Loading 2D fields into yt

yt_xarray can also load in 2D fields from xarray into a yt dataset. To do so, all you have to do is provide a selection dictionary that will reduce your chosen field down to 2D.

First, let’s import and create a test dataset with dimensions that yt will not know:

import xarray as xr
import yt_xarray
import yt
import numpy as np
from yt_xarray.sample_data import load_random_xr_data

fields = {'temp': ('x', 'y', 'z'),
          'precip': ('x', 'y'),
          'precip_t': ('x', 'y', 'time')}
dims = {'x': (0,1,15), 'y': (0, 1, 10), 'z': (0, 1, 15), 'time': (0, 1, 5)}
ds = load_random_xr_data(fields, dims, length_unit='m')
ds

<xarray.Dataset>
Dimensions:   (x: 15, y: 10, z: 15, time: 5)
Coordinates:
  * x         (x) float64 0.0 0.07143 0.1429 0.2143 ... 0.7857 0.8571 0.9286 1.0
  * y         (y) float64 0.0 0.1111 0.2222 0.3333 ... 0.6667 0.7778 0.8889 1.0
  * z         (z) float64 0.0 0.07143 0.1429 0.2143 ... 0.7857 0.8571 0.9286 1.0
  * time      (time) float64 0.0 0.25 0.5 0.75 1.0
Data variables:
    temp      (x, y, z) float64 0.326 0.4565 0.3212 ... 0.5516 0.8988 0.3817
    precip    (x, y) float64 0.2222 0.153 0.6116 0.9373 ... 0.2619 0.4644 0.4776
    precip_t  (x, y, time) float64 0.06265 0.5747 0.3099 ... 0.6112 0.5412
Attributes:
    geospatial_vertical_units:  m

xarray.Dataset

• Dimensions:
  • x: 15
  • y: 10
  • z: 15
  • time: 5
• Coordinates: (4)
  • x
    (x)
    float64
    0.0 0.07143 0.1429 ... 0.9286 1.0

    array([0.      , 0.071429, 0.142857, 0.214286, 0.285714, 0.357143, 0.428571,
           0.5     , 0.571429, 0.642857, 0.714286, 0.785714, 0.857143, 0.928571,
           1.      ])

  • y
    (y)
    float64
    0.0 0.1111 0.2222 ... 0.8889 1.0

    array([0.      , 0.111111, 0.222222, 0.333333, 0.444444, 0.555556, 0.666667,
           0.777778, 0.888889, 1.      ])

  • z
    (z)
    float64
    0.0 0.07143 0.1429 ... 0.9286 1.0

    array([0.      , 0.071429, 0.142857, 0.214286, 0.285714, 0.357143, 0.428571,
           0.5     , 0.571429, 0.642857, 0.714286, 0.785714, 0.857143, 0.928571,
           1.      ])

  • time
    (time)
    float64
    0.0 0.25 0.5 0.75 1.0

    array([0.  , 0.25, 0.5 , 0.75, 1.  ])

• Data variables: (3)
  • temp
    (x, y, z)
    float64
    0.326 0.4565 ... 0.8988 0.3817

    array([[[0.32598182, 0.45647945, 0.32115827, ..., 0.88881336,
             0.92916872, 0.28513419],
            [0.38498831, 0.34685493, 0.87686403, ..., 0.98561839,
             0.54748294, 0.99689153],
            [0.31439338, 0.57599412, 0.61936831, ..., 0.69753647,
             0.38056409, 0.33390803],
            ...,
            [0.47546163, 0.89790901, 0.63634617, ..., 0.54840389,
             0.76202432, 0.08428293],
            [0.26709556, 0.74359163, 0.69070315, ..., 0.0317331 ,
             0.75586292, 0.05447616],
            [0.11910449, 0.0917645 , 0.29458512, ..., 0.40982469,
             0.09189011, 0.3655462 ]],
    
           [[0.87044514, 0.23611204, 0.31439002, ..., 0.67843202,
             0.7560599 , 0.42542709],
            [0.01033324, 0.82882971, 0.99644576, ..., 0.88212835,
             0.02625913, 0.83780917],
            [0.23952358, 0.62684004, 0.46466989, ..., 0.68138232,
             0.82573242, 0.08835035],
    ...
            [0.17915621, 0.69549357, 0.58507922, ..., 0.57369955,
             0.74451177, 0.06025509],
            [0.86757176, 0.29572123, 0.43366542, ..., 0.68648574,
             0.86829538, 0.53135286],
            [0.35787316, 0.94107335, 0.96379985, ..., 0.58971186,
             0.95448084, 0.49333469]],
    
           [[0.15826958, 0.32700182, 0.71897271, ..., 0.85397326,
             0.64410334, 0.94763168],
            [0.80024134, 0.08549205, 0.75804989, ..., 0.12200411,
             0.45270574, 0.58819454],
            [0.59996043, 0.89448429, 0.09276963, ..., 0.60502974,
             0.990353  , 0.54049341],
            ...,
            [0.91469487, 0.73384255, 0.6063925 , ..., 0.54507691,
             0.78261869, 0.39183739],
            [0.55767915, 0.9977138 , 0.7264136 , ..., 0.11863228,
             0.81047226, 0.85380915],
            [0.16855151, 0.08524202, 0.41680344, ..., 0.55158289,
             0.89880299, 0.38173445]]])

  • precip
    (x, y)
    float64
    0.2222 0.153 ... 0.4644 0.4776

    array([[0.22217264, 0.15295026, 0.61163844, 0.93730817, 0.89668991,
            0.99214039, 0.29909278, 0.74475653, 0.78772396, 0.92523599],
           [0.60349196, 0.42634968, 0.29185248, 0.19094139, 0.92311216,
            0.18478802, 0.287947  , 0.97495314, 0.55180508, 0.25201517],
           [0.53524069, 0.86350264, 0.46597866, 0.3670259 , 0.80471548,
            0.49047687, 0.08645395, 0.32742848, 0.30951506, 0.59975655],
           [0.86342811, 0.0589786 , 0.78726538, 0.47453512, 0.14459543,
            0.46649236, 0.41696558, 0.4897827 , 0.1479404 , 0.21270995],
           [0.95588343, 0.20489127, 0.40611575, 0.69906597, 0.28250164,
            0.74225047, 0.58598055, 0.0120114 , 0.03130897, 0.7865828 ],
           [0.98573986, 0.59048428, 0.72063382, 0.86986312, 0.3742464 ,
            0.10676613, 0.31289239, 0.77811808, 0.97764   , 0.12539072],
           [0.9761179 , 0.92978316, 0.79764   , 0.04674653, 0.11431391,
            0.59860573, 0.38533898, 0.12136668, 0.81065811, 0.16082201],
           [0.73064325, 0.8902874 , 0.05787405, 0.3714906 , 0.54519502,
            0.4282663 , 0.75979749, 0.47763882, 0.16805363, 0.93937327],
           [0.11046918, 0.3349947 , 0.17486539, 0.17836109, 0.74995865,
            0.81552836, 0.22116431, 0.82393529, 0.63192804, 0.90413258],
           [0.83500225, 0.10964297, 0.1289645 , 0.584948  , 0.47078772,
            0.03665615, 0.80322784, 0.0269028 , 0.17663079, 0.22025834],
           [0.57186511, 0.40211841, 0.61111321, 0.10977642, 0.58406663,
            0.47139306, 0.50333583, 0.87887219, 0.91195282, 0.51042398],
           [0.79526473, 0.89726369, 0.44860379, 0.92487876, 0.43387699,
            0.12545196, 0.68936587, 0.93492633, 0.86996779, 0.04987222],
           [0.13350554, 0.68694498, 0.21468388, 0.66023543, 0.79047382,
            0.72261139, 0.46811995, 0.77589962, 0.63929388, 0.51696813],
           [0.89693036, 0.56999041, 0.17530841, 0.58617263, 0.52826595,
            0.42114383, 0.97652878, 0.87975487, 0.51975288, 0.90122874],
           [0.77234096, 0.16937556, 0.73391609, 0.18413409, 0.62468792,
            0.50926595, 0.68298584, 0.26191313, 0.46443739, 0.47759819]])

  • precip_t
    (x, y, time)
    float64
    0.06265 0.5747 ... 0.6112 0.5412

    array([[[0.06264953, 0.57472231, 0.30988857, 0.43984663, 0.46652574],
            [0.0148907 , 0.6961693 , 0.72306714, 0.11230994, 0.16941439],
            [0.85095602, 0.59788738, 0.92958948, 0.54044261, 0.77384652],
            [0.03190556, 0.97545942, 0.09147733, 0.36565172, 0.25514499],
            [0.6144058 , 0.45056175, 0.90317712, 0.54148154, 0.31898566],
            [0.18977621, 0.45823385, 0.34037081, 0.51997612, 0.90842733],
            [0.93161873, 0.57520764, 0.87303839, 0.21629675, 0.0749521 ],
            [0.75467147, 0.45284295, 0.12732986, 0.82941421, 0.42197446],
            [0.47566672, 0.67146299, 0.70596735, 0.245461  , 0.60361191],
            [0.93856972, 0.14548536, 0.70029593, 0.99838754, 0.70595241]],
    
           [[0.32006175, 0.30478899, 0.87314967, 0.28532945, 0.79084846],
            [0.76621621, 0.1266861 , 0.58858198, 0.3844011 , 0.18332656],
            [0.89438221, 0.06456629, 0.56048975, 0.41210946, 0.50529411],
            [0.54272234, 0.07349166, 0.12873135, 0.26850462, 0.25289834],
            [0.95480026, 0.96897922, 0.31296918, 0.78862981, 0.7994119 ],
            [0.6976959 , 0.64377239, 0.69186096, 0.94320494, 0.13334451],
            [0.76478768, 0.40522048, 0.57678473, 0.18880976, 0.94824664],
            [0.99692186, 0.24338042, 0.72406592, 0.1700075 , 0.93350633],
            [0.73566579, 0.55486404, 0.18187306, 0.93480677, 0.64788366],
    ...
            [0.74674532, 0.89504118, 0.790286  , 0.46579711, 0.35579101],
            [0.06344881, 0.27391038, 0.28223709, 0.81424824, 0.71082165],
            [0.56052773, 0.26008856, 0.01444782, 0.76641622, 0.0877623 ],
            [0.36106612, 0.47999344, 0.80452908, 0.65770762, 0.46378607],
            [0.54232961, 0.68520177, 0.12660172, 0.32549959, 0.29184018],
            [0.37479274, 0.25180252, 0.58934639, 0.97069459, 0.90866479],
            [0.16308039, 0.18029869, 0.16019955, 0.80090969, 0.67873992],
            [0.96711563, 0.36477498, 0.43678471, 0.3480698 , 0.01680719],
            [0.70280137, 0.08115505, 0.46118324, 0.30495649, 0.26845057]],
    
           [[0.60340268, 0.14121347, 0.91313358, 0.37061401, 0.0672742 ],
            [0.19131439, 0.13042009, 0.59203369, 0.14862896, 0.5212862 ],
            [0.01587425, 0.9739382 , 0.87057001, 0.68102086, 0.17955921],
            [0.56512958, 0.51157996, 0.64540682, 0.56254259, 0.38941955],
            [0.10208497, 0.76364434, 0.86817902, 0.79675218, 0.56293255],
            [0.86324389, 0.98853589, 0.53968984, 0.77275393, 0.77562859],
            [0.17281464, 0.87943392, 0.64488083, 0.71408766, 0.18671652],
            [0.16576267, 0.34148214, 0.85146243, 0.12557548, 0.08192725],
            [0.48146953, 0.22424038, 0.46755927, 0.06737267, 0.71746002],
            [0.84339285, 0.97812472, 0.68573179, 0.61120015, 0.54119445]]])

• Attributes: (1)

  geospatial_vertical_units :

  m

loading the "temp" field without any selection dictionary will load the full 3D field:

yt_ds = ds.yt.load_grid(fields=["temp",], length_unit = 'km')

yt_xarray : [INFO ] 2023-02-06 12:24:49,168:  Inferred geometry type is cartesian. To override, use ds.yt.set_geometry
yt_xarray : [INFO ] 2023-02-06 12:24:49,170:  Attempting to detect if yt_xarray will require field interpolation:
yt_xarray : [INFO ] 2023-02-06 12:24:49,170:      Cartesian geometry on uniform grid: yt_xarray will not interpolate.
yt : [INFO     ] 2023-02-06 12:24:49,261 Parameters: current_time              = 0.0
yt : [INFO     ] 2023-02-06 12:24:49,262 Parameters: domain_dimensions         = [15 10 15]
yt : [INFO     ] 2023-02-06 12:24:49,263 Parameters: domain_left_edge          = [-0.03571429 -0.05555556 -0.03571429]
yt : [INFO     ] 2023-02-06 12:24:49,264 Parameters: domain_right_edge         = [1.03571429 1.05555556 1.03571429]
yt : [INFO     ] 2023-02-06 12:24:49,265 Parameters: cosmological_simulation   = 0

print(yt_ds.domain_width)

[1.07142857 1.11111111 1.07142857] code_length

loading in the "precip" field, which is two dimensional, which load in the field with a dummy 3rd dimension:

yt_ds = ds.yt.load_grid(fields=["precip",], length_unit = 'km')

yt_xarray : [INFO ] 2023-02-06 12:24:49,284:  Attempting to detect if yt_xarray will require field interpolation:
yt_xarray : [INFO ] 2023-02-06 12:24:49,285:      Cartesian geometry on uniform grid: yt_xarray will not interpolate.
yt : [INFO     ] 2023-02-06 12:24:49,367 Parameters: current_time              = 0.0
yt : [INFO     ] 2023-02-06 12:24:49,368 Parameters: domain_dimensions         = [15 10  1]
yt : [INFO     ] 2023-02-06 12:24:49,369 Parameters: domain_left_edge          = [-0.03571429 -0.05555556 -0.5       ]
yt : [INFO     ] 2023-02-06 12:24:49,371 Parameters: domain_right_edge         = [1.03571429 1.05555556 0.5       ]
yt : [INFO     ] 2023-02-06 12:24:49,372 Parameters: cosmological_simulation   = 0

print(yt_ds.domain_width)

[1.07142857 1.11111111 1.        ] code_length

yt.SlicePlot(yt_ds, "z", ("stream", "precip"))

yt : [INFO     ] 2023-02-06 12:24:49,512 xlim = -0.035714 1.035714
yt : [INFO     ] 2023-02-06 12:24:49,513 ylim = -0.055556 1.055556
yt : [INFO     ] 2023-02-06 12:24:49,513 xlim = -0.035714 1.035714
yt : [INFO     ] 2023-02-06 12:24:49,514 ylim = -0.055556 1.055556
yt : [INFO     ] 2023-02-06 12:24:49,518 Making a fixed resolution buffer of (('stream', 'precip')) 800 by 800

Finally, you can also provide a selection dictionary to reduce a field to 2D. For example, you can load select a timestep to load for "precip_t" with:

yt_ds = ds.yt.load_grid(fields=["precip_t",], length_unit = 'km', geometry="cartesian", sel_dict={'time':0})

yt_xarray : [INFO ] 2023-02-06 12:24:50,271:  Attempting to detect if yt_xarray will require field interpolation:
yt_xarray : [INFO ] 2023-02-06 12:24:50,273:      Cartesian geometry on uniform grid: yt_xarray will not interpolate.
yt : [INFO     ] 2023-02-06 12:24:50,340 Parameters: current_time              = 0.0
yt : [INFO     ] 2023-02-06 12:24:50,341 Parameters: domain_dimensions         = [15 10  1]
yt : [INFO     ] 2023-02-06 12:24:50,343 Parameters: domain_left_edge          = [-0.03571429 -0.05555556 -0.5       ]
yt : [INFO     ] 2023-02-06 12:24:50,344 Parameters: domain_right_edge         = [1.03571429 1.05555556 0.5       ]
yt : [INFO     ] 2023-02-06 12:24:50,345 Parameters: cosmological_simulation   = 0

print(yt_ds.domain_width)

[1.07142857 1.11111111 1.        ] code_length

yt.SlicePlot(yt_ds, "z", ("stream", "precip_t"))

yt : [INFO     ] 2023-02-06 12:24:50,453 xlim = -0.035714 1.035714
yt : [INFO     ] 2023-02-06 12:24:50,454 ylim = -0.055556 1.055556
yt : [INFO     ] 2023-02-06 12:24:50,455 xlim = -0.035714 1.035714
yt : [INFO     ] 2023-02-06 12:24:50,455 ylim = -0.055556 1.055556
yt : [INFO     ] 2023-02-06 12:24:50,460 Making a fixed resolution buffer of (('stream', 'precip_t')) 800 by 800