Comparison to Xarray

---

For users coming from an Xarray background, much of UXarray’s design is familiar. This notebook showcases an example of transitioning a Structured Grid Xarray workflow to UXarray.

Imports

import matplotlib.pyplot as plt
import uxarray as ux
import xarray as xr

fig_size = 400
plot_kwargs = {"backend": "matplotlib", "aspect": 2, "fig_size": fig_size}

Data

It is common practice to resample unstructured grids to a structured representation for many analysis workflows to utilize familiar and reliable tools.

The datasets used in this example are meant to mimic this workflow, with a source Unstructured Grid and a Structured representation of that same grid.

Structured

base_path = "../../meshfiles/"
ds_path = base_path + "outCSne30.structured.nc"

xrds = xr.open_dataset(ds_path)
xrds

<xarray.Dataset> Size: 30kB
Dimensions:  (lat: 45, lon: 80)
Coordinates:
  * lat      (lat) int64 360B -90 -86 -82 -78 -74 -70 -66 ... 66 70 74 78 82 86
  * lon      (lon) float64 640B -180.0 -175.5 -171.0 ... 166.5 171.0 175.5
Data variables:
    psi      (lat, lon) float64 29kB ...

xarray.Dataset

• Dimensions:
  • lat: 45
  • lon: 80
• Coordinates: (2)
  • lat
    (lat)
    int64
    -90 -86 -82 -78 -74 ... 74 78 82 86

    units :

    degrees_north

    array([-90, -86, -82, -78, -74, -70, -66, -62, -58, -54, -50, -46, -42, -38,
           -34, -30, -26, -22, -18, -14, -10,  -6,  -2,   2,   6,  10,  14,  18,
            22,  26,  30,  34,  38,  42,  46,  50,  54,  58,  62,  66,  70,  74,
            78,  82,  86])

  • lon
    (lon)
    float64
    -180.0 -175.5 ... 171.0 175.5

    units :

    degrees_east

    array([-180. , -175.5, -171. , -166.5, -162. , -157.5, -153. , -148.5, -144. ,
           -139.5, -135. , -130.5, -126. , -121.5, -117. , -112.5, -108. , -103.5,
            -99. ,  -94.5,  -90. ,  -85.5,  -81. ,  -76.5,  -72. ,  -67.5,  -63. ,
            -58.5,  -54. ,  -49.5,  -45. ,  -40.5,  -36. ,  -31.5,  -27. ,  -22.5,
            -18. ,  -13.5,   -9. ,   -4.5,    0. ,    4.5,    9. ,   13.5,   18. ,
             22.5,   27. ,   31.5,   36. ,   40.5,   45. ,   49.5,   54. ,   58.5,
             63. ,   67.5,   72. ,   76.5,   81. ,   85.5,   90. ,   94.5,   99. ,
            103.5,  108. ,  112.5,  117. ,  121.5,  126. ,  130.5,  135. ,  139.5,
            144. ,  148.5,  153. ,  157.5,  162. ,  166.5,  171. ,  175.5])

• Data variables: (1)
  • psi
    (lat, lon)
    float64
    ...

    regrid_method :

    nearest_s2d

    [3600 values with dtype=float64]

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([-90, -86, -82, -78, -74, -70, -66, -62, -58, -54, -50, -46, -42, -38,
           -34, -30, -26, -22, -18, -14, -10,  -6,  -2,   2,   6,  10,  14,  18,
            22,  26,  30,  34,  38,  42,  46,  50,  54,  58,  62,  66,  70,  74,
            78,  82,  86],
          dtype='int64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([-180.0, -175.5, -171.0, -166.5, -162.0, -157.5, -153.0, -148.5, -144.0,
           -139.5, -135.0, -130.5, -126.0, -121.5, -117.0, -112.5, -108.0, -103.5,
            -99.0,  -94.5,  -90.0,  -85.5,  -81.0,  -76.5,  -72.0,  -67.5,  -63.0,
            -58.5,  -54.0,  -49.5,  -45.0,  -40.5,  -36.0,  -31.5,  -27.0,  -22.5,
            -18.0,  -13.5,   -9.0,   -4.5,    0.0,    4.5,    9.0,   13.5,   18.0,
             22.5,   27.0,   31.5,   36.0,   40.5,   45.0,   49.5,   54.0,   58.5,
             63.0,   67.5,   72.0,   76.5,   81.0,   85.5,   90.0,   94.5,   99.0,
            103.5,  108.0,  112.5,  117.0,  121.5,  126.0,  130.5,  135.0,  139.5,
            144.0,  148.5,  153.0,  157.5,  162.0,  166.5,  171.0,  175.5],
          dtype='float64', name='lon'))

• Attributes: (0)

Unstructured

base_path = "../../meshfiles/"
grid_filename = base_path + "outCSne30.grid.ug"
data_filename = base_path + "outCSne30.data.nc"

uxds = ux.open_dataset(grid_filename, data_filename)
uxds

<xarray.UxDataset> Size: 43kB
Dimensions:  (n_face: 5400)
Dimensions without coordinates: n_face
Data variables:
    psi      (n_face) float64 43kB ...

<xarray.UxDataset> Size: 43kB
Dimensions:  (n_face: 5400)
Dimensions without coordinates: n_face
Data variables:
    psi      (n_face) float64 43kB ...

uxarray.UxDataset

• Dimensions:
  • n_face: 5400
• Coordinates: (0)
• Data variables: (1)
  • psi
    (n_face)
    float64
    ...

    [5400 values with dtype=float64]

• Indexes: (0)
• Attributes: (0)

Show Grid Information

<uxarray.Grid>
Original Grid Type: UGRID
Grid Dimensions:
  * n_node: 5402
  * n_face: 5400
  * n_max_face_nodes: 4
  * n_nodes_per_face: (5400,)
Grid Coordinates (Spherical):
  * node_lon: (5402,)
  * node_lat: (5402,)
Grid Coordinates (Cartesian):
Grid Connectivity Variables:
  * face_node_connectivity: (5400, 4)
Grid Descriptor Variables:
  * n_nodes_per_face: (5400,)

uxarray.UxDataset.uxgrid

• Dimensions:
  • n_face: 5400
  • n_max_face_nodes: 4
  • n_node: 5402
• Spherical Coordinates: (2)
  • node_lon
    (n_node)
    float64
    -45.0 45.0 135.0 ... 138.0 135.0

    standard_name :

    longitude

    long_name :

    longitude of 2D mesh nodes

    units :

    degrees_east

    array([-45.        ,  45.        , 135.        , ..., 141.09968961,
           138.03317102, 135.        ])

  • node_lat
    (n_node)
    float64
    ...

    standard_name :

    latitude

    long_name :

    latitude of 2D mesh nodes

    units :

    degrees_north

    [5402 values with dtype=float64]

• Cartesian Coordinates: (0)
• Connectivity: (1)
  • face_node_connectivity
    (n_face, n_max_face_nodes)
    int64
    0 8 356 124 8 ... 5401 297 6 298

    cf_role :

    face_node_connectivity

    start_index :

    0

    _FillValue :

    -9223372036854775808

    array([[   0,    8,  356,  124],
           [   8,    9,  357,  356],
           [   9,   10,  358,  357],
           ...,
           [5399, 5400,  299,  300],
           [5400, 5401,  298,  299],
           [5401,  297,    6,  298]])

• Descriptors: (0)
• Attributes: (0)

Example Workflows

Below are two simple visualization workflows that someone would run into

• Creating a single plot

• Creating a pair of plots (two different color maps are used to mimic different data)

Xarray

xrds["psi"].plot(figsize=(12, 5), cmap="inferno")

<matplotlib.collections.QuadMesh at 0x7f1c543efaf0>

fig, axs = plt.subplots(nrows=2, figsize=(12, 10))

xrds["psi"].plot(cmap="inferno", ax=axs[0])
xrds["psi"].plot(cmap="cividis", ax=axs[1])

<matplotlib.collections.QuadMesh at 0x7f1c54182ef0>

UXarray

uxds["psi"].plot(width=1000, height=500, cmap="inferno")

The default plotting method works great, but we can chose to set exclude_antimeridian=False to include the entire grid and generate a better looking plot.

See also:

To learn more about this type of plotting functionality and supported parameters, please refer to the Polygon Section

uxds["psi"].plot(width=1000, height=500, cmap="inferno", exclude_antimeridian=False)

WARNING:param.main: exclude_antimeridian option not found for polygons plot with bokeh; similar options include: []

(
    uxds["psi"].plot(cmap="inferno", exclude_antimeridian=False, **plot_kwargs)
    + uxds["psi"].plot(cmap="cividis", exclude_antimeridian=False, **plot_kwargs)
).opts(fig_size=fig_size).cols(1)

WARNING:param.main: exclude_antimeridian option not found for polygons plot with matplotlib; similar options include: []

WARNING:param.main: fig_size option not found for polygons plot with matplotlib; similar options include: []

WARNING:param.main: exclude_antimeridian option not found for polygons plot with matplotlib; similar options include: []

WARNING:param.main: fig_size option not found for polygons plot with matplotlib; similar options include: []

Using hvPlot to conbine UXarray and Xarray Plots

One of the primary drawbacks to UXarray’s use of HoloViews for visualization is that there is no direct way to integrate plots generated with Xarray and UXarray together. This can be alleviated by using the hvPlot library, specifically hvplot.xarray, on Xarray’s data structures.

See also:

To learn more about hvPlot and xarray, please refer to the hvPlot Documentation

import holoviews as hv
import hvplot.xarray

hv.extension("bokeh")

By using xrds.hvplot() as opposed to xrds.plot(), we can create a simple figure showcasing our Structured Grid figure from Xarray and Unstructured Grid figure from UXarray in a single plot.

hv.extension("bokeh")
(
    xrds.hvplot(cmap="inferno", title="Xarray with hvPlot", width=1000, height=500)
    + uxds["psi"].plot(
        cmap="inferno",
        title="UXarray Plot",
        exclude_antimeridian=False,
        width=1000,
        height=500,
    )
).cols(1)

WARNING:param.main: exclude_antimeridian option not found for polygons plot with bokeh; similar options include: []