diff --git a/atl11_play.ipynb b/atl11_play.ipynb
index ce9017d..1d21651 100644
--- a/atl11_play.ipynb
+++ b/atl11_play.ipynb
@@ -30,7 +30,7 @@
     "import hvplot.pandas\n",
     "import hvplot.xarray\n",
     "\n",
-    "# import intake\n",
+    "import intake\n",
     "import geopandas as gpd\n",
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -123,6 +123,15 @@
    },
    "outputs": [],
    "source": [
+    "# Xarray open_dataset preprocessor to add fields based on input filename.\n",
+    "# Adapted from the intake.open_netcdf._add_path_to_ds function.\n",
+    "add_path_to_ds = lambda ds: ds.assign_coords(\n",
+    "    coords=intake.source.utils.reverse_format(\n",
+    "        format_string=\"ATL11.001z123/ATL11_{referencegroundtrack:04d}1x_{mincycle:02d}{maxcycle:02d}_{}_v{}.zarr\",\n",
+    "        resolved_string=ds.encoding[\"source\"],\n",
+    "    )\n",
+    ")\n",
+    "\n",
     "# Load dataset from all Zarr stores\n",
     "# Aligning chunks spatially along cycle_number (i.e. time)\n",
     "ds: xr.Dataset = xr.open_mfdataset(\n",
@@ -132,6 +141,7 @@
     "    combine=\"nested\",\n",
     "    concat_dim=\"ref_pt\",\n",
     "    parallel=\"True\",\n",
+    "    preprocess=add_path_to_ds,\n",
     "    backend_kwargs={\"consolidated\": True},\n",
     ")\n",
     "# ds = ds.unify_chunks().compute()\n",
@@ -306,11 +316,9 @@
    "source": [
     "# Save to Zarr/NetCDF formats for distribution\n",
     "ds_subset.to_zarr(\n",
-    "    store=f\"ATLXI/ds_subset_{placename}.zarr\", mode=\"w\", consolidated=True,\n",
+    "    store=f\"ATLXI/ds_subset_{placename}.zarr\", mode=\"w\", consolidated=True\n",
     ")\n",
-    "ds_subset.to_netcdf(\n",
-    "    path=f\"ATLXI/ds_subset_{placename}.nc\", engine=\"h5netcdf\",\n",
-    ")"
+    "ds_subset.to_netcdf(path=f\"ATLXI/ds_subset_{placename}.nc\", engine=\"h5netcdf\")"
    ]
   },
   {
@@ -324,7 +332,7 @@
     "    data=ds_subset.sel(cycle_number=7)[[*essential_columns]],\n",
     "    label=\"Cycle_7\",\n",
     "    kdims=[\"x\", \"y\"],\n",
-    "    vdims=[\"utc_time\", \"h_corr\", \"cycle_number\"],\n",
+    "    vdims=[\"utc_time\", \"h_corr\", \"cycle_number\", \"referencegroundtrack\"],\n",
     "    datatype=[\"xarray\"],\n",
     ")\n",
     "df_subset = points_subset.dframe()"
@@ -343,10 +351,12 @@
     "    title=f\"Elevation (metres) at Cycle 7\",\n",
     "    x=\"x\",\n",
     "    y=\"y\",\n",
-    "    c=\"h_corr\",\n",
-    "    cmap=\"Blues\",\n",
-    "    rasterize=True,\n",
+    "    c=\"referencegroundtrack\",\n",
+    "    cmap=\"Set3\",\n",
+    "    # rasterize=True,\n",
     "    hover=True,\n",
+    "    datashade=True,\n",
+    "    dynspread=True,\n",
     ")"
    ]
   },
@@ -726,13 +736,13 @@
     "    grid=agg_grid,\n",
     "    region=region.bounds(),\n",
     "    projection=f\"x1:{scale}\",\n",
-    "    frame=[\"afg\", f'WSne+t\"ICESat-2 Ice Surface Change over {region.name}\"',],\n",
+    "    frame=[\"afg\", f'WSne+t\"ICESat-2 Ice Surface Change over {region.name}\"'],\n",
     "    Q=True,\n",
     ")\n",
     "for subglacial_lake in subglacial_lakes:\n",
     "    fig.plot(data=subglacial_lake, L=True, pen=\"thinnest\")\n",
     "fig.colorbar(\n",
-    "    position=\"JCR+e\", frame=[\"af\", 'x+l\"Elevation Change from Cycle 5 to 6\"', \"y+lm\"],\n",
+    "    position=\"JCR+e\", frame=[\"af\", 'x+l\"Elevation Change from Cycle 5 to 6\"', \"y+lm\"]\n",
     ")\n",
     "fig.savefig(f\"figures/plot_atl11_dh56_{placename}.png\")\n",
     "fig.show(width=600)"
@@ -854,7 +864,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
diff --git a/atl11_play.py b/atl11_play.py
index 177d2b0..7d373af 100644
--- a/atl11_play.py
+++ b/atl11_play.py
@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: hydrogen
 #       format_version: '1.3'
-#       jupytext_version: 1.4.2
+#       jupytext_version: 1.5.0
 #   kernelspec:
 #     display_name: deepicedrain
 #     language: python
@@ -34,7 +34,7 @@
 import hvplot.pandas
 import hvplot.xarray
 
-# import intake
+import intake
 import geopandas as gpd
 import numpy as np
 import pandas as pd
@@ -63,6 +63,15 @@
 print(f"{len(stores)} reference ground track Zarr stores")
 
 # %%
+# Xarray open_dataset preprocessor to add fields based on input filename.
+# Adapted from the intake.open_netcdf._add_path_to_ds function.
+add_path_to_ds = lambda ds: ds.assign_coords(
+    coords=intake.source.utils.reverse_format(
+        format_string="ATL11.001z123/ATL11_{referencegroundtrack:04d}1x_{mincycle:02d}{maxcycle:02d}_{}_v{}.zarr",
+        resolved_string=ds.encoding["source"],
+    )
+)
+
 # Load dataset from all Zarr stores
 # Aligning chunks spatially along cycle_number (i.e. time)
 ds: xr.Dataset = xr.open_mfdataset(
@@ -72,6 +81,7 @@
     combine="nested",
     concat_dim="ref_pt",
     parallel="True",
+    preprocess=add_path_to_ds,
     backend_kwargs={"consolidated": True},
 )
 # ds = ds.unify_chunks().compute()
@@ -178,11 +188,9 @@
 # %%
 # Save to Zarr/NetCDF formats for distribution
 ds_subset.to_zarr(
-    store=f"ATLXI/ds_subset_{placename}.zarr", mode="w", consolidated=True,
-)
-ds_subset.to_netcdf(
-    path=f"ATLXI/ds_subset_{placename}.nc", engine="h5netcdf",
+    store=f"ATLXI/ds_subset_{placename}.zarr", mode="w", consolidated=True
 )
+ds_subset.to_netcdf(path=f"ATLXI/ds_subset_{placename}.nc", engine="h5netcdf")
 
 # %%
 # Look at Cycle Number 7 only for plotting
@@ -190,7 +198,7 @@
     data=ds_subset.sel(cycle_number=7)[[*essential_columns]],
     label="Cycle_7",
     kdims=["x", "y"],
-    vdims=["utc_time", "h_corr", "cycle_number"],
+    vdims=["utc_time", "h_corr", "cycle_number", "referencegroundtrack"],
     datatype=["xarray"],
 )
 df_subset = points_subset.dframe()
@@ -201,10 +209,12 @@
     title=f"Elevation (metres) at Cycle 7",
     x="x",
     y="y",
-    c="h_corr",
-    cmap="Blues",
-    rasterize=True,
+    c="referencegroundtrack",
+    cmap="Set3",
+    # rasterize=True,
     hover=True,
+    datashade=True,
+    dynspread=True,
 )
 
 
@@ -359,13 +369,13 @@
     grid=agg_grid,
     region=region.bounds(),
     projection=f"x1:{scale}",
-    frame=["afg", f'WSne+t"ICESat-2 Ice Surface Change over {region.name}"',],
+    frame=["afg", f'WSne+t"ICESat-2 Ice Surface Change over {region.name}"'],
     Q=True,
 )
 for subglacial_lake in subglacial_lakes:
     fig.plot(data=subglacial_lake, L=True, pen="thinnest")
 fig.colorbar(
-    position="JCR+e", frame=["af", 'x+l"Elevation Change from Cycle 5 to 6"', "y+lm"],
+    position="JCR+e", frame=["af", 'x+l"Elevation Change from Cycle 5 to 6"', "y+lm"]
 )
 fig.savefig(f"figures/plot_atl11_dh56_{placename}.png")
 fig.show(width=600)
diff --git a/atlxi_dhdt.ipynb b/atlxi_dhdt.ipynb
index 5333912..e249911 100644
--- a/atlxi_dhdt.ipynb
+++ b/atlxi_dhdt.ipynb
@@ -34,6 +34,7 @@
    "source": [
     "import dask\n",
     "import datashader\n",
+    "import intake\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import pygmt\n",
@@ -98,6 +99,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Xarray open_dataset preprocessor to add fields based on input filename.\n",
+    "add_path_to_ds = lambda ds: ds.assign_coords(\n",
+    "    coords=intake.source.utils.reverse_format(\n",
+    "        format_string=\"ATL11.001z123/ATL11_{referencegroundtrack:04d}1x_{mincycle:02d}{maxcycle:02d}_{}_v{}.zarr\",\n",
+    "        resolved_string=ds.encoding[\"source\"],\n",
+    "    )\n",
+    ")\n",
+    "\n",
     "# Load ATL11 data from Zarr\n",
     "ds: xr.Dataset = xr.open_mfdataset(\n",
     "    paths=\"ATL11.001z123/ATL11_*.zarr\",\n",
@@ -106,6 +115,7 @@
     "    combine=\"nested\",\n",
     "    concat_dim=\"ref_pt\",\n",
     "    parallel=\"True\",\n",
+    "    preprocess=add_path_to_ds,\n",
     "    backend_kwargs={\"consolidated\": True},\n",
     ")"
    ]
@@ -352,8 +362,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 27min 59s, sys: 2min 25s, total: 30min 25s\n",
-      "Wall time: 29min 12s\n"
+      "CPU times: user 2min 54s, sys: 1min 51s, total: 4min 45s\n",
+      "Wall time: 4min 11s\n"
      ]
     }
    ],
@@ -427,6 +437,9 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>maxcycle</th>\n",
+       "      <th>mincycle</th>\n",
+       "      <th>referencegroundtrack</th>\n",
        "      <th>x</th>\n",
        "      <th>x_atc</th>\n",
        "      <th>y</th>\n",
@@ -441,10 +454,16 @@
        "      <td>2.203888e+08</td>\n",
        "      <td>2.203888e+08</td>\n",
        "      <td>2.203888e+08</td>\n",
+       "      <td>2.203888e+08</td>\n",
+       "      <td>2.203888e+08</td>\n",
+       "      <td>2.203888e+08</td>\n",
        "      <td>2.161963e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
+       "      <td>6.083857e+00</td>\n",
+       "      <td>1.264584e+00</td>\n",
+       "      <td>7.191751e+02</td>\n",
        "      <td>2.434822e+05</td>\n",
        "      <td>3.012317e+07</td>\n",
        "      <td>5.866348e+04</td>\n",
@@ -453,6 +472,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
+       "      <td>3.175861e-01</td>\n",
+       "      <td>4.946184e-01</td>\n",
+       "      <td>3.978832e+02</td>\n",
        "      <td>1.062715e+06</td>\n",
        "      <td>1.445927e+06</td>\n",
        "      <td>9.249454e+05</td>\n",
@@ -461,6 +483,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
+       "      <td>5.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
        "      <td>-4.367936e+06</td>\n",
        "      <td>2.563874e+07</td>\n",
        "      <td>-2.551004e+06</td>\n",
@@ -469,6 +494,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
+       "      <td>6.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>3.700000e+02</td>\n",
        "      <td>-4.427290e+05</td>\n",
        "      <td>2.887657e+07</td>\n",
        "      <td>-5.929225e+05</td>\n",
@@ -477,6 +505,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
+       "      <td>6.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>7.450000e+02</td>\n",
        "      <td>1.882507e+05</td>\n",
        "      <td>3.012436e+07</td>\n",
        "      <td>3.427737e+04</td>\n",
@@ -485,6 +516,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
+       "      <td>6.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.073000e+03</td>\n",
        "      <td>1.020950e+06</td>\n",
        "      <td>3.138991e+07</td>\n",
        "      <td>7.047390e+05</td>\n",
@@ -493,6 +527,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
+       "      <td>7.000000e+00</td>\n",
+       "      <td>5.000000e+00</td>\n",
+       "      <td>1.387000e+03</td>\n",
        "      <td>4.003066e+06</td>\n",
        "      <td>3.457507e+07</td>\n",
        "      <td>4.000857e+06</td>\n",
@@ -504,15 +541,25 @@
        "</div>"
       ],
       "text/plain": [
-       "                  x         x_atc             y         y_atc       h_range\n",
-       "count  2.203888e+08  2.203888e+08  2.203888e+08  2.203888e+08  2.161963e+08\n",
-       "mean   2.434822e+05  3.012317e+07  5.866348e+04  6.939610e+01  2.457177e-01\n",
-       "std    1.062715e+06  1.445927e+06  9.249454e+05  2.746865e+03  1.304804e+01\n",
-       "min   -4.367936e+06  2.563874e+07 -2.551004e+06 -4.352500e+03  0.000000e+00\n",
-       "25%   -4.427290e+05  2.887657e+07 -5.929225e+05 -3.351000e+03  3.833008e-02\n",
-       "50%    1.882507e+05  3.012436e+07  3.427737e+04 -1.500000e+00  7.861328e-02\n",
-       "75%    1.020950e+06  3.138991e+07  7.047390e+05  3.350000e+03  1.630859e-01\n",
-       "max    4.003066e+06  3.457507e+07  4.000857e+06  4.348500e+03  3.588577e+04"
+       "           maxcycle      mincycle  referencegroundtrack             x  \\\n",
+       "count  2.203888e+08  2.203888e+08          2.203888e+08  2.203888e+08   \n",
+       "mean   6.083857e+00  1.264584e+00          7.191751e+02  2.434822e+05   \n",
+       "std    3.175861e-01  4.946184e-01          3.978832e+02  1.062715e+06   \n",
+       "min    5.000000e+00  1.000000e+00          1.000000e+00 -4.367936e+06   \n",
+       "25%    6.000000e+00  1.000000e+00          3.700000e+02 -4.427290e+05   \n",
+       "50%    6.000000e+00  1.000000e+00          7.450000e+02  1.882507e+05   \n",
+       "75%    6.000000e+00  1.000000e+00          1.073000e+03  1.020950e+06   \n",
+       "max    7.000000e+00  5.000000e+00          1.387000e+03  4.003066e+06   \n",
+       "\n",
+       "              x_atc             y         y_atc       h_range  \n",
+       "count  2.203888e+08  2.203888e+08  2.203888e+08  2.161963e+08  \n",
+       "mean   3.012317e+07  5.866348e+04  6.939610e+01  2.457177e-01  \n",
+       "std    1.445927e+06  9.249454e+05  2.746865e+03  1.304804e+01  \n",
+       "min    2.563874e+07 -2.551004e+06 -4.352500e+03  0.000000e+00  \n",
+       "25%    2.887657e+07 -5.929225e+05 -3.351000e+03  3.833008e-02  \n",
+       "50%    3.012436e+07  3.427737e+04 -1.500000e+00  7.861328e-02  \n",
+       "75%    3.138991e+07  7.047390e+05  3.350000e+03  1.630859e-01  \n",
+       "max    3.457507e+07  4.000857e+06  4.348500e+03  3.588577e+04  "
       ]
      },
      "execution_count": 18,
@@ -699,8 +746,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 18min 12s, sys: 2min 34s, total: 20min 47s\n",
-      "Wall time: 20min 42s\n"
+      "CPU times: user 9min 25s, sys: 3min 3s, total: 12min 28s\n",
+      "Wall time: 16min 41s\n"
      ]
     }
    ],
@@ -831,6 +878,302 @@
     "fig.show(width=600)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Along track plots of subglacial lake drainage/filling events\n",
+    "\n",
+    "Let's take a closer look at one potential\n",
+    "subglacial lake filling event at Whillans Ice Stream.\n",
+    "We'll plot a cross-section view of\n",
+    "ice surface height changes over time,\n",
+    "along an ICESat-2 reference ground track."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import holoviews as hv\n",
+    "import hvplot.pandas\n",
+    "import panel as pn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Subset dataset to geographic region of interest\n",
+    "placename: str = \"whillans2\"\n",
+    "region: deepicedrain.Region = regions[placename]\n",
+    "ds_subset: xr.Dataset = region.subset(ds=ds_dhdt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Quick facet plot of height over different cycles\n",
+    "# See https://xarray.pydata.org/en/stable/plotting.html#datasets\n",
+    "ds_subset.plot.scatter(\n",
+    "    x=\"x\", y=\"y\", hue=\"h_corr\", cmap=\"gist_earth\", col=\"cycle_number\", col_wrap=4\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  7,  59,  68,  74,  83, 120, 129, 135])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find reference ground tracks that have data up to cycle 7 (the most recent cycle)\n",
+    "rgts = np.unique(\n",
+    "    ar=ds_subset.sel(cycle_number=7).dropna(dim=\"ref_pt\").referencegroundtrack\n",
+    ")\n",
+    "rgts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Convert xarray.Dataset to pandas.DataFrame for easier analysis\n",
+    "df_many: pd.DataFrame = ds_subset.to_dataframe().dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dhdt_plot(\n",
+    "    cycle: int = 7,\n",
+    "    dhdt_variable: str = \"dhdt_slope\",\n",
+    "    dhdt_range: tuple = (1, 10),\n",
+    "    rasterize: bool = False,\n",
+    "    datashade: bool = False,\n",
+    ") -> hv.element.chart.Scatter:\n",
+    "    \"\"\"\n",
+    "    ICESat-2 rate of height change over time (dhdt) interactive scatter plot.\n",
+    "    Uses HvPlot, and intended to be used inside a Panel dashboard.\n",
+    "    \"\"\"\n",
+    "    df_ = df_many.query(\n",
+    "        expr=\"cycle_number == @cycle & \"\n",
+    "        \"abs(dhdt_slope) > @dhdt_range[0] & abs(dhdt_slope) < @dhdt_range[1]\"\n",
+    "    )\n",
+    "    return df_.hvplot.scatter(\n",
+    "        title=f\"ICESat-2 Cycle {cycle} {dhdt_variable}\",\n",
+    "        x=\"x\",\n",
+    "        y=\"y\",\n",
+    "        c=dhdt_variable,\n",
+    "        cmap=\"gist_earth\" if dhdt_variable == \"h_corr\" else \"BrBG\",\n",
+    "        clim=None,\n",
+    "        # by=\"cycle_number\",\n",
+    "        rasterize=rasterize,\n",
+    "        datashade=datashade,\n",
+    "        dynspread=datashade,\n",
+    "        hover=True,\n",
+    "        hover_cols=[\"referencegroundtrack\", \"dhdt_slope\", \"h_corr\"],\n",
+    "        colorbar=True,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Interactive holoviews scatter plot to find referencegroundtrack needed\n",
+    "# Tip: Hover over the points, and find those with high 'dhdt_slope' values\n",
+    "layout: pn.layout.Column = pn.interact(\n",
+    "    dhdt_plot,\n",
+    "    cycle=pn.widgets.IntSlider(name=\"Cycle Number\", start=2, end=7, step=1, value=7),\n",
+    "    dhdt_variable=pn.widgets.RadioButtonGroup(\n",
+    "        name=\"dhdt_variables\",\n",
+    "        value=\"dhdt_slope\",\n",
+    "        options=[\"referencegroundtrack\", \"dhdt_slope\", \"h_corr\"],\n",
+    "    ),\n",
+    "    dhdt_range=pn.widgets.RangeSlider(\n",
+    "        name=\"dhdt range ±\", start=0, end=20, value=(1, 10), step=0.5\n",
+    "    ),\n",
+    "    rasterize=pn.widgets.Checkbox(name=\"Rasterize\"),\n",
+    "    datashade=pn.widgets.Checkbox(name=\"Datashade\"),\n",
+    ")\n",
+    "dashboard: pn.layout.Column = pn.Column(\n",
+    "    pn.Row(\n",
+    "        pn.Column(layout[0][1], align=\"center\"),\n",
+    "        pn.Column(layout[0][0], layout[0][2], align=\"center\"),\n",
+    "        pn.Column(layout[0][3], layout[0][4], align=\"center\"),\n",
+    "    ),\n",
+    "    layout[1],\n",
+    ")\n",
+    "dashboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show dashboard in another browser tab\n",
+    "# dashboard.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking at Reference Ground Track 135\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Select one Reference Ground track to look at\n",
+    "rgt: int = 135\n",
+    "assert rgt in rgts\n",
+    "df_rgt: pd.DataFrame = df_many.query(expr=\"referencegroundtrack == @rgt\")\n",
+    "print(f\"Looking at Reference Ground Track {rgt}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select one laser pair (out of three) based on y_atc field\n",
+    "# df = df_rgt.query(expr=\"y_atc < -100\")  # left\n",
+    "df = df_rgt.query(expr=\"abs(y_atc) < 100\")  # centre\n",
+    "# df = df_rgt.query(expr=\"y_atc > 100\")  # right"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Interactive scatter plot of height along one laser pair track, over time\n",
+    "df.hvplot.scatter(\n",
+    "    x=\"x_atc\",\n",
+    "    y=\"h_corr\",\n",
+    "    by=\"cycle_number\",\n",
+    "    hover=True,\n",
+    "    hover_cols=[\"x\", \"y\", \"dhdt_slope\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Filter points to those with significant dhdt values > +/- 0.2 m/yr\n",
+    "df = df.query(expr=\"abs(dhdt_slope) > 0.2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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X/6j5kHNWiAcAAAAAMCIFJgAAAAAAcKMeRSa3snAaNqohr2w+HzQU3hzsXgIAAAAAML5nS3cAAAAAAABKVS9wHnPh9yFZTA5bd3n97xt/a1su50COBAAAAACYgAITAAAAAAC4w4hFJoecs+IS2Lg6B7yTB+pdTTapYewHeRIAAAAAYBpVznnpPgAAAAAAQAhnC6F3PV9ySOltkQrAW3U+eZtLcs7Vgt1ZTFVV5/+YeZAvAQAAAACmo8AEAAAAAAAmUFXVLrUXmvgGfgAAAAAAAFbl/wMb58k1NVrGWwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {
+      "image/png": {
+       "width": 500
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Plot 2D along track view of\n",
+    "# Ice Surface Height Changes over Time\n",
+    "fig = pygmt.Figure()\n",
+    "# Setup map frame, title, axis annotations, etc\n",
+    "fig.basemap(\n",
+    "    projection=\"X20c/10c\",\n",
+    "    region=[df.x_atc.min(), df.x_atc.max(), df.h_corr.min(), df.h_corr.max()],\n",
+    "    frame=[\n",
+    "        rf'WSne+t\"ICESat-2 Change in Ice Surface Height over Time at {region.name}\"',\n",
+    "        'xaf+l\"Along track x (m)\"',\n",
+    "        'yaf+l\"Height (m)\"',\n",
+    "    ],\n",
+    ")\n",
+    "fig.text(\n",
+    "    x=df.x_atc.mean(),\n",
+    "    y=df.h_corr.max(),\n",
+    "    text=f\"Reference Ground Track {rgt:04d}\",\n",
+    "    justify=\"TC\",\n",
+    "    D=\"jTC-0c/0.2c\",\n",
+    ")\n",
+    "\n",
+    "# Colors from https://colorbrewer2.org/#type=qualitative&scheme=Set1&n=7\n",
+    "cycle_colors = {3: \"#ff7f00\", 4: \"#984ea3\", 5: \"#4daf4a\", 6: \"#377eb8\", 7: \"#e41a1c\"}\n",
+    "for cycle, color in cycle_colors.items():\n",
+    "    df_ = df.query(expr=\"cycle_number == @cycle\").copy()\n",
+    "    if len(df_) > 0:\n",
+    "        # Get x, y, time\n",
+    "        data = np.column_stack(tup=(df_.x_atc, df_.h_corr))\n",
+    "        time_nsec = deepicedrain.deltatime_to_utctime(dataarray=df_.delta_time.mean())\n",
+    "        time_sec = np.datetime_as_string(arr=time_nsec.to_datetime64(), unit=\"s\")\n",
+    "        label = f'\"Cycle {cycle} at {time_sec}\"'\n",
+    "\n",
+    "        # Plot data points\n",
+    "        fig.plot(data=data, style=\"c0.05c\", color=color, label=label)\n",
+    "        # Plot line connecting points\n",
+    "        # fig.plot(data=data, pen=f\"faint,{color},-\", label=f'\"+g-1l+s0.15c\"')\n",
+    "\n",
+    "fig.legend(S=3, position=\"jBR+jBR+o0.2c\", box=\"+gwhite+p1p\")\n",
+    "fig.savefig(f\"figures/alongtrack_atl11_dh_{placename}_{rgt}.png\")\n",
+    "fig.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -841,6 +1184,7 @@
  ],
  "metadata": {
   "jupytext": {
+   "encoding": "# -*- coding: utf-8 -*-",
    "formats": "ipynb,py:hydrogen"
   },
   "kernelspec": {
@@ -858,7 +1202,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
diff --git a/atlxi_dhdt.py b/atlxi_dhdt.py
index dbaf924..3132072 100644
--- a/atlxi_dhdt.py
+++ b/atlxi_dhdt.py
@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 # ---
 # jupyter:
 #   jupytext:
@@ -36,6 +37,7 @@
 # %%
 import dask
 import datashader
+import intake
 import numpy as np
 import pandas as pd
 import pygmt
@@ -52,6 +54,14 @@
 # # Select essential points
 
 # %%
+# Xarray open_dataset preprocessor to add fields based on input filename.
+add_path_to_ds = lambda ds: ds.assign_coords(
+    coords=intake.source.utils.reverse_format(
+        format_string="ATL11.001z123/ATL11_{referencegroundtrack:04d}1x_{mincycle:02d}{maxcycle:02d}_{}_v{}.zarr",
+        resolved_string=ds.encoding["source"],
+    )
+)
+
 # Load ATL11 data from Zarr
 ds: xr.Dataset = xr.open_mfdataset(
     paths="ATL11.001z123/ATL11_*.zarr",
@@ -60,6 +70,7 @@
     combine="nested",
     concat_dim="ref_pt",
     parallel="True",
+    preprocess=add_path_to_ds,
     backend_kwargs={"consolidated": True},
 )
 
@@ -362,3 +373,177 @@
 fig.show(width=600)
 
 # %%
+
+# %% [markdown]
+# # Along track plots of subglacial lake drainage/filling events
+#
+# Let's take a closer look at one potential
+# subglacial lake filling event at Whillans Ice Stream.
+# We'll plot a cross-section view of
+# ice surface height changes over time,
+# along an ICESat-2 reference ground track.
+
+# %%
+import holoviews as hv
+import hvplot.pandas
+import panel as pn
+
+# %%
+# Subset dataset to geographic region of interest
+placename: str = "whillans2"
+region: deepicedrain.Region = regions[placename]
+ds_subset: xr.Dataset = region.subset(ds=ds_dhdt)
+
+# %%
+# Quick facet plot of height over different cycles
+# See https://xarray.pydata.org/en/stable/plotting.html#datasets
+ds_subset.plot.scatter(
+    x="x", y="y", hue="h_corr", cmap="gist_earth", col="cycle_number", col_wrap=4
+)
+
+# %%
+# Find reference ground tracks that have data up to cycle 7 (the most recent cycle)
+rgts = np.unique(
+    ar=ds_subset.sel(cycle_number=7).dropna(dim="ref_pt").referencegroundtrack
+)
+rgts
+
+# %%
+# Convert xarray.Dataset to pandas.DataFrame for easier analysis
+df_many: pd.DataFrame = ds_subset.to_dataframe().dropna()
+
+
+# %%
+def dhdt_plot(
+    cycle: int = 7,
+    dhdt_variable: str = "dhdt_slope",
+    dhdt_range: tuple = (1, 10),
+    rasterize: bool = False,
+    datashade: bool = False,
+) -> hv.element.chart.Scatter:
+    """
+    ICESat-2 rate of height change over time (dhdt) interactive scatter plot.
+    Uses HvPlot, and intended to be used inside a Panel dashboard.
+    """
+    df_ = df_many.query(
+        expr="cycle_number == @cycle & "
+        "abs(dhdt_slope) > @dhdt_range[0] & abs(dhdt_slope) < @dhdt_range[1]"
+    )
+    return df_.hvplot.scatter(
+        title=f"ICESat-2 Cycle {cycle} {dhdt_variable}",
+        x="x",
+        y="y",
+        c=dhdt_variable,
+        cmap="gist_earth" if dhdt_variable == "h_corr" else "BrBG",
+        clim=None,
+        # by="cycle_number",
+        rasterize=rasterize,
+        datashade=datashade,
+        dynspread=datashade,
+        hover=True,
+        hover_cols=["referencegroundtrack", "dhdt_slope", "h_corr"],
+        colorbar=True,
+    )
+
+
+# %%
+# Interactive holoviews scatter plot to find referencegroundtrack needed
+# Tip: Hover over the points, and find those with high 'dhdt_slope' values
+layout: pn.layout.Column = pn.interact(
+    dhdt_plot,
+    cycle=pn.widgets.IntSlider(name="Cycle Number", start=2, end=7, step=1, value=7),
+    dhdt_variable=pn.widgets.RadioButtonGroup(
+        name="dhdt_variables",
+        value="dhdt_slope",
+        options=["referencegroundtrack", "dhdt_slope", "h_corr"],
+    ),
+    dhdt_range=pn.widgets.RangeSlider(
+        name="dhdt range ±", start=0, end=20, value=(1, 10), step=0.5
+    ),
+    rasterize=pn.widgets.Checkbox(name="Rasterize"),
+    datashade=pn.widgets.Checkbox(name="Datashade"),
+)
+dashboard: pn.layout.Column = pn.Column(
+    pn.Row(
+        pn.Column(layout[0][1], align="center"),
+        pn.Column(layout[0][0], layout[0][2], align="center"),
+        pn.Column(layout[0][3], layout[0][4], align="center"),
+    ),
+    layout[1],
+)
+dashboard
+
+# %%
+# Show dashboard in another browser tab
+# dashboard.show()
+
+# %%
+# Select one Reference Ground track to look at
+rgt: int = 135
+assert rgt in rgts
+df_rgt: pd.DataFrame = df_many.query(expr="referencegroundtrack == @rgt")
+print(f"Looking at Reference Ground Track {rgt}")
+
+# %%
+# Select one laser pair (out of three) based on y_atc field
+# df = df_rgt.query(expr="y_atc < -100")  # left
+df = df_rgt.query(expr="abs(y_atc) < 100")  # centre
+# df = df_rgt.query(expr="y_atc > 100")  # right
+
+# %%
+# Interactive scatter plot of height along one laser pair track, over time
+df.hvplot.scatter(
+    x="x_atc",
+    y="h_corr",
+    by="cycle_number",
+    hover=True,
+    hover_cols=["x", "y", "dhdt_slope"],
+)
+
+# %%
+# Filter points to those with significant dhdt values > +/- 0.2 m/yr
+df = df.query(expr="abs(dhdt_slope) > 0.2")
+
+# %%
+# Plot 2D along track view of
+# Ice Surface Height Changes over Time
+fig = pygmt.Figure()
+# Setup map frame, title, axis annotations, etc
+fig.basemap(
+    projection="X20c/10c",
+    region=[df.x_atc.min(), df.x_atc.max(), df.h_corr.min(), df.h_corr.max()],
+    frame=[
+        rf'WSne+t"ICESat-2 Change in Ice Surface Height over Time at {region.name}"',
+        'xaf+l"Along track x (m)"',
+        'yaf+l"Height (m)"',
+    ],
+)
+fig.text(
+    x=df.x_atc.mean(),
+    y=df.h_corr.max(),
+    text=f"Reference Ground Track {rgt:04d}",
+    justify="TC",
+    D="jTC-0c/0.2c",
+)
+
+# Colors from https://colorbrewer2.org/#type=qualitative&scheme=Set1&n=7
+cycle_colors = {3: "#ff7f00", 4: "#984ea3", 5: "#4daf4a", 6: "#377eb8", 7: "#e41a1c"}
+for cycle, color in cycle_colors.items():
+    df_ = df.query(expr="cycle_number == @cycle").copy()
+    if len(df_) > 0:
+        # Get x, y, time
+        data = np.column_stack(tup=(df_.x_atc, df_.h_corr))
+        time_nsec = deepicedrain.deltatime_to_utctime(dataarray=df_.delta_time.mean())
+        time_sec = np.datetime_as_string(arr=time_nsec.to_datetime64(), unit="s")
+        label = f'"Cycle {cycle} at {time_sec}"'
+
+        # Plot data points
+        fig.plot(data=data, style="c0.05c", color=color, label=label)
+        # Plot line connecting points
+        # fig.plot(data=data, pen=f"faint,{color},-", label=f'"+g-1l+s0.15c"')
+
+fig.legend(S=3, position="jBR+jBR+o0.2c", box="+gwhite+p1p")
+fig.savefig(f"figures/alongtrack_atl11_dh_{placename}_{rgt}.png")
+fig.show()
+
+# %%
diff --git a/poetry.lock b/poetry.lock
index a6303fc..4206c21 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1771,7 +1771,7 @@ description = "N-D labeled arrays and datasets in Python"
 name = "xarray"
 optional = false
 python-versions = ">=3.6"
-version = "0.15.2.dev98+g4ce30073"
+version = "0.15.2.dev190-g6f6aae7d"
 
 [package.dependencies]
 numpy = ">=1.15"
@@ -1779,9 +1779,9 @@ pandas = ">=0.25"
 setuptools = ">=41.2"
 
 [package.source]
-reference = "4ce3007362af4e22dbe5836e95bebd91cde734b7"
+reference = "6f6aae7ddcd73d8c8e067c32a4320b15efd9e4c0"
 type = "git"
-url = "https://github.com/Mikejmnez/xarray.git"
+url = "https://github.com/weiji14/xarray.git"
 
 [[package]]
 category = "main"
diff --git a/pyproject.toml b/pyproject.toml
index c425095..a024d20 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,7 +28,7 @@ python = "^3.8"
 pyproj = "^2.6.0"
 toolz = "^0.10.0"
 tqdm = "^4.46.0"
-xarray = {git = "https://github.com/Mikejmnez/xarray.git", rev="4ce3007362af4e22dbe5836e95bebd91cde734b7"}
+xarray = {git = "https://github.com/weiji14/xarray.git", rev = "6f6aae7ddcd73d8c8e067c32a4320b15efd9e4c0"}
 xrviz = "^0.1.4"
 
 [tool.poetry.dev-dependencies]