10b3a  LSTM 32Node load model and test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Look at accelerometer data \n",
    "\n",
    "Finding Zero velocity times by rail axis acceleration noise levels, making summary statistics for the noise levels across the whole day files.  Spot check graphs to see what works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standard Header used on the projects\n",
    "\n",
    "#first the major packages used for math and graphing\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cycler import cycler\n",
    "import scipy.special as sp\n",
    "\n",
    "#Custome graph format style sheet\n",
    "#plt.style.use('Prospectus.mplstyle')\n",
    "\n",
    "#If being run by a seperate file, use the seperate file's graph format and saving paramaeters\n",
    "#otherwise set what is needed\n",
    "if not 'Saving' in locals():\n",
    "    Saving = False\n",
    "if not 'Titles' in locals():\n",
    "    Titles = True\n",
    "if not 'Ledgends' in locals():\n",
    "    Ledgends = True\n",
    "if not 'FFormat' in locals():\n",
    "    FFormat = '.png'\n",
    "\n",
    "#Standard cycle to make black and white images and dashed and line styles\n",
    "default_cycler = (cycler('color', ['0.00', '0.40', '0.60', '0.70']) + cycler(linestyle=['-', '-', '-', '-']))\n",
    "plt.rc('axes', prop_cycle=default_cycler)\n",
    "my_cmap = plt.get_cmap('gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extra Headers:\n",
    "import os as os\n",
    "import pywt as py\n",
    "import statistics as st\n",
    "import os as os\n",
    "import random\n",
    "import multiprocessing\n",
    "from joblib import Parallel, delayed\n",
    "import platform\n",
    "\n",
    "from time import time as ti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import CoreFunctions as cf\n",
    "from skimage.restoration import denoise_wavelet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-09-26 20:37:00.938303: I tensorflow/core/platform/cpu_feature_guard.cc:193] 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"
     ]
    }
   ],
   "source": [
    "from keras.layers import LSTM, Dense, RepeatVector, TimeDistributed, Masking\n",
    "from keras.models import Sequential\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing Platform\n",
    "Working is beinging conducted on several computers, and author needs to be able to run code on all without rewriting..  This segment of determines which computer is being used, and sets the directories accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "HostName = platform.node()\n",
    "\n",
    "if HostName == \"Server\":\n",
    "    Computer = \"Desktop\"   \n",
    "elif HostName[-6:] == 'wm.edu':\n",
    "    Computer = \"SciClone\"\n",
    "elif HostName == \"SchoolLaptop\":\n",
    "    Computer = \"LinLap\"\n",
    "elif HostName == \"WTC-TAB-512\":\n",
    "    Computer = \"PortLap\"\n",
    "else:\n",
    "    Computer = \"WinLap\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if Computer == \"SciClone\":\n",
    "    location = '/sciclone/home20/dchendrickson01/image/'\n",
    "elif Computer == \"WinLap\":\n",
    "    location = 'C:\\\\Data\\\\'\n",
    "elif Computer == \"Desktop\":\n",
    "    location = \"E:\\\\Backups\\\\Dan\\\\CraneData\\\\\"\n",
    "elif Computer == \"LinLap\":\n",
    "    location = '/home/dan/Output/'\n",
    "elif Computer == 'PortLap':\n",
    "    location = 'C:\\\\users\\\\dhendrickson\\\\Desktop\\\\AccelData\\\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if Computer ==  \"SciClone\":\n",
    "    rootfolder = '/sciclone/home20/dchendrickson01/'\n",
    "    folder = '/sciclone/scr10/dchendrickson01/Recordings2/'\n",
    "    imageFolder = '/sciclone/scr10/dchendrickson01/Move3Dprint/'\n",
    "elif Computer == \"Desktop\":\n",
    "    rootfolder = location\n",
    "    folder = rootfolder + \"Recordings2\\\\\"\n",
    "elif Computer ==\"WinLap\":\n",
    "    rootfolder = location\n",
    "    folder = rootfolder + \"Recordings2\\\\\"   \n",
    "elif Computer == \"LinLap\":\n",
    "    rootfolder = '/home/dan/Data/'\n",
    "    folder = rootfolder + 'Recordings2/'\n",
    "elif Computer =='PortLap':\n",
    "    rootfolder = location \n",
    "    folder = rootfolder + 'Recordings2\\\\'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Saving = False\n",
    "location = folder\n",
    "Titles = True\n",
    "Ledgends = True\n",
    "\n",
    "TimeSteps = 250\n",
    "StepSize = 15\n",
    "Features = 3\n",
    "f = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "BeforeTamping = ['221206 recording1.csv','221207 recording1.csv','221208 recording1.csv','221209 recording1.csv',\n",
    "         '221206 recording2.csv','221207 recording2.csv','221208 recording2.csv','221209 recording2.csv']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Specific Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Smooth = cf.Smoothing(ODataSet[:,3],2) #,50)\n",
    "def SmoothMoves(file):\n",
    "    #    if file[-3:] =='csv':\n",
    "    ODataSet = np.genfromtxt(open(folder+file,'r'), delimiter=',',skip_header=0,missing_values=0,invalid_raise=False)\n",
    "    SmoothX = denoise_wavelet(ODataSet[:,3], method='VisuShrink', mode='soft', wavelet_levels=3, wavelet='sym2', rescale_sigma='True')\n",
    "    SmoothY = denoise_wavelet(ODataSet[:,4], method='VisuShrink', mode='soft', wavelet_levels=3, wavelet='sym2', rescale_sigma='True')\n",
    "    SmoothZ = denoise_wavelet(ODataSet[:,5], method='VisuShrink', mode='soft', wavelet_levels=3, wavelet='sym2', rescale_sigma='True')\n",
    "    SmoothX -= np.average(SmoothX)\n",
    "    SmoothY -= np.average(SmoothY)\n",
    "    SmoothZ -= np.average(SmoothZ)\n",
    "    MoveMatrix = np.matrix([SmoothX, SmoothY, SmoothZ])\n",
    "    return MoveMatrix\n",
    "    #else:\n",
    "    #    pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "TimeSteps = 250\n",
    "StepSize = 15\n",
    "PredictSize = 25\n",
    "Features = 3\n",
    "\n",
    "# split a multivariate sequence into samples\n",
    "def split_sequences(sequences, n_steps, s_step = 1, y_steps = 1):\n",
    "    X, y = list(), list()\n",
    "    Steps_to_take = int(len(sequences) / s_step)\n",
    "    for j in range(Steps_to_take):\n",
    "        i = j * s_step\n",
    "        # find the end of this pattern\n",
    "        end_ix = i + n_steps\n",
    "        # check if we are beyond the dataset\n",
    "        if end_ix > len(sequences)-1:\n",
    "            break\n",
    "        # gather input and output parts of the pattern\n",
    "        seq_x = sequences[i:end_ix, :]\n",
    "        seq_y = sequences[end_ix:end_ix+y_steps :]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try LSTM Stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tf.config.threading.set_intra_op_parallelism_threads(32)\n",
    "#tf.config.threading.set_inter_op_parallelism_threads(32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-09-26 20:37:08.758483: I tensorflow/core/platform/cpu_feature_guard.cc:193] 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",
      "2023-09-26 20:37:08.762497: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "lstm_autoencoder2 = tf.keras.models.load_model(\"LSTM_smaller_predict\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3.31715171734492\n",
      "Sequence Made  5.435306672255198\n",
      "Moves Segmented  5.492642402648926\n",
      "Done Prep  5.774313918749491\n"
     ]
    }
   ],
   "source": [
    "LoopFiles = 9\n",
    "\n",
    "BeforeTamping[:2]\n",
    "\n",
    "loops = int(len(BeforeTamping) / LoopFiles) \n",
    "if len(BeforeTamping)%LoopFiles != 0:\n",
    "    loops += 1\n",
    "\n",
    "st = ti()\n",
    "\n",
    "Moves = []\n",
    "\n",
    "for k in range(loops):\n",
    "    if k == loops -1:\n",
    "        tfiles = BeforeTamping[k*LoopFiles:]\n",
    "    else:\n",
    "        tfiles = BeforeTamping[k*LoopFiles:(k+1)*LoopFiles]\n",
    "    #Results = Parallel(n_jobs=LoopFiles)(delayed(DeviationVelocity)(file) for file in tfiles)\n",
    "    Results = Parallel(n_jobs=LoopFiles)(delayed(SmoothMoves)(file) for file in tfiles)\n",
    "    #Results =[]\n",
    "    #for file in tfiles:\n",
    "    #    Results.append(DeviationVelocity(file))\n",
    "    #    print(file, (ti()-st)/60.0)\n",
    "    for result in Results:\n",
    "        Moves.append(result)\n",
    "    print(k, (ti()-st)/60.0)\n",
    "        \n",
    "Sequences = []\n",
    "Outputs = []\n",
    "for move in Moves:\n",
    "    Seq, Out = split_sequences(move.T,TimeSteps,StepSize)\n",
    "    Sequences.append(Seq)\n",
    "    Outputs.append(Out)\n",
    "print('Sequence Made ', (ti()-st)/60.0)\n",
    "MoveSegments = []\n",
    "for seq in Sequences:\n",
    "    for mv in seq:\n",
    "        MoveSegments.append(mv)\n",
    "NextDataPoint = []\n",
    "print('Moves Segmented ', (ti()-st)/60.0)\n",
    "for out in Outputs:\n",
    "    for pt in out:\n",
    "        NextDataPoint.append(np.reshape(pt,(1,3)))\n",
    "print('Done Prep ',(ti()-st)/60.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 11s 258ms/step\n"
     ]
    }
   ],
   "source": [
    "Check1 = np.asarray(MoveSegments[:1000])\n",
    "\n",
    "output_X = lstm_autoencoder2.predict(Check1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = np.linalg.norm(NextDataPoint[:1000] - output_X, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxes = np.max(scores, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold = 0.95\n",
    "\n",
    "sorted_scores = sorted(maxes)\n",
    "threshold_score = sorted_scores[round(len(maxes) * threshold)]\n",
    "\n",
    "plt.title(\"Reconstruction Error\")\n",
    "plt.plot(maxes)\n",
    "plt.plot([threshold_score]*len(scores), c='r')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(maxes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "858"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxes.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.title('Checking for move')\n",
    "plt.plot(Moves[0][:500000,:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yFBUVwdfXF/b29gCArKws7N+/Hy4uLnjssccAAGfOnMGpU6cQGhqKgQMH3nK/paWlKCsra9cZ0p3W2NgIS0t22lL71NXV4fz580hKSsLly5cRFRWFiRMndnW1zM5gMKCiogKenp5d9jnd1NSEK1eu4MKFCzh37hy0Wi0AIDg4GH/961/FcrW1tZDJZHjnnXdQXFyMgIAAzJ8/HzY2NhAEAT/88AO+/vprHDp0CNXV1QAAR0dHFBcXw8bG5o7VtyPf3/wE6iBBEFBaWorU1FSkpaUhNTUVtbW1mDNnDoYOHXrHjlNfX4/Lly+juroagwcPxtWrV5GamoqCggI4ODhAp9Nh9OjRGDZs2B05ntFoRGVlJfR6vbjY2Nhg8ODBYpkNGzZgypQpcHFxuSPHvFfU1NQgPj4eJ06cwNChQ00+mI8ePQqNRoNRo0YhMDAQAODp6YlBgwbB09NTLCeX3+gEtbCwuOlxSktLcenSJZw6dQpXr16Fi4sL3nnnHXFbcxAEAcXFxUhISMDBgwdRUlKCvn37oqqqCosWLYKXl5fZjk3SZTQaceXKFSQmJuLs2bOoq6sTH9NoNOLPJSUlcHd374oq3lE6nQ65ubnIzc1FXl4e8vLyUFpaCgCYMGECHn300U6tjyAI2LhxI86fP4/a2lpxvUKhQL9+/TBkyBCT8s0BJDY2Fh9++CHS09OxevVqjBgxAq+++ioSEhLEst7e3pg5cyZiYmKgUCg65wm1olPCy7p16/Dhhx9Co9Fg4MCBWLt2bZtfuj/88APeeOMN5OTkoE+fPnj//fcRHR3dGVVtU2ZmJr755psWXZ+2trbYvn07XFxc0KtXrw7v12AwICcnB9bW1rC2tsb58+dx7tw5qFQqyOVyTJo0CWVlZQCAkSNHIjIyEqtXr8Zrr72G5cuXY9GiRa32/BiNRuj1epSWlqK0tBTXr1+Ho6MjRo8eLZb54IMPUFJSgqqqKvy+E65Xr15ieBEEAVevXsXHH3+Ml19+WexFaE11dTWOHj2K2tpaTJ48WVzfWWOkZWVluHbtGrp37w4PD487vv+mpiakpqbi6NGj2LdvH/bv3w+1Wo1XXnkFOTk5WLlyJZYuXQqZTAZvb2/I5XKTsxNbW9sW7/+BAwfCyckJPXv2FNcVFhaisrISFy9exKVLl1BUVCQ+JpPJoFKpUF1dDQcHBxiNxjsSYurq6rB7927s3bsXffr0gVKpxNtvvy0eOzQ0FOHh4UhISMDAgQPx+uuvY+7cuW2+H25FEARotVrk5eXBYDCgtrYWNTU1qKioQElJCUpKSqDT6TBhwgSMGzfunu5tNBqNkMlkt3yOBoNB/JLMzs5GdnY2VCoVnnvuuTt6JvxHCIKAt99+GwUFBeI6Nzc3hIaGYtiwYVCr1RAEAf/5z3+wf/9+zJ49G8OHD+/CGrdNq9VCp9PBxsYGTU1NqKqqQmZmJjw9PVFYWIj8/HxcvnzZJKD91k8//YTw8HDY2tqarY719fXIzs4We2JlMhl0Oh1qa2vh4OCA/v37Y+DAgQgODoa1tXWL7Wtra9HY2AgfHx8sXrwYn3zyCc6fP49XX30VAGBnZ4cXX3wRjz32GPr372/WE6b2Mnt42bZtG1588UV8/vnnCA0Nxccff4yxY8ciPT291cSdkJCAmTNnYuXKlXj00UexZcsWTJ48GWfOnEG/fv3MXd022drawsvLS3wD9+nTB7169cKxY8dgYWGB0tJSMbzU1tbi+PHjsLGxwQMPPCDuo7i4GNXV1XB1dYWTkxOamprw448/orKyEqmpqfj444/FshMnToRMJkN9fT2cnZ0RFBSEgIAA2Nvb46GHHsKOHTvw2muv4dChQxgyZAhGjx6NkSNHYtOmTSgpKUFZWRkaGhpMnoOfn59JeCkrK0NlZSWAG294Ozs7dOvWDfb29nB3d0dWVpY4VNXQ0IDy8nL87W9/w4ABAyCTyVBTU4OmpiY0NDTAYDCgrq4O9fX18PHxAQDMnz8fNTU16Nu3L3r37g1fX1/0798fVlZWt/16NDY2Ijs7Gw0NDQgODkZWVha+/PJLKBQKuLq6ory8HF5eXhg9ejR69ux5W//htFotNmzYgO+//x6CIGD06NG4cuUK9uzZA+DGh4dOp0NeXh6++OILbN++Ha+//joiIiJw//3333L/MpkMfn5+AG58+F+/fh1xcXGora1FVlYWKioqxLJVVVUoKCjA/v378c477yAqKgrdu3fHm2++iW7dunX4uZWVlWHfvn3IysrCJ598Igbl1157DW5ublCpVHBxccGYMWMwevRo2NraIjU1FaWlpVi0aBH+/e9/Y8qUKbC2tsawYcNw3333wdLSEk5OTrC3t2/xRVxYWIh9+/bh4MGDSEtLQ2ZmJhobGyGXy6HT6cRy3bp1Q7du3VBTU4PKykp89tlniIyMxEsvvYSHH364yz9Am5qaoNPpUF5eLi4VFRUoLy9Ht27dMHr0aHh7e7e6bVZWFsrKyuDn5wc3NzcAQFFREX766SfY2triz3/+M3Q6Hc6dO4erV6+itrYW6enpSE5ORlZWFqysrLB06VIYjUZcvnwZMpkM169fx6RJkzBz5kxMnz79jgxlC4KAhoYG1NTUoLa2FtXV1SguLhaXhoYGKJVKyOVy+Pn5QSaToampCTY2NrCysoK3tze8vLwgl8tRVlaGw4cPo6mpCTKZDBUVFXB1dcXXX3+NxsZGPPDAA10aTJuammAwGGAwGFBTU4OLFy/i5MmTKC4ubrX8Dz/8gPLycgBAv379EBgYiLKyMnFIt3lYNyMjA9bW1vjkk0/uyHtWEASxlzw/Px9XrlzBmTNnUFtbi/feew9KpRLAje+PSZMmmXz26XQ6FBQUoHv37mKISUlJwfHjx9GzZ09ERkbi8uXL+P7771FUVARLS0s89dRT+Mtf/oJr166hvr4eGo2mS4fCmpl9zktoaCiGDh2Kf/7znwBunFn4+PjghRdewNKlS1uUf+KJJ1BdXS1+KQDA8OHDMWjQIHz++ectytfV1ZkkXr1eDx8fnzs+5+XKlSsIDw/HwoULUVVVhbfffhtGoxEAMHnyZAwbNgzHjh2DVquFm5sblEolHnzwQTQ0NODcuXNQKBRQKBTo3r077O3tYTAYUF1djU2bNkGv12Pu3LlISEjA8ePHERwcjIEDB4pL//79xXDU3INSUlKC9PR0GI1GBAUFwWAwYOnSpZDL5Zg1a5bJnAR7e3uo1Wqo1Wp4e3sjLCwMv/zyC44cOYLLly+jsLAQubm50Ol0LXpffis4OBiPPfYYNBoNqqurcfDgQQwYMABTpkxBcnIyNmzYIJYdMWIErl+/juzsbDQ2NmLIkCGIjY1FfX09Tpw4AR8fH/j6+sLHxwc9evSAr68vnJycbnrs2tpaZGRkwNnZGYIg4MyZM7h06ZL4wf/MM89Ar9fjlVdegbe3Nzw8PJCdnS2O8drb2+OFF14QezauXr2KnJwcNDU1wWg0QqFQwMbGBra2tnB1dYWvry8A4Ndff8V3332HHTt2iCFvxIgReOqpp3D9+nUIgoBHH31UnKfy73//G4sXLxbHhWUyGfr374+oqCj069cPMpkMDg4OcHNzg5+fH9RqtRhQd+3ahT179uDw4cPQaDSYO3cuAgIC8OuvvyItLQ35+fkYPXo0IiIiEBcXh/379wMAZsyYgZ49e6KqqgpBQUEYOXIkgoODkZOTg7q6Onh7e9+0bS9evIi///3viIiIQHV1Nf7v//4PHh4e+NOf/oT+/fvD19cXgwcPbvF/qa6uDps2bcKqVasQEBCACRMmICkpCV9++aVY5r333kNTUxOOHj0KZ2dnKJVK2NnZQaFQ4Pz58zhy5AgqKythZ2eH1atXAwA+++wzWFtbQ6FQIDQ0FH369MH169eRm5uLTZs2ob6+HrNnz4aFhQUEQcDgwYPFs/nm19HBwUHsCTIajaiqqoK9vb34AV5UVIT8/HzU19ejvr4eFhYWkMlk4lwypVKJmpoa8Ytar9cjNzcXV69eFUNKY2MjysrKTLrkgRvDfhYWFmhsbITRaMSwYcMwdepUWFlZQaPRwMXFBa6urrC0tERDQwN8fHzQu3dvODk5QSaTYefOnRAEAd9++y0SExNha2uL5557Dr1798aJEyewceNGAICHhwf+9re/obGxEefPnxd7Nf39/eHs7IydO3fC3t4eI0aMwNChQ+Hh4QGVSiV+sbWmqqoK8fHxSEpKQnp6Oq5cuSIGp5txdXXFAw88ALVajZ07d8LDwwMPPvggMjMzceDAATGorFu3DhYWFnj11VdRVlYGuVyON954A15eXsjNzcX169fF3kRPT08xNKtUKri5ucHV1VU84REEAUVFRcjOzkZ+fj6qqqpQU1MjBqyamhrxJKq+vl78ua6uTvzcbV6afwcABwcHaLVa8f95cHAwRo4cCaPRCIPBAEtLSxiNRtTX10Ov1+Pq1atwdHREjx494O3tje7du4thrbGxEQqFApmZmXjmmWcgCAIiIyPxxRdfiJ8tf0RBQQG++eYbZGdnt3jMyckJw4YNg1wux9ChQ+Hs7AyDwWDSSbBlyxZUVVVhwoQJ4rB1bm4u9u3bh+7du6OyshKzZs2CTCbDjBkzsGLFCvj7++ODDz4wOaarqyvefvvtO34C0ZE5L2YNL/X19bCzs8P27dtNhg+eeuopVFRU4Mcff2yxja+vL1588UUsWrRIXPfWW29h165dOH/+fIvyy5cvx4oVK1qsv9PhJSMjA3369GlX2XHjxolfkvn5+SZBbPz48QgKCkJ8fLx4tYmLiwv+/Oc/Y/DgwbC2tkZ+fj6sra1NJlStWLEChYWFLY5lY2MDf39/GAwGfP7558jNzYWPjw+CgoKQn5+P9PR0NDU1AbgxDBQYGIiEhASUl5dDLpcjLCwMrq6uOHLkCCoqKqBQKODl5YU//elPyM/Px7Zt22BrawtbW1vExsZCrVbj4sWLqK+vB3BjroaVlRWqqqrELw+FQgFra2vY2NiIXdiHDh1CVVUVbG1tcejQIbH+L7zwAqytrbFp0ybU1dWhe/fu8PHxgVqthpeXF5ydnVFfX49u3brBwcEBpaWluHbtmrh9cHAwlEol1Go1HBwcxN6W5jocOnQIFy9eRENDA1asWAGVSgUA+M9//oNff/31pq/hxIkTsW7dOnh7e+P+++/Ht99+i6qqKjz//PMIDQ2FlZUVPD09W/0yKC0txapVq7Blyxbk5ua29VaBvb09evfujdLSUvTp0wd9+vRBZmYm4uPj4ePjg379+sHb2xsODg6ws7ODSqWCQqGAnZ2deEa1detW9OvXDzU1Nfjkk0+QnZ2Nbt264eWXX4ZarUZFRQWcnJzg6ekJtVqN9PR0uLi4oKKiAnPmzEF9fT2mTJmCXr16YezYsRgzZky7J+QajUYkJCTgwoULyMzMFHtTLCwssGrVKgDA2bNnxaDfvXt3qNVqFBcXo6ysDGPGjIGfnx+SkpIgCALOnj0rBmhPT0+4u7vj+vXrCAwMxJAhQ7BmzRr07dsXwI1Jzs1lf7vf3NxcBAUFoX///qioqEB2djYyMzPR1NSE6upq9OrVC76+vkhJScGBAweQkpKCbt264R//+AcAYO3atWhoaEBTUxMeeeQRDBo0COnp6Thw4ADOnz8Pa2trrF27FsCNnmKZTAYLCwt4eXlBpVKhtLQU2dnZ2Lx5M5ydnfHuu++ivr4eL7zwgthuDz74ILy9vcX5S8CNoNutWzfI5XI4ODhg8ODB8PLygp2dHSwtLVFfX49Ro0ahV69e4rZKpRLBwcGwsbHB+++/D2trazg4OJgM9Q0aNAjPPfcc8vPzsWvXLnh4eKBHjx4YOHAgnJ2d4eHhgU2bNmH79u2wsLCAk5MTKisrxS/1379f/f394e3tDVdXV9jY2Ii9LWlpaVCr1YiMjEReXh527doFS0tLWFpaYvDgwaivr0dBQYHYm9OtWzcMHz4ccXFxCAgIgIWFBVxcXODn54fTp09j/fr14nEXLlwIGxsbHDp0CFlZWSgtLcXo0aPx6KOP4sKFC/jmm2/Esq+//jrs7e2xevVqsdckJCQEEydORGpqKrZu3SqWDQwMREBAgDiUdfz4caSkpMDW1hZOTk7o3bs3ysvLxWGv4OBgDB06FP7+/rCxscGxY8dw+fJlDBkyROxhraurw1dffQUAmDNnDrZt24bY2FjMmjULw4YNQ0FBAaZOnYqBAweivr4eu3fvhoWFBSZNmiSGgStXriAvLw89e/YUe/OLi4uxfft2sdersbFRPHkvKytDSkqKeMViaGgo5syZA71ej6KiItjb26Nbt25wdHSEhYWF2LtubW0NKysrKBQK5ObmYvbs2WhoaMD//d//oU+fPlCpVOLIx4ULF1BWVobc3Fz4+flh8eLF7fqM6Ii7JrwUFhaie/fuSEhIQFhYmLj+5ZdfxuHDh5GUlNRiG2tra3z11VeYOXOmuO7TTz/FihUrWu2+66yel/r6ely5ckX8cv7tIggCSkpKUFRUhIKCAiQlJUGv14vbyuVyyOVysZut+U1XX1+PwYMHw8nJCefOnUNjY6O4jZWVFVavXi2ebaxatQo6nU48E2k+K2n+vXmbS5cuIT4+Xhxf/emnn3D27FmT8WfgxqSr8ePHIyQkBIIgoH///hg8eDBsbGzEJO7s7IzHH39c3Gbfvn2wtLRE3759ER8fj4SEBDQ2NuLZZ58V58bU1dXB2tq61S7FyspKnDx5EmfOnEFubi40Gg3Cw8Mhk8mwdOlSsQt21KhReOKJJ3D06FFs27YNISEhGD58OHr16oXi4mKUlJSIY/0ajeaml/VZWVmhR48e6N27N6KiosQzJ3d3d9TW1qKsrAx2dnawsbFBfX292C1eUlKCL7/8EjqdDmPHjsXUqVOhUCgwdepUODg4dOh9o9FocPDgQfz8888oLi42mYek1WphaWkJGxsbhIaGwtXVFcCND9R58+a1OgTU2NgIjUYDpVIp9i7U1tbim2++QXFxMXJycnDgwAHk5OTgkUceQa9evbB3714xRAUFBWHRokUoKSnBG2+8AQCIjo7Gp59+ih49etzy+dTW1iIxMREajQZFRUUoLi42GdJq/tBsPjtet26dGFyqq6vRrVs3eHl5wWAwYPjw4Rg9erQ4CX7lypUAbvx/ae7BaP45NDQU0dHRqK2txalTp3D06FFcu3YNDQ0NMBqN6NGjB9RqNfLz83Ho0CEcP37cJJCcO3dODPHu7u5QKpXiB356ejoEQcCoUaPg4+Nj8n+leXi3vLwclpaWeOCBB+Dg4IDLly8DgMklp809fhqNBn379kVERAQuXryIixcvIi0tDXV1dWhqakJtbS0qKyvFISaNRiN+6djY2GD8+PHie0Emk6F3797inAUPD4+bdteXlZXhvffew9SpU9HY2Ijjx4+LJyoPPvggrl27hi1btojl3333Xbi6uuK9994Tz6qjoqIwefJkNDU1icPkw4YNw/nz59HY2Iiff/5Z7JmwsrKCj48PnJ2dMX36dCiVSlRWViIzMxPe3t7i59LNNF8Jk5aWhqtXr6KgoACOjo7o3r07cnJysHv3bly/fh3Ozs5YuHAhrK2t8frrr+P69euwsLDAhAkTEBUVhfz8fKSlpYk9pwMHDoSlpSU0Gg3kcjmsra1ha2sLhUIBuVwOJycnNDY2IisrC05OTrC2tkZqaiqUSiVGjx6NHj164OzZs/Dy8hLn4uh0Omzbtg0ymQxPPfWUOORy6tQpnDlzBn379hWnBwiCgISEBNjY2GDAgAGwsrLCiRMncOjQIbi4uCA9PR3ffPMNQkND8cQTT8BgMEAmkyE2NlZsm+YQ1b9/fyQkJGDdunUoLCzEmjVrANz4Dm0eYp08eTKioqJw4MABnDp1Ck5OTrh69Sreeecd5OXl4ZNPPmkxfaAtTzzxBJYvX44zZ87A399fbANBELBhwwYYjUZER0ffdEj0dvxPhZff6+xLpW8mPz8f//3vf03OJIEbZwDz5s0DcKN9PvroI/EMp/mLtnv37ujduzfc3d3/0LhiZWUljhw5gsbGRkyaNAnAjd6AAwcOQKvVimObFhYWyMjIQFFREXr37i12IxoMBvGLVa1Wi/v9/YRbnU6HkydPigEEADZu3IisrCwMHjwYI0aMMNn+t65cuYLdu3fDYDDAysoKNjY2sLS0FOc+2Nra4vr167h69ao4o93CwgKWlpaoq6tDdXU1qqqqTLqAf/97c7sHBQXh2WefRX19PV555RXxy3T48OGwsLBAamoqrK2t4e7uDjc3Nxw9ehQNDQ0YMWIENmzYgO+++068QsLT01PsIfH394erq2uL16i8vBwXLlzA1atXxWG+qqoqzJ8/XxxeOnr0KL799luT7RwcHPDcc8/9oUnfTU1N+Mc//oGMjAzI5XIMHz4cRqMROTk50Gg00Gg0yM/Px7Vr12BnZweZTIbc3Fy8/PLLWLlypXjG1zysd/nyZVhaWsLCwgIeHh7ihPn6+nosXLiwxfCik5MT1Go1+vbti7Fjx4rrO2OSdl1dnXiVR25uLnJycnDp0iVcvnxZnHPj6emJUaNGia9Z87CNSqVCUFCQuJ9t27aJJ0QNDQ1wcHCAs7MznJ2d4evri969e4vPKysrS3zfWllZoaGhAXV1dbC3t4eDg4N44pGbm4t33nnnpvV/+OGH8dhjj6GyshI7duxAUlISLC0tMWrUKERGRsLZ2bndbVFTU2MyMfTkyZNwd3eHQqEQz9g1Gg2ysrJQXl4OvV6PAwcOYNSoUVi8eDHq6upw6dIlVFVVIT09Hd27d8cbb7yBLVu2oLq6GoWFhdDpdOjZsyc8PDyg1+vh7u6OSZMm3ZHXOTc3FwkJCXj88cfFK+9ee+011NbWwsnJCS4uLujRowf8/f3FQKJUKk2CvlarhdFohFKpFHsQmy9btrKyQkZGBr799ls0NjYiKCgItra2GD16tHg14KVLl5CQkAB/f39ERESI+/3Pf/4DS0tLPPTQQ+J3S11dHWQyGaysrG75/BMSEvD999+jrq4OjY2NOHnyJFJSUtCnTx+4uLjA19cXU6dORXR0NHQ6HU6dOoW1a9fCwcEBv/zyCxobGxEbGwtfX1/06NFD7PUWBAE1NTUICgoS745bXl6OEydOIC0tDUVFRSafi81L8/v8t8NqjzzyCD777DPxpNxoNIqvQ319PTZv3oyGhgbMmTPHLLdLuGvCS2cMG/3e3RJemlVXV0Or1YrzPwICAsSJxxqNBm+99RZ69uyJGTNmiBM2zaF5HL2mpgZjxozBfffdZ5bjLF++3OSqmF69emHo0KG4ePEiJk2aJJ7dnzt3Dp999tlN9xMTE4OQkBB069YNWq0WOTk5GDx4cLs/IJuamlBYWIisrCxkZWUhNzcXer0e165dE69gmT59Ory9vfGvf/1LHMJTKpWIjIyElZUVNmzYAEtLS2zduhVXr141eV7NAgIC8OKLLwK4EUZ//PFHnDt3rtU6Pf/88xgwYACAG2fsX331Fezs7GBrawu1Wo3p06d36Ivq92pqarB582acPHkSAPDSSy+JX7bNmntE0tPToVQqMXjwYAiCgJSUFJw+fRrnz59vMVzQu3dvvPTSS+Lvmzdvho2NjTgMpVarO+0+Rx3VHGJv52qo29XU1AStVgutVitO5CwrK4NWq0VFRQXGjBmDhx9+GMCN13DDhg2YPHnybZ/Z1tbW4tVXX4XBYEBYWBj69u2Lnj17ttoj0tDQgD179uCXX34Rg6mtrS2GDx+OadOmITc3F9XV1eJ8FJlMhitXriAzMxOhoaFmu31CU1MTtmzZglOnTrU69+a3J4MAkJ6eDm9v75tOXBcEAZ999hnOnz+Pfv36YfLkyeLFBc2qqqqg1WphY2NjMl/kTlzRp9frsXXrVrHHrr6+HteuXcOJEydQU1MD4MZE9YcffhgJCQmYMGECrK2toVKp8PLLL98V32kNDQ135IKL1tw14QWAeHlc8xix0WiEr68vFixYcNMJuwaDAT/99JO4bsSIERgwYECrE3Z/724LL21JSUlBfn4+IiIi2ry/x51SUVGBgoICuLm5meUSYuDGB+alS5dw4sQJXLp0yeQMvX///uJdGcvLy5GRkSFecVVaWiqOszc2NiIsLMzksmFzOH36NPLz89GvXz9UVlaiuLgYBQUFaGxsRK9evRAeHm5SvqqqChkZGbh69SoyMjKQl5eHBx54ADExMQBu9CatWrVKnDjZr18/qNVqcWjvt2fE5uqREARBvEw9MjISwI2zw9LSUnTv3r3VbQwGA5YvXy52Q7u6umLkyJGwsrJCU1MT3N3d23XFFN1dqqqqsHXrVjHMNvPw8MDgwYMxcuRIqFQqNDQ0YN26dUhNTQVwIxCMGDECAQEBZvuS6qj6+nqcP38eJ06cECfKV1dXi0MvwI3P/uaQ7e7uDn9/f3Hx8vISP2ObrwR96KGHuuSKtea5Nc29MHZ2dpg0aRL27t2LH374ATk5OQBuDOH5+PjAz88PS5YsuWteC3O6q8LLtm3b8NRTT+Ff//oXhg0bho8//hjff/890tLS4OHhgSeffBLdu3cXx7sTEhIwevRovPfeexg/fjy2bt2Kd999t92XSkspvNzrKioqcOLECZw/fx5qtRrR0dHihNm7VUJCArKyshAVFSXOO7iZ5qsZfntW/9///hf333+/yQ3outqFCxewbt06BAQEYOLEiS16Y4Abl+4mJiYiJCQE9913X5dfhkx3TkZGBk6cOCEOqzUPm06aNAnR0dHi+0OhUGD27NmSCqq/PQnIzc3FF1980eqf2VAoFJg4caLJMFBXq6urE6/mar5pnNFoxPvvv4+ioiLU1dXB0tISb7311j1xI7/2uKvCCwD885//FG9SN2jQIKxZswahoaEAgDFjxqBnz57iH4YCblw///rrr4s3qfvggw/afZM6hhciU0eOHMF3330nfmkNHToUU6dOhaWlJf+P/I+pqanBpUuXkJSUhJiYGHGYMiEhQbyXlNRVVVUhJydHHDLOzs5GbW0tZs+ebTL38m70++H08ePH/0/8KYVmd1146UwML0QtabVa7N27F8eOHYMgCOIlrE8++SRCQkK6unpEZmM0GlFUVNRiYu/dqLa2FgcPHkRcXBxcXFzw8ssvt3pH3HsVwwvDC1Gr8vLysHXrVmRkZAAAHn30UUyYMKGLa0VEvyUIAgRB+J8bvmV4YXghuilBEHD+/HnodDo8+OCD/3MfkER0d+JflSaim5LJZOL9IIiIpIinXERERCQpDC9EREQkKQwvREREJCkML0RERCQpDC9EREQkKQwvREREJCkML0RERCQpDC9EREQkKQwvREREJCkML0RERCQpDC9EREQkKQwvREREJCkML0RERCQpDC9EREQkKQwvREREJCkML0RERCQpDC9EREQkKQwvREREJCkML0RERCQpDC9EREQkKQwvREREJCkML0RERCQpDC9EREQkKQwvREREJCkML0RERCQpDC9EREQkKQwvREREJCkML0RERCQpDC9EREQkKQwvREREJCkML0RERCQpDC9EREQkKQwvREREJClmCy9arRYxMTFwdHSEUqnE3LlzUVVV1eY269evx5gxY+Do6AiZTIaKigpzVY+IiIgkymzhJSYmBikpKYiLi8OePXtw5MgRzJs3r81tDAYDxo0bh1dffdVc1SIiIiKJkwmCINzpnaampiI4OBgnT57EkCFDAAD79u1DdHQ08vPz4eXl1eb2hw4dwkMPPYTy8nIolcoOHVuv18PJyQk6nQ6Ojo5/9CkQERFRJ+rI97dZel4SExOhVCrF4AIAERERkMvlSEpKuqPHqqurg16vN1mIiIjo3mWW8KLRaODu7m6yztLSEi4uLtBoNHf0WCtXroSTk5O4+Pj43NH9ExER0d2lQ+Fl6dKlkMlkbS5paWnmqmurli1bBp1OJy55eXmdenwiIiLqXJYdKbxkyRLMnj27zTL+/v5Qq9UoKSkxWd/Y2AitVgu1Wt3hSrZFoVBAoVDc0X0SERHR3atD4UWlUkGlUt2yXFhYGCoqKnD69GmEhIQAAOLj42E0GhEaGvrHakpEREQEM815CQoKwrhx4xAbG4vk5GQcP34cCxYswIwZM8QrjQoKChAYGIjk5GRxO41Gg3PnziEjIwMAcPHiRZw7dw5ardYc1SQiIiIJMtt9XjZv3ozAwECEh4cjOjoaI0eOxPr168XHGxoakJ6eDoPBIK77/PPPMXjwYMTGxgIARo0ahcGDB2P37t3mqiYRERFJjFnu89KVeJ8XIiIi6eny+7wQERERmQvDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSYpZw4tWq0VMTAwcHR2hVCoxd+5cVFVVtVn+hRdeQEBAAGxtbeHr64uFCxdCp9OZs5pEREQkIWYNLzExMUhJSUFcXBz27NmDI0eOYN68eTctX1hYiMLCQnz00Ue4dOkSNm3ahH379mHu3LnmrCYRERFJiEwQBMEcO05NTUVwcDBOnjyJIUOGAAD27duH6Oho5Ofnw8vLq137+eGHH/CnP/0J1dXVsLS0vGV5vV4PJycn6HQ6ODo63tZzICIios7Rke9vs/W8JCYmQqlUisEFACIiIiCXy5GUlNTu/TQ/iZsFl7q6Ouj1epOFiIiI7l1mCy8ajQbu7u4m6ywtLeHi4gKNRtOufZSWluLvf/97m0NNK1euhJOTk7j4+PjcVr2JiIjo7tbh8LJ06VLIZLI2l7S0tNuumF6vx/jx4xEcHIzly5fftNyyZcug0+nEJS8v77aPTURERHevW08i+Z0lS5Zg9uzZbZbx9/eHWq1GSUmJyfrGxkZotVqo1eo2t6+srMS4cePg4OCAnTt3wsrK6qZlFQoFFApFu+tPRERE0tbh8KJSqaBSqW5ZLiwsDBUVFTh9+jRCQkIAAPHx8TAajQgNDb3pdnq9HmPHjoVCocDu3bthY2PT0SoSERHRPcxsc16CgoIwbtw4xMbGIjk5GcePH8eCBQswY8YM8UqjgoICBAYGIjk5GcCN4BIZGYnq6mps2LABer0eGo0GGo0GTU1N5qoqERERSUiHe146YvPmzViwYAHCw8Mhl8sxbdo0rFmzRny8oaEB6enpMBgMAIAzZ86IVyL17t3bZF/Z2dno2bOnOatLREREEmC2+7x0Fd7nhYiISHruivu8EBEREZkDwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUmKWcOLVqtFTEwMHB0doVQqMXfuXFRVVbW5zV/+8hf06tULtra2UKlUmDRpEtLS0sxZTSIiIpIQs4aXmJgYpKSkIC4uDnv27MGRI0cwb968NrcJCQnBxo0bkZqail9++QWCICAyMhJNTU3mrCoRERFJhEwQBMEcO05NTUVwcDBOnjyJIUOGAAD27duH6Oho5Ofnw8vLq137uXDhAgYOHIiMjAz06tXrluX1ej2cnJyg0+ng6Oh4W8+BiIiIOkdHvr/N1vOSmJgIpVIpBhcAiIiIgFwuR1JSUrv2UV1djY0bN8LPzw8+Pj6tlqmrq4NerzdZiIiI6N5ltvCi0Wjg7u5uss7S0hIuLi7QaDRtbvvpp5/C3t4e9vb2+PnnnxEXFwdra+tWy65cuRJOTk7icrOQQ0RERPeGDoeXpUuXQiaTtbnc7gTbmJgYnD17FocPH8Z9992H6dOno7a2ttWyy5Ytg06nE5e8vLzbOjYRERHd3Sw7usGSJUswe/bsNsv4+/tDrVajpKTEZH1jYyO0Wi3UanWb2zf3ovTp0wfDhw+Hs7Mzdu7ciZkzZ7Yoq1AooFAoOvo0iIiISKI6HF5UKhVUKtUty4WFhaGiogKnT59GSEgIACA+Ph5GoxGhoaHtPp4gCBAEAXV1dR2tKhEREd2DzDbnJSgoCOPGjUNsbCySk5Nx/PhxLFiwADNmzBCvNCooKEBgYCCSk5MBAFlZWVi5ciVOnz6N3NxcJCQk4PHHH4etrS2io6PNVVUiIiKSELPe52Xz5s0IDAxEeHg4oqOjMXLkSKxfv158vKGhAenp6TAYDAAAGxsbHD16FNHR0ejduzeeeOIJODg4ICEhocXkXyIiIvrfZLb7vHQV3ueFiIhIeu6K+7wQERERmQPDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKwwsRERFJCsMLERERSYpZw4tWq0VMTAwcHR2hVCoxd+5cVFVVtWtbQRAQFRUFmUyGXbt2mbOaREREJCFmDS8xMTFISUlBXFwc9uzZgyNHjmDevHnt2vbjjz+GTCYzZ/WIiIhIgizNtePU1FTs27cPJ0+exJAhQwAAa9euRXR0ND766CN4eXnddNtz585h1apVOHXqFDw9Pc1VRSIiIpIgs/W8JCYmQqlUisEFACIiIiCXy5GUlHTT7QwGA2bNmoV169ZBrVbf8jh1dXXQ6/UmCxEREd27zBZeNBoN3N3dTdZZWlrCxcUFGo3mptstXrwYI0aMwKRJk9p1nJUrV8LJyUlcfHx8bqveREREdHfrcHhZunQpZDJZm0taWtofqszu3bsRHx+Pjz/+uN3bLFu2DDqdTlzy8vL+0LGJiIhIGjo852XJkiWYPXt2m2X8/f2hVqtRUlJisr6xsRFarfamw0Hx8fHIzMyEUqk0WT9t2jQ8+OCDOHToUIttFAoFFApFR54CERERSViHw4tKpYJKpbplubCwMFRUVOD06dMICQkBcCOcGI1GhIaGtrrN0qVL8cwzz5is69+/P1avXo0JEyZ0tKpERER0DzLb1UZBQUEYN24cYmNj8fnnn6OhoQELFizAjBkzxCuNCgoKEB4ejq+//hrDhg2DWq1utVfG19cXfn5+5qoqERERSYhZ7/OyefNmBAYGIjw8HNHR0Rg5ciTWr18vPt7Q0ID09HQYDAZzVoOIiIjuITJBEISursSdpNfr4eTkBJ1OB0dHx66uDhEREbVDR76/+beNiIiISFIYXoiIiEhSGF6IiIhIUhheiIiISFIYXoiIiEhSGF6IiIhIUhheiIiISFIYXoiIiEhSGF6IiIhIUhheiIiISFIYXoiIiEhSGF6IiIhIUhheiIiISFIYXoiIiEhSGF6IiIhIUhheiIiISFIYXoiIiEhSGF6IiIhIUhheiIiISFIYXoiIiEhSGF6IiIhIUhheiIiISFIYXoiIiEhSGF6IiIhIUhheiIiISFIYXoiIiEhSGF6IiIhIUhheiIiISFIYXoiIiEhSGF6IiIhIUhheiIiISFIYXoiIiEhSGF6IiIhIUhheiIiISFIYXoiIiEhSGF6IiIhIUhheiIiISFLMGl60Wi1iYmLg6OgIpVKJuXPnoqqqqs1txowZA5lMZrI8++yz5qwmERERSYilOXceExODoqIixMXFoaGhAU8//TTmzZuHLVu2tLldbGws/va3v4m/29nZmbOaREREJCFmCy+pqanYt28fTp48iSFDhgAA1q5di+joaHz00Ufw8vK66bZ2dnZQq9XmqhoRERFJmNmGjRITE6FUKsXgAgARERGQy+VISkpqc9vNmzfDzc0N/fr1w7Jly2AwGG5atq6uDnq93mQhIiKie5fZel40Gg3c3d1ND2ZpCRcXF2g0mptuN2vWLPTo0QNeXl64cOECXnnlFaSnp2PHjh2tll+5ciVWrFhxR+tOREREd68Oh5elS5fi/fffb7NMamrqH67QvHnzxJ/79+8PT09PhIeHIzMzE7169WpRftmyZXjxxRfF3/V6PXx8fP7w8YmIiOju1uHwsmTJEsyePbvNMv7+/lCr1SgpKTFZ39jYCK1W26H5LKGhoQCAjIyMVsOLQqGAQqFo9/6IiIhI2jocXlQqFVQq1S3LhYWFoaKiAqdPn0ZISAgAID4+HkajUQwk7XHu3DkAgKenZ0erSkRERPcgs03YDQoKwrhx4xAbG4vk5GQcP34cCxYswIwZM8QrjQoKChAYGIjk5GQAQGZmJv7+97/j9OnTyMnJwe7du/Hkk09i1KhRGDBggLmqSkRERBJi1pvUbd68GYGBgQgPD0d0dDRGjhyJ9evXi483NDQgPT1dvJrI2toa+/fvR2RkJAIDA7FkyRJMmzYNP/30kzmrSURERBIiEwRB6OpK3El6vR5OTk7Q6XRwdHTs6uoQERFRO3Tk+5t/24iIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkxWzhRavVIiYmBo6OjlAqlZg7dy6qqqpuuV1iYiIefvhhdOvWDY6Ojhg1ahRqamrMVU0iIiKSGLOFl5iYGKSkpCAuLg579uzBkSNHMG/evDa3SUxMxLhx4xAZGYnk5GScPHkSCxYsgFzODiIiIiK6QSYIgnCnd5qamorg4GCcPHkSQ4YMAQDs27cP0dHRyM/Ph5eXV6vbDR8+HI888gj+/ve//+Fj6/V6ODk5QafTwdHR8Q/vh4iIiDpPR76/zdKlkZiYCKVSKQYXAIiIiIBcLkdSUlKr25SUlCApKQnu7u4YMWIEPDw8MHr0aBw7dqzNY9XV1UGv15ssREREdO8yS3jRaDRwd3c3WWdpaQkXFxdoNJpWt8nKygIALF++HLGxsdi3bx/uv/9+hIeH4+rVqzc91sqVK+Hk5CQuPj4+d+6JEBER0V2nQ+Fl6dKlkMlkbS5paWl/qCJGoxEA8Je//AVPP/00Bg8ejNWrVyMgIABffvnlTbdbtmwZdDqduOTl5f2h4xMREZE0WHak8JIlSzB79uw2y/j7+0OtVqOkpMRkfWNjI7RaLdRqdavbeXp6AgCCg4NN1gcFBSE3N/emx1MoFFAoFO2oPREREd0LOhReVCoVVCrVLcuFhYWhoqICp0+fRkhICAAgPj4eRqMRoaGhrW7Ts2dPeHl5IT093WT9lStXEBUV1ZFqEhER0T3MLHNegoKCMG7cOMTGxiI5ORnHjx/HggULMGPGDPFKo4KCAgQGBiI5ORkAIJPJ8NJLL2HNmjXYvn07MjIy8MYbbyAtLQ1z5841RzWJiIhIgjrU89IRmzdvxoIFCxAeHg65XI5p06ZhzZo14uMNDQ1IT0+HwWAQ1y1atAi1tbVYvHgxtFotBg4ciLi4OPTq1ctc1SQiIiKJMct9XroS7/NCREQkPV1+nxciIiIic2F4ISIiIklheCEiIiJJYXghIiIiSWF4ISIiIklheCEiIiJJYXghIiIiSWF4ISIiIklheCEiIiJJYXghIiIiSWF4ISIiIklheCEiIiJJYXghIiIiSWF4ISIiIklheCEiIiJJYXghIiIiSWF4ISIiIklheCEiIiJJYXghIiIiSWF4ISIiIklheCEiIiJJYXghIiIiSWF4ISIiIklheCEiIiJJYXghIiIiSWF4ISIiIklheCEiIiJJYXghIiIiSWF4ISIiIklheCEiIiJJYXghIiIiSWF4ISIiIklheCEiIiJJsezqCkhJXV0dzpw5A0EQTJZbGThwIBwdHQEAhYWFyMnJgZubG+677z4AgNFoxKlTpyCTyW65ABD/9fX1FfdbXl6O/Px82Nvbw8/PzxxPn4iI6K7A8NIBRqMRZWVlHd6uqalJ/Lm2thZarRa2trbiOkEQUFJS0uH9urm5ieGluroa165dg0qlYnghIqJ7GsNLB1hbW+P+++8HAMjlcrEHBIDJzwBMemS6desm/uzu7g4bGxuT8CKTyTBgwABxG6PRKO6jtaX5sd/u19HREX369DFZR0REdC9ieOkACwsLeHl53dY+7O3tYW9vb7JOLpfD19f3tvbr6Ogo9sIQERHdy8w6YVer1SImJgaOjo5QKpWYO3cuqqqqblo+JyfnpnM9fvjhB3NWlYiIiCTCrOElJiYGKSkpiIuLw549e3DkyBHMmzfvpuV9fHxQVFRksqxYsQL29vaIiooyZ1WJiIhIImRCey6X+QNSU1MRHByMkydPYsiQIQCAffv2ITo6Gvn5+e0efhk8eDDuv/9+bNiwoV3l9Xo9nJycoNPpOIxCREQkER35/jZbz0tiYiKUSqUYXAAgIiICcrkcSUlJ7drH6dOnce7cOcydO/emZerq6qDX600WIiIiuneZLbxoNBq4u7ubrLO0tISLiws0Gk279rFhwwYEBQVhxIgRNy2zcuVKODk5iYuPj89t1ZuIiIjubh0OL0uXLr3ljdTS0tJuu2I1NTXYsmVLm70uALBs2TLodDpxycvLu+1jExER0d2rw5dKL1myBLNnz26zjL+/P9RqdYsbrzU2NkKr1UKtVt/yONu3b4fBYMCTTz7ZZjmFQgGFQnHL/REREdG9ocPhRaVSQaVS3bJcWFgYKioqcPr0aYSEhAAA4uPjYTQaERoaesvtN2zYgIkTJ7brWERERPS/w2xzXoKCgjBu3DjExsYiOTkZx48fx4IFCzBjxgzxSqOCggIEBgYiOTnZZNuMjAwcOXIEzzzzjLmqR0RERBJl1vu8bN68GYGBgQgPD0d0dDRGjhyJ9evXi483NDQgPT0dBoPBZLsvv/wS3t7eiIyMNGf1iIiISILMdp+XrsL7vBAREUnPXXGfFyIiIiJzYHghIiIiSbnn/qp08ygY77RLREQkHc3f2+2ZzXLPhZfKykoA4J12iYiIJKiyshJOTk5tlrnnJuwajUYUFhbCwcEBMpnsju5br9fDx8cHeXl5nAxsRmznzsO27jxs687Bdu48d7qtBUFAZWUlvLy8IJe3Pavlnut5kcvl8Pb2NusxHB0d+Z+iE7CdOw/buvOwrTsH27nz3Mm2vlWPSzNO2CUiIiJJYXghIiIiSWF46QCFQoG33nqLfwjSzNjOnYdt3XnY1p2D7dx5urKt77kJu0RERHRvY88LERERSQrDCxEREUkKwwsRERFJCsMLERERSQrDCxEREUkKw0s7rVu3Dj179oSNjQ1CQ0ORnJzc1VWSvOXLl0Mmk5ksgYGB4uO1tbV4/vnn4erqCnt7e0ybNg3FxcVdWGNpOHLkCCZMmAAvLy/IZDLs2rXL5HFBEPDmm2/C09MTtra2iIiIwNWrV03KaLVaxMTEwNHREUqlEnPnzkVVVVUnPgtpuFVbz549u8V7fNy4cSZl2Na3tnLlSgwdOhQODg5wd3fH5MmTkZ6eblKmPZ8Xubm5GD9+POzs7ODu7o6XXnoJjY2NnflU7mrtaecxY8a0eE8/++yzJmU6o50ZXtph27ZtePHFF/HWW2/hzJkzGDhwIMaOHYuSkpKurprk9e3bF0VFReJy7Ngx8bHFixfjp59+wg8//IDDhw+jsLAQU6dO7cLaSkN1dTUGDhyIdevWtfr4Bx98gDVr1uDzzz9HUlISunXrhrFjx6K2tlYsExMTg5SUFMTFxWHPnj04cuQI5s2b11lPQTJu1dYAMG7cOJP3+HfffWfyONv61g4fPoznn38eJ06cQFxcHBoaGhAZGYnq6mqxzK0+L5qamjB+/HjU19cjISEBX331FTZt2oQ333yzK57SXak97QwAsbGxJu/pDz74QHys09pZoFsaNmyY8Pzzz4u/NzU1CV5eXsLKlSu7sFbS99ZbbwkDBw5s9bGKigrByspK+OGHH8R1qampAgAhMTGxk2oofQCEnTt3ir8bjUZBrVYLH374obiuoqJCUCgUwnfffScIgiBcvnxZACCcPHlSLPPzzz8LMplMKCgo6LS6S83v21oQBOGpp54SJk2adNNt2NZ/TElJiQBAOHz4sCAI7fu82Lt3ryCXywWNRiOW+eyzzwRHR0ehrq6uc5+ARPy+nQVBEEaPHi389a9/vek2ndXO7Hm5hfr6epw+fRoRERHiOrlcjoiICCQmJnZhze4NV69ehZeXF/z9/RETE4Pc3FwAwOnTp9HQ0GDS7oGBgfD19WW734bs7GxoNBqTdnVyckJoaKjYromJiVAqlRgyZIhYJiIiAnK5HElJSZ1eZ6k7dOgQ3N3dERAQgOeeew5lZWXiY2zrP0an0wEAXFxcALTv8yIxMRH9+/eHh4eHWGbs2LHQ6/VISUnpxNpLx+/budnmzZvh5uaGfv36YdmyZTAYDOJjndXO99xflb7TSktL0dTUZPJCAICHhwfS0tK6qFb3htDQUGzatAkBAQEoKirCihUr8OCDD+LSpUvQaDSwtraGUqk02cbDwwMajaZrKnwPaG671t7PzY9pNBq4u7ubPG5paQkXFxe2fQeNGzcOU6dOhZ+fHzIzM/Hqq68iKioKiYmJsLCwYFv/AUajEYsWLcIDDzyAfv36AUC7Pi80Gk2r7/vmx8hUa+0MALNmzUKPHj3g5eWFCxcu4JVXXkF6ejp27NgBoPPameGFukxUVJT484ABAxAaGooePXrg+++/h62tbRfWjOjOmDFjhvhz//79MWDAAPTq1QuHDh1CeHh4F9ZMup5//nlcunTJZH4c3Xk3a+ffzsfq378/PD09ER4ejszMTPTq1avT6sdho1twc3ODhYVFi1nrxcXFUKvVXVSre5NSqcR9992HjIwMqNVq1NfXo6KiwqQM2/32NLddW+9ntVrdYjJ6Y2MjtFot2/42+fv7w83NDRkZGQDY1h21YMEC7NmzBwcPHoS3t7e4vj2fF2q1utX3ffNj9P+7WTu3JjQ0FABM3tOd0c4ML7dgbW2NkJAQHDhwQFxnNBpx4MABhIWFdWHN7j1VVVXIzMyEp6cnQkJCYGVlZdLu6enpyM3NZbvfBj8/P6jVapN21ev1SEpKEts1LCwMFRUVOH36tFgmPj4eRqNR/KCiPyY/Px9lZWXw9PQEwLZuL0EQsGDBAuzcuRPx8fHw8/Mzebw9nxdhYWG4ePGiSViMi4uDo6MjgoODO+eJ3OVu1c6tOXfuHACYvKc7pZ3v2NTfe9jWrVsFhUIhbNq0Sbh8+bIwb948QalUmsympo5bsmSJcOjQISE7O1s4fvy4EBERIbi5uQklJSWCIAjCs88+K/j6+grx8fHCqVOnhLCwMCEsLKyLa333q6ysFM6ePSucPXtWACD84x//EM6ePStcu3ZNEARBeO+99wSlUin8+OOPwoULF4RJkyYJfn5+Qk1NjbiPcePGCYMHDxaSkpKEY8eOCX369BFmzpzZVU/prtVWW1dWVgr/93//JyQmJgrZ2dnC/v37hfvvv1/o06ePUFtbK+6DbX1rzz33nODk5CQcOnRIKCoqEheDwSCWudXnRWNjo9CvXz8hMjJSOHfunLBv3z5BpVIJy5Yt64qndFe6VTtnZGQIf/vb34RTp04J2dnZwo8//ij4+/sLo0aNEvfRWe3M8NJOa9euFXx9fQVra2th2LBhwokTJ7q6SpL3xBNPCJ6enoK1tbXQvXt34YknnhAyMjLEx2tqaoT58+cLzs7Ogp2dnTBlyhShqKioC2ssDQcPHhQAtFieeuopQRBuXC79xhtvCB4eHoJCoRDCw8OF9PR0k32UlZUJM2fOFOzt7QVHR0fh6aefFiorK7vg2dzd2mprg8EgREZGCiqVSrCyshJ69OghxMbGtjjpYVvfWmttDEDYuHGjWKY9nxc5OTlCVFSUYGtrK7i5uQlLliwRGhoaOvnZ3L1u1c65ubnCqFGjBBcXF0GhUAi9e/cWXnrpJUGn05nspzPaWfb/VZiIiIhIEjjnhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgkheGFiIiIJIXhhYiIiCSF4YWIiIgk5f8BtSU6vAqYRYoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i=858\n",
    "plt.title('random Comparison')\n",
    "plt.plot(MoveSegments[i])\n",
    "plt.plot(output_X[i][:,0])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Simple",
   "language": "python",
   "name": "simple"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "178f6c3502586c94dc93af50f98dbd15c5205250cbf2345a6eb57380f8c77d96"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}