diff --git a/nblibrary/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb b/nblibrary/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb
index dc02a5d..a505626 100755
--- a/nblibrary/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb
+++ b/nblibrary/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb
@@ -930,58 +930,77 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plotScatter(seasonstr, caseSel = None): \n",
+    "def plotScatter(seasonstr, caseSel=None):\n",
     "    node_lats = uxgrid.face_lat.values\n",
     "    node_lons = uxgrid.face_lon.values\n",
     "\n",
     "    predictions = []\n",
-    "    \n",
-    "    fig,axs = plt.subplots(1,1,figsize=(8,8))\n",
-    "    \n",
-    "    \n",
-    "    CI_model = (\n",
-    "        couplingIndex_DS[\"CouplingIndex\"]\n",
-    "        .sel(season=seasonstr)\n",
-    "    )\n",
-    "    \n",
-    "    iSeason   = np.where(seasons == seasonstr)[0]\n",
+    "\n",
+    "    fig, axs = plt.subplots(1, 1, figsize=(8, 8))\n",
+    "\n",
+    "    CI_model = couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr)\n",
+    "\n",
+    "    iSeason = np.where(seasons == seasonstr)[0]\n",
     "    iStations = np.where(np.isfinite(terraCI_fluxnetConverted[:, iSeason]) == True)[0]\n",
-    "    \n",
+    "\n",
     "    for iPoint in range(len(iStations)):\n",
     "        this_lon = lon_fluxnet[iStations[iPoint]]  # lon1\n",
     "        this_lat = lat_fluxnet[iStations[iPoint]]  # lat1\n",
     "        obs_point = np.array((this_lon, this_lat))\n",
-    "    \n",
-    "        # Get subset of relevant points \n",
-    "        i = np.where( (node_lats>=(this_lat-2)) & (node_lats<=(this_lat+2)) & \n",
-    "                        (node_lons>=(this_lon-2)) & (node_lons<=(this_lon+2)))[0]\n",
-    "    \n",
+    "\n",
+    "        # Get subset of relevant points\n",
+    "        i = np.where(\n",
+    "            (node_lats >= (this_lat - 2))\n",
+    "            & (node_lats <= (this_lat + 2))\n",
+    "            & (node_lons >= (this_lon - 2))\n",
+    "            & (node_lons <= (this_lon + 2))\n",
+    "        )[0]\n",
+    "\n",
     "        minDistance = 100\n",
-    "        for iSelClose in range(len(i)): \n",
-    "            # Find point in uxarray? Use euclidian distance \n",
-    "            distance = np.linalg.norm(obs_point - np.array((node_lons[i[iSelClose]], node_lats[i[iSelClose]])) )\n",
-    "            \n",
-    "            if ( (distance<minDistance) & \n",
-    "                      (np.isfinite(couplingIndex_DS[\"CouplingIndex\"].sel(season='JJA').values[i[iSelClose]])==True) ): \n",
-    "                minDistance = distance \n",
+    "        for iSelClose in range(len(i)):\n",
+    "            # Find point in uxarray? Use euclidian distance\n",
+    "            distance = np.linalg.norm(\n",
+    "                obs_point - np.array((node_lons[i[iSelClose]], node_lats[i[iSelClose]]))\n",
+    "            )\n",
+    "\n",
+    "            if (distance < minDistance) & (\n",
+    "                np.isfinite(\n",
+    "                    couplingIndex_DS[\"CouplingIndex\"]\n",
+    "                    .sel(season=\"JJA\")\n",
+    "                    .values[i[iSelClose]]\n",
+    "                )\n",
+    "                == True\n",
+    "            ):\n",
+    "                minDistance = distance\n",
     "                selLon = node_lons[i[iSelClose]]\n",
     "                selLat = node_lats[i[iSelClose]]\n",
     "                selLoc = i[iSelClose]\n",
-    "    \n",
-    "        predictions = np.append( predictions, couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr).values[selLoc] )\n",
-    "        axs.plot(terraCI_fluxnetConverted[iPoint, iSeason], couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr).values[selLoc], \n",
-    "                 'bo', alpha=0.5)\n",
-    "    \n",
-    "    axs.set_xlabel('FLUXNET CI Value',fontsize=12)\n",
-    "    axs.set_ylabel('CESM CI Value',fontsize=12)\n",
-    "    axs.set_title('Individual station CI vs. nearest gridcell CI: '+seasonstr, fontsize=14)\n",
-    "    axs.set_xlim([-25,25])\n",
-    "    axs.set_ylim([-25,25])\n",
-    "    axs.plot(np.arange(-25,26), np.arange(-25,26),'k--')\n",
-    "    \n",
-    "    rmse = np.sqrt(((predictions - terraCI_fluxnetConverted[iStations, iSeason]) ** 2).mean())\n",
-    "    axs.text(0.05,0.95, \"RMSE: \"+str(rmse), transform=axs.transAxes)\n",
-    "    \n",
+    "\n",
+    "        predictions = np.append(\n",
+    "            predictions,\n",
+    "            couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr).values[selLoc],\n",
+    "        )\n",
+    "        axs.plot(\n",
+    "            terraCI_fluxnetConverted[iPoint, iSeason],\n",
+    "            couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr).values[selLoc],\n",
+    "            \"bo\",\n",
+    "            alpha=0.5,\n",
+    "        )\n",
+    "\n",
+    "    axs.set_xlabel(\"FLUXNET CI Value\", fontsize=12)\n",
+    "    axs.set_ylabel(\"CESM CI Value\", fontsize=12)\n",
+    "    axs.set_title(\n",
+    "        \"Individual station CI vs. nearest gridcell CI: \" + seasonstr, fontsize=14\n",
+    "    )\n",
+    "    axs.set_xlim([-25, 25])\n",
+    "    axs.set_ylim([-25, 25])\n",
+    "    axs.plot(np.arange(-25, 26), np.arange(-25, 26), \"k--\")\n",
+    "\n",
+    "    rmse = np.sqrt(\n",
+    "        ((predictions - terraCI_fluxnetConverted[iStations, iSeason]) ** 2).mean()\n",
+    "    )\n",
+    "    axs.text(0.05, 0.95, \"RMSE: \" + str(rmse), transform=axs.transAxes)\n",
+    "\n",
     "    return axs"
    ]
   },
@@ -1012,7 +1031,7 @@
     "        plotTCI_case(\"DJF\", iCase)\n",
     "\n",
     "        plotScatter(\"JJA\", iCase)\n",
-    "        plotScatter(\"DJF\", iCase)\n"
+    "        plotScatter(\"DJF\", iCase)"
    ]
   }
  ],