sircat.ipynb

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    "## sircat\n",
    "\n",
    "Makes a catalog of solar wind stream interaction regions (SIRs) and high speed solar wind streams (HSS) for the Wind, STEREO and MAVEN spacecraft since 2007.\n",
    "\n",
    "Authors: [C. Möstl](https://twitter.com/chrisoutofspace), A. J. Weiss, R. L. Bailey, [Austrian Space Weather Office](https://helioforecast.space) GeoSphere Austria; Lan Jian, NASA, USA, Maxim Grandin, University of Helsinki, Finland; Hui Huang, Beijing  University, China.\n",
    "\n",
    "\n",
    "**current status: need to include Allen catalog for PSP, and publish again. There are bugs!** \n",
    "\n",
    "If you want to use parts of this code for generating results for peer-reviewed scientific publications, please contact us per email (chris.moestl@outlook.com, lan.jian@nasa.gov, maxime.grandin@helsinki.fi) for co-authorships.\n",
    "\n",
    "\n",
    "part of https://github.com/cmoestl/heliocats, last update May 2023\n",
    "\n",
    "---\n",
    "\n",
    "### Installation \n",
    "In a command line, use \n",
    "\n",
    "    git clone https://github.com/cmoestl/heliocats\n",
    "    \n",
    "This notebook runs with the helio4 environment, see README for the heliocats package.\n",
    "\n",
    "Download the files from https://doi.org/10.6084/m9.figshare.11973693 and place them in the /data folder.\n",
    "\n",
    "\n",
    "### Updates\n",
    "\n",
    "Adding a new SIR event: change the source files, or add the sir and hss times in section 2 before the master file sircat/HELIO4CAST_SIRCAT_v10_master.xlsx is produced. Then delete the file for the respective spacecraft under sircat/indices_sircat, and run this notebook or script.\n",
    "\n",
    "Convert this notebook to a script with \"jupyter nbconvert --to script sircat.ipynb\" in a command line\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "### Data sources\n",
    "\n",
    "\n",
    "**PSP SIR list**: Allen et al. 2021: https://www.aanda.org/articles/aa/full_html/2021/06/aa39833-20/aa39833-20.html, list at https://sppgway.jhuapl.edu/event_list\n",
    "\n",
    "\n",
    "**STEREO SIR list**: Lan Jian, https://stereo-ssc.nascom.nasa.gov/data/ins_data/impact/level3/\n",
    "published in: L. K. Jian et al. https://doi.org/10.1007/s11207-019-1416-8, 2019.\n",
    "\n",
    "This catalog contains the SIR start and end times, as well as the Pt max time for the stream interface. We use their SIR start and ends time as our *sir_start_time* and *sir_end_time*, and set the *hss_start_time* with the Pt max time. For 4 Pt max times that were nan in the Jian et al. list, the *hss_start_time* has been set similar to the *sir_end_time*.\n",
    "\n",
    "**To do**: create our own *hss_end_time* by setting it as the first time when the total bulk speed drops below 450 km/s after *sir_end_time*. Lan: For the STEREO HSS catalog, you can opt to list only the events with the fastest speed reaching at least 500 km/s, to be consistent with Grandin et al. (2019).\"\n",
    "\n",
    "\n",
    "**Earth SIR/HSS list**: Maxim Grandin et al., 2018, https://doi.org/10.1029/2018JA026396\n",
    "\n",
    "This catalog directly gives the *hss_start_time* and the *hss_end_time*. This list was determined by an algorithm and there are no specifics about the the SIR times, instead the start time is determined as the start of the increasing speed and is thus is likely closer to an SIR start time than to a stream interface time, which we use as a *hss_start_time*. For simplicity, we have nevertheless taken the given start time as the hss_start_time. \n",
    "The times in the Earth SIR/HSS list have been modified to 1 hour earlier as these times were originally given for the magnetopause, but the Wind spacecraft is located at the L1 point. One hour is practically equivalent to the propagation time of a 400 km/s slow solar wind from the L1 point to the magnetopause.\n",
    "\n",
    "**To do**: In future updates, we may change hss_start_time to the sir_start_time and add a proper hss_start_time by searching for ptmax after a new sir_start_time. The Grandin et al. (2019) catalogue only contains events for which the solar wind speed reached at least 500 km/s. Lan: \"For Grandin et al. (2019), you can use the peak of total pressure to approximate the stream interface time.\"\n",
    "\n",
    "\n",
    "**MARS SIR/HSS list**: Hui Huang et al., 2019, https://doi.org/10.3847/1538-4357/ab25e9 (open access not available).\n",
    "This catalog gives the sir_start_time, hss_start_time (=stream interface time) and the sir_end_time. \n",
    "\n",
    "**To do**: Similar to the STEREO-list, with have added the hss_end_time.\n",
    "\n",
    "\n",
    "All other parameters are calculated from scratch from the spacecraft data via this notebook or script.\n",
    "\n",
    "---\n",
    "\n",
    "### Other resourcess\n",
    "\n",
    "\n",
    "**Great review on SIRs** by Ian G. Richardson: https://link.springer.com/article/10.1007/s41116-017-0011-z\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
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   "source": [
    "last_update='2023-May-3'"
   ]
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     "text": [
      "/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook sircat.ipynb to script\n",
      "[NbConvertApp] Writing 41801 bytes to sircat.py\n"
     ]
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   ],
   "source": [
    "#start with importing packages, get paths from config.py file and make directories \n",
    "\n",
    "import numpy as np\n",
    "import scipy.io\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.dates import  DateFormatter\n",
    "from datetime import timedelta\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "import astropy\n",
    "import astropy.constants as const\n",
    "from sunpy.time import parse_time\n",
    "import time\n",
    "import pickle\n",
    "import sys\n",
    "import os\n",
    "import urllib\n",
    "import json\n",
    "import importlib\n",
    "import pandas as pd\n",
    "import copy\n",
    "import openpyxl\n",
    "import h5py\n",
    "\n",
    "from heliocats import plot as hp\n",
    "importlib.reload(hp) #reload again while debugging\n",
    "\n",
    "from heliocats import data as hd\n",
    "importlib.reload(hd) #reload again while debugging\n",
    "\n",
    "from heliocats import cats as hc\n",
    "importlib.reload(hc) #reload again while debugging\n",
    "\n",
    "from heliocats import stats as hs\n",
    "importlib.reload(hs) #reload again while debugging\n",
    "\n",
    "#where the in situ data files are located is read \n",
    "#from config.py \n",
    "import config\n",
    "importlib.reload(config)\n",
    "from config import data_path\n",
    "from config import data_path_ML\n",
    "\n",
    "print(data_path)\n",
    "\n",
    "########### make directories first time if not there\n",
    "\n",
    "resdir='results'\n",
    "if os.path.isdir(resdir) == False: os.mkdir(resdir)\n",
    "\n",
    "datadir='data'\n",
    "if os.path.isdir(datadir) == False: os.mkdir(datadir)\n",
    "\n",
    "indexdir='sircat/indices_sircat' \n",
    "if os.path.isdir(indexdir) == False: os.mkdir(indexdir) \n",
    "\n",
    "catdir='sircat'\n",
    "if os.path.isdir(catdir) == False: os.mkdir(catdir)\n",
    "\n",
    "sirplotsdir='sircat/plots_sircat/' \n",
    "if os.path.isdir(sirplotsdir) == False: os.mkdir(sirplotsdir) \n",
    "\n",
    "#Convert this notebook to a script with jupyter nbconvert --to script icmecat.ipynb\n",
    "os.system('jupyter nbconvert --to script sircat.ipynb')    \n",
    "\n",
    "#in situ data files are updated via the icmecat.ipynb notebook \n",
    "    \n",
    "\n",
    "## (1) load data "
   ]
  },
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   "metadata": {},
   "source": [
    "## (1) load data from STEREO-B, STEREO-A, Wind, PSP, and MAVEN\n"
   ]
  },
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     "text": [
      "load STEREO-B data SCEQ\n",
      "load MAVEN data MSO\n",
      "load PSP data SCEQ\n",
      "psp Merging done\n",
      "load and merge STEREO-A data SCEQ\n",
      "STA Merging done\n",
      "Wind merging done\n",
      "\n",
      "\n",
      "time ranges of the in situ data: \n",
      "\n",
      "active spacecraft:\n",
      "Wind                  1995-01-01 2023-04-05\n",
      "STEREO-A              2007-01-01 2023-04-05\n",
      "Parker Solar Probe    2018-10-06 2022-10-30\n",
      "MAVEN                 2014-09-22 2018-01-31\n",
      "\n",
      "missions finished:\n",
      "STEREO-B              2007-01-01 2014-09-27\n",
      "\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "load_data=1\n",
    "\n",
    "if load_data > 0:    \n",
    "        \n",
    "    #print('load Ulysses RTN') #made with heliocats.data.save_ulysses_data\n",
    "    #fileuly='ulysses_1990_2009_rtn.p'\n",
    "    #[uly,huly]=pickle.load(open(data_path+fileuly, \"rb\" ) )      \n",
    "    \n",
    "    print('load STEREO-B data SCEQ') #yearly magplasma files from stereo science center, conversion to SCEQ \n",
    "    filestb='stereob_2007_2014_sceq.p'\n",
    "    [stb,hstb]=pickle.load(open(data_path+filestb, \"rb\" ) )      \n",
    " \n",
    "\n",
    "    ########### CURRENT ACTIVE SPACECRAFT    \n",
    "\n",
    "    \n",
    "    # ADD BepiColombo  \n",
    "    \n",
    "    \n",
    "    # ADD Solar Orbiter\n",
    "    \n",
    "       \n",
    "    print('load MAVEN data MSO') #removed magnetosphere by C. Simon Wedlund, 1 data point per orbit, MSO \n",
    "    #filemav='maven_2014_2018.p'\n",
    "    #[mav,hmav]=pickle.load(open(filemav, 'rb' ) )\n",
    "    #filemav='maven_2014_2018_removed.p'\n",
    "    #[mav,hmav]=pickle.load(open(filemav, 'rb' ) )    \n",
    "    filemav='maven_2014_2018_removed_smoothed.p'\n",
    "    [mav,hmav]=pickle.load(open(data_path+filemav, 'rb' ) )\n",
    "    \n",
    "    #print('load MSL RAD')\n",
    "    #MSL RAD\n",
    "    #rad=hd.load_msl_rad()#, rad.time,rad.dose_sol\n",
    "\n",
    "    \n",
    "    ##############################################\n",
    "    print('load PSP data SCEQ') #from heliosat, converted to SCEQ similar to STEREO-A/B\n",
    "    filepsp='psp_2018_2022_sceq.p'\n",
    "    [psp1,hpsp]=pickle.load(open(data_path+filepsp, \"rb\" ) ) \n",
    "    \n",
    "    #add file with mag only for 2022, plasma needs to be added read_psp.ipynb\n",
    "    \n",
    "    \n",
    "    filepsp2='psp_2022_add_mag_sceq.p'\n",
    "    psp2=pickle.load(open(data_path+filepsp2, \"rb\" ) )   \n",
    "    #cut off psp2 array so that it continues where psp1 stops\n",
    "    psp2=psp2[np.where(psp2.time > parse_time('2021-Dec-30 23:59').datetime)[0]]\n",
    "\n",
    "    #make merged array\n",
    "    psp=np.zeros(np.size(psp1.time)+np.size(psp2.time),dtype=[('time',object),('bx', float),('by', float),\\\n",
    "                ('bz', float),('bt', float),('vt', float),('np', float),('tp', float),\\\n",
    "                ('x', float),('y', float),('z', float),\\\n",
    "                ('r', float),('lat', float),('lon', float)])   \n",
    "\n",
    "    #convert to recarray\n",
    "    psp = psp.view(np.recarray)  \n",
    "    psp.time=np.hstack((psp1.time,psp2.time))\n",
    "    psp.bx=np.hstack((psp1.bx,psp2.bx))\n",
    "    psp.by=np.hstack((psp1.by,psp2.by))\n",
    "    psp.bz=np.hstack((psp1.bz,psp2.bz))\n",
    "    psp.bt=np.hstack((psp1.bt,psp2.bt))\n",
    "    psp.vt=np.hstack((psp1.vt,psp2.vt))\n",
    "    psp.np=np.hstack((psp1.np,psp2.np))\n",
    "    psp.tp=np.hstack((psp1.tp,psp2.tp))\n",
    "    psp.x=np.hstack((psp1.x,psp2.x))\n",
    "    psp.y=np.hstack((psp1.y,psp2.y))\n",
    "    psp.z=np.hstack((psp1.z,psp2.z))\n",
    "    psp.r=np.hstack((psp1.r,psp2.r))\n",
    "    psp.lon=np.hstack((psp1.lon,psp2.lon))\n",
    "    psp.lat=np.hstack((psp1.lat,psp2.lat))\n",
    "    \n",
    "    \n",
    "    #set plasma to nan for 2022\n",
    "    plasma_nan_time=parse_time('2021-12-30T00:00Z').datetime \n",
    "    plasma_nan_ind=np.where(psp.time >= plasma_nan_time)[0]\n",
    "    \n",
    "    psp.vt[np.hstack([plasma_nan_ind])]=np.nan\n",
    "    psp.np[np.hstack([plasma_nan_ind])]=np.nan\n",
    "    psp.tp[np.hstack([plasma_nan_ind])]=np.nan\n",
    "    \n",
    "    \n",
    "        \n",
    "    del psp1\n",
    "    del psp2\n",
    "    \n",
    "    print('psp Merging done')\n",
    "    \n",
    "        ########### STA\n",
    "    \n",
    "    print('load and merge STEREO-A data SCEQ') #yearly magplasma files from stereo science center, conversion to SCEQ \n",
    "    filesta1='stereoa_2007_2020_sceq.p'\n",
    "    sta1=pickle.load(open(data_path+filesta1, \"rb\" ) )  \n",
    "    \n",
    "    #beacon data\n",
    "    #filesta2=\"stereoa_2019_2020_sceq_beacon.p\"\n",
    "    #filesta2='stereoa_2019_2020_sept_sceq_beacon.p'\n",
    "    #filesta2='stereoa_2019_now_sceq_beacon.p'\n",
    "    #filesta2=\"stereoa_2020_august_november_sceq_beacon.p\" \n",
    "    filesta2='stereoa_2020_now_sceq_beacon.p'\n",
    "    \n",
    "    [sta2,hsta2]=pickle.load(open(data_path+filesta2, \"rb\" ) )  \n",
    "    #cutoff with end of science data\n",
    "    sta2=sta2[np.where(sta2.time >= parse_time('2020-Aug-01 00:00').datetime)[0]]\n",
    "\n",
    "    #make array\n",
    "    sta=np.zeros(np.size(sta1.time)+np.size(sta2.time),dtype=[('time',object),('bx', float),('by', float),\\\n",
    "                ('bz', float),('bt', float),('vt', float),('np', float),('tp', float),\\\n",
    "                ('x', float),('y', float),('z', float),\\\n",
    "                ('r', float),('lat', float),('lon', float)])   \n",
    "\n",
    "    #convert to recarray\n",
    "    sta = sta.view(np.recarray)  \n",
    "    sta.time=np.hstack((sta1.time,sta2.time))\n",
    "    sta.bx=np.hstack((sta1.bx,sta2.bx))\n",
    "    sta.by=np.hstack((sta1.by,sta2.by))\n",
    "    sta.bz=np.hstack((sta1.bz,sta2.bz))\n",
    "    sta.bt=np.hstack((sta1.bt,sta2.bt))\n",
    "    sta.vt=np.hstack((sta1.vt,sta2.vt))\n",
    "    sta.np=np.hstack((sta1.np,sta2.np))\n",
    "    sta.tp=np.hstack((sta1.tp,sta2.tp))\n",
    "    sta.x=np.hstack((sta1.x,sta2.x))\n",
    "    sta.y=np.hstack((sta1.y,sta2.y))\n",
    "    sta.z=np.hstack((sta1.z,sta2.z))\n",
    "    sta.r=np.hstack((sta1.r,sta2.r))\n",
    "    sta.lon=np.hstack((sta1.lon,sta2.lon))\n",
    "    sta.lat=np.hstack((sta1.lat,sta2.lat))\n",
    "    \n",
    "    del sta1\n",
    "    del sta2\n",
    "    \n",
    "    print('STA Merging done')\n",
    "\n",
    "\n",
    "\n",
    "    #### Wind\n",
    "\n",
    "    filewin=\"wind_1995_2021_heeq.p\" \n",
    "    [win1,hwin1]=pickle.load(open(data_path+filewin, \"rb\" ) )  \n",
    "\n",
    "    #add new data from 2021 October 31\n",
    "\n",
    "    filewin2=\"wind_2018_now_heeq.p\" \n",
    "    [win2,hwin2]=pickle.load(open(data_path+filewin2, \"rb\" ) )  \n",
    "\n",
    "    #function for spike removal, see list with times in that function\n",
    "    win2=hd.remove_wind_spikes_gaps(win2)\n",
    "\n",
    "    #merge Wind old and new data \n",
    "    win1=win1[np.where(win1.time < parse_time('2021-Oct-31 00:00').datetime)[0]]\n",
    "    #make array\n",
    "    win=np.zeros(np.size(win1.time)+np.size(win2.time),dtype=[('time',object),('bx', float),('by', float),\\\n",
    "                ('bz', float),('bt', float),('vt', float),('np', float),('tp', float),\\\n",
    "                ('x', float),('y', float),('z', float),\\\n",
    "                ('r', float),('lat', float),('lon', float)])   \n",
    "\n",
    "    #convert to recarray\n",
    "    win = win.view(np.recarray)  \n",
    "    win.time=np.hstack((win1.time,win2.time))\n",
    "    win.bx=np.hstack((win1.bx,win2.bx))\n",
    "    win.by=np.hstack((win1.by,win2.by))\n",
    "    win.bz=np.hstack((win1.bz,win2.bz))\n",
    "    win.bt=np.hstack((win1.bt,win2.bt))\n",
    "    win.vt=np.hstack((win1.vt,win2.vt))\n",
    "    win.np=np.hstack((win1.np,win2.np))\n",
    "    win.tp=np.hstack((win1.tp,win2.tp))\n",
    "    win.x=np.hstack((win1.x,win2.x))\n",
    "    win.y=np.hstack((win1.y,win2.y))\n",
    "    win.z=np.hstack((win1.z,win2.z))\n",
    "    win.r=np.hstack((win1.r,win2.r))\n",
    "    win.lon=np.hstack((win1.lon,win2.lon))\n",
    "    win.lat=np.hstack((win1.lat,win2.lat))\n",
    "    \n",
    "    #set plasma to nan thats not here already\n",
    "    plasma_nan_time=parse_time('2023-02-20T00:00Z').datetime \n",
    "    plasma_nan_ind=np.where(win.time >= plasma_nan_time)[0]\n",
    "    \n",
    "    win.vt[np.hstack([plasma_nan_ind])]=np.nan\n",
    "    win.np[np.hstack([plasma_nan_ind])]=np.nan\n",
    "    win.tp[np.hstack([plasma_nan_ind])]=np.nan\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    del win1\n",
    "    del win2\n",
    "\n",
    "\n",
    "    print('Wind merging done')\n",
    "            \n",
    "         \n",
    "print()\n",
    "    \n",
    "print()       \n",
    "print('time ranges of the in situ data: ')    \n",
    "print()\n",
    "print('active spacecraft:')\n",
    "print('Wind                 ',str(win.time[0])[0:10],str(win.time[-1])[0:10])\n",
    "print('STEREO-A             ',str(sta.time[0])[0:10],str(sta.time[-1])[0:10])\n",
    "print('Parker Solar Probe   ',str(psp.time[0])[0:10],str(psp.time[-1])[0:10])\n",
    "print('MAVEN                ',str(mav.time[0])[0:10],str(mav.time[-1])[0:10])\n",
    "#print('MSL/RAD              ',str(rad.time[0])[0:10],str(rad.time[-1])[0:10])\n",
    "print()\n",
    "print('missions finished:')\n",
    "#print('VEX                  ',str(vex.time[0])[0:10],str(vex.time[-1])[0:10])\n",
    "#print('MESSENGER            ',str(mes.time[0])[0:10],str(mes.time[-1])[0:10])\n",
    "print('STEREO-B             ',str(stb.time[0])[0:10],str(stb.time[-1])[0:10])\n",
    "#print('Ulysses              ',str(uly.time[0])[0:10],str(uly.time[-1])[0:10])\n",
    "print()\n",
    "# print('catalogs:')\n",
    "# print()\n",
    "# print('HELCATS HIGeoCAT     ',str(higeocat_time[0])[0:10],str(higeocat_time[-1])[0:10])\n",
    "\n",
    "\n",
    "\n",
    "print('done')"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (2) make SIRCAT masterfile from STEREO and Wind catalogs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we read raw STEREO SIR and Earth SIR catalogs from Robert Allen, Lan Jian, Maxim Grandin, and Hui Huang et al. and convert to master catalog xlsx file that contains all times in a consistent way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### read Allen PSP catalogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Event number', 'Start time', 'End time', 'Time of max P', 'R [au]',\n",
      "       'Max P [nPa]', 'Max B [nT]', 'Max np [cm^-3]', 'Min V [km/s]',\n",
      "       'Max V [km/s]', 'Ambiguous Marker', 'Notes', 'STEREO-A Conjunction',\n",
      "       'L1 conjunction'],\n",
      "      dtype='object')\n",
      "\n",
      "['SIR_PSP_ALLEN_20181114_01', 'PSP', '2018-11-14T20:40:00.000', nan, '2018-11-15T07:25:00.000', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]\n",
      "\n",
      "['SIR_WIND_ALLEN_20190927_01', 'Wind', '2019-09-27T05:00:00.000', nan, '2019-09-27T22:40:00.000', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]\n",
      "\n",
      "['SIR_STEREO_A_ALLEN_20190921_01', 'STEREO-A', '2019-09-21T06:20:00.000', nan, '2019-09-22T01:05:00.000', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]\n",
      "\n",
      "total number\n",
      "46\n"
     ]
    }
   ],
   "source": [
    "#make list for all basic times, ids etc. for master file\n",
    "rows_list1 = []\n",
    "\n",
    "def convert_time(p_time):\n",
    "    \n",
    "    #from Allen catalog format to datetime object\n",
    "    \n",
    "    p_time_obj=[]\n",
    "    for i in np.arange(0,len(p_time)):\n",
    "        p_str=p_time[i][0:10]+'T'+p_time[i][11:16]+'Z'\n",
    "        p_time_obj.append(parse_time(p_str).datetime)    \n",
    "        #print(p_time_obj[i])        \n",
    "        #dates with year 1 set to nan:\n",
    "        if mdates.date2num(p_time_obj[i])< 10: p_time_obj[i]=np.nan\n",
    "            \n",
    "    return p_time_obj\n",
    "\n",
    "\n",
    "#read all Allen catalogs\n",
    "psp_sir_file='sircat/sources/SIR_CIR_List_PSP.csv'\n",
    "psp_l1_sir_file='sircat/sources/SIR_CIR_List_L1_corr_PSP.csv'\n",
    "psp_sta_sir_file='sircat/sources/SIR_CIR_List_STA_corr_PSP.csv'\n",
    "\n",
    "#psp\n",
    "p_raw=pd.read_csv(psp_sir_file, header=46)\n",
    "#wind\n",
    "pw_raw=pd.read_csv(psp_l1_sir_file, header=51)\n",
    "#sta\n",
    "pa_raw=pd.read_csv(psp_sta_sir_file, header=51)\n",
    "\n",
    "print(p_raw.keys())\n",
    "print()\n",
    "\n",
    "\n",
    "\n",
    "########## PSP SIR catalog\n",
    "\n",
    "p_raw['Start time']=convert_time(p_raw['Start time'])\n",
    "p_raw['End time']=convert_time(p_raw['End time'])\n",
    "#p_raw['Time of max P']=convert_time(p_raw['Time of max P'])\n",
    "\n",
    "\n",
    "#generate and put all basic data for this event in a list\n",
    "\n",
    "#skip first event and last 4 due to no data available for us\n",
    "for i in np.arange(1,len(p_raw)-4):\n",
    "    \n",
    "    #make id for event    \n",
    "    id_time=parse_time(p_raw['Start time'][i]).isot\n",
    "    sc_idstring='SIR_PSP_ALLEN_'\n",
    "    sc_string='PSP'        \n",
    "    sircat_id=sc_idstring+id_time[0:4]+id_time[5:7]+id_time[8:10]+'_01'\n",
    "    \n",
    "    list1 = [sircat_id,sc_string,parse_time(p_raw['Start time'][i]).isot,np.nan, parse_time(p_raw['End time'][i]).isot,np.nan, np.nan,np.nan,np.nan,\\\n",
    "             np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,\\\n",
    "             np.nan,np.nan,np.nan]\n",
    "    \n",
    "    #print(list1)    \n",
    "    #append to full list\n",
    "    rows_list1.append(list1)\n",
    "    \n",
    "\n",
    "#remove more events for which we don't have data\n",
    "rows_list1.pop(22)\n",
    "rows_list1.pop(15)\n",
    "rows_list1.pop(8)\n",
    "rows_list1.pop(3)\n",
    "#print example for row\n",
    "print(rows_list1[0])\n",
    "\n",
    "\n",
    "############ Wind catalog\n",
    "print()\n",
    "pw_raw['Start time']=convert_time(pw_raw['Start time'])\n",
    "pw_raw['End time']=convert_time(pw_raw['End time'])\n",
    "#pw_raw['Time of max P']=convert_time(pw_raw['Time of max P'])\n",
    "#print(pw_raw['Start time'])\n",
    "#print(pw_raw['End time'])\n",
    "#print(pw_raw['Time of max P'])\n",
    "\n",
    "for i in np.arange(0,len(pw_raw)):\n",
    "    \n",
    "    #make id for event    \n",
    "    id_time=parse_time(pw_raw['Start time'][i]).isot\n",
    "    sc_idstring='SIR_WIND_ALLEN_'\n",
    "    sc_string='Wind'        \n",
    "    sircat_id=sc_idstring+id_time[0:4]+id_time[5:7]+id_time[8:10]+'_01'\n",
    "    \n",
    "    #put all data for this event in a list\n",
    "    list2 = [sircat_id,sc_string,parse_time(pw_raw['Start time'][i]).isot,np.nan,parse_time(pw_raw['End time'][i]).isot,np.nan, np.nan,np.nan,np.nan,\\\n",
    "             np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,\\\n",
    "             np.nan,np.nan,np.nan]\n",
    "    \n",
    "    #print(list1)    \n",
    "    #append to full list\n",
    "    rows_list1.append(list2)\n",
    "    \n",
    "    \n",
    "print(rows_list1[-1])\n",
    "\n",
    "\n",
    "\n",
    "############STA\n",
    "print()\n",
    "pa_raw['Start time']=convert_time(pa_raw['Start time'])\n",
    "pa_raw['End time']=convert_time(pa_raw['End time'])\n",
    "#pa_raw['Time of max P']=convert_time(pa_raw['Time of max P'])\n",
    "#print(pa_raw['Start time'])\n",
    "#print(pa_raw['End time'])\n",
    "#print(pa_raw['Time of max P'])\n",
    "\n",
    "\n",
    "for i in np.arange(0,len(pa_raw)):\n",
    "    \n",
    "    #make id for event    \n",
    "    id_time=parse_time(pa_raw['Start time'][i]).isot\n",
    "    sc_idstring='SIR_STEREO_A_ALLEN_'\n",
    "    sc_string='STEREO-A'        \n",
    "    sircat_id=sc_idstring+id_time[0:4]+id_time[5:7]+id_time[8:10]+'_01'\n",
    "    \n",
    "    #put all data for this event in a list\n",
    "    list3 = [sircat_id,sc_string,parse_time(pa_raw['Start time'][i]).isot,np.nan, parse_time(pa_raw['End time'][i]).isot, np.nan, np.nan,np.nan,np.nan,\\\n",
    "             np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,\\\n",
    "             np.nan,np.nan,np.nan,np.nan]\n",
    "    \n",
    "    \n",
    "    #print(list1)    \n",
    "    #append to full list\n",
    "    rows_list1.append(list3)\n",
    "\n",
    "\n",
    "\n",
    "print(rows_list1[-1])\n",
    "\n",
    "print()\n",
    "print('total number')\n",
    "print(len(rows_list1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### read Lan Jian STEREO SIR catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load Jian STEREO catalog from excel file: sircat/sources/STEREO_Level3_SIR_data.xlsx\n",
      "Events in STEREO SIR cat: 750\n",
      "\n",
      "['SIR_STEREO_A_JIAN_20200620_01', 'STEREO-A', '2020-06-18T06:28:00.000', nan, '2020-06-19T23:17:00.000', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]\n",
      "\n",
      "total event number\n",
      "750\n"
     ]
    }
   ],
   "source": [
    "#in this excel file some times were brought into a proper time format manually\n",
    "file='sircat/sources/STEREO_Level3_SIR_data.xlsx'\n",
    "print('load Jian STEREO catalog from excel file:', file)\n",
    "sraw=pd.read_excel(file, sheet_name='Sheet2')\n",
    "\n",
    "#get 2 times: HSS start (equivalent to SIR start as defined in the L. Jian catalog), HSS end (where speed again < 450km/s)\n",
    "\n",
    "print('Events in STEREO SIR cat:', sraw.shape[0])\n",
    "print()\n",
    "\n",
    "\n",
    "rows_list2 = []\n",
    "\n",
    "sc=sraw.loc[:,'spacecraft']\n",
    "year_start=sraw.loc[:,'year_start']\n",
    "stime=sraw.loc[:,'start_time']\n",
    "\n",
    "year_end=sraw.loc[:,'year_end']\n",
    "etime=sraw.loc[:,'end_time']\n",
    "\n",
    "year_pt=sraw.loc[:,'year_pt']\n",
    "ptime=sraw.loc[:,'pt_time']\n",
    "\n",
    "\n",
    "for i in np.arange(0,sraw.shape[0]):\n",
    "    \n",
    "\n",
    "    s=stime[i]    \n",
    "    y=year_start[i]\n",
    "    doy=int(s[0:3])\n",
    "    hour=int(s[-5:-3])\n",
    "    minute=int(s[-2:])\n",
    "    #print(y,doy,hour, min)\n",
    "    sir_start_time=datetime.datetime(y,1,1)+timedelta(days=doy-1)+timedelta(hours=hour)+timedelta(minutes=minute)\n",
    "\n",
    "    e=etime[i]    \n",
    "    y=year_end[i]\n",
    "    doy=int(e[0:3])\n",
    "    hour=int(e[-5:-3])\n",
    "    minute=int(e[-2:])\n",
    "    #print(y,doy,hour, min)\n",
    "    sir_end_time=datetime.datetime(y,1,1)+timedelta(days=doy-1)+timedelta(hours=hour)+timedelta(minutes=minute)\n",
    "\n",
    "    #print(i)\n",
    "    p=ptime[i]    \n",
    "    #print(ptime[i])\n",
    "    y=year_pt[i]\n",
    "    doy=int(p[0:3])\n",
    "    hour=int(p[-5:-3])\n",
    "    minute=int(p[-2:])\n",
    "    #print(y,doy,hour, min)\n",
    "    hss_start_time=datetime.datetime(y,1,1)+timedelta(days=doy-1)+timedelta(hours=hour)+timedelta(minutes=minute)\n",
    "    \n",
    "    \n",
    "    #make id for event    \n",
    "    id_time=parse_time(hss_start_time).isot\n",
    "    if sc[i]=='A': sc_idstring='SIR_STEREO_A_JIAN_'\n",
    "    if sc[i]=='B': sc_idstring='SIR_STEREO_B_JIAN_'\n",
    "\n",
    "    if sc[i]=='A': sc_string='STEREO-A'\n",
    "    if sc[i]=='B': sc_string='STEREO-B'\n",
    "        \n",
    "    sircat_id=sc_idstring+id_time[0:4]+id_time[5:7]+id_time[8:10]+'_01'\n",
    "    \n",
    "    #put all data for this event in a list\n",
    "    #list4 = [sircat_id,sc_string,parse_time(sir_start_time).isot,parse_time(hss_start_time).isot, parse_time(sir_end_time).isot,np.nan,np.nan,np.nan,np.nan,\\\n",
    "    #         np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,\\\n",
    "    #         np.nan,np.nan,np.nan]\n",
    "    \n",
    "    \n",
    "    #put all data for this event in a list without hss start time\n",
    "    list4 = [sircat_id,sc_string,parse_time(sir_start_time).isot,np.nan, parse_time(sir_end_time).isot,np.nan,np.nan,np.nan,np.nan,\\\n",
    "             np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,\\\n",
    "             np.nan,np.nan,np.nan]\n",
    "    \n",
    "    #print(list1)    \n",
    "    #append to full list\n",
    "    rows_list2.append(list4)\n",
    "\n",
    "print(rows_list2[-1])    \n",
    "    \n",
    "print()\n",
    "print('total event number')\n",
    "print(len(rows_list2))\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### read Grandin Wind catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load Grandin Earth HSS catalog from: sircat/sources/grandin_2018_list_modified.txt\n",
      "Events in Wind SIR/HSS cat: 586\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rows_list3 = []\n",
    "\n",
    "#Grandin et al. 2018 - OMNI\n",
    "#removed 2 SIRs due to data gap of Wind in oct 2014\n",
    "filewin='sircat/sources/grandin_2018_list_modified.txt'\n",
    "wraw=np.loadtxt(filewin,skiprows=9)\n",
    "print('load Grandin Earth HSS catalog from:', filewin)\n",
    "print('Events in Wind SIR/HSS cat:', wraw.shape[0])\n",
    "print()\n",
    "\n",
    "#2 times: SIR/HSS start, HSS end (where speed again < 450km/s)\n",
    "\n",
    "#begin with 2007\n",
    "begin2007=np.where(wraw[:,1]>=2007)[0][0]\n",
    "\n",
    "\n",
    "for i in np.arange(begin2007,len(wraw),1):\n",
    "\n",
    "    \n",
    "    #SIR HSS start time y,m,d,h,m - minus 1 hour for Wind at L1, not magnetopause\n",
    "    wstart=datetime.datetime(wraw[i,1].astype(int),wraw[i,2].astype(int), \\\n",
    "                             wraw[i,3].astype(int),wraw[i,4].astype(int), \\\n",
    "                             0)-datetime.timedelta(hours=1) \n",
    "    #SIR HSS end time y,m,d,h,m - minus 1 hour for Wind at L1, not magnetopause\n",
    "    wend=datetime.datetime(wraw[i,11].astype(int),wraw[i,12].astype(int), \\\n",
    "                             wraw[i,13].astype(int),wraw[i,14].astype(int), \\\n",
    "                             0)-datetime.timedelta(hours=1)\n",
    "\n",
    "\n",
    "    sc_idstring='SIR_WIND_GRANDIN_'\n",
    "    id_time=parse_time(wstart).isot\n",
    "    sircat_id=sc_idstring+id_time[0:4]+id_time[5:7]+id_time[8:10]+'_01'\n",
    "    sc_string='Wind'\n",
    "    \n",
    "    list5 = [sircat_id,sc_string,np.nan,parse_time(wstart).isot,np.nan,parse_time(wend).isot,np.nan,np.nan,np.nan,\\\n",
    "             np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,\\\n",
    "             np.nan,np.nan,np.nan]\n",
    "    \n",
    "    #print(list2)\n",
    "\n",
    "    rows_list3.append(list5)\n",
    "\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### read MAVEN catalog   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load MAVEN Huang SIR catalog from  sircat/sources/Huang_2019_SIR_MAVEN_table_1.xlsx\n",
      "Events in MAVEN SIR/HSS cat: 88\n",
      "\n",
      "['SIR_MAVEN_HUANG_20180129_01', 'MAVEN', '2018-01-29T09:42:00.000', '2018-01-29T17:07:00.000', '2018-01-30T15:17:00.000', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]\n",
      "\n",
      "total number\n",
      "364\n"
     ]
    }
   ],
   "source": [
    "from heliocats import data as hd\n",
    "importlib.reload(hd) #reload again while debugging\n",
    "\n",
    "#this is a recarray    \n",
    "mavsir_all=hd.load_maven_sir_huang()\n",
    "\n",
    "#check which events overlap with the available MAVEN data\n",
    "mavsir_ind=np.where(mavsir_all.start < mav.time[-1])[0]\n",
    "mavsir=mavsir_all[mavsir_ind]\n",
    "   \n",
    "print('Events in MAVEN SIR/HSS cat:', mavsir.shape[0])\n",
    "print()\n",
    "\n",
    "\n",
    "#go through all events\n",
    "for i in mavsir_ind:\n",
    "    \n",
    "    sc_idstring='SIR_MAVEN_HUANG_'\n",
    "    id_time=parse_time(mavsir.start[i][0]).isot\n",
    "    sircat_id=sc_idstring+id_time[0:4]+id_time[5:7]+id_time[8:10]+'_01'\n",
    "    sc_string='MAVEN'\n",
    "   \n",
    "    list6 = [sircat_id,sc_string,parse_time(mavsir.start[i][0]).isot,parse_time(mavsir.si[i][0]).isot,parse_time(mavsir.end[i][0]).isot,np.nan,np.nan,np.nan,np.nan,\\\n",
    "             np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,\\\n",
    "             np.nan,np.nan,np.nan]\n",
    "    \n",
    "    #print(list3)\n",
    "\n",
    "    rows_list3.append(list6)\n",
    "\n",
    "print(rows_list3[-1])\n",
    "\n",
    "print()\n",
    "print('total number')\n",
    "print(len(rows_list3))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combine to master file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SIRCAT master saved as sircat/HELIO4CAST_SIRCAT_v10_master.xlsx\n",
      "total events 1160\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "rows_list=rows_list1+rows_list2+rows_list3\n",
    "\n",
    "\n",
    "################ make pandas data frame for master file\n",
    "        \n",
    "parameters =['sircat_id','sc_insitu','sir_start_time','hss_start_time','sir_end_time',\\\n",
    "             'hss_end_time','hss_vtmax_time','sir_vtmax_time','sc_heliodistance',\\\n",
    "             'sc_long_heeq', 'sc_lat_heeq', \n",
    "             'hss_vtmax','hss_vtmean','hss_vtstd','hss_btmax','hss_btmean',\\\n",
    "             'hss_btstd','hss_bzmin', 'hss_bzmean','hss_bzstd','hss_duration',\\\n",
    "             'sir_vtmax','sir_vtmean', 'sir_vtstd','sir_btmax','sir_btmean',\\\n",
    "             'sir_btstd','sir_bzmin', 'sir_bzmean','sir_bzstd','sir_duration']\n",
    "\n",
    "\n",
    "master=pd.DataFrame(rows_list,columns=parameters)\n",
    "\n",
    "#sort by spacecraft indicator and start time\n",
    "master=master.sort_values(['sc_insitu','hss_start_time'])\n",
    "master = master.reset_index(drop=True) #drop extra index value\n",
    "\n",
    "master\n",
    "\n",
    "\n",
    "#save master file as Excel\n",
    "file='sircat/HELIO4CAST_SIRCAT_v10_master.xlsx'\n",
    "master.to_excel(file,sheet_name='SIRCATv1.0')\n",
    "print()\n",
    "print('SIRCAT master saved as '+file)\n",
    "print('total events', master.shape[0])\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (3) make SIRCAT \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load HELCATS SIRCAT from file: sircat/HELIO4CAST_SIRCAT_v10_master.xlsx\n",
      "done\n",
      "Get parameters for  PSP\n",
      "position\n",
      "hss\n",
      "sir\n",
      "Get parameters for  STEREO-B\n",
      "position\n",
      "hss\n",
      "sir\n",
      "Get parameters for  MAVEN\n",
      "position\n",
      "hss\n",
      "sir\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chris/python/heliocats/heliocats/cats.py:819: RuntimeWarning: All-NaN slice encountered\n",
      "  scat.at[sci[i],'sir_vtmax']=np.round(np.nanmax(sc.vt[sir_start_ind[i]:sir_end_ind[i]]),1)\n",
      "/Users/chris/python/heliocats/heliocats/cats.py:821: RuntimeWarning: Mean of empty slice\n",
      "  scat.at[sci[i],'sir_vtmean']=np.round(np.nanmean(sc.vt[sir_start_ind[i]:sir_end_ind[i]]),1)\n",
      "/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1878: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
      "  var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/Users/chris/python/heliocats/heliocats/cats.py:826: RuntimeWarning: All-NaN slice encountered\n",
      "  scat.at[sci[i],'sir_btmax']=np.round(np.nanmax(sc.bt[sir_start_ind[i]:sir_end_ind[i]]),1)\n",
      "/Users/chris/python/heliocats/heliocats/cats.py:828: RuntimeWarning: Mean of empty slice\n",
      "  scat.at[sci[i],'sir_btmean']=np.round(np.nanmean(sc.bt[sir_start_ind[i]:sir_end_ind[i]]),1)\n",
      "/Users/chris/python/heliocats/heliocats/cats.py:832: RuntimeWarning: All-NaN slice encountered\n",
      "  scat.at[sci[i],'sir_bzmin']=np.round(np.nanmin(sc.bz[sir_start_ind[i]:sir_end_ind[i]]),1)\n",
      "/Users/chris/python/heliocats/heliocats/cats.py:833: RuntimeWarning: Mean of empty slice\n",
      "  scat.at[sci[i],'sir_bzmean']=np.round(np.nanmean(sc.bz[sir_start_ind[i]:sir_end_ind[i]]),1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved as  sircat/plots_sircat/SIR_STEREO_A_ALLEN_20180808_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_ALLEN_20180811_01.png\n",
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      "saved as  sircat/plots_sircat/SIR_STEREO_A_ALLEN_20180907_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_ALLEN_20181026_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_ALLEN_20181102_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_ALLEN_20181122_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_ALLEN_20181201_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_ALLEN_20190730_01.png\n",
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      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20111116_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20111120_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20111123_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20111201_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20111208_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20111217_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20111219_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20111228_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20120106_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20120123_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20120204_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20120221_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20120226_01.png\n",
      "saved as  sircat/plots_sircat/SIR_STEREO_A_JIAN_20120309_01.png\n",
      "Unexpected exception formatting exception. Falling back to standard exception\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3505, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"/var/folders/tr/3wb8vxgd5sj97xz5dz09q0qm0000gn/T/ipykernel_2152/3255924089.py\", line 48, in <module>\n",
      "    hp.plot_sircat_events(sta,stai,scat,'STEREO-A',sirplotsdir)\n",
      "  File \"/Users/chris/python/heliocats/heliocats/plot.py\", line 306, in plot_sircat_events\n",
      "    plot_insitu_sircat_mag_plasma(sc[sir_start_ind[i]-60*2*24:sir_end_ind[i]+3*60*24],\\\n",
      "  File \"/Users/chris/python/heliocats/heliocats/plot.py\", line 572, in plot_insitu_sircat_mag_plasma\n",
      "    plt.savefig(plotfile)\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/matplotlib/pyplot.py\", line 960, in savefig\n",
      "    fig.canvas.draw_idle()  # Need this if 'transparent=True', to reset colors.\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/matplotlib/backend_bases.py\", line 2054, in draw_idle\n",
      "    self.draw(*args, **kwargs)\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py\", line 405, in draw\n",
      "    self.figure.draw(self.renderer)\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/matplotlib/artist.py\", line 74, in draw_wrapper\n",
      "    result = draw(artist, renderer, *args, **kwargs)\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/matplotlib/artist.py\", line 51, in draw_wrapper\n",
      "    return draw(artist, renderer)\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/matplotlib/figure.py\", line 3082, in draw\n",
      "    mimage._draw_list_compositing_images(\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/matplotlib/image.py\", line 131, in _draw_list_compositing_images\n",
      "    a.draw(renderer)\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/matplotlib/artist.py\", line 51, in draw_wrapper\n",
      "    return draw(artist, renderer)\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/matplotlib/axes/_base.py\", line 3100, in draw\n",
      "    mimage._draw_list_compositing_images(\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/matplotlib/image.py\", line 131, in _draw_list_compositing_images\n",
      "    a.draw(renderer)\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/matplotlib/artist.py\", line 51, in draw_wrapper\n",
      "    return draw(artist, renderer)\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/matplotlib/lines.py\", line 800, in draw\n",
      "    renderer.draw_path(gc, tpath, affine.frozen())\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py\", line 146, in draw_path\n",
      "    self._renderer.draw_path(gc, path, transform, rgbFace)\n",
      "KeyboardInterrupt\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 2102, in showtraceback\n",
      "    stb = self.InteractiveTB.structured_traceback(\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/IPython/core/ultratb.py\", line 1310, in structured_traceback\n",
      "    return FormattedTB.structured_traceback(\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/IPython/core/ultratb.py\", line 1199, in structured_traceback\n",
      "    return VerboseTB.structured_traceback(\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/IPython/core/ultratb.py\", line 1052, in structured_traceback\n",
      "    formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/IPython/core/ultratb.py\", line 978, in format_exception_as_a_whole\n",
      "    frames.append(self.format_record(record))\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/IPython/core/ultratb.py\", line 878, in format_record\n",
      "    frame_info.lines, Colors, self.has_colors, lvals\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/IPython/core/ultratb.py\", line 712, in lines\n",
      "    return self._sd.lines\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/stack_data/utils.py\", line 145, in cached_property_wrapper\n",
      "    value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/stack_data/core.py\", line 698, in lines\n",
      "    pieces = self.included_pieces\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/stack_data/utils.py\", line 145, in cached_property_wrapper\n",
      "    value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/stack_data/core.py\", line 649, in included_pieces\n",
      "    pos = scope_pieces.index(self.executing_piece)\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/stack_data/utils.py\", line 145, in cached_property_wrapper\n",
      "    value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/stack_data/core.py\", line 628, in executing_piece\n",
      "    return only(\n",
      "  File \"/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/executing/executing.py\", line 164, in only\n",
      "    raise NotOneValueFound('Expected one value, found 0')\n",
      "executing.executing.NotOneValueFound: Expected one value, found 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1350x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from heliocats import cats as hc\n",
    "importlib.reload(hc) #reload again while debugging\n",
    "\n",
    "from heliocats import plot as hp\n",
    "importlib.reload(hp) #reload again while debugging\n",
    "\n",
    "#load master file\n",
    "scat=hc.load_helio4cast_sircat_master_from_excel('sircat/HELIO4CAST_SIRCAT_v10_master.xlsx')\n",
    "scat\n",
    "\n",
    "####### 3a get indices for all spacecraft\n",
    "wini=np.where(scat.sc_insitu == 'Wind')[:][0] \n",
    "pspi=np.where(scat.sc_insitu == 'PSP')[:][0] \n",
    "stai=np.where(scat.sc_insitu == 'STEREO-A')[:][0]    \n",
    "stbi=np.where(scat.sc_insitu == 'STEREO-B')[:][0]    \n",
    "mavi=np.where(scat.sc_insitu == 'MAVEN')[:][0]    \n",
    "\n",
    "print('done')\n",
    "\n",
    "####### 3b get parameters for all spacecraft one after another\n",
    "# remove indices if the  events in the master file have changed\n",
    "#os.system('rm sircat/indices_sircat/SIRCAT_indices_Wind.p')\n",
    "#os.system('rm sircat/indices_sircat/SIRCAT_indices_STEREO-A.p')\n",
    "#os.system('rm sircat/indices_sircat/SIRCAT_indices_STEREO-B.p')\n",
    "#os.system('rm sircat/indices_sircat/SIRCAT_indices_MAVEN.p')\n",
    "#os.system('rm sircat/indices_sircat/SIRCAT_indices_PSP.p')\n",
    "\n",
    "#sir times\n",
    "scat=hc.get_sircat_parameters(psp,pspi,scat,'PSP')\n",
    "scat=hc.get_sircat_parameters(stb,stbi,scat,'STEREO-B')\n",
    "\n",
    "#both allen and jian cats\n",
    "#scat=hc.get_sircat_parameters(sta,stai,scat,'STEREO-A')\n",
    "\n",
    "\n",
    "scat=hc.get_sircat_parameters(mav,mavi,scat,'MAVEN')\n",
    "\n",
    "\n",
    "\n",
    "# ###### 3c make all plots if wanted\n",
    "#matplotlib.use('Agg')\n",
    "#hp.plot_sircat_events(psp,pspi,scat,'PSP',sirplotsdir)\n",
    "\n",
    "#hp.plot_sircat_events(stb,stbi,scat,'STEREO-B',sirplotsdir)\n",
    "#hp.plot_sircat_events(mav,mavi,scat,'MAVEN',sirplotsdir)\n",
    "\n",
    "\n",
    "hp.plot_sircat_events(sta,stai,scat,'STEREO-A',sirplotsdir)\n",
    "hp.plot_sircat_events(win,wini,scat,'Wind',sirplotsdir)\n",
    "\n",
    "#hss times\n",
    "#scat=hc.get_sircat_parameters(win,wini,scat,'Wind')\n",
    "scat\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#save as txt\n",
    "file='sircat/HELIO4CAST_SIRCAT_v10.txt'\n",
    "np.savetxt(file, scat.values.astype(str), fmt='%s' )\n",
    "print('SIRCAT saved as '+file)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#sir times\n",
    "scat=hc.get_sircat_parameters(psp,pspi,scat,'PSP')\n",
    "scat=hc.get_sircat_parameters(mav,mavi,scat,'MAVEN')\n",
    "\n",
    "\n",
    "#both allen and jian cats\n",
    "scat=hc.get_sircat_parameters(sta,stai,scat,'STEREO-A')\n",
    "\n",
    "\n",
    "# ###### 3c make all plots if wanted\n",
    "#matplotlib.use('Agg')\n",
    "#hp.plot_sircat_events(sta,stai,scat,'STEREO-A',sirplotsdir)\n",
    "#hp.plot_sircat_events(stb,stbi,scat,'STEREO-B',sirplotsdir)\n",
    "#hp.plot_sircat_events(win,wini,scat,'Wind',sirplotsdir)\n",
    "#hp.plot_sircat_events(mav,mavi,scat,'MAVEN',sirplotsdir)\n",
    "#hp.plot_sircat_events(psp,pspi,scat,'PSP',sirplotsdir)\n",
    "\n",
    "\n",
    "print('done')\n",
    "\n",
    "\n",
    "#kick out MAVEN events without data\n",
    "\n",
    "\n",
    "############### sort SIRCAt by date\n",
    "scat = scat.sort_values(by='hss_start_time',ascending=False)\n",
    "scat = scat.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (4) save SIRCAT "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4a save header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SIR CATALOGUE v1.0 \n",
      "\n",
      "This is the HELIO4CAST stream interaction region (SIR) and high speed stream (HSS) catalog,\n",
      "based on in situ magnetic field and bulk plasma observations in the heliosphere. \n",
      "It is a merged catalog created from individual ones made by Robert Allen et al., Lan Jian et al., Maxim Grandin et al. and Hui Huang et al. (see references).\n",
      "\n",
      "This is version 1.0, released 2020-06-10, updated 2023-May-3 doi: 10.6084/m9.figshare.12416906 \n",
      "\n",
      "The catalog is available as  python pandas dataframe (pickle), json, csv, xlsx, txt, html at \n",
      "https://helioforecast.space/sircat \n",
      "\n",
      "Number of events in SIRCAT: 1169 \n",
      "ICME observatories: Parker Solar Probe, Wind, STEREO-A, STEREO-B, MAVEN   \n",
      "Time ranges: Parker Solar Probe: Oct 2018 - May 2020, Wind: Jan 2007 - Sep 2019, STEREO-A/B: Jan 2007 - Sep 2019, MAVEN: Dec 2014 - Jan 2018. \n",
      "\n",
      "Authors: Christian Moestl, Andreas J. Weiss, R. L. Bailey, Martin A. Reiss, GeoSphere Austria / NASA Goddard. \n",
      "Robert Allen, JHU/APL, USA; Lan Jian, NASA, USA; Maxim Grandin, University of Helsinki, Finland; Hui Huang, Beijing University, China. \n",
      "\n",
      "Rules: If results are produced with this catalog for peer-reviewed scientific publications, \n",
      "please contact chris.moestl@outlook.com, robert.allen@jhuapl.edu, lan.jian@nasa.gov, maxime.grandin@helsinki.fi for possible co-authorships. \n",
      "\n",
      "This catalog has been made by getting the start and end times of each high speed stream from the \n",
      "individual catalogs, and then calculating all parameters again consistently from the data by us. \n",
      "The in situ data that were used for the creating catalog, with a size of 8 GB in total, including extra data \n",
      "files with magnetic field components in RTN coordinates and other spacecrat that are not used for producing this catalog, \n",
      "can be downloaded in python pickle format as recarrays from https://doi.org/10.6084/m9.figshare.11973693.v7 \n",
      "The python code for producing this catalog is available at https://github.com/cmoestl/heliocats sircat.ipynb \n",
      "\n",
      "Each sircat_id has a tag in it that indicates from which catalog the ICME times were taken: \n",
      "\n",
      "Parker Solar Probe: Allen et al. 2021, tag: ALLEN, \n",
      "Wind:       Grandin et al. (2019), tag: GRANDIN \n",
      "STEREO-A:   Jian et al. (2019), tag: JIAN. \n",
      "STEREO-B:   Jian et al. (2019), tag: JIAN. \n",
      "MAVEN:      Huang et al. (2019), tag: HUANG. \n",
      "\n",
      "References \n",
      "Allen et al. (2021), https://doi.org/10.1051/0004-6361/202039833 \n",
      "Grandin, M. et al. (2019), https://doi.org/10.1029/2018JA026396 \n",
      "Jian, L. et al. (2019), https://doi.org/10.1007/s11207-019-1416-8 \n",
      "Huang, H. et al. (2019), https://doi.org/10.3847/1538-4357/ab25e9 \n",
      "\n",
      "Comments: \n",
      "- The STEREO catalog contains the SIR start, stream interface and SIR end times. We use their stream interface time as our hss_start_time. \n",
      "- The MAVEN catalog has similar times as the STEREO catalog.\n",
      "- Earth SIR/HSS list: This catalog directly gives the hss_start_time and the hss_end_time, but no SIR times.  \n",
      "- The times in the Earth SIR/HSS list have been modified to 1 hour earlier as these times were \n",
      "originally given for the magnetopause, but the Wind spacecraft is located at the L1 point. \n",
      "One hour is practically equivalent to the propagation time of a 400 km/s slow solar wind \n",
      "from the L1 point to the magnetopause.\n",
      "- Spacecraft positions are given in Heliocentric Earth Equatorial Coordinates (HEEQ) coordinates. \n",
      "- The coordinate system for all magnetic field components is SCEQ, except for Wind (HEEQ, which is the equivalent for SCEQ for Earth). \n",
      "        Definition of SpaceCraft Equatorial Coordinates (SCEQ): \n",
      "        Z is the solar rotation axis. \n",
      "        Y is the cross product of Z and R, with R being the vector that points from the Sun to the spacecraft.\n",
      "        X completes the right handed triad (and points away from the Sun). \n",
      "This system is thus like HEEQ but centered on the respective in situ spacecraft, so the SCEQ X and Y \n",
      "base vectors are rotated by the HEEQ longitude of the in situ spacecraft from HEEQ X and Y.\n",
      "The Y vector is similar to the T vector in an RTN system for each spacecraft, but the X and Z vectors \n",
      "are rotated around Y compared to an RTN system. The differences between RTN and SCEQ for spacecraft within \n",
      "a few degrees of the solar equatorial plane are very small (within a few 0.1 nT usually).\n",
      "We choose SCEQ because it has the advantage that a comparison between multipoint CME events \n",
      "and for comparison to simulations there is always a similar reference plane (the solar equatorial plane). \n",
      "\n",
      " \n",
      "Parameters:\n",
      "00: sircat_id: The unique identifier for the observed stream interaction region (SIR). unit: string. \n",
      "01: sc insitu: The name of the in situ observing spacecraft. unit: string. \n",
      "02: sir_start_time: Stream interaction region start time. unit: UTC. \n",
      "03: hss_start_time: High speed stream start time, equal to the stream interface time (for STEREO, MAVEN catalogs). unit: UTC. \n",
      "04: sir_end_time: End time of the stream interaction region. unit: UTC. \n",
      "05: hss_end_time: High speed stream end time, criterion at Wind: speed < 450 km/s. unit: UTC. \n",
      "06: hss_vtmax_time: High speed stream maxmimum speed time. unit: UTC. \n",
      "07: sc_heliodistance: Heliocentric distance of the spacecraft at hss_start_time. unit: AU.\n",
      "08: sc_long_heeq: Heliospheric longitude of the spacecraft at hss_start_time, range [-180,180]. unit: degree (HEEQ).\n",
      "09: sc_lat_heeq: Heliospheric latitude of the spacecraft at hss_start_time, range [-90,90]. unit: degree (HEEQ).\n",
      "10: hss_vt_max: Maximum proton speed from hss_start_time to hss_end_time. unit: km/s.\n",
      "11: hss_vt_mean: Mean proton speed from hss_start_time to hss_end_time. unit: km/s.\n",
      "12: hss_vt_std: Standard deviation of proton speed from hss_start_time to hss_end_time. unit: km/s.\n",
      "13: hss_vt_mean: Mean proton speed from hss_start_time to hss_end_time. unit: km/s.\n",
      "14: hss_bt_max: Maximum total magnetic field from hss_start_time to hss_end_time. unit: nT.\n",
      "15: hss_bt_mean: Mean total magnetic field from hss_start_time to hss_end_time. unit: nT.\n",
      "16: hss_bt_std: Standard deviation of total magnetic field from hss_start_time to hss_end_time. unit: nT.\n",
      "17: hss_bz_min: Minimum Bz component (SCEQ) from hss_start_time to hss_end_time. unit: nT.\n",
      "18: hss_bz_mean: Mean Bz component (SCEQ) from hss_start_time to hss_end_time. unit: nT.\n",
      "19: hss_bz_std: Standard deviation of Bz component (SCEQ) from hss_start_time to hss_end_time. unit: nT.\n",
      "20: hss_duration: Duration of high speed stream from hss_start_time to hss_end_time. unit: hours.\n",
      "21: sir_vt_mean: Mean proton speed from hss_start_time to sir_end_time. unit: km/s.\n",
      "22: sir_vt_std: Standard deviation of proton speed from sir_start_time to hss_end_time. unit: km/s.\n",
      "23: sir_vt_mean: Mean proton speed from hss_start_time to sir_end_time. unit: km/s.\n",
      "24: sir_bt_max: Maximum total magnetic field from sir_start_time to hss_end_time. unit: nT.\n",
      "25: sir_bt_mean: Mean total magnetic field from sir_start_time to sir_end_time. unit: nT.\n",
      "26: sir_bt_std: Standard deviation of total magnetic field from sir_start_time to sir_end_time. unit: nT.\n",
      "27: sir_bz_min: Minimum Bz component (SCEQ) from sir_start_time to sir_end_time. unit: nT.\n",
      "28: sir_bz_mean: Mean Bz component (SCEQ) from sir_start_time to sir_end_time. unit: nT.\n",
      "29: sir_bz_std: Standard deviation of Bz component (SCEQ) from sir_start_time to sir_end_time. unit: nT.\n",
      "30: sir_duration: Duration of stream interaction region from sir_start_time to sir_end_time. unit: hours.\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "header saved as sircat/HELIO4CAST_SIRCAT_v10_header.txt\n",
      "\n",
      "header converted to HTML\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#save header and parameters as text file and prepare for html website\n",
    "header='SIR CATALOGUE v1.0 \\n\\n\\\n",
    "This is the HELIO4CAST stream interaction region (SIR) and high speed stream (HSS) catalog,\\n\\\n",
    "based on in situ magnetic field and bulk plasma observations in the heliosphere. \\n\\\n",
    "It is a merged catalog created from individual ones made by Robert Allen et al., Lan Jian et al., Maxim Grandin et al. and Hui Huang et al. (see references).\\n\\n\\\n",
    "This is version 1.0, released 2020-06-10, updated '+last_update+' doi: 10.6084/m9.figshare.12416906 \\n\\n\\\n",
    "The catalog is available as  python pandas dataframe (pickle), json, csv, xlsx, txt, html at \\n\\\n",
    "https://helioforecast.space/sircat \\n\\n\\\n",
    "Number of events in SIRCAT: '+str(len(scat))+' \\n\\\n",
    "ICME observatories: Parker Solar Probe, Wind, STEREO-A, STEREO-B, MAVEN   \\n\\\n",
    "Time ranges: Parker Solar Probe: Oct 2018 - May 2020, Wind: Jan 2007 - Sep 2019, STEREO-A/B: Jan 2007 - Sep 2019, MAVEN: Dec 2014 - Jan 2018. \\n\\n\\\n",
    "Authors: Christian Moestl, Andreas J. Weiss, R. L. Bailey, Martin A. Reiss, GeoSphere Austria / NASA Goddard. \\n\\\n",
    "Robert Allen, JHU/APL, USA; Lan Jian, NASA, USA; Maxim Grandin, University of Helsinki, Finland; Hui Huang, Beijing University, China. \\n\\n\\\n",
    "Rules: If results are produced with this catalog for peer-reviewed scientific publications, \\n\\\n",
    "please contact chris.moestl@outlook.com, robert.allen@jhuapl.edu, lan.jian@nasa.gov, maxime.grandin@helsinki.fi for possible co-authorships. \\n\\n\\\n",
    "This catalog has been made by getting the start and end times of each high speed stream from the \\n\\\n",
    "individual catalogs, and then calculating all parameters again consistently from the data by us. \\n\\\n",
    "The in situ data that were used for the creating catalog, with a size of 8 GB in total, including extra data \\n\\\n",
    "files with magnetic field components in RTN coordinates and other spacecrat that are not used for producing this catalog, \\n\\\n",
    "can be downloaded in python pickle format as recarrays from https://doi.org/10.6084/m9.figshare.11973693.v7 \\n\\\n",
    "The python code for producing this catalog is available at https://github.com/cmoestl/heliocats sircat.ipynb \\n\\n\\\n",
    "Each sircat_id has a tag in it that indicates from which catalog the ICME times were taken: \\n\\n\\\n",
    "Parker Solar Probe: Allen et al. 2021, tag: ALLEN, \\n\\\n",
    "Wind:       Grandin et al. (2019), tag: GRANDIN \\n\\\n",
    "STEREO-A:   Jian et al. (2019), tag: JIAN. \\n\\\n",
    "STEREO-B:   Jian et al. (2019), tag: JIAN. \\n\\\n",
    "MAVEN:      Huang et al. (2019), tag: HUANG. \\n\\n\\\n",
    "References \\n\\\n",
    "Allen et al. (2021), https://doi.org/10.1051/0004-6361/202039833 \\n\\\n",
    "Grandin, M. et al. (2019), https://doi.org/10.1029/2018JA026396 \\n\\\n",
    "Jian, L. et al. (2019), https://doi.org/10.1007/s11207-019-1416-8 \\n\\\n",
    "Huang, H. et al. (2019), https://doi.org/10.3847/1538-4357/ab25e9 \\n\\n\\\n",
    "Comments: \\n\\\n",
    "- The STEREO catalog contains the SIR start, stream interface and SIR end times. We use their stream interface time as our hss_start_time. \\n\\\n",
    "- The MAVEN catalog has similar times as the STEREO catalog.\\n\\\n",
    "- Earth SIR/HSS list: This catalog directly gives the hss_start_time and the hss_end_time, but no SIR times.  \\n\\\n",
    "- The times in the Earth SIR/HSS list have been modified to 1 hour earlier as these times were \\n\\\n",
    "originally given for the magnetopause, but the Wind spacecraft is located at the L1 point. \\n\\\n",
    "One hour is practically equivalent to the propagation time of a 400 km/s slow solar wind \\n\\\n",
    "from the L1 point to the magnetopause.\\n\\\n",
    "- Spacecraft positions are given in Heliocentric Earth Equatorial Coordinates (HEEQ) coordinates. \\n\\\n",
    "- The coordinate system for all magnetic field components is SCEQ, except for Wind (HEEQ, which is the equivalent for SCEQ for Earth). \\n\\\n",
    "        Definition of SpaceCraft Equatorial Coordinates (SCEQ): \\n\\\n",
    "        Z is the solar rotation axis. \\n\\\n",
    "        Y is the cross product of Z and R, with R being the vector that points from the Sun to the spacecraft.\\n\\\n",
    "        X completes the right handed triad (and points away from the Sun). \\n\\\n",
    "This system is thus like HEEQ but centered on the respective in situ spacecraft, so the SCEQ X and Y \\n\\\n",
    "base vectors are rotated by the HEEQ longitude of the in situ spacecraft from HEEQ X and Y.\\n\\\n",
    "The Y vector is similar to the T vector in an RTN system for each spacecraft, but the X and Z vectors \\n\\\n",
    "are rotated around Y compared to an RTN system. The differences between RTN and SCEQ for spacecraft within \\n\\\n",
    "a few degrees of the solar equatorial plane are very small (within a few 0.1 nT usually).\\n\\\n",
    "We choose SCEQ because it has the advantage that a comparison between multipoint CME events \\n\\\n",
    "and for comparison to simulations there is always a similar reference plane (the solar equatorial plane). \\n\\n '     \n",
    "\n",
    "\n",
    "parameters_text='Parameters:\\n\\\n",
    "00: sircat_id: The unique identifier for the observed stream interaction region (SIR). unit: string. \\n\\\n",
    "01: sc insitu: The name of the in situ observing spacecraft. unit: string. \\n\\\n",
    "02: sir_start_time: Stream interaction region start time. unit: UTC. \\n\\\n",
    "03: hss_start_time: High speed stream start time, equal to the stream interface time (for STEREO, MAVEN catalogs). unit: UTC. \\n\\\n",
    "04: sir_end_time: End time of the stream interaction region. unit: UTC. \\n\\\n",
    "05: hss_end_time: High speed stream end time, criterion at Wind: speed < 450 km/s. unit: UTC. \\n\\\n",
    "06: hss_vtmax_time: High speed stream maxmimum speed time. unit: UTC. \\n\\\n",
    "07: sc_heliodistance: Heliocentric distance of the spacecraft at hss_start_time. unit: AU.\\n\\\n",
    "08: sc_long_heeq: Heliospheric longitude of the spacecraft at hss_start_time, range [-180,180]. unit: degree (HEEQ).\\n\\\n",
    "09: sc_lat_heeq: Heliospheric latitude of the spacecraft at hss_start_time, range [-90,90]. unit: degree (HEEQ).\\n\\\n",
    "10: hss_vt_max: Maximum proton speed from hss_start_time to hss_end_time. unit: km/s.\\n\\\n",
    "11: hss_vt_mean: Mean proton speed from hss_start_time to hss_end_time. unit: km/s.\\n\\\n",
    "12: hss_vt_std: Standard deviation of proton speed from hss_start_time to hss_end_time. unit: km/s.\\n\\\n",
    "13: hss_vt_mean: Mean proton speed from hss_start_time to hss_end_time. unit: km/s.\\n\\\n",
    "14: hss_bt_max: Maximum total magnetic field from hss_start_time to hss_end_time. unit: nT.\\n\\\n",
    "15: hss_bt_mean: Mean total magnetic field from hss_start_time to hss_end_time. unit: nT.\\n\\\n",
    "16: hss_bt_std: Standard deviation of total magnetic field from hss_start_time to hss_end_time. unit: nT.\\n\\\n",
    "17: hss_bz_min: Minimum Bz component (SCEQ) from hss_start_time to hss_end_time. unit: nT.\\n\\\n",
    "18: hss_bz_mean: Mean Bz component (SCEQ) from hss_start_time to hss_end_time. unit: nT.\\n\\\n",
    "19: hss_bz_std: Standard deviation of Bz component (SCEQ) from hss_start_time to hss_end_time. unit: nT.\\n\\\n",
    "20: hss_duration: Duration of high speed stream from hss_start_time to hss_end_time. unit: hours.\\n\\\n",
    "21: sir_vt_mean: Mean proton speed from hss_start_time to sir_end_time. unit: km/s.\\n\\\n",
    "22: sir_vt_std: Standard deviation of proton speed from sir_start_time to hss_end_time. unit: km/s.\\n\\\n",
    "23: sir_vt_mean: Mean proton speed from hss_start_time to sir_end_time. unit: km/s.\\n\\\n",
    "24: sir_bt_max: Maximum total magnetic field from sir_start_time to hss_end_time. unit: nT.\\n\\\n",
    "25: sir_bt_mean: Mean total magnetic field from sir_start_time to sir_end_time. unit: nT.\\n\\\n",
    "26: sir_bt_std: Standard deviation of total magnetic field from sir_start_time to sir_end_time. unit: nT.\\n\\\n",
    "27: sir_bz_min: Minimum Bz component (SCEQ) from sir_start_time to sir_end_time. unit: nT.\\n\\\n",
    "28: sir_bz_mean: Mean Bz component (SCEQ) from sir_start_time to sir_end_time. unit: nT.\\n\\\n",
    "29: sir_bz_std: Standard deviation of Bz component (SCEQ) from sir_start_time to sir_end_time. unit: nT.\\n\\\n",
    "30: sir_duration: Duration of stream interaction region from sir_start_time to sir_end_time. unit: hours.\\n\\n\\n'\n",
    "\n",
    "print(header)\n",
    "print(parameters_text)\n",
    "\n",
    "\n",
    "#make header file\n",
    "file='sircat/HELIO4CAST_SIRCAT_v10_header.txt'\n",
    "with open(file, \"w\") as text_file:\n",
    "    text_file.write(header)\n",
    "    text_file.write(parameters_text)\n",
    "print()    \n",
    "print('header saved as '+file)\n",
    "print()    \n",
    "\n",
    "#Convert to html regarding line breaks, paragraph beginning and spaces\n",
    "header_spaces=header.replace(\" \", \"&nbsp;\")\n",
    "header_html= \"<p>\" +header_spaces.replace('\\n', '<br>')+ \"</p>\" \n",
    "parameters_spaces=parameters_text.replace(\" \", \"&nbsp;\")\n",
    "parameters_html= \"<p>\" +parameters_text.replace('\\n', '<br>')+ \"</p>\"\n",
    "print('header converted to HTML')\n",
    "print()    \n",
    "print()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4b save into different formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SIRCAT saved as sircat/HELIO4CAST_SIRCAT_v10_pandas.p\n",
      "SIRCAT saved as sircat/HELIO4CAST_SIRCAT_v10.xlsx\n",
      "SIRCAT saved as sircat/HELIO4CAST_SIRCAT_v10.json\n",
      "SIRCAT saved as sircat/HELIO4CAST_SIRCAT_v10.csv\n",
      "SIRCAT saved as sircat/HELIO4CAST_SIRCAT_v10.txt\n",
      "SIRCAT saved as sircat/HELIO4CAST_SIRCAT_v10_isot.json\n",
      "SIRCAT saved as sircat/HELIO4CAST_SIRCAT_v10_simple.html\n",
      "SIRCAT saved as sircat/HELIO4CAST_SIRCAT_v10.html\n"
     ]
    }
   ],
   "source": [
    "########## python formats\n",
    "\n",
    "# save ICMECAT as pandas dataframe with times as datetime objects as pickle\n",
    "file='sircat/HELIO4CAST_SIRCAT_v10_pandas.p'\n",
    "pickle.dump([scat,header,parameters], open(file, 'wb'))\n",
    "print('SIRCAT saved as '+file)\n",
    "\n",
    "\n",
    "#load sircat as pandas dataframe\n",
    "file='sircat/HELIO4CAST_SIRCAT_v10_pandas.p'\n",
    "[scat_pandas,h,p]=pickle.load( open(file, 'rb'))   \n",
    "scat.keys()\n",
    "scat\n",
    "\n",
    "\n",
    "####### get indices again for all spacecraft after sorting\n",
    "wini=np.where(scat.sc_insitu == 'Wind')[:][0] \n",
    "pspi=np.where(scat.sc_insitu == 'PSP')[:][0] \n",
    "stai=np.where(scat.sc_insitu == 'STEREO-A')[:][0]    \n",
    "stbi=np.where(scat.sc_insitu == 'STEREO-B')[:][0]    \n",
    "mavi=np.where(scat.sc_insitu == 'MAVEN')[:][0]   \n",
    "\n",
    "\n",
    "# # save SIRCAT as numpy array with times as matplotlib datetime as pickle\n",
    "# scat_num=copy.deepcopy(scat) \n",
    "# scat_num.icme_start_time=parse_time(scat_num.icme_start_time).plot_date\n",
    "# scat_num.mo_start_time=parse_time(scat_num.mo_start_time).plot_date\n",
    "# scat_num.mo_end_time=parse_time(scat_num.mo_end_time).plot_date\n",
    "# #convert to recarray\n",
    "# scat_num_rec=scat_num.to_records()\n",
    "# #create structured array\n",
    "# dtype1=[('index','i8'),('icmecat_id', '<U30'),('sc_insitu', '<U20')] +[(i, '<f8') for i in ic.keys()[2:len(ic.keys())]]\n",
    "# scat_num_struct=np.array(scat_num_rec,dtype=dtype1)\n",
    "\n",
    "\n",
    "\n",
    "# file='icmecat/HELIO4CAST_ICMECAT_v20_numpy.p'\n",
    "# pickle.dump([scat_num,scat_num_struct,header,parameters], open(file, 'wb'))\n",
    "# print('ICMECAT saved as '+file)\n",
    "\n",
    "\n",
    "################ save to different formats\n",
    "\n",
    "\n",
    "#get beginning of tags for STA to identify allen and jian events\n",
    "tag_list=[]\n",
    "for i in np.arange(0,len(scat)):\n",
    "    tag_list.append(scat.sircat_id[i][13]) #j\n",
    "\n",
    "stai_jian=np.where(np.logical_and(scat.sc_insitu == 'STEREO-A',np.array(tag_list)=='J'))[:][0] \n",
    "stai_allen=np.where(np.logical_and(scat.sc_insitu == 'STEREO-A',np.array(tag_list)=='A'))[:][0] \n",
    "\n",
    "#get indices of all SIR spacecraft in SIRCAT\n",
    "sir_sc=np.hstack([stai_jian,stbi,mavi])\n",
    "\n",
    "\n",
    "#get indices of all HSS spacecraft in SIRCAT\n",
    "hss_sc=np.hstack([pspi,wini,stai_allen])\n",
    "\n",
    "#copy pandas dataframe first to change time format consistent with HELIO4CAST\n",
    "scat_copy=copy.deepcopy(scat)\n",
    "\n",
    "for i in sir_sc:  \n",
    "    \n",
    "    scat_copy.at[i,'sir_start_time']=parse_time(scat.sir_start_time[i]).isot[0:16]+'Z'\n",
    "    scat_copy.at[i,'sir_end_time']=parse_time(scat.sir_end_time[i]).isot[0:16]+'Z'\n",
    "    \n",
    "for i in hss_sc:    \n",
    "    scat_copy.at[i,'hss_start_time']=parse_time(scat.hss_start_time[i]).isot[0:16]+'Z'\n",
    "    scat_copy.at[i,'hss_end_time']=parse_time(scat.hss_end_time[i]).isot[0:16]+'Z'\n",
    "    #scat_copy.at[i,'hss_vtmax_time']=parse_time(scat.hss_vtmax_time[i]).isot[0:16]+'Z'\n",
    "    \n",
    "scat_copy\n",
    "\n",
    "\n",
    "#save as Excel\n",
    "file='sircat/HELIO4CAST_SIRCAT_v10.xlsx'\n",
    "scat_copy.to_excel(file,sheet_name='SIRCATv1.0')\n",
    "print('SIRCAT saved as '+file)\n",
    "\n",
    "#save as json\n",
    "file='sircat/HELIO4CAST_SIRCAT_v10.json'\n",
    "scat_copy.to_json(file)\n",
    "print('SIRCAT saved as '+file)\n",
    "\n",
    "#save as csv\n",
    "file='sircat/HELIO4CAST_SIRCAT_v10.csv'\n",
    "scat_copy.to_csv(file)\n",
    "print('SIRCAT saved as '+file)\n",
    "\n",
    "#save as txt\n",
    "file='sircat/HELIO4CAST_SIRCAT_v10.txt'\n",
    "np.savetxt(file, scat_copy.values.astype(str), fmt='%s' )\n",
    "print('SIRCAT saved as '+file)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#copy pandas dataframe first to change time format consistent with HELIO4CAST\n",
    "scat_copy2=copy.deepcopy(scat)  \n",
    "\n",
    "for i in sir_sc:  \n",
    "    \n",
    "    scat_copy2.at[i,'sir_start_time']=parse_time(scat.sir_start_time[i]).iso[0:16]+'Z'\n",
    "    scat_copy2.at[i,'sir_end_time']=parse_time(scat.sir_end_time[i]).iso[0:16]+'Z'\n",
    "    \n",
    "for i in hss_sc:    \n",
    "    scat_copy2.at[i,'hss_start_time']=parse_time(scat.hss_start_time[i]).iso[0:16]+'Z'\n",
    "    scat_copy2.at[i,'hss_end_time']=parse_time(scat.hss_end_time[i]).iso[0:16]+'Z'\n",
    "    #scat_copy.at[i,'hss_vtmax_time']=parse_time(scat.hss_vtmax_time[i]).isot[0:16]+'Z'\n",
    "\n",
    "\n",
    "#save as json for webpage with different time format\n",
    "file='sircat/HELIO4CAST_SIRCAT_v10_isot.json'\n",
    "scat_copy2.to_json(file)\n",
    "print('SIRCAT saved as '+file)\n",
    "\n",
    "\n",
    "#save as html no header\n",
    "file='sircat/HELIO4CAST_SIRCAT_v10_simple.html'\n",
    "scat_copy.to_html(file)\n",
    "print('SIRCAT saved as '+file)\n",
    "\n",
    "\n",
    "############ save as html file with header\n",
    "#save as html\n",
    "file='sircat/HELIO4CAST_SIRCAT_v10.html'\n",
    "#ic.to_html(file,justify='center')\n",
    "\n",
    "#ichtml='{% extends \"_base.html\" %} \\n \\n {% block content %} \\n \\n \\n '\n",
    "ichtml = header_html\n",
    "ichtml += parameters_html\n",
    "ichtml += scat_copy.to_html()\n",
    "#ichtml +='\\n \\n {% endblock %}'\n",
    "\n",
    "\n",
    "with open(file,'w') as f:\n",
    "    f.write(ichtml)\n",
    "    f.close()\n",
    "    \n",
    "print('SIRCAT saved as '+file)    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4c load ICMECAT pickle files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sircat_id</th>\n",
       "      <th>sc_insitu</th>\n",
       "      <th>sir_start_time</th>\n",
       "      <th>hss_start_time</th>\n",
       "      <th>sir_end_time</th>\n",
       "      <th>hss_end_time</th>\n",
       "      <th>hss_vtmax_time</th>\n",
       "      <th>sc_heliodistance</th>\n",
       "      <th>sc_long_heeq</th>\n",
       "      <th>sc_lat_heeq</th>\n",
       "      <th>...</th>\n",
       "      <th>sir_vtmax</th>\n",
       "      <th>sir_vtmean</th>\n",
       "      <th>sir_vtstd</th>\n",
       "      <th>sir_btmax</th>\n",
       "      <th>sir_btmean</th>\n",
       "      <th>sir_btstd</th>\n",
       "      <th>sir_bzmin</th>\n",
       "      <th>sir_bzmean</th>\n",
       "      <th>sir_bzstd</th>\n",
       "      <th>sir_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SIR_PSP_ALLEN_20211114_01</td>\n",
       "      <td>PSP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-11-14 01:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-11-14 09:40:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3152</td>\n",
       "      <td>-44.68</td>\n",
       "      <td>3.59</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SIR_PSP_ALLEN_20210907_01</td>\n",
       "      <td>PSP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-09-07 01:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-09-08 09:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6727</td>\n",
       "      <td>-36.76</td>\n",
       "      <td>3.07</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SIR_PSP_ALLEN_20210821_01</td>\n",
       "      <td>PSP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-08-21 22:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-08-22 15:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4281</td>\n",
       "      <td>-40.27</td>\n",
       "      <td>2.12</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SIR_PSP_ALLEN_20210806_01</td>\n",
       "      <td>PSP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-08-06 02:20:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-08-06 06:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1936</td>\n",
       "      <td>78.21</td>\n",
       "      <td>2.65</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SIR_PSP_ALLEN_20210618_01</td>\n",
       "      <td>PSP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-06-18 01:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-06-18 19:00:00</td>\n",
       "      <td>2021-06-18 15:09:00</td>\n",
       "      <td>0.7821</td>\n",
       "      <td>55.07</td>\n",
       "      <td>3.55</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1164</th>\n",
       "      <td>SIR_WIND_GRANDIN_20070225_01</td>\n",
       "      <td>Wind</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-02-25 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-03-02 14:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9809</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-7.17</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>SIR_WIND_GRANDIN_20070212_01</td>\n",
       "      <td>Wind</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-02-12 08:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-02-18 01:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9772</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-5.74</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1166</th>\n",
       "      <td>SIR_WIND_GRANDIN_20070128_01</td>\n",
       "      <td>Wind</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-01-28 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-02-02 23:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9772</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-5.74</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167</th>\n",
       "      <td>SIR_WIND_GRANDIN_20070114_01</td>\n",
       "      <td>Wind</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-01-14 08:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-01-22 04:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9771</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-4.81</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1168</th>\n",
       "      <td>SIR_WIND_GRANDIN_20070109_01</td>\n",
       "      <td>Wind</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-01-09 04:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-01-12 05:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9771</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-4.81</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1169 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         sircat_id sc_insitu sir_start_time  \\\n",
       "0        SIR_PSP_ALLEN_20211114_01       PSP            NaN   \n",
       "1        SIR_PSP_ALLEN_20210907_01       PSP            NaN   \n",
       "2        SIR_PSP_ALLEN_20210821_01       PSP            NaN   \n",
       "3        SIR_PSP_ALLEN_20210806_01       PSP            NaN   \n",
       "4        SIR_PSP_ALLEN_20210618_01       PSP            NaN   \n",
       "...                            ...       ...            ...   \n",
       "1164  SIR_WIND_GRANDIN_20070225_01      Wind            NaN   \n",
       "1165  SIR_WIND_GRANDIN_20070212_01      Wind            NaN   \n",
       "1166  SIR_WIND_GRANDIN_20070128_01      Wind            NaN   \n",
       "1167  SIR_WIND_GRANDIN_20070114_01      Wind            NaN   \n",
       "1168  SIR_WIND_GRANDIN_20070109_01      Wind            NaN   \n",
       "\n",
       "           hss_start_time sir_end_time         hss_end_time  \\\n",
       "0     2021-11-14 01:30:00          NaN  2021-11-14 09:40:00   \n",
       "1     2021-09-07 01:30:00          NaN  2021-09-08 09:30:00   \n",
       "2     2021-08-21 22:00:00          NaN  2021-08-22 15:30:00   \n",
       "3     2021-08-06 02:20:00          NaN  2021-08-06 06:30:00   \n",
       "4     2021-06-18 01:30:00          NaN  2021-06-18 19:00:00   \n",
       "...                   ...          ...                  ...   \n",
       "1164  2007-02-25 00:00:00          NaN  2007-03-02 14:00:00   \n",
       "1165  2007-02-12 08:00:00          NaN  2007-02-18 01:00:00   \n",
       "1166  2007-01-28 00:00:00          NaN  2007-02-02 23:00:00   \n",
       "1167  2007-01-14 08:00:00          NaN  2007-01-22 04:00:00   \n",
       "1168  2007-01-09 04:00:00          NaN  2007-01-12 05:00:00   \n",
       "\n",
       "           hss_vtmax_time  sc_heliodistance  sc_long_heeq  sc_lat_heeq  ...  \\\n",
       "0                     NaN            0.3152        -44.68         3.59  ...   \n",
       "1                     NaN            0.6727        -36.76         3.07  ...   \n",
       "2                     NaN            0.4281        -40.27         2.12  ...   \n",
       "3                     NaN            0.1936         78.21         2.65  ...   \n",
       "4     2021-06-18 15:09:00            0.7821         55.07         3.55  ...   \n",
       "...                   ...               ...           ...          ...  ...   \n",
       "1164                  NaN            0.9809          0.05        -7.17  ...   \n",
       "1165                  NaN            0.9772          0.17        -5.74  ...   \n",
       "1166                  NaN            0.9772          0.17        -5.74  ...   \n",
       "1167                  NaN            0.9771          0.19        -4.81  ...   \n",
       "1168                  NaN            0.9771          0.19        -4.81  ...   \n",
       "\n",
       "      sir_vtmax  sir_vtmean  sir_vtstd  sir_btmax  sir_btmean  sir_btstd  \\\n",
       "0           NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "1           NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "2           NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "3           NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "4           NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "...         ...         ...        ...        ...         ...        ...   \n",
       "1164        NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "1165        NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "1166        NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "1167        NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "1168        NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "\n",
       "      sir_bzmin  sir_bzmean  sir_bzstd  sir_duration  \n",
       "0           NaN         NaN        NaN           NaN  \n",
       "1           NaN         NaN        NaN           NaN  \n",
       "2           NaN         NaN        NaN           NaN  \n",
       "3           NaN         NaN        NaN           NaN  \n",
       "4           NaN         NaN        NaN           NaN  \n",
       "...         ...         ...        ...           ...  \n",
       "1164        NaN         NaN        NaN           NaN  \n",
       "1165        NaN         NaN        NaN           NaN  \n",
       "1166        NaN         NaN        NaN           NaN  \n",
       "1167        NaN         NaN        NaN           NaN  \n",
       "1168        NaN         NaN        NaN           NaN  \n",
       "\n",
       "[1169 rows x 30 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#load sircat as pandas dataframe\n",
    "file='sircat/HELIO4CAST_SIRCAT_v10_pandas.p'\n",
    "[scat_pandas,h,p]=pickle.load( open(file, 'rb'))   \n",
    "scat.keys()\n",
    "scat\n",
    "\n",
    "#load icmecat as numpy array\n",
    "# file='icmecat/HELIO4CAST_ICMECAT_v20_numpy.p'\n",
    "# [ic_nprec,ic_np,h,p]=pickle.load( open(file, 'rb'))   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sircat_id', 'sc_insitu', 'sir_start_time', 'hss_start_time',\n",
       "       'sir_end_time', 'hss_end_time', 'hss_vtmax_time', 'sc_heliodistance',\n",
       "       'sc_long_heeq', 'sc_lat_heeq', 'hss_vtmax', 'hss_vtmean', 'hss_vtstd',\n",
       "       'hss_btmax', 'hss_btmean', 'hss_btstd', 'hss_bzmin', 'hss_bzmean',\n",
       "       'hss_bzstd', 'hss_duration', 'sir_vtmax', 'sir_vtmean', 'sir_vtstd',\n",
       "       'sir_btmax', 'sir_btmean', 'sir_btstd', 'sir_bzmin', 'sir_bzmean',\n",
       "       'sir_bzstd', 'sir_duration'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scat_pandas\n",
    "scat_pandas.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Duration [hours]')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make distribution plots\n",
    "plt.figure(20,figsize=(12,5), dpi=100)\n",
    "sns.histplot(scat.sc_heliodistance)\n",
    "plt.xlabel('Distance [AU]')\n",
    "\n",
    "plt.figure(21,figsize=(12,5), dpi=100)\n",
    "sns.histplot(scat.hss_vtmean,kde=True, label='HSS mean(Vt)')\n",
    "sns.histplot(scat.hss_vtstd, kde=True, label='HSS std(Vt)')\n",
    "sns.histplot(scat.hss_vtmax, kde=True, label='HSS max(Vt)')\n",
    "plt.legend()\n",
    "plt.xlabel('V [km/s]')\n",
    "plt.xlim(0,1200)\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(22,figsize=(12,5), dpi=100)\n",
    "sns.histplot(scat.sir_vtmean,kde=True, label='SIR mean(Vt)')\n",
    "sns.histplot(scat.sir_vtstd, kde=True, label='SIR std(Vt)')\n",
    "sns.histplot(scat.sir_vtmax, kde=True, label='SIR max(Vt)')\n",
    "plt.legend()\n",
    "plt.xlim(0,1200)\n",
    "plt.xlabel('V [km/s]')\n",
    "\n",
    "\n",
    "plt.figure(23,figsize=(12,5), dpi=100)\n",
    "sns.histplot(scat.hss_btmean,kde=True, label='HSS <Bt>')\n",
    "sns.histplot(scat.hss_bzmin,kde=True, label='HSS min Bz>')\n",
    "plt.legend()\n",
    "plt.xlabel('B [nT]')\n",
    "\n",
    "plt.figure(24,figsize=(12,5), dpi=100)\n",
    "sns.histplot(scat.hss_duration, kde=True, label='HSS')\n",
    "sns.histplot(scat.sir_duration, kde=True, label='SIR')\n",
    "plt.legend()\n",
    "plt.xlabel('Duration [hours]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}