diff --git a/.buildinfo b/.buildinfo index 8048b087a..35d95eda4 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 8d20069a5ad17e464a276db66857ed03 +config: 4d98c0a32dca65a16f68eb1fcde32261 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/.doctrees/environment.pickle b/.doctrees/environment.pickle index 69ef5e67b..d03abc31a 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 b22428b5c..343b3f536 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 e0ed2579b..131c3a3f4 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 8603a8b3e..6010e4966 100644 Binary files a/.doctrees/examples/connectivity.doctree and b/.doctrees/examples/connectivity.doctree differ diff --git a/.doctrees/examples/overlap_regridder.doctree b/.doctrees/examples/overlap_regridder.doctree index e2da143d8..4f072a9b8 100644 Binary files a/.doctrees/examples/overlap_regridder.doctree and b/.doctrees/examples/overlap_regridder.doctree differ diff --git a/.doctrees/examples/partitioning.doctree b/.doctrees/examples/partitioning.doctree index 584b213cd..bafb48394 100644 Binary files a/.doctrees/examples/partitioning.doctree and b/.doctrees/examples/partitioning.doctree differ diff --git a/.doctrees/examples/plotting.doctree b/.doctrees/examples/plotting.doctree index 92166bb85..cdcae3d05 100644 Binary files a/.doctrees/examples/plotting.doctree and b/.doctrees/examples/plotting.doctree differ diff --git a/.doctrees/examples/quick_overview.doctree b/.doctrees/examples/quick_overview.doctree index fb306c84c..e4e31c09e 100644 Binary files a/.doctrees/examples/quick_overview.doctree and b/.doctrees/examples/quick_overview.doctree differ diff --git a/.doctrees/examples/regridder_overview.doctree b/.doctrees/examples/regridder_overview.doctree index e7cc320ea..51c401908 100644 Binary files a/.doctrees/examples/regridder_overview.doctree and b/.doctrees/examples/regridder_overview.doctree differ diff --git a/.doctrees/examples/selection.doctree b/.doctrees/examples/selection.doctree index a4a503bed..c37109b5f 100644 Binary files a/.doctrees/examples/selection.doctree and b/.doctrees/examples/selection.doctree differ diff --git a/.doctrees/examples/sg_execution_times.doctree b/.doctrees/examples/sg_execution_times.doctree index 1deb24d7f..cd7c35a9e 100644 Binary files a/.doctrees/examples/sg_execution_times.doctree and b/.doctrees/examples/sg_execution_times.doctree differ diff --git a/.doctrees/examples/vector_conversion.doctree b/.doctrees/examples/vector_conversion.doctree index d0c4a068e..1b32947ad 100644 Binary files a/.doctrees/examples/vector_conversion.doctree and b/.doctrees/examples/vector_conversion.doctree differ diff --git a/.doctrees/sample_data/adh_san_diego.doctree b/.doctrees/sample_data/adh_san_diego.doctree index b2af6213e..c550293a5 100644 Binary files a/.doctrees/sample_data/adh_san_diego.doctree and b/.doctrees/sample_data/adh_san_diego.doctree differ diff --git a/.doctrees/sample_data/disk.doctree b/.doctrees/sample_data/disk.doctree index 99473b0e4..019f21fd2 100644 Binary files a/.doctrees/sample_data/disk.doctree and b/.doctrees/sample_data/disk.doctree differ diff --git a/.doctrees/sample_data/elevation_nl.doctree b/.doctrees/sample_data/elevation_nl.doctree index 7f7cb8b34..47119365b 100644 Binary files a/.doctrees/sample_data/elevation_nl.doctree and b/.doctrees/sample_data/elevation_nl.doctree differ diff --git a/.doctrees/sample_data/provinces_nl.doctree b/.doctrees/sample_data/provinces_nl.doctree index 76a243133..7b5895612 100644 Binary files a/.doctrees/sample_data/provinces_nl.doctree and b/.doctrees/sample_data/provinces_nl.doctree differ diff --git a/.doctrees/sample_data/sg_execution_times.doctree b/.doctrees/sample_data/sg_execution_times.doctree index 5d9987e86..869295028 100644 Binary files a/.doctrees/sample_data/sg_execution_times.doctree and b/.doctrees/sample_data/sg_execution_times.doctree differ diff --git a/.doctrees/sample_data/xoxo.doctree b/.doctrees/sample_data/xoxo.doctree index a30eb7b2c..9cdacc3a2 100644 Binary files a/.doctrees/sample_data/xoxo.doctree and b/.doctrees/sample_data/xoxo.doctree differ diff --git a/_modules/xugrid/ugrid/ugrid2d.html b/_modules/xugrid/ugrid/ugrid2d.html index fb7443331..020c47d8e 100644 --- a/_modules/xugrid/ugrid/ugrid2d.html +++ b/_modules/xugrid/ugrid/ugrid2d.html @@ -2482,10 +2482,16 @@
# Allocate face_node_connectivity
face_nodes = np.empty((ny * nx, 4), dtype=IntDType)
# Set connectivity in counterclockwise manner
- face_nodes[:, 0] = linear_index[:-1, 1:].ravel() # upper right
- face_nodes[:, 1] = linear_index[:-1, :-1].ravel() # upper left
- face_nodes[:, 2] = linear_index[1:, :-1].ravel() # lower left
- face_nodes[:, 3] = linear_index[1:, 1:].ravel() # lower right
+ left, right = slice(None, -1), slice(1, None)
+ lower, upper = slice(None, -1), slice(1, None)
+ if node_x[1] < node_x[0]: # x_decreasing
+ left, right = right, left
+ if node_y[ny + 1] < node_y[0]: # y_decreasing
+ lower, upper = upper, lower
+ face_nodes[:, 0] = linear_index[lower, left].ravel()
+ face_nodes[:, 1] = linear_index[lower, right].ravel()
+ face_nodes[:, 2] = linear_index[upper, right].ravel()
+ face_nodes[:, 3] = linear_index[upper, left].ravel()
return Ugrid2d(node_x, node_y, -1, face_nodes)
diff --git a/_sources/examples-dev/sg_execution_times.rst.txt b/_sources/examples-dev/sg_execution_times.rst.txt
index d60fbfb81..98628e23c 100644
--- a/_sources/examples-dev/sg_execution_times.rst.txt
+++ b/_sources/examples-dev/sg_execution_times.rst.txt
@@ -6,8 +6,8 @@
Computation times
=================
-**00:01.305** total execution time for **examples-dev** files:
+**00:01.224** total execution time for **examples-dev** files:
+----------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples-dev_voronoi.py` (``voronoi.py``) | 00:01.305 | 0.0 MB |
+| :ref:`sphx_glr_examples-dev_voronoi.py` (``voronoi.py``) | 00:01.224 | 0.0 MB |
+----------------------------------------------------------+-----------+--------+
diff --git a/_sources/examples-dev/voronoi.rst.txt b/_sources/examples-dev/voronoi.rst.txt
index c83900a11..b75a0922b 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 1.305 seconds)
+ **Total running time of the script:** (0 minutes 1.224 seconds)
.. _sphx_glr_download_examples-dev_voronoi.py:
diff --git a/_sources/examples/connectivity.rst.txt b/_sources/examples/connectivity.rst.txt
index ac3a339dd..8b1dfe976 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 1.665 seconds)
+ **Total running time of the script:** (0 minutes 1.659 seconds)
.. _sphx_glr_download_examples_connectivity.py:
diff --git a/_sources/examples/overlap_regridder.rst.txt b/_sources/examples/overlap_regridder.rst.txt
index fec47c4cd..66929fafe 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
-
+
@@ -280,7 +280,7 @@ To use our custom method, we provide at initialization of the OverlapRegridder:
.. code-block:: none
-
+
@@ -320,7 +320,7 @@ function can deal with NaN values! -- hence ``nanpercentile`` rather than
.. code-block:: none
-
+
@@ -331,7 +331,7 @@ function can deal with NaN values! -- hence ``nanpercentile`` rather than
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 3.981 seconds)
+ **Total running time of the script:** (0 minutes 4.012 seconds)
.. _sphx_glr_download_examples_overlap_regridder.py:
diff --git a/_sources/examples/partitioning.rst.txt b/_sources/examples/partitioning.rst.txt
index 554145828..de9f72d09 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
-
+
@@ -667,7 +667,7 @@ Note that partioning and merging does not preserve order!
<xarray.DataArray 'elevation' (mesh2d_nFaces: 5248)>
array([False, False, False, ..., False, False, False])
Coordinates:
- * mesh2d_nFaces (mesh2d_nFaces) int64 0 1 2 3 4 ... 5243 5244 5245 5246 5247
xarray.DataArray'elevation'- mesh2d_nFaces: 5248
- False False False False False False ... False False False False False
array([False, False, False, ..., False, False, False])
- mesh2d_nFaces(mesh2d_nFaces)int640 1 2 3 4 ... 5244 5245 5246 5247
array([ 0, 1, 2, ..., 5245, 5246, 5247])
- mesh2d_nFacesPandasIndex
PandasIndex(RangeIndex(start=0, stop=5248, step=1, name='mesh2d_nFaces'))
+ * mesh2d_nFaces (mesh2d_nFaces) int64 0 1 2 3 4 ... 5243 5244 5245 5246 5247
array([False, False, False, ..., False, False, False])
array([ 0, 1, 2, ..., 5245, 5246, 5247])
PandasIndex(RangeIndex(start=0, stop=5248, step=1, name='mesh2d_nFaces'))
array([ True, True, True, ..., True, True, True])
array([ 23882.79376058, 186048.98609163, 183280.61324667, ..., - 33842.56847139, 33139.63056206, 30303.5164253 ])
array([364821.96725663, 417102.96121876, 334623.01878379, ..., - 397494.51640391, 400187.85011645, 396399.29036318])
array([ 0, 1, 2, ..., 5245, 5246, 5247])
PandasIndex(RangeIndex(start=0, stop=5248, step=1, name='mesh2d_nFaces'))
array([ True, True, True, ..., True, True, True])
array([ 23882.79376058, 186048.98609163, 183280.61324667, ..., + 33842.56847139, 33139.63056206, 30303.5164253 ])
array([364821.96725663, 417102.96121876, 334623.01878379, ..., + 397494.51640391, 400187.85011645, 396399.29036318])
array([ 0, 1, 2, ..., 5245, 5246, 5247])
PandasIndex(RangeIndex(start=0, stop=5248, step=1, name='mesh2d_nFaces'))
array([ True, True, True, ..., True, True, True])
array([ 23882.79376058, 186048.98609163, 183280.61324667, ..., - 33842.56847139, 33139.63056206, 30303.5164253 ])
array([364821.96725663, 417102.96121876, 334623.01878379, ..., - 397494.51640391, 400187.85011645, 396399.29036318])
array([ 0, 1, 2, ..., 5245, 5246, 5247])
PandasIndex(RangeIndex(start=0, stop=5248, step=1, name='mesh2d_nFaces'))
array([ True, True, True, ..., True, True, True])
array([ 23882.79376058, 186048.98609163, 183280.61324667, ..., + 33842.56847139, 33139.63056206, 30303.5164253 ])
array([364821.96725663, 417102.96121876, 334623.01878379, ..., + 397494.51640391, 400187.85011645, 396399.29036318])
array([ 0, 1, 2, ..., 5245, 5246, 5247])
PandasIndex(RangeIndex(start=0, stop=5248, step=1, name='mesh2d_nFaces'))
<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_nEdges (mesh2d_nEdges) int64 0 1 2 3 4 5 ... 594 595 596 597 598 599 * 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
array([ 0, 1, 2, ..., 381, 382, 383])
array([ 0, 1, 2, ..., 597, 598, 599])
array([ 0, 1, 2, ..., 214, 215, 216])
array([ 1.93329198, 2.09140061, 1.87484204, 1.71955236, 1.71961656, + edge_z (mesh2d_nEdges) float64 1.989 1.875 1.8 ... 3.929 4.909 6.544
array([ 0, 1, 2, ..., 214, 215, 216])
array([ 0, 1, 2, ..., 597, 598, 599])
array([ 0, 1, 2, ..., 381, 382, 383])
array([ 1.93329198, 2.09140061, 1.87484204, 1.71955236, 1.71961656, 1.87394091, 2.14519674, 2.30021006, 2.24185487, 2.02372336, 1.68192173, 1.51366054, 1.49636083, 1.42590672, 1.4384199 , 1.61206453, 1.98452218, 2.34631843, 2.38859332, 2.67626878, @@ -496,7 +496,7 @@ faces. 7.75144002, 7.88800553, 7.04359085, 5.35779319, 3.29726906, 1.5076096 , 0.54807376, 0.63361455, 1.53104833, 2.68784153, 3.53975332, 3.82702868, 3.73040836, 3.74099464, 4.34093488, - 5.68812411, 7.49116681])
array([ 1.73730009, 1.91825084, 2.26876665, 5.31052091, 1.78990802, + 5.68812411, 7.49116681])
array([ 1.73730009, 1.91825084, 2.26876665, 5.31052091, 1.78990802, 0.56592199, 2.0239473 , 1.20054259, 1.50084278, 4.2032856 , 3.82037735, 3.69611343, 2.34307619, 2.45189748, 2.05010445, 1.23173146, 1.24293922, 4.96369209, 1.23243737, 1.31070306, @@ -536,7 +536,7 @@ faces. 1.91611618, 0.93777886, 0.82127919, 0.82409913, 0.93548072, 0.94143233, 0.96785184, 5.94683372, 6.36476797, 4.85117403, 5.39410053, 4.05700573, 4.22359378, 5.59335232, 4.86883751, - 7.30890722, 7.04320847, 5.40762661, 6.42392991])
array([ 1.98860502, 1.87511577, 1.7999506 , 1.81179977, 1.90641372, + 7.30890722, 7.04320847, 5.40762661, 6.42392991])
array([ 1.98860502, 1.87511577, 1.7999506 , 1.81179977, 1.90641372, 2.01522135, 1.95483397, 2.10396573, 2.1578564 , 2.07770379, 2.21438924, 1.76492656, 1.9903005 , 1.87706115, 1.74001569, 1.71509433, 1.64090366, 1.58755786, 1.5894138 , 1.77229325, @@ -576,7 +576,7 @@ faces. 3.32923738, 4.69300073, 6.14800061, 7.35257773, 7.91736337, 7.55634316, 6.24399649, 4.29381546, 2.28970536, 0.86825983, 0.43934876, 0.99050549, 2.09805846, 3.16064474, 3.73547458, - 3.7830263 , 3.6705139 , 3.92869759, 4.90866681, 6.54446841])
PandasIndex(RangeIndex(start=0, stop=384, step=1, name='mesh2d_nFaces'))
PandasIndex(RangeIndex(start=0, stop=600, step=1, name='mesh2d_nEdges'))
PandasIndex(RangeIndex(start=0, stop=217, step=1, name='mesh2d_nNodes'))
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'))
array([ 0, 1, 2, ..., 9137, 9138, 9139])
array(['2000-01-01T00:00:00.000000000', '2000-01-01T00:30:00.000000000', + face_node_connectivity (face, nmax_face) float64 ...
array([ 0, 1, 2, ..., 9137, 9138, 9139])
array(['2000-01-01T00:00:00.000000000', '2000-01-01T00:30:00.000000000', '2000-01-01T01:00:00.000000000', '2000-01-01T01:33:45.000000000', '2000-01-01T02:03:45.000000000', '2000-01-01T02:33:45.000000000', '2000-01-01T03:03:45.000000000', '2000-01-01T03:33:45.000000000', @@ -488,10 +488,10 @@ We'll start by fetching a dataset: '2000-01-01T21:02:30.000000000', '2000-01-01T21:32:30.000000000', '2000-01-01T22:02:30.000000000', '2000-01-01T22:32:30.000000000', '2000-01-01T23:02:30.000000000', '2000-01-01T23:32:30.000000000', - '2000-01-02T00:00:00.000000000'], dtype='datetime64[ns]')
[9140 values with dtype=float64]
[9140 values with dtype=float64]
[9140 values with dtype=float64]
[447860 values with dtype=float64]
[1 values with dtype=int32]
[50607 values with dtype=float64]
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + '2000-01-02T00:00:00.000000000'], dtype='datetime64[ns]')
[9140 values with dtype=float64]
[9140 values with dtype=float64]
[9140 values with dtype=float64]
[447860 values with dtype=float64]
[1 values with dtype=int32]
[50607 values with dtype=float64]
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ... 9130, 9131, 9132, 9133, 9134, 9135, 9136, 9137, 9138, 9139], - dtype='int64', name='node', length=9140))
PandasIndex(DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:30:00', + dtype='int64', name='node', length=9140))
PandasIndex(DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:30:00', '2000-01-01 01:00:00', '2000-01-01 01:33:45', '2000-01-01 02:03:45', '2000-01-01 02:33:45', '2000-01-01 03:03:45', '2000-01-01 03:33:45', @@ -516,7 +516,7 @@ We'll start by fetching a dataset: '2000-01-01 22:02:30', '2000-01-01 22:32:30', '2000-01-01 23:02:30', '2000-01-01 23:32:30', '2000-01-02 00:00:00'], - dtype='datetime64[ns]', name='time', freq=None))
array(['2000-01-01T00:00:00.000000000', '2000-01-01T00:30:00.000000000', + depth (time, node) float64 ...
array(['2000-01-01T00:00:00.000000000', '2000-01-01T00:30:00.000000000', '2000-01-01T01:00:00.000000000', '2000-01-01T01:33:45.000000000', '2000-01-01T02:03:45.000000000', '2000-01-01T02:33:45.000000000', '2000-01-01T03:03:45.000000000', '2000-01-01T03:33:45.000000000', @@ -943,7 +943,7 @@ separate the variables: '2000-01-01T21:02:30.000000000', '2000-01-01T21:32:30.000000000', '2000-01-01T22:02:30.000000000', '2000-01-01T22:32:30.000000000', '2000-01-01T23:02:30.000000000', '2000-01-01T23:32:30.000000000', - '2000-01-02T00:00:00.000000000'], dtype='datetime64[ns]')
[9140 values with dtype=float64]
[9140 values with dtype=float64]
array([ 0, 1, 2, ..., 9137, 9138, 9139])
[9140 values with dtype=float64]
[447860 values with dtype=float64]
PandasIndex(DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:30:00', + '2000-01-02T00:00:00.000000000'], dtype='datetime64[ns]')
[9140 values with dtype=float64]
[9140 values with dtype=float64]
array([ 0, 1, 2, ..., 9137, 9138, 9139])
[9140 values with dtype=float64]
[447860 values with dtype=float64]
PandasIndex(DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:30:00', '2000-01-01 01:00:00', '2000-01-01 01:33:45', '2000-01-01 02:03:45', '2000-01-01 02:33:45', '2000-01-01 03:03:45', '2000-01-01 03:33:45', @@ -968,7 +968,7 @@ separate the variables: '2000-01-01 22:02:30', '2000-01-01 22:32:30', '2000-01-01 23:02:30', '2000-01-01 23:32:30', '2000-01-02 00:00:00'], - dtype='datetime64[ns]', name='time', freq=None))
PandasIndex(RangeIndex(start=0, stop=9140, step=1, name='node'))
PandasIndex(RangeIndex(start=0, stop=9140, step=1, name='node'))
[9140 values with dtype=float64]
[9140 values with dtype=float64]
[9140 values with dtype=float64]
array([ 0, 1, 2, ..., 9137, 9138, 9139])
PandasIndex(RangeIndex(start=0, stop=9140, step=1, name='node'))
[9140 values with dtype=float64]
[9140 values with dtype=float64]
[9140 values with dtype=float64]
array([ 0, 1, 2, ..., 9137, 9138, 9139])
PandasIndex(RangeIndex(start=0, stop=9140, step=1, name='node'))
<xarray.DataArray (mesh2d_nFaces: 2)> array([1., 2.]) Coordinates: - * mesh2d_nFaces (mesh2d_nFaces) int64 0 1
array([1., 2.])
array([0, 1])
PandasIndex(RangeIndex(start=0, stop=2, step=1, name='mesh2d_nFaces'))
array([1., 2.])
array([0, 1])
PandasIndex(RangeIndex(start=0, stop=2, step=1, name='mesh2d_nFaces'))
<xarray.DataArray (mesh2d_nFaces: 2)> array([11., 12.]) Coordinates: - * mesh2d_nFaces (mesh2d_nFaces) int64 0 1
array([11., 12.])
array([0, 1])
PandasIndex(RangeIndex(start=0, stop=2, step=1, name='mesh2d_nFaces'))
array([11., 12.])
array([0, 1])
PandasIndex(RangeIndex(start=0, stop=2, step=1, name='mesh2d_nFaces'))
array([0, 1])
array([1., 2.])
PandasIndex(RangeIndex(start=0, stop=2, step=1, name='mesh2d_nFaces'))
array([0, 1])
array([1., 2.])
PandasIndex(RangeIndex(start=0, stop=2, step=1, name='mesh2d_nFaces'))
<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_nEdges (mesh2d_nEdges) int64 0 1 2 3 4 5 ... 594 595 596 597 598 599 * 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
array([ 0, 1, 2, ..., 381, 382, 383])
array([ 0, 1, 2, ..., 597, 598, 599])
array([ 0, 1, 2, ..., 214, 215, 216])
array([ 1.93329198, 2.09140061, 1.87484204, 1.71955236, 1.71961656, + edge_z (mesh2d_nEdges) float64 1.989 1.875 1.8 ... 3.929 4.909 6.544
array([ 0, 1, 2, ..., 214, 215, 216])
array([ 0, 1, 2, ..., 597, 598, 599])
array([ 0, 1, 2, ..., 381, 382, 383])
array([ 1.93329198, 2.09140061, 1.87484204, 1.71955236, 1.71961656, 1.87394091, 2.14519674, 2.30021006, 2.24185487, 2.02372336, 1.68192173, 1.51366054, 1.49636083, 1.42590672, 1.4384199 , 1.61206453, 1.98452218, 2.34631843, 2.38859332, 2.67626878, @@ -3162,7 +3162,7 @@ grid (nodes, faces, edges). 7.75144002, 7.88800553, 7.04359085, 5.35779319, 3.29726906, 1.5076096 , 0.54807376, 0.63361455, 1.53104833, 2.68784153, 3.53975332, 3.82702868, 3.73040836, 3.74099464, 4.34093488, - 5.68812411, 7.49116681])
array([ 1.73730009, 1.91825084, 2.26876665, 5.31052091, 1.78990802, + 5.68812411, 7.49116681])
array([ 1.73730009, 1.91825084, 2.26876665, 5.31052091, 1.78990802, 0.56592199, 2.0239473 , 1.20054259, 1.50084278, 4.2032856 , 3.82037735, 3.69611343, 2.34307619, 2.45189748, 2.05010445, 1.23173146, 1.24293922, 4.96369209, 1.23243737, 1.31070306, @@ -3202,7 +3202,7 @@ grid (nodes, faces, edges). 1.91611618, 0.93777886, 0.82127919, 0.82409913, 0.93548072, 0.94143233, 0.96785184, 5.94683372, 6.36476797, 4.85117403, 5.39410053, 4.05700573, 4.22359378, 5.59335232, 4.86883751, - 7.30890722, 7.04320847, 5.40762661, 6.42392991])
array([ 1.98860502, 1.87511577, 1.7999506 , 1.81179977, 1.90641372, + 7.30890722, 7.04320847, 5.40762661, 6.42392991])
array([ 1.98860502, 1.87511577, 1.7999506 , 1.81179977, 1.90641372, 2.01522135, 1.95483397, 2.10396573, 2.1578564 , 2.07770379, 2.21438924, 1.76492656, 1.9903005 , 1.87706115, 1.74001569, 1.71509433, 1.64090366, 1.58755786, 1.5894138 , 1.77229325, @@ -3242,7 +3242,7 @@ grid (nodes, faces, edges). 3.32923738, 4.69300073, 6.14800061, 7.35257773, 7.91736337, 7.55634316, 6.24399649, 4.29381546, 2.28970536, 0.86825983, 0.43934876, 0.99050549, 2.09805846, 3.16064474, 3.73547458, - 3.7830263 , 3.6705139 , 3.92869759, 4.90866681, 6.54446841])
PandasIndex(RangeIndex(start=0, stop=384, step=1, name='mesh2d_nFaces'))
PandasIndex(RangeIndex(start=0, stop=600, step=1, name='mesh2d_nEdges'))
PandasIndex(RangeIndex(start=0, stop=217, step=1, name='mesh2d_nNodes'))
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'))
<xarray.Dataset> Dimensions: () Data variables: - *empty*
[9140 values with dtype=float64]
[9140 values with dtype=float64]
array([ 0, 1, 2, ..., 9137, 9138, 9139])
[9140 values with dtype=float64]
PandasIndex(RangeIndex(start=0, stop=9140, step=1, name='node'))
[9140 values with dtype=float64]
[9140 values with dtype=float64]
array([ 0, 1, 2, ..., 9137, 9138, 9139])
[9140 values with dtype=float64]
PandasIndex(RangeIndex(start=0, stop=9140, step=1, name='node'))
[9140 values with dtype=float64]
[9140 values with dtype=float64]
array(['2000-01-01T00:00:00.000000000', '2000-01-01T00:30:00.000000000', + Conventions: CF-1.9 UGRID-1.0
[9140 values with dtype=float64]
[9140 values with dtype=float64]
array(['2000-01-01T00:00:00.000000000', '2000-01-01T00:30:00.000000000', '2000-01-01T01:00:00.000000000', '2000-01-01T01:33:45.000000000', '2000-01-01T02:03:45.000000000', '2000-01-01T02:33:45.000000000', '2000-01-01T03:03:45.000000000', '2000-01-01T03:33:45.000000000', @@ -4458,7 +4458,7 @@ before writing. '2000-01-01T21:02:30.000000000', '2000-01-01T21:32:30.000000000', '2000-01-01T22:02:30.000000000', '2000-01-01T22:32:30.000000000', '2000-01-01T23:02:30.000000000', '2000-01-01T23:32:30.000000000', - '2000-01-02T00:00:00.000000000'], dtype='datetime64[ns]')
array([ 0, 1, 2, ..., 9137, 9138, 9139])
[9140 values with dtype=float64]
[447860 values with dtype=float64]
PandasIndex(DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:30:00', + '2000-01-02T00:00:00.000000000'], dtype='datetime64[ns]')
array([ 0, 1, 2, ..., 9137, 9138, 9139])
[9140 values with dtype=float64]
[447860 values with dtype=float64]
PandasIndex(DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:30:00', '2000-01-01 01:00:00', '2000-01-01 01:33:45', '2000-01-01 02:03:45', '2000-01-01 02:33:45', '2000-01-01 03:03:45', '2000-01-01 03:33:45', @@ -4483,7 +4483,7 @@ before writing. '2000-01-01 22:02:30', '2000-01-01 22:32:30', '2000-01-01 23:02:30', '2000-01-01 23:32:30', '2000-01-02 00:00:00'], - dtype='datetime64[ns]', name='time', freq=None))
PandasIndex(RangeIndex(start=0, stop=9140, step=1, name='node'))
PandasIndex(RangeIndex(start=0, stop=9140, step=1, name='node'))
array([[ -8.83000004, -0.18999958, 44.04000092, ..., -9.72 , + * mesh2d_nFaces (mesh2d_nFaces) int64 0 1 2 3 4 ... 5243 5244 5245 5246 5247
array([[ -8.83000004, -0.18999958, 44.04000092, ..., -9.72 , -25.82999992, -10.44999999], [-18.83000004, -10.18999958, 34.04000092, ..., -19.72 , -35.82999992, -20.44999999], @@ -718,7 +718,7 @@ result. [-38.83000004, -30.18999958, 14.04000092, ..., -39.72 , -55.82999992, -40.44999999], [-48.83000004, -40.18999958, 4.04000092, ..., -49.72 , - -65.82999992, -50.44999999]])
[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'))
[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'))
array([[ 98.73378481, 24.75605825, nan, nan, + * mesh2d_nFaces (mesh2d_nFaces) int64 0 1 2 3 4 5 6 ... 91 92 93 94 95 96 97
array([[ 98.73378481, 24.75605825, nan, nan, nan, nan, 28.6866454 , 21.59076039, nan, nan, -10.30473318, -12.46283808, nan, nan, 1.98885124, -0.45315257, @@ -1191,12 +1191,12 @@ all additional dimensions. -50.05098298, -50.91804551, -39.44818058, -44.02645019, -34.95904013, -31.75848616, -53.71649682, -47.7613762 , -46.45744354, -42.33120932, -51.24098772, -50.25680056, - -45.92794405, -39.50867478]])
array([1, 2, 3, 4, 5])
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + -45.92794405, -39.50867478]])
array([1, 2, 3, 4, 5])
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, - 90, 91, 92, 93, 94, 95, 96, 97])
PandasIndex(Index([1, 2, 3, 4, 5], dtype='int64', name='layer'))
PandasIndex(RangeIndex(start=0, stop=98, 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'))
[1 values with dtype=float32]
[1 values with dtype=float64]
[1 values with dtype=float64]
array([4221])
array([0])
array([150000.])
array([463000.])
PandasIndex(Index([4221], dtype='int64', name='mesh2d_nFaces'))
[1 values with dtype=float32]
[1 values with dtype=float64]
[1 values with dtype=float64]
array([4221])
array([0])
array([150000.])
array([463000.])
PandasIndex(Index([4221], dtype='int64', name='mesh2d_nFaces'))
[6 values with dtype=float32]
[6 values with dtype=float64]
[6 values with dtype=float64]
array([1905, 1356, 372, 3057, 4198, 4113])
array([0, 1, 2, 3, 4, 5])
array([125000., 150000., 175000., 125000., 150000., 175000.])
array([400000., 400000., 400000., 465000., 465000., 465000.])
PandasIndex(Index([1905, 1356, 372, 3057, 4198, 4113], dtype='int64', name='mesh2d_nFaces'))
[6 values with dtype=float32]
[6 values with dtype=float64]
[6 values with dtype=float64]
array([1905, 1356, 372, 3057, 4198, 4113])
array([0, 1, 2, 3, 4, 5])
array([125000., 150000., 175000., 125000., 150000., 175000.])
array([400000., 400000., 400000., 465000., 465000., 465000.])
PandasIndex(Index([1905, 1356, 372, 3057, 4198, 4113], dtype='int64', name='mesh2d_nFaces'))
[3 values with dtype=float32]
[3 values with dtype=float64]
[3 values with dtype=float64]
array([1905, 2675, 4113])
array([0, 1, 2])
array([125000., 150000., 175000.])
array([400000., 430000., 465000.])
PandasIndex(Index([1905, 2675, 4113], dtype='int64', name='mesh2d_nFaces'))
[3 values with dtype=float32]
[3 values with dtype=float64]
[3 values with dtype=float64]
array([1905, 2675, 4113])
array([0, 1, 2])
array([125000., 150000., 175000.])
array([400000., 430000., 465000.])
PandasIndex(Index([1905, 2675, 4113], dtype='int64', name='mesh2d_nFaces'))
[10 values with dtype=float32]
[10 values with dtype=float64]
[10 values with dtype=float64]
array([3263, 3225, 3144, 2854, 3012, 4198, 4197, 2941, 3135, 4256])
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
array([100000., 110000., 120000., 130000., 140000., 150000., 160000., - 170000., 180000., 190000.])
array([465000., 465000., 465000., 465000., 465000., 465000., 465000., - 465000., 465000., 465000.])
PandasIndex(Index([3263, 3225, 3144, 2854, 3012, 4198, 4197, 2941, 3135, 4256], dtype='int64', name='mesh2d_nFaces'))
[10 values with dtype=float32]
[10 values with dtype=float64]
[10 values with dtype=float64]
array([3263, 3225, 3144, 2854, 3012, 4198, 4197, 2941, 3135, 4256])
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
array([100000., 110000., 120000., 130000., 140000., 150000., 160000., + 170000., 180000., 190000.])
array([465000., 465000., 465000., 465000., 465000., 465000., 465000., + 465000., 465000., 465000.])
PandasIndex(Index([3263, 3225, 3144, 2854, 3012, 4198, 4197, 2941, 3135, 4256], dtype='int64', name='mesh2d_nFaces'))
[100 values with dtype=float32]
[100 values with dtype=float64]
[100 values with dtype=float64]
array([1867, 1882, 1890, 1936, 1735, 1356, 1482, 1350, 1445, 1122, 2745, 1908, + unit: m NAP
[100 values with dtype=float32]
[100 values with dtype=float64]
[100 values with dtype=float64]
array([1867, 1882, 1890, 1936, 1735, 1356, 1482, 1350, 1445, 1122, 2745, 1908, 1912, 1824, 240, 1654, 1647, 1696, 1164, 164, 2750, 2716, 2687, 2708, 1792, 2666, 1668, 2628, 370, 110, 2825, 2738, 2695, 2705, 2655, 2675, 2836, 2889, 226, 3035, 4297, 2864, 2722, 3146, 3069, 2820, 2804, 2907, @@ -2292,12 +2292,12 @@ Two slices with a step results in broadcasting: 3250, 3259, 3138, 3140, 2964, 3002, 2953, 2935, 4229, 4251, 3291, 3246, 3089, 4302, 3088, 4218, 4117, 4203, 4241, 4260, 1945, 3377, 3378, 4675, 4562, 4173, 2939, 4096, 4093, 3677, 4928, 4876, 4813, 4838, 4559, 4770, - 4131, 4121, 4077, 1581])
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 4131, 4121, 4077, 1581])
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, - 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
array([100000., 110000., 120000., 130000., 140000., 150000., 160000., + 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
array([100000., 110000., 120000., 130000., 140000., 150000., 160000., 170000., 180000., 190000., 100000., 110000., 120000., 130000., 140000., 150000., 160000., 170000., 180000., 190000., 100000., 110000., 120000., 130000., 140000., 150000., 160000., 170000., @@ -2311,7 +2311,7 @@ Two slices with a step results in broadcasting: 170000., 180000., 190000., 100000., 110000., 120000., 130000., 140000., 150000., 160000., 170000., 180000., 190000., 100000., 110000., 120000., 130000., 140000., 150000., 160000., 170000., - 180000., 190000.])
array([400000., 400000., 400000., 400000., 400000., 400000., 400000., + 180000., 190000.])
array([400000., 400000., 400000., 400000., 400000., 400000., 400000., 400000., 400000., 400000., 410000., 410000., 410000., 410000., 410000., 410000., 410000., 410000., 410000., 410000., 420000., 420000., 420000., 420000., 420000., 420000., 420000., 420000., @@ -2325,7 +2325,7 @@ Two slices with a step results in broadcasting: 470000., 470000., 470000., 480000., 480000., 480000., 480000., 480000., 480000., 480000., 480000., 480000., 480000., 490000., 490000., 490000., 490000., 490000., 490000., 490000., 490000., - 490000., 490000.])
PandasIndex(Index([1867, 1882, 1890, 1936, 1735, 1356, 1482, 1350, 1445, 1122, 2745, 1908, + 490000., 490000.])
PandasIndex(Index([1867, 1882, 1890, 1936, 1735, 1356, 1482, 1350, 1445, 1122, 2745, 1908, 1912, 1824, 240, 1654, 1647, 1696, 1164, 164, 2750, 2716, 2687, 2708, 1792, 2666, 1668, 2628, 370, 110, 2825, 2738, 2695, 2705, 2655, 2675, 2836, 2889, 226, 3035, 4297, 2864, 2722, 3146, 3069, 2820, 2804, 2907, @@ -2334,7 +2334,7 @@ Two slices with a step results in broadcasting: 3089, 4302, 3088, 4218, 4117, 4203, 4241, 4260, 1945, 3377, 3378, 4675, 4562, 4173, 2939, 4096, 4093, 3677, 4928, 4876, 4813, 4838, 4559, 4770, 4131, 4121, 4077, 1581], - dtype='int64', name='mesh2d_nFaces'))
[20 values with dtype=float32]
[20 values with dtype=float64]
[20 values with dtype=float64]
array([1867, 1882, 1890, 1936, 1735, 1356, 1482, 1350, 1445, 1122, 2825, 2738, - 2695, 2705, 2655, 2675, 2836, 2889, 226, 3035])
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19])
array([100000., 110000., 120000., 130000., 140000., 150000., 160000., + unit: m NAP
[20 values with dtype=float32]
[20 values with dtype=float64]
[20 values with dtype=float64]
array([1867, 1882, 1890, 1936, 1735, 1356, 1482, 1350, 1445, 1122, 2825, 2738, + 2695, 2705, 2655, 2675, 2836, 2889, 226, 3035])
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19])
array([100000., 110000., 120000., 130000., 140000., 150000., 160000., 170000., 180000., 190000., 100000., 110000., 120000., 130000., - 140000., 150000., 160000., 170000., 180000., 190000.])
array([400000., 400000., 400000., 400000., 400000., 400000., 400000., + 140000., 150000., 160000., 170000., 180000., 190000.])
array([400000., 400000., 400000., 400000., 400000., 400000., 400000., 400000., 400000., 400000., 430000., 430000., 430000., 430000., - 430000., 430000., 430000., 430000., 430000., 430000.])
PandasIndex(Index([1867, 1882, 1890, 1936, 1735, 1356, 1482, 1350, 1445, 1122, 2825, 2738, + 430000., 430000., 430000., 430000., 430000., 430000.])
PandasIndex(Index([1867, 1882, 1890, 1936, 1735, 1356, 1482, 1350, 1445, 1122, 2825, 2738, 2695, 2705, 2655, 2675, 2836, 2889, 226, 3035], - dtype='int64', name='mesh2d_nFaces'))
array([ 0, 1, 2, ..., 5245, 5246, 5247])
array([ 1.17, 9.81, 54.04, ..., 0.28, -15.83, -0.45], dtype=float32)
array([ 23882.79376058, 186048.98609163, 183280.61324667, ..., - 33842.56847139, 33139.63056206, 30303.5164253 ])
array([364821.96725663, 417102.96121876, 334623.01878379, ..., - 397494.51640391, 400187.85011645, 396399.29036318])
PandasIndex(RangeIndex(start=0, stop=5248, step=1, name='mesh2d_nFaces'))
array([ 0, 1, 2, ..., 5245, 5246, 5247])
array([ 1.17, 9.81, 54.04, ..., 0.28, -15.83, -0.45], dtype=float32)
array([ 23882.79376058, 186048.98609163, 183280.61324667, ..., + 33842.56847139, 33139.63056206, 30303.5164253 ])
array([364821.96725663, 417102.96121876, 334623.01878379, ..., + 397494.51640391, 400187.85011645, 396399.29036318])
PandasIndex(RangeIndex(start=0, stop=5248, step=1, name='mesh2d_nFaces'))
array([10.725979 , -3.806918 , -0.46903867, 17.090816 , 0.3133026 , + unit: m NAP
array([10.725979 , -3.806918 , -0.46903867, 17.090816 , 0.3133026 , 52.252567 , 12.194658 , -1.7175047 , 11.021334 , 3.0442472 , - -1.4584134 , -0.9470762 ], dtype=float32)
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'))
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'))
00:01.305 total execution time for examples-dev files:
+00:01.224 total execution time for examples-dev files: