detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-11-03T12:05:26.745737Z",
     "start_time": "2024-11-03T12:05:21.598555Z"
    }
   },
   "source": [
    "from astropy.io import fits\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from scipy.fft import fft\n",
    "# import tsfel\n",
    "from scipy.signal import savgol_filter\n",
    "from sklearn.model_selection import KFold\n",
    "from tsfresh import extract_relevant_features\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D, LSTM, Dense, Dropout, Flatten\n",
    "from sklearn.model_selection import train_test_split\n",
    "print(tf.config.list_physical_devices())"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/tensor_py9/lib/python3.9/site-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
      "Dask dataframe query planning is disabled because dask-expr is not installed.\n",
      "\n",
      "You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
      "This will raise in a future version.\n",
      "\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-03T12:07:31.242246Z",
     "start_time": "2024-11-03T12:07:30.981866Z"
    }
   },
   "cell_type": "code",
   "source": [
    "file_path = \"./data/positive/kplr001871056-2011303113607_slc.fits\"\n",
    "with fits.open(file_path) as hdul:\n",
    "    data = hdul[1].data\n",
    "    header = hdul[1].header\n",
    "    #print(data[:5])\n",
    "    time = data['TIME']\n",
    "    flux = data['PDCSAP_FLUX']\n",
    "    \n",
    "    # Plot the light curve\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(time, flux, 'k-', markersize=1, lw=0.5)\n",
    "    plt.xlabel('Time (BJD - 2454833)')\n",
    "    plt.ylabel('Corrected Flux (e-/s)')\n",
    "    plt.title('Exoplanet Light Curve')\n",
    "    plt.show()\n",
    "    #print(header)  # Print header information"
   ],
   "id": "15b279be84f040b4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-02T15:51:36.607201Z",
     "start_time": "2024-11-02T15:51:36.515367Z"
    }
   },
   "cell_type": "code",
   "source": [
    "file_path = \"./data/negative/kplr000892772-2010078095331_llc.fits\"\n",
    "with fits.open(file_path) as hdul:\n",
    "    # Print information about the HDUs (Header/Data Units) in the file\n",
    "    hdul.info()\n",
    "\n",
    "    # Access the primary HDU data and header\n",
    "    data = hdul[1].data  # Usually the time-series data is in HDU 1\n",
    "    header = hdul[1].header\n",
    "    #print(header)\n",
    "    time = data['TIME']\n",
    "    flux = data['PDCSAP_FLUX']\n",
    "\n",
    "    # Plot the light curve\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(time, flux, 'k-', markersize=1, lw=0.5)\n",
    "    plt.xlabel('Time (BJD - 2454833)')\n",
    "    plt.ylabel('Corrected Flux (e-/s)')\n",
    "    plt.title('False Positive Light Curve')\n",
    "    plt.show()\n",
    "    #print(header)  # Print header information"
   ],
   "id": "4ce9df876135a281",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: ./data/negative/kplr000892772-2010078095331_llc.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU      58   ()      \n",
      "  1  LIGHTCURVE    1 BinTableHDU    161   4397R x 20C   [D, E, J, E, E, E, E, E, E, J, D, E, D, E, D, E, D, E, E, E]   \n",
      "  2  APERTURE      1 ImageHDU        48   (5, 4)   int32   \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-02T15:51:39.948408Z",
     "start_time": "2024-11-02T15:51:39.938984Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def load_fits_data(folder, label):\n",
    "    count = -1\n",
    "    for filename in os.listdir(folder):\n",
    "        if filename.endswith('.fits'):\n",
    "            with fits.open(os.path.join(folder, filename)) as hdul:\n",
    "                try:\n",
    "                    count += 1\n",
    "                    data = hdul[1].data\n",
    "                    time = data['TIME']\n",
    "                    flux = data['PDCSAP_FLUX']\n",
    "                    \n",
    "                    valid_indices = ~np.isnan(time) & ~np.isnan(flux)\n",
    "                    time = time[valid_indices]\n",
    "                    flux = flux[valid_indices]\n",
    "                    mean = np.mean(flux)\n",
    "                    std = np.std(flux)\n",
    "                    new_flux = (flux - mean) / std\n",
    "\n",
    "                    smoothed_flux = savgol_filter(flux, window_length=11, polyorder=2)\n",
    "                    smoothed_flux = (smoothed_flux - np.mean(smoothed_flux))/np.std(smoothed_flux)\n",
    "                    df = pd.DataFrame({\n",
    "                        'time': time,\n",
    "                        'flux': flux,\n",
    "                        'id': filename\n",
    "                    })\n",
    "                    \n",
    "                    \n",
    "                    print(\"datashape\", time.shape, flux.shape,)\n",
    "                    time_series.append([new_flux, smoothed_flux])\n",
    "                    labels.append(label)\n",
    "                except:\n",
    "                    print(\"got error for filename\", filename)\n",
    "    "
   ],
   "id": "4b4eb1b96855c71d",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-02T15:52:58.994576Z",
     "start_time": "2024-11-02T15:51:41.091920Z"
    }
   },
   "cell_type": "code",
   "source": [
    "positive_folder = \"./data/positive/\"\n",
    "negative_folder = \"./data/negative/\"\n",
    "time_series = []\n",
    "labels = []\n",
    "extracted_features_list = []\n",
    "\n",
    "load_fits_data(positive_folder, label=1)\n",
    "load_fits_data(negative_folder, label=0)\n",
    "# \n",
    "df = pd.DataFrame({\n",
    "    'time_series': time_series,\n",
    "    'label': labels\n",
    "})\n",
    "# print(df.shape, len(time_series))"
   ],
   "id": "ef682aeb32b23faa",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datashape (40771,) (40771,)\n",
      "datashape (4226,) (4226,)\n",
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      "datashape (1624,) (1624,)\n",
      "datashape (4366,) (4366,)\n",
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      "datashape (1624,) (1624,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4243,) (4243,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (52753,) (52753,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4366,) (4366,)\n",
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      "datashape (4226,) (4226,)\n",
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      "datashape (4272,) (4272,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (45615,) (45615,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (4244,) (4244,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (44035,) (44035,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (38645,) (38645,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (35655,) (35655,)\n",
      "datashape (7622,) (7622,)\n",
      "datashape (4609,) (4609,)\n",
      "datashape (43543,) (43543,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (46311,) (46311,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (4485,) (4485,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4242,) (4242,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4608,) (4608,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (45616,) (45616,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (51069,) (51069,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (49958,) (49958,)\n",
      "datashape (40771,) (40771,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (43978,) (43978,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (46462,) (46462,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (45419,) (45419,)\n",
      "datashape (29389,) (29389,)\n",
      "datashape (43543,) (43543,)\n",
      "datashape (4486,) (4486,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4485,) (4485,)\n",
      "got error for filename kplr002713049-2012179063303_llc.fits\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4242,) (4242,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (34810,) (34810,)\n",
      "datashape (52753,) (52753,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (43998,) (43998,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (46462,) (46462,)\n",
      "datashape (46462,) (46462,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: File may have been truncated: actual file length (442368) is smaller than the expected size (463680) [astropy.io.fits.file]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datashape (43543,) (43543,)\n",
      "datashape (44059,) (44059,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (7622,) (7622,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (45254,) (45254,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (32946,) (32946,)\n",
      "datashape (45613,) (45613,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (45186,) (45186,)\n",
      "datashape (1285,) (1285,)\n",
      "datashape (4243,) (4243,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (43543,) (43543,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (44035,) (44035,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (46806,) (46806,)\n",
      "datashape (43543,) (43543,)\n",
      "datashape (39122,) (39122,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (42289,) (42289,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (3550,) (3550,)\n",
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      "datashape (25891,) (25891,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (4242,) (4242,)\n",
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      "datashape (39984,) (39984,)\n",
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      "datashape (52753,) (52753,)\n",
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      "datashape (3550,) (3550,)\n",
      "datashape (3106,) (3106,)\n",
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      "datashape (29389,) (29389,)\n",
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      "datashape (4134,) (4134,)\n",
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      "datashape (4243,) (4243,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (43978,) (43978,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (4608,) (4608,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4070,) (4070,)\n",
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      "datashape (4245,) (4245,)\n",
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      "datashape (3106,) (3106,)\n",
      "datashape (44148,) (44148,)\n",
      "datashape (38646,) (38646,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (44875,) (44875,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (4486,) (4486,)\n",
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      "datashape (4473,) (4473,)\n",
      "datashape (49145,) (49145,)\n",
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      "datashape (4070,) (4070,)\n",
      "datashape (51068,) (51068,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (51069,) (51069,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (6151,) (6151,)\n",
      "datashape (42325,) (42325,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4243,) (4243,)\n",
      "datashape (1285,) (1285,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (53283,) (53283,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4486,) (4486,)\n",
      "datashape (1285,) (1285,)\n",
      "datashape (1285,) (1285,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (39984,) (39984,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (46156,) (46156,)\n",
      "datashape (44059,) (44059,)\n",
      "datashape (43998,) (43998,)\n",
      "datashape (43978,) (43978,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4486,) (4486,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (52751,) (52751,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4242,) (4242,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4609,) (4609,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (45615,) (45615,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4243,) (4243,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (43543,) (43543,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (44875,) (44875,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (46462,) (46462,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4485,) (4485,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (51069,) (51069,)\n",
      "datashape (42700,) (42700,)\n",
      "datashape (7622,) (7622,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (40433,) (40433,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (35656,) (35656,)\n",
      "datashape (44059,) (44059,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (7622,) (7622,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (44875,) (44875,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4243,) (4243,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (4610,) (4610,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4243,) (4243,)\n",
      "datashape (4610,) (4610,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (1285,) (1285,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4244,) (4244,)\n",
      "datashape (53281,) (53281,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (4610,) (4610,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (25891,) (25891,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (25891,) (25891,)\n",
      "datashape (1285,) (1285,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (49145,) (49145,)\n",
      "datashape (4608,) (4608,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4610,) (4610,)\n",
      "datashape (44148,) (44148,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (44035,) (44035,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (39984,) (39984,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (39122,) (39122,)\n",
      "datashape (4242,) (4242,)\n",
      "datashape (1285,) (1285,)\n",
      "datashape (42289,) (42289,)\n",
      "datashape (4486,) (4486,)\n",
      "datashape (45611,) (45611,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (42325,) (42325,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (25891,) (25891,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (45005,) (45005,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (49958,) (49958,)\n",
      "datashape (4486,) (4486,)\n",
      "datashape (4610,) (4610,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (25890,) (25890,)\n",
      "datashape (32600,) (32600,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (32946,) (32946,)\n",
      "datashape (51069,) (51069,)\n",
      "datashape (32600,) (32600,)\n",
      "datashape (46806,) (46806,)\n",
      "datashape (45186,) (45186,)\n",
      "datashape (4486,) (4486,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (1285,) (1285,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (25891,) (25891,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (46462,) (46462,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (35656,) (35656,)\n",
      "datashape (35656,) (35656,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4486,) (4486,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (7622,) (7622,)\n",
      "datashape (4609,) (4609,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (42289,) (42289,)\n",
      "datashape (42289,) (42289,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4242,) (4242,)\n",
      "datashape (38646,) (38646,)\n",
      "datashape (38645,) (38645,)\n",
      "datashape (53283,) (53283,)\n",
      "datashape (4486,) (4486,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (49958,) (49958,)\n",
      "datashape (4244,) (4244,)\n",
      "datashape (4486,) (4486,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4134,) (4134,)\n",
      "got error for filename kplr003544595-2010355172524_llc.fits\n",
      "datashape (25891,) (25891,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (39984,) (39984,)\n",
      "datashape (39122,) (39122,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (3534,) (3534,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: File may have been truncated: actual file length (425984) is smaller than the expected size (457920) [astropy.io.fits.file]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datashape (4473,) (4473,)\n",
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      "datashape (39984,) (39984,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (41710,) (41710,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (4243,) (4243,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4485,) (4485,)\n",
      "datashape (4610,) (4610,)\n",
      "got error for filename kplr002850321-2013098041711_llc.fits\n",
      "datashape (4441,) (4441,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4242,) (4242,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (1285,) (1285,)\n",
      "datashape (4242,) (4242,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (45005,) (45005,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4108,) (4108,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: File may have been truncated: actual file length (327680) is smaller than the expected size (440640) [astropy.io.fits.file]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datashape (4269,) (4269,)\n",
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      "datashape (3106,) (3106,)\n",
      "datashape (4486,) (4486,)\n",
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      "datashape (4108,) (4108,)\n",
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      "datashape (4108,) (4108,)\n",
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      "datashape (4366,) (4366,)\n",
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      "datashape (25891,) (25891,)\n",
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      "datashape (4108,) (4108,)\n",
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      "datashape (4486,) (4486,)\n",
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      "datashape (3106,) (3106,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4609,) (4609,)\n",
      "datashape (4609,) (4609,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (51069,) (51069,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4244,) (4244,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (469,) (469,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (1624,) (1624,)\n",
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      "datashape (4366,) (4366,)\n",
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      "datashape (4366,) (4366,)\n",
      "datashape (45419,) (45419,)\n",
      "datashape (4609,) (4609,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4485,) (4485,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4609,) (4609,)\n",
      "datashape (4473,) (4473,)\n",
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      "datashape (4610,) (4610,)\n",
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      "datashape (4134,) (4134,)\n",
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      "datashape (4366,) (4366,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (4609,) (4609,)\n",
      "datashape (44149,) (44149,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (1624,) (1624,)\n",
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      "datashape (4485,) (4485,)\n",
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      "datashape (4070,) (4070,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (7622,) (7622,)\n",
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      "datashape (4473,) (4473,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (3550,) (3550,)\n",
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      "datashape (469,) (469,)\n",
      "datashape (1284,) (1284,)\n",
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      "datashape (4485,) (4485,)\n",
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      "datashape (4108,) (4108,)\n",
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      "datashape (4366,) (4366,)\n",
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      "datashape (4226,) (4226,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (4609,) (4609,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (52753,) (52753,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (469,) (469,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (1285,) (1285,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (4610,) (4610,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (469,) (469,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (4244,) (4244,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (4609,) (4609,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (4244,) (4244,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (1285,) (1285,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4269,) (4269,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4486,) (4486,)\n",
      "datashape (4134,) (4134,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (4242,) (4242,)\n",
      "datashape (4486,) (4486,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (4486,) (4486,)\n",
      "datashape (1285,) (1285,)\n",
      "datashape (4485,) (4485,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (1624,) (1624,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4243,) (4243,)\n",
      "datashape (3550,) (3550,)\n",
      "datashape (4242,) (4242,)\n",
      "datashape (4272,) (4272,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (4485,) (4485,)\n",
      "datashape (4242,) (4242,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (4486,) (4486,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (39122,) (39122,)\n",
      "datashape (4242,) (4242,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (3534,) (3534,)\n",
      "datashape (4610,) (4610,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (3535,) (3535,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4441,) (4441,)\n",
      "datashape (1284,) (1284,)\n",
      "datashape (4366,) (4366,)\n",
      "got error for filename kplr002437209-2011116030358_slc.fits\n",
      "datashape (4609,) (4609,)\n",
      "datashape (4366,) (4366,)\n",
      "datashape (4609,) (4609,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: File may have been truncated: actual file length (4096000) is smaller than the expected size (5368320) [astropy.io.fits.file]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datashape (4609,) (4609,)\n",
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      "datashape (3106,) (3106,)\n",
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      "datashape (4441,) (4441,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4485,) (4485,)\n",
      "datashape (4108,) (4108,)\n",
      "datashape (3106,) (3106,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4226,) (4226,)\n",
      "datashape (4242,) (4242,)\n",
      "datashape (4473,) (4473,)\n",
      "datashape (4070,) (4070,)\n",
      "datashape (4269,) (4269,)\n",
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     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-02T15:54:08.761646Z",
     "start_time": "2024-11-02T15:53:57.946445Z"
    }
   },
   "cell_type": "code",
   "source": [
    "sequence_length = 200\n",
    "def split_into_segments(light_curve, sequence_length=200):\n",
    "    segments = []\n",
    "    #print(\"light_curve\", len(light_curve))\n",
    "    if len(light_curve[0]) < sequence_length:\n",
    "        return segments\n",
    "\n",
    "    frequency_spectrum = fft(light_curve[0])\n",
    "    magnitude = np.abs(frequency_spectrum)\n",
    "    phase = np.angle(frequency_spectrum)\n",
    "    magnitude_features = magnitude[:sequence_length]\n",
    "        \n",
    "    for i in range(0, len(light_curve[0]) - sequence_length + 1, sequence_length):\n",
    "        segment = []\n",
    "        for j in range(2):\n",
    "            feature = light_curve[j]\n",
    "            if(len(feature) < 10):\n",
    "                print(\"found less than 10\")\n",
    "            segment.append(feature[i:i + sequence_length])\n",
    "        segment.append(magnitude_features)\n",
    "        segments.append(np.array(segment))\n",
    "    return np.array(segments)\n",
    "\n",
    "light_curve_segments = []\n",
    "light_curve_labels = []\n",
    "count = 0\n",
    "c2 = 0\n",
    "total = 0\n",
    "for i in range(0, len(time_series)):\n",
    "    light_curve = time_series[i]\n",
    "    label = labels[i]\n",
    "    segments = split_into_segments(light_curve)\n",
    "    for segment in segments:\n",
    "        if(len(segment) != 3):\n",
    "            print(\"woah\", len(segment))\n",
    "        total += 1\n",
    "        if (len(segment[2]) < 10):\n",
    "            c2 +=1\n",
    "        else:\n",
    "            count += 1\n",
    "        if(len(segment[0]) < 200 or len(segment[1]) < 200 or len(segment[2]) < 10):\n",
    "            print(\"lengths \", len(segment[0]) ,len(segment[1]) ,len(segment[2]))\n",
    "        #print(\"segmemt details for light cureve\", i, len(segment[2]))\n",
    "        light_curve_segments.append(segment)\n",
    "        light_curve_labels.append(label)\n",
    "print(\"tot\", total, count, c2)\n",
    "print(len(light_curve_segments), len(light_curve_labels), len(light_curve_segments[0]), len(light_curve_segments[0][0]))\n",
    "\n",
    "light_curve_segments = np.array(light_curve_segments)\n",
    "light_curve_labels = np.array(light_curve_labels)\n",
    "\n",
    "num_features = light_curve_segments.shape[1]\n",
    "print(light_curve_segments.shape, light_curve_labels.shape)"
   ],
   "id": "8f058b6f3d935618",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tot 430293 430293 0\n",
      "430293 430293 3 200\n",
      "(430293, 3, 200) (430293,)\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-02T15:54:11.801938Z",
     "start_time": "2024-11-02T15:54:11.793473Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def get_ts_model():\n",
    "    with tf.device('/GPU:0'):\n",
    "        model = Sequential()\n",
    "        model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(sequence_length, num_features)))\n",
    "        model.add(MaxPooling1D(pool_size=2))\n",
    "        model.add(Dropout(0.2))\n",
    "    \n",
    "        model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))\n",
    "        model.add(MaxPooling1D(pool_size=2))\n",
    "        model.add(Dropout(0.2))\n",
    "        \n",
    "        model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))\n",
    "        model.add(MaxPooling1D(pool_size=2))\n",
    "        model.add(Dropout(0.2))\n",
    "        \n",
    "        # LSTM layer\n",
    "        model.add(LSTM(64, return_sequences=False))\n",
    "        model.add(Dropout(0.2))\n",
    "        \n",
    "        # Dense layers\n",
    "        model.add(Dense(64, activation='relu'))\n",
    "        model.add(Dropout(0.5))\n",
    "        model.add(Dense(1, activation='sigmoid'))  # Binary classification\n",
    "        \n",
    "        # Compile model\n",
    "        model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "        \n",
    "        # Summary\n",
    "        model.summary()\n",
    "        return model"
   ],
   "id": "edb63a46415169fd",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-02T15:54:12.606637Z",
     "start_time": "2024-11-02T15:54:12.600804Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def get_splits(lightcurves, labels):\n",
    "    print(lightcurves.shape)\n",
    "    # x = np.array([np.reshape(segment, (sequence_length, 1)) for segment in lightcurves])\n",
    "    x = np.transpose(lightcurves, (0, 2, 1))\n",
    "    print(\"x.shape\", x.shape)\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x, labels, test_size=0.2)\n",
    "    print(\"x_train.shape\", x_train.shape, y_train.shape)\n",
    "    return x_train, x_test, y_train, y_test\n",
    "    "
   ],
   "id": "3cae55ec74c4c775",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "x_train, x_test, y_train, y_test = get_splits(light_curve_segments, light_curve_labels)\n",
    "print(tf.config.list_physical_devices('GPU'))\n",
    "model = get_ts_model()\n",
    "history = model.fit(x_train, y_train, epochs=10, batch_size=128, validation_data=(x_test, y_test))"
   ],
   "id": "bfbe2fbb65919f65",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()"
   ],
   "id": "f41b01430f67f61",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "devices = tf.config.list_physical_devices()\n",
    "print(tf.config.list_physical_devices('GPU'))\n",
    "print(\"\\nDevices: \", devices)"
   ],
   "id": "72f1298f2dbfb61b",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-02T16:28:44.681943Z",
     "start_time": "2024-11-02T15:54:19.559799Z"
    }
   },
   "cell_type": "code",
   "source": [
    "kfold = KFold(n_splits=3, shuffle=True)\n",
    "scores = []\n",
    "X= np.transpose(light_curve_segments, (0, 2, 1))\n",
    "y = light_curve_labels\n",
    "histories = []\n",
    "k_fold_model = get_ts_model()\n",
    "new_x, test_x, new_y, test_y = train_test_split(X, y, test_size=0.2)\n",
    "print(\"x_train.shape\", new_x.shape, new_y.shape)\n",
    "\n",
    "for train_index, validation_index in kfold.split(new_x):\n",
    "    x_train, x_validation = new_x[train_index], new_x[validation_index]\n",
    "    y_train, y_validation = new_y[train_index], new_y[validation_index]\n",
    "    history = k_fold_model.fit(x_train, y_train, epochs=10, batch_size=64, validation_data=(x_validation, y_validation))\n",
    "    histories.append(history)\n",
    "    # score = model.evaluate(X_test, y_test, verbose=0)\n",
    "    # scores.append(score[1])\n",
    "# Make predictions on the test set\n",
    "y_pred = (k_fold_model.predict(test_x) > 0.5).astype(\"int32\")  # Convert probabilities to binary predictions\n",
    "\n",
    "# Calculate precision and recall\n",
    "precision = tf.keras.metrics.Precision()\n",
    "recall = tf.keras.metrics.Recall()\n",
    "\n",
    "# Update metrics with true labels and predictions\n",
    "precision.update_state(test_y, y_pred)\n",
    "recall.update_state(test_y, y_pred)\n",
    "\n",
    "# Get the final precision and recall values\n",
    "print(\"Precision:\", precision.result().numpy())\n",
    "print(\"Recall:\", recall.result().numpy())"
   ],
   "id": "6b39bc2efaa04316",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-02 21:24:19.601958: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n",
      "2024-11-02 21:24:19.602660: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 8.00 GB\n",
      "2024-11-02 21:24:19.603451: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 2.67 GB\n",
      "2024-11-02 21:24:19.605024: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2024-11-02 21:24:19.608965: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
      "/opt/anaconda3/envs/tensor_py9/lib/python3.9/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1mModel: \"sequential\"\u001B[0m\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001B[1m \u001B[0m\u001B[1mLayer (type)                   \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1mOutput Shape          \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m      Param #\u001B[0m\u001B[1m \u001B[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv1d (\u001B[38;5;33mConv1D\u001B[0m)                 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m198\u001B[0m, \u001B[38;5;34m64\u001B[0m)        │           \u001B[38;5;34m640\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling1d (\u001B[38;5;33mMaxPooling1D\u001B[0m)    │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m99\u001B[0m, \u001B[38;5;34m64\u001B[0m)         │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001B[38;5;33mDropout\u001B[0m)               │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m99\u001B[0m, \u001B[38;5;34m64\u001B[0m)         │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_1 (\u001B[38;5;33mConv1D\u001B[0m)               │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m97\u001B[0m, \u001B[38;5;34m128\u001B[0m)        │        \u001B[38;5;34m24,704\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling1d_1 (\u001B[38;5;33mMaxPooling1D\u001B[0m)  │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m48\u001B[0m, \u001B[38;5;34m128\u001B[0m)        │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001B[38;5;33mDropout\u001B[0m)             │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m48\u001B[0m, \u001B[38;5;34m128\u001B[0m)        │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_2 (\u001B[38;5;33mConv1D\u001B[0m)               │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m46\u001B[0m, \u001B[38;5;34m128\u001B[0m)        │        \u001B[38;5;34m49,280\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling1d_2 (\u001B[38;5;33mMaxPooling1D\u001B[0m)  │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m23\u001B[0m, \u001B[38;5;34m128\u001B[0m)        │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (\u001B[38;5;33mDropout\u001B[0m)             │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m23\u001B[0m, \u001B[38;5;34m128\u001B[0m)        │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm (\u001B[38;5;33mLSTM\u001B[0m)                     │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m64\u001B[0m)             │        \u001B[38;5;34m49,408\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (\u001B[38;5;33mDropout\u001B[0m)             │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m64\u001B[0m)             │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001B[38;5;33mDense\u001B[0m)                   │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m64\u001B[0m)             │         \u001B[38;5;34m4,160\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_4 (\u001B[38;5;33mDropout\u001B[0m)             │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m64\u001B[0m)             │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001B[38;5;33mDense\u001B[0m)                 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m1\u001B[0m)              │            \u001B[38;5;34m65\u001B[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">99</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">99</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">97</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">24,704</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling1d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">49,280</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling1d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">49,408</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Total params: \u001B[0m\u001B[38;5;34m128,257\u001B[0m (501.00 KB)\n"
      ],
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">128,257</span> (501.00 KB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Trainable params: \u001B[0m\u001B[38;5;34m128,257\u001B[0m (501.00 KB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">128,257</span> (501.00 KB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Non-trainable params: \u001B[0m\u001B[38;5;34m0\u001B[0m (0.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train.shape (344234, 200, 3) (344234,)\n",
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-02 21:24:48.079587: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m58s\u001B[0m 31ms/step - accuracy: 0.7041 - loss: 0.5840 - val_accuracy: 0.7760 - val_loss: 0.4401\n",
      "Epoch 2/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m51s\u001B[0m 28ms/step - accuracy: 0.7723 - loss: 0.4491 - val_accuracy: 0.8361 - val_loss: 0.2827\n",
      "Epoch 3/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m54s\u001B[0m 30ms/step - accuracy: 0.8232 - loss: 0.3249 - val_accuracy: 0.8637 - val_loss: 0.2321\n",
      "Epoch 4/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m53s\u001B[0m 29ms/step - accuracy: 0.8521 - loss: 0.2703 - val_accuracy: 0.8703 - val_loss: 0.2188\n",
      "Epoch 5/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m50s\u001B[0m 28ms/step - accuracy: 0.8609 - loss: 0.2474 - val_accuracy: 0.8826 - val_loss: 0.2109\n",
      "Epoch 6/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m52s\u001B[0m 29ms/step - accuracy: 0.8686 - loss: 0.2356 - val_accuracy: 0.8897 - val_loss: 0.1964\n",
      "Epoch 7/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m57s\u001B[0m 32ms/step - accuracy: 0.8762 - loss: 0.2260 - val_accuracy: 0.8902 - val_loss: 0.1994\n",
      "Epoch 8/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m59s\u001B[0m 33ms/step - accuracy: 0.8815 - loss: 0.2199 - val_accuracy: 0.9053 - val_loss: 0.1807\n",
      "Epoch 9/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m62s\u001B[0m 35ms/step - accuracy: 0.8858 - loss: 0.2158 - val_accuracy: 0.9178 - val_loss: 0.1665\n",
      "Epoch 10/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m61s\u001B[0m 34ms/step - accuracy: 0.8918 - loss: 0.2069 - val_accuracy: 0.9201 - val_loss: 0.1640\n",
      "Epoch 1/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m76s\u001B[0m 41ms/step - accuracy: 0.8960 - loss: 0.2035 - val_accuracy: 0.9358 - val_loss: 0.1442\n",
      "Epoch 2/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m62s\u001B[0m 35ms/step - accuracy: 0.9023 - loss: 0.1948 - val_accuracy: 0.9395 - val_loss: 0.1438\n",
      "Epoch 3/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m47s\u001B[0m 26ms/step - accuracy: 0.9064 - loss: 0.1906 - val_accuracy: 0.9361 - val_loss: 0.1432\n",
      "Epoch 4/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m56s\u001B[0m 31ms/step - accuracy: 0.9122 - loss: 0.1836 - val_accuracy: 0.9470 - val_loss: 0.1330\n",
      "Epoch 5/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m110s\u001B[0m 61ms/step - accuracy: 0.9154 - loss: 0.1780 - val_accuracy: 0.9537 - val_loss: 0.1202\n",
      "Epoch 6/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m70s\u001B[0m 39ms/step - accuracy: 0.9190 - loss: 0.1735 - val_accuracy: 0.9590 - val_loss: 0.1037\n",
      "Epoch 7/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m50s\u001B[0m 28ms/step - accuracy: 0.9228 - loss: 0.1685 - val_accuracy: 0.9656 - val_loss: 0.0953\n",
      "Epoch 8/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m55s\u001B[0m 31ms/step - accuracy: 0.9252 - loss: 0.1643 - val_accuracy: 0.9621 - val_loss: 0.1094\n",
      "Epoch 9/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m66s\u001B[0m 37ms/step - accuracy: 0.9292 - loss: 0.1576 - val_accuracy: 0.9671 - val_loss: 0.0861\n",
      "Epoch 10/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m84s\u001B[0m 47ms/step - accuracy: 0.9306 - loss: 0.1569 - val_accuracy: 0.9671 - val_loss: 0.0900\n",
      "Epoch 1/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m83s\u001B[0m 46ms/step - accuracy: 0.9310 - loss: 0.1572 - val_accuracy: 0.9753 - val_loss: 0.0807\n",
      "Epoch 2/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m76s\u001B[0m 42ms/step - accuracy: 0.9338 - loss: 0.1507 - val_accuracy: 0.9751 - val_loss: 0.0775\n",
      "Epoch 3/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m65s\u001B[0m 36ms/step - accuracy: 0.9365 - loss: 0.1463 - val_accuracy: 0.9765 - val_loss: 0.0710\n",
      "Epoch 4/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m62s\u001B[0m 35ms/step - accuracy: 0.9357 - loss: 0.1479 - val_accuracy: 0.9791 - val_loss: 0.0772\n",
      "Epoch 5/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m64s\u001B[0m 36ms/step - accuracy: 0.9392 - loss: 0.1427 - val_accuracy: 0.9814 - val_loss: 0.0612\n",
      "Epoch 6/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m63s\u001B[0m 35ms/step - accuracy: 0.9400 - loss: 0.1413 - val_accuracy: 0.9843 - val_loss: 0.0574\n",
      "Epoch 7/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m78s\u001B[0m 43ms/step - accuracy: 0.9422 - loss: 0.1372 - val_accuracy: 0.9826 - val_loss: 0.0579\n",
      "Epoch 8/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m79s\u001B[0m 44ms/step - accuracy: 0.9446 - loss: 0.1329 - val_accuracy: 0.9845 - val_loss: 0.0538\n",
      "Epoch 9/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m74s\u001B[0m 41ms/step - accuracy: 0.9432 - loss: 0.1355 - val_accuracy: 0.9851 - val_loss: 0.0548\n",
      "Epoch 10/10\n",
      "\u001B[1m1793/1793\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m81s\u001B[0m 45ms/step - accuracy: 0.9460 - loss: 0.1290 - val_accuracy: 0.9798 - val_loss: 0.0628\n",
      "\u001B[1m2690/2690\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m8s\u001B[0m 3ms/step\n",
      "Precision: 0.9890167\n",
      "Recall: 0.98120314\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "y_pred = (k_fold_model.predict(test_x) > 0.5).astype(\"int32\")  # Convert probabilities to binary predictions\n",
    "\n",
    "# Calculate precision and recall\n",
    "precision = tf.keras.metrics.Precision()\n",
    "recall = tf.keras.metrics.Recall()\n",
    "\n",
    "# Update metrics with true labels and predictions\n",
    "precision.update_state(test_y, y_pred)\n",
    "recall.update_state(test_y, y_pred)\n",
    "\n",
    "# Get the final precision and recall values\n",
    "print(\"Precision:\", precision.result().numpy())\n",
    "print(\"Recall:\", recall.result().numpy())"
   ],
   "id": "f73cfb6c85bb9fd0",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-02T16:34:40.331934Z",
     "start_time": "2024-11-02T16:34:39.328125Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_accuracy_list = []\n",
    "train_loss_list = []\n",
    "validation_accuracy_list = []\n",
    "validation_loss_list = []\n",
    "for history in histories:\n",
    "    train_accuracy_list.extend(history.history['accuracy'])\n",
    "    train_loss_list.extend(history.history['loss'])\n",
    "    validation_accuracy_list.extend(history.history['val_accuracy'])\n",
    "    validation_loss_list.extend(history.history['val_loss'])\n",
    "    \n",
    "plt.plot(train_accuracy_list)\n",
    "plt.plot(validation_accuracy_list)\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(train_loss_list)\n",
    "plt.plot(validation_loss_list)\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()"
   ],
   "id": "1a799e6914de3f3f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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oaCiffvopkydPpnPnzgwdOvSMx7fb7UycOJHo6GhWr15NQUFBrf47NUJCQliwYAEJCQls2rSJW2+9lZCQEP70pz8xadIkNm/ezBdffMFXX30FQFhYWJ1jlJSUMG7cOM4991zWrFlDVlYWt9xyC9OnT68V4pYtW0Z8fDzLli1j165dTJo0ibPOOotbb731jO9HRETaBoWcM6ksgccT6v2yAKBfY8/9l3TwDXJq15tuuomnnnqK5cuXM2rUKMBsqpo4cSKJiYncd999jn3vvvtuvvjiCxYvXuxUyPnqq6/Ytm0b+/btIykpCYDHH3+8Tj+aBx980HG/U6dO3HvvvSxatIg//elPBAQEEBwcjLe3N3Fxcac811tvvUVpaSlvvPEGQUHme3/hhRe4/PLL+fvf/05sbCwAERERvPDCC3h5edGzZ08uvfRSvv76a4UcERFxUMhpJXr27MmwYcN49dVXGTVqFLt372blypUsWbIEm83GE088waJFi0hLS6O8vJzy8nJHiDiTbdu20aFDB0fAAUhJSamz33vvvcecOXPYtWsXRUVFVFVVERoaWq/3sW3bNgYMGFCrtuHDh2O329m+fbsj5PTp0wcvLy/HPvHx8WzatKle5xIRkdbN4yFn7ty5PPXUU2RkZNCnTx/mzJnDiBEjTrrv8Vcpjrdt2zZ69uzZNAX6BJpXVBrg4NFS8koqiA31IybEv2Hnroebb76Z6dOn8+KLL/Laa6/RsWNHRo8ezVNPPcWzzz7LnDlz6NevH0FBQcyYMYOKigqnjnuy1dRPbH5bvXo111xzDY8++ihjx44lLCyMd955h6effrpe78EwjFM27R2/3cfHp85zdrsmXxQRkWM8GnIWLVrEjBkzmDt3LsOHD+df//oX48ePZ+vWrXTo0OGUr9u+fXutKwTt2rVruiItFqebjE7kH+iFUVlGKT4NPkZ9XH311fz+97/n7bff5vXXX+fWW2/FYrGwcuVKrrjiCm644QbA7GOzc+dOevXq5dRxe/fuzYEDB0hPTychwWy6+/7772vts2rVKjp27MjMmTMd204c7eXr64vNdvqO2L179+b111+nuLjYcTVn1apVWK1Wunfv7lS9IiIi4OHRVc888ww333wzt9xyC7169WLOnDm0b9+eefPmnfZ1MTExxMXFOW7HN1s0J+5e3iE4OJhJkybxl7/8hfT0dKZOnQpA165dWbp0KampqWzbto3bb7+dzMxMp4970UUX0aNHD2688UY2bNjAypUra4WZmnMcOHCAd955h927d/P888/z4Ycf1tqnU6dO7N27l/Xr15OdnU15eXmdc11//fX4+/szZcoUNm/ezLJly7j77ruZPHmyo6lKRETEGR4LORUVFaxdu5YxY8bU2j5mzBhSU1NP+9qBAwcSHx/P6NGjWbZs2Wn3LS8vp6CgoNbNXTyxvMPNN9/M0aNHueiiixxXwx566CEGDRrE2LFjGTlyJHFxcUyYMMHpY1qtVj788EPKy8s555xzuOWWW/jb3/5Wa58rrriCP/zhD0yfPp2zzjqL1NRUHnrooVr7/PrXv2bcuHGMGjWKdu3anXQYe2BgIF9++SW5ubmcffbZXHXVVYwePZoXXnih/h+GiIi0aRbjZB0u3CA9PZ3ExERWrVrFsGHDHNsff/xxXn/9dbZv317nNdu3b+fbb79l8ODBlJeX8+abbzJ//nyWL1/O+eeff9LzzJo1i0cffbTO9vz8/DqdYsvKyti7dy/Jycn4+zegD81JbE0voMpup2tMMIG+Hu8C1WY1xX9bERFxn4KCAsLCwk76+30qHv/VPbGT6ek6nvbo0YMePXo4HqekpHDw4EH+8Y9/nDLkPPDAA9xzzz2OxwUFBbRv394FlTvH38dKUbmdskqbQo6IiIgbeay5Kjo6Gi8vrzp9Q7KysurV9+Lcc89l586dp3zez8+P0NDQWjd3cufyDiIiInKMx0KOr68vgwcPrrM8wNKlS2s1X53JunXriI+Pd3V5LuPO5R1ERETkGI+2n9xzzz1MnjyZIUOGkJKSwksvvcSBAweYNm0aYDY1paWl8cYbbwAwZ84cOnXqRJ8+faioqOA///kP77//Pu+//74n38ZpHRthZTttU5yIiIi4lkdDzqRJk8jJyeGxxx4jIyODvn378tlnn9GxY0cAMjIyOHDggGP/iooK7rvvPtLS0ggICKBPnz58+umnXHLJJS6ty5V9sf18rFiwYLMbVNoMfL0VcjzBQ/3rRUTEgzw2uspTTtc722azsWPHDmJiYoiKinLZOXccLqSs0kanqCBCA3zO/AJxuZycHLKysujevXuznVdJREROrUWOrmpOvLy8CA8PJysrCzDnbHFF85KPUUVpVSWFxRZ8LX6NPp44zzAMSkpKyMrKIjw8XAFHRKQNUcg5Qc0K2TVBxxUKyyrJL62iyNeLgiBflx1XnBceHn7a1c9FRKT1Ucg5gcViIT4+npiYGCorK11yzDV7c5n12UaSIgJ5/aZzXHJMcZ6Pj4+u4IiItEEKOafg5eXlsh/GnklRpBXaSC8qxLD6EOCrH1wREZGm5tEFOtuKdiF+RAf7Yhiw/XChp8sRERFpExRy3KRXvNkT/JcM9y0QKiIi0pYp5LhJTcjZppAjIiLiFgo5btIzLgSAbRlqrhIREXEHhRw3cVzJySzQ7LsiIiJuoJDjJl3aBePjZaGwrIq0vFJPlyMiItLqKeS4ia+3lS7tggH4RU1WIiIiTU4hx43U+VhERMR9FHLcqFd8defjTIUcERGRpqaQ40bH5spRc5WIiEhTU8hxo55xZsjZm1NMSUWVh6sRERFp3RRy3Mhc3sEPw4Adh4s8XY6IiEirppDjZo5+Oep8LCIi0qQUctxMI6xERETcQyHHzWqu5KjzsYiISNNSyHGzms7HWt5BRESkaSnkuJmWdxAREXEPhRw3O355B61ILiIi0nQUcjygtzofi4iINDmFHA9wzHys5R1ERESajEKOB/R0zJWj5ioREZGmopDjATVXcvZpeQcREZEmo5DjAdHBx5Z32J6pqzkiIiJNQSHHQ3qpyUpERKRJKeR4SG91PhYREWlSCjke0lMLdYqIiDQphRwPcQwjzyjU8g4iIiJNQCHHQzpHVy/vUF7FoaNa3kFERMTVFHI8xNfbStcYNVmJiIg0FYUcVynOgVXPw9f/5/RLakZY/aJh5CIiIi6nkOMqVaWw9CFYNQdszk3w1ytOa1iJiIg0FYUcVwlJAG9/sFdB/gGnXnJsDStdyREREXE1hRxXsVohItm8n7PHqZfUDCPX8g4iIiKup5DjSlFdzL+5u53aPTrYj3Yh5vIOupojIiLiWgo5rhTZ2fyb69yVHICecdWdj7W8g4iIiEsp5LhSTcjJce5KDhxb3kGdj0VERFxLIceV6tlcBcc6HyvkiIiIuJZCjitFVoecvANgq3TqJT2PmytHyzuIiIi4jkKOK4XEHxtGnufcMPIu7czlHYq0vIOIiIhLKeS4ktVa787HPl5a3kFERKQpKOS4WgNGWNUs77BNI6xERERcRiHH1TTCSkREpFlQyHG1Boyw6hlXs7yDQo6IiIirKOS4Ws0IqwY0V+3PLaG4XMs7iIiIuIJCjqvVXMk5ut/pYeRRxy3vsP2w+uWIiIi4gkKOqwXHgXcAGDanh5GDJgUUERFxNYUcV2vAMHI41mS1JV0hR0RExBUUcppCVP1HWPVPDAdg46E819cjIiLSBinkNAXHlZx6hJykMMBcjbys0tYUVYmIiLQpCjlNoQEjrJIiAogM8qXKbvBLpjofi4iINJZCTlOoGWFVj+Yqi8VCv0Tzao6arERERBpPIacp1DRX1WM1coAB1U1WGw7mN0VVIiIibYpCTlMIiQefQHMY+dH9Tr+sf1I4oCs5IiIirqCQ0xQslgYNI+/f3rySs+tIEUWa+VhERKRRPB5y5s6dS3JyMv7+/gwePJiVK1c69bpVq1bh7e3NWWed1bQFNlRksvm3HiOsYkL8iQ/zxzBgS5qarERERBrDoyFn0aJFzJgxg5kzZ7Ju3TpGjBjB+PHjOXDg9DMF5+fnc+ONNzJ69Gg3VdoAkfXvfAzHhpJvPKSQIyIi0hgeDTnPPPMMN998M7fccgu9evVizpw5tG/fnnnz5p32dbfffjvXXXcdKSkpbqq0AaLqP4wcjvXL2aB+OSIiIo3isZBTUVHB2rVrGTNmTK3tY8aMITU19ZSve+2119i9ezePPPKIU+cpLy+noKCg1s0tGjAhIOhKjoiIiKt4LORkZ2djs9mIjY2ttT02NpbMzMyTvmbnzp3cf//9vPXWW3h7ezt1ntmzZxMWFua4tW/fvtG1O6WmuSrvAFRVOP2ymuUdDuSWcLTY+deJiIhIbR7veGyxWGo9NgyjzjYAm83Gddddx6OPPkr37t2dPv4DDzxAfn6+43bw4MFG1+yUkLjqYeT2eq1GHhboQ6eoQAA2qfOxiIhIgzl3OaQJREdH4+XlVeeqTVZWVp2rOwCFhYX89NNPrFu3junTpwNgt9sxDANvb2+WLFnChRdeWOd1fn5++Pn5Nc2bOJ2aYeSHN5tNVtFdnX5p/6Rw9uWUsPFQHud3b9eERYqIiLReHruS4+vry+DBg1m6dGmt7UuXLmXYsGF19g8NDWXTpk2sX7/ecZs2bRo9evRg/fr1DB061F2lOy+y/quRw7F+ORvUL0dERKTBPHYlB+Cee+5h8uTJDBkyhJSUFF566SUOHDjAtGnTALOpKS0tjTfeeAOr1Urfvn1rvT4mJgZ/f/8625uNRo6w0szHIiIiDefRkDNp0iRycnJ47LHHyMjIoG/fvnz22Wd07NgRgIyMjDPOmdOsNXCEVd/EUKwWOFxQzuGCMmJD/ZugOBERkdbNYhiG4eki3KmgoICwsDDy8/MJDQ1t2pPtWwULLoHwjjBjY71eOvbZb9l+uJCXbxzCxb3r9lESERFpSxry++3x0VWtWk1zVf7Beg0jh+Pny8lzcVEiIiJtg0JOUwqOBZ+g6mHkzq9GDtC/fTigzsciIiINpZDTlI5fjby+I6wSj13JaWMtiiIiIi6hkNPUomo6H9dvhFXP+BB8vCzklVRyMLe0CQoTERFp3RRymloDR1j5eXvRK97sWKXFOkVEROpPIaep1axhVc/mKjjW+VjLO4iIiNSfQk5Ta+CEgHBsUsANB/NcV4+IiEgboZDT1GqaqxoxjHxzWj42uzofi4iI1IdCTlMLjgXfYHMY+dF99Xpp13bBBPh4UVxhY8+RoqapT0REpJVSyGlqFgtEJpv369lk5e1lpW9iTedj9csRERGpD4Ucd2jgCCs41i9nk0ZYiYiI1ItCjju4YISVruSIiIjUj0KOO7hghNXWjAIqquwuLEpERKR1U8hxh0Y0V3WKCiTU35uKKjs7Dhe6uDAREZHWSyHHHWqaq/IPQVV5vV5qsViOzZejfjkiIiJOU8hxh+CY44aR1281cjhu5mP1yxEREXGaQo471BpG3vARVup8LCIi4jyFHHdpxAirAe3NKzk7DhdSWmFzZVUiIiKtlkKOuzRihFVcqD/RwX7Y7AZbM3Q1R0RExBkKOe7SiBFWFouFATXz5RxUyBEREXGGQo67OJqr6n8lB471y9moEVYiIiJOUchxl5rmqvyD9R5GDtC/ul/OxjRdyREREXGGQo67BLUzh5Fj1Hs1coD+iWbI2XOkmIKyStfWJiIi0gop5LiLxXKsX04DRlhFBfuRGB4AwGYNJRcRETkjhRx3asQIKzg2lFzz5YiIiJyZQo47NWKEFajzsYiISH0o5LhTIyYEhGPLO2zUlRwREZEzUshxJ0dz1d4GvbxfdefjtLxScorqP0JLRESkLVHIcaea5qr8g1BZVu+Xh/j70LldEKCrOSIiImeikONOQe3AN4SGDiMHGOBYrDPPVVWJiIi0Sgo57mSxQFRjOx+rX46IiIgzFHLczTHCqrHLO+RjGIaLihIREWl9FHLcrZEjrPokhOJttZBdVE5Gfv379YiIiLQVCjnu5hhh1bCQ4+/jRffYEEDz5YiIiJyOQo67OZqrGjaMHI71y9HMxyIiIqemkONuNc1V+YcaNIwcNPOxiIiIMxRy3C0oGvxCMYeRN+xqzvEjrOx2dT4WERE5GYUcd7NYIDLZvN/AEVY94kLw87ZSWFbF/twSFxYnIiLSeijkeEIjR1j5eFnpnRAKqMlKRETkVBRyPKGRI6wA+levY7XhoDofi4iInIxCjic0ckJAUOdjERGRM1HI8QRHc1XDQ86A9uaVnM3p+VTZ7K6oSkREpFVRyPGEmuaqgkNQWdqgQ3SODibYz5uySju7jhS5sDgREZHWQSHHEwKjwM+8EtPQ1citVgt9E6s7H6tfjoiISB0KOZ5w/DDyBo6wgmP9cjaoX46IiEgdCjme4ooRVsdNCigiIiK1KeR4Sk3n40aMsBpQfSXnl8wCyqtsLihKRESk9VDI8ZSaYeSNaK5KigggItCHSpvBLxmFLipMRESkdVDI8ZSoxl/JsVgsmi9HRETkFBRyPKWmuaogrcHDyAEGVPfL2aB+OSIiIrUo5HhKYOSxYeS5DVuNHKCfruSIiIiclEKOp1gsEFWzvEPD++XUXMnZlVVEcXmVKyoTERFpFRoUcg4ePMihQ4ccj3/88UdmzJjBSy+95LLC2gQXjLCKCfUnLtQfuwFb0gtcVJiIiEjL16CQc91117Fs2TIAMjMzufjii/nxxx/5y1/+wmOPPebSAls1F4ywguPny8lrZEEiIiKtR4NCzubNmznnnHMAePfdd+nbty+pqam8/fbbLFiwwJX1tW4uGGEFMKB9OKDOxyIiIsdrUMiprKzEz88PgK+++opf/epXAPTs2ZOMjAzXVdfauaC5CqBfoq7kiIiInKhBIadPnz7Mnz+flStXsnTpUsaNGwdAeno6UVFRLi2wVatpripIg4qSBh+mprlqf04J+3OKXVGZiIhIi9egkPP3v/+df/3rX4wcOZJrr72WAQMGAPDxxx87mrHECYGR4F+zGnnDh5GHB/pyQfd2ADz31U5XVCYiItLiNSjkjBw5kuzsbLKzs3n11Vcd22+77Tbmz59fr2PNnTuX5ORk/P39GTx4MCtXrjzlvt999x3Dhw8nKiqKgIAAevbsybPPPtuQt9A8WCwua7K6d0x3AD5cn8aOw1riQUREpEEhp7S0lPLyciIiIgDYv38/c+bMYfv27cTExDh9nEWLFjFjxgxmzpzJunXrGDFiBOPHj+fAgQMn3T8oKIjp06fz7bffsm3bNh588EEefPDBlj103WUjrMIZ1ycOw4BnluxwQWEiIiItm8UwDKO+LxozZgwTJ05k2rRp5OXl0bNnT3x8fMjOzuaZZ57hjjvucOo4Q4cOZdCgQcybN8+xrVevXkyYMIHZs2c7dYyJEycSFBTEm2++6dT+BQUFhIWFkZ+fT2hoqFOvaVLLHocVf4dBN8Kv/tmoQ+04XMjYOd9iGPDJ9PPoV91XR0REpKVryO93g67k/Pzzz4wYMQKA9957j9jYWPbv388bb7zB888/79QxKioqWLt2LWPGjKm1fcyYMaSmpjp1jHXr1pGamsoFF1xwyn3Ky8spKCiodWtWHM1VDe+TU6N7bAgTzkoE4B9Ltjf6eCIiIi1Zg0JOSUkJISEhACxZsoSJEyditVo599xz2b9/v1PHyM7OxmazERsbW2t7bGwsmZmZp31tUlISfn5+DBkyhLvuuotbbrnllPvOnj2bsLAwx619+/ZO1ec2LmquqjHjom54Wy2s2HGEH/fmuuSYIiIiLVGDQk7Xrl356KOPOHjwIF9++aXjakxWVla9m4AsFkutx4Zh1Nl2opUrV/LTTz8xf/585syZw8KFC0+57wMPPEB+fr7jdvDgwXrV1+RqJgQsTG/UMPIaHaOCuPpsM8g99eUvNKA1UkREpFVoUMh5+OGHue++++jUqRPnnHMOKSkpgHlVZ+DAgU4dIzo6Gi8vrzpXbbKysupc3TlRcnIy/fr149Zbb+UPf/gDs2bNOuW+fn5+hIaG1ro1K4GR4B9u3m/EMPLj/e7Cbvh6W1mz7ygrdhxxyTFFRERamgaFnKuuuooDBw7w008/8eWXXzq2jx492ukh3b6+vgwePJilS5fW2r506VKGDRvmdC2GYVBeXu70/s2Si5us4sL8ufHcjgA8vWSHruaIiEib5N3QF8bFxREXF8ehQ4ewWCwkJibWeyLAe+65h8mTJzNkyBBSUlJ46aWXOHDgANOmTQPMpqa0tDTeeOMNAF588UU6dOhAz549AXPenH/84x/cfffdDX0bzUNUF0j/GXJdE3IA7hjZhYU/HmBTWj5fbslkXN94lx1bRESkJWhQyLHb7fz1r3/l6aefpqioCICQkBDuvfdeZs6cidXq3AWiSZMmkZOTw2OPPUZGRgZ9+/bls88+o2NH8ypERkZGrTlz7HY7DzzwAHv37sXb25suXbrwxBNPcPvttzfkbTQfLpoQ8HhRwX7cfF4yz3+zi38s2cHFvePwsp6+r5OIiEhr0qB5ch544AFeeeUVHn30UYYPH45hGKxatYpZs2Zx66238re//a0panWJZjdPDsCGRfDhbdDxPPjtpy47bEFZJSP+voz80kqeuXoAEwcluezYIiIi7uS2eXJef/11/v3vf3PHHXfQv39/BgwYwJ133snLL7/MggULGnLItq1mhJULm6sAQv19uP0Cs7/PnK92UlFld+nxRUREmrMGhZzc3FxHv5jj9ezZk9xczc1SbzUdjwszoMK1q4hPHdaJ6GA/DuSW8O5PzWz4vIiISBNqUMgZMGAAL7zwQp3tL7zwAv379290UW3O8cPIXTDzca1D+3ozfZR5peif3+ykrNLm0uOLiIg0Vw3qePzkk09y6aWX8tVXX5GSkoLFYiE1NZWDBw/y2WefubrGtiGqC6StNZus4vq69NDXDu3Ayyv3kpZXyn9W7+eWEZ1denwREZHmqEFXci644AJ27NjBlVdeSV5eHrm5uUycOJEtW7bw2muvubrGtqFmhFX2Tpcf2s/bi9+P7gbA3OW7KSqvcvk5REREmpsGja46lQ0bNjBo0CBstubbJNIsR1cBrJ4HX9wPMb3hjlQ4w9IW9VVlszPm2W/Zk13MPRd353fVoUdERKQlcNvoKmkCA64F32DI2gq7v3H54b29rPzh4u4AvPztHvJKKlx+DhERkeZEIae5CAiHgZPN+9+/2CSnuLRfPL3iQyksr2L+CtdNPCgiItIcKeQ0J0NvB4sVdn8Nh7e6/PBWq4V7q6/mLEjdS1ZhmcvPISIi0lzUa3TVxIkTT/t8Xl5eY2qRyGToeRls+xhWz4Ur6g7Tb6zRvWIY2CGcdQfyePGbXTx6hWtHcomIiDQX9bqSExYWdtpbx44dufHGG5uq1rYhZbr5d+O7UJTl8sNbLBb+OKYHAG//eIBDR0tcfg4REZHmoF5XcjQ83A3anwOJQyDtJ1jzCox6wOWnGNY1muFdo1i1K4fnv97Jk1cNcPk5REREPE19cpobiwVS7jLvr/k3VJY2yWnuq76a897aQ+w+UtQk5xAREfEkhZzmqNevIKw9lGSbzVZNYGCHCC7qFYPdgGeX7miSc4iIiHiSQk5z5OUNQ6eZ979/EVw3X2Mt91Zfzfnfxgy2phc0yTlEREQ8RSGnuRo0GXxDIHs77Pq6SU7RKz6UywckAPD0ku1Ncg4RERFPUchprvzDYFD1SLXvXT+UvMYfLuqGl9XC179ksXZ/bpOdR0RExN0UcpqzmskB9yyDw1ua5BSd2wVz1aAkAG57Yy1r9x9tkvOIiIi4m0JOcxbR0eyEDPD93CY7zZ/G9aBPQig5xRVc+/JqPtmQ3mTnEhERcReFnOauZnLATe9C4eEmOUVUsB/v3p7CRb1iqaiyc/fCdbzwzU5cuEC9iIiI2ynkNHftz4akc8BWYc6b00SC/Lz51+TB3HxeMgD/WLKD+xZvpKLK3mTnFBERaUoKOS2BGyYHBPCyWnjost7834S+eFktvP/zISa/8gN5JRVNdk4REZGmopDTEvS8DMI7QGkubHinyU83+dyOvDJlCMF+3vywN5cr56ayL7u4yc8rIiLiSgo5LYGXNwy9w7y/ei7Ym74JaWSPGN67I4XE8AD2ZhczYe4qftyrIeYiItJyKOS0FANvAL9QyN4Bu75yyyl7xoXy4V3DGJAURl5JJTf8+wc+XHfILecWERFpLIWclsI/1C2TA54oJsSfd25LYXzfOCpsdv6waAPPLt2hkVciItLsKeS0JENvB4sX7F0BmZvcdtoAXy9evG4Qt1/QGYDnvt7JjEXrKau0ua0GERGR+lLIaUnCO0DvK8z7TTg54MlYrRYeGN+L2RP74W218N/16dzw7x/IKSp3ax0iIiLOUshpaRyTAy6Gggy3n/7aczqw4LfnEOLvzU/7j3Ll3FR2Hylyex0iIiJnopDT0iQNhvbngr0S1rzskRLO6xbNB3cMIykigAO5JVz54ipSd2d7pBYREZFTUchpiWomB/zpVajwzPw13WJD+Oiu4QzsEE5BWRXXvfwDU1/7kW93HFGnZBERaRYUclqinpdCeEcoPQobFnqsjOhgPxbeei6/GZyExQLLtx/hxld/5OJnv+WtH/ZTWqGOySIi4jkWo4393+6CggLCwsLIz88nNDTU0+U03Or58MWfIbILTP8JrJ7Nq/tzilmQuo/FPx2iqLwKgLAAH649pwM3pnQkITzAo/WJiEjL1pDfb4Wclqq8EJ7pA+X5cO070GO8pysCoLCsksU/HWJB6j4O5JYA5ppY4/rGcdPwZAZ1CMdisXi4ShERaWkUcpzQakIOwNKHYdVz0GkETP2fp6upxWY3+HrbYV5btY/v9+Q4tg9oH85Nwzsxvm88vt5qLRUREeco5DihVYWc/DR4rj/Yq+C2FZBwlqcrOqmt6QW8tmov/92QTkWVue5WbKgfN6Z04tpzOhAZ5OvhCkVEpLlTyHFCqwo5AO/fYs6Z038STHzJ09WcVnZROW//cIA3V+/nSKE5iaCft5UrByZyy4hkusaEeLhCERFprhRynNDqQk7az/DyKLB6w4xNEJrg6YrOqKLKzqeb0nn1u31sSssHwGKBcX3iuGtUV/omhnm4QhERaW4UcpzQ6kIOwGuXwP5VcN4f4KJZnq7GaYZhsHb/UV76dg9Lth52bB/Zox3TR3VlSKdID1YnIiLNiUKOE1plyPnlU3jnOvANhvF/hwHXeXxIeX3tOFzI3GW7+HhDOvbqb+S5nSOZPqobw7tGaUSWiEgbp5DjhFYZcuw2eG08HPzBfJw4BC55EhIHe7auBtiXXcz8Fbt5/+dDVNrMr+aA9uFMH9WVi3rFKOyIiLRRCjlOaJUhB6CqAn6YByuehIoiwAKDJsPoRyAo2tPV1Vt6XikvfbuHd9YcoKzSHJHVMy6EO0d15dJ+8XhZFXZERNoShRwntNqQU6MgA756BDYuMh/7h8GomTDkZvDy9mxtDZBdVM4r3+3lze/3O2ZS7hwdxLSRXbhyYCI+Xi2rWU5ERBpGIccJrT7k1DiwGj67DzI3mY9j+phNWJ3O82xdDZRfUsmC1H28lrqXvJJKABLDA7j9gs5cPaQ9/j5eHq5QRESakkKOE9pMyAGzr87aBfDN/5mLeQL0mQhj/gphiR4traGKy6t464f9vLxyr2OunVB/b/olhdErLpRe8eata0ywZlQWEWlFFHKc0KZCTo2SXPjmr7D2NTDs4BMII+6FYXeDt5+nq2uQskobi386yPwVe0jLK63zvI+Xha4xIfSKD6F3/LHwo9mVRURaJoUcJ7TJkFMjYwN89ic4uNp8HJEM456AHuM8W1cjVNrsbMsoqL4VsjXdvF9Y3X/nRLGhfo7AUxN+OkcHYVVHZhGRZk0hxwltOuQAGIa5DMSSh6Ao09zWbYwZdqK6eLY2FzEMg0NHSx3BZ1tGAdsyC9ifU3LS/ePD/LnirEQmDkqke6yWlhARaY4UcpzQ5kNOjfJC+PYp+H4u2CvByxd6T4B+V0GXC8HLx9MVulxhWSXbM83Qs7U6/PySWeAYog7QOz6UiYMS+dWABGJC/T1YrYiIHE8hxwkKOSfI3gmf/xl2f31sW0AE9L4C+l4FHYe3uNmT66Os0sayX7L4YF0ay7dnOSYgtFrgvG7tuHJgAmP7xBHo2/KG34uItCYKOU5QyDkJw4BDP5nNWFs+hOKsY8+FxJsjsvr9GhIGmStptlJHiyv438Z0PlyXxs8H8hzbA329GNcnjgkDExneNVoTEYqIeIBCjhMUcs7AVgX7VsLm92DrJ1Cef+y5yM7Q99fmFZ6Ynp6r0Q32ZRfz4bo0PlqfVqsvT0yIH1eclcCEgYn0jg/VMhMiIm6ikOMEhZx6qCqHXV+bgWf751B5XMfd2L7VgefXENHRczU2McMw+PlAHh+uO8T/NmY4JiIE6BEbwoSBiVzUK4auMcEKPCIiTUghxwkKOQ1UXgQ7voBN78Gur8zOyjWSzoGzroWBN7bIpSOcVVFlZ/n2LD5cl8bX27KosB3rsJwYHsAFPdoxsns7hneNJsiv9X4OIiKeoJDjBIUcFyjJhW2fmFd49q4Eqr9C8QPgVy9AfH+PlucO+SWVfLY5g882ZfDD3lwqqo4FHh8vC2d3imRkj3aM7BFDN13lERFpNIUcJyjkuFhhptlh+dunoCwfLF4w/PdwwZ/Bp20MwS6tsPH9nmyWbz/C8u1HOJBbez6exPAAzu/ejlE92jGsazTBusojIlJvCjlOUMhpIoWH4fM/wtb/mo+jusLlz0On4Z6ty80Mw2BvdrEZeHYcYfWeHF3lERFxAYUcJyjkNLFtn8Cn9x2bTXnITXDRo+DfNj/r0gobq/fksGx71kmv8kQH+9IvMYx+SeEMSAqjX1IYMSFt4wqYiEh9tMiQM3fuXJ566ikyMjLo06cPc+bMYcSIESfd94MPPmDevHmsX7+e8vJy+vTpw6xZsxg7dqzT51PIcYPSPFj6EPz8hvk4JAEuewZ6jPdoWZ52pqs8NeJC/emfFEb/JDP89E8MI0ILi4pIG9fiQs6iRYuYPHkyc+fOZfjw4fzrX//i3//+N1u3bqVDhw519p8xYwYJCQmMGjWK8PBwXnvtNf7xj3/www8/MHDgQKfOqZDjRnu/hY9/B0f3mo/7TITxT0JwO8/W1UyUVdrYmlHApkP5bDyUz8ZDeew6UsTJ/kW2jwygf2J4dfAJo29iGKH+rW/pDRGRU2lxIWfo0KEMGjSIefPmObb16tWLCRMmMHv2bKeO0adPHyZNmsTDDz/s1P4KOW5WUQLLZ8P3L4BhN5eMGDsbBlzTqmdPbqji8iq2pBew8VAeGw/lsyktn73ZxSfdt0u7IC7uHcevBiTQKz5EfXtEpFVryO+3x4Z5VFRUsHbtWu6///5a28eMGUNqaqpTx7Db7RQWFhIZGXnKfcrLyykvL3c8LigoaFjB0jC+gTDm/6DvRPjv3XB4E3w0zRyRddmz9Z9I0DCg9Kh5dejoPsjdCz6BMHiqea4WLsjPm3OSIzkn+dh3Or+kks3px672bDyUT1peKbuPFLN7xW7mr9hNl3ZBXD4ggcsHJNClXbAH34GISPPhsZCTnZ2NzWYjNja21vbY2FgyMzOdOsbTTz9NcXExV1999Sn3mT17No8++mijahUXSBgIty2D1Odh+d/NBUHnpsDoh+Cc28DqdWxfuw3yD9UOMkf3mY9z99VeaqLGpnfhmrchNMFNb8h9wgJ9GN41muFdox3bcorK+X5PDv/bkME327PYfaSYOV/tZM5XO+kdH8rlAxK4rH887SNbfvATEWkojzVXpaenk5iYSGpqKikpKY7tf/vb33jzzTf55ZdfTvv6hQsXcsstt/Df//6Xiy666JT7nexKTvv27dVc5UnZO+Hju+HA9+bjpLPNiQRz95pBJu9g7RmVTyY4DiKTIbwj7FwCpbnmtmsXQuKgpn8PzUhhWSVLtx7mkw3prNyZTZX92D/pQR3CuXxAApf2iycmVKO2RKTlalF9cioqKggMDGTx4sVceeWVju2///3vWb9+PStWrDjlaxctWsRvf/tbFi9ezKWXXlqv86pPTjNht8PaV2HpLKgorPu8l68ZYCI6mWEmIvnY/fCOtZumcvfCwmvgyC/g7Q8T5pprarVBR4sr+HxzJp9sSGf13hxHJ2aLBc5NjuLyAQmM7xun0Voi0uK0qJADZsfjwYMHM3fuXMe23r17c8UVV5yy4/HChQu56aabWLhwIRMmTKj3ORVympn8Q/DjS+ZMyceHmdCE2k1YZ1JWAO/fbF7VAbjgfnPWZau1ScpuCbIKyvh0UwafbEjn5wN5ju3eVgvndYtmZPd2JLcLplNUIInhAXh7td3PSkSavxYXcmqGkM+fP5+UlBReeuklXn75ZbZs2ULHjh154IEHSEtL4403zPlWFi5cyI033shzzz3HxIkTHccJCAggLCzMqXMq5LRidhssfdgcyQXQewJMmNcqOiQ31sHcEj7dlMHH69PZmlG3872Pl4X2EYF0jAqkU3QQnaKCqv8qAIlI89DiQg6YkwE++eSTZGRk0LdvX5599lnOP/98AKZOncq+fftYvnw5ACNHjjxpM9aUKVNYsGCBU+dTyGkDfn4T/vcHs19P/AC4ZiGEJXq6qmZjV1YRn27MYHN6Pvuyi9mfW3LSSQlreFsttI8MpNNxASg5OoghnSII9NU6XCLiHi0y5LibQk4bsT8VFt0AJTlmh+Rr3oakwZ6uqlmy2w0yCsrYn13M3pxi9ueUsDe7mP05xezLOXUA8vO2MqJbO8b0ieWiXrFEqp+PiDQhhRwnKOS0IUf3wdvXwJFtZofkK16Efld5uqoWxW43yCwoY1+2GXj25RSzN7uYbRkFHDpa6tjPaoGzO0Uypk8cY3rHaui6iLicQo4TFHLamLICeP8W2Pml+fj8P8HIB9p0h2RXMAyD7YcL+XLzYZZszWRLeu1+Pr3jQxnbJ44xfWLpGafZmEWk8RRynKCQ0wbZbfDVI5D6T/Nxr1/BlfPBN8izdbUiB3NLWLr1MF9uyWTNvlyOm6qHDpGBjOkdy5g+cQzuGIGXVYFHROpPIccJCjlt2Lq34JPfmx2S4/rDte+oQ3ITyC2u4Otth/lyy2FW7jxC+XF9eqKCfLmoVyyje8UwNDmKsEAtMioizlHIcYJCThu3/3tYdH11h+TY6g7JQzxdVatVUlHFtzuOsGTLYb7adpiCsirHcxYL9IwLZWhyJEOr1+uKCvbzYLUi0pwp5DhBIUc4ut+cITlrK3j5waVPw8AbtCp6E6u02flxby5fbsnku13Z7DlSd3X1bjHBDO0cydDkKIZ2jiQmREtRiIhJIccJCjkCQHkhvH8r7PjcfJx8gbkqelQXz9bVhmQVlvHj3lx+2JPLj3tz2X647vIenaODHKHnnORIEsIDPFCpiDQHCjlOUMgRB7utelX0J6CqzLyqc8EfYdjvwVtzvrhbbnGFGXr25vDDnly2ZRZw4v86tY8MYGhyFGe1D6dvYhg940Lw96nH8h8i0mIp5DhBIUfqyN0D/7sH9iwzH7frBZfPgQ7nerSsti6/pJI1+6pDz95cNqfl1xq1BeBltdAtJpi+iWH0Swyjb2IoveJDNROzSCukkOMEhRw5KcOATYvhiwegJNvcNvi3cNEsCAj3ZGVSrbCskrX7j7JmXy6b0grYnJZPbnFFnf2sFujSzgw+fRJC6ZcYRu+EUEL8NZJLpCVTyHGCQo6cVkkuLH0I1v3HfBwcC+OegD5XqmNyM2MYBhn5ZWxOy2dzuhl6Nqflk1VYftL9k6OD6JsYxoCkMM7tHEXv+FCsmrNHpMVQyHGCQo44Zd938MkMyNlpPu42xhyFFd7Bo2XJmWUVlLE5PZ/N1Vd7tqQXkJZXWme/8EAfUjpHMaxrNMO7RJEcHaSZmUWaMYUcJyjkiNOqymHlM/DdM2CrAJ9AGPUXGHoHeKnPR0uSU1TOlvQCNqXls3b/UX7Yk0Nxha3WPvFh/qR0iWJ4l2iGd40mLkzD10WaE4UcJyjkSL0d2QH/mwH7V5mP4/rD5c9B4iCPliUNV2mzs/FQPqm7slm1O5uf9+dRYau92nrndkEMqw49KV2iCA/UiDsRT1LIcYJCjjSI3Q7r/wNLHoKyPLBY4Zzb4cKZ4Bfi6eqkkUorbPy0P5fU3Tmk7spm0wkjuSwWc9HR4V2j6RkXQlJEIEkRAcSG+mstLhE3UchxgkKONErREfjyAXMkFkBQO4jtC6GJEJpQfTvufkCEOiy3QPmllazek8P3u3NYtSubnVlFJ93P22ohPtyfxPAAkiICq/8GkBgRQPuIQOLC/PHx0or3Iq6gkOMEhRxxiV1fmXPr5O0//X7eAScPPzX3Y3pr4sEWIKugjNTdOfywN4d92SUcyishI6+MqhMn7jmB1QJxof4kRpghqFNUEOd2jmRghwh8vRV+ROpDIccJCjniMpVlcPAHKEirvqVX36rvl+Sc+RiRneG6xRDdtenrFZey2Q0OF5SRllfKoaMlpB0trb5fStrRUg7llVJRZT/pawN8vBjaOZLzupqdnHvEhmg4u8gZKOQ4QSFH3KayFAoz6oafmvs5u6G8AAIi4dp3oMNQT1csLmS3G2QXlx8LPUdL2ZpRQOqubHJOmMQwOtiXYV2izdDTLZpErdElUodCjhMUcqTZKDoCb18N6T+Dtz9MfBl6/8rTVUkTs9sNth8uZNWubL7blc0Pe3Ipraw9nD05OojhXaM4r2s0KZ2jCQvUbM0iCjlOUMiRZqWiGN67uXo1dAuMmw3n3uHpqsSNKqrsrDtw1BF6NhzKx3ZcXx+rBfolhjG8azT9EsNIigikfWQAYQE+mrxQ2hSFHCco5EizY6uCz/8EP71iPj73LhjzV7CqY2pbVFBWyQ97ch2hZ9cpRnaF+Hmbo7gizeHs7auHtdc81lpd0too5DhBIUeaJcOA756Frx81H/eeAFf+C3w0625bl5lfxqpd2aTuzmFPdhEHc0vJLjr5+lzHCw/0cYSf9pGBxIb64+NlwQJgMf9aLRYsFhz3qb5vsViwWqh+ztwnKSKA/knhGhIvHqOQ4wSFHGnWNi6Gj+4AeyV0SIFr3obASE9XJc1MaYWNtLwSDh4t5VBuCYeOlnLwaAkHc82RXkdLKpvkvEG+Xpxbvd7XeV2j6R4brCYzcRuFHCco5Eizt/dbeOcGKM+HqG5ww3sQ0cnTVUkLUlRexaHjQs/B3FKyCsswDLAbBoYBBgZ2w7yICMax58Bxn+r7NrvBL5kFdcJTdLAfw7tWr/elUWHSxBRynKCQIy1C1jb4z1VQcAiCYuC6RVorSzzKbjfMIfC7s/luVw4/7s2hrLL2PECdogIZXj33T0rnKCKCNNGluI5CjhMUcqTFKMiAt34DhzeZK6D/5nXoPsbTVYkAUF5lY92BPFbtymbVSUaFWSzQJyHUEXg6RwcTG+aHn7eXB6uWlkwhxwkKOdKilBXAuzfCnmVg8YJLn4Yhv/V0VSJ1FNaMCttthp4dh08+Kiw62I+EcH/iw/yJDwuovm/+TQgPICZEi57KySnkOEEhR1ocWyV88ntY/5b5eMS9cOFDWvhTmrWa9b5W7cpm7YGjpOeV1mneOhkvq4XYED/iwwOIDzODT3igDz5WK15WC95eFrytVryr73tZLfh4Wav/WvCyWvGxWhz7+np50SEqkLAADalv6RRynKCQIy2SYcDyJ2DFE+bj/pPgVy9ocU9pMQzD4GhJJel5pWTkl5GRb671lZFn3k/PK+NwwZkXPW2ouFB/usUG0z02hB6xIXSLDaZbbAjBft5Ncj5xPYUcJyjkSIv28xvwyQwwbNDxPLOPjtW7+uZ13P1TbTvusZevefP2O+6vnxmcvPzAy0dXi8StbHaD7KLyOuGnsKwSm92g0m5gs9uptBnmY5sdm92gymZQZbdTVX3f3Nd8rqTCxpHCU88rlBgeQPfq8NOtOgB1jQkmwFd9h5obhRwnKORIi7fzK1g8BSpO3ufBpbz8TghBvuY6W75B0OsyGPxbCAhv+jpEGqGgrJKdh4vYebiQHYeL2HG4kB2HC8k6RfixWKB9RCDdY0NIDPfHarWYEycCVqs5OaK1ZsJEaiZOtBzbVv3Yy2ohJsSPjlGBdIgMIjrYV/MKNYJCjhMUcqRVOLwFfnrVXPvKXnXczebcY1ulOeFgVQXYyo/9tVfVrw7fYBh0o7neVniHpnmvIk0kr6TCEXp2Hi5k++FCdh4uqrNKvKsE+nrRITLQcesYFUiHqCA6RAaSGB6Ar7dmkz4dhRwnKOSInIbdXh16ysFWccLf48JQ7l74YT5kbTVfZ/GCPhNg2N2QMNCjb0GksXKKyh3h50hhuWPiRMdEikbtx8f/tTueN5vOMvLLOJBbQnp+Kaf7tbVaICE84Fj4iQyic7sgBneMIDrYz31vvhlTyHGCQo6IixgG7P4aUv8Je5Yf295phBl2ul6sRUZFqpVX2Ug7Wsr+3BIO5JRwILeE/TklHMgt5kBuyWlHnnVuF8Q5nSI5u1Mk5yRHkhQR0CabvRRynKCQI9IEMjbC9y/A5vePNXlF94Bh06Hf1VpoVOQ0DMPgSGG5IwCZf4vZlmE2oZ0oLtSfs5PNwHNOp0i6xQRjbQNzCynkOEEhR6QJ5R8ym7F+WgAV1f/jHBQDQ2+DITdrsVGRejpaXMFP+4+yZl8uP+7NZXNafp1h9mEBPpzdKYKzO0VydnIkfRPCWmX/HoUcJyjkiLhBWb453H31PChIM7f5BMLAG8xOypGdPVufSAtVUlHF+gN5/LgvlzX7cvl5fx6llbZa+/j7WBnYPoL4MH+8vczJEs2bBe+a+1YLPt7mpIq+3la8rebzNfsG+FpJCA8gKSKw2cwlpJDjBIUcETeyVcKWDyH1ecjcZG6zWKH3FTD6EYhM9mx9Ii1cpc3OlvQC1uzN5Ye9ufy0P5e8E1aLb6zwQB+SIgJIrA49SRHm38TwAJIiAwj1d89s0go5TlDIEfEAw4C9K8xOyru+Mrd5+ZpXdUbcB/76tyjiCna7wa4jRfy8/ygFZZVU2sxJE6uq/zoe2+1UVJmTKNbabjOosNkpLq8iLa/UqcAU6u/tCD+JEceC0JjesS7tIK2Q4wSFHBEPy9wESx4yFx0FCGoHFz4IAyebMzKLSLNRVF5F2tFSDh0t4VD137S80ur7peSeYk6h8EAf1j88xqW1KOQ4QSFHpBkwDNi5BL78C+TsMrfF9oWxj0PnCzxbm4g4reaKT+0gVIqft5VnJp3l0nMp5DhBIUekGbFVwpp/w/LZZmdlgB6Xwpj/g6gunq1NRJqVhvx+t74xZiLScnj5mP1yfrcezrndnDl5+6fw4lD4ciaU5nm6QhFpwRRyRMTzAiPhkifhzu/NmZLtlebkgv8cBGteAVs919QSEUEhR0Sak3Y94Ib34Pr3zBmTS3Lg03vgXyNg9zeerk5EWhiFHBFpfrpdDHesgvFPQUCEuRDom1fC29dA9i5PVyciLYRCjog0T14+5nIQv1sH594JVm/Y8TnMHQrLHlcTloickUKOiDRvAREwbjbcuRq6jzMXAF3xd3j9Msg76OnqRKQZU8gRkZYhuhtctwh+/Qr4hsCB72H+ebDtE09XJiLNlEKOiLQs/a6CaSshcTCU5cGiG+DTe6Gy1NOViUgzo5AjIi1PZDL89gsY/nvz8Zp/w8uj4ch2z9YlIs2KQo6ItEzevnDxY3DD++b6V1lb4F8XwNrXzWUjRKTNU8gRkZat60UwbRV0HgVVpfDJ7+C9m44tEyEibZZCjoi0fCGxcMMHcNGj5lDzLR/A/BFw6CdPV3ZM5mbYsEhD30XcSCFHRFoHqxXOm2H21QnvAHn74dWx8N2zYLd7rq7yQvjiAXPW5g9vgw9vB7vNc/WItCEKOSLSurQ/G25fCX2uNOfU+WoW/GciFB52fy3b/mcuNrp6Lhh2sFhh83vw8d2eDV4ibYRCjoi0PgHhcNVrcPnz4B0Ae5bB/OGw62v3nD//ECy8DhZdDwVpEN7R7CB91Wtm0Fn/Fnx2rzpIizQxj4ecuXPnkpycjL+/P4MHD2blypWn3DcjI4PrrruOHj16YLVamTFjhvsKFZGWxWKBwVPgtuUQ0xuKj5hXdL54wAwhTcFWBakvwAvnwPZPzf5B591jztbc9SLoMwGu/BdggZ9eNWtR0BFpMh4NOYsWLWLGjBnMnDmTdevWMWLECMaPH8+BAwdOun95eTnt2rVj5syZDBgwwM3VikiLFNMTbv0GhtxsPl49F57tC6//CtYvhPIi15wnbS28PBKWzITKYmh/Lkz7Di56BHwDj+3X/2r41T/N+z/MM5vTFHREmoTFMDz3r2vo0KEMGjSIefPmObb16tWLCRMmMHv27NO+duTIkZx11lnMmTOnXucsKCggLCyM/Px8QkNDG1K2iLRU2z83r7Ts/+7YNp8g6P0rGHANdBoBVq/6HbMsH775K/z4MmCAf7g5f8/AyWZn6FNZ829zpmaAkQ/AyPvr+25E2pSG/H57N3FNp1RRUcHatWu5//7a/7DHjBlDamqqy85TXl5OeXm543FBQYHLji0iLUyP8ebt6D7Y+C5sWAi5e8y/GxZCaCL0nwQDroV23U9/LMOArf+Fz/8MRZnmtv6TYMzfILjdmWs5+xaoKocv/wLLZ4O3H5z3h0a/RRE5xmPNVdnZ2dhsNmJjY2ttj42NJTMz02XnmT17NmFhYY5b+/btXXZsEWmhIjrBBX+Cu3+Gm5bA4N+Cf5jZSfi7Z+DFs+HlC82rMyW5dV9/dD+8fTUsnmIGnMjOMPkjmPiScwGnRspdMPph8/5Xs2D1vNPuLiL14/GOxxaLpdZjwzDqbGuMBx54gPz8fMft4MGDLju2iLRwFgt0GAqXz4F7d8BvFkD3cWDxMvvYfHYf/KM7vHO9ORy8ohi+m2MOC9+5BKw+cMGf4Y7vocuohtUw4l7zGABf3A9rXnHRmxMRjzVXRUdH4+XlVeeqTVZWVp2rO43h5+eHn5+fy44nIq2Uj785t06fK6HoCGxabDZhZW6EX/5n3qze5tw7AB3Pg8uePXOzljNGPmCuop76PHx6D3j7w8DrG39ckTbOY1dyfH19GTx4MEuXLq21fenSpQwbNsxDVYmIYDY5pdwJ01bCHakw7G4IjjUDTkAkXDEXpv7PNQEHzCtKFz8G59xuPv54Omx6zzXHFmnDPHYlB+Cee+5h8uTJDBkyhJSUFF566SUOHDjAtGnTALOpKS0tjTfeeMPxmvXr1wNQVFTEkSNHWL9+Pb6+vvTu3dsTb0FEWrvYPjDmrzB6FmRthYiOZv8dV7NYYPzfwVYOaxfAB7eBl6858ktEGsSjIWfSpEnk5OTw2GOPkZGRQd++ffnss8/o2LEjYE7+d+KcOQMHDnTcX7t2LW+//TYdO3Zk37597ixdRNoaL2+I79+057BY4NJnoaoCNrxtrqZ+zVvQfWzjjmu3QWUJ+IW4pk6RFsKj8+R4gubJEZFmz26D928xV1P38oPr3oEuF575dYYBhRnmFafDWyFrG2RtgSPboaoMOo80J0XscYkZ2kRakIb8fivkiIg0R7ZKWDzV7PDsHQA3vAedzjv2fOlRyPrFDDFZ26pDzVYoyzvzsUPiYdAUc9mL0ISmegciLqWQ4wSFHBFpMarKYdEN5nB1nyAYdCPk7DQDTWH6yV9j8YKorhDTy+xPFNPLXLvLYoWf34B1b5rreNXs22M8nH0zJI88/QzNIh6mkOMEhRwRaVEqy2DhJNizvO5zYe2PhZiY3hDbG6K6mcPhT6WqArZ9bC4Qun/Vse2RXWDIb+Gs6yEw0uVvQ6SxFHKcoJAjIi1ORbG5Ppat0gwyMX3MhUcbO8ora5sZdja8A+XVS954+UHfiWbfnaQhZmdokWZAIccJCjkiIicoL4LN75mzLWduPLY9rp8Zdvr9BvyCPVefCAo5TlHIERE5BcMwl7NY84o5squqzNzuF2qO7orqaq7TFdXF/BvUTld6xG0UcpygkCMi4oSSXFj/ltmclbvn5Pv4hkBk8rHQE9nlWAhSABIXU8hxgkKOiEg92O1mB+WM9ZCz2ww8uXsg/xBwmp+PmgAU2Rmiu0GnEdAhBbx93VU5lOWbtcb0Bm+tYdjSKeQ4QSFHRMQFKssgb/9xwaf6b84eyD/ISQOQb7A5IWH3sdD1YgiNd21Ndhukr4fdX8Our+HQGjBs0K4n/PrfZh8jabEUcpygkCMi0sSqyuHovmNXfTI3wa6vjs3PUyOuH3QbC93GmCO5rF71P1d+Guz+xgw2e5abkyQez8vPXA/MyxcumgVD79B8QC2UQo4TFHJERDzAbjebvHYuhZ1fQtrP1LraExABXS8yA0/Xi049V09lqdl8tqs62Bz5pfbzfmHQ+Xyzo3SX0eAbBB/fDds/M5/vPAomzHP9VSRpcgo5TlDIERFpBoqOmCFl5xLzKk9Z/rHnLFZIHGIGnm4Xg5ePebVm19ewP9W8MnP8vgmDoOtoM9gkDqm7LpdhwNrX4Iu/QFUpBETCr/4JvS5zz3sVl1DIcYJCjohIM2OrMvvP7Fxi3g5vPv3+oYlmoOk6GpIvcH6G5iM74P2bj80FNHgqjH3cvNojzZ5CjhMUckREmrn8NNi1FHYsMfvZGHZzcdKaYBPdveHD06sqYNlfYdXzgGHO/fPrf0PCQFe+A2kCCjlOUMgREWlBbFWAYTZZudKeFfDhNHOhU6s3XPggDPtdwzo/i1s05PdbXcxFRKT58vJ2fcAB6HwB3LEKev0K7FXw1Sx444rq+X+ktVDIERGRtikwEq5+A654EXyCYN9KmDcMtnzo6crERRRyRESk7bJYYOANMG0lJA42R3ktngof3QnlhZ6uruWpLIMtH8H6tz1dCaA+OZ4uR0REmgtbJaz4O6x82uzsHNEJLpsDHc4FnwBPV9d82e2w/zvYuAi2fgLl+RAcB/dsdWkfp4b8fnufeRcREZE2wMvH7IDc5UL44HZz1uY3J5hz8UQkQ0wv89aup/k3qpt71+JqbjI3m8Fm03tmB+4aoUnQ7ypz4ka/YM/Vh67keLocERFpjsry4cuZ8MunUJp78n2s3ubK67XCT29zUdITJyRsLfIOwub3YOO7kLX12Hb/MOg9AfpPMhdibYKlMzSE3AkKOSIi4jTDgKIsOLINsn4xf9iP/AJZ26C84OSv8fI15/KJ7g7e/mCrAHul2RxmqzQf2yqrt1Wceru3vxmeYvuat7i+EN3D/VePSo/C1v+awWb/qtrvs/s46H+1OTt1E6/0rpDjBIUcERFpNMOAgnQz7NQKQNuhsrjpzmv1NoNObB8z9MT2gdh+EBzT8AkST6ayzFxjbOO75izUtorqJyzmxIz9rzaH3weEu+6cZ6CQ4wSFHBERaTJ2O+QfMENPzk6w28wrHl4+5s3qU/txzf2TbS8rMJe4OLyl+u/m2mt8HS8wujr01ASfPuAdABVFUFEMlSXH7leUnGZ7sfk4d6/ZgbhGbF/o9xuzr01Ykns+yxMo5DhBIUdERFokwzAnKzw+9BzeAjm7zNFgrhaaaAab/lebocnDNLpKRESktbJYILy9eesx7tj2ihKzn1BN6MncbDadGXbwDTYXIPUNPHbfJ7B6W3D19iBzMkTf425B0RA/sEk6ELuTQo6IiEhL5hsIiYPMm9TSsiOaiIiIyCko5IiIiEirpJAjIiIirZJCjoiIiLRKCjkiIiLSKinkiIiISKukkCMiIiKtkkKOiIiItEoKOSIiItIqKeSIiIhIq6SQIyIiIq2SQo6IiIi0Sgo5IiIi0iop5IiIiEir5O3pAtzNMAwACgoKPFyJiIiIOKvmd7vmd9wZbS7kFBYWAtC+fXsPVyIiIiL1VVhYSFhYmFP7Woz6RKJWwG63k56eTkhICBaLxaXHLigooH379hw8eJDQ0FCXHrs10+dWf/rMGkafW8Poc2sYfW71d7rPzDAMCgsLSUhIwGp1rrdNm7uSY7VaSUpKatJzhIaG6gvdAPrc6k+fWcPoc2sYfW4No8+t/k71mTl7BaeGOh6LiIhIq6SQIyIiIq2SQo4L+fn58cgjj+Dn5+fpUloUfW71p8+sYfS5NYw+t4bR51Z/rv7M2lzHYxEREWkbdCVHREREWiWFHBEREWmVFHJERESkVVLIERERkVZJIcdF5s6dS3JyMv7+/gwePJiVK1d6uqRmbdasWVgsllq3uLg4T5fV7Hz77bdcfvnlJCQkYLFY+Oijj2o9bxgGs2bNIiEhgYCAAEaOHMmWLVs8U2wzcqbPberUqXW+f+eee65nim0mZs+ezdlnn01ISAgxMTFMmDCB7du319pH37e6nPnc9H2ra968efTv398x6V9KSgqff/6543lXfdcUclxg0aJFzJgxg5kzZ7Ju3TpGjBjB+PHjOXDggKdLa9b69OlDRkaG47Zp0yZPl9TsFBcXM2DAAF544YWTPv/kk0/yzDPP8MILL7BmzRri4uK4+OKLHWu0tVVn+twAxo0bV+v799lnn7mxwuZnxYoV3HXXXaxevZqlS5dSVVXFmDFjKC4uduyj71tdznxuoO/biZKSknjiiSf46aef+Omnn7jwwgu54oorHEHGZd81QxrtnHPOMaZNm1ZrW8+ePY3777/fQxU1f4888ogxYMAAT5fRogDGhx9+6Hhst9uNuLg444knnnBsKysrM8LCwoz58+d7oMLm6cTPzTAMY8qUKcYVV1zhkXpaiqysLAMwVqxYYRiGvm/OOvFzMwx935wVERFh/Pvf/3bpd01XchqpoqKCtWvXMmbMmFrbx4wZQ2pqqoeqahl27txJQkICycnJXHPNNezZs8fTJbUoe/fuJTMzs9Z3z8/PjwsuuEDfPScsX76cmJgYunfvzq233kpWVpanS2pW8vPzAYiMjAT0fXPWiZ9bDX3fTs1ms/HOO+9QXFxMSkqKS79rCjmNlJ2djc1mIzY2ttb22NhYMjMzPVRV8zd06FDeeOMNvvzyS15++WUyMzMZNmwYOTk5ni6txaj5fum7V3/jx4/nrbfe4ptvvuHpp59mzZo1XHjhhZSXl3u6tGbBMAzuuecezjvvPPr27Qvo++aMk31uoO/bqWzatIng4GD8/PyYNm0aH374Ib1793bpd63NrULeVCwWS63HhmHU2SbHjB8/3nG/X79+pKSk0KVLF15//XXuueceD1bW8ui7V3+TJk1y3O/bty9DhgyhY8eOfPrpp0ycONGDlTUP06dPZ+PGjXz33Xd1ntP37dRO9bnp+3ZyPXr0YP369eTl5fH+++8zZcoUVqxY4XjeFd81XclppOjoaLy8vOqky6ysrDopVE4tKCiIfv36sXPnTk+X0mLUjEbTd6/x4uPj6dixo75/wN13383HH3/MsmXLSEpKcmzX9+30TvW5nYy+byZfX1+6du3KkCFDmD17NgMGDOC5555z6XdNIaeRfH19GTx4MEuXLq21fenSpQwbNsxDVbU85eXlbNu2jfj4eE+X0mIkJycTFxdX67tXUVHBihUr9N2rp5ycHA4ePNimv3+GYTB9+nQ++OADvvnmG5KTk2s9r+/byZ3pczsZfd9OzjAMysvLXftdc1Gn6DbtnXfeMXx8fIxXXnnF2Lp1qzFjxgwjKCjI2Ldvn6dLa7buvfdeY/ny5caePXuM1atXG5dddpkREhKiz+wEhYWFxrp164x169YZgPHMM88Y69atM/bv328YhmE88cQTRlhYmPHBBx8YmzZtMq699lojPj7eKCgo8HDlnnW6z62wsNC49957jdTUVGPv3r3GsmXLjJSUFCMxMbFNf2533HGHERYWZixfvtzIyMhw3EpKShz76PtW15k+N33fTu6BBx4wvv32W2Pv3r3Gxo0bjb/85S+G1Wo1lixZYhiG675rCjku8uKLLxodO3Y0fH19jUGDBtUaPih1TZo0yYiPjzd8fHyMhIQEY+LEicaWLVs8XVazs2zZMgOoc5syZYphGOaw3kceecSIi4sz/Pz8jPPPP9/YtGmTZ4tuBk73uZWUlBhjxowx2rVrZ/j4+BgdOnQwpkyZYhw4cMDTZXvUyT4vwHjttdcc++j7VteZPjd9307upptucvxmtmvXzhg9erQj4BiG675rFsMwjAZeWRIRERFpttQnR0RERFolhRwRERFplRRyREREpFVSyBEREZFWSSFHREREWiWFHBEREWmVFHJERESkVVLIERHBXAzwo48+8nQZIuJCCjki4nFTp07FYrHUuY0bN87TpYlIC+bt6QJERADGjRvHa6+9Vmubn5+fh6oRkdZAV3JEpFnw8/MjLi6u1i0iIgIwm5LmzZvH+PHjCQgIIDk5mcWLF9d6/aZNm7jwwgsJCAggKiqK2267jaKiolr7vPrqq/Tp0wc/Pz/i4+OZPn16reezs7O58sorCQwMpFu3bnz88cdN+6ZFpEkp5IhIi/DQQw/x61//mg0bNnDDDTdw7bXXsm3bNgBKSkoYN24cERERrFmzhsWLF/PVV1/VCjHz5s3jrrvu4rbbbmPTpk18/PHHdO3atdY5Hn30Ua6++mo2btzIJZdcwvXXX09ubq5b36eIuJDr1hQVEWmYKVOmGF5eXkZQUFCt22OPPWYYhrnS87Rp02q9ZujQocYdd9xhGIZhvPTSS0ZERIRRVFTkeP7TTz81rFarkZmZaRiGYSQkJBgzZ848ZQ2A8eCDDzoeFxUVGRaLxfj8889d9j5FxL3UJ0dEmoVRo0Yxb968WtsiIyMd91NSUmo9l5KSwvr16wHYtm0bAwYMICgoyPH88OHDsdvtbN++HYvFQnp6OqNHjz5tDf3793fcDwoKIiQkhKysrIa+JRHxMIUcEWkWgoKC6jQfnYnFYgHAMAzH/ZPtExAQ4NTxfHx86rzWbrfXqyYRaT7UJ0dEWoTVq1fXedyzZ08Aevfuzfr16ykuLnY8v2rVKqxWK927dyckJIROnTrx9ddfu7VmEfEsXckRkWahvLyczMzMWtu8vb2Jjo4GYPHixQwZMoTzzjuPt956ix9//JFXXnkFgOuvv55HHnmEKVOmMGvWLI4cOcLdd9/N5MmTiY2NBWDWrFlMmzaNmJgYxo8fT2FhIatWreLuu+927xsVEbdRyBGRZuGLL74gPj6+1rYePXrwyy+/AObIp3feeYc777yTuLg43nrrLXr37g1AYGAgX375Jb///e85++yzCQwM5Ne//jXPPPOM41hTpkyhrKyMZ599lvvuu4/o6Giuuuoq971BEXE7i2EYhqeLEBE5HYvFwocffsiECRM8XYqItCDqkyMiIiKtkkKOiIiItErqkyMizZ5a1UWkIXQlR0RERFolhRwRERFplRRyREREpFVSyBEREZFWSSFHREREWiWFHBEREWmVFHJERESkVVLIERERkVZJIUdERERapf8H37C/eRsot9kAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "",
   "id": "c22c78d7057867b2",
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}