diff --git a/data_preproc_mapvis.ipynb b/data_preproc_mapvis.ipynb
index a1f3e63ef8c198178f39892498bfba953039ca32..f79931c33f331592e2473e7b1aef442d294436e8 100644
--- a/data_preproc_mapvis.ipynb
+++ b/data_preproc_mapvis.ipynb
@@ -67,12 +67,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Code for plotting some maps and frequency distributions\n",
-    "\n",
+    "import numpy as np\n",
     "import netCDF4 as nc\n",
     "\n",
     "PATH_TO_NC = '/p/scratch/deepacf/schultz1/'"
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -238,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -250,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -285,87 +285,161 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "\"\\n!module load Stages/2020  GCC/9.3.0  ParaStationMPI/5.4.7-1 Cartopy\\n%matplotlib inline\\nimport matplotlib.pyplot as plt\\n#import cartopy.crs as ccrs\\nimport numpy as np\\nimport cartopy\\n\\n# prep some data for contourf plot\\n# extents: upper-right of the map\\nx = np.linspace(-65, -30, 30)\\ny = np.linspace(-30, 15, 30)\\nmatrixLon, matrixLat = np.meshgrid(x, y)\\nmatrixTec = 10*np.sin(matrixLon**2 + matrixLat**2)/(matrixLon**2 + matrixLat**2)\\n\\nax = plt.axes(projection=cartopy.crs.PlateCarree())\\n\\n# prep increasing values of v covering values of Z (matrixTec)\\nv = np.arange(-0.15, 0.15, 0.025)\\n\\n# plot with appropriate parameters\\n# zorder: put the filled-contour on top\\n# alpha: set transparency to allow some visibility of graphics below\\ncp = plt.contourf(matrixLon, matrixLat, matrixTec, v,                   transform=cartopy.crs.PlateCarree(),                   zorder=2,                   alpha=0.65,                   cmap=plt.cm.copper)\\nplt.colorbar(cp)\\n\\nax.add_feature(cartopy.feature.LAND)\\nax.add_feature(cartopy.feature.OCEAN)\\nax.add_feature(cartopy.feature.COASTLINE)\\nax.add_feature(cartopy.feature.BORDERS, linestyle=':')\\nax.set_extent([-85, -30, -60, 15])\\nplt.title('TEC Map')\\nplt.show()\\n\""
+       "<Figure size 1008x792 with 2 Axes>"
       ]
      },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "\"\"\"\n",
-    "!module load Stages/2020  GCC/9.3.0  ParaStationMPI/5.4.7-1 Cartopy\n",
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "#import cartopy.crs as ccrs\n",
-    "import numpy as np\n",
-    "import cartopy\n",
-    "\n",
-    "# prep some data for contourf plot\n",
-    "# extents: upper-right of the map\n",
-    "x = np.linspace(-65, -30, 30)\n",
-    "y = np.linspace(-30, 15, 30)\n",
-    "matrixLon, matrixLat = np.meshgrid(x, y)\n",
-    "matrixTec = 10*np.sin(matrixLon**2 + matrixLat**2)/(matrixLon**2 + matrixLat**2)\n",
-    "\n",
-    "ax = plt.axes(projection=cartopy.crs.PlateCarree())\n",
-    "\n",
-    "# prep increasing values of v covering values of Z (matrixTec)\n",
-    "v = np.arange(-0.15, 0.15, 0.025)\n",
-    "\n",
-    "# plot with appropriate parameters\n",
-    "# zorder: put the filled-contour on top\n",
-    "# alpha: set transparency to allow some visibility of graphics below\n",
-    "cp = plt.contourf(matrixLon, matrixLat, matrixTec, v, \\\n",
-    "                  transform=cartopy.crs.PlateCarree(), \\\n",
-    "                  zorder=2, \\\n",
-    "                  alpha=0.65, \\\n",
-    "                  cmap=plt.cm.copper)\n",
-    "plt.colorbar(cp)\n",
-    "\n",
-    "ax.add_feature(cartopy.feature.LAND)\n",
-    "ax.add_feature(cartopy.feature.OCEAN)\n",
-    "ax.add_feature(cartopy.feature.COASTLINE)\n",
-    "ax.add_feature(cartopy.feature.BORDERS, linestyle=':')\n",
-    "ax.set_extent([-85, -30, -60, 15])\n",
-    "plt.title('TEC Map')\n",
-    "plt.show()\n",
-    "\"\"\"\n"
+    "\n",
+    "# plot contours with matplotlib.pyplot - without projection onto a map\n",
+    "fig, ax = plt.subplots()\n",
+    "fig.set_figwidth(14)\n",
+    "fig.set_figheight(11)\n",
+    "\n",
+    "cs = ax.contourf(lon, lat, val)\n",
+    "plt.title(name)\n",
+    "plt.colorbar(cs)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "272d2099bfe849d6843863d674ef5491",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "Map(center=[50.0, 0.0], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_…"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "%matplotlib inline\n",
+    "# plot contours with ipyleaflet using the contour levels obtained from matplotlib's contourf function\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
-    "cs = plt.contourf(lon, lat, val)\n",
-    "plt.title(name)\n",
-    "plt.colorbar()\n",
-    "plt.show()"
+    "from ipyleaflet import Map, basemaps, Polygon\n",
+    "\n",
+    "# create the contour path information\n",
+    "cs = plt.contourf(lat, lon, val.T)\n",
+    "plt.close()     # make sure plot is not displayed in Jupyter notebook\n",
+    "\n",
+    "# print(cs.allsegs[0][0][0:12])  # returns list of polygons per contour level; each polygon is a list of [x,y]tuples\n",
+    "# i.e. [level][polygon][x,y]\n",
+    "# Note that one Polygon from matplotlib's contour may contain several disconnected path segments. Therefore,\n",
+    "# we need to split these into individual polygons (function split_contours below).\n",
+    "# other useful information from the contour object:\n",
+    "# print(cs.get_array())  # get contour levels\n",
+    "\n",
+    "def split_contours(segs, kinds=None):\n",
+    "    \"\"\"takes a list of polygons and vertex kinds and separates disconnected vertices into separate lists.\n",
+    "    The input arrays can be derived from the allsegs and allkinds atributes of the result of a matplotlib\n",
+    "    contour or contourf call. They correspond to the contours of one contour level.\n",
+    "    \n",
+    "    Example:\n",
+    "    cs = plt.contourf(x, y, z)\n",
+    "    allsegs = cs.allsegs\n",
+    "    allkinds = cs.allkinds\n",
+    "    for i, segs in enumerate(allsegs):\n",
+    "        kinds = None if allkinds is None else allkinds[i]\n",
+    "        new_segs = split_contours(segs, kinds)\n",
+    "        # do something with new_segs\n",
+    "        \n",
+    "    More information:\n",
+    "    https://matplotlib.org/3.3.3/_modules/matplotlib/contour.html#ClabelText\n",
+    "    https://matplotlib.org/3.1.0/api/path_api.html#matplotlib.path.Path\n",
+    "    \"\"\"\n",
+    "    if kinds is None:\n",
+    "        return segs    # nothing to be done\n",
+    "    # search for kind=79 as this marks the end of one polygon segment\n",
+    "    # Notes: \n",
+    "    # 1. we ignore the different polygon styles of matplotlib Path here and only\n",
+    "    # look for polygon segments.\n",
+    "    # 2. the Path documentation recommends to use iter_segments instead of direct\n",
+    "    # access to vertices and node types. However, since the ipyleaflet Polygon expects\n",
+    "    # a complete polygon and not individual segments, this cannot be used here\n",
+    "    # (it may be helpful to clean polygons before passing them into ipyleaflet's Polygon,\n",
+    "    # but so far I don't see a necessity to do so)\n",
+    "    new_segs = []\n",
+    "    for i, seg in enumerate(segs):\n",
+    "        segkinds = kinds[i]\n",
+    "        boundaries = [0] + list(np.nonzero(segkinds == 79)[0])\n",
+    "        for b in range(len(boundaries)-1):\n",
+    "            new_segs.append(seg[boundaries[b]+(1 if b>0 else 0):boundaries[b+1]])\n",
+    "    return new_segs\n",
+    "        \n",
+    "# set-up the map and overlay contours\n",
+    "zoom = 4\n",
+    "center = [50., 0.]\n",
+    "# map = Map(basemap=basemaps.NASAGIBS.ViirsTrueColorCR, center=center, zoom=zoom) # loads current satellite image\n",
+    "# map = Map(basemap=basemaps.NASAGIBS.ViirsEarthAtNight2012, center=center, zoom=zoom)\n",
+    "map = Map(basemap=basemaps.Esri.WorldImagery, center=center, zoom=zoom)\n",
+    "# map = Map(basemap=basemaps.CartoDB.Positron, center=center, zoom=zoom)\n",
+    "\n",
+    "# add contours as polygons\n",
+    "# hardwired colors for now: these correspons to the 8-level default of matplotlib with an added orange color\n",
+    "colors = [\"#48186a\", \"#424086\", \"#33638d\", \"#26828e\", \"#1fa088\", \"#3fbc73\", \"#84d44b\", \"#d8e219\", \"#fcae1e\"]\n",
+    "allsegs = cs.allsegs\n",
+    "allkinds = cs.allkinds\n",
+    "\n",
+    "nclevs = len(cs.allsegs)\n",
+    "for clev in range(nclevs):\n",
+    "    kinds = None if allkinds is None else allkinds[clev]\n",
+    "    segs = split_contours(allsegs[clev], kinds)\n",
+    "    polygons = Polygon(\n",
+    "                    locations=[p.tolist() for p in segs],\n",
+    "                    # locations=segs[14].tolist(),\n",
+    "                    color=colors[min(clev, 4)],\n",
+    "                    weight=1,\n",
+    "                    opacity=0.8,\n",
+    "                    fill_color=colors[clev],\n",
+    "                    fill_opacity=0.2 + 0.6*clev/nclevs\n",
+    "    )\n",
+    "    map.add_layer(polygons);\n",
+    "map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1261, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# inspect polygons\n",
+    "clev = cs.allsegs[6]\n",
+    "# for i in range(len(clev)):\n",
+    "#    print(f'\\n\\nPolygon no {i}:')\n",
+    "#    print(clev[i])\n",
+    "print(clev[14].shape)"
    ]
   },
   {