keras_g3_conv_fpga.ana.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import itertools\n",
    "\n",
    "from event_display import  table_display, table_display_compare, get_bin_centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pics_prefix = os.path.join(\"plots\", \"v1_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time open 0.338362455368042\n",
      "Time split 0.279801607131958\n",
      "(50000, 11, 11)\n",
      "(50000, 11, 11)\n",
      "(50000, 11, 11, 1)\n",
      "(50000, 11, 11, 1)\n",
      "Inputs shape new = (50000, 11, 11, 1)\n"
     ]
    }
   ],
   "source": [
    "from geant3_parser import build_true_answers_train_set\n",
    "from geant3_parser import Geant3DataFile\n",
    "\n",
    "def norm_func(e):\n",
    "    return np.float64(np.log(e) / 11)\n",
    "\n",
    "file_name = os.path.join('data', 'shower_geant3_new.dat')\n",
    "data_file = Geant3DataFile(file_name, skip_lines=3)\n",
    "input_data, answers, values = build_true_answers_train_set(data_file, 50000, norm_func=norm_func, rnd_shift=((-2,2), (-2,2)))\n",
    "print(np.shape(answers))\n",
    "print(np.shape(input_data))\n",
    "\n",
    "# Remove x,y\n",
    "\n",
    "# Reshape to events x 11 x 11 x 1 form\n",
    "input_data = np.reshape(input_data, (len(input_data), 11, 11, 1))  # -1 => autodetermine\n",
    "answers = np.reshape(answers, (len(answers), 11, 11, 1))  # -1 => autodetermine\n",
    "print(np.shape(answers))\n",
    "print(np.shape(input_data))\n",
    "\n",
    "\n",
    "# Pad with 1 row and column of zeroes, so it divides by 2, events x 12 x 12 x 1 now\n",
    "#input_data = np.pad(input_data, ((0,0), (0,1), (0,1), (0,0)), mode='constant', constant_values=0)\n",
    "#answers = np.pad(answers, ((0,0), (0,1), (0,1), (0,0)), mode='constant', constant_values=0)\n",
    "\n",
    "# printout shape\n",
    "print(f\"Inputs shape new = {np.shape(input_data)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-24 01:11:53.409714: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
      "2022-07-24 01:11:54.361672: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2022-07-24 01:11:54.362413: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n",
      "2022-07-24 01:11:54.410604: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:0a:00.0 name: NVIDIA GeForce GTX 1080 Ti computeCapability: 6.1\n",
      "coreClock: 1.683GHz coreCount: 28 deviceMemorySize: 10.91GiB deviceMemoryBandwidth: 451.17GiB/s\n",
      "2022-07-24 01:11:54.410638: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
      "2022-07-24 01:11:54.412276: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10\n",
      "2022-07-24 01:11:54.412326: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10\n",
      "2022-07-24 01:11:54.413902: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
      "2022-07-24 01:11:54.414195: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
      "2022-07-24 01:11:54.415878: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
      "2022-07-24 01:11:54.416814: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10\n",
      "2022-07-24 01:11:54.420439: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7\n",
      "2022-07-24 01:11:54.420958: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
      "2022-07-24 01:11:54.421330: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-07-24 01:11:54.423097: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:0a:00.0 name: NVIDIA GeForce GTX 1080 Ti computeCapability: 6.1\n",
      "coreClock: 1.683GHz coreCount: 28 deviceMemorySize: 10.91GiB deviceMemoryBandwidth: 451.17GiB/s\n",
      "2022-07-24 01:11:54.423129: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
      "2022-07-24 01:11:54.423145: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10\n",
      "2022-07-24 01:11:54.423153: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10\n",
      "2022-07-24 01:11:54.423162: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
      "2022-07-24 01:11:54.423170: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
      "2022-07-24 01:11:54.423178: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
      "2022-07-24 01:11:54.423186: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10\n",
      "2022-07-24 01:11:54.423194: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7\n",
      "2022-07-24 01:11:54.423435: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
      "2022-07-24 01:11:54.423458: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
      "2022-07-24 01:11:54.804943: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2022-07-24 01:11:54.804973: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n",
      "2022-07-24 01:11:54.804978: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n",
      "2022-07-24 01:11:54.805554: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8871 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce GTX 1080 Ti, pci bus id: 0000:0a:00.0, compute capability: 6.1)\n",
      "2022-07-24 01:11:54.805927: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2022-07-24 01:11:55.210720: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n",
      "2022-07-24 01:11:55.230190: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 3493245000 Hz\n",
      "2022-07-24 01:11:55.300480: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10\n",
      "2022-07-24 01:11:55.408620: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7\n",
      "2022-07-24 01:11:55.902015: W tensorflow/stream_executor/gpu/asm_compiler.cc:63] Running ptxas --version returned 256\n",
      "2022-07-24 01:11:55.996427: W tensorflow/stream_executor/gpu/redzone_allocator.cc:314] Internal: ptxas exited with non-zero error code 256, output: \n",
      "Relying on driver to perform ptx compilation. \n",
      "Modify $PATH to customize ptxas location.\n",
      "This message will be only logged once.\n"
     ]
    }
   ],
   "source": [
    "# Load NN model\n",
    "from keras.models import load_model\n",
    "num_events = 700000\n",
    "name = \"conv_bce-sgm_fpga_2dense\"\n",
    "model = load_model(os.path.join(\"trained_models\", f\"g3_{name}_{num_events}.hd5\"))\n",
    "\n",
    "# Run ML on data\n",
    "ml_events = model.predict(input_data)\n",
    "# reshape events\n",
    "ml_events = np.reshape(ml_events, (len(ml_events), 11, 11, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max true 410.00000000000017 max eval 478.4071350097656\n",
      "sum true 410.00000000000017 sum eval 478.4071350097656\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 864x648 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# exponentiate data\n",
    "event_num = 5\n",
    "true_event = np.exp(answers[event_num]*11)\n",
    "ml_event = np.exp(ml_events[event_num]*11)\n",
    "\n",
    "#print(ml_event)\n",
    "#print(\"true event\")\n",
    "#print(true_event)\n",
    "\n",
    "# clear low data for display\n",
    "ml_event[ml_event<2] = 0\n",
    "true_event[true_event<1.5] = 0\n",
    "\n",
    "table_display_compare(true_event, ml_event, cal_size=11)\n",
    "print(f\"max true {np.max(true_event)} max eval {np.max(ml_event)}\")\n",
    "print(f\"sum true {np.sum(true_event)} sum eval {np.sum(ml_event)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def data_to_energies(data):\n",
    "    # We took log(hit) to make hits linear. /11 as a scale. Now reverting it    \n",
    "    denorm_data = np.exp(data*11)\n",
    "\n",
    "    # Get rid of array form of cells\n",
    "    # For ML you have arrays like \n",
    "    # [num_pictures][x_pixels][y_pixels][colors]  where colors=3 for RGB,  colors=1 for BW\n",
    "    # We use colors=1 (we just have an energy deposit in a cell) and we have num_pictures=num_events, [xy]_pixels=[xy]_cells\n",
    "    # No we want to remove this useless [1]\n",
    "    # (len(data), 12, 12, 1) => (len(data), 12, 12)\n",
    "    reshaped = np.reshape(denorm_data, (len(data), 11, 11))\n",
    "\n",
    "    # Sum along (x_pixels, y_pixels) axis => basicly summ everything in one event\n",
    "    sums = np.sum(reshaped, axis=(1,2))\n",
    "    return sums\n",
    "\n",
    "sr_e = data_to_energies(input_data)             # SR = simple reco = energy from original data, summed together\n",
    "ml_e = data_to_energies(ml_events)              # Energy from ML (sum everything)\n",
    "true_e = 1000*np.reshape(values[:,0], len(input_data))    # True energy, 1000 to convert from GeV to\n",
    "\n",
    "# create pandas table... for fun\n",
    "pd = pd.DataFrame({\"sr_e\": sr_e, \"ml_e\": ml_e, \"true_e\":true_e})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x432 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using tuple unpacking for multiple Axes\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15,6))\n",
    "\n",
    "ax1.hist(sr_e, bins=10)\n",
    "ax2.hist(ml_e, bins=100, range=(0,10000))\n",
    "ax3.hist2d(sr_e, ml_e/sr_e, bins=(15, 100))\n",
    "#plt.savefig(pics_prefix + \"e_2dhist.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(11, 19, 19)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build numpy 2d histogram\n",
    "err_bins = np.arange(0.4, 1.4, 0.05)\n",
    "e_bins = np.arange(0, 6000, 500)\n",
    "err_bin_centers = get_bin_centers(err_bins)\n",
    "e_bin_centers = get_bin_centers(e_bins)\n",
    "de = ml_e/sr_e\n",
    "dee_hist, de_bin_edges, e_bin_edges = np.histogram2d(sr_e, de, bins=[e_bins, err_bins])\n",
    "e_bins_num, dee_bins_num = dee_hist.shape\n",
    "e_bins_num, dee_bins_num, len(err_bin_centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total subplots: 11 plot rows=3 columns=4\n"
     ]
    },
    {
     "data": {
      "text/plain": "[]"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1152x864 with 12 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_columns = 4\n",
    "plot_rows = int(e_bins_num / plot_columns) + 1\n",
    "(int(e_bins_num/plot_columns) + 1)*plot_columns\n",
    "print(f\"Total subplots: {e_bins_num} plot rows={plot_rows} columns={plot_columns}\")\n",
    "fig, axes = plt.subplots(plot_rows, plot_columns, figsize=(plot_columns*4, plot_rows*4), sharey=True)\n",
    "\n",
    "from statistics import NormalDist\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "# Fit fragment is taken from here\n",
    "# https://gist.github.com/cpascual/a03d0d49ddd2c87d7e84b9f4ad2df466\n",
    "def gauss(x, H, A, x0, sigma):\n",
    "    return H + A * np.exp(-(x - x0) ** 2 / (2 * sigma ** 2))\n",
    "\n",
    "def gauss_fit(x, y):\n",
    "    mean = sum(x * y) / sum(y)\n",
    "    sigma = np.sqrt(sum(y * (x - mean) ** 2) / sum(y))\n",
    "    popt, pcov = curve_fit(gauss, x, y, p0=[min(y), max(y), mean, sigma])\n",
    "    return popt\n",
    "\n",
    "# axes is a 2d array consisting of rows and colums [[...], [...], ...]\n",
    "# flatten it as we need to feel it one by one\n",
    "axes = list(itertools.chain.from_iterable(axes))\n",
    "sigmas = []\n",
    "dee_peaks = []\n",
    "for i in range(e_bins_num):\n",
    "    hist1d = dee_hist[i]\n",
    "    ax = axes[i]\n",
    "    # (!) using ax.step() for histograms\n",
    "    # But commented ax.plot() to check ax.step() centers correctness\n",
    "    # ax.plot(err_bin_centers, hist1d, \"-o\")    \n",
    "    ax.step(err_bins[1:], hist1d)\n",
    "\n",
    "    # Fit using Gaussian function\n",
    "    H, A, x0, sigma = gauss_fit(err_bin_centers, hist1d)\n",
    "    std = np.std(hist1d)    \n",
    "\n",
    "    # Plot gauss\n",
    "    smooth_gauss_x = np.arange(err_bins[0], err_bins[-1], (err_bins[-1]-err_bins[0])/1000)\n",
    "    smooth_gauss_y = gauss(smooth_gauss_x, H, A, x0, sigma)\n",
    "    ax.plot(smooth_gauss_x, smooth_gauss_y, '--r', label='fit')\n",
    "\n",
    "    # titles\n",
    "    ax.set_title(f\"Eml/E for E={e_bins[i]}-{e_bins[i+1]}\\n sgm={sigma:.3f} peak={x0:.4f}\", wrap=True)  \n",
    "\n",
    "    # Add our error\n",
    "    sigmas.append(sigma)\n",
    "    dee_peaks.append(x0)\n",
    "    \n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig(pics_prefix + \"ml_div_true_fit\")\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Sigma by energy')"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(e_bin_centers, sigmas, \"--o\")\n",
    "plt.title(\"Sigma by energy\")\n",
    "#plt.savefig(pics_prefix + \"errors\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 0, 'Energy, MeV')"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigma_div_e = sigmas/e_bin_centers\n",
    "\n",
    "plt.plot(e_bin_centers, sigma_div_e, \"--o\")\n",
    "plt.xlabel(\"Energy, MeV\")\n",
    "#plt.savefig(pics_prefix + \"errors_div_energy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a=0.0013670558278376182 b=0.15526242564945655 c=-3.414446507413421e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7fe0c404c4c0>]"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calo_error(x, a, b, c):\n",
    "    return a/np.sqrt(x) + b/x + c\n",
    "\n",
    "popt, pcov = curve_fit(calo_error, e_bin_centers, sigma_div_e, p0=[1, 1, 0])\n",
    "a, b, c = popt\n",
    "print(f\"a={a} b={b} c={c}\")\n",
    "\n",
    "# Plot gauss\n",
    "smooth_cal_x = np.arange(e_bin_centers[0], e_bin_centers[-1], (e_bin_centers[-1]-e_bin_centers[0])/1000)\n",
    "smooth_cal_y = calo_error(smooth_cal_x, a, b, c)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(e_bin_centers, sigma_div_e, \"o\")\n",
    "ax.plot(smooth_cal_x, smooth_cal_y, '--r', label='fit')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(0.4, 1.4)"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(e_bin_centers, dee_peaks , \"--o\")\n",
    "plt.title(\"Mean MLE/E by energy\")\n",
    "plt.ylim(0.4,1.4)\n",
    "#plt.savefig(pics_prefix + \"ml_div_true_peaks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
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