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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Small Hamming Weight Denominations for CoinJoins\n",
+        "\n",
+        "Standardizing on a small set of denominations has advantages, among other use cases, for privacy in CoinJoin transactions. Given an arbitrary amount, if instead of creating a single output this amount is approximated from below by a sum of standard denominations each such output will be less linkable to the input cluster which funded it.\n",
+        "\n",
+        "The fewer denominations there are to choose from, the more likely it is that any one denomination will be chosen by more than one user, which improves privacy. But this comes at a cost of requiring more outputs to represent a given balance.\n",
+        "\n",
+        "Too few denominations, and the approximations become too crude and lossy. This notebook examines the gap sizes of the sums of combinations up to size $k$ of elements from a set of values with Hamming weight 1. In order to approximate values ranging up to $n$ sats, the size of the set of denominations smaller than $n$ scales as $O(\\log n )$.\n",
+        "\n",
+        "## Sub-transaction model and implications for privacy\n",
+        "\n",
+        "In the sub-transaction model ([1](https://m.flrn.cc/academic/2017_CoinJoin.pdf), [2](https://medium.com/@laurentmt/introducing-boltzmann-85930984a159)), for every repeated value with multiplicity $k$ in the inputs or the outputs of a sub-transaction mapping as a whole, $k!$ equivalent mappings can be obtained by permution. Clearly this does not change the sum of any sub-transaction, and therefore preserves condition.\n",
+        "\n",
+        "Within a sub-transaction in the mapping permutations have no effect on sub-transaction assignment. The product of the factorials of those must be divided out of the equivalent mappings estimate.\n",
+        "\n",
+        "In base 2, a value with multiplicity 2 within a single sub-transaction is are exchangeable with the next denomination up from some other sub-transaction. Base 3 and 10 have somewhat more complex arithmetic relationships which imply yet more permutations of subsets of coins.\n",
+        "\n",
+        "If an arbitrary output value (i.e. with large Hamming weight) can be constructed -- or more generally than in Maurer et al, approximated -- by summing together $k$ distinct denominations, and each of which has multiplicity $m$ in the outputs of the sub-transaction mapping as a whole, then a number number of equivalent mappings is $k \\times m!$. This counts permutations over sets of subsets of outputs, specifically when a subset of size $k$ is exchanged with a subset of size $1$.\n",
+        "\n",
+        "Although this preserves the nominal sum of the sub-transaction its effective cost and therefore inferred fee will differ. A high variance in sub-transaction is less likely, so unlike 1:1 exchanges, k:1 exchanges are not perfectly symmetric. Users who attempt to recover every last sat are more susceptible to such analysis.\n",
+        "\n",
+        "More generally, sets which generate dense subset sum instances place fewer constraints on sub-transaction mapping enumeration. A dense subset sum instance with non-trivial multiplicities implies that for every input or output, the number of distinct sub-transactions it appears in across all non-derived sub-transaction mappings is large. This is because for any random subset of inputs or outputs it may co-occur with, subset sum is likely still be dense after removing this random subset (due to the non trivial multiplicities).\n",
+        "\n",
+        "## Radix CoinJoin\n",
+        "\n",
+        "A logarithmic (in the range of values) number of small Hamming weight values with non-trivial multiplicity, taken from a geometric progression can therefore provide cover for any number of arbitrary valued inputs and outputs. Larger Hamming weight values with multiplicity 1, can of course contribute to subset sum density, but a geometric progression with high multiplicity is much more robust and trivial to analyze.\n",
+        "\n",
+        "Ignoring any hypothetical UTXO sharing, the anonymity set size of a CoinJoin transaction is constrained by the number of inputs or the number outputs (the smaller of the two). Even if the number estimated number of distinct sub-transactions in which a coin appears is very large, this only means that the sub-transaction mappings will not be the limiting constraint on anonymity set size for this coin. Therefore even loose lower bounds on this value are sufficient in practice.\n",
+        "\n",
+        "## Mixed bases and preferred values\n",
+        "\n",
+        "If using only a single base, when a small fee cost is subtracted from a single denomination this will result in a remaining balance with a large Hamming weight in that base. To get around this to some extent, values from multiple bases can be used. Additionally, not all Hamming weight 1 values are required, a [preferred value series](https://en.wikipedia.org/wiki/Preferred_number) can approximate values well with a smaller number of denominations.\n",
+        "\n",
+        "Currency conversions often results in excessively precise bitcoin amounts. In some base $b$, a value $v$ will have expected weight $\\frac{(b-1) \\log_b v}{b}$. Values which have a small weight in one base usually have a large weight in another, in particular bases 2 and 3. The result of subtracting a value with weight 1 in base 3 from a value with a large weight in base 2 can often have a weight that is smaller by more than 1 (but it may also be higher).\n",
+        "\n",
+        "Concretely, the 1-2-5 series in base 10 is useful for representing values with a decimal bias. Base 2 and 3 are suitable for decomposing arbitrary values with a high decimal Hamming weight. They are also complementary to each other, the 1-2 series in base 3 has a high hamming weight in base 2 and powers of 2 have a high hamming weight in base 3. These additional values also improve the overall density, reducing the sizes of combinations required for good approximations. Beyond this, additional denominations provide diminishing returns.\n",
+        "\n",
+        "As we will see in detail, within any practical range, most values can usualy be approximated reasonably well even with only 3 outputs. The vast majority can be closely approximated with 4. Practically all values are addressible with 5, and with combinations of size 6, gaps larger than the dust threshold (for 1 sat/vbyte) vanish entirely.\n",
+        "\n",
+        "This python implementation is optimized for simplicity, not performance or memory use, and the nature of this problem lends itself to precomputation. In an optimized implementation it's possible to exhaustively enumerate all possibilities even as part of an online computation. This is worth noting because for privacy choosing uniformly at random between an unbiased selection of acceptable choices is better than always picking the best choice according to the cost function. A black box optimization approach may introduce such a bias depending on the cost function."
+      ],
+      "metadata": {
+        "id": "AVfGSV7Mw6ed"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Parameters\n",
+        "\n",
+        "The minimal feerate can be controlled in order to vary the dust threshold."
+      ],
+      "metadata": {
+        "id": "Jc--XxUuZtdl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title bitcoin network parameters\n",
+        "max_sats = 2099999997690000\n",
+        "dust_feerate = 3.0\n",
+        "expected_feerate = 10.0\n",
+        "min_vbytes = 98 # 294 sats @ 3 sat/vb for p2wpkh per https://bitcoin.stackexchange.com/a/41082\n",
+        "dust = int(min_vbytes * dust_feerate)"
+      ],
+      "metadata": {
+        "id": "XVuJ0fzfZkpw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We will generate denominations from the dust threshold to some maximum value. Setting a large `max_denom` smooths the gap distribution and emphasizes the gaps between the larger magnitude values, which tend to have smaller relative differences. `max_combination_value` is set because sumsets are sparse at the upper extremity, since the largest sums involve repetitions of the largest denominations."
+      ],
+      "metadata": {
+        "id": "0ZHm5ZFOZzLC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title denomination parameters\n",
+        "min_denom = dust\n",
+        "max_denom = int(1e8) # reducing this reveals some interesting structure in the plots below\n",
+        "max_combination_size = 6 # 7 is slow but doable, and consumes lots of ram for larger values of max_denom with this naive implementation. results are not very interesting, median gap size is 1 sat.\n",
+        "max_combination_value = max_denom * 2"
+      ],
+      "metadata": {
+        "id": "QC-xsypy1QWh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The final parameters set a cutoff for the smallest gaps. The smallest possible gap is of size 1. Setting a higher cutoff omits negligible gaps (e.g. below `dust`) improving readability of the individual histograms with regards to larger gaps. Low values aid in comparing between sumset degrees."
+      ],
+      "metadata": {
+        "id": "qNRqZishaH2I"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title analysis parameters\n",
+        "min_diff = 2 # setting to dust hides small gaps, focusing on more significant ones\n",
+        "min_diff_ratio = 0 # 1e-4 is a basis point"
+      ],
+      "metadata": {
+        "id": "bnD-gLR5Zo5I"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate denominations"
+      ],
+      "metadata": {
+        "id": "O9TyP4-pnTbq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title import boilerplate\n",
+        "from math import log, ceil\n",
+        "\n",
+        "from itertools import combinations_with_replacement, product\n",
+        "\n",
+        "import pandas as pd\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import numpy as np"
+      ],
+      "metadata": {
+        "id": "xDlB6YbryO-2",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Denominations are based on [preferred value series](https://en.wikipedia.org/wiki/Preferred_number) in base 2, 3, and 10."
+      ],
+      "metadata": {
+        "id": "QA2SfyYTcVMG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TbSBw69PJbat"
+      },
+      "outputs": [],
+      "source": [
+        "# @title helper functions\n",
+        "def powers(b: int) -> set[int]:\n",
+        "  \"\"\"\n",
+        "  Generates powers of the given base within the min/max denomination range\n",
+        "  \"\"\"\n",
+        "  return { b**i for i in range(int(ceil(log(min_denom)/log(b))), int(log(max_denom)/log(b))+1) }\n",
+        "\n",
+        "def multiples(vs: set[int], cs: set[int]) -> set[int]:\n",
+        "  \"\"\"\n",
+        "  Computes the products of pairs of values from two sets.\n",
+        "  \"\"\"\n",
+        "  return { c*v for (c,v) in product(cs,vs) if c*v in range(min_denom, max_denom+1) }"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title denominations of different bases\n",
+        "binary  = multiples(powers(2),  {1})\n",
+        "ternary = multiples(powers(3),  {1,2})\n",
+        "decimal = multiples(powers(10), {1,2,5})"
+      ],
+      "metadata": {
+        "id": "ZA_70YeCKTVP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# these aditional Hamming weight 2 values plug the proportionally largest gaps\n",
+        "# between the denominations this has some impact on the decomposition of smaller\n",
+        "#  values (max_denom <= 1e6 or so) and so seem appropriate for a future with a\n",
+        "# lower dust feerate, but marginal benefit today\n",
+        "\n",
+        "#binary = binary | multiples({3}, { 2**i for i in [ 10, 13, 15, 18 ]})\n",
+        "\n",
+        "# the addition of 3 * 2^n arguably over-represents base 2 values\n",
+        "# https://en.wikipedia.org/wiki/Preferred_number#Computer_engineering\n",
+        "\n",
+        "#binary  = multiples(powers(2), {1,3})"
+      ],
+      "metadata": {
+        "id": "8OrEjHZjLuWX"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The combined set of denominations is just a union of these Hamming weight 1 values in these 3 useful bases."
+      ],
+      "metadata": {
+        "id": "aMwxprZTLLcx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title combined denomination set\n",
+        "combined = binary | ternary | decimal"
+      ],
+      "metadata": {
+        "id": "lpnr1fajvulN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title denomination values\n",
+        "palette = {'binary': 'tab:red', 'ternary': 'tab:orange', 'decimal': 'tab:green', 'combined': 'tab:blue'}\n",
+        "origin = { v: 'binary' for v in binary } | { v: 'ternary' for v in ternary } | { v: 'decimal' for v in decimal }\n",
+        "\n",
+        "combined_index = {value: index for index, value in enumerate(sorted(combined))}\n",
+        "\n",
+        "plt.figure(figsize=(8,3))\n",
+        "sns.scatterplot(data=pd.DataFrame([{'value': v, 'index': combined_index[v], 'set': origin[v]} for v in sorted(combined) ]), x='index', y='value', hue='set', palette=palette)\n",
+        "\n",
+        "plt.yscale('log')\n",
+        "plt.ylabel('sats')\n",
+        "plt.title('Denomination values')\n",
+        "plt.grid(True, which='both', alpha=0.5)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 333
+        },
+        "id": "1syuD194yYeb",
+        "outputId": "1f65cbcf-fe68-4e4d-8375-b99b757df366",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x300 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title denomination set sizes\n",
+        "data = {\n",
+        "    'set': ['binary', 'ternary', 'decimal', 'combined'],\n",
+        "    'size': [len(binary), len(ternary), len(decimal), len(combined)],\n",
+        "    'color': [palette['binary'], palette['ternary'], palette['decimal'], palette['combined']]\n",
+        "}\n",
+        "df_sets = pd.DataFrame(data)\n",
+        "\n",
+        "plt.figure(figsize=(8, 2))\n",
+        "ax = sns.barplot(x='set', y='size', hue='set', data=df_sets, palette=palette, legend=False)\n",
+        "plt.xlabel('Base')\n",
+        "plt.ylabel('Number of denominations')\n",
+        "plt.title(f'Sizes of denomination base sets for values ≤ {max_denom/1e8} BTC')\n",
+        "\n",
+        "for container in ax.containers:\n",
+        "    ax.bar_label(container, fmt='%.0f', label_type='center')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 256
+        },
+        "cellView": "form",
+        "id": "BG6NcZ3dM9hg",
+        "outputId": "dadd0287-816e-45d7-b8dc-5ac169732b40"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x200 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title denominations table\n",
+        "from google.colab import data_table\n",
+        "data_table.enable_dataframe_formatter()\n",
+        "\n",
+        "def hamming_weight(n, base):\n",
+        "    \"\"\"Calculates the Hamming weight of n in a given base.\"\"\"\n",
+        "    if n == 0:\n",
+        "        return 0\n",
+        "    weight = 0\n",
+        "    while n > 0:\n",
+        "        weight += n % base != 0\n",
+        "        n //= base\n",
+        "    return weight\n",
+        "\n",
+        "pd.DataFrame([{\n",
+        "      'set': origin[x],\n",
+        "      'value': \"{0:.8f}\".format(x/1e8),\n",
+        "      'binary (little endian)': \"{0:b}\".format(x)[::-1],\n",
+        "      'popcount': bin(x).count('1'),\n",
+        "      'weight (base 3)': hamming_weight(x, 3),\n",
+        "      'weight (base 10)': hamming_weight(x, 10)\n",
+        "  } for x in sorted(combined) ])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 669
+        },
+        "id": "p1oXz8IlnZhp",
+        "outputId": "28201543-bd60-461e-c6e4-78764d6fd30c",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        set       value       binary (little endian)  popcount  \\\n",
+              "0    binary  0.00000512                   0000000001         1   \n",
+              "1   ternary  0.00000729                   1001101101         6   \n",
+              "2   decimal  0.00001000                   0001011111         6   \n",
+              "3    binary  0.00001024                  00000000001         1   \n",
+              "4   ternary  0.00001458                  01001101101         6   \n",
+              "5   decimal  0.00002000                  00001011111         6   \n",
+              "6    binary  0.00002048                 000000000001         1   \n",
+              "7   ternary  0.00002187                 110100010001         5   \n",
+              "8    binary  0.00004096                0000000000001         1   \n",
+              "9   ternary  0.00004374                0110100010001         5   \n",
+              "10  decimal  0.00005000                0001000111001         5   \n",
+              "11  ternary  0.00006561                1000010110011         6   \n",
+              "12   binary  0.00008192               00000000000001         1   \n",
+              "13  decimal  0.00010000               00001000111001         5   \n",
+              "14  ternary  0.00013122               01000010110011         6   \n",
+              "15   binary  0.00016384              000000000000001         1   \n",
+              "16  ternary  0.00019683              110001110011001         8   \n",
+              "17  decimal  0.00020000              000001000111001         5   \n",
+              "18   binary  0.00032768             0000000000000001         1   \n",
+              "19  ternary  0.00039366             0110001110011001         8   \n",
+              "20  decimal  0.00050000             0000101011000011         6   \n",
+              "21  ternary  0.00059049             1001010101100111         9   \n",
+              "22   binary  0.00065536            00000000000000001         1   \n",
+              "23  decimal  0.00100000            00000101011000011         6   \n",
+              "24  ternary  0.00118098            01001010101100111         9   \n",
+              "25   binary  0.00131072           000000000000000001         1   \n",
+              "26  ternary  0.00177147           110111111100110101        13   \n",
+              "27  decimal  0.00200000           000000101011000011         6   \n",
+              "28   binary  0.00262144          0000000000000000001         1   \n",
+              "29  ternary  0.00354294          0110111111100110101        13   \n",
+              "30  decimal  0.00500000          0000010010000101111         7   \n",
+              "31   binary  0.00524288         00000000000000000001         1   \n",
+              "32  ternary  0.00531441         10001111110110000001        10   \n",
+              "33  decimal  0.01000000         00000010010000101111         7   \n",
+              "34   binary  0.01048576        000000000000000000001         1   \n",
+              "35  ternary  0.01062882        010001111110110000001        10   \n",
+              "36  ternary  0.01594323        110010111100101000011        11   \n",
+              "37  decimal  0.02000000        000000010010000101111         7   \n",
+              "38   binary  0.02097152       0000000000000000000001         1   \n",
+              "39  ternary  0.03188646       0110010111100101000011        11   \n",
+              "40   binary  0.04194304      00000000000000000000001         1   \n",
+              "41  ternary  0.04782969      10011110110111110001001        14   \n",
+              "42  decimal  0.05000000      00000010110100100011001         8   \n",
+              "43   binary  0.08388608     000000000000000000000001         1   \n",
+              "44  ternary  0.09565938     010011110110111110001001        14   \n",
+              "45  decimal  0.10000000     000000010110100100011001         8   \n",
+              "46  ternary  0.14348907     110101100100111101011011        15   \n",
+              "47   binary  0.16777216    0000000000000000000000001         1   \n",
+              "48  decimal  0.20000000    0000000010110100100011001         8   \n",
+              "49  ternary  0.28697814    0110101100100111101011011        15   \n",
+              "50   binary  0.33554432   00000000000000000000000001         1   \n",
+              "51  ternary  0.43046721   10000010111010110000100101        11   \n",
+              "52  decimal  0.50000000   00000001000011110101111101        12   \n",
+              "53   binary  0.67108864  000000000000000000000000001         1   \n",
+              "54  ternary  0.86093442  010000010111010110000100101        11   \n",
+              "55  decimal  1.00000000  000000001000011110101111101        12   \n",
+              "\n",
+              "    weight (base 3)  weight (base 10)  \n",
+              "0                 4                 3  \n",
+              "1                 1                 3  \n",
+              "2                 4                 1  \n",
+              "3                 6                 3  \n",
+              "4                 1                 4  \n",
+              "5                 4                 1  \n",
+              "6                 6                 3  \n",
+              "7                 1                 4  \n",
+              "8                 7                 3  \n",
+              "9                 1                 4  \n",
+              "10                6                 1  \n",
+              "11                1                 4  \n",
+              "12                5                 4  \n",
+              "13                7                 1  \n",
+              "14                1                 5  \n",
+              "15                8                 5  \n",
+              "16                1                 5  \n",
+              "17                5                 1  \n",
+              "18               10                 5  \n",
+              "19                1                 5  \n",
+              "20                9                 1  \n",
+              "21                1                 4  \n",
+              "22                7                 5  \n",
+              "23                7                 1  \n",
+              "24                1                 5  \n",
+              "25                9                 5  \n",
+              "26                1                 6  \n",
+              "27                7                 1  \n",
+              "28                9                 6  \n",
+              "29                1                 6  \n",
+              "30               11                 1  \n",
+              "31                9                 6  \n",
+              "32                1                 6  \n",
+              "33                9                 1  \n",
+              "34               10                 6  \n",
+              "35                1                 6  \n",
+              "36                1                 7  \n",
+              "37               10                 1  \n",
+              "38               11                 6  \n",
+              "39                1                 7  \n",
+              "40               11                 6  \n",
+              "41                1                 7  \n",
+              "42                7                 1  \n",
+              "43                8                 6  \n",
+              "44                1                 7  \n",
+              "45                9                 1  \n",
+              "46                1                 7  \n",
+              "47                9                 8  \n",
+              "48               12                 1  \n",
+              "49                1                 8  \n",
+              "50                7                 8  \n",
+              "51                1                 7  \n",
+              "52               11                 1  \n",
+              "53               12                 7  \n",
+              "54                1                 7  \n",
+              "55               13                 1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7eb84c00-58a4-4d1e-9b4d-0994e59f0bd0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>set</th>\n",
+              "      <th>value</th>\n",
+              "      <th>binary (little endian)</th>\n",
+              "      <th>popcount</th>\n",
+              "      <th>weight (base 3)</th>\n",
+              "      <th>weight (base 10)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.00000512</td>\n",
+              "      <td>0000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.00000729</td>\n",
+              "      <td>1001101101</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.00001000</td>\n",
+              "      <td>0001011111</td>\n",
+              "      <td>6</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.00001024</td>\n",
+              "      <td>00000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.00001458</td>\n",
+              "      <td>01001101101</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.00002000</td>\n",
+              "      <td>00001011111</td>\n",
+              "      <td>6</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.00002048</td>\n",
+              "      <td>000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.00002187</td>\n",
+              "      <td>110100010001</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.00004096</td>\n",
+              "      <td>0000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.00004374</td>\n",
+              "      <td>0110100010001</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.00005000</td>\n",
+              "      <td>0001000111001</td>\n",
+              "      <td>5</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.00006561</td>\n",
+              "      <td>1000010110011</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.00008192</td>\n",
+              "      <td>00000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.00010000</td>\n",
+              "      <td>00001000111001</td>\n",
+              "      <td>5</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.00013122</td>\n",
+              "      <td>01000010110011</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.00016384</td>\n",
+              "      <td>000000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.00019683</td>\n",
+              "      <td>110001110011001</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.00020000</td>\n",
+              "      <td>000001000111001</td>\n",
+              "      <td>5</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.00032768</td>\n",
+              "      <td>0000000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.00039366</td>\n",
+              "      <td>0110001110011001</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.00050000</td>\n",
+              "      <td>0000101011000011</td>\n",
+              "      <td>6</td>\n",
+              "      <td>9</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.00059049</td>\n",
+              "      <td>1001010101100111</td>\n",
+              "      <td>9</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.00065536</td>\n",
+              "      <td>00000000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.00100000</td>\n",
+              "      <td>00000101011000011</td>\n",
+              "      <td>6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.00118098</td>\n",
+              "      <td>01001010101100111</td>\n",
+              "      <td>9</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.00131072</td>\n",
+              "      <td>000000000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>9</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.00177147</td>\n",
+              "      <td>110111111100110101</td>\n",
+              "      <td>13</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.00200000</td>\n",
+              "      <td>000000101011000011</td>\n",
+              "      <td>6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.00262144</td>\n",
+              "      <td>0000000000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>9</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.00354294</td>\n",
+              "      <td>0110111111100110101</td>\n",
+              "      <td>13</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.00500000</td>\n",
+              "      <td>0000010010000101111</td>\n",
+              "      <td>7</td>\n",
+              "      <td>11</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.00524288</td>\n",
+              "      <td>00000000000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>9</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.00531441</td>\n",
+              "      <td>10001111110110000001</td>\n",
+              "      <td>10</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>33</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.01000000</td>\n",
+              "      <td>00000010010000101111</td>\n",
+              "      <td>7</td>\n",
+              "      <td>9</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.01048576</td>\n",
+              "      <td>000000000000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.01062882</td>\n",
+              "      <td>010001111110110000001</td>\n",
+              "      <td>10</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.01594323</td>\n",
+              "      <td>110010111100101000011</td>\n",
+              "      <td>11</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>37</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.02000000</td>\n",
+              "      <td>000000010010000101111</td>\n",
+              "      <td>7</td>\n",
+              "      <td>10</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>38</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.02097152</td>\n",
+              "      <td>0000000000000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>11</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.03188646</td>\n",
+              "      <td>0110010111100101000011</td>\n",
+              "      <td>11</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>40</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.04194304</td>\n",
+              "      <td>00000000000000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>11</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>41</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.04782969</td>\n",
+              "      <td>10011110110111110001001</td>\n",
+              "      <td>14</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>42</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.05000000</td>\n",
+              "      <td>00000010110100100011001</td>\n",
+              "      <td>8</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>43</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.08388608</td>\n",
+              "      <td>000000000000000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>8</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.09565938</td>\n",
+              "      <td>010011110110111110001001</td>\n",
+              "      <td>14</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>45</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.10000000</td>\n",
+              "      <td>000000010110100100011001</td>\n",
+              "      <td>8</td>\n",
+              "      <td>9</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>46</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.14348907</td>\n",
+              "      <td>110101100100111101011011</td>\n",
+              "      <td>15</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>47</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.16777216</td>\n",
+              "      <td>0000000000000000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>9</td>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>48</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.20000000</td>\n",
+              "      <td>0000000010110100100011001</td>\n",
+              "      <td>8</td>\n",
+              "      <td>12</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.28697814</td>\n",
+              "      <td>0110101100100111101011011</td>\n",
+              "      <td>15</td>\n",
+              "      <td>1</td>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.33554432</td>\n",
+              "      <td>00000000000000000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>51</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.43046721</td>\n",
+              "      <td>10000010111010110000100101</td>\n",
+              "      <td>11</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>52</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>0.50000000</td>\n",
+              "      <td>00000001000011110101111101</td>\n",
+              "      <td>12</td>\n",
+              "      <td>11</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>53</th>\n",
+              "      <td>binary</td>\n",
+              "      <td>0.67108864</td>\n",
+              "      <td>000000000000000000000000001</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>54</th>\n",
+              "      <td>ternary</td>\n",
+              "      <td>0.86093442</td>\n",
+              "      <td>010000010111010110000100101</td>\n",
+              "      <td>11</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>55</th>\n",
+              "      <td>decimal</td>\n",
+              "      <td>1.00000000</td>\n",
+              "      <td>000000001000011110101111101</td>\n",
+              "      <td>12</td>\n",
+              "      <td>13</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7eb84c00-58a4-4d1e-9b4d-0994e59f0bd0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7eb84c00-58a4-4d1e-9b4d-0994e59f0bd0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7eb84c00-58a4-4d1e-9b4d-0994e59f0bd0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-10df01da-685a-4ea2-ae2a-401f244da08c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-10df01da-685a-4ea2-ae2a-401f244da08c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-10df01da-685a-4ea2-ae2a-401f244da08c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
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+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate sumsets\n",
+        "\n",
+        "`{0}` is unioned with the denominations in order to obtain the sumsets from combinations of size $\\leq k$ as opposed to $=k$."
+      ],
+      "metadata": {
+        "id": "9GmIGoKZajjd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title compute sumsets naively\n",
+        "\n",
+        "sumsets = []\n",
+        "for k in range(1, max_combination_size+1):\n",
+        "  subsets = combinations_with_replacement({0} | combined, k)\n",
+        "  sumset = { s for s in map(sum, subsets) if s in range(1,max_combination_value+1) }\n",
+        "  sumsets.append(np.array(sorted(sumset)))"
+      ],
+      "metadata": {
+        "id": "W2F-Uml1PplS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title (fail to) compute sumsets more efficiently\n",
+        "import numpy as np\n",
+        "from numpy.fft import fft, ifft\n",
+        "from math import log2\n",
+        "\n",
+        "def sumset_k_fft(nums, k):\n",
+        "  \"\"\"\n",
+        "  Compute sumset via FFT, same as above but supposedly faster\n",
+        "  \"\"\"\n",
+        "  nums = np.array(sorted(nums))\n",
+        "  max_sum = np.max(nums) * k\n",
+        "  poly = np.zeros(2**ceil(log2(max_sum)))\n",
+        "  poly[nums] = 1\n",
+        "  F = fft(poly)\n",
+        "  Fk = F ** k\n",
+        "  result = np.rint(ifft(Fk).real).astype(int)\n",
+        "  sums = np.where(result > 0)[0]\n",
+        "  sums = sums[(sums >= min_denom) & (sums <= max_combination_value)]\n",
+        "  return sums\n",
+        "\n",
+        "# this is actually slower, so don't do it\n",
+        "#sumsets = [sumset_k_fft({0} | combined, k) for k in range(1, max_combination_size+1)]"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "k1BmOBIGJ28r"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title compute gaps naively\n",
+        "# gaps = [np.array([ d for d in (b - a for a,b in zip(s, s[1:])) if min_diff <= d ]) for s in sumsets]"
+      ],
+      "metadata": {
+        "id": "QwdjhVjav5aU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title compute gaps efficiently\n",
+        "gaps = []\n",
+        "for s in sumsets:\n",
+        "  diffs = np.diff(s)\n",
+        "  gaps.append(diffs[diffs >= min_diff])"
+      ],
+      "metadata": {
+        "id": "2fSksykoJPzg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "For the relative gap size, omit gaps below the dust threshold. These gaps are neglibible and unrecoverable (since an additional outputs has a cost), and are therefore noise. To to improve clarity of the plots they are filtered out of the relative differences."
+      ],
+      "metadata": {
+        "id": "qYl5Qqgosaeq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title compute normalized gaps naively\n",
+        "#relative_gaps = [np.array([ p for d, p in ((b-a, (b-a)/b) for a,b in zip(s, s[1:])) if dust <= d and min_diff_ratio <= p ]) for s in sumsets]"
+      ],
+      "metadata": {
+        "id": "lDKMabw7v8Bs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title compute normalized gaps efficiently\n",
+        "\n",
+        "relative_gaps = []\n",
+        "for s in sumsets:\n",
+        "    diffs = np.diff(s)\n",
+        "    relative_diffs = diffs / s[1:]\n",
+        "    mask = (diffs >= dust) & (relative_diffs >= min_diff_ratio)\n",
+        "    relative_gaps.append(relative_diffs[mask])"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "JDXeC1IUvqHx"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Plots"
+      ],
+      "metadata": {
+        "id": "ulHMFsxrhgV0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title dataframe boilerplate\n",
+        "\n",
+        "all_gaps_data = np.concatenate(gaps)\n",
+        "all_gaps_k_values = np.concatenate([np.full(len(g_arr), i + 1) for i, g_arr in enumerate(gaps)])\n",
+        "df_gaps = pd.DataFrame({'d': all_gaps_data, 'k': all_gaps_k_values})\n",
+        "\n",
+        "all_rel_gaps_data = np.concatenate(relative_gaps)\n",
+        "all_rel_gaps_k_values = np.concatenate([np.full(len(rg_arr), i + 1) for i, rg_arr in enumerate(relative_gaps)])\n",
+        "df_rel_gaps = pd.DataFrame({'r': all_rel_gaps_data, 'k': all_rel_gaps_k_values})"
+      ],
+      "metadata": {
+        "id": "MlK-T-Nkqo3d",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Gaps"
+      ],
+      "metadata": {
+        "id": "_TqArs_lOTp8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Count and sizes\n",
+        "\n",
+        "Here we compare the gaps (ignoring gaps of size 1) of the sumsets of different degrees. As larger combinations are permitted the number of distinct sums grows and with them the number of gaps between these sums. Because these values are all in a fixed range, the sizes of the more numerous gaps of sumsets of higher degrees are of course smaller.\n",
+        "\n",
+        "Note that for $k \\in \\{1, 6\\}$ the scatterplots are a little misleading. For $k=1$ there aren't that many distinct values, so even though these values are smoothly distributed the quantile values aren't. For $k=6$ several quantiles are all entirely gaps of size 1, then several for 2 and 3, the spacings these in the scatter plot are a visual artifact arising from plotting integer values."
+      ],
+      "metadata": {
+        "id": "RpUP7EWnPTh_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title\n",
+        "gap_sizes = list(map(len, gaps))\n",
+        "gap_sizes_larger_than_dust = list(map(lambda x: max(len(x),1), [ [x for x in s if dust <= x] for s in gaps ]))\n",
+        "ks = list(range(1, max_combination_size + 1))\n",
+        "\n",
+        "plt.figure(figsize=(8, 4))\n",
+        "fig, ax1 = plt.subplots(figsize=(8, 4))\n",
+        "\n",
+        "ax1.set_xlabel('Combination Size (k)')\n",
+        "ax1.tick_params(axis='y')\n",
+        "ax1.set_xticks(ks)\n",
+        "ax1.grid(axis='y', alpha=0.5)\n",
+        "ax1.set_yscale(\"log\")\n",
+        "\n",
+        "ax1.plot(ks, gap_sizes, label=\"# gaps\")\n",
+        "ax1.plot(ks, gap_sizes_larger_than_dust, label=f\"# gaps ≥ dust ({dust})\")\n",
+        "\n",
+        "mean_gap_sizes = df_gaps.groupby('k')['d'].mean()\n",
+        "median_gap_sizes = df_gaps.groupby('k')['d'].median()\n",
+        "quantiles = df_gaps.groupby('k')['d'].quantile([i/20 for i in range(1, 20)])\n",
+        "\n",
+        "ax1.plot(ks, mean_gap_sizes.values, label='mean gap size')\n",
+        "\n",
+        "ax1.scatter(ks, median_gap_sizes.values, color='black', marker='x', label='median gap size')\n",
+        "\n",
+        "for i in range(1, 20):\n",
+        "    if i == 5: # skip median\n",
+        "      continue\n",
+        "    quantile_values = quantiles.loc[(slice(None), i/20)].values\n",
+        "    ax1.scatter(ks, quantile_values, marker='.', color='black', alpha=0.2, label=f'i×5-th percentile' if i == 1 else None)\n",
+        "\n",
+        "fig.suptitle('Gaps by Combination Size', y=1.05)\n",
+        "fig.legend(loc=\"lower left\", bbox_to_anchor=(1,1), bbox_transform=ax1.transAxes)\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 521
+        },
+        "id": "VqHJpf05hOxI",
+        "outputId": "11241de8-a5d2-4bea-de55-884d850d7f78",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Gap sizes distribution\n",
+        "\n",
+        "The next two plots show the gaps in more detail, both their sizes and their sizes in relation to the next larger value. When decomposing into low Hamming weight values, the total balance being decomposed may fall in one of these gaps. The distribution of their sizes bounds the loss from the decomposition. Here too small integer values create visual artifacts, the empty bins for $k>3 are empty because the populated bins interspersing them correspond to successive integer values."
+      ],
+      "metadata": {
+        "id": "ziqJgAo40jxL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "bins = 300"
+      ],
+      "metadata": {
+        "id": "BWIYY82T6kM2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "e812467a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 974
+        },
+        "outputId": "a709c2aa-2b21-40ba-e3ac-55863b92cda6",
+        "cellView": "form"
+      },
+      "source": [
+        "# @title\n",
+        "min_gap = df_gaps['d'].min()\n",
+        "gap_bins = np.logspace(np.log10(min_gap), np.log10(df_gaps['d'].max()), bins)\n",
+        "g_abs_hist_binned = sns.FacetGrid(df_gaps, col='k', col_wrap=1, height=1.7, aspect=6)\n",
+        "g_abs_hist_binned.map(plt.hist, 'd', bins=gap_bins)\n",
+        "g_abs_hist_binned.set(xscale=\"log\", yscale=\"log\")\n",
+        "g_abs_hist_binned.fig.suptitle(f'Gap sizes ≥ {min_gap} sats', y=1.05)\n",
+        "for i, ax in enumerate(g_abs_hist_binned.axes.flat):\n",
+        "  k = i+1\n",
+        "  ax.grid(True, which='both', alpha=0.5)\n",
+        "  if min_diff < dust:\n",
+        "    ax.axvline(x=dust, color='black', linestyle='--', label=f'dust ({dust} sats = {min_vbytes} vB × {dust_feerate} sat/vB)')\n",
+        "  ax.axvline(x=k*dust, color='orange', linestyle='--', label='k × dust')\n",
+        "  ax.axvline(x=k*(expected_feerate * min_vbytes), color='red', linestyle='--', label=f'k × {min_vbytes} vB × {expected_feerate} sat/vB')\n",
+        "  if i == 0:\n",
+        "    ax.legend()\n",
+        "    ax.secondary_xaxis('top').spines['top'].set_visible(False)\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1020x1020 with 6 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Relative gap size\n",
+        "\n",
+        "The next plot displays gaps larger than the dust threshold as a fraction of the larger value. No quantization artifacts arise in this plot, but there is a cutoff imposed by dividing by values no larger than the maximum combination value."
+      ],
+      "metadata": {
+        "id": "_LwNFWgWO_1j"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title\n",
+        "ratio_bins = np.logspace(np.log10(df_rel_gaps['r'].min()), np.log10(df_rel_gaps['r'].max()), bins)\n",
+        "g_ratio_hist_binned = sns.FacetGrid(df_rel_gaps, col='k', col_wrap=1, height=1.5, aspect=7)\n",
+        "g_ratio_hist_binned.map(plt.hist, 'r', bins=ratio_bins)\n",
+        "g_ratio_hist_binned.set(xscale=\"log\", yscale=\"log\")\n",
+        "g_ratio_hist_binned.fig.suptitle(f'Relative gap sizes (b-a)/b ≥ {min_diff_ratio} for gaps (a,b) where (b-a) ≥ {dust}', y=1.05)\n",
+        "for i, ax in enumerate(g_ratio_hist_binned.axes.flat):\n",
+        "  ax.grid(True, which='both', alpha=0.5)\n",
+        "  ax.axvline(x=1e-4, color='black', linestyle='--', label='1‱')\n",
+        "  ax.axvline(x=1e-2, color='red', linestyle='--', label='1%')\n",
+        "  if i == 0:\n",
+        "    ax.legend()\n",
+        "    ax.secondary_xaxis('top').spines['top'].set_visible(False)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 859
+        },
+        "id": "c6XLNw5Ywu5f",
+        "outputId": "c7f9c97b-82be-4bdb-df98-a56b9e6b1c7b",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1050x900 with 6 Axes>"
+            ],
+            "image/png": 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jzp0715BkvP/++55lkgy73W5s2rSpVttrrrnGOO6444zS0tJayy+77DIjPT3dZ+4fPnzYsNlsxh//+Mc66wJ9To915MgRo7Kystayn3/+2cjMzDTGjRtXp72/r0m1ffv2GQsXLjR+97vfGW3atDEkGS1btjQuu+wy49lnn20wvvoUFRUZiYmJxu9+9zvPMpfLZbRo0cI466yzarUtLS01mjdvXuc98+OPPxqpqanGY489ZhjG/+a5Tz75JKiYADRtnEoAWMzQoUPVpk0bZWVlafTo0WrevLlee+01dejQQdLRQxVXr16tSy+9VAcOHFBpaalKS0v1008/adiwYSoqKvJ5F4Oav2Q4nU6VlpbqtNNOk2EY+uyzz4KKecyYMSopKal1W8TFixfL7XZrzJgxYYl7+fLlatasmcaPH+9ZZrfbNWHCBJ/7eOjQIZWWluo3v/mNJHk9hPnYPiZNmiRJPm+LVX1EwFtvvRX0+d1r1qzR1KlTNWnSJP3ud7+TFNjz1KJFC23atElFRUUBjVt9WH/No1BqOvXUU9W3b1/P444dO+q3v/2t3nrrLblcLp99B/rct2zZUgcPHqz1HH7xxRf65ptvvP76e91119X65fyGG25QXFyc37cw69ixo2644Qa98cYb+umnn/TGG29oxIgRevbZZzVy5Ei1atVKVVVVPvs4ePCgEhIS6iyvPhe+5mk5/lq5cqWqqqp0yy231LoC//jx45WWlqalS5fWap+QkKCrr766wX5/+OEH/fe//9Xvf//7WnefGDx4sHr37l2nfc3X7+eff9b+/fs1cOBAr6+dP3nSokULrV27Vj/88EODsdb0008/KS4uzusdMwKJsT41b8lZ/Qt6VVVVnavMV79HSktLffY3cOBAFRcXa8uWLZKOHhkwaNAgDRw4UO+++66ko0cRGIZR54iBlJQUXXXVVZ7H8fHxys3N1ddff+1ZtmjRIp100knq0aOHZ04oLS3VmWeeKUl1DrsfPHiwevbs6XlsGIZeeuklnX/++TIMo1Yfw4YN0/79+30+f3v37pVhGPXOGZL/z+mxHA6H57B7t9utvXv36siRI+rXr1+9c4bU8GtSLT09XZdeeqmeeeYZ7d69Wx9++KFuvPFGffTRR/rd736nzMxMr6fC+VJRUaFLLrlESUlJta4bYLfb9Yc//EGrVq3S1KlTVVRUpPXr1+vSSy/1zCs154fq6xFce+21AY0PwJooDAAWU1BQoBUrVmjx4sU699xzVVpaWutLyLZt22QYhv7f//t/atOmTa3/qs9drr64mTfffPONxo4dq4yMDKWkpKhNmzaeUwBqnhsfiOHDhys9PV0LFy70LFu4cKH69Omj7t27hyXuXbt26bjjjvMcslmta9euddru3btXN998szIzM5WUlKQ2bdrohBNOqHcfu3XrVutxly5dZLfbfd5e6oQTTtDkyZP15JNPqnXr1ho2bJgKCgr8fg6/++47jRkzRqeffroeeOABz/JAnqe77rpL+/btU/fu3dW7d2/dfvvt+vzzz/0aX1K95+ge+3xIUvfu3VVRUaE9e/aoqqpKu3fvrvVf9RfBQJ/76hhqngawdOlSZWZmql+/fg3GlpKSouOOO67BW4Ed69ChQ3rnnXe0fPlyvfXWWzpw4IASEhI0aNCgBm+Nl5SUVOe87uo+q9cHqvoc6BNPPLHW8vj4eHXu3LnOOdLHH398rXOYG+rX2/vE27I33nhDv/nNb5SYmKiMjAy1adNGjz76qF/vG6l2nkjSrFmz9MUXXygrK0u5ubmaMWNGrS+8wQgkRm/sdrs6d+5cJ25JdfLIW356U/1l/91335XT6dRnn32mgQMHatCgQZ7CwLvvvqu0tDTl5OTU2rZDhw51+m/ZsmWta2cUFRVp06ZNdeaE6riPnTur33PV9uzZo3379unxxx+v00d1gcnX/Hvs83Esf57TPXv21JozysvLPW3nz5+vU045xXOtlDZt2mjp0qV+zxn+MAxD69ev97znd+3aJZvNpl//+td1rh/gi8vl0mWXXaYvv/xSixcvVvv27Wutv+uuu3TNNddo1qxZ6t69u/r166e4uDhdc801kuQpdn300Ud69tln9c9//jPit+MEEJu4xgBgMbm5uZ4vRBdeeKHOOOMMXXHFFdqyZYtSUlLkdrslSbfddpuGDRvmtQ9vH/ilox9ozj77bO3du1d33HGHevTooebNm+v777/X2LFjPX0HKiEhQRdeeKGWLFmiRx55RMXFxXr//fd19913e9qEEnegLr30Un3wwQe6/fbb1adPH8/zNnz4cL/20d8PnPfff7/Gjh2rV199Vf/5z3900003ea5TUH2EhzdVVVUaPXq0EhIS9OKLL9a6w0Mgz9OgQYO0fft2z/hPPvmk/vnPf2ru3Lk+f4Fq1aqVpKO/tvqKsz4ffPCBhgwZUmvZjh07lJ2dHfBz//PPPys5ObnWl+k333xTw4cPD/iDf0O+/fZbvfnmm1q6dKlWrVqliooKHX/88Z5rDQwdOlTNmzdvsJ/jjjvO69EtP/74oyTV+aLQGIIpPjTk3Xff1QUXXKBBgwbpkUce0XHHHadmzZrp6aef1vPPPx9Un5deeqkGDhyoJUuW6D//+Y/uvfde/eMf/9DLL79c58KSNbVq1UpHjhzRgQMHlJqa2qgx+lL95byhL47t27fXCSecoMLCQmVnZ8swDJ166qlq06aNbr75Zu3atUvvvvuuTjvttDpfAus7173ml3C3263evXvXKiLWdOw1LY7Nj+r33lVXXeX1uiySPNe68CYjI0M2m82vC33Wp3///rUKXNOnT9eMGTP03HPPaezYsbrwwgt1++23q23btnI4HLrnnnvqXHRX8v81kY4WI//zn/9o6dKlWrZsmUpKSpSamqqzzz5bN9xwg0aOHOnz+hzejB8/Xm+88YYWLFjgOWKjpvj4eD355JP629/+pq1btyozM1Pdu3fXFVdcIbvd7pm///SnP2ngwIE64YQTPMWT6qMgfvzxR33zzTfq2LFjQLEBaNooDAAWVv3haMiQIZozZ46mTJni+VWmWbNmPq8q781///tfbd26VfPnz9fvf/97z3JvV7EP9EvZmDFjNH/+fK1atUpfffWVDMPwnEYgKaS4JalTp05as2aNKioqah01sG3btlrtfv75Z61atUozZ86sdRspX4fbFxUV1fqFbdu2bXK73XWu2O5N79691bt3b02bNk0ffPCBTj/9dM2dO1d//etf693mpptu0oYNG1RYWFjnQ2mgz1NGRoauvvpqXX311SovL9egQYM0Y8YMn4WBHj16SDr6Zd7b4eTenqutW7cqOTlZbdq0UUJCQp2cadeuXVDP/Y4dO3TSSSd5Hu/bt08ffPBBrcOSj42tZlGivLxcP/74o84999x6x5COHsFy2WWXyW63Kzc3V1OnTtXIkSP1q1/9yud23vTp00dr1qxRWVlZrQsQrl271rM+UJ06dZIkbdmypdYvr1VVVdqxY0dQ75ma/R77PvG27KWXXlJiYqLeeuutWkcpHXuf9moN5Um14447TjfeeKNuvPFGlZSU6Ne//rX+9re/+SwM1MzRml9YA43RG7fbra+//trzi3Z13JLqvOd37NghSbVytD4DBw5UYWGhTjjhBPXp00epqanKyclRenq6li9frk8//VQzZ870O86aunTpoo0bN+qss84KqmDWpk0bpaamyuVyBZVLcXFx6tKli+f5OJY/z+mCBQtqHUZfneeLFy9W586d9fLLL9fat+ojpI7l72vy3Xff6YQTTtCRI0fUrVs3XXHFFRo5cqQGDx4c9C1kb7/9dj399NOaPXu2Lr/8cp9tMzMzPfO7y+XS22+/rQEDBniOGPjmm2+0a9euOkd3SNIFF1yg9PT0kO+gAqBp4dgiwOLy8vKUm5ur2bNn69ChQ2rbtq3y8vL02GOPeX6hrKn6EF5vqn+ZqvlLlGEYevDBB+u0rf7l1N8PJkOHDlVGRoYWLlyohQsXKjc3t9YHnlDilqRhw4bp8OHDeuKJJzzL3G6357aK1bzto3T0ys/1ObaPhx9+WJJ8fnEpKyvTkSNHai3r3bu37Ha718PMqz399NN67LHHVFBQ4LmKfU2BPE/H3gIwJSVFXbt29Tm+dPSq3vHx8Vq3bp3X9R9++GGtc3u//fZbvfrqqzrnnHPkcDjUsmVLDR06tNZ/iYmJQT33n376qU477TTP4//85z+SpHPOOcdr+8cff1yHDx/2PH700Ud15MgRn6+VdPTQ6vnz53vOMZ42bVpQRQFJGj16tFwulx5//HHPssrKSj399NMaMGBAvXck8GXo0KGKj4/XQw89VOv5+9e//qX9+/f7dbV9b9q3b69evXrpmWeeqXXo9jvvvKP//ve/tdo6HA7ZbLZa15HYuXNnnSveV2soT1wuV51Dwdu2bav27ds3mKOnnnqqJNXJ0UBjrM+cOXM8fxuGoTlz5qhZs2Y666yzarVbv3690tPT/bpzxcCBA7Vz504tXLjQc2qB3W7XaaedpgceeECHDx+uc30Bf1166aX6/vvva81/1Q4ePCin0+lze4fDoYsvvlgvvfRSrdvHVmto/pWOvib1zRlSw8/p6aefXmvOqC4MeJs31q5dqw8//NDrOP6+JklJSZo1a5a2bt2qrVu36p///KeGDh0adFHg3nvv1X333ac///nPuvnmmwPa9r777tOPP/6oP/7xj55ljz/+uJYsWVLrv+rr29x3331asGBBUHECaLo4YgCAbr/9dl1yySWaN2+err/+ehUUFOiMM85Q7969NX78eHXu3FnFxcX68MMP9d1332njxo1e++nRo4e6dOmi2267Td9//73S0tL00ksveT08tPqiYjfddJOGDRsmh8Ohyy67rN4YmzVrplGjRumFF16Q0+nUfffdV6dNsHFLR0+ryM3N1R//+Edt27ZNPXr00Guvvaa9e/dK+t8RDmlpaRo0aJBmzZqlw4cP6/jjj9d//vOfen/pko7+AnXBBRdo+PDh+vDDD/Xcc8/piiuuqHMucE2rV6/WxIkTdckll6h79+46cuSInn32Wc8HcG9KS0t14403qmfPnkpISNBzzz1Xa/1FF12k5s2b+/089ezZU3l5eerbt68yMjK0bt06z63hfElMTNQ555yjlStXer2VWK9evTRs2LBat6GT1OCvnYE+9+vXr9fevXv129/+1rNs6dKlOuOMM+q93WNVVZXOOussXXrppdqyZYseeeQRnXHGGbrgggt8xpaenq6Kigq99NJLPttJRw8V9nUrswEDBuiSSy7R1KlTVVJSoq5du2r+/PnauXOn/vWvfzXYvzdt2rTR1KlTNXPmTA0fPlwXXHCBZ//69+9f6+J0gbr77rv129/+Vqeffrquvvpq/fzzz5ozZ4569epVq1gwcuRIPfDAAxo+fLiuuOIKlZSUqKCgQF27dvV67YqG8uTAgQPq0KGDRo8erZycHKWkpGjlypX65JNPdP/99/uMuXPnzurVq5dWrlypcePGBRVj9e0f16xZo7y8PM/yxMRELV++XPn5+RowYICWLVumpUuX6s9//nOtIx2ko0dTnX/++X79Sl/9pX/Lli21TqMaNGiQli1bpoSEBPXv37/Bfrz53e9+pxdffFHXX3+91qxZo9NPP10ul0ubN2/Wiy++qLfeesvrNTlq+vvf/641a9ZowIABGj9+vHr27Km9e/fq008/1cqVKz1zaX1++9vf6tlnn9XWrVtrHRkgBfacHuu8887Tyy+/rIsuukgjR47Ujh07NHfuXPXs2bNWflbz9zWJj49XUlKSVq1apVWrVvlsO3LkSJ8FvSVLluhPf/qTunXrppNOOqnO3H322Wd7jg547rnn9NJLL2nQoEGenH/xxRd17bXX1vq3wVvxs7oQP3jw4AZfTwAWFLH7HwAwla/bFLlcLqNLly5Gly5dPLcE3L59u/H73//eaNeundGsWTPj+OOPN8477zxj8eLFnu283a7wyy+/NIYOHWqkpKQYrVu3NsaPH29s3Lixzi3/jhw5YkyaNMlo06aNYbPZat26UMfcrrDaihUrDEmGzWardXvFmvyJuz579uwxrrjiCiM1NdVIT083xo4da7z//vuGJOOFF17wtPvuu++Miy66yGjRooWRnp5uXHLJJcYPP/xQJ+7q28Z9+eWXxujRo43U1FSjZcuWxsSJE42DBw/6jOXrr782xo0bZ3Tp0sVITEw0MjIyjCFDhhgrV66s1a7m7Qp37Njhuc2bt/927NgR0PP017/+1cjNzTVatGhhJCUlGT169DD+9re/1bqdX31efvllw2azGd98802t5ZKMCRMmGM8995zRrVs3IyEhwfjVr35VK4d88fe5NwzDuOOOO4yOHTsabrfbMIyjtzhr27atMWvWrDr9Vr8/3nnnHeO6664zWrZsaaSkpBhXXnml8dNPPzUYV/X2/vzX0GtvGIZx8OBB47bbbjPatWtnJCQkGP379zeWL1/u13Pk7XaF1ebMmWP06NHDaNasmZGZmWnccMMNxs8//1yrzeDBg42TTz7Zr7GqvfDCC0aPHj2MhIQEo1evXsZrr71mXHzxxUaPHj1qtfvXv/7led179OhhPP300554a/InTyorK43bb7/dyMnJMVJTU43mzZsbOTk5xiOPPOJXzA888ICRkpJS5zZ6/sb4xz/+0bDZbMZXX33lWZafn280b97c2L59u3HOOecYycnJRmZmpjF9+vRat4k0DMP46quvPLeQ9Vfbtm0NSUZxcbFn2XvvvWdIMgYOHFinfX2vZX5+fp1bSVZVVRn/+Mc/jJNPPtlISEgwWrZsafTt29eYOXOmsX//fk+76tfGm+LiYmPChAlGVlaW0axZM6Ndu3bGWWedZTz++OMN7ltlZaXRunVr4//+7//qxOrvc+qN2+027r77bqNTp06ePHrjjTe8PgeBvCYNzbc1/1u2bJnPvqrzq77/aub92rVrjUGDBhktW7Y0EhMTjZycHGPu3Lmeec4XblcIwBebYdRzCVgAgF555RVddNFFeu+993T66acHtG31L4p79uwJ6KrUTYHL5VLPnj116aWX6v/+7/8iPn5lZaWys7M1ZcoUz2G5H3/8sQYMGKBNmzbVutUaGkefPn3Upk0br9cYaYjNZtOECRNqHT4ebvv371fnzp01a9YszxXdA5Gbm6tOnTpp0aJFQY1/yy23qLCwUOvXrw/7hTBj1f/93//p6aefVlFRkc+jahoLrwkAK+MaAwDwi2PvD+9yufTwww8rLS1Nv/71r02KKjY5HA7dddddKigo8Hq4bmN7+umn1axZM11//fW1lt99990UBcLs8OHDda6H8fbbb2vjxo21DrGPNunp6frTn/6ke++9N+A7ppSVlWnjxo1eT5Xxx08//aQnn3xSf/3rX/kCWsOtt96q8vJyvfDCCxEfm9cEgNVxxAAA/OLaa6/VwYMHdeqpp6qyslIvv/yyPvjgA919992aOnVqwP1Z+YgBWMfOnTs1dOhQXXXVVWrfvr02b96suXPnKj09XV988YXn9pWBiMQRAwAA4H+4+CAA/OLMM8/U/fffrzfeeEOHDh1S165d9fDDDzd4sT3Aylq2bKm+ffvqySef1J49e9S8eXONHDlSf//734MqCgAAgMjjiAEAAAAAACyMawwAAAAAAGBhFAYAAAAAALAwCgMAAAAAAFgYhQEAAAAAACyMwgAAAAAAABZGYQAAAAAAAAujMAAAAAAAgIVRGAAAAAAAwMIoDAAAAAAAYGEUBgAAAAAAsDAKAwAAAAAAWBiFAQAAAAAALIzCAAAAAAAAFkZhAAAAi8vLy9Mtt9xidhgAAMAkFAYAAEDE3XTTTerbt68SEhLUp08fs8MBAMDSKAwAAABTjBs3TmPGjDE7DAAALI/CAAAAqGXp0qVKT0/XggULGm2Mhx56SBMmTFDnzp0bbQwAAOAfCgMAAMDj+eef1+WXX64FCxboyiuvrLddSkqKz/+uv/76CEYNAABCEWd2AAAAIDoUFBToL3/5i15//XUNHjzYZ9sNGzb4XJ+WlhbGyAAAQGOiMAAAALR48WKVlJTo/fffV//+/Rts37Vr1whEBQAAIoFTCQAAgH71q1+pTZs2euqpp2QYRoPtOZUAAICmgyMGAACAunTpovvvv195eXlyOByaM2eOz/acSgAAQNNBYQAAAEiSunfvrjVr1igvL09xcXGaPXt2vW1DPZVg27ZtKi8v1+7du3Xw4EFPoaFnz56Kj48PqW8AABAYCgMAAMDjxBNP1OrVqz1HDtx///2NMs61116rd955x/P4V7/6lSRpx44dys7ObpQxAQCAdzbDnxMJAQAAAABAk8TFBwEAAAAAsDAKAwAAAAAAWBiFAQAAAAAALIzCAAAAAAAAFkZhAAAAAAAAC6MwAAAAAACAhVEYAAAAAADAwigMAAAAAABgYRQGAAAAAACwMAoDDdixY4eGDBminj17qnfv3nI6nWaHBAvKzs7WKaecoj59+mjIkCFmhwOLqqioUKdOnXTbbbeZHQosZt++ferXr5/69OmjXr166YknnjA7JFjMt99+q7y8PPXs2VOnnHKKFi1aZHZIsKCLLrpILVu21OjRo80OBU2QzTAMw+wgotngwYP117/+VQMHDtTevXuVlpamuLg4s8OCxWRnZ+uLL75QSkqK2aHAwv7yl79o27ZtysrK0n333Wd2OLAQl8ulyspKJScny+l0qlevXlq3bp1atWpldmiwiB9//FHFxcXq06ePdu/erb59+2rr1q1q3ry52aHBQt5++20dOHBA8+fP1+LFi80OB00MRwz4sGnTJjVr1kwDBw6UJGVkZFAUAGBJRUVF2rx5s0aMGGF2KLAgh8Oh5ORkSVJlZaUMwxC/ayCSjjvuOPXp00eS1K5dO7Vu3Vp79+41NyhYTl5enlJTU80OA01UTBcGCgsLdf7556t9+/ay2Wx65ZVX6rQpKChQdna2EhMTNWDAAH388cd+919UVKSUlBSdf/75+vWvf6277747jNGjqWjsPJQkm82mwYMHq3///lqwYEGYIkdTEYkcvO2223TPPfeEKWI0NZHIwX379iknJ0cdOnTQ7bffrtatW4cpejQFkcjBauvXr5fL5VJWVlaIUaMpiWQOAo0hpgsDTqdTOTk5Kigo8Lp+4cKFmjx5sqZPn65PP/1UOTk5GjZsmEpKSjxtqs9XPPa/H374QUeOHNG7776rRx55RB9++KFWrFihFStWRGr3ECMaOw8l6b333tP69ev12muv6e6779bnn38ekX1DbGjsHHz11VfVvXt3de/ePVK7hBgTiXmwRYsW2rhxo3bs2KHnn39excXFEdk3xIZI5KAk7d27V7///e/1+OOPN/o+IbZEKgeBRmM0EZKMJUuW1FqWm5trTJgwwfPY5XIZ7du3N+655x6/+vzggw+Mc845x/N41qxZxqxZs8ISL5qmxsjDY912223G008/HUKUaMoaIwenTJlidOjQwejUqZPRqlUrIy0tzZg5c2Y4w0YTEol58IYbbjAWLVoUSphowhorBw8dOmQMHDjQeOaZZ8IVKpqoxpwH16xZY1x88cXhCBOoJaaPGPClqqpK69ev19ChQz3L7Ha7hg4dqg8//NCvPvr376+SkhL9/PPPcrvdKiws1EknndRYIaMJCkceOp1OHThwQJJUXl6u1atX6+STT26UeNH0hCMH77nnHn377bfauXOn7rvvPo0fP1533nlnY4WMJiYcOVhcXOyZB/fv36/CwkKdeOKJjRIvmp5w5KBhGBo7dqzOPPNM/e53v2usUNFEhSMHgcbWZK+kV1paKpfLpczMzFrLMzMztXnzZr/6iIuL0913361BgwbJMAydc845Ou+88xojXDRR4cjD4uJiXXTRRZKOXpl7/Pjx6t+/f9hjRdMUjhwEQhGOHNy1a5euu+46z0UHJ02apN69ezdGuGiCwpGD77//vhYuXKhTTjnFc+74s88+Sx7CL+H6t3jo0KHauHGjnE6nOnTooEWLFunUU08Nd7iwqCZbGAiXESNGcBVumKpz587auHGj2WEAkqSxY8eaHQIsKDc3Vxs2bDA7DFjYGWecIbfbbXYYsLiVK1eaHQKasCZ7KkHr1q3lcDjqXJyouLhY7dq1MykqWA15CLORgzAbOQizkYMwGzmIWNBkCwPx8fHq27evVq1a5Vnmdru1atUqDrlBxJCHMBs5CLORgzAbOQizkYOIBTF9KkF5ebm2bdvmebxjxw5t2LBBGRkZ6tixoyZPnqz8/Hz169dPubm5mj17tpxOp66++moTo0ZTQx7CbOQgzEYOwmzkIMxGDiLmmXtThNCsWbPGkFTnv/z8fE+bhx9+2OjYsaMRHx9v5ObmGh999JF5AaNJIg9hNnIQZiMHYTZyEGYjBxHrbIZhGJEoQAAAAAAAgOjTZK8xAAAAAAAAGkZhAAAAAAAAC6MwAAAAAACAhVEYAAAAAADAwigMAAAAAABgYRQGAAAAAACwMAoDAAAAAABYmCULA5WVlZoxY4YqKyvNDgUWRQ7CbOQgogF5CLORgzAbOYhoYTMMwzA7iEgrKytTenq69u/fr7S0NLPDgQWRgzAbOYhoQB7CbOQgzEYOIlrE9BEDBQUFIa2PdZHev3CPF2p/wWzv7zbhakcORvd45GDsIwcbLwf9bUsOxnYOhtonOWg+M/YvmuZCctB85GD056BfjBh20kknBbV+//79hiRj//79jRFWxDS0/9E+Xqj9BbO9v9uEqx05GN3jkYPkoNnjRXMO+ts22Bw0jKaRh7Geg6H2SQ6aL9I52BhjkoPkoNljNvUc9EfMHTFgGIbKyspkWO8MCAAAAAAAwi7mCgMHDhxQenq69u3bp6lTp8rlcnlt53K56l3frFkzPfroo2rWrFljh9tofO1fLIwXan/BbO/vNuFqRw5G93jkIDlo9njRnIP+tg0lB6XYz8NYz8FQ+yQHzRfpHGyMMclBctDsMZt6Dvor5goDNfXv3z+o9fHx8crLy1N8fHxjhBUxDe1/tI8Xan/BbO/vNuFqRw5G93jkIDlo9njRnIP+tg02B6WmkYexnoOh9kkOmi/SOdgYY5KD5KDZYzb1HPRHTBcGAACBMwxDTqdTFRUVnJYFwLoMQ3I6ZauoOPo3AFhYnNkBAAAiq6KiQunp6ZLE7ZEAWFdFhRzp6TpRkmv/fom5EICFccQAAAAAAAAWRmEAAAAAAAAL41QCAAAAAEDEGYahI0eOBHxVf7fbrUOHDsnhcIQcQyj9BbNtINs01NbhcMhmswUUc30oDAAAAAAAIqqqqko//vijKioqAtquupiwa9eusHwpDqW/YLYNZBt/2iYlJcntdgcUtzcUBgAAAAAAEeN2u7Vjxw45HA61b99e8fHxAX2xrqysVEJCQtgKA8H2F8y2gWzjq61hGKqqqtKePXtUVVUlt9sd0hEUFAYAAAAAABFT/UU2KytLycnJAW1bfavlxMTEsBUGgu0vmG0D2aahtklJSYqLi9P27dt1+PBhNWvWLJDwa6EwAAAW43A4dPHFF6u8vDws5+YBQExyOGRcfLEOlJerOXMhYAq7nWvhh6r6OawuIgSLwgAAWExiYqIWLlyooqIiJSYmmh0OAJgjMVHuhQv1Q1GRujEXArC4iJdo9u3bp379+qlPnz7q1auXnnjiiUiHAAAAAAAAfhHxIwZSU1NVWFio5ORkOZ1O9erVS6NGjVKrVq0iHQoAAAAAAJYX8SMGHA6H5wITlZWVMgwj5PMhAAD+czqdiouL00knnSSn02l2OABgDqdTjrg49TjpJIm5EICfCgsLdf7556t9+/ay2+167bXXaq2/77771LZtW2VmZur++++vtW7t2rXq16+fjhw5EsmQ/RJwYaDmE2Gz2fTKK6/UaVNQUKDs7GwlJiZqwIAB+vjjj2ut37dvn3JyctShQwfdfvvtat26ddA7AAAAAABAJDidTuXk5KigoKDOus8//1x33nmnXnjhBT3//POaNm2a/vvf/0qSjhw5ouuvv16PPvqo4uKi71J/AUdU/USMGzdOo0aNqrN+4cKFmjx5subOnasBAwZo9uzZGjZsmLZs2aK2bdtKklq0aKGNGzequLhYo0aN0ujRo5WZmel1vMrKSlVWVnoel5WVSZJcLpfcbrdcLpfX7Xytb2jbWBDpfQj3eKH2F8z2/m4TrnbkYHSPZ+UcrPnY5XLFbB6Sg42Xg/62DWUeDDSeaBTrORhqnzGfgy6XHDXaKQbz0Iz3UDTNhTGfgwHGE42Cjd/lcnmOHD/26HFfRzM6HA4lJCRIOnoVfl9t7Xa7kpKSGuy3+mh2f49iHz58uIYPH15rWfW2X331lU455RQNGTJEknTKKafoq6++Uq9evTRr1iwNHDhQ/fr18xw535DqNr7aVq+r7zOdv3egshkhHMdvs9m0ZMkSXXjhhZ5lAwYMUP/+/TVnzhxJ8tyfctKkSZoyZUqdPm688UadeeaZGj16tNcxZsyYoZkzZ9ZZvnbtWlVVVSkjI8PrbS7cbrf27t3rdb2vdbEi0vsQ7vFC7S+Y7f3dJlztyMHoHs/KOVhRUaG+fftKkj755BOlpKT4FX+0IQcbLwf9bRvKPBjsPkSTWM/BUPuM9Ry0VVToxF/mwq8++US2GJwLzXgPRdNcGOs5GOw+RJNg43e73Tpy5Ig6duzo+aJfrfqLujfDhg3TkiVLdOTIEcXFxal169aqqKjw2nbgwIF66623PI87duyo0tLSOu0qKio8/QUqOTlZzz//vOf78ObNm3XWWWdp7dq1crvd+s1vfqPVq1crPj5eF154od5//32lpqYGNF5DbSsrK7Vjxw41a9bMa7sePXr4NU5Yj2GoqqrS+vXrNXXqVM8yu92uoUOH6sMPP5QkFRcXKzk5Wampqdq/f78KCwt1ww031Nvn1KlTNXnyZM/jsrIyZWVlqXPnziopKVHXrl29VkFcLpe2bdvmdb2vdbEi0vsQ7vFC7S+Y7f3dJlztyMHoHs/KOVizYt6lSxelpaX5FX+0IQcbLwf9bRvKPBjsPkSTWM/BUPuM+Rw8Zi50xOBcaMZ7KJrmwpjPwSD3IZoEG/+hQ4e0a9cuJSQkBHTr5JpHDBxbUDiW3W73q++a/dlsNr9jqTlO9bZ9+vTR3/72N51//vkyDEN33323+vTpo7PPPluzZs3SO++8o5kzZ8rhcOjBBx/U4MGD9eOPP2r8+PHavHmzhg8frn/+859q1qyZHnvsMT300ENKSEjQww8/rNNPP93r+IZhyOFwqGPHjmrevHnA8VcLa2GgtLRULperzmkBmZmZ2rx5syRp165duu666zyHjUyaNEm9e/eut8+EhASvL7rD4ZDdbpfD4ag3CX2tb2jbWBDpfQj3eKH2F8z2/m4TrnbkYHSPZ9UcPPZvctC88aI5B/1tG8o8GGg80SjWczDUPmM6B5vIXGjGeyia5sKYzsEg4olGwcTvcDhks9k8/9VUXl7e4HbS0aPXS0pKfMZVs++dO3d6bVezv2AKA8due8MNN9T64Xv+/PlKTU3VaaedphNPPFEff/yxvv76a11xxRXasWOHbrrpJv3617/WkiVLdM455+jxxx/X4MGDNX36dH322Wdav369xowZox07dig+Pr7e+EPNoYhf9SA3N1cbNmyI9LAAAAAAgCjX0K/eNc+ED+QX8vraNuYd8kpLSzVz5kwVFhZq7dq16t69u7p166asrCwdPnxYW7Zs0erVqzVz5kzFx8frkksu0erVq+V2uzVo0CAdd9xxGjp0qKqqqrRlyxafP6iHKqyFgdatW8vhcKi4uLjW8uLiYrVr1y6cQwEAguRwODRixAg5nc6Y/XUCAELmcMj4ZS5MYi4E0AhuvfVW3XrrrerQoYM++eQTHT582LPuyJEjcrvdcrvdnuVVVVV1lhmGocOHD8vtdjdqrGG9wkV8fLz69u2rVatWeZa53W6tWrVKp556ajiHAgAEKTExUa+//roee+yxgM7rA4AmJTFR7tdf13ePPSYxFwLwU3l5uTZs2OA5Cn7Xrl3asGGDvvnmm1rtVqxYoa1bt2rChAmSpP79+2vz5s1atmyZ/vWvf8nhcOjEE0/UGWecoYceekhfffWV5s2bpzPOOENnnHGGVq9erffee0+PP/64mjVrphNPPLFR9yvgIwbKy8u1bds2z+MdO3Zow4YNysjIUMeOHTV58mTl5+erX79+ys3N1ezZs+V0OnX11VeHNXAAAAAAACJp3bp1ntsRStIdd9yhO+64Q/n5+Zo3b54k6eDBg5o4caIWLlzouVtDhw4d9PDDD2vcuHGKj4/XvHnzlJSUpIceekhXXXWVBgwYoPPOO0833nijkpKSNHXqVI0aNUopKSl65plnGv3HnIALA8c+EdV3DKh+IsaMGaM9e/bozjvv1O7du9WnTx8tX768zgUJAQAAAACIJXl5eZ7rEhiGoUOHDikxMbHWhQuTkpK0ZcuWOttee+21uuaaazzbSFLnzp31wQcf1Gk7depUTZkypVbbxhRwYaDmE1GfiRMnauLEiUEHBQBoPE6nU23btpVhGNq9e3fM3q4QAELidMretq26G4aM3bsl5kIAFhbxuxIAAMxXUVFhdggAYDpbRYVsklxmBwIAJgvrxQcBAAAAAEBsoTAAAAAAAICFURgAAAAAAMDCKAwAAAAAAGBhFAYAAAAAALAw7koAABZjt9s1aNAgHTx4UHY79WEAFmW3y/hlLkxgLgRgcRQGAMBikpKStHr1ahUVFSkpKcnscADAHElJcq9erW+KitSNuRCAxVEeBQAAAADAwigMAAAAAADgh8LCQp1//vlq37697Ha7XnvttVrr77vvPrVt21Zt27bV/fffX2vd2rVr1a9fPx05ciSSIfuFUwkAwGKcTqeys7Plcrm0c+dOpaWlmR0SAESe0yl7dra6ulzSzp0ScyEAPzidTuXk5GjcuHE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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Sums and combinations"
+      ],
+      "metadata": {
+        "id": "CkakPy1EO6H0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Density of distinct sums\n",
+        "\n",
+        "The next plot shows the density of distinct sums in the integers. The previous plots show the gap sizes over the entire range, but not all balances decompose as readily, and towards the end of the range the density tapers off (see `max_combination_value` parameter). The initial ramp up is determined by the size of the smallest denomination (see also `dust_feerate` parameter)\n",
+        "\n",
+        "This plot gives a clearer indication of the expected loss as a function of the balance being decomposed into a sum of standard values. The distinct constructible sums are assigned into bins (ranges of natural numbers). To plot each bin, this number of distinct values is divided by the size of the bin (number of distinct values, or difference between smallest and largest values).\n",
+        "\n",
+        "A density of 1 implies every possible integer value in the bin can be constructed exactly, and a density $\\geq\\mathrm{dust}^{-1}$ means every value can be approximated from below with error smaller than the dust threshold.\n",
+        "\n",
+        "More intuitively, anything above the dashed line means values can be well approximated often enough that the loss is on average negligible (the cost of an additional output will be higher even at the minimum relay fee). The difference in costs will be linear in $k$, smaller combinations are more efficient representations of a given value but concrete efficiency depends on the feerate, and at higher feerates."
+      ],
+      "metadata": {
+        "id": "oR9gLKlHzPl2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title\n",
+        "min_sum = min_denom\n",
+        "max_sum = max_combination_value\n",
+        "\n",
+        "log_bins = np.logspace(np.log10(min_sum), np.log10(max_sum), bins)\n",
+        "\n",
+        "plt.figure(figsize=(12, 5))\n",
+        "\n",
+        "colors = plt.cm.plasma(np.linspace(0, 1, max_combination_size))[::-1]\n",
+        "\n",
+        "# similar to density=True except there's no division by the total entry count,\n",
+        "# which allows comparing the relative densities of different combination sizes\n",
+        "for i in range(max_combination_size -1 , -1, -1):\n",
+        "    k = i + 1\n",
+        "    if len(log_bins) > 1:\n",
+        "        counts, bin_edges = np.histogram(sumsets[i], bins=log_bins)\n",
+        "        bin_widths = np.diff(bin_edges)\n",
+        "        density = counts / bin_widths\n",
+        "        plt.bar(bin_edges[:-1], density, width=bin_widths, color=colors[i], label=f'k = {k}', align='edge')\n",
+        "\n",
+        "plt.xscale('log')\n",
+        "plt.yscale('log')\n",
+        "plt.ylim(1e-5, 1)\n",
+        "plt.xlabel('Binned sums (sats)')\n",
+        "plt.ylabel('Density per bin (count / bin width)')\n",
+        "plt.title(f'Density of distinct sums by combination size (k)')\n",
+        "plt.axhline(y=1/dust, color='black', linestyle='--', label=f'dust⁻¹ ({dust}⁻¹)')\n",
+        "\n",
+        "plt.axhspan(ymin=0, ymax=1/dust, facecolor='lightgrey', alpha=0.5)\n",
+        "\n",
+        "plt.legend(loc='lower left')\n",
+        "plt.grid(True, which='both', alpha=0.5)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 444
+        },
+        "id": "-QcHCbT7ov3z",
+        "outputId": "e85729b6-fe35-49bb-ad58-525f39a0d48f",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Distinct combinations vs. distinct sums\n",
+        "\n",
+        "The following plot displays the possible combinations (as opposed to the distinct sums of the previous plot), which gives an indication for how many ways of constructing a possible sum there are as the combination size increases."
+      ],
+      "metadata": {
+        "id": "RXPRZr3HzC1_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title\n",
+        "plt.figure(figsize=(8, 5))\n",
+        "fig, ax1 = plt.subplots(figsize=(8, 5))\n",
+        "\n",
+        "ks = list(range(1, max_combination_size + 1))\n",
+        "num_combinations = [ len(combined)**k for k in range(1, max_combination_size+1) ]\n",
+        "combination_density = [ len(combined)**k / max_combination_value for k in range(1, max_combination_size+1) ]\n",
+        "\n",
+        "\n",
+        "ax1.set_xlabel('Combination Size (k)', fontsize=11)\n",
+        "ax1.set_yscale(\"log\")\n",
+        "ax1.plot(ks, num_combinations, marker='.', label=f\"combinations ({len(combined)}ᵏ)\")\n",
+        "ax1.plot(ks, list(map(len, sumsets)), marker='.', label=\"distinct sums\")\n",
+        "plt.axhline(y=max_combination_value, color='black', linestyle='--', label=\"distinct integers in the range\")\n",
+        "ax1.legend()\n",
+        "for i, k in enumerate(ks):\n",
+        "    ax1.text(k, num_combinations[i], f'{num_combinations[i]:,}', ha='right', va='bottom')\n",
+        "    if k > 1:\n",
+        "      ax1.text(k, len(sumsets[i]), f'{len(sumsets[i]):,}', ha='right', va='top')\n",
+        "\n",
+        "ax1.grid(True, which='both', alpha=0.5)\n",
+        "\n",
+        "\n",
+        "fig.suptitle(f'Combinations with replacement summing to ≤{max_combination_value/1e8} BTC')\n",
+        "\n",
+        "ax2 = ax1.twinx()\n",
+        "exponent = int(np.floor(np.log10(max_combination_value)))\n",
+        "mantissa = max_combination_value / (10**exponent)\n",
+        "def sup(n):\n",
+        "    \"\"\"Converts an integer to a unicode superscript string.\"\"\"\n",
+        "    superscript_map = {\n",
+        "        '0': '⁰', '1': '¹', '2': '²', '3': '³', '4': '⁴', '5': '⁵', '6': '⁶',\n",
+        "        '7': '⁷', '8': '⁸', '9': '⁹', '-': '⁻'\n",
+        "    }\n",
+        "    return ''.join(superscript_map.get(digit, digit) for digit in str(n))\n",
+        "ax2.set_ylabel(f'Density (y × ({mantissa:.1f} × 10{sup(exponent)}){sup(-1)})', fontsize=11)\n",
+        "ax2.set_yscale(\"log\")\n",
+        "ax2.scatter(ks, combination_density, alpha=0)\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 533
+        },
+        "id": "wXTvLfIn5hvA",
+        "outputId": "929cc132-620f-411b-c50f-b524f2cf93d2",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file