Quadmesh

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import hvplot.xarray  # noqa

import xarray as xr

ds = xr.tutorial.open_dataset('rasm')
ds

<xarray.Dataset>
Dimensions:  (time: 36, y: 205, x: 275)
Coordinates:
  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00
    xc       (y, x) float64 ...
    yc       (y, x) float64 ...
Dimensions without coordinates: y, x
Data variables:
    Tair     (time, y, x) float64 ...
Attributes:
    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...
    institution:               U.W.
    source:                    RACM R1002RBRxaaa01a
    output_frequency:          daily
    output_mode:               averaged
    convention:                CF-1.4
    references:                Based on the initial model of Liang et al., 19...
    comment:                   Output from the Variable Infiltration Capacity...
    nco_openmp_thread_number:  1
    NCO:                       netCDF Operators version 4.7.9 (Homepage = htt...
    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d...

xarray.Dataset

• Dimensions:
  • time: 36
  • y: 205
  • x: 275
• Coordinates: (3)
  • time
    (time)
    object
    1980-09-16 12:00:00 ... 1983-08-...

    long_name :

    time

    type_preferred :

    int

    array([cftime.DatetimeNoLeap(1980, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1980, 10, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1980, 11, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1980, 12, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 1, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 2, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 3, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 4, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 5, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 6, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 7, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 8, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 10, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 11, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1981, 12, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 1, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 2, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 3, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 4, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 5, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 6, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 7, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 8, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 9, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 10, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 11, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1982, 12, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 1, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 2, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 3, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 4, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 5, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 6, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 7, 17, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1983, 8, 17, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • xc
    (y, x)
    float64
    ...

    long_name :

    longitude of grid cell center

    units :

    degrees_east

    [56375 values with dtype=float64]

  • yc
    (y, x)
    float64
    ...

    long_name :

    latitude of grid cell center

    units :

    degrees_north

    [56375 values with dtype=float64]

• Data variables: (1)
  • Tair
    (time, y, x)
    float64
    ...

    units :

    C

    long_name :

    Surface air temperature

    type_preferred :

    double

    time_rep :

    instantaneous

    [2029500 values with dtype=float64]

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1980-09-16 12:00:00, 1980-10-17 00:00:00, 1980-11-16 12:00:00,
                 1980-12-17 00:00:00, 1981-01-17 00:00:00, 1981-02-15 12:00:00,
                 1981-03-17 00:00:00, 1981-04-16 12:00:00, 1981-05-17 00:00:00,
                 1981-06-16 12:00:00, 1981-07-17 00:00:00, 1981-08-17 00:00:00,
                 1981-09-16 12:00:00, 1981-10-17 00:00:00, 1981-11-16 12:00:00,
                 1981-12-17 00:00:00, 1982-01-17 00:00:00, 1982-02-15 12:00:00,
                 1982-03-17 00:00:00, 1982-04-16 12:00:00, 1982-05-17 00:00:00,
                 1982-06-16 12:00:00, 1982-07-17 00:00:00, 1982-08-17 00:00:00,
                 1982-09-16 12:00:00, 1982-10-17 00:00:00, 1982-11-16 12:00:00,
                 1982-12-17 00:00:00, 1983-01-17 00:00:00, 1983-02-15 12:00:00,
                 1983-03-17 00:00:00, 1983-04-16 12:00:00, 1983-05-17 00:00:00,
                 1983-06-16 12:00:00, 1983-07-17 00:00:00, 1983-08-17 00:00:00],
                dtype='object', length=36, calendar='noleap', freq=None))

• Attributes: (11)

  title :

  /workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc

  institution :

  U.W.

  source :

  RACM R1002RBRxaaa01a

  output_frequency :

  daily

  output_mode :

  averaged

  convention :

  CF-1.4

  references :

  Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.

  comment :

  Output from the Variable Infiltration Capacity (VIC) model.

  nco_openmp_thread_number :

  1

  NCO :

  netCDF Operators version 4.7.9 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

  history :

  Fri Aug 7 17:57:38 2020: ncatted -a bounds,,d,, rasm.nc Tue Dec 27 14:15:22 2016: ncatted -a dimensions,,d,, rasm.nc rasm.nc Tue Dec 27 13:38:40 2016: ncks -3 rasm.nc rasm.nc history deleted for brevity

quadmesh can be slower that image, but it allows you to plot values on an irregular grid by representing each value as a polygon.

ds.Tair.hvplot.quadmesh(x='xc', y='yc', geo=True, widget_location='bottom')

To reduce the render time or the size of the saved plot, use rasterize to aggregate the values to the pixel. It is recommended that when rasterizing geo plots, you project before rasterizing, by setting project=True.

ds.Tair.hvplot.quadmesh(x='xc', y='yc', geo=True, widget_location='bottom', rasterize=True, project=True)

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Download this notebook from GitHub (right-click to download).