diff --git a/ConjugateMap.ipynb b/ConjugateMap.ipynb
deleted file mode 100644
index 27ee411..0000000
--- a/ConjugateMap.ipynb
+++ /dev/null
@@ -1,1159 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {
-    "81116f74-46c6-4f80-a610-b6aac8fdbe1f.PNG": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "d5dd5daf-c6a0-4b89-9773-d97314daff7b",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Geomagnetic Conjugate Map\n",
-    "The purpose of this notebook is to gather relevant stations and mapping information for a geomagnetic conjugate map, from Lanzerotti 1987:\n",
-    "\n",
-    "![lanzerotti1987.PNG](attachment:81116f74-46c6-4f80-a610-b6aac8fdbe1f.PNG)\n",
-    "\n",
-    "Papers on field line tracing: \n",
-    "- https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008SW000391\n",
-    "- https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/jgra.50137\n",
-    "- https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014JA020264"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7c2caf78-4aad-4dc3-955f-8930304a22e8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "import json\n",
-    "import ogr\n",
-    "import geopandas as gpd\n",
-    "import aacgmv2\n",
-    "import datetime as dt\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import plotly.graph_objects as go\n",
-    "\n",
-    "# smag = __import__('supermag-api')          # SuperMAG python API\n",
-    "# logon = 'kd8oxt'                              # SuperMAG ID\n",
-    "\n",
-    "# # For pulling data from CDAweb:\n",
-    "# from ai import cdas\n",
-    "# import datetime\n",
-    "# from matplotlib import pyplot as plt\n",
-    "\n",
-    "import gpxpy\n",
-    "import gpxpy.gpx\n",
-    "\n",
-    "import geopandas\n",
-    "\n",
-    "# converting from geographic to GSM\n",
-    "from geopack import geopack as gp \n",
-    "from geopack import t89\n",
-    "\n",
-    "# Import functions:\n",
-    "import datetime\n",
-    "from conjCalcFunctions import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "63f37086-aa0c-4450-b943-b93660a6befc",
-   "metadata": {},
-   "source": [
-    "The following functions are imported from `conjCalcFunctions.py`:\n",
-    " - `findconj()` : function to compute conjugate points for a given location at a given time and date. \n",
-    " - `conjcalc()` : function to take in a dataframe and add columns for all stages of calculating conjugate points.\n",
-    " - `calc_mlat_rings()` : function to calculate magnetic graticules for a given latitude and datetime."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cdb4ef66-2a41-40f7-898f-53ceabcc6284",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(calc_mlat_rings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b2a0e98a-519e-4f5d-91e6-71976b2bef44",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ut = dt.datetime(1980, 11, 3, 18, 0, 0)\n",
-    "lat, lon = [-64, -64]\n",
-    "\n",
-    "print(\"Geopack: \")\n",
-    "print(findconj(lat, lon, ut, method=\"geopack\"))\n",
-    "print(\"AACGM: \")\n",
-    "print(findconj(lat, lon, ut, method=\"aacgm\"))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "29685dad-9706-447d-b1e1-c8423bbb6947",
-   "metadata": {},
-   "source": [
-    "help(conjcalc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6cbd391c-2afa-4919-9cb9-6a3e6af93d43",
-   "metadata": {},
-   "source": [
-    "## Stations and Features\n",
-    "First, let's collect stations and features of interest into pandas dataframes. (Make sure to include dates of operation wherever possible - want to make sure that we have that information when creating historical maps later!)\n",
-    "\n",
-    "I'm collecting station information here: https://docs.google.com/spreadsheets/d/1DYVxUyF0DDpw7SoCTTIANWcNzMouLW6NB8Slq00Icc8/edit?usp=sharing\n",
-    "\n",
-    "A version of this is also included in the `\\input` folder."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ac2c57a2-ae9a-494b-bfa0-687ff259d751",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "stations = pd.read_csv('https://docs.google.com/spreadsheets/d/' + \n",
-    "                   '1DYVxUyF0DDpw7SoCTTIANWcNzMouLW6NB8Slq00Icc8'+\n",
-    "                   # '/edit#gid=0'\n",
-    "                   # '0Ak1ecr7i0wotdGJmTURJRnZLYlV3M2daNTRubTdwTXc' +\n",
-    "                   '/export?gid=0&format=csv',\n",
-    "                   # Set first column as rownames in data frame\n",
-    "                   index_col=0,\n",
-    "                   # Parse column values to datetime\n",
-    "                   parse_dates=['Start', 'End']\n",
-    "                  )\n",
-    "# Comment in this line instead of the line above to use the local copy:\n",
-    "# stations = pd.read_csv('input/PGC Conjugate Map Points.csv',\n",
-    "#                    # Set first column as rownames in data frame\n",
-    "#                    index_col=0,\n",
-    "#                    # Parse column values to datetime\n",
-    "#                    parse_dates=['Start', 'End']\n",
-    "#                   )\n",
-    "stations[\"Source\"] = \"Manual\"\n",
-    "stations.head() "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ccc1a84-d772-4c80-b690-3f87ffee84f8",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Northern Hemisphere Stations\n",
-    "- [ ] All-sky cameras\n",
-    "- [X] Full set of magnetometer networks from SuperMAG\n",
-    "\n",
-    "Let's pull some more Northern hemisphere stations from the list of instruments in the Madrigal database, at http://cedar.openmadrigal.org/instMetadata. (It would be good form to use the `madrigalWeb` python package for this if we need more data.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "22cf401c-c026-4ba3-b45d-9096de3d2a59",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# madrigal = pd.read_csv(\"madrigal.csv\")\n",
-    "madrigal = pd.read_html(\"http://cedar.openmadrigal.org/instMetadata\")[0]\n",
-    "madrigal = madrigal.rename(columns={\"3-letter mnemonic\": \"ID\", \"Latitude\": \"GLAT\", \"Longitude (-180-180)\":\"GLON\"})\n",
-    "madrigal = madrigal.set_index(\"Name\")\n",
-    "madrigal[\"Source\"] = \"Madrigal\"\n",
-    "\n",
-    "# add to stations list:\n",
-    "stations = pd.concat([stations, madrigal], ignore_index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52f7a372-fbf7-4f78-baf2-596bb121bb03",
-   "metadata": {},
-   "source": [
-    "Downloading SUPERMAG's list of stations from https://supermag.jhuapl.edu/mag/?fidelity=low&tab=stationinfo&start=2001-01-01T00%3A00%3A00.000Z&interval=23%3A59#:\n",
-    "\n",
-    "(I've edited the operators column of the spreadsheet to make it digestible to pandas...)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "385511e1-73de-4c76-8e13-8cc4368d5f1a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "supermag = pd.read_csv(\"20230803-11-15-supermag-stations.csv\", sep = ',') \n",
-    "\n",
-    "supermag = supermag.rename(columns={\"STATION-NAME\":\"Name\", \"IAGA\": \"ID\", \"GEOLAT\": \"GLAT\", \"GEOLON\":\"GLON\", \"OP1\":\"Network\"})\n",
-    "supermag = supermag.set_index(\"Name\")\n",
-    "supermag[\"Category\"] = \"Magnetometers\"\n",
-    "supermag[\"Source\"] = \"SuperMAG\"\n",
-    "\n",
-    "# add to stations list:\n",
-    "stations = pd.concat([stations, supermag], ignore_index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b8ebdbf-4a74-4e54-a972-e0ceaf0d6052",
-   "metadata": {},
-   "source": [
-    "Alternatively, we can pull data using the SuperMAG API:"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "38e7d425-b31b-450c-b04f-c62555ef069b",
-   "metadata": {},
-   "source": [
-    "start=[2019,11,15,10,40,00] # alt: start='2019-11-15T10:40'\n",
-    "(status,gmags) = smag.SuperMAGGetInventory(logon, start,3600)\n",
-    "print(gmags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d3addc8-2e69-4f5a-98b8-a259127f0b55",
-   "metadata": {},
-   "source": [
-    "### Antarctic Stations\n",
-    "We'll import COMNAP data from https://github.com/PolarGeospatialCenter/comnap-antarctic-facilities/tree/master"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b9b17536-7cda-48ce-a003-2bd402ff5d29",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = \"https://github.com/PolarGeospatialCenter/comnap-antarctic-facilities/raw/master/dist/COMNAP_Antarctic_Facilities_Master.xls\"\n",
-    "\n",
-    "comnap_df = pd.read_excel(url)\n",
-    "\n",
-    "comnap = comnap_df.rename(columns={\"English Name\":\"Name\", \"Type\": \"Category\", \"Latitude (DD)\": \"GLAT\", \"Longitude (DD)\":\"GLON\", \"Operator (primary)\":\"Nation\"})\n",
-    "comnap = comnap.set_index(\"Name\")\n",
-    "comnap[\"Source\"] = \"COMNAP\"\n",
-    "\n",
-    "# add to stations list:\n",
-    "stations = pd.concat([stations, comnap], ignore_index=False)\n",
-    "\n",
-    "# stations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2b24e558-e1ff-4baf-af56-3c524be894cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import plotly.express as px\n",
-    "fig = px.scatter_geo(comnap_df,\n",
-    "                    lat=comnap_df[\"Latitude (DD)\"],\n",
-    "                    lon=comnap_df[\"Longitude (DD)\"],\n",
-    "                    hover_name=\"English Name\",\n",
-    "                    color = \"Type\")\n",
-    "fig.update_geos(projection_type=\"orthographic\")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2ddda896-6f40-4b31-badc-9dcf516c8b9f",
-   "metadata": {},
-   "source": [
-    "## Finalizing station table\n",
-    "Before we start doing coordinate transforms, let's drop stations that have NaN longitude values and aggregate entries to the extent we can. \n",
-    "This next line reduces the large station table to a fairly representative set of test values - it can be commmented in for testing purposes, or out to create the full table."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8ebc24a0-9a2c-408e-9d33-6230f9c1b9a9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# stations = stations.loc[['PG0', 'PG1', 'PG2', 'PG3', 'PG4', 'PG5', 'Vernadsky', 'South Pole Station', 'Syowa', 'Palmer', 'McMurdo', 'Cape Disappointment', 'South Pole Station', 'Bor', 'Clyde River']]\n",
-    "stations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61a257f9-3737-4739-b3d6-02080cb855e0",
-   "metadata": {},
-   "source": [
-    "We can filter out the stations from a single network if we like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a881490e-d35e-435f-aca0-3201f47722ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stations[stations.Network==\"AALPIP\"]\n",
-    "pd.unique(stations.Institution)\n",
-    "# stations[stations.Institution==\"BAS\"]"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "ed0c0f0c-09da-4b13-9db5-d57254ec7afe",
-   "metadata": {},
-   "source": [
-    "try:\n",
-    "    stations['GLON'] = pd.to_numeric(stations['GLON'], errors='coerce')\n",
-    "except Exception as e:\n",
-    "    # stations = stations.dropna(subset=['GLON'])\n",
-    "    print(e)\n",
-    "\n",
-    "stations['GLON'] = stations['GLON'].apply(lambda glon: glon - 360 if glon > 180 else glon)\n",
-    "stations"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "c1ec99c8-09b7-4b34-b495-7b456040633c",
-   "metadata": {},
-   "source": [
-    "# Testing conjcalc() with just lat and lon data....\n",
-    "\n",
-    "df2 = stations[['GLAT', 'GLON']].copy()\n",
-    "# conjcalc(df2)\n",
-    "# print(df2)\n",
-    "conjcalc(df2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f779e464-3d15-42f1-8404-1453348d7c4c",
-   "metadata": {},
-   "source": [
-    "### Eclipse Paths\n",
-    "Shunrong Zhang computed paths at various elevations for relevant eclipses. These can be found in the `/input` file."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "e3cd5544-e625-4d18-acec-e1db7b96e1dc",
-   "metadata": {},
-   "source": [
-    "# url = 'http://aeronomy.haystack.mit.edu/share/eclipse_2017/eclipse20170821.txt.000km.hiRes'\n",
-    "# filename = 'input/eclipse20170821.txt.000km.hiRes'\n",
-    "filename = 'input/eclipse20240408.txt.150km.hiRes'\n",
-    "header = [\"YEAR/M/DD\", \"HH:MM:SS\",  \"UTC\", \"MIN Magni\", \"LAT\", \"LONG\"]\n",
-    "#Min Magni = minimum obscuration magnitude (ratio), based on the fraction of the visible solar disk area screened by the Moon\n",
-    "eclipse = pd.read_csv(filename, sep='\\s+', names = header)\n",
-    "\n",
-    "df = eclipse[['LAT', 'LONG']]\n",
-    "gpx = gpxpy.gpx.GPX()\n",
-    "\n",
-    "# Create first track in our GPX:\n",
-    "gpx_track = gpxpy.gpx.GPXTrack()\n",
-    "gpx.tracks.append(gpx_track)\n",
-    "\n",
-    "# Create first segment in our GPX track:\n",
-    "gpx_segment = gpxpy.gpx.GPXTrackSegment()\n",
-    "gpx_track.segments.append(gpx_segment)\n",
-    "\n",
-    "# Create points:\n",
-    "for idx in df.index:\n",
-    "    gpx_segment.points.append(gpxpy.gpx.GPXTrackPoint(df.loc[idx, 'LAT'], df.loc[idx, 'LONG']))\n",
-    "\n",
-    "# print(gpx.to_xml())\n",
-    "# with open('output/eclipse.gpx', 'w') as f:\n",
-    "#     f.write(gpx.to_xml())\n",
-    "\n",
-    "fig = px.scatter_geo(df, lat=df[\"LAT\"], lon=df[\"LONG\"])\n",
-    "fig.update_geos(projection_type=\"orthographic\")\n",
-    "fig.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be87dd69-f842-4fa7-93e3-8298dc395045",
-   "metadata": {},
-   "source": [
-    "Code to convert all the eclipse paths to .gpx files (only needed to run this once. Run `pip install gpxpy` if you need to rerun it)."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "f67ba71e-43f6-4387-ae81-a1b084b6bb75",
-   "metadata": {},
-   "source": [
-    "# import required module\n",
-    "import os\n",
-    "# assign directory\n",
-    "directory = 'input'\n",
-    "\n",
-    "\n",
-    "os.listdir(directory)\n",
-    "# iterate over files in\n",
-    "# that directory\n",
-    "for filename in os.listdir(directory):\n",
-    "    try:\n",
-    "        f = os.path.join(directory, filename)\n",
-    "        header = [\"YEAR/M/DD\", \"HH:MM:SS\",  \"UTC\", \"MIN Magni\", \"LAT\", \"LONG\"] #Min Magni = minimum obscuration magnitude (ratio), based on the fraction of the visible solar disk area screened by the Moon\n",
-    "        eclipse = pd.read_csv(f, sep='\\s+', names = header)\n",
-    "        df = eclipse[['LAT', 'LONG']]\n",
-    "        gpx = gpxpy.gpx.GPX()\n",
-    "\n",
-    "        # Create first track in our GPX:\n",
-    "        gpx_track = gpxpy.gpx.GPXTrack()\n",
-    "        gpx.tracks.append(gpx_track)\n",
-    "\n",
-    "        # Create first segment in our GPX track:\n",
-    "        gpx_segment = gpxpy.gpx.GPXTrackSegment()\n",
-    "        gpx_track.segments.append(gpx_segment)\n",
-    "\n",
-    "        # Create points:\n",
-    "        for idx in df.index:\n",
-    "            gpx_segment.points.append(gpxpy.gpx.GPXTrackPoint(df.loc[idx, 'LAT'], df.loc[idx, 'LONG']))\n",
-    "\n",
-    "        # print(gpx.to_xml())\n",
-    "\n",
-    "        with open('output/'+filename+'.gpx', 'w') as f:\n",
-    "            f.write(gpx.to_xml())\n",
-    "        print('Writing ' + filename + \" to gpx. \")\n",
-    "    except Exception as e:\n",
-    "        print(e)\n",
-    "        continue\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7bb29aa8-d983-44c8-b7f6-1924b3f6a81c",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Maps\n",
-    "\n",
-    "Let's begin by plotting all the points on a globe map using plotly express."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "76044c14-aea5-47b5-9e62-ff0d4e5c6239",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.scatter_geo(stations.reset_index(),\n",
-    "                    lat=stations[\"GLAT\"],\n",
-    "                    lon=stations[\"GLON\"],\n",
-    "                    hover_name=\"Name\",\n",
-    "                    hover_data=[\"Category\"],\n",
-    "                    color = \"Category\")\n",
-    "fig.update_geos(projection_type=\"orthographic\")\n",
-    "fig.update_geos(lataxis_showgrid=True, lonaxis_showgrid=True) # geographic graticules\n",
-    "\n",
-    "fig.show()\n",
-    "fig.write_html(\"output/all-stations.html\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44767742-27ec-4091-b6b5-b3abba6caed8",
-   "metadata": {},
-   "source": [
-    "### Antarctic Coastline"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "fa2a55b7-fa25-4ba6-bd68-3d3fcfeb7b29",
-   "metadata": {},
-   "source": [
-    "gdf = geopandas.read_file(\"stanford-yk702xd7587-geojson.json\")\n",
-    "gdf.geometry[0].exterior"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3758a394-179a-4af5-9637-fba2f62649d3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gdf = geopandas.read_file(\"stanford-yk702xd7587-geojson.json\")\n",
-    "gdf.geometry\n",
-    "\n",
-    "# df.geometry[0].exterior\n",
-    "\n",
-    "points = []\n",
-    "for polygon in gdf.geometry[0]:\n",
-    "    for point in polygon.exterior.coords:\n",
-    "        points.append({\"lat\": point[1], \"lon\": point[0]})\n",
-    "Antarctic_coastline = pd.DataFrame(points)\n",
-    "\n",
-    "with open('stanford-yk702xd7587-geojson.json') as f:\n",
-    "  Antarctic_coast = json.load(f)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d6ff95b-4a21-47b8-ba1a-545d48d7b318",
-   "metadata": {},
-   "source": [
-    "We can visualize the geometry of the coastline directly in Jupyter notebook. Yep, looks like Antarctica:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "30d5086e-a8e4-41a1-ad87-50f7567aae53",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gdf.geometry[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "596a9986-a229-4a8e-9e58-fc9ee5a18a1e",
-   "metadata": {},
-   "source": [
-    "Let's compute the conjugate points for this outline, then export them as a .gpx file. That shape is *huge*, so let's  reduce the resolution so it's easier to run computations on."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9c9308b2-2f2b-4cba-950f-9aee79252039",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# foo = conjcalc(Antarctic_coastline.sample(20000).sort_index(), latname = 'lat', lonname='lon')\n",
-    "foo = conjcalc(Antarctic_coastline.sample(10000).sort_values('lon'), latname = 'lat', lonname='lon')\n",
-    "# foo = conjcalc(Antarctic_coastline.sample(10000).sort_values('lon'), latname = 'lat', lonname='lon')\n",
-    "\n",
-    "df = foo[['PLAT', 'PLON']]\n",
-    "\n",
-    "gpx = gpxpy.gpx.GPX()\n",
-    "\n",
-    "# Create first track in our GPX:\n",
-    "gpx_track = gpxpy.gpx.GPXTrack()\n",
-    "gpx.tracks.append(gpx_track)\n",
-    "\n",
-    "# Create first segment in our GPX track:\n",
-    "gpx_segment = gpxpy.gpx.GPXTrackSegment()\n",
-    "gpx_track.segments.append(gpx_segment)\n",
-    "\n",
-    "# Create points:\n",
-    "for idx in df.index:\n",
-    "    gpx_segment.points.append(gpxpy.gpx.GPXTrackPoint(df.loc[idx, 'PLAT'], df.loc[idx, 'PLON']))\n",
-    "\n",
-    "# print(gpx.to_xml())\n",
-    "\n",
-    "with open('output/conj-Ant.gpx', 'w') as f:\n",
-    "    f.write(gpx.to_xml())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1e8cedab-3c8d-40a6-91e9-4ce794e8c3ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ant=foo.copy()\n",
-    "ant\n",
-    "\n",
-    "fig = px.scatter_geo(ant,\n",
-    "                    lat=ant[\"PLAT\"],\n",
-    "                    lon=ant[\"PLON\"],\n",
-    "                    # hover_name=\"Name\",\n",
-    "                    # hover_data=[\"Hemisphere\"],\n",
-    "                    hover_data = [\"PLAT\", \"PLON\"],\n",
-    "                    )\n",
-    "fig.update_geos(projection_type=\"orthographic\")\n",
-    "fig.update_geos(lataxis_showgrid=True, lonaxis_showgrid=True) # geographic graticules\n",
-    "\n",
-    "fig.show()\n",
-    "fig.write_html(\"output/AntarcticConjugate.html\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22850e98-4913-4832-9c05-db1a902318f7",
-   "metadata": {},
-   "source": [
-    "### Coordinate Conversions\n",
-    "Now to run the conversions and save the results as .CSV files:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d4a0e74f-76ff-498a-a10f-1241415174bb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n2s = conjcalc(stations, dtime = dt.datetime(2020, 1, 1), mode = 'N2S', method = 'aacgm', is_saved = True)\n",
-    "n2s.to_csv('output/stations_N2S_aacgm.csv') #save as .csv\n",
-    "s2n = conjcalc(stations, dtime = dt.datetime(2020, 1, 1), mode = 'S2N', method = 'aacgm', is_saved = True)\n",
-    "s2n.to_csv('output/stations_S2N_aacgm.csv') #save as .csv\n",
-    "# conjcalc(stations)\n",
-    "# stations[stations.index==\"PG0\"].GLON\n",
-    "\n",
-    "\n",
-    "stations = conjcalc(stations, is_saved = True)\n",
-    "# stations.to_csv('output/stations.csv') #save as .csv"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a11d6d38-c797-4dc9-8ed3-39885bd9d044",
-   "metadata": {},
-   "source": [
-    "And map:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "94a382c6-e232-45a6-8b02-53122ebc6aea",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.scatter_geo(stations.reset_index(),\n",
-    "                    lat=stations[\"PLAT\"],\n",
-    "                    lon=stations[\"PLON\"],\n",
-    "                    hover_name=\"Name\",\n",
-    "                    hover_data=[\"Category\"],\n",
-    "                    color = \"Hemisphere\",\n",
-    "                    symbol = 'Network'\n",
-    "                    )\n",
-    "fig.update_geos(projection_type=\"orthographic\")\n",
-    "fig.update_geos(lataxis_showgrid=True, lonaxis_showgrid=True) # geographic graticules\n",
-    "\n",
-    "fig.show()\n",
-    "fig.write_html(\"output/conj-stations.html\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6a8ddf22-a2df-48fc-a4bd-e3f321c9d485",
-   "metadata": {},
-   "source": [
-    "As a sanity check, let's look at just the conjugate points for southern stations, color-coded by source."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "584d51c2-fc6b-48e0-8fe1-4b07bc787b57",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.scatter_geo(stations[stations['Hemisphere']=='S'].reset_index(),\n",
-    "                    lat=stations[stations['Hemisphere']=='S'][\"PLAT\"],\n",
-    "                    lon=stations[stations['Hemisphere']=='S'][\"PLON\"],\n",
-    "                    hover_name=\"Name\",\n",
-    "                    # hover_data=[\"Hemisphere\"],\n",
-    "                    hover_data = [\"GLAT\", \"GLON\"],\n",
-    "                    # color = \"Source\",\n",
-    "                     color = 'Hemisphere',\n",
-    "                    symbol = 'Network'\n",
-    "                    )#stations[\"GLAT\"])\n",
-    "fig.update_geos(projection_type=\"orthographic\")\n",
-    "fig.update_geos(lataxis_showgrid=True, lonaxis_showgrid=True) # geographic graticules\n",
-    "\n",
-    "fig.show()\n",
-    "fig.write_html(\"output/conj-stations-south.html\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7595664f-1e83-4675-998f-dbf1596b5119",
-   "metadata": {},
-   "source": [
-    "## Compare `aacgm` vs `geopack`\n",
-    "Let's see how much difference there is in the results between these two approaches."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e781cfc6-c841-47e4-9281-2fdd47a35277",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# run conjcalc for aacgm\n",
-    "foo = conjcalc(stations, method = \"aacgm\", mode = 'flip')\n",
-    "foo['PLAT_aacgm'] = foo['PLAT']\n",
-    "foo['PLON_aacgm'] = foo['PLON']\n",
-    "# run conjcalc with geopack\n",
-    "foo = conjcalc(stations, method = \"geopack\", mode = 'flip')\n",
-    "foo = foo.rename(columns={\"PLAT\": \"PLAT_geopack\", \"PLON\": \"PLON_geopack\"})\n",
-    "\n",
-    "# flight path chart\n",
-    "import plotly.graph_objects as go\n",
-    "import pandas as pd\n",
-    "\n",
-    "fig = go.Figure()\n",
-    "\n",
-    "fig.add_trace(go.Scattergeo(\n",
-    "    # locationmode = 'USA-states',\n",
-    "    lon = foo['PLON_aacgm'],\n",
-    "    lat = foo['PLAT_aacgm'],\n",
-    "    hoverinfo = 'text',\n",
-    "    text = foo['ID'],\n",
-    "    mode = 'markers',\n",
-    "    marker = dict(\n",
-    "        size = 2,\n",
-    "        color = 'rgb(255, 0, 0)',\n",
-    "        line = dict(\n",
-    "            width = 3,\n",
-    "            color = 'rgba(68, 68, 68, 0)'\n",
-    "        )\n",
-    "    )))\n",
-    "\n",
-    "fig.add_trace(go.Scattergeo(\n",
-    "    # locationmode = 'USA-states',\n",
-    "    lon = foo['PLON_geopack'],\n",
-    "    lat = foo['PLAT_geopack'],\n",
-    "    hoverinfo = 'name',\n",
-    "    text = foo['ID'],\n",
-    "    mode = 'markers',\n",
-    "    marker = dict(\n",
-    "        size = 2,\n",
-    "        color = 'rgb(0, 255, 0)',\n",
-    "        line = dict(\n",
-    "            width = 3,\n",
-    "            color = 'rgba(68, 68, 68, 0)'\n",
-    "        )\n",
-    "    )))\n",
-    "\n",
-    "flight_paths = []\n",
-    "for i in range(len(foo)):\n",
-    "    fig.add_trace(\n",
-    "        go.Scattergeo(\n",
-    "            # locationmode = 'USA-states',\n",
-    "            lon = [foo['PLON_aacgm'][i], foo['PLON_geopack'][i]],\n",
-    "            lat = [foo['PLAT_aacgm'][i], foo['PLAT_geopack'][i]],\n",
-    "            mode = 'lines',\n",
-    "            line = dict(width = 1,color = 'red'),\n",
-    "            # opacity = float(foo['cnt'][i]) / float(foo['cnt'].max()),\n",
-    "        )\n",
-    "    )\n",
-    "\n",
-    "fig.update_layout( \n",
-    "    title_text = 'AACGM vs geopack',\n",
-    "    showlegend = False,\n",
-    "    geo = dict(\n",
-    "        # scope = 'north america',\n",
-    "        projection_type = 'orthographic',\n",
-    "        showland = True,\n",
-    "        landcolor = 'rgb(243, 243, 243)',\n",
-    "        countrycolor = 'rgb(204, 204, 204)',\n",
-    "    ),\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e8859b1c-1b4f-4f6d-b4e1-bdb54987ddad",
-   "metadata": {},
-   "source": [
-    "## Graticules\n",
-    "It can be useful to visualize magnetic latitude over time. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ac323c59-6c98-4729-90c2-81fea68352ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rings = calc_mlat_rings(list(range(-90, 90, 5)), ut = datetime.datetime(2020, 1, 1), is_saved = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba5cceeb-7c27-4515-9010-bd245be0af8b",
-   "metadata": {},
-   "source": [
-    "## Animated Maps\n",
-    "We can calculate and map the locations of conjugate points over time. This will make it easier to visualize shifts in Earth's magnetic field.\n",
-    "\n",
-    " - [ ] Animate shift in magnetic graticules over time.\n",
-    " - [ ] Animate shift in conjugate Antarctic coastline over time. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8d05ebc3-315e-4818-b6ef-e0b8ba2daadb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# stations_over_time = pd.DataFrame()\n",
-    "\n",
-    "# for year in range(1900, 2020, 1):\n",
-    "#     try:\n",
-    "#         print(\"Computing for \" + str(year) + \"...................................................................\")\n",
-    "#         # run conjcalc for the year of interest\n",
-    "#         data = conjcalc(stations, dtime = dt.datetime(year, 1, 1), is_verbose = False, mode='S2N')\n",
-    "#         # add year column to table\n",
-    "#         data = pd.DataFrame(data)\n",
-    "#         data['Year'] = year\n",
-    "#         # append table to big table\n",
-    "#         # stations_over_time.concat(data)\n",
-    "#         stations_over_time = pd.concat([stations_over_time, data])\n",
-    "#         print('Appending to main table.........................................')\n",
-    "#         print(stations_over_time.shape)\n",
-    "#         # stations_over_time = pd.concat(data)\n",
-    "        \n",
-    "#     except Exception as e:\n",
-    "#         print(e)\n",
-    "\n",
-    "\n",
-    "# # write DataFrame to an excel sheet \n",
-    "# stations_over_time\n",
-    "# stations_over_time.to_csv('output/stations-over-time.csv')\n",
-    "# stations_over_time.head()\n",
-    "\n",
-    "\n",
-    "# Read .csv file:\n",
-    "stations_over_time = pd.read_csv('output/stations-over-time.csv')\n",
-    "\n",
-    "# Create the animation\n",
-    "world_map = px.scatter_geo(stations_over_time.reset_index(), lon='PLON', lat='PLAT', \n",
-    "                           color = 'Hemisphere',\n",
-    "                           symbol = 'Network',\n",
-    "                           # size = 'Year',  \n",
-    "                           animation_frame='Year', animation_group='Year', projection='orthographic', hover_name='Name', hover_data = ['ID', 'Network'], \n",
-    "                          # symbol = 'Category'\n",
-    "                          )\n",
-    "# world_map = px.scatter_geo(world, lon='long', lat='lat', size='mass', color='orange', animation_frame='year', animation_group='group', projection='orthographic', hover_name='name')\n",
-    "world_map.update_layout(title='Conjugate Points Over Time', title_x=0.5, title_font=dict(size=10))\n",
-    "\n",
-    "# Display the animation\n",
-    "world_map.show()\n",
-    "world_map.write_html(\"output/animated-map.html\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5191c0db-d7d1-4606-85f5-8e3d8e210699",
-   "metadata": {},
-   "source": [
-    "Let's do the same thing again, but this time as a map of Northern conjugate points superimposed on Antarctica:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a88b698-f87a-4ac9-a78f-4ae29b35d8d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stations_over_time = pd.DataFrame()\n",
-    "    \n",
-    "\n",
-    "for year in range(1900, 2020, 1):\n",
-    "    try:\n",
-    "        print(\"Computing for \" + str(year) + \"...................................................................\")\n",
-    "        # run conjcalc for the year of interest\n",
-    "        data = conjcalc(stations, dtime = dt.datetime(year, 1, 1), is_verbose = False, mode='N2S')\n",
-    "        # add year column to table\n",
-    "        data = pd.DataFrame(data)\n",
-    "        data['Year'] = year\n",
-    "        # append table to big table\n",
-    "        # stations_over_time.concat(data)\n",
-    "        stations_over_time = pd.concat([stations_over_time, data])\n",
-    "        print('Appending to main table.........................................')\n",
-    "        print(stations_over_time.shape)\n",
-    "        # stations_over_time = pd.concat(data)\n",
-    "        \n",
-    "    except Exception as e:\n",
-    "        print(e)\n",
-    "\n",
-    "\n",
-    "# write DataFrame to an excel sheet \n",
-    "stations_over_time\n",
-    "stations_over_time.to_csv('output/stations-over-time-northern-conjugates.csv')\n",
-    "stations_over_time.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2283323d-0355-45b0-8b80-6a6e186286b7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stations_over_time = pd.read_csv('output/stations-over-time-northern-conjugates.csv')\n",
-    "# Create the animation\n",
-    "world_map = px.scatter_geo(stations_over_time.reset_index(), lon='PLON', lat='PLAT', \n",
-    "                           color = 'Hemisphere',\n",
-    "                           symbol = 'Network',\n",
-    "                           # size = 'Year',  \n",
-    "                           animation_frame='Year', animation_group='Year', projection='orthographic', hover_name='Name', hover_data = ['ID', 'Network'])\n",
-    "# world_map = px.scatter_geo(world, lon='long', lat='lat', size='mass', color='orange', animation_frame='year', animation_group='group', projection='orthographic', hover_name='name')\n",
-    "world_map.update_layout(title='Conjugate Points Over Time', title_x=0.5, title_font=dict(size=10))\n",
-    "\n",
-    "# Display the animation\n",
-    "world_map.show()\n",
-    "world_map.write_html(\"output/animated-map-northern-conjugates.html\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "60e12dc5-5d77-4d7f-8a40-dea21a062ea6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stations_over_time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a7cf4b6d-2300-490c-9153-87e772b14a83",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(calc_mlat_rings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1ab028b0-3427-4cfa-88bf-e5db5ec31183",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ANIMATED MAP OF MAGNETIC GRATICULES OVER TIME\n",
-    "\n",
-    "rings_over_time = pd.DataFrame()\n",
-    "    \n",
-    "\n",
-    "for year in range(1900, 2020, 5):\n",
-    "    try:\n",
-    "        print(\"Computing for \" + str(year) + \"...................................................................\")\n",
-    "        # run conjcalc for the year of interest\n",
-    "        rings = calc_mlat_rings(list(range(-90, 90, 5)), ut = datetime.datetime(year, 1, 1), is_saved = True)\n",
-    "        # add year column to table\n",
-    "        df = pd.DataFrame.from_dict(rings, orient='index')\n",
-    "        df = df.explode(['glats', 'glons'])\n",
-    "        df.reset_index(inplace=True)\n",
-    "        df = df.rename(columns = {'index':'MLAT'})\n",
-    "        df['Year'] = year\n",
-    "        # append table to big table\n",
-    "        # stations_over_time.concat(data)\n",
-    "        rings_over_time = pd.concat([rings_over_time, df])\n",
-    "        print('Appending to main table.........................................')\n",
-    "        print(rings_over_time.shape)\n",
-    "        # stations_over_time = pd.concat(data)\n",
-    "\n",
-    "    except Exception as e:\n",
-    "        print(e)\n",
-    "\n",
-    "\n",
-    "# write DataFrame to an excel sheet \n",
-    "rings_over_time\n",
-    "rings_over_time.to_csv('output/graticules/graticules-over-time.csv')\n",
-    "rings_over_time.head()\n",
-    "\n",
-    "# Create the animation of magnetic graticules\n",
-    "df = rings_over_time.copy()\n",
-    "world_map = px.scatter_geo(df, lon='glons', lat='glats', \n",
-    "                           color = 'MLAT',\n",
-    "                           # size = 'Year',  \n",
-    "                           animation_frame='Year', animation_group='Year', \n",
-    "                           projection='orthographic', \n",
-    "                           hover_name='MLAT'\n",
-    "                          )\n",
-    "# world_map = px.scatter_geo(world, lon='long', lat='lat', size='mass', color='orange', animation_frame='year', animation_group='group', projection='orthographic', hover_name='name')\n",
-    "world_map.update_layout(title='Shift in Magnetic Latitude Over Time', title_x=0.5, title_font=dict(size=20))\n",
-    "\n",
-    "# Display the animation\n",
-    "world_map.show()\n",
-    "world_map.write_html(\"output/animated-graticules.html\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d0763b1-33db-40d7-a00c-aba2fa192d5c",
-   "metadata": {},
-   "source": [
-    "## Timelapse map of conjugate Antarctic coastline\n",
-    "Let's use the same format to examine the movement of the conjugate Antarctic coastline over time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d8246578-30dd-44f6-87c3-cd64e8384e32",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# # CONJUGATE COASTLINE OVER TIME\n",
-    "gdf = geopandas.read_file(\"stanford-yk702xd7587-geojson.json\")\n",
-    "gdf.geometry\n",
-    "\n",
-    "# df.geometry[0].exterior\n",
-    "\n",
-    "points = []\n",
-    "for polygon in gdf.geometry[0]:\n",
-    "    for point in polygon.exterior.coords:\n",
-    "        points.append({\"lat\": point[1], \"lon\": point[0]})\n",
-    "Antarctic_coastline = pd.DataFrame(points)\n",
-    "Antarctic_coastline = Antarctic_coastline.sample(10000)\n",
-    "\n",
-    "\n",
-    "stations_over_time = pd.DataFrame()\n",
-    "for year in range(1900, 2020, 10):\n",
-    "    try:\n",
-    "        print(\"Computing for \" + str(year) + \"...................................................................\")\n",
-    "        # run conjcalc for the year of interest\n",
-    "        data = conjcalc(Antarctic_coastline, latname = 'lat', lonname = 'lon', dtime = dt.datetime(year, 1, 1), is_verbose = False, mode='S2N')\n",
-    "        # add year column to table\n",
-    "        data = pd.DataFrame(data)\n",
-    "        data['Year'] = year\n",
-    "        # append table to big table\n",
-    "        # stations_over_time.concat(data)\n",
-    "        stations_over_time = pd.concat([stations_over_time, data])\n",
-    "        print('Appending to main table.........................................')\n",
-    "        print(stations_over_time.shape)\n",
-    "        # stations_over_time = pd.concat(data)\n",
-    "        \n",
-    "    except Exception as e:\n",
-    "        print(e)\n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0befdb7d-ed98-4fd8-b6aa-01fb07d0d927",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create the animation\n",
-    "world_map = px.scatter_geo(stations_over_time.reset_index(), lon='PLON', lat='PLAT', \n",
-    "                           # color = 'Hemisphere',\n",
-    "                           # size = 'Year',  \n",
-    "                           animation_frame='Year', animation_group='Year', projection='orthographic', \n",
-    "                           #hover_name='Name', hover_data = ['ID', 'Network']\n",
-    "                          )\n",
-    "# world_map = px.scatter_geo(world, lon='long', lat='lat', size='mass', color='orange', animation_frame='year', animation_group='group', projection='orthographic', hover_name='name')\n",
-    "world_map.update_layout(title='Geomagnetic Conjugate of Antarctic Coastline Over Time', title_x=0.5, title_font=dict(size=20))\n",
-    "\n",
-    "# Display the animation\n",
-    "world_map.show()\n",
-    "world_map.write_html(\"output/animated-map-conjugate-coastline.html\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5fbb7e08-daa2-4278-b159-215ccf170c6d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a 2D representation\n",
-    "world_map = px.scatter_geo(stations_over_time.reset_index(), lon='PLON', lat='PLAT', \n",
-    "                           color = 'Year',\n",
-    "                           # size = 'Year',  \n",
-    "                           # animation_frame='Year', animation_group='Year', \n",
-    "                           projection='orthographic'\n",
-    "                           #hover_name='Name', hover_data = ['ID', 'Network']\n",
-    "                          )\n",
-    "# world_map = px.scatter_geo(world, lon='long', lat='lat', size='mass', color='orange', animation_frame='year', animation_group='group', projection='orthographic', hover_name='name')\n",
-    "world_map.update_layout(title='Geomagnetic Conjugate of Antarctic Coastline Over Time', title_x=0.5, title_font=dict(size=20))\n",
-    "\n",
-    "\n",
-    "world_map.update_traces(marker=dict(size=2))\n",
-    "# Display the map b\n",
-    "world_map.show()\n",
-    "world_map.write_html(\"output/overlaid-conjugate-coastline.html\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "871bb332-a182-4fd0-b305-7711a2d031d9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.unique(stations['Category'].to_list())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8793a258-65fc-4cee-956d-802b9537b74f",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Citations\n",
-    "- Please cite this item as 'Gerrish, L., Ireland, L., Fretwell, P., & Cooper, P. (2023). High resolution vector polylines of the Antarctic coastline (7.7) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/70ac5759-34ee-4f39-9069-2116db592340'. If using for a graphic or if short on space, please cite as 'data from the SCAR Antarctic Digital Database, 2023'\n",
-    "\n",
-    "- Shepherd, S. G. (2014), Altitude‐adjusted corrected geomagnetic coordinates: Definition and functional approximations, Journal of Geophysical Research: Space Physics, 119, 7501–7521, doi:10.1002/2014JA020264.\n",
-    "\n",
-    "- Connors, M., Schofield, I., Reiter, K. et al. The AUTUMNX magnetometer meridian chain in Québec, Canada. Earth Planet Sp 68, 2 (2016). https://doi.org/10.1186/s40623-015-0354-4\n",
-    "\n",
-    "- Gjerloev, J. W. (2012), The SuperMAG data processing technique, J. Geophys. Res., 117, A09213, doi:10.1029/2012JA017683.\n",
-    "- Gjerloev, J. W. (2009), A Global Ground-Based Magnetometer Initiative, EOS, 90, 230-231, doi:10.1029/2009EO270002."
-   ]
-  }
- ],
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