groningen aardbevingen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4da5a85f",
   "metadata": {},
   "source": [
    "# Groningen aardbeving analyse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "130609b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset 1 KNMI: https://dataplatform.knmi.nl/dataset/aardbevingen-cijfers-1\n",
    "# api 1 'Earthquakes - facts and figures for the Groningen gas field'\n",
    "# https://api.dataplatform.knmi.nl/open-data/v1/datasets/aardbevingen_cijfers/versions/1/files\n",
    "\n",
    "# dataset 2 KNMI: https://dataplatform.knmi.nl/dataset/aardbevingen-nederland-2\n",
    "# api 2 'Earthquakes - last 100 earthquakes in the Netherlands and near surrounding'\n",
    "# https://api.dataplatform.knmi.nl/open-data/v1/datasets/aardbevingen_nederland/versions/2/files\n",
    "\n",
    "# background info\n",
    "# https://developer.dataplatform.knmi.nl/example-scripts\n",
    "\n",
    "# temp key\n",
    "# eyJvcmciOiI1ZTU1NGUxOTI3NGE5NjAwMDEyYTNlYjEiLCJpZCI6IjYzNmIyMjNiNjEzYjQ3OTQ4MDk1NGUxYTRkN2I5ZGYxIiwiaCI6Im11cm11cjEyOCJ9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c2c23480",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import logging\n",
    "import sys\n",
    "from datetime import datetime\n",
    "from pathlib import Path\n",
    "import requests\n",
    "import json\n",
    "import pandas as pd   \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ffb77a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.basicConfig()\n",
    "logger = logging.getLogger(__name__)\n",
    "logger.setLevel(\"INFO\")\n",
    "\n",
    "api_url = \"https://api.dataplatform.knmi.nl/open-data\"\n",
    "api_version = \"v1\"\n",
    "\n",
    "def download_file(datasetname, datasetversion, filenumber):\n",
    "    # Parameters\n",
    "    api_key = \"eyJvcmciOiI1ZTU1NGUxOTI3NGE5NjAwMDEyYTNlYjEiLCJpZCI6IjYzNmIyMjNiNjEzYjQ3OTQ4MDk1NGUxYTRkN2I5ZGYxIiwiaCI6Im11cm11cjEyOCJ9\"\n",
    "    dataset_name = datasetname\n",
    "    dataset_version = datasetversion\n",
    "    max_keys = \"10\"\n",
    "\n",
    "    # Use list files request to request first 10 files of the day.\n",
    "    timestamp = datetime.utcnow().date().strftime(\"%Y%m%d\")\n",
    "    start_after_filename_prefix = f\"KMDS__OPER_P___10M_OBS_L2_{timestamp}\"\n",
    "    list_files_response = requests.get(\n",
    "        f\"{api_url}/{api_version}/datasets/{dataset_name}/versions/{dataset_version}/files\",\n",
    "        headers={\"Authorization\": api_key},\n",
    "        params={\"maxKeys\": max_keys, \"startAfterFilename\": start_after_filename_prefix},\n",
    "    )\n",
    "    list_files = list_files_response.json()\n",
    "\n",
    "    logger.info(f\"List files response:\\n{list_files}\")\n",
    "    dataset_files = list_files.get(\"files\")\n",
    "\n",
    "    # Retrieve first file in the list files response\n",
    "    filename = dataset_files[filenumber].get(\"filename\")\n",
    "    logger.info(f\"Retrieve file with name: {filename}\")\n",
    "    endpoint = f\"{api_url}/{api_version}/datasets/{dataset_name}/versions/{dataset_version}/files/{filename}/url\"\n",
    "    get_file_response = requests.get(endpoint, headers={\"Authorization\": api_key})\n",
    "    if get_file_response.status_code != 200:\n",
    "        logger.error(\"Unable to retrieve download url for file\")\n",
    "        logger.error(get_file_response.text)\n",
    "        sys.exit(1)\n",
    "\n",
    "    download_url = get_file_response.json().get(\"temporaryDownloadUrl\")\n",
    "    dataset_file_response = requests.get(download_url)\n",
    "    if dataset_file_response.status_code != 200:\n",
    "        logger.error(\"Unable to download file using download URL\")\n",
    "        logger.error(dataset_file_response.text)\n",
    "        sys.exit(1)\n",
    "\n",
    "    # Write dataset file to disk\n",
    "    p = Path(\"aantal_aardbevingen_\" + str(filenumber) + \".json\")\n",
    "    p.write_bytes(dataset_file_response.content)\n",
    "    logger.info(f\"Successfully downloaded dataset file to {p}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "fa77716a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:__main__:List files response:\n",
      "{'isTruncated': True, 'resultCount': 10, 'files': [{'filename': 'aantal-aardbevingen-20200625144101.json', 'size': 5787, 'lastModified': '2020-06-25T15:23:29+00:00'}, {'filename': 'aantal-aardbevingen-20200625150100.json', 'size': 5787, 'lastModified': '2020-06-25T15:23:30+00:00'}, {'filename': 'aantal-aardbevingen-20200626000824.json', 'size': 5787, 'lastModified': '2020-06-26T00:08:31+00:00'}, {'filename': 'aantal-aardbevingen-20200627000828.json', 'size': 5787, 'lastModified': '2020-06-27T00:09:30+00:00'}, {'filename': 'aantal-aardbevingen-20200628000829.json', 'size': 5787, 'lastModified': '2020-06-28T00:09:30+00:00'}, {'filename': 'aantal-aardbevingen-20200629000828.json', 'size': 5787, 'lastModified': '2020-06-29T00:09:30+00:00'}, {'filename': 'aantal-aardbevingen-20200630061724.json', 'size': 5787, 'lastModified': '2020-06-30T06:17:30+00:00'}, {'filename': 'aantal-aardbevingen-20200630152024.json', 'size': 5787, 'lastModified': '2020-06-30T15:20:31+00:00'}, {'filename': 'aantal-aardbevingen-20200703071821.json', 'size': 5787, 'lastModified': '2020-07-03T07:18:31+00:00'}, {'filename': 'aantal-aardbevingen-20200714152928.json', 'size': 5787, 'lastModified': '2020-07-14T15:30:32+00:00'}], 'maxResults': 10, 'startAfterFilename': 'KMDS__OPER_P___10M_OBS_L2_20220907'}\n",
      "INFO:__main__:Retrieve file with name: aantal-aardbevingen-20200625150100.json\n",
      "INFO:__main__:Successfully downloaded dataset file to aantal_aardbevingen_1.json\n"
     ]
    }
   ],
   "source": [
    "download_file(\"aardbevingen_cijfers\", 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c95c456f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:__main__:List files response:\n",
      "{'isTruncated': False, 'resultCount': 1, 'files': [{'filename': 'SEISM_OPER_P___EQ100___L2.nc', 'size': 78640, 'lastModified': '2022-08-28T07:35:44+00:00'}], 'maxResults': 10, 'startAfterFilename': 'KMDS__OPER_P___10M_OBS_L2_20220907'}\n",
      "INFO:__main__:Retrieve file with name: SEISM_OPER_P___EQ100___L2.nc\n",
      "INFO:__main__:Successfully downloaded dataset file to SEISM_OPER_P___EQ100___L2.nc\n"
     ]
    }
   ],
   "source": [
    "download_file(\"aardbevingen_nederland\", 2, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "6c0739d9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1.0</th>\n",
       "      <th>1.5</th>\n",
       "      <th>2.0</th>\n",
       "      <th>2.5</th>\n",
       "      <th>3.0</th>\n",
       "      <th>3.5</th>\n",
       "      <th>max</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1991</td>\n",
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       "   1.0  1.5  2.0  2.5  3.0  3.5  max  year\n",
       "0    0    0    0    1    0    0    0  1991"
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     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('aantal_aardbevingen_0.json','r') as f: data = json.loads(f.read())\n",
    "df = data['data']\n",
    "df = pd.json_normalize(df['1991'])\n",
    "store = df.assign(year=[1991])\n",
    "store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "e4e67410",
   "metadata": {},
   "outputs": [
    {
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       "      <th>twofive</th>\n",
       "      <th>three</th>\n",
       "      <th>threefive</th>\n",
       "      <th>max</th>\n",
       "      <th>year</th>\n",
       "      <th>Total</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>1991</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1992</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1993</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1994</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>37</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>32</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>48</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  onefive  two  twofive  three  threefive  max  year  Total\n",
       "0    0        0    0        1      0          0    0  1991      1\n",
       "0    0        2    0        0      0          0    0  1992      4\n",
       "0    4        7    2        2      0          0    0  1993     22\n",
       "0    3       13    4        1      2          0    0  1994     36\n",
       "0    1        6    3        1      0          0    0  1995     17\n",
       "0    6        3    1        1      0          0    0  1996     14\n",
       "0    6        9    6        0      0          0    0  1997     30\n",
       "0    4        5    3        2      1          0    0  1998     20\n",
       "0    4        6    4        1      0          0    0  1999     21\n",
       "0    1        5    4        1      2          0    0  2000     18\n",
       "0    4        4    2        0      0          0    0  2001     14\n",
       "0    5        2    2        1      0          0    0  2002     12\n",
       "0   20       13    8        2      2          2    0  2003     60\n",
       "0    6        7    5        1      0          0    0  2004     26\n",
       "0    7       13    7        4      0          0    0  2005     44\n",
       "0    6       20   15        4      1          0    1  2006     67\n",
       "0    7        9    6        5      1          0    0  2007     37\n",
       "0    5       20    4        2      1          1    0  2008     53\n",
       "0    5       20   14        2      2          1    0  2009     64\n",
       "0    6       18   10        3      2          0    0  2010     57\n",
       "0   14       37   22        4      2          1    0  2011    117\n",
       "0   42       32   17        2      0          0    1  2012    126\n",
       "0   42       48   17        7      3          2    0  2013    167\n",
       "0   20       40   10        4      4          1    0  2014    119\n",
       "0   65       25   13        6      1          1    0  2015    136\n",
       "0   79       23   10        3      0          0    0  2016    138\n",
       "0   70       35   14        3      1          0    0  2017    158\n",
       "0   61       13   11        2      1          1    0  2018    102\n",
       "0   50       24    9        0      1          1    0  2019    109\n",
       "0   20       12    5        2      1          0    0  2020     52"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = 1992\n",
    "while(count <2021):\n",
    "    df = pd.json_normalize(data1[str(count)])\n",
    "    df =  df.assign(year=[count])\n",
    "    store = store.append(df)\n",
    "    count += 1\n",
    "\n",
    "store.columns = ['one', 'onefive', 'two', 'twofive', 'three', 'threefive', 'max', 'year']\n",
    "store['Total'] = store['one'] + store['onefive'] + store['onefive'] + store['two'] + store['twofive'] + store['three'] + store['threefive'] + store['max']\n",
    "\n",
    "store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "1d7b0ebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting of data\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "x = store['year']\n",
    "plt.plot(x, store['one'], label = '1')\n",
    "plt.plot(x, store['onefive'], label = '1.5')\n",
    "plt.plot(x, store['two'], label = '2')\n",
    "plt.plot(x, store['twofive'], label = '2.5')\n",
    "plt.plot(x, store['three'], label = '3')\n",
    "plt.plot(x, store['threefive'], label = '3.5')\n",
    "plt.plot(x, store['max'], label = '>3.5')\n",
    "plt.plot(x, store['Total'], label = 'Total')\n",
    "\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('amount of earthquakes')\n",
    "plt.title('earthquakes in Groningen')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "d410c430",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM,Dropout,Dense\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "f80e3ac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = store[['Total', 'year']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "c9a53c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x286ff82e9c8>]"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['year'],df['Total'],  label = 'total amount of earthquakes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "id": "b9865bb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = store[['Total', 'year']]\n",
    "\n",
    "scaler=MinMaxScaler(feature_range=(0,1))\n",
    "df.index=df.year\n",
    "df.drop('year',axis=1,inplace=True)\n",
    "final_data = df.values\n",
    "\n",
    "\n",
    "train_data=final_data[0:25,:]\n",
    "valid_data=final_data[25:,:]\n",
    "scaler=MinMaxScaler(feature_range=(0,1))\n",
    "scaled_data=scaler.fit_transform(final_data)\n",
    "\n",
    "x_train_data,y_train_data=[],[]\n",
    "for i in range(20,len(train_data))\n",
    "    x_train_data.append(scaled_data[i-20:i,0])\n",
    "    y_train_data.append(scaled_data[i,0])\n",
    "\n",
    "x_train_data = np.asarray(x_train_data)\n",
    "y_train_data = np.asarray(y_train_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "id": "70198a5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_model=Sequential()\n",
    "lstm_model.add(LSTM(units=50,return_sequences=True,input_shape=(np.shape(x_train_data)[0],1)))\n",
    "lstm_model.add(LSTM(units=50))\n",
    "lstm_model.add(Dense(1))\n",
    "\n",
    "model_data=df[len(df)-len(valid_data)-25:].values\n",
    "model_data=model_data.reshape(-1,1)\n",
    "model_data=scaler.transform(model_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "id": "6e544419",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 1160, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 1146, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 1135, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 993, in train_step\n        y_pred = self(x, training=True)\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 296, in assert_input_compatibility\n        f'Input {input_index} of layer \"{layer_name}\" is '\n\n    ValueError: Input 0 of layer \"sequential_56\" is incompatible with the layer: expected shape=(None, 5, 1), found shape=(None, 20)\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_8252\\759227698.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mlstm_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'adam'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mlstm_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     68\u001b[0m             \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m             \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     71\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m             \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mtf__train_function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m     13\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m                     \u001b[0mretval_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfscope\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m                 \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 1160, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 1146, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 1135, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 993, in train_step\n        y_pred = self(x, training=True)\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 296, in assert_input_compatibility\n        f'Input {input_index} of layer \"{layer_name}\" is '\n\n    ValueError: Input 0 of layer \"sequential_56\" is incompatible with the layer: expected shape=(None, 5, 1), found shape=(None, 20)\n"
     ]
    }
   ],
   "source": [
    "lstm_model.compile(loss='mean_squared_error',optimizer='adam')\n",
    "lstm_model.fit(x_train_data,y_train_data,epochs=1,batch_size=3,verbose=2)\n",
    "X_test=[]\n",
    "\n",
    "for i in range(0,model_data.shape[0]):\n",
    "    X_test.append(model_data[i-25:i,0])\n",
    "X_test=np.array(X_test)\n",
    "X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "id": "64b9e535",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lstm_model.fit(x_train_data,y_train_data,epochs=1,batch_size=3,verbose=2)\n",
    "#x_train_data.shape\n",
    "#y_train_data \n",
    "#model_data.shape[0]\n",
    "y_train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "id": "70ff767c",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 2041, in predict_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 2027, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 2015, in run_step  **\n        outputs = model.predict_step(data)\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 1983, in predict_step\n        return self(x, training=False)\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 296, in assert_input_compatibility\n        f'Input {input_index} of layer \"{layer_name}\" is '\n\n    ValueError: Input 0 of layer \"sequential_29\" is incompatible with the layer: expected shape=(None, 20, 1), found shape=(None, 0, 1)\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_8252\\4078296469.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpredicted_earthquakes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlstm_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mpredicted_earthquakes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscaler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredicted_earthquakes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     68\u001b[0m             \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m             \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     71\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m             \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mtf__predict_function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m     13\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m                     \u001b[0mretval_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfscope\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m                 \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 2041, in predict_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 2027, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 2015, in run_step  **\n        outputs = model.predict_step(data)\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\training.py\", line 1983, in predict_step\n        return self(x, training=False)\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\alexd\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 296, in assert_input_compatibility\n        f'Input {input_index} of layer \"{layer_name}\" is '\n\n    ValueError: Input 0 of layer \"sequential_29\" is incompatible with the layer: expected shape=(None, 20, 1), found shape=(None, 0, 1)\n"
     ]
    }
   ],
   "source": [
    "predicted_earthquakes=lstm_model.predict(X_test)\n",
    "predicted_earthquakes=scaler.inverse_transform(predicted_earthquakes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "id": "4711d432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 1)"
      ]
     },
     "execution_count": 554,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values.shape"
   ]
  }
 ],
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