{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "827134aa",
   "metadata": {},
   "source": "# Counting the universal set\n\n*The vignette `vignette(\"universal-set\")` of the R package clobe, ported to `psp.clobe`.*\n\nTo judge whether a model is too flexible, we need to know how many ordinal patterns *could* have been observed. This set of all possible patterns is the **universal set**, and its size is the `universal` argument of `gdistance()`. This notebook explains how to count it. It assumes you know what an inequality matrix is; if not, start with [inequality-matrices.ipynb](inequality-matrices.ipynb).\n\n`psp.clobe` gives three ways to get at the universal set:\n\n| Tool | What it gives | Use it when |\n|---|---|---|\n| `fubini()`, `semiorders()` | the number of patterns, instantly | any pattern could be measured |\n| `orderings()` | every pattern, as matrices | you need the patterns themselves |\n| `brutes()` | the patterns a particular measurement scale can produce | the scale is coarse compared with the threshold |"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d00f81a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:27.301959Z",
     "iopub.status.busy": "2026-10-15T15:58:27.301853Z",
     "iopub.status.idle": "2026-10-15T15:58:28.395501Z",
     "shell.execute_reply": "2026-10-15T15:58:28.394980Z"
    }
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   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "import math\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from psp import clobe\n",
    "\n",
    "sys.path.insert(0, \"..\")  # for notebooks/figure_style.py\n",
    "from figure_style import aqua, blue, figures, ink, light, muted, orange, reference, titled\n",
    "\n",
    "show = figures(\"clobe-universal-set\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78a3ef90",
   "metadata": {},
   "source": "## Building block: splitting stimuli into groups\n\nHow many ways are there to split $n$ stimuli into $k$ groups, where the groups have no order and none is empty? This is the **Stirling number of the second kind**, $S(n, k)$. For example, 3 stimuli can be split into 2 groups in 3 ways: {1, 2}{3}, {1, 3}{2} and {2, 3}{1}."
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0fbdd5d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:28.397220Z",
     "iopub.status.busy": "2026-10-15T15:58:28.397014Z",
     "iopub.status.idle": "2026-10-15T15:58:28.399672Z",
     "shell.execute_reply": "2026-10-15T15:58:28.399316Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clobe.stirling2(3, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b355b2d",
   "metadata": {},
   "source": "There is a simple way to compute them. Take the last stimulus. Either it is in a group of its own, and the other $n - 1$ stimuli form the remaining $k - 1$ groups; or it joins one of the $k$ groups formed by the others. So\n\n$$\nS(n, k) = S(n-1, k-1) + k \\, S(n-1, k), \\qquad S(0, 0) = 1.\n$$\n\nThis is the standard recurrence of the Stirling numbers of the second kind (Graham, Knuth & Patashnik, 1994, chap. 6; OEIS A008277), and the one `stirling2()` uses."
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb6b7101",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:28.400555Z",
     "iopub.status.busy": "2026-10-15T15:58:28.400460Z",
     "iopub.status.idle": "2026-10-15T15:58:28.404519Z",
     "shell.execute_reply": "2026-10-15T15:58:28.404174Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>k</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>90</td>\n",
       "      <td>65</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "k  1   2   3   4   5  6\n",
       "n                      \n",
       "1  1   0   0   0   0  0\n",
       "2  1   1   0   0   0  0\n",
       "3  1   3   1   0   0  0\n",
       "4  1   7   6   1   0  0\n",
       "5  1  15  25  10   1  0\n",
       "6  1  31  90  65  15  1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([[clobe.stirling2(n, k) for k in range(1, 7)] for n in range(1, 7)],\n",
    "             index=pd.Index(range(1, 7), name=\"n\"), columns=pd.Index(range(1, 7), name=\"k\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acce99f8",
   "metadata": {},
   "source": "## Weak orderings: the Fubini numbers\n\nWith `ordering=\"weak\"`, only exact ties are \"about the same\", and a pattern is a ranking with ties. To build one, split the $n$ stimuli into $j$ tied groups ($S(n, j)$ ways) and rank the groups from top to bottom ($j!$ ways). Adding up over the possible numbers of groups gives the **Fubini numbers** (also called ordered Bell numbers; OEIS A000670):\n\n$$\na(n) = \\sum_{j=1}^{n} j! \\, S(n, j).\n$$\n\nFor 3 stimuli: one group (all tied) in 1 way; two groups in $2! \\times 3 = 6$ ways; three groups (no ties) in $3! = 6$ ways. That makes 13."
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7cb06f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:28.405308Z",
     "iopub.status.busy": "2026-10-15T15:58:28.405213Z",
     "iopub.status.idle": "2026-10-15T15:58:28.407310Z",
     "shell.execute_reply": "2026-10-15T15:58:28.406996Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 13, 75, 541, 4683, 47293, 545835]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[clobe.fubini(n) for n in range(1, 9)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860cd7c2",
   "metadata": {},
   "source": "If the dependent variable can take fewer distinct values than there are stimuli, there cannot be more tied groups than values. `levels` stops the sum there:"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c39a6ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:28.408228Z",
     "iopub.status.busy": "2026-10-15T15:58:28.408128Z",
     "iopub.status.idle": "2026-10-15T15:58:28.410000Z",
     "shell.execute_reply": "2026-10-15T15:58:28.409687Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clobe.fubini(4, levels=3)  # 4 stimuli, but only 3 possible values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ebcfff2",
   "metadata": {},
   "source": "## Semiorders\n\nWith the default `ordering=\"semiorder\"`, stimuli within the threshold of each other are \"about the same\", and this relation need not be transitive. Counting these patterns takes two steps.\n\n### Step 1: the shape of a semiorder\n\nIgnore for a moment which stimulus is which, and sort the stimuli from smallest to largest value. For the stimulus in position $p$, let its **reach** $r_p$ be the last position that is still about the same as it. Two rules hold:\n\n- $r_p \\ge p$, because a stimulus is about the same as itself.\n- $r_1 \\le r_2 \\le \\ldots \\le r_n$: if a stimulus is about the same as something further up, then so is every stimulus between them, because their values are closer.\n\nEvery such sequence describes a different shape, and every shape has one. For 3 stimuli there are 5 shapes:\n\n| $r_1, r_2, r_3$ | meaning |\n|---|---|\n| 1, 2, 3 | all different: $1 < 2 < 3$ |\n| 1, 3, 3 | $1 < 2 \\approx 3$ |\n| 2, 2, 3 | $1 \\approx 2 < 3$ |\n| 2, 3, 3 | $1 \\approx 2$, $2 \\approx 3$, but $1 < 3$ |\n| 3, 3, 3 | all about the same |\n\nThe number of such sequences for $n$ stimuli is the $n$-th **Catalan number**,\n\n$$\nC_n = \\frac{1}{n+1} \\binom{2n}{n}: \\quad 1, 2, 5, 14, 42, 132, \\ldots\n$$\n\nwhich counts many \"staircase\" structures in mathematics. That the semiorders of $n$ unlabelled items are counted by the Catalan numbers is a classical result of Wine and Freund (1957), as Hu (2014) credits it; Fishburn (1985) treats semiorders and the interval orders they belong to at length.\n\n### Step 2: labelling the stimuli\n\nA shape becomes a pattern when we decide which stimulus sits in which position. There are $n!$ ways to do that, but some give the same pattern: if two stimuli are about the same as each other and relate identically to everything else, swapping them changes nothing. Removing these duplicates gives the number of **labelled semiorders**. Chandon, Lemaire and Pouget (1978) counted them; their result is usually stated as the exponential generating function $C(1 - e^{-x})$, where $C$ is the generating function of the Catalan numbers (Stanley, 1999, Problem 6.30; OEIS A006531). Reading off its coefficients gives an exact formula, derived from the two steps above in Appendix A:\n\n$$\ns(n) = \\sum_{j=0}^{n} (-1)^{n-j} \\, C_j \\, j! \\, S(n, j).\n$$\n\nIts terms alternate in sign; the subtractions remove the double counting. For 3 stimuli:"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "715e59b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:28.410913Z",
     "iopub.status.busy": "2026-10-15T15:58:28.410821Z",
     "iopub.status.idle": "2026-10-15T15:58:28.412946Z",
     "shell.execute_reply": "2026-10-15T15:58:28.412624Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, -12, 30], 19)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terms = [(-1) ** (3 - j) * math.comb(2 * j, j) // (j + 1) * math.factorial(j) * clobe.stirling2(3, j)\n",
    "         for j in range(4)]\n",
    "terms, sum(terms)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9206dbc8",
   "metadata": {},
   "source": "`semiorders()` computes this, and `orderings()` lists the patterns themselves using exactly the two steps above. They agree:"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fae99107",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:28.413816Z",
     "iopub.status.busy": "2026-10-15T15:58:28.413726Z",
     "iopub.status.idle": "2026-10-15T15:58:28.419300Z",
     "shell.execute_reply": "2026-10-15T15:58:28.418868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([1, 3, 19, 183, 2371, 38703, 763099, 17648823], 2371)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[clobe.semiorders(n) for n in range(1, 9)], clobe.orderings(5).n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90e2329",
   "metadata": {},
   "source": "### How fast the universal set grows"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04e46031",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:28.420108Z",
     "iopub.status.busy": "2026-10-15T15:58:28.420007Z",
     "iopub.status.idle": "2026-10-15T15:58:28.425587Z",
     "shell.execute_reply": "2026-10-15T15:58:28.425186Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weak_orderings</th>\n",
       "      <th>semiorders</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stimuli</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <th>3</th>\n",
       "      <td>13</td>\n",
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       "      <th>4</th>\n",
       "      <td>75</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>541</td>\n",
       "      <td>2371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4683</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>47293</td>\n",
       "      <td>763099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>545835</td>\n",
       "      <td>17648823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         weak_orderings  semiorders\n",
       "stimuli                            \n",
       "1                     1           1\n",
       "2                     3           3\n",
       "3                    13          19\n",
       "4                    75         183\n",
       "5                   541        2371\n",
       "6                  4683       38703\n",
       "7                 47293      763099\n",
       "8                545835    17648823"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"weak_orderings\": [clobe.fubini(k) for k in range(1, 9)],\n",
    "              \"semiorders\": [clobe.semiorders(k) for k in range(1, 9)]},\n",
    "             index=pd.Index(range(1, 9), name=\"stimuli\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eece8aaf",
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     "iopub.status.idle": "2026-10-15T15:58:29.108236Z",
     "shell.execute_reply": "2026-10-15T15:58:29.107729Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 770x528 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 530,
       "width": 670
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = np.arange(1, 13)\n",
    "fig, ax = plt.subplots(figsize=(7, 4.8))\n",
    "for name, count, colour, marker in [(\"semiorders\", clobe.semiorders, blue, \"o\"),\n",
    "                                    (\"weak orderings\", clobe.fubini, orange, \"s\")]:\n",
    "    values = [count(int(n)) for n in k]\n",
    "    ax.plot(k, values, \"-\" + marker, color=colour, linewidth=2, markersize=6,\n",
    "            markeredgecolor=\"white\", label=name)\n",
    "    ax.annotate(name, (k[-1], values[-1]), xytext=(8, 0), textcoords=\"offset points\",\n",
    "                color=ink, va=\"center\")\n",
    "ax.plot(k, 3.0 ** (k * (k - 1) / 2), \":\", color=muted, linewidth=1.5)\n",
    "ax.annotate(\"every matrix, 3^M\", (7, 3.0 ** 21), xytext=(-6, 0), textcoords=\"offset points\",\n",
    "            color=muted, ha=\"right\", va=\"center\", fontsize=10)\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set(xticks=k, xlim=(0.5, 15.5), ylim=(0.5, 1e18), xlabel=\"stimuli\")\n",
    "ax.legend(loc=\"upper left\")\n",
    "titled(ax, \"The universal set grows faster than exponentially\",\n",
    "       \"patterns of k stimuli, log scale; most matrices are not patterns\")\n",
    "show(fig, \"growth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a7a6b9f",
   "metadata": {},
   "source": "Semiorders outnumber weak orderings, because they also include the non-transitive \"about the same\" chains, and both grow faster than exponentially. With 8 stimuli there are over 17 million semiorders.\n\nIn R, the counts are doubles, which hold whole numbers exactly only up to $2^{53} \\approx 9 \\times 10^{15}$, so the R functions warn from 17 stimuli for `fubini()` and 14 for `semiorders()`. Here the counts are Python integers, which have no limit, so they are exact however many stimuli there are:"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "909103f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:29.109256Z",
     "iopub.status.busy": "2026-10-15T15:58:29.109130Z",
     "iopub.status.idle": "2026-10-15T15:58:29.111633Z",
     "shell.execute_reply": "2026-10-15T15:58:29.111252Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13977223809217503353398375258316496402242148303, True)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clobe.semiorders(30), clobe.semiorders(30) > 2**53"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43b114c0",
   "metadata": {},
   "source": "## When the measurement scale matters\n\n`semiorders(n)` assumes that any values at all could be measured. Real data are often coarser: a proportion of 20 trials can only be 0, 0.05, 0.10, and so on. Then some semiorders cannot occur. `brutes()` finds out which can, by trying every way of assigning the possible values to the stimuli.\n\nFor 4 stimuli, a scale of 0, 0.1, ..., 1 with a threshold of 0.1 produces every semiorder:"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "366e1326",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:29.112682Z",
     "iopub.status.busy": "2026-10-15T15:58:29.112579Z",
     "iopub.status.idle": "2026-10-15T15:58:29.115967Z",
     "shell.execute_reply": "2026-10-15T15:58:29.115571Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(183, 183)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale = torch.arange(11, dtype=torch.float64) / 10\n",
    "clobe.brutes(scale, 4, (0.1, -0.1), return_matrices=False).n, clobe.semiorders(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "289a23e6",
   "metadata": {},
   "source": "For 5 stimuli, it does not:"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "78afb55a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:29.116900Z",
     "iopub.status.busy": "2026-10-15T15:58:29.116798Z",
     "iopub.status.idle": "2026-10-15T15:58:29.122010Z",
     "shell.execute_reply": "2026-10-15T15:58:29.121583Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2131, 2371)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clobe.brutes(scale, 5, (0.1, -0.1), return_matrices=False).n, clobe.semiorders(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06aa80e4",
   "metadata": {},
   "source": "### Why some patterns are impossible\n\nThe problem is not the range of the scale but its *resolution* relative to the threshold. When the threshold is exactly one step, any two values are either within one step (about the same) or at least two steps apart (different); there is nothing in between. Here is a semiorder of 5 stimuli that such a scale cannot produce:"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a2868465",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:29.122857Z",
     "iopub.status.busy": "2026-10-15T15:58:29.122755Z",
     "iopub.status.idle": "2026-10-15T15:58:29.126240Z",
     "shell.execute_reply": "2026-10-15T15:58:29.125845Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 2., -1., -1., -1.,  0.],\n",
       "        [ 2.,  2., -1.,  0.,  0.],\n",
       "        [ 2.,  2.,  2.,  0.,  1.],\n",
       "        [ 2.,  2.,  2.,  2.,  0.],\n",
       "        [ 2.,  2.,  2.,  2.,  2.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clobe.imac(torch.tensor([0.0, 3, 6, 4, 2]), (2, -2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3efa6af",
   "metadata": {},
   "source": "Try to realise it with whole-numbered values and a threshold of 1 (stimuli numbered from 1 here, as in the matrix rows 0 to 4):\n\n- Stimulus 1 is smaller than 2, so they are at least 2 steps apart. Stimulus 5 is about the same as both, so it is within 1 step of each. Both can only hold if 2 is exactly 2 steps above 1, with 5 in the middle: $v_2 = v_1 + 2$, $v_5 = v_1 + 1$.\n- In the same way, stimulus 4 is about the same as both 2 and 3, while $2 < 3$, so $v_3 = v_2 + 2$ and $v_4 = v_2 + 1 = v_1 + 3$.\n- But then $v_4 - v_5 = 2$, so 4 and 5 are different, while the pattern says they are about the same.\n\nWith a threshold of *two* steps there is room in between, and the values $(0, 3, 6, 4, 2)$ above produce the pattern. A wider range of one-step values does not help:"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "42b4b487",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:29.127030Z",
     "iopub.status.busy": "2026-10-15T15:58:29.126928Z",
     "iopub.status.idle": "2026-10-15T15:58:29.248501Z",
     "shell.execute_reply": "2026-10-15T15:58:29.248062Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2131, 2371)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(clobe.brutes(torch.arange(21.0), 5, (1, -1), return_matrices=False).n,\n",
    " clobe.brutes(torch.arange(13.0), 5, (2, -2), return_matrices=False).n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae9431c8",
   "metadata": {},
   "source": "How fine does the scale have to be? For 5 stimuli, the share of the semiorders a scale can produce, as the threshold spans more of its steps (the scale runs from 0 to 10 thresholds, so its range is never what limits it):"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b56988a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:29.249707Z",
     "iopub.status.busy": "2026-10-15T15:58:29.249586Z",
     "iopub.status.idle": "2026-10-15T15:58:29.988143Z",
     "shell.execute_reply": "2026-10-15T15:58:29.987673Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JhR1gULcnkUgwceJEPPXUU7j77rudnngTEhIwbtw4/PjHP8YDDzyADRs2OE2zuroa7777Ln7605/6Na9paWlOp+3evRv9+/fH6NGjkZ2djczMTAwbNgzZ2dnIyMjo9f3RJCUl4eabb3Y4zdFNnjWJRIKHH37Y4bTw8HAMGjQIFy9edDi9ubnZu4xa6c0B2MLCQqd9QMlksoDlo7S0FN9++y3y8/Nx7tw5lJaWorW1Fa2trdDpdDbzuuqXrL6+3u95c3dD/vjjj3d6He76Hlu0aJHbNNzNs2PHDrz44otu0+ku++Lll1/2eN5AnXO3b9/udJrJZEJKSorbNJwdbwCg0Whw6tQp5ObmepwnVwJVLoE4RhzpLnXVkZ5WVzzRE6/fzgTq2OjN56aecK/bm8vfE73pmO1O90mOeHPPcvjwYZfT58+f7zaN+fPnY+XKlR6vs013ratA97zvIxILayF1e/369XN7wbIWGRmJVatWYdKkScjPz3c63zfffOP3AOzkyZMRGRnptEWQ0WjEiRMncOLECZvvg4ODMWHCBMyaNQt33XUXRo8e7dd8dQfTpk1zGtB09bQXMN8suroxcPSEv01ra6tH+XOkNw/CFRwc3KXr37NnD37zm99g7969fkmvqanJL+lYc/YDo820adM6vY5Lly65nD5ixAi3aQwcONDleaekpMTl8t1pX2RlZWHUqFEezx+oc667uuCqdY6nqqurO51Gm+5SLv44Rqx1p7rqTE+rK57oiddvZwJ1bHSXY1CM+tYT7nV7c/l7ojcds93hPskZb+9Z3G3L8OHD3abhyTyOdNe62l3v+4jE0nubd9ENLTQ0FL/73e9cznPgwAG/XGyshYSE4H/+53+8Xk6n02H//v14+eWXkZOTgylTpuD48eN+zVtXS09PdzpNLpe7bG06dOhQl2m7CpQKPWjE6BvFb37zG8ycOdNvQRTAfMPlb7W1tS6nDx48uNPrqKurczk9KSnJo3QSExOdTnO1Hd1tX0yYMMGr+QN1znVXF/zBn6NHd5dy8ccx0qa71VVnelpd8URvun4H6tjoLsegP3Ssbz3hXrc3l78netMx29X3Sa54e8/S0NDgcrqrPHozjyPdta521/s+IrEwAEu91oIFCyCXy51O1+l0qKqq8vt6f/vb32LevHmdSuPgwYOYNGkSvv32Wz/lqutFR0e7nO5qX8XGxrpcVqPR+JSnzhLjx3tv99prr3n1qlFXamxsdDk9MjKy0+tw1wIvLCzMo3RczedsHd1xXwwYMMDrZQJxzhWjpWRHHV+d76xAlEsgjhGge9ZVZ3piXXGnt12/A3Wf1pvPTT3hXrc3l787vemY7cr7JHe8vWdx1Wc6ACgUCrdpeLq9HXXXutpd7/uIxMIALPVaCoUCycnJLucR4zW+4OBgbN68GX/84x9dvqbjjsFgwAMPPOD2aWlP0ZlX3kNDQ11OF6uVq6u+AwFx+rrqzSorK/H//t//czpdIpHgkUcewfbt21FZWQmVSgW1Wm3598gjjwQus3D/A8YfA/lFRUW5nO7pgAau5nO0ju66L9yVhyOBOOf6kq+uFohyCcQx0l3rqjM9sa640xOv364E6j6tN5+besK9bm8uf3d60zHbVfdJnvB2OVcDSAGebYuvA11117raXe/7iMTCACz1au4GURLrJiIoKAi//vWvUVZWhk8++QQPPPAABg4c6HU6TU1N+O9//ytCDskT7m5yiouLA5ST3uGrr75yOgIrALz55pv48MMPMWfOHCQnJ0OhUCA0NNTyz99dhrjjrp+0q1evdnod8fHxLqd7+jqXq1fLHG1Hd90Xvg58J/Y511VdSEhIsAn4+frvBz/4gdf5dacrywXwzzHSXeuqMz21rtxoAnWf1pvPTT3hXrc3l/+Noqvukzzh7T2Lu9bF7rpbAHzvSqC71tXuet9HJBYOwkW9llarRWVlpct53LWQ7aywsDAsX74cy5cvB2BuMVlUVISLFy/i4sWLOH36NLZv3+5yJOadO3fiZz/7maj5vFGFhIS4nO7uiai7kVnJ1q5du5xOS05OxtNPP+1y+StXrvg5R66lpqa6nH7gwAGMHDmyU+sYNGiQy+mFhYVu+9EsKytz+WqZo3X0tH3hKbHOua72U21tLRoaGjwaQbiriFUugThGelpd7el15UYTqPu03nxu6gn3ur25/Hu7rrpPEoO79RQVFbkdZKuoqMjv6+7JdbUnnH+IrLEFLPVa27dvd9kXTXBwsOgB2I6ioqKQm5uL+++/H7/97W+xevVqlJWV4eabb3a6jD9aEJFj7l57OXPmjNNpZWVlWLdunZ9z1LtVVFQ4nZaamupyYIf6+nocO3ZMjGw5NXfuXJfTP/zww06vY/bs2S6nf/fdd27TcDfPrFmz7L7rafvCV/46586ZM8fletauXdvpvAaSv8olEMdIT6urva2u3GgCdZ/Wm89NPeFetzeXf2/TVfdJYpg0aZLL6du2bXObxtatW31a941SV3vC+YdubAzAUre2bt06/OY3v/G643CDwYDf//73LueZNWuW2xaQgRAaGoqnnnrK6fTW1lan09zlP9ADdfQ07gLwK1eudNgNgU6nw6OPPupzP0zdQXZ2ts1rutb/brvtNlHW6WrgBnet1F577bWA1+eZM2e6HBDh8OHD+Oyzzzq1jlmzZrkcDOOTTz5xef4TBAFvvfWWy3XcdNNNdt/1tH3hT76cc2fNmuWyT71XXnnFp/5Oq6qq8JOf/ATbt2/3ell/86VcAnGM9LS6eiPUlRtNZ+7TxF5PT6lvgSrDzujN5d+TddV9khhyc3Nd/nZbtWqVy22pra3Fl19+6dO6b+S62hPOP3TjYACWurWWlha8/PLLSEtLw29/+1uPXiXUarV45JFHcOTIEZfzLV261F/ZtKiursbcuXPx6aefuuyzrqOCggKn05KSkpxOc9eCs7Cw0OM83IiGDBnisgwvXLiARYsW4fjx4zAajVCpVNi8eTOmTp3q8xPo7kKr1Tr9J1bAIjEx0em0iooKbNy40eG0zZs3469//asoeXJFoVDghz/8oct5nnzySWzZssVtWpcvX8ZDDz1k931UVJTD79vU19fj6aefdtpf9auvvuqyhd+ECRMctrjoafvCmUCdcyMiIvDYY485Xebq1atYtmyZxwPz5eXl4bnnnkNaWhpef/11vz/MCVS5BOIY6Wl1tafVlRtNoI6N3nxuCvS9ri96c/nfaLrqPkkMkZGRuPPOO51Ob2howI9//GOH0wRBwNNPPw2lUunTuntLXe0J5x8ilwSibmzlypUCAMs/qVQqzJ07V/jLX/4iHDx4UKiqqhKMRqOg1+uFixcvCm+99ZaQmZlps4yjf+np6YJWq/V7fisqKizrCAkJEebMmSP84Q9/ENauXSucPXtWaGxsFAwGgyAIgqDRaIT8/HzhxRdfFORyudO8/uAHP3C6vsLCQpfbOXToUGH//v0eb2tDQ4PL9F599VWny5aWlvq8rCAIQkhIiNNln3rqKZfLZmdnO1127ty5Lpe9+eab3daXtrrnyXzW/9asWSNKWXdm2TZDhgxxuvyCBQvcLu+L//f//p/LfMfGxgpvv/22UFFRIZhMJuHSpUvCL3/5S5fHh/XxJoaamhohMjLS5bolEolw7733Ct99953Q0NAgCIL5+L506ZLw2WefCXfeeacQFBQkREdHO1xHSUmJ222cN2+esHv3bkGtVgsGg0E4ffq08MQTT7gtl02bNnWrfeGPumstkOfca9euCaGhoS7zP3DgQOG1114TCgsLLefdpqYmobi4WPjqq6+En/3sZ8LIkSPtlvvmm2+82u7uVC5iHyM9sa72pLoiCD3z+u3r/gnUsdGbz02Bvtf1RW8uf0G4sY5ZQeia+6TO5tmZAwcOuM3TzTffLBw6dEjQarWCVqsV9u/fL8yfP9/tcgCEl156yem6u6Ku9uT7PiIxMABL3VrHAKyjf94GxmQymfDdd9+Jkl/ri4Krf578MGz7t337dqfrMxqNQnR0tEfphISE2PzbvXu3XXo3YgB29erVXtUfb/4xAGuroKBAkEgkHpWdt8e1WAFYQRCEtWvXepUfmUzm8HtnAVhBEIR///vfHqfvaV5cHTddtS/EvBF39c9f59wVK1Z4VRbO6kLHf2IGYANRLmIeIz21rvaUuiIIPfP67Y8ArJjHRm8+NwV623zRm8tfEG6sY7ZNoO+T/JFnZx5++GGP8ieRSDy+/rX9cxWAFYTA19Weft9H5G/sgoB6PJPJ5PG8EokE//3vfzF//nwRc+SeXq/3aL4lS5a47DRdKpXivvvu8yitjq+Ze1Nuvdkdd9yBnJwcn5Z19poQOZadnY0HH3zQo3k71s/k5GQsXLhQjGy5tWTJErz++usuB/uxZjQavV7H008/jeeee86jeT05dufOnYs333zT6fSeui985a9z7sMPP4zf/e53Hq/Xl7oQSP4qFzGPkZ5aV3tbXbnR+OvYCNR6umN9C1QZdkZvLv/eJtD3SWL6xz/+gdTUVLfzCebGcjbfuepGwBM3Sl3tCecfujExAEs3jJSUFGzYsMFlP0LdycyZMz0aQfqXv/wl4uLiApCj3ikoKAgrVqxAdHS0x8vIZDK8/fbbePzxx0XMWe/0n//8B2PHjvVqmfj4eGzevBn9+vUTKVfuPfvss9i8ebOo/US98cYb+Ne//uVykARPPPfcc9i8ebPLQSuAnrsvxOLpOfell17CqlWr3PbB3Vt4Wi5iHiM9ta7eaHXlRuPpsRGo9fTE+haoMuyM3lz+PU2g75PEEhsbix07dmDAgAFeLffwww/j1Vdf7fT6WVfNesL5h3ofBmCpW1u2bBnWrFmDO++8E2FhYT6lkZCQgBdeeAFnzpzBrbfe6ucc+l9cXBxeffVVfP/994iJiXE7/8CBA7Fp0yYMHDhQ/Mz1UqNHj8bOnTuRnp7udt709HTs2LHD5Wia5Fx4eDh27dqFe+65x6P5p06diiNHjmDMmDEi58y9BQsWoKCgAL/61a8QHx8vyjqeeeYZnDhxAvfffz9kMplXy86aNQvbtm3DG2+84dGPip68L/zJ23MuANx9990oKCjAc889h4iICJ/Wm5iYiMceewwbN27s8rcyHPGlXMQ6RnpyXb0R6sqNxpdjI1Dr6Sn1LVBl2Bm9ufx7skDeJ4lp8ODBOHz4MJYsWeJ2XoVCgVdffRUrVqyAVOqf8M2NXFd7wvmHeq+grs4AkSuhoaFYtmwZli1bBr1ej6NHj2Lv3r04ffo0Ll68iMuXL6O5uRlqtRqCICAiIgIxMTFIS0vDxIkTMWXKFNx8880ICQkJSH779OmDy5cv48KFCzh//rzl/4qKCjQ3N0OpVEKpVEKtVkMikUChUCA2NhaZmZkYMWIEFixYgHnz5nmd39zcXJw9exarVq3CN998g7y8PFRWVnL0VC+MGTMGhYWF+Pjjj7F27VocO3YMdXV1kEql6NevH8aMGYPly5dj8eLFXX7T1tNFRkbiiy++wE9/+lN88MEH2LVrF0pLS6FWqxEXF4eUlBTMmDED9957L6ZPn+7xa82BkJiYiJdffhm//vWvsXXrVuzduxcHDx5EaWkpGhoa0NLSArlcjtjYWMu2jBs3DhMnTsTEiRM9Wkd2djY+/fRT/PnPf8bmzZuxZ88e5OXloba2Fg0NDTAajYiOjkZ8fDyysrIwc+ZM3HTTTRg9erTX29OT9wXQdedcABgwYADeeOMN/N///R+2bNmC/fv349ChQ6ioqEB9fT1UKhWkUinCw8MRHx+PtLQ0ZGRkYMyYMZg+fTqysrJEK8+uLBexjpGeXFe7c1250QTq2OjN56au3DZP9ebyp8DeJ4kpJSUFa9euxZ49e/DJJ59g165dKCsrg1arRVJSEoYMGYI777wT9913H5KTk/2+/p5YV3vC+YfIFYnQsWMRoh6orY8cfz0VFJvRaIRUKhX9omU0Gp32gRMcHOywvDQajdP05HK5y6fNnVlWq9Xa9XPUJigoCEFBzp8X6XQ6p/09yWQynwOmbflxtp8EQYBWq3W6vLMybtNVZQ2IV2adYTQaXebbYDDAYDA4nCaRSLrFzZS7bfAHk8kk+rlOzH3R2brri0Cdc60ZDAaX563uoCvKxd/HSG+oq92hrvTE67eY+yeQ92m99dzUFdvmrZ5c/jxmnfPnfVJX3LO4y7+rPLnbf97oDnXVVz3h/EM3JgZgiYiIiIiIiIiIiETSM5oLEhEREREREREREfVADMASERERERERERERiYQBWCIiIiIiIiIiIiKRMABLREREREREREREJBIGYImIiIiIiIiIiIhEwgAsERERERERERERkUgYgCUiIiIiIiIiIiISCQOwRERERERERERERCJhAJaIiIiIiIiIiIhIJAzAEhEREREREREREYmEAVgiIiIiIiIiIiIikTAAS0RERERERERERCQSBmCJiIiIiIiIiIiIRMIALBEREREREREREZFIGIAlIiIiIiIiIiIiEgkDsEREREREREREREQiYQCWiIiIiIiIiIiISCQMwBIRERERERERERGJhAFYIiIiIiIiIiIiIpEwAEtEREREREREREQkEgZgiYiIiIiIiIiIiETCACwRERERERERERGRSBiAJSIiIiIiIiIiIhIJA7BEREREREREREREImEAloiIiIiIiIiIiEgkDMASERERERERERERiYQBWCIiIiIiIiIiIiKRMABLREREREREREREJJKgrs4AEVFPVF1Ti3+//zEAICoyAj/78ZNdnCPyhlqjwV/+8R/L59//8qddmBsi/9m8dScOHcsDAMyaNgmzp0/p4hzdGF7959toaVUBAH785CNIjI/r4hx1Xm/cpt5GEAQUFJ1DfkERKqqqoVapYTSZAAA5I4fjjlsXdnEOiYiIqA0DsEREPjAYjahvaAQAmK7/2KGeQxAEy/4j6k1UarWlbqvVmq7NzA2kobEZypYWAIDRaOzi3PhHb9ym3kSv1+O9jz5H0flih9PbgudERETUPTAAS+Qn23fvx54DhwEAU3LHYcGcmaIsQ/7XrFTitX+9BwAIDpbj1z97totzRERERIHw5rsforauAQDwxEP3YUC/lC7OkWc2fr/DafCViIiIuh8GYIn8RG3V6kilUou2DPmf0Wiy7Ifg4OCuzQwREREFTGOT0nIPYDAYujYzHjKZTDhw+Ljl8/BhGZg9fQriYqIhk8kAACEhvJ8hIiLqThiAJSIiIiIi6iEqq2qg1pi7GAkNCcFjD9yDED5AJiIi6takXZ0BIiIiIiIi8kzz9b55ASA+LpbBVyIioh6AAVgiIiIiIqIewqBv7ypBLucLjURERD0Br9jUrW38fgeOHD8JAJgzYypmTs2F0WjE6cKzyD9dhNr6eqhUakREhGPQgP6YOC4H/fv28Wod18orcepMES5fKUVjczNMJhPCFAr0SU5C5pBU5IwcjqAg54fKBytX4eq1MpvRpg8cOY6Tpwvt5k2Ij8WzTz7q0zLOVFRW41RhES6VXEWzsgVanQ7hYQokJSZgWMYQ5IzKhtxF/gHH5WwymVB0rhj5Z4pQXVMLpbIFBqMR99+1BEPT01BdU4t/v/8xACAqMgI/+/GTAIC6+gYczcvHxUtX0NjcDIlEgtjoaAzNGIIpE8chNDTEZV7aKFtacCK/AJcuX0VNbT00Wi3kQUEIDw9DRHgY+iQnIStzCAYN6A+p1LdnSfkFRfj6m80wmUyW7/R6PV76898dzv/I/XchddAAl2n6a/vbaDRanDpThLMXLqK2rh6trSrI5XJER0UiLXUgxo4eicT4OK/S9IS/y1+n0+HUmbM4X3wJVTW1UKnUkEqliIwMR1RkJIakDsLwoRmIjYl2mY5KrUb+6UKcv1iC6ppaqNUahIaGIDoqCmmDB2JczkjExcb4qRRsddW+aNPdylDsc4AnnOWhvLIKR0/k4+q1MjQ3tyAoKAiJCXEYlZ2FMaOyLX0keptuTW0dTuQX4GLJFTQrW6DWaJCZnobldy2xS6NZqUTeqTM4X1yChsYmaLRahIcpkBAXh2GZ5nOzt63WSq6U4mhePq6VVaC1VYXw8DAkJsRjzMhsDB+W4fG50HrgwdCQEPzyp8+4XeabLdtwLO8UAGDerGmYPnmi22UEQcC54ks4e74YpWUVaGlthcFgRFREOCIjI9C/XwqGD8306LptMplQdL4YReeKUVZRiZbWVgBARHg4Bvbvi5HZw5CeOthtOoHUrGzB8ZOncPb8RTQ1K2EymRATHYVhmenIHZeD8PAwp8u+9d+VqKyqAQDcPH8OcsfleLTOc8WX8NmadQCA8DAFnn/2KZ+vkY50ZpvavPrPt9HSqgIA/PjJR5AYHweVSo2802dQeO4CGpuaoWpVQSqV4rc//5/r6/VvnVW2tOJvb74DAGhsarZ8//7Hnzu855sxJRdzZ051uJ5A1c3Ssgq8//HnAMz3Km2ulVXY3bf07ZOMpx5d7jAdf52bfNmPHZVXVqGg6BxKLl9FU3OLJS+RkRFISohH1tAMDBk80OV9uHX5nC48i8tXSqFsaYFOp0dYmOL6/Uo6Ro/IcnvuJyIiEhMDsNSttapUloER1Bo1Kqtr8NFna3CtvNJmvuraOly6fBW79h3EjCm5uOPWBW5vsorOXcCmrTtx+eo1h9NLrpTi4JHjWL8pCsvvvgPDMoY4nK9Z2T54QxuNRguNRms3b9sNpC/L2K+3BV+u34STp89AEASbaTUALl+9hiPHT2LD5q24547FGDF8qMN0APtyrq6pw8pVXzksG51OBwAwGI2WZUwmEwRBwLZd+7Bp6067QSwqKqtReO4Ctu3aix88fL/bIOaOPQew8bvt0Fn9wLB3Bpu37kTa4IH4ydNPuEzPGZ1OZ7cfBEGw+66Nq8E5/Ln9bfYcOIzNW3dafuBYK6uoROG5C9j0/U5MzR2POxYvdBto95S/y3/PgcPYsm0XlC2t9hOvH8pHT+QDAH742APIHpZpN1tTUzO2bN+Nw8dP2vzwbFNaVoGConP49rvtmDElF0tume/XH1pdtS+s19/dylDMc4CnOubBaDRiw+at2Ln3oN158Vp5BfJOncHWXXvxyP13ISU5yeN0TSYTvv1uO7bv3m/zwAYwn8+tCYKA77bvxtade+2OobZz87GTp7Bh8zYsXbwQ43JGut1OnV6PVV9/Y3lQZlFbh5IrpThy/CTSBg/Ew/ctc5sWYDvwoCI01KNlWlutrxMa1zMDuHCxBF9t2Iyyikq7adU1tQCAvFNn8M3mbZg9fQqWLl7oMq016zeiorLawdQaFF+6jB17DiAjbTAevPdOtw8hAuHoiXysWbfRrqwqqqpRdL4Y3+/Yg3vvXIyckdkOl8/KzEDh2QsAgL0HDnscgN1/6KhlP40eMdyvwdfOblObhsZmKK+/Qm80GpF3qgCrvv4WrSrb82uQ1fnH33VWEEwOr/XNyha77wDzoKmOBLJuGgwGh3m2Pl+1CQ+zD4T7+9zky35sU9/QiC83bMLpM2ftptVY/b199370TUnGL3/iPOBe39CIL9dvwulC+7RwfbsOHT2B+LhY3L/sdmSmp7ncLiIiIrGwCwLqMZqalfjXuyvsgq/WBEHA7v2H8PEXX9n9+LZ24WIJ/vPBSqfBV2uNTc1464OVOFN03qd8i6Gyugav/etd5J0qcLmdgDn/7370GQ4ePeFR2s3NLXjz3Q+dl42T1a3f+D02bN7qMkipbGnFWx+sRENjk9N5duw5gLXfbnET/Gun13ePEYv9tf2AOeDzyeq1WLNuo8OAX8d59x48gv+895FfRm/2Z/mbTCZ89PmXWLNuo+PAoQMGg9Hh9/98dwX2HTrqMHDYcZ279h3Eh5+u8Wh97nTlvmhLs6eUoT+PAV99uX4Tduw54PK8WF5RhTff+RCV1TVO53GU7tade+2CrwBszokmkwkrPvsSG7/f4fYYalYqseKzNdi2a5/L+QwGA95d8al98LWDS5ev4l/vfQSV2n1wVGz7Dh7Fv977yGHw1RGD0XmdOXj0BP713kdOAly2Lly6jFf/+TZq6uo9zqsYTp4+g5WrvnYZqG5VqfDfT1bj+MnTDqdPHDcawXI5AOBKaRnKK6rcrreltdUmoDVt0ngvc+6cP7bJkfzThfjw0zV2QTvA6e1Gt9GT6qYY5yZr3uzHy1ev4W9vvuMw+OqIs2saAJReK8dr/3rXafDVWl19A/79/sfIO1Xg0XqJiIj8jS1gqcfYf+gYBEGAVCrF5AljkTNyOGJjYqDVanHheguDtpZIJ/ILMCR1MGZMcfyKpPH6j2ipVIrsrEwMy0jHgH59EB4WBqPJhPr6Rpy9UIwDR05Ap9NdD8J8jZd+8RO712cff/Ae6PUG7NhzAHsOHAYATJk4DvPnzLBbb1trMl+WaaPWaPDuis8srR1CgoMxcXwOsjLSER8fC3lQEFpaW3Gx5Ar2HjS3hBEEAau//gYD+qWgf98Ul+W879BRCIIAeVAQJk8chxHDhyIuJgZBQeZ8REZG2C3T1KzE9j37AQDDMoZg4rgc9E1JhjwoCDV19Th45ATyCwot+d+8dSfud/DKrl6vx+ZtOy2fY2OiMX3yRKQOGoDICPN6la0taGlpRWV1DYrOFTsOiHho1Igs/O7Fn6BZ2YK//9v8amOwXI5f/ezHDuePcrDt/tz+Npu37cLhY3mWzxlDUjEuZyT6pfRBeJgCer0BFVXVOHoiH2fOmh8MFJdcwbqN32HZ7bd4XQ5t/F3+6zd9b3n9EzDX1ckTx2JYRjpiY6MhkUjQ3KxEU7MSxSVXUHTugtO02taTlJiAMaOykTZ4IGKiohAUFIRWVSuulJbj4NHjliBFfkEhDh09gUkTxvpcHkDX7Ys2PaUM/X0M+KKpWYl9h44CAAb0S8GMqZPQv28KZDIpqqprcODwcRSdLwZgDgR//PmX+NmPn3TbUrqpWYm9B48AANIGD8TkiePQv28KFNevBcHBcsu823bvw4n89uBTRHg4Zk+fjMz0VIQpFGhqVuJ04VnsPXjUEqjesHkr+vftg2GZ6Q7Xv2X7bpy7cMnyOSEuFrNnTEXqoAGQy4NQV9eAY3mncDz/NGpq61DbxcHHM2fPY/W6by1BcIlEgtEjhmP0iCwkJyUiJFiO5pYWKJWtKC0rx5mz5yGBxGFaFy6W4IuvNrTX3YR45E4Yg9SBAyzXovqGRhQUncPBIydgMBigbGnF+x99jl/874/82vrTG5u+3wlBEBAZEY45M6YgPS0VYYpQNDQ24WRBIQ4cPm5pNf7Zl+sxaGB/JMTF2qQRplBg7OgROHT9/HPgyHEsu/1ml+s9cvwkDEZzsCpjSCqSEhO61TY5svH7HRAEAbEx0Zg5dRKGpA5ERHg4JBIJJBLH9cIfIsLD8bsXfwIA+Ne7K1Bb3wAAeOKhex3eJykUtq1uu6Ju9u+XYsnz+Ysllq4m+vftgyceus9m3o5vT4lxbrLm6X5sbGrGuys+s3mgOHBAP+SOy8HA/n0RpgiDSq2GUtmCqppaFJ2/4PThp7KlFe9+9Jml1bJCEYpJ48cgMz0N8bGxkMlkULa0oPjSZew5cATNSnOXGZ+sWot+KSlISox3u11ERET+xAAs9RiCIEAul+OpR+7H0A7dAQwc0A+TJozBv979CNfKKwAA327ZhtzxOQ77sZIAGDt6BG6/eb7Dvg5TkpOQnZWJWdMmW27MW1pVOHw8DzOnTrKZNyoyEgAsP8YBIDQ0BPEufnj4skybjd/tQE1tnXm7+/fFkw/fj+joKJt5EhPikTpoIKZPycUHK79A0bliGIxGbPxuh9M+wdoIgoCI8DA884OH3QZrrZcBgLuW3IIZU3JtpiUlJiB7WCa+XL8Ju/cfAgAczy/APUsX2wU+rpVXWLphiIwIxy/+50d2/cklo/0H5cK5s6DvREvDkOBghMQF2/4Qkkg82g/W/LX9gPlV7e+27wZg/gH14D1LMXb0CLv5+qYkY1zOSBw5fhKfrF4LQRCw79AxzJkx1ec+UP1Z/levlWPHngOWzwMH9DPX1ahIm/naXgOfeP31WmetJxPiYrH01oUYMXyogx/l5vo+Y8pEfLVhs+WhxvY9+zsVgO3KfQH0rDL05zHgq7Y85I4fg/uX3W5zXKckJyFnZDa+37EH32zZBsDc5cLhYycxJXecR+kumDsTty6Y63Q+ZUsrNm/dZfncv28fPPODhxERHm75LikxARlDUjFhzGj86/2PoFKpIQgC1qzbiN+88JzdfmlqVtq0QstMT8NTjy63tIwEgD5JicjOysTI7GH46PMvO/VQqrP0BgM+W7PeUmYR4WF44qH7MCR1kM18bYHBMaOycduimxyeR4xGIz5ZvdayPbOnT8HtN99kV2f6JCVi+NAMTMudgH+9twLKllZzH8B5pzx+bd/fBEFA35RkPPvkI3b7f2jGEIwdNQJv//cT6PR66HQ6fLN5Kx5dfrddOlMnjbcEYI/m5eP2W+a77N7k4JH2N12m5vqv9Svgv21ylO7QjDT84OH7ve4TuTOkUqnlWi+1qlNRkZFu7wG6qm7Kg4IseYu0anUbZPW9I2KcmzrydD9+/c1mS7cFEokESxcvwqxpkxzOOxLmvnud3Wes2/idpf/e9NRBePyhe222ybxd8RiSOggzpuTi7Q8/waXLV6HT67Fl+y48dO+dLreJiIjI39gFAfUot8yfbRd8bRMeFobHHrjb8tRfrdHYtByzljEkFY8uv9ttcCQ+LtamFVtXd0PQ2qrCwSPHAZiDh08+stwu+GotJDgYD9+3zHIzXHjugs3AX87cs3Sxx8HXNpMmjLULvFi7Zf4cyK8HDXQ6HcodvLKn1eosf/fvm+LRYB7+7mvTV/7YfsDcSqUteHHrgjkOA37WJo7LQe74MQDa+2DzlT/Lf+vOvZa/Y6Kj8KPHHrALHDrirM/jHz3+IEZmD3P5I1AqlWLp4oWWH6KVVTWou96qyRdduS+AnleG/joGOqNPUiLuXbrYaeuy+XNm2PSPu+/QEY/SHZqR5jL4CgAHjhyzBL/lcjkef9A+GNBmQP++uO/O2yyfq2vrLK1zrR06egLG6y0aw8PC8Ojyu2yCr9bGjh7hNJARKIeP5VneRJFIJHj8wXvtgq+OODqPHD952vKmR1ZmOpYuXugyYN83JRlLFy9qX97J9T8QgmQyPLb8Hqf7P2NIKm5d2F6f8guKHPY9OnjgAMu1WKUyD57nzKXLVyzdakRGhGP0iKzObIIdf21TR+FhYXj0/rsDGnztrJ5WN8U4N3XkyX6srqmzGWx24dyZHp2zHJ0fGhqbLPf4EeFh+MHD9zvdJsDcOvbR5Xdb9lP+6UK/dRVERETkKQZgqccIDQlxO+pyYkI8xoxqH/zhrJObRm9e/cpMT7X8ffVaucfLiaHofLGl767RI4d7FIwJDwtDetpgAOZXkEuulrqcPy42BqNHDPc6b+5uohWKUJuRrusb7IM6CVYjyF++es0yAnRP4I/tN5lMln7MpFIppuZO8Gjdo7Pbf2hfvHzVo2Uc8Vf5G41GFFq9Cn/T7Okufxh5wtNjViaT2QR8fD1mu3pf9MQy9Mcx0Fmzp09xO1r2/DnTLX+XllVYRix3l647Z8+1X2/G5Yy0OZ4cyRmZjT7JiZbPRefsr1fWgY/JE8e6rQNzZ07rstfuAdj0wzhy+DDLtccX+WeKLH9Pd9KdUEejRmRZHjBc6sTx11mjRmQhOcn16//TJk1AmEIBwHy8X7hY4nC+qVb9uB64/gDWkQNWrV9zx4/xaNR4b/hzm6xNHJfj0cO+7qSn1U0xzk0debIfC4rOWbWOD8e82dNdzu/K6cJzlhbI43JGISxM4XaZmOgoDBrQD4B5YMPSsgqf109EROSL7tF0jMgDaakDEexBC4mszHTLaOBXSstczms0GnGu+BLOnr+IiqoqtLS0QqPVOX2Fs1Wlgslk6rIfuBdLrlj+Lig6h9+/8nr7xOs3tdaDHbTd6Fr3n9XcbDtid0dDUgd53e9asFyOfil93M7X1vUCAKivv+puLSE+DpnpqThfXAK1RoO/vvEWRo8Yjqyh6Rg8cAASE+JE7RPOV/7a/vLKapsWyq+88Vb7RKtBhTruY+sBKtztX1f8Vf5l5ZXQ6dpb07obEdsb1TV1OHWmEKVlFaitb4BWq7UbCEylah+tuu1VR2919b7oaWXor2Ogs7KGuu+rMHXQQISGhli627hSWmbTKrYjiUSCdA9acVoHqrOHZXiQWyB7WKblQcfVa7bXK0EQUGqVZpYH/TBGRUagf98+XfawsORy+wO+nJHeP8izdsnqerdm3UZ8/c0W8wcXx581rU4HjUZr1297IHiyr+RyOdLTBuPU9WDeldIyh6POTxgzCus3fg+NVoviS5dRU1ePxA4BNI1Gi7xTZwCY66u/ux8A/LtN1jI6EaTvKj2tbvr73OSIJ/ux5Ep74Dk7K9Npa35PWN8PHz95ytIHOwCX98PWfc+2tdYnIiIKFAZgqcdITkx0PxOA5KT2+VyNGl5QeA5r1m+0vEbmKY1G69GTdjFYjxyuUqltgiSe0mhdBz1iXHRp4EzHwSmckcnaA9cmo+Mg90P3LsM7H36C0rIK6A0GHDt5CsdOml8zU4SGInXwAGRlZmDs6GybYE5X8tf2N1rtX5PJ5NNgOu72rzv+KP/G5mbL35EREU4HL/NGs1KJNes22ry+6AmNj0G+rt4XPa0M/XkO8FWwXI7YmGi380kkEiQlxFuCEq6uE4C53oeEuA6UaDRam5HF+yQleZDj9v57Adi9rq3R2qZpfW1zJTkpsUsCsFqdDmpN+0OLfn3dB+Sd0ev1Ng8Ora993tBouyYA666lqM185rip0wcdISEhGJczEvsPmwciPXjkOG5bdJPNPMdOnrI8sBmakea2haMv/LlN1ny55+hKPa1uinFucsST/djWXysA9EtJ9igfztNqL/eWVpXTgbpc8fX+gIiIyFfsgoB6DE9vVK0HtjIYDA477z+WdwrvfvSZ18FXADAJXTfAiUrtfcC1o44tMTqyHtHbU/5slRodFYmf/fhJPHD3HchIG2zTr5pao0Hh2Qv4asMmvPSnv+PL9Ztsflh0FX9tf6tf9m/nlvdH+Vu3HPU0MOdKa6sKr//nA68Dh4Dvx2tX74ueVobdoWW6N8GM0ND2MtVoXPeL7ck5sWOw3dO8uMqHdZ/M3qUZ+IAjALv+xRWhvtdbXx4uOuLueieW0BDPtt16X7mqh9Mmt3eBcvjYSbu3dA5adU0wbZJn3aV4y9/b1Ebuwz1HV+ppdVOMc5MjnuxH6wc0CkXnGjL4Yz90zdmBiIhuZGwBSz2G3sNAm3VASCqV2nXer1KpsWbdRsvNb2hICMaOHoG01EGIj41BeFgYguRBkFoFFH7/yutd9kPOmnWfbvPnzMCUia5H73akq1rvekMmkyF3/Bjkjh8DnU6Hq9fKcfnqNVwsuYzzxSXQ6fUwGI3Yvf8QrpVX4NknH/HraOpdxbquxsXG4LmnHvU6DX+UQ2fL37qrEOvX6H31zZZtqLFqgZqRNhgjhg9Fn+QkREZEICRYbrP+jd/vsHRD4quu3he9oQwDrWM3Cq7nbb9OBMs7P/hPx1dpdToPr1dW+7ZjFztyue21S6/XezRQkd7Ddftbx0C1p2XgSFCHbf/J048jOsr7lpKe9JMuBk/vV6z3lasulvr3TcHggf1x+eo1NCuVOFN0HiOzhwEArpVXWlo8R0dFYkTW0E7k3EVe/bxNPVVPq5tinJt8z4v/rmvW98O3LbrJ7SCZjkT0sL6HiYio52MAlnoMT1urWs8X7iDYeLrwrKUlaWREBJ5/9knExcY4TU+r1XaL4CtgHtm4jbKl1TJSeW8WHByM9LTBSE8bjHmzpkGr02H/oaNYt/F7CIKAiyVXcDTvFCZdH32+J7Pev03NSkRFRlhGje8qvpS/9XY0K1ug1el8HuHaaDTieP5py+c7b1uEWdMmu1zGH8drV++L3lCGgabWaKBSqT16yGRznQjv/EOp0NAQSKVSS8vE+oYGJCXGe5ePDvlWhIbaplnf6NFAbPWNjW7nsX7AKHjYDqxji9yOFKGhCJLJYDCa+0Guqavz+LX1jsIUCptt12p1Pep6V9fQiIHXB/txpd7q9fXwMNfBoKmTJuDy1WsAzKPatwVgDxw5Zpln8oSxoj2MFGObvCFGnfVFT6ubYpybfGV9Xaup9b5bH2dptbaquvU+ICIiasMuCKjHuHT5qtPBsawVX7ps+dvRoDBlFVWWvydNGOMy+Ap4MYq6L6/gernMwP59LX8Xnj3vUXn0NiHBwZgzYyqmT24fefhM0XkXS7jXHV6fBsytnNoGeDMajSg8d6GLc2TPk/K33g6TyYSz5y/6vL66+kZLP23hYWGYMSXX7TKlZZ3v/7Kr90VvKMOucPHyFbfz1NTVo8lqgDRPBg9zRyqVom+f9j4Tz3sw+nvH+fr1TbFL07ofxuIS99um0+txtdT9vgsOaQ/ma7U6GI1GF3ObVVRVu5wukUgwwPoa1YljRiKR2FzvThee8zmtrnDRg33V9gCrjbt6OHb0CEt3JIXnitHU1Ay9Xo/jeeaHKxKJBJN9eCvGU2JskzfEqLNtvLkF6Gl1U4xzk6+sA/hF5zt3TbXZB0Xdex8QERG1YQCWeozGpmYUuLnJ0mi0Nq/NpjsYlVVr1R9WmAd9UO09eMSj/Fm/suyo31l/LDN8aPtI3U3NSuw7dMzF3L3bgH7tPwh8Hem+jfV+MHi478SgUIRi8MD+ls/fbd/t0Y/MruCq/ENDQ5A6aIDl89ade3x+WGB9vCqut+Rx5VzxJVRV1/q0LmtdvS96Qxl2BU/O13sPHLb8HRMd5bcBi9LTUi1/Hz520qbcHSmvqMIFqyCHo+uV9XcHjxx3WwePHs/3aPC30JAQy2vFgiC4DbhfK69EeWWVy3kAICsz3fL34WMnfR6gCACyhraP1n7oWJ5PfbZ3lWN5p+z6xO2o8OwF1NU3WD4PSRvkcv5guRwTx+YAMD+UOXQsDydPF1re6MkeluH2gXJniLFN3hCrzgLe3wP0tLopxrnJF9bnh6rqWuSdOuNzWsOHte+D6ppaHMs71am8ERERBQIDsNSjfLl+E5qsRlG1JggC1qzfaBnRWiqVYuK4HLv5Iq1GEz9deNblq7a79x/2+AbR+lW7Wg9frfJ2mZQ+SRhudeO/9tstHrcyKrlyFVu27fJo3q5y8nQhDhx2H2QAzEGiNtavovki1CooZTKZbH5ABtq8mdMsf5eWVeDTNes8+kGoUqmxftP3To8PT/iz/K1fcb9SWoY16zb6FEC0Pl5r6xtwrbzS6bx19Q34ZNXXXq/Dma7cF0DvKMNAKzpXbDMgUUcXLpZg9/72AOzkCWP9tu4pue2tD5uVSnzx9TdO95darcHKVV9Zrj/hYWEYMyrbbr5JVvmrrK7Bxu93OF1/dU0tNmzZ6lFeJRKJzUOUnXsPOp1Xrdbg09Vfe9QtxbTJEyxddeh0Ory/8gu0+jA6OQBMnzzREnDT6/V458NPPTqmTCYT9h8+hoIubJnYqlJh9dpvne7/ZqUSq9d9a/mcNngg+iQluk3XeoCtQ0dP4MDh9oewU3PFGXyrjVjb5Cmx6iwAhFndi3nyanxPq5tinJt8kTZ4IAZZtYL94qsNnr9lZpfWIJuHlF98tQHFJZc9WvbCxRJs27XPp/USERF1BvuApR6lobEJr/37Pdx+83yMyh5m+aFXeq0c336/HYVn24ORU3LHITYm2i6NzCGp+G77bgDmbg3eXfEZbr5pNvr3S4FEIoHJZMK1sgrs2HsAx0+etlvemb4ptq94nS48ixFZQ12+3u7LMktuXYBLl69Co9XCYDDg7f9+gonjcjBl4jgMGtDP0v+bIAioqq7FhUslOHoiHyVXSm1aH3RHTc1KfLl+IzZv24kJY0dj+LAMDOrfz7KfTSYTSssqsHv/IZvWDlmZGc6S9IhMJkNyUgIqKs2vK278fgeW3X6zRy2k/W1k9jCMyMpEwfXX+o+eyMe18grMmzkNWUMz7Po9K7lSipMFhcg7dQY6na5TI2D7s/xHj8hCZnoazl8P1O47dBTXyiswf/YMZGakWfozNRqNaFa24GLJFZw5ex6TJozF0PQ0Szox0VFISohHdW0dAODt/67E4oXzMGb0CMvgIs3KFpzIP43N23b5bYRqoGv3BdA7yrArfP7VBlRUVWPWtMmWFoEtra04dPQENm/dZQk8RISHY8ZU990xeColOQkTxo62vIVxLO8UWlpaccuCORg0oD8kEomlO4v1m763aWW8cN5Mu8FyAKB/3z4YNSILpwqKAABbd+5FQ2MTFsyZiT7J5uCWRqNF3qkCbNi8zat9lzMy2/K6+In8AoSGhGDhvFmW66ZWq0VB0Xls/G67zQBurkRGRGDh3Jn4Zss2AMDV0jL85fX/YP6cGRg9IgtRkeaBhwRBQKtKhdKyChSePY/oqCjMmzWtQ1rhuHXBHHz9zRYAQHllFf78j/9gzowpGDMqG4kJ7f1Y6vR6lF4rR+G5Czhy/CQam5px3523O8yjyWTC71953fJ5zoypmOnHetDm2MlTaFWpcMuCuZagk95gQEHhOazb+J1Nq8mbb5rtUZp9khORnjoIxSVXUFvfgNrrDwtjY6JtWgSKRYxt8oYYdRYA+vZJtpxnd+8/hIz0VCS6aBkvVt0UixjnJl8tXbwIb7z9X5hMJqjUarz+1geYOTUXuePGWM5pgPm8Vl1Ti8LzF1BdU4eH7r3TLq1lt92M19/6AHqDAVqdDm++swJTJo5D7vgxGNAvxXI/bDKZUFlVg/MXS3Dk+EmUlpX7NGgXERFRZzEASz3G8GEZOH/hEhoam7DiszWQyWQIDwuDVquFtsNoqslJCbh90XyH6WSmpyF10ACUXCkFABQUnUNB0TkEyWQIDw+DSq2xGe139IjhOF141m3Ls0ED+iMqMgLNyhYYjUa8u+IzBMvlCA8PswRUE+Jj8eyTj3ZqmZTkJDx8/zL8d+Uq6A0GCIKAw8fycPhYHmQymSVoqFKru+3r6+40NjVj68692LpzLyQSCRSKUMikMrSqVHb7oX/fFOSOz+n0OkdlZ1kCsEdP5ONY3ilERkQgKKh9QJNH7r/LpsWFWB66bxnefGeF5RXLispqrLzeKlGhCEVIcDDUao1dvfcXf5S/RCLBI/ffhdf/874l8Hf56jW8+9FnkEgkltbfrSqVTSulnJH2LW0WzJ1p2f6mZiU+Wb0Wn6xei4jwcEsgp02YQoEB/VNw7sIlu3R80ZX7oreUYaBERoQjIT4OJVdKsXPvQezcexBhYQrIpFK0tNqWkVQqxX3LbvNoUCtv3LXkFpReK0dldQ0A4OyFizh74SKC5XIoFKFobVVZBqlqMyo7CzOnTnKa5j133IqrpWVovN7C7ljeKRzLOwVFaCjk8iC0tLYflyHBwRg8qL9H+27yhDHYufeAJWh24MhxHDhyHBHh4ZBIJDbHe0R4OPqmtAepXLlp9nSUVVTiRH4BAPP5ZPXab7F67bdQKEIRLJejpVVlc32aPmWiw7RmT5+C6to67Dt4FIC5rn+zZRu+2bINwcHBCFOEQqfTQ63ReNzaURAEm0ChWuP/Bw6jsrNw6kwRis4Xo+h8MYKDgxEaEoJWlcruujxr2iQMzRjicdpTJ02w6w94ysRxbrsW6Swxt8lTYtXZUdnDsGufuUVteWUV/u+V1xEeFoYQq35nZ0zJxdyZUy2fxaibYhLj3OSLtMEDcdftt2D1um8hCAL0ej227dqHbbv2WfKiVmugs7oPT0p0PJjfwAH9sPzuO7By1dcwGo0wmUzYd+go9h06iiCZDGFhiuvXN/UNOWYCERF1P+yCgHqM1EED8NB9d1pe+zK3+lLaBT769+2D5556FKGhIU7TevyBe+xGZzYYjWhqVtoEX3NGZuPh++yfujsik8mwdPEim9arOr0eDY1NqG9oRH1DIxqblJ1eBgBGZA3FT575gU1rAcBcJsqWFihbWux+EA1JHYRFN83yaFu6Ss7I4Zg2aQKCrEZxFgQBKpUaypYWuxvojLTBeOYHD/ll1Oc5M6bY9AUpCAKalUrLfqhvaAxY/7CK0FD85OnHMTV3vN2ParVag8amZrt6HxEehiW3zEdMdJTP6/V3+UdGhONnP34SI7KG2nwvCAJaWlvR0trq0Q/TieNybH74tmlpbbUJHMZER+GHjy13+mPNF121L9r0hjIMFJlMhiceus9moBdz3bUtI7lcjgfvWYpR2Vl+z4MiNBT/+6PHkTEk1eZ7nV6PpmalTYBDIpFgau54PPbA3S7feoiKjMRzTz1q1yJPrdGgWdl+XIYpFHj8wXtsBu5yJSQkBI8/eC8iwm1Hqm9pbbU53uNiY/D04w+6bBFore3BwYK5M+3ODWq1Bk3NSq8eDt5zx2Lcc8diu2u6TqdDY1MzVGq1zf6VyWSYNmkCRmYP7ZiU4/zC/4Mw3rpwrk33FjqdDs1K++2ePmUili5e5FXaOSOH2zw4kEqlfu1Kwxkxt8lTYtXZjCGpdi0iW1Uqm+u/Wm0fqBe7bvqTGOcmX02bPMHhfmzLi3Xw1Z1xOSPx3FOP2vXlbbj+ZoiypdXu3mVYxhDMmzXd9w0gIiLyEVvAUo8yZtQI9O+bgh17DuDUmSI0K82D/0gkEvRL6YPc8TmYPnmi24BcdHQUXnjuh9i55wCO5p1CdU3761ahISHIzEjDpPFjMHL4MK/yNy5nJGKio7BjzwEUl1z26HVQX5YBzIMg/eqnP0Z+QRHyThWg+NJlS3kA7SPfpqelYsLY0TYjxnZX0VGRuGfpYtw8fw6OnshHQeFZXC4tswmKR0VGIG3wQEwcm4OR2d7tH1fCFAq88OxT2LprL/ILilBbV9+lrVbkcjnuvfM2zJ05FfsOHcX54ksor6y2+SERHRWJ1EEDMHrEcIwakdXp1wTFKP+wMAWeenQ5iksu4+CREzhffMnSkg8AgmQyREVFYkjqIGRlptu8Om9tyS0LMDQ9DTv2HMDFkiuWQeva6vnokcMxc+okKEJDcdTPg3F0xb6w1hvKMFCiIiPw06efwN6DR3E0Lx/XyiqsWsSFYWR2FubPnu63gbccCQ8Pw3NPPYrTZ87i0PE8XCgugVrTPnhRVGQkhmUOwcwpuTbBYlcSE+Lx4k+fwa69B3H0RL6lFRtgfv189IgszJ05DTHRUR73Cw6YRxJ/8X+fxvc79yK/oBBNzeYHfhKJBIkJ8Rg3egTmzJhqDjAddpOYFYlEglsXmAN2ew4cRtH5YlRW1VjOqRKJBBHhYejfLwXDh2ZilJtzybTJEzAuZyQOHDmGwnMXcPnKNZsgTWhoCAb274vsYZkYP2Y0oqz6Pe6ouqbOJp/ZWZlO5/WVRCLB/XctwagRWdh38CjOXyyxnEeDgoIwLCMNs6ZPcXqsuhIUFIQxo7Ox94B5wLkRw4ci2g8Pe9wRc5u8IVadffi+ZUhPG4zDx/JQVlHl8QNXf9ZNsYlxbvLV6BFZGJaRhn2HjlnuNazLXBEaiuSkBAzLTMfILNcB67TBA/HbF57DifwC5BcUovjSFbS0tlqmy2Qy9EtJRmZ6GiaMHY2+fZJF2y4iIiJXJEJ3eC+GyInV6761/Mi4ZcEcLJw7y2a6Tq+HWq1BeJgCQUG+P0/Q6nRQqzVQhIYgJMS+5Wx9Q6Plh2NsTLTHr/qp1RpotFpLAEAmk7ltFefLMm0MBgNaVWpIpVKEhyk8zmerSgWNxjwqbphCAYUi1O0yRqPREgSSSqUO+9vtSNnSCt311oIR4WEOy7qjttejjQYjQq+/8h0Ier351UGDwWjZ91GREZb+UAO1/dYEQYBKrYZBbzC/yhuAshCj/NvqqVwe5FM/u0ajEa0q9fXX8O3ruSf12WQy2YzQHh8X61UeumJfWOsOZdgVx0BH5ZVV+PPf/w3A3Hr3D79+3ma6yWRCS6sK8qAgj85rbXzZNlc0Gi20Wi0UYQq/BOf1BgNUrSqEhYfZjOAO2O47hSLUq/qh1mig0+kRER5m9yCztVUFjda764Q1k8l0vbW14zrnLY1GC41Wi5DgYK/ysmvfIXy1YRMAYOzoEXh0+d2dykebhsYmy3U7JjrKrvxUKjWMJhMirLoY8tWrb76Dq6VlAICnH38QWUPF6f9VrG1yl643xKizRqPRkm7b9d+bY8nXuukNnV4P5fWH7kFBQYiOivQpnc6cm/y5HwFz11l6vaHT9/SA+R5OpVIj6Pq5X+wuOoiIiDzBFrDUowXL5X75MRsSHOwysNQ2kIu3FIpQr2++fVmmja834eFhYZb+JD0lk8m8DlqZBy3yrs9Fc0sp//bT6Am5XG4JtjoSqO23Zt3vZ6CIUf6d+bEImMveVSsiT+qzVCr1ev9Z64p9Ya07lGFXHAPekkqlPrU482XbXAkNDXHZLY635EFBTls9+nI+b6MIDYUi1PH1Jzw8DOHhvtd5qVSKyAj/tf7ztUzPX7xkyc/NN83xW37cBenDwvwzqOPV0jJL8DU+LhbDRBxcU6xt6uwDDWti1FmZTGa+7vl4qvL38e5IsFzul3NUZ/Lqz/0ImIPk8NPYp3K5HNHR/nsThYiIyB/4OJCIiIiIej2TyYTii5cBmPtF7tgXfE/w7ffbLX9PnjBWlD46iYiIiMj/2AKWiIiIiHq9q9fKodZoEBQUhEXzZnV1dryi0+mwZftuFJ0rBmBuATkld3wX54qIiIiIPMUALBERERH1eil9kvC7F3/S6S48AqXofDG++GoDTCYTmpUtNgP/zZiae71LDyIiIiLqCRiAJSIiIqJeLyQ4GCFxgR0wrzN0Oj3qGxrtvu+bktzjWvASERER3egYgCUiIiIi6uZCgoMxdvQI3H7LfAS7GDiUiIiIiLofiSAIQldngsiZVpUKGo0WgHl0VIXC8Ui3RER04zEajWhsagZgHtXe36NyE3UlnU4HZUsrACAoKAiREeGQSjl+LhEREVFPxAAsERERERERERERkUj4GJ2IiIiIiIiIiIhIJAzAEhEREREREREREYmEAVgiIiIiIiIiIiIikTAAS0RERERERERERCQSBmCJiIiIiIiIiIiIRMIALBEREREREREREZFIGIAlIiIiIiIiIiIiEgkDsEREREREREREREQiYQCWiIiIiIiIiIiISCQMwBIRERERERERERGJhAFYIiIiIiIiIiIiIpEwAEtEREREREREREQkEgZgiYiIiIiIiIiIiETCACwRERERERERERGRSBiAJSIiIiIiIiIiIhIJA7BEREREREREREREImEAloiIiIiIiIiIiEgkDMASERERERERERERiYQBWCIiIiIiIiIiIiKRMABLREREREREREREJBIGYImIiIiIiIiIiIhEEtTVGSAi6o30egMamlsAALFREZDLebolIqLA4DWIiIi6Cq9BRI6xBSwRERERERERERGRSBiAJSIiIiIiIiIiIhIJA7BEREREREREREREImEAloiIiIiIiIiIiEgkDMASERERERERERERiYQBWCIiIiIiIiIiIiKRMABLREREREREREREJBIGYImIiIiIiIiIiIhEwgAsERERERERERERkUgYgCUiIiIiIiIiIiISCQOwRERERERERERERCJhAJaIiIiIiIiIiIhIJAzAEhEREREREREREYmEAVgiIiIiIiIiIiIikTAAS0RERERERERERCQSBmCJiIiIiIiIiIiIRMIALBEREREREREREZFIGIAlIiIiIiIiIiIiEgkDsEREREREREREREQiYQCWiIiIiIiIiIiISCQMwBIRERERERERERGJhAFYIiIiIiIiIiIiIpEwAEtEREREREREREQkEgZgiYiIiIiIiIiIiETCACwRERERERERERGRSBiAJSIiIiIiIiIiIhIJA7BEREREREREREREImEAloiIiIiIiIiIiEgkDMASERERERERERERiYQBWCIiIiIiIiIiIiKRMABLREREREREREREJBIGYImIiIiIiIiIiIhEwgAsERERERERERERkUgYgCUiIiIiIiIiIiISCQOwRERERERERERERCJhAJaIiIiIiIiIiIhIJAzAEhEREREREREREYmEAVgiIiIiIiIiIiIikTAAS0RERERERERERCQSBmCJiIiIiIiIiIiIRMIALBEREREREREREZFIGIAlIiIiIiIiIiIiEgkDsEREREREREREREQiYQCWiIiIiIiIiIiISCQMwBIRERERERERERGJhAFYIiIiIiIiIiIiIpEwAEtEREREREREREQkEgZgiYiIiIiIiIiIiETCACwRERERERERERGRSBiAJSIiIiIiIiIiIhIJA7BEREREREREREREImEAloiIiIiIiIiIiEgkDMASERERERERERERiYQBWCIiIiIiIiIiIiKRMABLREREREREREREJBIGYImIiIiIiIiIiIhEwgAsERERERERERERkUgYgCUiIiIiIiIiIiISCQOwRERERERERERERCJhAJaIiIiIiIiIiIhIJAzAEhEREREREREREYmEAVgiIiIiIiIiIiIikQR1dQa6u+ZmJcorKlFbV4dWlQomkwnh4eFISkzAkNTBkMvlXqV35NgJVFVXe7XM8KxhGJI62KtlBEHApZLLqKyqgVKpRHR0NPr1TcHAAf28SidQ6RIREREREREREfVGDMA6sGPXXuw7cAjniy+hvqHB6XzBwXJMnZyLe+5cgoED+nuU9sYtW3H0eJ5X+Xn84eUeB2BNJhM2bNyC9d9uRlV1jd30QQP645677sCs6VO9yoNY6RIREREREREREfVmDMA6sHPPPhzPy3c7n06nx87d+7B3/yH84NEHsfjmBQHInXNarQ6//9NfcfJUgdN5rpRew1///iZOnDyFn/z4h5BIJF2WLhERERERERERUW/HAKwHQkNDMCQtFYkJ8VCEhqKhoRFnis5B2dICADAYDHjrvQ8REhyM+fNme5zumNEj0a9vitv5hqQN9ii9V//xpk2QNCQkBDmjRiA6Ogo1NbU4VVAIo9EIANi2Yzeio6Lw+MPLuyxdot6qvLIKX67fBIPBiOHDMjF3xpSuzlKn7NizH2fOXgAA3L/sdsTHxXZxjoiIiIiIiIh6DgZgnQgNDcGs6VMxY9oUjMweDpnMdrwynU6HtRs2YuXnq2EyCQCA9z5cidyJ4xAdFeXROubOmoE5s6b7Jb/7DhzCgcNHLZ/Hj83BC//7Y0RGRli+q6quwct//QeKL14CAHy9/lvMmjHVZfcGYqVL1Jup1RpcuFgCAEhOSuzi3HReZVUNzhebj2+tTtfFuQkMvV6P0rJyXL5ahtq6ejQpldDr9AgJCUZ0VBQGD+yPkcOHIiQkxO/rrm9oxKXLV1FRVQ1lSytaWlohCALCw8PQLyUZ2cOGIikx3m06m7fuRHHJFa/XHxMdhQfvWep0uslkQnVNHcoqKlFWUYmKyiro9AYAQOaQVCyYO9PrdRIREREREfVmDMA6cOui+fj5T59FZESE03mCg4Nxz7I7EBwcjPc+XAkAaFWpsO/AYdyy8KZAZdVi5WerLX8PHjQQv33xZ3YDhCUnJeIPv30RT//vC2hobIIgCFj52Wr87tc/D3i6RETd1YefrkG+Vct+R3bvB0JDQnDTnOmYN3MapFKp03k9VVvfgDfe+gCNTc0u51v77XcYOXwY7l5yC6KjnT/wK6+ssgTOvZHSJ8nptA8/XY3Theeg1+sdTo8ID/N6fURERERERL1d538x9kK5E8a5DL5au/3WRTav456/cFGsbDl1vvgiSsvKLZ8feeBeuyBpm+joKNy19HbL5+N5+WhuVgY0XSKi7qymts5l8LWNRqvFN5u34cNPV8NkMnV6vTqdzm3wFQAEQcCpM0V47d/voa7e+UCRvhqVneV0WlV1rdPgKxERERERETnGFrCdJJVKMSQt1fIjWKkMfNDx4OFjlr9jY6IxfmyOy/nnzZmJ91d8ApPJBKPRiCPHTmDeHPtXRsVKl6i369snGT989AEoVWqPuySh7kUul2NAvxQMGtAfMdFRiIwIR1iYAhqNFiVXSnH4eB40Gi0A4OTpQuzcewBzZ07r9HolEglSkpMwcEA/JCbEISoyEpHh4TAYjaiprUXeqTO4es38YKyhsQmfrF6L//nhYw7TWjRvNqZOmuB2nSaTCe9//IUlsJo7fozL+aVSKRIT4tAvpQ/6pfRBU7MSew4c9nJLiYiIiIiIbhwMwPqBVqu1/B0ZGRnw9V8obm91OyI7y+2rsBHh4RiSNhgXrr+aeuHiJYeBUrHSJertFIpQZKanoaG5pauzQj54dPldiIuNgUwmczh9XM5IzJ05Ff95/2NUVtcAALbvPoDZ06d0qiuChLhY/PE3LyAq0vkbGPNmTcf3O/bgmy3bAADFly7jWnkl+vftYzdv35Rk9EWy2/WePnPWEnxNTx2ExPg4p/M+uvxuxMZGI9jqbYhDx/LcroOIiIiIiOhGxi4IOkmj1aL4+mA7AJCZMcTjZQUIuHipBLv3HsDm77dhz/6DOHvuAvTXBzPx1NXSa5a/UwcN8miZ1EEDHS4fiHSJiLqzxIR4p8HXNrEx0Xj4/mWWz8qWFlRV13ZqvcHBwS6Dr23mz5mBAf1SLJ8vXynt1HoPHj1h+dtd69fkpASb4CsRERERERG5xxawnbT5u21oVakAACEhIZg6aaLHy77x73dgMNj3MyiXyzFp4jjcuWQxMtNdB3SNRiNq6+otnz0dcT05uX2QlarrLbgCkW6gVdfUofDceZSVV6KltRUyWRBioqMwND0Nw4dlOAyytKpU+PDT1RAE96OBW/v8y/WorW+ARAI8dO+diHLSGtqXPLW5cLEEW7bvBgBMmzQeY0aNgMlkwoVLl3Gm6Dwam5qgUqsxcngWJowdhQ8/WQ2TICA6KhIP3XunR9vx2Zp1qGtohEQCPHzfXYiMCPdouY5qautwuvAcyiur0NraCsDcSjoiIhz9Uvpg+NAMhIUp3KYjRnkVnS9G4dkLaGhqgjxIjj5JCZgycZzdgEaCIODs+WIUnruA+oYmSCQS9O2ThInjcpDgopVgeWUVvly/CQaDEcOHZWLujCkut9FkMuHshYs4f+ES6huboNVpER4Whr59kpCdNRQpyc4HRXK1nY7qxcypuXbLny++hNOF51DX0ACZVIrYmGiMGpGF9NTBLtfrjE6vx7nzF3HhUgkam5qh1ekQHhaGAf1SMCo7y6bfbEc++vxLNCtboAgNxRMP3QsAaGpqxvH8AlRUVqFZ2QKD0YgfPrrcab/UgdC/bwriYmNQ39AIwByETYHrfeUvgwcNQGlZBQCgVa3yOZ1mZQvOnD0PwBwAHjMq2y/5IyIiIiIionYMwHZCRUUlVn6+2vL5jttuRkxMtMfLOwq+AoBer8fe/Yew/+Bh3LV0CR66/25IJBKH86rUapvPER4GyyLC2+frmIaY6XrC2xbAjjQrW7Bu4xbkFxRCEOyn795/CInxcbj7jsUYkmrbujdYHgytVofLV80teGdOneQ2AFZTW4cDR44DMAdlFKEKu+3oTJ7aNDQ2WUY1H5qehsqqGnz8xVc2g6UBQEJcHORBcgiCYJl/au54DOzfz+V2lFdWWVrDZaQNRmhIiNf7Q6fX46sNm3AsL9/hdraRSiVYOG825jnpN1OM8qqqrsVHn39pV14A8P3OPbh7yWKMHzMKAFDf0IgPP12NsopKm/nyCwqxdddeLLv9Fkx00i+yUtmKC9dbxicnJTo91gHzK+Rfrt+I6to6h9PXb9qK0SOysOz2WxAe5niEeW/qhfX+bFa24NM1ay15tbZz70EMTU/D8rvvgMnUvgMMBqPLOnHk+Els2roDzUr77heOnsjHuo3fY+qkCbht4TynwfOLJVfQ0NiE8DAFdDo9duzZj83bdtkNdKXR6gCYz417Dx5BQdE5AEBWZjpmTZvsNI/+ZL0NwfJgv5y/PKFWaSx/hynCfF7vwSPHLeU6ekQWpFKZ12lZD1hmMgkBKwMics76uuPqGkRERORvvAZRbySXdz58ygCsj1pbVfjdn161DMIyJG0w7rvLs5aSgDlQ2a9fCmKjoxETEw293oCq6hpcuHjJ0qesySRg1ZdrYdDr8fgjDzhMR6PR2HwOCQn2aP0hwe3zqdUau+lipeuJzvabWVtXj09XfYVmNwOi1dTV4+0PP8Hdd9yG9CGpNtOys4ZZArD7Dh3FvNmu+7Lde6h9wLLs4cPstsEfeQKAFqugS11jE958d4XDNDU6PRqaWzB61Eicux6Y273/MG5dNN/l+nftP2T5O2f0SJ/2xZq1G3D2fLHb+UwmAdW1dQ7XIUZ51Tc241/vrXA6yrzBYMTnX62HTC5HbEwMPvjoU0vrdkfzrvr6G4SGhmFA/75205UqtcvPbQrPnsfabzbZBRY7yi8oQmlZBR6+/x6HD0O8rReA+QHJik++sAwg6Mi54kt4+8NPEB/X3tq3uUWFUIXjerFl2w4cPX7S5baYTCbsPXAYFZVVuOfOJQ77TG0L+JoEAes3m7tncaSxuQUhIea+S0vLKyyB5PDw8ID0v9vS2mopv5CQEIQoFAFZr1qtxplz5larEokEycnJPq/XuvuBrGFDfUpHpW7vB12nN7DvY6Juxtk1iIiISGy8BlFvkRQf0+k0GID1gV6vxx9feQ2l18oAAJEREfjV8//r0auwN82dhYfuvxtpqYMdtmpVqdXY/N02fPLFl5ZA7Ffrv8XE8WMxcsRwu/k7tgyUwHFL2Y4k0vb5BME++CNWumLT6XRY/fUGS/BJJpMha2gGBg8cgIiIcJhMJlRV1yL/9Bk0NjXBaDRi3cbN+OHjD9u03h2eNRTfbd8Jg8GI02fOYs7M6U4H1xEEAafPFAIwjw4+ImuYKHnq6PDREzAajQgNDUHOyBHom9IHitBQQAJL9weZ6WmIjopCU3Mzzpw9h5vmzHIaTNfr9Sg4cxYAEB0V6bb7C0dq6+ptgq+pgwYiIz0NMdFRkMlkUKnUaGltRXVNLYov2be6FLO8Dh09DqPRiKjICOSMGoHkpKTradXgyPET0On0EAQBO3fvg0wmQ6tKhajIyOvzJsJkMqGyuhpHjuVBr78+7959eOi+u70uJ8Dcwvabzd9Zgq9BQTKMGpGNQQP6IyQkGE3NSpwpOourpWWW+dd9uxkP3LvMVbIe1QsA2LJ1h03wdfDAARieNRRRkRHQaLUoKbmK04VFKK+o8qhv06PH82yCr4kJ8Rg+bCgSE+IhlwehpaUVxZdKcPZ8MQRBQPGly9h/6CimT7HvEqGNVqvD3gPmhwKpgwZi2NAMREdFWlqddmX3AwBw6Mgxq9ajw932G+sP18rKsen77ZYHXOPHjkZMh64zPHX1WpmlDsTGRLttIU9ERERERES+YQDWS3q9AX985e/IP30GABAWpsAfXvolUlLsR6B2ZNpk58EGAAhTKHDnksUYPmwofvnSH6DTmVt3ff7lWocB2NCQEJvPWp3Oo3xote3zKULt++EUK11PxEa5H4TGma0796Cu3tx3bWJ8HB5/6D4kJcTbzXfzvFn47Mt1yC8ohFqtwanTBbhl/lyrOSIwcvgw5J06g5bWVlRVVWH40AyH6zx/8RKams3BwuxhmejXx7a/XP/lCYgIC7X8bTQa0S+lD558ZLnLflqnThqPTd/vgE6nx6WSEkyZOM7hfEdOnITmetB/2qQJiI/xPqhz9Wr7YEBTJo7DsttvcTqvyWSCsqUV0R32t5jllZmehkfvv9suCD0tdxz+/p/3oNPpca3c3K/msMx0PHzfMptW3QAwdeI4/OM/70GvN+Bq6TXIZRK7wG9Dh75tI8MUCAqyDc5t2brdcnxHRUbiR489YNfX8k0zp2L3/kNYv+l7AEDJlauoqa5GZnqay+10Vy8qq2tw5vrr+gBw+83zMXPqJJt5ZkyagMtXr+Hdjz61tPQHgKiIMLtjVKVWY+fe/ZbPtyyYi9nTpkAqtX1wM3vaJJwvvoQPPvkCer0Bh44ew/zZ0+zON23LmUwmSCTAfXfejgljRzvcljYh8mCrv+WdOo944tLlqzh8zNx6NCI8DLcumIuIcMddRPjq/Y8/h95gfp1fr9ejrr4Rypb21qUzpuTitkXz7crZU1uKzlr+njR+DOKiHfdb7U6Yon3/BcuDRC97InLPYDBaWh05ugYRERGJhdcgIscYgPWCXm/Ay3/9O44ezwMAKBQK/OG3v/SppaA7WcMysWTxzVj91XoAQMGZQqjUaoQpbAM7ig6fnb0y3ZHK6lWAUEWo3XSx0vWEr31rCIKAfde7ApBKpXjy0eXo42TwMLk8CA/duxS/f+UqmpUtyDt1BktuWWAzz+QJY5F3yhxoP37yNEaPyHKY1vGTp22Wsc6/v/Nk3cJOKpXisQfuQVys636Hp02agO+374bBaMThYyccDsIEAIePmeu1PCgI0yZP8Gk/KKwCMf369nGbRsdAqJjlpQgNxWMP3O2wH9V+fftg7OiROHT9dewwhQKPLb8bCgd1eEC/FIweMRzH8k5BEMzBzKxM233Q8SYjKEhmUxZ6gwEnr9ctAHjw3qXobzWqvbV5s6bhWnmFpZ4dzctHdlam0+30pF6cyG+vszkjszFvluN+eDOGDMbSxYvw2Zp1TrcFAI4fPGV5+DJ5wlgsnOu8y47srEwsnDsL32zZBo1Gi/PFlzAuZ6TNPNZvB0yZOB5Tch0/NLA2e/pk5IwyP6SKiYrySx89ztTU1WPFZ6thMgmQSCR46L5liPXhgYU7xSVXoHPw8Cs5KQEP33cXBjipM57QaLTILygCYC7vSR3OXd6wrX8SUcueiLzn6LxNREQUCLwGEbVz/E412WnrduDI9RZPitBQ/N9vfoGsYZlulvTd9CntLdIMBiMqK6vt5pHLgxAd1f7Dv7rG/avCAFBVXWP529Fo7mKlK6byyipL67ChGWlOA3dtgoODMfz6/qtvaLS0Ym0zNGOI5dXe04VnHQ4qptW2BzEiwsMxfJhtK1l/58la5pBUJCXatwztKDIiHDnXRzYvLavA1Wv2A1BVVFaj5Iq59erYnJEuX+V3ZcjgQZag5eatu3As75SlKw1PiFleI7OHOR3ECgAG9Gvvy3XUiCyHwdf2edsDXy0trS7z6MjV0jJLy8aUPkkYluH6Ic7cGVMtf1+6fNXlvJ7UC+s0Zk2b5GJOYOLY0QgLc92a/dyFS5a/Z0x1nR5gDvq2aat3zkzNHe82PcAclByWMQTDMoagT7LretMZDY1N+Pd7H6Gl1fxQauniRcjKTBdtfY5UVdfizXc+xLdbtrntP9iZE/kFluDu0PQ0xMXG+DGHREREREREZI2PIjyg0+nwh7+8huN5+QCA0NAQ/O43v0D28GFuluwc64FvADgdkGjggH44fcY8sFBp6TWP0r56rX2+gf37BzRdsVRYBagrq2rwn/c/tnwWILT9YfNdTW295XNTsxLRUe2v4EqlUowfMwrbdu2DwWDAifwCTJs0wWadeacLLUGM8WNG2fUB6e88WXM0+JMz0ydPxLG8UwCAA4ePYWD/22ym7z981PL3DBd9croTGhqCh+69Eys+XYOW1lZ89PmXkEql6NsnCX1T+mBg/34Ymp7mNEAmZnk56sbAmiK0vfWuu3lDQ9qDs552z2Gt1qrv1SGDB7mdv3+/FIQEB0Or06Gh0dzvrbP+Rj2pF7V15jKTSqUYNMB1v58ymQyDBvRD0TnnA6tVVLXvt3Ubt0AqMT/bc7bPTMb2oKGroLlUKkW/vp517xIIDY1N+Oc7H1r6Tb395pvcBrA744ePLofxeoBVr9OjvrER5y+WoKDwHNQaDb7bsQclV6/hR489gKAg7y7nB48et/ydO36MX/NNREREREREthiAdUOr1eH3f34VJ6+/shsSEoLf/frnGJnt+HV0f+oYcA130r/gkLTBOH3G3Aqz7X9XtFodzl+4aLN8INMVS6tV9wcNjU1oaGzyanm1RmP3Xe74Mdi2ax8A4Mjxk3YB2CPXX9tvmzcQeWoT5kUXD2mDB6J/3xTLq+x33LoAIdf73dTr9Th6whycTR00AAO9COw6MiJrKH7zwnPYc+Aw8k8Xorq2DtfKK3GtvBJHrg/S1L9vHyy6aTZGdTiOxCwvb/oe8qqfIsH9LB21DaAEwGX/vW0kEgkiI8KhrTcHe1VqNSIjHPez6Um9aFu/IjTUo8Cds3W1sd5v1q1hPaFxsc9CQ0KcDn4XaLX1DXjznQ9R39AIALh1wVzMmzVd1HVmDEm1+27m1Ekor6zC2//9BA2NTThffAk79hzA/DkzPE63oqoal6+aH5YpQkOddq9CRERERERE/tE9ftl2UxqNBi/98S92wddRI7LdLOkfbX3NAuYATFJigsP5cie0v6JbVV2DwrPnHM7XZu/+g9Dr9VbLjw1oumKRdTJQIwj2kbQ+SYkYPNDckrfkSqlNVwz1DY0oLrkCwBxQ7O+gpZ4YeWpj3U+mJ6ZNNgePNVotjln1W3vi1BlL9wqdaf1qLSY6Crctugm//fn/4E//7xd45omHcMv8ORiSOggSiQTXyivx3kefY/PWnTbLiVle3Yl1gNdgNHq0TFuXBeblnQdNPakXbcsbrNJ0uW696/k6s99MfqzjYqmuqcUbb31gCb7eMn8OFrjo51Zsffsk494721uxHzlx0qvlDx45Yfl77OgRkMvl/soaEREREREROcAWsE6o1Rq89MdXUFBobvkZEhyMl371AkaPDEzwtbKq2jIAFwBkDEmz6ZPV2ojhWYiLjUV9g/m12JWfrcaffv8bh8ELrVaHVV+ts3wePmwoEhMcB3bFSlcsEVYtCcfljMQkLwPAzga0yR03xtJa7Mjxk7h14TzL320BP2ev8IqVJ19MGDMK6zd9D7VagwOHj1n61jxw2DzoVVRkBHJGDvfb+tpERoRjWGY6hmWmY+G8WaisrsG/3/sIjU3N2LJ9N8aPHY3E6/0Fd6fyElOEVWv2Kg/6V1ZrNGhWmvvGDQoKgiLUtwHu2oSHh0Gt0UCr06FZqURUpONuG9rU1Na5nB4REQ61RgOpVIofPvaAV4FTV/3ydgfllVX417sfWfomvvmm2Vg4b1bXZgrAkMEDLX9bd8PhjtFoxNET+ZbPkyaw+wEiIiIiIiKxMQDrgFqtwf/7w59xpsjc4jMkOBgv/foF5Iwa0al0TxcUIjk5yWlL1jZHjp3Am2+/b/nBDwB33HaL0/llMinuvesO/Ofd/wIA8k+fwdvvr8CTjz0Mmay9ZZpGq8Wr//gXysorLN8tv3dZwNMVS+rA9j5n6xsa3Q5s5KmxOSPw9TeboTcYcOREPm5ZMBcSicTyOr1MJsO4nFEBzZMvgoODkTtuDHbtO4ir18pRWlaBoCCZZUCmKbnjve5H0hd9khIxY0ouNmzeCpPJhHMXLloCsN2pvMRkPeDX+QuXoNFoEWrVB21Hp86ctQT7/RFkHtAvxdIP7KkzZ+261rBWV9+AsopKl+mlDhqAmto6mEwmyOVBSE8d3Ok8dgelZRX493sfoVVlHnBr0U2zseim2V2cK7Om5vbrQ7AXLVhPF55DS6t54DhzC/8Bfs8bERERERER2WIA1oGX//p3S/AVAMaNzUHptTKUXitzu2x4WDjmOOkX8JMv1uD0mSIkJSYgM30IoqOjERUZgYiIcOj1BlRV1+BUwRmbQCYATM6dgJnTp7hc76L5c7Ft525LH6zfbPoOx06cxJRJExEbE43qmlrs3X/Qpk/N6VMnYczokV2Srhiio6OQOmgASq6UouRKKQ4ePYHJHrSgrG9oxOWr1zB2tOMAe5hCgRHDhyHvVIG5z8WLJZAHyVBzPYA1fFiG0348xcqTr6ZPnoDd+w9BEAQcOHzM8iq8TCbDNA9Hm3el6NwFREZGoH9f10FC69fptVqt5e/uVl5iiY+LRXJSAqqqa6HRavHNlm24a4njhyytKhU2frfd8jkrM73T6x8+NAN5p84AAL7bvhtjRmU7bIkqCALWbfzObdcOOSOGWx5IfL1hM5576jGXAWUAMJlMOHj0BMaOHtHpFr1iuHz1Gv7zwceW/nIX3TQbN3eT4CsAbN211/K3s0HtHOHgW0RERERERIHHAKwDV0qv2Xw+cOgIDhw64tGyKX2SnQZg21TX1Nr0JerK9KmT8Pz/PON2PplMht/96ud4/lcvofx6a7WKyip8te4bh/NnZw3FT599usvSFcvNN83Gv9//GADw+ZfrcfVaGWZOnYQ+SbYBCpPJhCulZTh09ASO5p3C+JyRLoN3ueNzkHeqAIC564EgqxHoc8e5DmKIlSdfJCUmYGh6Gs5euIhjJ09ZBjgaPSIL0dGOu7jwRml5Bb7dsh0js4dhWu54pKcNtulfUm8w4OSpM9ixe397njp0VdGdyktMs6dNwRdfbwAA7DlwGEajEYsXzrMZbO/y1VJ8tma95QFHsFxu6cu3M8bmjMT6Td+jpVWFxqZm/PPtD3H/XUswaEA/yzzNSiW+2rAFJ08Xuk1vxPChGNi/r6Vl9atvvoNbF8zFiKxMu/5Fm5VKnCo4iz0HDqOiqtpvA0Dt3n8Yp64PFpg9LANzZkz1Oa1Ll6/irf+uhEZjfjjgj+CrJ/nbsHkr+iQlYmjGEERH2XcLIQgCrpRew3fb96DA6iHhhLGjPcpDY1Mzzp43P0yTSqWYOM6z5YiIiIiIiKhzGIANoFEjs1FbV4+Kyiq382YNy8TS227F1MkTPU4/JiYa/3ztz/hgxafYumOXwwF2FKGhuOO2W3DvXXd4/Lq5WOmKYVhmOm6aPR1bd+6FIAjYd/Ao9h08isiIcMTHxUIeFAS1RoPaugZorFpeupOVmY7oqEg0NStx8nShZdChiPAwjMjK7JI8+Wra5Ik4e+GiJbgE+G/wLcAcJDpVUIRTBUUICgpCUkI8whShUGk0qK9vtNnG+LhYDBtq26Kzu5WXWCZPHIvj+adx4WIJAGD/4WM4ePQE+vZJQmhICOobmyyDPrVZetsiREZEdHrdwXI5lt1+C1Z8tgaAuZ/Tv735DuLjYhEbHQWVRoPKqhqYTCZIpVKkJCe57IZAIpHgkeV347U330WrSoXqmlr895NVCJLJkJgQj/AwBYwmE5qVLairb+h0/h2pqqnB+eJLAID42JhOpfXeR59bjg+5XI6LJVfw5rsr3C43e9pkjBg+1Of85Z06Y+kaQhEaivDwMESEhyEoKAgajRa19fU2xy1g7v5hqoet1w8fy4PJZAIADB+a7rbvX0eKzhdj2659Nt81K5WWvy9cvGxXVkMz0jB/9gyv10VERERERNRbMADrwE1zZqKlpdWnZV21Ilx+zzIsv2cZmpqace5CMWrr6tHcrISypQUymRTh4eHo1zcFwzLSkZAQ79P6wxQKPPujJ/DoQ/fh6PE8VFZVo6WlFVFRkejfty/GjR2N0BDXrwYHMl0xLF44D6GhIdj43Q5LsEHZ0gqlk30aHxeLsTmuu0yQSqWYMHY0tu3aB51OZ/l+/JhRkFm1hg1knnw1cvhQxMZEW1pV9u/bB0NSB/kl7eFDM5FfUISrpebuOgwGA8qdPHCIiozAEw/dB7mDgH13Ki+xSKVSPPHgvXjv489RfOkyAHOr3mvl9oFOiUSCWxfO9TjQ5olxOSNRW1+Pb7e0d29QV99gEyCVSCRYunghysor3fYDmxgfh/99+nH8d+UqVFRVAwAMRqPl746kUikmTxjbLbsf0On1lr/1er0lcOrOGBeDNFp34yALcn/OUGs01x80OB9ga+TwYXjo3js9OgcJgoBDx/Isn3PHezfAXZumZqXL8lC2tEBZ3GLzXVRk5x8aEBERERER9WQMwDrw0P33iJp+dHQUJvr449dTEeHhmD1jWo9J158kEgnmz56BnBHDsWPPAZwuPGfTQgsAgmQyDB7YH7kTxmJczkiHQcCOJo7LsWv5NdFN9wNi58kXUqkUuePHYMu2XQD82/q1f98+eP7HT+LM2fPYf+gYzhVfgt4qmAUAsTHRGJczEvNmTXPY7yjQvcpLTGFhCjz75CM4ePQE9hw4jPIK22C1XC7H0PQ0LJw3y6Z7AH9ZMGcmBvbvh2+/224JmrcZPLA/br5pNrKGZuCzNes8Sq9PUiJ+/j8/xJHj+Th49DiuXiu3BNDbxMZEY/SILEybNBHJSa4HJOxN2spXIpE4DaQvuWU+is4V42LJFVTV1DrsezciPAxDM4Zg8sRxGJqe5vH6iy9dtgRzPWm5T0RERERERP4jEdyNrkLUCzQ0NkGpbIFOr0eYQoGEhDivRg5vU3zpMgxGIwBAKpEg04sAiD/y1KxssbQoTUqIR5yPr1q///EXyC8oRFiYAn/49fM+lYUn9AYDamrq0KpSQS6XIzYm2mHflp4Qu7yalUqUV5pbayYnJiA2JtrpvE1NzaiorgFgDjrGdGj5rlZrcOnyVShVakRHRSF98ADI5e4DxMqWVjQ0NkGn0yE8LAwJ8bF2fag6znvn60VTsxL1DY2QSqWIjYm2abVYWVWDxuZmAOZX3kOCgz1KU6vToa6+ASq1GkEyGWKio+3KypFLl69ApzcgSCZDetpgj9ZVVV2LhiZzq+6YqCivBqbq6MLFEhg7BI494aguAEBrqwq//L9XIAgCJowdjYfuvdNtWjq9HkplC5QtrdDr9QgNCUFUVKTPx09tfYNVADYc/fv28Smdpmal01bNzkRHRiKlT5JP6yMi3+j1BjQ0m1ujx0ZFeHQNIiIi8gdeg4gcYwCW6AZTXlGFV954CyaTCbOmTcKdt93c1VnqlXjjQW2OnzyNFZ+tQVBQEH77wnM+PzghIvIUr0FERNRVeA0ickza1RkgosBRtrTgoy++hMlkgkQiwZSJ/utTlIgcO3u+GAAwc0oug69EREREREQ3ID6KIOrlvt2yDSVXr0Gr1aKsogoGgwEAMCJrKF8LJgqAaZMnYtyYURg8oH9XZ4WIiIiIiIi6AAOwRL3ctfJKu1HLQ4KDseSWBV2UI6IbixgDqBEREREREVHPwQAs0Q0mIT4Oy+9agqTE+K7OChERERERERFRr8cALFEvd+vCeZg1fTIkEgmiIiPQJykREomkq7NFRERERERERHRDYACWqJfr37dPV2eBiIiIiIiIiOiGJe3qDBARERERERERERH1VgzAEhEREREREREREYmEAVgiIiIiIiIiIiIikTAAS0RERERERERERCQSBmCJiIiIiIiIiIiIRMIALBEREREREREREZFIGIAlIiIiIiIiIiIiEgkDsEREREREREREREQiYQCWiIiIiIiIiIiISCRBXZ0Boo5efu1Nr5eZMGY05s+ZYff99zv24Ghevtfp/fpnz9p919DYhP988LHXad00azomjsux+3712m9x4VKJV2nFREfhmScetvu+5MpVfPbleq/zdtfttyAzPc3u+3c+/BS19fVepTV44AAsv2uJ3fd5pwqwaetOr/P25MP3IzEh3u77V15/Cwajwau0ckZm45b5c+y+37FnPw4ePeF13n7+3A8hl8ttvlO2tOCf73xo+SwIgNFoAgDIZFJIJI7Tmj1tCqbkjrP7/qsNm3H2QrFX+YoID8f//PAxu+9Lr5Xj41VfeZUWACy9dSGyhmbYff/BylWorK72Kq3+fVPw8H3L7L4/daYI32zZ5nXeHlt+D1L6JNl9/9q/3oVGq/UqrZHDh+G2RTfZfb/nwGHsPXjE67z99OkfQKEItflOrdbg7/95z+u0pk+eiBlTcu2+37B5K04XnvUqrdCQEPzsx0/afV9RWY3/frrK67wtXjgPo7Kz7L7/6PMvca28wqu0+iQl4fEH77H7vujcBXz97Rav8/bQPXdiQP++dt+/8fZ/0dLa6lVawzLScedti+y+P3D4OHbuO+B13p576lFERkTYfKfX6/HXf77tdVqTJ4zFnBlT7b7f+P0OnDx9xqu0gmRB+MX//sju+5raOrz70Wde5+3mm2ZjzKgRdt9/umYdLl8t9SqthLg4PPXocrvvzxdfwpr1G73O2/3LbkfqoIF23//7/Y/Q2NTsVVoZaam4+45b7b4/cvwktu7a63Xenn78IcTGRNt97+29kSAAw4cNxbTJE+2m8d7INd4biXdv5CneG/HeyBXeG3X/eyNPfwe14b0R741c8de9EdC5uJGj+yBvMQBL3U5lVY3XyzQrW5x+70t6jhiNRp/SUqnVDr9vaGz0Oj293vHNtVan9ylvWp3O4fe19fVepxcVGenwe5VK41PeDAajw++rqmugN3j3I6N5kNLh98qWVp/yJjj4zmQ0+ZRWq0rl8PvGpmYf9oHG4fc6vW/1w9nNep0P9SNMoXD4vVqj9SlvzupAVU0t1GrH5eDMgH72N6MA0OJr/RDsa4ggCD6l1dLi+IbYl/rR8YdPG73B4FPe1BrH9aO+wftzmzzI8e2IRutb/dDp9Q6/r66pQ7PS8fnAmT5J9j9mAfOx60veTNd/kFgT4Nu1T+mkfjQ3K/22DwwGH699KsfHoS/1wxmtTufjtc9x/aiprUddfYNXaSXExTr8XqVW+5Q3o9Hxtc+XtAYN6O/we94bucZ7I94bucJ7I94bucJ7I94bucJ7o66/NwpE3MgVBmCJiIiIbnAag4DsXxbZfR8uNMG+nZF7v1tbgfL19unlGFsR52VaxVVah3mLF8ow2oe8PfHBVTRI7INsk406OA6HOLerqAX/dJC3/qYqZPqQtwWvXoRGUmX3vX07Rfe+OtGKv5y0b3GVYarHAB/Sc7QPQoUWTPEhrT9/U4VnNtqnN8rYggQv07pWr3OYt1ihEmN8yNszH19DncQ+WDTRqEWEg/ldOVTcircd5K2vqQLDfMjb7a9fQqukzu77mUYBMh/SIwqkjsdpkKCDfTs09/6zvRYv7LQ/rrJMTUjxMq1mtdHh+SNSqMcEH/L2q9XlqPwyxO77sUYVYrxM60yZxmHeEoUyjPQhbw++cwVNEvuHG1ONBtjn2LXvTzfj7w7yNtBUjXQf8jbrzxegk4TZfCcVDJjlQ1of7K7Dr/ba522oqRH9vEyL90a+3RuRcwzAUrfTJznR62WiIh3fEkdFRviUniMymcyntJw93Y6NifE6vZjoKIffhwTLfcpbSHCww+8T4ry9BABxsTEOvw8LC/Upb0FBjn9KJCclev2aXVSU4xYokRHhPuXN0Vs0UpnUJi1PX70JDwtz+H1MdJTXeYsID3f4fbDct/oRGuL4diw+Ls7rljbO6ociNMSnvDl7Ip2cmOD1a3bOjqsIX+uHg50tkUh8SisiwvE+9aV+ONuf8qAgn/KmCHWcXlxsjNPWbc7EOznnhIb4Vj+CO7wG2yYpMR5hYY5buzjjrH6Eh4X5lDepzL77fQl8u/ZFOqkfUVGRDtMrrnJ+bDgL35ggRQscl4Ereonj64tGEo4Wwbv01HB8DjciyKe8GZ3c/qoQCaOXYSyNxPE+MCDYp7wJDq8w8CktHRzXdR1CfUrPEQESn9IywH/1QwvH11F/1w9n9dAVZ/VDLwn2ejsB8/HoiApRkMDcQig92bNQSlfcG3mK90Y3xr2RAF/PbY73gRYKr9MzwvH9ggky385tEsfpmc9tjlvXO6Nx8sjHCLmP5zbH1zcVIqF3Ug5O8yZxfIzqEeLjtc/Ruc2364vza5/39YP3Rr7dG8XGxDj8Pkyh8On8IZM5Xn93jRu5IhEcvQ9ARESdotcb0NBsfsUhNioCcjmfdxFR13LUUoKIepczf7bvf5KoO+A1iOjGwOuQc44foxIRERERERERERFRpzEAS0RERERERERERCQSBmCJiIiIiIiIiIiIRMIALBEREREREREREZFIGIAlIiIiIiIiIiIiEgkDsEREREREREREREQiYQCWiIiIiIiIiIiISCQMwBIRERERERERERGJhAFYIiIiIiIiIiIiIpEwAEtEREREREREREQkEgZgiYiIiIiIiIiIiETCACwRERERERERERGRSBiAJSIiIiIiIiIiIhIJA7BEREREREREREREImEAloiIiIiIiIiIiEgkDMASERERERERERERiYQBWCIiIiIiIiIiIiKRMABLREREREREREREJBIGYImIiIiIiIiIiIhEwgAsERERERERERERkUgYgCUiIiIiIiIiIiISCQOwRERERERERERERCJhAJaIiIiIiIiIiIhIJAzAEhEREREREREREYmEAVgiIiIiIiIiIiIikTAAS0RERERERERERCQSBmCJiIiIiIiIiIiIRMIALBEREREREREREZFIgro6A0RERERE3U1wkART0sMxOSMcI/sr0D9OjiiFDGqdCY0qI4rKNTh8sRXfnmxGq9YkWj4kEmBIUghGDQhFZp9QpCUGIz4iCNFhMkSHySCTAEqNCRWNepwp02DbGSUOXWyFIHiW/vYX09EnWt6pPL66qQor9tY7nR4eIkVqYvD1fyFITTD/PTA+GCHy9vYgj7x7BUdLVJ3KCxFRb5HdLxQzhkVg9AAF0pNDEBsug0kAmtVGlNTocPKKChvymnC1Ti9qPhIjgzB6oAJZfUMxJCkYfaLllmtQWLAUKq0JtS0GnKvQ4MCFVnx3WgmVzrPr4q9vS8b9k+M6lb8Tl1V48J0rTqcHSYGB8devP1bXosGJwYhWyCzzrdxfj798W9WpvBC50uUBWJVajbq6eihbWmAwGBAXG4v+/fp2dbaIiIiI6AY0ckAo7p4Yi4UjoxAWYv+ymFwhQ5RChoHxwVgwMgo/W5SEf2+rxcf76z0OenpjxtAI/OfhAS7nCZFLkRAZhJEDFLh3UiyKq7T4v3UVOH5Z7f8MOaDRu97wL59NxcD44IDkhYioJ0uMDMKyCTFYOj4GfWMdPxwLC5aiT7Qck9PD8dTsBGw+1YyXv6lCk8ooSp62/SIdQTKJ0+ltwdghSSG4eXQ0fnGrEf/eVoOV+xtEyU9Har3rYO//LkjCozPiA5IXIle6JACr0Wiw+fvt2LV3Py5eugyTqf2AWTR/Hp790ROWzxWVVdi7/xAAoG9KMqZNmRTw/BIRERFR7xccJMEXT6d6tUx4iAw/vyUZOQMVeP6LMhjFawzrsfTkEHzwxCD8ek05NuY3i7oug1HA9jNKUddBRHSj+OOyFEzLjPB4fqlUgltyojFmUBgeevcyKhoNIubOM5GhMrx4ax8M76fAr9aUi/Jw0trWAl6DqGcIeAD29JkivPLaP1Hf4NnTkIT4OHyzaQvq6hsQHCxH9vBhiI2JETeTRERERHTDU2qM+P60ErvOKnG2XIsapQGhcgmGpYTijvHRWJwTDanU3Cpo/sgo/G+DHq9trvZrHowmAecqNMi/qsb5Si0uVWtR12pEY6sBjSojgmQSpMTIMWaQAvdNisPwfqEAALlMgj/cmYKicg0u1eicpj/3L8Ve5ad/rBybnx9i2e79F1pRo3T9g1+pMaLgmhqXanS4XKNFSY0OJTU65AxS4Hd3pHi1fiKiG4XJJOBAcSu2n1HiWIkKlU0GmAQBA+KCMTc7Eg9Ni7O8Qt83Vo63HxmIO/95CQY/PwisatbjdKkGReUaFFdpUdVsQKPKgMZWIwwmAbHhQcjqG4pbc6KwcGSU5fpw25hoFJVp8PF+513UvLyhCi9v8O61/6+fS8XQFPO1Tq0zYZObB416o4CL1eZrj+UaVKtDrdKA73+e7tW6iTojoAHY43n5+N3Lf4XR6HnTeLlcjsU3L8SKTz6HTqfHxi1b8cC9d4mYSyIiIiK6kTWrjfhwTx0+P9QApcb2l6zeKOBoiQpHS1TYWqDEP+7vD3mQ+cfmg1PjsOpQA641+K8/vn3nW7HvfInT6QaTYAlorj3ehJeW9MFdE2MBmLsmeHRGPH77VYXf8rN0fIzlxzUAfH2s0e0yd//rssPvs68Hi4mIqJ3JJODb/Ga8t7PW4QO0C1VaXKjSYv3xRnzwxCAMSjB38ZKeHIK7c2Px2UH/vvo//68XXU6vbjagurkFu8+2YFN+M954oD9k168TT8yKx6cH6/32dkh2v1BL8BUAvi9w3w/7G9/X4I3va+y+jwzlmPQUWAGrcXX19XjltX9agq8hISG4a+nt+Mcrf8QjD9znctlZM6ZCIjEfwHv3HRQ9r0RERER04xEE4LvTzVj8j0t4d1edXfC1o51FLXh7Z63ls1wmwYKRUWJn0ylBMLcmqrVqkTo1I9xv6UskwG1joy2f61oM2FXEVz+JiPzlYrUWD793Bb9cXe7y7QUAqGgy4CefXoPR1P6O/y05XXcNAszXxQ0nmiyf4yPMrWP9Zen4GJvPa481OZ6RqBsKWAB29Vfr0dLaCgCIiozE3//yf3j0wfswNDMd4eFhLpdNSkzAwAH9AQClZeWoqOTIdERERETkX3qjgJ9+VmYTwHRn1eEGmKx+/GZ1catOvVHA8csqy+eEyCBInI+d4pWpGeFIiWkfFOabvCa/v+pKRHQj++vGapzwYgDFc5Va5F9tnz8rpevfLDh4sdXmc1KUf168Dg6SYNHo9gDz1TodjpaoXCxB1L0EJABrNJqwc/c+y+enn3oMqYMHeZVGZvoQy9/ni103gSciIiIiCoSGViOa1O3da8WGybowN2bWraF0BsFvA6DcMS7G5rMn3Q8QEZG4rFvKhsilCAvu2lfrjUbbi45a55+L0E3ZkZY+bwFgLa9B1MME5Mi8WFJiaf0aHxeLaZNzbaZLPHgsn5yUaPm7rNx//VgREREREflKIgHCQ9pvqZvVno91IAapBMgZ2P522alSz1tSuRIdJsPsrPaRufOvqnGx2vXrsUREJL5oRfs1SG8UoNJ17asJ41Pbr0F6o4DCMv9ch+6w6n7AYBSw/gS7H6CeJSAB2Oqa9r6xhmZmQCr1frXW3RS0tLT4JV9ERERERJ0xaoACwUHt97YXqrRdmBvgJwuT0De2vZuAj/c5H33aG4tzohAib99OtjwiIup6Uon5OtTmYhdfgyamhWHZhFjL5w0nmtCk7nxAuG+MHLlp7TGhA8WtqGr2vLsgou7AP51xuNHa2t4HSESEbwMBGAzWB5efOrIiIiIiIuqE5ZNjbT5vPxPYQankMgmSooKQM1CBe3JjMc6q5dGKvXXYddY/DResux9Q6UzYdKrZL+kSEZHvbhoRieTo9odu2wJ8DZJJgWiFDJl9QrBodDRuHxsNucwcryks0+CvG/0zfs8d46MhlbbHgb4+2uiXdIkCKSABWIWi/YmMVmP/RMaTgQEam9pv8qIiI/2SLyIiIiIiX01MC8OiUe0Dguw914JzleK2PnpwaixevLWPy3nKG/T459ZqfJPnnyDp8L6hGGY1ivXWgma0ajn6FhFRVwoPkeL5RcmWz0qNEV8cbhB1nX1j5Nj6i3SX86h1Jqw63IB/ba2BWu+f/l9vHxtt+bu+xYBdZwMbaCbyh4AEYGOj2w+Wa+XlPqVxofiS5e/EhPhO54mIiIiIyFcJETK8ck9fS4scldaEP31T2cW5Ao5easXfNlej4JrGb2kuHR9t8/nrY+x3j4ioq728LMWmy5l/bKlGQ2vX9kNe1aTHP76rxsaTzTD5aQDIyenh6BcbbPn87ckm6Lt2M4l8EpA+YIcMSYVMZh6truTyVdTW2fZFJXHTpUBdfT0KCossn4dnDfV/JomIiIiIPKCQS/DvhwcgKar9h+/LGypxtU7fhbkym5AWjlXPpOLtRwagT3Tn21oEB0lw8+j2AOyVWh2Olag6nS4REfnuJwsScdOI9jcwtp1RYtXhxq7L0HXJ0XL85e5+2PT8EExO9637yY46PgT8ig8BqYcKSAvYMIUCw4dl4vSZIphMJqz6ci2eeepxj5dfsfILmEzm15wG9OuLlD7JbpYgIiIiIvK/4CAJ3nxoAEb0b+9i64PdtVgXoNGYV+5vwMr97a+YhgVLkBwtx5hBCtw1MdYyGMv0oRH49EeD8dA7V1DW4HtgeF52JKLDZJbPa483+pwWERF13iPT4/DErATL54JravxytW9vGnurvFGP7F+2N46TyySIC5chq28oFo6Kws2joyCTSjAgLhhvPzIAL64ux+ZO9BkeFSrFnOHtXVCeLlWjuIsHGiPyVUBawALA0ttvtfy96btt2Lhlq9tlBEHAp6u+xPZde9rTWXKriyWIiIiIiMQhl0nwxgP9bVr1fHawHn/fUtNleVLpBJTU6PD1sSbc95/L+Num9gFP+kTL8ee7+nYq/aXjYyx/G4wC1gco0ExERPbunxyLF25ub5B2rkKDJz8shUrXNf1y640CqpoN2HW2BS+uLscDb19GQ6t5APUgmQT/tzQFKTG+t/u7dUw0QuXtYauvjjV2NstEXSZgAdjcCeMwZvRIAObA6r/f+QAv/fEVHDx8DE1K2w6Ua+vqsWPXXvz0xd/i0y++tHyfkZ6GubNmBCrLREREREQAALkMeP2BfpgxNMLy3ZdH/j979x3lVnWucfiVNEXTey8u496Nu43BGDCmm04oCQQSQoCbkBAgAZIQElpCChAIvYRueo8NpuPee+9lmqcXjUbS/cPhzEymWDOjczTl96x1191b2vs7n3ODdHl9tE+J/vRuYJ7wHCjPfHVYry9puEN2XL9IHdM3oo0drcuID9Gk/pHG/JutVSoor+90jwCA9vve5ATddlbDQxi357t09VN7VFbddQ5EXbO3Vr+Ze9CYR4bbddnUxA7XO2dcvDGuqfPqo9WBebgkEAyWHEHwnVt/+TP94tY7tP/AkX8gly5fqaXLVzZZ89G8T/TRvE+a7Y2Pi9Ovb/q5QkIsbRkAAAC93JHwNVszhjT8DPKtZaX63VvBf+hWS174tkTnT0ww5lMHRGnFrpp21zlnXLzxkDFJepM7jwAgKL43OUG3n90Qvu4ocOmHT+7W4SA/dKslX26u1J7iOuUmHXlwVkfPgh2cHq5hWU5jPm9duSpdwbnTFwgEy+6AlaSYmGg9cM8fdMyYUe3al5Odpb/ed5fS01JN6gwAAAB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",
      "text/plain": [
       "<Figure size 770x462 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 479,
       "width": 688
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "steps = [1, 2, 3]\n",
    "realised = [clobe.brutes(torch.arange(10.0 * t + 1), 5, (t, -t), return_matrices=False,\n",
    "                         device=device).n for t in steps]\n",
    "fig, ax = plt.subplots(figsize=(7, 4.2))\n",
    "bars = ax.bar([str(t) for t in steps], realised, color=blue, width=0.6)\n",
    "ax.bar_label(bars, labels=[f\"{n:,}\" for n in realised], padding=-22, color=\"white\")\n",
    "ax.axhline(clobe.semiorders(5), color=muted, linestyle=\"--\", linewidth=1.2)\n",
    "ax.annotate(f\"every semiorder: {clobe.semiorders(5):,}\", (-0.4, clobe.semiorders(5)), xytext=(0, 6),\n",
    "            textcoords=\"offset points\", color=muted, fontsize=10)\n",
    "ax.set(ylim=(0, 2800), xlabel=\"threshold, in steps of the scale\", ylabel=\"patterns it can produce\")\n",
    "titled(ax, \"5 stimuli: a coarse scale misses semiorders\",\n",
    "       \"patterns the scale can produce, by brute force\")\n",
    "show(fig, \"scale-resolution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8c53f44",
   "metadata": {},
   "source": "So, in practice:\n\n- If the threshold spans several steps of your measurement scale, the universal set is `semiorders(n)` (or `fubini(n)` for weak orderings). You can confirm this with `brutes()`.\n- If the threshold is close to a single step, use `brutes()` with the values your scale can actually produce.\n\n## Computing it\n\n`brutes()` evaluates every assignment of values to stimuli: with $m$ possible values and $n$ stimuli, that is $m^n$ assignments. It works through them in chunks of `chunk_size` assignments as tensor operations, on the CPU or, with `device=\"cuda\"`, on the GPU, which is where it is fastest. Even so, $m^n$ grows quickly: 11 values and 8 stimuli is about 214 million assignments; 21 values and 8 stimuli is about 38 billion."
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "beafb449",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:29.989234Z",
     "iopub.status.busy": "2026-10-15T15:58:29.989117Z",
     "iopub.status.idle": "2026-10-15T15:58:30.536795Z",
     "shell.execute_reply": "2026-10-15T15:58:30.535962Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu: 11^7 = 19,487,171 assignments, 437,179 patterns, 0.5 s\n",
      "cuda: 11^7 = 19,487,171 assignments, 437,179 patterns, 0.0 s\n"
     ]
    }
   ],
   "source": [
    "devices = [\"cpu\"] + ([\"cuda\"] if torch.cuda.is_available() else [])\n",
    "for device in devices:\n",
    "    start = time.perf_counter()\n",
    "    n = clobe.brutes(scale, 7, (0.1, -0.1), return_matrices=False, device=device).n\n",
    "    print(f\"{device}: {scale.numel()}^7 = {scale.numel() ** 7:,} assignments, {n:,} patterns, \"\n",
    "          f\"{time.perf_counter() - start:.1f} s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6af15db3",
   "metadata": {},
   "source": "Two arguments help with large problems:\n\n- `return_matrices=False` returns only the count. For 8 stimuli, the matrices alone would take several gigabytes of memory.\n- `chunk_size` sets how many assignments are evaluated at once; lower it if the device runs out of memory.\n\nAnd `orderings()` constructs the patterns directly, so its cost depends on the number of patterns rather than on $m^n$. Use it when every pattern can be measured."
  },
  {
   "cell_type": "markdown",
   "id": "58061534",
   "metadata": {},
   "source": "## Appendix A: where the semiorder count comes from\n\nThe count of labelled semiorders is the result of Chandon, Lemaire and Pouget (1978), in the form of an exponential generating function given by Stanley (1999, Problem 6.30) and OEIS A006531. This appendix shows how it follows from the two steps of the main text.\n\n**1. Shapes.** Sorted by value, an unlabelled semiorder of $n$ items is fixed by its reach sequence, and there are $C_n$ of those (Wine & Freund, 1957). Write $C(y) = \\sum_n C_n y^n$.\n\n**2. Twins.** Two items are *twins* if they relate in the same way to every other item. Merging every set of twins into one item leaves a *twin-free* semiorder, whose items have strictly different values. Let $r_j$ be the number of unlabelled twin-free semiorders of $j$ items, and $R(z) = \\sum_j r_j z^j$. Every unlabelled semiorder is a twin-free one with each item replaced by a run of $k \\ge 1$ twins, which contributes $y + y^2 + \\dots = y / (1 - y)$ for each item, so\n\n$$\nC(y) = R\\left(\\frac{y}{1 - y}\\right), \\qquad R(z) = C\\left(\\frac{z}{1 + z}\\right).\n$$\n\n**3. Labels.** A labelled semiorder of $n$ stimuli is a split of the stimuli into $j$ groups of twins ($S(n, j)$ ways), a twin-free semiorder of $j$ items ($r_j$ ways), and an assignment of the groups to its items ($j!$ ways, as its items are all distinguishable):\n\n$$\ns(n) = \\sum_{j=0}^{n} S(n, j) \\, j! \\, r_j .\n$$\n\nSince $\\sum_n S(n, j) \\, x^n / n! = (e^x - 1)^j / j!$, the exponential generating function is\n\n$$\n\\sum_n s(n) \\frac{x^n}{n!} = R(e^x - 1) = C\\left(\\frac{e^x - 1}{e^x}\\right) = C(1 - e^{-x}).\n$$\n\n**4. Coefficients.** Expanding $C(1 - e^{-x}) = \\sum_j C_j (1 - e^{-x})^j$ and using $(1 - e^{-x})^j = (-1)^j j! \\sum_n S(n, j) (-1)^n x^n / n!$, the coefficient of $x^n / n!$ is\n\n$$\ns(n) = \\sum_{j=0}^{n} (-1)^{n-j} \\, C_j \\, j! \\, S(n, j),\n$$\n\nthe formula of the main text, which `semiorders()` evaluates in exact integer arithmetic. Weak orderings need no step 2: a weak ordering is a ranking of tied groups, so $\\sum_j j! \\, S(n, j)$ counts them directly (OEIS A000670), as `fubini()` does.\n\nThe counts agree with the published sequences:"
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bf5fc8d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-15T15:58:30.538817Z",
     "iopub.status.busy": "2026-10-15T15:58:30.538605Z",
     "iopub.status.idle": "2026-10-15T15:58:30.562268Z",
     "shell.execute_reply": "2026-10-15T15:58:30.561855Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'the counts match OEIS A006531 and A000670'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oeis_semiorders = [1, 1, 3, 19, 183, 2371, 38703, 763099, 17648823, 468603091]  # OEIS A006531\n",
    "oeis_weak = [1, 1, 3, 13, 75, 541, 4683, 47293, 545835, 7087261]  # OEIS A000670\n",
    "assert [clobe.semiorders(n) for n in range(10)] == oeis_semiorders\n",
    "assert [clobe.fubini(n) for n in range(10)] == oeis_weak\n",
    "assert [clobe.orderings(n).n for n in range(1, 7)] == oeis_semiorders[1:7]\n",
    "\"the counts match OEIS A006531 and A000670\""
   ]
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   "source": "## Appendix B: sources\n\n| Result | Source | Checked against |\n|---|---|---|\n| Recurrence of the Stirling numbers of the second kind | Graham, Knuth & Patashnik (1994, chap. 6) | OEIS A008277, which states the same recurrence |\n| Weak orderings are counted by $\\sum_j j! \\, S(n, j)$, the Fubini numbers | OEIS A000670 | the sequence and its formula on OEIS; the counts above |\n| Unlabelled semiorders are counted by the Catalan numbers | Wine & Freund (1957) | its Crossref record (doi:10.1214/aoms/1177707051), and the attribution in Hu (2014) |\n| Labelled semiorders are counted by the coefficients of $C(1 - e^{-x})$ | Chandon, Lemaire & Pouget (1978); Stanley (1999, Problem 6.30) | the Numdam record of Chandon et al.; OEIS A006531, which lists both and gives the formula; the counts above |\n| Measurement scales that cannot realise every semiorder | the R package clobe (`vignette(\"universal-set\")`) | `brutes()` in this notebook |\n\n## References\n\nChandon, J. L., Lemaire, J., & Pouget, J. (1978). Dénombrement des quasi-ordres sur un ensemble fini. *Mathématiques et Sciences Humaines, 62*, 61-80. http://www.numdam.org/item/MSH_1978__62__61_0/\n\nDome, L., & Wills, A. J. (2025). g-Distance: On the comparison of model and human heterogeneity. *Psychological Review, 132*(3), 632-655. https://doi.org/10.1037/rev0000550\n\nFishburn, P. C. (1985). *Interval orders and interval graphs: A study of partially ordered sets*. Wiley.\n\nGraham, R. L., Knuth, D. E., & Patashnik, O. (1994). *Concrete mathematics: A foundation for computer science* (2nd ed.). Addison-Wesley.\n\nHu, Y. (2014). On the number of fixed-length semiorders. *Journal of Integer Sequences, 17*, Article 14.1.6. https://cs.uwaterloo.ca/journals/JIS/VOL17/Hu/hu3.html\n\nOEIS Foundation Inc. (2026). The On-Line Encyclopedia of Integer Sequences: A000670 (Fubini numbers), A006531 (semiorders on n elements), A008277 (Stirling numbers of the second kind). https://oeis.org\n\nStanley, R. P. (1999). *Enumerative combinatorics* (Vol. 2). Cambridge University Press.\n\nWine, R. L., & Freund, J. E. (1957). On the enumeration of decision patterns involving n means. *The Annals of Mathematical Statistics, 28*(1), 256-259. https://doi.org/10.1214/aoms/1177707051"
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