WIP: Add gallery example OR tutorial showing the usage of pygmt.grdmask

examples/gallery/images/grdmask.py

@@ -0,0 +1,160 @@
+"""
+Create grid masks from spatial shapes
+=====================================
+
+The functionn :func:`pygmt.grdmask` allows to create a grid mask based on spatial
+shapes given as closed polygons. These polygons can be provided as NumPy arrays or
+GeoPandas DataFrame. For the nodes falling inside, outside, and on the edges, different
+values can be defined. The create mask can then be applied to a desired grid.
+
+To create a land-water mask based on the GMT built-in shoreline data you can directly
+use the function :func:`pygmt.grdlandmask` explained in the gallery example
+:doc:` Create 'wet-dry' mask grid </gallery/images/gradlandmask>`.
+"""
+
+# %%
+import geopandas
+import numpy as np
+import pygmt
+from shapely.geometry import Point
+
+# %%
+# Polygons based on NumPy arrays
+# ------------------------------
+
+# Define a study region
+region = [125, 135, 25, 36]
+
+# Define two closed polygons, here a square and a triangle.
+# Use an np.nan to separate the polygons
+polygon = np.array(
+    [
+        [129, 31],
+        [134, 31],
+        [134, 35],
+        [129, 35],
+        [129, 31],
+        [np.nan, np.nan],
+        [126, 26],
+        [131, 26],
+        [131, 30],
+        [126, 26],
+    ],
+)
+
+# Download elevation grid
+grid = pygmt.datasets.load_earth_relief(region=region, resolution="30s")
+
+# Create a grid mask based on the two polygons defined above
+# Set all grid nodes outside the polygons to NaN
+mask_out = pygmt.grdmask(region=region, data=polygon, spacing="30s", outside="NaN")
+# Set all grid nodes inside the polygons to NaN
+# Set the outside parameter to a value larger 0 to keep the nodes outside unchanged
+mask_in = pygmt.grdmask(
+    region=region, data=polygon, spacing="30s", inside="NaN", outside=1
+)
+
+# Apply the grid mask to the downloaded elevation grid by multiplying the two grids
+grid_mask_out = grid * mask_out
+grid_mask_in = grid * mask_in
+
+
+fig = pygmt.Figure()
+pygmt.makecpt(cmap="SCM/oleron", series=[-2000, 2000])
+
+# Plot the elevation grid
+fig.basemap(region=region, projection="M12c", frame=True)
+fig.grdimage(grid=grid, cmap=True)
+fig.plot(data=polygon, pen="2p,darkorange")
+
+fig.shift_origin(xshift="+w+2c")
+
+# Plot the masked elevation grid outside
+fig.basemap(region=region, projection="M12c", frame=True)
+fig.grdimage(grid=grid_mask_out, cmap=True)
+fig.plot(data=polygon, pen="2p,darkorange")
+
+fig.shift_origin(xshift="+w+2c")
+
+# Plot the masked elevation grid inside
+fig.basemap(region=region, projection="M12c", frame=True)
+fig.grdimage(grid=grid_mask_in, cmap=True)
+fig.plot(data=polygon, pen="2p,darkorange")
+
+fig.colorbar(frame=True)
+fig.show()
+
+
+# %%
+# US state Missouri based on GeoPandas polygon geometry
+# -----------------------------------------------------
+
+region = [-126, -66, 25, 49]
+
+provider = "https://naciscdn.org/naturalearth"
+states = geopandas.read_file(
+    f"{provider}/50m/cultural/ne_50m_admin_1_states_provinces.zip"
+)
+missouri = states[states["name"] == "Missouri"]
+
+grid = pygmt.datasets.load_earth_relief(region=region, resolution="01m")
+mask = pygmt.grdmask(region=region, data=missouri, spacing="01m", outside="NaN")
+mask_lonlat = mask.rename(new_name_or_name_dict={"x": "lon", "y": "lat"})
+grid_mask = grid * mask_lonlat
+
+
+fig = pygmt.Figure()
+pygmt.makecpt(cmap="SCM/oleron", series=[-2000, 2000])
+
+# Plot the elevation grid
+fig.basemap(projection="L-96/35/33/41/12c", region=region, frame=True)
+fig.grdimage(grid=grid, cmap=True)
+fig.plot(data=missouri, pen="1p,darkorange")
+
+fig.shift_origin(xshift="+w+1c")
+
+# Plot the masked elevation grid
+# fig.basemap(projection="L-96/35/33/41/12c", region=region, frame=True)
+fig.basemap(projection="M10c", region=[-96.5, -88.5, 35.8, 41], frame=True)
+fig.grdimage(grid=grid_mask, cmap=True)
+fig.plot(data=missouri, pen="1p,darkorange")
+
+fig.colorbar(frame=True)
+fig.show()
+
+
+# %%
+# Circle based on GeoPandas polygon geometry
+# ------------------------------------------
+
+region = [125, 135, 25, 36]
+
+# Create a point and buffer it
+point = geopandas.GeoSeries([Point(126.5, 33.5)])
+circle = point.buffer(0.6)  # 10 is the radius
+
+grid = pygmt.datasets.load_earth_relief(region=region, resolution="30s")
+mask = pygmt.grdmask(region=region, data=circle, spacing="30s", outside="NaN")
+mask_lonlat = mask.rename(new_name_or_name_dict={"x": "lon", "y": "lat"})
+grid_mask = grid * mask_lonlat
+
+
+fig = pygmt.Figure()
+pygmt.makecpt(cmap="SCM/oleron", series=[-2000, 2000])
+
+# Plot the elevation grid
+fig.basemap(region=region, projection="M12c", frame=True)
+fig.grdimage(grid=grid, cmap=True)
+fig.plot(data=circle, pen="2p,darkorange")
+
+fig.shift_origin(xshift="+w+2c")
+
+# Plot the masked elevation grid
+fig.basemap(region=[125.5, 127.5, 32.5, 34.5], projection="M12c", frame=True)
+fig.grdimage(grid=grid_mask, cmap=True)
+fig.plot(data=circle, pen="2p,darkorange")
+
+fig.colorbar(frame=True)
+fig.show()
+
+# sphinx_gallery_thumbnail_number = 1