https://github.com/xCDAT/xcdat/issues/582

import xcdat as xc
import numpy as np

inputfile = "/Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_data/CMIP5_demo_data/ts_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc"
data_var = "ts"

ds = xc.open_dataset(inputfile)

ds[data_var].isel(time=0).plot()

<matplotlib.collections.QuadMesh at 0x14a814910>

ds

<xarray.Dataset> Size: 209MB
Dimensions:    (time: 1872, bnds: 2, lat: 145, lon: 192)
Coordinates:
  * lat        (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0
  * lon        (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1
  * time       (time) object 15kB 1850-01-16 12:00:00 ... 2005-12-16 12:00:00
Dimensions without coordinates: bnds
Data variables:
    time_bnds  (time, bnds) object 30kB ...
    lat_bnds   (lat, bnds) float64 2kB ...
    lon_bnds   (lon, bnds) float64 3kB ...
    ts         (time, lat, lon) float32 208MB ...
Attributes: (12/28)
    institution:            CSIRO (Commonwealth Scientific and Industrial Res...
    institute_id:           CSIRO-BOM
    experiment_id:          historical
    source:                 ACCESS1-0 2011. Atmosphere: AGCM v1.0 (N96 grid-p...
    model_id:               ACCESS1-0
    forcing:                GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4...
    ...                     ...
    table_id:               Table Amon (27 April 2011) 9c851218e3842df9a62ef3...
    title:                  ACCESS1-0 model output prepared for CMIP5 historical
    parent_experiment:      pre-industrial control
    modeling_realm:         atmos
    realization:            1
    cmor_version:           2.8.0

xarray.Dataset

• Dimensions:
  • time: 1872
  • bnds: 2
  • lat: 145
  • lon: 192
• Coordinates: (3)
  • lat
    (lat)
    float64
    -90.0 -88.75 -87.5 ... 88.75 90.0

    bounds :

    lat_bnds

    units :

    degrees_north

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,
           -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,
           -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,
           -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,
           -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,
           -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,
           -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,
           -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,
             0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,
            11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,
            22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,
            33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,
            45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,
            56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,
            67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,
            78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,
            90.  ])

  • lon
    (lon)
    float64
    0.0 1.875 3.75 ... 356.2 358.1

    bounds :

    lon_bnds

    units :

    degrees_east

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,
            15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,
            30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,
            45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,
            60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,
            75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,
            90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,
           105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,
           120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,
           135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,
           150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,
           165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,
           180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,
           195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,
           210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,
           225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,
           240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,
           255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,
           270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,
           285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,
           300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,
           315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,
           330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,
           345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])

  • time
    (time)
    object
    1850-01-16 12:00:00 ... 2005-12-...

    bounds :

    time_bnds

    axis :

    T

    long_name :

    time

    standard_name :

    time

    array([cftime.DatetimeProlepticGregorian(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeProlepticGregorian(2005, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2005, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2005, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Data variables: (4)
  • time_bnds
    (time, bnds)
    object
    ...

    [3744 values with dtype=object]

  • lat_bnds
    (lat, bnds)
    float64
    ...

    [290 values with dtype=float64]

  • lon_bnds
    (lon, bnds)
    float64
    ...

    [384 values with dtype=float64]

  • ts
    (time, lat, lon)
    float32
    ...

    standard_name :

    surface_temperature

    long_name :

    Surface Temperature

    comment :

    ""skin"" temperature (i.e., SST for open ocean)

    units :

    K

    cell_methods :

    time: mean

    cell_measures :

    area: areacella

    history :

    2012-01-17T03:16:09Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).

    associated_files :

    baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_ACCESS1-0_historical_r0i0p0.nc areacella: areacella_fx_ACCESS1-0_historical_r0i0p0.nc

    [52116480 values with dtype=float32]

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,
           -78.75,
           ...
            78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,
             90.0],
          dtype='float64', name='lat', length=145))

  • lon
    PandasIndex

    PandasIndex(Index([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,
              15.0,  16.875,
           ...
            341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,
            356.25, 358.125],
          dtype='float64', name='lon', length=192))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1850-01-16 12:00:00, 1850-02-15 00:00:00, 1850-03-16 12:00:00,
                 1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,
                 1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,
                 1850-10-16 12:00:00,
                 ...
                 2005-03-16 12:00:00, 2005-04-16 00:00:00, 2005-05-16 12:00:00,
                 2005-06-16 00:00:00, 2005-07-16 12:00:00, 2005-08-16 12:00:00,
                 2005-09-16 00:00:00, 2005-10-16 12:00:00, 2005-11-16 00:00:00,
                 2005-12-16 12:00:00],
                dtype='object',
                length=1872,
                calendar='proleptic_gregorian',
                freq=None))

• Attributes: (28)

  institution :

  CSIRO (Commonwealth Scientific and Industrial Research Organisation, Australia), and BOM (Bureau of Meteorology, Australia)

  institute_id :

  CSIRO-BOM

  experiment_id :

  historical

  source :

  ACCESS1-0 2011. Atmosphere: AGCM v1.0 (N96 grid-point, 1.875 degrees EW x approx 1.25 degree NS, 38 levels); ocean: NOAA/GFDL MOM4p1 (nominal 1.0 degree EW x 1.0 degrees NS, tripolar north of 65N, equatorial refinement to 1/3 degree from 10S to 10 N, cosine dependent NS south of 25S, 50 levels); sea ice: CICE4.1 (nominal 1.0 degree EW x 1.0 degrees NS, tripolar north of 65N, equatorial refinement to 1/3 degree from 10S to 10 N, cosine dependent NS south of 25S); land: MOSES2 (1.875 degree EW x 1.25 degree NS, 4 levels

  model_id :

  ACCESS1-0

  forcing :

  GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)

  parent_experiment_id :

  piControl

  parent_experiment_rip :

  r1i1p1

  branch_time :

  109207.0

  contact :

  The ACCESS wiki: http://wiki.csiro.au/confluence/display/ACCESS/Home. Contact Tony.Hirst@csiro.au regarding the ACCESS coupled climate model. Contact Peter.Uhe@csiro.au regarding ACCESS coupled climate model CMIP5 datasets.

  history :

  CMIP5 compliant file produced from raw ACCESS model output using the ACCESS Post-Processor and CMOR2. 2012-01-17T03:16:09Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.

  references :

  See http://wiki.csiro.au/confluence/display/ACCESS/ACCESS+Publications

  initialization_method :

  1

  physics_version :

  1

  tracking_id :

  3ba59dfe-51df-471d-ae2f-74959d08bd9f

  version_number :

  v20120115

  product :

  output

  experiment :

  historical

  frequency :

  mon

  creation_date :

  2012-01-17T03:16:09Z

  Conventions :

  CF-1.4

  project_id :

  CMIP5

  table_id :

  Table Amon (27 April 2011) 9c851218e3842df9a62ef38b1e2575bb

  title :

  ACCESS1-0 model output prepared for CMIP5 historical

  parent_experiment :

  pre-industrial control

  modeling_realm :

  atmos

  realization :

  1

  cmor_version :

  2.8.0

ds.isel(time=0).spatial.average(data_var)[data_var].item()

285.88807821517435

da = ds[data_var].isel(time=0).copy()

# Define the latitude and longitude ranges
lat_range = (20, 70)
lon_range = (210, 320)

# Create masks for the latitude and longitude ranges
lat_mask = (da.lat >= lat_range[0]) & (da.lat <= lat_range[1])
lon_mask = (da.lon >= lon_range[0]) & (da.lon <= lon_range[1])

# Use broadcasting to apply the masks across the array dimensions
da.values[np.ix_(lat_mask, lon_mask)] = np.nan

da.plot()

<matplotlib.collections.QuadMesh at 0x14a944c10>

ds[data_var].values[0] = da

ds[data_var].isel(time=0).plot()

<matplotlib.collections.QuadMesh at 0x14a9b3f50>

ds.isel(time=0).spatial.average(data_var)[data_var].item()

286.89966412418875

ds.isel(time=0).spatial.average(data_var, skipna=True)[data_var].item()

286.89966412418875

ds.isel(time=0).spatial.average(data_var, skipna=False)[data_var].item()

nan

https://docs.xarray.dev/en/stable/generated/xarray.DataArray.mean.html

ds.temporal.average(data_var)[data_var].plot()

<matplotlib.collections.QuadMesh at 0x149eda710>

ds.temporal.average(data_var, skipna=True)[data_var].plot()

<matplotlib.collections.QuadMesh at 0x149f29710>

ds.temporal.average(data_var, skipna=False)[data_var].plot()

<matplotlib.collections.QuadMesh at 0x14aaa8810>