diff --git a/Autoencoder.ipynb b/Autoencoder.ipynb
deleted file mode 100644
index 2ebd684..0000000
--- a/Autoencoder.ipynb
+++ /dev/null
@@ -1,445 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "b6d8016c-6a09-4717-891b-6f091c5a944e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras import layers, models\n",
-    "\n",
-    "import scipy\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "id": "7eabca9a-7e3d-49c8-b0b4-4d360dfc51a9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Test signal generator\n",
-    "\n",
-    "def signal(A, t):\n",
-    "    return A * np.sin(30*t) * np.exp(-t) + np.random.randn(t.shape[0])/2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "id": "ea8f0359-e795-4ab3-aa8a-b8fc2a8d153b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A1 = 3\n",
-    "t1 = np.linspace(0, 5, 40000)\n",
-    "s1 = signal(A1, t1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "id": "eee901c5-9be0-4439-8b4a-5b35964f613f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot(x, y, title):\n",
-    "    plt.figure()\n",
-    "    plt.plot(x, y, linewidth=0.7, c='blue')\n",
-    "    plt.title(title)\n",
-    "    plt.savefig(title + '.png')\n",
-    "    plt.show()\n",
-    "\n",
-    "plot(t1, s1, 'Example')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "id": "6681ea57-27e9-480a-ad1f-ece1749ce3dc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def data_generator(n_samples):\n",
-    "    data = np.empty([n_samples, 40000])\n",
-    "    for i in range(n_samples):\n",
-    "        data[i] = signal(A1, t1)\n",
-    "\n",
-    "    return data\n",
-    "\n",
-    "data = data_generator(200)\n",
-    "\n",
-    "# normalise\n",
-    "data = data / np.max(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "id": "c0e83f5b-9b4e-4bfd-8de4-5b45231a709f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(t1, data[10], 'Example')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "id": "8d5233d0-cf1a-4a93-9f90-979160f1a7ee",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "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: \"functional_31\"</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"functional_31\"\u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "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",
-       "│ input_layer_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40000</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ functional_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</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\">10,281,408</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ functional_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40000</span>)          │    <span style=\"color: #00af00; text-decoration-color: #00af00\">10,321,344</span> │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "</pre>\n"
-      ],
-      "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",
-       "│ input_layer_33 (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40000\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ functional_29 (\u001b[38;5;33mFunctional\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │    \u001b[38;5;34m10,281,408\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ functional_30 (\u001b[38;5;33mFunctional\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40000\u001b[0m)          │    \u001b[38;5;34m10,321,344\u001b[0m │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">20,602,752</span> (78.59 MB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m20,602,752\u001b[0m (78.59 MB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "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\">20,602,752</span> (78.59 MB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m20,602,752\u001b[0m (78.59 MB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "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"
-      ],
-      "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Define the input shape\n",
-    "input_shape = (40000,)\n",
-    "\n",
-    "# Build the encoder\n",
-    "def build_encoder(input_shape):\n",
-    "    inputs = layers.Input(shape=input_shape)\n",
-    "    encoded = layers.Dense(256, activation='relu')(inputs)  \n",
-    "    encoded = layers.Dense(128, activation='relu')(encoded)  \n",
-    "    encoded = layers.Dense(64, activation='relu')(encoded)  \n",
-    "    return models.Model(inputs, encoded)\n",
-    "\n",
-    "# Build the decoder\n",
-    "def build_decoder():\n",
-    "    encoded_inputs = layers.Input(shape=(64,))\n",
-    "    decoded = layers.Dense(128, activation='relu')(encoded_inputs)\n",
-    "    decoded = layers.Dense(256, activation='relu')(decoded)\n",
-    "    decoded = layers.Dense(40000, activation='linear')(decoded) \n",
-    "    return models.Model(encoded_inputs, decoded)\n",
-    "\n",
-    "# Build the autoencoder\n",
-    "def build_autoencoder(input_shape):\n",
-    "    encoder = build_encoder(input_shape)\n",
-    "    decoder = build_decoder()\n",
-    "    \n",
-    "    autoencoder_input = layers.Input(shape=input_shape)\n",
-    "    encoded = encoder(autoencoder_input)\n",
-    "    reconstructed = decoder(encoded)\n",
-    "    \n",
-    "    autoencoder = models.Model(autoencoder_input, reconstructed)\n",
-    "    return autoencoder\n",
-    "\n",
-    "# Create the autoencoder\n",
-    "autoencoder = build_autoencoder(input_shape)\n",
-    "\n",
-    "# Compile the autoencoder\n",
-    "autoencoder.compile(optimizer='adam', loss='mse')\n",
-    "\n",
-    "# Display architecture\n",
-    "autoencoder.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "id": "1e032edc-7aa6-4606-a9ae-10755b729c44",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 192ms/step - loss: 0.0311 - val_loss: 0.0214\n",
-      "Epoch 2/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 162ms/step - loss: 0.0179 - val_loss: 0.0134\n",
-      "Epoch 3/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 154ms/step - loss: 0.0134 - val_loss: 0.0125\n",
-      "Epoch 4/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 162ms/step - loss: 0.0122 - val_loss: 0.0118\n",
-      "Epoch 5/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 155ms/step - loss: 0.0117 - val_loss: 0.0117\n",
-      "Epoch 6/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 163ms/step - loss: 0.0116 - val_loss: 0.0116\n",
-      "Epoch 7/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 163ms/step - loss: 0.0115 - val_loss: 0.0115\n",
-      "Epoch 8/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 160ms/step - loss: 0.0113 - val_loss: 0.0114\n",
-      "Epoch 9/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 157ms/step - loss: 0.0113 - val_loss: 0.0114\n",
-      "Epoch 10/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 162ms/step - loss: 0.0113 - val_loss: 0.0114\n",
-      "Epoch 11/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 157ms/step - loss: 0.0113 - val_loss: 0.0114\n",
-      "Epoch 12/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 158ms/step - loss: 0.0113 - val_loss: 0.0114\n",
-      "Epoch 13/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 157ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 14/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 160ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 15/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 164ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 16/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 155ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 17/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 159ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 18/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 155ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 19/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 154ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 20/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 156ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 21/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 159ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 22/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 159ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 23/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 154ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 24/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 156ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 25/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 155ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 26/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 155ms/step - loss: 0.0112 - val_loss: 0.0114\n",
-      "Epoch 27/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 162ms/step - loss: 0.0111 - val_loss: 0.0114\n",
-      "Epoch 28/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 164ms/step - loss: 0.0111 - val_loss: 0.0114\n",
-      "Epoch 29/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 155ms/step - loss: 0.0111 - val_loss: 0.0114\n",
-      "Epoch 30/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 157ms/step - loss: 0.0111 - val_loss: 0.0115\n",
-      "Epoch 31/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 158ms/step - loss: 0.0111 - val_loss: 0.0114\n",
-      "Epoch 32/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 158ms/step - loss: 0.0110 - val_loss: 0.0114\n",
-      "Epoch 33/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 160ms/step - loss: 0.0110 - val_loss: 0.0114\n",
-      "Epoch 34/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 166ms/step - loss: 0.0109 - val_loss: 0.0114\n",
-      "Epoch 35/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 165ms/step - loss: 0.0109 - val_loss: 0.0114\n",
-      "Epoch 36/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 167ms/step - loss: 0.0108 - val_loss: 0.0114\n",
-      "Epoch 37/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 174ms/step - loss: 0.0108 - val_loss: 0.0114\n",
-      "Epoch 38/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 159ms/step - loss: 0.0108 - val_loss: 0.0114\n",
-      "Epoch 39/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 161ms/step - loss: 0.0107 - val_loss: 0.0115\n",
-      "Epoch 40/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 163ms/step - loss: 0.0107 - val_loss: 0.0115\n",
-      "Epoch 41/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 157ms/step - loss: 0.0107 - val_loss: 0.0115\n",
-      "Epoch 42/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 174ms/step - loss: 0.0107 - val_loss: 0.0114\n",
-      "Epoch 43/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 170ms/step - loss: 0.0105 - val_loss: 0.0115\n",
-      "Epoch 44/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 161ms/step - loss: 0.0105 - val_loss: 0.0115\n",
-      "Epoch 45/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 162ms/step - loss: 0.0104 - val_loss: 0.0115\n",
-      "Epoch 46/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 169ms/step - loss: 0.0103 - val_loss: 0.0115\n",
-      "Epoch 47/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 162ms/step - loss: 0.0103 - val_loss: 0.0115\n",
-      "Epoch 48/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 169ms/step - loss: 0.0102 - val_loss: 0.0115\n",
-      "Epoch 49/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 163ms/step - loss: 0.0101 - val_loss: 0.0116\n",
-      "Epoch 50/50\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 164ms/step - loss: 0.0101 - val_loss: 0.0116\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<keras.src.callbacks.history.History at 0x255cb756630>"
-      ]
-     },
-     "execution_count": 156,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "autoencoder.fit(data, data, epochs=50, batch_size=50, validation_split=0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "fc11a70e-b56e-4595-8b2c-0de8d56b9838",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n"
-     ]
-    }
-   ],
-   "source": [
-    "reconstructed = autoencoder.predict(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "id": "9dccc169-5d08-420a-a962-c654cf129efd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "plt.plot(t1, data[0], c='blue', label='Original signal', linewidth=0.7)\n",
-    "plt.plot(t1, reconstructed[0], c='red', label='Reconstructed signal', linewidth=0.7)\n",
-    "plt.legend(loc='best')\n",
-    "plt.title('Reconstruction example')\n",
-    "plt.savefig('Reconstruction_ex.png')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5ece85ed-eb24-4a24-b1c4-f0d3e16d95ca",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Classifier.ipynb b/Classifier.ipynb
deleted file mode 100644
index 1e14f4c..0000000
--- a/Classifier.ipynb
+++ /dev/null
@@ -1,373 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "9f49724a-a1cc-4973-8919-68d31c6186f1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# imports\n",
-    "\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras import layers, models\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "import scipy\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "id": "8a233e36-5810-4cfb-b6d1-452acf432793",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# three types of fake data for testing\n",
-    "\n",
-    "def signal1(A, t):\n",
-    "    return A * np.sin(30*t) * np.exp(-t) + np.random.randn(t.shape[0])\n",
-    "\n",
-    "def signal2(A, t):\n",
-    "    return A * np.sin(30.5*t) * np.exp(-t)+ np.random.randn(t.shape[0])\n",
-    "\n",
-    "def signal3(A, t):\n",
-    "    return A * np.sin(31*t) * np.exp(-t) + np.random.randn(t.shape[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "id": "f9d9086a-538e-4410-afd3-333093722ff1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# tests\n",
-    "A = 2\n",
-    "t = np.linspace(0.0001, 5, 40000)\n",
-    "s1 = signal1(A, t)\n",
-    "s2 = signal2(A, t)\n",
-    "s3 = signal3(A, t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "d7510935-dd2c-4484-96b5-eefec9837f33",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot fn for convenience\n",
-    "def plot(x, y, title):\n",
-    "    plt.figure()\n",
-    "    plt.plot(x, y, linewidth=0.7, c='blue')\n",
-    "    plt.title(title)\n",
-    "    plt.savefig(title + '.png')\n",
-    "    plt.show()\n",
-    "\n",
-    "plot(t, s1, 'Example1')\n",
-    "plot(t, s2, 'Example2')\n",
-    "plot(t, s3, 'Example3')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "3cfb129b-0ce8-4790-94ae-cb31fa3e0ec4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Number of samples and length of each signal\n",
-    "n_samples = 200\n",
-    "signal_length = 40000\n",
-    "\n",
-    "# Initialize arrays\n",
-    "X_data = np.zeros((n_samples, signal_length))\n",
-    "y_labels = np.zeros(n_samples)\n",
-    "\n",
-    "# time array\n",
-    "t = np.linspace(0, 1, signal_length)\n",
-    "\n",
-    "# Generate samples\n",
-    "for i in range(n_samples):\n",
-    "    if i < n_samples // 3:\n",
-    "        X_data[i] = signal1(A, t)\n",
-    "        y_labels[i] = 0\n",
-    "    elif i < 2 * n_samples // 3:\n",
-    "        X_data[i] = signal2(A, t)\n",
-    "        y_labels[i] = 1\n",
-    "    else:\n",
-    "        X_data[i] = signal3(A, t)\n",
-    "        y_labels[i] = 2\n",
-    "\n",
-    "# One-hot encode the labels\n",
-    "y_labels_one_hot = to_categorical(y_labels, num_classes=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "092a184e-a79f-462a-8017-741471a05ae9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "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: \"functional_14\"</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"functional_14\"\u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "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",
-       "│ input_layer_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40000</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_56 (<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\">256</span>)            │    <span style=\"color: #00af00; text-decoration-color: #00af00\">10,240,256</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_57 (<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\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_58 (<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\">8,256</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_59 (<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\">3</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">195</span> │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "</pre>\n"
-      ],
-      "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",
-       "│ input_layer_14 (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40000\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_56 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │    \u001b[38;5;34m10,240,256\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_57 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m32,896\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_58 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_59 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)              │           \u001b[38;5;34m195\u001b[0m │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">10,281,603</span> (39.22 MB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m10,281,603\u001b[0m (39.22 MB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "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\">10,281,603</span> (39.22 MB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m10,281,603\u001b[0m (39.22 MB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "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"
-      ],
-      "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "input_shape = (40000,)\n",
-    "\n",
-    "# Build the classifier model\n",
-    "def build_classifier(input_shape):\n",
-    "    inputs = layers.Input(shape=input_shape)\n",
-    "    x = layers.Dense(256, activation='relu')(inputs)\n",
-    "    x = layers.Dense(128, activation='relu')(x)\n",
-    "    x = layers.Dense(64, activation='relu')(x)\n",
-    "    \n",
-    "    # Output layer (one hot encoding)\n",
-    "    outputs = layers.Dense(3, activation='softmax')(x)\n",
-    "    \n",
-    "    model = models.Model(inputs, outputs)\n",
-    "    return model\n",
-    "\n",
-    "classifier = build_classifier(input_shape)\n",
-    "\n",
-    "# Compile the model\n",
-    "classifier.compile(optimizer='adam', \n",
-    "                   loss='categorical_crossentropy', \n",
-    "                   metrics=['CategoricalAccuracy'])\n",
-    "\n",
-    "# Display model\n",
-    "classifier.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "ec34ffe0-d4ed-484d-b82a-c3270083f082",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/10\n",
-      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 103ms/step - CategoricalAccuracy: 0.4166 - loss: 9.3746 - val_CategoricalAccuracy: 0.0000e+00 - val_loss: 62.7976\n",
-      "Epoch 2/10\n",
-      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - CategoricalAccuracy: 0.5618 - loss: 10.2041 - val_CategoricalAccuracy: 0.6750 - val_loss: 0.6425\n",
-      "Epoch 3/10\n",
-      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - CategoricalAccuracy: 0.6952 - loss: 1.6685 - val_CategoricalAccuracy: 1.0000 - val_loss: 0.0000e+00\n",
-      "Epoch 4/10\n",
-      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - CategoricalAccuracy: 0.9716 - loss: 0.0921 - val_CategoricalAccuracy: 1.0000 - val_loss: 1.3485e-05\n",
-      "Epoch 5/10\n",
-      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - CategoricalAccuracy: 0.9812 - loss: 0.0438 - val_CategoricalAccuracy: 0.3000 - val_loss: 1.4833\n",
-      "Epoch 6/10\n",
-      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - CategoricalAccuracy: 0.9966 - loss: 0.0041 - val_CategoricalAccuracy: 0.2500 - val_loss: 1.5841\n",
-      "Epoch 7/10\n",
-      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - CategoricalAccuracy: 1.0000 - loss: 1.0457e-06 - val_CategoricalAccuracy: 1.0000 - val_loss: 8.7625e-04\n",
-      "Epoch 8/10\n",
-      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - CategoricalAccuracy: 1.0000 - loss: 1.6579e-05 - val_CategoricalAccuracy: 1.0000 - val_loss: 2.3305e-06\n",
-      "Epoch 9/10\n",
-      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - CategoricalAccuracy: 1.0000 - loss: 9.9684e-04 - val_CategoricalAccuracy: 1.0000 - val_loss: 5.0664e-08\n",
-      "Epoch 10/10\n",
-      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - CategoricalAccuracy: 1.0000 - loss: 0.0012 - val_CategoricalAccuracy: 1.0000 - val_loss: 2.9802e-09\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<keras.src.callbacks.history.History at 0x28680244e60>"
-      ]
-     },
-     "execution_count": 136,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# training\n",
-    "classifier.fit(X_data, y_labels_one_hot, epochs=10, batch_size=32, validation_split=0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "id": "2bd36515-2777-4d49-82ec-b50f1e0904a0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.0000000e+00, 1.0902163e-08, 1.0000000e+00]], dtype=float32)"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# test on newly generated data\n",
-    "test = signal3(A, t)\n",
-    "test = np.reshape(test, (1, 40000))\n",
-    "\n",
-    "classifier.predict(test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "931bb66b-7171-4630-9753-d9ff7fa3cb78",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/preprocessed/lunar/data/Autoencoder.ipynb b/preprocessed/lunar/data/Autoencoder.ipynb
new file mode 100644
index 0000000..96dedc1
--- /dev/null
+++ b/preprocessed/lunar/data/Autoencoder.ipynb
@@ -0,0 +1,416 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b6d8016c-6a09-4717-891b-6f091c5a944e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras import layers, models\n",
+    "\n",
+    "import scipy\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "from obspy import read\n",
+    "from datetime import datetime, timedelta\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7eabca9a-7e3d-49c8-b0b4-4d360dfc51a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This section is to test the autoencoder - it generates data that is somewhat similar to the real data\n",
+    "\n",
+    "# Test signal generator\n",
+    "\n",
+    "# def signal(A, t):\n",
+    "#     return A * np.sin(30*t) * np.exp(-t) + np.random.randn(t.shape[0])/2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ea8f0359-e795-4ab3-aa8a-b8fc2a8d153b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A1 = 3\n",
+    "# t1 = np.linspace(0, 5, 40000)\n",
+    "# s1 = signal(A1, t1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6681ea57-27e9-480a-ad1f-ece1749ce3dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def data_generator(n_samples):\n",
+    "#     data = np.empty([n_samples, 40000])\n",
+    "#     for i in range(n_samples):\n",
+    "#         data[i] = signal(A1, t1)\n",
+    "\n",
+    "#     return data\n",
+    "\n",
+    "# data = data_generator(200)\n",
+    "\n",
+    "# # normalise\n",
+    "# data = data / np.max(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92594999-3a15-4e0f-aff6-8b7b31860c71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convenience plot function\n",
+    "def plot(x, y, title):\n",
+    "    plt.figure()\n",
+    "    plt.plot(x, y, linewidth=0.7, c='blue')\n",
+    "    plt.title(title)\n",
+    "    plt.savefig(title + '.png')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "c0e83f5b-9b4e-4bfd-8de4-5b45231a709f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(46376,)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# test_filename = 'xa.s12.00.mhz.1970-03-25HR00_evid00003_trimmed_7000_sec'\n",
+    "\n",
+    "data_directory = './'\n",
+    "mseed_file = f'{data_directory}{test_filename}.mseed'\n",
+    "st = read(mseed_file)\n",
+    "st\n",
+    "\n",
+    "tr = st.traces[0].copy()\n",
+    "tr_times = tr.times()\n",
+    "tr_data = tr.data\n",
+    "\n",
+    "# plot(tr_times, tr_data, 'Mseed Example')\n",
+    "\n",
+    "print(tr_times.shape)\n",
+    "\n",
+    "def read_all_mseed_files(data_directory, target_length=None):\n",
+    "    # List all files in the directory with \".mseed\" extension\n",
+    "    mseed_files = [f for f in os.listdir(data_directory) if f.endswith('.mseed')]\n",
+    "    \n",
+    "    data_matrix = []\n",
+    "    \n",
+    "    # Loop through all the mseed files and extract time and data series\n",
+    "    for filename in mseed_files:\n",
+    "        st = read(os.path.join(data_directory, filename))\n",
+    "        tr = st.traces[0].copy()  \n",
+    "        tr_data = tr.data  \n",
+    "        \n",
+    "        if target_length is None:\n",
+    "            target_length = len(tr_data)  # Set target length to the first trace's length\n",
+    "        \n",
+    "        # Pad or trim the data to the target length\n",
+    "        if len(tr_data) < target_length:\n",
+    "            # Pad with zeros if shorter\n",
+    "            tr_data = np.pad(tr_data, (0, target_length - len(tr_data)), mode='constant')\n",
+    "        else:\n",
+    "            # Trim if longer\n",
+    "            tr_data = tr_data[:target_length]\n",
+    "        \n",
+    "        data_matrix.append(tr_data)\n",
+    "    \n",
+    "    # Convert the list to a numpy matrix\n",
+    "    data_matrix = np.array(data_matrix)\n",
+    "    \n",
+    "    return data_matrix\n",
+    "\n",
+    "data = read_all_mseed_files(data_directory, 46376)\n",
+    "\n",
+    "plot(tr_times, data[3], 'Example 3')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "8d5233d0-cf1a-4a93-9f90-979160f1a7ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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: \"functional_6\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"functional_6\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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",
+       "│ input_layer_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46376</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ functional_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │    <span style=\"color: #00af00; text-decoration-color: #00af00\">48,080,512</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ functional_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46376</span>)          │    <span style=\"color: #00af00; text-decoration-color: #00af00\">48,126,760</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "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",
+       "│ input_layer_7 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46376\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ functional_4 (\u001b[38;5;33mFunctional\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │    \u001b[38;5;34m48,080,512\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ functional_5 (\u001b[38;5;33mFunctional\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46376\u001b[0m)          │    \u001b[38;5;34m48,126,760\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">96,207,272</span> (367.00 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m96,207,272\u001b[0m (367.00 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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\">96,207,272</span> (367.00 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m96,207,272\u001b[0m (367.00 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define the input shape\n",
+    "input_shape = (46376,)\n",
+    "\n",
+    "# Build the encoder\n",
+    "def build_encoder(input_shape):\n",
+    "    inputs = layers.Input(shape=input_shape)\n",
+    "    encoded = layers.Dense(1024, activation='relu')(inputs)  \n",
+    "    encoded = layers.Dense(512, activation='relu')(encoded)  \n",
+    "    encoded = layers.Dense(128, activation='relu')(encoded)  \n",
+    "    return models.Model(inputs, encoded)\n",
+    "\n",
+    "# Build the decoder\n",
+    "def build_decoder():\n",
+    "    encoded_inputs = layers.Input(shape=(128,))\n",
+    "    decoded = layers.Dense(512, activation='relu')(encoded_inputs)\n",
+    "    decoded = layers.Dense(1024, activation='relu')(decoded)\n",
+    "    decoded = layers.Dense(46376, activation='linear')(decoded) \n",
+    "    return models.Model(encoded_inputs, decoded)\n",
+    "\n",
+    "# Build the autoencoder\n",
+    "def build_autoencoder(input_shape):\n",
+    "    encoder = build_encoder(input_shape)\n",
+    "    decoder = build_decoder()\n",
+    "    \n",
+    "    autoencoder_input = layers.Input(shape=input_shape)\n",
+    "    encoded = encoder(autoencoder_input)\n",
+    "    reconstructed = decoder(encoded)\n",
+    "    \n",
+    "    autoencoder = models.Model(autoencoder_input, reconstructed)\n",
+    "    return autoencoder\n",
+    "\n",
+    "# Create the autoencoder\n",
+    "autoencoder = build_autoencoder(input_shape)\n",
+    "\n",
+    "# Compile the autoencoder\n",
+    "autoencoder.compile(optimizer='adam', loss='mse')\n",
+    "\n",
+    "# Display architecture\n",
+    "autoencoder.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "1e032edc-7aa6-4606-a9ae-10755b729c44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 638ms/step - loss: 1.7035e-17 - val_loss: 2.1754e-17\n",
+      "Epoch 2/10\n",
+      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 609ms/step - loss: 2.0816e-17 - val_loss: 2.1793e-17\n",
+      "Epoch 3/10\n",
+      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 607ms/step - loss: 1.4756e-17 - val_loss: 2.1855e-17\n",
+      "Epoch 4/10\n",
+      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 610ms/step - loss: 9.2580e-18 - val_loss: 2.1905e-17\n",
+      "Epoch 5/10\n",
+      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 611ms/step - loss: 2.1401e-17 - val_loss: 2.1988e-17\n",
+      "Epoch 6/10\n",
+      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 634ms/step - loss: 1.5923e-17 - val_loss: 2.2044e-17\n",
+      "Epoch 7/10\n",
+      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 621ms/step - loss: 1.0468e-17 - val_loss: 2.2061e-17\n",
+      "Epoch 8/10\n",
+      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 631ms/step - loss: 1.0075e-17 - val_loss: 2.2054e-17\n",
+      "Epoch 9/10\n",
+      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 606ms/step - loss: 1.5476e-17 - val_loss: 2.2053e-17\n",
+      "Epoch 10/10\n",
+      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 625ms/step - loss: 1.9096e-17 - val_loss: 2.2073e-17\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x1f87f14b620>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "autoencoder.fit(data, data, epochs=10, batch_size=10, validation_split=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "fc11a70e-b56e-4595-8b2c-0de8d56b9838",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "reconstructed = autoencoder.predict(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "9dccc169-5d08-420a-a962-c654cf129efd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(tr_times, data[0], c='blue', label='Original signal', linewidth=0.7)\n",
+    "plt.plot(tr_times, reconstructed[0], c='red', label='Reconstructed signal', linewidth=0.7)\n",
+    "plt.legend(loc='best')\n",
+    "plt.title('Reconstruction example')\n",
+    "plt.savefig('Reconstruction_ex.png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ece85ed-eb24-4a24-b1c4-f0d3e16d95ca",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/preprocessed/lunar/data/Classifier.ipynb b/preprocessed/lunar/data/Classifier.ipynb
new file mode 100644
index 0000000..9bb6ccd
--- /dev/null
+++ b/preprocessed/lunar/data/Classifier.ipynb
@@ -0,0 +1,452 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9f49724a-a1cc-4973-8919-68d31c6186f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras import layers, models\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "import scipy\n",
+    "import pandas as pd\n",
+    "from obspy import read\n",
+    "from datetime import datetime, timedelta\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8a233e36-5810-4cfb-b6d1-452acf432793",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# three types of fake data for testing\n",
+    "\n",
+    "# def signal1(A, t):\n",
+    "#     return A * np.sin(30*t) * np.exp(-t) + np.random.randn(t.shape[0])\n",
+    "\n",
+    "# def signal2(A, t):\n",
+    "#     return A * np.sin(30.5*t) * np.exp(-t)+ np.random.randn(t.shape[0])\n",
+    "\n",
+    "# def signal3(A, t):\n",
+    "#     return A * np.sin(31*t) * np.exp(-t) + np.random.randn(t.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f9d9086a-538e-4410-afd3-333093722ff1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tests\n",
+    "A = 2\n",
+    "# t = np.linspace(0.0001, 5, 40000)\n",
+    "# s1 = signal1(A, t)\n",
+    "# s2 = signal2(A, t)\n",
+    "# s3 = signal3(A, t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d7510935-dd2c-4484-96b5-eefec9837f33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot fn for convenience\n",
+    "def plot(x, y, title):\n",
+    "    plt.figure()\n",
+    "    plt.plot(x, y, linewidth=0.7, c='blue')\n",
+    "    plt.title(title)\n",
+    "    plt.savefig(title + '.png')\n",
+    "    plt.show()\n",
+    "\n",
+    "# plot(t, s1, 'Example1')\n",
+    "# plot(t, s2, 'Example2')\n",
+    "# plot(t, s3, 'Example3')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3cfb129b-0ce8-4790-94ae-cb31fa3e0ec4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Number of samples and length of each signal\n",
+    "# n_samples = 200\n",
+    "# signal_length = 40000\n",
+    "\n",
+    "# # Initialize arrays\n",
+    "# X_data = np.zeros((n_samples, signal_length))\n",
+    "# y_labels = np.zeros(n_samples)\n",
+    "\n",
+    "# # time array\n",
+    "# t = np.linspace(0, 1, signal_length)\n",
+    "\n",
+    "# # Generate samples\n",
+    "# for i in range(n_samples):\n",
+    "#     if i < n_samples // 3:\n",
+    "#         X_data[i] = signal1(A, t)\n",
+    "#         y_labels[i] = 0\n",
+    "#     elif i < 2 * n_samples // 3:\n",
+    "#         X_data[i] = signal2(A, t)\n",
+    "#         y_labels[i] = 1\n",
+    "#     else:\n",
+    "#         X_data[i] = signal3(A, t)\n",
+    "#         y_labels[i] = 2\n",
+    "\n",
+    "# # One-hot encode the labels\n",
+    "# y_labels_one_hot = to_categorical(y_labels, num_classes=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "f09275a3-2673-4d61-9903-8719b9e1b161",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_filename = 'xa.s12.00.mhz.1970-03-25HR00_evid00003_trimmed_7000_sec'\n",
+    "\n",
+    "data_directory = './'\n",
+    "mseed_file = f'{data_directory}{test_filename}.mseed'\n",
+    "st = read(mseed_file)\n",
+    "st\n",
+    "\n",
+    "tr = st.traces[0].copy()\n",
+    "tr_times = tr.times()\n",
+    "tr_data = tr.data\n",
+    "\n",
+    "# plot(tr_times, tr_data, 'Mseed Example')\n",
+    "\n",
+    "# print(tr_times.shape)\n",
+    "\n",
+    "def read_all_mseed_files(data_directory, target_length=None):\n",
+    "    # List all files in the directory with \".mseed\" extension\n",
+    "    mseed_files = [f for f in os.listdir(data_directory) if f.endswith('.mseed')]\n",
+    "    \n",
+    "    data_matrix = []\n",
+    "    \n",
+    "    # Loop through all the mseed files and extract time and data series\n",
+    "    for filename in mseed_files:\n",
+    "        st = read(os.path.join(data_directory, filename))\n",
+    "        tr = st.traces[0].copy()  \n",
+    "        tr_data = tr.data  \n",
+    "        \n",
+    "        if target_length is None:\n",
+    "            target_length = len(tr_data)  # Set target length to the first trace's length\n",
+    "        \n",
+    "        # Pad or trim the data to the target length\n",
+    "        if len(tr_data) < target_length:\n",
+    "            # Pad with zeros if shorter\n",
+    "            tr_data = np.pad(tr_data, (0, target_length - len(tr_data)), mode='constant')\n",
+    "        else:\n",
+    "            # Trim if longer\n",
+    "            tr_data = tr_data[:target_length]\n",
+    "        \n",
+    "        data_matrix.append(tr_data)\n",
+    "    \n",
+    "    # Convert the list to a numpy matrix\n",
+    "    data_matrix = np.array(data_matrix)\n",
+    "    \n",
+    "    return data_matrix\n",
+    "\n",
+    "X_data = read_all_mseed_files(data_directory, 46376)\n",
+    "\n",
+    "# plot(tr_times, data[3], 'Example 3')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "47c0ecea-e0fa-4ebf-99db-f6c8e77a72e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 0., 0.])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "\n",
+    "# read csv\n",
+    "df = pd.read_csv('catalog.csv', header=None, names=['data'], skiprows=1)\n",
+    "\n",
+    "# split cells\n",
+    "df['label'] = df['data'].apply(lambda x: x.split(',')[-1].strip())\n",
+    "\n",
+    "# Step 3: One-hot encode the labels\n",
+    "encoder = OneHotEncoder(sparse_output=False)\n",
+    "y_label = encoder.fit_transform(df['label'].values.reshape(-1, 1))\n",
+    "\n",
+    "\n",
+    "\n",
+    "y_label[4]\n",
+    "\n",
+    "# one-hot format: [deep, impact, shallow]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "092a184e-a79f-462a-8017-741471a05ae9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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: \"functional_2\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"functional_2\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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",
+       "│ input_layer_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46376</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (<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\">256</span>)            │    <span style=\"color: #00af00; text-decoration-color: #00af00\">11,872,512</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (<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\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_10 (<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\">8,256</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_11 (<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\">3</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">195</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "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",
+       "│ input_layer_2 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46376\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │    \u001b[38;5;34m11,872,512\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m32,896\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)              │           \u001b[38;5;34m195\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">11,913,859</span> (45.45 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m11,913,859\u001b[0m (45.45 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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\">11,913,859</span> (45.45 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m11,913,859\u001b[0m (45.45 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "input_shape = (46376,)\n",
+    "\n",
+    "# Build the classifier model\n",
+    "def build_classifier(input_shape):\n",
+    "    inputs = layers.Input(shape=input_shape)\n",
+    "    x = layers.Dense(256, activation='relu')(inputs)\n",
+    "    x = layers.Dense(128, activation='relu')(x)\n",
+    "    x = layers.Dense(64, activation='relu')(x)\n",
+    "    \n",
+    "    # Output layer (one hot encoding)\n",
+    "    outputs = layers.Dense(3, activation='softmax')(x)\n",
+    "    \n",
+    "    model = models.Model(inputs, outputs)\n",
+    "    return model\n",
+    "\n",
+    "classifier = build_classifier(input_shape)\n",
+    "\n",
+    "# Compile the model\n",
+    "classifier.compile(optimizer='adam', \n",
+    "                   loss='categorical_crossentropy', \n",
+    "                   metrics=['CategoricalAccuracy'])\n",
+    "\n",
+    "# Display model\n",
+    "classifier.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "ec34ffe0-d4ed-484d-b82a-c3270083f082",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 374ms/step - CategoricalAccuracy: 0.3743 - loss: 1.0963 - val_CategoricalAccuracy: 0.8750 - val_loss: 1.0815\n",
+      "Epoch 2/10\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - CategoricalAccuracy: 0.8576 - loss: 1.0794 - val_CategoricalAccuracy: 0.8750 - val_loss: 1.0604\n",
+      "Epoch 3/10\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - CategoricalAccuracy: 0.8160 - loss: 1.0603 - val_CategoricalAccuracy: 0.8750 - val_loss: 1.0354\n",
+      "Epoch 4/10\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - CategoricalAccuracy: 0.8576 - loss: 1.0334 - val_CategoricalAccuracy: 0.8750 - val_loss: 1.0057\n",
+      "Epoch 5/10\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - CategoricalAccuracy: 0.8264 - loss: 1.0080 - val_CategoricalAccuracy: 0.8750 - val_loss: 0.9703\n",
+      "Epoch 6/10\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - CategoricalAccuracy: 0.8264 - loss: 0.9747 - val_CategoricalAccuracy: 0.8750 - val_loss: 0.9285\n",
+      "Epoch 7/10\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - CategoricalAccuracy: 0.8264 - loss: 0.9361 - val_CategoricalAccuracy: 0.8750 - val_loss: 0.8798\n",
+      "Epoch 8/10\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - CategoricalAccuracy: 0.8368 - loss: 0.8870 - val_CategoricalAccuracy: 0.8750 - val_loss: 0.8243\n",
+      "Epoch 9/10\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - CategoricalAccuracy: 0.8368 - loss: 0.8350 - val_CategoricalAccuracy: 0.8750 - val_loss: 0.7626\n",
+      "Epoch 10/10\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - CategoricalAccuracy: 0.8368 - loss: 0.7783 - val_CategoricalAccuracy: 0.8750 - val_loss: 0.6969\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x179aad80c50>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# training\n",
+    "classifier.fit(X_data, y_label, epochs=10, batch_size=32, validation_split=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "2bd36515-2777-4d49-82ec-b50f1e0904a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "[[0.23078983 0.55988955 0.20932065]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test\n",
+    "\n",
+    "test = X_data[8]\n",
+    "test = test.reshape(1, -1)\n",
+    "\n",
+    "# Now call predict\n",
+    "prediction = classifier.predict(test)\n",
+    "\n",
+    "print(prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "931bb66b-7171-4630-9753-d9ff7fa3cb78",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "077d77a2-c3bc-4606-b4bb-f420ff123817",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "711329a8-0b67-4afe-b455-3f8a296a622e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/preprocessed/lunar/data/catalog.csv b/preprocessed/lunar/data/catalog.csv
new file mode 100644
index 0000000..4a45425
--- /dev/null
+++ b/preprocessed/lunar/data/catalog.csv
@@ -0,0 +1,77 @@
+filename,time_abs(%Y-%m-%dT%H:%M:%S.%f),time_rel(sec),evid,mq_type
+xa.s12.00.mhz.1970-01-19HR00_evid00002,1970-01-19T20:25:00.000000,73500.0,evid00002,impact_mq
+xa.s12.00.mhz.1970-03-25HR00_evid00003,1970-03-25T03:32:00.000000,12720.0,evid00003,impact_mq
+xa.s12.00.mhz.1970-03-26HR00_evid00004,1970-03-26T20:17:00.000000,73020.0,evid00004,impact_mq
+xa.s12.00.mhz.1970-04-25HR00_evid00006,1970-04-25T01:14:00.000000,4440.0,evid00006,impact_mq
+xa.s12.00.mhz.1970-04-26HR00_evid00007,1970-04-26T14:29:00.000000,52140.0,evid00007,deep_mq
+xa.s12.00.mhz.1970-06-15HR00_evid00008,1970-06-15T19:00:00.000000,68400.0,evid00008,impact_mq
+xa.s12.00.mhz.1970-06-26HR00_evid00009,1970-06-26T20:01:00.000000,72060.0,evid00009,impact_mq
+xa.s12.00.mhz.1970-07-20HR00_evid00010,1970-07-20T05:06:00.000000,18360.0,evid00010,impact_mq
+xa.s12.00.mhz.1970-07-20HR00_evid00011,1970-07-20T11:44:00.000000,42240.0,evid00011,deep_mq
+xa.s12.00.mhz.1970-09-26HR00_evid00013,1970-09-26T19:57:00.000000,71820.0,evid00013,impact_mq
+xa.s12.00.mhz.1970-10-24HR00_evid00014,1970-10-24T11:31:00.000000,41460.0,evid00014,impact_mq
+xa.s12.00.mhz.1970-11-12HR00_evid00015,1970-11-12T12:50:00.000000,46200.0,evid00015,impact_mq
+xa.s12.00.mhz.1970-12-11HR00_evid00017,1970-12-11T07:22:00.000000,26520.0,evid00017,impact_mq
+xa.s12.00.mhz.1970-12-27HR00_evid00019,1970-12-27T20:34:00.000000,74040.0,evid00019,deep_mq
+xa.s12.00.mhz.1970-12-31HR00_evid00021,1970-12-31T15:41:00.000000,56460.0,evid00021,deep_mq
+xa.s12.00.mhz.1971-01-15HR00_evid00022,1971-01-15T12:40:00.000000,45600.0,evid00022,impact_mq
+xa.s12.00.mhz.1971-01-28HR00_evid00023,1971-01-28T14:59:00.000000,53940.0,evid00023,deep_mq
+xa.s12.00.mhz.1971-01-29HR00_evid00024,1971-01-29T18:21:00.000000,66060.0,evid00024,impact_mq
+xa.s12.00.mhz.1971-02-09HR00_evid00026,1971-02-09T03:42:00.000000,13320.0,evid00026,impact_mq
+xa.s12.00.mhz.1971-03-25HR00_evid00028,1971-03-25T15:18:00.000000,55080.0,evid00028,impact_mq
+xa.s12.00.mhz.1971-04-13HR00_evid00029,1971-04-13T12:55:00.000000,46500.0,evid00029,impact_mq
+xa.s12.00.mhz.1971-04-17HR00_evid00030,1971-04-17T07:04:00.000000,25440.0,evid00030,shallow_mq
+xa.s12.00.mhz.1971-05-12HR00_evid00031,1971-05-12T08:05:00.000000,29100.0,evid00031,impact_mq
+xa.s12.00.mhz.1971-05-12HR00_evid00032,1971-05-12T09:45:00.000000,35100.0,evid00032,impact_mq
+xa.s12.00.mhz.1971-05-13HR00_evid00033,1971-05-13T03:00:00.000000,10800.0,evid00033,impact_mq
+xa.s12.00.mhz.1971-05-23HR00_evid00034,1971-05-23T22:20:00.000000,80400.0,evid00034,impact_mq
+xa.s12.00.mhz.1971-06-12HR00_evid00035,1971-06-12T10:51:00.000000,39060.0,evid00035,impact_mq
+xa.s12.00.mhz.1971-09-25HR00_evid00042,1971-09-25T08:57:00.000000,32220.0,evid00042,impact_mq
+xa.s12.00.mhz.1971-10-18HR00_evid00043,1971-10-18T03:19:00.000000,11940.0,evid00043,impact_mq
+xa.s12.00.mhz.1971-10-20HR00_evid00044,1971-10-20T18:08:00.000000,65280.0,evid00044,impact_mq
+xa.s12.00.mhz.1971-10-31HR00_evid00045,1971-10-31T05:30:00.000000,19800.0,evid00045,impact_mq
+xa.s12.00.mhz.1971-11-14HR00_evid00046,1971-11-14T04:17:00.000000,15420.0,evid00046,impact_mq
+xa.s12.00.mhz.1972-01-04HR00_evid00049,1972-01-04T06:35:00.000000,23700.0,evid00049,impact_mq
+xa.s12.00.mhz.1972-03-12HR00_evid00052,1972-03-12T18:00:00.000000,64800.0,evid00052,impact_mq
+xa.s12.00.mhz.1972-05-11HR00_evid00055,1972-05-11T13:35:00.000000,48900.0,evid00055,impact_mq
+xa.s12.00.mhz.1972-06-16HR00_evid00060,1972-06-16T16:11:00.000000,58260.0,evid00060,impact_mq
+xa.s12.00.mhz.1972-07-17HR00_evid00067,1972-07-17T07:50:00.000000,28200.0,evid00067,impact_mq
+xa.s12.00.mhz.1972-07-17HR00_evid00068,1972-07-17T21:56:00.000000,78960.0,evid00068,impact_mq
+xa.s12.00.mhz.1972-07-28HR00_evid00070,1972-07-28T02:23:00.000000,8580.0,evid00070,impact_mq
+xa.s12.00.mhz.1972-07-31HR00_evid00071,1972-07-31T18:08:00.000000,65280.0,evid00071,impact_mq
+xa.s12.00.mhz.1972-12-02HR00_evid00083,1972-12-02T07:58:00.000000,28680.0,evid00083,impact_mq
+xa.s12.00.mhz.1972-12-03HR00_evid00084,1972-12-03T02:38:00.000000,9480.0,evid00084,impact_mq
+xa.s12.00.mhz.1973-01-18HR00_evid00088,1973-01-18T23:01:00.000000,82860.0,evid00088,impact_mq
+xa.s12.00.mhz.1973-01-31HR00_evid00091,1973-01-31T00:44:00.000000,2640.0,evid00091,impact_mq
+xa.s12.00.mhz.1973-03-01HR00_evid00093,1973-03-01T07:15:00.000000,26100.0,evid00093,deep_mq
+xa.s12.00.mhz.1973-03-13HR00_evid00094,1973-03-13T08:01:00.000000,28860.0,evid00094,shallow_mq
+xa.s12.00.mhz.1973-03-24HR00_evid00097,1973-03-24T19:23:00.000000,69780.0,evid00097,impact_mq
+xa.s12.00.mhz.1973-05-14HR00_evid00104,1973-05-14T00:44:00.000000,2640.0,evid00104,impact_mq
+xa.s12.00.mhz.1973-06-05HR00_evid00107,1973-06-05T02:38:00.000000,9480.0,evid00107,impact_mq
+xa.s12.00.mhz.1973-06-05HR00_evid00108,1973-06-05T11:10:00.000000,40200.0,evid00108,deep_mq
+xa.s12.00.mhz.1973-06-18HR00_evid00109,1973-06-18T01:42:00.000000,6120.0,evid00109,impact_mq
+xa.s12.00.mhz.1973-06-27HR00_evid00112,1973-06-27T18:36:00.000000,66960.0,evid00112,impact_mq
+xa.s12.00.mhz.1973-07-03HR00_evid00113,1973-07-03T18:15:00.000000,65700.0,evid00113,impact_mq
+xa.s12.00.mhz.1973-07-04HR00_evid00114,1973-07-04T02:46:00.000000,9960.0,evid00114,impact_mq
+xa.s12.00.mhz.1973-07-20HR00_evid00117,1973-07-20T19:02:00.000000,68520.0,evid00117,deep_mq
+xa.s12.00.mhz.1973-07-28HR00_evid00120,1973-07-28T00:52:00.000000,3120.0,evid00120,impact_mq
+xa.s12.00.mhz.1973-07-29HR00_evid00121,1973-07-29T23:31:00.000000,84660.0,evid00121,impact_mq
+xa.s12.00.mhz.1973-08-21HR00_evid00127,1973-08-21T12:17:00.000000,44220.0,evid00127,impact_mq
+xa.s12.00.mhz.1974-01-10HR00_evid00136,1974-01-10T23:21:00.000000,84060.0,evid00136,impact_mq
+xa.s12.00.mhz.1974-02-07HR00_evid00137,1974-02-07T06:21:00.000000,22860.0,evid00137,impact_mq
+xa.s12.00.mhz.1974-02-12HR00_evid00138,1974-02-12T22:30:00.000000,81000.0,evid00138,impact_mq
+xa.s12.00.mhz.1974-03-25HR00_evid00140,1974-03-25T16:58:00.000000,61080.0,evid00140,impact_mq
+xa.s12.00.mhz.1974-04-08HR00_evid00141,1974-04-08T08:40:00.000000,31200.0,evid00141,impact_mq
+xa.s12.00.mhz.1974-04-19HR00_evid00142,1974-04-19T18:34:00.000000,66840.0,evid00142,impact_mq
+xa.s12.00.mhz.1974-04-26HR00_evid00144,1974-04-26T09:18:00.000000,33480.0,evid00144,deep_mq
+xa.s12.00.mhz.1974-04-27HR00_evid00145,1974-04-27T14:18:00.000000,51480.0,evid00145,impact_mq
+xa.s12.00.mhz.1974-06-25HR00_evid00149,1974-06-25T00:23:00.000000,1380.0,evid00149,impact_mq
+xa.s12.00.mhz.1974-07-06HR00_evid00150,1974-07-06T02:57:00.000000,10620.0,evid00150,impact_mq
+xa.s12.00.mhz.1974-07-06HR00_evid00151,1974-07-06T14:14:00.000000,51240.0,evid00151,impact_mq
+xa.s12.00.mhz.1974-07-11HR00_evid00152,1974-07-11T00:52:00.000000,3120.0,evid00152,shallow_mq
+xa.s12.00.mhz.1974-07-17HR00_evid00153,1974-07-17T12:05:00.000000,43500.0,evid00153,impact_mq
+xa.s12.00.mhz.1974-10-14HR00_evid00156,1974-10-14T17:43:00.000000,63780.0,evid00156,impact_mq
+xa.s12.00.mhz.1975-04-12HR00_evid00191,1975-04-12T18:15:00.000000,65700.0,evid00191,impact_mq
+xa.s12.00.mhz.1975-05-04HR00_evid00192,1975-05-04T10:05:00.000000,36300.0,evid00192,impact_mq
+xa.s12.00.mhz.1975-06-24HR00_evid00196,1975-06-24T16:03:00.000000,57780.0,evid00196,impact_mq
+xa.s12.00.mhz.1975-06-26HR00_evid00198,1975-06-26T03:24:00.000000,12240.0,evid00198,impact_mq