use sample meshes for examples (#1339)

* use sample meshes for examples

* add changelog news fragment

changelog/1339.documentation.rst

@@ -0,0 +1,3 @@
+Simplified several :ref:`gv-examples` by using ``geovista.pantry.meshes``
+instead of loading ``geovista.pantry.data`` to construct sample
+meshes. (:user:`bjlittle`)

src/geovista/__init__.pyi

@@ -10,17 +10,15 @@ from .pantry.textures import (
 )
 from .report import Report
 
-
 __version__: str
 GEOVISTA_IMAGE_TESTING: bool
 
-
 __all__ = [
-    "__version__",
-    "GeoPlotter",
     "GEOVISTA_IMAGE_TESTING",
+    "GeoPlotter",
     "Report",
     "Transform",
+    "__version__",
     "black_marble",
     "blue_marble",
     "checkerboard",

src/geovista/cache/__init__.py

@@ -44,7 +44,7 @@
 BASE_URL: str = "https://github.com/bjlittle/geovista-data/raw/{version}/assets/"
 """Base URL for :mod:`geovista` resources."""
 
-DATA_VERSION: str = "2025.02.1"
+DATA_VERSION: str = "2025.02.2"
 """The ``geovista-data`` repository version for :mod:`geovista` resources."""
 
 GEOVISTA_CACHEDIR: str = "GEOVISTA_CACHEDIR"

src/geovista/cache/registry.txt

@@ -36,35 +36,35 @@ rasters/bahamas_rgb.tif.bz2 b4798063e478ad25398a2bf8a464f68f9890c5295ba70ad28b42
 rasters/fuji_dem.tif.bz2 2e51a278e62304d671e15be8f6cdbea0cb7d477643b97b95c4d4706fa58b4f0a
 rasters/japan_map.png.bz2 7b39023b5d26e79475d2dba92269593d5ce46c14ed5859079c79d1fa0ec0987f
 
-tests/images/examples.test__clouds.png 571b052c2bfe6e16bcbb3405a91082feb3c08c901bb869e9cb61d558826adf0d
+tests/images/examples.test__clouds.png e41d62c3d11add1f9928efef9b784b35d67b59d181bd6fa6d71c04467c46e089
 tests/images/examples.test__clouds_robin.png dd4b9cd16f735e55aa67f5b6ff90eeb7646bdb65bbb1c6df524a5746d9efc36c
-tests/images/examples.test__contour.banded_contour.png cb8a092afa55098e0b2ddacf4fc860619ed7e6938e1e5bc54567d0b85b04ad7d
+tests/images/examples.test__contour.banded_contour.png d34d24f1442007a0bdd7abe9f5df7fdff98b7c6744e02b600170bc0360abee81
 tests/images/examples.test__curvilinear.from_2d__orca_moll.png 2d218333c1033f37fc899e6ff4785cf61fbdda1d3e1d95449fd6c5e6e766a63a
 tests/images/examples.test__curvilinear.from_2d__orca.png 22442553c36ba79180db6a07178613b77fce7f84232c848995b3021ce7cd93a8
-tests/images/examples.test__extraction.wedge_manifold.png 17d71ec72d0e496924a69ed9be9dc42003a8339e5fee44f77329f5d8efb311f4
+tests/images/examples.test__extraction.wedge_manifold.png 7da4e392b3da445b215221ffa9b51d8117699da3a2f3810b624dee731c959640
 tests/images/examples.test__point_cloud.from_points__orca_cloud_eqc.png d7cbc1365588e6b4cfc3e393776e2ca18671d45121e83a6450beebe7e6bfa4a8
 tests/images/examples.test__point_cloud.from_points__orca_cloud.png 5473cc27d56b3ccb7bc59c98fbb01737f3290971072dae769ba26f264f059747
 tests/images/examples.test__rectilinear.from_1d__oisst_eqc.png f2ec2b420a621ea69b764ee00e5fb271174013c9018c4396a3fb48fd53a6f6af
 tests/images/examples.test__rectilinear.from_1d__oisst.png 674f25f62cd416002315ec1a583a4118681ebcc47afe89879da36f0f6bd5004e
 tests/images/examples.test__rectilinear.from_1d__synthetic_face_m1_n1.png a4bf7bb7aa9ecf861c2f7fa5ea68e3e9ec5750ad347915b13bfb502c122f46d3
 tests/images/examples.test__rectilinear.from_1d__synthetic_face_m1_n1_robin.png f7572747f575b0f6a9fa96661219312bd7f2a3082b4a2e0f8449089b75618df4
-tests/images/examples.test__rectilinear.from_1d__synthetic_node_m1_n1_moll.png a36b01283ffac2ae8f1a6642fdea177f1aaf3c4558a9670e26e54205fa14cc04
-tests/images/examples.test__rectilinear.from_1d__synthetic_node_m1_n1.png b194552e2328f09691aef0c757a166204c09e692c341c1787bf4315bf4ef0934
-tests/images/examples.test__rectilinear.from_2d__synthetic_face_m1n1.png eb8a81c7fa779f4bb8c886c1898dc0b5fc8b471f6a6b045db40abd1cf5a351e5
+tests/images/examples.test__rectilinear.from_1d__synthetic_node_m1_n1_moll.png d37abc2b311b75b72e519787369f11aff86132acb8094eda48f5023a23d1815c
+tests/images/examples.test__rectilinear.from_1d__synthetic_node_m1_n1.png e2b13ebdb355926d3591df726235a97c8cd4396c001b2f2e2e9b7bf4f56e95e2
+tests/images/examples.test__rectilinear.from_2d__synthetic_face_m1n1.png 0a31754360b3af9d7282a97c0afeec69046ae2346c96b342b9daa306323e164e
 tests/images/examples.test__rectilinear.from_2d__synthetic_face_m1n1_robin.png 0a542a94392f51d76fe1f0e80ab61108d16fc2367bd52063358768e851e087aa
 tests/images/examples.test__rectilinear.from_2d__synthetic_node_m1n1_moll.png 0e5f1350b0d90fa6cca0ef0e0a6e0fa2963c9e8879a7633ca60835734bf9c759
 tests/images/examples.test__rectilinear.from_2d__synthetic_node_m1n1.png e4801d13f076251e395ce1b1e9c5ffedbc38332b72ace2d829e941a23c0d691d
-tests/images/examples.test__scalar_data.earthquakes.png 12c75a49b0668691063b7895a63bb63d29b618792652f86ddb99e0b80e69ed36
+tests/images/examples.test__scalar_data.earthquakes.png 61342f57dd1dac1cc7d9eb3bfa5cecf053bb56e354f3bad658fdf628cef10b8e
 tests/images/examples.test__scalar_data.earthquakes_wink1.png c5ec52c697e572b8a3e67be7772a1e2659b5954ba5932a68e9781a9c197b18ab
 tests/images/examples.test__scalar_data.from_tiff__dem.png 6a927cec0c68f5b7adbb07f7b8a8253b43d028d3a5f610636ffcf565410139aa
 tests/images/examples.test__scalar_data.from_tiff__rgb.png 52b04c390500417f69cb2a40eda7e6b473620cdd74b2524f158644c04d528514
-tests/images/examples.test__spatial_index.uber_h3.png d93a475382420059d2f79f722351e9078a8e13d8f6e174a32cabc68de1b12995
+tests/images/examples.test__spatial_index.uber_h3.png 8e8deb4418aeb530d0a3fdd5e43819426eed8f9680d18590f96d0087d8a18170
 tests/images/examples.test__unstructured.dynamico.png 0eed8e6e5c87fdf65291e8b1ed8c7aa642fd6e6d57e27f05e37793a97f9c75ac
 tests/images/examples.test__unstructured.dynamico_poly.png cad9c8baa23ac7e7563bfedcd538d2850f154a6ee468fdc84651ba3bbc3e6c91
 tests/images/examples.test__unstructured.fesom_fouc.png 50df3477fe8b6d5189eb3071a1b910c6c57cb59145e2a0611a4bf3690446548c
-tests/images/examples.test__unstructured.fesom.png a2a36b2480ce76780a91bb0ae6e8711c26e1f89b698e662c9a7f0441f14c3148
+tests/images/examples.test__unstructured.fesom.png cc8b1896f0931cf0c832ddb15f874a503f2104019c77421db494d5f06d7d7d6c
 tests/images/examples.test__unstructured.icon_eqc.png 600b2efd3be6f67409ffa2deb192984c17231c304b08e544816f5bbceebbbb64
-tests/images/examples.test__unstructured.icon.png 1cca507842b07c5a7c4d49b91adfffa07f926189fbc4e9d4b9c53d6fc42992d6
+tests/images/examples.test__unstructured.icon.png d0bece98005ca2528a6721801286f80a9cceb61d71fdea3f074f142ee642587b
 tests/images/examples.test__unstructured.lam_pacific_moll.png bfe0b2269f343ca10bdea10a695a5bc696ca8f329fc1305bc16616d0686254aa
 tests/images/examples.test__unstructured.lam_pacific.png 3c0abe5ccc0fa55f06982402bf23fd47c072b34c378997c8f21b339254b59ac9
 tests/images/examples.test__unstructured.lfric_sst_bonne.png db2631001aed41029e7293726b89cfee8de75ad70393b8cf64b0423aa05e45a8
@@ -78,8 +78,8 @@ tests/images/examples.test__vector_data.vectors.png 72f1a30dd57b8c6cc635e866aa1f
 tests/images/examples.test__vector_data.wind_arrows.png e73f77f2cb804e7d629be36ef8b0fcef1d64d653db005d29d2a2188575734809
 tests/images/examples.test__vector_data.winds_3d.png eb08f0d00a89d3429ab4e541d1b6c791d569d6bc6e359bf98d43f2937dff6753
 tests/images/examples.test__warp.from_unstructured__fvcom.png 18ae7a4041f847760f4baf5e549156231867639893767010c8c03ba65306942f
-tests/images/examples.test__warp.from_unstructured__lfric_orog_eqc.png f15eed048b1c7ed45e2b831f0c794a519bea7a5eba537b2a2c5abb3999c57308
-tests/images/examples.test__warp.from_unstructured__lfric_orog.png 5e0aa856d978057d547d32afd885fb1eb13c0bbf6b9470dbf6454b635460a64b
+tests/images/examples.test__warp.from_unstructured__lfric_orog_eqc.png 9a2f2878914d817dc2780968eafa438aa120eb655029593843597e779be6c26c
+tests/images/examples.test__warp.from_unstructured__lfric_orog.png 93edeee02f044f83c977849d8c07824687242d6c0984d4dd53eb4c39ef52d706
 tests/images/geoplotter.test_view_poi.test_lam_polar__eqc.png 602bba11b641ac202308adf9d0833152af6ccc370123dc8d618424172119a092
 tests/images/geoplotter.test_view_poi.test_lam_polar__moll.png 429b2a7fa80aba2b712f49341ccb0daa6f70a0a5141da014618ac5cad17e50c9
 tests/images/geoplotter.test_view_poi.test_lam_polar__None.png 5f2ec2b36c900946cdfab0b52615e7c955692573f788691f0de2bfd6753ddf16

src/geovista/examples/contour/banded_contour.py

@@ -12,6 +12,7 @@
 
 📋 Summary
 ^^^^^^^^^^
+
 Uses an unstructured Met Office LFRic C48 cubed-sphere mesh of surface altitude
 data.
 

src/geovista/examples/extraction/wedge_manifold.py

@@ -12,10 +12,8 @@
 
 📋 Summary
 ^^^^^^^^^^
-Creates a mesh from 1-D latitude and longitude unstructured points and
-connectivity.
 
-It uses an unstructured Met Office LFRic C48 cubed-sphere of surface altitude
+Uses an unstructured Met Office LFRic C48 cubed-sphere mesh of surface altitude
 data.
 
 Three separate geodesic wedge manifolds are constructed to extract the cells of the
@@ -43,7 +41,7 @@
 
     component: coastlines, component: manifold, component: texture,
     domain: orography,
-    load: unstructured
+    sample: unstructured
 
 ----
 
@@ -53,7 +51,7 @@
 
 import geovista as gv
 from geovista.geodesic import wedge
-from geovista.pantry.data import lfric_orog
+from geovista.pantry.meshes import lfric_orog
 import geovista.theme
 
 
@@ -65,17 +63,8 @@ def main() -> None:
     .. versionadded:: 0.6.0
 
     """
-    # Load the sample data.
-    sample = lfric_orog()
-
-    # Create the mesh from the sample data.
-    mesh = gv.Transform.from_unstructured(
-        sample.lons,
-        sample.lats,
-        connectivity=sample.connectivity,
-        data=sample.data,
-        name=sample.name,
-    )
+    # Load the sample mesh.
+    mesh = lfric_orog()
 
     # Calculate the sample data range.
     clim = mesh.get_data_range()
@@ -92,7 +81,7 @@ def main() -> None:
     region_3 = bbox_3.enclosed(mesh, preference="center")
 
     p = gv.GeoPlotter()
-    sargs = {"title": f"{sample.name} / {sample.units}", "fmt": "%.1f"}
+    sargs = {"title": "Surface Altitude / m", "fmt": "%.0f"}
 
     # Add the 3 extracted regions.
     p.add_mesh(region_1, clim=clim, scalar_bar_args=sargs)

src/geovista/examples/warp/from_unstructured__lfric_orog.py

@@ -12,14 +12,12 @@
 
 📋 Summary
 ^^^^^^^^^^
-Creates a mesh from 1-D latitude and longitude unstructured points and
-connectivity.
 
-It uses an unstructured Met Office LFRic C48 cubed-sphere of surface altitude
+Uses an unstructured Met Office LFRic C48 cubed-sphere mesh of surface altitude
 data.
 
-The resulting mesh contains quad cells and is constructed from CF UGRID
-unstructured cell points and connectivity.
+The mesh contains quad cells and is constructed from CF UGRID unstructured cell
+points and connectivity.
 
 Note that the scalar elevation values are located on the mesh nodes/points
 which results in the rendered colours being interpolated across the cell faces.
@@ -35,7 +33,7 @@
 
     domain: orography,
     filter: warp,
-    load: unstructured
+    sample: unstructured
 
 ----
 
@@ -44,7 +42,7 @@
 from __future__ import annotations
 
 import geovista as gv
-from geovista.pantry.data import lfric_orog
+from geovista.pantry.meshes import lfric_orog
 import geovista.theme
 
 
@@ -56,25 +54,16 @@ def main() -> None:
     .. versionadded:: 0.1.0
 
     """
-    # Load the sample data.
-    sample = lfric_orog()
-
-    # Create the mesh from the sample data.
-    mesh = gv.Transform.from_unstructured(
-        sample.lons,
-        sample.lats,
-        connectivity=sample.connectivity,
-        data=sample.data,
-        name=sample.name,
-    )
+    # Load the sample mesh.
+    mesh = lfric_orog()
 
     # Warp the mesh nodes by the surface altitude.
     mesh.compute_normals(cell_normals=False, point_normals=True, inplace=True)
-    mesh.warp_by_scalar(scalars=sample.name, inplace=True, factor=2e-5)
+    mesh.warp_by_scalar(inplace=True, factor=2e-5)
 
     # Plot the unstructured mesh.
     p = gv.GeoPlotter()
-    sargs = {"title": f"{sample.name} / {sample.units}"}
+    sargs = {"title": "Surface Altitude / m", "fmt": "%.0f"}
     p.add_mesh(mesh, scalar_bar_args=sargs)
     p.add_axes()
     p.add_text(

src/geovista/examples/warp/from_unstructured__lfric_orog_eqc.py

@@ -14,14 +14,11 @@
 📋 Summary
 ^^^^^^^^^^
 
-Creates a mesh from 1-D latitude and longitude unstructured points and
-connectivity.
-
-It uses an unstructured Met Office LFRic C48 cubed-sphere of surface altitude
+Uses an unstructured Met Office LFRic C48 cubed-sphere mesh of surface altitude
 data.
 
-The resulting mesh contains quad cells and is constructed from CF UGRID unstructured
-cell points and connectivity.
+The mesh contains quad cells and is constructed from CF UGRID unstructured cell
+points and connectivity.
 
 The mesh is transformed onto an Equidistant Cylindrical (Plate Carrée)
 projection.
@@ -40,7 +37,7 @@
 
     domain: orography,
     filter: warp,
-    load: unstructured,
+    sample: unstructured,
     projection: crs, projection: transform
 
 ----
@@ -50,7 +47,7 @@
 from __future__ import annotations
 
 import geovista as gv
-from geovista.pantry.data import lfric_orog
+from geovista.pantry.meshes import lfric_orog
 import geovista.theme
 from geovista.transform import transform_mesh
 
@@ -63,28 +60,19 @@ def main() -> None:
     .. versionadded:: 0.1.0
 
     """
-    # Load the sample data.
-    sample = lfric_orog()
-
-    # Create the mesh from the sample data.
-    mesh = gv.Transform.from_unstructured(
-        sample.lons,
-        sample.lats,
-        connectivity=sample.connectivity,
-        data=sample.data,
-        name=sample.name,
-    )
+    # Load the sample mesh.
+    mesh = lfric_orog()
 
     # Transform the mesh to the Plate Carrée projection.
     mesh = transform_mesh(mesh, crs := "esri:54001")
 
     # Warp the mesh nodes by the surface altitude.
     mesh.compute_normals(cell_normals=False, point_normals=True, inplace=True)
-    mesh.warp_by_scalar(scalars=sample.name, inplace=True, factor=200)
+    mesh.warp_by_scalar(inplace=True, factor=200)
 
     # Plot the unstructured mesh.
     p = gv.GeoPlotter(crs=crs)
-    sargs = {"title": f"{sample.name} / {sample.units}"}
+    sargs = {"title": "Surface Altitude / m", "fmt": "%.0f"}
     p.add_mesh(mesh, scalar_bar_args=sargs)
     p.add_axes()
     p.add_text(