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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:

    @@ -1928,7 +1938,7 @@

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

    • @@ -2603,7 +2618,7 @@

    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:

    @@ -2896,7 +2916,7 @@

    Aggregation Methods -../../_images/6d1efa34007820b31892be9b794f1405f9f55b0715317520ad6d61b41c18bc7b.png +../../_images/bac5bbe4b0be794255ff58fcd62e0488a8de4ecf26350af9f8ad9e71b946be37.png

    There are many other combinations of aggregation methods and dimensions on which to perform these methods. In this example, we compute the temporal minimum:

    @@ -2989,7 +3009,7 @@

    Aggregation Methods @@ -3414,7 +3439,7 @@

    Aggregation Methods @@ -4040,7 +4070,7 @@

    Aggregation Methods @@ -4427,7 +4462,7 @@

    GroupBy: Split, Apply, Combine -../../_images/42db7a05f163d0a9972d2f6c78c4243d27e3ddeafa52c5f897820cb995704e65.png +../../_images/f23ff868623f5a05d47840d28ac114f66e150026d0e8fa277847b7284840fb0a.png
    @@ -4440,8 +4475,8 @@

    Split
    -
    DataArrayGroupBy, grouped over 'month'
-12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12.
+
    <DataArrayGroupBy, grouped over 1 grouper(s), 12 groups in total:
+    'month': 12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12>
    @@ -4458,8 +4493,8 @@

    Split

    -
    DataArrayGroupBy, grouped over 'month'
-12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12.
+
    <DataArrayGroupBy, grouped over 1 grouper(s), 12 groups in total:
+    'month': 12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12>
    @@ -4566,7 +4601,7 @@

    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:

    @@ -4980,7 +5020,7 @@

    Compute climatology -../../_images/dacb09ffeb7cc274fd435bac11066b35c056ef785bc99e226d28758b0d5f6f19.png +../../_images/efe156f0f0069edefc9595cf15ad668b0afac211821007a80c2a4c5d23858262.png

    In this example, we plot the zonal mean climatology:

    @@ -4991,7 +5031,7 @@

    Compute climatology -../../_images/4511a7c4f4a90c37d91eaddcbb65cb5b0fa21b652beb0cbb162c0d5f38be81eb.png +../../_images/58b0582cbd22d8dfd439516165d31e7e03cbaac4e3e925851cadb0cf0eed896b.png

    Finally, this example calculates and plots the difference between the climatology for January and the climatology for December:

    @@ -5002,7 +5042,7 @@

    Compute climatology -../../_images/0f1646738258316c5500f3227e5323e8072fdc1e05751022133c695001e119e5.png +../../_images/18c656706e6140a109cc78056d77143e57ca04f60d50dd2ec5172996608c94ab.png

    @@ -5100,7 +5140,7 @@

    Compute anomaly
    @@ -5698,7 +5743,7 @@

    Compute anomaly -../../_images/03e0ba1a7a3d3684429c4097fa92b5b112d73dcba404302904891fc7bd864527.png +../../_images/d32d64e292ca45635d354bd0202a805f33b079636bac0e38d4b5fecf90bf2211.png

    In this example, we compute and plot our dataset’s mean global anomaly over time. In order to specify global data, we must provide both lat and lon to the mean() method’s dim keyword argument:

    @@ -5710,7 +5755,7 @@

    Compute anomaly -../../_images/84c359d819db4e57bcd3cc844301ef1ee11e587b9737b09ef81ccaef72a396b2.png +../../_images/6971ff27c8b252b9a6c0fb94a73aa35a99c6b5ad3e200a7f864200a033b49d7b.png
    @@ -5812,7 +5857,7 @@

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

    @@ -6155,7 +6205,7 @@

    Compute anomaly -../../_images/926073eda22aae6e8c04cbc0bad6466dbf7df2a3cd1b11cde61001e9514b36cb.png +../../_images/2cad55a30eeed7d3239a2f673d6212ab88457c5afbf15c4158c6ee4100e0cf5c.png

    @@ -6180,8 +6230,8 @@

    Other high level computation functionality
    <string>:6: FutureWarning: 'AS' is deprecated and will be removed in a future version. Please use 'YS' instead of 'AS'.
    -
    DataArrayResample, grouped over '__resample_dim__'
-15 groups with labels 2000-01-01, 00:00:00, ..., 201....
+
    <DataArrayResample, grouped over 1 grouper(s), 15 groups in total:
+    '__resample_dim__': 15 groups with labels 2000-01-01, 00:00:00, ..., 201...>
    @@ -6275,7 +6325,7 @@

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

    @@ -6792,7 +6847,7 @@

    Other high level computation functionality
    @@ -7399,7 +7459,7 @@

    Other high level computation functionality -../../_images/e36841c2ab5523c6ee7d6199a2b6df3ae4ed067bd951a871e119b572a7052193.png +../../_images/e7f9305aae09497d8cdbe9eec96cbb2bf833d84921fc76a063da4fc56a994cf0.png

    @@ -7496,7 +7556,7 @@

    Masking Data

    Using where with one condition

    @@ -8134,7 +8199,7 @@

    Using whereUsing whereUsing whereUsing 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:

    @@ -8573,7 +8643,7 @@

    Using whereUsing whereUsing whereUsing 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.

    @@ -8934,7 +9009,7 @@

    Using where

    -../../_images/a8fefa7f7ac9eafa0b1f31439bc2d6162f13cffb7ecbf1467af0a7242d6c4a08.png +../../_images/2288b73f103a2fdcb26a6393df9baa9a5a14c6af6c44a4fb6fef66ca8b67d0b2.png
    @@ -8948,7 +9023,7 @@

    Using where
    -../../_images/801f913e40a422af45c4839bba030727f045a6b171916d30d8420a3cca375c14.png +../../_images/a52dd74641b2c473ee1eaddb89c71fdcb7d25fd5d5e33465b977ea62751b85c2.png

    In addition to using DataArrays and Datasets in Boolean conditions provided to .where(), we can also use coordinate variables. In the following example, we make use of Boolean conditions containing latitude and longitude coordinates. This greatly simplifies the masking of regions outside of the Niño 3.4 region:

    @@ -8962,7 +9037,7 @@

    Using where
    -../../_images/c269c5b23d9973acaad34e11d0c11e2051cd7f7cdb5031776269f64e8cea2781.png +../../_images/72b5c58fb048162bc542ff42273e72641a6ff28bd3fc82c323e9625740abfcde.png
    @@ -8976,7 +9051,7 @@

    Using where
    -../../_images/23fc554c5c0d9dc542581badafc9491f6d1b4684083e48ed1a0df0770ea75dd2.png +../../_images/a881aac640226ef1f12cf238bc70b7f981f4e965e0f23e49566b7cb303f88415.png
    @@ -9343,7 +9418,7 @@

    Resources and References

    - Last updated on 15 August 2024. + Last updated on 30 September 2024.

    @@ -9472,7 +9547,7 @@

    Contribute

    By the     Project Pythia Community. - Last updated on 15 August 2024. + Last updated on 30 September 2024.

    diff --git a/core/xarray/dask-arrays-xarray.html b/core/xarray/dask-arrays-xarray.html index 0031ca9c3..31bb4366a 100644 --- a/core/xarray/dask-arrays-xarray.html +++ b/core/xarray/dask-arrays-xarray.html @@ -79,7 +79,7 @@ - + @@ -574,91 +574,91 @@

    Create a dask.a
    -
    array([[[0.5184325 , 0.046638  , 0.92287728, ..., 0.26504938,
-         0.11275941, 0.64598662],
-        [0.36080487, 0.34902886, 0.10096936, ..., 0.20152136,
-         0.09257283, 0.5231039 ],
-        [0.28464014, 0.34994686, 0.41933859, ..., 0.00297687,
-         0.37455909, 0.23149486],
+
    array([[[0.37775005, 0.62947627, 0.00564539, ..., 0.63902856,
+         0.67911821, 0.39010697],
+        [0.62792836, 0.27409267, 0.8949886 , ..., 0.09977237,
+         0.16074566, 0.87851831],
+        [0.3687918 , 0.37670046, 0.73744562, ..., 0.66370758,
+         0.54146716, 0.21264393],
         ...,
-        [0.25791855, 0.17016204, 0.32902413, ..., 0.85523763,
-         0.26152203, 0.61347891],
-        [0.47225935, 0.64672366, 0.38823705, ..., 0.83971964,
-         0.72834442, 0.02006107],
-        [0.89105212, 0.93748693, 0.34070346, ..., 0.67460795,
-         0.65267089, 0.26918091]],
-
-       [[0.20276396, 0.97495025, 0.83348235, ..., 0.79141013,
-         0.73407996, 0.84101567],
-        [0.1300822 , 0.87159931, 0.84615933, ..., 0.11586209,
-         0.53483724, 0.20767494],
-        [0.34475167, 0.9258658 , 0.92136022, ..., 0.71635699,
-         0.90253889, 0.5599674 ],
+        [0.92399536, 0.40318325, 0.39307817, ..., 0.68658235,
+         0.48706422, 0.22189892],
+        [0.5854838 , 0.22443556, 0.36006811, ..., 0.12131069,
+         0.76905228, 0.62287681],
+        [0.94817314, 0.0709042 , 0.62100584, ..., 0.77854617,
+         0.07562664, 0.02695677]],
+
+       [[0.38703277, 0.267349  , 0.32514936, ..., 0.76035085,
+         0.3018308 , 0.96791738],
+        [0.07186054, 0.89945941, 0.1748418 , ..., 0.2498013 ,
+         0.95092865, 0.28224382],
+        [0.87054342, 0.54723073, 0.31845415, ..., 0.47210787,
+         0.31991325, 0.27184289],
         ...,
-        [0.79994722, 0.96237263, 0.33556596, ..., 0.61299915,
-         0.73628045, 0.93800097],
-        [0.88155331, 0.59421056, 0.76679875, ..., 0.46440536,
-         0.69745599, 0.90212257],
-        [0.47661935, 0.18081637, 0.53304333, ..., 0.26563674,
-         0.19132535, 0.43214162]],
-
-       [[0.50699012, 0.3297785 , 0.67899552, ..., 0.09478629,
-         0.3093938 , 0.83359792],
-        [0.9180401 , 0.7858792 , 0.67798925, ..., 0.37375471,
-         0.61493394, 0.80724763],
-        [0.18845279, 0.10460723, 0.08533754, ..., 0.5850158 ,
-         0.81645454, 0.28815911],
+        [0.06234781, 0.14399073, 0.8728681 , ..., 0.50860255,
+         0.99872604, 0.5677752 ],
+        [0.6796088 , 0.84436146, 0.66573142, ..., 0.35731503,
+         0.99264546, 0.68723472],
+        [0.70605769, 0.17267298, 0.50173173, ..., 0.64453587,
+         0.57265394, 0.7721373 ]],
+
+       [[0.55696528, 0.11449379, 0.92694985, ..., 0.56894983,
+         0.78417975, 0.90158576],
+        [0.97069143, 0.27683271, 0.43138915, ..., 0.87241519,
+         0.08523579, 0.57394352],
+        [0.3081765 , 0.42206902, 0.48404974, ..., 0.57270428,
+         0.13133028, 0.4325785 ],
         ...,
-        [0.81745359, 0.2880636 , 0.82478271, ..., 0.57549814,
-         0.14798728, 0.81974411],
-        [0.52847182, 0.26870524, 0.11085541, ..., 0.58577687,
-         0.01771122, 0.86287587],
-        [0.28215681, 0.25559091, 0.79044868, ..., 0.365979  ,
-         0.19072596, 0.27908382]],
+        [0.69132501, 0.86504342, 0.71498216, ..., 0.16195653,
+         0.8167124 , 0.46589804],
+        [0.8186041 , 0.78035331, 0.82799896, ..., 0.72694233,
+         0.88050152, 0.04019538],
+        [0.36028821, 0.9725241 , 0.0482341 , ..., 0.72844726,
+         0.46208066, 0.2338216 ]],
 
        ...,
 
-       [[0.33012431, 0.51973913, 0.24952384, ..., 0.60782786,
-         0.71486703, 0.20484314],
-        [0.53139472, 0.95897191, 0.35299349, ..., 0.63374876,
-         0.22394387, 0.37111759],
-        [0.36489702, 0.21424433, 0.24540218, ..., 0.43499409,
-         0.66539727, 0.84712377],
+       [[0.82827969, 0.05411067, 0.38735053, ..., 0.61610288,
+         0.44349064, 0.59900705],
+        [0.1858905 , 0.4364763 , 0.15521085, ..., 0.13154387,
+         0.50222195, 0.55482662],
+        [0.1464492 , 0.21933656, 0.45191851, ..., 0.99233026,
+         0.84373942, 0.5920736 ],
         ...,
-        [0.84607521, 0.41973524, 0.82169955, ..., 0.42272746,
-         0.29744027, 0.58905831],
-        [0.43978006, 0.25926513, 0.33069262, ..., 0.09908567,
-         0.34537431, 0.28847673],
-        [0.6345535 , 0.13785228, 0.04295492, ..., 0.8178551 ,
-         0.00206408, 0.84727722]],
-
-       [[0.85951687, 0.25394909, 0.74833589, ..., 0.67763157,
-         0.32160838, 0.44660319],
-        [0.08180601, 0.33190328, 0.46235854, ..., 0.95111018,
-         0.5478879 , 0.05179331],
-        [0.43907554, 0.73884568, 0.85300404, ..., 0.84021008,
-         0.16226672, 0.38182654],
+        [0.54022002, 0.62662158, 0.6142112 , ..., 0.3567014 ,
+         0.89024009, 0.92264671],
+        [0.6303727 , 0.28802464, 0.87613499, ..., 0.87552449,
+         0.49147178, 0.22429148],
+        [0.0028435 , 0.05307869, 0.11296118, ..., 0.49984947,
+         0.5127456 , 0.05895227]],
+
+       [[0.44989569, 0.89490845, 0.62328457, ..., 0.75377221,
+         0.51749495, 0.34975961],
+        [0.61061326, 0.46773184, 0.8138154 , ..., 0.01388832,
+         0.90689683, 0.04750638],
+        [0.43359481, 0.03486677, 0.26643238, ..., 0.83410552,
+         0.14569936, 0.74684874],
         ...,
-        [0.23430133, 0.18054579, 0.45854211, ..., 0.9290759 ,
-         0.45167987, 0.0390323 ],
-        [0.19037445, 0.30813956, 0.97728872, ..., 0.29257147,
-         0.211952  , 0.98639107],
-        [0.3273913 , 0.90867446, 0.01622696, ..., 0.14972616,
-         0.1149099 , 0.46032708]],
-
-       [[0.73450837, 0.81303307, 0.29175881, ..., 0.51376636,
-         0.05080381, 0.02931205],
-        [0.62772822, 0.44428469, 0.70541899, ..., 0.4494359 ,
-         0.39641965, 0.02969298],
-        [0.85905116, 0.27198219, 0.66480523, ..., 0.84380808,
-         0.44344909, 0.3715538 ],
+        [0.30822774, 0.80036316, 0.32556191, ..., 0.04074299,
+         0.72155602, 0.99098984],
+        [0.81099648, 0.08259551, 0.74629791, ..., 0.25439544,
+         0.34994799, 0.27550888],
+        [0.03059158, 0.08737646, 0.16837435, ..., 0.47028463,
+         0.64096187, 0.10868479]],
+
+       [[0.3260913 , 0.39496692, 0.54801696, ..., 0.20827316,
+         0.67386874, 0.01592301],
+        [0.96184637, 0.88117674, 0.55066756, ..., 0.40500808,
+         0.85953224, 0.60947702],
+        [0.98404854, 0.04910576, 0.60134113, ..., 0.94485435,
+         0.143324  , 0.24691185],
         ...,
-        [0.95016907, 0.1192507 , 0.27533283, ..., 0.23711665,
-         0.68480273, 0.74402174],
-        [0.22431953, 0.71082402, 0.26514418, ..., 0.76844777,
-         0.1616924 , 0.84333815],
-        [0.17677655, 0.23865375, 0.41142664, ..., 0.16364663,
-         0.05405567, 0.47781669]]])
+        [0.55055961, 0.9509038 , 0.86233821, ..., 0.49973668,
+         0.17814505, 0.85317038],
+        [0.19923308, 0.93725078, 0.2403799 , ..., 0.05756981,
+         0.52054081, 0.53621674],
+        [0.6271708 , 0.46798374, 0.14842478, ..., 0.20882582,
+         0.35622212, 0.06915404]]])
    @@ -878,11 +878,11 @@

    Compute the result -
    CPU times: user 336 ms, sys: 34.5 ms, total: 371 ms
-Wall time: 177 ms
+
    CPU times: user 319 ms, sys: 33.8 ms, total: 353 ms
+Wall time: 160 ms
 
    
 
    
-
    np.float64(12002302.585679188)
+
    np.float64(11998641.406183477)
 
    
 
    
 
    
@@ -1112,7 +1112,7 @@ 
    Testing a bigger calculation
-../../_images/a814d8a11c4cdade8655a9946a2d92ed02ae1aeee3d5b4bd9ce35175df758a9e.png
+../../_images/8500a4193e11ba6083e68af00fbb291a09c7934fb23f36e0c74b2eedfdb9583e.png
    @@ -1123,34 +1123,34 @@

    Testing a bigger calculation -
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+
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    Testing a bigger calculation
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+
    [##################################      ] | 85% Completed | 6.30 s
 
    
 
    
-
    [##################################      ] | 85% Completed | 6.37 s
+
    [##################################      ] | 85% Completed | 6.40 s
 
    
 
    
-
    [##################################      ] | 87% Completed | 6.47 s
+
    [##################################      ] | 85% Completed | 6.50 s
 
    
 
    
-
    [####################################    ] | 90% Completed | 6.57 s
+
    [##################################      ] | 85% Completed | 6.60 s
 
    
 
    
-
    [#####################################   ] | 93% Completed | 6.67 s
+
    [##################################      ] | 85% Completed | 6.70 s
 
    
 
    
-
    [####################################### ] | 98% Completed | 6.77 s
+
    [###################################     ] | 87% Completed | 6.80 s
 
    
 
    
-
    [########################################] | 100% Completed | 6.87 s
+
    [####################################    ] | 90% Completed | 6.91 s
+
    
+
    
+
    [#####################################   ] | 93% Completed | 7.01 s
+
    
+
    
+
    [####################################### ] | 98% Completed | 7.11 s
+
    
+
    
+
    [########################################] | 100% Completed | 7.21 s
 
    
 
    
 
    
@@ -1459,7 +1468,7 @@ 
    Reading data with 
-
    /home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:286: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
+
    /home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:284: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
   var = coder.decode(var, name=name)
 
    
 
    
@@ -1545,7 +1554,7 @@ 
    Reading data with 
@@ -1960,7 +1974,7 @@ 
    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.
    
@@ -2189,7 +2203,7 @@ 
    Reading data with 
-
    /home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:286: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
+
    /home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:284: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
   var = coder.decode(var, name=name)
 
    
 
    
@@ -2275,7 +2289,7 @@ 
    Reading data with 
@@ -2656,7 +2675,7 @@ 
    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.
    
 
    
@@ -2992,7 +3011,7 @@ 
    Parallel and lazy computation using Parallel and lazy computation using

    + * lon (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
    @@ -3333,7 +3357,7 @@

    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:

    @@ -3448,7 +3472,7 @@

    Parallel and lazy computation using Parallel and lazy computation using

    + * lon (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
    @@ -3789,10 +3818,10 @@

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

    @@ -3814,10 +3843,10 @@

    Parallel and lazy computation using -
    [                                        ] | 0% Completed | 118.65 us
+
    [                                        ] | 0% Completed | 113.68 us
 
    
 
    
-
    [########################################] | 100% Completed | 101.06 ms
+
    [########################################] | 100% Completed | 101.22 ms
 
    
 
    
 
    
@@ -4231,7 +4260,7 @@ 
    Resources and references
     
    
-  Last updated on 15 August 2024.
+  Last updated on 30 September 2024.
   
    
 
    
   
    
@@ -4360,7 +4389,7 @@ 
    Contribute
    
         
           By the     Project Pythia Community.
         
-          Last updated on 15 August 2024.
+          Last updated on 30 September 2024.
         
    
       
    
     
    
diff --git a/core/xarray/enso-xarray.html b/core/xarray/enso-xarray.html
index ba2b1ff3d..390e1eaba 100644
--- a/core/xarray/enso-xarray.html
+++ b/core/xarray/enso-xarray.html
@@ -79,7 +79,7 @@
     
   
   
-    
+    
   
   
   
@@ -571,7 +571,7 @@ 
    The Niño 3.4 Index
-
    /home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:286: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
+
    /home/runner/miniconda3/envs/pythia-book-dev/lib/python3.11/site-packages/xarray/conventions.py:284: SerializationWarning: variable 'tos' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.
   var = coder.decode(var, name=name)
 
    
 
    
@@ -657,7 +657,7 @@ 
    The Niño 3.4 Index
@@ -1193,7 +1198,7 @@ 
    The Niño 3.4 Index
-../../_images/af797c843c10fc3a041e7d876ef1ff15a4803277287ae57eb431630fa80aa59b.png
+../../_images/8324d7444d1406b8c61c6d284ece049df09332c0930344d114058e8b5dd72365.png
 
    
 
    
 
@@ -1290,7 +1295,7 @@ 
    Select the Niño 3.4 regionwhere() method:
    
 
    
@@ -1891,7 +1901,7 @@ 
    Select the Niño 3.4 region
@@ -2614,7 +2629,7 @@ 
    Select the Niño 3.4 region
-../../_images/6ab5cbcb09cc1725e45bdeb0fbe5f30b9a353bcc2d18484b8f3a014ccf384b1f.png
+../../_images/d668effa4a111a845404e56ed6660915a0ff26f741902a08b940001360580f6f.png
 
    
 
    
 
@@ -2648,7 +2663,7 @@ 
    Compute the anomalies
    -../../_images/c415355f301c9836dfc57494b3a535c7915d164b2011850ca53978f0ad907beb.png +../../_images/6f60427a2c7f0df930bbf0370719c8fd71384e2bd8521e5d1e060680eb167992.png

    Since the ENSO index conveys deviations from a norm, the calculation of Niño data requires a standard deviation. In this example, we calculate the standard deviation of the SST in the Niño 3.4 region data, across the entire time period of the data array:

    @@ -2742,7 +2757,7 @@

    Compute the anomaliesCompute the anomaliesCompute the anomaliesCompute the anomalies
    <xarray.DataArray 'tos' ()> Size: 4B
-array(1.8436452, dtype=float32)

    +np.float32(1.8436452)

    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:

    @@ -3071,7 +3091,7 @@

    Visualize the computed Niño 3.4 index -../../_images/a14c93028a3b8c6d5d3dbf64972a8086a1f7a8fee31e7a1d8609269c5006f9ec.png +../../_images/12db7892253b21fea805f070c320da981d6f998733c7a0ea91cd2ddc2f2d051a.png

    @@ -3406,7 +3426,7 @@

    Resources and References

    - Last updated on 15 August 2024. + Last updated on 30 September 2024.

    @@ -3535,7 +3555,7 @@

    Contribute

    By the     Project Pythia Community. - Last updated on 15 August 2024. + Last updated on 30 September 2024.

    diff --git a/core/xarray/xarray-intro.html b/core/xarray/xarray-intro.html index b293695bf..bc60c851d 100644 --- a/core/xarray/xarray-intro.html +++ b/core/xarray/xarray-intro.html @@ -79,7 +79,7 @@ - + @@ -589,25 +589,25 @@

    Generate a random numpy array -
    array([[[286.07106204, 282.75500686, 288.18891049, 284.05197917],
-        [278.48204891, 282.65242598, 285.29732392, 285.475873  ],
-        [283.40070349, 281.00077496, 273.93393173, 284.0856337 ]],
+
    array([[[278.53939144, 289.37839116, 279.54475234, 286.50733947],
+        [284.42126856, 283.12304617, 285.58635498, 289.95324852],
+        [279.15685414, 282.6490363 , 281.87024619, 285.57278422]],
 
-       [[290.52115799, 283.15972197, 277.44088762, 282.93208247],
-        [288.76696041, 277.41754149, 281.73321988, 282.24259059],
-        [284.45823934, 286.92590881, 283.20372245, 286.58946405]],
+       [[282.27062792, 281.80483037, 277.68170639, 289.59344242],
+        [283.66876135, 281.58134494, 280.40314149, 279.91108801],
+        [276.78117617, 275.89965719, 275.45636479, 283.17199269]],
 
-       [[291.47517803, 279.70639511, 287.76468271, 282.81506218],
-        [285.80476031, 289.90157391, 285.35962822, 287.00348634],
-        [279.9727862 , 277.35418621, 283.05533678, 279.36171177]],
+       [[275.93455711, 281.41122416, 281.65497204, 288.34307561],
+        [285.55221198, 272.51234016, 288.83309115, 279.4433927 ],
+        [283.63045841, 281.99692603, 289.2331882 , 274.00140312]],
 
-       [[288.68920074, 285.25886117, 287.41329887, 278.36207998],
-        [278.66514635, 275.35627092, 289.10312878, 280.83394782],
-        [288.21987607, 289.48820921, 286.79336075, 285.80212405]],
+       [[282.67893211, 283.07211767, 280.66169864, 277.7804871 ],
+        [270.70408333, 282.74308622, 284.5239253 , 283.83908513],
+        [288.36490953, 275.8851595 , 286.12125746, 282.77229587]],
 
-       [[288.05380058, 283.76417328, 281.50969427, 276.34018994],
-        [279.46247977, 284.41041746, 277.43249079, 276.85689554],
-        [276.32422962, 280.27054557, 274.30511871, 285.39457587]]])
+       [[278.63064282, 275.27172022, 283.45178899, 284.3312277 ],
+        [278.55168355, 282.56319522, 288.63738696, 285.0321086 ],
+        [288.76420588, 285.24881482, 275.71430688, 290.07058394]]])
    @@ -706,7 +706,7 @@

    Wrap the array: first attempt @@ -1127,7 +1132,7 @@

    Assign dimension namesAssign dimension namesAssign dimension namesAssign dimension names
    <xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[286.07106204, 282.75500686, 288.18891049, 284.05197917],
-        [278.48204891, 282.65242598, 285.29732392, 285.475873  ],
-        [283.40070349, 281.00077496, 273.93393173, 284.0856337 ]],
-
-       [[290.52115799, 283.15972197, 277.44088762, 282.93208247],
-        [288.76696041, 277.41754149, 281.73321988, 282.24259059],
-        [284.45823934, 286.92590881, 283.20372245, 286.58946405]],
-
-       [[291.47517803, 279.70639511, 287.76468271, 282.81506218],
-        [285.80476031, 289.90157391, 285.35962822, 287.00348634],
-        [279.9727862 , 277.35418621, 283.05533678, 279.36171177]],
-
-       [[288.68920074, 285.25886117, 287.41329887, 278.36207998],
-        [278.66514635, 275.35627092, 289.10312878, 280.83394782],
-        [288.21987607, 289.48820921, 286.79336075, 285.80212405]],
-
-       [[288.05380058, 283.76417328, 281.50969427, 276.34018994],
-        [279.46247977, 284.41041746, 277.43249079, 276.85689554],
-        [276.32422962, 280.27054557, 274.30511871, 285.39457587]]])
-Dimensions without coordinates: time, lat, lon

    +array([[[278.53939144, 289.37839116, 279.54475234, 286.50733947], + [284.42126856, 283.12304617, 285.58635498, 289.95324852], + [279.15685414, 282.6490363 , 281.87024619, 285.57278422]], + + [[282.27062792, 281.80483037, 277.68170639, 289.59344242], + [283.66876135, 281.58134494, 280.40314149, 279.91108801], + [276.78117617, 275.89965719, 275.45636479, 283.17199269]], + + [[275.93455711, 281.41122416, 281.65497204, 288.34307561], + [285.55221198, 272.51234016, 288.83309115, 279.4433927 ], + [283.63045841, 281.99692603, 289.2331882 , 274.00140312]], + + [[282.67893211, 283.07211767, 280.66169864, 277.7804871 ], + [270.70408333, 282.74308622, 284.5239253 , 283.83908513], + [288.36490953, 275.8851595 , 286.12125746, 282.77229587]], + + [[278.63064282, 275.27172022, 283.45178899, 284.3312277 ], + [278.55168355, 282.56319522, 288.63738696, 285.0321086 ], + [288.76420588, 285.24881482, 275.71430688, 290.07058394]]]) +Dimensions without coordinates: time, lat, lon

    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.

    @@ -1575,7 +1585,7 @@

    Initialize the .xr-sections { padding-left: 0 !important; display: grid; - grid-template-columns: 150px auto auto 1fr 20px 20px; + grid-template-columns: 150px auto auto 1fr 0 20px 0 20px; } .xr-section-item { @@ -1583,7 +1593,8 @@

    Initialize the } .xr-section-item input { - display: none; + display: inline-block; + opacity: 0; } .xr-section-item input + label { @@ -1595,6 +1606,10 @@

    Initialize the color: var(--xr-font-color2); } +.xr-section-item input:focus + label { + border: 2px solid var(--xr-font-color0); +} + .xr-section-item input:enabled + label:hover { color: var(--xr-font-color0); } @@ -1858,51 +1873,51 @@

    Initialize the fill: currentColor; } 
    <xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[286.07106204, 282.75500686, 288.18891049, 284.05197917],
-        [278.48204891, 282.65242598, 285.29732392, 285.475873  ],
-        [283.40070349, 281.00077496, 273.93393173, 284.0856337 ]],
+array([[[278.53939144, 289.37839116, 279.54475234, 286.50733947],
+        [284.42126856, 283.12304617, 285.58635498, 289.95324852],
+        [279.15685414, 282.6490363 , 281.87024619, 285.57278422]],
 
-       [[290.52115799, 283.15972197, 277.44088762, 282.93208247],
-        [288.76696041, 277.41754149, 281.73321988, 282.24259059],
-        [284.45823934, 286.92590881, 283.20372245, 286.58946405]],
+       [[282.27062792, 281.80483037, 277.68170639, 289.59344242],
+        [283.66876135, 281.58134494, 280.40314149, 279.91108801],
+        [276.78117617, 275.89965719, 275.45636479, 283.17199269]],
 
-       [[291.47517803, 279.70639511, 287.76468271, 282.81506218],
-        [285.80476031, 289.90157391, 285.35962822, 287.00348634],
-        [279.9727862 , 277.35418621, 283.05533678, 279.36171177]],
+       [[275.93455711, 281.41122416, 281.65497204, 288.34307561],
+        [285.55221198, 272.51234016, 288.83309115, 279.4433927 ],
+        [283.63045841, 281.99692603, 289.2331882 , 274.00140312]],
 
-       [[288.68920074, 285.25886117, 287.41329887, 278.36207998],
-        [278.66514635, 275.35627092, 289.10312878, 280.83394782],
-        [288.21987607, 289.48820921, 286.79336075, 285.80212405]],
+       [[282.67893211, 283.07211767, 280.66169864, 277.7804871 ],
+        [270.70408333, 282.74308622, 284.5239253 , 283.83908513],
+        [288.36490953, 275.8851595 , 286.12125746, 282.77229587]],
 
-       [[288.05380058, 283.76417328, 281.50969427, 276.34018994],
-        [279.46247977, 284.41041746, 277.43249079, 276.85689554],
-        [276.32422962, 280.27054557, 274.30511871, 285.39457587]]])
+       [[278.63064282, 275.27172022, 283.45178899, 284.3312277 ],
+        [278.55168355, 282.56319522, 288.63738696, 285.0321086 ],
+        [288.76420588, 285.24881482, 275.71430688, 290.07058394]]])
 Coordinates:
   * time     (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
   * lat      (lat) float64 24B 25.0 40.0 55.0
-  * lon      (lon) float64 32B -120.0 -100.0 -80.0 -60.0
    + 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'))
    • @@ -2000,7 +2015,7 @@

    Set useful attributesSet useful attributesSet useful attributesSet useful attributes
    <xarray.DataArray (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[286.07106204, 282.75500686, 288.18891049, 284.05197917],
-        [278.48204891, 282.65242598, 285.29732392, 285.475873  ],
-        [283.40070349, 281.00077496, 273.93393173, 284.0856337 ]],
+array([[[278.53939144, 289.37839116, 279.54475234, 286.50733947],
+        [284.42126856, 283.12304617, 285.58635498, 289.95324852],
+        [279.15685414, 282.6490363 , 281.87024619, 285.57278422]],
 
-       [[290.52115799, 283.15972197, 277.44088762, 282.93208247],
-        [288.76696041, 277.41754149, 281.73321988, 282.24259059],
-        [284.45823934, 286.92590881, 283.20372245, 286.58946405]],
+       [[282.27062792, 281.80483037, 277.68170639, 289.59344242],
+        [283.66876135, 281.58134494, 280.40314149, 279.91108801],
+        [276.78117617, 275.89965719, 275.45636479, 283.17199269]],
 
-       [[291.47517803, 279.70639511, 287.76468271, 282.81506218],
-        [285.80476031, 289.90157391, 285.35962822, 287.00348634],
-        [279.9727862 , 277.35418621, 283.05533678, 279.36171177]],
+       [[275.93455711, 281.41122416, 281.65497204, 288.34307561],
+        [285.55221198, 272.51234016, 288.83309115, 279.4433927 ],
+        [283.63045841, 281.99692603, 289.2331882 , 274.00140312]],
 
-       [[288.68920074, 285.25886117, 287.41329887, 278.36207998],
-        [278.66514635, 275.35627092, 289.10312878, 280.83394782],
-        [288.21987607, 289.48820921, 286.79336075, 285.80212405]],
+       [[282.67893211, 283.07211767, 280.66169864, 277.7804871 ],
+        [270.70408333, 282.74308622, 284.5239253 , 283.83908513],
+        [288.36490953, 275.8851595 , 286.12125746, 282.77229587]],
 
-       [[288.05380058, 283.76417328, 281.50969427, 276.34018994],
-        [279.46247977, 284.41041746, 277.43249079, 276.85689554],
-        [276.32422962, 280.27054557, 274.30511871, 285.39457587]]])
+       [[278.63064282, 275.27172022, 283.45178899, 284.3312277 ],
+        [278.55168355, 282.56319522, 288.63738696, 285.0321086 ],
+        [288.76420588, 285.24881482, 275.71430688, 290.07058394]]])
 Coordinates:
   * time     (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
   * lat      (lat) float64 24B 25.0 40.0 55.0
   * lon      (lon) float64 32B -120.0 -100.0 -80.0 -60.0
 Attributes:
     units:          kelvin
-    standard_name:  air_temperature
    + 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'))
  • units :
    kelvin
    standard_name :
    air_temperature

    • @@ -2427,7 +2447,7 @@

    Issues with preservation of attributesdocumentation page.

    @@ -2863,7 +2888,7 @@

    Create a pressure @@ -3290,7 +3320,7 @@

    Create a Datase .xr-sections { padding-left: 0 !important; display: grid; - grid-template-columns: 150px auto auto 1fr 20px 20px; + grid-template-columns: 150px auto auto 1fr 0 20px 0 20px; } .xr-section-item { @@ -3298,7 +3328,8 @@

    Create a Datase } .xr-section-item input { - display: none; + display: inline-block; + opacity: 0; } .xr-section-item input + label { @@ -3310,6 +3341,10 @@

    Create a Datase color: var(--xr-font-color2); } +.xr-section-item input:focus + label { + border: 2px solid var(--xr-font-color0); +} + .xr-section-item input:enabled + label:hover { color: var(--xr-font-color0); } @@ -3579,48 +3614,48 @@

    Create a Datase * lat (lat) float64 24B 25.0 40.0 55.0 * lon (lon) float64 32B -120.0 -100.0 -80.0 -60.0 Data variables: - Temperature (time, lat, lon) float64 480B 286.1 282.8 288.2 ... 274.3 285.4 - Pressure (time, lat, lon) float64 480B 1.005e+03 997.3 ... 993.7 997.9 + 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.)

    @@ -3717,7 +3752,7 @@

    Access Data variables and Coordinates in a Access Data variables and Coordinates in a 
    <xarray.DataArray 'Pressure' (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[1004.9947503 ,  997.34305551, 1005.2570497 , 1004.94932052],
-        [ 999.57936157, 1008.00887056,  995.79943783, 1001.72584549],
-        [1000.97883329,  997.63416122,  999.87628649,  995.75668156]],
+array([[[ 996.15816272, 1002.18760854, 1012.93802387,  986.76977459],
+        [1008.05155746,  998.24449116, 1011.1755782 , 1000.87734423],
+        [ 999.68735314,  997.52369302,  997.3385019 , 1004.8646271 ]],
 
-       [[ 996.99848906,  998.99171043, 1005.54123355, 1002.47329747],
-        [1001.19873086, 1002.6421458 , 1003.67919972, 1008.1781423 ],
-        [ 998.95022522,  996.69139569,  998.70696184,  999.66190854]],
+       [[1002.76643568,  998.56430674, 1007.42574341, 1004.46735136],
+        [ 996.71499824, 1006.43539625,  991.999029  ,  993.35091554],
+        [ 998.74902701,  997.30526524, 1009.69477736, 1000.00715494]],
 
-       [[1003.82128876, 1009.50147864,  998.26626214, 1010.68459148],
-        [1002.6011393 ,  999.48914922, 1012.1218076 ,  999.87554602],
-        [ 995.50402431,  998.72653344,  992.28434786, 1007.10859161]],
+       [[1002.57135033,  996.03813525, 1002.8499856 ,  998.77229612],
+        [1004.5932332 , 1002.55073323, 1003.83609714, 1002.52131806],
+        [1001.35835361, 1000.15754707,  991.03029186,  989.09619347]],
 
-       [[1006.63645914, 1010.22534761,  995.65591661,  997.57897502],
-        [ 994.61555351,  999.01768804,  993.38474362, 1000.31994312],
-        [ 994.56472116, 1002.27774107,  998.34375863,  996.64867096]],
+       [[ 998.42352674,  996.87529166,  998.2111554 , 1002.26616799],
+        [ 993.55403079,  997.97428306,  997.30919509, 1000.04049624],
+        [ 996.30043273,  999.05351058,  995.453286  ,  995.07222965]],
 
-       [[ 999.58204854,  990.81696617, 1004.11327168, 1000.97634993],
-        [1000.68621438,  995.89839319,  996.55767565, 1007.06295562],
-        [ 999.48492163,  998.19131491,  993.7209654 ,  997.93656611]]])
+       [[ 999.69982937, 1001.7700656 , 1006.05521327,  989.51726457],
+        [ 998.24514493,  999.26971534, 1002.97473355,  993.16692268],
+        [1003.34862296, 1002.51657572, 1001.91339674,  992.92192685]]])
 Coordinates:
   * time     (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
   * lat      (lat) float64 24B 25.0 40.0 55.0
   * lon      (lon) float64 32B -120.0 -100.0 -80.0 -60.0
 Attributes:
     units:          hPa
-    standard_name:  air_pressure
    + 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'))
  • units :
    hPa
    standard_name :
    air_pressure

    • In addition, you can access DataArrays through a dictionary syntax, as shown in this example:

    @@ -4139,7 +4179,7 @@

    Access Data variables and Coordinates in a Access Data variables and Coordinates in a 
    <xarray.DataArray 'Pressure' (time: 5, lat: 3, lon: 4)> Size: 480B
-array([[[1004.9947503 ,  997.34305551, 1005.2570497 , 1004.94932052],
-        [ 999.57936157, 1008.00887056,  995.79943783, 1001.72584549],
-        [1000.97883329,  997.63416122,  999.87628649,  995.75668156]],
+array([[[ 996.15816272, 1002.18760854, 1012.93802387,  986.76977459],
+        [1008.05155746,  998.24449116, 1011.1755782 , 1000.87734423],
+        [ 999.68735314,  997.52369302,  997.3385019 , 1004.8646271 ]],
 
-       [[ 996.99848906,  998.99171043, 1005.54123355, 1002.47329747],
-        [1001.19873086, 1002.6421458 , 1003.67919972, 1008.1781423 ],
-        [ 998.95022522,  996.69139569,  998.70696184,  999.66190854]],
+       [[1002.76643568,  998.56430674, 1007.42574341, 1004.46735136],
+        [ 996.71499824, 1006.43539625,  991.999029  ,  993.35091554],
+        [ 998.74902701,  997.30526524, 1009.69477736, 1000.00715494]],
 
-       [[1003.82128876, 1009.50147864,  998.26626214, 1010.68459148],
-        [1002.6011393 ,  999.48914922, 1012.1218076 ,  999.87554602],
-        [ 995.50402431,  998.72653344,  992.28434786, 1007.10859161]],
+       [[1002.57135033,  996.03813525, 1002.8499856 ,  998.77229612],
+        [1004.5932332 , 1002.55073323, 1003.83609714, 1002.52131806],
+        [1001.35835361, 1000.15754707,  991.03029186,  989.09619347]],
 
-       [[1006.63645914, 1010.22534761,  995.65591661,  997.57897502],
-        [ 994.61555351,  999.01768804,  993.38474362, 1000.31994312],
-        [ 994.56472116, 1002.27774107,  998.34375863,  996.64867096]],
+       [[ 998.42352674,  996.87529166,  998.2111554 , 1002.26616799],
+        [ 993.55403079,  997.97428306,  997.30919509, 1000.04049624],
+        [ 996.30043273,  999.05351058,  995.453286  ,  995.07222965]],
 
-       [[ 999.58204854,  990.81696617, 1004.11327168, 1000.97634993],
-        [1000.68621438,  995.89839319,  996.55767565, 1007.06295562],
-        [ 999.48492163,  998.19131491,  993.7209654 ,  997.93656611]]])
+       [[ 999.69982937, 1001.7700656 , 1006.05521327,  989.51726457],
+        [ 998.24514493,  999.26971534, 1002.97473355,  993.16692268],
+        [1003.34862296, 1002.51657572, 1001.91339674,  992.92192685]]])
 Coordinates:
   * time     (time) datetime64[ns] 40B 2018-01-01 2018-01-02 ... 2018-01-05
   * lat      (lat) float64 24B 25.0 40.0 55.0
   * lon      (lon) float64 32B -120.0 -100.0 -80.0 -60.0
 Attributes:
     units:          hPa
-    standard_name:  air_pressure
    + 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'))
  • units :
    hPa
    standard_name :
    air_pressure

    • Dataset objects are mainly used for loading data from files, which will be covered later in this tutorial.

    @@ -4571,7 +4616,7 @@

    NumPy-like selectionNumPy-like selectionNumPy-like selectionNumPy-like selection
    <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[290.52115799, 283.15972197, 277.44088762, 282.93208247],
-       [288.76696041, 277.41754149, 281.73321988, 282.24259059],
-       [284.45823934, 286.92590881, 283.20372245, 286.58946405]])
+array([[282.27062792, 281.80483037, 277.68170639, 289.59344242],
+       [283.66876135, 281.58134494, 280.40314149, 279.91108801],
+       [276.78117617, 275.89965719, 275.45636479, 283.17199269]])
 Coordinates:
     time     datetime64[ns] 8B 2018-01-02
   * lat      (lat) float64 24B 25.0 40.0 55.0
   * lon      (lon) float64 32B -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.

    @@ -4963,7 +5013,7 @@

    Selecting with .xr-sections { padding-left: 0 !important; display: grid; - grid-template-columns: 150px auto auto 1fr 20px 20px; + grid-template-columns: 150px auto auto 1fr 0 20px 0 20px; } .xr-section-item { @@ -4971,7 +5021,8 @@

    Selecting with } .xr-section-item input { - display: none; + display: inline-block; + opacity: 0; } .xr-section-item input + label { @@ -4983,6 +5034,10 @@

    Selecting with color: var(--xr-font-color2); } +.xr-section-item input:focus + label { + border: 2px solid var(--xr-font-color0); +} + .xr-section-item input:enabled + label:hover { color: var(--xr-font-color0); } @@ -5246,18 +5301,18 @@

    Selecting with fill: currentColor; } 
    <xarray.DataArray (lat: 3, lon: 4)> Size: 96B
-array([[290.52115799, 283.15972197, 277.44088762, 282.93208247],
-       [288.76696041, 277.41754149, 281.73321988, 282.24259059],
-       [284.45823934, 286.92590881, 283.20372245, 286.58946405]])
+array([[282.27062792, 281.80483037, 277.68170639, 289.59344242],
+       [283.66876135, 281.58134494, 280.40314149, 279.91108801],
+       [276.78117617, 275.89965719, 275.45636479, 283.17199269]])
 Coordinates:
     time     datetime64[ns] 8B 2018-01-02
   * lat      (lat) float64 24B 25.0 40.0 55.0
   * lon      (lon) float64 32B -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:

    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:

    @@ -7344,7 +7424,7 @@

    One more selection method: One more selection method: One more selection method: One more selection method: 
    <xarray.DataArray (time: 3, lat: 2, lon: 2)> Size: 96B
-array([[[282.75500686, 288.18891049],
-        [282.65242598, 285.29732392]],
+array([[[289.37839116, 279.54475234],
+        [283.12304617, 285.58635498]],
 
-       [[283.15972197, 277.44088762],
-        [277.41754149, 281.73321988]],
+       [[281.80483037, 277.68170639],
+        [281.58134494, 280.40314149]],
 
-       [[279.70639511, 287.76468271],
-        [289.90157391, 285.35962822]]])
+       [[281.41122416, 281.65497204],
+        [272.51234016, 288.83309115]]])
 Coordinates:
   * time     (time) datetime64[ns] 24B 2018-01-01 2018-01-02 2018-01-03
   * lat      (lat) float64 16B 25.0 40.0
   * lon      (lon) float64 16B -100.0 -80.0
 Attributes:
     units:          kelvin
-    standard_name:  air_temperature
    + [[281.41122416, 281.65497204], + [272.51234016, 288.83309115]]])

    • 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:

    @@ -7774,7 +7859,7 @@

    Access netCDF data with Access netCDF data with Access netCDF data with Access netCDF data with

    @@ -8234,7 +8324,7 @@

    Subsetting the .xr-sections { padding-left: 0 !important; display: grid; - grid-template-columns: 150px auto auto 1fr 20px 20px; + grid-template-columns: 150px auto auto 1fr 0 20px 0 20px; } .xr-section-item { @@ -8242,7 +8332,8 @@

    Subsetting the } .xr-section-item input { - display: none; + display: inline-block; + opacity: 0; } .xr-section-item input + label { @@ -8254,6 +8345,10 @@

    Subsetting the color: var(--xr-font-color2); } +.xr-section-item input:focus + label { + border: 2px solid var(--xr-font-color0); +} + .xr-section-item input:enabled + label:hover { color: var(--xr-font-color0); } @@ -8529,17 +8624,17 @@

    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:

    @@ -8633,7 +8728,7 @@

    Subsetting the .xr-sections { padding-left: 0 !important; display: grid; - grid-template-columns: 150px auto auto 1fr 20px 20px; + grid-template-columns: 150px auto auto 1fr 0 20px 0 20px; } .xr-section-item { @@ -8641,7 +8736,8 @@

    Subsetting the } .xr-section-item input { - display: none; + display: inline-block; + opacity: 0; } .xr-section-item input + label { @@ -8653,6 +8749,10 @@

    Subsetting the color: var(--xr-font-color2); } +.xr-section-item input:focus + label { + border: 2px solid var(--xr-font-color0); +} + .xr-section-item input:enabled + label:hover { color: var(--xr-font-color0); } @@ -8942,7 +9042,7 @@

    Subsetting the 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:

    @@ -9083,7 +9183,7 @@

    Subsetting the .xr-sections { padding-left: 0 !important; display: grid; - grid-template-columns: 150px auto auto 1fr 20px 20px; + grid-template-columns: 150px auto auto 1fr 0 20px 0 20px; } .xr-section-item { @@ -9091,7 +9191,8 @@

    Subsetting the } .xr-section-item input { - display: none; + display: inline-block; + opacity: 0; } .xr-section-item input + label { @@ -9103,6 +9204,10 @@

    Subsetting the color: var(--xr-font-color2); } +.xr-section-item input:focus + label { + border: 2px solid var(--xr-font-color0); +} + .xr-section-item input:enabled + label:hover { color: var(--xr-font-color0); } @@ -9383,7 +9488,7 @@

    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
    • @@ -9527,7 +9632,7 @@

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

    • Info

    @@ -9934,7 +10044,7 @@

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

    • @@ -10255,10 +10370,10 @@

    Simple visualization with
    -
    [<matplotlib.lines.Line2D at 0x7f5f336fd790>]
+
    [<matplotlib.lines.Line2D at 0x7f647e5dd4d0>]
 
    
 
    
-../../_images/f67cbd438ed8005f711922d5a2c13722fca88bf8c50715ff77c4cda2883962e8.png
+../../_images/ca2e0c5a4a29bfb2704a6c998687b070753a4493d011510c506f49cdee82f0d1.png

    In the figure shown above, Xarray has generated a line plot, which uses the mean temperature profile and the 'isobaric' coordinate variable as axes. In addition, the axis labels and unit information have been read automatically from the DataArray’s metadata.

    @@ -10279,10 +10394,10 @@

    Customizing the plot

    -
    [<matplotlib.lines.Line2D at 0x7f5f33729d90>]
+
    [<matplotlib.lines.Line2D at 0x7f647e4ac290>]
 
    
 
    
-../../_images/99ebd8e23d40f0053e832cc06ff05177d26e536b2b9d803a62dd9e006ab6fa68.png
+../../_images/371cb749acf39f890ddbda648cea4d5ac3565267c5364d084f7d914ad6031033.png
    @@ -10297,10 +10412,10 @@

    Plotting 2-D data -
    <matplotlib.collections.QuadMesh at 0x7f5f2b62a210>
+
    <matplotlib.collections.QuadMesh at 0x7f647e6632d0>
 
    
 
    
-../../_images/c5e51602db51af9960e932efb3aafa5f58719850cd4c3ffabdd1665d1efa7f40.png
+../../_images/1c765f54dd8ab497cc54e712e6e28ac82cddc894000a0507d13d40d479ca20b7.png

    The figure above is generated by Matplotlib’s pcolormesh method, which was automatically called by Xarray’s plot method. This occurred because Xarray recognized that the DataArray object calling the plot method contained two distinct coordinate variables.

    @@ -10696,7 +10811,7 @@

    Resources and references

    - Last updated on 15 August 2024. + Last updated on 30 September 2024.

    @@ -10825,7 +10940,7 @@

    Contribute

    By the     Project Pythia Community. - Last updated on 15 August 2024. + Last updated on 30 September 2024.

    diff --git a/foundations/conda.html b/foundations/conda.html index 1636560f3..cff4861ce 100644 --- a/foundations/conda.html +++ b/foundations/conda.html @@ -79,7 +79,7 @@ - + @@ -534,13 +534,14 @@

    What are Packages?

    Installing Conda

    -

    We recommend you install Miniconda. You can do that by following the instructions for your machine.

    -

    Miniconda only comes with the conda package management system; it is a pared-down version of the full Anaconda Python distribution.

    +

    We recommend you install Miniforge. You can do that by following the instructions for your machine.

    +

    Miniforge uses the conda package management system and is based on Miniconda, which is a pared-down version of the full Anaconda Python distribution.

    Installing Anaconda takes longer and uses up more disk space, but provides you with more functionality, including Spyder (a Python-specific integrated development environment or IDE) and Jupyter, in addition to other immediately installed packages. Also, the interface of Anaconda is great if you are uncomfortable with the terminal.

    -

    We recommend Miniconda for two reasons:

    +

    We recommend Miniforge for these reasons:

    1. It’s quicker and takes up less disk space.

    2. It encourages you to install only the packages you need in reproducible isolated environments for specific projects. This is generally a more robust way to work with open source tools.

      3. +

    3. It uses conda-forge as the default channel for packages.

    Once you have conda via the Miniconda installer, the next step is to create an environment and install packages.

    @@ -919,7 +920,7 @@

    Resources and References

    - Last updated on 15 August 2024. + Last updated on 30 September 2024.

    @@ -1048,7 +1049,7 @@

    Contribute

    By the     Project Pythia Community. - Last updated on 15 August 2024. + Last updated on 30 September 2024.

    diff --git a/foundations/getting-started-github.html b/foundations/getting-started-github.html index 2b09b8f67..493beda2e 100644 --- a/foundations/getting-started-github.html +++ b/foundations/getting-started-github.html @@ -81,7 +81,7 @@ - + @@ -771,7 +771,7 @@

    Getting Started with GitHub

    - Last updated on 15 August 2024. + Last updated on 30 September 2024.

    @@ -900,7 +900,7 @@

    Contribute

    By the     Project Pythia Community. - Last updated on 15 August 2024. + Last updated on 30 September 2024.

    diff --git a/foundations/getting-started-jupyter.html b/foundations/getting-started-jupyter.html index 8405fb06b..84db8c8b1 100644 --- a/foundations/getting-started-jupyter.html +++ b/foundations/getting-started-jupyter.html @@ -79,7 +79,7 @@ - + @@ -911,7 +911,7 @@

    Resources and references

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    @@ -1040,7 +1040,7 @@

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    diff --git a/foundations/getting-started-python.html b/foundations/getting-started-python.html index 73ae79f38..8c9efec45 100644 --- a/foundations/getting-started-python.html +++ b/foundations/getting-started-python.html @@ -81,7 +81,7 @@ - + @@ -784,7 +784,7 @@

    Topics

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    @@ -913,7 +913,7 @@

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    diff --git a/foundations/github/basic-git.html b/foundations/github/basic-git.html index ace2f89e2..5afa8d5ed 100644 --- a/foundations/github/basic-git.html +++ b/foundations/github/basic-git.html @@ -79,7 +79,7 @@ - + @@ -1255,7 +1255,7 @@

    References

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    @@ -1384,7 +1384,7 @@

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    diff --git a/foundations/github/contribute-to-pythia.html b/foundations/github/contribute-to-pythia.html index d143c5555..10b5350e6 100644 --- a/foundations/github/contribute-to-pythia.html +++ b/foundations/github/contribute-to-pythia.html @@ -79,7 +79,7 @@ - + @@ -891,7 +891,7 @@

    Summary

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    @@ -1020,7 +1020,7 @@

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    diff --git a/foundations/github/git-branches.html b/foundations/github/git-branches.html index 8e5fec4aa..3ff595216 100644 --- a/foundations/github/git-branches.html +++ b/foundations/github/git-branches.html @@ -79,7 +79,7 @@ - + @@ -1047,7 +1047,7 @@

    Resources and references

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    @@ -1176,7 +1176,7 @@

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    diff --git a/foundations/github/github-advanced.html b/foundations/github/github-advanced.html index c27023089..ad06adba3 100644 --- a/foundations/github/github-advanced.html +++ b/foundations/github/github-advanced.html @@ -77,7 +77,7 @@ - + @@ -816,7 +816,7 @@

    References

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    @@ -945,7 +945,7 @@

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    diff --git a/foundations/github/github-cloning-forking.html b/foundations/github/github-cloning-forking.html index e8e17bdcb..efec1fb32 100644 --- a/foundations/github/github-cloning-forking.html +++ b/foundations/github/github-cloning-forking.html @@ -79,7 +79,7 @@ - + @@ -994,7 +994,7 @@

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    References

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    @@ -1081,7 +1081,7 @@

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    diff --git a/foundations/github/github-repos.html b/foundations/github/github-repos.html index bbcd61bbf..faefd21e2 100644 --- a/foundations/github/github-repos.html +++ b/foundations/github/github-repos.html @@ -79,7 +79,7 @@ - + @@ -891,7 +891,7 @@

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    diff --git a/foundations/github/github-setup-advanced.html b/foundations/github/github-setup-advanced.html index 358dc0633..4eb7c522f 100644 --- a/foundations/github/github-setup-advanced.html +++ b/foundations/github/github-setup-advanced.html @@ -79,7 +79,7 @@ - + @@ -930,7 +930,7 @@

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    @@ -1059,7 +1059,7 @@

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    diff --git a/foundations/github/github-workflows.html b/foundations/github/github-workflows.html index a90e6b0ba..752490629 100644 --- a/foundations/github/github-workflows.html +++ b/foundations/github/github-workflows.html @@ -79,7 +79,7 @@ - + @@ -1259,7 +1259,7 @@

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    @@ -1388,7 +1388,7 @@

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    diff --git a/foundations/github/review-pr.html b/foundations/github/review-pr.html index df93dcc30..4b8136032 100644 --- a/foundations/github/review-pr.html +++ b/foundations/github/review-pr.html @@ -79,7 +79,7 @@ - + @@ -924,7 +924,7 @@

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    @@ -1053,7 +1053,7 @@

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    diff --git a/foundations/github/what-is-github.html b/foundations/github/what-is-github.html index 182f1a980..6e144e66b 100644 --- a/foundations/github/what-is-github.html +++ b/foundations/github/what-is-github.html @@ -81,7 +81,7 @@ - + @@ -922,7 +922,7 @@

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    @@ -1051,7 +1051,7 @@

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    diff --git a/foundations/how-to-run-python.html b/foundations/how-to-run-python.html index 41ff17a5d..ba8d96521 100644 --- a/foundations/how-to-run-python.html +++ b/foundations/how-to-run-python.html @@ -79,7 +79,7 @@ - + @@ -877,7 +877,7 @@

    Resources and References

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    @@ -1006,7 +1006,7 @@

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    diff --git a/foundations/jupyter.html b/foundations/jupyter.html index a38d8d00a..79d85913d 100644 --- a/foundations/jupyter.html +++ b/foundations/jupyter.html @@ -79,7 +79,7 @@ - + @@ -528,7 +528,7 @@

    Prerequisites

    Installing Python in Jupyter

    To run a Jupyter session, you will need to install some necessary packages into your Conda environment.

    -

    Install miniconda by following the instructions for your machine.

    +

    Install miniforge by following the instructions for your machine.

    Learn more about Conda here

    Next, create a Conda environment with Jupyter Lab installed. In the terminal, type:

    $ conda create --name pythia_foundations_env jupyterlab
@@ -539,9 +539,7 @@ 
    Installing Python in JupyterAnaconda, and select LAUNCH under the Jupyter panel in the GUI.
    
-
    Anaconda Navigator
    
-
    In both methods, a new window should open automatically in your default browser. You can change the browser when launching from the terminal with (for example):
    
+
    A new window should open automatically in your default browser. You can change the browser when launching from the terminal with (for example):
    
 
    jupyter lab —browser=chrome
 
    
 
    
@@ -608,7 +606,8 @@ 
    What’s Next?
 
    Resources and References
    
 
 
 
@@ -899,7 +898,7 @@ 
    Resources and References
     
    
-  Last updated on 15 August 2024.
+  Last updated on 30 September 2024.
    @@ -1028,7 +1027,7 @@

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    diff --git a/foundations/jupyterlab.html b/foundations/jupyterlab.html index 81672932b..104c84c92 100644 --- a/foundations/jupyterlab.html +++ b/foundations/jupyterlab.html @@ -79,7 +79,7 @@ - + @@ -1167,7 +1167,7 @@

    Resources and references

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    @@ -1296,7 +1296,7 @@

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    diff --git a/foundations/markdown.html b/foundations/markdown.html index 9cf1b0200..71137487f 100644 --- a/foundations/markdown.html +++ b/foundations/markdown.html @@ -79,7 +79,7 @@ - + @@ -757,7 +757,7 @@

    Formatted Text in the Notebook with Markdown

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    @@ -886,7 +886,7 @@

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    diff --git a/foundations/overview.html b/foundations/overview.html index 2bee6506e..81ffe269d 100644 --- a/foundations/overview.html +++ b/foundations/overview.html @@ -79,7 +79,7 @@ - + @@ -783,7 +783,7 @@

    Topics

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    @@ -912,7 +912,7 @@

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    diff --git a/foundations/quickstart.html b/foundations/quickstart.html index c874b5af1..873e1fa67 100644 --- a/foundations/quickstart.html +++ b/foundations/quickstart.html @@ -81,7 +81,7 @@ - + @@ -932,7 +932,7 @@

    Some basic graphics with Matplotlib
    Text(0.5, 1.0, 'Our first Pythonic plot')
    -../_images/6ce9c3ee255664d524dbac5887525ce42ef5b7fa89b4829acd81f68c6defc7cf.png +../_images/b73f628da3bddae47133b691495965d00d5318a7499974c540d548b5be389bd2.png @@ -1272,7 +1272,7 @@

    Resources and references

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    @@ -1401,7 +1401,7 @@

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    diff --git a/foundations/terminal.html b/foundations/terminal.html index b76517746..daee50839 100644 --- a/foundations/terminal.html +++ b/foundations/terminal.html @@ -79,7 +79,7 @@ - + @@ -526,7 +526,7 @@

    Prerequisites

    Installing Python in the Terminal

    -

    If you are running Python in the terminal, it is best to install Miniconda. You can do that by following the instructions for your machine.

    +

    If you are running Python in the terminal, it is best to install Miniforge. You can do that by following the instructions for your machine.

    Learn more about Conda here

    Then create a Conda environment with Python installed by typing the following into your terminal:

    $ conda create --name pythia_foundations_env python
@@ -887,7 +887,7 @@ 
    Resources and References
     
    
-  Last updated on 15 August 2024.
+  Last updated on 30 September 2024.
    @@ -1016,7 +1016,7 @@

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    diff --git a/foundations/why-python.html b/foundations/why-python.html index 3dc9fb918..6d9007542 100644 --- a/foundations/why-python.html +++ b/foundations/why-python.html @@ -79,7 +79,7 @@ - + @@ -796,7 +796,7 @@

    Open source

    - Last updated on 15 August 2024. + Last updated on 30 September 2024.

    @@ -925,7 +925,7 @@

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    diff --git a/genindex.html b/genindex.html index e7a63c5d7..8eb3c06bc 100644 --- a/genindex.html +++ b/genindex.html @@ -76,7 +76,7 @@ - + @@ -651,7 +651,7 @@

    Index

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    @@ -780,7 +780,7 @@

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    diff --git a/landing-page.html b/landing-page.html index 6ae5f17dd..438ca3119 100644 --- a/landing-page.html +++ b/landing-page.html @@ -80,7 +80,7 @@ - + @@ -781,7 +781,7 @@

    A community learning resource for Python-based computing in the geosciences<

    - Last updated on 15 August 2024. + Last updated on 30 September 2024.

    @@ -910,7 +910,7 @@

    Contribute

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    diff --git a/preamble/how-to-cite.html b/preamble/how-to-cite.html index b13b7b48d..234ac8185 100644 --- a/preamble/how-to-cite.html +++ b/preamble/how-to-cite.html @@ -79,7 +79,7 @@ - + @@ -758,7 +758,7 @@

    How to Cite This Book

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    @@ -887,7 +887,7 @@

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    diff --git a/preamble/how-to-use.html b/preamble/how-to-use.html index 406beb712..8b70ddb81 100644 --- a/preamble/how-to-use.html +++ b/preamble/how-to-use.html @@ -79,7 +79,7 @@ - + @@ -900,7 +900,7 @@

    Interacting with Jupyter Books locally

    - Last updated on 15 August 2024. + Last updated on 30 September 2024.

    @@ -1029,7 +1029,7 @@

    Contribute

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    diff --git a/search.html b/search.html index 126f11863..17e838983 100644 --- a/search.html +++ b/search.html @@ -78,7 +78,7 @@ - + @@ -676,7 +676,7 @@

    Search

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    @@ -805,7 +805,7 @@

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    diff --git a/searchindex.js b/searchindex.js index 813f3b289..192e01811 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"2nd Derivative": [[15, "nd-derivative"]], "A community learning resource for Python-based computing in the geosciences": [[48, "a-community-learning-resource-for-python-based-computing-in-the-geosciences"]], "A content subsection": [[1, "a-content-subsection"]], "A figure with two different regional maps": [[3, "a-figure-with-two-different-regional-maps"]], "A little history and nomenclature": [[28, "a-little-history-and-nomenclature"]], "A simple query of just the year:": [[7, "a-simple-query-of-just-the-year"]], "A very first Python program": [[45, "a-very-first-python-program"]], "About version control and git": [[28, "about-version-control-and-git"]], "Access Data variables and Coordinates in a Dataset": [[23, "access-data-variables-and-coordinates-in-a-dataset"]], "Access netCDF data with xr.open_dataset": [[23, "access-netcdf-data-with-xr-open-dataset"]], "Add a Box Around the Text": [[9, "add-a-box-around-the-text"]], "Add a fancy background image to the map.": [[3, "add-a-fancy-background-image-to-the-map"]], "Add cartographic features to the map": [[3, "add-cartographic-features-to-the-map"]], "Add in the cartographic shapefiles": [[3, "add-in-the-cartographic-shapefiles"]], "Add some predefined features": [[3, "add-some-predefined-features"]], "Adding Annotations": [[9, "adding-annotations"]], "Adding labels and a grid": [[11, "adding-labels-and-a-grid"]], "Adding labels to an Axes object": [[11, "adding-labels-to-an-axes-object"]], "Adding titles to each subplot": [[11, "adding-titles-to-each-subplot"]], "Additional Resources": [[10, "additional-resources"]], "Advanced GitHub Topics": [[31, null]], "Aggregation Methods": [[20, "aggregation-methods"]], "Aggregation operations": [[23, "aggregation-operations"]], "An example of plotting data": [[3, "an-example-of-plotting-data"]], "Animations": [[10, "animations"]], "Annotations, Colorbars, and Advanced Layouts": [[9, null]], "Another content subsection": [[1, "another-content-subsection"]], "Apply & Combine": [[20, "apply-combine"]], "Applying operations to a DataFrame": [[18, "applying-operations-to-a-dataframe"]], "Approximate selection and interpolation": [[23, "approximate-selection-and-interpolation"]], "Arithmetic": [[14, "arithmetic"]], "Arithmetic Operations": [[20, "arithmetic-operations"]], "Array comparisons": [[13, "array-comparisons"]], "Array math functions": [[14, "array-math-functions"]], "Arrays of numbers with NumPy": [[45, "arrays-of-numbers-with-numpy"]], "Assign dimension names": [[23, "assign-dimension-names"]], "Auxiliary Coordinates": [[5, "auxiliary-coordinates"]], "Basic Colorbars": [[9, "basic-colorbars"]], "Basic Line Plots": [[11, "basic-line-plots"]], "Basic Python data types": [[45, "basic-python-data-types"]], "Basic Statistics": [[18, "basic-statistics"]], "Basic Version Control with git": [[28, null]], "Basic concepts: map projections and GeoAxes": [[3, "basic-concepts-map-projections-and-geoaxes"]], "Basic flow control": [[45, "basic-flow-control"]], "Blocked algorithms": [[21, "blocked-algorithms"]], "Blocking": [[15, "blocking"]], "Building the book locally": [[0, "building-the-book-locally"]], "Building the site": [[0, "building-the-site"]], "Calculating ENSO with Xarray": [[22, null]], "Calculating coastal tides with the timedelta class": [[7, "calculating-coastal-tides-with-the-timedelta-class"]], "Cartopy": [[2, null]], "Cartopy\u2019s set_extent method": [[3, "cartopys-set-extent-method"]], "Cell Bounds": [[5, "cell-bounds"]], "Character strings (str)": [[45, "character-strings-str"]], "Checking out a previous commit": [[28, "checking-out-a-previous-commit"]], "Choosing a Python Platform": [[40, "choosing-a-python-platform"]], "Classes of colormaps": [[9, "classes-of-colormaps"]], "Clone, branch, change, commit, push": [[37, "clone-branch-change-commit-push"]], "Cloning a repository": [[32, "cloning-a-repository"]], "Cloning and Forking a Repository": [[32, null]], "Cloning and forking": [[32, "cloning-and-forking"]], "Cloning the remote repository": [[37, "cloning-the-remote-repository"]], "Code Cells": [[42, "code-cells"]], "Colormap Overview": [[9, "colormap-overview"]], "Command Mode Shortcuts": [[42, "command-mode-shortcuts"]], "Commands we used in this tutorial": [[28, "commands-we-used-in-this-tutorial"]], "Committing": [[28, "committing"]], "Compare across branches": [[28, "compare-across-branches"]], "Compare across commits": [[28, "compare-across-commits"]], "Comparing versions": [[28, "comparing-versions"]], "Complete Workflow": [[30, "complete-workflow"]], "Computations and Masks with Xarray": [[20, null]], "Compute anomaly": [[20, "compute-anomaly"]], "Compute climatology": [[20, "compute-climatology"]], "Compute the anomalies": [[22, "compute-the-anomalies"]], "Compute the result": [[21, "compute-the-result"]], "Configuring Your GitHub Account": [[36, null]], "Consoles": [[42, "consoles"]], "Constants": [[14, "constants"]], "Content section": [[31, "content-section"]], "Contour and Filled Contour Plots": [[11, "contour-and-filled-contour-plots"]], "Contribute to Project Pythia via GitHub": [[29, null]], "Contributing a new Jupyter Notebook": [[0, "contributing-a-new-jupyter-notebook"]], "Controlling the output format with strftime()": [[7, "controlling-the-output-format-with-strftime"]], "Coordinate System Information": [[5, "coordinate-system-information"]], "Coordinate variables": [[5, "coordinate-variables"]], "Core libraries": [[16, "core-libraries"]], "Create Some Sample Data": [[9, "create-some-sample-data"]], "Create a DataArray with named Coordinates": [[23, "create-a-dataarray-with-named-coordinates"]], "Create a Dataset object": [[23, "create-a-dataset-object"]], "Create a Pull Request": [[29, "create-a-pull-request"]], "Create a conda environment": [[0, "create-a-conda-environment"]], "Create a dask.array object": [[21, "create-a-dask-array-object"]], "Create a map with a specified projection": [[3, "create-a-map-with-a-specified-projection"]], "Create a new branch": [[37, "create-a-new-branch"]], "Create a new feature branch": [[28, "create-a-new-feature-branch"]], "Create a pressure DataArray using the same coordinates": [[23, "create-a-pressure-dataarray-using-the-same-coordinates"]], "Create a regional map centered over New York State": [[3, "create-a-regional-map-centered-over-new-york-state"]], "Create an array of \u2018data\u2019": [[14, "create-an-array-of-data"]], "Create regional maps": [[3, "create-regional-maps"]], "Creating a Conda Environment": [[24, "creating-a-conda-environment"]], "Creating a New Branch": [[30, "creating-a-new-branch"]], "Creating and filling a variable": [[5, "creating-and-filling-a-variable"]], "Creating the file and dimensions": [[5, "creating-the-file-and-dimensions"]], "Creation and basic setup": [[5, "creation-and-basic-setup"]], "Creation of a DataArray object": [[23, "creation-of-a-dataarray-object"]], "Custom Colorbars": [[9, "custom-colorbars"]], "Customize your Plot": [[18, "customize-your-plot"]], "Customizing colors": [[11, "customizing-colors"]], "Customizing the plot": [[23, "customizing-the-plot"]], "Danger": [[1, null], [5, null], [18, null], [42, null]], "Dask Arrays with Xarray": [[21, null], [21, "id1"]], "Dask Shortcomings": [[21, "dask-shortcomings"]], "Data Formats": [[4, null]], "Data Setup": [[20, "data-setup"]], "Datetime": [[6, null]], "Dealing with Time Zones": [[7, "dealing-with-time-zones"]], "Deleting Branches": [[30, "deleting-branches"]], "Dictionaries (dict)": [[45, "dictionaries-dict"]], "Did you know?": [[38, null], [39, null], [39, null]], "Discussions": [[33, "discussions"]], "Displaying Images": [[11, "displaying-images"]], "Domain-specific libraries": [[16, "domain-specific-libraries"]], "Email notifications": [[36, "email-notifications"]], "Example usage of the datetime object": [[7, "example-usage-of-the-datetime-object"]], "Executing Jupyter": [[26, "executing-jupyter"]], "Exercise with dask.arrays": [[21, "exercise-with-dask-arrays"]], "Exploratory Data Analysis": [[18, "exploratory-data-analysis"]], "Explore some of Cartopy\u2019s map projections": [[3, "explore-some-of-cartopys-map-projections"]], "Extend Matplotlib\u2019s axes into georeferenced GeoAxes": [[3, "extend-matplotlibs-axes-into-georeferenced-geoaxes"]], "Extending to higher dimensions": [[15, "extending-to-higher-dimensions"]], "Extending to the DataFrame": [[18, "extending-to-the-dataframe"]], "FOSS + VCS + Web = GitHub": [[39, "foss-vcs-web-github"]], "FOSS and VCS on the Internet": [[39, "foss-and-vcs-on-the-internet"]], "Figure and Axes": [[11, "figure-and-axes"]], "Finding the difference between min and max": [[15, "finding-the-difference-between-min-and-max"]], "Floating point numbers (float)": [[45, "floating-point-numbers-float"]], "Forking 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