.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/regridder_overview.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_examples_regridder_overview.py>`
to download the full example code.
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_examples_regridder_overview.py:
Regridding overview
===================
`Regridding`_ is the process of converting gridded data from one grid to
another grid. Xugrid provides tools for 2D and 3D regridding of structured
gridded data, represented as xarray objects, as well as (`layered`_)
unstructured gridded data, represented as xugrid objects.
A number of regridding methods are provided, based on area or volume overlap,
as well as interpolation routines. It currently only supports Cartesian
coordinates. See e.g. `xESMF`_ instead for regridding with a spherical Earth
representation (note: EMSF is `not available`_ via conda-forge on Windows).
Here are a number of quick examples of how to get started with regridding.
We'll start by importing a few essential packages.
.. GENERATED FROM PYTHON SOURCE LINES 20-26
.. code-block:: Python
import matplotlib.pyplot as plt
import xarray as xr
import xugrid as xu
.. GENERATED FROM PYTHON SOURCE LINES 27-29
We will take a look at a sample dataset: a triangular grid with the surface
elevation of the Netherlands.
.. GENERATED FROM PYTHON SOURCE LINES 29-33
.. code-block:: Python
uda = xu.data.elevation_nl()
uda.ugrid.plot(vmin=-20, vmax=90, cmap="terrain")
.. image-sg:: /examples/images/sphx_glr_regridder_overview_001.png
:alt: regridder overview
:srcset: /examples/images/sphx_glr_regridder_overview_001.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
<matplotlib.collections.PolyCollection object at 0x7f74d436bd10>
.. GENERATED FROM PYTHON SOURCE LINES 34-37
Xugrid provides several "regridder" classes which can convert gridded data
from one grid to another grid. Let's generate a very simple coarse mesh that
covers the entire Netherlands.
.. GENERATED FROM PYTHON SOURCE LINES 37-56
.. code-block:: Python
def create_grid(bounds, nx, ny):
"""Create a simple grid of triangles covering a rectangle."""
import numpy as np
from matplotlib.tri import Triangulation
xmin, ymin, xmax, ymax = bounds
dx = (xmax - xmin) / nx
dy = (ymax - ymin) / ny
x = np.arange(xmin, xmax + dx, dx)
y = np.arange(ymin, ymax + dy, dy)
y, x = [a.ravel() for a in np.meshgrid(y, x, indexing="ij")]
faces = Triangulation(x, y).triangles
return xu.Ugrid2d(x, y, -1, faces)
grid = create_grid(uda.ugrid.total_bounds, 7, 7)
.. GENERATED FROM PYTHON SOURCE LINES 57-62
CentroidLocatorRegridder
------------------------
An easy way of regridding is by simply looking in which cell of the original
the centroids of the new grid fall.
.. GENERATED FROM PYTHON SOURCE LINES 62-68
.. code-block:: Python
fig, ax = plt.subplots()
uda.ugrid.plot(vmin=-20, vmax=90, cmap="terrain", ax=ax)
grid.plot(ax=ax, color="red")
ax.scatter(*grid.centroids.T, color="red")
.. image-sg:: /examples/images/sphx_glr_regridder_overview_002.png
:alt: regridder overview
:srcset: /examples/images/sphx_glr_regridder_overview_002.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
<matplotlib.collections.PathCollection object at 0x7f74d40d6420>
.. GENERATED FROM PYTHON SOURCE LINES 69-70
Xugrid provides the CentroidLocatorRegridder for this:
.. GENERATED FROM PYTHON SOURCE LINES 70-75
.. code-block:: Python
regridder = xu.CentroidLocatorRegridder(source=uda, target=grid)
result = regridder.regrid(uda)
result.ugrid.plot(vmin=-20, vmax=90, cmap="terrain", edgecolor="red")
.. image-sg:: /examples/images/sphx_glr_regridder_overview_003.png
:alt: regridder overview
:srcset: /examples/images/sphx_glr_regridder_overview_003.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
<matplotlib.collections.PolyCollection object at 0x7f74d3e164b0>
.. GENERATED FROM PYTHON SOURCE LINES 76-81
OverlapRegridder
----------------
Such a regridding is not appropriate when the new grid cells are
so large. Let's try the OverlapOverregridder instead.
.. GENERATED FROM PYTHON SOURCE LINES 81-86
.. code-block:: Python
regridder = xu.OverlapRegridder(source=uda, target=grid)
mean = regridder.regrid(uda)
mean.ugrid.plot(vmin=-20, vmax=90, cmap="terrain", edgecolor="red")
.. image-sg:: /examples/images/sphx_glr_regridder_overview_004.png
:alt: regridder overview
:srcset: /examples/images/sphx_glr_regridder_overview_004.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
<matplotlib.collections.PolyCollection object at 0x7f74dcf64980>
.. GENERATED FROM PYTHON SOURCE LINES 87-89
By default, the OverlapRegridder computes an area weighted mean.
Let's try again, now with the minimum:
.. GENERATED FROM PYTHON SOURCE LINES 89-94
.. code-block:: Python
regridder = xu.OverlapRegridder(source=uda, target=grid, method="minimum")
minimum = regridder.regrid(uda)
minimum.ugrid.plot(vmin=-20, vmax=90, cmap="terrain", edgecolor="red")
.. image-sg:: /examples/images/sphx_glr_regridder_overview_005.png
:alt: regridder overview
:srcset: /examples/images/sphx_glr_regridder_overview_005.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
<matplotlib.collections.PolyCollection object at 0x7f74d3e16090>
.. GENERATED FROM PYTHON SOURCE LINES 95-96
Or the maximum:
.. GENERATED FROM PYTHON SOURCE LINES 96-101
.. code-block:: Python
regridder = xu.OverlapRegridder(source=uda, target=grid, method="maximum")
maximum = regridder.regrid(uda)
maximum.ugrid.plot(vmin=-20, vmax=90, cmap="terrain", edgecolor="red")
.. image-sg:: /examples/images/sphx_glr_regridder_overview_006.png
:alt: regridder overview
:srcset: /examples/images/sphx_glr_regridder_overview_006.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
<matplotlib.collections.PolyCollection object at 0x7f74ddbf6ae0>
.. GENERATED FROM PYTHON SOURCE LINES 102-107
All regridders also work for multi-dimensional data.
Let's pretend our elevation dataset contains multiple layers, for example to
denote multiple geological strata. We'll generate five layers, each with a
thickness of 10.0 meters.
.. GENERATED FROM PYTHON SOURCE LINES 107-114
.. code-block:: Python
thickness = xr.DataArray(
data=[10.0, 10.0, 10.0, 10.0, 10.0],
coords={"layer": [1, 2, 3, 4, 5]},
dims=["layer"],
)
.. GENERATED FROM PYTHON SOURCE LINES 115-117
We need to make that the face dimension remains last, so we transpose the
result.
.. GENERATED FROM PYTHON SOURCE LINES 117-121
.. code-block:: Python
bottom = (uda - thickness.cumsum("layer")).transpose()
bottom
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</style><pre class='xr-text-repr-fallback'><xarray.DataArray (layer: 5, mesh2d_nFaces: 5248)> Size: 210kB
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],
[-28.83000004, -20.18999958, 24.04000092, ..., -29.72 ,
-45.82999992, -30.44999999],
[-38.83000004, -30.18999958, 14.04000092, ..., -39.72 ,
-55.82999992, -40.44999999],
[-48.83000004, -40.18999958, 4.04000092, ..., -49.72 ,
-65.82999992, -50.44999999]], shape=(5, 5248))
Coordinates:
mesh2d_face_x (mesh2d_nFaces) float64 42kB ...
mesh2d_face_y (mesh2d_nFaces) float64 42kB ...
* layer (layer) int64 40B 1 2 3 4 5
* mesh2d_nFaces (mesh2d_nFaces) int64 42kB 0 1 2 3 4 ... 5244 5245 5246 5247</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>layer</span>: 5</li><li><span class='xr-has-index'>mesh2d_nFaces</span>: 5248</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cb73e63b-6dbf-4273-b5c2-ba372f52d55c' class='xr-array-in' type='checkbox' checked><label for='section-cb73e63b-6dbf-4273-b5c2-ba372f52d55c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-8.83 -0.19 44.04 -9.01 -7.4 ... -49.07 -13.81 -49.72 -65.83 -50.45</span></div><div class='xr-array-data'><pre>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],
[-28.83000004, -20.18999958, 24.04000092, ..., -29.72 ,
-45.82999992, -30.44999999],
[-38.83000004, -30.18999958, 14.04000092, ..., -39.72 ,
-55.82999992, -40.44999999],
[-48.83000004, -40.18999958, 4.04000092, ..., -49.72 ,
-65.82999992, -50.44999999]], shape=(5, 5248))</pre></div></div></li><li class='xr-section-item'><input id='section-c0ac154a-8af9-4de4-9e1d-e88076f6e87a' class='xr-section-summary-in' type='checkbox' checked><label for='section-c0ac154a-8af9-4de4-9e1d-e88076f6e87a' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mesh2d_face_x</span></div><div class='xr-var-dims'>(mesh2d_nFaces)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-168da6a6-fc20-4d9e-92d1-a3b7172a5a27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-168da6a6-fc20-4d9e-92d1-a3b7172a5a27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-546d069e-da4c-4b15-95c8-ae8db3d095dd' class='xr-var-data-in' type='checkbox'><label for='data-546d069e-da4c-4b15-95c8-ae8db3d095dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>[5248 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mesh2d_face_y</span></div><div class='xr-var-dims'>(mesh2d_nFaces)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2b313852-18f7-48a5-a1a5-34506d0dc9c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b313852-18f7-48a5-a1a5-34506d0dc9c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7726c7d8-6631-4095-86ce-9747bdbc2d36' class='xr-var-data-in' type='checkbox'><label for='data-7726c7d8-6631-4095-86ce-9747bdbc2d36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>[5248 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>layer</span></div><div class='xr-var-dims'>(layer)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5</div><input id='attrs-5190e7e3-b2dc-4143-83f3-db9fd765e2e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5190e7e3-b2dc-4143-83f3-db9fd765e2e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-108c58db-e897-49cb-a2d9-ac75636a195d' class='xr-var-data-in' type='checkbox'><label for='data-108c58db-e897-49cb-a2d9-ac75636a195d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mesh2d_nFaces</span></div><div class='xr-var-dims'>(mesh2d_nFaces)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 5244 5245 5246 5247</div><input id='attrs-7818ec95-5162-4294-ad2c-7ca43f5bdb68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7818ec95-5162-4294-ad2c-7ca43f5bdb68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eac49619-9a86-4c29-b7a3-55ceb506a055' class='xr-var-data-in' type='checkbox'><label for='data-eac49619-9a86-4c29-b7a3-55ceb506a055' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 5245, 5246, 5247], shape=(5248,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f058693-7d24-4de5-b442-e1ea2f2b6f0d' class='xr-section-summary-in' type='checkbox' ><label for='section-3f058693-7d24-4de5-b442-e1ea2f2b6f0d' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>layer</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0f83e9b7-8b46-4b90-bebd-65f490302306' class='xr-index-data-in' type='checkbox'/><label for='index-0f83e9b7-8b46-4b90-bebd-65f490302306' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5], dtype='int64', name='layer'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mesh2d_nFaces</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d744399b-a068-47b1-b1b5-f614f4fca340' class='xr-index-data-in' type='checkbox'/><label for='index-d744399b-a068-47b1-b1b5-f614f4fca340' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=5248, step=1, name='mesh2d_nFaces'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a079e0d4-fd7d-4203-942a-c5209da2da7e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a079e0d4-fd7d-4203-942a-c5209da2da7e' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
</div>
<br />
<br />
.. GENERATED FROM PYTHON SOURCE LINES 122-124
We can feed the result to the regridder, which will automatically regrid over
all additional dimensions.
.. GENERATED FROM PYTHON SOURCE LINES 124-128
.. code-block:: Python
mean_bottom = xu.OverlapRegridder(source=bottom, target=grid).regrid(bottom)
mean_bottom
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-47.89456194, -50.43064765, -51.51684987, -50.9563055 ,
-50.24352139, -49.97530487, -54.90399917, -50.87557562,
-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]])
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 97</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>layer</span>: 5</li><li><span class='xr-has-index'>mesh2d_nFaces</span>: 98</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bd43e616-56de-4790-a687-4c623306715a' class='xr-array-in' type='checkbox' checked><label for='section-bd43e616-56de-4790-a687-4c623306715a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>98.73 24.76 nan nan nan nan ... -42.33 -51.24 -50.26 -45.93 -39.51</span></div><div class='xr-array-data'><pre>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,
-7.76052909, -7.60938601, -13.9746031 , -12.82278477,
-11.86447095, -13.9699236 , nan, nan,
-11.32250825, -12.61944108, nan, nan,
-10.39512475, -8.94184232, 7.71162015, 4.05908219,
11.63407438, 21.07322886, 8.79753456, 16.4475533 ,
16.63484974, 15.75826454, -6.61134759, -5.75454656,
7.20785073, -0.81124398, nan, nan,
69.34102272, nan, nan, nan,
8.73683325, 17.46905942, nan, -11.27368816,
nan, nan, nan, nan,
nan, nan, -8.92800775, -11.78767776,
-4.59914228, -11.1621325 , nan, nan,
nan, nan, 20.30399372, 14.92605801,
4.65570035, -0.30620094, -9.73834196, -10.55580261,
-11.23052693, -11.40449714, -12.85176061, -11.33053948,
-7.89456194, -10.43064765, -11.51684987, -10.9563055 ,
...
-51.86447095, -53.9699236 , nan, nan,
-51.32250825, -52.61944108, nan, nan,
-50.39512475, -48.94184232, -32.28837985, -35.94091781,
-28.36592562, -18.92677114, -31.20246544, -23.5524467 ,
-23.36515026, -24.24173546, -46.61134759, -45.75454656,
-32.79214927, -40.81124398, nan, nan,
29.34102272, nan, nan, nan,
-31.26316675, -22.53094058, nan, -51.27368816,
nan, nan, nan, nan,
nan, nan, -48.92800775, -51.78767776,
-44.59914228, -51.1621325 , nan, nan,
nan, nan, -19.69600628, -25.07394199,
-35.34429965, -40.30620094, -49.73834196, -50.55580261,
-51.23052693, -51.40449714, -52.85176061, -51.33053948,
-47.89456194, -50.43064765, -51.51684987, -50.9563055 ,
-50.24352139, -49.97530487, -54.90399917, -50.87557562,
-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]])</pre></div></div></li><li class='xr-section-item'><input id='section-bc6b6db8-266d-4001-9996-53c08ebbcba4' class='xr-section-summary-in' type='checkbox' checked><label for='section-bc6b6db8-266d-4001-9996-53c08ebbcba4' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>layer</span></div><div class='xr-var-dims'>(layer)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5</div><input id='attrs-36bef1c7-adc8-4c01-9746-ff0ca6d1e318' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36bef1c7-adc8-4c01-9746-ff0ca6d1e318' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1e1119b-527b-4730-9808-ac6817d605bd' class='xr-var-data-in' type='checkbox'><label for='data-c1e1119b-527b-4730-9808-ac6817d605bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mesh2d_nFaces</span></div><div class='xr-var-dims'>(mesh2d_nFaces)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 92 93 94 95 96 97</div><input id='attrs-4d5696c5-6525-42e1-8dea-ccf4468e62d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d5696c5-6525-42e1-8dea-ccf4468e62d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-572b7550-c684-4d48-8b9d-7d0d60cf6467' class='xr-var-data-in' type='checkbox'><label for='data-572b7550-c684-4d48-8b9d-7d0d60cf6467' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1342e6b2-251a-40fb-beeb-11d537d14d7b' class='xr-section-summary-in' type='checkbox' ><label for='section-1342e6b2-251a-40fb-beeb-11d537d14d7b' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>layer</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-811ed03f-a30f-4b94-9c4b-a980d84eb985' class='xr-index-data-in' type='checkbox'/><label for='index-811ed03f-a30f-4b94-9c4b-a980d84eb985' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5], dtype='int64', name='layer'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mesh2d_nFaces</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-5629c4ec-3469-4760-be1b-ea8b3f70436a' class='xr-index-data-in' type='checkbox'/><label for='index-5629c4ec-3469-4760-be1b-ea8b3f70436a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=98, step=1, name='mesh2d_nFaces'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-16cd7fe0-554a-46f0-8216-079468fda26c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-16cd7fe0-554a-46f0-8216-079468fda26c' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
</div>
<br />
<br />
.. GENERATED FROM PYTHON SOURCE LINES 129-131
Let's take a slice to briefly inspect our original layer bottom elevation,
and the aggregated mean.
.. GENERATED FROM PYTHON SOURCE LINES 131-140
.. code-block:: Python
section_y = 475_000.0
section = bottom.ugrid.sel(y=section_y)
section_mean = mean_bottom.ugrid.sel(y=section_y)
fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(10, 5), sharex=True, sharey=True)
section.plot.line(x="mesh2d_s", hue="layer", ax=ax0)
section_mean.plot.line(x="mesh2d_s", hue="layer", ax=ax1)
.. image-sg:: /examples/images/sphx_glr_regridder_overview_007.png
:alt: regridder overview
:srcset: /examples/images/sphx_glr_regridder_overview_007.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
[<matplotlib.lines.Line2D object at 0x7f74ddb15d30>, <matplotlib.lines.Line2D object at 0x7f74ddb156a0>, <matplotlib.lines.Line2D object at 0x7f74d4294950>, <matplotlib.lines.Line2D object at 0x7f74d4295790>, <matplotlib.lines.Line2D object at 0x7f74ddb15850>]
.. GENERATED FROM PYTHON SOURCE LINES 141-149
BarycentricInterpolator
-----------------------
All examples above show reductions: from a fine grid to a coarse grid.
However, xugrid also provides interpolation to generate smooth fine
representations of a coarse grid.
To illustrate, we will zoom in to a part of the Netherlands.
.. GENERATED FROM PYTHON SOURCE LINES 149-153
.. code-block:: Python
part = uda.ugrid.sel(x=slice(125_000, 225_000), y=slice(440_000, 500_000))
part.ugrid.plot(vmin=-20, vmax=90, cmap="terrain")
.. image-sg:: /examples/images/sphx_glr_regridder_overview_008.png
:alt: regridder overview
:srcset: /examples/images/sphx_glr_regridder_overview_008.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
<matplotlib.collections.PolyCollection object at 0x7f74dcf340e0>
.. GENERATED FROM PYTHON SOURCE LINES 154-158
We can clearly identify the individual triangles that form the grid. To get a
smooth presentation, we can use the BarycentricInterpolator.
We will generate a fine grid.
.. GENERATED FROM PYTHON SOURCE LINES 158-161
.. code-block:: Python
grid = create_grid(part.ugrid.total_bounds, nx=100, ny=100)
.. GENERATED FROM PYTHON SOURCE LINES 162-164
We use the centroids of the fine grid to interpolate between the centroids of
the triangles.
.. GENERATED FROM PYTHON SOURCE LINES 164-169
.. code-block:: Python
regridder = xu.BarycentricInterpolator(part, grid)
interpolated = regridder.regrid(part)
interpolated.ugrid.plot(vmin=-20, vmax=90, cmap="terrain")
.. image-sg:: /examples/images/sphx_glr_regridder_overview_009.png
:alt: regridder overview
:srcset: /examples/images/sphx_glr_regridder_overview_009.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
<matplotlib.collections.PolyCollection object at 0x7f74dcdeb7a0>
.. GENERATED FROM PYTHON SOURCE LINES 170-175
Arbitrary grids
---------------
The above examples all feature triangular source and target grids. However,
the regridders work for any collection of (convex) faces.
.. GENERATED FROM PYTHON SOURCE LINES 175-186
.. code-block:: Python
grid = create_grid(part.ugrid.total_bounds, nx=20, ny=15)
voronoi_grid = grid.tesselate_centroidal_voronoi()
regridder = xu.CentroidLocatorRegridder(part, voronoi_grid)
result = regridder.regrid(part)
fig, ax = plt.subplots()
result.ugrid.plot(vmin=-20, vmax=90, cmap="terrain")
voronoi_grid.plot(ax=ax, color="red")
.. image-sg:: /examples/images/sphx_glr_regridder_overview_010.png
:alt: regridder overview
:srcset: /examples/images/sphx_glr_regridder_overview_010.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
<matplotlib.collections.LineCollection object at 0x7f74ddbdd2b0>
.. GENERATED FROM PYTHON SOURCE LINES 187-193
Re-use
------
The most expensive step of the regridding process is finding and computing
overlaps. A regridder can be used repeatedly, provided the source topology
is kept the same.
.. GENERATED FROM PYTHON SOURCE LINES 193-198
.. code-block:: Python
part_other = part - 50.0
result = regridder.regrid(part_other)
result.ugrid.plot(vmin=-20, vmax=90, cmap="terrain")
.. image-sg:: /examples/images/sphx_glr_regridder_overview_011.png
:alt: regridder overview
:srcset: /examples/images/sphx_glr_regridder_overview_011.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
<matplotlib.collections.PolyCollection object at 0x7f74d419c0e0>
.. GENERATED FROM PYTHON SOURCE LINES 199-205
.. _Xarray: https://docs.xarray.dev/en/stable/index.html
.. _Xugrid: https://deltares.github.io/xugrid/
.. _Regridding: https://climatedataguide.ucar.edu/climate-tools/regridding-overview
.. _layered: https://ugrid-conventions.github.io/ugrid-conventions/#3d-layered-mesh-topology
.. _xESMF: https://xesmf.readthedocs.io/en/latest/index.html
.. _not available: https://github.com/conda-forge/esmf-feedstock/issues/64
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 9.819 seconds)
.. _sphx_glr_download_examples_regridder_overview.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: regridder_overview.ipynb <regridder_overview.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: regridder_overview.py <regridder_overview.py>`
.. container:: sphx-glr-download sphx-glr-download-zip
:download:`Download zipped: regridder_overview.zip <regridder_overview.zip>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_