aare/python/tests/ClusterFinderCUDA.ipynb

    {
        "cells":
            [
                {
                    "cell_type": "markdown",
                    "id": "title",
                    "metadata": {},
                    "source": [
                        "# ClusterFinder — CPU vs CUDA\n",
                        "\n",
                        "Two finders over the same frames and the same pedestal:\n",
                        "\n",
                        "| | |\n",
                        "|---|---|\n",
                        "| **`ClusterFinderMT`** + `ClusterCollector` | multithreaded CPU baseline |\n",
                        "| **`ClusterFinderCUDA.find_clusters_batched`** | batched across CUDA streams, over a pinned input buffer, so H2D / kernel / D2H overlap |\n",
                        "\n",
                        "**Part 1** times them. **Part 2** compares what they found."
                    ]
                },
                {
                    "cell_type": "code",
                    "execution_count": 1,
                    "id": "imports",
                    "metadata": {},
                    "outputs": [],
                    "source": [
                        "import sys; sys.path.append('/home/ferjao_k/aare/build')\n",
                        "\n",
                        "import time\n",
                        "from pathlib import Path\n",
                        "\n",
                        "import boost_histogram as bh\n",
                        "import matplotlib.pyplot as plt\n",
                        "import numpy as np\n",
                        "from tqdm import tqdm\n",
                        "\n",
                        "from aare import (File, ClusterFinderMT, ClusterCollector, ClusterFinderCUDA)"
                    ]
                },
                {
                    "cell_type": "markdown",
                    "id": "md-config",
                    "metadata": {},
                    "source": [
                        "## Configuration"
                    ]
                },
                {
                    "cell_type": "code",
                    "execution_count": 2,
                    "id": "config",
                    "metadata": {},
                    "outputs": [
                        {
                            "name": "stdout",
                            "output_type": "stream",
                            "text": [
                                "Image size:      (400, 400)   cluster (3, 3)\n",
                                "Pedestal frames: 600\n",
                                "Data frames:     100000  of 100000 in file\n"
                            ]
                        }
                    ],
                    "source": [
                        "base = Path('/mnt/sls_det_storage/moench_data/2603_MaxIVBeamtime/2026032408/process/xrf/')\n",
                        "f = File(base / 'Cu_factor_10_data_master_0.json')\n",
                        "pd = File(base / 'Cu_factor_10_pedestal_master_0.json')\n",
                        "\n",
                        "n_frames_pd  = 600                  # pedestal frames, taken from the head of the file\n",
                        "N            = 100000                 # data frames, read after those\n",
                        "cluster_size = (3, 3)\n",
                        "rows, cols   = f.rows, f.cols\n",
                        "image_size   = (rows, cols)\n",
                        "\n",
                        "N_SIGMA    = 5\n",
                        "N_STREAMS  = 4                       # H2D / kernel / D2H overlap\n",
                        "BATCH_SIZE = 2000                    # frames per find_clusters_batched call\n",
                        "\n",
                        "# CPU workers. ClusterFinderMT gives each thread its own ClusterFinder with its\n",
                        "# own pedestal, and data frames are handed out round-robin -- so at high thread\n",
                        "# counts each pedestal tracks on only 1/n of the frames while the CUDA finder's\n",
                        "# single pedestal sees all of them, and the cluster counts drift apart. Keep it\n",
                        "# low when comparing counts; raise it to measure CPU throughput.\n",
                        "N_THREADS = 24\n",
                        "\n",
                        "# Fixed per-frame output slot the kernel writes into, copied WHOLE on every D2H.\n",
                        "# Too low does not error -- it TRUNCATES. The CUDA cell prints the peak actually\n",
                        "# reached, which is how you size this for a new dataset.\n",
                        "CAP = 3000\n",
                        "\n",
                        "capacity = 3_000                    # initial ClusterVector capacity, CPU side\n",
                        "N_BINS   = 200\n",
                        "E_MIN, E_MAX = -2, 4000              # spectrum range, ADU\n",
                        "\n",
                        "print(f'Image size:      {image_size}   cluster {cluster_size}')\n",
                        "print(f'Pedestal frames: {n_frames_pd}')\n",
                        "print(f'Data frames:     {N}  of {f.total_frames} in file')"
                    ]
                },
                {
                    "cell_type": "markdown",
                    "id": "md-build",
                    "metadata": {},
                    "source": [
                        "## Build the two finders"
                    ]
                },
                {
                    "cell_type": "code",
                    "execution_count": 3,
                    "id": "build",
                    "metadata": {},
                    "outputs": [],
                    "source": [
                        "cf_cpu = ClusterFinderMT(image_size, #only positional argument here i.e. need to use the keyword for next ones\n",
                        "                         cluster_size=cluster_size, \n",
                        "                         n_sigma=N_SIGMA,\n",
                        "                         capacity=capacity, \n",
                        "                         min_pedestal_samples=n_frames_pd, \n",
                        "                         n_threads=N_THREADS)\n",
                        "\n",
                        "sink = ClusterCollector(cf_cpu)\n",
                        "\n",
                        "cf_cuda = ClusterFinderCUDA(image_size, \n",
                        "                            cluster_size=cluster_size, \n",
                        "                            n_sigma=N_SIGMA,\n",
                        "                            max_clusters_per_frame=CAP, \n",
                        "                            n_streams=N_STREAMS, \n",
                        "                            min_pedestal_samples=n_frames_pd, \n",
                        "                            algorithm=\"fixed_window\", \n",
                        "                            time_kernels=False)"
                    ]
                },
                {
                    "cell_type": "markdown",
                    "id": "md-ped",
                    "metadata": {},
                    "source": [
                        "## Pedestal\n",
                        "\n",
                        "Identical frames into both, so the implementation is the only thing that differs."
                    ]
                },
                {
                    "cell_type": "code",
                    "execution_count": 4,
                    "id": "train",
                    "metadata": {},
                    "outputs": [
                        {
                            "name": "stdout",
                            "output_type": "stream",
                            "text": [
                                "Pedestal (600 frames): 2.39s\n"
                            ]
                        }
                    ],
                    "source": [
                        "pd.seek(0)\n",
                        "t0 = time.perf_counter()\n",
                        "for _ in range(n_frames_pd):\n",
                        "    pd_img = pd.read_frame()\n",
                        "    cf_cpu.push_pedestal_frame(pd_img.copy())\n",
                        "    cf_cuda.push_pedestal_frame(pd_img.copy())\n",
                        "print(f'Pedestal ({n_frames_pd} frames): {time.perf_counter() - t0:.2f}s')"
                    ]
                },
                {
                    "cell_type": "markdown",
                    "id": "md-io",
                    "metadata": {},
                    "source": [
                        "## Read the data frames\n",
                        "\n",
                        "Kept out of both timing loops: the two finders run over the same in-memory array."
                    ]
                },
                {
                    "cell_type": "code",
                    "execution_count": 5,
                    "id": "io",
                    "metadata": {},
                    "outputs": [
                        {
                            "name": "stdout",
                            "output_type": "stream",
                            "text": [
                                "Reading 100000 frames: 54.30s  (1,842 FPS, 32.00 GB at 0.59 GB/s)\n"
                            ]
                        }
                    ],
                    "source": [
                        "f.seek(0)\n",
                        "t0 = time.perf_counter()\n",
                        "data = f.read_n(N)\n",
                        "t_io = time.perf_counter() - t0\n",
                        "gb = f.bytes_per_frame * N / 1e9\n",
                        "print(f'Reading {N} frames: {t_io:.2f}s  ({N / t_io:,.0f} FPS, '\n",
                        "      f'{gb:.2f} GB at {gb / t_io:.2f} GB/s)')"
                    ]
                },
                {
                    "cell_type": "markdown",
                    "id": "md-cpu",
                    "metadata": {},
                    "source": [
                        "## 1 — CPU, multithreaded"
                    ]
                },
                {
                    "cell_type": "code",
                    "execution_count": 6,
                    "id": "cpu",
                    "metadata": {},
                    "outputs": [
                        {
                            "name": "stderr",
                            "output_type": "stream",
                            "text": [
                                "100%|██████████████████████████████████████████████████████████████████████████| 100000/100000 [00:23<00:00, 4235.66it/s]\n"
                            ]
                        },
                        {
                            "name": "stdout",
                            "output_type": "stream",
                            "text": [
                                "CPU MT (24 threads): 23.62s   4,235 FPS   233,104,694 clusters (2331.0/frame)\n"
                            ]
                        }
                    ],
                    "source": [
                        "t0 = time.perf_counter()\n",
                        "# Pass the frame number: find_clusters(frame) defaults it to 0, and every\n",
                        "# resulting cluster is then stamped frame 0. Part 2 groups by it.\n",
                        "for i, frame in enumerate(tqdm(data)):\n",
                        "    cf_cpu.find_clusters(frame, i)\n",
                        "t_cpu = time.perf_counter() - t0\n",
                        "\n",
                        "cf_cpu.stop(); sink.stop()\n",
                        "clusters_cpu = sink.steal_clusters()\n",
                        "\n",
                        "hist_cpu = bh.Histogram(bh.axis.Regular(N_BINS, E_MIN, E_MAX))\n",
                        "n_clusters_cpu = 0\n",
                        "for cv in clusters_cpu:\n",
                        "    hist_cpu.fill(cv.sum())\n",
                        "    n_clusters_cpu += cv.size\n",
                        "\n",
                        "print(f'CPU MT ({N_THREADS} threads): {t_cpu:.2f}s   {N / t_cpu:,.0f} FPS   '\n",
                        "      f'{n_clusters_cpu:,} clusters ({n_clusters_cpu / N:.1f}/frame)')"
                    ]
                },
                {
                    "cell_type": "markdown",
                    "id": "md-cuda",
                    "metadata": {},
                    "source": [
                        "## 2 — CUDA, batched + multi-streamed\n",
                        "\n",
                        "`register_input_buffer` page-locks the input once so H2D runs at full DMA\n",
                        "bandwidth instead of through the driver's staging buffer. It is paid once,\n",
                        "outside the loop, and released when the dataset is done."
                    ]
                },
                {
                    "cell_type": "code",
                    "execution_count": 7,
                    "id": "cuda",
                    "metadata": {},
                    "outputs": [
                        {
                            "name": "stdout",
                            "output_type": "stream",
                            "text": [
                                "CUDA batched: 3.35s   29,810 FPS   233,095,737 clusters (2331.0/frame)\n",
                                "  input pinned in 0.84s, once, outside the loop\n",
                                "  vs CPU: 7.04x\n",
                                "  peak clusters in one frame: 2,545 of CAP 3,000\n"
                            ]
                        }
                    ],
                    "source": [
                        "t0 = time.perf_counter()\n",
                        "cf_cuda.register_input_buffer(data)\n",
                        "t_pin = time.perf_counter() - t0\n",
                        "\n",
                        "clusters_cuda = []\n",
                        "t0 = time.perf_counter()\n",
                        "for start in range(0, N, BATCH_SIZE):\n",
                        "    stop = min(start + BATCH_SIZE, N)\n",
                        "    clusters_cuda.extend(\n",
                        "        cf_cuda.find_clusters_batched(data[start:stop], first_frame=start))\n",
                        "t_cuda = time.perf_counter() - t0\n",
                        "\n",
                        "cf_cuda.unregister_input_buffer()\n",
                        "\n",
                        "hist_cuda = bh.Histogram(bh.axis.Regular(N_BINS, E_MIN, E_MAX))\n",
                        "energies = [np.asarray(cv.sum()).ravel() for cv in clusters_cuda if cv.size > 0]\n",
                        "if energies:\n",
                        "    hist_cuda.fill(np.concatenate(energies))\n",
                        "n_clusters_cuda = sum(cv.size for cv in clusters_cuda)\n",
                        "\n",
                        "print(f'CUDA batched: {t_cuda:.2f}s   {N / t_cuda:,.0f} FPS   '\n",
                        "      f'{n_clusters_cuda:,} clusters ({n_clusters_cuda / N:.1f}/frame)')\n",
                        "print(f'  input pinned in {t_pin:.2f}s, once, outside the loop')\n",
                        "print(f'  vs CPU: {t_cpu / t_cuda:.2f}x')\n",
                        "\n",
                        "# One ClusterVector per frame, so this IS the per-frame maximum.\n",
                        "peak = max((cv.size for cv in clusters_cuda), default=0)\n",
                        "print(f'  peak clusters in one frame: {peak:,} of CAP {CAP:,}')\n",
                        "if peak >= CAP:\n",
                        "    print('  !! CAP REACHED -- clusters were dropped; raise CAP and re-run.')"
                    ]
                },
                {
                    "cell_type": "markdown",
                    "id": "md-summary",
                    "metadata": {},
                    "source": [
                        "## Summary"
                    ]
                },
                {
                    "cell_type": "code",
                    "execution_count": 8,
                    "id": "summary",
                    "metadata": {},
                    "outputs": [
                        {
                            "name": "stdout",
                            "output_type": "stream",
                            "text": [
                                "                             s        FPS   us/frame      clusters\n",
                                "CPU MT (24 thr)          23.62      4,235      236.2   233,104,694\n",
                                "CUDA batched              3.35     29,810       33.5   233,095,737\n",
                                "\n",
                                "speedup: 7.04x\n"
                            ]
                        }
                    ],
                    "source": [
                        "print(f'{\"\":<22} {\"s\":>7} {\"FPS\":>10} {\"us/frame\":>10} {\"clusters\":>13}')\n",
                        "for name, t, n in (('CPU MT (%d thr)' % N_THREADS, t_cpu, n_clusters_cpu),\n",
                        "                   ('CUDA batched', t_cuda, n_clusters_cuda)):\n",
                        "    print(f'{name:<22} {t:7.2f} {N / t:10,.0f} {t * 1e6 / N:10.1f} {n:>13,}')\n",
                        "print(f'\\nspeedup: {t_cpu / t_cuda:.2f}x')"
                    ]
                },
                {
                    "cell_type": "markdown",
                    "id": "md-part2",
                    "metadata": {},
                    "source": [
                        "# Part 2 — Where the two disagree\n",
                        "\n",
                        "The two implementations are not bit-identical by construction: the CUDA finder\n",
                        "updates its pedestal once per frame, the CPU finder per pixel, so once the\n",
                        "pedestals drift apart a few pixels land on opposite sides of the threshold.\n",
                        "\n",
                        "Three views, cheapest first: the **spectra**, the **cluster sets**, and **where\n",
                        "in the run** the difference accumulates."
                    ]
                },
                {
                    "cell_type": "markdown",
                    "id": "md-spec",
                    "metadata": {},
                    "source": [
                        "## Spectra\n",
                        "\n",
                        "Both curves should lie on top of each other; the ratio panel is the sensitive\n",
                        "view."
                    ]
                },
                {
                    "cell_type": "code",
                    "execution_count": 9,
                    "id": "spectra",
                    "metadata": {},
                    "outputs": [
                        {
                            "data": {
                                "image/png":
                                    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d0TQBiByCvOOgJ2F6JVBrHPSfgrt5pp6A6ghYenLhTa/CaY2fXv0CEJg2BdKTbR2RTkfZvP32203/Kw3utI+WTq2gV9mDBXna5O7iiy82J+3aVExrhvRkRE/SvOlJt56oaG2TXpDRkyod2VPXe39H9Yq/BjJ6VV1rHPSqva7TkR31BLFVq1bmRE/7sWlfFj35mzdvngluChNQaVCggYaOrqgj4+lFI30fWoumI2tq3xv3lAbh0ikJ9LdHR67U/nLav0abZOlvl56o6sh6gUY/9KbvV2vENCDS9OkVfg2qtIYk0HD6Wiurfdx0pD9Ns45emZ9wPxOlv7kaIGgtn57Ya+2HjnSoLSncfdfC/Yz0opuOHqj9jTSfNTjUgE7Lm+7r7rvvDlkWQtE816Zt7lE7NWjQICTQFBPxRoN39xQcegFB+3ppQOW+KKA1pO7/izq9hPbJ1DzQvNWRILXZoH5+3mVGAyn9LHSUVq15++2338yIsFoLrN8FbxoEefdTLcjrtRzrOv190X6cWiOn/8/1/7eWJd2Xm37fNEDXcqHlUNOtI7Rq7ZaWoUhzT2Giv4HalFLLio6Yq+Vc30+kaCsjrcXTZpjaLFOPpSPC6meqF1D0Vn+LdWRO7fenfV51lFINdLWGWPMq3EnqAcRArEd+iSeaXbNnz/Y81hHtdJ2OKua96Mh43iPgeY8kps8FGvkLgC8dVVBH66tevbqVlpZmvls6stxDDz1kbdu2LeRk6Podu/rqq83IcTqq5Q033GAtW7bMZ3TNrVu3Wr1797bq169v9q2jx5111lnWs88+6zMRsk56rCPzlSxZ0rxeR0B00xHtHnzwQTOSpaZRj3XmmWda9957rxkp1Pu348477yzQR6wj5LVt29aMFlmsWDFzXH1PCxYs8Gyj+aNpD2dERx2d7+mnnzaj5hUvXty8X33vt912m/Xbb795ttPjXHbZZQHT9OOPP5qJnfX1mrc62t9rr71mjrV69eo822ve6XP9+vUL6z2H+5m406ijZOqohZr3NWvWtMaMGZNnn+F+RjqioB6nUaNGnu1atGhhffDBB/mWBffomu+8807A9/Xtt99aLVu2NO/p1FNPNZ/PK6+8EnB0zUB5H6j8uEdnfOqpp6xY0e+dpiHQ4j+67bp166xu3bqZ8nzCCSdY7dq1s5YvXx5wv/q/Uj93/Rx05Mh77rnHjC7rTR8HG4k3nNe796HP6Ta6rb4m0CiaSn8/NM2adn0P+l70PYWbT8FG1wz2+T3xxBOmTOt3XydSf/nllwN+rwtSNoKV0y+++MKUO/1O6wTvWkb1sXs7naBdv8OaP/redQRY/T5perxHfAVgLy79E4vgMh7pVUNtjtmtWzfzWK/mXn/99abZkPdVP/cVMv9O/O7JRvOb0BUA4oVOkKw1IDowhX/TWq0l0L55OueY1sZGitZ2am2ue5JwJB5t0q01XlrjlF9tKgAkIpprHgcdQU6bqGjH4/z62OlogdosKr9+QQBgV9rUTptJalNF7eOkQZb2M9Z+c94BnjZz1N883V6bgkYywAOU/j/VZsgEeAAQGEFePvREZt26dZ7HeuKyatUq08dCBwXQmjwdRviZZ54xQZ+OmKZ9SPQfj/bFcNO+LNqePtRInQBgZ9qPT+ew0758Oiqh9pvT+ey0n5837d+nA0XoxS9GEkY0aDkEAARHc818aIdu7XDsTzsqa+d5HQxCO+tPmzZNNm3aZEZx04EjtKO2+wqjdvTXwR80GHzsscfyOyQAAAAAxGeQp6Pf6QhSOjqbzkPTsmVLMzJavXr18h3Ra+DAgaYvnDYd0pGz+vXr57ONjqg2bNgwMwKmznOjwVV+o8cBAAAAQLzzHfO6iGmwpkNsf/PNN2boa23+0759ezlw4EDQ12hzSW0Gqc2AtN/HkCFDTMd+DerclixZYua90WGUtVO23nbv3l2WLl1aRO8MAAAAAGLDVs01dU4WnaNFgz+d1ygQnXNJBy/5+eefPeu0Fk+DOQ3ulAZ4Ou/NRx995NnGPdeUjgIHAAAAAE5lq4FXdMJNpYOaBKOBnNb2eevQoYNMnjzZ9I/TgQF0G5341n+bsWPHBtynTlKui5v2odOJQbV/nU6bAAAAAACxpvVz+/btM13WkpKS7B/kaYK1n92//vUvM/9RMDpiW8WKFX3W6WNt6qkjW+oIlsG20fXB+gbqQCkAAAAAYHcbN26UqlWr2j/Iu+uuu+T777+Xr776Kt9t/WvX3C1OvdcH2iZYrdzgwYNNgOldo1i9enXZsGGDmbzcDjT9mq7SpUtTu0h+Ow7lm/x2Mso3+e1klG/y28ksG55/a5c0jVNKliwZcjtbBHl333236We3aNGikBGpqlSpUp4aOZ2MPCUlxTSvDLWNf+2eW7FixcziTz9QOwV5yk6FzMnIb/LbySjf5LeTUb7JbyejfJPfrtw4IL94ICnWBVVr8HQaBZ1AvFatWvm+Rueg05E4vc2bN0+aNm1q+uOF2kanaAAAAAAAJ4tpkKfTJ7zxxhvy1ltvmSpHrX3T5dChQz5NKXUSce+RNP/66y/TvFJH2JwyZYoZdOW+++7zbNO/f38T1OmcezoHn94uWLBABgwYUOTvEQAAAAASJsibNGmSaefapk0bM2CKe5k5c6Znm82bN5u+cW5a2zd37lxZuHChnH322fLII4/IuHHj5KqrrvJsozV2M2bMkFdffVXOOussmTp1qtln8+bNi/w9AgAAAEDCzpNnF9qhUfu+aQBqpz55duv46WTkN/ntZJRv8tvJKN+xz++srCwzrRWiN3y+toDjfNCZ+Z2amirJycnHHafYYuAVAAAAxP8JsXa72bNnT6yT4mg6n/POnTtjnYyEkR2D/D755JPNQJLHE1gS5AEAAOC4uQO8ChUqyAknnEBNU5QCaa0p1ZoeavKcl9+WZcnBgwfNrABKu7EVFkEeAAAAjoueCLsDPPeUVog8gjzn53eJEiXMrQZ6+n0K1XTTtgOvAAAAIP65++BpDR6A4+P+Hh1P31aCPAAAAEQETQgBe3yPCPIAAAAAwEEI8gAAAIAE1apVK3nrrbdinYyE8b///U+aNGliRu2MJoI8AAAASKKPDHr33XfLaaedJsWKFZNq1apJly5d5NNPP/VsU7NmTdOMThftM9WoUSN58cUXPc8//PDDcvbZZ+fZtw5Io69ZuHBh0OO3adPGbPPEE0/kea5Tp07mOd3/n3/+aeZRS0pK8qTFf9HtChJw6Hu/9tprzeNdu3aZfKhXr555j9WrV5d77rnHzMnm7fLLLzfPFS9e3IwA2bNnT/nnn388z+uUA5deeqlUqVLFk5933XWXmeMtlJdeesnkhc7/pu8l0HQcu3fvNsfTueJ00fvBpu3YuXOnVK1aNei+/C1ZskQuuugiOfHEE800Bm3btpVDhw55nl+xYoVccskl5jkdYOjWW2+V/fv3F+jYnTt3NuuiHVgT5AEAACBhaeB07rnnymeffSZPPvmk/PDDD/Lxxx+bE/w777zTZ9uRI0fK5s2b5fvvv5du3bpJv379ZObMmRFJhwZCr776qs86DZw0Xe6h9HWbjRs3mvWajn//+9/SsGFDc9+93HfffWEfc9y4cXLTTTeZoNF9PF2efvppkw9Tp041edGnTx+f12nevP322/Lrr7/KrFmz5Pfff5err77a87zur2vXrvL+++/L2rVrzX4WLFhg8isUnT5Ag8MhQ4YE3eb//u//ZNWqVSZduuh9DfQC6dOnj5x11llh5YUGeHrs9u3by7fffivfffed+fy98+biiy+W008/XZYuXWqOvWbNGundu3eBj615/vzzz0tUWcgjPT3d0qzRW7vIzs62du/ebW5BfjsN5Zv8djLKN/mdCOX74MGD1k8//WQdOnTIijcdO3a0Tj31VGv//v15ntP35lajRg3r2Wef9Xm+Tp061rXXXmvuDx8+3GrcuHHAfeh55eeffx40Da1bt7Zuv/1265RTTrG++uorz/rHHnvM6tKli9mv7l/z++jRo57zwWDHDMf27dstl8tl/fjjjyG3e/vtt620tDRz3GDmzJlj9pWRkRF0m+eee86qWrVqWGnTvNI8885/pWVM13/zzTeedUuWLDHrfvnlF59tJ06caPL1008/Dbgvf82bN7cefPBBn3Xe+f3iiy9aFSpUsLKysjzPr1y50uz7t99+K9Cx//zzT7P+999/D5gW/R4F+z6FG6dQkwcAAICEpM0TtUZGa2y0iZ4/bZYXijZXPJ5h7r2lpaXJ9ddf71ObpzVgN998s0TDV199ZZpkNmjQIOR22lRTm0+mpKQEzcM333xTWrZsaZqSBqK1YO+++660bt36uNKstW3aRLN58+aedeeff75Zt3jxYs+6n376ydS6Tps2zVMTF4rOSae1czovnb6PihUrmrRqHrkdOXLEfEbe+3PPaee9XTjHrlGjhjnWl19+KdHCZOgA4G3dpyLTc/omSHKayJBN5A8AFFLDhz6Wo1la8VB0UpNdsmbkpWFtu27dOjPhdf369Qt0jMzMTHnjjTdMk8bbb79dIkWb+P3rX/+S5557TpYvX24CrMsuu6xA/ewK0kxVg5lQQZD2K3vkkUfktttuy/PcAw88IOPHjzdNLDXQ0v59/q677jqZM2eO6demfRxfeeWV40qz9h/U4MifrtPn3MHYddddJ0899ZTpN/jHH3/ku1/3NprP2lRV+1ZqkKbNM7U5qJYP7as3cOBAs9/+/fvLgQMHPM1KtZlsQY996qmnms8gWqjJA4DcE5G6Qz+Sm6Z+JxmZ2SJZGZJx5DB5AwAOpgFeQeYl08DmpJNOMjU4Wvv3n//8J2AAVFjah6tOnTry3//+V6ZMmWL6mgWrHTteGnhpTWQwOkiKBphnnHGGDB8+PM/z+t5Xrlwp8+bNk+TkZLnxxhs9+en27LPPmsFK3nvvPdNvT4Ok4xXos9LjutcPHjzY1E7ecMMNYe/TPdKlfpbaX05Hv9S06wA0WpuqtO/ja6+9Js8884ypAa1UqZIZqEcDZX3/BT22liENkKOFmjwAEJEHrZcl22XJCOsWaZT5hvyQcoMc/1SkAJDYwq1RixUNqDQ4+Pnnn81AKvnRwEYH2tCTfB0MxTvg0CaN/qNQKvfIitqkMBzaPHPChAmm2Z8OABIt5cqVMyNVBrJv3z4zCIkGtLNnzw4YaOrrdalbt64JbHRQmG+++UZatGjh2UYDIV20JkxHo7zwwgtl2LBhnoFkCkr3tXXr1jzrt2/fboItpQPV/PDDDyZQVu7AU9M6dOhQGTFiRJ7Xu9OjAa03fV8bNmzwGfRFF02DNu/Vz3/MmDFSq1atAh9bm7mWL19eooUgDwBE5KqknKGtrx/xrrnNyHvREgDgMGXLlpUOHTqYoEqnCvDvl6cBmne/PD1Z19EVA9FA5u+//zbNBjUYcdNRGrVJZLDX+dMgQkfIbNy4cZ6gI5K0tkrTqoFemTJlfGrwNE906gMdHTNUbZ+bO5jR5orHs01+NIDUQFqD3/POO8+s0750uk770ikd7fOQ17QHmv8aOGv/t9q1awfcr06PodM96Gih3nRkUM0Lf+6AUmtbNX90WoWCHPvw4cOmZlM/g2ghyAMAAEDCmjhxogkQNGjQATO0yaT2uZs/f75MmjTJ1PKFQ4fe15ofnXPuscceM0GDTrWgAZtOHVCyZMmw9qMBl/bxilYzTTcNMLQm6euvvzZzt7lr8PR9aDNC7XOoAZ97bjvdVpslaoCli/Yd1LRqv7OHHnrIBDHuWry5c+ea2q5mzZqZ2kCtlbz//vvlggsuMAFVMBp06qJ9JZXWimm+af82Dcg1f7WG8ZZbbvHMUahz1Wn6tWml8g/kduzYYW71tcEG0tEaOa2l1WapGlxrnzxtmvnLL7/IjBkzPNtpH0QtK/qetHzoa3RuQ/d+wz221nhqEO1d6xlpBHkAEpb3gAA/pIhP88zdkvPPOOdaHQDAqbSpnfYb08BM553TAEsDGp07T4O8cOnok9o/TQfj0FEydcRGHUWxb9++JsApiPxG9YwEDdi0lklHxnQHeTrYi9aMKf+ax/Xr15sATfuS6UiZGhDp4CPa1FEDLw2GNHBRus3LL78s9957r6m506acV155pQwaNChPcKWjibrnmnvhhRd8mjS2atXK3Hpvo+nVWlcNRt0Ts2vwVVAuv2MPGDDA1LBpmrUppQZ7+nl6B24a3Or71gnQteZWA81gc/SFMn36dFNGtNlvtLh0HoWo7T1O6RULbTftHjLWDvRj0vRousLtHAzyO17EqnzrQCuDXa/K9cmfSporUzKsFEkbsdPznFr7WEdxGn5PyG8no3zHJr/15F5HCtSAKZzmfSh8fmdlZZkALRL/L7W2TQcU0eBOA9KipOVF+0RqLZ/e2vHYVoTz291/UAPEZcuWefry+dNgU4PqQN+ncOMURtcEkNCSXC5JS0ky0yWkFePEBACQOLRv2eTJk30GFykqOj+hNrUs6gAv1sfW4E2bCAcL8CKF5poAEtpj2b2l17CZsU4GAAAx0bVr15gcV/spxkq/GB5b+366B42JJoI8AAmrjWt57r28TTJXpOS00RfJO1QzAACAnRHkAUhY45Ofzb33YJ7n0iSzyNMDAAAQCfTJAwAAAAAHIcgDAAAAAAchyAMAAAAAByHIAwAAAAAHIcgDAAAAAAchyAOQsLZKWbMEcn3WcLMAABAPPvvsM6lfv75kZ2fHOikJo1mzZvLuu++KHRHkAUhY7TLHmSWQ1VYdswAAnG/Lli1y9913y2mnnSbFihWTatWqSZcuXeTTTz/1bONyueS9997L89oBAwZImzZtPI979+5tttUlNTVVKlasKJdccolMmTIlaADWvn17SU5Olm+++abQ7+H++++XoUOHSlJSzum9Bh963PLly0upUqWkRYsW8sknn/i8Rrdp2rSpnHzyyXLiiSfK2WefLa+//rrPNpMmTZKzzjrL7MO9n48++ijf9HzxxRdy7rnnSvHixU2+vvDCCz7PHz16VEaOHCm1a9c22zRu3Fg+/vhjn20efvhhT166l0qVKoU8rnf+u7yWhg0bFuh916xZ0+SlfoZ6697PnXfe6dlm2LBhMmjQIFsG1gR5ABDEmORxIo+UP7Y8fip5BQAO8+eff5pgRGvCnnzySfnhhx9MsNG2bVufE/qCuPTSS2Xz5s1m3xoQ6b769+8vnTt3lsxM33lYN2zYIEuWLJG77rpLJk+eXKjjLV68WH777Te55pprPOsWLVpkgry5c+fK8uXLTRo0cF25cqVnm7Jly5rAUI///fffy0033WQW72CwatWq8sQTT8iyZcvMctFFF0nXrl1lzZo1QdOzfv166dSpk1x44YXmeEOGDJF77rlHZs2a5dnmwQcflBdffFGef/55+emnn6Rfv35yxRVX+KRPaXCmeele9PMJ5bnnnvPZfuPGjeZ9eudNOO/7u+++k3/++ce8Xm/nz59v1nvv57LLLpP09PQ8wbMdMBk6AIQjK4N8AgAHuuOOO0wNzbfffmtqdbyDi5tvvrlQ+9TaQHeN06mnnirnnHOOnH/++dKuXTuZOnWq9O3b17Ptq6++aoK/22+/Xc477zwZO3asTzrCMWPGDFMbqDVibrofb48//rjMmTNHPvjgA2nSpIlZ510DqTQQfe211+Srr76SDh06mHUaGHp77LHHTO2e1jp6145501q76tWre9LQoEEDEyA+/fTTctVVV5l1WnOmgZYGg0rfvwZLzzzzjLzxxhuefaWkpORbe+etdOnSZnHT2tfdu3ebIM4tnPetNaCWZUlWVpapZR09erSpdWzdurXndbpe0z99+nTp2LGj2Ak1eQAS1o8pN5glmIFZ94gM256zJKcVadoAANG3a9cuU2unNXaBAittzhcpWgOmTRK9+3BpEKFB3g033GD609WtW1fefvvtAu9ba+20+WEo2qRw3759phYrEE2LNk/99ddfpVWrVgG30YBHA8oDBw6YZpvBaA2ZBp3eNHjSQE+baaojR474BKWqRIkSJtDypjWUVapUkVq1asm1114rf/zxhxTE5MmT5eKLL5YaNWoU+n1nZGSYwFODfr0g4E0D8y+//FLshpo8AAgiIytb6g7N6XfwY0pOe3tCPQAoIG3uHkzfBSKVG+fcn3G9yG/zAm/X6j8ire/Pub/idZEPBwbertKZIrd8FnbS1q1bZ07yNcAqCnocbR7otmDBAjl48KCn9kiDPQ1KvGudwqHNQjUQCkVryDQ46969u896bW6otY0adGnN1MSJE00zT2/aRFKDusOHD8tJJ50ks2fPljPOOCNkH0fti+hNH2tT1R07dkjlypXNex4zZowJrLSGTAMtrWnUQNKtefPmMm3aNBP8bt26VR599FFp2bKlaSp6yimn5JsvmzdvNs1l33rrrTzPhfO+vWsD9+zZY/r7+dN9aJNbDaLd/SHtwD4pAQAbSU12SVrysZ/IF7M6yyvZvk1WAADxTQM85V87E83jeR9LA7oePXqYJonquuuuk6VLl5papYI4dOhQnloxb9qcUAcxmTlzplSoUMHnuZIlS8qqVatMHzRtijlw4EBZuHChzzb16tUz22gTTW1W2atXL9OPLhT/PPXPa+07V6dOHRP4pqWlmT6JGtxqwOWmTSC1eeeZZ55pauM+/PBDs16bVoZj6tSppja2W7dueZ4L53276aA5mpZAgbTWPmqAp8GinVCTBwABrBl5qc/jukP1P5TIHeQWABSMNnkPx7VvhrfdOT1zlgjQIEODjp9//jlgIOAfFGjtjz+t4fHuAxaKHkebHbqbimoNkTZf1D5ublqTpUGF9gELV7ly5Uy/s0A0sOvTp4+88847JlDyp7VPp59+urmvo0xqGkeNGuXTb02DMPc22ixUAyMN0nTglEC0D53W5nnbtm2bCWbdNXDa503fv9YO7ty50wRQOlKlO38C0Sa1GvBpE878WJZl8rFnz54m/YV53+qvv/4yNa7BpkrQz/GEE04wwZ6dUJMHAACAhKT907TZ4IQJE0xTxkABnJvWOGlw4x9I6MiVWtOVHx29U5s9ugceefPNN83IlatXrzY1Su5FByvRmir/UThD0YFUAtWsaQ2eNjHU5oo6EmQ49D3lVyuV3zbatNM9GqXbvHnzTICoUxJ40xpIbfKo71dH39SRO4PRY2owps09w5nCYd26dSbADUew96SfhdZ+Bsu/H3/80QysYzfU5AFAGDq73B3B7TV6FgDg+GhfLO3npQNo6LxtOiecBhwapGgNmwYV6r777jPNFDXY00FFtInkSy+9JL///nueqRY0WNCaLK2V075kOriL1hLpKJo33nijp6nm1VdfLY0aNfJ5rQ4Q8sADD5imiaECHm8aqPo3YdQAT4+lNW46sqe7Zk1rnNw1j5omDby0T5wOLqLTLWgfOO+aRZ3+QJsq6tyBOnCLDryizRr957TzptMhjB8/3jSBvOWWW8xALPp+NU1u2ix106ZNphZNb7U5qTZ71Pn+3DTPdXRPHalTawK1T97evXvN55CfyZMnmz59/vkb7vtWmh7NV81Hd5Nafzroiv8gM7ZgIY/09HRtNGxu7SI7O9vavXu3uQX57TSxKt9HHiprlkhva3f8npDfTkb5jk1+Hzx40Prpp5+sQ4cOWfHon3/+se68806rRo0aVlpamnXqqadal19+ufX555/7bDdjxgyradOmVqlSpawKFSpYHTp0sJYtW+azTa9evcx5pC4pKSlW+fLlrYsvvtiaMmWKlZWVZbbR1+jz3377bcD0dOnSxSyB8vvo0aN5/l/u2rXLKlGihPXLL7941rVu3dqTDu9F0+c2dOhQ6/TTT7eKFy9ulSlTxmrRooV5j95uvvlmT77oe2nXrp01b948n22GDx9utvG2cOFCq0mTJuZ1NWvWtCZNmpTn+QYNGljFihWzTjnlFKtnz57Wpk2bfLbp0aOHVblyZSs1NdWqUqWKdeWVV1pr1qzJ99h79uwx+fHSSy8FzN9w3rf6+OOPTZ5556u3v//+26Rt48aNViTp9yjY9yncOMWlf2IVYOpwr0899ZSp5tbRb3SknlDtobW6OVBHSx3dxz0ho3awDDQiUX4dUr3pFQK9wqHtrkuVKiV2oB+TpkfTVVSdgxMZ+Z0Y+d116ERzO+ex/HvaZQzP6UOQNmKnxDvKN/ntZJTv2OS3zgunIzxqf6pwz7dQuPx2z9vm//9Sa8D0swjWTy6a3KNO6nm4k45thchv9Z///MfkudboRpL2U9QJ5QN9n8KNU2LaXFPbPut8IRqUudsnh6LVzU888YTnsVal6+u9Z55X+ob9RyXiBweAv58leOduAADiiU4srn0L3UFJUdL+b1p5EwuxPHaFChVMk1I7immQp+17CzI7fDgz2CuNtHVUHwAAACAR6Dmy9p+LBa11ipVYHvs///mP2FVcj64ZbAb7/fv3m3U6YpF2cF25cmXM0gjAvsYnP2MWAAAAJ4nb0TWDzWCvIx5pm1ydQ0PbrGoTzwsuuMAMT6tzoQSiIyB5D5mqr3O3w41hl0Uf7rTYJT1OR34nRn63dq3yHD8/luS0xXfCd5DyTX47GeU7tvnNuUrR5j2cmd9WiO9TuOmI2yAv2Az2OkSsLm4a4OncFc8//7yMGzcu4L50GNURI0bkWa8dGu3yBdJ0aA2lYuAV8ttpYlW+j5aobm5TA0xu6299iabmtlYY29odvyfkt5NRvmOT3zoMvQ43r/3BdEH0aD7D2fmdlZVljqtTVvjP3eeujHJkkJffDPb+s9k3a9ZMfvvtt6DbDB482Mzj4Z15OheItm220+iaitE1yW8nilX5zji0wdymefX1DebaffeY21/C2Nbu+D0hv52M8h2b/NbRNXft2mV+w4t60I9ERB47O79dLpeJYUqWLJln8Mhwz5PiMsgryAz2+uOzatUq03wzGP1h0sWfZqKdas3c6bFTmpyM/HZefjd86GM5mnWsdv6HlJxGmOEc091c0ynfP8o3+e1klO+iz289j9KTUu1OU758eXMR3im/l3aS35D+iO/8tizL1Ipv377dfJ/0e+V/3LgI8rR6X4M179FxNCArW7asmdlea9g2bdpkZqAPdwZ7bXapzTW1/53WyGkTTd2nDikLILFpgJeRlS1pyTljThXk5/pEORi1dAFAvNMTUp3TS4O8f/75J9bJcTRtxqf5Defm9wknnGBioeM5bkyDvGXLlknbtm09j91NJnv16mX63OkPxYYNOc2pvPvJzZo1ywyoEsiePXvk1ltvlS1btpimX02aNDFzZ5x33nlRfjcA4oEGeGsfy5265ZHwfzyXptyaey//OT0BIBFp7Z2emOo8xvTLi15Nj/bT0mZ81OQ5M7+Tk5MlJSXluI8X0yCvTZs2IQc2CTRzvQZuBw8Gv6L+7LPPmgUA/K1I6S1pkimyYqzIOT1FWtl3fhsAiEd6YpqammoWRJ6eN+tAHNpPiyAv+qw4zu+47JMHAIWhAV6aK/PYitb3k5EAAMBxaNALIKFkWCk5tXgAAAAORZAHAAAAAA5CkAcAAAAADkKQBwAAAAAOwsArABLGz1YNc9u4EK/tlPm0uV0Q4TQBAABEGkEegIRxTdZj5nZtIV67QSpFPD0AAADRQHNNAAAAAHAQavIAJIwysrfQr301OacWUKRjxNIDAAAQDQR5ABLGlyl35N7rUeDXNnP9HPH0AAAARAPNNQEAAADAQQjyAAAAAMBBCPIAAAAAwEEI8gAAAADAQQjyAAAAAMBBGF0TAMLwZPb/mdsHyS0AAGBzBHkAEka7zOfM7ZeFeO207E7mliAPAADYHUEegISxVU45rtffmDRX5JEbcx4kp4kM2RSZhAEAAEQQQR4AFFRWBnkGAABsi4FXACSMN5MfNkthmSabw7bn1OIBAADYFDV5ABJGY9e6WCcBAAAg6qjJA4AwZWRlS92hH0lGZrZZAAAA7IiaPAAIQ2qyy3NdbGl2fUlyueQCcg4AANgQQR4AhGHNyEs99+sOzbldS84BAAAborkmAAAAADgINXkAUEDVZQt5BgAAbIsgD0DCeCw7ZyLzEce5n7kp9+Xeu+m40wQAABBpBHkAEsb07PYRCfIAAADsjD55AAAAAOAg1OQBSBh9k97PvdcxxikBAACIHoI8AAljQNLbufcmxTglAAAA0UNzTQAAAABwEII8AAAAAHAQmmsCQAE1z3zJ3K4k5wAAgA0R5AFAAR2QE8gzAABgWzTXBAAAAAAHoSYPQML42jrT3LY9zv3MSXkg9x5TMQAAAPshyAOQMG7LygnO1h7nfmrLpoikBwAAIBporgkAAAAADhLTIG/RokXSpUsXqVKlirhcLnnvvfdCbr9w4UKznf/yyy+/+Gw3a9YsOeOMM6RYsWLmdvbs2VF+JwDiQW352ywAAABOFtMg78CBA9K4cWMZP358gV7366+/yubNmz1LnTp1PM8tWbJEevToIT179pTVq1eb2+7du8vSpUuj8A4AxJM5KYPMAgAA4GQx7ZPXsWNHsxRUhQoV5OSTTw743NixY+WSSy6RwYMHm8d6+8UXX5j106dPP+40AwAAAICdxeXAK02aNJHDhw+bppgPPvigtG3b1qcm79577/XZvkOHDibIC+bIkSNmcdu7d6+5tSzLLHbgTotd0uN05Lcz89sSl+d4dthPrFC+yW8no3yT305G+Sa/rTDPPeIqyKtcubK89NJLcu6555qg7PXXX5d27dqZvnqtWrUy22zZskUqVqzo8zp9rOuDGTVqlIwYMSLP+vT0dNucxGk69u/fb+5rP0SQ305SVOX7aInq5jY1Pf249jOmWM6FpIHHuZ9Y4feE/HYyyjf57WSUb/J7b25llKOCvHr16pnFrUWLFrJx40Z5+umnPUFeoJNE/UKEOnHUJp0DBw70ybxq1apJ6dKlpVSpUmIH7mBT00SQR347TbTK95nDP5ajWccu1KxO2WDq4NJKlz6u/c480MTcjjjO/cQKvyfkt5NRvslvJ6N8k9+uMM+T4irIC+T888+XN954w/O4UqVKeWrttm3blqd2z5uOwqmLP/fonXbhPaIoyG+niUb5zsjSxZK05JwxppIkJ+A73mO4m2vG83eR3xPy28ko3+S3k1G+Ezu/XWGmI+7nyVu5cqVpxulduzd//nyfbebNmyctW7aMQeoAxJoGeGsf62iWtJQksxyvAUkzzQIAAGBHMa3J0z4469at8zxev369rFq1SsqWLSvVq1c3zSg3bdok06ZNM8/r4Ck1a9aUhg0bSkZGhqnB0znxdHHr37+/abo5evRo6dq1q8yZM0cWLFggX331VUzeIwAbGRyZOfL6Jn0Qkf0AAAA4LshbtmyZz8iY7n5xvXr1kqlTp5o58DZs2OB5XgO7++67zwR+JUqUMMHehx9+KJ06dfJsozV2M2bMMKNuDhs2TGrXri0zZ86U5s2bF/G7AxBrK1J6S5pkijySJFKmlshd38Y6SQAAAM4O8tq0aRNy9EoN9Lzdf//9ZsnP1VdfbRYAiU0DvDRXprkHAACQKOJ+4BUACCXDSpG0YdvJJAAAkDDifuAVAAAAAMAxBHkAAAAA4CA01wTgWAOz7ja34yO833nWeea2c4T3CwAAEAkEeQAca54VnVF1B2bdY24J8gAAgB3RXBMAAAAAHISaPACOdX/SG7n3OkZ0v41dv0VlvwAAAJFAkAfAsW5M+jgq+30zeUTuvZxmmwAAAHZCc00AAAAAcBCCPAAAAABwEII8AAAAAHAQgjwAAAAAcBCCPAAAAABwEEbXBOBY/7NamtsrI7zfjNyfzrQI7xcAACASCPIAONagrDuiEuSdkznV3K6N8H4BAAAigSAPAArrmfoiB3fm3E9OExmyibwEAAAxR5AHwLGauX7KvdcxugfKyoju/gEAAAqAIA+AY72a/HjuvX9H5wD//iXn9pHy0dk/AABAITC6JgAAAAA4CDV5AFAIGVnZUnfoR+b+jynZ5pbRNgEAgB0Q5AFAAaUmu3waQvQ5ep+kJLnkVXISAADYAEEeABTQmpGX+jyuO1RELLIRAADYA33yAAAAAMBBqMkD4FgHpESR9JUbkfRK0UzVAAAAEAaCPACO1TzzZXO7NsrHuSppYZSPAAAAED6aawIAAACAgxDkAQAAAICD0FwTgGMtTbkl997fMU4JAABA0SHIA+BYJ8qhWCcBAACgyNFcEwAAAAAchJo8ADhO07MvNre9yEkAAGADBHkAcJwey+5tbgnyAACAHdBcEwAAAAAchJo8ADhObVzLc+91JC8BAEDMEeQBcKybsoaY2zejfJzxyc/m3nswykcCAADIH0EeAMf6zjoj1kkAAAAocvTJAwAAAAAHoSYPgGM9kTwx9x595QAAQOKIaU3eokWLpEuXLlKlShVxuVzy3nvvhdz+3XfflUsuuUTKly8vpUqVkhYtWsgnn3zis83UqVPNvvyXw4cPR/ndALCbzq7FZgEAAEgkMQ3yDhw4II0bN5bx48eHHRRqkDd37lxZvny5tG3b1gSJK1eu9NlOA8DNmzf7LMWLF4/SuwAAAAAA+4hpc82OHTuaJVxjx471efz444/LnDlz5IMPPpAmTZp41mvNXaVKlSKaVgAIZquUNbfVyCIAAGADcT3wSnZ2tuzbt0/Kls05wXLbv3+/1KhRQ6pWrSqdO3fOU9MHAJHULnOcWQAAAOwgrgdeeeaZZ0yTz+7du3vW1a9f3/TLO/PMM2Xv3r3y3HPPyQUXXCCrV6+WOnXqBNzPkSNHzOKmr1OWZZnFDtxpsUt6nI78dkZ+W+Ly7D+aXJKz/3j5flK+yW8no3yT305G+Sa/rTDPNeI2yJs+fbo8/PDDprlmhQoVPOvPP/98s7hpgHfOOefI888/L+PGBb7SPmrUKBkxYkSe9enp6bY5adN0aA2luzkqyG8niVb5PlqiurlNTU+XaKpyguX5zYgH/J6Q305G+Sa/nYzyTX7vza2McmSQN3PmTOnTp4+88847cvHFF4fcNikpSZo1aya//fZb0G0GDx4sAwcO9Mm8atWqSenSpc0gLnbgDjY1TQR55LfTRKt8Tz6QMxl6n9KlJZo+zuhlbtNKb5d4wO8J+e1klG/y28ko3+S3K8zzpJR4rMG7+eabze1ll10W1pdh1apVpvlmMMWKFTOLP/f0C3bhPSUEyG+niUb5fjL7enPbN8rfGXdzzXj6bvJ7Qn47GeWb/HYyyndi57crHoI8bZ61bt06z+P169ebgEwHUqlevbqpYdu0aZNMmzbNPK+B3Y033mj62WmTzC1btpj1JUqUMDUASptd6nPa/05r5LSJpu5zwoQJMXqXAAAAAJAgo2suW7bMTH3gnv5Am0zq/Yceesg81vntNmzY4Nn+xRdflMzMTLnzzjulcuXKnqV///6ebfbs2SO33nqrNGjQQNq3b2+CRJ1f77zzzovBOwQQS+1dS80CAACQSGJak9emTZuQA5voKJneFi5cmO8+n332WbMAwJjk53Mz4WEyAwAAJIy4nicPAAAAAOCLIA8AAAAAHCTuRtcEALu5JvMRczsn1gkBAAAgyAOA4/ez1CIbAQCAbVCTB8BRGj70sRzNyhnQ6YcUncMOAAAgsRDkAXAUDfAysrIlLTlJNkpFE+TVjvIxxyc/k3uvY5SPBAAAkD+CPACOowHe2sc6FlnQ1ca1skiOAwAAEA5G1wQAAAAAByHIA+BcmUdyFgAAgARCc00AjrIipbekSabII0kiWRkiyWkiw7bHOlkAAABFhpo8AI6iAV6aKzPngQZ4ugAAACQQavIAOE6GlSJp1N4BAIAERZAHAMdpYvYV5nYAOQkAAGyAIA+Ao3TNfMLcflSEx5yYfZW5JcgDAAB2QJAHwFF+l6qxTgIAAEBMEeQBwHG6yvV57r2imXwdAAAgFII8AI7yYvLoIg+4RiRPzhnR85FXj63UUT2HbCqyNAAAALgR5AFwlAtcPxT5MTP0p9TS6Rty6fx8AAAAMUKQBwDH6ZzMqeZ27Yjc2sOXLyJPAQBAzBDkAUCk3fIZeQoAAGKGIA8AIiAjK1vqDj02cUNqskvWjLyUvAUAAEUuqTAvWrFihfzww7F+L3PmzJFu3brJkCFDJCODvigAEosGdGnJx35OT8raIydlpcc0TQAAIHEVqibvtttuk0GDBsmZZ54pf/zxh1x77bVyxRVXyDvvvCMHDx6UsWPHRj6lAGBT/jV2GcNPyb3XIybpAQAAia1QNXlr166Vs88+29zXwK5Vq1by1ltvydSpU2XWrFmRTiMAhG1sdnezAAAAJKpC1eRZliXZ2dnm/oIFC6Rz587mfrVq1WTHjh2RTSEAFMAr2Zeb2/vJNQAAkKAKVZPXtGlTefTRR+X111+XL774Qi677DKzfv369VKxYsVIpxEAAAAAEM2avGeffVZuuOEGee+992To0KFy+umnm/X//e9/pWXLloXZJQBExHVJ83Lv5c5ZBwAAkGAKFeQ1btzYZ3RNt6eeekpSUpiVAUDsDE2alnvvWT4GAACQkArVXPO0006TnTt35ll/+PBhqVu3biTSBQAAAAAohEJVu/3555+SlZWVZ/2RI0fk77//LswuAcAx2mU+Z26/jHVCAABAQipQkPf+++977n/yySdSunRpz2MN+j799FOpVatWZFMIAHFmq7jnyQMAALB5kNetWzdz63K5pFevXj7PpaamSs2aNeWZZ56JbAoBAAAAANEJ8txz42lt3XfffSflypUryMsBICG8mfxw7j1G+AQAAHHSJ0/nwwMAO1pt5Uzp0iyGaWjsWhfDowMAgERX6PkOtP+dLtu2bfPU8LlNmTIlEmkDgAK7PiunFm0teQcAABJUoYK8ESNGyMiRI6Vp06ZSuXJl00cPAAAAABCnQd4LL7wgU6dOlZ49e0Y+RQBwHCpK3jk8AQAAEkmhgryMjAxp2bJl5FMDAMfp05T+ufduIC8BAEBCSirMi/r27StvvfVW5FMDAAAAACj6IO/w4cMyZswYad26tdx9990ycOBAnyVcixYtki5dukiVKlVMv7733nsv39d88cUXcu6550rx4sXltNNOM01H/c2aNUvOOOMMKVasmLmdPXt2gd8jABTWY9k3mgUAACBugrzvv/9ezj77bElKSpIff/xRVq5c6VlWrVoV9n4OHDggjRs3lvHjx4c9dUOnTp3kwgsvNMcaMmSI3HPPPSaoc1uyZIn06NHD9BdcvXq1ue3evbssXbq0MG8VAApsenZ7swAAAMRNn7zPP/88Igfv2LGjWcKltXbVq1eXsWPHmscNGjSQZcuWydNPPy1XXXWVWafPXXLJJTJ48GDzWG+19k/XT58+PSLpBgAAAADHzZMXC1pL176979XxDh06yOTJk+Xo0aOSmppqtrn33nvzbOMODAM5cuSIWdz27t1rbi3LMosduNNil/Q4Hfkdv/ltSc6ULrH8rvRNej83DZeKHVG+yW8no3yT305G+Sa/rTDPbwoV5LVt2zbk3HifffZZVD6BLVu2SMWKFX3W6ePMzEzZsWOHmbMv2Da6PphRo0aZuf/8paen2yao0nTs37/f3GdeQvLbaSJZvo+WqG5uU9PTJVZuOnGp5zfEjvg9Ib+djPJNfjsZ5Zv83ptbGRWVIE/743nTWjTti6f983r16hXV3Pc/AXQHYd7rA20T6sRRm3R6DxijmVetWjUpXbq0lCpVSuzA/T41TQR55LfTRLJ8t9j3gLldUrq0xErGoQ3mNi2GaQiF3xPy28ko3+S3k1G+yW9XmOdJhQrynn322YDrH374Yc/V+GioVKlSnhq5bdu2SUpKipxyyikht/Gv3fOmo3DqEigT7RRQudNjpzQ5Gfkdn/m9S3IuzMTye+KSvBef7IbyTX47GeWb/HYyyndi57crzHQUanTNYG644QaZMmWKREuLFi1k/vz5PuvmzZsnTZs2Nf3xQm3D5O0AAAAAEkFEB17RQU90/rpwaa3funXrfKZI0GafZcuWNaNoajPKTZs2ybRp08zz/fr1M9MtaNPKW265xRxPB13xHjWzf//+0qpVKxk9erR07dpV5syZIwsWLJCvvvoqkm8VgE29kzw09174I/cCAABIogd5V155ZZ72wZs3bzbTGQwbNizs/ej2OoiLm7tfnPbrmzp1qtnnhg05fVtUrVq1ZO7cuWb0zAkTJphJ1MeNG+eZPkFpjd2MGTPkwQcfNGmpXbu2zJw5U5o3b16YtwogzjRw/RXrJAAAAMRfkKeDI3jTSdHr1asnI0eOzDPFQSht2rQJOXqlBnr+WrduLStWrAi536uvvtosABALX1tnmttjl7AAAABsHuS9+uqrkU8JADjEbVk5I3yujXVCAABAQjquPnnLly+Xn3/+2YzycsYZZ0iTJk0ilzIAAAAAQNEEeTolwbXXXisLFy6Uk08+2TS51El/tX+d9ocrX758YXYLAI5QW/6OdRIAAEACK9QUCnfffbeZMHzNmjWya9cu2b17t5kIXdfdc889kU8lAMSROSmDzAIAABA3NXkff/yxmZagQYMGnnXaXFNHvCzIwCsAEGnDs/qY21FkLQAASFCFCvKys7M9k49703X6HADEyiwrZ0xLgjwAAJCoCtVc86KLLjKTjv/zzz+edTppuc5f165du0imDwAAAAAQ7Zq88ePHS9euXaVmzZpSrVo1M7qmTlp+5plnyhtvvFGYXQJARNyRNCv3XkdyFAAAJKRCBXka2OmE5PPnz5dffvnFjK6pffIuvvjiyKcQAArgjqTZufdeId8AAEBCKlBzzc8++8wEczqKprrkkkvMSJs6omazZs2kYcOG8uWXX0YrrQAAAACASAZ5Y8eOlVtuuUVKlSqV57nSpUvLbbfdJmPGjCnILgHAcZpkvmoWAAAA2wd5q1evlksvvTTo8zp9wvLlyyORLgCIW0cl1Swy/jyRR8qLPH5qrJMEAAASSIH65G3dujXg1AmenaWkyPbt2yORLgBwhqyMWKcAAAAkmALV5J166qnyww8/BH3++++/l8qVK0ciXQAQ/+76ViQ5LdapAAAACaZANXmdOnWShx56SDp27CjFixf3ee7QoUMyfPhw6dy5c6TTCABBNXzoYzmaZXkej0tqIkkukfbkGQAASFAFCvIefPBBeffdd6Vu3bpy1113Sb169cwceT///LNMmDBBsrKyZOjQodFLLQD40QAvIytb0pJzGibck/1vSU12yZoY55Smqe7Qj+THlGzzmPo8AABgyyCvYsWKsnjxYrn99ttl8ODBZn48pYFehw4dZOLEiWYbAChKGuCtfcw+k59rkOluDa+/kvoIAADAtpOh16hRQ+bOnSu7d++WdevWmUCvTp06UqZMmeikEAAKYvPqnNvKjWOWb2tGHhuF+K4H7za342OWGgAAkGgKHOS5aVCnE6ADgK28cnHO7TB7jPQ7z2oe6yQAAIAEU+ggDwDsYEVKb0mTTJFHko5NWcCIlgAAIIER5AGIaxrgpbkyjw1togGejYK8+5PeyL1nnz6DAADA2QjyAMS1Rpk5QdTah+0ZRN2Y9HGskwAAABJMgSZDBwAAAADYG0EeAAAAADgIzTUBxLVPU+7JvfdbjFMCAABgDwR5AOJaRdkV6yQAAADYCs01AQAAAMBBqMkDgCj6n9XS3F5JLgMAgCJCkAcAUTQo6w5zS5AHAACKCs01AQAAAMBBqMkDgChq5vop9549J2sHAADOQ5AHIK7dlXWvuX1J7OnV5Mdz7/07xikBAACJgiAPQFxbaJ0b6yQAAADYCn3yAAAAAMBBqMkDENeGJk3NvUefNwAAAEWQByCuXZe0INZJAAAAsBWaawIAAACAg1CTBwBRdEBKmNs0chkAACRKTd7EiROlVq1aUrx4cTn33HPlyy+/DLpt7969xeVy5VkaNmzo2Wbq1KkBtzl8+HARvSMAOKZ55stmAQAASIggb+bMmTJgwAAZOnSorFy5Ui688ELp2LGjbNiwIeD2zz33nGzevNmzbNy4UcqWLSvXXHONz3alSpXy2U4XDSIBAAAAwOliGuSNGTNG+vTpI3379pUGDRrI2LFjpVq1ajJp0qSA25cuXVoqVarkWZYtWya7d++Wm266yWc7rbnz3k4XAAAAAEgEMQvyMjIyZPny5dK+fXuf9fp48eLFYe1j8uTJcvHFF0uNGjV81u/fv9+sq1q1qnTu3NnUEgJwplnZbcxiV0tTbjELAACA4wde2bFjh2RlZUnFihV91uvjLVu25Pt6bYL50UcfyVtvveWzvn79+qZf3plnnil79+41TTwvuOACWb16tdSpUyfgvo4cOWIWN32dsizLLHbgTotd0uN05Hf85PfD2X3M7bU2/W6cIDn9ge303aV8k99ORvkmv52M8k1+W2GeT8R8dE1tWumfcP91gWggd/LJJ0u3bt181p9//vlmcdMA75xzzpHnn39exo0bF3Bfo0aNkhEjRuRZn56ebpsTM02H1lCqcPIH5Hc8OZ7yXeUEy/N9taOjJaqb21QbpY/fE/LbySjf5LeTUb7J7725lVG2DfLKlSsnycnJeWrttm3blqd2L1ABnzJlivTs2VPS0kIPTJ6UlCTNmjWT3377Leg2gwcPloEDB/pknvYN1D6AOoiLHbiDTU0TQR757TTHU75rHvoh97WdxI4yDuUMJJVWurTYBb8n5LeTUb7JbyejfJPfrjDPk2IW5GlwplMmzJ8/X6644grPen3ctWvXkK/94osvZN26dWbQlnC+DKtWrTLNN4MpVqyYWfy5p1+wC+8pIUB+O01hy/eLyU/lvn6Q2JFLcgJYu31v+T0hv52M8k1+OxnlO7Hz22X3IE9p7ZnWxjVt2lRatGghL730kpk+oV+/fp4atk2bNsm0adPyDLjSvHlzadSoUZ59arNLba6p/e+0Rk6baGqQN2HChCJ7XwAAAAAQKzEN8nr06CE7d+6UkSNHmoFUNGibO3euZ7RMXec/Z572u5k1a5YZUCWQPXv2yK233mqagWrTryZNmsiiRYvkvPPOK5L3BAAAAACxFPOBV+644w6zBBtcxZ8GbgcPHgy6v2effdYsAGAHN2UNMbdvxjohAAAgYcQ8yAMAJ/vOOiPWSQAAAAmGIA8AouyJ5Ikij9x4bEVymsiQTeQ7AACICoI8AHFtt5Q0t6EnXrGRrIxYpwAAADgcQR6AuHZh5iRzu1bsa1DWHXLlyA9yHjxSPtbJAQAADkeQBwBRlpGVLXWHfmTu/5iSbW7TyHUAABAlBHkAEEWpyTppaZLn8atZHSTZ5ZK+5DoAAIgSgjwAcW1FSu/ce1vFjtaMvNTncd2hImIJQR4AAIgagjwAcS1NMmOdBAAAAFshyAOAItTetTT3XkfyHQAARAVBHgAUoTHJz+fee5h8BwAAUXFsNAAAAAAAQNwjyAMAAAAAByHIAwAAAAAHoU8egLh2fdZwc/tOrBMCAABgEwR5AOLaaqtOrJMAAABgKwR5AFCENkhFc3s6uQ4AAKKEIA9AXBuTPC6u5p3rlPmMuV0b64QAAADHIsgDENfau76NdRIAAABshSAPQFxp+NDHcjTL8jz+IUXEJfEjVY7GOgkAAMDhCPIAxBUN8DKysiUtOWcGmHgK8NTKlJty7+2McUoAAIBTEeQBiDsa4K19LLcP3iNM9wkAAOCNsyMAAAAAcBCCPABxZUVKb+ns+urYiqyMWCYHAADAdgjyAMSVNMmUu5P/e2zFhfeJJKfFMkkAAAC2QpAHIO6cKjuOPWg3TGTIplgmBwAAwFYI8gAAAADAQRhdEwCKUNfMJ8ztR+Q6AACIEoI8AChCv0tV8hsAAEQVzTUBAAAAwEGoyQMQV36XU81tA4lPLyaPzr2XO5k7AABAhBHkAYgrXTNzgqS1Ep8ucP0Q6yQAAACHo7kmAAAAADgINXkA4sqJcjDWSQAAALA1avIAxJWlKbeaJZ6luTJFHikv8tXYWCcFAAA4EDV5AFCEMvRn1xJJI9cBAECUEOQBQBE6J3OqZGRlS1pmkpkR/bpP7pVkl0seGjmGzwEAAEQEQR4AFKHUZJdPS/khSdNE14gQ5AEAgMggyAOAIrRm5KU+jzOGk/0AACCyGHgFAAAAABwk5kHexIkTpVatWlK8eHE599xz5csvvwy67cKFC8XlcuVZfvnlF5/tZs2aJWeccYYUK1bM3M6ePbsI3gkAAAAAJHiQN3PmTBkwYIAMHTpUVq5cKRdeeKF07NhRNmzYEPJ1v/76q2zevNmz1KlTx/PckiVLpEePHtKzZ09ZvXq1ue3evbssXbq0CN4RgGjrlPm0WQAAAGDDIG/MmDHSp08f6du3rzRo0EDGjh0r1apVk0mTJoV8XYUKFaRSpUqeJTk52fOc7uOSSy6RwYMHS/369c1tu3btzHoA8W+DVDILAAAAbDbwSkZGhixfvlwGDRrks759+/ayePHikK9t0qSJHD582DTFfPDBB6Vt27Y+NXn33nuvz/YdOnQIGeQdOXLELG579+41t5ZlmcUO3GmxS3qcjvy2b367dJK53Nc4wSorpyVCsyJ8P5TvokV+k99ORvkmv53MsuH5d7hpiVmQt2PHDsnKypKKFSv6rNfHW7ZsCfiaypUry0svvWT67mlQ9vrrr5taOu2r16pVK7ONvrYg+1SjRo2SESNG5Fmfnp5umw9V07F//35zX/shgvx2koKU75dKTja36ektxQkeKDbM3M5OTy+yY/J7UrTIb/LbySjf5LeTWTY8/3ZXRtl+CgX/DNPMDJaJ9erVM4tbixYtZOPGjfL00097gryC7lNpk86BAwf6ZJ42Gy1durSUKlVK7MAdbGqa7FLInIz8tm9+n3/oC3ObVrq0OME/B12e915UKN9Fi/wmv52M8k1+O5llw/PvcNMRsyCvXLlypi+dfw3btm3b8tTEhXL++efLG2+84XmsffQKuk8dhVMXf+7RO+3Ce0RRkN9OE275djfXdMr3oILsisn74fekaJHf5LeTUb7Jbydz2ez8O9x0xGzglbS0NNPscv78+T7r9XHLluE3w9JRObUZp3ftnv8+582bV6B9AkBR+TSlv1kAAAAiJabNNbWJpE5x0LRpUxOcaX87nT6hX79+nmaUmzZtkmnTppnHOnhKzZo1pWHDhmbgFq3B0znxdHHr37+/abo5evRo6dq1q8yZM0cWLFggX331VczeJwAAAAAkRJCn89nt3LlTRo4caea7a9SokcydO1dq1Khhntd13nPmaWB33333mcCvRIkSJtj78MMPpVOnTp5ttMZuxowZZtTNYcOGSe3atc18fM2bN4/JewQAAACAouSy7DJ8pI3owCvawVJH17TTwCuaHjt1/HQy8tu++Z0x/BRzmzZipzhBLN4P5btokd/kt5NRvslvJ7NseP4dbpwS89E1AaAgnsz+P3P7INkGAAAQEEEegLgyLTuneTZBHgAAgM1G1wQAAAAARB41eQDiyo1Jc3PvdRQnuDBzorldGuuEAAAAxyDIAxBX7k96K/fe8+IEu8UegzsBAADnoLkmAAAAADgINXkAEEPvJA91VPNTAAAQewR5ABBDDVx/kf8AACCiCPIAIMbSXJkij5TPeZCcJjJkU6yTBAAA4hhBHgDEUIakiGWJuDKzTbCXobd8IgAA4DgQ5AGwtYYPfSxHsyzP48lJ9SXJ5ZILxBmaW6953l9X+UxSXC4ZFetEAQCAuEaQB8DWNADKyMqWtOScwYD7ZD8oqckuWSPOsGbkpZ77dXUMFksI8gAAwHEhyANgexrgrX2M0ScBAADCQZAHwNZWpPSWNNGBSXKn9Wx4hUjrB0ROqS1Oc0fSrNx7BLQAAKDwmAwdgK1pgGdGn3RbM1vkxVbiRHckzTYLAADA8aAmD4DtZVgpkjZse6yTAQAAEBeoyQMAAAAAB6EmD4CtNc98ydyujHVCAAAA4gRBHgBbOyAnxDoJAAAAcYXmmgAAAADgINTkAbC1OSkPJMy0AgutJua2fawTAgAA4hpBHgBbqy2bJFHclfVvc7s21gkBAABxjeaaAAAAAOAg1OQBgE00kPWxTgIAAHAAgjwAsIl3Uobl3rsjxikBAADxjOaaAAAAAOAgBHkAAAAA4CA01wRga4Oy+pnbMZIY0lyZIo+Uz3kwbHuskwMAAOIQQR4AW/uf9a+ECfIy9CfZEkmLdUIAAEBcI8gDAJs4J3OqZGRlS1pmbkv6oR9JarJL1oy8NNZJAwAAcYQgD4CtDUiamXuvozidBnTeXaUXJN8tLpeu+y2m6QIAAPGFIA+ArfVN+kAShX+NXcbw3TFLCwAAiF+MrgkAAAAADkKQBwAAAAAOQpAHAAAAAA5CkAcAAAAADkKQBwAAAAAOwuiaAGxtnnWeue0sieeurHvN7UuxTggAAIgrMa/JmzhxotSqVUuKFy8u5557rnz55ZdBt3333XflkksukfLly0upUqWkRYsW8sknn/hsM3XqVDOvlP9y+PDhIng3ACJtYNY9ZklEC61zzQIAABA3Qd7MmTNlwIABMnToUFm5cqVceOGF0rFjR9mwYUPA7RctWmSCvLlz58ry5culbdu20qVLF/NabxoAbt682WfRIBIAAAAAnC6mzTXHjBkjffr0kb59+5rHY8eONTVzkyZNklGjRuXZXp/39vjjj8ucOXPkgw8+kCZNmnjWa81dpUqViuAdAIi2xq7fcu91TLjMHpo0NWHfOwAAiMMgLyMjw9TGDRo0yGd9+/btZfHixWHtIzs7W/bt2ydly5b1Wb9//36pUaOGZGVlydlnny2PPPKITxDo78iRI2Zx27t3r7m1LMssduBOi13S43Tkd2zz+8zhH8vRrJz7q1NGistsc7ckmmuTPjW3kf7eU76LFvlNfjsZ5Zv8djLLhuff4aYlZkHejh07TBBWsWJFn/X6eMuWLWHt45lnnpEDBw5I9+7dPevq169v+uWdeeaZJlh77rnn5IILLpDVq1dLnTp1Au5Haw1HjBiRZ316erptPlRNhwav7ppKkN9O4l++yxfLlqPZlqQmuWRfcnWzPjU9XRLN0RLRee/8nhQt8pv8djLKN/ntZJYNz7/dlVG2H13TP8M0M8PJxOnTp8vDDz9smmtWqFDBs/788883i5sGeOecc448//zzMm7cuID7Gjx4sAwcONAn86pVqyalS5c2/fvswB1saprsUsicjPyObX6/n9FH0iRT0lKSRI5miCSn6ZOSaDIObZA0V6bI83VzVjT+P5Euzx73finfRYv8Jr+djPJNfjuZZcPz73DTEbMgr1y5cpKcnJyn1m7btm15avcCDdiiffneeecdufjii0Num5SUJM2aNZPffnP368mrWLFiZvHnHpnTLrxHCwX57TQ+I+KKJcVcR0UkTSQ5NWdJwHJ/VJL1P4y4MrPM41nfbZRHl30ia0Zeetz75vekaJHf5LeTUb7Jbydz2ez82/ZBXlpampkyYf78+XLFFVd41uvjrl27hqzBu/nmm83tZZddFlYEvmrVKtN8E0B8OCczZ8CRtQ8n9oAjza3XPH0TVUZWtqQl26MJOQAAsK+YNtfUJpI9e/aUpk2bmjnvXnrpJTN9Qr9+/TzNKDdt2iTTpk0zjzWwu/HGG00/O22S6a4FLFGihKlGVdq3Tp/T/nfa7FKbaGqQN2HChBi+UwAoOP8au5sefCL3XmIHvwAAwMZBXo8ePWTnzp0ycuRIM5ddo0aNzBx4OjKm0nXec+a9+OKLkpmZKXfeeadZ3Hr16mUGW1F79uyRW2+91QSAGvjpqJo6v955550Xg3cIAJHzYvJTufd8RyUGAADw5rLsMnykjWgNoAaIOrqmnQZe0fTYqeOnk5Hfsc3vrcNrmvUVR/xZxCmxt4zhp5jbtBE7j2s/lO+iRX6T305G+Sa/ncyy4fl3uHFKzEfXBAB/ZWQfmQIAAFBISYV9IQAAAADAfgjyAAAAAMBBCPIAAAAAwEHokwcAcWK3lDS3FWOdEAAAYGsEeQAQJy7MnGRu18Y6IQAAwNYI8gDYzm1Z/zG3r8Y6IXb1+KkiWRkiyWkiQzbFOjUAAMBmCPIA2M7XVuNYJ8G2MrKyZf+RTDnJlSEZmdmSFusEAQAA22HgFQCIE6nJLklLTpJzMqfKEYtrdAAAIDDOEgDYzoikV3LvdYxxSuxlzchLPfczhsc0KQAAwMYI8gDYzlVJC2OdBAAAgLhFkAcg5s4c/rGUL5Yt/xx0iSUu+SFFxBXrRAEAAMQpgjwAMXc0y5Kj2Za4QzsCvPxdn5XTXvOdKH82AAAg/hDkAbCF1CSX/HLBF+JaPkUkKzNnegAEtdqqQ+4AAICAGF0TgP1ogEeQBwAAUCjU5AGIudauFVLKVVyk4xMinUbHOjlxYUzyOOmctFjkEa8aTyZHBwAABHkA7OD55LGyN6m6iPw71kmJG21cK8xcea7MbPM4zZXJ5OgAAMCgJg8A4lBz6zUzYI3bDyk3MGANAAAwCPIAIM4nRlcTh3Uxt3fEKD0AAMA+CPIAwAHGZvcwtwR5AACA0TUBAAAAwEGoyQMAB+js+kruTv6vyCM3HlvJaJsAACQkgjwAMbdVysohKSXlYp2QODYy+RVJFR1hM+cxo20CAJC4CPIAFLmGD33sMzLk0awxUjPNJZ/xWRQao20CAAA3gjwARW6pq5ekpmR6hvw/U16XlGQ+iEiOtvnz8FPNbQOyFQCAhEOQB6DIpUmmaU5o+oyJyC8Pd5T09HQ+iQjqmjna3K4lVwEASDgEeQBiIsNKkbRh23MeWMeabiJy0rL2S8bwU8z9o5IiJ47YSvYCAJAACPIAwIFSk12SmjtLjqk1JY4GACBhEOQBgKP76F3lqc0DitqB4RXNqK9u/7NayqCsO8z9Zq6f5KXkJ6lhBoAoIMgDUOSuyXzE3M4h74uMmVJh+CnSKfNp2SgVpXpJkXkPdLR1cPC5dY4MzLrHPG7s+k3eTB4RcNsMSTGji/oPPoPYjZx7f9IbcmPSx3Ki1iLnNs/2d5frv3Ki67DPRQiaFQNAZBDkASjSK/oLrSayOuteSUvOaUqI6NMTZ+/mmhlZ2TLEmiIZD98ortwnYn1y7V/jo8FBZ9diT5CX7+szsqTu0I/ky5TbpYzs83ku1u/Nyfw/twsz75KPs8+T3JbCJrjzzv8rc5ec1/b2Cf70QoQOyqSfo7vJMYE7ABQOQR6AIqEngu6+YRrg6QkcioZ3gLNARBo99JEkuVx5Tq61RuXJ7P+T6a7LonJy7T0/4oqU3vJQVl/5n/Uv83ht6mFz6z7pdwcHax9z1zbqbeCAr8lDH0tacuBOh+73Jus+FTm9XcTfUyLSz3GOa6BUl615aupSklxyYkqy9B35pmf7nDF08/IPvDVgzJDUnP1lZcs3KbdKxvCccqEI1gEgfAR5AIqMngi2H7mAYf1j7IcRl0p6egtJK11aXC6XT21MVrYlB7Ky5NFhd8v9SW8d18m1O6ibk/KA1JZNskLjyhQx8yNq8HV38n/lf5k5Qd7ErCukV9JHPscJFhz48w1I//R5bn/ue+s39Tv52sqQEUmvSFKSSI8Rswr8fpBDP1NL57V05a2pG38cmaT7ODF32g8tOy4JfCHipqwh8p11BjV9ABACQR4AJDjvwOptrRXzatvpfXKtvrMayE1ZQ8396rJF5qbcF3S/rowXJENO8PyncZ+yp6VoW740qZW8X9aO8K6pizztq+euPVRXJi2UYrn9ExW1QwVvkilZk+UyecZTyxpuMF4QOYH730HToDV9njahAIA8CPIARIV307w7kmbJgOTMgIMvwF6O1YrpCfzzeU/wCyA1OUlOTE6WBiO+l1jxb3Z6YHiKuCzfAPa1YT3ksezeZh39wHK+u9WzNsqclEEmT/ybZOrnKkXc3Nr7QoQ2BH33oS5yZdIiyRiekyaCdQDwxRkXgIjwDwbmuUrLWLlK5shF5rG7WVc0rvojevybaV6Q25zumJuCvnal2Pv9+JfZf1nLZIDMkozh1+d53VYpK5dazzt2IBB3XnxtnSkHMv4jR11aU5bDv0nmGom9Dq5vPUGnd23ztOxL5ans620/eiwARBtBHoDjrq17J3moNE7yHTijvKTLwynT5KkRT+XWCr1CgAdbcQctvXIXHe0xWK2lZVmeETx/TLkh4DbxVJsUaDRTI3dgpM3JNSRtxE7P82lxFKxrU84mmT/aavRYAChqBHkAgsqvqV6xjPFyQEqJJOe92m/HE0MglFBBQMcQI3i6a5O+yGosd+YO/z8++Rlp48qpy7TEJUdLVJeMQxtM0DEx+wqZmH2Vee4q1+cyInlywH3+bNWQa7IeM/fLyF75MiVnEvFA2mU+J1slp5/hm8kPS2PXuoDbPZZ9o0zPbi83yRUyIGWWJEtOjZ37+9t25MK4GxjJ+3PrKyJjH/pIkpMCD9qyQSpKp8xnaJYLwPFiHuRNnDhRnnrqKdm8ebM0bNhQxo4dKxdeeGHQ7b/44gsZOHCgrFmzRqpUqSL333+/9OvXz2ebWbNmybBhw+T333+X2rVry2OPPSZXXHFFEbwbIH5q365LmidDk6YF3Ga1dbpcn/WwlM16ypwIXpn8Vcg+V41HrjCPCergVL7NNI/VcHlfEBmffbXEi1elq7xhdfN5X2mOGz32/jyjx3oGbck6yuA7ABwtpkHezJkzZcCAASbQu+CCC+TFF1+Ujh07yk8//STVq1fPs/369eulU6dOcsstt8gbb7whX3/9tdxxxx1Svnx5ueqqnKuiS5YskR49esgjjzxiArvZs2dL9+7d5auvvpLmzZvH4F0mJvc/VfdVY9U36X0ZkPR2wO21H8htWQ+Y+7Xlb0+H/0DiqdlNsJow7TfyZHZOk6/2rqUyJvn5gK93X3VWqXJUVqYE7//UNfMJ+V2qmvsvJo+WC1w/BNzuhqxr5MWsLmENTLcrubw8knS7XDtiTsDnl+a/CyAh6G+S77eko09Tz9T0dM+UFQNEzHJsuycD7rNxnv6PPYIe/8sgx/Y3IndJJN7/L07Xxw99LJKVFXD02EC/pee5fo6b/zkAYIsgb8yYMdKnTx/p21cbWIipxfvkk09k0qRJMmrUqDzbv/DCCyb40+1UgwYNZNmyZfL00097gjx97pJLLpHBgwebx3qrtX+6fvr06UX6/uJ9RMTjaR7k6d8RYa9kdZKxmVeJDP1I5qb820zGG8jArLtlnpUT1N+f9IbcmPRxwO3+Z7WUQVk577GZ6yd5NfnxgM2rDkgJaZ75sud1S1NukRPlUMB9uudwCjTBsx1oMyatfRsx8lkR0SWvZnlOLgHAGY7VXu7Mt0l626ScJrfeQaAam91dXsm+nGafAGwrZmeeGRkZsnz5chk0yLfGpn379rJ48eKAr9FaOn3eW4cOHWTy5Mly9OhRSU1NNdvce++9ebZxB4aJINg/rXD6gaxJypRVUlu65/YDKSx3/44RjzzrddVYry5PCrh92zxBxS0Bt5ugV2BD9Iuxm3ezW5lR4PyvAutljZxLG+58eTjg60/Pky95m4m55fQE8t5nYPfnLgCQ6ALV0Hn/lh4YXjxoEKjNPrvL/ICjsXo3e1cVZad8mtI/aDouzJwou7V/s4gZyKqB66+A2w3P6iOzrLaeqWnuSJodcLuFVhO5K+vf5n4DWS/vpAwLeuxGmW947n+aco9UlF0Bt7sr615ZaJ1r7g9NmirXJS0IeFF0VnYbGZ6d8x/uAtdqeTFZB9/Ka7eUlAszj50TrEjpbWpVA7k+a7istuo4Nqj2P29rnvmSHNA5RkVkTsoDUls2eZ7zzu/BWbfJ/6x/mfUDkmZK36QPAu5/nnWeDMy6x9xv7PpN3kweEZetoxAnQd6OHTskKytLKlas6LNeH2/ZsiXga3R9oO0zMzPN/ipXrhx0m2D7VEeOHDGLW3p6uudWm9nYgaZj59g2knF4o2e0MG+9M4fKr1LD3F+eetj8VGRYyT7bHM7OkOzsA+b+EdcRSQ8YLCVLBflbvhvc0jw6JH8ETdP/fB7NC1iv5c7LSFn87xZej5YEqUsT0RD1WJjaMuh27XRyZ5/t+nry+2B6uqTmNq9K8tlOrQm6z4k+j1qazyLS+eA0mt/uPNL8BvntJJTvOM7vgWsDhh23ab/GxxdI5tEMSc9t/eJvn5Ul2Vk5/3Oz5FDQ7czzmQclW0ew0tclZ0u6ezJHP0eyjki2lbPPw0kZkp4UeLv9XsfOzOfY2Zk526m9WdlSPMA5hsrIOuw59qGkTM+x9e8+V7ZYRzT8sORgdqbnXCPDdTjIuYbIXrF8jq1pTAty7Mysw3Ikc5+MSH1Ztg/6v4DbaDeISdlXmvsdXN/Iw8lTAm63XqrI/2XmBN/aImdBiOC7e+ajslEqmPsTkp+Rc1y/BtzuuexrZEb2Jeb+tUnzpX/SOwG3W2HVkztzg+9qsk3eTnnQ3E9zZfmct2Wb8pCTF/uysiXdK1+889u7PBwKUR4OWJnHyoPXZ6LH1RKyfVBZyZAUaZ157Azmo5R/y8myL+D+BmTdI0utRub+4KTX5fIk3wbjbrOy28rT2deZ+/9yrZKnkn3PkNy2SRnpmjna83hJyq0SjPf57pPJE+RC1+qA203O7mxq29Xlri9lcPLrAbf71aouvbNyPgd9v/q+vWmpPnT7Cludn+zdu9fc5hujWDGyadMmTZm1ePFin/WPPvqoVa9evYCvqVOnjvX444/7rPvqq6/MfjZv3mwep6amWm+99ZbPNm+88YZVrFixoGkZPny42QcLeUAZoAxQBigDlAHKAGWAMkAZoAyIzfNg48aNIWOtmNXklStXTpKTk/PUsG3bti1PTZxbpUqVAm6fkpIip5xySshtgu3T3W9PR+x0y87Oll27dpl92ilqr1atmmzcuFFKlcpp0gHy2yko3+S3k1G+yW8no3yT306214bn31qDt2/fPjPLQCgxC/LS0tLk3HPPlfnz5/tMb6CPu3btGvA1LVq0kA8+8G1vPG/ePGnatKnpj+feRvfh3S9Pt2nZMqf5YSDFihUzi7eTTz5Z7EgLmF0KWSIgv8lvJ6N8k99ORvkmv52M8p3Y+V26dOl8t4npkH9ae9azZ08TpGlw9tJLL8mGDRs8895pDdumTZtk2rScubx0/fjx483rdBoFHWRFB13xHjWzf//+0qpVKxk9erQJFufMmSMLFiwwUygAAAAAgNPFNMjT+ex27twpI0eONJOhN2rUSObOnSs1auR0qNR1GvS51apVyzyvtXQTJkww1ZTjxo3zTJ+gtMZuxowZ8uCDD5oJ0XUydJ2PjznyAAAAACSCmE/epZOZ6xLI1KlT86xr3bq1rFiRM8pNMFdffbVZnESbkw4fPjxPs1KQ305A+Sa/nYzyTX47GeWb/HayYnF8/u3S0VdinQgAAAAAQGTo9F8AAAAAAIcgyAMAAAAAByHIAwAAAAAHIcgDAAAAAAchyAMAAAAAByHIAwAAAAAHIcgDAAAAAAchyAMAAAAAByHIAwAAAAAHIcgDAAAAAAchyAMAAAAAByHIAwAAAAAHIcgDAAAAAAchyAMAAAAAByHIAwAAAAAHIcgDAAAAAAchyAMAAAAAB7F9kLdo0SLp0qWLVKlSRVwul7z33nsht9+8ebP83//9n9SrV0+SkpJkwIABRZZWAAAAAIg12wd5Bw4ckMaNG8v48ePD2v7IkSNSvnx5GTp0qHkdAAAAACSSFLG5jh07miVcNWvWlOeee87cnzJlShRTBgAAAAD2Y/uaPAAAAACAg2ryioI28dTFLTs7W3bt2iWnnHKK6QcIAAAAALFmWZbs27fPjFei448EQ5AnIqNGjZIRI0YU5ecDAAAAAIWyceNGqVq1atDnCfJEZPDgwTJw4EBPpqSnp0v16tXlr7/+klKlSokdaO3ijh07pFy5ciGjdpDf8YjyTX47GeWb/HYyyjf57WTZNjz/3rt3r9SoUUNKliwZcjuCPBEpVqyYWfydfPLJtgryMjIyTJrsUsicjPwmv52M8k1+Oxnlm/x2Mso3+Z2UGwfk16XM9kHe/v37Zd26dZ7H69evl1WrVknZsmVNbZvWwm3atEmmTZvm2Uafd792+/bt5nFaWpqcccYZMXkPAAAAAFBUbB/kLVu2TNq2bet57G5W2atXL5k6daqZ/HzDhg0+r2nSpInn/vLly+Wtt94y1Zp//vlnEaYcAAAAAIqe7YO8Nm3amFFkgtFAz1+o7QEAAADAyejcBQAAAAAOQpAHAAAAAA5CkAcAAAAADkKQBwAAAAAOQpAHAAAAAA5CkAcAAAAADkKQBwAAAAAOQpAHAAAAAA5CkAcAAAAADkKQBwAAAAAOQpAHAAAAAA5CkAcAAAAADkKQBwAAAAAOQpAHAAAAAA5CkAcAAAAADkKQBwAAAAAOQpAHAAAAAA5CkAcAAAAADkKQBwAAAAAOQpAHAAAAAA5CkAcAAAAADpISyZ3dfPPNAdeXLl1a6tWrJzfccIOcdNJJkTwkAAAAACBaNXm7d+8OuKxatUoeeughE+j98ccfkTwkAAAAACBaNXmzZ88O+tyhQ4fkxhtvlEGDBsnbb78dycMCAAAAAIq6T16JEiXkgQcekG+++aaoDgkAAAAACadIB14pW7as7NmzpygPCQAAAAAJpUiDvMWLF0vt2rWL8pAAAAAAkFAi2ifv+++/D7g+PT1dvvvuO3n88cfl0UcfjeQhAQAAAADRCvLOPvtscblcYllWnufKly9v+uT169cvkocEAAAAAEQryFu/fn3QefJOPvnkSB4KAAAAABDtIK9GjRrm9siRI5KZmSknnnhiJHcPAAAAACjKgVd27Nghl112mZx00klSqlQpadmyJZOfAwAAAEC8BnmDBw+W5cuXy4gRI+Spp54yQd9tt90WyUMAAAAAAIqqueYnn3wiU6ZMkU6dOpnHetuoUSM5evSopKamRvJQAAAAAIBoB3n//POPNGnSxPO4fv36kpaWZta7++sBAAAAQJF461qR3X6DQ5apJfJ/Mxz9AUS0uaZOnZCS4hs36uPs7OxIHgYAAAAA8rd7vciuP4491vv+QZ8DpUQ6yGvXrp1PoHfw4EHp0qWLqdFzW7FiRSQPCwAAAACBlT1N5M6lOfcnNE+IXIpokDd8+PA867p27RrJQwAAAAAAYhnkAQAAAADitE/e4cOH5f3335d9+/bleW7v3r3mOZ0oHQAAAAAQB0Heiy++KM8995yULFkyz3M6Ofq4cePk5ZdfjuQhAQAAAADRCvLefPNNGTBgQNDn9blp06ZF8pAAAAAAgGgFeb/99ps0btw46PNnnXWW2QYAAAAAEAcDr2RmZsr27dulevXqAZ/X53QbAAAAAEj0ScvjIshr2LChLFiwQM4999yAz8+fP99sAwAIA//wAACJNmm5zmmnvCcwR2yDvJtvvlkGDhxoArnOnTv7PPfBBx/Io48+KmPGjInkIQHAeYGc2/Zfcm7L1z/2WJdgE7lyxRMA4KRJyzXQ8/6fF43/c285swYxokHerbfeKosWLZLLL79c6tevL/Xq1ROXyyU///yzrF27Vrp37262AYCEDdzyC+S86TrvfzSh9ssVT+D4OfRkD4hL+t0riv9zu51ZgxjRIE+98cYbJsh76623TGBnWZYJ9kaMGGGCPABwPP9/GKH4B3KhhNqmqK54Ak4O6vwvuoQ62SMgBML7boT6X+S9rf//Tf/XBGvFEtUaRJdIuZYi1zwtkuhBntJgjoAOQML+Q3P/o3L/w4jFFU//pp0EfEhUoYIx/wsy/hddQp1UOvTqP3DcvL8b+X0vvLfVxf9/WSyU8UrDrvUixWtLPIpKkAcACX31Pxb/qPyvePpfHQUSxYzrRQ4liRz+XUSs/Gvn8rsg419D7r3e/+p/QgbN8VvTgSjWTLu/G+F8L4r6omh+vN/zhPMlXhHkAUBB/wH6nzQWpMllTP5JJcjJJxJHqOZguv7oSSKpErx2zh245desOtTFGrvUOhS1PDWY8VvTAS/UTDsOQR4AFPQfoB2DOiBRa8/9mybv+lOkysUiN80TSUrKuy/vwCy/QK2g3/FgtX7u4zrlN8OnBjN+azrgp7A108G6LCCmCPIAIFx2a1JS6JPPBG5eVZABAWDfJtHeF1r8ty1bS+SkisH3Ha3PO1SwGGj6E8peXnw/i1aoQU9CbRuo9tz79UVRy83AR0Ub5FWpUkW6du1qRtds166dpKWlRXL3AFB0nHRlMs8w1AnUvCpUsODfN4uThvhsEu2/LjtbZNu2aKc2/3Tk93uC4xuwIxS+y+HJb9AT74uDoX47Y3ERlOalRRvk6bQJOun5PffcI1u3bpUOHTqYgO+yyy6TsmXLRvJQABBdsbgyGS15hqE+X2TfVpFJLXMGpnBSrUJBaoD8p53Ib/J5p+SRXTm5SXRRDgVv93lC8/tcgw3YUZDALVQAkN9+nFSbGE6eBQvO/P/f+f922uECaiwmTk/UIK9NmzZmeeaZZ2TNmjXy/vvvy4QJE6Rv377SokULTy1f7drhX0HWydWfeuopWb58uWzevFlmz54t3bp1C/maL774QgYOHGjSoLWL999/v/Tr1y8C7xBFgitwsIt4bp4Ziv7jM6MP7ndeMJPfkPjhnsQEqlmiuV30OfU7l0j990LNE5pf7XmoE/6C1twE61+W334iVZtoB8dT2xXJ8hfqYlqkLqAW1cTpcSRqffIaNmxolsGDB5taPQ34dBk6dKicdtppMnr0aFPDl58DBw5I48aN5aabbpKrrroq3+3Xr18vnTp1kltuucVMzP7111/LHXfcIeXLlw/r9bABquCB6Lr2zZzmbBUq5AxMEe/BTKB+JeEECvm9J+/nA9UQhptP/kP6h9rWyfKr4YnnJtEFFeqktqhOTqNZYxXsO+gf1Ba0xcTxTFnhP6JqqP0UZPh/u18Yj9Y0H96BW6jvbn4X0yIlkWrL7TTwSsWKFU3QpcvBgwflk08+kWLFioX12o4dO5olXC+88IJUr15dxo4dax43aNBAli1bJk8//TRBXjwJVQWfiCdHiD4n9cErqEgFM3ZokhmtZrWh5iEMlk+e59aKlDpLpFiCX2EOVcMT702iCyrU96eomp1FssbKu/l3QX87Q12UCTeQyE9BRlSNZlNUp1wY98+/UHnK+VrijK55wgknyBVXXBG1/S9ZskTat2/vs077Bk6ePFmOHj0qqanuiXMQNyfX3j8c4QyUEO4PbqSuYtK81Bmc1AcvmsGMHQKUgjTJjFU+eStfT6TcuTmjmWrNqffJe36/YXYIqiMpkZpjxkOzM+8aq1DNR93pCtbs2bv5d36/nYWtAcpvYJBQ35NIfX+Opylqfmk8HgWprYtE4BzL36Nw57iE86ZQ2LJli6k59KaPMzMzZceOHVK5cuU8rzly5IhZ3Pbu3Wtus7OzzWIHmg7LsmyTngLT5kqBfuy02djuP3PmNdJhr1XZ2jnPud/rtW8de41eKdSRAd3z8mz/9dhJlD99Tq+ge8/h4z6m8j6u7lOHlc89ZvaMG8Q6lCTZ/s2r9LX+78U/DX77CjsfAj3v/ZyD2aN8u3LK3u2LfVfH63cuUvnt/f0L9B2MSTm14Wfln0/++b19u1d+u9xP5P39C+d3LBRb/2Z4vW/H/54ch0DfOaXvJ7//IYX9PMqcduxxfuXQXV7d/y+7v55TvsuX952XMFD++x/H//99ON8p97be+/L/nvilsWDl0vtxgOcC/fb4f07K/7sd6tzAX4hzgbzluwDpL0j+25F3+vNNuyv05xjy8THZ4hJLXLb6PQk3LS5LS0qccLlc+Q68UrduXdN/T/sCumm/vH/9619m4JZKlSrlec3DDz8sI0aMyLN+4sSJUqJECbED/Zg0UE1JSTH5EHe2rhHJPCKSktte6eihnNvUEsfWV2yY/352rsvZ3pu+9pTT89/W+5jK+7h+6bOOHpbM5BMkJUm/3n6v9d+Pfxp0X0r3m18aQj32z5dQ770g+WJDMSvf3vlWkHIY544rv/Mr05Eqd4HKtFucfVZ58tv79ya/9xIqH/zl99sUCzH4jsX9/0t/3v9TQv0vLej/Be/95sd/X177sUV+H8//QP988M/vYM8VZD/5vTbQvtyfs99nrCftmSklJaVstWO/J+GmP5FsLcDnGCKfrK1rJDOpuKSUO802vyeHDh0y442kp6dLqVKlEifIa9WqlTRp0kSee+45zzp9Tffu3U1/wEDNNQPV5FWrVk12794dMvOKOmrfvn27GUAmyftKWbxwX+FyX/2KRY1VqCugfs9paLe9XAspf9WTOfldkPR6v1d3rYf3VfpQNXfez/u/1r/G0PtxoNpEfV2gq402FLPy7Z/Htq4FsWl+e5fhQDXrhc3TQN8db3H0WeXJ72j9/vnv1w6/AzH4jsX9/8tQeej/mYb6zP3z3v+1/v+XEzW//fPB/394sOcKsp/8XhtqX/7nJ7v+ku1V2kn5XlNz8jvQcYKVl0QyqQCfY4jPJnvSBbK9eO1j+W0DGqeUKVMm3yCvyJpr6o/Ahx9+aPrGvffee1E7jk7VoHP1eZs3b540bdo0aH88HQQm0EAw+mHa5QN1B7l2S1OB+9m50/5/04s+TaGO6f9cdra4tm07lt8FSm9ux/NJLbxG8fqm4GkqUzP3ml3udZjydYMM727lfc60t7eO5bcdO4vbonxbImVrBv98HCxi+e1dhr3LpGcwkp9zvgv+ZS3ckRYd8tn45He0fv/892v6rfx+LP+L4nse7Le/iD/HuPp/mR/v/wX6e6WPg/0v9fnt9/t98/+/4P6eRiCP4ju//fPB+3Go5wqyH7/H+fbX89o2z2d8vmlAeCy//Y4TqrwkFKsAn2Poz9U3v2Mv3HREPcj77bffZMqUKfLaa6+ZmjEdBKUg9u/fL+vWrfOZImHVqlVmcnUdRVObZW7atEmmTZtmntf58MaPH2/mydPRPHUgFg0sp0+PQVDhdKFO0qI1D0o8iNQoXgUZ3r2g89J4K8joiQwyg0iNQBmqTCbab0a0hBq0KloYwCjynDLoTiIId1CT4x0RM9RoppQXRDPI07aib7/9tgmuvvnmG8nKypJnn31Wbr75ZjnppJMKtC+d/qBt27aexxq8qV69esnUqVNNP7sNGzZ4nq9Vq5bMnTtX7r33XjMRu06GPm7cOKZPiIZQo0zFYrQ7u7DDey7IvDQFGgre78TcDiMtFkQiT5Ngt6AvkX8jiop33kZrWP6gNXeMoGlLkZqSAMc3rUB+U0XlN+poQUYzRcKKaJD37bffyiuvvCIzZ840A6DccMMN8s4770jVqlXl4osvLnCAp9q0aWM69QajgZ6/1q1by4oVKwp8LBQC/8ztqSAnbgUaCt7vxDzeJhulliG2COhix/8kMFLzH/Kdsp9gQ8wXNAhBwYTz3YnEZ6N9WrdtE6lQIUGbYSImQV7Lli3l7rvvNsFevXoBhrQHYH8FPcmLt4nquTCBRBTN+Q/5TsVHdwG7/zY7HZ9N0duV2HPqRTTIu+iii0wTzW3btknPnj1N/zu7DDeKCKCpG+zQ5wfA8fM/4Q9VKx+qdj9BT55si0CucEI1lYxUE1c+m/gcHyGORTTI01EsN27cKK+++qrcfvvtpm9ejx49zHMEew5Asxzk1+fHbrgwAYTPv8+eW6hBchL05AkOEqqpJE1c49f/UXMd8YFXdH65hx56yCzz5883I2vqBJldu3aVq6++2iznnHNOpA+LokKzHMQTLkwA4QkVqDFIDhI1GCBQQByL6hQKl1xyiVl06oQ33njDBHyjR482o20CcKBojN53vLgwAeQv1t9TAEBEFclk6Doruw7IogujXsYZ7+Zu9L1AQWoC6KMHAADsaJfzpxOJeJCXnZ1tpjV499135c8//zR98XTuOm2mqYOx0FQzjpu70fcCkRrIAQAAIBbKJMZ0IhEN8nQ+u8svv9xMRt64cWM588wzzbqff/5ZevfubQK/9957L5KHRKQxsS3iHbXPAAAgwZunRzTI0xq8RYsWyaeffipt27b1ee6zzz6Tbt26ybRp0+TGG2+M5GERSQxUgXhH7TMAAEhwEQ3ypk+fLkOGDMkT4Lnn0Bs0aJC8+eabBHl2x0AViHeUYQAAkMCSIrmz77//Xi699NKgz3fs2FFWr14dyUMCiIeOze5Fm1ICAAAgfmrydu3aJRUrVgz6vD6n0ykASABFNdomE54DAABEL8jT+e904vNgkpOTJTMzM5KHBJDoo23SjxQAACC6o2vqKJrFihUL+PyRI0cieTgAyEEfPAAAgOgEeb169cp3G0bWtBmaugEAAACOEtEg79VXX43k7lAUaOqGeMOFCQAAgKIL8hCnaOqGoh5t0z0wS2EmJOXCBAAAQNEFeTo/nsvlyrO+dOnSUq9ePbnzzjulWrVqkTwkgHgcbfN4R9rkwgQAAEDRBHlnn312wPV79uyRuXPnyvjx4+Wrr74Kuh0AB/OutYvWSJsAAACIbJD37LPPhnxea/KGDBliAj4ACAt98AAAAOzbJ++2226TDh06FOUh4Y8TZsQb+uABAADYN8grUaKEHD58uCgPCf/AbvsvObfl6x/r2+TdVwqIxSAs4hIp11Lkmqfzllndjj54AAAA9gzy5s2bJ3Xr1i3KQ8K/JkSDu8KOaghEiv+Fhe2/ihwpJjKppYhYvhcjuBABAAAQuyDv/fffD7g+PT1dvvvuO5k8ebJMnTo1kodEuKgJgZ34X2R46zqRQ0kih3/PeczFCAAAAHsEed26dQu4vmTJklK/fn0T4F1zzTWRPCQAJ7j2TZFt20QqVBBJSop1agAAAOJaRIO87OzsSO4OAAAAAFBAXDIHAAAAAAeJaJD32WefyRlnnCF79+4N2C+vYcOG8uWXX0bykAAAAACAaAV5Y8eOlVtuuUVKlSqV57nSpUubefLGjBkTyUMCAAAAAKLVJ2/16tUyevTooM+3b99enn46dx4sRA8TngMAAAAJK6I1eVu3bpXU1NSgz6ekpMj27dsjeUiEmhfPjXnGAAAAgIQR0Zq8U089VX744Qc5/fTTAz7//fffS+XKlSN5SATDvHgAAABAQopoTV6nTp3koYceksOHD+d57tChQzJ8+HDp3LlzJA8JAAAAAIhWTd6DDz4o7777rtStW1fuuusuqVevnrhcLvn5559lwoQJkpWVJUOHDo3kIQEAAAAA0QryKlasKIsXL5bbb79dBg8eLJZlmfUa6HXo0EEmTpxotgEAAAAAxEGQp2rUqCFz586V3bt3y7p160ygV6dOHSlTpkykDwUAAAAAiHaQ56ZBXbNmzaK1e3hjygQAAAAA0Rh4BTHClAkAAAAAol2ThyLGlAkAAAAAqMkDAAAAAGehuSYAAAAAOAhBHgAAAAA4CEEeAAAAADgIA684YdqEXX/kDLwCAAAAIOFRkxevvKdN0ACvTK1YpwgAAACADVCTF8+YNgEAAACAH2ryAAAAAMBBCPIAAAAAwEForhkvZlwvcihJ5PDvImIx2AoAAACAgAjy4mmglaMniaTmPmawFQAAAAABEOTFk5IVRW6aJ5JEK1sAAAAAgREtAAAAAICDEOQBAAAAgIMQ5AEAAACAgxDkAQAAAICDxEWQN3HiRKlVq5YUL15czj33XPnyyy9Dbj9hwgRp0KCBlChRQurVqyfTpk0rsrQCAAAAQCzZfnTNmTNnyoABA0ygd8EFF8iLL74oHTt2lJ9++kmqV6+eZ/tJkybJ4MGD5eWXX5ZmzZrJt99+K7fccouUKVNGunTpEpP3AAAAAABFxfY1eWPGjJE+ffpI3759Te3c2LFjpVq1aiaYC+T111+X2267TXr06CGnnXaaXHvtteb1o0ePLvK0AwAAAEBRs3WQl5GRIcuXL5f27dv7rNfHixcvDviaI0eOmGad3rTZptboHT16NKrpBQAAAIBYs3VzzR07dkhWVpZUrFjRZ70+3rJlS8DXdOjQQV555RXp1q2bnHPOOSZInDJlignwdH+VK1cOGBjq4paenm5u9+zZI9nZ2WIH2YeyZK+VKWl79kgSk6FHP7+zs2Xv3r2SlpZGfhcB8rtokd/kt5NRvslvJ6N8F3F+H7Lf+beenyrLsuI3yHNzuVw+j/VN+a9zGzZsmAkAzz//fLOdBoS9e/eWJ598UpKTkwO+ZtSoUTJixIg862vUqCH2skzk7rdjnQgAAAAgQSyz5fn3vn37pHTp0kGfd1n5hYExbq55wgknyDvvvCNXXHGFZ33//v1l1apV8sUXXwR9rdbcbd261dTcvfTSS/LAAw+YmrlAUbh/TZ5eJdm1a5eccsopQYPJWETt2hdx48aNUqpUqVgnx/HIb/LbySjf5LeTUb7JbyejfJPflmWZAK9KlSohaxdtXZOnTeV0yoT58+f7BHn6uGvXriFfm5qaKlWrVjX3Z8yYIZ07dw6aEcWKFTOLt5NPPlnsSAM8gjzy26ko3+S3k1G+yW8no3yT305Wymbn36Fq8OIiyFMDBw6Unj17StOmTaVFixamVm7Dhg3Sr18/87xOl7Bp0ybPXHhr1641g6w0b95cdu/ebUbn/PHHH+W1116L8TsBAAAAgOizfZCnUyHs3LlTRo4cKZs3b5ZGjRrJ3LlzPf3ldJ0GfW46UMszzzwjv/76q6nNa9u2rRmJs2bNmjF8FwAAAABQNGwf5Kk77rjDLIFMnTrV57HOpbdy5UpxGm1OOnz48DzNSkF+OwHlm/x2Mso3+e1klG/y28mKxfH5t60HXgEAAAAAFIw9JnwAAAAAAEQEQR4AAAAAOAhBHgAAAAA4CEFenJg4caLUqlVLihcvbuYO/PLLL2OdpLjz8MMPm8ntvZdKlSp5ntfuqbqNTi5ZokQJadOmjaxZs8ZnH0eOHJG7775bypUrJyeeeKJcfvnl8vfff8fg3djPokWLpEuXLib/NG/fe+89n+cjlb86NYpOq6JzxOii9/fs2SOJJr/87t27d57yfv755/tsQ36Hb9SoUdKsWTMpWbKkVKhQQbp162ZGcfZGGS/a/KaMR86kSZPkrLPO8swFplNWffTRR57nKdtFm9+U7ej+trhcLhkwYIDzy7cOvAJ7mzFjhpWammq9/PLL1k8//WT179/fOvHEE62//vor1kmLK8OHD7caNmxobd682bNs27bN8/wTTzxhlSxZ0po1a5b1ww8/WD169LAqV65s7d2717NNv379rFNPPdWaP3++tWLFCqtt27ZW48aNrczMTCvRzZ071xo6dKjJP/1pmT17ts/zkcrfSy+91GrUqJG1ePFis+j9zp07W4kmv/zu1auXySvv8r5z506fbcjv8HXo0MF69dVXrR9//NFatWqVddlll1nVq1e39u/f79mGMl60+U0Zj5z333/f+vDDD61ff/3VLEOGDDHnHZr/irJdtPlN2Y6Ob7/91qpZs6Z11llnmXNpN6eWb4K8OHDeeeeZwuWtfv361qBBg2KWpngN8vQLGUh2drZVqVIl80V3O3z4sFW6dGnrhRdeMI/37NljfoQ16HbbtGmTlZSUZH388cdF8A7ih3/QEan81Yscuu9vvvnGs82SJUvMul9++cVKVMGCvK5duwZ9Dfl9fPQCkeb7F198YR5Txos2vxVlPLrKlCljvfLKK5TtIs5vRdmOvH379ll16tQxQVrr1q09QZ6Tf7tprmlzGRkZsnz5cmnfvr3Pen2sk7yjYH777TdTHa9NX6+99lr5448/zPr169fLli1bfPJZ50Rp3bq1J5/1czh69KjPNrqvRo0a8VnkI1L5u2TJEtMEonnz5p5ttAmiruP7kNfChQtNU7e6devKLbfcItu2bfM8R34fn/T0dHNbtmxZyngM8tuNMh55WVlZMmPGDDlw4IBpRsjvd9HmtxtlO7LuvPNOueyyy+Tiiy/2We/k8h0Xk6Ensh07dpgfgIoVK/qs18daKBE+/eJNmzbNnPBu3bpVHn30UWnZsqVpd+3Oy0D5/Ndff5n7uk1aWpqUKVOGz6KAIpW/eqtBiz9dx/fBV8eOHeWaa66RGjVqmH9iw4YNk4suusj8s9J/YOR34Wnl6cCBA+Vf//qX+SdPGS/6/FaU8cj64YcfTJBx+PBhOemkk2T27NlyxhlneE5Q+f0umvxWlO3ImjFjhqxYsUK+++67hDo/IciLE9pJ1P+fnv86hKY/mm5nnnmm+XGtXbu2vPbaa54BKQqTz3wW4YtE/gbans8grx49enju64lx06ZNTcD34YcfypVXXkl+H4e77rpLvv/+e/nqq6/yPEcZL7r8poxHVr169WTVqlVmoIhZs2ZJr1695IsvvvA8T9kumvzWQI+yHTkbN26U/v37y7x588zghcE4sXzTXNPmdBSf5OTkPFcBtNmV/1UHFIyOjqTBnjbhdI+yGSqfdRttPqujJ/FZFEyk8le30VpYf9u3b+f7kI/KlSubIE/LO/ldeDq62vvvvy+ff/65VK1a1bOeMl60+R0IZfz4aE3F6aefbi4I6QiEjRs3lueee46yXcT5HQhlu/CWL19uziN0ZPqUlBSzaDA9btw4c999fuHE8xOCvDj4EdCCOX/+fJ/1+libGqLwdDjcn3/+2fx4ah89/YJ657N+ofWHwJ3P+jmkpqb6bLN582b58ccf+SzyEan81dpX7Zvz7bfferZZunSpWcf3IbSdO3eaK5pa3snvgtOrsVqj9O6778pnn31myjRlPHb5HQhlPPKfgf6f5Pe7aPM7EMp24bVr1840jdVaU/eigfX1119v7p922mnOPT+JyXAvKNQUCpMnTzaj9wwYMMBMofDnn3+SkwXw73//21q4cKH1xx9/mNGPdFhbHTLXnY86spKOpvTuu++aIXSvu+66gEPoVq1a1VqwYIEZQveiiy5iCgWvkatWrlxpFv1pGTNmjLnvnuojUvmrQxTr8Mc6apUuZ555ZkJOoRAqv/U5Le86hPP69eutzz//3GrRooUZ/pn8Lpzbb7/dlF/9DfGeluLgwYOebSjjRZfflPHIGjx4sLVo0SLze/H999+bIf115MB58+aZ5ynbRZfflO3oa+01uqaTyzdBXpyYMGGCVaNGDSstLc0655xzfIaRRnjc855owFylShXryiuvtNasWeN5XofR1WkWdCjdYsWKWa1atTJfdm+HDh2y7rrrLqts2bJWiRIlzJd3w4YNfASWZQIJDTb8Fx0KOpL5q3O9XX/99SZA10Xv7969O+E+g1D5rSfC7du3t8qXL2/Ku84vpuv985L8Dl+gvNZF53Jzo4wXXX5TxiPr5ptv9pxj6O9Gu3btPAGeomwXXX5Ttos+yMt26PmJS//Epg4RAAAAABBp9MkDAAAAAAchyAMAAAAAByHIAwAAAAAHIcgDAAAAAAchyAMAAAAAByHIAwAAAAAHIcgDAAAAAAchyAMAAAAAByHIAwAghtq0aSMul8ssq1ativln0bt3b0963nvvvVgnBwBQCAR5AIC44R2AeC+XXnqpxLNbbrlFNm/eLI0aNcrzXPv27SU5OVm++eabkPmRmpoqFStWlEsuuUSmTJki2dnZPtsGC9oGDBhgAk235557zqQFABC/CPIAAHFFAzoNQryX6dOnR/WYGRkZUd3/CSecIJUqVZKUlBSf9Rs2bJAlS5bIXXfdJZMnTw6ZH3/++ad89NFH0rZtW+nfv7907txZMjMzC5yW0qVLm7QAAOIXQR4AIK4UK1bMBCHeS5kyZXxqrF555RW54oorTPBUp04def/993328dNPP0mnTp3kpJNOMrVfPXv2lB07dnie15otDawGDhwo5cqVM7VjSvej+ytRooQJpl577TVzvD179siBAwekVKlS8t///tfnWB988IGceOKJsm/fvgK/11dffdUEa7fffrvMnDnTHCNYfpx66qlyzjnnyJAhQ2TOnDkm4Js6dWqBjwkAiH8EeQAAxxkxYoR0795dvv/+exPMXX/99bJr1y7znNZ6tW7dWs4++2xZtmyZfPzxx7J161azvTcN4LRm7euvv5YXX3zR1JRdffXV0q1bN9N37rbbbpOhQ4d6ttdA7tprrzWBmTd9rK8rWbJkgd6DZVnmtTfccIPUr19f6tatK2+//XZYr73oooukcePG8u677xbomAAAZyDIAwDElf/973+mBs57eeSRR/L0Vbvuuuvk9NNPl8cff9zUgH377bfmuUmTJpkaL12vwVOTJk1MH7bPP/9c1q5d69mHvvbJJ5+UevXqme1eeOEFc/+pp54ytxrQ6XG89e3bVz755BP5559/zGOtHdT03nzzzQV+nwsWLJCDBw9Khw4dzGMN9oI12QxE06yBKQAg8RDkAQDiijaT1Jo07+XOO+/02eass87yqWHTWrRt27aZx8uXLzcBnXeQqAGR+v333z2va9q0qc8+f/31V2nWrJnPuvPOOy/P44YNG8q0adPM49dff12qV68urVq1KvD71ICuR48enn56GrQuXbrUpCPcmkBtSgoASDy+PbwBALA5Ddq0li0UHWnSmwY77tEm9bZLly4yevToPK+rXLmyz3HyC5p0nT+tzRs/frwMGjTINLe86aabChxsadNSHQnz6NGjpubRLSsry9Q6Bkq7v59//llq1arleayBbnp6ep7ttD+hDrYCAHAOavIAAAlFm2quWbNGatasaYJF78U/sPOmtX3fffedzzrt0+dPm1XqqJjjxo0zx+nVq1eB0/jmm29K1apVZfXq1T41lmPHjjV9BfMbNfOzzz6TH374Qa666qqQ6dcgVWs2tfkpAMA5CPIAAHHlyJEjsmXLFp/Fe2TM/GjTTq0p0+aP2k/vjz/+kHnz5pl+c1pTFowOtPLLL7/IAw88YPru6SAo7tErvWvqdKTPK6+8Uv7zn/+YOe40WCtMU00drEXnzfNeNI1a8/bhhx/myY9NmzbJihUrTF/Drl27mlE5b7zxRs929913n9mv1jJq+jWA1BFEtYmqf3NXAEB8I8gDAMQVHQ1Tm1V6L//617/Cfn2VKlXMiJka0OmgJho86bxy2mQxKSn4v0Vt+qjTI+iIldrnT5tRukfX1GkMvPXp08fMrVeYAVe0Zk0DMO9aOO8mlxo4eg/A4s4PrZnUOfO0v6HWIuo0CjqJupuOHqpBqdYEat9C3Y8GeF9++aXUqFGjwOkEANiXywrUoQAAAOTrscceM6Nubty4MU9zSw0cdZTNtLS0kPvQOfl0OgdtimknWjs5e/ZsM2UEACC+UJMHAECYJk6caPq1aRNPHTlTp1Pw7nOnUx5oP7xRo0aZ5p35BXje+9VRPrUfXaz169fPpAUAEL+oyQMAIEz33nuvzJw50/Tp06kRevbsKYMHD/ZMc/Dwww+b2j2dMkGbS4YTLGlfukOHDpn7us9wA8No0akm9u7da+5rM9BQg9EAAOyJIA8AAAAAHITmmgAAAADgIAR5AAAAAOAgBHkAAAAA4CAEeQAAAADgIAR5AAAAAOAgBHkAAAAA4CAEeQAAAADgIAR5AAAAAOAgBHkAAAAAIM7x/+xXTgFvoafsAAAAAElFTkSuQmCC",
                                "text/plain": [
                                    "<Figure size 900x650 with 2 Axes>"
                                ]
                            },
                            "metadata": {},
                            "output_type": "display_data"
                        }
                    ],
                    "source": [
                        "fig, (ax_spec, ax_ratio) = plt.subplots(\n",
                        "    2, 1, figsize=(9, 6.5), sharex=True, gridspec_kw={'height_ratios': [3, 1]})\n",
                        "\n",
                        "edges = hist_cpu.axes[0].edges\n",
                        "cpu_vals, cuda_vals = hist_cpu.values(), hist_cuda.values()\n",
                        "\n",
                        "ax_spec.stairs(cpu_vals, edges, label=f'CPU MT  ({n_clusters_cpu:,})',\n",
                        "               color='C0', linewidth=1.4)\n",
                        "ax_spec.stairs(cuda_vals, edges, label=f'CUDA  ({n_clusters_cuda:,})',\n",
                        "               color='C1', linestyle='--', linewidth=1.4)\n",
                        "ax_spec.set_ylabel('Counts')\n",
                        "ax_spec.set_title(f'Cluster energy spectrum — {N:,} frames')\n",
                        "ax_spec.legend()\n",
                        "ax_spec.grid(alpha=0.2)\n",
                        "\n",
                        "with np.errstate(divide='ignore', invalid='ignore'):\n",
                        "    ratio = np.where(cpu_vals > 0, cuda_vals / cpu_vals, np.nan)\n",
                        "ax_ratio.stairs(ratio, edges, color='C1', linewidth=1.2)\n",
                        "ax_ratio.axhline(1.0, color='gray', linewidth=0.5)\n",
                        "ax_ratio.set_ylabel('CUDA / CPU')\n",
                        "ax_ratio.set_xlabel('Energy [ADU]')\n",
                        "ax_ratio.set_ylim(0.9, 1.1)\n",
                        "ax_ratio.grid(alpha=0.3)\n",
                        "\n",
                        "plt.tight_layout()\n",
                        "plt.show()"
                    ]
                },
                {
                    "cell_type": "markdown",
                    "id": "md-sets",
                    "metadata": {},
                    "source": [
                        "## Cluster sets\n",
                        "\n",
                        "Every cluster is keyed by `(frame, x, y)`, so the two results can be differenced\n",
                        "directly — no per-frame Python loop.\n",
                        "\n",
                        "A cluster whose centre moved by one pixel was *relabelled*, not gained or lost,\n",
                        "so those are counted separately from genuine adds and drops."
                    ]
                },
                {
                    "cell_type": "code",
                    "execution_count": 10,
                    "id": "sets",
                    "metadata": {},
                    "outputs": [
                        {
                            "name": "stdout",
                            "output_type": "stream",
                            "text": [
                                "CPU   233,104,694 clusters\n",
                                "CUDA  233,095,737 clusters      net -8,957  (0.0038%)\n",
                                "\n",
                                "exact-position mismatches   CPU-only  167,059   CUDA-only  158,102\n",
                                "  of which shifted by 1 px            126,836             126,667\n",
                                "  genuine add / drop                   40,223              31,435\n",
                                "\n",
                                "frames touched by a genuine difference: 48,682 of 100,000\n",
                                "[43.3s]\n"
                            ]
                        }
                    ],
                    "source": [
                        "def cluster_keys(vectors):\n",
                        "    \"\"\"One int64 key per cluster: frame * rows*cols + y * cols + x.\n",
                        "\n",
                        "    Centres are unique within a frame, so keys are globally unique and the sets\n",
                        "    below need only a sort, never a np.unique.\n",
                        "    \"\"\"\n",
                        "    n = len(vectors)\n",
                        "    sizes = np.fromiter((cv.size for cv in vectors), np.int64, n)\n",
                        "    frames = np.repeat(\n",
                        "        np.fromiter((cv.frame_number for cv in vectors), np.int64, n), sizes)\n",
                        "    xy = [np.asarray(cv) for cv in vectors if cv.size]\n",
                        "    if not xy:\n",
                        "        return np.empty(0, np.int64), frames\n",
                        "    x = np.concatenate([v['x'] for v in xy]).astype(np.int64)\n",
                        "    y = np.concatenate([v['y'] for v in xy]).astype(np.int64)\n",
                        "    return frames * (rows * cols) + y * cols + x, frames\n",
                        "\n",
                        "\n",
                        "t0 = time.perf_counter()\n",
                        "k_cpu, fr_cpu = cluster_keys(clusters_cpu)\n",
                        "k_cuda, fr_cuda = cluster_keys(clusters_cuda)\n",
                        "\n",
                        "# Sort ONCE. np.setdiff1d and np.isin each re-sort their argument on every call,\n",
                        "# and the shift check below calls one eight times per direction -- about twenty\n",
                        "# sorts of a 27-million-element array. Two sorts plus binary search instead.\n",
                        "s_cpu = np.sort(k_cpu)\n",
                        "s_cuda = np.sort(k_cuda)\n",
                        "\n",
                        "\n",
                        "def present_in(vals, sorted_keys):\n",
                        "    \"\"\"Which of `vals` appear in an already-sorted key array.\"\"\"\n",
                        "    if sorted_keys.size == 0:\n",
                        "        return np.zeros(vals.size, dtype=bool)\n",
                        "    i = np.searchsorted(sorted_keys, vals)\n",
                        "    np.clip(i, 0, sorted_keys.size - 1, out=i)\n",
                        "    return sorted_keys[i] == vals\n",
                        "\n",
                        "\n",
                        "cpu_only = s_cpu[~present_in(s_cpu, s_cuda)]\n",
                        "cuda_only = s_cuda[~present_in(s_cuda, s_cpu)]\n",
                        "\n",
                        "\n",
                        "def drop_shifted(only, sorted_other):\n",
                        "    \"\"\"Drop mismatches that have a counterpart within 1 px in the other set.\"\"\"\n",
                        "    keep = np.ones(only.size, dtype=bool)\n",
                        "    for dy in (-1, 0, 1):\n",
                        "        for dx in (-1, 0, 1):\n",
                        "            if dx or dy:\n",
                        "                keep &= ~present_in(only + dy * cols + dx, sorted_other)\n",
                        "    return only[keep]\n",
                        "\n",
                        "\n",
                        "cpu_real = drop_shifted(cpu_only, s_cuda)\n",
                        "cuda_real = drop_shifted(cuda_only, s_cpu)\n",
                        "touched = np.unique(np.concatenate([cpu_real, cuda_real]) // (rows * cols)).size\n",
                        "\n",
                        "print(f'CPU  {k_cpu.size:>12,} clusters')\n",
                        "print(f'CUDA {k_cuda.size:>12,} clusters      net {k_cuda.size - k_cpu.size:+,}'\n",
                        "      f'  ({abs(k_cuda.size - k_cpu.size) / k_cpu.size:.4%})')\n",
                        "print()\n",
                        "print(f'exact-position mismatches   CPU-only {cpu_only.size:>8,}   '\n",
                        "      f'CUDA-only {cuda_only.size:>8,}')\n",
                        "print(f'  of which shifted by 1 px  {cpu_only.size - cpu_real.size:>17,}   '\n",
                        "      f'{cuda_only.size - cuda_real.size:>17,}')\n",
                        "print(f'  genuine add / drop        {cpu_real.size:>17,}   {cuda_real.size:>17,}')\n",
                        "print(f'\\nframes touched by a genuine difference: {touched:,} of {N:,}')\n",
                        "print(f'[{time.perf_counter() - t0:.1f}s]')"
                    ]
                },
                {
                    "cell_type": "markdown",
                    "id": "md-drift",
                    "metadata": {},
                    "source": [
                        "## Where in the run it accumulates\n",
                        "\n",
                        "Cumulative `CUDA − CPU` against frame index. A curve that rises and then flattens\n",
                        "means the two pedestals settled into agreement and the difference is a start-up\n",
                        "transient; a steadily sloping line would mean a persistent per-frame bias."
                    ]
                },
                {
                    "cell_type": "code",
                    "execution_count": 11,
                    "id": "drift",
                    "metadata": {},
                    "outputs": [
                        {
                            "data": {
                                "image/png":
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",
                                "text/plain": [
                                    "<Figure size 900x360 with 1 Axes>"
                                ]
                            },
                            "metadata": {},
                            "output_type": "display_data"
                        },
                        {
                            "name": "stdout",
                            "output_type": "stream",
                            "text": [
                                "first half: -3,925      second half: -5,032\n"
                            ]
                        }
                    ],
                    "source": [
                        "cnt_cpu = np.bincount(fr_cpu, minlength=N)\n",
                        "cnt_cuda = np.bincount(fr_cuda, minlength=N)\n",
                        "cum = np.cumsum(cnt_cuda.astype(np.int64) - cnt_cpu.astype(np.int64))\n",
                        "\n",
                        "fig, ax = plt.subplots(figsize=(9, 3.6))\n",
                        "ax.plot(cum, color='C1', linewidth=1.2)\n",
                        "ax.axhline(0, color='gray', linewidth=0.5)\n",
                        "ax.set_xlabel('Frame')\n",
                        "ax.set_ylabel('cumulative CUDA - CPU')\n",
                        "ax.set_title(f'Cluster-count difference accumulating over {N:,} frames '\n",
                        "             f'(final {cum[-1]:+,})')\n",
                        "ax.grid(alpha=0.25)\n",
                        "plt.tight_layout()\n",
                        "plt.show()\n",
                        "\n",
                        "half = N // 2\n",
                        "print(f'first half: {cum[half - 1]:+,}      second half: {cum[-1] - cum[half - 1]:+,}')"
                    ]
                },
                {
                    "cell_type": "code",
                    "execution_count": null,
                    "id": "e7b0da01-1787-4260-890d-533accda0e17",
                    "metadata": {},
                    "outputs": [],
                    "source": []
                }
            ],
        "metadata": {
            "kernelspec": {
                "display_name": "Python 3 (ipykernel)",
                "language": "python",
                "name": "python3"
            },
            "language_info": {
                "codemirror_mode": {
                    "name": "ipython",
                    "version": 3
                },
                "file_extension": ".py",
                "mimetype": "text/x-python",
                "name": "python",
                "nbconvert_exporter": "python",
                "pygments_lexer": "ipython3",
                "version": "3.11.15"
            }
        },
        "nbformat": 4,
        "nbformat_minor": 5
    }