✨ Active subglacial lake mega-cluster analysis

Look at not one but multiple active subglacial lakes! Examples include: Lake 78, Subglacial Lake Conway, Subglacial Lake Mercer and Subglacial Lake Whillans on the Siple Coast; and Recovery 2 on Recovery Glacier. Also renumbered all the lake ids for the single lakes. The subglacial_lakes.feature file splits the single and mega-cluster lake tests. Note that the mega-cluster test won't run on CI until deepicedrain v0.4.0 is released. Really required a lot of refactoring to handle multiple lake polygons. Bounding box region is now determined using a convex hull so that it works for single or multiple polygons. Lakes are dissolved into a MultiPolygon (geopandas v0.8.0 requiring a dissolve field, problematic for Lake 78 that spans Whillans and Mercer basins). The lake outlines are saved to an OGR GMT format which is actually quite nice as it can be shared around.
weiji14 · Dec 5, 2020 · c93eded · c93eded
1 parent 1dac092
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diff --git a/.gitignore b/.gitignore
@@ -35,3 +35,8 @@ ATL11.001
 ATL11.001z123
 ATLXI
 Quantarctica3
+
+# Subglacial Lake grid files and figures
+figures/**/*.gif
+figures/**/*.nc
+figures/**/*.png
diff --git a/atlxi_lake.ipynb b/atlxi_lake.ipynb
@@ -922,7 +922,7 @@
    "outputs": [],
    "source": [
     "# Save or load dhdt data from Parquet file\n",
-    "placename: str = \"siple_coast\"  # \"Recovery\"  # \"Whillans\"\n",
+    "placename: str = \"siple_coast\"  # \"slessor_downstream\"  #  \"Recovery\"  # \"Whillans\"\n",
     "try:\n",
     "    drainage_basins: gpd.GeoDataFrame = drainage_basins.set_index(keys=\"NAME\")\n",
     "    region: deepicedrain.Region = deepicedrain.Region.from_gdf(\n",
@@ -942,7 +942,8 @@
    "outputs": [],
    "source": [
     "# Antarctic subglacial lake polygons with EPSG:3031 coordinates\n",
-    "antarctic_lakes: gpd.GeoDataFrame = deepicedrain.catalog.subglacial_lakes.read()"
+    "antarctic_lakes: gpd.GeoDataFrame = deepicedrain.catalog.subglacial_lakes.read()\n",
+    "antarctic_lakes = antarctic_lakes.set_crs(epsg=3031, allow_override=True)"
    ]
   },
   {
@@ -986,24 +987,30 @@
     "# Choose one draining/filling lake\n",
     "draining: bool = False\n",
     "placename: str = \"Whillans\"  # \"Slessor\"  # \"Kamb\"  # \"Mercer\"  #\n",
-    "lakes: gpd.GeoDataFrame = antarctic_lakes.query(expr=\"basin_name == @placename\")\n",
-    "lake = lakes.loc[lakes.inner_dhdt.idxmin() if draining else lakes.inner_dhdt.idxmax()]\n",
-    "lake = lakes.query(expr=\"inner_dhdt < 0\" if draining else \"inner_dhdt > 0\").loc[48]\n",
+    "lakes: gpd.GeoDataFrame = antarctic_lakes  # .query(expr=\"basin_name == @placename\")\n",
+    "\n",
+    "lake_ids: int = (44,)  # single lake\n",
+    "lake_ids: tuple = (41, 43, 45)  # lake mega-cluster\n",
+    "# TODO handle Lake 78 cross-basin by using dissolve(by=None) available\n",
+    "# in geopandas v0.9.0 https://github.com/geopandas/geopandas/pull/1568\n",
+    "lake = lakes.loc[list(lake_ids)].dissolve(by=\"basin_name\", as_index=False).squeeze()\n",
     "lakedict = {\n",
-    "    21: \"Mercer 2b\",  # filling lake\n",
-    "    40: \"Lower Subglacial Lake Conway\",  # draining lake\n",
-    "    41: \"Subglacial Lake Conway\",  # draining lake\n",
-    "    48: \"Subglacial Lake Whillans\",  # filling lake\n",
-    "    50: \"Whillans IX\",  # filling lake\n",
-    "    63: \"Kamb 1\",  # filling lake\n",
-    "    65: \"Kamb 12\",  # filling lake\n",
-    "    97: \"MacAyeal 1\",  # draining lake\n",
-    "    109: \"Slessor 45\",  # draining lake\n",
-    "    116: \"Slessor 23\",  # filling lake\n",
-    "    151: \"Recovery IV\",  # draining lake\n",
-    "    156: \"Recovery 2\",  # filling lake\n",
+    "    (15, 19): \"Subglacial Lake Mercer\",  # filling lake\n",
+    "    (32,): \"Whillans 7\",  # draining lake\n",
+    "    (34, 35): \"Subglacial Lake Conway\",  # draining lake\n",
+    "    (41, 43, 45): \"Subglacial Lake Whillans\",  # filling lake\n",
+    "    (16, 46, 48): \"Lake 78\",  # filling lake\n",
+    "    (44,): \"Whillans IX\",  # filling lake\n",
+    "    (62,): \"Kamb 1\",  # filling lake\n",
+    "    # (65): \"Kamb 12\",  # filling lake\n",
+    "    (84,): \"MacAyeal 1\",  # draining lake\n",
+    "    (95,): \"Slessor 45\",  # draining lake\n",
+    "    (101,): \"Slessor 23\",  # filling lake\n",
+    "    (141,): \"Recovery IV\",  # draining lake\n",
+    "    (143, 144): \"Recovery 2\",  # filling lake\n",
     "}\n",
-    "region = deepicedrain.Region.from_gdf(gdf=lake, name=lakedict[lake.name])\n",
+    "region = deepicedrain.Region.from_gdf(gdf=lake, name=lakedict[lake_ids])\n",
+    "assert (lake.inner_dhdt < 0 and draining) or (lake.inner_dhdt > 0 and not draining)\n",
     "\n",
     "print(lake)\n",
     "lake.geometry"
@@ -1019,7 +1026,12 @@
    "source": [
     "# Subset data to lake of interest\n",
     "placename: str = region.name.lower().replace(\" \", \"_\")\n",
-    "df_lake: cudf.DataFrame = region.subset(data=df_dhdt)"
+    "df_lake: cudf.DataFrame = region.subset(data=df_dhdt)\n",
+    "\n",
+    "# Save lake outline to OGR GMT file format\n",
+    "outline_points: str = f\"figures/{placename}/{placename}.gmt\"\n",
+    "if not os.path.exists(path=outline_points):\n",
+    "    lakes.loc[list(lake_ids)].to_file(filename=outline_points, driver=\"OGR_GMT\")"
    ]
   },
   {
@@ -1044,7 +1056,7 @@
    ],
    "source": [
     "# Generate gridded time-series of ice elevation over lake\n",
-    "cycles: tuple = (3, 4, 5, 6, 7, 8)\n",
+    "cycles: tuple = (3, 4, 5, 6, 7, 8, 9)\n",
     "os.makedirs(name=f\"figures/{placename}\", exist_ok=True)\n",
     "ds_lake: xr.Dataset = deepicedrain.spatiotemporal_cube(\n",
     "    table=df_lake.to_pandas(),\n",
@@ -1092,15 +1104,9 @@
     "    time_nsec: pd.Timestamp = df_lake[f\"utc_time_{cycle}\"].to_pandas().mean()\n",
     "    time_sec: str = np.datetime_as_string(arr=time_nsec.to_datetime64(), unit=\"s\")\n",
     "\n",
-    "    grid = f\"figures/{placename}/h_corr_{placename}_cycle_{cycle}.nc\"\n",
-    "    points = pd.DataFrame(\n",
-    "        data=np.vstack(lake.geometry.boundary.coords.xy).T, columns=(\"x\", \"y\")\n",
-    "    )\n",
-    "    outline_points = pygmt.grdtrack(points=points, grid=grid, newcolname=\"z\")\n",
-    "    outline_points[\"z\"] = outline_points.z.fillna(value=outline_points.z.median())\n",
-    "\n",
+    "    # grid = ds_lake.sel(cycle_number=cycle).z\n",
     "    fig = deepicedrain.plot_icesurface(\n",
-    "        grid=grid,  # ds_lake.sel(cycle_number=cycle).z\n",
+    "        grid=f\"figures/{placename}/h_corr_{placename}_cycle_{cycle}.nc\",\n",
     "        grid_region=grid_region,\n",
     "        diff_grid=ds_lake_diff.sel(cycle_number=cycle).z,\n",
     "        diff_grid_region=diff_grid_region,\n",

diff --git a/atlxi_lake.py b/atlxi_lake.py
@@ -331,7 +331,7 @@
 
 # %%
 # Save or load dhdt data from Parquet file
-placename: str = "siple_coast"  # "Recovery"  # "Whillans"
+placename: str = "siple_coast"  # "slessor_downstream"  #  "Recovery"  # "Whillans"
 try:
     drainage_basins: gpd.GeoDataFrame = drainage_basins.set_index(keys="NAME")
     region: deepicedrain.Region = deepicedrain.Region.from_gdf(
@@ -347,29 +347,36 @@
 # %%
 # Antarctic subglacial lake polygons with EPSG:3031 coordinates
 antarctic_lakes: gpd.GeoDataFrame = deepicedrain.catalog.subglacial_lakes.read()
+antarctic_lakes = antarctic_lakes.set_crs(epsg=3031, allow_override=True)
 
 # %%
 # Choose one draining/filling lake
 draining: bool = False
 placename: str = "Whillans"  # "Slessor"  # "Kamb"  # "Mercer"  #
-lakes: gpd.GeoDataFrame = antarctic_lakes.query(expr="basin_name == @placename")
-lake = lakes.loc[lakes.inner_dhdt.idxmin() if draining else lakes.inner_dhdt.idxmax()]
-lake = lakes.query(expr="inner_dhdt < 0" if draining else "inner_dhdt > 0").loc[48]
+lakes: gpd.GeoDataFrame = antarctic_lakes  # .query(expr="basin_name == @placename")
+
+lake_ids: int = (44,)  # single lake
+lake_ids: tuple = (41, 43, 45)  # lake mega-cluster
+# TODO handle Lake 78 cross-basin by using dissolve(by=None) available
+# in geopandas v0.9.0 https://github.com/geopandas/geopandas/pull/1568
+lake = lakes.loc[list(lake_ids)].dissolve(by="basin_name", as_index=False).squeeze()
 lakedict = {
-    21: "Mercer 2b",  # filling lake
-    40: "Lower Subglacial Lake Conway",  # draining lake
-    41: "Subglacial Lake Conway",  # draining lake
-    48: "Subglacial Lake Whillans",  # filling lake
-    50: "Whillans IX",  # filling lake
-    63: "Kamb 1",  # filling lake
-    65: "Kamb 12",  # filling lake
-    97: "MacAyeal 1",  # draining lake
-    109: "Slessor 45",  # draining lake
-    116: "Slessor 23",  # filling lake
-    151: "Recovery IV",  # draining lake
-    156: "Recovery 2",  # filling lake
+    (15, 19): "Subglacial Lake Mercer",  # filling lake
+    (32,): "Whillans 7",  # draining lake
+    (34, 35): "Subglacial Lake Conway",  # draining lake
+    (41, 43, 45): "Subglacial Lake Whillans",  # filling lake
+    (16, 46, 48): "Lake 78",  # filling lake
+    (44,): "Whillans IX",  # filling lake
+    (62,): "Kamb 1",  # filling lake
+    # (65): "Kamb 12",  # filling lake
+    (84,): "MacAyeal 1",  # draining lake
+    (95,): "Slessor 45",  # draining lake
+    (101,): "Slessor 23",  # filling lake
+    (141,): "Recovery IV",  # draining lake
+    (143, 144): "Recovery 2",  # filling lake
 }
-region = deepicedrain.Region.from_gdf(gdf=lake, name=lakedict[lake.name])
+region = deepicedrain.Region.from_gdf(gdf=lake, name=lakedict[lake_ids])
+assert (lake.inner_dhdt < 0 and draining) or (lake.inner_dhdt > 0 and not draining)
 
 print(lake)
 lake.geometry
@@ -379,13 +386,17 @@
 placename: str = region.name.lower().replace(" ", "_")
 df_lake: cudf.DataFrame = region.subset(data=df_dhdt)
 
+# Save lake outline to OGR GMT file format
+outline_points: str = f"figures/{placename}/{placename}.gmt"
+if not os.path.exists(path=outline_points):
+    lakes.loc[list(lake_ids)].to_file(filename=outline_points, driver="OGR_GMT")
 
 # %% [markdown]
 # ## Create an interpolated ice surface elevation grid for each ICESat-2 cycle
 
 # %%
 # Generate gridded time-series of ice elevation over lake
-cycles: tuple = (3, 4, 5, 6, 7, 8)
+cycles: tuple = (3, 4, 5, 6, 7, 8, 9)
 os.makedirs(name=f"figures/{placename}", exist_ok=True)
 ds_lake: xr.Dataset = deepicedrain.spatiotemporal_cube(
     table=df_lake.to_pandas(),
@@ -413,15 +424,9 @@
     time_nsec: pd.Timestamp = df_lake[f"utc_time_{cycle}"].to_pandas().mean()
     time_sec: str = np.datetime_as_string(arr=time_nsec.to_datetime64(), unit="s")
 
-    grid = f"figures/{placename}/h_corr_{placename}_cycle_{cycle}.nc"
-    points = pd.DataFrame(
-        data=np.vstack(lake.geometry.boundary.coords.xy).T, columns=("x", "y")
-    )
-    outline_points = pygmt.grdtrack(points=points, grid=grid, newcolname="z")
-    outline_points["z"] = outline_points.z.fillna(value=outline_points.z.median())
-
+    # grid = ds_lake.sel(cycle_number=cycle).z
     fig = deepicedrain.plot_icesurface(
-        grid=grid,  # ds_lake.sel(cycle_number=cycle).z
+        grid=f"figures/{placename}/h_corr_{placename}_cycle_{cycle}.nc",
         grid_region=grid_region,
         diff_grid=ds_lake_diff.sel(cycle_number=cycle).z,
         diff_grid_region=diff_grid_region,

diff --git a/deepicedrain/features/subglacial_lakes.feature b/deepicedrain/features/subglacial_lakes.feature
@@ -15,24 +15,39 @@ Feature: Mapping Antarctic subglacial lakes
 
 
   Scenario Outline: Subglacial Lake Animation
-    Given some altimetry data at <location> spatially subsetted to <lake_name> with <lake_id>
+    Given some altimetry data at <location> spatially subsetted to <lake_name> with <lake_ids>
     When it is turned into a spatiotemporal cube over ICESat-2 cycles <cycles>
     And visualized at each cycle using a 3D perspective at <azimuth> and <elevation>
     Then the result is an animation of ice surface elevation changing over time
 
     Examples:
-    | location            | lake_name                      | lake_id | cycles | azimuth | elevation |
-    # | whillans_downstream | Mercer 2b                      | 21      | 3-8    | 157.5   | 45        |
-    # | whillans_downstream | Lower Subglacial Lake Conway   | 40      | 3-8    | 157.5   | 45        |
-    # | whillans_downstream | Subglacial Lake Conway         | 41      | 3-8    | 157.5   | 45        |
-    # | whillans_downstream | Subglacial Lake Whillans       | 48      | 3-8    | 157.5   | 45        |
-    # | whillans_upstream   | Whillans IX                    | 50      | 3-8    | 157.5   | 45        |
-    # | whillans_upstream   | Kamb 8                         | 61      | 3-8    | 157.5   | 45        |
-    # | whillans_upstream   | Kamb 1                         | 62      | 3-8    | 157.5   | 45        |
-    | whillans_upstream   | Kamb 34                        | 63      | 4-7    | 157.5   | 45        |
-    # | siple_coast         | Kamb 12                        | 65      | 3-8    | 157.5   | 45        |
-    # | siple_coast         | MacAyeal 1                     | 97      | 3-8    | 157.5   | 60        |
-    # | slessor_downstream  | Slessor 45                     | 109     | 3-8    | 202.5   | 60        |
-    # | slessor_downstream  | Slessor 23                     | 116     | 3-8    | 202.5   | 60        |
-    | slessor_downstream  | Recovery IV                    | 141     | 3-8    | 247.5   | 45        |
-    # | slessor_downstream  | Recovery 2                     | 156     | 3-8    | 202.5   | 45        |
+    | location            | lake_name                | lake_ids | cycles | azimuth | elevation |
+    # | whillans_downstream | Mercer XV                | 17       | 3-8    | 157.5   | 45        |
+    # | whillans_upstream   | Whillans 7               | 32       | 3-8    | 157.5   | 45        |
+    # | whillans_upstream   | Whillans 6               | 33       | 3-8    | 157.5   | 45        |
+    # | whillans_upstream   | Whillans X               | 38       | 3-8    | 157.5   | 45        |
+    # | whillans_upstream   | Whillans XI              | 49       | 3-8    | 157.5   | 45        |
+    # | whillans_upstream   | Whillans IX              | 44       | 3-8    | 157.5   | 45        |
+    # | whillans_upstream   | Kamb 8                   | 61       | 3-8    | 157.5   | 45        |
+    # | whillans_upstream   | Kamb 1                   | 62       | 3-8    | 157.5   | 45        |
+    | whillans_upstream   | Kamb 34                  | 63       | 4-7    | 157.5   | 45        |
+    # | siple_coast         | Kamb 12                  | 65       | 3-8    | 157.5   | 45        |
+    # | siple_coast         | MacAyeal 1               | 84       | 3-8    | 157.5   | 60        |
+    # | slessor_downstream  | Slessor 45               | 95       | 3-8    | 202.5   | 60        |
+    # | slessor_downstream  | Slessor 23               | 101      | 3-8    | 202.5   | 60        |
+    | slessor_downstream  | Recovery IV              | 141      | 3-8    | 247.5   | 45        |
+
+
+  Scenario Outline: Subglacial Lake Mega-Cluster Animation
+    Given some altimetry data at <location> spatially subsetted to <lake_name> with <lake_ids>
+    When it is turned into a spatiotemporal cube over ICESat-2 cycles <cycles>
+    And visualized at each cycle using a 3D perspective at <azimuth> and <elevation>
+    Then the result is an animation of ice surface elevation changing over time
+
+    Examples:
+    | location            | lake_name                | lake_ids    | cycles | azimuth | elevation |
+    # | whillans_downstream | Lake 78                  | 16,46,48    | 3-8    | 157.5   | 45        |
+    # | whillans_downstream | Subglacial Lake Conway   | 34,35       | 3-8    | 157.5   | 45        |
+    | whillans_downstream | Subglacial Lake Mercer   | 15,19       | 3-8    | 157.5   | 45        |
+    # | whillans_downstream | Subglacial Lake Whillans | 41,43,45    | 3-8    | 157.5   | 45        |
+    # | slessor_downstream  | Recovery 2               | 143,144     | 3-8    | 202.5   | 45        |
diff --git a/deepicedrain/spatiotemporal.py b/deepicedrain/spatiotemporal.py
@@ -76,7 +76,7 @@ def from_gdf(
             import pygmt
 
             xmin, xmax, ymin, ymax = pygmt.info(
-                table=np.vstack(gdf.geometry.exterior.coords.xy).T,
+                table=np.vstack(gdf.geometry.convex_hull.exterior.coords.xy).T,
                 spacing=float(spacing),
             )
         except (ImportError, TypeError):