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@@ -611,7 +611,7 @@ Dataset and calling the :py:meth:`UgridDataArray.ugrid.plot()` method. .. code-block:: none - + @@ -646,7 +646,7 @@ the edges results in a different kind of plot: .. code-block:: none - + @@ -688,7 +688,7 @@ We can put them side by side to illustrate the differences: .. code-block:: none - + @@ -718,7 +718,7 @@ filled contours for data associated with the face dimension: .. code-block:: none - + @@ -749,7 +749,7 @@ We can also overlay this data with the edges: .. code-block:: none - + @@ -824,7 +824,7 @@ All these (2D) plots are illustrated here for completeness' sake: .. code-block:: none - + @@ -857,7 +857,7 @@ The ``surface`` methods generate 3D surface plots: .. code-block:: none - + @@ -891,7 +891,7 @@ used: .. code-block:: none - + @@ -927,7 +927,7 @@ take an xarray DataArray and a xugrid grid as arguments. .. code-block:: none - + @@ -963,14 +963,14 @@ somewhere in the unstructured topology, and plot the resulting timeseries: .. code-block:: none - [] + [] .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 11.103 seconds) + **Total running time of the script:** (0 minutes 11.487 seconds) .. _sphx_glr_download_examples_plotting.py: diff --git a/_sources/examples/quick_overview.rst.txt b/_sources/examples/quick_overview.rst.txt index df90aa2c1..bc50bd862 100644 --- a/_sources/examples/quick_overview.rst.txt +++ b/_sources/examples/quick_overview.rst.txt @@ -464,7 +464,7 @@ We'll start by fetching a dataset: elevation (node) float64 ... depth (time, node) float64 ... mesh2d int32 ... - face_node_connectivity (face, nmax_face) float64 ...


    • @@ -919,7 +919,7 @@ separate the variables: * node (node) int64 0 1 2 3 4 5 6 ... 9133 9134 9135 9136 9137 9138 9139 Data variables: elevation (node) float64 ... - depth (time, node) float64 ...


    • @@ -1361,7 +1361,7 @@ We can then grab one of the data variables as usual for xarray: Coordinates: node_x (node) float64 ... node_y (node) float64 ... - * node (node) int64 0 1 2 3 4 5 6 7 ... 9133 9134 9135 9136 9137 9138 9139 + * node (node) int64 0 1 2 3 4 5 6 7 ... 9133 9134 9135 9136 9137 9138 9139

    @@ -1771,7 +1771,7 @@ some data by hand here: 
    <xarray.DataArray (mesh2d_nFaces: 2)>
     array([1., 2.])
     Coordinates:
-      * mesh2d_nFaces  (mesh2d_nFaces) int64 0 1
    + * mesh2d_nFaces (mesh2d_nFaces) int64 0 1

    @@ -1809,7 +1809,7 @@ Plotting .. code-block:: none - + @@ -1860,7 +1860,7 @@ To select based on the topology, use the ``.ugrid`` attribute: .. code-block:: none - + @@ -2258,7 +2258,7 @@ Computation on DataArrays is unchanged from xarray: 
    <xarray.DataArray (mesh2d_nFaces: 2)>
     array([11., 12.])
     Coordinates:
-      * mesh2d_nFaces  (mesh2d_nFaces) int64 0 1
    + * mesh2d_nFaces (mesh2d_nFaces) int64 0 1

    @@ -2720,7 +2720,7 @@ Conversion from Geopandas is easy too: Coordinates: * mesh2d_nFaces (mesh2d_nFaces) int64 0 1 Data variables: - test (mesh2d_nFaces) float64 1.0 2.0 + test (mesh2d_nFaces) float64 1.0 2.0

    @@ -3116,13 +3116,13 @@ grid (nodes, faces, edges). 
    <xarray.Dataset>
     Dimensions:        (mesh2d_nNodes: 217, mesh2d_nFaces: 384, mesh2d_nEdges: 600)
     Coordinates:
+      * mesh2d_nFaces  (mesh2d_nFaces) int64 0 1 2 3 4 5 ... 378 379 380 381 382 383
       * mesh2d_nNodes  (mesh2d_nNodes) int64 0 1 2 3 4 5 ... 211 212 213 214 215 216
       * mesh2d_nEdges  (mesh2d_nEdges) int64 0 1 2 3 4 5 ... 594 595 596 597 598 599
-      * mesh2d_nFaces  (mesh2d_nFaces) int64 0 1 2 3 4 5 ... 378 379 380 381 382 383
     Data variables:
         node_z         (mesh2d_nNodes) float64 1.933 2.091 1.875 ... 5.688 7.491
         face_z         (mesh2d_nFaces) float64 1.737 1.918 2.269 ... 5.408 6.424
-        edge_z         (mesh2d_nEdges) float64 1.989 1.875 1.8 ... 3.929 4.909 6.544
    • mesh2d_nFaces
      PandasIndex
      PandasIndex(RangeIndex(start=0, stop=384, step=1, name='mesh2d_nFaces'))
    • mesh2d_nNodes
      PandasIndex
      PandasIndex(RangeIndex(start=0, stop=217, step=1, name='mesh2d_nNodes'))
    • mesh2d_nEdges
      PandasIndex
      PandasIndex(RangeIndex(start=0, stop=600, step=1, name='mesh2d_nEdges'))


    • @@ -3634,7 +3634,7 @@ a grid object: 
    <xarray.Dataset>
     Dimensions:  ()
     Data variables:
-        *empty*
    + *empty*

    @@ -4029,7 +4029,7 @@ We can then add variables one-by-one, as we might with an xarray Dataset: node_y (node) float64 ... * node (node) int64 0 1 2 3 4 5 6 ... 9133 9134 9135 9136 9137 9138 9139 Data variables: - elevation (node) float64 ... + elevation (node) float64 ...

    @@ -4434,7 +4434,7 @@ before writing. elevation (node) float64 ... depth (time, node) float64 ... Attributes: - Conventions: CF-1.9 UGRID-1.0
  • Conventions :
    CF-1.9 UGRID-1.0


    • @@ -4495,7 +4495,7 @@ before writing. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.565 seconds) + **Total running time of the script:** (0 minutes 0.580 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 f014438cb..5d7a0e0c3 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 - + @@ -151,7 +151,7 @@ the centroids of the new grid fall. .. code-block:: none - + @@ -182,7 +182,7 @@ Rexgrid provides the CentroidLocatorRegridder for this: .. code-block:: none - + @@ -217,7 +217,7 @@ so large. Let's try the OverlapOverregridder instead. .. code-block:: none - + @@ -249,7 +249,7 @@ Let's try again, now with the minimum: .. code-block:: none - + @@ -280,7 +280,7 @@ Or the maximum: .. code-block:: none - + @@ -709,7 +709,7 @@ result. mesh2d_face_x (mesh2d_nFaces) float64 ... mesh2d_face_y (mesh2d_nFaces) float64 ... * layer (layer) int64 1 2 3 4 5 - * mesh2d_nFaces (mesh2d_nFaces) int64 0 1 2 3 4 ... 5243 5244 5245 5246 5247
    • mesh2d_face_x
      (mesh2d_nFaces)
      float64
      ...
      standard_name :
      projection_x_coordinate
      [5248 values with dtype=float64]
    • mesh2d_face_y
      (mesh2d_nFaces)
      float64
      ...
      standard_name :
      projection_y_coordinate
      [5248 values with dtype=float64]
    • layer
      (layer)
      int64
      1 2 3 4 5
      array([1, 2, 3, 4, 5])
    • mesh2d_nFaces
      (mesh2d_nFaces)
      int64
      0 1 2 3 4 ... 5244 5245 5246 5247
      array([   0,    1,    2, ..., 5245, 5246, 5247])
    • layer
      PandasIndex
      PandasIndex(Index([1, 2, 3, 4, 5], dtype='int64', name='layer'))
    • mesh2d_nFaces
      PandasIndex
      PandasIndex(RangeIndex(start=0, stop=5248, step=1, name='mesh2d_nFaces'))


    • @@ -1151,7 +1151,7 @@ all additional dimensions. -45.92794405, -39.50867478]]) Coordinates: * layer (layer) int64 1 2 3 4 5 - * mesh2d_nFaces (mesh2d_nFaces) int64 0 1 2 3 4 5 6 ... 91 92 93 94 95 96 97
    • layer
      PandasIndex
      PandasIndex(Index([1, 2, 3, 4, 5], dtype='int64', name='layer'))
    • mesh2d_nFaces
      PandasIndex
      PandasIndex(RangeIndex(start=0, stop=98, step=1, name='mesh2d_nFaces'))


    • @@ -1233,7 +1233,7 @@ and the aggregated mean. .. code-block:: none - [, , , , ] + [, , , , ] @@ -1270,7 +1270,7 @@ To illustrate, we will zoom in to a part of the Netherlands. .. code-block:: none - + @@ -1323,7 +1323,7 @@ the triangles. .. code-block:: none - + @@ -1364,7 +1364,7 @@ the regridders work for any collection of (convex) faces. .. code-block:: none - + @@ -1400,7 +1400,7 @@ is kept the same. .. code-block:: none - + @@ -1416,7 +1416,7 @@ is kept the same. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 7.902 seconds) + **Total running time of the script:** (0 minutes 8.160 seconds) .. _sphx_glr_download_examples_regridder_overview.py: diff --git a/_sources/examples/selection.rst.txt b/_sources/examples/selection.rst.txt index 78488aaf3..9567a686c 100644 --- a/_sources/examples/selection.rst.txt +++ b/_sources/examples/selection.rst.txt @@ -85,7 +85,7 @@ elevation of the Netherlands. .. code-block:: none - + @@ -130,7 +130,7 @@ A subset of the unstructured grid is returned by using slices without a step: .. code-block:: none - + @@ -161,7 +161,7 @@ In such a case the entire grid is returned. .. code-block:: none - + @@ -196,7 +196,7 @@ This means we can easily select along a single dimension: .. code-block:: none - + @@ -226,7 +226,7 @@ Or, using ``None`` if we only care about the start: .. code-block:: none - + @@ -660,7 +660,7 @@ Two values will select a point: mesh2d_x (mesh2d_nFaces) float64 1.5e+05 mesh2d_y (mesh2d_nFaces) float64 4.63e+05 Attributes: - unit: m NAP + unit: m NAP

    @@ -1066,7 +1066,7 @@ of six points: mesh2d_x (mesh2d_nFaces) float64 1.25e+05 1.5e+05 ... 1.5e+05 1.75e+05 mesh2d_y (mesh2d_nFaces) float64 4e+05 4e+05 ... 4.65e+05 4.65e+05 Attributes: - unit: m NAP + unit: m NAP

    @@ -1472,7 +1472,7 @@ To select points without broadcasting, use ``.ugrid.sel_points`` instead: mesh2d_x (mesh2d_nFaces) float64 1.25e+05 1.5e+05 1.75e+05 mesh2d_y (mesh2d_nFaces) float64 4e+05 4.3e+05 4.65e+05 Attributes: - unit: m NAP + unit: m NAP

    @@ -1876,9 +1876,9 @@ We can sample points along a line as well by providing slices **with** a step: mesh2d_x (mesh2d_nFaces) float64 1e+05 1.1e+05 ... 1.8e+05 1.9e+05 mesh2d_y (mesh2d_nFaces) float64 4.65e+05 4.65e+05 ... 4.65e+05 Attributes: - unit: m NAP + unit: m NAP

    @@ -2284,7 +2284,7 @@ Two slices with a step results in broadcasting: mesh2d_x (mesh2d_nFaces) float64 1e+05 1.1e+05 ... 1.8e+05 1.9e+05 mesh2d_y (mesh2d_nFaces) float64 4e+05 4e+05 4e+05 ... 4.9e+05 4.9e+05 Attributes: - unit: m NAP
  • unit :
    m NAP


    • @@ -2738,15 +2738,15 @@ As well as a slice with a step and multiple values: mesh2d_x (mesh2d_nFaces) float64 1e+05 1.1e+05 ... 1.8e+05 1.9e+05 mesh2d_y (mesh2d_nFaces) float64 4e+05 4e+05 4e+05 ... 4.3e+05 4.3e+05 Attributes: - unit: m NAP
  • unit :
    m NAP


    • @@ -2930,7 +2930,7 @@ thousands faces: .. code-block:: none - + @@ -2952,7 +2952,7 @@ face dimension for 2D topologies. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 3.958 seconds) + **Total running time of the script:** (0 minutes 4.194 seconds) .. _sphx_glr_download_examples_selection.py: diff --git a/_sources/examples/sg_execution_times.rst.txt b/_sources/examples/sg_execution_times.rst.txt index f3b977df3..c3c664062 100644 --- a/_sources/examples/sg_execution_times.rst.txt +++ b/_sources/examples/sg_execution_times.rst.txt @@ -6,22 +6,22 @@ Computation times ================= -**00:47.960** total execution time for **examples** files: +**00:50.359** total execution time for **examples** files: +----------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_vector_conversion.py` (``vector_conversion.py``) | 00:15.527 | 0.0 MB | +| :ref:`sphx_glr_examples_vector_conversion.py` (``vector_conversion.py``) | 00:16.155 | 0.0 MB | +----------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_plotting.py` (``plotting.py``) | 00:11.103 | 0.0 MB | +| :ref:`sphx_glr_examples_plotting.py` (``plotting.py``) | 00:11.487 | 0.0 MB | +----------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_regridder_overview.py` (``regridder_overview.py``) | 00:07.902 | 0.0 MB | +| :ref:`sphx_glr_examples_regridder_overview.py` (``regridder_overview.py``) | 00:08.160 | 0.0 MB | +----------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_selection.py` (``selection.py``) | 00:03.958 | 0.0 MB | +| :ref:`sphx_glr_examples_selection.py` (``selection.py``) | 00:04.194 | 0.0 MB | +----------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_overlap_regridder.py` (``overlap_regridder.py``) | 00:03.790 | 0.0 MB | +| :ref:`sphx_glr_examples_overlap_regridder.py` (``overlap_regridder.py``) | 00:04.029 | 0.0 MB | +----------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_partitioning.py` (``partitioning.py``) | 00:03.615 | 0.0 MB | +| :ref:`sphx_glr_examples_partitioning.py` (``partitioning.py``) | 00:04.029 | 0.0 MB | +----------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_connectivity.py` (``connectivity.py``) | 00:01.499 | 0.0 MB | +| :ref:`sphx_glr_examples_connectivity.py` (``connectivity.py``) | 00:01.726 | 0.0 MB | +----------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_examples_quick_overview.py` (``quick_overview.py``) | 00:00.565 | 0.0 MB | +| :ref:`sphx_glr_examples_quick_overview.py` (``quick_overview.py``) | 00:00.580 | 0.0 MB | +----------------------------------------------------------------------------+-----------+--------+ diff --git a/_sources/examples/vector_conversion.rst.txt b/_sources/examples/vector_conversion.rst.txt index 1711b69fd..9ee40e5cb 100644 --- a/_sources/examples/vector_conversion.rst.txt +++ b/_sources/examples/vector_conversion.rst.txt @@ -73,7 +73,7 @@ We'll once again use the surface elevation data example. .. code-block:: none - + @@ -518,9 +518,9 @@ GeoDataFrame. Data variables: elevation (mesh2d_nFaces) float32 1.17 9.81 54.04 ... 0.28 -15.83 -0.45 mesh2d_face_x (mesh2d_nFaces) float64 2.388e+04 1.86e+05 ... 3.03e+04 - mesh2d_face_y (mesh2d_nFaces) float64 3.648e+05 4.171e+05 ... 3.964e+05 + mesh2d_face_y (mesh2d_nFaces) float64 3.648e+05 4.171e+05 ... 3.964e+05

    @@ -582,7 +582,7 @@ burn into the grid. .. code-block:: none - + @@ -978,9 +978,9 @@ we want to compute the average surface elevation per province: Coordinates: * id (id) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 Attributes: - unit: m NAP
    • id
      (id)
      float64
      0.0 1.0 2.0 3.0 ... 9.0 10.0 11.0
      array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])
    • id
      PandasIndex
      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'))
  • unit :
    m NAP


    • @@ -1087,7 +1087,7 @@ We can also use such "masks" to e.g. modify specific parts of the grid data: .. code-block:: none - + @@ -1233,7 +1233,7 @@ burn operation. .. code-block:: none - + @@ -1342,7 +1342,7 @@ compare to the grid faces. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 15.527 seconds) + **Total running time of the script:** (0 minutes 16.155 seconds) .. _sphx_glr_download_examples_vector_conversion.py: diff --git a/_sources/sample_data/adh_san_diego.rst.txt b/_sources/sample_data/adh_san_diego.rst.txt index 26e3cf481..cda3b0beb 100644 --- a/_sources/sample_data/adh_san_diego.rst.txt +++ b/_sources/sample_data/adh_san_diego.rst.txt @@ -41,7 +41,7 @@ It contains a static dataset (bed elevation) and a time varying dataset .. code-block:: none - + @@ -71,7 +71,7 @@ It contains a static dataset (bed elevation) and a time varying dataset .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.299 seconds) + **Total running time of the script:** (0 minutes 0.334 seconds) .. _sphx_glr_download_sample_data_adh_san_diego.py: diff --git a/_sources/sample_data/disk.rst.txt b/_sources/sample_data/disk.rst.txt index 1ffef9117..1bc7c5042 100644 --- a/_sources/sample_data/disk.rst.txt +++ b/_sources/sample_data/disk.rst.txt @@ -39,7 +39,7 @@ of a disk. It contains data on the nodes, faces, and edges. .. code-block:: none - + @@ -68,7 +68,7 @@ of a disk. It contains data on the nodes, faces, and edges. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.158 seconds) + **Total running time of the script:** (0 minutes 0.170 seconds) .. _sphx_glr_download_sample_data_disk.py: diff --git a/_sources/sample_data/elevation_nl.rst.txt b/_sources/sample_data/elevation_nl.rst.txt index 94dadc9da..6ad936af6 100644 --- a/_sources/sample_data/elevation_nl.rst.txt +++ b/_sources/sample_data/elevation_nl.rst.txt @@ -39,7 +39,7 @@ of the Netherlands. .. code-block:: none - [] + [] @@ -67,7 +67,7 @@ of the Netherlands. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.780 seconds) + **Total running time of the script:** (0 minutes 0.809 seconds) .. _sphx_glr_download_sample_data_elevation_nl.py: diff --git a/_sources/sample_data/provinces_nl.rst.txt b/_sources/sample_data/provinces_nl.rst.txt index 0710726f6..3b2e15b35 100644 --- a/_sources/sample_data/provinces_nl.rst.txt +++ b/_sources/sample_data/provinces_nl.rst.txt @@ -58,7 +58,7 @@ Netherlands, including water, presented as geopandas GeoDataFrame. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.082 seconds) + **Total running time of the script:** (0 minutes 0.088 seconds) .. _sphx_glr_download_sample_data_provinces_nl.py: diff --git a/_sources/sample_data/sg_execution_times.rst.txt b/_sources/sample_data/sg_execution_times.rst.txt index 14339cf17..30da677d2 100644 --- a/_sources/sample_data/sg_execution_times.rst.txt +++ b/_sources/sample_data/sg_execution_times.rst.txt @@ -6,16 +6,16 @@ Computation times ================= -**00:01.384** total execution time for **sample_data** files: +**00:01.472** total execution time for **sample_data** files: +---------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_sample_data_elevation_nl.py` (``elevation_nl.py``) | 00:00.780 | 0.0 MB | +| :ref:`sphx_glr_sample_data_elevation_nl.py` (``elevation_nl.py``) | 00:00.809 | 0.0 MB | +---------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_sample_data_adh_san_diego.py` (``adh_san_diego.py``) | 00:00.299 | 0.0 MB | +| :ref:`sphx_glr_sample_data_adh_san_diego.py` (``adh_san_diego.py``) | 00:00.334 | 0.0 MB | +---------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_sample_data_disk.py` (``disk.py``) | 00:00.158 | 0.0 MB | +| :ref:`sphx_glr_sample_data_disk.py` (``disk.py``) | 00:00.170 | 0.0 MB | +---------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_sample_data_provinces_nl.py` (``provinces_nl.py``) | 00:00.082 | 0.0 MB | +| :ref:`sphx_glr_sample_data_provinces_nl.py` (``provinces_nl.py``) | 00:00.088 | 0.0 MB | +---------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_sample_data_xoxo.py` (``xoxo.py``) | 00:00.065 | 0.0 MB | +| :ref:`sphx_glr_sample_data_xoxo.py` (``xoxo.py``) | 00:00.070 | 0.0 MB | +---------------------------------------------------------------------+-----------+--------+ diff --git a/_sources/sample_data/xoxo.rst.txt b/_sources/sample_data/xoxo.rst.txt index a6dd0cb25..d736cb345 100644 --- a/_sources/sample_data/xoxo.rst.txt +++ b/_sources/sample_data/xoxo.rst.txt @@ -55,7 +55,7 @@ directory if it's not there already. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.065 seconds) + **Total running time of the script:** (0 minutes 0.070 seconds) .. _sphx_glr_download_sample_data_xoxo.py: diff --git a/api/xugrid.burn_vector_geometry.html b/api/xugrid.burn_vector_geometry.html index ecdaf599c..159f7ddcc 100644 --- a/api/xugrid.burn_vector_geometry.html +++ b/api/xugrid.burn_vector_geometry.html @@ -653,7 +653,7 @@
    -xugrid.burn_vector_geometry(gdf: geopandas.GeoDataframe, like: xugrid.Ugrid2d | xugrid.UgridDataArray | xugrid.UgridDataset, column: str = None, fill: int | float = nan, all_touched: bool = False) → None[source]#
    +xugrid.burn_vector_geometry(gdf: geopandas.GeoDataframe, like: 'xugrid.Ugrid2d' | 'xugrid.UgridDataArray' | 'xugrid.UgridDataset', column: str | None = None, fill: int | float = nan, all_touched: bool = False) → None[source]#

    Burn vector geometries (points, lines, polygons) into a Ugrid2d mesh.

    If no column argument is provided, a value of 1.0 will be burned in to the mesh.

    @@ -662,7 +662,7 @@

    xugrid.burn_vector_geometry

    • gdf (geopandas.GeoDataFrame) – Polygons, points, and/or lines to be burned into the grid.

    • like (UgridDataArray, UgridDataset, or Ugrid2d) – Grid to burn the vector data into.

      • -

    • column (str) – Name of the geodataframe column of which to the values to burn into +

    • column (str, optional) – Name of the geodataframe column of which to the values to burn into grid.

    • fill (int, float, optional, default value np.nan.) – Fill value for nodata areas.

    • all_touched (bool, optional, default value False.) – All mesh faces (cells) touched by polygons will be updated, not just diff --git a/examples-dev/sg_execution_times.html b/examples-dev/sg_execution_times.html index 72672a90d..b2b842863 100644 --- a/examples-dev/sg_execution_times.html +++ b/examples-dev/sg_execution_times.html @@ -393,11 +393,11 @@

      Computation times#

      -

      00:01.247 total execution time for examples-dev files:

      +

      00:01.366 total execution time for examples-dev files:

      - + diff --git a/examples-dev/voronoi.html b/examples-dev/voronoi.html index d1023547a..62871bfb0 100644 --- a/examples-dev/voronoi.html +++ b/examples-dev/voronoi.html @@ -798,7 +798,7 @@

      Infinite rays
      (-1.5, 1.5)
      -

      Total running time of the script: (0 minutes 1.247 seconds)

      +

      Total running time of the script: (0 minutes 1.366 seconds)

       -connectivity
      <matplotlib.collections.PolyCollection object at 0x7f0a7d9e8b10>
+connectivity
      <matplotlib.collections.PolyCollection object at 0x7fae2173fe50>
 
      
 
      
 
      By default, the border value for binary dilation is also set to
@@ -514,7 +514,7 @@ 
      Binary erosion and dilationuda.ugrid.plot()
      -connectivity
      <matplotlib.collections.PolyCollection object at 0x7f0a8838b750>
+connectivity
      <matplotlib.collections.PolyCollection object at 0x7fae2170a7d0>
 
      
 
      
 
      Now let’s run two dilations: one with the default border, and one with the
@@ -527,7 +527,7 @@ 
      Binary erosion and dilationiter1_boundary.ugrid.plot(ax=ax1)
      -connectivity
      <matplotlib.collections.PolyCollection object at 0x7f0a7d104150>
+connectivity
      <matplotlib.collections.PolyCollection object at 0x7fae22712e10>
 
      
 
      
 
@@ -543,7 +543,7 @@ 
      Connected Componentslabeled.ugrid.plot(cmap="RdBu")
      -connectivity
      <matplotlib.collections.PolyCollection object at 0x7f0a7d9a3010>
+connectivity
      <matplotlib.collections.PolyCollection object at 0x7fae21548610>
 
      
 
      
 
@@ -555,7 +555,7 @@ 
      Centroidal Voronoi Tesselationxugrid.plot.line(voronoi_grid, color="black")
      -connectivity
      <matplotlib.collections.LineCollection object at 0x7f0a7d126850>
+connectivity
      <matplotlib.collections.LineCollection object at 0x7fae221bd390>
 
      
 
      
 
      There are two alternative flavors to consider. We can fully ignore the
@@ -576,7 +576,7 @@ 
      Centroidal Voronoi Tesselationxugrid.plot.line(convex_exterior, ax=ax1, color="black")
      -connectivity
      <matplotlib.collections.LineCollection object at 0x7f0a7d102210>
+connectivity
      <matplotlib.collections.LineCollection object at 0x7fae21318350>
 
      
 
      
 
@@ -592,7 +592,7 @@ 
      Triangulationxugrid.plot.line(triangulation, ax=ax1, color="black")
      -connectivity
      <matplotlib.collections.LineCollection object at 0x7f0a7c4cca90>
+connectivity
      <matplotlib.collections.LineCollection object at 0x7fae223e9c10>
 
      
 
      
 
@@ -623,7 +623,7 @@ 
      Laplace interpolationuda.ugrid.plot.line(ax=ax, color="black")
      -connectivity
      <matplotlib.collections.LineCollection object at 0x7f0a7c4ca350>
+connectivity
      <matplotlib.collections.LineCollection object at 0x7fae22147e10>
 
      
 
      
 
      We can now use Laplace interpolation to fill the gaps in the grid.
      
@@ -631,7 +631,7 @@ 
      Laplace interpolationfilled.ugrid.plot(cmap="gist_rainbow", vmin=2.5, vmax=7.5)
      -connectivity
      <matplotlib.collections.PolyCollection object at 0x7f0a7d375750>
+connectivity
      <matplotlib.collections.PolyCollection object at 0x7fae2155ab90>
 
      
 
      
 
      Laplace interpolation can also be used on the nodes of a grid.
@@ -647,7 +647,7 @@ 
      Laplace interpolationdisk_filled.ugrid.plot(ax=ax2)
      -connectivity
      <matplotlib.collections.PolyCollection object at 0x7f0a7d18ee10>
+connectivity
      <matplotlib.collections.PolyCollection object at 0x7fae211a3550>
 
      
 
      
 
@@ -665,7 +665,7 @@ 
      Reverse-Cuthill McKeeax.imshow(connectivity, cmap="Greys")
      -connectivity
      <matplotlib.image.AxesImage object at 0x7f0a7d77fcd0>
+connectivity
      <matplotlib.image.AxesImage object at 0x7fae213ff690>
 
      
 
      
 
      The bandwidth of this matrix is poor. Connections are all over the place: low
@@ -679,10 +679,10 @@ 
      Reverse-Cuthill McKeeax.imshow(connectivity, cmap="Greys")
      -connectivity
      <matplotlib.image.AxesImage object at 0x7f0a7d486250>
+connectivity
      <matplotlib.image.AxesImage object at 0x7fae222caad0>
 
      
 
      
-
      Total running time of the script: (0 minutes 1.499 seconds)
      
+
      Total running time of the script: (0 minutes 1.726 seconds)
      -overlap regridder
      <matplotlib.collections.LineCollection object at 0x7f0a7cfc6b10>
+overlap regridder
      <matplotlib.collections.LineCollection object at 0x7fae213e3210>
 
      
 
      
 
      
@@ -523,7 +523,7 @@ 
      Relative overlapresult.ugrid.plot()
      -overlap regridder
      <matplotlib.collections.PolyCollection object at 0x7f0a8993d750>
+overlap regridder
      <matplotlib.collections.PolyCollection object at 0x7fae2b1f9390>
 
      
 
      
 
@@ -565,7 +565,7 @@ 
      Custom reductionsresult.ugrid.plot(vmin=-20, vmax=90, cmap="terrain")
      -overlap regridder
      <matplotlib.collections.PolyCollection object at 0x7f0a7e33f050>
+overlap regridder
      <matplotlib.collections.PolyCollection object at 0x7fae2d5c8790>
 
      
 
      
 
      Not every reduction uses the weights argument. For example, computing an
@@ -582,10 +582,10 @@ 
      Custom reductionsresult.ugrid.plot(vmin=-20, vmax=90, cmap="terrain")
      -overlap regridder
      <matplotlib.collections.PolyCollection object at 0x7f0a7d833750>
+overlap regridder
      <matplotlib.collections.PolyCollection object at 0x7fae212e8a50>
 
      
 
      
-
      Total running time of the script: (0 minutes 3.790 seconds)
      
+
      Total running time of the script: (0 minutes 4.029 seconds)
      -partitioning
      <matplotlib.collections.PolyCollection object at 0x7f0a7e361a10>
+partitioning
      <matplotlib.collections.PolyCollection object at 0x7fae2b1f8250>
 
      
 
      
 
      Let’s start by dividing the grid into four parts:
      
@@ -476,7 +476,7 @@ 
      Partition the gridlabels.ugrid.plot()
      -partitioning
      <matplotlib.collections.PolyCollection object at 0x7f0a7d47b750>
+partitioning
      <matplotlib.collections.PolyCollection object at 0x7fae227c9650>
 
      
 
      
 
      Not quite the twelve provinces of the Netherlands!
      
@@ -501,7 +501,7 @@ 
      Merging partitionsmerged.ugrid.plot(vmin=-20, vmax=90, cmap="terrain")
      -partitioning
      <matplotlib.collections.PolyCollection object at 0x7f0a88d13750>
+partitioning
      <matplotlib.collections.PolyCollection object at 0x7fae220fab90>
 
      
 
      
 
@@ -522,7 +522,7 @@ 
      Partitioning grids without datamerged_grid.plot()
      -partitioning
      <matplotlib.collections.LineCollection object at 0x7f0a7c588610>
+partitioning
      <matplotlib.collections.LineCollection object at 0x7fae21337dd0>
 
      
 
      
 
@@ -899,7 +899,7 @@ 
      Preserving orderTotal running time of the script: (0 minutes 3.615 seconds)
      
+
      Total running time of the script: (0 minutes 4.029 seconds)
      • mesh2d_nFaces
        PandasIndex
        PandasIndex(RangeIndex(start=0, stop=384, step=1, name='mesh2d_nFaces'))
      • mesh2d_nNodes
        PandasIndex
        PandasIndex(RangeIndex(start=0, stop=217, step=1, name='mesh2d_nNodes'))
      • mesh2d_nEdges
        PandasIndex
        PandasIndex(RangeIndex(start=0, stop=600, step=1, name='mesh2d_nEdges'))


      • @@ -968,7 +968,7 @@

      UgridDataArrayuda.ugrid.plot() -plotting
      <matplotlib.collections.PolyCollection object at 0x7f0a7d6d5750>
+plotting
      <matplotlib.collections.PolyCollection object at 0x7fae2133d850>
 
      
 
      
 
      Like Xarray, the axes and the colorbar are labeled automatically using the
@@ -980,7 +980,7 @@ 
      UgridDataArray
      ds["edge_z"].ugrid.plot()
      -plotting
      <matplotlib.collections.LineCollection object at 0x7f0a7c4a9f10>
+plotting
      <matplotlib.collections.LineCollection object at 0x7fae215e8d10>
 
      
 
      
 
      The method called by default depends on the type of the data:
      
@@ -1009,7 +1009,7 @@ 
      UgridDataArrayds["node_z"].ugrid.plot(ax=ax2)
      -plotting
      <matplotlib.collections.PolyCollection object at 0x7f0a88cfcf10>
+plotting
      <matplotlib.collections.PolyCollection object at 0x7fae214578d0>
 
      
 
      
 
      We can also exactly control the type of plot we want. For example, to plot
@@ -1017,7 +1017,7 @@ 
      UgridDataArray
      ds["face_z"].ugrid.plot.contourf()
      -plotting
      <matplotlib.tri._tricontour.TriContourSet object at 0x7f0a7d871950>
+plotting
      <matplotlib.tri._tricontour.TriContourSet object at 0x7fae2158b990>
 
      
 
      
 
      We can also overlay this data with the edges:
      
@@ -1026,7 +1026,7 @@ 
      UgridDataArrayds["face_z"].ugrid.plot.line(color="black")
      -plotting
      <matplotlib.collections.LineCollection object at 0x7f0a883f7f10>
+plotting
      <matplotlib.collections.LineCollection object at 0x7fae2ebabe90>
 
      
 
      
 
      In general, there has to be data associated with the mesh topology before a
@@ -1077,7 +1077,7 @@ 
      Other types of plotds["node_z"].ugrid.plot.scatter(ax=axes[4, 2])
      -plotting
      <matplotlib.collections.PathCollection object at 0x7f0a7da95410>
+plotting
      <matplotlib.collections.PathCollection object at 0x7fae227dbe10>
 
      
 
      
 
      The surface methods generate 3D surface plots:
      
@@ -1088,7 +1088,7 @@ 
      Other types of plotds["node_z"].ugrid.plot.surface(ax=ax1)
      -plotting
      <mpl_toolkits.mplot3d.art3d.Poly3DCollection object at 0x7f0a887cd4d0>
+plotting
      <mpl_toolkits.mplot3d.art3d.Poly3DCollection object at 0x7fae21686fd0>
 
      
 
      
 
@@ -1100,7 +1100,7 @@ 
      Additional Arguments
      ds["face_z"].ugrid.plot(cmap="RdBu", levels=8, yincrease=False)
      -plotting
      <matplotlib.collections.PolyCollection object at 0x7f0a7d08d750>
+plotting
      <matplotlib.collections.PolyCollection object at 0x7fae22028250>
 
      
 
      
 
@@ -1114,7 +1114,7 @@ 
      As a functionxugrid.plot.pcolormesh(grid, da)
      -plotting
      <matplotlib.collections.PolyCollection object at 0x7f0a7d09d890>
+plotting
      <matplotlib.collections.PolyCollection object at 0x7fae2228c190>
 
      
 
      
 
@@ -1128,10 +1128,10 @@ 
      Xarray DataArray plotsdepth.isel(node=1000).plot()
      -node_x = 4.84e+05, node_y = 3.614e+06, node = 1000
      [<matplotlib.lines.Line2D object at 0x7f0a7d07c4d0>]
+node_x = 4.84e+05, node_y = 3.614e+06, node = 1000
      [<matplotlib.lines.Line2D object at 0x7fae221fbfd0>]
 
      
 
      
-
      Total running time of the script: (0 minutes 11.103 seconds)
      
+
      Total running time of the script: (0 minutes 11.487 seconds)

      Centroid Voronoi Tesselation (CVT) (voronoi.py)

      00:01.247

      00:01.366

      0.0 MB
      - + - + - + - + - + - + - + - + diff --git a/examples/vector_conversion.html b/examples/vector_conversion.html index 47af9a9a1..371567f47 100644 --- a/examples/vector_conversion.html +++ b/examples/vector_conversion.html @@ -448,7 +448,7 @@ uda.ugrid.plot(vmin=-20,vmax=90,cmap="terrain") -vector conversion
      <matplotlib.collections.PolyCollection object at 0x7f0a7d9ed750>
+vector conversion
      <matplotlib.collections.PolyCollection object at 0x7fae2d52d790>
 
      
 
      
 
      
@@ -859,9 +859,9 @@ 
      Conversion from GeoDataFrame
@@ -897,7 +897,7 @@ 
      “Rasterizing”, or “burning” vector geometriesburned.ugrid.plot()
      -vector conversion
      <matplotlib.collections.PolyCollection object at 0x7f0a883d5750>
+vector conversion
      <matplotlib.collections.PolyCollection object at 0x7fae21634890>
 
      
 
      
 
      This makes it very easy to classify and group data. Let’s say
@@ -1277,9 +1277,9 @@ 
      “Rasterizing”, or “burning” vector geometries
      • id
        (id)
        float64
        0.0 1.0 2.0 3.0 ... 9.0 10.0 11.0
        array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])
      • id
        PandasIndex
        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'))
    • unit :
      m NAP


      • This is a convenient way to create masks for specific regions:

      @@ -1319,7 +1319,7 @@

      “Rasterizing”, or “burning” vector geometriesmodified.ugrid.plot(vmin=-20, vmax=90, cmap="terrain") -vector conversion
      <matplotlib.collections.PolyCollection object at 0x7f0a7c57f190>
+vector conversion
      <matplotlib.collections.PolyCollection object at 0x7fae225c5a90>
 
      
 
      
 
      Note that all_touched=True is less suitable when differently valued
@@ -1379,7 +1379,7 @@ 
      “Rasterizing”, or “burning” vector geometriesburned.ugrid.plot()
      -vector conversion
      <matplotlib.collections.PolyCollection object at 0x7f0a88753750>
+vector conversion
      <matplotlib.collections.PolyCollection object at 0x7fae2222a110>
 
      
 
      
 
@@ -1436,7 +1436,7 @@ 
      Snap to gridTotal running time of the script: (0 minutes 15.527 seconds)
      
+
      Total running time of the script: (0 minutes 16.155 seconds)
      -

      Total running time of the script: (0 minutes 0.082 seconds)

      +

      Total running time of the script: (0 minutes 0.088 seconds)

      Vector geometry conversion (vector_conversion.py)

      00:15.527

      00:16.155

      0.0 MB

      Plot unstructured mesh data (plotting.py)

      00:11.103

      00:11.487

      0.0 MB

      Regridding overview (regridder_overview.py)

      00:07.902

      00:08.160

      0.0 MB

      Select unstructured data (selection.py)

      00:03.958

      00:04.194

      0.0 MB

      OverlapRegridder (overlap_regridder.py)

      00:03.790

      00:04.029

      0.0 MB

      Partitioning (partitioning.py)

      00:03.615

      00:04.029

      0.0 MB

      Connectivity (connectivity.py)

      00:01.499

      00:01.726

      0.0 MB

      Quick overview (quick_overview.py)

      00:00.565

      00:00.580

      0.0 MB
      - + - + - + - + - + diff --git a/sample_data/xoxo.html b/sample_data/xoxo.html index cefcb01a7..a8c86018a 100644 --- a/sample_data/xoxo.html +++ b/sample_data/xoxo.html @@ -449,7 +449,7 @@ ax.set_aspect(1) -

      Total running time of the script: (0 minutes 0.065 seconds)

      +

      Total running time of the script: (0 minutes 0.070 seconds)

      Elevation NL (elevation_nl.py)

      00:00.780

      00:00.809

      0.0 MB

      ADH San Diego (adh_san_diego.py)

      00:00.299

      00:00.334

      0.0 MB

      Disk (disk.py)

      00:00.158

      00:00.170

      0.0 MB

      Pronvinces NL (provinces_nl.py)

      00:00.082

      00:00.088

      0.0 MB

      Xoxo (xoxo.py)

      00:00.065

      00:00.070

      0.0 MB

    xugrid.burn_vector_geometry#