOilB7bTaWlpo7FgZ7arVwdfV6qBaufIQiUTFuLdERERfDkGCbM2aNsH4saOweu0G9O47AGWsrSESiXDlyjW0atMRAYGBEIvFmDplIurWrV3wComIiIiICACQlZUFz0Cf/7d331FRXG0YwJ+lF0E6UqR3QVApNuzdKLZYE6NGUzU9+qX33osxRhONGjXRKBoTewMbRaVIk67Se5POfH8AGwkLLMtShOd3jufIzL1z3y13dvbduffiZHT93WrXbidBEASp6xtp9cdEp8GY4uKBcQ6D0F9dsxOjJSIi6rvkttb2C88/AxcXZ2z8YTOiY2IBAPkFBcgvKIC9nS2eWfcUZkyfKq/miIiIiIh6raLyMpyNu4GTMeE4HRuB7JIiqeuKRCK4mwzEOFsnzPYcjcFmllBQUOjEaImIiAiQY5INAKZNnYxpUycjLy+//u61OgEmJgNgbCz9ap9ERERERH2NIAiIybyDUzHhOBEdhqCUeNTW1UldX0ddExOdBmOyszsmOLlBobwMAKBnaNJZIRMREdF/yDXJ1khfX6/Dq4gSEREREfVmpZUVCIyPxsmGRQvSCvPaVd/NzBJTGuZWG2ZhCyVFRfG+/IYkGxEREXWdTkmyERERERFRc4k5meKk2sWEGFTV1khdt5+qGsY5uGKyszsmOg2GqQ5/1CYiIupJ5JJkW/+/16UuKxKJ8MlH78mjWSIiIiKiHq2iugoXE2NxMiYcp2LCkZSb1a76DsammOLsgcnO7vCxdoCKEn8jJyIi6qnk8in95wH/Vvc3LgsuCAKTbERERETUq90pyBXfrRYQH4W7VVVS11VXVoGvvQsmO7tjktNgWOpzbmMiIqL7hVySbLt3bpO4vU6oQ3p6Bs4HXMA/R49jzeqVGD92jDyaJCIiIiLqEapraxCcHI8TMWE4GROO2My0dtW31DPEFBcPTHJ2x2hbZ6irqHRSpERERNSZ5JJk8/HxanX//HlzMHrUSLz51ruYOnmSPJokIiIiIuo2WcWFOBUbgZMxYTgbdwMlFeVS11VWVMRIWydMdnbHZGcP2BkOEI/8ICIiovtXl03qsPDBefj5l+34+tvv8eu2LV3VLBERERFRh9XW1eHarSTx3Gphd5LbVd+kv25DUs0dY+wHQUtNvZMiJSIiou7SpTOn2tvbIfDCpa5skoiIiIhIJvllJTgTG4mTMeE4HReB/LJSqesqKijA28penFhzMRnIu9WIiIh6uS5Nst1JS0NdXW1XNklEREREJBVBEBCZlipetCA0NQF1giB1fYN+Wpjk5I5Jzu6Y4OgGHQ3NToyWiIiIepouSbLV1tZi68/bERkZBS/PYV3RJBERERFRm4orynEu7gZOxoThdGwEMosL21V/6EAbTHJ2xxQXD3iYW0FBQaFzAiUiIqIeTy5JtmUPr2xxX2lZGW7fvoPi4hIoKSni2WeekkeTRERERETtJggC4rLScTImDKdiwnE56SZq2jHSor+6BiY4umGyswcmOLnBSKt/J0ZLRERE9xO5JNmuBIW0ul9ZSQk+3p54dt3T8Pb2lEeTRERERERSuVtViQsJMTgZE4aTMeG4lZ/brvqDTAbWz63m4gEvSzsoKSp2UqRERER0P5NLki3g7IkW9ykrK0NXVwfKysryaIqIiIiIqE3JuVniudUuJMSgsqZa6rqaKqoY5+CKSc7186uZ6eh1YqRERETUW8glyWZmZiqPwxARERERyaSyphqXk+JwMiYcp2LCEZ+d0a769kYmmNSwEugIG0eoKvEHYiIiImqfLl1dlIiIiIhIXtIK88Vzq52/GYWyqkqp66oqKWO0nTOmOHtgkvNgWBsYd2KkRERE1BcwyUZERERE94Wa2lqEpCbgZHT93GpRGbfbVX+grgGmuLhjsrMHRts5Q0NFtZMiJSIior5ILkk2O0c3meuKRCLEx0bIIwwiIiIi6mVySopwOjYSJ2LCcDYuEkXld6Wuq6SgiBE2Dpjk7I4pLh5wMDKFSCTqxGiJiIioL5NLks3EZABEIhHS0tLF27S1tQAAxcUl4m2cu42IiIiIWlNXV4frt5PFc6tdu53UrvoDtHXEc6uNdXCFtpp6J0VKRERE1JRckmxnTx3F2mdeQFVVFZ5+6nHMmT0LWlr9AAAlJaU4dPgINm7aDBdnZ2z87ksoctlzIiIiImpQeLcMZ+IicSomHKdiw5FbWtJ2pQYKIhE8Le0wuSGx5mZmybvViIiIqFvIJcn24+atCLxwCX//9SesLC2b7NPS6oeHli3GqFEjMHPWPPyw6SesW/ukPJolIiIiovuQIAiISr+FkzHhOBkTjuCUeNQJgtT19TT7YZJTfVJtvKMr9DS1OjFaIiIiIunIJcl2wP8wRgz3bpZgu5e1lSVGjvDBwUN/MclGRERE1MeUVJQjID5KnFjLKCpoV30Pc2vxMNChFjZQVFDopEiJiIiIZCOXJFtGRiYGuTi3WU5dXR0ZGZnyaJKIiIiIejBBEJCQk4kT0WE4FROOS0mxqK6tlbq+lpo6xju6YoqzByY6DYaxtk7nBUtEREQkB3JJsunp6eJKUAjKysqgqakpsUxZWRmuBIVAT09XHk0SERERUQ9TXlWFC4kxONVwt1pKXna76jsPMBfPreZtbQ9lRblcqhIRERF1CblcuUyfNgXbtu/E6seexltvvgonR4cm++NuxuOddz9AQUEBVjzykDyaJCIiIqIe4FZ+jngIaGB8NMqrq6Suq6GigjH2g8SJNXNdg06MlIiIiKhzySXJ9uy6p3Hp8hUEh4TigdnzYWdrA1NTU4hEIqSlpyMhIRGCIMDR0R7PrntaHk0SERERUTeoqqnBleQ4cWLtZlZ6u+rbGBhjsrMHJru4Y6SNI9SUVTopUiIiIqKuJZckm5ZWP/yxZxe++PIb7D9wEPEJiYhPSBTvV1dXw/x5c/DSC89BS6ufPJokIiIioi6SUVQgHgJ67uYNlFZWSF1XRVEJo+ycxXer2RoO6MRIiYiIiLqP3Ca66NdPE2+9+So2rH8BkTeikJVVPweHsbER3FwHQU1NTV5NEREREVEnqq2rQ2hqgvhutci01HbVN9PRx2Rnd0xxcYevnQs0VXkdSERERL2f3GeTVVNTg5fnMHkfloiIiIg6UW5pMc7ERuJkTDjOxEWi4G6p1HUVFRQw3NoBk53dMcnZHc4DzCESiToxWiIiIqKeh0s2EREREfVBdXV1iEhLxcmYcJyIDsO120kQBEHq+kZa/THRaTAmO7tjvKMr+qtLXmGeiIiIqK+QKcn27Xc/QCQS4eGHlkBHRwfffveD1HVFIhHWrX1SlmaJiIiIqAOKystwNu4GTsaE43RsBLJLiqSuKxKJMMzCVjy32mAzSygoKHRitERERET3F5mSbN80JNlmzpwOHR0d8d/S/PrJJBsRERFR1xAEATGZd3Cq4W61oJR41NbVSV1fR11TfLfaBCc3GPTT7sRoiYiIiO5vMiXZPv34fQCAkaFhk7+JiIiIqHuVVVYgID5avGhBWmFeu+q7mVliSsPcasMsbKGkqNhJkRIRERH1LjIl2ebPm9Pq30RERETUdRJzMsVJtYsJMaiqrZG6bj9VNYxzcMVkZ3dMdBoMUx29ToyUiIiIqPfiwgdERERE95mK6ipcSorDyehwnIwJQ1JuVrvqOxibYrKzO6Y4e8DH2gEqSrwkJCIiIuooXlERERER3QfuFOSK71YLiI/C3aoqqeuqK6vA194Fk53dMclpMCz1jToxUiIiIqK+SW5Jturqahz5+yiuBIUgOzsbVS1d+IlE+G3HL/JqloiIiKhXqq6tQXByPE7GhONETBhiM9PaVd9SzxBTXDwwydkdo22doa6i0kmREhEREREgpyRbbl4eHlr+KBISEttcYVQkEsmjSSIiIqJeJ6u4EKdiI3AyJgxn426gpKJc6rrKiooYaeuEyc7umOzsATvDAbzuIiIiIupCckmyffLpl4iPT8BgN1esWb0StrY26KepKY9DExEREfVatXV1uHYrCSdjwnEqJhxhd5LbVd+kv25DUs0dY+wHQUtNvZMiJSIiIqK2yCXJdj4gEMZGRti142doMrlGRERE1KL8shKcjbuBE9FhOB0XgfyyUqnrKohE8Layr1+0wMUDLiYDebcaERERUQ8hlyRbSUkpJowfywQbERER0X8IgoDItFTxogWhqQmoa2N6jXsZ9NPCJCd3THJ2xwRHN+ho8HqLiIiIqCeSS5LN0mIg7pZLP2cIERERUW9WXFGO8zcb7laLjUBmcWG76g8daINJDXereZhbQUFBoXMCJSIiIiK5kUuSbeGD8/HFV98iIyMTJiYD5HFIIiIiovuGIAiIy0rHyZgwnIoJx+Wkm6ipq5W6fn91DUxwdMNkZw9McHKDkVb/ToyWiIiIiDqDXJJsK1c8jMgbUVi2fBXefvNVjB41kr+4EhERUa92t6oSFxJicDImDCdjwnErP7dd9QeZDKxftMDFA16WdlBSVOykSImIiIioK8glyTZuwjQAQFp6OlatfhJKioowNDKEgqh5ok0kEuHcmWPyaJaIiIioS6XkZeNEdH1S7UJCDCprqqWuq6miirEOgzDZ2QOTnN1hpqPXiZESERERUVeTS5LtTlpak7+ra2qQnp4hj0MTERERdZvKmmpcTorDyZhwnIoJR3x2+65v7I1MMMnZHZOd3THCxhGqSsqdFCkRERERdTe5JNkSb96Qx2GIiIiIul1aYT5ONSTVzsdHobSyQuq6qkrKGG3njCnOHpjkPBjWBsadGCkRERER9SRySbIRERER3a9qamsRkpqAkw3DQKMybrer/kBdA0xxccdkZw+MtnOGhopqJ0VKRERERD0Zk2xERETU5+SUFOF0bCROxoThTFwkisrvSl1XSUERI2wcGoaBesDR2BQikagToyUiIiKi+wGTbERERNTr1dXV4frtZPHcatfvJEMQBKnrD9DWEc+tNtbBFdpq6p0YLRERERHdj+SSZLNzdJO6rEgkQnxshDyaJSIiol6svKoKIbeTUVpZAdOSYniYW0NdRUXq+oV3y3AmLrJ+frXYcOSWlkhdV0EkgqelHSY3JNbczCx5txoRERERtUouSTYTkwESLzzr6uqQk5ODmppaAICZmak8miMiIqJeLL0wH5sCjuG3oAAUlpeJt+uoa2KZzxg8NXY6TPrrNqsnCAKiM27jZEw4TkSHISQ1AbV1dVK3q6fZD5Oc6pNq4x1doaepJZfHQ0RERER9g1ySbIHnTra4r7a2FhcvXcY7732EwW6u+OqLT+TRJBEREfVCkWmpWLD5U+SUFjfbV1heho3njuKP0IvY//h6uJlZoqSiHAHxUTgZE46TMeHIKCpoV3se5tbiYaBDLWygqKAgr4dCRERERH1Mp8/JpqioiDG+o7Hlx+/xgN8C/LztVzy68pHObpaIiIjuM+mF+S0m2O6VU1qMmd+/D1dTC1y9lYjq2lqp29BSU8d4R1dMdvbAJKfBMNbW6WDURERERET1umzhAxsba7gPdsPvv+9nko2IiIia2RRwrM0EW6PSygpcSb4pVVmnAWaY4uyByc7u8La2h7Ii130iIiIiIvnr0qtMXV0dhIVz0QMiIiJqqryqCr8FBcjlWBoqKhhjPwiTnd0xyckdA/UM5HJcIiIiIqLWdFmSraysDNfDwqGtzUmEiYiIqKmwO8lNFjloLxsDY0x29sBkF3eMtHGEmrL0q5ASEREREcmDXJJsaenpLe4rK7uLpKRk/LTlF+Tk5GLB/LnyaJKIiIh6geKKchy7cQ0/XTghU/3lw8dh3fiZsDUcIOfIiIiIiIjaRy5JtjHjpkAkErVaRhAEODjYY/1Lz8ujSSIiIrpPFVeU43jUNfiHBeN0bASqamtkPtZiz9FMsBERERFRjyCXJJu3lydayrEpKyvDyMgII4Z744GZ06GiwuEbREREfU1JRTmOR12Hf3gQTsdGorKmusPH1NXQhLu5tRyiIyIiIiLqOLkk2fb8tl0ehyEiIqJepLSyAsejr+NQWDBOxoTLJbF2r2XeY6HOH++IiIiIqIfgGvZEREQkN6WVFTgRHQb/sCCciglHhZSJtUEmAzHByQ2/BQcgv6y0zfJGWv3x5NhpHQ2XiIiIiEhumGQjIiKiDimrrMCJmHAcCg/CyehwlFdXSVXPeYA55nj4wM/dGw7GpgCABUNHYsHmT5FTWtxiPSOt/tj/2Msw6a8rl/iJiIiIiOSBSTYiIiJqt7tVlTgZEw7/sCCciA6TOrHmNMAMc9x94OfhDUdjs2b73cwsce7F97Hp/DH8FnweBXfLxPt0NTSxzHssnhw7jQk2IiIiIupxmGQjIiIiqdytqsSpmAj4hwfhRPR13K2SLrHmYGwqTqw5DzBvs7xJf128O3sJXpk2H4E3QlFSWQEzYzO4m1tzDjYiIiIi6rGYZCMiIqIWlVdV4VRsOA6FB+N41HWUVVVKVc/eyBRzPLwxx8NHqsSaJOoqKvAcWL96qJ6hiUzHICIiIiLqKkyyERERURMV1VU4HRsB/7BgHI++jtLKCqnq2RuZwM/dB3PcveFsYg6RSNTJkRIRERER9RxMshEREREqqqtwJi4S/mFBOBYlfWLN1nAA5njUJ9ZcTAYysUZEREREfRaTbERERH1UZU01zsRG4lB4MP65cVXqxJqNgbE4sTbI1IKJNSIiIiIiMMlGRETUp1TWVONc3A34hwfhnxvXUFJRLlU9a32j+sSahw9cmVgjIiIiImpGpiTb+v+9LnODIpEIn3z0nsz1iYiIqH2qampw7uYN+IfVJ9aKK+5KVc9K3whz3L3h5+GDwWaWTKwREREREbVCpiTbnwf8JW5vvPgWBKHF7UyyERERdb6qmhqcv3kD/g1DQYvKpUusWeoZws+9flVQd3MrJtaIiIiIiKQkU5Jt985tzbYdO34SO3btxmA3V/jNfgDm5qYAgDt30nH4r78RHhGJRx5ehqlTJnUsYiIiIpKourYG529Gwz88CH9HhkqdWBuoa4A5HvWJNQ9zaybWiIiIiIhkIFOSzcfHq8nfFy5exq7de/HWG69g+cPLmpVf8chD2LFrN957/2NMmDBOpkCJiIioueraGgTER8M/LAh/R15FYXmZVPXMdfUxx90Hfu7eGGphw8QaEREREVEHyWXhgx82bYazs5PEBFuj5Q8txZ8H/LHxh80YPWqEPJolIiLqk6praxCYEINDYcE4EhmKgrulUtUz09FvGArqjWEWtkysERERERHJkVySbDeiYjBh/Ng2y1lbWeHM2fPyaJKIiKhPqamtxYXEGPiHBeFIZCjyy6RLrJnq6NUn1tx9MMzCBgoKCp0cKRERERFR3ySXJJtIJEJSUnKb5ZKSkvmrORERkZRqamtxMTFWnFjLKyuRqp5Jf134uXvDz90bXpZ2TKwREREREXUBuSTZhngMxoWLl7Ft+06sXPGwxDK/7vgNUdExGOM7qsPt1dTU4MixM7hwKQTZOXlQU1OFk4MN5s6aBmurge061t3ychz55zSCQsOQk5MHFVUVWFsNxMyp4+ExeFCHYyUiImqPmtpaXEqKhX9YMI5EhiC3VMrEmrYuZrl7YY6HD7yZWCMiIiIi6nJySbI9/9w6XAkKwQcffYqDh/7CrJnTYWZWv7poenoG/vr7H0RFxUBZWRnPPbu2Q23V1NTgo89/QHRsPLS1tTDUwxWFRUUIuRqBa2FRePm5x+Hu5izVsYqKS/Duh18jPTMbWlr94DbICXcrKhAXl4gbUXFYstAPs2dwNVQiIupctXV1uJQYi0PhwfgrIgQ5pcVS1RugrYPZgxsSa1b2TKwREREREXUjuSTZ3Ae7YcuP3+OlDa8iKioa0dExTfYLggB9fT18/umHcB/s1qG2Dv99CtGx8bC1tsCr69dCQ10dAHDxSii+//FXbPxpB7759C2oq6u1eayt2/ciPTMbri4OeH7tamho1B8rOfU2Pv78B+zddxiuLg6wsbLoUMxERET/VVtXhytJcfAPD8JfEaHILimSqp6xVn/MbhgK6mPtAEUm1oiIiIiIegS5JNkAwNd3FM6dPoajx04gOOQqsrKyAADGxsbw8hyKGdOnQr0hISar2tpaHD1xFgCwcvkicYINAEYN98SFSyEIi4jGucArmD5lXKvHyssvQOi1CCgoKGD1iiXiBBsAWFsOxDy/adi+az8OHzmJ59Y+2qG4iYiIgPrEWlDyTfiHBeGviBBkSZlYM+ynjdnuXpjj7oPhNo5MrBERERER9UByS7IBgLq6OubN9cO8uX7yPKxYXHwSSsvuwshQH7bWze8uG+EzFGER0Qi9HtFmki055TYAwMhQH8ZGBs32u7o4AgDCIqJRU1MDJSW5PlVERNRH1NXVISglHofCgnA4IgSZxYVS1TPsp41ZDUNBRzCxRkRERETU491XmaPUW2kA6u80k6Rx+62Gcq2prKwCAGhqakjc36+fZn25qipkZGZjoLlpu+MlIqK+qa6uDsGpCfV3rIWHIKO4QKp6Bv20MGuwF/zcvTHSxglKioqdHCkREREREcmLXJNsgRcu4bfdvyM8IgIF+QWYPfsBfPrx+wCAgMALCAi8iDWProSxsZFMx8/NywcA6OnpSNyvp1u/vbTsLioqKqGmptrisbS0+gEAcnLzJe7Pycn79/+5+W0m2d587wuJ2+fPGg8TYwPk52S0Wv9+UVNTDQC95vEQdRX2nd6vTqhDWNot/BMTgaNxkVIPBdVV18Q0J1dMdxoMbwsbKCnUJ9aK87M7M9z7BvsOkWzYd4hkw75DJJve2Hf0DE3aXUduSbYPPvoU27bvhCAI0NTQQHVNTZP9Bvr62LZ9J0xNTLBq5XKZ2qioqAQAqKpKTp7dm1Qrr6hoNclmb2sFFRVlFBeX4Or1SAwb0nRBhtPnLjY5FhER0X/VJ9Zu42hsBI7GRiBT6sSaBqY4umKGkzt8LP9NrBERERER0f1LLkm2Q4eP4JdtOzBokAs+ev8duLg4wc6xadLKxcUZA4yNcebceZmTbPKkrq6GaZPG4vA/p/Dj1l1Y8fCDcHd1RnlFJU6fu4DzF4KgqKiI2tpaKIhEbR7v3TdelLi9MYsrSwa0J+ptj4eoq7Dv9B6CIODqrUT4hwXjUHgw0grz2q4EQFejHx5w88QcD2+MtnOGsuJ9NWNDt2HfIZIN+w6RbNh3iGTDvlNPLlf4u3bvhZaWFn7+aSMMDQ1bLOfk5Ij4hASZ22m8M62yslLi/sY73QBAXU2tzeM9OO8BFBYVI+BiML7/8dcm+6ZMHIPEpBQkJt9qcd42IiLqGwRBwLVbSfAPD8Kh8GDcKZAusaajrokHBnvCz90bY+xdmFgjIiIiIurF5HK1Hxd7E8OGDWk1wQYA2tpayM2V7ouJJAb6egCA/PxCifvzC+q399PUaHWoaCMlJUU8ueZhTJk0FtfDbqCgsAhaWpoY6uEGBztrPPXc6wAAczMuekBE1NcIgoDrt5NxKDwI/mHBuF2QK1W9/uoamOnmiTnuPhjrwMQaEREREVFfIZcr/9q6OqioqLRZLic3V6pyLbG0MAMAJKfelri/cbtFQzlp2VpbwNbaosm2nJw8FBQWwdTEGHq6/WWIloiI7jeCICD8Tgr8w4LgHx6EW/nSJda01TQw020Y5nj4YKz9IKgoMbFGRERERNTXyOVbwMCB5oiKjkFtbS0UFSVP3lxRUYHYmDjY2ljL3I6jvQ36aWogOycPicm3miXGLgddAwB4DhkscxuN/jlxFgAwafzoDh+LiIh6LkEQEJGWCv+w+qGgKXnSrepZn1gbCj93H4xzcGVijYiIiIioj5PLN4Ipkybghx+34PsfNuPZdU9JLPP9D5tRWFSEx6dOlrkdRUVFTJ8yHvsO/o1tO37Hq+vXQkNdHQBw8UoowiKioaXVD+N8h4vrJCSlYNNPOwEAX3z8RpPj5eTmQ1lJCTo62uJtdXV1OHbyHI6fCoDlQDNMnuArc7xERNQzCYKAyLRUHAoPhn9YEJKlTKxpqaljhutQzHH3wThHV6gqKXdypEREREREdL+QS5Jt9eqV8D98BN99vwlh4RGYOnkiACAzMwsH/Q/j+InTOHX6DKysLPHQssUdamv2zEmIirmJ6Nh4PL/hPTg72qGoqBixNxOhqKiIp9Y8DHX1fxc9qKqsRnqm5C9P0TE38dO2PbC2NIeBvh4EQUBCciry8wthZjoA6194EkpKku/MIyKi+4sgCIhKvwX/hsRaUm6WVPX6qaphhuswzPHwxnhHNybWiIiIiIhIIrkk2bS1tLBj+xY8vfZ5BARcQGDgRQDApctXcOnyFQiCACdHB2ze9B3UG+48kzlgJSW88tJTOHL0NC5cDsW165FQVVOF5xA3zPObDmurgVIfy9rKAsO9hyAhMQW30zKgqKCIAQMMMX3yOEydNAbKyvwiRUR0PxMEAdEZt8VDQRNyMqWq109VDdMHDYWfhzcmOLpBTVn2+USJiIiIiKhvENXWVAryOpggCDhz5hwCLlzE7dtpEIQ6mJgMwOhRIzFt6mQoKCjIq6n7Rn5OBgBAz9CkmyORj972eIi6CvtO1xEEATEZd+AfHoxD4UGIz86Qqp6miiqmDRqKOR7emOA4GOodWKiH5Id9h0g27DtEsmHfIZIN+049uc7SLBKJMHHieEycOF6ehyUiImpTTOad+lVBw4IRn50uVR1NFVVMHTQEc9x9MNGJiTUiIiIiIpJdlyyFVlBQCG1trRZXHiUiIpJFbGaaeChoXFaaVHU0VFQw1WUI/Nx9MMl5MDRUVDs5SiIiIiIi6gvkkmS7ERWN8wEXMHXyRNjZ2Yq3nz5zDq+98TZyc/PQr18/vPTCsx1e+ICIiPq2uKw0HAoLhn94EGIzpUusqSurYIqLB+Z4+GCyszsTa0REREREJHdySbLt2Lkbhw7/hUUL54u3ZWRkYt2zL6KyshJqamooKSnB2+9+ABcXJwwd4iGPZomIqI+Iz85oGAoahJjMO1LVUVdWwWQXd8xxr0+saaqqtV2JiIiIiIhIRnJJsoWFhcPJyREG+vribX8e8EdlZSXWPv0Enn92LS5fCcLyFWuw/dddTLIREVGbErIz4B8ehENhwYjKuC1VHTUlZUx28YCfuzemuHigHxNrRERERETUReSSZMvJzcVwH+8m2y5cvAxVVVU88dijAIARw33g4TEYkTei5NEkERH1Qok5mTgUHgz/sCDcSL8lVR01JWVMcnaHn4c3proMYWKNiIiIiIi6hVySbJWVVVBWVhb/XVNTg8gbUfBwd4O6urp4u5mpKaKjY+XRJBER9RJJOVk4FB4E//BgRKalSlVHVUkZk5wGY46HD6a4eEBLTb3tSkRERERERJ1ILkm2AQOMkZCYKP47JPQqKioq4O3t1aRcZWUl1NV4hwERUV+XnJuFQ+HBOBQejPA7KVLVUVFUwiTnwZjj7oMpg4ZAm4k1IiIiIiLqQeSSZPMcNhQHDh7C1l+2Y9TIEfjyq28hEokwftyYJuXiExJhbGwkjyaJiOg+k5qX3TAUNBhhd5KlqqOiqIQJTm6Y4+6DaYOGQFtdo5OjJCIiIiIiko1ckmxPPPYo/v7nGD7+5AsAgCAI8B09Eu6D3cRlUlNvITk5BYsXPSiPJomI6D5wKz8H/mHBOBQehOu3pUusKSsqYoKjG+Z4+GD6oKFMrBERERER0X1BLkk2Gxtr7Nu7E9u270R+QSFcXV3w+JpVTcpcuHgZzk6OmDhhnDyaJCKiHup2fm79qqDhwbh2K0mqOsqKihjvWH/H2nTXIeivrtnJURIREREREcmXqLamUujuIHqz/JwMAICeoUk3RyIfve3xEHWV3t537hTkwj88GIfCgnH1VmLbFQAoKShinMMgzPHwwQzXYdDRYGKNmuvtfYeos7DvEMmGfYdINuw79eRyJxsREfU9dwrycDiifo610NQEqeooKShibGNibdBQ6Gr26+QoiYiIiIiIugaTbEREJLW0wnwcDg+Gf3gQQlKkS6wpKihgrH19Ym2m6zAm1oiIiIiIqFeSW5KtsrIK23/diWPHTyI5JRWlpaUSy4lEIsTHRsirWSIi6mTphfk4HBGCQ+HBCEq+KVUdRQUF+Nq5iBNr+v20OjlKIiIiIiKi7iWXJFt5eTmWPLQCN25EQ1FRAcrKyhAEAUZGhsjJyYUg1E/7ZmZmKo/miIiok2UUFeCviBD4hwXhipSJNQWRCL72Lpjj7oMH3DyZWCMiIiIioj5FLkm2n7ZuQ2RkFB5cMA9vv/kq3njzXRw89BcuXziL8vJyHDp8BJ9/8Q2GDR2Cr774RB5NEhGRnGUWF+Kv8BD4h9cn1hp/IGmNgkgEXzsX+Hl44wE3Txj00+6CSImIiIiIiHoeuSTZjh0/CX19Pbzz1utQVVUBRCLxPnV1dSxe9CBcXJwx/8Gl8HAfjEeWL5NHs0RE1EFZxYU4EhkK/7AgXEqKkzqxNsrWCXM86u9YM9Tq3wWREhERERER9WxySbLdvn0bPt7e9Qk2AAoKCgCA2tpaKCoqAgAGu7nCc9hQ7Nt/gEk2IqJulF1ShCMRIfAPC8bFpFipEmsikQijbJwwx8MbDwz2ghETa0RERERERE3IJcmmIFKApqaG+G8NdXUAQH5BAQwNDMTbjYwMcSMqSh5NEhFRO+SUFInvWLuYGIs6KRNrI20c4efujVmDvWCsrdP5gRIREREREd2n5JJkMzI2QmZWlvhvC4uBAIAbN6IxftwY8fbExCSoNyTgiIioc+WWFuNIZCgOhQUjMCFa6sTaCGsH+Ln7YJa7FwYwsUZERERERCQVuSTZ3FwHISDwAmpqaqCkpIQxvqPw/ocCPv38Kww0N8OAAQPw687fEBMbh7FjfOXRJBERSZBXWlJ/x1p4EC4kxKC2rq7NOiKRCD5W9pjj4YNZg71g0l+3CyIlIiIiIiLqXeSSZBs/bgwO//U3zp4LwORJE2Bra4MF8+di/58HMW3mHHE5ZWVlvPTCM/JokoiIGuSXleBI5FX4hwUhMCFaqsQaAPhYO2BOw1BQUx29To6SiIiIiIiod5NLkm3WAzMwaeJ4KCsri7d9+P7bsLO1wbHjJ1FYVARbG2s88fhquLg4y6NJIqI+raCsFH/fqE+snY+Pkjqx5m1lDz93b8x294YZE2tERERERERyI5ckm0gkgoaGRpNtioqKWLN6JdasXimPJoiI+rzCu2X/JtZuRqGmrlaqep6Wdpjj4Y3Zg71hrqvfyVESERERERH1TXJJshERUecovFuGf+65Y626VrrE2jALW8zx8IGfuxfMdQ3arkBEREREREQdItckW2VlFY4eO4GQkFBkZNavNjpggBG8vTwxfdoUqKqqyrM5IqL7QnlVFUJuJ6O0sgKmJcXwMLeGuopKi+WLystw9MZ1+IcH4WxcpNSJtaEWNvBz94afuzcs9AzlFT4RERERERFJQW5JtitBwXjxpVeQlZ0NQRCa7Ptj3wF8+tlX+PyzDzFyxHB5NUlE1KOlF+ZjU8Ax/BYUgMLyMvF2HXVNLPMZg6fGThev5FlcfhdHo67BPywIZ9qRWBsy0Bp+7vV3rFnqG3XK4yAiIiIiIqK2ySXJFh0dg5WPPoGqqio4OtrDb9YDMDc3AwDcuZOGw3/9jdi4m1j92NPY//suLn5ARL1eZFoqFmz+FDmlxc32FZaXYeO5o/g99CIe952Cq7cScSY2ElW1NVId28Pcun6ONXdvWDGxRkRERERE1CPIJcn21Tffo6qqChtefgGPrVnVbP/jjz2KLVu34eNPv8BX33yPLZs3yqNZIqIeKb0wv8UE271yS4vxwdH9Uh3T3dxKPBTU2sBYHmESERERERGRHMklyRYSeg1Ojg4SE2yN1qxeCf9DfyEk9Jo8miQi6rE2BRxrM8EmDTczS8xx94afuw9sDJlYIyIiIiIi6snkkmSrra2Bvb1dm+Xs7e1w6/ZteTRJRNQjlVdV4begAJnru5paNKwK6g1bwwFyjIyIiIiIiIg6k1ySbA729khLS2uzXFp6Ouzt2k7GERHdr07EXG+yyIG0HvYZi7XjZ8LeyKQToiIiIiIiIqLOpiCPg6xauRzXrofjxMnTLZY5eeoMrl0Lw6qVy+XRJBFRj3G3qhL7rl7E3B8/xspfv5fpGDPdPJlgIyIiIiIiuo/JdCdbWnp6k789PAZjxSMPYe0zL2Da1MnwmzUTZg2ri6alpePwX3/j2PETWPHIQxgyxL3jURMRdTNBEBCUfBO7QwLhHxaE0sqKDh1PW01dTpERERERERFRdxDV1lQK7a1k6+AKkUjUbLsgCBK337tPJBIhPjai/ZHep/JzMgAAeoa94w6V3vZ4iNrrTkEu9oZewN6QC0jKzZLLMXU1NHHjzW+hrqIil+MR9Sb83CGSDfsOkWzYd4hkw75TT6Y72by9PNFCLo2IqNcpq6zA35FXsSckEAEJ0RCEdv820apl3mOZYCMiIiIiIrrPyZRk2/PbdjmHQUTUs8gyHNTB2BRLvXwx1sEVC3/6DDmlxW3WMdLqjyfHTpNHyERERERERNSN5LK6KBFRb3E7v3E4aCCS87LbLN9fXQPzh4zAEi9fDLWwEQ+Z3//4eizY/GmriTYjrf7Y/9jLMOmvK7f4iYiIiIiIqHt0WpItKTkFuTm58Pb27KwmiIjkoqyyAkciQ+uHg8ZHt1leQSTCRKfBWOzli+mDhkBNuflQTzczS5x78X1sOn8MvwWfR8HdMvE+XQ1NLPMeiyfHTmOCjYiIiIiIqJfotCTbph+34KD/YSTERXZWE0REMhMEAZeT4rA7JBCHw4OlGg7qaGyGJV6+eHDYSKmSYyb9dfHu7CV4Zdp8BN4IRUllBcyMzeBubs052IiIiIiIiHoZDhcloj7lVn4O9oZcwN7QC0iRYjiojrom5g8djiVeYzBkoHWLKyi3Rl1FBZ4DrQFwtR0iIiIiIqLeikk2Iur1Sisr8FdECPaGBCIwIabN8goiESY5u2Ox52hMa2E4KBEREREREdG9mGQjol6prq6uyXDQsqrKNus4GpthqbcvHhw2CgO0dTo/SCIiIiIiIuo1mGQjol4lNS+7YXXQC0jNz2mzvI66JhYMHYEl3r7wMJdtOCgRERERERFRpyXZFi2cj+E+Xp11eCIiscbhoHuCA3Ehse3hoIoKCpjkNBhLvMZg6iAPqCopd0GURERERERE1JvJJclWUVEBNTW1Jts8hw2F57Chzcomp6TC2spSHs0SUR9WV1eHS0lx2BMSgMPhIVINB3UaYIal3mPw4NCRMOZwUCIiIiIiIpIjuSTZnlr7PLb+tBEKCgqtlktOScWyh1bi0oUz8miWiPqglLzshtVBA3ErP7fN8roa/eqHg3r5wt3cisNBiYiIiIiIqFPIJcl2PiAQG155A5998kGLZW7duo1lD61Edk7bcyQREd2rtLICh8ODsSckEBcTY9ssr6iggMnO7lji5YspLhwOSkRERERERJ1PLkm26dOm4KD/YRgY6GPDyy8023/79h0seWgFsrKz8dor6+XRJBH1cnV1dbiYGIs9IYE4HBGMu1VVbdZxHmBePxx02EgYafXvgiiJiIiIiIiI6sklyfbl5x+joKAAW7Zug5GhIVaueFi8rzHBlpmZhf+tfxGrVi6XR5NE1Esl52aJVwe9XdD2cFA9zX5YMGQElniPwWAzSw4HJSIiIiIiom4hlySbiooKNm/6HouWPIwPP/4MBgb6mPXADNy5k4alD69ERkYmXn7xOaxZvVIezRFRL1NSUY5D4cHYGxKIS0lxbZZXVFDAFGcP8XBQFaVOWyiZiIiIiIiISCpy+2bar58mtv28GQsWLcPLG15DRUUlvt/4I9LTM/DCc+vwxOOr5dUUEfUCdXV1uJAYgz0hgfgrIkSq4aAuJgOx1NsXC4ZyOCgRERERERH1LHK9/cPIyBDbf/kJCxc9hFdeexOCIOCZdU/h6acel2czRHQfS87Nwp6G1UHvFOS1WV5fUwsLho7EEq/RcONwUCIiIiIiIuqhZEqypaWnt7hPVVUF777zBp5/cT38Zs/CgvlzmpU3MzWVpVkiuk8VV5TjUFgQ9oZewGUphoMqKShiikv96qCTnTkclIiIiIiIiHo+mb65jhk3pc27SQRBwIGDh3Dg4KEm20UiEeJjI2RplojuI3V1dQhM+Hc4aHl128NBXU0tsMTLFwuGjoAhh4MSERERERHRfUSmJJu3lyc4YouIJEnKycKe0EDsDbmAtELphoM+OGwklnj5ws3MsgsiJCIiIiIiIpI/mZJse37bLucwiOh+VlxRDv+wIOwJCURQ8s02yyspKGKqiweWePtikpM7h4MSERERERHRfY/fbIlIJrV1dQhMiMae4EAciQyVajiom5mleDioQT/tLoiSiIiIiIiIqGswyUZE7ZKYk4m9IYHYG3pRquGgBv208OCwUVjiORquHA5KREREREREvZRckmyHDh/Bl199h/fefQNjfEdLLBMQeAFvvPke1r/8PGbOmCaPZomoixSX38XBhuGgwSnxbZZXUlDEtEFDsMTLF5OcB0NZkfl8IiIiIiIi6t3k8s3X//ARlJWVYcRwnxbLDPfxRmlZKQ76H2aSjeg+UFtXh4D4KOwJCcSRiFBU1FS3Wcfd3AqLPUdjPoeDEhERERERUR8jlyTbzbh4ODo5QFlZucUyKioqcHJyRGxc25OiE1H3ScjOwN7QC9gbegHphfltljfsp10/HNRrNAaZWnRBhEREREREREQ9j1ySbHn5+fD29myznKGBAa5dC5NHk0QkR8Xld3Eg7Ar2hAQiJCWhzfLKioqY6jIES719MdGJw0GJiIiIiIiI5PLNWEtLC+npGW2Wy8zMgoaGujyaJKIOqq2rw/n4KOwJDsTfkdINB/Uwt8Zir9GYP2QE9PtpdUGURERERERERPcHuSTZXF1dcOnSZSQmJsHW1kZimcTEJFy7HoYRw73l0SQRyehmVjr2hgTi96sXkVFU0GZ5w37aWOg5Cks8feFiOrALIiQiIiIiIiK6/8glybZ44QIEBFzA6sefxtdffgr3wW5N9kdE3sCzz7+Muro6LFq4QB5NElE7FJWX4eD1IOwOCURoqnTDQacNGoqlXr6Y4OTG4aBEREREREREbZDLN+epUybBb/YDOHT4COY/uBR2tjawsrICAKSkpCAhMQmCIGDWAzMwY/pUeTRJRG2oravDuZs3sCckEH9HXkWlFMNBhwy0xhIvX8wbMhx6mhwOSkRERERERCQtud2e8sVnH8He3hZbtm5DfEIi4hMSxfv699fGmtUr8cRjq+XVHBG1IC4rDXtDLuCP0IvIKG57OKiRVn8sHDYKi71Gw8WEw0GJiIiIiIiIZCG3JJtIJMKTj6/B6lUrcONGNNIz6hdCMDUxgaurC5SVleXVFBH9R+HdMhwMu4LdwYG4eiuxzfIqikqY5joES73GYIKjG5QUFbsgSiIiIiIiIqLeS+4TLSkrK2PIEHcMGeIu70MT0T1q6+pwNu4G9oQE4J8b16QaDjp0oA2WePtinsdw6Gr264IoiYiIiIiIiPoGzmZOdJ+Jy0rDnuBA/HH1IjKLC9ssb6zVHws9R2Gxly+cB5h3foBEREREREREfZBMSbZvv/sBIpEIDz+0BDo6Ovj2ux+krisSibBu7ZOyNEvUZxXeLcOB65exOyQQ124ltVleRVEJ013rVwcdz+GgRERERERERJ1OpiTbNw1Jtpkzp0NHR0f8tyAIbdZlko1IOjW1tTgbF4k9oRfwT+RVVNXWtFlnqIUNlnr5Yi6HgxIRERERERF1KZmSbJ9+/D4AwMjQsMnfRNRxsZlp2BMSgD9CLyKrpKjN8gO0dRpWB/WF0wCzLoiQiIiIiIiIiP5LpiTb/HlzWv2biNqnoKwUB8KuYE9wIK7dlm446Ay3YVji5YvxDq4cDkpERERERETUzbjwAVE3aRwOujskEEdvXJNqOOgwC1ss9a4fDqqjodkFURIRERERERGRNJhkI+piMZl3sCc4EPuuSjcc1ERbt2F10NFwNOZwUCIiIiIiIqKeSKYk2/r/vS5zgyKRCJ989J7M9YnuR/llJThw/Qr2hATi+u3kNsurKiljZsNw0HEOrlBUUOiCKImIiIiIiIhIVjIl2f484C9zg0yyUV9RU1uLM3GR2B0SgGM3rks1HNTT0g5LvXwxx8OHw0GJiIiIiIiI7iMyJdl279wm7zjapaamBkeOncGFSyHIzsmDmpoqnBxsMHfWNFhbDWzXscrK7uLI0dMIvR6J7Jxc1NUJ0NPVgauLA/wemAwjQ4NOehTUW0Vn3MaekEDsu3oJ2VIOB13kWb86qIOxaRdESERERERERETyJlOSzcfHS95xSK2mpgYfff4DomPjoa2thaEerigsKkLI1QhcC4vCy889Dnc3Z6mOVVhUjLfe/wrZObnQ1uoHV2dHKCopICn5Ns6cv4TLQdfw2vq1sLWx7ORHRfe7/LIS/Hmtfjho2J22h4OqKSljppsnlnj7Yqz9IA4HJSIiIiIiIrrP3XcLHxz++xSiY+Nha22BV9evhYa6OgDg4pVQfP/jr9j40w588+lbUFdXa/NYBw8fQ3ZOLga7OuH5dauhpqoKAKitrcUvO/7AmfOX8Otv+/HuGy926mOi+1N1bQ1Ox0Zgd3AgjkdfR3VtbZt1vKzssMTLF3M9fNBfncNBiYiIiIiIiHoLuSTZ0jMycOVKMDw83GFjbSWxTFJSMsLCIzBiuA9MTAbI1E5tbS2OnjgLAFi5fJE4wQYAo4Z74sKlEIRFRONc4BVMnzKuzePFxCUAAPxmThEn2ABAUVERC+bOwJnzl5CYfAuCIEAkEskUM/U+0em3sTukfnXQnNLiNsub9NfFYs/RWOzlC3sjky6IkIiIiIiIiIi6mlySbNu278S27Ttx8viRFsuIFBTw8obXsGb1SvxvvWx3hsXFJ6G07C6MDPVha23RbP8In6EIi4hG6PUIqZJsSkptP/x+mhpMsBHySkvw5/XL2BMSiPA7KW2WV1NSxgODPbHYi8NBiYiIiIiIiPoCuSTZAi9cgqODPaytWp67zNrKEo6O9ggMvChzki31Vlr9sSwlL27QuP1WQ7m2uLs6IznlNg79fQK2NpZQVVUBUH/H3P6D/wAAxo8dIVOsdP+rrq3BqZgI7AmRfjiot5W9eDiotrpGF0RJRERERERERD2BfIaLpmdg9Ki2k1FWlpa4fCVI5nZy8/IBAHp6OhL36+nWby8tu4uKikqoqalKLNdo9szJuJmYjIgbsXjmpbdgZ2sFJUVFJCbfQmlpGR6YPhEL5z0gVWxvvveFxO3zZ42HibEB8nMypDpOT1dTUw0AvebxSBKbnYE/I0JxKOoa8u+WtVl+gFZ/zHUbhnmuw2CtbwgAqCktQn5p2yuLUt/RF/oOUWdg3yGSDfsOkWzYd4hk0xv7jp5h+6d7kkuSra6u7Tt8AEAkEqGyskrmdioqKgEAqqqSk2f3JtXKKyraTLKpq6thw/NPYvuufTgbcBnXwm6I91lbDYSdjRUUOMyvT8i/W4a/oq7jz8hQRGelt1leVUkJUx3dMN/NE8MtbTkclIiIiIiIiKiPk0uSzdzMDGFhEaitrYWioqLEMrW1tbh+PVzmRQ86Q25ePj79ajPy8wuwesViDHF3haqKMuITU7Bz95/4euPPWDB3Bub7TW/zWC2tQNqYxZUlA9oT9abHU11bg5Mx4dgTHIgTMWFSDQf1sXbAEi9fzHH35nBQapfe1HeIuhL7DpFs2HeIZMO+QyQb9p16ckmyjR3ji5+3/Ypvv/sBzz+3TmKZb7/fhOycHDyyfJnM7TTemVZZWSlxf+OdbgCgrqbW5vE2bdmF23fS8dzTq+DjNUS83WOwC0xNjLD+tY9w8PAxjPQZBpMBRjLHTT3LjbRU7A4JxP5rl5BbWtJmeTMdfSz2Go3FnqNha9hzksRERERERERE1HPIJcm2+tFHsP/AQWzc9BMib0Rjwfw5sLa2AgAkJ6fgzwOHcD4gEP37a+Ox1atkbsdAXw8AkJ9fKHF/fkH99n6aGm0OFc3LK0B0bDyUlJTgNcy92X4jQwPY2loiOiYe0bHxTLLd53JLi7H/Wv3qoJFpqW2WV1dWwazBXlji5QtfO2cOGyYiIiIiIiKiVsklyWZoaIitm3/A40+tw/mAQAQEXmiyXxAE6OvrYdPGb2BsLHuyytLCDACQnHpb4v7G7RYN5VqT15CQU1NVaTGBoqmhDgAoK7vb3lCpB6iqqcHJmDDsCQnEiehw1Egxd+DwhuGgfh4+0FZT74IoiYiIiIiIiKg3kEuSDQCGDHHH6RP/YO/v+3Dx0mWkp9ePxzU1NcHoUSOxcOF8aGtpdagNR3sb9NPUQHZOHhKTb8HW2qLJ/stB1wAAnkMGt3ks3f7aAOpXIs3MysEAY8Mm+2tra5GccgcAYGig16G4qWtFpqVid3D9cNC8sraHg5rr6mOR52gs8fSFjaFxF0RIRERERERERL2N3JJsAKCl1Q9rVq/EmtUr5XlYMUVFRUyfMh77Dv6NbTt+x6vr10JDvf5uo4tXQhEWEQ0trX4Y5ztcXCchKQWbftoJAPji4zfE2w0N9WFlaY6U1DvYsm0Pnlv7KLT6aQIAampq8dvv/sjNy4eGujoGuzp3yuMh+ckpKRIPB72RfqvN8urKKpjtXj8cdLQth4MSERERERERUcfINcnWFWbPnISomJuIjo3H8xveg7OjHYqKihF7MxGKiop4as3DUFf/d9GDqspqpGdmSzzWYyuX4oNPv6s/1vp3YGtjBRUVZSSn3EZefgEUFRXx2Kol0NTkKpI9UVVNDU5E1w8HPRkj3XDQETaOWOLli9nu3hwOSkRERERERERyc98l2ZSUlPDKS0/hyNHTuHA5FNeuR0JVTRWeQ9wwz286rK0GSn0sa6uB+OS9V3Dk2GlE3IhFTFwCBEGATn8t+I70wsxpE2BpYd6Jj4baSxCE+uGgDauD5peVtllnoK6BeHVQawMOByUiIiIiIiIi+RPV1lQK3R1Eb5afUz83nZ6hSTdHIh/d9XiyS4qw7+ol7A0JRFSG5IUv7qWhooLZg72xxMsXo2ydOByUul1vOxcQdRX2HSLZsO8QyYZ9h0g27Dv17rs72ajvqKqpwfHo6+LhoLV1dW3WGWnjiCXeYzB7sBe0OByUiIiIiIiIiLoIk2zUowiCgIi0VOwJDsD+65elGg5qoWeAxZ6+WOw1Glb6Rl0QJRERERERERFRU0yyUY+QVVyIfdcuYU9wIGIy77RZXkNFBX7u3ljiNQYjbRw5HJSIiIiIiIiIuhWTbNRtKmuqcTwqDHtCAnAqNkKq4aCjbJ2wxMsXszgclIiIiIiIiIh6ECbZqEsJgoDwOynYExKI/dcuo+Bu28NBLfUMxauDWnI4KBERERERERH1QEyyUZfIKi7EH1cvYk9IIGIz09osr6miitnu3ljq5YsRHA5KRERERERERD0ck2wktfKqKoTcTkZpZQVMS4rhYW4NdRWVFstX1lTjWFT96qCnpRwO6mvnjMUNw0H7qarJM3wiIiIiIiIiok7DJBu1Kb0wH5sCjuG3oAAUlpeJt+uoa2KZzxg8NXY6TPrrAqgfDnr9djL2NgwHvbd8Syz1DLHEq351UAs9w057HEREREREREREnYVJNmpVZFoqFmz+FDmlxc32FZaXYeO5o/gj9CJ+XPYkItNSsSckEHFZ0g0H9fPwwVIvXwy3duBwUCIiIiIiIiK6rzHJRi1KL8xvMcF2r5zSYszf/IlUxxxj74LFnr54YLAnh4MSERERERERUa/BJBu1aFPAsTYTbNKw0jfCEi9fLPIcxeGgRERERERERNQrMclGEpVXVeG3oACZ6/dTVYOfuzeWeo/BcGsHiEQiOUZHRERERERERNSzMMlGEoXdSZZq0YL/8jC3whNjpmGm2zBocjgoEREREREREfURTLKRRCUV5TLV+9+0+Zji4iHfYIiIiIiIiIiIejgu6UgSaampy1RPW8Z6RERERERERET3MybZSCIPc2voqGu2q46uhibcza07KSIiIiIiIiIiop6LSTaSSF1FBct8xrSrzjLvsVBXUemkiIiIiIiIiIiIei4m2ahFT42dDsN+2lKVNdLqjyfHTuvkiIiIiIiIiIiIeiYm2ahFJv11sf/x9W0m2oy0+mP/Yy/DpL9uF0VGRERERERERNSzMMlGrXIzs8S5F9/H2nEzoKvRdI42XQ1NrB03A2dfeA+uZpbdFCERERERERERUfdT6u4AqOcz6a+Ld2cvwSvT5iPwRihKKitgZmwGd3NrzsFGRERERERERAQm2agd1FVU4DmwfvVQPUOTbo6GiIiIiIiIiKjn4HBRIiIiIiIiIiKiDmKSjYiIiIiIiIiIqIOYZCMiIiIiIiIiIuogJtmIiIiIiIiIiIg6iEk2IiIiIiIiIiKiDmKSjYiIiIiIiIiIqIOYZCMiIiIiIiIiIuogJtmIiIiIiIiIiIg6iEk2IiIiIiIiIiKiDmKSjYiIiIiIiIiIqIOYZCMiIiIiIiIiIuogUW1NpdDdQfRm2Rm3AQhQUlLu7lDkoqamGgB6zeMh6irsO0SyYd8hkg37DpFs2HeIZNMb+46Ssgq0dfTbVYd3snUyBQUFQCTq7jDkJiMrFxlZud0dBtF9h32HSDbsO0SyYd8hkg37DpFs2HfqKXV3AL2dgbFZd4cgV1//uBsA8O4bL3ZzJET3F/YdItmw7xDJhn2HSDbsO0SyYd+pxzvZiIiIiIiIiIiIOohJNiIiIiIiIiIiog5iko2IiIiIiIiIiKiDmGQjIiIiIiIiIiLqICbZiIiIiIiIiIiIOkhUW1MpdHcQRERERERERERE9zPeyUZERERERERERNRBTLIRERERERERERF1EJNsREREREREREREHcQkGxERERERERERUQcxyUZERERERERERNRBTLIRERERERERERF1kFJ3B0D3h5qaGhw5dgYXLoUgOycPamqqcHKwwdxZ02BtNbC7wyPqNskptxEZHYekpFQkJt9Cbl4+AODbz96GoaF+i/WysnOw/+BR3IiOQ1nZXejp6cDH0wNzZ0+DmppqV4VP1C1qamoRHXsT18KiEB0bj+zsXNTV1UFPTweDXZ0xa8YkGBroSazLvkN93elzF3EjOg63bqejqLgEFRUV0NTUhK21BSaNH42hHq4S67HvEDUlCALe//Q7RMfEAwA+//A1mJkOaFaOfYf6uk1bdiLgYnCL+yeOG4XVKxY3295X+46otqZS6O4gqGerqanBR5//gOjYeGhra8HZ0Q6FRUWIu5kERUVFvPzc43B3c+7uMIm6xRff/ITQ65HNtreWZEtOuY33Pv4W5RUVsLYcCGNjAyQkpiI3Lx8DzU3w9qvPQ0NDvbNDJ+o2kVGx+PCzjQAAfT1d8Y81iUmpKCgsgrq6Gv734lNwsLNuUo99hwh48X/vISsnFwPNTaGnowMVVWVk5+QhKfkWAGDmtAl4aPHcJnXYd4iaO3X2An7+9XeIRCIIgiAxyca+Q/Rvkm2wqzN0+ms12+/i7ICxo32abOvLfYd3slGbDv99CtGx8bC1tsCr69dCQ72+M1y8Eorvf/wVG3/agW8+fQvq6mrdHClR17O3s8ZAc1PYWFvAxtoCr771KYqKS1osX1dXh+9+3I7yigosXjALfg9MAVCfzP7qu59xLfwGdv9xSOKvQUS9hUgkgo+nB2ZMm9AkkVZVVY1fdvyO8xeC8N2m7fjqkzehpKQIgH2HqNHjqx+Chblps7sAYm8m4uMvfsDfx85guNcQ2NlaAWDfIZIkL78Au/84BHc3Z6SlZ4lHItyLfYeoKb+Zk+HibN9mub7edzgnG7WqtrYWR0+cBQCsXL5InGADgFHDPeEx2AUlJaU4F3ilu0Ik6lazZ07GwvkPwHPoYOjp6rRZPvRaBDIyszHQ3ASzZ04Wb1dSUsLqlYuhqKiAc4GXUVJa1olRE3UvVxdHPLf20WZ3qqmoKGPV8oXQUFdHbl4+biYkifex7xDVc7CzljjMxsnBFiO8hwIAbkTHibez7xA1t3X7XtTV1eHRR1r+ks++QySbvt53mGSjVsXFJ6G07C6MDPVha23RbP8In/qLudDrEV0dGtF96VrYDQCAj9cQiESiJvt0dfrD0cEWtbV1uB5+ozvCI+p2KioqMBlgCAAoKCwSb2ffIWqbomL9nZ9KSv8OVmHfIWoq8GIwwiKisXDeAy3O/wmw7xDJqq/3HSbZqFWpt9IAANaWkhc3aNx+q6EcEbUuRco+lco+RX1UXV0dcnLrh+3o9NcWb2ffIWpdSuodXAm+BgUFBbgPdvl3O/sOkVhhUTF27P4TttYWmDZ5bKtl2XeImgq+Go7tu/Zj6/a9OHj4OBKSUiSW6+t9h3OyUasa5yfQ09ORuL9xeFxp2V1UVFT26lVCiOShsU/pt9GncvMKuigiop4l8GIwiktKoa3Vr8lwUvYdoqbOBV5BTGw8amprkZObj4TEFCgqKmLV8oUYaGYiLse+Q/SvbTv3obyiAmtWLoGCQuv3m7DvEDV1/NT5Jn//ceAIPAa74OnHlqNfP03x9r7ed5hko1ZVVFQCAFRVJSfP7k2qlVdUMMlG1AZp+1RFRUWXxUTUU+Tk5GHX3oMAgIXzH4CysrJ4H/sOUVM345MQcDFY/LeqigoeXjoP48eMaFKOfYeoXlBIGIJDwzB75mRYWpi3WZ59h6iepYU5VtlaYZCzA/T1dFFcUoro2Hj8vv8vhEVE47OvN+OtV58TJ677et9hko2IiIi63d3ycnz+7U8oLbsLH68hmDhuVHeHRNSjPbZqKR5btRQVlZXIzMzB8VPnsXX7XoRcDccL61ZDRUWlu0Mk6jFKS8uwbecfGGBsiPl+07s7HKL7yoyp45v8baiqh7GjfeA2yBEbXv8INxOSERwajuHeQ7opwp6Fc7JRqxqzzJWVlRL3N2apAUBdTa1LYiK6n0nbp9TYn6gPqaqqxudf/4Rbt9Ph6uKApx97uFkZ9h0iydRUVWFlaY7HH12G8WNGIDwyBn8fO/vvfvYdIuzYcwBFxSV49JFFUFFRbrsC2HeI2qKnq4OxvsMBAGERUeLtfb3v8E42apWBfv2KO/n5hRL35xfUb++nqcGhokRSMNDXQ1nZXeTlF0ocqtDYpwz0dbs4MqLuUVNTi6++34qYuATY21rhxWcfazJMtBH7DlHbfEd542zAZVy9HoG5s6cCYN8hAoCr1yKhrKyMA4eO4cChY032FRUVAwA2bdkJFRUVTJ00Bj5eQ9h3iKRgMsAIQNMV4ft63+GdbNQqSwszAEBy6m2J+xu3WzSUI6LWWUnZpyzZp6gPqKurw8affkVYRDQsB5ph/QtPQq2F+TvYd4japq3VDwBQXFIq3sa+Q1SvuroaMXEJzf5V19QAABKTbyEmLgF5DTcXsO8Qta2s7C4AQFX13ykK+nrfYZKNWuVob4N+mhrIzslDYvKtZvsvB10DAHgOGdzVoRHdl4Z6uAIAgkKuQxCEJvsKCosQdzMRiooK8Bg8qDvCI+oygiBgy7Y9uBJ8HaYDjPDKy0+jn6ZGi+XZd4jaFh0bDwAwNjYUb2PfIQJ+3vQp9mz/TuK/xpE7n3/4GvZs/048/xT7DlHrBEFAcGgYAMDGykK8va/3HSbZqFWKioqYPqX+g2bbjt9xt7xcvO/ilVCERURDS6sfxjWMxSai1g0b4gaTAUa4fScDh/8+Kd5eU1ODrdv3ora2DuN8R4jvRiDqrXbtPYhzgVdgZKiP1zasQ39trVbLs+8QAUkptxByNRy1tbXN9l0Lu4Hf/zwCAJhwzwqj7DtEsmHfIaq/6+zi5VDUNNzx2ehueTm2bNuDxORbUFVVwThffu40EtXWVAptF6O+rKamBh99/gOiY+Ohra0FZ0c7FBUVI/ZmIhQVFfHSs4/BY7BLd4dJ1C2uhd3AwcPHxX8np95GbW0tLC3MoaxUP+3lEPdBmOc3TVwmKeUW3vv4W1RUVMLaaiAGGBkiPjEFuXn5GGhugrdffR4aGupd/liIukrotQh88e0WAICLsz0M9CTPyeE5dDC8hrmL/2bfob4u5Go4vvxuKzQ11GFtNRD9tbVRdvcuMjKzkZWdCwCYOW0CHlo8t0k99h2ilq178S3k5uXj8w9fg5npgCb72HeorxN/7mhqwMbKAtpa/VBYVIyUW3dQVnYXamqqeObJlRji3vSutL7cd5hkI6nU1NTgyNHTuHA5FNnZuVBVU4WTvQ3m+U2HtdXA7g6PqNucD7yCH3/+rdUyY0Z548k1TVdLzMzKwX7/f3AjKg5ld8uhp6sDHy8PzJs9jYuIUK8nTb8BgPl+07Fg7owm29h3qC/LLyjCmXMXEROXgMzsHJQUl0KkIIKuTn842FljwrhRcHKwlViXfYdIstaSbAD7DvVtWdm5OHbyHBKTUpGTm4/SsrtQVFSAoYE+3AY5YvrkcTA01JdYt6/2HSbZiIiIiIiIiIiIOohzshEREREREREREXUQk2xEREREREREREQdxCQbERERERERERFRBzHJRkRERERERERE1EFMshEREREREREREXUQk2xEREREREREREQdxCQbERERERERERFRBzHJRkRERERERERE1EFMshEREREREREREXUQk2xEREREREREREQdxCQbERERERERERFRBzHJRkTt4jtuMmzsB3V3GD3KX0f+gd+8hXBxGwYb+0FwHzq8u0OSWU95fbs6jry8fLy8/lUMHzUO9k6DYWM/CL9s29Fl7cvD199uhI39IFwJCu7uUORq/58HYWM/CF9/u7Fb2l+ybAVs7Afhzp20bmn/5fWv3lev6507abCxH4Qly1Z0dyjUyRrPOfv/PNjdoXSp7j4nEcnb6TPnYGM/CHv2/tFke1FREV5a/wrchw6Hm4cXnnjqWWRkZEo8Rk1NDWbMmotpM/xQXV0tscw33/0AWwdXRETekPtjIOpJmGQjIuqA8IhIPP/iBsTHJ2L0qBGYP9cPs2fN6O6w6B7SJOz+9+ob+PPgIejo6OCBmdMxf64f7O1suyhC6sts7AfBd9zk7g6j010JCoaN/SC8vP7V7g6lT+nI+6uv/ujS3Yl1oq5UU1ODjz75HBYDB+LBBfOa7HvuxQ04cPAwHOxt4eHujhMnT2HVmidRV1fX7Djbd+xCbOxNvPv2G1BWVpbY1qoVy6GtrYUPP/qsUx4LUU+h1N0BEBHdz86cOYe6ujq89earWPTg/O4Oh2RQVVWFc+cDYWZmir8P/wlFRcXuDonuMWXKJAzxcIeurm53h0JSMDY2wsljf0FNXa27Q6FOtvyhpZg1czoMjQy7OxQiktG+/QeQlJSMD957C0pK/6YGIiJv4Pz5QCxaOB8fffAugPo70b75diOOnziF6dOmiMtmZWXj229/wNw5s+Ht7dliW1pa/bD84WX47vtNOHsuAOPHjem8B0bUjXgnGxFRB2RkZgEALC0GdnMkJKuc3FzU1tbC3MyMCbYeSFtLC7a2NtDTY5LtfqCsrAxbWxuYmZp2dyjUyfT0dGFrawNtLa3uDoWIZLRj1x6oqqpi5szpTbZHR8cAABYu+PcH5MULFwAAYmJim5R9/8NPoKCoiFf+91Kb7c2bM7uh3d0dipuoJ+OdbESExMQkbN7yC0JCryIzMwuqqqowNDCAh8dgPPzQEgx2c5VY76D/YWz7dRcSEhKhqqqC4T7e2LD+BVhZWjYpV1FRgUN//Y2zZwMQFxeHrOwcKCoqwMbGGrNnzcQjDy9r8usZUD+vz5jxU+Dj7YWtP23Edxt/xPHjJ5GRkYmxY32xedN34mPv2LkbR/45huTkZNTVCbC1scaC+XOwbOnididNoqJi8ONPWxEcEorCwkLo6OjAx9sLTzz2KFxcnMXlvv52I7797gfx30sfWin+/6cfv48F8+e22VZ7Y4+KisFff/+Dy1eCkJ6eieLiIujr6cPLaxiefGINnBwdJLZTW1uLAwcPw//QYUTHxKK8vAKGhgYY5OKChQ/Ow4TxYyXWk/b1bU3j8/Tpx+/DyckRX3/zPa5dD0NlZRUcHezx6KpHMHPGNKmPd/rMOZw8dQbXr4chMysbVVVVMDU1wYTxY/Hk42uaJGKuBAU3eV3+O9woKT6qybag4BDx32Zmpgg8d1K8T9r3xb1tmZmZ4szJf/DT1m04fPgIbt2+AxtrK/z914Emz8ugQS74+pvvERJ6FTXVNRjs7ob1Lz0v7nf79h/Ezl27kZiUDA11dUyZPBEbNrwo9RfbwsJCbPt1F46fOIX09AzU1tZCX18PTo4OmOM3CzOmT5XqOMHBoTh67ASCQ0KRmZWFstIyGBkbYfSoEXjqicdgbm7WrM6SZSsQFByCgLMnEB4Rie07diEuLh6lpaUIu3oZ0TGxWPrQSsyf64cNG17EV19/j3PnA5CTk4uHli3Gm6+/gv1/HsT6/72OZ9Y9heeeeRoA8OTTz+L4iVP46otP4Df7AYnxrnn8aZw+cw7ff/ul+DHK8hhakpGRiS0/b0Ng4CVkZGZAQaQAfQN9uLkOwsIH52P0qBGt1m98XACQlpbe5L343/dfo2vXw/Dtdz/gelgEqqurMcjFCc+sexq+o0dKbCMrKxubt/yMc+cDkZ6eATU1Nbi5DsLqRx/B2DG+Uj9WAAi8cAnbtu9E3M2byMvNg5aWFgYMMIaPjxcef+xRGBoYAGh67t7z23YA9fPK/XnwEADgz4OHxP8HgPlz/fDZpx+K/y4uLsbWn7fjxMnTuHX7DhQVFeDk6IBlS5dgjp/k17o1Kamp2LJ1Oy5duozMrGyoqanC3MwM48eNwcoVy6GrqwOgfu6hQ4eP4Oy5ACQmJSM7OweqqqpwcLDDogfnSzynN55j5s/1wxtvvIKvv/4ex0+cQl5eHoyNjTFnziyse/qJZp9vjc/H7l3boKKi0q7XVNpzkSzvr/8+rkaSzp1A0/P7f5+f2tpa7Nt/AH8ePISbNxNQVVUFi4HmmPXADKx+dAXU1Nq+01HaOACgsrIKO3b+hsN//Y2k5JT68tZW8Jv9AB5+aClUVVXabK/xvdvo3v8DQMDZE83OEVlZ2fj8y29wPiAQxcUlGGhujmVLF2HFIw9JbEOe1yztuXbr6Pk7KjoGW7ZuQ2xcHNTV1DFmzGj8b8OLMDQwQEVFBX7Y9BP+OvIPMjKzYGxkhKVLFuKxNasgEok69Tlo/Kw9e+ooNm/5BQcOHkJaWjp0dPpj4vhxeP75dTDQ15dY98LFy/h1xy6EhUWguKQYBgYGGDd2DNY9/QQGDDBuUvbez6G5frPw9bcbcenyFeTl5ePV/72MVSuXo7q6GgcOHsIf+w7g1u07KCkpgZ6uLiwsBmL0qBFY+/QTTY5ZV1eHffsPYN/+g7h5Mx7VNTWwGGiOaVMnY/WjK6Gl1a/FGJYserBd77uWXL12HXFxNzFj+tRm1xRFRcUAAG3tf7fr6PQHABQWFYm3BV64hL//OYZ33n69xef6XpaWFhji4Y7AwIu4cyetXZ+7RPcLJtmI+rioqBg8uPghVFRUwMHBHhMnjENNTQ3S0zPgf+gvWFoMlJhk++yLr7Fl6zZ4eQ7FhPFjERkZheMnTuHqtes49vehJsmO1Fu38cqrb8LAQB821tZwc3NFYWERwsIj8MGHn+Ly5SBs2byxxYuxJctWICX1Fny8PeHi4gwdHR0AQH5+AR5ZuQZR0THQ19PDsKFDoKSkhLCwCLz97oe4fCUYP3z/tcTjSvLP0eN4/sX1qK6ugZvbIAz38UZySiqO/H0UJ06cwtdffYZpU+vntnFxdsL8uX4IvXoNqbduY4zvKPGXTEtLizbbkiX2jZs24+SpM3B0sIf7YFeoqqoiKTkFfx35BydPncH2nzc3u02/oqICax5fi4uXLkNFRQXDhg2Bvp4eMjIzcfHSJRQVFUlMsrXn9ZXG9bAIvP7muzA1NcHoUSORk5OLkNCrWPfsi0hJvYWnn3xMquOs/99rqKyshIO9PUaPGoGqqipEx8Ti519+xfETp3Bw/17o6+sBAAwNDDB/rh+OHj+Bu3fLMX+uX7PjzZ/rh7K7d3Hs+EkYGOhjrO9oAIDuPY+vPe+Le9XV1eGJp57FpctX4OPtCQcH+2aTAUdE3sCbb7/fcBE+Eikpqbh06QqWPbwS/n/+jj2/78POXbvh7eWFMb6jcPXadez5fR9Sbt3Cbzt+afP5Kisrw9wFS5CaegvGxkYY7uMFVVVVZGZl4fKVINwtL5c6yfb+R5/iZtxNODo5wMtzGAARbsbHY+/v+3Hs+Ens//032NhYS6y7ecvP+G337xg6xAPjx41BckoqcM97Oy+/AHPmLUJlRSU8PYdCEABtbe0WY5k7ZzaOnzgF/8NHJCbZ8vMLEBB4Adra2pg4YbxcHsO9MjIyMctvAfILCmBpMRCjR42CoqICMjIyceLkaWhoaLSZZLO0tMD8uX748+AhaGioY/rUf7/Q60roX2fPBmDbrzvhYG+HsWNGI/XWLVy9FoZVq5/Aju1bMGK4T5Py4eERWLXmSRQUFMLSYiDGjR2D4uIihF69houXLuP1Vzdg1crlbT5WANi95w+8/uY7UFBQwNChHhg2dAhKSktx69Zt/LJtB6ZOmSQ+/0ni6TkUObm5CAi8CEuLgfAcNrTJvkYpqalYvmIN7txJg8mAARg1cjiqqqpwPSwCL7y0AZE3buCN1/4nVcwAcPLUGTz7/MuoqKiAxcCBmDhhHCoqKpCckorvf9iMkSOHY7iPNwAg9Op1vP3uhzAxGQArK0t4uA9GXl4erl4Lw9Wr1xEReQPvvv2GxHaKS0qw4MGlKCgohKfnUFRUViIkJBTffb8JmZlZ+OSj9yTWa+9r2p5zUXvfX/eS5tzZmsrKSqx5fC0uXLwELS0tuLkNQj9NTURGRuHLr7/DufOB2LXj5zYTbdLGUV5ejuUrVuPqtTBoaWmJE5SXrwTjw48/w4lTp7Fj25Y229PQ0MD8uX44H3gBubl5mDZ1MjQ1NJrsv1d6egZmz30QSkpK8PH2Qn5BAUJDr+Ld9z9CSUkJ1q19skl5eV6ztPfarSPnvh27duOXbTvgOWwoxo0dg4jIGzjofxg3bkThz317sGLVY0hMSoKPtxesrK0QHByKTz77EpWVlXhm3VOd9hw0EgQBa595AefOB2LEcG+4ODshJPQq9vy+DwEXLmL/77/B2NioSZ0vv/oW3/+wGUpKShjs5gpjYyMkJiVjz94/cPLUaez97VeJz0dycgr85i2CpqYGvL08cfduOdQbhsa/+PIrOPL3UWhoqMPLcxi0tbWRm5eHhIREXL12vUmSra6uDk+vex7HT5yCmpoahvt4Q0NDHUHBofhu4484euwE9vz2q/h65l7tfd+15vSZcwAgPg/ey9TUBACQmJQMW1ub+v8nJgGA+E7lysoqvP3uB3BzG4RlSxZJ3e7w4d64HhaOM2fPYfnDy6SuR3TfqK2pFPiP//iv7/574YUXBUtrO2HTpk3N9mVm3BFiYqKabBs5eoxgaW0neAz1FG5Ehou3l98tEVY9ulqwtLYTvvzqqyZ1srMyhHPnzgrVVeVNthfk5wjLH1khWFrbCf7+B5vsS01JEiyt7QRLazth5iw/ISc7s1l8je29vH6DUFJc8O9xC3KFh5c/Ilha2wk7d+6U6nlIu3NLcB7kJlha2wn79v3RZN/u3b8JltZ2wiBXdyEj/Y7E5+/ixcB2Pe+yxB4QcL5Z+7U1lcKJ48cEOwcnYcLEKUJNdUWTff975dX65/CB2cLt2ylN9hUV5gsBAec7/Pq29u+LL78Uv47vvPtuk/fAxYuBgoPTIMHa1kGICA+TGMd/j/f330eaPF+1NZVCZUWZ8NlnnwmW1nbC/155tVmdlo713/fawkVL5Pa+aHzMo3zHCqkpSa0+L5t/2txk3wcffihYWtsJk6dME4Z5+Qix9/TBvNwsYdyESYKltZ1w6eKFNp//33/fK1ha2wmrHl0tVFXebbKvtKRQCAkJkvq1PHnyhJCfl91kW011hbBz507B0tpOeHj5I83qLFy0RLC0thPsHJyEc2fPNNt/8WKg+Hl4ZMWqZq/tvY/hiy+/bPJ+9BjqKdjYOQqZmWnN6mzb9kv9++F/r8jtMdz7Oja+fq+/8Uaz8gX5OUJY2DWpn1dLazth5OgxLe5vPMdY2dgL+/fva7Lv+43fC5bWdsKixU3fu0WF+YL38JGClY29sHPnzibnhfibscLI0WMEGzvHZuf3lv6NHD1GsLKxF65dDWm2Lzr6RpPXoKX+1Phav/DCixLbqK4qF6ZNnylYWtsJn3/+uVBRXirel552W5j5wGzB0tpOOHv2tFQxJyclCE4uboKVjb2w/dftzc6NYWHXhDu3U8V/JyXGC8HBV5odJzPjjjB9xgOCpbWdEBoS3OL7d9Wjq4XSksJ/n+f4OGGQq7tgZWPf7Bwgy2vakXNRa++vtl731s6djf3g99/3Ntn+5ptvCZbWdsLKVY8KuTlZ4u13y0qEF158SbC0thM++vhjucXx9jvvCJbWdsIsvzlN2svOyhBmzJwlWFrbCe+9/77U7Unq8/f+azwnWVrbCa+8+ppQWVEm3hcUdFmwtnUQXFwHNzufyfOapb3Xbh059zk6uwpXrly653UsFu+bPGWaMP/BhUJBQa54f2REmGBr79Tpz0Hj+9vS2k4Y5uUj3IyLaRLjmsceFyyt7YQ1jz3RpM7ffx8RLK3thDHjJgjR0Tea7Gt8PubMnd/ia75+w4Ym56famn/PeyNG+Ta7Xq2uKhcuXAhosm3r1q2CpbWdMNp3nHArNVm8vaS4QPw8PP7Ek3J537X2b+68BYKltZ3Ez6zMzDTBycVNmDp9hpCYeFPISL8jLFn2kGBt6yBERUUKtTWVwtdffy1Y2zoI169flbrN2ppK4fjxoxIfI//xX2/5xznZiPq4vPwCAJB414WhoSEc7O0k1nvu2bVwdnYS/62ioiL+lS4oKLhJWX19PfiOHgkFhaanHG1tbbz5Rv1Kc8eOtzx05Z03X2t251RMTCxOnzkHB3s7vP/um01+YdbW0sKnH38AFWVl/LZ7b4vHvdfv+/7E3bvlGD9uLOb959fyRQsXYPSokSgtK8Pvf+yX6nitkTX2USOHw0jCBNMTJ47H9GlTkZiUhPiERPH27Owc7Nt/ACrKytj0wzcwNTFpUq9fP02MGjlcYozteX2lYTJgANa/9HyT98BwH28sWjgfdXV1Us/NMW3q5GZ3EygpKeGF55+BsbERjrfyPpJFR98XL7/4XKtDIYYO8cDqVSuabHvy8dUAgJvxCXj+2bWwv6cP6ujoYOmShQCAK8Ehbcaf39C/Rwz3aTYER11dHUOHeLR5jEYTxo9F//79m2wTiURYumQhhg31wIWLl1FaWiax7oL58+DrO6rFY6soK+O9d99o9tq2WF5FBTNnTENtbS3++uufZvsP+v8FAJgzZ5bcHsO98vPzAQCjRjY/b2pra8PNVf4rIU6fNgVzG+ayafToykegra2Na9fCmtwluf/Pg8jKysbihQuwdMnCJneF2NhY47VX1qO2thZ7f98nVdv5+QXQ0tKCu/vgZvscHexbvYtNWmfOnkNMbBx8R4/E88+ta7I6nbGxET784B0AwG+7f5fqeD9v+xXl5eVYvOhBPLxsSbM7Y9xcB8HEZID4b0tLCwwbOqTZcQwNDbFh/YsAWv6c0tTUwEcfvgt1dXXxNhtrK8yZMwuCICCohb7ante0Kz+jOiIvLx979v4BfT09fPnFJ+LhuACgqqqCd99+HQYG+tj7+z6JKxS2V3l5ufgxv/v2G03a09fXw9tvvQYA2L3nd1RUVHS4vXuZmprgjdc2NBkO7DlsKMb4jkZZ2V1ERv47nFXe1yztvXbryLlv5YqHG+5+q6eqqoqVKx4GACQkJuHD995uMtTQxcUZ48b6dvpzcK91a58U323VGOM7b70OFRUVnDx1Gmlp6eJ9G3/YDAD44rOP4Ohg3+Q4S5csxMQJ43A9LFw8J9m9dHV18PqrG5qtnpnX8JkwyMW52fWqgoJCs7tSt/26EwCwYf0LMDP7d/5KDQ0NfPDe21BWVsLxE6eaxN2oPe+7tsTExgEAbCXctWdoYIBn1z2F2NibmDBpBoaPGodLl65g+UNL4eTogNu372DT5q1YsvjBJndNVlZWtdm3bW3rV2//79xuRL0Fh4sS9XFuri44dz4Ab779Pl54fh28PIe1uPT2vcaPbb4ikK1N/QVOVnaOxDrhEZG4ciUYaenpKC+vAAQBAgQAQHJKisQ6Bgb6GDLEvdn2wAuXAAATJoxrNt8NABgZGcLKyhJxN+NRUVHR5jCR4JBQAGhxzp/58/xw4eIlBIWEYl2rR2pbR2IvLinBmTPnERsbi6LiYtRU1wAAbsbHA6gfytB4cX0lKBg1NTUYO9a33ZOQy/L6tmbatMlQUWk+J86c2bOwY+du8fMvjbS0dJw5dx7JySkoK7uLutpaAEBNTS3yCwpQVFTU7MuErDr6vpg8aUKrxx8jIfHUv39/6OrqoKCgEGPGjG62v3FOvOys7DaiB1xdXQAAP239BQYG+hg/bmyzeV7aIzcvD6fPnENCQiJKSkpRW1P//svJyUNdXR1SU29h0KDmc9RNmdz68+Di4tzu9+hcv9nY9dte+B/6q8mwx6TkFIRHRMJi4MAmXww7+hju5TqoPon22edfQ1FBAaNGjZBqjqmOGDe2+RxqKioqsBhojhtR0SgoKBQn4QMCLwIApk6dJPFY3l71w8rDwyOlatt1kAtCQq/i5Q2v4dFVj7Q4/2NHiGOe0nzoNVD/5VVTUwNh4RFSHe/CxcsAgIUL2p4fs5EgCAgODkVI6FVkZWejoqISEASUltUnH1r6nHIdNEjiXERtnTPb85p25WdURwQFh6CquhojRvhInDdSXV0dbq6uOHvuPFJSUqUant2ayBvRuHu3HDY21hKnthg2dAisrCyRkpKKyBtREs8Jshrh4w1VVdVm221trXHufACysv89R8v7mkWWazdZz32N0yjcq3FaDDNTU9jZ2Tbbb2VV/znVmc/BvSRNG2BsbIQRw71xPuACQkKvwczMFLl5eYiKjoGRkWGLPzJ5e3ni9JlzCAuPbDbn6qiRI6Cpqdmsjo21NTQ01HH2XAA2//Qz/GY/0Gxet0bpGRm4cycNampqEqeaMDMzhY+3Ny5cvISQ0KtNknBA+953rbl79y7Ky8uhoqLS4g9cj61ZBRcXZ5w5cw61dbUYNXIEpkyeCAB4570PoampiZdffA5A/bx/77z3IWJi46CiooIpkyfi/XfflDj1g27D3G55eflSxUp0v2GSjaiPe2zNKlwPi8CFi5fw0PJHxRNjjx41AvPnz2l291Ojxrka7tWvX/2FR1VVVZPtZWVleOa5l3H23PkW42jpF9SWvnjfvnMHAPDj5q34cfPWFo8LAIWFRRgwoPWLtayGhMVAc3OJ+y0G1q8empUp3cVLa2SN/fiJU9jwyhsoLi5usXxpaan4/+npGQAAayvpFypo1J7XVxrmZpLv5jIzr399MxtWaW3LF19+gx9/+hm1DYk1SUpLy+SWZOvI+0JfX7/NLwkDBgyQuF1DQwMFBYUwkbBfU7P+Ylia12HkiOF4bPVKbP3lVzz3wnooKirC3t4Ww328McdvVouLmkiy87c9+PCjz1BZWdlimXvff/dqK4H23y8R0hgyxB1WVpa4ERWN+PgE8R1//v6HATS/iw3o2GO41/x5frh8JQiHDh/BmifWQllZCS7Ozhgxwgfz5/o1uaNCXiT1SQDiL3z3vh/uNJxjVqx6vNVj5hcUSNX2u++8gSeeegZ/HvDHnwf8oaurg6FDPDBu7BjMnTNL6jsQW3P7ThoA4PU338Hrb77TYrnKSunOP+Lzn7WVVOVzcnLw+JPPtJrEa+m90fJr03pfbc9r2pWfUR1x+3b9e+/I30dx5O+jrZbNLyiADTqWZMvKqv/sGNjKHcMWA82RkpIqfg7lpT2vn7yvWdp77daRc5+kZFHjXHUtJZIazwmd+Rw00tbWbnEhILOGa4/MzEwAQFrDeSY7O6fZQhr/VSDh/NjSa66l1Q+ffPQ+XnntLXzy2Zf45LMvYW5uBi/PYZg2dTImThgnvpO/sY+amZo2G+HRaOBA84a4m18bted915ri4pKGeq2fv0ePGtHsjskTJ0/jzNnz+PzTD6GtrY3s7Bw8+tiTsLK0xMbvvkJGRiY+//Ib1NTU4Ifvv252zH796n/sKy0rQ11dXYvPA9H9ikk2oj5OU1MTO7ZvQVhYBM6eO4+g4FCEhUcgJPQqfvhxC7775nNMmtj8LpT2fCB++vlXOHvuPIYNG4Jn1z0NZydHaGtrQVlZGVVVVXAaNASCIEis21KSoq6uvryH+2CJt7nfS9IdVN1JltjTMzLw3AvrUVdXi1c2vISJE8ZhwABjqKurQyQS4bMvvsamH7e0+Dy2V0+84Pnn6HFs3PQTjI2N8Nor6zF0qAf09fTFq8YtWLgM166Hye056Cg1tea/NP+XgkLrkzvL43X434aXsHTJIpw6fRaXrwQh9Oo1bP91F7b/ugtPPrFG/Ct0a8IjIvH2Ox9AU0MDb7/5KkYM94GRkaG4fz77/Mv468g/4jtT/6utZKOahF/lpTHXbxa++uZ7HDz0F9a/9DwEQYD/4SPiffJ8DPdSVFTEV198giceX43TZ87hypVgXLt+HeERkfhpyy94563XsWyp9JNAS6M974W6hj4wedKEVlehbWsC/EaODvY4/s8hXLh4GQGBFxASchVnzp7H6TPnsPGHzfh9zw7xl0JZCQ3Di0aOHA4TY8lf3DvT/159C2HhEZg0cTweW7MKdrY20NLSgqKiIpKSUzBpyky0dGpRaMck7U3q9cDzbEfVCfWvo4O9XZvDpnUbFjG6X4na0yflfM3Snmu3jp77Wnuftuu81AOu2xpj0NXVwYRxkldWb2QvYbqU1j7LZs6YhlEjh+PM2fO4cPEygkNCcdD/MA76H4aPtxd2bN8i1UiRtrTnfdeaxlVDy6SYIuFe5eXlePf9j+Dt5Skeur5z126Uld3Fxu++goVFfcK/qKgI3238ESmpqc1WpS8pqU/o9tPU7JXnQSIm2YgIAODhMRgeHvXz7ZSVlWHzTz/j+x8249XX35aYZGuPo8dOQFFRET//9EOz28ZTU2/JdEzThrl0xo71xbP/Wb1KFsbGRkhKSsbtO3fEz8O9Gn+BNR5g1Gxfe8kS+9mzAaisrMTqR1dgzeqVzfZLeh4bf+1MTkntQLTykZbefF4RAEi7U799gHHbz2vjfEjvv/sWJk4Y12y/rO+l1nTl+6IzWVgMxKqVy7Fq5XLU1tbi+IlTePHlV/Dj5q2Y6zdL4nCfex07fhKCIOClF5/FooULmu1PvSX/514ac/xm4etvN+LQ4SN4+cXnEBJ6DXfupGHYUI9mq/x2xmNwdLCHo4M9nnpiDSorq/DH/j/x9jsf4L0PPsasWTNaTXB1JpMBA5CUlIxVK5bDx8dLLsdUUVHBhPFjxasRZ2Rk4vU338XZc+fx2Rdf49uvP+/Q8RvnR5vrNwvz583paLgwNTVBcnIKkpNTJM4ld6+7d+/ifEAg9PX1sWnjN83mL+yMc0t73S/nIpMB9Z87bm6u+OyTDzq9PeOGhGzjnZCSNO777wqTXUne1yyNpLl26ynn7856DoqLi1FSUipxKoS0tPrXvvGOu8bzjKamJj779EO5xdBIR0cH8+b6iZNPEZE38OxzLyEoOAR/7DuAZUsXiftoWnp6i3dxNd6N3NKdgvKgoaEBdXV1lJeXo6ysTOIwWEm+2/gjcnJy8MvWH8Xb4hOSoK+nJ06wARCfdxMTkpol2QqLigBA4uqpRL0BU8dE1IympiZeeP4ZqKmpITc3r8NzJhQVFUFTU1PivAyH/vpbpmOOHj0SAHDq1Bm53LnUOEeR/6EjEvcfOHgIAODTUK4jZIm98YJE0vDBvLx88fxD9xox3BtKSkq4fOkK0jMyOhBxxx09dqLJJN6NDje8/l5SPK+FhfXPgalJ8+cg8MIl8cTD/9X4y3FNw/wz7dGV74uuoqioiBnTp2L4cG8IgoCb8Qlt1ilqfP9JGD6ekJCI6Ojumbx44EBzDBs2BBkZmQgKDsGhQ/ULHsyd49esbGc/BlVVFTy8bAlsbaxRVVWF5OQUqeopKyuhtqbl4c+y8G04xxw/eUqux72XickAPLO2fjGUuLibbZYX98MWhnr7jq6fn/DEydNyia9xeNP+A/5tli0pKUVdXR2MjQybJdgA4NBhyf2/K8l6LurI+0uWc+fIET5QUlJCQOAFuS000Focbq4uUFdXR1JSMiIibzTbf/16OJKTU6ChoS71giT/vlfb/5nREnlfs0jS0rVbTzl/d+ZzcFjCtWROTg4uXwmGSCSC57ChAOqTVvZ2trhzJ03iwgbyNtjNFYsXPQgAiLtZf540NTGBubkZKioqJC6mkpaejitB9XHLcw5BSZydHAEAiUnJUpVPTEzCz79sx8pHHm6yuIZIBJRXlDcpK+7/Eu70TWhYpOu/c94R9RZMshH1cbt+24uU1OZ3Ol2+EoSKigr009QU31IuKxsbGxQXF8O/4Qtwo/MBgfj5l19lOqb7YDeMGzsGUdEx2PDKGygsLGxWJik5Bf8cPS7V8RY9OL9h0trzzeLct/8gAgIvQlNTQ+KvwF0Re+PQigMHDzWZv660tAwbXnld4jxthoaGeHD+XFRVV+PJp59tNrdHWVkZLl660uHHI42MjEx8/sU3TVacCg4Oxd4/9kNBQQEPP7SkzWM0Pgc7f9vT5DipqbfwxpvvtljP2Kj+V+OExKR2x92V74vOcPzEKYRevdbsC012do74C0ZL8y7eq3Fy8j179zWZ7yU3Lw8vbXhVpgSmvMxrWJ3x9z/+xN9HjzesPDq1WTl5PoYDBw9JXBUtISERt++kQSQSSUyIS2JkZITcvDzxF2F5WLJ4IYyMDLFz1x5s276z2WMTBAEhoVcRevVam8cqLy/Htu07JZ6nzp4PBNDyHEH3auyHiS30w6lTJsHJyQEnT53BZ198jfLy8mZloqJicD4gsM22AGDViuVQU1PDnr37sHvPH836QOSNKGRk1M/TZGCgD21tbcTdjMeV/6yevG//Qfx1pPkKtl1N1nNRR95fspw7jYwMsXjRAuTk5OLpdS+In+N7ZWRkipOCHY1DXV0dixbOBwC89fb7Td6nBQWFeOvd+rvpli5ZJPVE+sYNi00kJrT/M6Ml8r5mac+1W085f8v7ObjXdxs3IemeHzYqKyvx1jsfoKqqChMnjGuyyvezzzwNAFj33EsIC2s+B2NpaRl+3/dnu5LEUVEx+Ofo8WZzRlZXV4t/BL33s3bF8ocAAJ9+9lWTO/3Ly8vxxpvvobq6BlOnTJJpvtL28PZuWAQnQrpFcN58+z0YGBjgmf/ciejgYI+7d8tx9NgJAPWfMQf9D0MkEjVb6RYAroeFA4Dc7rQm6mk4XJSoj9uz9w+8+fZ7sLa2gr2dHVRVVZCeno7rDRceL734bIfnkFj71ONY9+yLeOGl/2Hnrj0wNzfDrVu3ER4RiSceX93mBLgt+fLzj7Dy0Sew/8+DOHbskOZWHwAACT1JREFUBJydnWBiMgB3797FzZsJuHX7NiZPmoAZ05t/4f6vAQOM8enHH+D5F9fjhZf+h+07dsHSwgIpqamIjIyCsrISPvvkQ/FKbx3V3tgnThgHZydHREXHYNyEqfD0HAZAQHBIKBQVlbBg/lzs//Ngs3Zef20DklNScCUoBOMmToOn51Do6+khIzMTMTGxcB00CKNGDpfLY2rNksUL8euOXTh15ixcB7kgJycHwSFXUVdXh+efXSvVHQaPPLIMfx48hL2/78eVoBC4DnJBYWERgoND4OHhDkNDfVy9Ftas3uRJExAUHIKHlz+KESN8xBMyf/xhy4m5Rl39vpC3oOAQbP91FwwNDeDi4gyd/v1RUFCAkNBrKC8vx/RpUyQOPfuvB+fPxbZtO3HufADGT5wOD4/BqKioRHBICIyNjTFl8kS53YHUXjOmT8Xb734ovtto2tTJEhe+kOdjOH7iFF5a/yrMzEzh5OgATU1N5OTkIPTqNVRX12D1oyukfk9MnjQB23/dhQf8FmDY0CFQU1ODrq4ONrz8gvRPwn9oafXDTz9+j9WPPYX3PvgYP239BQ72dtDV1UVBQQGio2ORl5+P11/dIL7DoyXV1dV474OP8dEnn8PZ2RGWFhaoq6vDzZvxSEhMgoaGOp5d93SbMZmbm8HZyRE3oqIxe+5CONjbQklJGcOGDsGDC+ZCUVERm3/4Do+segybftyCvb/vg5OTIwwNDFBcXILY2DhkZmVhxSMPYeyY5qty/pelpQW+/PxjPP/iBrz+5jvYsvUXuLoOQkVFBZIahpHu3rUNJiYDoKioiKeeWIOPP/0CDy1/FD7enjAwMEDczXjcvBnfoc8peZH1XNSR95es587XXtmAtLQMnD13HhMmz8AgF2eYmpqgqqoaSUlJSEhMgrOTo3hIXUfjePnF5xARcQPXrodh3MTpGO7jDaA+4VRSUgJPz6F44Tnp11ydPHki/jx4CM+/uAGjR48U34W/4eUXoKurI/Vx/kue1yztuXbrSedveT4HjUxNTTDIxRkzHpiLESN80E9TE6FXryErKxumpiZ4563Xm5SfMX0qnn92Lb7+diPmPbgEzk6OsLS0gKKiIu7cSUNMTCyqqqsxfepkqROzaenpWPvMC9DU1ICb6yAYGRmhvLwcYeERyMnJhaWlBZYsflBcfsUjDyE45CpOnDyFKdNmY8Rwb6ipqSE4JBS5uXmws7XBu++8IfVzIKuJE8bhx81bcflyEB5e1vqPnf6HjuDylWD88P3XzRa7efihJdi2fSeef2E9Dhw8hKysbNyIiobf7Ackztd55UowFBQU2pwXj+h+xSQbUR/3wnPrcPrsOVwPi0BwSAjKyytgbGSEqVMmYcUjD8nlVvWZM6ZBR0cH3373Q/2Xlvh42NvZ4YvPPsLcObNl/vKio6OD3/fsEN9pEBt3E2Hh4dDX04eZmQnmz/PDzJnTpT7ejOlTYWExED9u3orgkFBER8dCR6c/Zs6YhiceWy1xaXtZtTd2ZWVl7N29A998txGnz5zDufMB0NPVxeRJE/H8s2ux94/9EttRV1fHju1b8ce+Azh46DAiwiNRVV0NQ0MDjPEdjYUPzpPbY2rNEI/BWLRwPr765nucD7iAqqoquLq64NGVj2DWAzOkOoaVpSUO+/+Bzz7/Gteuh+HkqTMwMzPFE0+swZOPr8GKVY9JrPfI8mUoKS3FocNHcOLEKVQ1DFuVJskGdO37Qt4WzJsDFWVlhIReQ3R0DIoKi6Crpwv3wa5YtHABHpCyf/Tv3x/+B/bi8y+/xaXLV3D6zDkYGRli4YPz8ey6p/De+x938iNpmba2NiZOGCf+BX3e3NkSy8nzMTy68hGYmpjg6rXrCAuLQElJCQwMDTBy5Ag8vGyJeN4yaTQuPHHy1Bn8c/Q4ampqYGZm2qEkG1A/TOnoEX/8umMXTp85h6vXrqOuToChgQFcXV0wccJ4qb7Iamho4N2338CVoGBEx8SKV4k2GTAAyx9eilUrljeZh6c1mzZ+g48//QIhIVcRHR2Duro61NbU4MEFcwHUD//9y38fdv22F8eOn0RkZBSqqqpgYKAPa2tLrFq5HDNnTJP6OZg2dTLs7Wzx09ZtuHjpMk6cPAVNTU2Ym5lh3don4eToIC772JpVMDU1wdZftiMyMgoiBQUMcnHCz1s2wd7OttuTbIBs56KOvL9kPXeqqqpg608b8deRf/DnAX9ERcUg8sYN6OjoYMAAYzzx+GrMnC7969hWHOrq6vht5y/4dcdvOPzX3wgIvACRSARrK0v4zX4Ayx9eJl4gRxpTJk/E22++ij2/78fZcwHiFTnXPvV4h5Js8rxmac+1W086f8v7ug0ARCIRvv/2S2z6cQv8Dx9Belo6+uv0x+JFC/D8s2thaNj8B491a5/EqFEj8OuO3xAaeg2JieegrqEOY2NjzJkzG1OnTIJWO+bUHOIxGC+9+ByuXAlGUnIyrodFQENDHWamplj5yMNYunRRkzk6FRQU8MP3X+GPfX9i3/6DCAoOQXV1DSwGmmPxwgVYs3qVxDnm5G3Y0CFwdHTAmbPnUVxcLHFaF6B+SP1Hn3yGsWN9MW3q5Gb7DfT1sXP7Fnz48ecIvHAJGhrqWLLoQbz6ysvNyqakpiIsPAJjx/o2ucOQqDcR1dZU9oxl2IiIqNf5+tuN+Pa7H/Dpx+9jwfy53R0OERER9RI29oNgZmaKwHPN5zYj6ezZ+wdee+MdvPfOm3JfFVuSr77+Dt9t/BE/b9mE8ePGdHp7RN2Bc7IRERERERER9TEPLpgHGxtr/LTlF4kLVMlTSUkpduzaDW8vTybYqFdjko2IiIiIiIioj1FSUsIrG17C7Tt3JM7tK0+/bN+B4uISicNIiXoTzslGRERERERE1AdNnDAOSfFRnd7Os+uewrP/WZmUqDfinGxEREREREREREQdxOGiREREREREREREHcQkGxERERERERERUQcxyUZERERERERERNRBTLIRERERERERERF1EJNsREREREREREREHcQkGxERERERERERUQcxyUZERERERERERNRBTLIRERERERERERF1EJNsREREREREREREHcQkGxERERERERERUQcxyUZERERERERERNRBTLIRERERERERERF10P8BoXqlgCJb/T8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1260x588 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 4.2))\n",
    "ax.plot(fragmentation.fragment_share * 100, fragmentation.click_based / 1e6, color=SIGNAL, lw=2.2, marker=\"o\")\n",
    "ax.axhline(truth[\"unique\"] / 1e6, color=INK, lw=1.4, ls=\"--\")\n",
    "ax.text(1, truth[\"unique\"] / 1e6 + 0.012, \"the truth\", color=INK, fontsize=10)\n",
    "ax.axhline(read[\"sum\"] / 1e6, color=NAIVE, lw=1.2, ls=\":\")\n",
    "ax.text(1, read[\"sum\"] / 1e6 - 0.012, \"the sum\", color=NAIVE, fontsize=10, va=\"top\")\n",
    "ax.set_xlabel(\"share of each platform's arrivals the site cannot tie to the same person (%)\")\n",
    "ax.set_ylabel(\"click-based unique reach (millions)\")\n",
    "ax.set_title(\"Cookie fragmentation pushes the click-based figure up, keenness pushes it down\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_fragmentation.png\", dpi=140)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0138a0e7",
   "metadata": {},
   "source": [
    "Two biases of unknown size pushing opposite ways is not a licence to call it a wash. It is\n",
    "the reason the method reports the range with its assumptions stated, and the reason the\n",
    "your-data section asks for the site's identity situation rather than assuming it. A site\n",
    "with a logged-in identity - an email captured on both arrivals, a customer account - does not\n",
    "have the fragmentation problem; a site with only a cookie has it, and mobile-app traffic\n",
    "makes it worse."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eba7634e",
   "metadata": {},
   "source": [
    "## Consent: who the site is allowed to recognise\n",
    "\n",
    "In opt-in regions the cookie that recognises a returning visitor needs consent, so the site\n",
    "sees visitors-from-both only among the people who consented. The single-platform arrival\n",
    "counts do not need it: a landing with a click tag in its URL is counted on the server side,\n",
    "cookie or no cookie, and the platforms report their own click counts besides. So the shared\n",
    "count shrinks by the consent rate $s$ while the single counts do not, the click-based overlap\n",
    "comes out $s$ times too small, and the unique reach climbs. Dividing the consented shared count\n",
    "by the consent rate undoes that only if the people who declined overlap the way consenting ones\n",
    "do, which nothing you hold measures - here it works because consent is drawn independently of\n",
    "everything else. So the platform reports the shared count as consent-limited rather than\n",
    "corrected, and the your-data section divides only if you set a consent rate, and says what that\n",
    "assumes. (Counting all three from the consented cookie data instead flips the\n",
    "direction - the overlap comes out $1/s$ too big - and the same division fixes it.)\n",
    "\n",
    "If consent differs between the two platforms' audiences - one platform skews to a region\n",
    "with lower consent - the shared visitors' consent rate sits somewhere between the two, and\n",
    "the correction is a small band rather than a number. Simulated on the worked market with two\n",
    "regions:"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uniform consent: visitors via A consent at 70%, via B at 70%, the shared visitors at 73%\n",
      "  uncorrected, the click-based reach is 887,599 - +14% against the full-consent figure 776,226\n",
      "  corrected with A's consent rate: 756,716; with B's: 757,537 - a band 0.1% wide\n",
      "skewed consent: visitors via A consent at 75%, via B at 65%, the shared visitors at 67%\n",
      "  uncorrected, the click-based reach is 912,627 - +18% against the full-consent figure 776,226\n",
      "  corrected with A's consent rate: 820,101; with B's: 762,332 - a band 7.4% wide\n"
     ]
    }
   ],
   "source": [
    "CONSENT_HIGH, CONSENT_LOW = 0.90, 0.50\n",
    "LOW_REGION_SHARE_A, LOW_REGION_SHARE_B = 0.30, 0.70\n",
    "\n",
    "\n",
    "def consent_scenario(market, generator, low_share_a, low_share_b):\n",
    "    \"\"\"Each person sits in a high- or low-consent region; platform B's audience leans to the low\n",
    "    one. The site sees visitors-from-both only among consenting people; the single-platform\n",
    "    arrival counts are taken from the server side and need no consent.\"\"\"\n",
    "    n = len(market)\n",
    "    # region membership follows platform membership so the two audiences skew differently\n",
    "    p_low = np.where(market.on_b & ~market.on_a, low_share_b,\n",
    "                     np.where(market.on_a & ~market.on_b, low_share_a, (low_share_a + low_share_b) / 2))\n",
    "    in_low = generator.random(n) < p_low\n",
    "    consent = generator.random(n) < np.where(in_low, CONSENT_LOW, CONSENT_HIGH)\n",
    "    both = market.visit_a & market.visit_b\n",
    "    seen_both = int((both & consent).sum())\n",
    "    consent_rate_a = float(consent[market.visit_a].mean())\n",
    "    consent_rate_b = float(consent[market.visit_b].mean())\n",
    "    consent_rate_both = float(consent[both].mean())\n",
    "    return {\"seen_both\": seen_both, \"true_both\": int(both.sum()), \"consent_rate_a\": consent_rate_a,\n",
    "            \"consent_rate_b\": consent_rate_b, \"consent_rate_both\": consent_rate_both}\n",
    "\n",
    "\n",
    "consent = consent_scenario(market, np.random.default_rng(SEED + 5), LOW_REGION_SHARE_A, LOW_REGION_SHARE_B)\n",
    "consent_uniform = consent_scenario(market, np.random.default_rng(SEED + 6), 0.5, 0.5)\n",
    "consent_out = {}\n",
    "for label, sc in [(\"uniform\", consent_uniform), (\"skewed\", consent)]:\n",
    "    biased = dict(report, visitors_both=sc[\"seen_both\"])\n",
    "    r_biased = readings(biased)\n",
    "    corr_a = readings(dict(report, visitors_both=sc[\"seen_both\"] / sc[\"consent_rate_a\"]))\n",
    "    corr_b = readings(dict(report, visitors_both=sc[\"seen_both\"] / sc[\"consent_rate_b\"]))\n",
    "    consent_out[label] = {\n",
    "        \"consent_rate_a\": sc[\"consent_rate_a\"], \"consent_rate_b\": sc[\"consent_rate_b\"],\n",
    "        \"consent_rate_both\": sc[\"consent_rate_both\"], \"seen_both\": sc[\"seen_both\"],\n",
    "        \"uncorrected_click_based\": r_biased[\"click_based\"],\n",
    "        \"uncorrected_error_vs_full_pct\": (r_biased[\"click_based\"] / read[\"click_based\"] - 1) * 100,\n",
    "        \"corrected_with_rate_a\": corr_a[\"click_based\"], \"corrected_with_rate_b\": corr_b[\"click_based\"],\n",
    "        \"corrected_band_width_pct\": abs(corr_a[\"click_based\"] - corr_b[\"click_based\"]) / read[\"click_based\"] * 100,\n",
    "        # The band is how far the two corrections sit apart, not how far either is from the full-consent\n",
    "        # reach; the shared visitors' own consent rate decides that.\n",
    "        \"corrected_miss_vs_full_pct\": (min(corr_a[\"click_based\"], corr_b[\"click_based\"]) / read[\"click_based\"] - 1) * 100\n",
    "        if abs(min(corr_a[\"click_based\"], corr_b[\"click_based\"]) / read[\"click_based\"] - 1)\n",
    "        >= abs(max(corr_a[\"click_based\"], corr_b[\"click_based\"]) / read[\"click_based\"] - 1)\n",
    "        else (max(corr_a[\"click_based\"], corr_b[\"click_based\"]) / read[\"click_based\"] - 1) * 100,\n",
    "    }\n",
    "    print(f\"{label} consent: visitors via A consent at {sc['consent_rate_a'] * 100:.0f}%, via B at \"\n",
    "          f\"{sc['consent_rate_b'] * 100:.0f}%, the shared visitors at {sc['consent_rate_both'] * 100:.0f}%\")\n",
    "    print(f\"  uncorrected, the click-based reach is {r_biased['click_based']:,.0f} - \"\n",
    "          f\"{consent_out[label]['uncorrected_error_vs_full_pct']:+.0f}% against the full-consent figure \"\n",
    "          f\"{read['click_based']:,.0f}\")\n",
    "    print(f\"  corrected with A's consent rate: {corr_a['click_based']:,.0f}; with B's: {corr_b['click_based']:,.0f} \"\n",
    "          f\"- a band {consent_out[label]['corrected_band_width_pct']:.1f}% wide\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3386ccbb",
   "metadata": {},
   "source": [
    "Uniform consent is undone by the division here because this market's consent is independent of\n",
    "everything else - an assumption, not something the data shows. Skewed consent leaves a band the width\n",
    "of the gap between the two platforms' consent rates - small next to the keenness range, and\n",
    "stated rather than hidden."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9290b130",
   "metadata": {},
   "source": [
    "## Caveats to watch out for\n",
    "\n",
    "Everything measured above, collected in one place before the your-data section.\n",
    "\n",
    "**The click-based figure is a floor, not an estimate, unless keenness is even.** People\n",
    "reached on both platforms click more, so the visitors from both overstate the people on both\n",
    "and the unique reach comes out low - by a fifth on the worked market, by more where keenness\n",
    "is spread more unevenly. No report shows the keenness.\n",
    "\n",
    "**The chance formula is a ceiling, and only with a population you do not have.** Audiences\n",
    "overlap more than chance, and the population the formula divides by is a guess; with the\n",
    "guess off by a factor of two either way the formula is a coin toss.\n",
    "\n",
    "**The visitor share is the overlap times the click rates.** Read off the analytics report it\n",
    "says the audiences barely overlap. It is the sum wearing a different hat.\n",
    "\n",
    "**Cookie fragmentation runs the other way.** Every arrival the site cannot tie to the same\n",
    "person shrinks the shared count, and the click-based figure climbs toward the sum. A third of\n",
    "arrivals untied and the two biases cancel on the worked market by accident.\n",
    "\n",
    "**Below a hundred-odd shared visitors the figure is noise.** The floor is a precision floor,\n",
    "and more visitors do nothing for the keenness bias.\n",
    "\n",
    "**Consent scales the shared count.** Dividing by the rate assumes decliners overlap like\n",
    "consenters, which nothing held can check; skewed consent leaves a small band on top.\n",
    "\n",
    "**The reach numbers are estimates of accounts.** Both platforms say so in their own\n",
    "documentation; the article carries the quotes. Every reading here inherits that.\n",
    "\n",
    "**None of this is an experiment.** Reach says who saw the ad, not what the ad did."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a27693e9",
   "metadata": {},
   "source": [
    "## Run it on your own figures\n",
    "\n",
    "Everything above ran on a synthetic market. This section runs the same method on your data,\n",
    "on your machine - nothing is uploaded anywhere. It needs two things.\n",
    "\n",
    "**The reach numbers**, one per platform, typed in from each platform's own reporting for the\n",
    "same window. Both platforms label their reach an estimate; take it as given.\n",
    "\n",
    "**Your site's visitors by platform**, in either of two shapes:\n",
    "\n",
    "1. **A ready-made visitor file** - one line per visitor per platform they arrived from, so a\n",
    "   visitor who came from both platforms has two lines:\n",
    "\n",
    "   | column | what it holds | example |\n",
    "   |---|---|---|\n",
    "   | `visitor_id` | the identity your site recognises a returning visitor by | `v00017` |\n",
    "   | `platform` | which platform's ad that arrival came from | `tiktok` |\n",
    "   | `converted` | optional: 1 if this visitor became a customer in the window, else 0 | `1` |\n",
    "   | `segment` | optional: a segment both platforms report reach by and the site sees - a device class, a region, an age band | `mobile web` |\n",
    "\n",
    "   A `platforms` column holding `meta|tiktok` on one line per visitor is accepted too. With\n",
    "   the `converted` column present the notebook also reports the customers each platform\n",
    "   brought alone and what a new customer cost; without it, only visitors. With the `segment`\n",
    "   column present AND each platform's reach typed in per segment, it also computes the\n",
    "   stratified floor - the click-based figure per segment, summed - which is the one narrowing\n",
    "   the reports allow.\n",
    "\n",
    "2. **A raw landing log** - one line per paid arrival, which the notebook consolidates into\n",
    "   the visitor file above by reading the platform off the landing URL:\n",
    "\n",
    "   | column | what it holds | example |\n",
    "   |---|---|---|\n",
    "   | `timestamp` | when the arrival happened | `2026-05-03T14:02:11` |\n",
    "   | `visitor_id` | the identity the site recognises the visitor by | `v00017` |\n",
    "   | `landing_url` | the URL the visitor landed on, click tags and all | `https://shop.example/?ttclid=abc123` |\n",
    "   | `converted` | optional: 1 if this visitor bought in the window (on any of their lines), else 0 | `0` |\n",
    "   | `segment` | optional: the visitor's segment, as above | `app` |\n",
    "\n",
    "   Meta's `fbclid`, TikTok's `ttclid`, Google's `gclid` (and `gbraid`/`wbraid`), Microsoft's\n",
    "   `msclkid` and LinkedIn's `li_fat_id` are recognised, and so is a `utm_source` naming the\n",
    "   platform. Arrivals the notebook cannot place are reported and left out.\n",
    "\n",
    "From a GA4 export in BigQuery, the landing log is one query away: each event carries\n",
    "`user_pseudo_id`, the page location, and the collected traffic source with its `gclid`, and a\n",
    "join on the window's `purchase` events fills the `converted` column; the give-to-your-AI\n",
    "brief carries the query. Two honest notes on that identity: `user_pseudo_id`\n",
    "is a browser, not a person, so the fragmentation caveat applies in full; and in opt-in regions\n",
    "it exists only for consenting visitors, so set the consent rate below.\n",
    "\n",
    "Optional inputs: the population the two platforms draw from (for the chance ceiling - without\n",
    "it the ceiling is the sum), each platform's spend (for the cost per new person), the share of\n",
    "visitors who consented to the cookie (default one), and whether the site's identity is a\n",
    "cookie or a login."
   ]
  },
  {
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   "source": [
    "# ---- your inputs ----------------------------------------------------------------------------\n",
    "YOUR_VISITOR_FILE = None          # e.g. Path(\"visitors.csv\"); None runs the demo below\n",
    "YOUR_LANDING_LOG = None           # e.g. Path(\"landings.csv\"); the consolidator builds the visitor file\n",
    "YOUR_REACH = {}                   # e.g. {\"meta\": 714202, \"tiktok\": 481237}\n",
    "YOUR_REACH_BY_SEGMENT = {}        # e.g. {\"meta\": {\"app\": 367661, \"mobile web\": 189056, \"desktop\": 157485}, \"tiktok\": {...}}\n",
    "YOUR_POPULATION = None            # e.g. 2_400_000 - the population both platforms draw from\n",
    "YOUR_SPEND = {}                   # e.g. {\"tiktok\": 24000.0}\n",
    "YOUR_CONSENT_RATE = 1.0           # share of visitors the site was allowed to recognise; below 1 the\n",
    "                                  # shared count is divided by it, which assumes decliners overlap\n",
    "                                  # the way consenters do - nothing you hold can check that\n",
    "YOUR_IDENTITY = \"cookie\"          # \"cookie\" or \"login\"\n",
    "# ---------------------------------------------------------------------------------------------\n",
    "\n",
    "PLATFORM_TAGS = {\"fbclid\": \"meta\", \"ttclid\": \"tiktok\", \"gclid\": \"google\", \"gbraid\": \"google\",\n",
    "                 \"wbraid\": \"google\", \"msclkid\": \"microsoft\", \"li_fat_id\": \"linkedin\"}\n",
    "SOURCE_ALIASES = {\"facebook\": \"meta\", \"instagram\": \"meta\", \"meta\": \"meta\", \"fb\": \"meta\", \"ig\": \"meta\",\n",
    "                  \"tiktok\": \"tiktok\", \"google\": \"google\", \"youtube\": \"google\", \"bing\": \"microsoft\",\n",
    "                  \"microsoft\": \"microsoft\", \"linkedin\": \"linkedin\"}\n",
    "COLUMN_ALIASES = {\n",
    "    \"visitor_id\": [\"visitor_id\", \"user_id\", \"client_id\", \"user_pseudo_id\", \"visitor\", \"cookie_id\"],\n",
    "    \"platform\": [\"platform\", \"source_platform\", \"channel\"],\n",
    "    \"platforms\": [\"platforms\"],\n",
    "    \"landing_url\": [\"landing_url\", \"page_location\", \"url\", \"landing_page\"],\n",
    "    \"timestamp\": [\"timestamp\", \"time\", \"event_timestamp\", \"date\", \"datetime\"],\n",
    "    \"source\": [\"source\", \"utm_source\", \"manual_source\", \"session_source\"],\n",
    "    \"converted\": [\"converted\", \"customer\", \"is_customer\", \"purchased\", \"bought\"],\n",
    "    \"segment\": [\"segment\", \"device\", \"device_category\", \"region\", \"age_band\", \"stratum\"],\n",
    "}\n",
    "TRUE_WORDS = {\"1\", \"1.0\", \"true\", \"yes\", \"y\", \"t\"}\n",
    "\n",
    "\n",
    "class YourDataError(ValueError):\n",
    "    \"\"\"A plain-words refusal: the input cannot support the reading.\"\"\"\n",
    "\n",
    "\n",
    "# ---- export reader (shared across every Stochastic Strata notebook; do not edit here) ----------\n",
    "# One reader for the file you bring. Ad platforms and analytics tools do not hand over clean tables:\n",
    "# a Google Ads download opens with a title line and a date-range line and closes with \"Total:\" rows,\n",
    "# a GA4 download opens with \"#\" lines and writes dates as 20261019, a European download separates\n",
    "# with semicolons and writes 1.234,56, and Excel turns numbers into \"1,234.56\" or \"€12.30\". This\n",
    "# reader finds the real header row, sets the title, comment and total rows aside, turns formatted\n",
    "# numbers back into numbers, and reads dates in the platforms' own forms. Where a file is genuinely\n",
    "# ambiguous - 06/07/2026 could be the 6th of July or the 7th of June - it stops and asks rather than\n",
    "# guessing, because a guessed date silently moves a day to the wrong weekday.\n",
    "#\n",
    "#     table, notes = read_export(\"my_export.csv\", columns={\"date\": [\"day\", \"date\"], ...})\n",
    "#     for note in notes: print(note)\n",
    "#\n",
    "# `columns` maps the names this notebook uses to the names your file might use (case and spacing do\n",
    "# not matter, and a currency suffix such as \"(EUR)\" is ignored). `date_columns` are read as dates,\n",
    "# `id_columns` are kept as text. Everything else that reads as numbers becomes numbers; a share\n",
    "# written \"49.41%\" becomes 0.4941. A share the platform shows only as \"< 10%\" or \"> 90%\" is read as\n",
    "# 0.10 or 0.90 and the direction is kept in a column ending \"_bound\", because it is a limit, not a\n",
    "# measurement. Every row set aside, and why, comes back in `notes`.\n",
    "import csv as _csv\n",
    "import io as _io\n",
    "import re as _re\n",
    "from pathlib import Path as _Path\n",
    "\n",
    "import numpy as _np\n",
    "import pandas as _pd\n",
    "\n",
    "\n",
    "class ExportReadError(ValueError):\n",
    "    \"\"\"The file cannot be read without guessing; the message says what to change.\"\"\"\n",
    "\n",
    "\n",
    "_TOTAL_ROW = _re.compile(r\"^\\s*(grand\\s+)?totals?\\b\", _re.I)\n",
    "_CURRENCY = _re.compile(r\"[€$£¥₹]|\\b(EUR|USD|GBP|CHF|SEK|NOK|DKK|PLN|AUD|CAD)\\b\", _re.I)\n",
    "_EMPTY = {\"\", \"--\", \"-\", \"—\", \"–\", \"n/a\", \"na\", \"nan\", \"none\", \"null\", \"(not set)\"}\n",
    "_MONTHS = {m: i for i, m in enumerate(\n",
    "    [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"], 1)}\n",
    "\n",
    "\n",
    "def _norm(name):\n",
    "    name = _re.sub(r\"\\((?:[A-Z]{3}|[€$£])\\)\", \"\", str(name))\n",
    "    return _re.sub(r\"[\\s_\\-./]+\", \" \", name).strip().lower()\n",
    "\n",
    "\n",
    "def _decode(raw):\n",
    "    if raw[:2] in (b\"\\xff\\xfe\", b\"\\xfe\\xff\"):\n",
    "        return raw.decode(\"utf-16\")\n",
    "    for encoding in (\"utf-8-sig\", \"cp1252\", \"latin-1\"):\n",
    "        try:\n",
    "            return raw.decode(encoding)\n",
    "        except UnicodeDecodeError:\n",
    "            continue\n",
    "    return raw.decode(\"utf-8\", errors=\"replace\")\n",
    "\n",
    "\n",
    "def _rows_from(source):\n",
    "    \"\"\"Every row of the file as a list of strings, whatever the container.\"\"\"\n",
    "    if isinstance(source, _pd.DataFrame):\n",
    "        return [list(map(str, source.columns))] + source.astype(str).values.tolist(), \",\"\n",
    "    path = _Path(source)\n",
    "    if not path.exists():\n",
    "        raise ExportReadError(f\"There is no file at {path}. Check the name and the folder.\")\n",
    "    if path.suffix.lower() in (\".xlsx\", \".xlsm\", \".xls\"):\n",
    "        sheet = _pd.read_excel(path, header=None, dtype=str).fillna(\"\")\n",
    "        return sheet.values.tolist(), \",\"\n",
    "    text = _decode(path.read_bytes())\n",
    "    lines = [ln for ln in text.splitlines() if not ln.lstrip().startswith(\"#\")]\n",
    "    counts = {d: sum(ln.count(d) for ln in lines[:60]) for d in (\",\", \";\", \"\\t\")}\n",
    "    delimiter = max(counts, key=counts.get) if any(counts.values()) else \",\"\n",
    "    return [row for row in _csv.reader(_io.StringIO(\"\\n\".join(lines)), delimiter=delimiter)], delimiter\n",
    "\n",
    "\n",
    "def _find_header(rows, wanted):\n",
    "    \"\"\"The first row that names the columns: the one matching most expected names, else the first\n",
    "    row as wide as the data below it that is mostly words rather than numbers.\"\"\"\n",
    "    widths = [len([c for c in r if str(c).strip()]) for r in rows]\n",
    "    if wanted:\n",
    "        best, best_hits = None, 0\n",
    "        for i, row in enumerate(rows[:40]):\n",
    "            names = {_norm(c) for c in row if str(c).strip()}\n",
    "            hits = sum(1 for options in wanted.values() if names & set(options))\n",
    "            if hits > best_hits:\n",
    "                best, best_hits = i, hits\n",
    "        if best is not None:\n",
    "            return best\n",
    "    body = [w for w in widths if w > 1]\n",
    "    width = max(set(body), key=body.count) if body else 1\n",
    "    for i, row in enumerate(rows[:40]):\n",
    "        cells = [str(c).strip() for c in row if str(c).strip()]\n",
    "        if len(cells) >= max(2, width - 1) and sum(_looks_number(c) for c in cells) <= len(cells) / 2:\n",
    "            return i\n",
    "    return 0\n",
    "\n",
    "\n",
    "def _looks_number(text):\n",
    "    if _re.fullmatch(r\"\\d{1,4}[./\\-]\\d{1,2}[./\\-]\\d{1,4}\", str(text).strip()):\n",
    "        return False  # a date such as 22.09.2026, not a number with separators\n",
    "    return bool(_re.fullmatch(r\"[<>]?\\s*[-+(]?[€$£]?\\s*[\\d.,\\s']+\\s*%?\\)?\", str(text).strip()))\n",
    "\n",
    "\n",
    "def _decimal_comma(values):\n",
    "    \"\"\"True when these values write 1.234,56 (a comma followed by one or two final digits, or dots\n",
    "    grouping thousands with a comma after them), False when they write 1,234.56, None when they do\n",
    "    not say (whole numbers only, or a lone '1.340').\"\"\"\n",
    "    comma_votes = dot_votes = 0\n",
    "    for v in values:\n",
    "        s = _re.sub(r\"[^\\d.,]\", \"\", v)\n",
    "        if \",\" in s and \".\" in s:\n",
    "            comma_votes += s.rfind(\",\") > s.rfind(\".\")\n",
    "            dot_votes += s.rfind(\".\") > s.rfind(\",\")\n",
    "        elif _re.search(r\",\\d{1,2}$\", s):\n",
    "            comma_votes += 1\n",
    "        elif _re.search(r\"\\.\\d{1,2}$\", s) or _re.search(r\"\\.\\d{4,}$\", s):\n",
    "            dot_votes += 1\n",
    "    if comma_votes == dot_votes:\n",
    "        return None\n",
    "    return comma_votes > dot_votes\n",
    "\n",
    "\n",
    "def _to_number(text, comma_decimal):\n",
    "    s = str(text).strip().replace(\" \", \" \")\n",
    "    if s.lower() in _EMPTY:\n",
    "        return _np.nan, None, True\n",
    "    bound = None\n",
    "    if s[:1] in \"<>\":\n",
    "        bound, s = s[0], s[1:].strip()\n",
    "    negative = s.startswith(\"(\") and s.endswith(\")\")\n",
    "    s = _CURRENCY.sub(\"\", s).strip(\"() \").replace(\" \", \"\").replace(\"'\", \"\")\n",
    "    percent = s.endswith(\"%\")\n",
    "    s = s.rstrip(\"%\")\n",
    "    if comma_decimal:\n",
    "        s = s.replace(\".\", \"\").replace(\",\", \".\")\n",
    "    else:\n",
    "        s = s.replace(\",\", \"\")\n",
    "    try:\n",
    "        value = float(s)\n",
    "    except ValueError:\n",
    "        return _np.nan, None, False\n",
    "    if negative:\n",
    "        value = -value\n",
    "    return (value / 100.0 if percent else value), bound, True\n",
    "\n",
    "\n",
    "def _parse_dates(values, column, dayfirst):\n",
    "    text = [str(v).strip() for v in values]\n",
    "    present = [t for t in text if t.lower() not in _EMPTY]\n",
    "    if not present:\n",
    "        return _pd.Series([_pd.NaT] * len(text)), []\n",
    "    notes = []\n",
    "    if all(_re.fullmatch(r\"\\d{8}\", t) for t in present):\n",
    "        return _pd.to_datetime(_pd.Series(text), format=\"%Y%m%d\", errors=\"coerce\"), notes\n",
    "    if all(_re.fullmatch(r\"\\d{10}(\\.\\d+)?|\\d{13}|\\d{16}\", t) for t in present):\n",
    "        digits = len(present[0].split(\".\")[0])\n",
    "        unit = {10: \"s\", 13: \"ms\", 16: \"us\"}[digits]\n",
    "        notes.append(f\"'{column}' holds epoch times in {dict(s='seconds', ms='milliseconds', us='microseconds')[unit]}; read as such.\")\n",
    "        return _pd.to_datetime(_pd.to_numeric(_pd.Series(text), errors=\"coerce\"), unit=unit), notes\n",
    "    cleaned = [_re.sub(r\"\\s+(UTC|GMT|Z)$\", \"\", t) for t in text]\n",
    "    slash = [t for t in cleaned if _re.fullmatch(r\"\\d{1,2}[/.\\-]\\d{1,2}[/.\\-]\\d{2,4}(\\s.*)?\", t)]\n",
    "    if slash and len(slash) >= len(present) * 0.9:\n",
    "        firsts = [int(_re.split(r\"[/.\\-]\", t)[0]) for t in slash]\n",
    "        seconds = [int(_re.split(r\"[/.\\-]\", t)[1]) for t in slash]\n",
    "        dotted = all(\".\" in t.split(\" \")[0] for t in slash)\n",
    "        if dayfirst is None:\n",
    "            if max(firsts) > 12 and max(seconds) <= 12:\n",
    "                dayfirst = True\n",
    "            elif max(seconds) > 12 and max(firsts) <= 12:\n",
    "                dayfirst = False\n",
    "            elif dotted:\n",
    "                dayfirst = True\n",
    "            else:\n",
    "                raise ExportReadError(\n",
    "                    f\"The dates in '{column}' (for example {slash[0]}) could be day-first or month-first, \"\n",
    "                    \"and nothing in the file says which. Re-run with dayfirst=True for 6 July written \"\n",
    "                    \"06/07, or dayfirst=False for June 7 written 06/07 - or export the dates as \"\n",
    "                    \"2026-07-06.\")\n",
    "        return _pd.to_datetime(_pd.Series(cleaned), dayfirst=dayfirst, errors=\"coerce\"), notes\n",
    "    parsed = _pd.to_datetime(_pd.Series(cleaned), errors=\"coerce\", format=\"mixed\")\n",
    "    return parsed, notes\n",
    "\n",
    "\n",
    "def read_export(source, columns=None, date_columns=(), id_columns=(), dayfirst=None,\n",
    "                max_unreadable_share=0.02):\n",
    "    \"\"\"Read a downloaded report into a table with this notebook's column names. Returns\n",
    "    (table, notes); raises ExportReadError with a plain-words fix when it cannot read without\n",
    "    guessing.\"\"\"\n",
    "    columns = columns or {}\n",
    "    # Each name's candidates in the caller's order of preference, the name itself first.\n",
    "    wanted = {name: list(dict.fromkeys([_norm(name)] + [_norm(a) for a in aliases]))\n",
    "              for name, aliases in columns.items()}\n",
    "    rows, delimiter = _rows_from(source)\n",
    "    rows = [r for r in rows if any(str(c).strip() for c in r)]\n",
    "    if not rows:\n",
    "        raise ExportReadError(\"The file is empty.\")\n",
    "    notes = []\n",
    "    h = _find_header(rows, wanted)\n",
    "    if h:\n",
    "        notes.append(f\"set aside {h} line(s) above the header (a report title or date range).\")\n",
    "    header = [str(c).strip() for c in rows[h]]\n",
    "    body, totals = [], 0\n",
    "    for r in rows[h + 1:]:\n",
    "        first = next((str(c) for c in r if str(c).strip()), \"\")\n",
    "        if _TOTAL_ROW.match(first):\n",
    "            totals += 1\n",
    "            continue\n",
    "        if [str(c).strip() for c in r] == header:\n",
    "            continue\n",
    "        body.append([str(c) for c in r] + [\"\"] * (len(header) - len(r)))\n",
    "    if totals:\n",
    "        notes.append(f\"set aside {totals} total row(s).\")\n",
    "    frame = _pd.DataFrame([r[:len(header)] for r in body], columns=header)\n",
    "    rename = {}\n",
    "    for name, options in wanted.items():\n",
    "        by_norm = {}\n",
    "        for c in frame.columns:\n",
    "            by_norm.setdefault(_norm(c), c)\n",
    "        match = next((by_norm[o] for o in options if o in by_norm and by_norm[o] not in rename), None)\n",
    "        if match is not None:\n",
    "            rename[match] = name\n",
    "    frame = frame.rename(columns=rename)\n",
    "    missing = [name for name in columns if name not in frame.columns]\n",
    "    if missing:\n",
    "        seen = \", \".join(f\"'{c}'\" for c in header[:12])\n",
    "        raise ExportReadError(\n",
    "            f\"Could not find the column(s) {', '.join(missing)}. The file's columns are {seen}. \"\n",
    "            \"Rename the column in the file, or add its name to the list this cell accepts.\")\n",
    "    dates = set(date_columns)\n",
    "    # The file's own convention, for a column that cannot tell by itself: a clicks column of whole\n",
    "    # numbers says nothing, but its \"1.340\" means one thousand three hundred and forty when the\n",
    "    # file's other columns write decimals with a comma.\n",
    "    every = [str(v) for c in frame.columns if c not in date_columns for v in frame[c].tolist()\n",
    "             if _looks_number(v)]\n",
    "    file_comma = _decimal_comma(every)\n",
    "    if file_comma is None:\n",
    "        file_comma = delimiter == \";\"\n",
    "    ids = set(id_columns) | {c for c in frame.columns if _re.search(r\"(^|[ _])(id|pseudo id)$\", _norm(c))}\n",
    "    for col in frame.columns:\n",
    "        values = frame[col].tolist()\n",
    "        if col in dates:\n",
    "            parsed, extra = _parse_dates(values, col, dayfirst)\n",
    "            notes += extra\n",
    "            bad = int(parsed.isna().sum() - sum(str(v).strip().lower() in _EMPTY for v in values))\n",
    "            if bad > max(1, max_unreadable_share * len(values)):\n",
    "                example = next(v for v, p in zip(values, parsed) if _pd.isna(p) and str(v).strip())\n",
    "                raise ExportReadError(f\"{bad} value(s) in '{col}' are not dates I can read, for example '{example}'.\")\n",
    "            frame[col] = parsed.values\n",
    "            continue\n",
    "        if col in ids:\n",
    "            continue\n",
    "        present = [v for v in values if str(v).strip().lower() not in _EMPTY]\n",
    "        if not present or sum(_looks_number(v) for v in present) < 0.5 * len(present):\n",
    "            continue\n",
    "        comma = _decimal_comma(present)\n",
    "        if comma is None:\n",
    "            comma = file_comma\n",
    "        parsed = [_to_number(v, comma) for v in values]\n",
    "        unreadable = [v for v, (_, _, ok) in zip(values, parsed) if not ok]\n",
    "        if unreadable and col not in columns:\n",
    "            notes.append(f\"'{col}' mixes numbers with labels such as '{unreadable[0]}'; kept as text.\")\n",
    "            continue\n",
    "        if unreadable:\n",
    "            notes.append(f\"{len(unreadable)} value(s) in '{col}' could not be read as numbers, for example \"\n",
    "                         f\"'{unreadable[0]}'; they are left empty.\")\n",
    "            if len(unreadable) > max(1, max_unreadable_share * len(values)):\n",
    "                raise ExportReadError(\n",
    "                    f\"{len(unreadable)} of {len(values)} values in '{col}' are not numbers I can read, for \"\n",
    "                    f\"example '{unreadable[0]}'. Fix them in the file, or export the column unformatted.\")\n",
    "        frame[col] = [p[0] for p in parsed]\n",
    "        bounds = [p[1] for p in parsed]\n",
    "        if any(bounds):\n",
    "            # An object column, so a day with no limit stays None: a newer pandas would infer a string\n",
    "            # column here and turn None into NaN, which is truthy and reads as a limit.\n",
    "            frame[f\"{col}_bound\"] = _pd.Series(bounds, index=frame.index, dtype=object)\n",
    "            notes.append(f\"'{col}' has values the platform shows only as a limit (for example \"\n",
    "                         f\"'{next(v for v, b in zip(values, bounds) if b)}'); kept as the limit, with the \"\n",
    "                         f\"direction in '{col}_bound'.\")\n",
    "    if dates:\n",
    "        undated = frame[list(dates)].isna().all(axis=1)\n",
    "        if undated.any():\n",
    "            frame = frame[~undated].reset_index(drop=True)\n",
    "            notes.append(f\"set aside {int(undated.sum())} row(s) with no date (GA4 writes its grand total \"\n",
    "                         \"this way, under the header).\")\n",
    "    return frame, notes\n",
    "# ---- end of export reader -------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "def _platform_key(name):\n",
    "    key = str(name).strip().lower()\n",
    "    return SOURCE_ALIASES.get(key, key)\n",
    "\n",
    "\n",
    "def _read_ids_as_text(path):\n",
    "    \"\"\"Any visitor file or landing log, read by the shared reader with every column kept as text, so a\n",
    "    numeric id such as GA4's 1234567890.1760951712 is never shortened by a float.\"\"\"\n",
    "    try:\n",
    "        frame, notes = read_export(path)\n",
    "    except ExportReadError as problem:\n",
    "        raise YourDataError(str(problem)) from None\n",
    "    for note in notes:\n",
    "        print(\"  \" + note)\n",
    "    for c in frame.columns:\n",
    "        if \"id\" in str(c).lower():\n",
    "            frame[c] = frame[c].astype(str)\n",
    "    return frame\n",
    "\n",
    "\n",
    "def _find_column(frame, name, required=True):\n",
    "    lowered = {c.lower().strip(): c for c in frame.columns}\n",
    "    for alias in COLUMN_ALIASES[name]:\n",
    "        if alias in lowered:\n",
    "            return lowered[alias]\n",
    "    if required:\n",
    "        raise YourDataError(f\"the file needs a '{name}' column (or one of {COLUMN_ALIASES[name]}); \"\n",
    "                            f\"it has {list(frame.columns)}\")\n",
    "    return None\n",
    "\n",
    "\n",
    "def platform_of(url, source=None):\n",
    "    \"\"\"Which platform an arrival came from, read off the click tag in the URL or the utm source.\"\"\"\n",
    "    text = str(url or \"\").lower()\n",
    "    for tag, platform in PLATFORM_TAGS.items():\n",
    "        if f\"{tag}=\" in text:\n",
    "            return platform\n",
    "    for key in (\"utm_source=\",):\n",
    "        if key in text:\n",
    "            value = text.split(key, 1)[1].split(\"&\", 1)[0]\n",
    "            if value in SOURCE_ALIASES:\n",
    "                return SOURCE_ALIASES[value]\n",
    "    if source is not None and str(source).lower().strip() in SOURCE_ALIASES:\n",
    "        return SOURCE_ALIASES[str(source).lower().strip()]\n",
    "    return None\n",
    "\n",
    "\n",
    "def consolidate_landing_log(path):\n",
    "    \"\"\"One line per paid arrival -> one line per visitor per platform, with the platform read off\n",
    "    the landing URL (click tags) or a source column. Repeat arrivals from the same platform by the\n",
    "    same visitor collapse to one; arrivals that name no platform are reported and dropped.\"\"\"\n",
    "    frame = _read_ids_as_text(path)\n",
    "    vid = _find_column(frame, \"visitor_id\")\n",
    "    url = _find_column(frame, \"landing_url\", required=False)\n",
    "    src = _find_column(frame, \"source\", required=False)\n",
    "    if url is None and src is None:\n",
    "        raise YourDataError(\"the landing log needs a 'landing_url' column (or a 'source' column) to read the platform from\")\n",
    "    platforms = [platform_of(row[url] if url else None, row[src] if src else None) for _, row in frame.iterrows()]\n",
    "    frame = frame.assign(platform=platforms)\n",
    "    unplaced = int(frame.platform.isna().sum())\n",
    "    placed = frame.dropna(subset=[\"platform\"])[[vid, \"platform\"]].rename(columns={vid: \"visitor_id\"})\n",
    "    placed = placed.drop_duplicates()\n",
    "    placed.attrs[\"customers\"] = _customers_in(frame, vid)\n",
    "    placed.attrs[\"segments\"] = _segments_in(frame, vid)\n",
    "    print(f\"consolidated {len(frame):,} arrivals into {len(placed):,} visitor-platform pairs across \"\n",
    "          f\"{placed.platform.nunique()} platform(s); {unplaced:,} arrival(s) named no platform and were left out\")\n",
    "    return placed\n",
    "\n",
    "\n",
    "def _segments_in(frame, vid):\n",
    "    \"\"\"Each visitor's segment (the first named on their lines) - or None when the file has none.\"\"\"\n",
    "    seg = _find_column(frame, \"segment\", required=False)\n",
    "    if seg is None:\n",
    "        return None\n",
    "    named = frame.dropna(subset=[seg])\n",
    "    return {str(v): str(g).strip().lower() for v, g in zip(named[vid], named[seg]) if str(g).strip()}\n",
    "\n",
    "\n",
    "def _customers_in(frame, vid):\n",
    "    \"\"\"The visitors flagged as converted on any of their lines - or None when the file has no flag.\"\"\"\n",
    "    conv = _find_column(frame, \"converted\", required=False)\n",
    "    if conv is None:\n",
    "        return None\n",
    "    flagged = (frame[conv].astype(str).str.lower().str.strip().str.replace(r\"\\.0$\", \"\", regex=True)\n",
    "               .isin(TRUE_WORDS))\n",
    "    return set(frame.loc[flagged, vid].astype(str))\n",
    "\n",
    "\n",
    "def read_visitor_file(path):\n",
    "    \"\"\"The ready-made shape: visitor_id + platform, or visitor_id + platforms ('meta|tiktok').\"\"\"\n",
    "    frame = _read_ids_as_text(path)\n",
    "    vid = _find_column(frame, \"visitor_id\")\n",
    "    plat = _find_column(frame, \"platform\", required=False)\n",
    "    plats = _find_column(frame, \"platforms\", required=False)\n",
    "    if plat is None and plats is None:\n",
    "        raise YourDataError(\"the visitor file needs a 'platform' column (one line per visitor per platform) \"\n",
    "                            \"or a 'platforms' column ('meta|tiktok')\")\n",
    "    customers = _customers_in(frame, vid)\n",
    "    segments = _segments_in(frame, vid)\n",
    "    if plats is not None and plat is None:\n",
    "        rows = []\n",
    "        for _, row in frame.iterrows():\n",
    "            for p in str(row[plats]).replace(\",\", \"|\").replace(\";\", \"|\").split(\"|\"):\n",
    "                if p.strip():\n",
    "                    rows.append({\"visitor_id\": row[vid], \"platform\": p.strip().lower()})\n",
    "        frame = pd.DataFrame(rows)\n",
    "    else:\n",
    "        frame = frame[[vid, plat]].rename(columns={vid: \"visitor_id\", plat: \"platform\"})\n",
    "        frame[\"platform\"] = frame.platform.astype(str).str.lower().str.strip()\n",
    "    frame = frame.dropna().drop_duplicates()\n",
    "    if frame.empty:\n",
    "        raise YourDataError(\"the visitor file has no rows after dropping blanks\")\n",
    "    frame.attrs[\"customers\"] = customers\n",
    "    frame.attrs[\"segments\"] = segments\n",
    "    return frame\n",
    "\n",
    "\n",
    "def visitor_sets(pairs):\n",
    "    \"\"\"Per platform, the set of visitor ids that arrived from it.\"\"\"\n",
    "    return {p: set(g.visitor_id.astype(str)) for p, g in pairs.groupby(\"platform\")}\n",
    "\n",
    "\n",
    "def customers_of(pairs):\n",
    "    \"\"\"The set of converted visitor ids the file carried, or None when it carried no flag.\"\"\"\n",
    "    return pairs.attrs.get(\"customers\")\n",
    "\n",
    "\n",
    "def segments_of(pairs):\n",
    "    \"\"\"Each visitor's segment as the file named it, or None when it carried no segment column.\"\"\"\n",
    "    return pairs.attrs.get(\"segments\")\n",
    "\n",
    "\n",
    "def report_reach(sets, reach, population=None, spend=None, consent_rate=1.0, identity=\"cookie\",\n",
    "                 customers=None, segments=None, reach_by_segment=None, floor=SHARED_VISITOR_FLOOR):\n",
    "    \"\"\"The measured layer for any number of platforms, and the estimated range for each pair\n",
    "    with a reach figure. Refuses in plain words where the data cannot carry a reading.\"\"\"\n",
    "    spend = {_platform_key(k): v for k, v in (spend or {}).items()}\n",
    "    reach = {_platform_key(k): v for k, v in (reach or {}).items()}\n",
    "    platforms = sorted(sets)\n",
    "    unmatched = [k for k in reach if k not in sets]\n",
    "    if unmatched:\n",
    "        print(f\"  the reach figures name {unmatched}, but the visitors came from {platforms}: use those names \"\n",
    "              \"(or 'facebook', 'instagram', 'fb' for meta)\")\n",
    "    if len(platforms) < 2:\n",
    "        raise YourDataError(f\"the visitors came from {len(platforms)} platform(s); de-duplicating needs at least two\")\n",
    "    if not (0 < consent_rate <= 1):\n",
    "        raise YourDataError(\"the consent rate must be a share between zero and one\")\n",
    "    everyone = set().union(*sets.values())\n",
    "    print(\"the exact part - your visitors, de-duplicated\")\n",
    "    print(f\"  {len(everyone):,} distinct visitors arrived from {len(platforms)} platforms \"\n",
    "          f\"({sum(len(s) for s in sets.values()):,} platform-arrivals, so \"\n",
    "          f\"{sum(len(s) for s in sets.values()) - len(everyone):,} were counted twice or more)\")\n",
    "    out = {\"unique_visitors\": len(everyone), \"alone\": {}, \"customers_alone\": {}, \"pairs\": {}}\n",
    "    for p in platforms:\n",
    "        others = set().union(*(sets[q] for q in platforms if q != p))\n",
    "        alone = sets[p] - others\n",
    "        out[\"alone\"][p] = len(alone)\n",
    "        line = f\"  {p}: {len(sets[p]):,} visitors, of whom {len(alone):,} came from no other platform\"\n",
    "        if p in spend and alone:\n",
    "            line += f\" - {spend[p] / len(alone):,.2f} per new visitor\"\n",
    "        print(line)\n",
    "    if customers is not None:\n",
    "        print(\"the exact part - your customers, by the platforms that brought them\")\n",
    "        print(f\"  {len(customers & everyone):,} of the {len(everyone):,} visitors became customers\")\n",
    "        for p in platforms:\n",
    "            others = set().union(*(sets[q] for q in platforms if q != p))\n",
    "            bought_alone = len((sets[p] - others) & customers)\n",
    "            out[\"customers_alone\"][p] = bought_alone\n",
    "            line = f\"  {p}: {bought_alone:,} customers came from no other platform\"\n",
    "            if p in spend:\n",
    "                line += (f\" - {spend[p] / bought_alone:,.2f} per new customer\" if bought_alone\n",
    "                         else \" - no cost per new customer: it brought none alone\")\n",
    "            print(line)\n",
    "        shared = len(customers & everyone) - sum(out[\"customers_alone\"].values())\n",
    "        print(f\"  {shared:,} customers arrived via more than one platform; splitting them is attribution, \"\n",
    "              \"another method - report them as shared\")\n",
    "    print(\"  reach figures must be each platform's de-duplicated reach for the whole window, at campaign or \"\n",
    "          \"account level - never a sum of ad-set or campaign rows, which counts the same people twice\")\n",
    "    if consent_rate < 1:\n",
    "        print(\"  the consent rate divides the shared count, which assumes the people who declined overlap the way \"\n",
    "              \"consenting ones do - nothing held can check it - and only holds if each platform's visitor count \"\n",
    "              \"was taken on the server side; if both counts come from the consented cookie too (GA4 ids), leave \"\n",
    "              \"the rate at 1, because dividing then makes the overlap too big\")\n",
    "    if identity == \"cookie\":\n",
    "        print(\"  note: the identity is a cookie, so a person split across devices or app browsers is \"\n",
    "              \"two visitors here; the shared counts are floors and the per-platform-alone counts are ceilings\")\n",
    "    for i, a in enumerate(platforms):\n",
    "        for b in platforms[i + 1:]:\n",
    "            if a not in reach or b not in reach:\n",
    "                print(f\"\\n{a} x {b}: no reach figure for both platforms, so only the visitor counts above apply\")\n",
    "                continue\n",
    "            va, vb, vab = len(sets[a]), len(sets[b]), len(sets[a] & sets[b])\n",
    "            if reach[a] < va or reach[b] < vb:\n",
    "                raise YourDataError(f\"{a} x {b}: a platform's reach ({reach[a]:,} / {reach[b]:,}) is smaller than its \"\n",
    "                                    f\"visitors ({va:,} / {vb:,}) - the reach figure or the window is wrong\")\n",
    "            corrected_both = vab / consent_rate\n",
    "            observed = {\"reach_a\": reach[a], \"reach_b\": reach[b], \"visitors_a\": va, \"visitors_b\": vb,\n",
    "                        \"visitors_both\": corrected_both}\n",
    "            print(f\"\\n{a} x {b}: {va:,} visitors from {a}, {vb:,} from {b}, {vab:,} from both\"\n",
    "                  + (f\" ({corrected_both:,.0f} after dividing by the consent rate)\" if consent_rate < 1 else \"\"))\n",
    "            if corrected_both < floor:\n",
    "                print(f\"  the estimate is REFUSED: {vab:,} shared visitors is below the floor of {floor}; the \"\n",
    "                      f\"sampling error alone would be more than {FLOOR_TARGET_HALF_WIDTH * 100:.0f} per cent\")\n",
    "                out[\"pairs\"][f\"{a}x{b}\"] = {\"refused\": True, \"visitors_both\": vab}\n",
    "                continue\n",
    "            r = readings(observed, population=population)\n",
    "            iv = click_interval(observed)\n",
    "            ceiling_key = \"chance\" if population else \"sum\"\n",
    "            if r[\"click_overlap_capped\"]:\n",
    "                print(f\"  WARNING: the click-based overlap ({r['click_overlap_uncapped']:,.0f}) is larger than the smaller \"\n",
    "                      \"platform's whole reach, which is impossible: the people on both platforms click far more than \"\n",
    "                      \"others, so the floor has collapsed to the larger reach and says little. Read the range as \"\n",
    "                      \"'at least the larger reach'.\")\n",
    "            print(f\"  unique reach sits between {r['click_based']:,.0f} (if the people on both platforms click like \"\n",
    "                  f\"anyone else) and {r[ceiling_key]:,.0f} \"\n",
    "                  + (\"(if the audiences overlap only by chance)\" if population else \"(the sum, with no overlap at all)\"))\n",
    "            print(f\"  the click-based end's sampling interval is {iv['unique_low']:,.0f} to {iv['unique_high']:,.0f}\")\n",
    "            print(f\"  people {b} added that {a} had not reached: between {r['net_new_b_click']:,.0f} and \"\n",
    "                  f\"{reach[b] - (r['chance_overlap'] if population else 0):,.0f}\")\n",
    "            pair = {\"refused\": False, \"click_based\": r[\"click_based\"], \"ceiling\": r[ceiling_key],\n",
    "                    \"ceiling_kind\": ceiling_key, \"net_new_b_low\": r[\"net_new_b_click\"],\n",
    "                    \"interval\": [iv[\"unique_low\"], iv[\"unique_high\"]]}\n",
    "            if b in spend:\n",
    "                lo = cost_per_net_new(spend[b], reach[b], r[\"click_overlap\"])\n",
    "                hi = cost_per_net_new(spend[b], reach[b], r[\"chance_overlap\"] if population else 0)\n",
    "                top = \"no upper bound (the floor collapsed)\" if not np.isfinite(lo) else f\"{lo * 100:.1f} cents\"\n",
    "                print(f\"  an extra person reached on {b} cost between {hi * 100:.1f} cents and {top} \"\n",
    "                      \"(an awareness figure, not an acquisition cost)\")\n",
    "                pair[\"cost_per_net_new_cents\"] = [hi * 100, lo * 100]\n",
    "            if segments and reach_by_segment and a in reach_by_segment and b in reach_by_segment:\n",
    "                pair[\"stratified\"] = _stratified_pair(sets[a], sets[b], segments, reach_by_segment[a], reach_by_segment[b])\n",
    "            out[\"pairs\"][f\"{a}x{b}\"] = pair\n",
    "    print(\"\\nthe total across everyone who saw an ad is not estimated: two markets with these exact reports can \"\n",
    "          \"differ by a fifth, and only a panel that sees the same people on both platforms can tell them apart\")\n",
    "    return out\n",
    "\n",
    "\n",
    "def _stratified_pair(set_a, set_b, segments, reach_a_by_seg, reach_b_by_seg):\n",
    "    \"\"\"The click-based floor per segment, summed - the one narrowing the reports allow.\"\"\"\n",
    "    ra_by = {str(k).strip().lower(): v for k, v in reach_a_by_seg.items()}\n",
    "    rb_by = {str(k).strip().lower(): v for k, v in reach_b_by_seg.items()}\n",
    "    names = sorted(set(ra_by) & set(rb_by))\n",
    "    unplaced = sum(1 for v in set_a | set_b if segments.get(v) not in names)\n",
    "    rows = []\n",
    "    for name in names:\n",
    "        in_seg = {v for v, g in segments.items() if g == name}\n",
    "        rows.append({\"segment\": name, \"reach_a\": ra_by[name], \"reach_b\": rb_by[name],\n",
    "                     \"visitors_a\": len(set_a & in_seg), \"visitors_b\": len(set_b & in_seg),\n",
    "                     \"visitors_both\": len(set_a & set_b & in_seg)})\n",
    "    st = stratified_floor(rows)\n",
    "    print(f\"  stratified by segment ({', '.join(names)}; {unplaced:,} visitors in no listed segment were left out):\")\n",
    "    for r in st[\"segments\"]:\n",
    "        print(f\"    {r['segment']:12s} visitors {r['visitors_a']:,} / {r['visitors_b']:,} / {r['visitors_both']:,} shared -> \"\n",
    "              f\"overlap {r['click_overlap']:,.0f}\" + (\" (capped at the smaller reach)\" if r[\"capped\"] else \"\"))\n",
    "    if st[\"refused\"]:\n",
    "        print(f\"    the stratified figure is REFUSED: its combined sampling half-width is {st['relative_half_width'] * 100:.0f} per cent, \"\n",
    "              f\"over the {FLOOR_TARGET_HALF_WIDTH * 100:.0f} the pooled figure refuses at; the pooled floor stands\")\n",
    "    else:\n",
    "        print(f\"    stratified floor on unique reach: {st['click_based']:,.0f} (half-width {st['relative_half_width'] * 100:.0f} per cent); \"\n",
    "              f\"the pooled floor with the between-segment part of the keenness taken out, and still a floor\")\n",
    "    return {\"refused\": st[\"refused\"], \"click_based\": st[\"click_based\"], \"click_overlap\": st[\"click_overlap\"],\n",
    "            \"relative_half_width\": st[\"relative_half_width\"], \"unplaced_visitors\": unplaced,\n",
    "            \"segments\": st[\"segments\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "39b915a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T17:01:27.088899Z",
     "iopub.status.busy": "2026-10-24T17:01:27.087570Z",
     "iopub.status.idle": "2026-10-24T17:01:27.109460Z",
     "shell.execute_reply": "2026-10-24T17:01:27.107653Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no file set - the demo below runs on exports written from the synthetic market\n"
     ]
    }
   ],
   "source": [
    "if YOUR_VISITOR_FILE is not None or YOUR_LANDING_LOG is not None:\n",
    "    pairs = read_visitor_file(YOUR_VISITOR_FILE) if YOUR_VISITOR_FILE else consolidate_landing_log(YOUR_LANDING_LOG)\n",
    "    your_result = report_reach(visitor_sets(pairs), YOUR_REACH, population=YOUR_POPULATION, spend=YOUR_SPEND,\n",
    "                               consent_rate=YOUR_CONSENT_RATE, identity=YOUR_IDENTITY, customers=customers_of(pairs),\n",
    "                               segments=segments_of(pairs), reach_by_segment=YOUR_REACH_BY_SEGMENT)\n",
    "else:\n",
    "    print(\"no file set - the demo below runs on exports written from the synthetic market\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3c4d02a",
   "metadata": {},
   "source": [
    "### Exercised twice, on data whose answers are known\n",
    "\n",
    "Before trusting the loaders, run them on exports written from the market above, whose answers\n",
    "this notebook already knows: the ready-made visitor file must reproduce the worked market's\n",
    "counts, its customers and its click-based figure exactly, the landing log must consolidate to\n",
    "the same counts, a thin export must be refused, and a file with the wrong columns must fail\n",
    "in plain words rather than produce a number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c9664e57",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T17:01:27.116827Z",
     "iopub.status.busy": "2026-10-24T17:01:27.116423Z",
     "iopub.status.idle": "2026-10-24T17:01:33.861394Z",
     "shell.execute_reply": "2026-10-24T17:01:33.860452Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clean visitor file\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the exact part - your visitors, de-duplicated\n",
      "  34,312 distinct visitors arrived from 2 platforms (34,647 platform-arrivals, so 335 were counted twice or more)\n",
      "  meta: 22,368 visitors, of whom 22,033 came from no other platform\n",
      "  tiktok: 12,279 visitors, of whom 11,944 came from no other platform - 2.01 per new visitor\n",
      "the exact part - your customers, by the platforms that brought them\n",
      "  1,013 of the 34,312 visitors became customers\n",
      "  meta: 663 customers came from no other platform\n",
      "  tiktok: 341 customers came from no other platform - 70.38 per new customer\n",
      "  9 customers arrived via more than one platform; splitting them is attribution, another method - report them as shared\n",
      "  reach figures must be each platform's de-duplicated reach for the whole window, at campaign or account level - never a sum of ad-set or campaign rows, which counts the same people twice\n",
      "  note: the identity is a cookie, so a person split across devices or app browsers is two visitors here; the shared counts are floors and the per-platform-alone counts are ceilings\n",
      "\n",
      "meta x tiktok: 22,368 visitors from meta, 12,279 from tiktok, 335 from both\n",
      "  unique reach sits between 776,226 (if the people on both platforms click like anyone else) and 1,052,230 (if the audiences overlap only by chance)\n",
      "  the click-based end's sampling interval is 727,796 to 819,640\n",
      "  people tiktok added that meta had not reached: between 62,024 and 338,028\n",
      "  an extra person reached on tiktok cost between 7.1 cents and 38.7 cents (an awareness figure, not an acquisition cost)\n",
      "  stratified by segment (app, desktop, mobile web; 0 visitors in no listed segment were left out):\n",
      "    app          visitors 13,891 / 7,899 / 277 shared -> overlap 241,755\n",
      "    desktop      visitors 3,373 / 1,678 / 13 shared -> overlap 35,280\n",
      "    mobile web   visitors 5,104 / 2,702 / 45 shared -> overlap 76,023\n",
      "    stratified floor on unique reach: 842,381 (half-width 12 per cent); the pooled floor with the between-segment part of the keenness taken out, and still a floor\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "the total across everyone who saw an ad is not estimated: two markets with these exact reports can differ by a fifth, and only a panel that sees the same people on both platforms can tell them apart\n",
      "\n",
      "agrees with the worked market: unique visitors True, customers via B alone True, click-based reach True, stratified floor True\n"
     ]
    }
   ],
   "source": [
    "DEMO = HERE / \"_your_data_demo\"\n",
    "DEMO.mkdir(exist_ok=True)\n",
    "\n",
    "\n",
    "def write_demo_exports(market, generator):\n",
    "    ids = np.arange(len(market))\n",
    "    visit_a, visit_b, buy = market.visit_a.to_numpy(), market.visit_b.to_numpy(), market.buy.to_numpy()\n",
    "    seg = market.segment.to_numpy()\n",
    "    rows = [{\"visitor_id\": f\"v{i:07d}\", \"platform\": \"meta\", \"converted\": int(buy[i]), \"segment\": seg[i]} for i in ids[visit_a]]\n",
    "    rows += [{\"visitor_id\": f\"v{i:07d}\", \"platform\": \"tiktok\", \"converted\": int(buy[i]), \"segment\": seg[i]} for i in ids[visit_b]]\n",
    "    visitors_csv = pd.DataFrame(rows).sample(frac=1.0, random_state=1)\n",
    "    visitors_csv.to_csv(DEMO / \"visitors.csv\", index=False)\n",
    "    # the raw log: one arrival per visit for a quarter of the visitors (to keep the demo small),\n",
    "    # click tags in the URL, a few utm-only arrivals, one repeat, one arrival from no platform\n",
    "    in_log = generator.random(len(market)) < 0.25\n",
    "    log = []\n",
    "    for i in ids[visit_a & in_log]:\n",
    "        tag = f\"fbclid=IwAR{i:x}\" if generator.random() < 0.9 else \"utm_source=facebook&utm_medium=paid\"\n",
    "        log.append({\"timestamp\": f\"2026-05-{generator.integers(1, 29):02d}T{generator.integers(0, 24):02d}:{generator.integers(0, 60):02d}:00\",\n",
    "                    \"visitor_id\": f\"v{i:07d}\", \"landing_url\": f\"https://shop.example/?{tag}\", \"converted\": int(buy[i]),\n",
    "                    \"device\": seg[i]})\n",
    "    for i in ids[visit_b & in_log]:\n",
    "        tag = f\"ttclid=E.C.P.{i:x}\" if generator.random() < 0.9 else \"utm_source=tiktok&utm_medium=paid\"\n",
    "        log.append({\"timestamp\": f\"2026-05-{generator.integers(1, 29):02d}T{generator.integers(0, 24):02d}:{generator.integers(0, 60):02d}:00\",\n",
    "                    \"visitor_id\": f\"v{i:07d}\", \"landing_url\": f\"https://shop.example/landing?{tag}\", \"converted\": int(buy[i]),\n",
    "                    \"device\": seg[i]})\n",
    "    log.append(dict(log[0]))  # a repeat arrival by the same visitor from the same platform\n",
    "    log.append({\"timestamp\": \"2026-05-09T10:00:00\", \"visitor_id\": \"v9999999\", \"landing_url\": \"https://shop.example/?utm_source=newsletter\",\n",
    "                \"converted\": 0, \"device\": \"desktop\"})\n",
    "    pd.DataFrame(log).sample(frac=1.0, random_state=2).to_csv(DEMO / \"landings.csv\", index=False)\n",
    "    log_expected = {\"meta\": int((visit_a & in_log).sum()), \"tiktok\": int((visit_b & in_log).sum()),\n",
    "                    \"both\": int((visit_a & visit_b & in_log).sum()),\n",
    "                    \"tiktok_customers_alone\": int((visit_b & ~visit_a & buy & in_log).sum())}\n",
    "    # a thin export: a small slice of the visitors, so the shared count sits under the floor\n",
    "    thin = visitors_csv[visitors_csv.visitor_id.isin(visitors_csv.visitor_id.drop_duplicates().sample(3000, random_state=3))]\n",
    "    thin.to_csv(DEMO / \"visitors_thin.csv\", index=False)\n",
    "    pd.DataFrame({\"id\": [\"a\", \"b\"], \"channel_name\": [\"meta\", \"tiktok\"]}).to_csv(DEMO / \"broken.csv\", index=False)\n",
    "    return log_expected\n",
    "\n",
    "\n",
    "log_expected = write_demo_exports(market, np.random.default_rng(SEED + 9))\n",
    "print(\"clean visitor file\")\n",
    "clean_pairs = read_visitor_file(DEMO / \"visitors.csv\")\n",
    "clean_sets = visitor_sets(clean_pairs)\n",
    "reach_by_segment = {\"meta\": {r[\"segment\"]: r[\"reach_a\"] for r in segments}, \"tiktok\": {r[\"segment\"]: r[\"reach_b\"] for r in segments}}\n",
    "clean_result = report_reach(clean_sets, {\"meta\": report[\"reach_a\"], \"tiktok\": report[\"reach_b\"]},\n",
    "                            population=N_MARKET, spend={\"tiktok\": SPEND_B_EUR}, customers=customers_of(clean_pairs),\n",
    "                            segments=segments_of(clean_pairs), reach_by_segment=reach_by_segment)\n",
    "clean_pair = clean_result[\"pairs\"][\"metaxtiktok\"]\n",
    "clean_agrees = (clean_result[\"unique_visitors\"] == visitors[\"unique_visitors\"]\n",
    "                and clean_result[\"customers_alone\"][\"tiktok\"] == customers[\"customers_b_only\"]\n",
    "                and abs(clean_pair[\"click_based\"] - read[\"click_based\"]) < 0.5\n",
    "                and abs(clean_pair[\"stratified\"][\"click_based\"] - strat[\"click_based\"]) < 0.5)\n",
    "print(f\"\\nagrees with the worked market: unique visitors {clean_result['unique_visitors'] == visitors['unique_visitors']}, \"\n",
    "      f\"customers via B alone {clean_result['customers_alone']['tiktok'] == customers['customers_b_only']}, \"\n",
    "      f\"click-based reach {abs(clean_pair['click_based'] - read['click_based']) < 0.5}, \"\n",
    "      f\"stratified floor {abs(clean_pair['stratified']['click_based'] - strat['click_based']) < 0.5}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1002de53",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T17:01:33.866181Z",
     "iopub.status.busy": "2026-10-24T17:01:33.865749Z",
     "iopub.status.idle": "2026-10-24T17:01:36.956543Z",
     "shell.execute_reply": "2026-10-24T17:01:36.955726Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "landing log, consolidated\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "consolidated 8,667 arrivals into 8,665 visitor-platform pairs across 2 platform(s); 1 arrival(s) named no platform and were left out\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  counts from the log: {'meta': 5628, 'tiktok': 3037}, shared 84, customers via tiktok alone 86 - same as the visitors the log was written for ({'meta': 5628, 'tiktok': 3037, 'both': 84, 'tiktok_customers_alone': 86}): True\n",
      "\n",
      "thin export\n",
      "the exact part - your visitors, de-duplicated\n",
      "  3,000 distinct visitors arrived from 2 platforms (3,035 platform-arrivals, so 35 were counted twice or more)\n",
      "  meta: 1,947 visitors, of whom 1,912 came from no other platform\n",
      "  tiktok: 1,088 visitors, of whom 1,053 came from no other platform\n",
      "  reach figures must be each platform's de-duplicated reach for the whole window, at campaign or account level - never a sum of ad-set or campaign rows, which counts the same people twice\n",
      "  note: the identity is a cookie, so a person split across devices or app browsers is two visitors here; the shared counts are floors and the per-platform-alone counts are ceilings\n",
      "\n",
      "meta x tiktok: 1,947 visitors from meta, 1,088 from tiktok, 35 from both\n",
      "  the estimate is REFUSED: 35 shared visitors is below the floor of 97; the sampling error alone would be more than 20 per cent\n",
      "\n",
      "the total across everyone who saw an ad is not estimated: two markets with these exact reports can differ by a fifth, and only a panel that sees the same people on both platforms can tell them apart\n",
      "\n",
      "broken export\n",
      "  refused in plain words: the file needs a 'visitor_id' column (or one of ['visitor_id', 'user_id', 'client_id', 'user_pseudo_id', 'visitor', 'cookie_id']); it has ['id', 'channel_name']\n",
      "\n",
      "one platform only\n",
      "  refused in plain words: the visitors came from 1 platform(s); de-duplicating needs at least two\n",
      "\n",
      "reach smaller than visitors\n",
      "the exact part - your visitors, de-duplicated\n",
      "  34,312 distinct visitors arrived from 2 platforms (34,647 platform-arrivals, so 335 were counted twice or more)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  meta: 22,368 visitors, of whom 22,033 came from no other platform\n",
      "  tiktok: 12,279 visitors, of whom 11,944 came from no other platform\n",
      "  reach figures must be each platform's de-duplicated reach for the whole window, at campaign or account level - never a sum of ad-set or campaign rows, which counts the same people twice\n",
      "  note: the identity is a cookie, so a person split across devices or app browsers is two visitors here; the shared counts are floors and the per-platform-alone counts are ceilings\n",
      "  refused in plain words: meta x tiktok: a platform's reach (1,000 / 481,237) is smaller than its visitors (22,368 / 12,279) - the reach figure or the window is wrong\n"
     ]
    }
   ],
   "source": [
    "print(\"landing log, consolidated\")\n",
    "log_pairs = consolidate_landing_log(DEMO / \"landings.csv\")\n",
    "log_sets = visitor_sets(log_pairs)\n",
    "log_counts = {p: len(s) for p, s in log_sets.items()}\n",
    "log_both = len(log_sets[\"meta\"] & log_sets[\"tiktok\"])\n",
    "log_customers_alone = len((log_sets[\"tiktok\"] - log_sets[\"meta\"]) & customers_of(log_pairs))\n",
    "log_agrees = (log_counts == {\"meta\": log_expected[\"meta\"], \"tiktok\": log_expected[\"tiktok\"]} and log_both == log_expected[\"both\"]\n",
    "              and log_customers_alone == log_expected[\"tiktok_customers_alone\"])\n",
    "print(f\"  counts from the log: {log_counts}, shared {log_both}, customers via tiktok alone {log_customers_alone} - \"\n",
    "      f\"same as the visitors the log was written for ({log_expected}): {log_agrees}\")\n",
    "print(\"\\nthin export\")\n",
    "thin_result = report_reach(visitor_sets(read_visitor_file(DEMO / \"visitors_thin.csv\")),\n",
    "                           {\"meta\": report[\"reach_a\"], \"tiktok\": report[\"reach_b\"]}, population=N_MARKET)\n",
    "print(\"\\nbroken export\")\n",
    "try:\n",
    "    read_visitor_file(DEMO / \"broken.csv\")\n",
    "except YourDataError as e:\n",
    "    print(f\"  refused in plain words: {e}\")\n",
    "print(\"\\none platform only\")\n",
    "try:\n",
    "    report_reach({\"meta\": clean_sets[\"meta\"]}, {\"meta\": report[\"reach_a\"]})\n",
    "except YourDataError as e:\n",
    "    print(f\"  refused in plain words: {e}\")\n",
    "print(\"\\nreach smaller than visitors\")\n",
    "try:\n",
    "    report_reach(clean_sets, {\"meta\": 1000, \"tiktok\": report[\"reach_b\"]})\n",
    "except YourDataError as e:\n",
    "    print(f\"  refused in plain words: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64335f86",
   "metadata": {},
   "source": [
    "## The numbers this bundle publishes\n",
    "\n",
    "Two files carry every number the article, the widget and the video show, so none of them is\n",
    "ever typed by hand. `data/checks.json` holds pinned cases with their expected readings, which\n",
    "the widget must reproduce from the same formulas; `data/worked_example.json` holds every\n",
    "headline figure the article quotes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6e45e2c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T17:01:36.966436Z",
     "iopub.status.busy": "2026-10-24T17:01:36.965657Z",
     "iopub.status.idle": "2026-10-24T17:01:39.027278Z",
     "shell.execute_reply": "2026-10-24T17:01:39.026436Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote data/checks.json - the worked market (estimated), its even-keenness twin (estimated), a lightly overlapping pair (estimated), a thin pair (refused), a very keen pair (estimated)\n"
     ]
    }
   ],
   "source": [
    "def expected_for(observed, population, spend_b, customers=None):\n",
    "    r = readings(observed, population=population)\n",
    "    iv = click_interval(observed)\n",
    "    v = visitor_counts(observed)\n",
    "    out = {\"unique_visitors\": v[\"unique_visitors\"], \"net_new_visitors_b\": v[\"net_new_visitors_b\"],\n",
    "           # the two exact costs: None where B brought nobody alone, since no finite cost exists then\n",
    "           \"cost_per_net_new_visitor_eur\": round(spend_b / v[\"net_new_visitors_b\"], 4) if v[\"net_new_visitors_b\"] else None,\n",
    "           \"cost_per_new_customer_eur\": (round(spend_b / customers[\"customers_b_only\"], 4)\n",
    "                                         if customers and customers[\"customers_b_only\"] else None),\n",
    "           \"share_of_visitors_from_both\": round(v[\"share_of_visitors_from_both\"], 6),\n",
    "           \"sum\": r[\"sum\"], \"visitor_share\": round(r[\"visitor_share\"], 4),\n",
    "           \"chance_overlap\": round(r[\"chance_overlap\"], 4), \"chance\": round(r[\"chance\"], 4),\n",
    "           \"click_overlap\": round(r[\"click_overlap\"], 4), \"click_based\": round(r[\"click_based\"], 4),\n",
    "           \"net_new_b_click\": round(r[\"net_new_b_click\"], 4),\n",
    "           \"interval_unique_low\": round(iv[\"unique_low\"], 4), \"interval_unique_high\": round(iv[\"unique_high\"], 4),\n",
    "           \"relative_half_width\": round(iv[\"relative_half_width\"], 6),\n",
    "           \"refused\": bool(observed[\"visitors_both\"] < SHARED_VISITOR_FLOOR),\n",
    "           \"click_overlap_capped\": bool(r[\"click_overlap_capped\"]),\n",
    "           \"cost_per_net_new_cents_low\": round(cost_per_net_new(spend_b, observed[\"reach_b\"], r[\"chance_overlap\"]) * 100, 4),\n",
    "           # None where the floor credits B with no new people at all (the cap binds): no finite cost exists\n",
    "           \"cost_per_net_new_cents_high\": (round(cost_per_net_new(spend_b, observed[\"reach_b\"], r[\"click_overlap\"]) * 100, 4)\n",
    "                                           if np.isfinite(cost_per_net_new(spend_b, observed[\"reach_b\"], r[\"click_overlap\"])) else None)}\n",
    "    for g in [0.5, 1.0, 2.0]:\n",
    "        rg = readings(observed, keenness_inflation=g)\n",
    "        out[f\"click_based_at_inflation_{g}\"] = round(rg[\"click_based\"], 4)\n",
    "    return out\n",
    "\n",
    "\n",
    "light_market = add_customers(build_market(3_000_000, -1.0, -1.6, 0.2, 0.3, 0.3, 0.04, 0.035, np.random.default_rng(SEED + 21)),\n",
    "                             BUY_RATE, np.random.default_rng(SEED + 31))\n",
    "light_report, light_truth, light_customers = observe(light_market), truth_of(light_market), customer_counts(light_market)\n",
    "assert light_report[\"visitors_both\"] >= SHARED_VISITOR_FLOOR, light_report\n",
    "thin_market = add_customers(build_market(200_000, -1.5, -2.0, 0.9, 0.7, 0.6, 0.01, 0.01, np.random.default_rng(SEED + 22)),\n",
    "                            BUY_RATE, np.random.default_rng(SEED + 32))\n",
    "thin_report, thin_truth, thin_customers = observe(thin_market), truth_of(thin_market), customer_counts(thin_market)\n",
    "keen_market = add_customers(build_market(N_MARKET, ALPHA_A, ALPHA_B, 1.3, 1.0, 0.9, CLICK_A, CLICK_B, np.random.default_rng(SEED + 23)),\n",
    "                            BUY_RATE, np.random.default_rng(SEED + 33))\n",
    "keen_report, keen_truth, keen_customers = observe(keen_market), truth_of(keen_market), customer_counts(keen_market)\n",
    "assert readings(keen_report)[\"click_overlap_capped\"], keen_report\n",
    "\n",
    "checks = {\n",
    "    \"method\": \"cross-platform de-duplicated reach: inclusion-exclusion over the site's visitors (exact), the \"\n",
    "              \"click-based capture-recapture overlap v_AB*R_A*R_B/(v_A*v_B) as a floor on unique reach, the \"\n",
    "              \"chance-overlap R_A*R_B/population as a ceiling, a log-scale sampling interval from the three \"\n",
    "              \"counts, and a refusal below the shared-visitor floor\",\n",
    "    \"tolerance\": 1e-4,\n",
    "    \"constants\": {\"shared_visitor_floor\": SHARED_VISITOR_FLOOR, \"floor_target_half_width\": FLOOR_TARGET_HALF_WIDTH,\n",
    "                  \"interval_level\": RANGE_LEVEL},\n",
    "    \"reach_cases\": [\n",
    "        {\"name\": \"the worked market\", \"note\": \"keen double-reached people: the click-based figure runs low\",\n",
    "         \"population\": N_MARKET, \"spend_b\": SPEND_B_EUR, \"observed\": report, \"customers\": customers,\n",
    "         \"truth\": {\"overlap\": truth[\"overlap\"], \"unique\": truth[\"unique\"]},\n",
    "         \"expected\": expected_for(report, N_MARKET, SPEND_B_EUR, customers)},\n",
    "        {\"name\": \"its even-keenness twin\", \"note\": \"the same reports; the click-based figure is exact here\",\n",
    "         \"population\": N_MARKET, \"spend_b\": SPEND_B_EUR, \"observed\": twin_report, \"customers\": None,\n",
    "         \"truth\": {\"overlap\": twin_truth[\"overlap\"], \"unique\": twin_truth[\"unique\"]},\n",
    "         \"expected\": expected_for(twin_report, N_MARKET, SPEND_B_EUR)},\n",
    "        {\"name\": \"a lightly overlapping pair\", \"note\": \"little correlation, keenness nearly even\",\n",
    "         \"population\": 3_000_000, \"spend_b\": 9_000.0, \"observed\": light_report, \"customers\": light_customers,\n",
    "         \"truth\": {\"overlap\": light_truth[\"overlap\"], \"unique\": light_truth[\"unique\"]},\n",
    "         \"expected\": expected_for(light_report, 3_000_000, 9_000.0, light_customers)},\n",
    "        {\"name\": \"a thin pair\", \"note\": \"too few shared visitors - the estimate is refused\",\n",
    "         \"population\": 200_000, \"spend_b\": 3_000.0, \"observed\": thin_report, \"customers\": thin_customers,\n",
    "         \"truth\": {\"overlap\": thin_truth[\"overlap\"], \"unique\": thin_truth[\"unique\"]},\n",
    "         \"expected\": expected_for(thin_report, 200_000, 3_000.0, thin_customers)},\n",
    "        {\"name\": \"a very keen pair\", \"note\": \"the click-based overlap exceeds the smaller reach - the floor collapses\",\n",
    "         \"population\": N_MARKET, \"spend_b\": SPEND_B_EUR, \"observed\": keen_report, \"customers\": keen_customers,\n",
    "         \"truth\": {\"overlap\": keen_truth[\"overlap\"], \"unique\": keen_truth[\"unique\"]},\n",
    "         \"expected\": expected_for(keen_report, N_MARKET, SPEND_B_EUR, keen_customers)},\n",
    "    ],\n",
    "}\n",
    "(DATA / \"checks.json\").write_text(json.dumps(checks, indent=1), encoding=\"utf-8\")\n",
    "print(\"wrote data/checks.json -\", \", \".join(f\"{c['name']} ({'refused' if c['expected']['refused'] else 'estimated'})\" for c in checks[\"reach_cases\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d6feacec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T17:01:39.037275Z",
     "iopub.status.busy": "2026-10-24T17:01:39.036839Z",
     "iopub.status.idle": "2026-10-24T17:01:39.109664Z",
     "shell.execute_reply": "2026-10-24T17:01:39.109082Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote data/worked_example.json\n"
     ]
    }
   ],
   "source": [
    "worked = {\n",
    "    \"label\": \"synthetic - generated in this notebook with the answer built in\",\n",
    "    \"seed\": SEED,\n",
    "    \"market\": {\"n_market\": N_MARKET, \"correlation\": CORRELATION, \"keen_spread\": KEEN_SPREAD,\n",
    "               \"keen_follows\": KEEN_FOLLOWS, \"click_a\": CLICK_A, \"click_b\": CLICK_B, \"spend_b_eur\": SPEND_B_EUR,\n",
    "               \"reach_share_a_pct\": round(report[\"reach_a\"] / N_MARKET * 100, 2),\n",
    "               \"reach_share_b_pct\": round(report[\"reach_b\"] / N_MARKET * 100, 2)},\n",
    "    \"report\": report,\n",
    "    \"truth\": {**truth, \"overlap_share_of_unique_pct\": round(truth[\"overlap\"] / truth[\"unique\"] * 100, 2),\n",
    "              \"overlap_share_of_b_pct\": round(truth[\"overlap\"] / report[\"reach_b\"] * 100, 2),\n",
    "              \"overlap_share_of_a_pct\": round(truth[\"overlap\"] / report[\"reach_a\"] * 100, 2),\n",
    "              \"chance_overlap_true_population\": round(read[\"chance_overlap\"], 1),\n",
    "              \"overlap_over_chance\": round(truth[\"overlap\"] / read[\"chance_overlap\"], 3)},\n",
    "    \"visitors\": {**visitors, \"share_of_visitors_from_both_pct\": round(visitors[\"share_of_visitors_from_both\"] * 100, 3),\n",
    "                 \"visit_rate_a_pct\": round(report[\"visitors_a\"] / report[\"reach_a\"] * 100, 3),\n",
    "                 \"visit_rate_b_pct\": round(report[\"visitors_b\"] / report[\"reach_b\"] * 100, 3)},\n",
    "    \"customers\": {**customers, \"buy_rate\": BUY_RATE,\n",
    "                  \"buy_rate_observed_pct\": round(customers[\"customers\"] / visitors[\"unique_visitors\"] * 100, 2)},\n",
    "    \"readings\": {key: {\"unique\": round(read[key], 1), \"overlap\": round(report[\"reach_a\"] + report[\"reach_b\"] - read[key], 1),\n",
    "                       \"error_pct\": round((read[key] / truth[\"unique\"] - 1) * 100, 2)}\n",
    "                 for key in [\"sum\", \"visitor_share\", \"chance\", \"click_based\"]},\n",
    "    \"interval\": {k: round(float(v), 4) for k, v in interval.items()},\n",
    "    # the click-based formula read left to right: B's reach, times the share of B's visitors who\n",
    "    # also came from A, divided by A's click rate - which turns \"also visited from A\" into \"also\n",
    "    # reached by A\" - against what was true\n",
    "    \"formula_walk\": {\"share_of_b_visitors_from_a_pct\": round(report[\"visitors_both\"] / report[\"visitors_b\"] * 100, 3),\n",
    "                     \"click_rate_a_pct\": round(report[\"visitors_a\"] / report[\"reach_a\"] * 100, 3),\n",
    "                     \"one_over_click_rate_a\": round(report[\"reach_a\"] / report[\"visitors_a\"], 2),\n",
    "                     \"formula_share_of_b_also_reached_by_a_pct\": round(read[\"click_overlap\"] / report[\"reach_b\"] * 100, 2),\n",
    "                     \"true_share_of_b_also_reached_by_a_pct\": round(truth[\"overlap\"] / report[\"reach_b\"] * 100, 2),\n",
    "                     \"formula_overlap\": round(read[\"click_overlap\"], 1), \"true_overlap\": truth[\"overlap\"]},\n",
    "    \"keenness_inflation_worked\": round(keenness_inflation(report, truth[\"overlap\"]), 4),\n",
    "    \"visitors_both_over_even_keenness\": round(1 + keenness_inflation(report, truth[\"overlap\"]), 4),\n",
    "    \"click_based_at_inflation\": {str(g): round(readings(report, keenness_inflation=g)[\"click_based\"], 1) for g in [0.5, 1.0, 2.0]},\n",
    "    \"range\": {\"low\": round(read[\"click_based\"], 1), \"high\": round(read[\"chance\"], 1),\n",
    "              \"width_pct_of_truth\": round((read[\"chance\"] - read[\"click_based\"]) / truth[\"unique\"] * 100, 2),\n",
    "              \"truth_inside\": bool(read[\"click_based\"] <= truth[\"unique\"] <= read[\"chance\"])},\n",
    "    \"money\": {k: {kk: round(float(vv), 3) for kk, vv in v.items()} for k, v in money.items()},\n",
    "    \"twins\": {\"report\": twin_report, \"truth\": twin_truth, \"gap_pct\": round(twins_gap_pct, 2),\n",
    "              \"report_matches\": {k: abs(report[k] - twin_report[k]) / max(report[k], 1) * 100 for k in report}},\n",
    "    \"keenness_sweep\": keen_sweep.round(4).to_dict(orient=\"records\"),\n",
    "    \"population\": {**pop_summary, \"n_planned\": N_POP, \"decision_tolerance\": DECISION_TOLERANCE},\n",
    "    \"inflation\": {k: round(float(v), 4) for k, v in inflation.items()},\n",
    "    \"observable\": observable,\n",
    "    \"stratified\": {\"segment_noise\": SEGMENT_NOISE, \"segments\": [{k: (round(float(v), 1) if isinstance(v, float) else v)\n",
    "                                                                   for k, v in r.items()} for r in strat[\"segments\"]],\n",
    "                   \"click_overlap\": round(strat[\"click_overlap\"], 1), \"click_based\": round(strat[\"click_based\"], 1),\n",
    "                   \"error_pct\": round((strat[\"click_based\"] / truth[\"unique\"] - 1) * 100, 2),\n",
    "                   \"pooled_error_pct\": round((read[\"click_based\"] / truth[\"unique\"] - 1) * 100, 2),\n",
    "                   \"relative_half_width_pct\": round(strat[\"relative_half_width\"] * 100, 2), \"refused\": strat[\"refused\"]},\n",
    "    \"floor\": {\"shared_visitor_floor\": SHARED_VISITOR_FLOOR, \"target_half_width_pct\": FLOOR_TARGET_HALF_WIDTH * 100,\n",
    "              \"table\": floor_table.round(2).to_dict(orient=\"records\")},\n",
    "    \"fragmentation\": {\"rows\": fragmentation.round(2).to_dict(orient=\"records\"),\n",
    "                      \"cancel_share\": float(crossing.fragment_share), \"cancel_error_pct\": round(float(crossing.error_pct), 2)},\n",
    "    \"consent\": {k: {kk: round(float(vv), 4) for kk, vv in v.items()} for k, v in consent_out.items()},\n",
    "    \"your_data\": {\"clean_agrees\": bool(clean_agrees),\n",
    "                  \"log_agrees\": bool(log_agrees),\n",
    "                  \"thin_refused\": bool(thin_result[\"pairs\"][\"metaxtiktok\"][\"refused\"]),\n",
    "                  \"thin_shared_visitors\": int(thin_result[\"pairs\"][\"metaxtiktok\"][\"visitors_both\"]),\n",
    "                  \"clean_customers_alone\": clean_result[\"customers_alone\"],\n",
    "                  \"clean_stratified_agrees\": bool(abs(clean_pair[\"stratified\"][\"click_based\"] - strat[\"click_based\"]) < 0.5),\n",
    "                  \"cost_per_net_new_cents_range\": [round(x, 2) for x in clean_pair[\"cost_per_net_new_cents\"]]},\n",
    "}\n",
    "\n",
    "\n",
    "def _plain(o):\n",
    "    if isinstance(o, dict):\n",
    "        return {str(k): _plain(v) for k, v in o.items()}\n",
    "    if isinstance(o, (list, tuple)):\n",
    "        return [_plain(v) for v in o]\n",
    "    if isinstance(o, (np.integer,)):\n",
    "        return int(o)\n",
    "    if isinstance(o, (np.floating,)):\n",
    "        return float(o)\n",
    "    if isinstance(o, (np.bool_,)):\n",
    "        return bool(o)\n",
    "    return o\n",
    "\n",
    "\n",
    "(DATA / \"worked_example.json\").write_text(json.dumps(_plain(worked), indent=1), encoding=\"utf-8\")\n",
    "print(\"wrote data/worked_example.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19493fc5",
   "metadata": {},
   "source": [
    "## What this notebook did, and what it did not\n",
    "\n",
    "It counted the site's visitors across two platforms exactly, and showed that count is the one\n",
    "number in cross-platform reach that carries no estimate. It turned the visitors from both\n",
    "platforms into an estimate of the people on both, showed that estimate is exact when keenness\n",
    "is even and a floor when it is not, and paired it with the chance formula's ceiling to make\n",
    "a range the truth sat inside on nearly every market where the two assumptions held. It built\n",
    "two markets with identical reports and different reaches, which is the whole reason it\n",
    "refuses a single total. And it measured what fragmentation, thin data and consent do.\n",
    "\n",
    "It did not measure what the ads did - reach is exposure, not effect. It did not estimate the\n",
    "total unique reach across everyone who saw an impression, and it will not: that number needs\n",
    "an instrument that sees the same people's impressions on both platforms, which is a panel or\n",
    "a clean room, not a website. And every number in it is from a synthetic market; run the last\n",
    "section on yours."
   ]
  }
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