more_hek_xrs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa2eb16c-6b61-438f-abea-f93d5119d5c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Import in the usual stuff\n",
    "import glob\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sunpy import timeseries as ts\n",
    "from sunpy.net import Fido\n",
    "from sunpy.net import attrs as a\n",
    "from astropy.time import Time\n",
    "from astropy.time import TimeDelta\n",
    "from sunpy.time import parse_time\n",
    "\n",
    "#  Here just assuming it save to your sunpy/data dir but can of course change\n",
    "outdir='/Users/iain/sunpy/data/'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbc5e597-0991-4e49-922f-3127d4bd583e",
   "metadata": {},
   "source": [
    "---\n",
    "Testing that can get GOES XRS 15 data for the 2014 X-class flares...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "21ba3f8d-8173-478c-a7fc-34616b1047cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# All X-flares over GOES 15 time 2010-09 to 2020-03\n",
    "trange=a.Time(\"2010-09-01 00:00\",\"2020-03-01 00:00\")\n",
    "\n",
    "res = Fido.search(trange,a.hek.EventType(\"FL\"),\n",
    "                  a.hek.FL.GOESCls > \"X1.0\",a.hek.FRM.Name == \"SSW Latest Events\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f54b71ef-f668-4b26-8962-a9e5fec68e5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "hekres = res[\"hek\"]\n",
    "shrt_res = hekres[\"event_starttime\", \"event_peaktime\",\"event_endtime\", \"fl_goescls\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "114d88b2-f10a-4a5e-aca0-1fa842c84f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>HEKTable length=37</i>\n",
       "<table id=\"table140631859286560\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>event_starttime</th><th>event_peaktime</th><th>event_endtime</th><th>fl_goescls</th></tr></thead>\n",
       "<thead><tr><th>Time</th><th>Time</th><th>Time</th><th>str4</th></tr></thead>\n",
       "<tr><td>2011-02-15 01:44:00.000</td><td>2011-02-15 01:45:00.000</td><td>2011-02-15 01:56:00.000</td><td>X2.2</td></tr>\n",
       "<tr><td>2011-03-09 23:13:00.000</td><td>2011-03-09 23:16:00.000</td><td>2011-03-09 23:23:00.000</td><td>X1.5</td></tr>\n",
       "<tr><td>2011-08-09 07:48:00.000</td><td>2011-08-09 08:05:00.000</td><td>2011-08-09 08:08:00.000</td><td>X6.9</td></tr>\n",
       "<tr><td>2011-09-06 22:12:00.000</td><td>2011-09-06 22:20:00.000</td><td>2011-09-06 22:24:00.000</td><td>X2.1</td></tr>\n",
       "<tr><td>2011-09-06 22:12:00.000</td><td>2011-09-06 22:20:00.000</td><td>2011-09-06 22:24:00.000</td><td>X2.1</td></tr>\n",
       "<tr><td>2011-09-07 22:32:00.000</td><td>2011-09-07 22:38:00.000</td><td>2011-09-07 22:44:00.000</td><td>X1.8</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>2014-12-20 00:11:00.000</td><td>2014-12-20 00:24:00.000</td><td>2014-12-20 00:28:00.000</td><td>X1.8</td></tr>\n",
       "<tr><td>2015-03-11 16:11:00.000</td><td>2015-03-11 16:22:00.000</td><td>2015-03-11 16:29:00.000</td><td>X2.1</td></tr>\n",
       "<tr><td>2015-05-05 22:05:00.000</td><td>2015-05-05 22:11:00.000</td><td>2015-05-05 22:15:00.000</td><td>X2.7</td></tr>\n",
       "<tr><td>2017-09-06 08:57:00.000</td><td>2017-09-06 09:10:00.000</td><td>2017-09-06 09:17:00.000</td><td>X2.2</td></tr>\n",
       "<tr><td>2017-09-06 11:53:00.000</td><td>2017-09-06 12:02:00.000</td><td>2017-09-06 12:10:00.000</td><td>X9.3</td></tr>\n",
       "<tr><td>2017-09-07 14:20:00.000</td><td>2017-09-07 14:36:00.000</td><td>2017-09-07 14:55:00.000</td><td>X1.3</td></tr>\n",
       "<tr><td>2017-09-10 15:35:00.000</td><td>2017-09-10 16:06:00.000</td><td>2017-09-10 16:31:00.000</td><td>X8.2</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<sunpy.net.hek.hek.HEKTable object at 0x7fe7b34758e0>\n",
       "    event_starttime          event_peaktime     ... fl_goescls\n",
       "----------------------- ----------------------- ... ----------\n",
       "2011-02-15 01:44:00.000 2011-02-15 01:45:00.000 ...       X2.2\n",
       "2011-03-09 23:13:00.000 2011-03-09 23:16:00.000 ...       X1.5\n",
       "2011-08-09 07:48:00.000 2011-08-09 08:05:00.000 ...       X6.9\n",
       "2011-09-06 22:12:00.000 2011-09-06 22:20:00.000 ...       X2.1\n",
       "2011-09-06 22:12:00.000 2011-09-06 22:20:00.000 ...       X2.1\n",
       "2011-09-07 22:32:00.000 2011-09-07 22:38:00.000 ...       X1.8\n",
       "2011-09-22 10:29:00.000 2011-09-22 11:01:00.000 ...       X1.4\n",
       "                    ...                     ... ...        ...\n",
       "2014-12-20 00:11:00.000 2014-12-20 00:24:00.000 ...       X1.8\n",
       "2015-03-11 16:11:00.000 2015-03-11 16:22:00.000 ...       X2.1\n",
       "2015-05-05 22:05:00.000 2015-05-05 22:11:00.000 ...       X2.7\n",
       "2017-09-06 08:57:00.000 2017-09-06 09:10:00.000 ...       X2.2\n",
       "2017-09-06 11:53:00.000 2017-09-06 12:02:00.000 ...       X9.3\n",
       "2017-09-07 14:20:00.000 2017-09-07 14:36:00.000 ...       X1.3\n",
       "2017-09-10 15:35:00.000 2017-09-10 16:06:00.000 ...       X8.2\n",
       "Length = 37 rows"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shrt_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f450e3ac-dfe9-4e91-bda8-cceb28b8d644",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    event_starttime          event_peaktime     ... fl_goescls\n",
      "----------------------- ----------------------- ... ----------\n",
      "2014-01-07 18:04:00.000 2014-01-07 18:32:00.000 ...       X1.2\n",
      "2014-01-07 18:04:00.000 2014-01-07 18:30:00.000 ...       X1.2\n",
      "2014-06-10 11:36:00.000 2014-06-10 11:42:00.000 ...       X2.2\n",
      "2014-06-10 12:36:00.000 2014-06-10 12:52:00.000 ...       X1.5\n",
      "2014-09-10 17:21:00.000 2014-09-10 17:45:00.000 ...       X1.6\n",
      "2014-10-19 04:17:00.000 2014-10-19 05:01:00.000 ...       X1.1\n",
      "2014-10-22 14:02:00.000 2014-10-22 14:28:00.000 ...       X1.6\n",
      "2014-10-24 21:07:00.000 2014-10-24 21:40:00.000 ...       X3.1\n",
      "2014-10-26 10:04:00.000 2014-10-26 10:56:00.000 ...       X2.0\n",
      "2014-10-27 14:12:00.000 2014-10-27 14:47:00.000 ...       X2.0\n",
      "2014-11-07 16:53:00.000 2014-11-07 17:25:00.000 ...       X1.6\n",
      "2014-12-20 00:11:00.000 2014-12-20 00:27:00.000 ...       X1.8\n",
      "2014-12-20 00:11:00.000 2014-12-20 00:24:00.000 ...       X1.8\n"
     ]
    }
   ],
   "source": [
    "# Here are the 2014 ones\n",
    "print(shrt_res[18:31])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ab3d53d-699d-489a-b481-366706566edd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For first event the GOES 15 data is online:\n",
    "# 2sec data:\n",
    "# https://satdat.ngdc.noaa.gov/sem/goes/data/science/xrs/goes15/gxrs-l2-irrad_science/2014/04/sci_gxrs-l2-irrad_g15_d20140401_v0-0-0.nc\n",
    "# avg1min science data:\n",
    "# https://satdat.ngdc.noaa.gov/sem/goes/data/science/xrs/goes15/xrsf-l2-avg1m_science/2014/01/sci_xrsf-l2-avg1m_g15_d20140107_v1-0-0.nc\n",
    "\n",
    "\n",
    "# Note the upcoming change of urls with:\n",
    "# https://satdat.ngdc.noaa.gov/sem/goes/ -> https://www.ncei.noaa.gov/data/goes-space-environment-monitor/access/\n",
    "# i.e. https://solarnews.nso.edu/upcoming-ngdc-satdat-url-change/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c1290705-bf57-4328-bb09-ad84755249f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results from 1 Provider:\n",
      "\n",
      "1 Results from the XRSClient:\n",
      "Source: <13: https://umbra.nascom.nasa.gov/goes/fits \n",
      "13, 14, 15: https://satdat.ngdc.noaa.gov/sem/goes/data/science/ \n",
      "16, 17: https://data.ngdc.noaa.gov/platforms/solar-space-observing-satellites/goes/\n",
      "\n",
      "       Start Time               End Time        Instrument ... Source Provider\n",
      "----------------------- ----------------------- ---------- ... ------ --------\n",
      "2014-01-07 00:00:00.000 2014-01-07 23:59:59.999        XRS ...   GOES     NOAA\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88d94940c26a4c979e46d4da399edb6f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Files Downloaded:   0%|          | 0/1 [00:00<?, ?file/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Can fido get it for me?\n",
    "gtstart=Time(shrt_res[18][\"event_starttime\"],scale='utc')-TimeDelta(10*60,format='sec')\n",
    "gtend=Time(shrt_res[18][\"event_endtime\"],scale='utc')+TimeDelta(10*60,format='sec')\n",
    "\n",
    "# Search and get the XRS data\n",
    "tflrange=a.Time(gtstart.iso,gtend.iso)\n",
    "rg15 = Fido.search(tflrange, a.Instrument(\"XRS\"), a.goes.SatelliteNumber(15))\n",
    "print(rg15)\n",
    "fg15 = Fido.fetch(rg15,path=outdir,overwrite=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67278297-5cf1-401a-8192-c00185e18a3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/Users/iain/sunpy/data/sci_gxrs-l2-irrad_g15_d20140107_v0-0-0.nc']\n"
     ]
    }
   ],
   "source": [
    "print(fg15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e34a78d4-a2da-4f65-a79f-8a3a510fd61f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: SunpyDeprecationWarning: .index is deprecatd. Use .time instead to get an astropy.time.Time object, or ts.to_dataframe().index to get a pandas DateTimeIndex. [sunpy.timeseries.timeseriesbase]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg15=glob.glob(outdir+'sci_gxrs-l2-irrad_g15_d20140107_v0-0-0.nc')\n",
    "g15 = ts.TimeSeries(fg15, concatenate=True)\n",
    "\n",
    "tg15=g15.truncate(tflrange.start.iso,tflrange.end.iso)\n",
    "\n",
    "tg_tims=tg15.index\n",
    "tg_x05=tg15.quantity(\"xrsa\").value\n",
    "tg_x18=tg15.quantity(\"xrsb\").value\n",
    "\n",
    "# And then plot it all\n",
    "tfl=a.Time(shrt_res[18][\"event_starttime\"],shrt_res[18][\"event_endtime\"])\n",
    "\n",
    "plt.rcParams.update({'font.size': 18,'font.family':\"sans-serif\",\\\n",
    "                         'font.sans-serif':\"Arial\",'mathtext.default':\"regular\"})\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(10, 6))\n",
    "plt.plot(tg_tims,tg_x05,drawstyle='steps-post',marker=None,color='darkblue',lw=2,label='$0.5-4\\;\\AA$')\n",
    "plt.plot(tg_tims,tg_x18,drawstyle='steps-post',marker=None,color='firebrick',lw=2,label='$1-8\\;\\AA$')\n",
    "\n",
    "ax.set_ylabel(\"GOES15 XRS [$\\mathrm{W\\;m^{-2}}$] \")\n",
    "ax.set_xlabel(\"Start Time \"+tflrange.start.iso[:-4])\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_ylim([1e-8,5e-3])\n",
    "ax.set_xlim([tflrange.start.datetime,tflrange.end.datetime])\n",
    "myFmt = matplotlib.dates.DateFormatter('%H:%M')\n",
    "majorx= matplotlib.dates.MinuteLocator(interval=15)\n",
    "minorx= matplotlib.dates.MinuteLocator(interval=5)\n",
    "ax.xaxis.set_major_locator(majorx)\n",
    "ax.xaxis.set_minor_locator(minorx)\n",
    "ax.xaxis.set_major_formatter(myFmt)\n",
    "ax.axvline(parse_time(shrt_res[18][\"event_peaktime\"]).datetime,color='black',ls='-.')\n",
    "ax.axvspan(tfl.start.datetime,tfl.end.datetime,color='black',alpha=0.2, label='GOES Flare')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5870722a-6cb7-474d-9c90-fdb2e500095f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2c19e6d5-041d-48e4-8630-43d14db514cb",
   "metadata": {},
   "source": [
    "---\n",
    "Testing if there is missing data from NOAA server for date in 2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "842c6dbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results from 1 Provider:\n",
      "\n",
      "0 Results from the XRSClient:\n",
      "Source: <13: https://umbra.nascom.nasa.gov/goes/fits \n",
      "13, 14, 15: https://satdat.ngdc.noaa.gov/sem/goes/data/science/ \n",
      "16, 17: https://data.ngdc.noaa.gov/platforms/solar-space-observing-satellites/goes/\n",
      "\n",
      "<No columns>\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rg=Fido.search(a.Time(\"2011-03-09 23:00\",\"2011-03-10 02:10\"), a.Instrument(\"XRS\"))\n",
    "print(rg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0ba5bd84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Was there at some point\n",
    "# https://www.solarmonitor.org/data/2011/03/10/pngs/goes/goes_xrays_20110310.png\n",
    "# but mostly missing files in the noaa directory for that month:\n",
    "# https://satdat.ngdc.noaa.gov/sem/goes/data/science/xrs/goes15/gxrs-l2-irrad_science/2011/03/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b50d15ae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e4e1416",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    event_starttime          event_peaktime          event_endtime      fl_goescls\n",
      "----------------------- ----------------------- ----------------------- ----------\n",
      "2011-03-09 23:13:00.000 2011-03-09 23:23:00.000 2011-03-09 23:29:00.000       X1.5\n",
      "    event_starttime          event_peaktime          event_endtime      fl_goescls\n",
      "----------------------- ----------------------- ----------------------- ----------\n",
      "2012-10-23 03:13:00.000 2012-10-23 03:17:00.000 2012-10-23 03:21:00.000       X1.8\n",
      "43\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><i>HEKTable length=43</i>\n",
       "<table id=\"table140633510925264\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>event_starttime</th><th>event_peaktime</th><th>event_endtime</th><th>fl_goescls</th></tr></thead>\n",
       "<thead><tr><th>Time</th><th>Time</th><th>Time</th><th>str4</th></tr></thead>\n",
       "<tr><td>2011-02-15 01:44:00.000</td><td>2011-02-15 01:56:00.000</td><td>2011-02-15 02:06:00.000</td><td>X2.2</td></tr>\n",
       "<tr><td>2011-08-09 07:48:00.000</td><td>2011-08-09 08:05:00.000</td><td>2011-08-09 08:08:00.000</td><td>X6.9</td></tr>\n",
       "<tr><td>2011-09-06 22:12:00.000</td><td>2011-09-06 22:20:00.000</td><td>2011-09-06 22:24:00.000</td><td>X2.1</td></tr>\n",
       "<tr><td>2011-09-06 22:12:00.000</td><td>2011-09-06 22:20:00.000</td><td>2011-09-06 22:24:00.000</td><td>X2.1</td></tr>\n",
       "<tr><td>2011-09-07 22:32:00.000</td><td>2011-09-07 22:38:00.000</td><td>2011-09-07 22:44:00.000</td><td>X1.8</td></tr>\n",
       "<tr><td>2011-09-22 10:29:00.000</td><td>2011-09-22 11:01:00.000</td><td>2011-09-22 11:44:00.000</td><td>X1.4</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>2014-12-20 00:11:00.000</td><td>2014-12-20 00:28:00.000</td><td>2014-12-20 00:55:00.000</td><td>X1.8</td></tr>\n",
       "<tr><td>2015-03-11 16:11:00.000</td><td>2015-03-11 16:22:00.000</td><td>2015-03-11 16:29:00.000</td><td>X2.1</td></tr>\n",
       "<tr><td>2015-05-05 22:05:00.000</td><td>2015-05-05 22:11:00.000</td><td>2015-05-05 22:15:00.000</td><td>X2.7</td></tr>\n",
       "<tr><td>2017-09-06 08:57:00.000</td><td>2017-09-06 09:10:00.000</td><td>2017-09-06 09:17:00.000</td><td>X2.2</td></tr>\n",
       "<tr><td>2017-09-06 11:53:00.000</td><td>2017-09-06 12:02:00.000</td><td>2017-09-06 12:10:00.000</td><td>X9.3</td></tr>\n",
       "<tr><td>2017-09-07 14:20:00.000</td><td>2017-09-07 14:36:00.000</td><td>2017-09-07 14:55:00.000</td><td>X1.3</td></tr>\n",
       "<tr><td>2017-09-10 15:35:00.000</td><td>2017-09-10 16:06:00.000</td><td>2017-09-10 16:31:00.000</td><td>X8.2</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<sunpy.net.hek.hek.HEKTable object at 0x7fe7ca77a9d0>\n",
       "    event_starttime          event_peaktime     ... fl_goescls\n",
       "----------------------- ----------------------- ... ----------\n",
       "2011-02-15 01:44:00.000 2011-02-15 01:56:00.000 ...       X2.2\n",
       "2011-08-09 07:48:00.000 2011-08-09 08:05:00.000 ...       X6.9\n",
       "2011-09-06 22:12:00.000 2011-09-06 22:20:00.000 ...       X2.1\n",
       "2011-09-06 22:12:00.000 2011-09-06 22:20:00.000 ...       X2.1\n",
       "2011-09-07 22:32:00.000 2011-09-07 22:38:00.000 ...       X1.8\n",
       "2011-09-22 10:29:00.000 2011-09-22 11:01:00.000 ...       X1.4\n",
       "2011-09-24 09:21:00.000 2011-09-24 09:40:00.000 ...       X1.9\n",
       "                    ...                     ... ...        ...\n",
       "2014-12-20 00:11:00.000 2014-12-20 00:28:00.000 ...       X1.8\n",
       "2015-03-11 16:11:00.000 2015-03-11 16:22:00.000 ...       X2.1\n",
       "2015-05-05 22:05:00.000 2015-05-05 22:11:00.000 ...       X2.7\n",
       "2017-09-06 08:57:00.000 2017-09-06 09:10:00.000 ...       X2.2\n",
       "2017-09-06 11:53:00.000 2017-09-06 12:02:00.000 ...       X9.3\n",
       "2017-09-07 14:20:00.000 2017-09-07 14:36:00.000 ...       X1.3\n",
       "2017-09-10 15:35:00.000 2017-09-10 16:06:00.000 ...       X8.2\n",
       "Length = 43 rows"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trange=a.Time(\"2010-03-01 00:00\",\"2020-03-01 00:00\")\n",
    "res = Fido.search(trange,a.hek.EventType(\"FL\"),a.hek.FL.GOESCls > \"X1.0\",a.hek.OBS.Observatory == \"GOES\")\n",
    "hekres = res[\"hek\"]\n",
    "shrt_res = hekres[\"event_starttime\", \"event_peaktime\",\"event_endtime\", \"fl_goescls\"]\n",
    "\n",
    "# These were the ones that returned no G15 XRS data?\n",
    "print(shrt_res[1])\n",
    "print(shrt_res[15])\n",
    "\n",
    "# get rid of them from the list\n",
    "ind=list(range(len(shrt_res)))\n",
    "ind2=[i for i in ind if not (i ==1 or i==15)]\n",
    "new_res=shrt_res[ind2]\n",
    "\n",
    "print(len(new_res))\n",
    "new_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0062e8f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0c278a6",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}