alert.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b5f18158-1cd4-45b2-92f8-30385fc0f06e",
   "metadata": {},
   "source": [
    "## Alerts for geomagnetic storms "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a036a00-365a-49d6-bdfe-0c28d7071b01",
   "metadata": {},
   "source": [
    "uses environment 'envs/env_alert.yml'\n",
    "\n",
    "run data_update_web_hf.pynb to produce a new Dst file\n",
    "\n",
    "for testing, delete the alert_list.p files\n",
    "\n",
    "\n",
    "Issues: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea062b3d-4703-49cb-bf92-7e40ee84c4be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "telegram alerts will be sent\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook alert.ipynb to script\n",
      "[NbConvertApp] Writing 12611 bytes to alert.py\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "######### CHECK: do you want to debug or actually send alerts\n",
    "\n",
    "#for debugging, set to False\n",
    "\n",
    "telegram=True\n",
    "#telegram=False\n",
    "\n",
    "if not telegram:\n",
    "    print('no telegram alerts will be sent')\n",
    "\n",
    "    \n",
    "if telegram: \n",
    "    print('telegram alerts will be sent')\n",
    "\n",
    "    \n",
    "import os\n",
    "\n",
    "os.system('jupyter nbconvert --to script alert.ipynb')  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b5d61a37-fa1a-46fa-ab71-2a82553b93a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thresholds set to  -50    -100\n",
      "system is mac\n",
      "/Users/chris/python/data/insitu_python/\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import numpy as np\n",
    "import datetime\n",
    "from datetime import timedelta\n",
    "import seaborn as sns\n",
    "import urllib\n",
    "import pandas as pd\n",
    "import sys\n",
    "import importlib\n",
    "import email\n",
    "import smtplib\n",
    "import requests\n",
    "\n",
    "\n",
    "#Plotly imports\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import plotly.express as px\n",
    "\n",
    "\n",
    "from email.mime.multipart import MIMEMultipart\n",
    "from email.mime.text import MIMEText\n",
    "\n",
    "from alerts import alert_server_ids as aid\n",
    "\n",
    "#Dst threshold definition for defining an alert\n",
    "threshold1=-50 #for real time application\n",
    "\n",
    "threshold2=-100 #for extra alert\n",
    "\n",
    "#threshold3=-150 #for extra alert\n",
    "\n",
    "#threshold4=-200 #for extra alert\n",
    "\n",
    "\n",
    "print('thresholds set to ',threshold1, '  ', threshold2)#'  ', threshold3,'  ', threshold4 )\n",
    "\n",
    "\n",
    "##### check for system type\n",
    "#server\n",
    "if sys.platform == 'linux': \n",
    "    print('system is linux')\n",
    "    from config_server import data_path\n",
    "    matplotlib.use('Agg') \n",
    "   \n",
    "        \n",
    "#mac\n",
    "if sys.platform =='darwin':  \n",
    "    print('system is mac')\n",
    "    from config_local import data_path    \n",
    "    #matplotlib.use('Agg') \n",
    "    %matplotlib inline     \n",
    "\n",
    "print(data_path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca1d76e6-a7c0-49bd-9e48-21159e508472",
   "metadata": {},
   "source": [
    "### get Dst data and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a98bb89-40d3-4d0a-91e0-68d1cff36638",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34.0 -165.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tr/3wb8vxgd5sj97xz5dz09q0qm0000gn/T/ipykernel_6058/602292105.py:65: UserWarning:\n",
      "\n",
      "This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get current dst last 35 days\n",
    "filenoaa='noaa_dst_last_35files_now.p'\n",
    "n=pickle.load(open(data_path+filenoaa, \"rb\" ) )  \n",
    "\n",
    "#########################\n",
    "\n",
    "## TBD load Nc values from Dst file\n",
    "\n",
    "\n",
    "###################\n",
    "\n",
    "sns.set_context('talk')\n",
    "sns.set_style('darkgrid')\n",
    "fig, ax1=plt.subplots(1,figsize=(13,7),dpi=100)\n",
    "\n",
    "ax1.axhline(y=threshold1, color='yellowgreen', linestyle='--',label='threshold 1')\n",
    "ax1.axhline(y=threshold2, color='orange', linestyle='--',label='threshold 2')\n",
    "ax1.axhline(y=-150, color='red', linestyle='--',label='')\n",
    "\n",
    "ax1.axvline(x=datetime.datetime.utcnow(), color='k', linestyle='--',alpha=0.5, linewidth=1.0)\n",
    "\n",
    "\n",
    "ax1.plot(n.time,n.dst,color='royalblue',linewidth=1.5,alpha=1.0)\n",
    "ax1.plot(n.time,n.dst,'ok',markersize=5)\n",
    "\n",
    "#ax1.axhline(y=threshold3, color='b', linestyle='--',label='threshold 3')\n",
    "#ax1.axhline(y=threshold4, color='k', linestyle='--',label='threshold 4')\n",
    "\n",
    "\n",
    "plotmin=np.nanmin(n.dst)\n",
    "plotmax=np.nanmax(n.dst)\n",
    "print(plotmax, plotmin)\n",
    "\n",
    "\n",
    "ax1.set_ylim(plotmin-50,plotmax+30)\n",
    "plt.ylabel('Dst [nT]')\n",
    "ax1.xaxis_date()\n",
    "\n",
    "ax1.set_xlim(datetime.datetime.utcnow()-datetime.timedelta(days=15),datetime.datetime.utcnow()+datetime.timedelta(days=1))\n",
    "#ax1.set_xlim(datetime.datetime.utcnow()-datetime.timedelta(days=5),datetime.datetime.utcnow()+datetime.timedelta(days=0))\n",
    "\n",
    "plt.title('Latest geomagnetic storms',fontsize=15)\n",
    "\n",
    "\n",
    "ax1.xaxis_date()\n",
    "ax1.xaxis.set_major_locator(mdates.DayLocator())\n",
    "myformat = mdates.DateFormatter('%b %d')\n",
    "ax1.xaxis.set_major_formatter(myformat)\n",
    "plt.xticks(rotation=35)\n",
    "\n",
    "ax1.set_xlabel(datetime.datetime.utcnow().year)\n",
    "\n",
    "fsize=12\n",
    "\n",
    "logo = plt.imread('logo/GSA_Basislogo_Positiv_RGB_XXS.png')\n",
    "newax = fig.add_axes([0.87,0.90,0.08,0.08], anchor='NE', zorder=1)\n",
    "newax.imshow(logo)\n",
    "newax.axis('off')\n",
    "\n",
    "\n",
    "\n",
    "plt.figtext(0.09,0.01,'Austrian Space Weather Office   GeoSphere Austria', color='black', ha='left',fontsize=fsize-2, style='italic')\n",
    "plt.figtext(0.98,0.01,'helioforecast.space', color='black', ha='right',fontsize=fsize-2, style='italic')\n",
    "plt.figtext(0.10,0.93,'last update: '+str(datetime.datetime.utcnow())[0:16]+ ' UT', ha='left', fontsize=10)\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "plt.savefig('alerts/alert_dst.png',dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d998673b-0b0f-4ff1-bf5f-fabe34958c4d",
   "metadata": {},
   "source": [
    "### alert functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6bd4da5b-eb60-41c0-978c-54e2d1fb17ea",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#with outlook as sender, gmail does not work\n",
    "#use outlook.com aswo-space@outlook.com account for testing\n",
    "\n",
    "def send_alert_email(time,dstval):\n",
    "\n",
    "    ######### get email list from file if needed\n",
    "    #email_file_path='alerts/alert_email_list.txt'\n",
    "    #with open(email_file_path, \"r\") as file:\n",
    "    #    email_list = []\n",
    "    #    for line in file:\n",
    "    #        # Remove leading and trailing whitespace (e.g., newline characters)\n",
    "    #        string = line.strip()\n",
    "    #        email_list.append(string)\n",
    "    #print(email_list)      \n",
    "    recipients=email_list\n",
    "    \n",
    "    msg = MIMEMultipart()\n",
    "    msg['From'] = aid.smtp_username\n",
    "    \n",
    "    msg['Subject'] = 'Geomagnetic storm alert, Austrian Space Weather Office, GeoSphere Austria'\n",
    "\n",
    "    \n",
    "    time_formatted=time.strftime(\"%Y %b %d %H:00 UT\")\n",
    "    \n",
    "    email_body =\"\"\" Dear Space Weather enthusiast, \n",
    "    \n",
    "    there is currently an ongoing geomagnetic storm with \n",
    "    \n",
    "    Dst {} nT at  {},\n",
    "\n",
    "    Happy aurora hunting! \n",
    "    \n",
    "    The ASWO team\n",
    "    https://helioforecast.space\n",
    "    \"\"\".format(dstval, time_formatted)\n",
    "        \n",
    "    msg.attach(MIMEText(email_body, 'plain'))\n",
    "\n",
    "    server = smtplib.SMTP(aid.smtp_server, aid.smtp_port)\n",
    "    server.starttls()  # Use TLS encryption\n",
    "    server.login(aes.smtp_username, aid.smtp_password)\n",
    "    \n",
    "    #add list for all into one string\n",
    "    msg['Bcc'] = ', '.join(recipients)\n",
    "    try:\n",
    "        server.sendmail(aes.smtp_username, recipients, msg.as_string())\n",
    "        print(\"Email sent to \", recipients)\n",
    "    except Exception as e:\n",
    "        print(\"Error sending email:\", str(e), recipients)\n",
    "        \n",
    "    server.quit()\n",
    "       \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "67242f59-5d70-40b8-b8b2-4ea7e0958882",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def send_telegram_message(time,dstval):\n",
    "    \n",
    "    \n",
    "\n",
    "    # Replace 'YOUR_BOT_TOKEN' with the actual token you received from BotFather\n",
    "    bot_token = aid.bot_token\n",
    "\n",
    "    # Replace 'YOUR_CHAT_ID' with the chat ID of the user or group where you want to send the message\n",
    "    chat_id = aid.chat_id\n",
    "\n",
    "    time_formatted=time.strftime(\"%Y %b %d %Hh UT\")\n",
    "    \n",
    "    dstval=int(dstval)\n",
    "    \n",
    "    # The message you want to send\n",
    "    message_text = \"\"\" Hi, there is an ongoing geomagnetic storm: \n",
    "Dst {} nT at {} \n",
    "Happy aurora hunting!     \n",
    "The ASWO team\n",
    "https://helioforecast.space/solarwind\"\"\".format(dstval, time_formatted)\n",
    "\n",
    "    # Create the URL for the Telegram Bot API endpoint\n",
    "    api_url = f'https://api.telegram.org/bot{bot_token}/sendMessage'\n",
    "\n",
    "    # Prepare the parameters for the message\n",
    "    params = {\n",
    "        'chat_id': chat_id,\n",
    "        'text': message_text\n",
    "    }\n",
    "\n",
    "    # Send the message using a POST request\n",
    "    response = requests.post(api_url, data=params)\n",
    "\n",
    "    # Check the response from the Telegram API\n",
    "    if response.status_code == 200:\n",
    "        print('Message sent successfully to Geomagnetic storm')\n",
    "    else:\n",
    "        print('Failed to send the message.')\n",
    "        print(response.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a4dbc1f-1f1a-489d-9344-3fe86e0fa648",
   "metadata": {
    "tags": []
   },
   "source": [
    "### alert for threshold 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4e9a1f07-f9fc-4bf4-89e1-114cd789ca70",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "------------------------------------------------------------------\n",
      "start Dst alert check at 2024-Apr-02 11:54:41 UT\n",
      "\n",
      "latest Dst data (NOAA/Kyoto): \n",
      "2024-Apr-01 18 UT -19 nT\n",
      "2024-Apr-01 19 UT -19 nT\n",
      "2024-Apr-01 20 UT -19 nT\n",
      "2024-Apr-01 21 UT -20 nT\n",
      "2024-Apr-01 22 UT -20 nT\n",
      "2024-Apr-01 23 UT -21 nT\n",
      "2024-Apr-02 00 UT -21 nT\n",
      "2024-Apr-02 01 UT -24 nT\n",
      "2024-Apr-02 02 UT -24 nT\n",
      "2024-Apr-02 03 UT -25 nT\n",
      "2024-Apr-02 04 UT -20 nT\n",
      "2024-Apr-02 05 UT -20 nT\n",
      "2024-Apr-02 06 UT -20 nT\n",
      "2024-Apr-02 07 UT -17 nT\n",
      "2024-Apr-02 08 UT -20 nT\n",
      " \n",
      " \n",
      "Dst above threshold -50 nT, no alert triggered\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# for testing\n",
    "#n.dst[-1]=-55\n",
    "\n",
    "time_now=datetime.datetime.utcnow()\n",
    "\n",
    "print(' ')\n",
    "print('------------------------------------------------------------------')\n",
    "print('start Dst alert check at',time_now.strftime(\"%Y-%b-%d %H:%M:%S UT\"))\n",
    "\n",
    "\n",
    "print()\n",
    "time_now_num=mdates.date2num(time_now)\n",
    "\n",
    "print('latest Dst data (NOAA/Kyoto): ')\n",
    "for i in np.arange(-15,0):\n",
    "    formatted_date = n.time[i].strftime(\"%Y-%b-%d %H UT\")\n",
    "    print(formatted_date, int(n.dst[i]),'nT')\n",
    "print(' ')\n",
    "\n",
    "\n",
    "print(' ')\n",
    "\n",
    "if n.dst[-1]> threshold1: \n",
    "    print('Dst above threshold',threshold1,'nT, no alert triggered')\n",
    "\n",
    "if n.dst[-1]<= threshold1: \n",
    "    \n",
    "    print('------------ Alert triggered')\n",
    "    print('Dst is ',int(n.dst[-1]), ' nT, below the threshold of <=',threshold1,' nT')\n",
    "    print(' ')\n",
    "    print('Was alert already sent in last 12 hours?')\n",
    "    print(' ')\n",
    "\n",
    "    #read list of sent out alert times from a pickle file\n",
    "    file_path = 'alerts/alert_list.p'\n",
    "    \n",
    "    if os.path.exists(file_path):\n",
    "        print('Alerts file exists')\n",
    "        atime_list=pickle.load(open(file_path, \"rb\" ) )   \n",
    "        atime_list_num=mdates.date2num(atime_list)\n",
    "    else:\n",
    "        print('Alerts file does not exist, file created')\n",
    "        #create a dummy list if the file is not there, and create the pickle file\n",
    "        atime_list=[datetime.datetime(2023, 9, 15, 18, 0), \\\n",
    "                     datetime.datetime(2023, 9, 25, 7, 0)]        \n",
    "        atime_list_num=mdates.date2num(atime_list)\n",
    "        pickle.dump(atime_list, open('alerts/alert_list.p', 'wb') )\n",
    "    \n",
    "    \n",
    "    \n",
    "    print('latest alerts from pickle file')\n",
    "    for i in np.arange(0,len(atime_list)):\n",
    "        print(atime_list[i].strftime(\"%Y-%m-%dT%H:%MZ\") )\n",
    "    \n",
    "    \n",
    "    print('')\n",
    "    \n",
    "    \n",
    "    #go through all times and check whether one was in the last 12 hours or 0.5 days in matplotlib times\n",
    "    if np.nanmin(time_now_num-atime_list_num) > 0.5:   \n",
    "        \n",
    "        print('no, alert will be sent')       \n",
    "                     \n",
    "        print('Alert time and Dst written to file alerts/alert_list.txt and alert_list.p')\n",
    "        \n",
    "        alert_time_for_file=n.time[-1].strftime(\"%Y-%m-%dT%H:%MZ\")\n",
    "        print(alert_time_for_file+' '+str(int(n.dst[-1])))        \n",
    "                \n",
    "        #write this to a text file\n",
    "        with open('alerts/alert_list.txt', 'a') as file:\n",
    "            file.write(alert_time_for_file)\n",
    "            file.write(' '+str(int(n.dst[-1]))+'\\n')\n",
    "            \n",
    "        #append to list and write to pickle file    \n",
    "        atime_list.append(n.time[-1])\n",
    "        pickle.dump(atime_list, open('alerts/alert_list.p', 'wb') )            \n",
    "        \n",
    "        \n",
    "        #if telegram switch is on, send message\n",
    "        if telegram: \n",
    "            send_telegram_message(n.time[-1],n.dst[-1])\n",
    "\n",
    "        \n",
    "                        \n",
    "        #write into file the time of the sent alert\n",
    "    else: \n",
    "        print('yes, no alert sent')\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c11eb9e3-b13e-45b8-8724-b342910a4268",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Alert for threshold 2, same setup as for threshold 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "051f750d-79c2-405b-b18a-f5956fe0658c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "------------------------------------------------------------------\n",
      "start Dst alert check at 2024-Apr-02 11:54:41 UT\n",
      " \n",
      "Dst above threshold -100 nT, no alert triggered\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# for testing\n",
    "#n.dst[-1]=-150\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print(' ')\n",
    "print('------------------------------------------------------------------')\n",
    "print('start Dst alert check at',time_now.strftime(\"%Y-%b-%d %H:%M:%S UT\"))\n",
    "print(' ')\n",
    "\n",
    "if n.dst[-1]> threshold2: \n",
    "    print('Dst above threshold',threshold2,'nT, no alert triggered')\n",
    "\n",
    "if n.dst[-1]<= threshold2: \n",
    "    \n",
    "    print('------------ Alert triggered')\n",
    "    print('Dst is ',int(n.dst[-1]), ' nT, below the threshold of <=',threshold2,' nT')\n",
    "    print(' ')\n",
    "    print('Was alert already sent in last 12 hours?')\n",
    "    print(' ')\n",
    "\n",
    "    #read list of sent out alert times from a pickle file\n",
    "    file_path = 'alerts/alert_list2.p'\n",
    "    \n",
    "    if os.path.exists(file_path):\n",
    "        print('Alerts file exists')\n",
    "        atime_list=pickle.load(open(file_path, \"rb\" ) )   \n",
    "        atime_list_num=mdates.date2num(atime_list)\n",
    "    else:\n",
    "        print('Alerts file does not exist, file created')\n",
    "        #create a dummy list if the file is not there, and create the pickle file\n",
    "        atime_list=[datetime.datetime(2023, 9, 15, 18, 0), \\\n",
    "                     datetime.datetime(2023, 9, 25, 7, 0)]        \n",
    "        atime_list_num=mdates.date2num(atime_list)\n",
    "        pickle.dump(atime_list, open('alerts/alert_list2.p', 'wb') )\n",
    "    \n",
    "    \n",
    "    \n",
    "    print('latest alerts from pickle file')\n",
    "    for i in np.arange(0,len(atime_list)):\n",
    "        print(atime_list[i].strftime(\"%Y-%m-%dT%H:%MZ\") )\n",
    "    \n",
    "    \n",
    "    print('')\n",
    "    \n",
    "    \n",
    "    #go through all times and check whether one was in the last 12 hours or 0.5 days in matplotlib times\n",
    "    if np.nanmin(time_now_num-atime_list_num) > 0.5:   \n",
    "        \n",
    "        print('no, alert will be sent')       \n",
    "                     \n",
    "        print('Alert time and Dst written to file alerts/alert_list.txt and alert_list.p')\n",
    "        \n",
    "        alert_time_for_file=n.time[-1].strftime(\"%Y-%m-%dT%H:%MZ\")\n",
    "        print(alert_time_for_file+' '+str(int(n.dst[-1])))        \n",
    "                \n",
    "        #write this to a text file\n",
    "        with open('alerts/alert_list2.txt', 'a') as file:\n",
    "            file.write(alert_time_for_file)\n",
    "            file.write(' '+str(int(n.dst[-1]))+'\\n')\n",
    "            \n",
    "        #append to list and write to pickle file    \n",
    "        atime_list.append(n.time[-1])\n",
    "        pickle.dump(atime_list, open('alerts/alert_list2.p', 'wb') )            \n",
    "        \n",
    "        \n",
    "        #if telegram switch is on, send message\n",
    "        if telegram: \n",
    "            send_telegram_message(n.time[-1],n.dst[-1])\n",
    "        \n",
    "                        \n",
    "        #write into file the time of the sent alert\n",
    "    else: \n",
    "        print('yes, no alert sent')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b09f565-39c9-4a9c-826d-4a1f0af6b4f1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      " \n",
      "end Dst alert program\n",
      "------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    " \n",
    " \n",
    "print(' ')\n",
    "print(' ')\n",
    "print('end Dst alert program')\n",
    "\n",
    "print('------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f5bfb28-39dc-47e7-afd9-caf221a42d8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "651a02ed-9715-4763-81d6-c455f0ce1eb0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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