prafu.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-24 14:08:57.969664: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-05-24 14:09:05.217202: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "# for data transformation\n",
    "import numpy as np\n",
    "# for visualizing the data\n",
    "import matplotlib.pyplot as plt\n",
    "# for opening the media file\n",
    "import scipy.io.wavfile as wavfile\n",
    "import tensorflow \n",
    "from scipy.fftpack import fft, dct\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44100\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fs, aud = wavfile.read('//speech/db/mul/audio/music/tency1/part1/adele_set-fire-to-the-rain-(moto-blanco-edit)_40287/multi/synth bass.wav')\n",
    "# select left channel only\n",
    "aud = aud[:,0]\n",
    "# trim the first 125 seconds\n",
    "first = aud[:int(Fs*125)]\n",
    "\n",
    "print(type(first))\n",
    "\n",
    "dct_a = dct(first, 1)\n",
    "fft_a = fft(first)\n",
    "plt.plot(fft_a[1:1000])\n",
    "plt.show()\n",
    "plt.plot(dct_a[1:1000])\n",
    "print(Fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-02 17:07:20.902161: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-06-02 17:07:23.662593: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'aud' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mtf\u001b[39;00m \n\u001b[0;32m----> 3\u001b[0m tf\u001b[39m.\u001b[39msignal\u001b[39m.\u001b[39mstft(aud,Fs,frame_step \u001b[39m=\u001b[39m \u001b[39m0.1\u001b[39m,fft_length\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,window_fn\u001b[39m=\u001b[39mtf\u001b[39m.\u001b[39msignal\u001b[39m.\u001b[39mhann_window,\n\u001b[1;32m      4\u001b[0m     pad_end\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m,\n\u001b[1;32m      5\u001b[0m     name\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m\n\u001b[1;32m      6\u001b[0m )\n\u001b[1;32m      7\u001b[0m \u001b[39m#\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'aud' is not defined"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf \n",
    "tf.signal.stft(aud,Fs,frame_step = 0.1,fft_length=None,window_fn=tf.signal.hann_window,\n",
    "    pad_end=False,\n",
    "    name=None\n",
    ")\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dearadhp/.local/lib/python3.8/site-packages/matplotlib/axes/_axes.py:7773: RuntimeWarning: divide by zero encountered in log10\n",
      "  Z = 10. * np.log10(spec)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "powerSpectrum, frequenciesFound, time, imageAxis = plt.specgram(first, Fs=Fs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "import tensorflow as tf \n",
    "import tensorflow.keras as keras\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11490434/11490434 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "(x_train,y_train),(x_test,y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train/255\n",
    "y_train = y_train/255\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-11 16:35:45.488220: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 11387 MB memory:  -> device: 0, name: NVIDIA TITAN X (Pascal), pci bus id: 0000:02:00.0, compute capability: 6.1\n",
      "2023-05-11 16:35:45.490128: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 10370 MB memory:  -> device: 1, name: NVIDIA GeForce GTX 1080 Ti, pci bus id: 0000:04:00.0, compute capability: 6.1\n",
      "2023-05-11 16:35:45.490701: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 11387 MB memory:  -> device: 2, name: NVIDIA TITAN X (Pascal), pci bus id: 0000:84:00.0, compute capability: 6.1\n",
      "2023-05-11 16:35:45.491249: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 11437 MB memory:  -> device: 3, name: NVIDIA GeForce GTX TITAN X, pci bus id: 0000:83:00.0, compute capability: 5.2\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential([keras.layers.Flatten(input_shape=(28, 28)),keras.layers.Dense(128, activation='relu'),keras.layers.Dense(10, activation='softmax')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.SGD.\n"
     ]
    }
   ],
   "source": [
    "opt = keras.optimizers.SGD(lr=0.1)\n",
    "model.compile(optimizer=opt,loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-11 16:37:03.364478: I tensorflow/compiler/xla/service/service.cc:169] XLA service 0x7f36737fff60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-05-11 16:37:03.364531: I tensorflow/compiler/xla/service/service.cc:177]   StreamExecutor device (0): NVIDIA TITAN X (Pascal), Compute Capability 6.1\n",
      "2023-05-11 16:37:03.364542: I tensorflow/compiler/xla/service/service.cc:177]   StreamExecutor device (1): NVIDIA GeForce GTX 1080 Ti, Compute Capability 6.1\n",
      "2023-05-11 16:37:03.364550: I tensorflow/compiler/xla/service/service.cc:177]   StreamExecutor device (2): NVIDIA TITAN X (Pascal), Compute Capability 6.1\n",
      "2023-05-11 16:37:03.364558: I tensorflow/compiler/xla/service/service.cc:177]   StreamExecutor device (3): NVIDIA GeForce GTX TITAN X, Compute Capability 5.2\n",
      "2023-05-11 16:37:07.340738: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:424] Loaded cuDNN version 8800\n",
      "2023-05-11 16:37:07.561122: E tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:114] *** WARNING *** You are using ptxas 10.1.243, which is older than 11.1. ptxas before 11.1 is known to miscompile XLA code, leading to incorrect results or invalid-address errors.\n",
      "\n",
      "2023-05-11 16:37:07.684909: I ./tensorflow/compiler/jit/device_compiler.h:180] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n",
      "2023-05-11 16:37:07.699972: E tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:114] *** WARNING *** You are using ptxas 10.1.243, which is older than 11.1. ptxas before 11.1 is known to miscompile XLA code, leading to incorrect results or invalid-address errors.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1/1875 [..............................] - ETA: 3:18:54 - loss: 2.7710 - accuracy: 0.0000e+00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-11 16:37:07.774077: E tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:114] *** WARNING *** You are using ptxas 10.1.243, which is older than 11.1. ptxas before 11.1 is known to miscompile XLA code, leading to incorrect results or invalid-address errors.\n",
      "\n",
      "2023-05-11 16:37:07.862427: E tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:114] *** WARNING *** You are using ptxas 10.1.243, which is older than 11.1. ptxas before 11.1 is known to miscompile XLA code, leading to incorrect results or invalid-address errors.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 12s 3ms/step - loss: 0.0205 - accuracy: 0.0984 - val_loss: 2717.2671 - val_accuracy: 0.0980\n",
      "Epoch 2/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 7.9928e-04 - accuracy: 0.0987 - val_loss: 3002.7063 - val_accuracy: 0.0980\n",
      "Epoch 3/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 4.5358e-04 - accuracy: 0.0987 - val_loss: 3172.3599 - val_accuracy: 0.0980\n",
      "Epoch 4/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 3.1642e-04 - accuracy: 0.0987 - val_loss: 3293.8274 - val_accuracy: 0.0980\n",
      "Epoch 5/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 2.4264e-04 - accuracy: 0.0987 - val_loss: 3388.6897 - val_accuracy: 0.0980\n",
      "Epoch 6/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 1.9656e-04 - accuracy: 0.0987 - val_loss: 3466.6648 - val_accuracy: 0.0980\n",
      "Epoch 7/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 1.6506e-04 - accuracy: 0.0987 - val_loss: 3532.9033 - val_accuracy: 0.0980\n",
      "Epoch 8/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 1.4217e-04 - accuracy: 0.0987 - val_loss: 3590.5676 - val_accuracy: 0.0980\n",
      "Epoch 9/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 1.2479e-04 - accuracy: 0.0987 - val_loss: 3641.5916 - val_accuracy: 0.0980\n",
      "Epoch 10/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 1.1116e-04 - accuracy: 0.0987 - val_loss: 3687.3948 - val_accuracy: 0.0980\n",
      "Epoch 11/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 1.0019e-04 - accuracy: 0.0987 - val_loss: 3728.9517 - val_accuracy: 0.0980\n",
      "Epoch 12/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 9.1159e-05 - accuracy: 0.0987 - val_loss: 3766.9895 - val_accuracy: 0.0980\n",
      "Epoch 13/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 8.3608e-05 - accuracy: 0.0987 - val_loss: 3802.0459 - val_accuracy: 0.0980\n",
      "Epoch 14/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 7.7200e-05 - accuracy: 0.0987 - val_loss: 3834.5752 - val_accuracy: 0.0980\n",
      "Epoch 15/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 7.1692e-05 - accuracy: 0.0987 - val_loss: 3864.9192 - val_accuracy: 0.0980\n",
      "Epoch 16/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 6.6911e-05 - accuracy: 0.0987 - val_loss: 3893.3503 - val_accuracy: 0.0980\n",
      "Epoch 17/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 6.2721e-05 - accuracy: 0.0987 - val_loss: 3920.1133 - val_accuracy: 0.0980\n",
      "Epoch 18/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 5.9017e-05 - accuracy: 0.0987 - val_loss: 3945.3772 - val_accuracy: 0.0980\n",
      "Epoch 19/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 5.5723e-05 - accuracy: 0.0987 - val_loss: 3969.3176 - val_accuracy: 0.0980\n",
      "Epoch 20/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 5.2772e-05 - accuracy: 0.0987 - val_loss: 3992.0569 - val_accuracy: 0.0980\n",
      "Epoch 21/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 5.0115e-05 - accuracy: 0.0987 - val_loss: 4013.7175 - val_accuracy: 0.0980\n",
      "Epoch 22/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 4.7710e-05 - accuracy: 0.0987 - val_loss: 4034.3877 - val_accuracy: 0.0980\n",
      "Epoch 23/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 4.5523e-05 - accuracy: 0.0987 - val_loss: 4054.1609 - val_accuracy: 0.0980\n",
      "Epoch 24/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 4.3525e-05 - accuracy: 0.0987 - val_loss: 4073.1140 - val_accuracy: 0.0980\n",
      "Epoch 25/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 4.1693e-05 - accuracy: 0.0987 - val_loss: 4091.3159 - val_accuracy: 0.0980\n",
      "Epoch 26/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 4.0009e-05 - accuracy: 0.0987 - val_loss: 4108.8223 - val_accuracy: 0.0980\n",
      "Epoch 27/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 3.8452e-05 - accuracy: 0.0987 - val_loss: 4125.6777 - val_accuracy: 0.0980\n",
      "Epoch 28/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 3.7012e-05 - accuracy: 0.0987 - val_loss: 4141.9365 - val_accuracy: 0.0980\n",
      "Epoch 29/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 3.5673e-05 - accuracy: 0.0987 - val_loss: 4157.6396 - val_accuracy: 0.0980\n",
      "Epoch 30/30\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 3.4428e-05 - accuracy: 0.0987 - val_loss: 4172.8203 - val_accuracy: 0.0980\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, batch_size=32, epochs=30,validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'keras.callbacks.History'>\n"
     ]
    }
   ],
   "source": [
    "print(type(history))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<keras.callbacks.History object at 0x7f3e10067a30>\n"
     ]
    }
   ],
   "source": [
    "print(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pydub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'req'...\n",
      "remote: Enumerating objects: 9, done.\u001b[K\n",
      "remote: Counting objects: 100% (9/9), done.\u001b[K\n",
      "remote: Compressing objects: 100% (7/7), done.\u001b[K\n",
      "remote: Total 9 (delta 0), reused 0 (delta 0), pack-reused 0\u001b[K\n",
      "Unpacking objects: 100% (9/9), 2.44 KiB | 73.00 KiB/s, done.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "!git clone https://github.com/PrafulAradhyamth/req.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import librosa\n",
    "import librosa.display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create function to convert Hz to Mels\n",
    "def freqToMel(f):\n",
    "    return 1127 * math.log(1 + (f/700))\n",
    "\n",
    "# Vectorize function to apply to numpy arrays\n",
    "freqToMelv = np.vectorize(freqToMel)\n",
    "\n",
    "# Observing 0 to 10,000 Hz\n",
    "Hz = np.linspace(0,1e4) \n",
    "# Now we just apply the vectorized function to the Hz variable\n",
    "Mel = freqToMelv(Hz) \n",
    "\n",
    "# Plotting the figure:\n",
    "fig, ax = plt.subplots(figsize = (20,10))\n",
    "ax.plot(Hz, Mel)\n",
    "plt.title('Hertz to Mel')\n",
    "plt.xlabel('Hertz Scale')\n",
    "plt.ylabel('Mel Scale')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using env_mask from wave form articles\n",
    "def env_mask(wav, threshold):\n",
    "    # Absolute value\n",
    "    wav = np.abs(wav)\n",
    "    # Point wise mask determination.\n",
    "    mask = wav > threshold\n",
    "    return wav[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "#Load in the sounds\n",
    "guitar, _ = librosa.load(\"//speech/db/mul/audio/music/tency1/part0/rag-n-bone-man_human_51837/rag'n'bone man - human - 51837 - nbv.wav\")\n",
    "\n",
    "\n",
    "# Mask audio to trim out dead noise (simple data cleaning)\n",
    "guitar = env_mask(guitar, 0.005)\n",
    "\n",
    "\n",
    "# Create Mel Spectrograms of sounds\n",
    "print(type(guitar))\n",
    "\n",
    "\n",
    "# Plot mel spectrograms\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'Musical-Instrument-Classification-Using-Deep-Learning'...\n",
      "remote: Enumerating objects: 19, done.\u001b[K\n",
      "remote: Counting objects: 100% (19/19), done.\u001b[K\n",
      "remote: Compressing objects: 100% (17/17), done.\u001b[K\n",
      "remote: Total 19 (delta 7), reused 0 (delta 0), pack-reused 0\u001b[K\n",
      "Unpacking objects: 100% (19/19), 1.11 MiB | 4.89 MiB/s, done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/bissessk/Musical-Instrument-Classification-Using-Deep-Learning.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: stempeg in ./.local/lib/python3.8/site-packages (0.2.3)\n",
      "Requirement already satisfied: numpy>=1.6 in ./.local/lib/python3.8/site-packages (from stempeg) (1.24.3)\n",
      "Requirement already satisfied: ffmpeg-python>=0.2.0 in ./.local/lib/python3.8/site-packages (from stempeg) (0.2.0)\n",
      "Requirement already satisfied: future in ./.local/lib/python3.8/site-packages (from ffmpeg-python>=0.2.0->stempeg) (0.18.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install stempeg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "Warning",
     "evalue": "An error occurs with ffprobe (see ffprobe output below)\n\nffprobe version 4.2.7-0ubuntu0.1 Copyright (c) 2007-2022 the FFmpeg developers\n  built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)\n  configuration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\n  libavutil      56. 31.100 / 56. 31.100\n  libavcodec     58. 54.100 / 58. 54.100\n  libavformat    58. 29.100 / 58. 29.100\n  libavdevice    58.  8.100 / 58.  8.100\n  libavfilter     7. 57.100 /  7. 57.100\n  libavresample   4.  0.  0 /  4.  0.  0\n  libswscale      5.  5.100 /  5.  5.100\n  libswresample   3.  5.100 /  3.  5.100\n  libpostproc    55.  5.100 / 55.  5.100\n: No such file or directory\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mError\u001b[0m                                     Traceback (most recent call last)",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/stempeg/read.py:216\u001b[0m, in \u001b[0;36mread_stems\u001b[0;34m(filename, start, duration, stem_id, always_3d, dtype, ffmpeg_format, info, sample_rate, reader, multiprocess)\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[39mif\u001b[39;00m info \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 216\u001b[0m     metadata \u001b[39m=\u001b[39m Info(filename)\n\u001b[1;32m    217\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/stempeg/read.py:328\u001b[0m, in \u001b[0;36mInfo.__init__\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[39msuper\u001b[39m(Info, \u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m()\n\u001b[0;32m--> 328\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minfo \u001b[39m=\u001b[39m ffmpeg\u001b[39m.\u001b[39;49mprobe(filename)\n\u001b[1;32m    329\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maudio_streams \u001b[39m=\u001b[39m [\n\u001b[1;32m    330\u001b[0m     stream \u001b[39mfor\u001b[39;00m stream \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minfo[\u001b[39m'\u001b[39m\u001b[39mstreams\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m    331\u001b[0m     \u001b[39mif\u001b[39;00m stream[\u001b[39m'\u001b[39m\u001b[39mcodec_type\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39maudio\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m    332\u001b[0m ]\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/ffmpeg/_probe.py:23\u001b[0m, in \u001b[0;36mprobe\u001b[0;34m(filename, cmd, **kwargs)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[39mif\u001b[39;00m p\u001b[39m.\u001b[39mreturncode \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m---> 23\u001b[0m     \u001b[39mraise\u001b[39;00m Error(\u001b[39m'\u001b[39m\u001b[39mffprobe\u001b[39m\u001b[39m'\u001b[39m, out, err)\n\u001b[1;32m     24\u001b[0m \u001b[39mreturn\u001b[39;00m json\u001b[39m.\u001b[39mloads(out\u001b[39m.\u001b[39mdecode(\u001b[39m'\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m'\u001b[39m))\n",
      "\u001b[0;31mError\u001b[0m: ffprobe error (see stderr output for detail)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mWarning\u001b[0m                                   Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mstempeg\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mst\u001b[39;00m \n\u001b[0;32m----> 2\u001b[0m S, rate \u001b[39m=\u001b[39m st\u001b[39m.\u001b[39;49mread_stems(\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/stempeg/read.py:222\u001b[0m, in \u001b[0;36mread_stems\u001b[0;34m(filename, start, duration, stem_id, always_3d, dtype, ffmpeg_format, info, sample_rate, reader, multiprocess)\u001b[0m\n\u001b[1;32m    220\u001b[0m     ffmpeg\u001b[39m.\u001b[39mprobe(filename)\n\u001b[1;32m    221\u001b[0m \u001b[39mexcept\u001b[39;00m ffmpeg\u001b[39m.\u001b[39m_run\u001b[39m.\u001b[39mError \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m--> 222\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mWarning\u001b[39;00m(\n\u001b[1;32m    223\u001b[0m         \u001b[39m'\u001b[39m\u001b[39mAn error occurs with ffprobe (see ffprobe output below)\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\n\u001b[1;32m    224\u001b[0m         \u001b[39m.\u001b[39mformat(e\u001b[39m.\u001b[39mstderr\u001b[39m.\u001b[39mdecode()))\n\u001b[1;32m    226\u001b[0m \u001b[39m# check number of audio streams in file\u001b[39;00m\n\u001b[1;32m    227\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mstreams\u001b[39m\u001b[39m'\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m metadata\u001b[39m.\u001b[39minfo \u001b[39mor\u001b[39;00m metadata\u001b[39m.\u001b[39mnb_audio_streams \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n",
      "\u001b[0;31mWarning\u001b[0m: An error occurs with ffprobe (see ffprobe output below)\n\nffprobe version 4.2.7-0ubuntu0.1 Copyright (c) 2007-2022 the FFmpeg developers\n  built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)\n  configuration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\n  libavutil      56. 31.100 / 56. 31.100\n  libavcodec     58. 54.100 / 58. 54.100\n  libavformat    58. 29.100 / 58. 29.100\n  libavdevice    58.  8.100 / 58.  8.100\n  libavfilter     7. 57.100 /  7. 57.100\n  libavresample   4.  0.  0 /  4.  0.  0\n  libswscale      5.  5.100 /  5.  5.100\n  libswresample   3.  5.100 /  3.  5.100\n  libpostproc    55.  5.100 / 55.  5.100\n: No such file or directory\n"
     ]
    }
   ],
   "source": [
    "import stempeg as st \n",
    "S, rate = st.read_stems(\"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import librosa\n",
    "import librosa.display\n",
    "import scipy as sp  \n",
    "from scipy.io.wavfile import read  \n",
    "from scipy.io.wavfile import write     \n",
    "from scipy import signal \n",
    "\n",
    "#/home/dearadhp/New-Sound-Added-With-Guassian-Noise.wav\n",
    "noisy_file,audio_rate = librosa.load(\"//speech/db/mul/audio/music/tency2/part4/halestorm_the-reckoning_49309/multi/pads.wav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Amplitude')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bNsHBwaHU/aZOnYq0tDTNz40bN0wYJREREZmSrdQB6MrT0xM2Nja4ffu21vbbt2/D19e3zGPnzJmD2bNnY/v27WjcuHGZ+6pUKqhUqgrHS0RERObPYlqE7O3tERYWplXo/LjwuWXLlqUe99lnn+Hjjz9GdHQ0wsPDTREqUbl+PHIDb6yNRU5+odShEBHJmsW0CAHAxIkTMWzYMISHh6NFixaYP38+srKyMHz4cADA0KFD4efnh1mzZgEAPv30U0ybNg3r1q1DYGCgppbI2dkZzs7Okj0Oosk/nQAANKtxDaPaBkkcDQlcYcNiCYKAy3eypA6DLJhFJUIDBw7EnTt3MG3aNCQlJaFp06aIjo7WFFBfv34dSuWTRq7FixcjLy8P/fv31zrP9OnTMWPGDFOGTlSi9Ox8qUMgsmjbz5Y8j5ycxVy+Bwc7JUJrVJY6FItgUYkQAIwdOxZjx44t8b7du3dr3Y6Pjzd+QEREJJm/T+s2athS5ReqYWejexVLSlYeBi37FwAQP7unscKyKhZTI0RERCSWs4np+N8fZ5D6IE/qUEo1e8s51HlvC04n6D7v2d1MTg2iLyZCREQkO92/3Ifl+6/i/c2npA6lVEv2XAYAzNl63uTXnvLzCSRn5Jj8ulJgIkRERBaronXuZxK5ekBJNhy+gf8+GtRh7ZgIERERUTEXb2dKHYJJMBEiIiKzkJNfiFup2VKHQTLDRIiIiMxC9y/3ofXsnXoVB5M4Lt7OkDoEyTARIiIis3D17sOJEbecfDgkPu5GKi4ll/0BbQmTYRaqBew6n4yULNOOUDuToHv907Pz9hoxEvPGRIhISgqF1BEQmaXk9Bz0XXQAkV9Y/gf0mn+vYfjKw2j28TYIJszceny1D2q1BWSKEmMiRERUxKlbaYj4ZDt+ir0pdSiytTomHtdTHkgdhmi2nErU/P7h72f0Pp6pjHExESIi2Sv6QTN+wzHcTs/F2z8elyweuUvPKcC1e6ZJhK7cyUJegdok1wKAVf/Em+xaFWXK1ispMREiIiqigF0JZuHyHd2GbgsitJcs23elwucoS3IGZ3s2Z0yEiIgqKDk9B6ducaSTmL7efdlk1/rn8l2jnv/KnSyt2+k5xltsmVWH+mMiRCQDcmnilkqLT3ag14L95Y5wInnJLyy5y23zsVsmjoTKwkSIyMrtv3gXTT78W+owLEZFalOOXk8VLxAymcTUHByOTxH1nLvOJ6POe1uw5t9rop7XlOTy9YmJEJGVe3nFQaTnFEgdBpFxiPBpfeVuFgYsicH+i+J1kY1ZexQARFnUdff5O7jDOiOjYSJERCSRQrWA0auPYN62C1KHIqlCtWBQjdW9zFxc0rGoWhcvrzgo2rnENvPP0ofd38nIRWEpRf4VmaosMY2rzxMRkRHtuZCMbWdu48sdF6UORVIf/n4avRbs1/u4sP9tx4mb2gnUbglmcNbXtF9P49q9rPJ3LOJeKY8p7kYqms/cjkHL/hUjNFliIkQkIY7wsD7L9l7BsG8PIbegsNx9c/JNN3+NOVsdI14dTdSjGZzL8u3+q6Jdz1AjVh3Wa/99F+9i2q+nig18WHfw4XN36OrDGqdjN1JFiU9OmAgREYlo5l9nsefCHWw6ypFB5uqjP/Sf3Vlsl+/o1yIEPEwYD14tvag7ITUb//3phN7nfbzGm1zZSh0AEZG1mL3lnOb37PzyW4SyclnEbq2M1dqb+WjgQ1p2frHWoS4GLpz6nQXNdm0MbBEiIhKJPrUpBYVqTC7y7X3Nv9dw3UTLSpA4ElKzEX0qyeQLm6rVApp8+DeafrQNuUWWB8ksIbFesPNSha+XV6DGxdsZVjsfGRMhIpK9irzBZxg4S/COc8lat9/ffArtPt9lcBxkeq1m78Rra2Lx63HTdoPmFZmo8XZ62SO7vth2AQmp2aXen5VbgNMJZY/YG7X6CJ6dtxe/WGl3LxMhIqIKWLjLsG/cplzokwxXUivL0/ZfvGeCSAxXWjft9zHxaDB9Kw7H3y/z+L0X7gAAvouJFzs0s8BEiIjokRwd6nqelpJp2FDtXCZCOsvIycdn0edwJiHdpNf9+3QSGk7fii/+Pq/3sYqKTOBTjkvJhs2dlPogT/Maj712Hx/8elrMsCwWEyEiokcWi7jQZ3kfg5lGXHjT2oT/bzu+3n0ZPb7aV+Fz/XOp/NmjC9UC8gvV+ODXh7NCf2VAnY2uaZAgCLhfQm1ZaeuUAdCac0mhw5XWH7yO+LtZaPrRNoR8EA1BEBBj5IVmLQkTISI8fDMasuKgZlp8XSWn52Dixjgcu1520zJZBkO/aZNxGdp6llegxsmbaZpi5u1nbuM/y8ufPbrnV/vQavZOFBRq144du36/zP/1e5m5+P14AnILCnVe+WP6b6cR+vE2bD2dpNl26lYa6ry3RcczlG/5/qvoMGe35vacv8/jQZ5urZ/fW2l3WFFMhIjwcB6NfRfv4s+TiaVOVV+SyT+dwC/HbuH5r/9BWnY+5m27gCsiTvlPpqPP352kp8vUA2+sjUXvhfuxfP8VqNUCRq0+otO5zyVl4E5GrtZsztl5hXj+63/w/Nf/FOtCFR6lPQOWxODN9cfw5faLOtUWAU8mk/ws+snUCx8beZ6jRbt0b/mcVWRKCGvFRIgIQNHPQH169q/cfZL0TPv1FL7ccbHMuTwEQcDZxCd1DkYsIyA93MvKQ7OPt+HPk4la22Ov3cf2M7cx6YfjpX6wGZo+iZF2FaoFrNh/1aB1uizdGzq03m4/+3Bk3qoD8ei/5J8KXS8r78nf/+nWlLuP6sSuPJqYMPpUEkxFMPCV9LWI3cCWjhMqEpXjQV4Bluy5gq4NfNCgmlup+x15NPKioIyWhZ+P3sLbPx4XPUaqmKfXq3qs3+InH56ezvaY2qOeaNfc82gkTkkK1QJO3ExFQz832NmU/n3159ibmtaD+Nk9RYvN3KnVQpnPX0mOXk8VNYaiUy7s1TOW0hSqBdgo9ft29O+V0meaFpuu3WmWhi1CRACe/n4efSoRfRbuR/zdLMzffhFf7biInl9pLwr5zd7LuJFS+vwcJdGlv91aJy2zdAl6rsRd3qih2Gul15p8tvUcnv/6H7xTznIJZ5NKHkWlfpRIlVVway70nUbg+3+voeGMrUaKRneb40qfU0dtwP/w5TtZqDctGvsu3inz7/bAgJGNYrHWGjomQkRPUSiA19YcxYmbaZj4Q1yJ3Q6ZuQX45C9x+s5/ir2JKT+fQKFaQHpOPjrM2W30GgEyb0v3XAEA/HLMsAnsvtp5EX0WHsCkH8y/9bHu+/oVBX+w+ZRZtExsOpagdXvsuidddRUp7h6y4lCZrVcrD0i7YGzvBfux/6J1jThjIkRUhoyckutCCkT6pr1o1yW8/eNxbDh8A3+cSMAPh2/g2r0HWGEGq2OTcZX22hLD42kAfjueUM6e8mDMOX0e++PEk/qyRD1bD/WRni3ttAsnb6Xh5RXlj7yzJEyEiESSZ0BylF9keO79rDyDmtTJNH4/noAu8/bgXmau1nb+yeQnTaRkxBKGpsthPAcTISKI82F2JyO3/J3KoW/NEZnWhduZeg09Ls0iA5fl0BVnrTZcaf/HRROCjnN2i1LLx5mdzQMTIZKlxtVLH/31NEPf7y4lZ2LtwWt6daP9GHtD532jTyXii20XWFxtYroWIJe1GObnW/VfsgHQb3V7XV24nYHv/om3iMJqU2g+c3uJ2+X6X5ZlBvVYxsbh8yRLzqqKvfR1qe+I/GIPgIcjeIa0DNTpvDn5un8YvbbmYXFm88DKaFvHS+fjSDxnE9Px89GbJd4n9qKqn289h0W7LuOz/o3xYri/aOd9PO9VoVrAiDY1RTuvOblVxurrutr5aE4isj5sESKC7t/2HjyaVO2bvVd0PvexG6k67XfdwG4xMbrkyDATTTgq63GX3HQjdaecuJlqlPOW5UbKA5Nf01D3nmqN22dlI6fkjIkQ0VMmlzF3y6BlD0dLGGP47oM8440iIuMor9tTEAS8/eNxLN1jnFl8LTkJTkjNRtvPdkkdhs52nWeLkLVi1xjRU36KfdLVcTE5U2um6OM6tu4QAUDMlXua19Or7WuJfv6iQ7YtzVEjL1Qs9tpxh66abgZnMi22CJHsXb/3oMy1ga4+Wj/I1IxZBJ36QPyiWyruQa7+LYdqLv4qik5zd0sdAlkItgiR7LX7XL/m+dazd5pksdTTCelo6Kf76DZ96FOUTdoMXeRSV9ctqG7GnF27x+eRdMMWISI93UrNxs37ZRc2/1m0y0Io8ddiNhzWHjrfa8F+HLxyr9x4OHretHadE2eBzdJsPV32yuXZ+YXILbD8Ic0KWUzVJz7+u4uPiRCREYwpsu5QRaw5eF3ze3pOPj6NPoezidoLbW45Zbl1Ipao6Fw+ZX0o3X9g2OzDuizP8Pfp2waduyymWIaCKu7KHWm66q0ZEyEiCzHzj7NYvPsyun+5T2v74XjjFp2SYUqbX0gMXIqFSDysESKyEKcS0qQOgfCwRmjVgauY8fuZcvc1JGFZ9U98ufuM3xAn+grgpm4PYgMUmQu2CBGZSFZuAet5rIAgQKckCAD+PPmk2zJb5Lmnfow1XosTkZwwESIyslup2bh6NwsNpm/FyVts1ZGT0wlP6rnKWntMDMv0mO3cHLBBiMwFEyEiIzt4NQXrDl4z6TUT07Ilm//I2unTqGfKBXFn/nVW532T0nKKz4rNzIRkijVCRCZQYMRJ8op+2Gbk5CP+7gP0XrgfAHB8WhejXVeu9FlMVYy/en6hWq+17cpzJD4F/ZfEAACuzuoh2nmJLBUTISITWHkg3iTX6TpvLxKKDL++cZ+TyklJjKHOa/+9hs+3nhchmofWH3oyX1XRrjtdbDp2E/F3H+CtZ+tWOA4WS5O5YCJEZMaKflbo8sGR8NQcNHFcG83inb+dKer54u89Sc5y9WjdAoC3Nh4HALQP9kKzGpVFjYtIKqwRIrICiWnZJdajnE/KkCAaKknUykO4l6n/avE39WjVKyhUI//p2p+nxF6r+LxTaQZOFklkjtgiRGTh0nMK0HLWTkyIrFPsPmOvi0W6i7/3AGH/244m1fVbP26fHvMF1X5vi75haZh+yQv2jZF5YIsQkRkrFAQU6lhoPX/7xWLb1vx7vYQ9WZ8hpeM3zXMKBb1eE3z9kBVhIkRkxv48kYjg97egUC3gcjKHw5O49Ol2I7JWTISIzFyBWsDxm6nIzrf8FcfJvBwxcJ06MRqE2CpJ5oKJEJEF2Ho6Sev2znPirz5O1unC7dIL5r//17QTfRKZIyZCRBZg6R7tCfVGrDoiUSRkaVbsuyr6ORUiNOeojTjJKJE+mAgREZFe3V1i9Gpx0VgyF0yEiIhIr/mFxKjvYaE2mQuLS4QWLVqEwMBAODg4ICIiAocOHSp139OnT6Nfv34IDAyEQqHA/PnzTRcoEZEZ0HUuqSsmXqTX9PMWEZXMohKhjRs3YuLEiZg+fTqOHj2KJk2aoGvXrkhOTi5x/wcPHiAoKAizZ8+Gr6+viaMlIpLej7E3cfmOuMt0MIkha2JRidAXX3yB0aNHY/jw4ahfvz6WLFkCJycnfPvttyXu37x5c3z++ed46aWXoFKpTBwtEZH0BAHoPHeP1GGQlcnIsZ5lViwmEcrLy0NsbCwiIyM125RKJSIjIxETEyPadXJzc5Genq71Q0RETzyuEVp/6Do2Hi559nKybncy9F83z1xZTCJ09+5dFBYWwsfHR2u7j48PkpKSSjlKf7NmzYKbm5vmx9/fX7RzExFZAwWAtOx8TP3lJN75+SSycgv0PkdhCYsEE0nBYhIhU5k6dSrS0tI0Pzdu3JA6JDKCTAPeuInkrOCpVe1zi8x0Xt6K90TmzGJWn/f09ISNjQ1u39aeUff27duiFkKrVCrWE8lAZg4TISJ9LCsyMWOBCJMhCmwRIjNhMS1C9vb2CAsLw44dOzTb1Go1duzYgZYtW0oYGRGR+Wvz6U7cSs02+PhPo8+JGA2R+bCYFiEAmDhxIoYNG4bw8HC0aNEC8+fPR1ZWFoYPHw4AGDp0KPz8/DBr1iwADwusz5w5o/n91q1biIuLg7OzM2rXri3Z4yDp8bsoyc3N+9n4rJxkRhAELNh5CU383dG+rlep+2WwRZWsiEUlQgMHDsSdO3cwbdo0JCUloWnTpoiOjtYUUF+/fh1K5ZNGroSEBISGhmpuz5kzB3PmzEH79u2xe/duU4dPZDaOXU9Ft4acW0tuCsvp0lq48xK+2HYBABA/u2ep+208cgPNAyuLGhtZFjHWmzMXFpUIAcDYsWMxduzYEu97OrkJDAxkPzSVSO6vi6Q0w7tIyHrNfZQEAcD2M7fRMcQbNkrr+cAjKonF1AgRkXjknQbK158nE3Xed9TqI1gdE2+0WPgaJHPBRIiISCb0bQjdfvZ2idufbiO6ds+ABVSZCZGZYCJEsiT392CZ9wxSBT1dHvLcogPSBEIkAiZCRESkF1YN0c37BrQCmikmQkQyxAYh0kVuvvFmjOZr0LINWXFI6hBEw0SIZEnuXUM7Sqn9ICrqyLX7UodAZHQWN3yeiCrun8v3pA6BLERadj7WHdReYV6hUECEVTaIzAITIZIlgQ3zRDpp8uHfJW4/cOluhc4r97m8yHywa4xkie/BRBUz6cfjUodAJAomQiRLN+9zZmUiImIiREREejqbmC51CESiYSJERER6SUzLkToEItEwESIiogo7dStNr/1ZpkfmgokQERFVWL/F/+i1PwcskLlgIkRERBWWW2C8WaiJjMmgRCg1NRXLly/H1KlTkZKSAgA4evQobt26JWpwRERknTiXF5kLvSdUPHHiBCIjI+Hm5ob4+HiMHj0aHh4e+OWXX3D9+nWsXr3aGHESERERiU7vFqGJEyciKioKFy9ehIODg2Z7jx49sHfvXlGDIyIiIjImvROhw4cP49VXXy223c/PD0lJSaIERURE1o3F0mQu9E6EVCoV0tOLT6Z14cIFeHl5iRIUERFZNyZCZC70ToT69OmDjz76CPn5+QAerkJ8/fp1vPPOO+jXr5/oARIREREZi96J0Ny5c5GZmQlvb29kZ2ejffv2qF27NlxcXDBz5kxjxEhERERkFHqPGnNzc8O2bduwf/9+nDhxApmZmWjWrBkiIyONER8RERGR0eidCD3Wpk0btGnTRsxYiMiEBEGAQqGQOgwiIknplAh99dVXOp9w3LhxBgdDRKaz63wyOoX4SB0GyZSvmwNupWZLHQaRbonQvHnztG7fuXMHDx48gLu7O4CHM007OTnB29ubiRCRhTh2PZWJEEnG3dFO6hCIAOhYLH316lXNz8yZM9G0aVOcPXsWKSkpSElJwdmzZ9GsWTN8/PHHxo6XiIjMVE5+odQhEOlN71FjH3zwARYsWIDg4GDNtuDgYMybNw/vv/++qMERkfHcus9uCRKXmpMDkQXSOxFKTExEQUFBse2FhYW4ffu2KEERkfH9coyLJBMR6Z0Ide7cGa+++iqOHj2q2RYbG4vXX3+dQ+iJiIhk4nxShtQhiELvROjbb7+Fr68vwsPDoVKpoFKp0KJFC/j4+GD58uXGiJGIiIjMzPubT0odgij0nkfIy8sLf/31Fy5cuIBz584BAEJCQlC3bl3RgyMiIuvEaiLLp4B1zENm8ISKdevWZfJDREREFk3vRGjEiBFl3v/tt98aHAwREcmDdbQlyFtqdp7UIYhC70To/v37Wrfz8/Nx6tQppKamolOnTqIFRkTGdzczF57OKqnDICILdOF2ptQhiELvRGjTpk3FtqnVarz++uuoVauWKEERkWnM+usc5r7YROowiIgko/eosRJPolRi4sSJxZbiICLz9vPRm3jpmxjOCExEsiVKIgQAly9fLnGiRSIyb/9eScGvcZxckUyLo8bIXOjdNTZx4kSt24IgIDExEX/++SeGDRsmWmBEZDo5+WqpQyAikoTeidCxY8e0biuVSnh5eWHu3LnljigjIvN0NjFd6hCIiCShdyK0a9cuY8RBRBIqULOjgoj0l5NfCAc7G6nDqBC9a4Q6deqE1NTUYtvT09M5fJ6IiEhGNhy6LnUIFaZ3IrR7927k5RWfRCknJwf79u0TJSgiMq2fYm8iJcs6JkcjItNJz7H8QVI6d42dOHFC8/uZM2eQlJSkuV1YWIjo6Gj4+fmJGx0RmUyzj7dhQmQdTIjk0jlkmMS0HNTycpY6DDIhwQp61XVOhJo2bQqFQgGFQlFiF5ijoyMWLFgganBEZFrzt19kIkQGO5OQzkRIZuZtv4AxHWvB1ka02XhMTudE6OrVqxAEAUFBQTh06BC8vLw099nb28Pb2xs2NpZdMEVERIZTW0PzAOlt1/k7eLa+j9RhGEznRCggIADAw+U0iIiInsZESJ5ir923/kTot99+Q/fu3WFnZ4fffvutzH379OkjSmBERGRZrt/LljoEksCSPZcxpXuI1GEYTKdEqG/fvkhKSoK3tzf69u1b6n4KhQKFhVyziIhIjuZtv4DxkXWkDoNILzolQkW7w9g1RkRERNbCcsu8icgoTt1KkzoEkgGB9URkJnRqEfrqq690PuG4ceMMDoaIpNdrwX7N7/Gze0oYCRFZivxCNewsdAi9TonQvHnzdDqZQqFgIkRERCQzA5fG4Jc3WksdhkF0SoSuXr1q7DiIyAydupWGpLQcRFrw0FgiMr6j11OlDsFgFWrHEgSB/bxEVqzXgv0YtfoITt5k3RARlc1S8wGDEqEVK1agYcOGcHBwgIODAxo2bIjly5eLHRsRmYneC/eXvxORHhQKhdQhkMhqTv1L6hAMovPM0o9NmzYNX3zxBd588020bNkSABATE4O33noL169fx0cffSR6kEREZBkEQdApybHU1gOyPnonQosXL8ayZcswaNAgzbY+ffqgcePGePPNN5kIEVmpW6nZsFMqkJyRi4Z+blKHQ2Zq25nb6NLAV+owSCK7ziejQ10vi2rx0zsRys/PR3h4eLHtYWFhKCgoECUoIjI/rWfv1LrNofVUkl3n7zARkrHhKw/jrci6FjXDuN41QkOGDMHixYuLbf/mm28wePBgUYIiIvPXae5uqUMgM7T+0HWpQyCJzdt+QeoQ9FKhYulRo0Zh1KhRaNSoEZYtWwalUomJEydqfoxh0aJFCAwMhIODAyIiInDo0KEy9//xxx8REhICBwcHNGrUCH/9ZZnFXETm5sqdLARO+VPr535WntRhEZEZCJzyJ3LyLWPtUb27xk6dOoVmzZoBAC5fvgwA8PT0hKenJ06dOqXZzxj9gxs3bsTEiROxZMkSREREYP78+ejatSvOnz8Pb2/vYvv/888/GDRoEGbNmoVevXph3bp16Nu3L44ePYqGDRuKHh+R3IV+vE3z+xcvNsELzapLGA1JZd/FO2hbx0vqMEhiIR9EAwCuzuph1jVDCsGCSvcjIiLQvHlzLFy4EMDDBWD9/f3x5ptvYsqUKcX2HzhwILKysvDHH39otj3zzDNo2rQplixZotM109PT4ebmhrS0NLi6uorzQEhygVP+lDoE2fNyUaFjsBfa1/WGv4cjrt17AB9XBwBAsK8LnFW2UCoAQQAUCuN8ueLrwLjmD2yKjiHemr9l0b/hiFWHsfNcsoTRkaktebkZujbwNVlSpOvnt94tQlLJy8tDbGwspk6dqtmmVCoRGRmJmJiYEo+JiYkp1kXXtWtXbN68udTr5ObmIjc3V3M7PT29YoGX4pO/zuKXozf1PEq3F48urzFdX4a6nav8nXR93euymzl/syDd3cnIxQ9HbuKHI/r+HxjO09kewMPXEF9FxjdhY5zUIZAZeW3N0WLbPCrZQ6kA3u1RT7IWZL0ToZycHCxYsAC7du1CcnIy1Gq11v1HjxZ/oGK4e/cuCgsL4eOjPdW/j48Pzp07V+IxSUlJJe6flJRU6nVmzZqFDz/8sOIBlyMztwB3M1lPQWRK/J8jMi8pj+oKc/LV5expPHonQiNHjsTff/+N/v37o0WLFlb37Xzq1KlarUjp6enw9/cX/TrjOtXBsJaBOu8vQLceTF06OnXaR8TriXUeMWN6btEBnc5F5snRzgbZRQoxPZ1V8HVTwd3RHv4ejgAUuJyciaY13FHJ3hY+riqE1qgM4OHrSBAAtSCg51ecMZtICq4Otvisf2PU9HQGAPi4qiSLRe9E6I8//sBff/2F1q1Nu8qsp6cnbGxscPv2ba3tt2/fhq9vyXNW+Pr66rU/AKhUKqhUxv+D+Lo5wNfNwejXIZJKXR9nRI9vB6VSgYJCNWxtKrS0IVmY8uaZem7hfhznGnZWz9fVAf++21nqMMqkdyLk5+cHFxcXY8RSJnt7e4SFhWHHjh3o27cvgIfF0jt27MDYsWNLPKZly5bYsWMHJkyYoNm2bds2zdIgRCSudaMj0KqWZ7HtTILkhZNtEgAc++BZVK5kL3UY5dI7EZo7dy7eeecdLFmyBAEBAcaIqVQTJ07EsGHDEB4ejhYtWmD+/PnIysrC8OHDAQBDhw6Fn58fZs2aBQAYP3482rdvj7lz56Jnz57YsGEDjhw5gm+++cakcRNZq/jZPXVeW4rkIbCKk9QhkBkw9yHzRemdCIWHhyMnJwdBQUFwcnKCnZ2d1v0pKSmiBfe0gQMH4s6dO5g2bRqSkpLQtGlTREdHawqir1+/DqXyyTfPVq1aYd26dXj//ffx7rvvok6dOti8eTPnECISwYkZXQBwFB9p6xhSfE43kh9Lel/QOxEaNGgQbt26hU8++QQ+Pj4mf7Bjx44ttSts9+7dxbYNGDAAAwYMMHJURNavXV0v1PV2xuH4FPw6to3U4ZCZmtQlWOoQSGIX/tdd6hD0onci9M8//yAmJgZNmjQxRjxEZKZWj2ghdQhkAZxVFjM9HRnB8WldYG9rWTWBekcbEhKC7OxsY8RCRGYq2Mf0AySIyPK4OdmVv5OZ0TsRmj17NiZNmoTdu3fj3r17SE9P1/ohIuuzZXxbqUMgIjIKvdswu3XrBgDo3Fl7XoDHI0cKCy1jtVkiKt/Pr7eEp7MKSqXlFD6ShbCgYlrSTczUTlKHYBC9E6Fdu3aVet/JkycrFAwRmZewAA+pQyAiC1HVzVHqEAyidyLUvn17rdsZGRlYv349li9fjtjY2FJHdBERERGZG4NLu/fu3Ythw4ahatWqmDNnDjp16oR///1XzNiIiMiCdOIcQrJ16D3zXkajLHq1CCUlJWHVqlVYsWIF0tPT8eKLLyI3NxebN29G/fr1jRUjEZnY4fci4WEBU+OT5WKFkHXxdrHctTN1bhHq3bs3goODceLECcyfPx8JCQlYsGCBMWMjIgmc/agbvFxUsGGBNOlJn1cMa6XJXOjcIrRlyxaMGzcOr7/+OurUqWPMmIhIQo72NlKHQBZqeOuaOu/LPIjMhc4tQvv370dGRgbCwsIQERGBhQsX4u7du8aMjYiILEjlSpY3mR5VnKuDZc8mrnMi9Mwzz2DZsmVITEzEq6++ig0bNqBatWpQq9XYtm0bMjIyjBknEZnA7BcaSR0CWbDkjFypQyAJWPo8Y3qPGqtUqRJGjBiB/fv34+TJk5g0aRJmz54Nb29v9OnTxxgxEpGJvNSihtQhkAW7nJwpdQhEeqvQymjBwcH47LPPcPPmTaxfv16smIhIAgPD/aUOgYgs0PBWuteGmSNRloi1sbFB37598dtvv4lxOiKSwKf9G0sdAlk4BYeCydLYTrWlDqFCREmEiMiyhfhydXmqOKZB8mTpU20wESIitK7tKXUIJDNsPSJzwUSIiIhEwdxGfqpYwQz0TISICD0a+UodAlkBvWaWNloUZErWkPwyESIiNKjmJnUIZAXY3SU/giB1BBXHRIhI5j7v3xgOdlxWgyqOeZD8dGlg+a3JTISIZO6ZoCpSh0BWgnmQ/LBGiIgs2vKh4fD3cJI6DLIWbBKSFX8PR4xuGyR1GBVm2SulEVGFtAjykDoEsiJ6FUszZ7J40ePboZLK8tMItggRydSM3vXh6sDVwkk8SmY3ZIGYCBHJVLeGVaUOgayMvS0/Usjy8FVLJFPeLiqpQyArE1GTXa1keZgIEcmU0sLXByLzY+lrTpE8MREiIiJRsERIXqxl/jEmQkREJAqFHuPG9NmXzJO1tAAyESIiIlHo9bloHZ+hZAWYCBERkTiY3JAFYiJERESi4DxCZImYCBERkSiYBpElYiJERESiUOjTIiQYLw4ifTARIiIiUag4szRZIL5qiYhIFNawACfJDxMhIiIyPRYUkZlgIkRERESyxUSIiIiIZIuJEBEREckWEyEiIjI5lgiRuWAiRERERLLFRIiIiCrsmSAPqUMgMggTISIiqrA1IyP02p/LkpG5YCJEREQVZmvDjxOyTHzlEhERkWwxESIiIiK9dKnvI3UIomEiRERERHqxphovJkJERESkF4UVzQTFRIiIiMq0d3JHqUMgMhomQkREMtGtga9Bx9Wo4lRs28znG1YoFmtqUSDLxkSIiEgmSkpoDFWvqmuFjremGhM5sqa/HxMhIhmypjcx0l1Zf3ZfVwe0qe1psljIslnTewgTISIiwj9TOsHN0c6gY0e0rilyNGTurKlrk4kQkQxZz1sY6aWMP7xSqcD4yDrFto/rXHwbAAjCk9+HtAyoaGREkmEiREQkE+V9i6/r44IL/+uuuT1/YFNMfLZusf1mv9BI67atkqk1WS4mQkQypLCmDn4Slb1t+R8L/cOqmyASMmtW9BbCRIhIhqzoPYz0oG/+G1DKKLOnF1g1JK9mLm7ZlFb0B7SVOgAiMj0reg8jI/j59Va4npKF0BqVpQ6FzNR/uwZLHYJoLKZFKCUlBYMHD4arqyvc3d0xcuRIZGZmlnnMN998gw4dOsDV1RUKhQKpqammCZaIyAzpmv+GBVTG86G6d3+xq1V+/D3Em5NKahaTCA0ePBinT5/Gtm3b8Mcff2Dv3r145ZVXyjzmwYMH6NatG959910TRUlkGapUUkkdAklA3O4MofxdiCyARXSNnT17FtHR0Th8+DDCw8MBAAsWLECPHj0wZ84cVKtWrcTjJkyYAADYvXu3iSIlsgwvP1ND6hDIxPw9HDGyTU0s3HVJ9HOzPYgsmUW0CMXExMDd3V2TBAFAZGQklEolDh48KOq1cnNzkZ6ervVDZG1UtjZSh0AmtndyR1SuZC91GBrWNCEfWTaLSISSkpLg7e2ttc3W1hYeHh5ISkoS9VqzZs2Cm5ub5sff31/U8xMRmVrzwMpmV8djZuGQjEmaCE2ZMgUKhaLMn3Pnzpk0pqlTpyItLU3zc+PGDZNen4hIbENbBmp+d7AT/23fkKSmfV0v0eMgMoSkNUKTJk1CVFRUmfsEBQXB19cXycnJWtsLCgqQkpICX19fUWNSqVRQqVhISkTWo1fjqprfR7Suia93X5YwmodUduyeJfMgaSLk5eUFL6/yvxW0bNkSqampiI2NRVhYGABg586dUKvViIiIMHaYREQWrWi3mJ2NEVqEWO9DFswiaoTq1auHbt26YfTo0Th06BAOHDiAsWPH4qWXXtKMGLt16xZCQkJw6NAhzXFJSUmIi4vDpUsPR0mcPHkScXFxSElJkeRxEJkL1mdQRQkcPU9WwiISIQBYu3YtQkJC0LlzZ/To0QNt2rTBN998o7k/Pz8f58+fx4MHDzTblixZgtDQUIwePRoA0K5dO4SGhuK3334zefxEROamflVXUc7DxJosmUXMIwQAHh4eWLduXan3BwYGQnjqK8qMGTMwY8YMI0dGRGQ5xEpaHIrU+Djas96HLJfFtAgRkf48nVn4T8ZRdPSZi8pivlMTFcNEiMiKta3jKXUIZMYqUuZTvbL1rDVF8sZEiMgCvN+znkHHuTjwmzoZh4OdDY5P74LTH3Y1aLJGV742yUwwESIycx8/1wDtDJx8LqJmFZGjIblaPLgZ7GwU+HpwM802N0c7VDKwW6xno6rl70RkAkyEiETWPLCyqOfrEKy9vExoDXet21XKWD/qmSAPUWMh6/Jc04fTjwT7uJS7b/dGVXHu4+7oIVICY2uE+YyIDMG2SSKRmXpyuQHh/liyR/qZgskyBHk5a34f1aYm6lV1RVN/d52OtVFynDxZHyZCRGbO3laJ7PxCze0eDavi2PVUzW1+sSZ99GpUFYmp2QgLqAxbGyXX/CLZ41sokZnzcXVA9cqOmtsvPxNQ4XOa20rkZDpKpQKvtq+F8EB2mxIBbBEiMmtNHnVZONnbIvb9SNjZKqF86utLaUsd6FL3QUQkd2wRIhJZsK9xEpAqziq4OtjBRsfWnJqelYwSBxGRNWEiRCSyJjoWnhpKjNE2ETXZLSIX341oIXUIRGaNiRBRKWLfj5Q6hAopq+FIZct/fVOrSFnWgLDqBh33TJCHWRdDt6rFea5Ienw3JCrBmpERqFKBdboCq2gvP7Dr7Q4VjKh0EUH6f5iwVtr0DO2qjJ/dE58PaIIfXm2p97FLh4QbdE1TqVzGHFhEpsJEiKgE3q4VW6zU1dFO8/s73UIM/hCs6+1c7j7t63phNbs/rF4LPbszz33cDW5FXodEVDImQkQiUwD4tF/jJ7cNbH0Z3joQ7/esr9O+JS3BwVYfy/fFi0103te5yFIXIb4ucLCzMUZIRFaHiRCREdSr6lrhc0zv3QBuTvxGL2f6LGfx77udjRiJcTBXJ3PARIioBL5uDsW2vdMtRPN7iK8LOod4F9vHUO4iJDw+rioUXQGhrKU+HO05hZip/adFjXL36RTirfU604ezgYufEskdEyGiImyVCuyc1B6uDsUTk4giC5g62ttg5vON9Dp3m9qepd63dUI7vc5Vkv3vdMKZj7ppbXMp4XEAgJ+7Y4nbSTxTu2snNCNa18RPr5Vd8PxtVHMMauGvuc21vYiMj4kQURF+lR21FqUU09cvN8OXLzU1yrkBwM5GWawuxN5WiX+mdMKBKZ2Mdl0q2avta2ndVioVOi1r4e5kj6VDwrAyqjnsnpoz6oNeutWMWYpJXYKlDoGIiRCRWAaEVUcND6dS6zpcHezwXFO/Eu/zclaJ/yH3qDGhmrsjW4AsTNcGvuhYQtfryDY1JYjGeDj7OZkDdioTGaCk9b0+H9AEgiAYtKCpUqnAyDY1kZiajeX7r4oQIVmrpv7uiLuRqrWNiS6R4dgiRFSG5oGVS71PQPFsqKQkKLCK7t96q4nwgdbzUYvUKCtrPTAX1UoopDelTW+0ws5J7fFsfR/Ntj/HtZEwIiLLxkSIqAxzBjyZx6W0Vd5L8+NrLfFBr/ro2sCn/J1FtPA/oTg+rQtCa5SexJHhNr7aUqug2dQUCgWCvJy1xgS6Oz2coblj8MP5pIa3DjR9YAYKeGoWdjEYsxaPrA+7xojKEKBHa87Tmgd6oLkOxbFiUygUnH/IiPw9nDDrhcao6VkJn/x1TupwtHwzNBzxd7NQW4cZyc2FoxEmfmxdxghNqrgqVrY0ChMhIgM83Ti07a2KD38HOBu0nIXWcK/wOexslKjj41LxYExI35ZWIrGxa4xIBN6uuteNiLkauGcFFoYl3SwYFGqS69gp+Xasq6b+7lKHUGFvRdaVOgR6hP95REU8PQleWcqaubkslVQV7wp4XAPy8XMNKnwuKl3PRlVhZ8NmOnNSr6orfnm9VZn7lDQhqrkR431AV/qsWSdHTISIHtk8pjW6NdR9bScfVxXa1fVCZD0fuDoY3stsyNII03rVx7EPnkV3PdaiIv2F1nBH53qmLXbX1eh2QQCAbg18JY7EtOxsFFCWM+O2va1pP9rqG7C24OCIACNEUjJj1GFZE9YIkewdmNIJ9zJz0bi6u+4HPZovaPWIFnpf7+mWJEd7/d+kFAoFKltZwaK5enp258da1Sq/IHdy12B8vvW82CEBeFiMf+yDZ0VZp44qxsOA/0VD/u/JONgiRLLn5+6oXxJEBKChnxv+eLPs+Xt8Sqgdi6wn3mK9lSvZGzSBpzkpaT6usvRqbHgraHlTWbzZqXaJ2yd3DYaDHT8urRX/skREpXCyL7vRvKGfW5n3Px/qh+GtA7F0SJhm21eDQrFyePNi+3q5svBdFyPbBBl03KH3OmNyV91rAJ92fHoXjGxTE/v+27HCgxRqeXFpEXPCRIiIqBT9wkpeG05XNkoFpvdugK5F6nic7G3RMVi7VSiynjem97auBVWNxaac+qDSeLs4lDs9RWndoACgsrXBB73qw9/DCbsndzAohseacbJTs8JEiMjE3u4aDCfWB1gEla1p/k7LhzWHt4u0S3fIXYivC6J0nJHbkAEOZL6YCJEsebtUrGm7InPA1fSshJMzulbo+kTWwhQTKro5ll9QHj2hncmG3ZtDWdfAcOmWiTE3TIRIlvRZCNUYijbvm8F7IpUgoqbpl0ch49g+sb1Rzx/oKf56acbWgq9vDSZCRBL54sUm8KhkjyVFCmnJfCjN4Ws7icKrgi3A5ZncNQRN9Jjt2tDJWCvi6SVcujX0rXDLuLVgIkTyZAafcS80q47Y9yNNXjhpYK2p1eOHgjRsyyhQlpI+dXxujnb4dUxrfGWi5ViKalbDHa+2L38kXds62kv7VFLZImZqZ2OFZVHM8xVIZEZ6NPJFIz83NKn+ZKi0WHUNlj4HjLXo3aSaydYUI22ezrpPRjiohenqWsqbOqEkfZpUM0IkpfN1dcDaUc9gavd65e4b5PmkHOCbR63Qho7AszYsfScqx9eDwyA8mkna2OoZMFU/GUfRif6GtQzAdzHXdF4oU5+alLo+znrHJld+7o4mvJrxqrgfv5VUr+yIm/ezdT6uTW1P7L90F3+/1Q51fVygVgvlLjfyWKtaVTS/hweyPqgoJkJEOjBVy80zQVXw9eBmqOnJCdfMyfTeDTCkZaDOE+HV9mZyIxYXB1tk5BRIHYZR9GxcFUv3XNF5/+9HtkBOvlqzPIeuSRAAsygHMFfsGiMyMz0aVWXLkJlRKhWo7e3MrkwJ1PPV73+hrKJlY/z1qlcWp5Vq3egIhPi64I0OtUrdR6FQGL5GWZEGLr6KtTERIjJAJRUnRLQ2hiY5Ib4umt9ffqYGvh+p/0K8clbW875jknYXo79H+cPUVY+Kryu6NpiudYAvNKuu97kff9Gp4/3ktdOqlieiJ7RDWID4gyecHUrv/Hm/Zz34lrAmnpwwESLSw8L/hKJBNVfMfqGx1KGQyAQDK+CLLsvwv76Nio3OKc3jD+pngqqUs6d81fLS7mLs3Vj3YuTGfu4iR6Obn19viU/7NSpzn+aPanReCPXDuz1CsOmNVkaNqU1tz1LvG9U2CKtLSN7/eLMNBkfUMGZYZoOJEJEeejWuhj/HtUUga3isiqFJUEVse6s93u9ZD1O6G74QqBx0ebRivI+rSq+amLkvNkHr2lXw3YjyW+g+62/4F5unIwoL8MDA5rolEEqlAq+0q4VQI0+hoW9rp4vKFg393DDzee2ELqDKwxa5zvW8SzrMYrFYmmSJfeT0NFPPZ+Pv4YRRbQ1bSV1OoloFooaHk95dRv4eTlg76hmd9nW0K7mrO7SGO45dTzX5sPiSdA4xXfJRq5Ri/x9fbYltZ2+jb9OKLUZsbpgIEREBCKxi2DIJrJ82LlsbJbo08NX9ABH/Hj+91gpZeQUmW4MMKP31NK5zHaNdU9cGUW9XBwyOCDBaHFJh1xgRETi5JRVno1SYNAkyFyPb1JQ6BJNiixAREdFTTF81Zh6WDw1HZH0fqcMwKbYIERERPTJ3QBO4OdpplqEwVHkjx8xVgIFdxJaMLUJEJHty/fZPxfULq44XmvlVuKt0YPMauJuZh8+3nhcpMjIWtggREYGTZEpFiqkLyiNlvZjCSGNaXR2f1Do58bWuhYkQyVJkvYd94O5O8iuEpJKpbG2w6+0OUodBFcSS95I52Nlg21vtsH1iO6hsmQgVxa4xkqXhrQPhV9kR4UaYzp4slyGL3XpUsjdCJKQPH1cVbqfnlrufTSkTMro5Gv6FqKzGI5WtebU11PFxKX8nGTKvvxKRidjaKNGjUVV4y2iNnc/6N4ZCASwbGi51KFZl5vON0DKoCp9XibSqVQUxUzrrtG8NDyd0DNZeAiWqVSDa1il9CYqK+E9EDX7ZsgBMhIhk4sVwf1z8X3d0rievobHG5ufuiPWvPINnZTbk2JzouvSGQqHAyuEtENUqULNtRp8GRqsJcrK3xU+vF19HTMlPXrPCrjEiGTH1MhKWonIJtWKPVwgn82ZJ82D6uTsi2NcFwWV0UUUEecDFwRYZOQUmjEze+K5IRLK1YFAo2tf1wqRngzXb/nizDd7oUAuTugSXcSSZK3NOjGb3a4Rvo5qX2QLlZG+Lox88a8KoiIkQEclW7ybV8N2IFqhcpOC5oZ8b/tstBM4qNphbkmm96sPT2R7/69tQ6lAqzM6MWm5Ht3243IYx1zqTGv/TiYjI4o1oUxPDWwdyzTgDVHV/MmjEwU57aP27PerhpRY1EGTAiEpLYT5pJxERUQVYUxL09Og2Y7ItUmz+9FOoUChQy8vZqp7bp1lMIpSSkoLBgwfD1dUV7u7uGDlyJDIzM8vc/80330RwcDAcHR1Ro0YNjBs3DmlpaSaMmoiIzI2PiNNmONkbZ3LCpxOPwCqGtcg09XcXIRrrZjFdY4MHD0ZiYiK2bduG/Px8DB8+HK+88grWrVtX4v4JCQlISEjAnDlzUL9+fVy7dg2vvfYaEhIS8NNPP5k4eiIiMhfDWwfiyp3MCq2yPqN3fWw7extDngkUL7BSHJjSCW56zoLf0M8Vw1vVRKcQbyNFZT0sIhE6e/YsoqOjcfjwYYSHP5y0bMGCBejRowfmzJmDatWqFTumYcOG+PnnnzW3a9WqhZkzZ+Lll19GQUEBbG0t4qETEZHIHOxs8PmAJhU6R1TrmohqXVOvYwxdR8zP3VHvY5zsbNEvrLpB15Mbi+gai4mJgbu7uyYJAoDIyEgolUocPHhQ5/OkpaXB1dW1zCQoNzcX6enpWj9ERGReJkTWgb2NEu/1qC91KGbhq0GhBh9rW2SGRxcH+a2/aBHNIklJSfD21m7es7W1hYeHB5KSknQ6x927d/Hxxx/jlVdeKXO/WbNm4cMPPzQ4ViIiMr4JkXUxtmNtThL6SJ8m1fBsPR/Umxat97H2tkqsHRWBvEJ1hdZds1SSvoKmTJkChUJR5s+5c+cqfJ309HT07NkT9evXx4wZM8rcd+rUqUhLS9P83Lhxo8LXJyIi8TEJ0uZYpHDbUc8i7ta1PdExWJ71RJK2CE2aNAlRUVFl7hMUFARfX18kJydrbS8oKEBKSgp8fX3LPD4jIwPdunWDi4sLNm3aBDu7srNdlUoFlUqlU/xERETGMLJNTew8l4zOehY7zx3QBMv2XbGKiSVNRdJEyMvLC15e5c+V0LJlS6SmpiI2NhZhYWEAgJ07d0KtViMiIqLU49LT09G1a1eoVCr89ttvcHCQz0rjRERkuVrX9sSh9zrDs5J+X8z7hVVnkbSeLKJdsV69eujWrRtGjx6NQ4cO4cCBAxg7dixeeuklzYixW7duISQkBIcOHQLwMAnq0qULsrKysGLFCqSnpyMpKQlJSUkoLCyU8uEQEdEjcu2O0YW3iwOUSuudyNBcWESxNACsXbsWY8eORefOnaFUKtGvXz989dVXmvvz8/Nx/vx5PHjwAABw9OhRzYiy2rVra53r6tWrCAwMNFnsRERUsqEtA+Dr5oA31h6VOhSSKYtJhDw8PEqdPBEAAgMDIQiC5naHDh20bhMRkfmxtVGiR6OqUodBMmYRXWNERESWqoaHEwCgaQ13aQOhEjERIiIiyS0dEiZ1CEazc1J7nP2oG5xVFtMJIytMhIiISHJdG/ji7EfdpA7DKGxtlHrP60Omw0SIiIjMgqO9Dfb9tyOCPCvhY86DQybCdjoiIjIb/h5O2Pl2B6nDIBlhixARERHJFhMhIiIiki0mQkRERCRbTISIiIhItpgIERERkWwxESIiIiLZYiJEREREssVEiIiIiGSLiRARERHJFhMhIiIiki0mQkRERCRbTISIiIhItpgIERERkWwxESIiIiLZspU6AHMnCAIAID09XeJIiIiISFePP7cff46XholQOTIyMgAA/v7+EkdCRERE+srIyICbm1up9yuE8lIlmVOr1UhISICLiwsUCoVo501PT4e/vz9u3LgBV1dX0c5LxfG5Ng0+z6bB59k0+DybjrGea0EQkJGRgWrVqkGpLL0SiC1C5VAqlahevbrRzu/q6sp/MhPhc20afJ5Ng8+zafB5Nh1jPNdltQQ9xmJpIiIiki0mQkRERCRbTIQkolKpMH36dKhUKqlDsXp8rk2Dz7Np8Hk2DT7PpiP1c81iaSIiIpIttggRERGRbDERIiIiItliIkRERESyxUSIiIiIZIuJkEQWLVqEwMBAODg4ICIiAocOHZI6JKuzd+9e9O7dG9WqVYNCocDmzZulDskqzZo1C82bN4eLiwu8vb3Rt29fnD9/XuqwrM7ixYvRuHFjzaRzLVu2xJYtW6QOy+rNnj0bCoUCEyZMkDoUqzJjxgwoFAqtn5CQEEliYSIkgY0bN2LixImYPn06jh49iiZNmqBr165ITk6WOjSrkpWVhSZNmmDRokVSh2LV9uzZgzFjxuDff//Ftm3bkJ+fjy5duiArK0vq0KxK9erVMXv2bMTGxuLIkSPo1KkTnnvuOZw+fVrq0KzW4cOHsXTpUjRu3FjqUKxSgwYNkJiYqPnZv3+/JHFw+LwEIiIi0Lx5cyxcuBDAw/XM/P398eabb2LKlCkSR2edFAoFNm3ahL59+0oditW7c+cOvL29sWfPHrRr107qcKyah4cHPv/8c4wcOVLqUKxOZmYmmjVrhq+//hr/+9//0LRpU8yfP1/qsKzGjBkzsHnzZsTFxUkdCluETC0vLw+xsbGIjIzUbFMqlYiMjERMTIyEkRGJIy0tDcDDD2kyjsLCQmzYsAFZWVlo2bKl1OFYpTFjxqBnz55a79UkrosXL6JatWoICgrC4MGDcf36dUni4KKrJnb37l0UFhbCx8dHa7uPjw/OnTsnUVRE4lCr1ZgwYQJat26Nhg0bSh2O1Tl58iRatmyJnJwcODs7Y9OmTahfv77UYVmdDRs24OjRozh8+LDUoVitiIgIrFq1CsHBwUhMTMSHH36Itm3b4tSpU3BxcTFpLEyEiEg0Y8aMwalTpyTr67d2wcHBiIuLQ1paGn766ScMGzYMe/bsYTIkohs3bmD8+PHYtm0bHBwcpA7HanXv3l3ze+PGjREREYGAgAD88MMPJu/qZSJkYp6enrCxscHt27e1tt++fRu+vr4SRUVUcWPHjsUff/yBvXv3onr16lKHY5Xs7e1Ru3ZtAEBYWBgOHz6ML7/8EkuXLpU4MusRGxuL5ORkNGvWTLOtsLAQe/fuxcKFC5GbmwsbGxsJI7RO7u7uqFu3Li5dumTya7NGyMTs7e0RFhaGHTt2aLap1Wrs2LGDff1kkQRBwNixY7Fp0ybs3LkTNWvWlDok2VCr1cjNzZU6DKvSuXNnnDx5EnFxcZqf8PBwDB48GHFxcUyCjCQzMxOXL19G1apVTX5ttghJYOLEiRg2bBjCw8PRokULzJ8/H1lZWRg+fLjUoVmVzMxMrW8XV69eRVxcHDw8PFCjRg0JI7MuY8aMwbp16/Drr7/CxcUFSUlJAAA3Nzc4OjpKHJ31mDp1Krp3744aNWogIyMD69atw+7du7F161apQ7MqLi4uxerbKlWqhCpVqrDuTURvv/02evfujYCAACQkJGD69OmwsbHBoEGDTB4LEyEJDBw4EHfu3MG0adOQlJSEpk2bIjo6ulgBNVXMkSNH0LFjR83tiRMnAgCGDRuGVatWSRSV9Vm8eDEAoEOHDlrbV65ciaioKNMHZKWSk5MxdOhQJCYmws3NDY0bN8bWrVvx7LPPSh0akd5u3ryJQYMG4d69e/Dy8kKbNm3w77//wsvLy+SxcB4hIiIiki3WCBEREZFsMREiIiIi2WIiRERERLLFRIiIiIhki4kQERERyRYTISIiIpItJkJEREQkW0yEiIiMZMeOHahXrx4KCwtFPe9LL72EuXPninpOIlPbu3cvevfujWrVqkGhUGDz5s16n0MQBMyZMwd169aFSqWCn58fZs6cqdc5mAgRkcGioqKgUCiK/UixcKI5+u9//4v3339fsz7VqlWr4O7uXuK++nwQvP/++5g5cybS0tJEipTI9LKystCkSRMsWrTI4HOMHz8ey5cvx5w5c3Du3Dn89ttvaNGihV7n4BIbRFQh3bp1w8qVK7W2PT1Nfl5eHuzt7U0ZluT279+Py5cvo1+/fqKfu2HDhqhVqxbWrFmDMWPGiH5+IlPo3r07unfvXur9ubm5eO+997B+/XqkpqaiYcOG+PTTTzXL+Zw9exaLFy/GqVOnEBwcDAAGLfrMFiEiqhCVSgVfX1+tn86dO2Ps2LGYMGECPD090bVrVwDAqVOn0L17dzg7O8PHxwdDhgzB3bt3NefKysrC0KFD4ezsjKpVq2Lu3Lno0KEDJkyYoNmnpJYTd3d3rfXjbty4gRdffBHu7u7w8PDAc889h/j4eM39UVFR6Nu3L+bMmYOqVauiSpUqGDNmDPLz8zX75Obm4p133oG/vz9UKhVq166NFStWQBAE1K5dG3PmzNGKIS4uTqs1bMOGDXj22Wfh4OCg93M6Y8aMElvaij7G3r17Y8OGDXqfm8hSjB07FjExMdiwYQNOnDiBAQMGoFu3brh48SIA4Pfff0dQUBD++OMP1KxZE4GBgRg1ahRSUlL0ug4TISIyiu+++w729vY4cOAAlixZgtTUVHTq1AmhoaE4cuQIoqOjcfv2bbz44ouaYyZPnow9e/bg119/xd9//43du3fj6NGjel03Pz8fXbt2hYuLC/bt24cDBw7A2dkZ3bp1Q15enma/Xbt24fLly9i1axe+++47rFq1SivRGDp0KNavX4+vvvoKZ8+exdKlS+Hs7AyFQoERI0YUawVbuXIl2rVrh9q1awMA9u3bh/DwcAOeuYcrcycmJmp+5syZAycnJ63ztWjRAocOHUJubq5B1yAyZ9evX8fKlSvx448/om3btqhVqxbefvtttGnTRvO/d+XKFVy7dg0//vgjVq9ejVWrViE2Nhb9+/fX72ICEZGBhg0bJtjY2AiVKlXS/PTv319o3769EBoaqrXvxx9/LHTp0kVr240bNwQAwvnz54WMjAzB3t5e+OGHHzT337t3T3B0dBTGjx+v2QZA2LRpk9Z53NzchJUrVwqCIAjff/+9EBwcLKjVas39ubm5gqOjo7B161ZN3AEBAUJBQYFmnwEDBggDBw4UBEEQzp8/LwAQtm3bVuLjvnXrlmBjYyMcPHhQEARByMvLEzw9PYVVq1ZpxbR69Wqt41auXCkA0Hq+Hv+U9LgEQRBiYmIEBwcHYePGjVrbjx8/LgAQ4uPjS4yRyJI8/fr/448/SvxfsbW1FV588UVBEARh9OjRmvePx2JjYwUAwrlz53S+NmuEiKhCOnbsiMWLF2tuV6pUCYMGDUJYWJjWfsePH8euXbvg7Oxc7ByXL19GdnY28vLyEBERodnu4eGh6fvX1fHjx3Hp0iW4uLhobc/JycHly5c1txs0aKApYgaAqlWr4uTJkwAednPZ2Nigffv2JV6jWrVq6NmzJ7799lu0aNECv//+O3JzczFgwADNPtnZ2SV2i7m4uJTYylWnTp1i265fv46+ffvi7bff1mo5AwBHR0cAwIMHD0qMkciSZWZmwsbGBrGxsVr/pwA07yFVq1aFra0t6tatq7mvXr16AB7+7+j63sFEiIgqpFKlSpruoKe3F5WZmYnevXvj008/LbZv1apVdR5pplAo8PAL5BNFa3syMzMRFhaGtWvXFju2aBG3nZ1dsfOq1WoAT5KMsowaNQpDhgzBvHnzsHLlSgwcOBBOTk6a+z09PXH//v1ixymVyhKfr6dlZWWhT58+aNmyJT766KNi9z+ug3i6MJ3IGoSGhqKwsBDJyclo27Ztifu0bt0aBQUFuHz5MmrVqgUAuHDhAgAgICBA52sxESIik2jWrBl+/vlnBAYGwta2+FtPrVq1YGdnh4MHD6JGjRoAgPv37+PChQtaLTNeXl5ITEzU3L548aJWq0izZs2wceNGeHt7w9XV1aBYGzVqBLVajT179iAyMrLEfXr06IFKlSph8eLFiI6Oxt69e7XuDw0NxZkzZwy6viAIePnll6FWq/H9999DoVAU2+fUqVOoXr06PD09DboGkdQyMzO1vgBdvXoVcXFx8PDwQN26dTF48GAMHToUc+fORWhoKO7cuYMdO3agcePG6NmzJyIjI9GsWTOMGDEC8+fPh1qtxpgxY/Dss89qtRKVh8XSRGQSY8aMQUpKCgYNGoTDhw/j8uXL2Lp1K4YPH47CwkI4Oztj5MiRmDx5Mnbu3IlTp04hKioKSqX221SnTp2wcOFCHDt2DEeOHMFrr72m1bozePBgeHp64rnnnsO+fftw9epV7N69G+PGjcPNmzd1ijUwMBDDhg3DiBEjsHnzZs05fvjhB80+NjY2iIqKwtSpU1GnTh20bNlS6xxdu3bF/v37DXquZsyYge3bt2Pp0qXIzMxEUlISkpKSkJ2drdln37596NKli0HnJzIHR44cQWhoKEJDQwEAEydORGhoKKZNmwbg4QCEoUOHYtKkSQgODkbfvn1x+PBhzRclpVKJ33//HZ6enmjXrh169uyJevXq6T2akokQEZlEtWrVcODAARQWFqJLly5o1KgRJkyYAHd3d02y8/nnn6Nt27bo3bs3IiMj0aZNm2K1RnPnzoW/vz/atm2L//znP3j77be1uqScnJywd+9e1KhRAy+88ALq1auHkSNHIicnR68WosWLF6N///544403EBISgtGjRyMrK0trn5EjRyIvLw/Dhw8vdvzgwYNx+vRpnD9/Xp+nCQCwZ88eZGZmolWrVqhatarmZ+PGjQAe1jtt3rwZo0eP1vvcROaiQ4cOEASh2M/j0Zt2dnb48MMPcfXqVeTl5SEhIQG//PILGjVqpDlHtWrV8PPPPyMjIwNJSUlYuXIlPDw89IpDITzd2U5EZEY6dOiApk2bYv78+VKHUsy+ffvQuXNn3LhxAz4+PsXunzx5MtLT07F06VJRr7t48WJs2rQJf//9t6jnJZIjtggREekpNzcXN2/exIwZMzBgwIASkyAAeO+99xAQEKApwhaLnZ0dFixYIOo5ieSKiRARkZ7Wr1+PgIAApKam4rPPPit1P3d3d7z77rvF6pwqatSoUXpPK0BEJWPXGBEREckWW4SIiIhItpgIERERkWwxESIiIiLZYiJEREREssVEiIiIiGSLiRARERHJFhMhIiIiki0mQkRERCRbTISIiIhItv4P40DzxKpX1SEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(noisy_file)   \n",
    "plt.title('Original Signal')  \n",
    "plt.xlabel('Frequency(Hz)')  \n",
    "plt.ylabel('Amplitude')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zZcsWlZSUmDUpKSlq3769GjdubNZcup+LNRf3UxFXV1d5enraPQAAQN1Vq6GpoKBAe/bs0Z49eyT9vPh7z549yszMlM1mU2xsrGbPnq3Vq1dr//79euihhxQYGGh+wq5jx46KjIzUmDFj9MUXX+izzz5TTEyMhg0bpsDAQEnS8OHD5eLioqioKKWnp2vZsmWaP3++Jk2aZPYxYcIEJScna+7cufr66681c+ZM7dixQzExMTX9KwEAAA6qVheC79ixQ7feeqv5/GKQGTlypJKSkjR16lSdO3dOY8eOVW5urm6++WYlJyfL7ZKP9L/33nuKiYlR//79Va9ePQ0dOlQLFiww5728vLR+/XpFR0erR48e8vHxUVxcnN29nP7whz9oyZIlmj59up566im1bdtWq1atUufOnWvgtwAAAK4GDnOfpqsd92kCAODqUyfu0wQAAOBICE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABQ4dmkpLS/XMM88oJCRE7u7uat26tZ577jkZhmHWGIahuLg4BQQEyN3dXWFhYTp48KDddk6fPq0RI0bI09NT3t7eioqKUkFBgV3Nvn371LdvX7m5uSkoKEgJCQk1cowAAODq4NCh6cUXX9TChQv12muv6auvvtKLL76ohIQEvfrqq2ZNQkKCFixYoMTERG3btk0NGzZURESECgsLzZoRI0YoPT1dKSkpWrNmjbZs2aKxY8ea8/n5+QoPD1dwcLB27typl156STNnztRbb71Vo8cLAAAcl8249LKNg7nzzjvl5+enf/zjH+bY0KFD5e7urn/9618yDEOBgYF6/PHHNXnyZElSXl6e/Pz8lJSUpGHDhumrr75Sp06dtH37dvXs2VOSlJycrAEDBuj48eMKDAzUwoUL9fTTTysrK0suLi6SpCeffFKrVq3S119/banX/Px8eXl5qdX0OXJyc6vi3wQAAKgOpYWFOjL7KeXl5cnT0/OytQ59pekPf/iDNmzYoG+++UaStHfvXn366ae64447JElHjx5VVlaWwsLCzNd4eXmpd+/eSktLkySlpaXJ29vbDEySFBYWpnr16mnbtm1mTb9+/czAJEkRERHKyMjQmTNnqv04AQCA43Ou7QYu58knn1R+fr46dOggJycnlZaW6vnnn9eIESMkSVlZWZIkPz8/u9f5+fmZc1lZWfL19bWbd3Z2VpMmTexqQkJCym3j4lzjxo3L9VZUVKSioiLzeX5+/u85VAAA4OAc+krTBx98oPfee09LlizRrl27tHjxYv3tb3/T4sWLa7s1xcfHy8vLy3wEBQXVdksAAKAaOXRomjJlip588kkNGzZMXbp00YMPPqiJEycqPj5ekuTv7y9Jys7Otntddna2Oefv76+cnBy7+QsXLuj06dN2NRVt49J9/NK0adOUl5dnPo4dO/Y7jxYAADgyhw5N58+fV7169i06OTmprKxMkhQSEiJ/f39t2LDBnM/Pz9e2bdsUGhoqSQoNDVVubq527txp1qSmpqqsrEy9e/c2a7Zs2aKSkhKzJiUlRe3bt6/wrTlJcnV1laenp90DAADUXQ4dmu666y49//zz+uijj/Ttt99q5cqVmjdvngYPHixJstlsio2N1ezZs7V69Wrt379fDz30kAIDAzVo0CBJUseOHRUZGakxY8boiy++0GeffaaYmBgNGzZMgYGBkqThw4fLxcVFUVFRSk9P17JlyzR//nxNmjSptg4dAAA4GIdeCP7qq6/qmWee0WOPPaacnBwFBgbqkUceUVxcnFkzdepUnTt3TmPHjlVubq5uvvlmJScny+2Sj/2/9957iomJUf/+/VWvXj0NHTpUCxYsMOe9vLy0fv16RUdHq0ePHvLx8VFcXJzdvZwAAMC1zaHv03Q14T5NAABcferMfZoAAAAcBaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABc5WioYMGVLpDScmJsrX17fSrwMAAHBElkLTqlWrdO+998rd3d3SRpcsWaKCggJCEwAAqDMshSZJWrBggeUQ9OGHH15xQwAAAI7I0pqmjRs3qkmTJpY3um7dOl133XVX3BQAAICjsXSl6ZZbbqnURm+++eYragYAAMBRVfrTc7fddptmzZpVbvzMmTO67bbbqqQpAAAAR2N5TdNFmzZt0v79+7V792699957atiwoSSpuLhYmzdvrvIGAQAAHMEV3afpk08+UVZWlm666SZ9++23VdwSAACA47mi0BQQEKDNmzerS5cu6tWrlzZt2lTFbQEAADiWSocmm80mSXJ1ddWSJUs0YcIERUZG6o033qjy5gAAABxFpdc0GYZh93z69Onq2LGjRo4cWWVNAQAAOJpKh6ajR4/Kx8fHbmzo0KFq3769du7cWWWNAQAAOJJKh6bg4OAKxzt37qzOnTv/7oYAAAAckeXQZPVLe1esWHHFzQAAADgqy6HJy8vL7vmSJUt01113ycPDo8qbAgAAcDSWQ9OiRYvsnn/44YdKSEhQq1atqrwpAAAAR3NF92kCAAC41hCaAAAALCA0AQAAWGB5TdPq1avtnpeVlWnDhg06cOCA3fif/vSnqukMAADAgVgOTYMGDSo39sgjj9g9t9lsKi0t/d1NAQAAOBrLoamsrKw6+wAAAHBorGkCAACwwFJoWr16tUpKSixvdO3atfrpp5+uuCkAAABHYyk0DR48WLm5uZY3OmzYMJ08efJKewIAAHA4ltY0GYahUaNGydXV1dJGCwsLf1dTAAAAjsZSaBo5cmSlNjpixAh5enpeUUMAAACOyFJo+uX3zgEAAFxr+PQcAACABQ4fmr7//ns98MADatq0qdzd3dWlSxft2LHDnDcMQ3FxcQoICJC7u7vCwsJ08OBBu22cPn3afMvQ29tbUVFRKigosKvZt2+f+vbtKzc3NwUFBSkhIaFGjg8AAFwdHDo0nTlzRn369FH9+vW1bt06ffnll5o7d64aN25s1iQkJGjBggVKTEzUtm3b1LBhQ0VERNgtRh8xYoTS09OVkpKiNWvWaMuWLRo7dqw5n5+fr/DwcAUHB2vnzp166aWXNHPmTL311ls1erwAAMBx2QzDMGq7iV/z5JNP6rPPPtN///vfCucNw1BgYKAef/xxTZ48WZKUl5cnPz8/JSUladiwYfrqq6/UqVMnbd++XT179pQkJScna8CAATp+/LgCAwO1cOFCPf3008rKypKLi4u571WrVunrr7+21Gt+fr68vLzUavocObm5VcHRAwCA6lZaWKgjs59SXl7eb36IrdJXmo4cOXLFjVXW6tWr1bNnT/35z3+Wr6+vbrzxRr399tvm/NGjR5WVlaWwsDBzzMvLS71791ZaWpokKS0tTd7e3mZgkqSwsDDVq1dP27ZtM2v69etnBiZJioiIUEZGhs6cOVPdhwkAAK4ClQ5Nbdq00a233qp//etf1X4/piNHjmjhwoVq27atPv74Y40bN05//etftXjxYklSVlaWJMnPz8/udX5+fuZcVlaWfH197eadnZ3VpEkTu5qKtnHpPn6pqKhI+fn5dg8AAFB3VTo07dq1S127dtWkSZPk7++vRx55RF988UV19KaysjJ1795dc+bM0Y033qixY8dqzJgxSkxMrJb9VUZ8fLy8vLzMR1BQUG23BAAAqlGlQ9MNN9yg+fPn68SJE3rnnXd08uRJ3XzzzercubPmzZunU6dOVVlzAQEB6tSpk91Yx44dlZmZKUny9/eXJGVnZ9vVZGdnm3P+/v7Kycmxm79w4YJOnz5tV1PRNi7dxy9NmzZNeXl55uPYsWNXcogAAOAqccWfnnN2dtaQIUO0fPlyvfjiizp06JAmT56soKAgPfTQQ1Xy3XN9+vRRRkaG3dg333yj4OBgSVJISIj8/f21YcMGcz4/P1/btm1TaGioJCk0NFS5ubnauXOnWZOamqqysjL17t3brNmyZYvdlxKnpKSoffv2dp/Uu5Srq6s8PT3tHgAAoO664tC0Y8cOPfbYYwoICNC8efM0efJkHT58WCkpKTpx4oTuvvvu393cxIkT9fnnn2vOnDk6dOiQlixZorfeekvR0dGSJJvNptjYWM2ePVurV6/W/v379dBDDykwMFCDBg2S9POVqcjISI0ZM0ZffPGFPvvsM8XExGjYsGEKDAyUJA0fPlwuLi6KiopSenq6li1bpvnz52vSpEm/+xgAAEDdYOlrVC41b948LVq0SBkZGRowYIDeffddDRgwQPXq/Zy/QkJClJSUpJYtW/7u5nr16qWVK1dq2rRpevbZZxUSEqJXXnlFI0aMMGumTp2qc+fOaezYscrNzdXNN9+s5ORkuV3ysf/33ntPMTEx6t+/v+rVq6ehQ4dqwYIF5ryXl5fWr1+v6Oho9ejRQz4+PoqLi7O7lxMAALi2Vfo+TW3bttXDDz+sUaNGKSAgoMKa4uJiLV26tNJf9Hs14z5NAABcfSpzn6ZKX2n65VeUVMTFxeWaCkwAAKDuq/SapkWLFmn58uXlxpcvX27ePwkAAKCuqXRoio+Pl4+PT7lxX19fzZkzp0qaAgAAcDSVDk2ZmZkKCQkpNx4cHGzePwkAAKCuqXRo8vX11b59+8qN7927V02bNq2SpgAAABxNpUPT/fffr7/+9a/auHGjSktLVVpaqtTUVE2YMEHDhg2rjh4BAABqXaU/Pffcc8/p22+/Vf/+/eXs/PPLy8rK9NBDD7GmCQAA1FmVDk0uLi5atmyZnnvuOe3du1fu7u7q0qWL+dUmAAAAdVGlQ9NF7dq1U7t27aqyFwAAAIdV6dBUWlqqpKQkbdiwQTk5OSorK7ObT01NrbLmAAAAHEWlQ9OECROUlJSkgQMHqnPnzrLZbNXRFwAAgEOpdGh6//339cEHH2jAgAHV0Q8AAIBDqvQtB1xcXNSmTZvq6AUAAMBhVTo0Pf7445o/f74Mw6iOfgAAABxSpd+e+/TTT7Vx40atW7dO119/verXr283v2LFiiprDgAAwFFUOjR5e3tr8ODB1dELAACAw6p0aFq0aFF19AEAAODQKr2mSZIuXLigTz75RG+++abOnj0rSTpx4oQKCgqqtDkAAABHUekrTd99950iIyOVmZmpoqIi3X777fLw8NCLL76ooqIiJSYmVkefAAAAtarSV5omTJignj176syZM3J3dzfHBw8erA0bNlRpcwAAAI6i0lea/vvf/2rr1q1ycXGxG2/ZsqW+//77KmsMAADAkVT6SlNZWZlKS0vLjR8/flweHh5V0hQAAICjqXRoCg8P1yuvvGI+t9lsKigo0IwZM/hqFQAAUGdV+u25uXPnKiIiQp06dVJhYaGGDx+ugwcPysfHR0uXLq2OHgEAAGpdpUNT8+bNtXfvXr3//vvat2+fCgoKFBUVpREjRtgtDAcAAKhLKh2aJMnZ2VkPPPBAVfcCAADgsCodmt59993Lzj/00ENX3AwAAICjqnRomjBhgt3zkpISnT9/Xi4uLmrQoAGhCQAA1EmV/vTcmTNn7B4FBQXKyMjQzTffzEJwAABQZ13Rd8/9Utu2bfXCCy+UuwoFAABQV1RJaJJ+Xhx+4sSJqtocAACAQ6n0mqbVq1fbPTcMQydPntRrr72mPn36VFljAAAAjqTSoWnQoEF2z202m5o1a6bbbrtNc+fOraq+AAAAHEqlQ1NZWVl19AEAAODQqmxNEwAAQF1W6StNkyZNslw7b968ym4eAADAIVU6NO3evVu7d+9WSUmJ2rdvL0n65ptv5OTkpO7du5t1Nput6roEAACoZZUOTXfddZc8PDy0ePFiNW7cWNLPN7wcPXq0+vbtq8cff7zKmwQAAKhtlV7TNHfuXMXHx5uBSZIaN26s2bNn8+k5AABQZ1U6NOXn5+vUqVPlxk+dOqWzZ89WSVMAAACOptKhafDgwRo9erRWrFih48eP6/jx4/r3v/+tqKgoDRkypDp6BAAAqHWVXtOUmJioyZMna/jw4SopKfl5I87OioqK0ksvvVTlDQIAADiCSoemBg0a6I033tBLL72kw4cPS5Jat26thg0bVnlzAAAAjuKKb2558uRJnTx5Um3btlXDhg1lGEZV9gUAAOBQKh2afvzxR/Xv31/t2rXTgAEDdPLkSUlSVFQUtxsAAAB1VqVD08SJE1W/fn1lZmaqQYMG5vh9992n5OTkKm0OAADAUVR6TdP69ev18ccfq3nz5nbjbdu21XfffVdljQEAADiSSl9pOnfunN0VpotOnz4tV1fXKmkKAADA0VQ6NPXt21fvvvuu+dxms6msrEwJCQm69dZbq7Q5AAAAR1Hpt+cSEhLUv39/7dixQ8XFxZo6darS09N1+vRpffbZZ9XRIwAAQK2r9JWmzp0765tvvtHNN9+su+++W+fOndOQIUO0e/dutW7dujp6BAAAqHWVutJUUlKiyMhIJSYm6umnn66ungAAABxOpa401a9fX/v27auuXgAAABxWpd+ee+CBB/SPf/yjOnoBAABwWJVeCH7hwgW98847+uSTT9SjR49y3zk3b968KmsOAADAUVQ6NB04cEDdu3eXJH3zzTd2czabrWq6AgAAcDCW3547cuSIDMPQxo0bf/WRmppanb3qhRdekM1mU2xsrDlWWFio6OhoNW3aVI0aNdLQoUOVnZ1t97rMzEwNHDhQDRo0kK+vr6ZMmaILFy7Y1WzatEndu3eXq6ur2rRpo6SkpGo9FgAAcHWxHJratm2rU6dOmc/vu+++cuGkOm3fvl1vvvmmunbtajc+ceJE/ec//9Hy5cu1efNmnThxQkOGDDHnS0tLNXDgQBUXF2vr1q1avHixkpKSFBcXZ9YcPXpUAwcO1K233qo9e/YoNjZWf/nLX/Txxx/X2PEBAADHZjk0GYZh93zt2rU6d+5clTdUkYKCAo0YMUJvv/22GjdubI7n5eXpH//4h+bNm6fbbrtNPXr00KJFi7R161Z9/vnnkn7+rrwvv/xS//rXv3TDDTfojjvu0HPPPafXX39dxcXFkqTExESFhIRo7ty56tixo2JiYnTPPffo5ZdfrpHjAwAAjq/Sn56rDdHR0Ro4cKDCwsLsxnfu3KmSkhK78Q4dOqhFixZKS0uTJKWlpalLly7y8/MzayIiIpSfn6/09HSz5pfbjoiIMLcBAABgeSG4zWYrt9C7JhZ+v//++9q1a5e2b99ebi4rK0suLi7y9va2G/fz81NWVpZZc2lgujh/ce5yNfn5+frpp5/k7u5ebt9FRUUqKioyn+fn51f+4AAAwFXDcmgyDEOjRo2Sq6urpJ8XYD/66KPlbjmwYsWKKmvu2LFjmjBhglJSUuTm5lZl260K8fHxmjVrVm23AQAAaojlt+dGjhwpX19feXl5ycvLSw888IACAwPN5xcfVWnnzp3KyclR9+7d5ezsLGdnZ23evFkLFiyQs7Oz/Pz8VFxcrNzcXLvXZWdny9/fX5Lk7+9fbsH6xee/VePp6VnhVSZJmjZtmvLy8szHsWPHquKQAQCAg7J8pWnRokXV2UeF+vfvr/3799uNjR49Wh06dNATTzyhoKAg1a9fXxs2bNDQoUMlSRkZGcrMzFRoaKgkKTQ0VM8//7xycnLk6+srSUpJSZGnp6c6depk1qxdu9ZuPykpKeY2KuLq6mpedQMAAHVfpW9uWZM8PDzUuXNnu7GGDRuqadOm5nhUVJQmTZqkJk2ayNPTU+PHj1doaKhuuukmSVJ4eLg6deqkBx98UAkJCcrKytL06dMVHR1thp5HH31Ur732mqZOnaqHH35Yqamp+uCDD/TRRx/V7AEDAACH5dChyYqXX35Z9erV09ChQ1VUVKSIiAi98cYb5ryTk5PWrFmjcePGKTQ0VA0bNtTIkSP17LPPmjUhISH66KOPNHHiRM2fP1/NmzfX3//+d0VERNTGIQEAAAdkM355AyZckfz8fHl5eanV9DlycrBF6wAAoGKlhYU6Mvsp5eXlydPT87K1V8V9mgAAAGoboQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwwKFDU3x8vHr16iUPDw/5+vpq0KBBysjIsKspLCxUdHS0mjZtqkaNGmno0KHKzs62q8nMzNTAgQPVoEED+fr6asqUKbpw4YJdzaZNm9S9e3e5urqqTZs2SkpKqu7DAwAAVxGHDk2bN29WdHS0Pv/8c6WkpKikpETh4eE6d+6cWTNx4kT95z//0fLly7V582adOHFCQ4YMMedLS0s1cOBAFRcXa+vWrVq8eLGSkpIUFxdn1hw9elQDBw7Urbfeqj179ig2NlZ/+ctf9PHHH9fo8QIAAMdlMwzDqO0mrDp16pR8fX21efNm9evXT3l5eWrWrJmWLFmie+65R5L09ddfq2PHjkpLS9NNN92kdevW6c4779SJEyfk5+cnSUpMTNQTTzyhU6dOycXFRU888YQ++ugjHThwwNzXsGHDlJubq+TkZEu95efny8vLS62mz5GTm1vVHzwAAKhypYWFOjL7KeXl5cnT0/OytQ59pemX8vLyJElNmjSRJO3cuVMlJSUKCwszazp06KAWLVooLS1NkpSWlqYuXbqYgUmSIiIilJ+fr/T0dLPm0m1crLm4jYoUFRUpPz/f7gEAAOquqyY0lZWVKTY2Vn369FHnzp0lSVlZWXJxcZG3t7ddrZ+fn7KyssyaSwPTxfmLc5eryc/P108//VRhP/Hx8fLy8jIfQUFBv/sYAQCA47pqQlN0dLQOHDig999/v7ZbkSRNmzZNeXl55uPYsWO13RIAAKhGzrXdgBUxMTFas2aNtmzZoubNm5vj/v7+Ki4uVm5urt3VpuzsbPn7+5s1X3zxhd32Ln667tKaX37iLjs7W56ennJ3d6+wJ1dXV7m6uv7uYwMAAFcHh77SZBiGYmJitHLlSqWmpiokJMRuvkePHqpfv742bNhgjmVkZCgzM1OhoaGSpNDQUO3fv185OTlmTUpKijw9PdWpUyez5tJtXKy5uA0AAACHvtIUHR2tJUuW6H/+53/k4eFhrkHy8vKSu7u7vLy8FBUVpUmTJqlJkyby9PTU+PHjFRoaqptuukmSFB4erk6dOunBBx9UQkKCsrKyNH36dEVHR5tXih599FG99tprmjp1qh5++GGlpqbqgw8+0EcffVRrxw4AAByLQ19pWrhwofLy8vTHP/5RAQEB5mPZsmVmzcsvv6w777xTQ4cOVb9+/eTv768VK1aY805OTlqzZo2cnJwUGhqqBx54QA899JCeffZZsyYkJEQfffSRUlJS1K1bN82dO1d///vfFRERUaPHCwAAHNdVdZ8mR8Z9mgAAuPrU2fs0AQAA1BZCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoekXXn/9dbVs2VJubm7q3bu3vvjii9puCQAAOABC0yWWLVumSZMmacaMGdq1a5e6deumiIgI5eTk1HZrAACglhGaLjFv3jyNGTNGo0ePVqdOnZSYmKgGDRronXfeqe3WAABALSM0/a/i4mLt3LlTYWFh5li9evUUFhamtLS0cvVFRUXKz8+3ewAAgLrLubYbcBQ//PCDSktL5efnZzfu5+enr7/+ulx9fHy8Zs2aVW68rKiw2noEAABV6+J/tw3D+M1aQtMVmjZtmiZNmmQ+//7779WpUyd9+9KztdgVAAC4EmfPnpWXl9dlawhN/8vHx0dOTk7Kzs62G8/Ozpa/v3+5eldXV7m6uprPGzVqpGPHjsnDw0M2m63a+0XVyM/PV1BQkI4dOyZPT8/abgcWcM6uPpyzq9O1ct4Mw9DZs2cVGBj4m7WEpv/l4uKiHj16aMOGDRo0aJAkqaysTBs2bFBMTMxvvr5evXpq3rx5NXeJ6uLp6Vmn/yjURZyzqw/n7Op0LZy337rCdBGh6RKTJk3SyJEj1bNnT/2///f/9Morr+jcuXMaPXp0bbcGAABqGaHpEvfdd59OnTqluLg4ZWVl6YYbblBycnK5xeEAAODaQ2j6hZiYGEtvx6FucHV11YwZM+zWp8Gxcc6uPpyzqxPnrTybYeUzdgAAANc4bm4JAABgAaEJAADAAkITAACABYQmAAAACwhNqPNmzpwpm81m9+jQoYM5X1hYqOjoaDVt2lSNGjXS0KFDy90ZHtVry5YtuuuuuxQYGCibzaZVq1bZzRuGobi4OAUEBMjd3V1hYWE6ePCgXc3p06c1YsQIeXp6ytvbW1FRUSooKKjBo7j2/NZ5GzVqVLl/e5GRkXY1nLeaFR8fr169esnDw0O+vr4aNGiQMjIy7Gqs/E3MzMzUwIED1aBBA/n6+mrKlCm6cOFCTR5KrSA04Zpw/fXX6+TJk+bj008/NecmTpyo//znP1q+fLk2b96sEydOaMiQIbXY7bXn3Llz6tatm15//fUK5xMSErRgwQIlJiZq27ZtatiwoSIiIlRY+H9fkD1ixAilp6crJSVFa9as0ZYtWzR27NiaOoRr0m+dN0mKjIy0+7e3dOlSu3nOW83avHmzoqOj9fnnnyslJUUlJSUKDw/XuXPnzJrf+ptYWlqqgQMHqri4WFu3btXixYuVlJSkuLi42jikmmUAddyMGTOMbt26VTiXm5tr1K9f31i+fLk59tVXXxmSjLS0tBrqEJeSZKxcudJ8XlZWZvj7+xsvvfSSOZabm2u4uroaS5cuNQzDML788ktDkrF9+3azZt26dYbNZjO+//77Guv9WvbL82YYhjFy5Ejj7rvv/tXXcN5qX05OjiHJ2Lx5s2EY1v4mrl271qhXr56RlZVl1ixcuNDw9PQ0ioqKavYAahhXmnBNOHjwoAIDA9WqVSuNGDFCmZmZkqSdO3eqpKREYWFhZm2HDh3UokULpaWl1Va7uMTRo0eVlZVld468vLzUu3dv8xylpaXJ29tbPXv2NGvCwsJUr149bdu2rcZ7xv/ZtGmTfH191b59e40bN04//vijOcd5q315eXmSpCZNmkiy9jcxLS1NXbp0sfu2jIiICOXn5ys9Pb0Gu6953BEcdV7v3r2VlJSk9u3b6+TJk5o1a5b69u2rAwcOKCsrSy4uLvL29rZ7jZ+fn7KysmqnYdi5eB5++XVGl56jrKws+fr62s07OzurSZMmnMdaFBkZqSFDhigkJESHDx/WU089pTvuuENpaWlycnLivNWysrIyxcbGqk+fPurcubMkWfqbmJWVVeG/x4tzdRmhCXXeHXfcYf7ctWtX9e7dW8HBwfrggw/k7u5ei50BdduwYcPMn7t06aKuXbuqdevW2rRpk/r371+LnUGSoqOjdeDAAbs1nrg83p7DNcfb21vt2rXToUOH5O/vr+LiYuXm5trVZGdny9/fv3YahJ2L5+GXn9659Bz5+/srJyfHbv7ChQs6ffo059GBtGrVSj4+Pjp06JAkzlttiomJ0Zo1a7Rx40Y1b97cHLfyN9Hf37/Cf48X5+oyQhOuOQUFBTp8+LACAgLUo0cP1a9fXxs2bDDnMzIylJmZqdDQ0FrsEheFhITI39/f7hzl5+dr27Zt5jkKDQ1Vbm6udu7cadakpqaqrKxMvXv3rvGeUbHjx4/rxx9/VEBAgCTOW20wDEMxMTFauXKlUlNTFRISYjdv5W9iaGio9u/fbxd4U1JS5OnpqU6dOtXMgdSW2l6JDlS3xx9/3Ni0aZNx9OhR47PPPjPCwsIMHx8fIycnxzAMw3j00UeNFi1aGKmpqcaOHTuM0NBQIzQ0tJa7vracPXvW2L17t7F7925DkjFv3jxj9+7dxnfffWcYhmG88MILhre3t/E///M/xr59+4y7777bCAkJMX766SdzG5GRkcaNN95obNu2zfj000+Ntm3bGvfff39tHdI14XLn7ezZs8bkyZONtLQ04+jRo8Ynn3xidO/e3Wjbtq1RWFhoboPzVrPGjRtneHl5GZs2bTJOnjxpPs6fP2/W/NbfxAsXLhidO3c2wsPDjT179hjJyclGs2bNjGnTptXGIdUoQhPqvPvuu88ICAgwXFxcjOuuu8647777jEOHDpnzP/30k/HYY48ZjRs3Nho0aGAMHjzYOHnyZC12fO3ZuHGjIancY+TIkYZh/HzbgWeeecbw8/MzXF1djf79+xsZGRl22/jxxx+N+++/32jUqJHh6elpjB492jh79mwtHM2143Ln7fz580Z4eLjRrFkzo379+kZwcLAxZswYu4+pGwbnraZVdL4kGYsWLTJrrPxN/Pbbb4077rjDcHd3N3x8fIzHH3/cKCkpqeGjqXk2wzCMmr66BQAAcLVhTRMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJwFVr1KhRGjRoUI3vNykpSTabTTabTbGxsTWyz5kzZ5r7fOWVV2pknwDsOdd2AwBQEZvNdtn5GTNmaP78+aqt+/N6enoqIyNDDRs2rJH9TZ48WY8++qh69epVI/sDUB6hCYBDOnnypPnzsmXLFBcXp4yMDHOsUaNGatSoUW20JunnUFeT3+h+8XidnJxqbJ8A7PH2HACH5O/vbz68vLzMkHLx0ahRo3Jvz/3xj3/U+PHjFRsbq8aNG8vPz09vv/22zp07p9GjR8vDw0Nt2rTRunXr7PZ14MAB3XHHHWrUqJH8/Pz04IMP6ocffqh0z2+88Ybatm0rNzc3+fn56Z577jHnysrKFB8fr5CQELm7u6tbt2768MMP7V6fnp6uO++8U56envLw8FDfvn11+PDhSvcBoHoQmgDUKYsXL5aPj4+++OILjR8/XuPGjdOf//xn/eEPf9CuXbsUHh6uBx98UOfPn5ck5ebm6rbbbtONN96oHTt2KDk5WdnZ2br33nsrtd8dO3bor3/9q5599lllZGQoOTlZ/fr1M+fj4+P17rvvKjExUenp6Zo4caIeeOABbd68WZL0/fffq1+/fnJ1dVVqaqp27typhx9+WBcuXKi6Xw6A34W35wDUKd26ddP06dMlSdOmTdMLL7wgHx8fjRkzRpIUFxenhQsXat++fbrpppv02muv6cYbb9ScOXPMbbzzzjsKCgrSN998o3bt2lnab2Zmpho2bKg777xTHh4eCg4O1o033ihJKioq0pw5c/TJJ58oNDRUktSqVSt9+umnevPNN3XLLbfo9ddfl5eXl95//33Vr19fkizvG0DNIDQBqFO6du1q/uzk5KSmTZuqS5cu5pifn58kKScnR5K0d+9ebdy4scL1UYcPH7YcXG6//XYFBwerVatWioyMVGRkpAYPHqwGDRro0KFDOn/+vG6//Xa71xQXF5vBas+ePerbt68ZmAA4HkITgDrll6HDZrPZjV38VF5ZWZkkqaCgQHfddZdefPHFctsKCAiwvF8PDw/t2rVLmzZt0vr16xUXF6eZM2dq+/btKigokCR99NFHuu666+xe5+rqKklyd3e3vC8AtYPQBOCa1r17d/373/9Wy5Yt5ez8+/4kOjs7KywsTGFhYZoxY4a8vb2Vmpqq22+/Xa6ursrMzNQtt9xS4Wu7du2qxYsXq6SkhKtNgINiITiAa1p0dLROnz6t+++/X9u3b9fhw4f18ccfa/To0SotLbW8nTVr1mjBggXas2ePvvvuO7377rsqKytT+/bt5eHhocmTJ2vixIlavHixDh8+rF27dunVV1/V4sWLJUkxMTHKz8/XsGHDtGPHDh08eFD//Oc/7W6zAKB2caUJwDUtMDBQn332mZ544gmFh4erqKhIwcHBioyMVL161v+/0tvbWytWrNDMmTNVWFiotm3baunSpbr++uslSc8995yaNWum+Ph4HTlyRN7e3urevbueeuopSVLTpk2VmpqqKVOm6JZbbpGTk5NuuOEG9enTp1qOG0Dl2Yzaup0uAFylkpKSFBsbq9zc3Brfd8uWLRUbG1tjX98C4P/w9hwAXIG8vDw1atRITzzxRI3sb86cOWrUqJEyMzNrZH8AyuNKEwBU0tmzZ5WdnS3p57flfHx8qn2fp0+f1unTpyVJzZo1k5eXV7XvE4A9QhMAAIAFvD0HAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYMH/B4qIBGndjvr6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.fft import fft, ifft\n",
    "\n",
    "FourierTransformation = fft(noisy_file)  \n",
    "\n",
    "from scipy import signal\n",
    "f, t,Zxx = signal.spectrogram(noisy_file,audio_rate, nperseg = 1000)\n",
    "\n",
    "plt.pcolormesh(t, f, np.abs(Zxx), vmin=min(noisy_file), vmax=max(noisy_file))\n",
    "plt.title('STFT Magnitude')\n",
    "plt.ylabel('Frequency [Hz]')\n",
    "plt.xlabel('Time [sec]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_441627/2104435373.py:1: DeprecationWarning: scipy.linspace is deprecated and will be removed in SciPy 2.0.0, use numpy.linspace instead\n",
      "  scale = sp.linspace(0, audio_rate, len(noisy_file))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Amplitude')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scale = sp.linspace(0, audio_rate, len(noisy_file))  \n",
    "\n",
    "plt.stem(scale[0:5000], np.abs(FourierTransformation[0:5000]), 'r')    \n",
    "plt.title('Signal spectrum after FFT')  \n",
    "plt.xlabel('Frequency(Hz)')  \n",
    "plt.ylabel('Amplitude')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "GuassianNoise = np.random.rand(len(FourierTransformation))  \n",
    "\n",
    "\n",
    "NewSound =  noisy_file - GuassianNoise\n",
    "\n",
    "Frequency = audio_rate\n",
    "\n",
    "write(\"New-Sound-Added-With-Guassian-Noise.wav\", Frequency, NewSound)  \n",
    "\n",
    "b,a = signal.butter(5, 1000/(Frequency/2), btype='highpass')  \n",
    "\n",
    "filteredSignal = signal.lfilter(b,a,NewSound)  \n",
    "plt.plot(filteredSignal) # plotting the signal.  \n",
    "plt.title('Highpass Filter')  \n",
    "plt.xlabel('Frequency(Hz)')  \n",
    "plt.ylabel('Amplitude')  \n",
    "\n",
    "\n",
    "c,d = signal.butter(5, 380/(Frequency/2), btype='lowpass') # ButterWorth low-filter  \n",
    "newFilteredSignal = signal.lfilter(c,d,filteredSignal) # Applying the filter to the signal  \n",
    "plt.plot(newFilteredSignal) # plotting the signal.  \n",
    "plt.title('Lowpass Filter')  \n",
    "plt.xlabel('Frequency(Hz)')  \n",
    "plt.ylabel('Amplitude')  \n",
    "\n",
    "write(\"New-Filtered-Sound.wav\", Frequency, newFilteredSignal)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'audioprocessing'...\n",
      "warning: You appear to have cloned an empty repository.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/PrafulAradhyamth/audioprocessing.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}