Comparison to Xarray

In this tutorial, you’ll learn about:

• The differences and similarities between UXarray’s and Xarray’s plotting routines

• Using hvPlot with Xarray

Related Documentation

• UXarray Plotting User Guide

Prerequisites

Concepts	Importance	Notes
Xarray	Necessary

Time to learn: 5 minutes

---

Introduction

For users coming from an Xarray background, much of UXarray’s design is familiar. This notebook showcases an example of transitioning a visualization of a structured grid using Xarray into a visualization of an unstructured grid using UXarray.

import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import uxarray as ux
import xarray as xr

Data

We use two variations of the outCSne30 grid for this example. One of them is the original unstructured cube sphere, with the other being a remapped structured version.

Xarray

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)

UXarray

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)

Visualization

Xarray

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

<matplotlib.collections.QuadMesh at 0x7f7b6275f0a0>

UXarray

uxds["psi"].plot(width=800, height=400, backend="matplotlib", cmap="inferno")

Using hvPlot to combine UXarray & Xarray Plots

Since UXarray is written using hvPlot, we can visualize Xarray and UXarray plots together by using hvplot.xarray.

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")

(
    xrds.hvplot(cmap="inferno", title="Xarray with hvPlot", width=800, height=400)
    + uxds["psi"].plot(
        cmap="inferno",
        title="UXarray Plot",
        width=800,
        height=400,
        periodic_elements="split",
    )
).cols(1)