Plotting Quantified Data

import xarray as xr
from astropy import units as u

import astropy_xarray  # noqa: F401

# to be able to read unit attributes following the CF conventions
# import cf_xarray.units  # must be imported before astropy_xarray
u.set_enabled_aliases(
    {
        "degK": u.Kelvin,
        "K": u.Kelvin,
        "degrees_north": u.deg,
        "degrees_east": u.deg,
    },
)

xr.set_options(display_expand_data=False)

<xarray.core.options.set_options at 0x78ab32e15150>

load the data

ds = xr.tutorial.open_dataset("air_temperature")
data = ds.air
data

<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)> Size: 31MB
[3869000 values with dtype=float64]
Coordinates:
  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0
  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0
  * time     (time) datetime64[ns] 23kB 2013-01-01 ... 2014-12-31T18:00:00
Attributes:
    long_name:     4xDaily Air temperature at sigma level 995
    units:         degK
    precision:     2
    GRIB_id:       11
    GRIB_name:     TMP
    var_desc:      Air temperature
    dataset:       NMC Reanalysis
    level_desc:    Surface
    statistic:     Individual Obs
    parent_stat:   Other
    actual_range:  [185.16 322.1 ]

xarray.DataArray

'air'

• time: 2920
• lat: 25
• lon: 53

• ...

  [3869000 values with dtype=float64]

• Coordinates: (3)
  • lat
    (lat)
    float32
    75.0 72.5 70.0 ... 20.0 17.5 15.0

    standard_name :

    latitude

    long_name :

    Latitude

    units :

    degrees_north

    axis :

    Y

    array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,
           45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,
           15. ], dtype=float32)

  • lon
    (lon)
    float32
    200.0 202.5 205.0 ... 327.5 330.0

    standard_name :

    longitude

    long_name :

    Longitude

    units :

    degrees_east

    axis :

    X

    array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
           225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
           250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
           275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
           300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
           325. , 327.5, 330. ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2013-01-01 ... 2014-12-31T18:00:00

    standard_name :

    time

    long_name :

    Time

    array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',
           '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',
           '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],
          shape=(2920,), dtype='datetime64[ns]')

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,
           45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,
           15.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
           225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,
           250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,
           275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,
           300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,
           325.0, 327.5, 330.0],
          dtype='float32', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 06:00:00',
                   '2013-01-01 12:00:00', '2013-01-01 18:00:00',
                   '2013-01-02 00:00:00', '2013-01-02 06:00:00',
                   '2013-01-02 12:00:00', '2013-01-02 18:00:00',
                   '2013-01-03 00:00:00', '2013-01-03 06:00:00',
                   ...
                   '2014-12-29 12:00:00', '2014-12-29 18:00:00',
                   '2014-12-30 00:00:00', '2014-12-30 06:00:00',
                   '2014-12-30 12:00:00', '2014-12-30 18:00:00',
                   '2014-12-31 00:00:00', '2014-12-31 06:00:00',
                   '2014-12-31 12:00:00', '2014-12-31 18:00:00'],
                  dtype='datetime64[ns]', name='time', length=2920, freq=None))

• Attributes: (11)

  long_name :

  4xDaily Air temperature at sigma level 995

  units :

  degK

  precision :

  2

  GRIB_id :

  11

  GRIB_name :

  TMP

  var_desc :

  Air temperature

  dataset :

  NMC Reanalysis

  level_desc :

  Surface

  statistic :

  Individual Obs

  parent_stat :

  Other

  actual_range :

  [185.16 322.1 ]

quantify the data

Note: this example uses the data provided by the xarray.tutorial functions. As such, the units attributes follow the CF conventions, which astropy does not understand by default. To still be able to read them, registry be aliases can be used. For more information, see cf-xarray.

quantified = data.astropy.quantify()
quantified

<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)> Size: 31MB
[K] 241.2 242.5 243.5 244.0 244.1 243.9 ... 297.9 297.4 297.2 296.5 296.2 295.7
Coordinates:
  * lat      (lat) float32 100B [°] 75.0 72.5 70.0 67.5 ... 22.5 20.0 17.5 15.0
  * lon      (lon) float32 212B [°] 200.0 202.5 205.0 ... 325.0 327.5 330.0
  * time     (time) datetime64[ns] 23kB 2013-01-01 ... 2014-12-31T18:00:00
Indexes:
    lat      AstropyIndex(PandasIndex)
    lon      AstropyIndex(PandasIndex)
Attributes:
    long_name:     4xDaily Air temperature at sigma level 995
    precision:     2
    GRIB_id:       11
    GRIB_name:     TMP
    var_desc:      Air temperature
    dataset:       NMC Reanalysis
    level_desc:    Surface
    statistic:     Individual Obs
    parent_stat:   Other
    actual_range:  [185.16 322.1 ]

xarray.DataArray

'air'

• time: 2920
• lat: 25
• lon: 53

• [K] 241.2 242.5 243.5 244.0 244.1 ... 297.4 297.2 296.5 296.2 295.7

  <Quantity [[[241.2 , 242.5 , 243.5 , ..., 232.8 , 235.5 , 238.6 ],
              [243.8 , 244.5 , 244.7 , ..., 232.8 , 235.3 , 239.3 ],
              [250.  , 249.8 , 248.89, ..., 233.2 , 236.39, 241.7 ],
              ...,
              [296.6 , 296.2 , 296.4 , ..., 295.4 , 295.1 , 294.7 ],
              [295.9 , 296.2 , 296.79, ..., 295.9 , 295.9 , 295.2 ],
              [296.29, 296.79, 297.1 , ..., 296.9 , 296.79, 296.6 ]],
  
             [[242.1 , 242.7 , 243.1 , ..., 232.  , 233.6 , 235.8 ],
              [243.6 , 244.1 , 244.2 , ..., 231.  , 232.5 , 235.7 ],
              [253.2 , 252.89, 252.1 , ..., 230.8 , 233.39, 238.5 ],
              ...,
              [296.4 , 295.9 , 296.2 , ..., 295.4 , 295.1 , 294.79],
              [296.2 , 296.7 , 296.79, ..., 295.6 , 295.5 , 295.1 ],
              [296.29, 297.2 , 297.4 , ..., 296.4 , 296.4 , 296.6 ]],
  
             [[242.3 , 242.2 , 242.3 , ..., 234.3 , 236.1 , 238.7 ],
              [244.6 , 244.39, 244.  , ..., 230.3 , 232.  , 235.7 ],
              [256.2 , 255.5 , 254.2 , ..., 231.2 , 233.2 , 238.2 ],
              ...,
  ...
              ...,
              [294.79, 295.29, 297.49, ..., 295.49, 295.39, 294.69],
              [296.79, 297.89, 298.29, ..., 295.49, 295.49, 294.79],
              [298.19, 299.19, 298.79, ..., 296.09, 295.79, 295.79]],
  
             [[245.79, 244.79, 243.49, ..., 243.29, 243.99, 244.79],
              [249.89, 249.29, 248.49, ..., 241.29, 242.49, 244.29],
              [262.39, 261.79, 261.29, ..., 240.49, 243.09, 246.89],
              ...,
              [293.69, 293.89, 295.39, ..., 295.09, 294.69, 294.29],
              [296.29, 297.19, 297.59, ..., 295.29, 295.09, 294.39],
              [297.79, 298.39, 298.49, ..., 295.69, 295.49, 295.19]],
  
             [[245.09, 244.29, 243.29, ..., 241.69, 241.49, 241.79],
              [249.89, 249.29, 248.39, ..., 239.59, 240.29, 241.69],
              [262.99, 262.19, 261.39, ..., 239.89, 242.59, 246.29],
              ...,
              [293.79, 293.69, 295.09, ..., 295.29, 295.09, 294.69],
              [296.09, 296.89, 297.19, ..., 295.69, 295.69, 295.19],
              [297.69, 298.09, 298.09, ..., 296.49, 296.19, 295.69]]] K>

• Coordinates: (3)
  • lat
    (lat)
    float32
    [°] 75.0 72.5 70.0 ... 17.5 15.0

    standard_name :

    latitude

    long_name :

    Latitude

    axis :

    Y

    <Quantity [75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5,
               50. , 47.5, 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5,
               25. , 22.5, 20. , 17.5, 15. ] deg>

  • lon
    (lon)
    float32
    [°] 200.0 202.5 ... 327.5 330.0

    standard_name :

    longitude

    long_name :

    Longitude

    axis :

    X

    <Quantity [200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. ,
               222.5, 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5,
               245. , 247.5, 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. ,
               267.5, 270. , 272.5, 275. , 277.5, 280. , 282.5, 285. , 287.5,
               290. , 292.5, 295. , 297.5, 300. , 302.5, 305. , 307.5, 310. ,
               312.5, 315. , 317.5, 320. , 322.5, 325. , 327.5, 330. ] deg>

  • time
    (time)
    datetime64[ns]
    2013-01-01 ... 2014-12-31T18:00:00

    standard_name :

    time

    long_name :

    Time

    array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',
           '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',
           '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],
          shape=(2920,), dtype='datetime64[ns]')

• Indexes: (3)
  • lat
    AstropyIndex(PandasIndex)

    <astropy_xarray.index.AstropyIndex object at 0x78ab15e47ad0>

  • lon
    AstropyIndex(PandasIndex)

    <astropy_xarray.index.AstropyIndex object at 0x78ab15e1d910>

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 06:00:00',
                   '2013-01-01 12:00:00', '2013-01-01 18:00:00',
                   '2013-01-02 00:00:00', '2013-01-02 06:00:00',
                   '2013-01-02 12:00:00', '2013-01-02 18:00:00',
                   '2013-01-03 00:00:00', '2013-01-03 06:00:00',
                   ...
                   '2014-12-29 12:00:00', '2014-12-29 18:00:00',
                   '2014-12-30 00:00:00', '2014-12-30 06:00:00',
                   '2014-12-30 12:00:00', '2014-12-30 18:00:00',
                   '2014-12-31 00:00:00', '2014-12-31 06:00:00',
                   '2014-12-31 12:00:00', '2014-12-31 18:00:00'],
                  dtype='datetime64[ns]', name='time', length=2920, freq=None))

• Attributes: (10)

  long_name :

  4xDaily Air temperature at sigma level 995

  precision :

  2

  GRIB_id :

  11

  GRIB_name :

  TMP

  var_desc :

  Air temperature

  dataset :

  NMC Reanalysis

  level_desc :

  Surface

  statistic :

  Individual Obs

  parent_stat :

  Other

  actual_range :

  [185.16 322.1 ]

work with the data

monthly_means = quantified.astropy.sel(time="2013").groupby("time.month").mean()
monthly_means

<xarray.DataArray 'air' (month: 12, lat: 25, lon: 53)> Size: 127kB
[K] 244.5 244.7 244.7 244.5 244.2 243.8 ... 298.0 297.9 297.9 297.5 297.4 297.4
Coordinates:
  * lat      (lat) float32 100B [°] 75.0 72.5 70.0 67.5 ... 22.5 20.0 17.5 15.0
  * lon      (lon) float32 212B [°] 200.0 202.5 205.0 ... 325.0 327.5 330.0
  * month    (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12
Indexes:
    lat      AstropyIndex(PandasIndex)
    lon      AstropyIndex(PandasIndex)
Attributes:
    long_name:     4xDaily Air temperature at sigma level 995
    precision:     2
    GRIB_id:       11
    GRIB_name:     TMP
    var_desc:      Air temperature
    dataset:       NMC Reanalysis
    level_desc:    Surface
    statistic:     Individual Obs
    parent_stat:   Other
    actual_range:  [185.16 322.1 ]

xarray.DataArray

'air'

• month: 12
• lat: 25
• lon: 53

• [K] 244.5 244.7 244.7 244.5 244.2 ... 297.9 297.9 297.5 297.4 297.4

  <Quantity [[[244.46677419, 244.66354839, 244.67024194, ..., 242.49145161,
               243.40637097, 244.67580645],
              [247.07322581, 247.0225    , 246.7275    , ..., 240.58209677,
               242.04483871, 244.70717742],
              [250.37943548, 249.83483871, 249.1075    , ..., 241.98435484,
               244.76709677, 249.00508065],
              ...,
              [295.83814516, 295.15096774, 294.92314516, ..., 295.3683871 ,
               294.88451613, 294.2683871 ],
              [296.46951613, 296.31701613, 295.84822581, ..., 295.58774194,
               295.34072581, 294.86556452],
              [297.05346774, 297.04201613, 296.73532258, ..., 296.30459677,
               296.09741935, 296.0391129 ]],
  
             [[240.73392857, 240.70133929, 240.41151786, ..., 241.60517857,
               242.71982143, 243.94455357],
              [241.933125  , 242.069375  , 241.91303571, ..., 241.01428571,
               242.32482143, 244.72758929],
              [245.32357143, 245.02607143, 244.36955357, ..., 243.41589286,
               245.76607143, 249.65857143],
  ...
              [297.63608333, 296.93116667, 296.7535    , ..., 297.92608333,
               297.47741667, 297.17683333],
              [298.21066667, 298.02866667, 297.66666667, ..., 298.22433333,
               298.16691667, 297.93108333],
              [298.4495    , 298.49558333, 298.34641667, ..., 298.74291667,
               298.76191667, 298.85766667]],
  
             [[249.14024194, 249.15282258, 249.05653226, ..., 239.47241935,
               240.96516129, 242.97443548],
              [251.73677419, 252.01983871, 252.22677419, ..., 238.96032258,
               241.60685484, 245.26169355],
              [254.08983871, 253.47693548, 253.15709677, ..., 238.44758065,
               242.58806452, 247.91266129],
              ...,
              [297.08112903, 296.26193548, 296.02008065, ..., 296.38370968,
               295.96524194, 295.50241935],
              [297.49048387, 297.32459677, 296.90685484, ..., 296.75870968,
               296.63508065, 296.31379032],
              [297.71451613, 297.74741935, 297.49137097, ..., 297.50306452,
               297.40758065, 297.365     ]]] K>

• Coordinates: (3)
  • lat
    (lat)
    float32
    [°] 75.0 72.5 70.0 ... 17.5 15.0

    standard_name :

    latitude

    long_name :

    Latitude

    axis :

    Y

    <Quantity [75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5,
               50. , 47.5, 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5,
               25. , 22.5, 20. , 17.5, 15. ] deg>

  • lon
    (lon)
    float32
    [°] 200.0 202.5 ... 327.5 330.0

    standard_name :

    longitude

    long_name :

    Longitude

    axis :

    X

    <Quantity [200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. ,
               222.5, 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5,
               245. , 247.5, 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. ,
               267.5, 270. , 272.5, 275. , 277.5, 280. , 282.5, 285. , 287.5,
               290. , 292.5, 295. , 297.5, 300. , 302.5, 305. , 307.5, 310. ,
               312.5, 315. , 317.5, 320. , 322.5, 325. , 327.5, 330. ] deg>

  • month
    (month)
    int64
    1 2 3 4 5 6 7 8 9 10 11 12

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

• Indexes: (3)
  • lat
    AstropyIndex(PandasIndex)

    <astropy_xarray.index.AstropyIndex object at 0x78ab17f22290>

  • lon
    AstropyIndex(PandasIndex)

    <astropy_xarray.index.AstropyIndex object at 0x78ab17f220d0>

  • month
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))

• Attributes: (10)

  long_name :

  4xDaily Air temperature at sigma level 995

  precision :

  2

  GRIB_id :

  11

  GRIB_name :

  TMP

  var_desc :

  Air temperature

  dataset :

  NMC Reanalysis

  level_desc :

  Surface

  statistic :

  Individual Obs

  parent_stat :

  Other

  actual_range :

  [185.16 322.1 ]

Most operations will preserve the units but there are some which will drop them (see the duck array integration status page). To work around that there are unit-aware versions on the .astropy accessor. For example, to select data use .astropy.sel instead of .sel:

monthly_means.sel(
    lat=u.Quantity(4350, "arcmin"),
    lon=u.Quantity(12000, "arcmin"),
)

<xarray.DataArray 'air' (month: 12)> Size: 96B
[K] 247.1 241.9 250.7 257.5 267.7 274.8 276.8 273.6 273.0 269.3 258.6 251.7
Coordinates:
    lat      float32 4B [°] np.float32(72.5)
    lon      float32 4B [°] np.float32(200.0)
  * month    (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12
Attributes:
    long_name:     4xDaily Air temperature at sigma level 995
    precision:     2
    GRIB_id:       11
    GRIB_name:     TMP
    var_desc:      Air temperature
    dataset:       NMC Reanalysis
    level_desc:    Surface
    statistic:     Individual Obs
    parent_stat:   Other
    actual_range:  [185.16 322.1 ]

xarray.DataArray

'air'

• month: 12

• [K] 247.1 241.9 250.7 257.5 267.7 ... 273.6 273.0 269.3 258.6 251.7

  <Quantity [247.07322581, 241.933125  , 250.65564516, 257.54741667,
             267.72048387, 274.75166667, 276.82798387, 273.56016129,
             273.01616667, 269.29532258, 258.63808333, 251.73677419] K>

• Coordinates: (3)
  • lat
    ()
    float32
    [°] np.float32(72.5)

    standard_name :

    latitude

    long_name :

    Latitude

    axis :

    Y

    <Quantity 72.5 deg>

  • lon
    ()
    float32
    [°] np.float32(200.0)

    standard_name :

    longitude

    long_name :

    Longitude

    axis :

    X

    <Quantity 200. deg>

  • month
    (month)
    int64
    1 2 3 4 5 6 7 8 9 10 11 12

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

• Indexes: (1)
  • month
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))

• Attributes: (10)

  long_name :

  4xDaily Air temperature at sigma level 995

  precision :

  2

  GRIB_id :

  11

  GRIB_name :

  TMP

  var_desc :

  Air temperature

  dataset :

  NMC Reanalysis

  level_desc :

  Surface

  statistic :

  Individual Obs

  parent_stat :

  Other

  actual_range :

  [185.16 322.1 ]

plot

xarray’s plotting functions will cast the data to numpy.ndarray, so we need to “dequantify” first.

monthly_means.astropy.dequantify(format="unicode").plot.imshow(col="month", col_wrap=4)

<xarray.plot.facetgrid.FacetGrid at 0x78ab15eb0990>