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  • In addition, there are many other arithmetic operations that can be performed on DataArrays. In this example, we demonstrate squaring the original Celsius values of our air temperature data:

    @@ -2548,7 +2548,7 @@

    Arithmetic Operations + dtype='float64', name='lon', length=360))

  • @@ -3178,7 +3178,7 @@

    Aggregation Methodsdim keyword argument; this means that the function was applied over all of the dataset’s dimensions. In other words, the aggregation method computed the mean of every element of the temperature dataset across every temporal and spatial data point. However, if a dimension name is used with the dim keyword argument, the aggregation method computes an aggregation along the given dimension. In this next example, we use aggregation to calculate the temporal mean across all spatial data; this is performed by providing the dimension name 'time' to the dim keyword argument:

    @@ -3578,7 +3578,7 @@

    Aggregation Methods @@ -4019,7 +4019,7 @@

    Aggregation Methods @@ -4627,7 +4627,7 @@

    Aggregation Methods @@ -5191,7 +5191,7 @@

    Compute climatologyDataArray containing the climatology data, we can plot the data in different ways. In this example, we plot the climatology at a specific latitude-longitude point:

    @@ -5711,7 +5711,7 @@

    Compute anomaly
    @@ -6392,7 +6392,7 @@

    Compute anomaly.weighted() method and the newly loaded CESM2 dataset to weight the grid cell data as described above:

    @@ -6892,7 +6892,7 @@

    Other high level computation functionalityrolling method to compute averages in a moving window of 5 months of data:

    @@ -7408,7 +7408,7 @@

    Other high level computation functionality
    @@ -8077,7 +8077,7 @@

    Masking Data

    Using where with one condition

    @@ -8715,15 +8715,15 @@

    Using where + dtype='float64', name='lon', length=360))

  • cell_measures :
    area: areacello
    cell_methods :
    area: mean where sea time: mean
    comment :
    Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
    description :
    This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
    frequency :
    mon
    id :
    tos
    long_name :
    Sea Surface Temperature
    mipTable :
    Omon
    out_name :
    tos
    prov :
    Omon ((isd.003))
    realm :
    ocean
    standard_name :
    sea_surface_temperature
    time :
    time
    time_label :
    time-mean
    time_title :
    Temporal mean
    title :
    Sea Surface Temperature
    type :
    real
    units :
    degC
    variable_id :
    tos
  • As shown in the previous example, methods like .isel() and .sel() return data of a different shape than the original data provided to them. However, .where() preserves the shape of the original data by masking the values with a Boolean condition. Data values for which the condition is True are returned identical to the values passed in. On the other hand, data values for which the condition is False are returned as a preset example value. (This example value defaults to nan, but can be set to other values as well.)

    Before testing .where(), it is helpful to look at the official documentation. As stated above, the .where() method takes a Boolean condition. (Boolean conditions use operators such as less-than, greater-than, and equal-to, and return a value of True or False.) Most uses of .where() check whether or not specific data values are less than or greater than a constant value. As stated in the documentation, the data values specified in the Boolean condition of .where() can be any of the following:

    @@ -9158,7 +9158,7 @@

    Using where + dtype='float64', name='lon', length=360))
  • cell_measures :
    area: areacello
    cell_methods :
    area: mean where sea time: mean
    comment :
    Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.
    description :
    This may differ from "surface temperature" in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.
    frequency :
    mon
    id :
    tos
    long_name :
    Sea Surface Temperature
    mipTable :
    Omon
    out_name :
    tos
    prov :
    Omon ((isd.003))
    realm :
    ocean
    standard_name :
    sea_surface_temperature
    time :
    time
    time_label :
    time-mean
    time_title :
    Temporal mean
    title :
    Sea Surface Temperature
    type :
    real
    units :
    degC
    variable_id :
    tos
  • In this example, we use Matplotlib to plot the original, unmasked data, as well as the masked data created in the previous example.

    @@ -9433,7 +9433,7 @@

    Contribute

    By the
    Project Pythia Community. - Last updated on 6 November 2023. + Last updated on 13 November 2023.

    diff --git a/core/xarray/dask-arrays-xarray.html b/core/xarray/dask-arrays-xarray.html index 73632a1ab..7d6bbd813 100644 --- a/core/xarray/dask-arrays-xarray.html +++ b/core/xarray/dask-arrays-xarray.html @@ -896,91 +896,91 @@

    Create a dask.a
    -
    array([[[3.10861580e-01, 4.62187397e-02, 3.61207401e-01, ...,
    -         4.54767781e-03, 3.72388645e-01, 7.88959165e-01],
    -        [9.37173416e-01, 7.77113954e-01, 7.94731495e-02, ...,
    -         4.23010812e-01, 5.65556305e-01, 6.40084130e-01],
    -        [5.63430947e-01, 8.55678653e-01, 1.45171347e-01, ...,
    -         3.12687006e-01, 1.04705296e-01, 9.98657987e-01],
    +
    array([[[0.42143919, 0.13620924, 0.10031389, ..., 0.087292  ,
    +         0.37711759, 0.97013988],
    +        [0.22744123, 0.0382843 , 0.89135076, ..., 0.69763335,
    +         0.34858082, 0.92716003],
    +        [0.64264561, 0.19968489, 0.3260333 , ..., 0.75814589,
    +         0.66916473, 0.02304138],
             ...,
    -        [7.31899227e-01, 9.96797704e-01, 1.88527852e-01, ...,
    -         5.86733258e-01, 9.46789774e-01, 9.66163910e-01],
    -        [3.24809006e-01, 8.50447446e-01, 5.14765912e-01, ...,
    -         7.29030994e-01, 4.43170579e-01, 4.45963337e-01],
    -        [5.78973326e-01, 2.76610054e-01, 8.42891632e-01, ...,
    -         8.21174628e-02, 5.13117022e-01, 7.79630960e-01]],
    -
    -       [[3.73552998e-02, 7.23417551e-01, 1.68418866e-01, ...,
    -         8.95936984e-01, 9.51938048e-02, 8.71762149e-02],
    -        [9.37194929e-01, 7.85197906e-01, 4.17018732e-01, ...,
    -         2.08220021e-01, 7.79881815e-01, 6.44952502e-01],
    -        [5.45526664e-01, 4.60077861e-01, 3.92786368e-01, ...,
    -         1.45889355e-01, 8.45740167e-01, 5.37536442e-01],
    +        [0.28598827, 0.2251065 , 0.06001736, ..., 0.42004888,
    +         0.52776677, 0.53055924],
    +        [0.49215523, 0.03522311, 0.71469466, ..., 0.57939491,
    +         0.5563092 , 0.80699414],
    +        [0.58666928, 0.14656976, 0.19668953, ..., 0.16677838,
    +         0.90796189, 0.87776925]],
    +
    +       [[0.39289028, 0.80941095, 0.05557023, ..., 0.08481391,
    +         0.14264737, 0.053984  ],
    +        [0.49677097, 0.49013555, 0.36014213, ..., 0.58853938,
    +         0.57179086, 0.64218975],
    +        [0.41423814, 0.58758346, 0.61097874, ..., 0.32891682,
    +         0.69626215, 0.71016101],
             ...,
    -        [4.74557549e-01, 9.48063314e-01, 1.83343789e-01, ...,
    -         3.29390117e-01, 9.02023087e-01, 3.21139907e-01],
    -        [4.41305602e-01, 4.09628539e-01, 7.22179913e-01, ...,
    -         4.28838124e-01, 5.07901849e-01, 2.43095506e-01],
    -        [8.67174859e-01, 2.98604359e-01, 8.70947553e-01, ...,
    -         7.01424991e-02, 2.49644544e-01, 2.54752389e-01]],
    -
    -       [[1.97009279e-02, 6.45395476e-02, 7.93768653e-01, ...,
    -         9.25454121e-01, 6.87322330e-01, 4.16714652e-01],
    -        [6.80678699e-01, 3.13206517e-01, 4.88641930e-01, ...,
    -         1.27324685e-01, 6.46336443e-01, 4.68169757e-01],
    -        [4.72979568e-04, 7.23982653e-01, 5.40976358e-01, ...,
    -         1.39567487e-01, 5.39110891e-01, 8.37144811e-01],
    +        [0.75030761, 0.94422799, 0.95711159, ..., 0.3500043 ,
    +         0.45192243, 0.20545104],
    +        [0.45561367, 0.48950205, 0.4029536 , ..., 0.17661388,
    +         0.07250772, 0.21790246],
    +        [0.20370323, 0.61578717, 0.50361923, ..., 0.39805472,
    +         0.56645654, 0.53966719]],
    +
    +       [[0.19620076, 0.0666889 , 0.74439493, ..., 0.03615842,
    +         0.68133034, 0.06902867],
    +        [0.71654917, 0.07153285, 0.80696254, ..., 0.12060149,
    +         0.1868439 , 0.11060421],
    +        [0.98816485, 0.55784584, 0.27353335, ..., 0.89018513,
    +         0.77627354, 0.7150142 ],
             ...,
    -        [9.01493230e-01, 2.88294316e-01, 7.82227402e-01, ...,
    -         2.30871839e-01, 1.00966990e-01, 5.95481676e-01],
    -        [2.02442761e-01, 9.27632620e-01, 3.03255986e-01, ...,
    -         8.96207175e-01, 2.25803430e-01, 9.21365603e-01],
    -        [7.99167381e-01, 3.16776781e-02, 6.53425672e-01, ...,
    -         8.62834475e-01, 6.99696629e-01, 5.65242628e-01]],
    +        [0.3897645 , 0.06000704, 0.56892347, ..., 0.67798108,
    +         0.48440959, 0.03056809],
    +        [0.60289047, 0.98678517, 0.25562977, ..., 0.94717736,
    +         0.27316361, 0.38002531],
    +        [0.80308378, 0.91206751, 0.49879164, ..., 0.88193646,
    +         0.11190437, 0.80450152]],
     
            ...,
     
    -       [[4.33965416e-01, 5.78697070e-01, 3.35992525e-01, ...,
    -         2.29814105e-01, 5.15971312e-01, 4.99903526e-01],
    -        [8.10365235e-01, 5.58478194e-01, 6.89359311e-01, ...,
    -         5.57949248e-01, 6.86869512e-01, 1.32313496e-01],
    -        [8.29086259e-02, 2.41109754e-01, 3.09447545e-01, ...,
    -         9.39385901e-01, 5.90143847e-01, 9.94182445e-01],
    +       [[0.91510803, 0.38776117, 0.96931156, ..., 0.65440314,
    +         0.56883723, 0.90357987],
    +        [0.99425279, 0.77755371, 0.45753634, ..., 0.02551885,
    +         0.35612656, 0.37100206],
    +        [0.65616547, 0.98679746, 0.0482911 , ..., 0.0926201 ,
    +         0.26563931, 0.9337811 ],
             ...,
    -        [2.40390212e-01, 3.04075422e-01, 9.82268998e-01, ...,
    -         1.77199178e-01, 3.19764030e-01, 5.40884763e-01],
    -        [7.67658520e-01, 1.53553896e-02, 5.92192642e-01, ...,
    -         5.20688966e-01, 1.22626526e-01, 7.02481466e-01],
    -        [4.26306698e-01, 8.90516881e-01, 9.41942790e-01, ...,
    -         7.36487974e-01, 2.01469270e-01, 1.14475477e-01]],
    -
    -       [[1.20668207e-01, 9.22661909e-01, 7.65695488e-02, ...,
    -         3.51092700e-02, 1.76882450e-01, 5.12395345e-01],
    -        [3.21858794e-01, 9.49372555e-01, 5.57648195e-01, ...,
    -         4.09968275e-01, 3.59964839e-01, 9.71877276e-01],
    -        [4.43895198e-01, 5.34140804e-01, 8.69353858e-01, ...,
    -         8.81844578e-01, 2.16019844e-01, 7.09331197e-01],
    +        [0.75902268, 0.86980696, 0.10968507, ..., 0.94428644,
    +         0.13192885, 0.65292052],
    +        [0.39046035, 0.38843513, 0.98771831, ..., 0.2995084 ,
    +         0.12613788, 0.00920443],
    +        [0.59074445, 0.05103007, 0.55890939, ..., 0.15435436,
    +         0.44107968, 0.89374796]],
    +
    +       [[0.34425678, 0.60619601, 0.23608674, ..., 0.61264431,
    +         0.03670536, 0.71149416],
    +        [0.84224834, 0.38943307, 0.73123407, ..., 0.08315865,
    +         0.7979252 , 0.27080958],
    +        [0.70377799, 0.67602931, 0.27884151, ..., 0.48586315,
    +         0.97009118, 0.24702396],
             ...,
    -        [1.12176216e-01, 5.52377572e-01, 6.74717020e-01, ...,
    -         3.27637200e-01, 5.92243094e-01, 5.91498308e-01],
    -        [9.34292392e-01, 8.00036045e-01, 8.47647316e-02, ...,
    -         1.32727250e-01, 3.48854974e-01, 2.26435822e-01],
    -        [6.15585578e-01, 2.54074147e-01, 6.07375135e-01, ...,
    -         6.69022436e-01, 6.34016198e-01, 3.48613207e-01]],
    -
    -       [[6.36021416e-01, 4.91441514e-01, 7.12245481e-01, ...,
    -         7.85474533e-01, 7.48410229e-01, 6.78399432e-01],
    -        [5.45154959e-01, 4.97560960e-02, 6.59270471e-01, ...,
    -         4.41292406e-01, 3.27654258e-01, 6.61526491e-01],
    -        [6.89645590e-01, 6.90952187e-01, 8.48275392e-01, ...,
    -         9.96430987e-01, 9.18703649e-01, 6.61512186e-01],
    +        [0.7271311 , 0.62472154, 0.82127014, ..., 0.79561243,
    +         0.58201948, 0.11994003],
    +        [0.77571353, 0.29659044, 0.16858462, ..., 0.03509655,
    +         0.96542874, 0.60235304],
    +        [0.74051964, 0.64162158, 0.57827627, ..., 0.10545466,
    +         0.13565184, 0.54938321]],
    +
    +       [[0.88550215, 0.48544486, 0.27483288, ..., 0.94207327,
    +         0.2197403 , 0.88901708],
    +        [0.9086715 , 0.45290384, 0.21155873, ..., 0.63723179,
    +         0.91189146, 0.06634373],
    +        [0.30588579, 0.14040023, 0.74065885, ..., 0.93346999,
    +         0.53291038, 0.91259223],
             ...,
    -        [3.76034564e-01, 9.04574293e-02, 2.87600814e-01, ...,
    -         7.94632937e-02, 6.65163270e-01, 6.43658379e-01],
    -        [1.45235633e-01, 8.11151072e-01, 3.70637029e-01, ...,
    -         3.94801359e-01, 3.07235226e-01, 2.01839369e-01],
    -        [1.23552913e-01, 8.58421626e-01, 9.04081165e-01, ...,
    -         8.69282551e-02, 8.41164494e-01, 1.11858824e-01]]])
    +        [0.34114822, 0.88907572, 0.71208087, ..., 0.99564156,
    +         0.50239451, 0.67997821],
    +        [0.35838468, 0.81217344, 0.32012856, ..., 0.15056891,
    +         0.66253709, 0.31932228],
    +        [0.95520776, 0.2197996 , 0.74141581, ..., 0.51346375,
    +         0.52688604, 0.79815545]]])
     
    @@ -1201,11 +1201,11 @@

    Compute the result -
    CPU times: user 408 ms, sys: 72 ms, total: 480 ms
    -Wall time: 362 ms
    +
    CPU times: user 397 ms, sys: 43.2 ms, total: 440 ms
    +Wall time: 326 ms
     
    -
    12001890.563126594
    +
    11998136.015103415
     
    @@ -1303,7 +1303,7 @@

    Exercise with d

    -../../_images/2d5c38d27e4f67aa9e812db6c8e3aaca1677ef1c52d83660e6b5963338129ba8.png +../../_images/08295070d60aad8abf9e7e6f3a47a7576717dcb496755a2a9f4cad9622ef9c06.png

    @@ -1435,7 +1435,7 @@

    Testing a bigger calculation -../../_images/670d7ffd4e0b48625437966143601052b4f8b50898a794c594a8ca30f58135f3.png +../../_images/e7bff1cbdf65b433dd0190f0dffbcc77c07cffe4170652cf705423bb1fd20d96.png

    @@ -1446,79 +1446,79 @@

    Testing a bigger calculation -
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    @@ -1527,34 +1527,34 @@ 

    Testing a bigger calculation
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    -
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    @@ -1569,445 +1569,367 @@ 

    Testing a bigger calculation
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    @@ -2525,7 +2447,7 @@ 

    Reading data with @@ -2639,7 +2561,7 @@

    Reading data with chunks keyword argument, this technique does not specify how to chunk the data, and therefore the resulting Dask Array contains only one chunk.

    Correct usage of the chunks keyword argument specifies how many values in each dimension are contained in a single chunk. In this example, specifying the chunks keyword argument as chunks={'time':90} indicates to Xarray and Dask that 90 time slices are allocated to each chunk on the temporal axis.

    @@ -3254,7 +3176,7 @@

    Reading data with @@ -3334,7 +3256,7 @@

    Reading data with .chunks method on an Xarray DataArray. In this example, we show that the tos DataArray now contains two chunks on the time dimension, with each chunk containing 90 time slices.

    @@ -3955,7 +3877,7 @@

    Parallel and lazy computation using @@ -4010,7 +3932,7 @@

    Parallel and lazy computation using

  • lon
    (lon)
    float64
    0.5 1.5 2.5 ... 357.5 358.5 359.5
    array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])
    • lat
      PandasIndex
      PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
      +        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])
    • lon
      (lon)
      float64
      0.5 1.5 2.5 ... 357.5 358.5 359.5
      array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])
    • lat
      PandasIndex
      PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
              ...
               80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
      -      dtype='float64', name='lat', length=180))
    • lon
      PandasIndex
      PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
      +      dtype='float64', name='lat', length=180))
    • lon
      PandasIndex
      PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
              ...
              350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
      -      dtype='float64', name='lon', length=360))
  • + dtype='float64', name='lon', length=360))
  • As shown in the above example, the result of the applied operations is an Xarray DataArray that contains a Dask Array, an identical object type to the object that the operations were performed on. This is true for any operations that can be applied to Xarray DataArrays, including subsetting operations; this next example illustrates this:

    @@ -4410,7 +4332,7 @@

    Parallel and lazy computation using @@ -4465,10 +4387,10 @@

    Parallel and lazy computation using dask.visualize() function, as shown in this example:

    @@ -4490,13 +4412,10 @@

    Parallel and lazy computation using -
    [                                        ] | 0% Completed | 171.60 us
    -
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    -
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    @@ -4727,7 +4646,7 @@ 

    Contribute

    By the Project Pythia Community. - Last updated on 6 November 2023. + Last updated on 13 November 2023.

    diff --git a/core/xarray/enso-xarray.html b/core/xarray/enso-xarray.html index 843e738aa..d710d8df1 100644 --- a/core/xarray/enso-xarray.html +++ b/core/xarray/enso-xarray.html @@ -1184,7 +1184,7 @@

    The Niño 3.4 Index @@ -1816,7 +1816,7 @@

    Select the Niño 3.4 regionwhere() method:

    +array(1.8436452, dtype=float32)

    The final step of the Niño 3.4 index calculation involves normalizing the data. In this example, we perform this normalization by dividing the smoothed anomaly data by the standard deviation calculated above:

    @@ -3458,7 +3458,7 @@

    Contribute

    By the
    Project Pythia Community. - Last updated on 6 November 2023. + Last updated on 13 November 2023.

    diff --git a/core/xarray/xarray-intro.html b/core/xarray/xarray-intro.html index bf6693595..63af3094d 100644 --- a/core/xarray/xarray-intro.html +++ b/core/xarray/xarray-intro.html @@ -1045,25 +1045,25 @@

    Generate a random numpy array -
    array([[[279.63412829, 290.7046724 , 286.43974556, 296.28430389],
    -        [283.24333356, 280.20612121, 287.72768408, 285.10619103],
    -        [284.78555959, 275.31457191, 286.5608933 , 285.73541362]],
    +
    array([[[281.44679453, 283.11536689, 284.01850013, 289.8298498 ],
    +        [282.70756685, 283.20576487, 291.81755832, 285.02631717],
    +        [285.69388088, 284.14632098, 278.13131305, 281.10133276]],
     
    -       [[282.67290387, 288.46831948, 276.8655069 , 273.37775686],
    -        [280.96565121, 285.91572322, 288.85729449, 276.21387254],
    -        [285.73164123, 291.8848124 , 284.03610165, 282.6622474 ]],
    +       [[280.66906637, 284.93058015, 276.68848833, 276.66236221],
    +        [283.34123739, 291.60938161, 277.22557956, 290.42866197],
    +        [280.72574254, 274.09877448, 281.28988749, 283.67354461]],
     
    -       [[289.46700092, 280.70764428, 286.89791924, 277.81629337],
    -        [284.88403254, 284.24057635, 282.80027431, 283.08771725],
    -        [282.59679367, 279.51983498, 284.08911381, 293.44005496]],
    +       [[282.84050648, 279.30885996, 280.84365279, 280.73150794],
    +        [281.8302336 , 282.80715014, 283.5039265 , 279.93251871],
    +        [291.07012189, 288.5609298 , 287.85293737, 280.55810674]],
     
    -       [[287.03692363, 275.34607996, 281.47190024, 276.5785568 ],
    -        [279.19184386, 283.62160477, 279.99321562, 273.42665989],
    -        [282.02013315, 284.86617306, 289.03291922, 278.33550939]],
    +       [[277.02317109, 287.05092039, 280.94338341, 283.98219401],
    +        [277.9119452 , 282.05633022, 273.53337508, 286.59784112],
    +        [288.95916858, 281.41584733, 278.06662746, 290.35808187]],
     
    -       [[285.68549639, 287.28614193, 280.89544557, 271.85350156],
    -        [282.33265811, 289.26371298, 283.19104827, 280.99816794],
    -        [285.17058947, 287.9149947 , 278.89551489, 277.45003043]]])
    +       [[280.5249207 , 282.14682868, 293.63348948, 277.90083911],
    +        [281.19412104, 283.6462338 , 281.18273795, 275.91265444],
    +        [285.27116401, 284.23420659, 283.32168626, 280.16233094]]])
     
    @@ -1444,44 +1444,44 @@

    Wrap the array: first attempt @@ -1864,44 +1864,44 @@

    Assign dimension names
    <xarray.DataArray (time: 5, lat: 3, lon: 4)>
    -array([[[279.63412829, 290.7046724 , 286.43974556, 296.28430389],
    -        [283.24333356, 280.20612121, 287.72768408, 285.10619103],
    -        [284.78555959, 275.31457191, 286.5608933 , 285.73541362]],
    +array([[[281.44679453, 283.11536689, 284.01850013, 289.8298498 ],
    +        [282.70756685, 283.20576487, 291.81755832, 285.02631717],
    +        [285.69388088, 284.14632098, 278.13131305, 281.10133276]],
     
    -       [[282.67290387, 288.46831948, 276.8655069 , 273.37775686],
    -        [280.96565121, 285.91572322, 288.85729449, 276.21387254],
    -        [285.73164123, 291.8848124 , 284.03610165, 282.6622474 ]],
    +       [[280.66906637, 284.93058015, 276.68848833, 276.66236221],
    +        [283.34123739, 291.60938161, 277.22557956, 290.42866197],
    +        [280.72574254, 274.09877448, 281.28988749, 283.67354461]],
     
    -       [[289.46700092, 280.70764428, 286.89791924, 277.81629337],
    -        [284.88403254, 284.24057635, 282.80027431, 283.08771725],
    -        [282.59679367, 279.51983498, 284.08911381, 293.44005496]],
    +       [[282.84050648, 279.30885996, 280.84365279, 280.73150794],
    +        [281.8302336 , 282.80715014, 283.5039265 , 279.93251871],
    +        [291.07012189, 288.5609298 , 287.85293737, 280.55810674]],
     
    -       [[287.03692363, 275.34607996, 281.47190024, 276.5785568 ],
    -        [279.19184386, 283.62160477, 279.99321562, 273.42665989],
    -        [282.02013315, 284.86617306, 289.03291922, 278.33550939]],
    +       [[277.02317109, 287.05092039, 280.94338341, 283.98219401],
    +        [277.9119452 , 282.05633022, 273.53337508, 286.59784112],
    +        [288.95916858, 281.41584733, 278.06662746, 290.35808187]],
     
    -       [[285.68549639, 287.28614193, 280.89544557, 271.85350156],
    -        [282.33265811, 289.26371298, 283.19104827, 280.99816794],
    -        [285.17058947, 287.9149947 , 278.89551489, 277.45003043]]])
    -Dimensions without coordinates: time, lat, lon
    + [[280.5249207 , 282.14682868, 293.63348948, 277.90083911], + [281.19412104, 283.6462338 , 281.18273795, 275.91265444], + [285.27116401, 284.23420659, 283.32168626, 280.16233094]]])

      • This DataArray is already an improvement over a NumPy array; the DataArray contains names for each of the dimensions (or axes in NumPy parlance). An additional improvement is the association of coordinate-value arrays with data upon creation of a DataArray. In the next example, we illustrate the creation of NumPy arrays representing the coordinate values for each dimension of the DataArray, and how to associate these coordinate arrays with the data in our DataArray.

        @@ -2311,51 +2311,51 @@

        Initialize the fill: currentColor; }
        <xarray.DataArray (time: 5, lat: 3, lon: 4)>
        -array([[[279.63412829, 290.7046724 , 286.43974556, 296.28430389],
        -        [283.24333356, 280.20612121, 287.72768408, 285.10619103],
        -        [284.78555959, 275.31457191, 286.5608933 , 285.73541362]],
        +array([[[281.44679453, 283.11536689, 284.01850013, 289.8298498 ],
        +        [282.70756685, 283.20576487, 291.81755832, 285.02631717],
        +        [285.69388088, 284.14632098, 278.13131305, 281.10133276]],
         
        -       [[282.67290387, 288.46831948, 276.8655069 , 273.37775686],
        -        [280.96565121, 285.91572322, 288.85729449, 276.21387254],
        -        [285.73164123, 291.8848124 , 284.03610165, 282.6622474 ]],
        +       [[280.66906637, 284.93058015, 276.68848833, 276.66236221],
        +        [283.34123739, 291.60938161, 277.22557956, 290.42866197],
        +        [280.72574254, 274.09877448, 281.28988749, 283.67354461]],
         
        -       [[289.46700092, 280.70764428, 286.89791924, 277.81629337],
        -        [284.88403254, 284.24057635, 282.80027431, 283.08771725],
        -        [282.59679367, 279.51983498, 284.08911381, 293.44005496]],
        +       [[282.84050648, 279.30885996, 280.84365279, 280.73150794],
        +        [281.8302336 , 282.80715014, 283.5039265 , 279.93251871],
        +        [291.07012189, 288.5609298 , 287.85293737, 280.55810674]],
         
        -       [[287.03692363, 275.34607996, 281.47190024, 276.5785568 ],
        -        [279.19184386, 283.62160477, 279.99321562, 273.42665989],
        -        [282.02013315, 284.86617306, 289.03291922, 278.33550939]],
        +       [[277.02317109, 287.05092039, 280.94338341, 283.98219401],
        +        [277.9119452 , 282.05633022, 273.53337508, 286.59784112],
        +        [288.95916858, 281.41584733, 278.06662746, 290.35808187]],
         
        -       [[285.68549639, 287.28614193, 280.89544557, 271.85350156],
        -        [282.33265811, 289.26371298, 283.19104827, 280.99816794],
        -        [285.17058947, 287.9149947 , 278.89551489, 277.45003043]]])
        +       [[280.5249207 , 282.14682868, 293.63348948, 277.90083911],
        +        [281.19412104, 283.6462338 , 281.18273795, 275.91265444],
        +        [285.27116401, 284.23420659, 283.32168626, 280.16233094]]])
         Coordinates:
           * time     (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-05
           * lat      (lat) float64 25.0 40.0 55.0
        -  * lon      (lon) float64 -120.0 -100.0 -80.0 -60.0
        + dtype='datetime64[ns]', name='time', freq='D'))

      • PandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
      • PandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
      • @@ -2735,54 +2735,54 @@

        Set useful attributes
        <xarray.DataArray (time: 5, lat: 3, lon: 4)>
        -array([[[279.63412829, 290.7046724 , 286.43974556, 296.28430389],
        -        [283.24333356, 280.20612121, 287.72768408, 285.10619103],
        -        [284.78555959, 275.31457191, 286.5608933 , 285.73541362]],
        +array([[[281.44679453, 283.11536689, 284.01850013, 289.8298498 ],
        +        [282.70756685, 283.20576487, 291.81755832, 285.02631717],
        +        [285.69388088, 284.14632098, 278.13131305, 281.10133276]],
         
        -       [[282.67290387, 288.46831948, 276.8655069 , 273.37775686],
        -        [280.96565121, 285.91572322, 288.85729449, 276.21387254],
        -        [285.73164123, 291.8848124 , 284.03610165, 282.6622474 ]],
        +       [[280.66906637, 284.93058015, 276.68848833, 276.66236221],
        +        [283.34123739, 291.60938161, 277.22557956, 290.42866197],
        +        [280.72574254, 274.09877448, 281.28988749, 283.67354461]],
         
        -       [[289.46700092, 280.70764428, 286.89791924, 277.81629337],
        -        [284.88403254, 284.24057635, 282.80027431, 283.08771725],
        -        [282.59679367, 279.51983498, 284.08911381, 293.44005496]],
        +       [[282.84050648, 279.30885996, 280.84365279, 280.73150794],
        +        [281.8302336 , 282.80715014, 283.5039265 , 279.93251871],
        +        [291.07012189, 288.5609298 , 287.85293737, 280.55810674]],
         
        -       [[287.03692363, 275.34607996, 281.47190024, 276.5785568 ],
        -        [279.19184386, 283.62160477, 279.99321562, 273.42665989],
        -        [282.02013315, 284.86617306, 289.03291922, 278.33550939]],
        +       [[277.02317109, 287.05092039, 280.94338341, 283.98219401],
        +        [277.9119452 , 282.05633022, 273.53337508, 286.59784112],
        +        [288.95916858, 281.41584733, 278.06662746, 290.35808187]],
         
        -       [[285.68549639, 287.28614193, 280.89544557, 271.85350156],
        -        [282.33265811, 289.26371298, 283.19104827, 280.99816794],
        -        [285.17058947, 287.9149947 , 278.89551489, 277.45003043]]])
        +       [[280.5249207 , 282.14682868, 293.63348948, 277.90083911],
        +        [281.19412104, 283.6462338 , 281.18273795, 275.91265444],
        +        [285.27116401, 284.23420659, 283.32168626, 280.16233094]]])
         Coordinates:
           * time     (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-05
           * lat      (lat) float64 25.0 40.0 55.0
           * lon      (lon) float64 -120.0 -100.0 -80.0 -60.0
         Attributes:
             units:          kelvin
        -    standard_name:  air_temperature
        + dtype='datetime64[ns]', name='time', freq='D'))

      • PandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
      • PandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
      • units :
        kelvin
        standard_name :
        air_temperature
      • @@ -3596,54 +3596,54 @@

        Create a pressure @@ -4028,48 +4028,48 @@

        Create a Datase * lat (lat) float64 25.0 40.0 55.0 * lon (lon) float64 -120.0 -100.0 -80.0 -60.0 Data variables: - Temperature (time, lat, lon) float64 279.6 290.7 286.4 ... 278.9 277.5 - Pressure (time, lat, lon) float64 1.002e+03 996.7 ... 1.002e+03 992.0

        + dtype='datetime64[ns]', name='time', freq='D'))
      • lat
        PandasIndex
        PandasIndex(Index([25.0, 40.0, 55.0], dtype='float64', name='lat'))
      • lon
        PandasIndex
        PandasIndex(Index([-120.0, -100.0, -80.0, -60.0], dtype='float64', name='lon'))
      • As listed in the rich display above, the new Dataset object is aware that both DataArrays share the same coordinate axes. (Please note that if this page is not run as a Jupyter Notebook, the rich display may be unavailable.)

        @@ -4448,54 +4448,54 @@

        Access Data variables and Coordinates in a DataArrays through a dictionary syntax, as shown in this example:

        @@ -4869,54 +4869,54 @@

        Access Data variables and Coordinates in a Dataset objects are mainly used for loading data from files, which will be covered later in this tutorial.

        @@ -5300,18 +5300,18 @@

        NumPy-like selection
        <xarray.DataArray (lat: 3, lon: 4)>
        -array([[282.67290387, 288.46831948, 276.8655069 , 273.37775686],
        -       [280.96565121, 285.91572322, 288.85729449, 276.21387254],
        -       [285.73164123, 291.8848124 , 284.03610165, 282.6622474 ]])
        +array([[280.66906637, 284.93058015, 276.68848833, 276.66236221],
        +       [283.34123739, 291.60938161, 277.22557956, 290.42866197],
        +       [280.72574254, 274.09877448, 281.28988749, 283.67354461]])
         Coordinates:
             time     datetime64[ns] 2018-01-02
           * lat      (lat) float64 25.0 40.0 55.0
           * lon      (lon) float64 -120.0 -100.0 -80.0 -60.0
         Attributes:
             units:          kelvin
        -    standard_name:  air_temperature

        + standard_name: air_temperature

        This example reveals one of the major shortcomings of index selection. In order to retrieve the correct data using index selection, anyone using a DataArray must have precise knowledge of the axes in the DataArray, including the order of the axes and the meaning of their indices.

        By using named coordinates, as shown in the next set of examples, we can avoid this cumbersome burden.

        @@ -5691,18 +5691,18 @@

        Selecting with fill: currentColor; }
        <xarray.DataArray (lat: 3, lon: 4)>
        -array([[282.67290387, 288.46831948, 276.8655069 , 273.37775686],
        -       [280.96565121, 285.91572322, 288.85729449, 276.21387254],
        -       [285.73164123, 291.8848124 , 284.03610165, 282.6622474 ]])
        +array([[280.66906637, 284.93058015, 276.68848833, 276.66236221],
        +       [283.34123739, 291.60938161, 277.22557956, 290.42866197],
        +       [280.72574254, 274.09877448, 281.28988749, 283.67354461]])
         Coordinates:
             time     datetime64[ns] 2018-01-02
           * lat      (lat) float64 25.0 40.0 55.0
           * lon      (lon) float64 -120.0 -100.0 -80.0 -60.0
         Attributes:
             units:          kelvin
        -    standard_name:  air_temperature

        + standard_name: air_temperature

        This method yields the same result as the index selection, however:

        @@ -6479,20 +6479,20 @@

        Interpolation

        Info

        @@ -6876,29 +6876,29 @@

        Slicing along coordinates

        Info

        @@ -7280,18 +7280,18 @@

        One more selection method:
        <xarray.DataArray (lat: 3, lon: 4)>
        -array([[282.67290387, 288.46831948, 276.8655069 , 273.37775686],
        -       [280.96565121, 285.91572322, 288.85729449, 276.21387254],
        -       [285.73164123, 291.8848124 , 284.03610165, 282.6622474 ]])
        +array([[280.66906637, 284.93058015, 276.68848833, 276.66236221],
        +       [283.34123739, 291.60938161, 277.22557956, 290.42866197],
        +       [280.72574254, 274.09877448, 281.28988749, 283.67354461]])
         Coordinates:
             time     datetime64[ns] 2018-01-02
           * lat      (lat) float64 25.0 40.0 55.0
           * lon      (lon) float64 -120.0 -100.0 -80.0 -60.0
         Attributes:
             units:          kelvin
        -    standard_name:  air_temperature

        + standard_name: air_temperature

        This selection technique is similar to NumPy’s index-based selection, as shown below:

        temp[1,:,:]
        @@ -7670,29 +7670,29 @@ 

        One more selection method:
        <xarray.DataArray (time: 3, lat: 2, lon: 2)>
        -array([[[290.7046724 , 286.43974556],
        -        [280.20612121, 287.72768408]],
        +array([[[283.11536689, 284.01850013],
        +        [283.20576487, 291.81755832]],
         
        -       [[288.46831948, 276.8655069 ],
        -        [285.91572322, 288.85729449]],
        +       [[284.93058015, 276.68848833],
        +        [291.60938161, 277.22557956]],
         
        -       [[280.70764428, 286.89791924],
        -        [284.24057635, 282.80027431]]])
        +       [[279.30885996, 280.84365279],
        +        [282.80715014, 283.5039265 ]]])
         Coordinates:
           * time     (time) datetime64[ns] 2018-01-01 2018-01-02 2018-01-03
           * lat      (lat) float64 25.0 40.0
           * lon      (lon) float64 -100.0 -80.0
         Attributes:
             units:          kelvin
        -    standard_name:  air_temperature
        + [[279.30885996, 280.84365279], + [282.80715014, 283.5039265 ]]])

        • time
          (time)
          datetime64[ns]
          2018-01-01 2018-01-02 2018-01-03
          array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
          +       '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
        • lat
          (lat)
          float64
          25.0 40.0
          array([25., 40.])
        • lon
          (lon)
          float64
          -100.0 -80.0
          array([-100.,  -80.])
        • time
          PandasIndex
          PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
        • lat
          PandasIndex
          PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
        • lon
          PandasIndex
          PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
      • units :
        kelvin
        standard_name :
        air_temperature
      • In contrast with the previous example, this example shows a useful advantage of using the .loc attribute. When using the .loc attribute, you can specify data slices using a syntax similar to NumPy in addition to, or instead of, using the slice function. Both of these slicing techniques are illustrated below:

        @@ -8066,29 +8066,29 @@

        One more selection method:
        <xarray.DataArray (time: 3, lat: 2, lon: 2)>
        -array([[[290.7046724 , 286.43974556],
        -        [280.20612121, 287.72768408]],
        +array([[[283.11536689, 284.01850013],
        +        [283.20576487, 291.81755832]],
         
        -       [[288.46831948, 276.8655069 ],
        -        [285.91572322, 288.85729449]],
        +       [[284.93058015, 276.68848833],
        +        [291.60938161, 277.22557956]],
         
        -       [[280.70764428, 286.89791924],
        -        [284.24057635, 282.80027431]]])
        +       [[279.30885996, 280.84365279],
        +        [282.80715014, 283.5039265 ]]])
         Coordinates:
           * time     (time) datetime64[ns] 2018-01-01 2018-01-02 2018-01-03
           * lat      (lat) float64 25.0 40.0
           * lon      (lon) float64 -100.0 -80.0
         Attributes:
             units:          kelvin
        -    standard_name:  air_temperature
        + [[279.30885996, 280.84365279], + [282.80715014, 283.5039265 ]]])

        • time
          (time)
          datetime64[ns]
          2018-01-01 2018-01-02 2018-01-03
          array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
          +       '2018-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
        • lat
          (lat)
          float64
          25.0 40.0
          array([25., 40.])
        • lon
          (lon)
          float64
          -100.0 -80.0
          array([-100.,  -80.])
        • time
          PandasIndex
          PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', name='time', freq='D'))
        • lat
          PandasIndex
          PandasIndex(Index([25.0, 40.0], dtype='float64', name='lat'))
        • lon
          PandasIndex
          PandasIndex(Index([-100.0, -80.0], dtype='float64', name='lon'))
      • units :
        kelvin
        standard_name :
        air_temperature
      • As described above, the arguments to .loc must be in the order of the DataArray’s dimensions. Attempting to slice data without ordering arguments properly can cause errors, as shown below:

        @@ -8521,10 +8521,10 @@

        Access netCDF data with

      • Originating_or_generating_Center :
        US National Weather Service, National Centres for Environmental Prediction (NCEP)
        Originating_or_generating_Subcenter :
        North American Regional Reanalysis Project
        GRIB_table_version :
        0,131
        Generating_process_or_model :
        North American Regional Reanalysis (NARR)
        Conventions :
        CF-1.6
        history :
        Read using CDM IOSP GribCollection v3
        featureType :
        GRID
        History :
        Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2) Original Dataset = DatasetScan#narr-a_221_19930313_0000_000.grb; Translation Date = 2018-01-04T23:57:16.043Z
        geospatial_lat_min :
        10.753308882144761
        geospatial_lat_max :
        46.8308828962289
        geospatial_lon_min :
        -153.88242040519995
        geospatial_lon_max :
        -42.666108129242815
      • @@ -8966,17 +8966,17 @@

        Subsetting the positive: down Grib_level_type: 100 _CoordinateAxisType: Pressure - _CoordinateZisPositive: down

        + dtype='float32', name='isobaric1'))
      • units :
        hPa
        long_name :
        Isobaric surface
        positive :
        down
        Grib_level_type :
        100
        _CoordinateAxisType :
        Pressure
        _CoordinateZisPositive :
        down
      • (As described earlier in this tutorial, we can also use dictionary syntax to select specific DataArrays; in this case, we would write ds['isobaric1'].)

        Many of the subsetting operations usable on DataArrays can also be used on Datasets. However, when used on Datasets, these operations are performed on every DataArray in the Dataset, as shown below:

        @@ -9378,7 +9378,7 @@

        Subsetting the geospatial_lat_min: 10.753308882144761 geospatial_lat_max: 46.8308828962289 geospatial_lon_min: -153.88242040519995 - geospatial_lon_max: -42.666108129242815

      • Originating_or_generating_Center :
        US National Weather Service, National Centres for Environmental Prediction (NCEP)
        Originating_or_generating_Subcenter :
        North American Regional Reanalysis Project
        GRIB_table_version :
        0,131
        Generating_process_or_model :
        North American Regional Reanalysis (NARR)
        Conventions :
        CF-1.6
        history :
        Read using CDM IOSP GribCollection v3
        featureType :
        GRID
        History :
        Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2) Original Dataset = DatasetScan#narr-a_221_19930313_0000_000.grb; Translation Date = 2018-01-04T23:57:16.043Z
        geospatial_lat_min :
        10.753308882144761
        geospatial_lat_max :
        46.8308828962289
        geospatial_lon_min :
        -153.88242040519995
        geospatial_lon_max :
        -42.666108129242815
      • As shown above, the subsetting operation performed on the Dataset returned a new Dataset. If only a single DataArray is needed from this new Dataset, it can be retrieved using the familiar dot notation:

        @@ -9818,7 +9818,7 @@

        Subsetting the Grib1_TableVersion: 131 Grib1_Parameter: 11 Grib1_Level_Type: 100 - Grib1_Level_Desc: Isobaric surface

        + dtype='float32', name='x', length=268))
      • long_name :
        Temperature @ Isobaric surface
        units :
        K
        description :
        Temperature
        grid_mapping :
        LambertConformal_Projection
        Grib_Variable_Id :
        VAR_7-15-131-11_L100
        Grib1_Center :
        7
        Grib1_Subcenter :
        15
        Grib1_TableVersion :
        131
        Grib1_Parameter :
        11
        Grib1_Level_Type :
        100
        Grib1_Level_Desc :
        Isobaric surface
      • @@ -10252,19 +10252,19 @@

        Aggregation operations + dtype='float32', name='isobaric1'))

      • Info

        @@ -10658,19 +10658,19 @@

        Aggregation operations + dtype='float32', name='isobaric1'))

      • @@ -10688,7 +10688,7 @@

        Simple visualization with

        -
        -
        [<matplotlib.lines.Line2D at 0x7fe0f464b8c0>]
        +
        [<matplotlib.lines.Line2D at 0x7f6c809e3d10>]
         
        ../../_images/eb496d8fdeeb149deaf794d8b93cb8049dace6688a85b73b496e402a0672a856.png @@ -10730,7 +10730,7 @@

        Plotting 2-D data - diff --git a/foundations/conda.html b/foundations/conda.html index bcb3aca4e..f5c63f5ca 100644 --- a/foundations/conda.html +++ b/foundations/conda.html @@ -989,7 +989,7 @@

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Foundations Contributor\u2019s Guide","Project Pythia Notebook Template","Cartopy","Introduction to Cartopy","Data Formats","NetCDF and CF: The Basics","Datetime","Times and Dates in Python","Matplotlib","Annotations, Colorbars, and Advanced Layouts","Histograms, Pie Charts, and Animations","Matplotlib Basics","NumPy","Intermediate NumPy","NumPy Basics","NumPy Broadcasting","Overview","Pandas","Introduction to Pandas","Xarray","Computations and Masks with Xarray","Dask Arrays with Xarray","Calculating ENSO with Xarray","Introduction to Xarray","Installing and Managing Python with Conda","Getting Started with GitHub","Getting Started with Jupyter","Getting Started with Python","Basic Version Control with git","Contribute to Project Pythia via GitHub","Git Branches","Advanced GitHub Topics","Cloning and Forking a Repository","Issues and Discussions","Opening a Pull Request on GitHub","GitHub Repositories","Configuring Your GitHub Account","GitHub Workflows","Reviewing Pull Requests","What is GitHub?","Installing and Running Python","Python in Jupyter","JupyterLab","Formatted Text in the Notebook with Markdown","Overview","Quickstart: Zero to Python","Python in the Terminal","Why Python?","Pythia Foundations","How to Cite This Book","How to Use This 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