diff --git a/.doctrees/environment.pickle b/.doctrees/environment.pickle index 9fb315a3..763eb614 100644 Binary files a/.doctrees/environment.pickle and b/.doctrees/environment.pickle differ diff --git a/.doctrees/examples-dev/sg_execution_times.doctree b/.doctrees/examples-dev/sg_execution_times.doctree index 75d67403..3447e86b 100644 Binary files a/.doctrees/examples-dev/sg_execution_times.doctree and b/.doctrees/examples-dev/sg_execution_times.doctree differ diff --git a/.doctrees/examples-dev/voronoi.doctree b/.doctrees/examples-dev/voronoi.doctree index 0fc23313..9b405370 100644 Binary files a/.doctrees/examples-dev/voronoi.doctree and b/.doctrees/examples-dev/voronoi.doctree differ diff --git a/.doctrees/examples/connectivity.doctree b/.doctrees/examples/connectivity.doctree index 53b93687..4e87b9bf 100644 Binary files a/.doctrees/examples/connectivity.doctree and b/.doctrees/examples/connectivity.doctree differ diff --git a/.doctrees/examples/overlap_regridder.doctree 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a/_downloads/97eabe2c66fc9328b1e51f27fdee52f6/partitioning.zip b/_downloads/97eabe2c66fc9328b1e51f27fdee52f6/partitioning.zip index 6f5e6106..e92797ce 100644 Binary files a/_downloads/97eabe2c66fc9328b1e51f27fdee52f6/partitioning.zip and b/_downloads/97eabe2c66fc9328b1e51f27fdee52f6/partitioning.zip differ diff --git a/_downloads/9be12df0ec6c323839ed0cb99cc89228/elevation_nl.zip b/_downloads/9be12df0ec6c323839ed0cb99cc89228/elevation_nl.zip index d90007aa..9d840b7f 100644 Binary files a/_downloads/9be12df0ec6c323839ed0cb99cc89228/elevation_nl.zip and b/_downloads/9be12df0ec6c323839ed0cb99cc89228/elevation_nl.zip differ diff --git a/_downloads/a50ba9731493d1c74010dcadb8694b20/selection.zip b/_downloads/a50ba9731493d1c74010dcadb8694b20/selection.zip index 574b8cf6..a948b26a 100644 Binary files a/_downloads/a50ba9731493d1c74010dcadb8694b20/selection.zip and b/_downloads/a50ba9731493d1c74010dcadb8694b20/selection.zip differ diff --git a/_downloads/cc393383c363f7c590c6ef714836f52a/xoxo.zip b/_downloads/cc393383c363f7c590c6ef714836f52a/xoxo.zip index 241128f7..90920787 100644 Binary files a/_downloads/cc393383c363f7c590c6ef714836f52a/xoxo.zip and b/_downloads/cc393383c363f7c590c6ef714836f52a/xoxo.zip differ diff --git a/_downloads/d2379110fee2f2fbaf724f0142daaa8d/plotting.zip b/_downloads/d2379110fee2f2fbaf724f0142daaa8d/plotting.zip index 796146f1..329da7f2 100644 Binary files a/_downloads/d2379110fee2f2fbaf724f0142daaa8d/plotting.zip and b/_downloads/d2379110fee2f2fbaf724f0142daaa8d/plotting.zip differ diff --git a/_downloads/f25be90ee220882246ef4778e276e863/vector_conversion.zip b/_downloads/f25be90ee220882246ef4778e276e863/vector_conversion.zip index 1c4aa028..0e834b38 100644 Binary files a/_downloads/f25be90ee220882246ef4778e276e863/vector_conversion.zip and b/_downloads/f25be90ee220882246ef4778e276e863/vector_conversion.zip differ diff --git a/_modules/xugrid/core/dataarray_accessor.html b/_modules/xugrid/core/dataarray_accessor.html index 546c7426..fb418548 100644 --- a/_modules/xugrid/core/dataarray_accessor.html +++ b/_modules/xugrid/core/dataarray_accessor.html @@ -871,7 +871,7 @@
else: ds = self.obj.to_dataset() - variables = [varname for varname, var in ds.variables.items() if dim in var.dims] + variables = [var for var in ds.data_vars if dim in ds[var].dims] # TODO deal with time-dependent data, etc. # Basically requires checking which variables are static, which aren't. # For non-static, requires repeating all geometries. diff --git a/_modules/xugrid/core/dataset_accessor.html b/_modules/xugrid/core/dataset_accessor.html index 7ae3b806..094f1e35 100644 --- a/_modules/xugrid/core/dataset_accessor.html +++ b/_modules/xugrid/core/dataset_accessor.html @@ -998,7 +998,7 @@
else:
raise ValueError("invalid topology dimension on grid")
- variables = [varname for varname, var in ds.variables.items() if dim in var.dims]
+ variables = [var for var in ds.data_vars if dim in ds[var].dims]
if variables:
data = ds[variables].to_dataframe(dim_order=dim_order)
else:
diff --git a/_modules/xugrid/ugrid/conventions.html b/_modules/xugrid/ugrid/conventions.html
index 0e028b7f..84d75698 100644
--- a/_modules/xugrid/ugrid/conventions.html
+++ b/_modules/xugrid/ugrid/conventions.html
@@ -578,6 +578,9 @@ Source code for xugrid.ugrid.conventions
def _get_topology(ds: xr.Dataset) -> List[str]:
+ # looping over ds.variables.items() is way faster for datasets with many
+ # variables than accessing each var with ds[var]
+ # https://github.com/Deltares/xugrid/issues/285
return [varname for varname, var in ds.variables.items() if var.attrs.get("cf_role") == "mesh_topology"]
diff --git a/_sources/examples-dev/sg_execution_times.rst.txt b/_sources/examples-dev/sg_execution_times.rst.txt
index 741e456c..cf1fb50c 100644
--- a/_sources/examples-dev/sg_execution_times.rst.txt
+++ b/_sources/examples-dev/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
Computation times
=================
-**00:01.167** total execution time for 1 file **from examples-dev**:
+**00:01.147** total execution time for 1 file **from examples-dev**:
.. container::
@@ -33,5 +33,5 @@ Computation times
- Time
- Mem (MB)
* - :ref:`sphx_glr_examples-dev_voronoi.py` (``voronoi.py``)
- - 00:01.167
+ - 00:01.147
- 0.0
diff --git a/_sources/examples-dev/voronoi.rst.txt b/_sources/examples-dev/voronoi.rst.txt
index 1f1ac611..ab2a21f3 100644
--- a/_sources/examples-dev/voronoi.rst.txt
+++ b/_sources/examples-dev/voronoi.rst.txt
@@ -630,7 +630,7 @@ The figure shows:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.167 seconds)
+ **Total running time of the script:** (0 minutes 1.147 seconds)
.. _sphx_glr_download_examples-dev_voronoi.py:
diff --git a/_sources/examples/connectivity.rst.txt b/_sources/examples/connectivity.rst.txt
index ee4576d0..3516ae47 100644
--- a/_sources/examples/connectivity.rst.txt
+++ b/_sources/examples/connectivity.rst.txt
@@ -129,7 +129,7 @@ By default, the border value for binary erosion is set to ``False`` (equal to
.. code-block:: none
-
+
@@ -165,7 +165,7 @@ start by setting a single value in the center of the grid to ``True``.
.. code-block:: none
-
+
@@ -200,7 +200,7 @@ alternative border value:
.. code-block:: none
-
+
@@ -238,7 +238,7 @@ analyse connected parts of the mesh.
.. code-block:: none
-
+
@@ -272,7 +272,7 @@ Tesselation.
.. code-block:: none
-
+
@@ -316,7 +316,7 @@ the original.
.. code-block:: none
-
+
@@ -355,7 +355,7 @@ We can break down one of the Voronoi tesselations from above into triangles:
.. code-block:: none
-
+
@@ -409,7 +409,7 @@ the upper and lower parts:
.. code-block:: none
-
+
@@ -439,7 +439,7 @@ We can now use Laplace interpolation to fill the gaps in the grid.
.. code-block:: none
-
+
@@ -477,7 +477,7 @@ interpolation.
.. code-block:: none
-
+
@@ -518,7 +518,7 @@ To illustrate, let's take a look at the connectivity matrix of the Xoxo grid.
.. code-block:: none
-
+
@@ -554,14 +554,14 @@ locality:
.. code-block:: none
-
+
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.390 seconds)
+ **Total running time of the script:** (0 minutes 1.413 seconds)
.. _sphx_glr_download_examples_connectivity.py:
diff --git a/_sources/examples/overlap_regridder.rst.txt b/_sources/examples/overlap_regridder.rst.txt
index 9c7397d9..a49446ed 100644
--- a/_sources/examples/overlap_regridder.rst.txt
+++ b/_sources/examples/overlap_regridder.rst.txt
@@ -112,7 +112,7 @@ some bathymetry) of the Netherlands, and a coarser target grid.
.. code-block:: none
-
+
@@ -202,7 +202,7 @@ conservative methods, such as conductance:
.. code-block:: none
-
+
@@ -298,7 +298,7 @@ OverlapRegridder:
.. code-block:: none
-
+
@@ -373,7 +373,7 @@ Then, provide it as the regridder method as above:
.. code-block:: none
-
+
@@ -384,7 +384,7 @@ Then, provide it as the regridder method as above:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 4.118 seconds)
+ **Total running time of the script:** (0 minutes 4.005 seconds)
.. _sphx_glr_download_examples_overlap_regridder.py:
diff --git a/_sources/examples/partitioning.rst.txt b/_sources/examples/partitioning.rst.txt
index c693546b..b8205da3 100644
--- a/_sources/examples/partitioning.rst.txt
+++ b/_sources/examples/partitioning.rst.txt
@@ -76,7 +76,7 @@ into several parts.
.. code-block:: none
-
+
@@ -145,7 +145,7 @@ We can easily plot this data to visualize the partitions:
.. code-block:: none
-
+
@@ -213,7 +213,7 @@ merge these partitions back into one whole for post-processing:
.. code-block:: none
-
+
@@ -275,7 +275,7 @@ data:
.. code-block:: none
-
+
@@ -668,7 +668,7 @@ Note that partioning and merging does not preserve order!
<xarray.DataArray 'elevation' (mesh2d_nFaces: 5248)> Size: 5kB
array([False, False, False, ..., False, False, False])
Coordinates:
- * mesh2d_nFaces (mesh2d_nFaces) int64 42kB 0 1 2 3 4 ... 5244 5245 5246 5247
+ * mesh2d_nFaces (mesh2d_nFaces) int64 42kB 0 1 2 3 4 ... 5244 5245 5246 5247
<xarray.Dataset> Size: 19kB Dimensions: (mesh2d_nNodes: 217, mesh2d_nFaces: 384, mesh2d_nEdges: 600) Coordinates: + * mesh2d_nEdges (mesh2d_nEdges) int64 5kB 0 1 2 3 4 5 ... 595 596 597 598 599 * mesh2d_nFaces (mesh2d_nFaces) int64 3kB 0 1 2 3 4 5 ... 379 380 381 382 383 * mesh2d_nNodes (mesh2d_nNodes) int64 2kB 0 1 2 3 4 5 ... 212 213 214 215 216 - * mesh2d_nEdges (mesh2d_nEdges) int64 5kB 0 1 2 3 4 5 ... 595 596 597 598 599 Data variables: node_z (mesh2d_nNodes) float64 2kB 1.933 2.091 1.875 ... 5.688 7.491 face_z (mesh2d_nFaces) float64 3kB 1.737 1.918 2.269 ... 5.408 6.424 - edge_z (mesh2d_nEdges) float64 5kB 1.989 1.875 1.8 ... 4.909 6.544
PandasIndex(RangeIndex(start=0, stop=600, step=1, name='mesh2d_nEdges'))
PandasIndex(RangeIndex(start=0, stop=384, step=1, name='mesh2d_nFaces'))
PandasIndex(RangeIndex(start=0, stop=217, step=1, name='mesh2d_nNodes'))
<xarray.DataArray (mesh2d_nFaces: 2)> Size: 16B array([1., 2.]) Coordinates: - * mesh2d_nFaces (mesh2d_nFaces) int64 16B 0 1+ * mesh2d_nFaces (mesh2d_nFaces) int64 16B 0 1
<xarray.DataArray (mesh2d_nFaces: 2)> Size: 16B array([11., 12.]) Coordinates: - * mesh2d_nFaces (mesh2d_nFaces) int64 16B 0 1+ * mesh2d_nFaces (mesh2d_nFaces) int64 16B 0 1
<xarray.Dataset> Size: 19kB Dimensions: (mesh2d_nNodes: 217, mesh2d_nFaces: 384, mesh2d_nEdges: 600) Coordinates: + * mesh2d_nEdges (mesh2d_nEdges) int64 5kB 0 1 2 3 4 5 ... 595 596 597 598 599 * mesh2d_nFaces (mesh2d_nFaces) int64 3kB 0 1 2 3 4 5 ... 379 380 381 382 383 * mesh2d_nNodes (mesh2d_nNodes) int64 2kB 0 1 2 3 4 5 ... 212 213 214 215 216 - * mesh2d_nEdges (mesh2d_nEdges) int64 5kB 0 1 2 3 4 5 ... 595 596 597 598 599 Data variables: node_z (mesh2d_nNodes) float64 2kB 1.933 2.091 1.875 ... 5.688 7.491 face_z (mesh2d_nFaces) float64 3kB 1.737 1.918 2.269 ... 5.408 6.424 - edge_z (mesh2d_nEdges) float64 5kB 1.989 1.875 1.8 ... 4.909 6.544
PandasIndex(RangeIndex(start=0, stop=600, step=1, name='mesh2d_nEdges'))
PandasIndex(RangeIndex(start=0, stop=384, step=1, name='mesh2d_nFaces'))
PandasIndex(RangeIndex(start=0, stop=217, step=1, name='mesh2d_nNodes'))
<xarray.Dataset> Size: 0B Dimensions: () Data variables: - *empty*+ *empty*
[5248 values with dtype=float64]
[5248 values with dtype=float64]
array([1, 2, 3, 4, 5])
array([ 0, 1, 2, ..., 5245, 5246, 5247])
PandasIndex(Index([1, 2, 3, 4, 5], dtype='int64', name='layer'))
PandasIndex(RangeIndex(start=0, stop=5248, step=1, name='mesh2d_nFaces'))
PandasIndex(Index([1, 2, 3, 4, 5], dtype='int64', name='layer'))
PandasIndex(RangeIndex(start=0, stop=98, step=1, name='mesh2d_nFaces'))
array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.])
PandasIndex(Index([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0], dtype='float64', name='id'))
00:01.167 total execution time for 1 file from examples-dev:
+00:01.147 total execution time for 1 file from examples-dev:
<xarray.DataArray 'elevation' (mesh2d_nFaces: 5248)> Size: 5kB array([False, False, False, ..., False, False, False]) Coordinates: - * mesh2d_nFaces (mesh2d_nFaces) int64 42kB 0 1 2 3 4 ... 5244 5245 5246 5247
The topology is equivalent, but the nodes, edges, and faces are in a @@ -1287,9 +1287,9 @@
Total running time of the script: (0 minutes 3.870 seconds)
<matplotlib.collections.PolyCollection object at 0x7f4850247c50>
+<matplotlib.collections.PolyCollection object at 0x7f253cfe2fc0>
Like Xarray, the axes and the colorbar are labeled automatically using the
@@ -985,7 +985,7 @@
UgridDataArrayds["edge_z"].ugrid.plot()
<matplotlib.collections.LineCollection object at 0x7f484e423650>
+<matplotlib.collections.LineCollection object at 0x7f2531ef31a0>
The method called by default depends on the type of the data:
@@ -1015,7 +1015,7 @@ UgridDataArrayds["node_z"].ugrid.plot(ax=ax2)
<matplotlib.collections.PolyCollection object at 0x7f484e3fda00>
+<matplotlib.collections.PolyCollection object at 0x7f2531f4b140>
We can also exactly control the type of plot we want. For example, to plot
@@ -1023,7 +1023,7 @@
UgridDataArrayds["face_z"].ugrid.plot.contourf()
<matplotlib.tri._tricontour.TriContourSet object at 0x7f4858f5fbc0>
+<matplotlib.tri._tricontour.TriContourSet object at 0x7f2531ef2e40>
We can also overlay this data with the edges:
@@ -1032,7 +1032,7 @@ UgridDataArrayds["face_z"].ugrid.plot.line(color="black")
<matplotlib.collections.LineCollection object at 0x7f48590ace90>
+<matplotlib.collections.LineCollection object at 0x7f253cbba600>
In general, there has to be data associated with the mesh topology before a
@@ -1083,7 +1083,7 @@
Other types of plotds["node_z"].ugrid.plot.scatter(ax=axes[4, 2])
<matplotlib.collections.PathCollection object at 0x7f485885a1b0>
+<matplotlib.collections.PathCollection object at 0x7f253cbc1970>
The surface
methods generate 3D surface plots:
@@ -1094,7 +1094,7 @@ Other types of plotds["node_z"].ugrid.plot.surface(ax=ax1)
<mpl_toolkits.mplot3d.art3d.Poly3DCollection object at 0x7f484e55e090>
+<mpl_toolkits.mplot3d.art3d.Poly3DCollection object at 0x7f253be7f350>
@@ -1106,7 +1106,7 @@ Additional Argumentsds["face_z"].ugrid.plot(cmap="RdBu", levels=8, yincrease=False)
<matplotlib.collections.PolyCollection object at 0x7f484f420710>
+<matplotlib.collections.PolyCollection object at 0x7f2532d35af0>
@@ -1120,7 +1120,7 @@ As a functionxugrid.plot.pcolormesh(grid, da)
<matplotlib.collections.PolyCollection object at 0x7f484f6ae1b0>
+<matplotlib.collections.PolyCollection object at 0x7f2532def590>
@@ -1134,10 +1134,10 @@ Xarray DataArray plotsdepth.isel(node=1000).plot()
[<matplotlib.lines.Line2D object at 0x7f484f6d8a40>]
+[<matplotlib.lines.Line2D object at 0x7f25331ee150>]
-Total running time of the script: (0 minutes 12.696 seconds)
+Total running time of the script: (0 minutes 12.170 seconds)