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

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

    [5400 values with dtype=float64]

• Indexes: (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 0x7f3321464100>

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 0x7f331ff7feb0>

UXarray

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