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diff --git a/_downloads/d2379110fee2f2fbaf724f0142daaa8d/plotting.zip b/_downloads/d2379110fee2f2fbaf724f0142daaa8d/plotting.zip
index e44f76da..a89c29ba 100644
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diff --git a/_downloads/f25be90ee220882246ef4778e276e863/vector_conversion.zip b/_downloads/f25be90ee220882246ef4778e276e863/vector_conversion.zip
index 54f6b9b3..88bd29a1 100644
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diff --git a/_sources/examples-dev/sg_execution_times.rst.txt b/_sources/examples-dev/sg_execution_times.rst.txt
index 8655d5e3..77a0c190 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
=================
-**00:01.334** total execution time for 1 file **from examples-dev**:
+**00:01.341** 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``)
- - 00:01.334
+ - 00:01.341
- 0.0
diff --git a/_sources/examples-dev/voronoi.rst.txt b/_sources/examples-dev/voronoi.rst.txt
index 52412ea8..d7dfae39 100644
--- a/_sources/examples-dev/voronoi.rst.txt
+++ b/_sources/examples-dev/voronoi.rst.txt
@@ -666,7 +666,7 @@ The figure shows:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.334 seconds)
+ **Total running time of the script:** (0 minutes 1.341 seconds)
.. _sphx_glr_download_examples-dev_voronoi.py:
diff --git a/_sources/examples/connectivity.rst.txt b/_sources/examples/connectivity.rst.txt
index 2a690ed6..c6dc9f04 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
- 00:01.334 total execution time for 1 file from examples-dev: 00:01.341 total execution time for 1 file from examples-dev: The topology is equivalent, but the nodes, edges, and faces are in a
@@ -1279,9 +1279,9 @@ <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
@@ -1080,9 +1080,9 @@ original topology. ``reindex_like`` looks at the coordinates of both
Coordinates:
mesh2d_face_x (mesh2d_nFaces) float64 42kB 2.388e+04 1.86e+05 ... 3.03e+04
mesh2d_face_y (mesh2d_nFaces) float64 42kB 3.648e+05 ... 3.964e+05
- * 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
@@ -1491,9 +1491,9 @@ reorder the data after merging.
Coordinates:
mesh2d_face_x (mesh2d_nFaces) float64 42kB 2.388e+04 1.86e+05 ... 3.03e+04
mesh2d_face_y (mesh2d_nFaces) float64 42kB 3.648e+05 ... 3.964e+05
- * 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
@@ -1510,7 +1510,7 @@ partitions.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 3.847 seconds)
+ **Total running time of the script:** (0 minutes 3.827 seconds)
.. _sphx_glr_download_examples_partitioning.py:
diff --git a/_sources/examples/plotting.rst.txt b/_sources/examples/plotting.rst.txt
index 70890217..ad9c0b43 100644
--- a/_sources/examples/plotting.rst.txt
+++ b/_sources/examples/plotting.rst.txt
@@ -457,13 +457,13 @@ faces.
<xarray.Dataset> Size: 19kB
Dimensions: (mesh2d_nNodes: 217, mesh2d_nFaces: 384, mesh2d_nEdges: 600)
Coordinates:
+ * 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
* 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
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=217, step=1, name='mesh2d_nNodes'))
PandasIndex(RangeIndex(start=0, stop=600, step=1, name='mesh2d_nEdges'))
PandasIndex(RangeIndex(start=0, stop=384, step=1, name='mesh2d_nFaces'))
@@ -618,7 +618,7 @@ Dataset and calling the :py:meth:`UgridDataArray.ugrid.plot()` method.
.. code-block:: none
-
@@ -933,7 +933,7 @@ separate the variables:
* node (node) int64 73kB 0 1 2 3 4 5 6 ... 9134 9135 9136 9137 9138 9139
Data variables:
elevation (node) float64 73kB ...
- depth (time, node) float64 4MB ...
@@ -1382,7 +1382,7 @@ We can then grab one of the data variables as usual for xarray:
Coordinates:
node_x (node) float64 73kB ...
node_y (node) float64 73kB ...
- * node (node) int64 73kB 0 1 2 3 4 5 6 ... 9134 9135 9136 9137 9138 9139
+ * node (node) int64 73kB 0 1 2 3 4 5 6 ... 9134 9135 9136 9137 9138 9139
@@ -1799,7 +1799,7 @@ some data by hand here:
<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
@@ -1837,7 +1837,7 @@ Plotting
.. code-block:: none
- <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
@@ -2762,7 +2762,7 @@ Conversion from Geopandas is easy too:
Coordinates:
* mesh2d_nFaces (mesh2d_nFaces) int64 16B 0 1
Data variables:
- test (mesh2d_nFaces) float64 16B 1.0 2.0
+ test (mesh2d_nFaces) float64 16B 1.0 2.0
@@ -3165,13 +3165,13 @@ grid (nodes, faces, edges).
<xarray.Dataset> Size: 19kB
Dimensions: (mesh2d_nNodes: 217, mesh2d_nFaces: 384, mesh2d_nEdges: 600)
Coordinates:
+ * 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
* 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
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=217, step=1, name='mesh2d_nNodes'))
PandasIndex(RangeIndex(start=0, stop=600, step=1, name='mesh2d_nEdges'))
PandasIndex(RangeIndex(start=0, stop=384, step=1, name='mesh2d_nFaces'))
@@ -3690,7 +3690,7 @@ a grid object:
<xarray.Dataset> Size: 0B
Dimensions: ()
Data variables:
- *empty*
+ *empty*
@@ -4092,7 +4092,7 @@ We can then add variables one-by-one, as we might with an xarray Dataset:
node_y (node) float64 73kB ...
* node (node) int64 73kB 0 1 2 3 4 5 6 ... 9134 9135 9136 9137 9138 9139
Data variables:
- elevation (node) float64 73kB ...
+ elevation (node) float64 73kB ...
@@ -4504,7 +4504,7 @@ before writing.
elevation (node) float64 73kB ...
depth (time, node) float64 4MB ...
Attributes:
- Conventions: CF-1.9 UGRID-1.0
@@ -4565,7 +4565,7 @@ before writing.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.562 seconds)
+ **Total running time of the script:** (0 minutes 0.569 seconds)
.. _sphx_glr_download_examples_quick_overview.py:
diff --git a/_sources/examples/regridder_overview.rst.txt b/_sources/examples/regridder_overview.rst.txt
index 95344de7..4f7cee75 100644
--- a/_sources/examples/regridder_overview.rst.txt
+++ b/_sources/examples/regridder_overview.rst.txt
@@ -79,7 +79,7 @@ elevation of the Netherlands.
.. code-block:: none
- [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'))
@@ -1165,7 +1165,7 @@ all additional dimensions.
-45.92794405, -39.50867478]])
Coordinates:
* layer (layer) int64 40B 1 2 3 4 5
- * mesh2d_nFaces (mesh2d_nFaces) int64 784B 0 1 2 3 4 5 ... 92 93 94 95 96 97PandasIndex(Index([1, 2, 3, 4, 5], dtype='int64', name='layer'))
PandasIndex(RangeIndex(start=0, stop=98, step=1, name='mesh2d_nFaces'))
@@ -1247,7 +1247,7 @@ and the aggregated mean.
.. code-block:: none
- [
@@ -1080,7 +1080,7 @@ of six points:
mesh2d_x (mesh2d_nFaces) float64 48B 1.25e+05 1.5e+05 ... 1.75e+05
mesh2d_y (mesh2d_nFaces) float64 48B 4e+05 4e+05 ... 4.65e+05 4.65e+05
Attributes:
- unit: m NAP
+ unit: m NAP
@@ -1493,7 +1493,7 @@ To select points without broadcasting, use ``.ugrid.sel_points`` instead:
mesh2d_x (mesh2d_nFaces) float64 24B 1.25e+05 1.5e+05 1.75e+05
mesh2d_y (mesh2d_nFaces) float64 24B 4e+05 4.3e+05 4.65e+05
Attributes:
- unit: m NAP
+ unit: m NAP
@@ -1904,9 +1904,9 @@ We can sample points along a line as well by providing slices **with** a step:
mesh2d_x (mesh2d_nFaces) float64 80B 1e+05 1.1e+05 ... 1.8e+05 1.9e+05
mesh2d_y (mesh2d_nFaces) float64 80B 4.65e+05 4.65e+05 ... 4.65e+05
Attributes:
- unit: m NAP
+ unit: m NAP
@@ -2319,7 +2319,7 @@ Two slices with a step results in broadcasting:
mesh2d_x (mesh2d_nFaces) float64 800B 1e+05 1.1e+05 ... 1.9e+05
mesh2d_y (mesh2d_nFaces) float64 800B 4e+05 4e+05 ... 4.9e+05 4.9e+05
Attributes:
- unit: m NAP
@@ -2780,15 +2780,15 @@ As well as a slice with a step and multiple values:
mesh2d_x (mesh2d_nFaces) float64 160B 1e+05 1.1e+05 ... 1.9e+05
mesh2d_y (mesh2d_nFaces) float64 160B 4e+05 4e+05 ... 4.3e+05 4.3e+05
Attributes:
- unit: m NAP
@@ -2972,7 +2972,7 @@ thousands faces:
.. code-block:: none
-
@@ -589,7 +589,7 @@ burn into the grid.
.. code-block:: none
- 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'))
@@ -1101,7 +1101,7 @@ We can also use such "masks" to e.g. modify specific parts of the grid data:
.. code-block:: none
- Computation times#
-<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
Preserving orderTotal running time of the script: (0 minutes 3.847 seconds)
Total running time of the script: (0 minutes 3.827 seconds)
<matplotlib.collections.PolyCollection object at 0x7f416df63c20>
+
<matplotlib.collections.PolyCollection object at 0x7f93485ab8f0>
Like Xarray, the axes and the colorbar are labeled automatically using the
@@ -971,7 +971,7 @@
UgridDataArrayds["edge_z"].ugrid.plot()
<matplotlib.collections.LineCollection object at 0x7f416d0cabd0>
+
<matplotlib.collections.LineCollection object at 0x7f93472bffb0>
The method called by default depends on the type of the data:
@@ -1001,7 +1001,7 @@ UgridDataArrayds["node_z"].ugrid.plot(ax=ax2)
<matplotlib.collections.PolyCollection object at 0x7f416d293260>
+
<matplotlib.collections.PolyCollection object at 0x7f933d40db50>
We can also exactly control the type of plot we want. For example, to plot
@@ -1009,7 +1009,7 @@
UgridDataArrayds["face_z"].ugrid.plot.contourf()
<matplotlib.tri._tricontour.TriContourSet object at 0x7f4178204aa0>
+
<matplotlib.tri._tricontour.TriContourSet object at 0x7f93483db6b0>
We can also overlay this data with the edges:
@@ -1018,7 +1018,7 @@ UgridDataArrayds["face_z"].ugrid.plot.line(color="black")
<matplotlib.collections.LineCollection object at 0x7f4177d8cf80>
+
<matplotlib.collections.LineCollection object at 0x7f933d71cb30>
In general, there has to be data associated with the mesh topology before a
@@ -1069,7 +1069,7 @@
Other types of plotds["node_z"].ugrid.plot.scatter(ax=axes[4, 2])
<matplotlib.collections.PathCollection object at 0x7f4177922150>
+
<matplotlib.collections.PathCollection object at 0x7f93482b76e0>
The surface
methods generate 3D surface plots:
@@ -1080,7 +1080,7 @@ Other types of plotds["node_z"].ugrid.plot.surface(ax=ax1)
<mpl_toolkits.mplot3d.art3d.Poly3DCollection object at 0x7f416d0721b0>
+
<mpl_toolkits.mplot3d.art3d.Poly3DCollection object at 0x7f933e8d9a30>
@@ -1092,7 +1092,7 @@ Additional Argumentsds["face_z"].ugrid.plot(cmap="RdBu", levels=8, yincrease=False)
<matplotlib.collections.PolyCollection object at 0x7f416e341370>
+
<matplotlib.collections.PolyCollection object at 0x7f9348273ce0>
@@ -1106,7 +1106,7 @@ As a functionxugrid.plot.pcolormesh(grid, da)
<matplotlib.collections.PolyCollection object at 0x7f416e0f9370>
+
<matplotlib.collections.PolyCollection object at 0x7f933e587b90>
@@ -1120,10 +1120,10 @@ Xarray DataArray plotsdepth.isel(node=1000).plot()
[<matplotlib.lines.Line2D object at 0x7f416ddff0b0>]
+
[<matplotlib.lines.Line2D object at 0x7f9347e4e6f0>]
-Total running time of the script: (0 minutes 14.170 seconds)
+Total running time of the script: (0 minutes 14.066 seconds)