diff --git a/_images/0ef04d327aa1df4db6d1f8f5ac11166ed822a5e6cc46fcf186fa90620f6fa990.png b/_images/0ef04d327aa1df4db6d1f8f5ac11166ed822a5e6cc46fcf186fa90620f6fa990.png deleted file mode 100644 index 0b95c385..00000000 Binary files a/_images/0ef04d327aa1df4db6d1f8f5ac11166ed822a5e6cc46fcf186fa90620f6fa990.png and /dev/null differ diff --git a/_images/4e3d37849e8b3a241fd297a196bdc609b3f3c91d7aff5b9a3d145f9483dd0808.png b/_images/4e3d37849e8b3a241fd297a196bdc609b3f3c91d7aff5b9a3d145f9483dd0808.png new file mode 100644 index 00000000..96e27e80 Binary files /dev/null and b/_images/4e3d37849e8b3a241fd297a196bdc609b3f3c91d7aff5b9a3d145f9483dd0808.png differ diff --git a/advanced/accessors/01_accessor_examples.html b/advanced/accessors/01_accessor_examples.html index 39479e4a..8bde4a8e 100644 --- a/advanced/accessors/01_accessor_examples.html +++ b/advanced/accessors/01_accessor_examples.html @@ -944,17 +944,17 @@

Example 1: accessing scipy functionality.stats clearly differentiates our new “accessor method” from core xarray methods.

@@ -1550,19 +1550,19 @@

Example 2: creating your own workflows
@@ -1943,9 +1943,9 @@

Example 2: creating your own workflows diff --git a/advanced/apply_ufunc/automatic-vectorizing-numpy.html b/advanced/apply_ufunc/automatic-vectorizing-numpy.html index f2d4f653..05d0f696 100644 --- a/advanced/apply_ufunc/automatic-vectorizing-numpy.html +++ b/advanced/apply_ufunc/automatic-vectorizing-numpy.html @@ -994,7 +994,7 @@

Load data @@ -1435,7 +1435,7 @@

Review @@ -1946,7 +1946,7 @@

Vectorization with
@@ -1365,7 +1365,7 @@

Introduction @@ -1819,7 +1819,7 @@

Introduction @@ -2223,27 +2223,27 @@

Returning multiple variables"lat" is removed (or reduced over).

@@ -2628,18 +2628,18 @@

Returning multiple variables diff --git a/advanced/apply_ufunc/core-dimensions.html b/advanced/apply_ufunc/core-dimensions.html index a25311f0..a33841a8 100644 --- a/advanced/apply_ufunc/core-dimensions.html +++ b/advanced/apply_ufunc/core-dimensions.html @@ -942,17 +942,17 @@

Setup title: 4x daily NMC reanalysis (1948) description: Data is from NMC initialized reanalysis\n(4x/day). These a... platform: Model - references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...

@@ -1403,27 +1403,27 @@

Reducing with n 260.4 260.2 259.9 259.5 259.0 258.6 ... 298.0 297.9 297.8 297.3 297.3 297.3 Coordinates: * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0 - * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0 + dtype='float32', name='lon'))
    • @@ -1945,27 +1945,27 @@

    Reducing with n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0 * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0 Data variables: - air (lat, lon) float32 260.4 260.2 259.9 259.5 ... 297.3 297.3 297.3 + dtype='float32', name='lon'))

    • diff --git a/advanced/apply_ufunc/dask_apply_ufunc.html b/advanced/apply_ufunc/dask_apply_ufunc.html index fc54c96f..0e5e0229 100644 --- a/advanced/apply_ufunc/dask_apply_ufunc.html +++ b/advanced/apply_ufunc/dask_apply_ufunc.html @@ -589,7 +589,7 @@

    Setup

    Client

    -

    Client-29205f05-71da-11ee-8d3f-002248a75747

    +

    Client-59d5cdc2-7d38-11ee-8ec0-000d3a3396c2

    @@ -624,22 +624,22 @@

    Client

    LocalCluster

    -

    e4198b87

    +

    7b1396ad

    @@ -661,14 +661,14 @@

    Scheduler Info

    Scheduler

    -

    Scheduler-1da82266-12d7-44f4-b2ab-59b767a58d60

    +

    Scheduler-e6b330e3-38f5-43e3-b07b-6cd19e0b002c

    Dashboard: http://127.0.0.1:8787/status - Workers: 2 + Workers: 4
    - Total threads: 2 + Total threads: 4 - Total memory: 6.76 GiB + Total memory: 15.61 GiB
    @@ -676,7 +676,7 @@

    Scheduler

    Dashboard:http://127.0.0.1:8787/status @@ -684,7 +684,7 @@

    Scheduler

    Started: Just now
    - Comm: tcp://127.0.0.1:46151 + Comm: tcp://127.0.0.1:34045 - Workers: 2 + Workers: 4
    - Total threads: 2 + Total threads: 4
    - Total memory: 6.76 GiB + Total memory: 15.61 GiB
    @@ -707,7 +707,7 @@

    Worker: 0

    @@ -752,7 +752,7 @@

    Worker: 1

    - Comm: tcp://127.0.0.1:41663 + Comm: tcp://127.0.0.1:38027 Total threads: 1 @@ -715,21 +715,21 @@

    Worker: 0

    - Dashboard: http://127.0.0.1:34297/status + Dashboard: http://127.0.0.1:46783/status - Memory: 3.38 GiB + Memory: 3.90 GiB
    - Nanny: tcp://127.0.0.1:40923 + Nanny: tcp://127.0.0.1:33299
    - Local directory: /tmp/dask-scratch-space/worker-y2fz9dzb + Local directory: /tmp/dask-scratch-space/worker-5htrl223
    + + + + + + +
    - Comm: tcp://127.0.0.1:34483 + Comm: tcp://127.0.0.1:42901 Total threads: 1 @@ -760,21 +760,111 @@

    Worker: 1

    - Dashboard: http://127.0.0.1:36549/status + Dashboard: http://127.0.0.1:41889/status - Memory: 3.38 GiB + Memory: 3.90 GiB
    - Nanny: tcp://127.0.0.1:40257 + Nanny: tcp://127.0.0.1:36845
    - Local directory: /tmp/dask-scratch-space/worker-w91hdg6n + Local directory: /tmp/dask-scratch-space/worker-q4fh1dye +
    + +
    +

    + +
    +
    +
    +
    + +

    Worker: 2

    +     + + + + + + + + + + + + + + + + + + + + + +
    + Comm: tcp://127.0.0.1:34519 + + Total threads: 1 +
    + Dashboard: http://127.0.0.1:39465/status + + Memory: 3.90 GiB +
    + Nanny: tcp://127.0.0.1:39213 +
    + Local directory: /tmp/dask-scratch-space/worker-btcvlx7a +
    +
    +
    +
    + +
    +
    +
    +
    + +

    Worker: 3

    +     + + + + + + + + + + + + + + + @@ -1205,17 +1295,17 @@

    Worker: 1

    title: 4x daily NMC reanalysis (1948) description: Data is from NMC initialized reanalysis\n(4x/day). These a... platform: Model - references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    + Comm: tcp://127.0.0.1:37367 + + Total threads: 1 +
    + Dashboard: http://127.0.0.1:44077/status + + Memory: 3.90 GiB +
    + Nanny: tcp://127.0.0.1:45607 +
    + Local directory: /tmp/dask-scratch-space/worker-p96hc0ug
    + dtype='datetime64[ns]')
    • air
      (time, lat, lon)
      float32
      dask.array<chunksize=(100, 25, 53), meta=np.ndarray>
      long_name :
      4xDaily Air temperature at sigma level 995
      units :
      degK
      precision :
      2
      GRIB_id :
      11
      GRIB_name :
      TMP
      var_desc :
      Air temperature
      dataset :
      NMC Reanalysis
      level_desc :
      Surface
      statistic :
      Individual Obs
      parent_stat :
      Other
      actual_range :
      [185.16 322.1 ]
    • @@ -1329,16 +1419,16 @@

    Worker: 1

    -
    • lat
      PandasIndex
      PandasIndex(Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,
+
    • lat
      PandasIndex
      PandasIndex(Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,
        45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,
        15.0],
-      dtype='float32', name='lat'))
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
+      dtype='float32', name='lat'))
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
        225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,
        250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,
        275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,
        300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,
        325.0, 327.5, 330.0],
-      dtype='float32', name='lon'))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 06:00:00',
+      dtype='float32', name='lon'))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 06:00:00',
                '2013-01-01 12:00:00', '2013-01-01 18:00:00',
                '2013-01-02 00:00:00', '2013-01-02 06:00:00',
                '2013-01-02 12:00:00', '2013-01-02 18:00:00',
@@ -1349,7 +1439,7 @@ 
      Worker: 1
      
                '2014-12-30 12:00:00', '2014-12-30 18:00:00',
                '2014-12-31 00:00:00', '2014-12-31 06:00:00',
                '2014-12-31 12:00:00', '2014-12-31 18:00:00'],
-              dtype='datetime64[ns]', name='time', length=2920, freq=None))
  • Conventions :
    COARDS
    title :
    4x daily NMC reanalysis (1948)
    description :
    Data is from NMC initialized reanalysis + dtype='datetime64[ns]', name='time', length=2920, freq=None))
  • Conventions :
    COARDS
    title :
    4x daily NMC reanalysis (1948)
    description :
    Data is from NMC initialized reanalysis (4x/day). These are the 0.9950 sigma level values.
    platform :
    Model
    references :
    http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
    • @@ -1909,7 +1999,7 @@

    A simple example @@ -2023,26 +2113,26 @@

    A simple example

    Understanding what’s happening#

    @@ -2444,7 +2534,7 @@

    Understanding what’s happening @@ -2558,26 +2648,26 @@

    Understanding what’s happening @@ -3147,10 +3237,10 @@

    Basics @@ -3625,20 +3715,20 @@

    Basics @@ -4212,12 +4302,12 @@

    Understanding execution + dtype='datetime64[ns]', name='time', length=2920, freq=None))

    • Note that we received an Xarray object back (integrated) but our wrapper function was called with a numpy array of shape (1,1,1).

    @@ -4705,20 +4795,20 @@

    Understanding execution + dtype='datetime64[ns]', name='time', length=2920, freq=None))

    • We see that integrate_wrapper is called many times! As many times as there are blocks in the array in fact, which is 30 here (ds.air.data.numblocks).

    Our function is independently executed on each block of the array, and then the results are concatenated to form the final result.

    @@ -5180,7 +5270,7 @@

    Adding new dimensions + dtype='datetime64[ns]', name='time', length=2920, freq=None))
    • @@ -5843,7 +5933,7 @@

    Dimensions that change size @@ -5957,21 +6047,21 @@

    Dimensions that change size

    Tip

    @@ -6382,15 +6472,15 @@

    Automatic Vectorizing + dtype='datetime64[ns]', name='time', length=2920, freq=None))

    • Again, it is important to understand the conceptual flow of information between the variuus packages when executing interped.compute() which looks ilke

    xarray.apply_ufunc ↔ dask.array.apply_gufunc ↔ numpy.vectorize ↔ numpy.interp

    diff --git a/advanced/apply_ufunc/example-interp.html b/advanced/apply_ufunc/example-interp.html index 2b8b6cc8..1c4681b5 100644 --- a/advanced/apply_ufunc/example-interp.html +++ b/advanced/apply_ufunc/example-interp.html @@ -960,7 +960,7 @@

    Load datanp.interp which expects 1D numpy arrays. This functionality is already implemented in xarray so we use that capability to make sure we are not making mistakes.

    @@ -1382,7 +1382,7 @@

    Load datalat at a time.

    @@ -1968,7 +1968,7 @@

    Core dimensions @@ -2478,7 +2478,7 @@

    Automatic vectorization with + dtype='float64', name='lat'))

    • Notice that the printed input shapes are all 1D and correspond to one vector along the lat dimension.

    The result is now an xarray object with coordinate values copied over from data. This is why apply_ufunc is so convenient; it takes care of a lot of boilerplate necessary to apply functions that consume and produce numpy arrays to xarray objects.

    diff --git a/advanced/apply_ufunc/numba-vectorization.html b/advanced/apply_ufunc/numba-vectorization.html index 5df2fc27..2baac660 100644 --- a/advanced/apply_ufunc/numba-vectorization.html +++ b/advanced/apply_ufunc/numba-vectorization.html @@ -921,9 +921,9 @@

    Load data @@ -1319,9 +1319,9 @@

    vectorize + [ 49., 64., 81., 100.]])
    • x
      (x)
      int64
      12 13 14
      array([12, 13, 14])
    • x
      PandasIndex
      PandasIndex(Index([12, 13, 14], dtype='int64', name='x'))

    • @@ -1751,10 +1751,10 @@

    guvectorize + [ 4, 8, 12]])
    • x
      (x)
      int64
      12 13 14
      array([12, 13, 14])
    • x
      PandasIndex
      PandasIndex(Index([12, 13, 14], dtype='int64', name='x'))

    • Notice the following:

      @@ -2143,7 +2143,7 @@

      With dask @@ -2201,7 +2201,7 @@

      With dask diff --git a/advanced/apply_ufunc/simple_numpy_apply_ufunc.html b/advanced/apply_ufunc/simple_numpy_apply_ufunc.html index 6ebffa6e..ac37b599 100644 --- a/advanced/apply_ufunc/simple_numpy_apply_ufunc.html +++ b/advanced/apply_ufunc/simple_numpy_apply_ufunc.html @@ -946,17 +946,17 @@

      Setup title: 4x daily NMC reanalysis (1948) description: Data is from NMC initialized reanalysis\n(4x/day). These a... platform: Model - references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...

    @@ -1459,7 +1459,7 @@

    A simple example: pure numpyDataArray.copy

    @@ -1903,7 +1903,7 @@

    A simple example: pure numpy

    Caution

    @@ -2343,7 +2343,7 @@

    apply_ufunc @@ -2797,7 +2797,7 @@

    How does apply_ufunc work?wrapper receives the underlying numpy array (ds.air.data), and the integer 1.

    Essentially, apply_ufunc does the following:

    @@ -3314,17 +3314,17 @@

    Handling datasets
    @@ -3753,17 +3753,17 @@

    Handling datasets @@ -4202,7 +4202,7 @@

    Passing positional and keyword arguments
    @@ -1364,12 +1364,12 @@

    It Works!
    @@ -1744,7 +1744,7 @@

    It Works! @@ -1085,7 +1085,7 @@

    Reduced memory usage with dask
    @@ -1458,7 +1458,7 @@

    Reduced memory usage with dask @@ -1497,7 +1497,7 @@

    Reduced memory usage with dask
    @@ -1871,7 +1871,7 @@

    Reduced memory usage with dask @@ -1926,7 +1926,7 @@

    Reduced memory usage with dask
    @@ -2300,7 +2300,7 @@

    Reduced memory usage with dask
    @@ -2674,7 +2674,7 @@

    Reduced memory usage with dask

    Client

    -

    Client-3fb01c2c-71da-11ee-8de7-002248a75747

    +

    Client-6aeacac2-7d38-11ee-8f8e-000d3a3396c2

    @@ -588,22 +588,22 @@

    Client

    LocalCluster

    -

    12d4b9f9

    +

    5f84521f

    @@ -625,14 +625,14 @@

    Scheduler Info

    Scheduler

    -

    Scheduler-6041dcc1-9197-4f21-a5f9-ce3ee998cb3e

    +

    Scheduler-7fe34e38-1fe4-499b-8a33-7d5a90a8a328

    Dashboard: http://127.0.0.1:8787/status - Workers: 2 + Workers: 4
    - Total threads: 2 + Total threads: 4 - Total memory: 6.76 GiB + Total memory: 15.61 GiB
    @@ -640,7 +640,7 @@

    Scheduler

    Dashboard:http://127.0.0.1:8787/status @@ -648,7 +648,7 @@

    Scheduler

    Started: Just now
    - Comm: tcp://127.0.0.1:41873 + Comm: tcp://127.0.0.1:40105 - Workers: 2 + Workers: 4
    - Total threads: 2 + Total threads: 4
    - Total memory: 6.76 GiB + Total memory: 15.61 GiB
    @@ -671,7 +671,7 @@

    Worker: 0

    @@ -716,7 +716,7 @@

    Worker: 1

    - Comm: tcp://127.0.0.1:43605 + Comm: tcp://127.0.0.1:35821 Total threads: 1 @@ -679,21 +679,21 @@

    Worker: 0

    - Dashboard: http://127.0.0.1:39695/status + Dashboard: http://127.0.0.1:46237/status - Memory: 3.38 GiB + Memory: 3.90 GiB
    - Nanny: tcp://127.0.0.1:33621 + Nanny: tcp://127.0.0.1:37631
    - Local directory: /tmp/dask-scratch-space/worker-hv57u97_ + Local directory: /tmp/dask-scratch-space/worker-deaxzcgz
    + + + + + + +
    - Comm: tcp://127.0.0.1:37085 + Comm: tcp://127.0.0.1:35357 Total threads: 1 @@ -724,21 +724,111 @@

    Worker: 1

    - Dashboard: http://127.0.0.1:36595/status + Dashboard: http://127.0.0.1:45725/status - Memory: 3.38 GiB + Memory: 3.90 GiB
    - Nanny: tcp://127.0.0.1:34345 + Nanny: tcp://127.0.0.1:42675
    - Local directory: /tmp/dask-scratch-space/worker-gju0bjlr + Local directory: /tmp/dask-scratch-space/worker-ak05to77 +
    + +
    +

    + +
    +
    +
    +
    + +

    Worker: 2

    +     + + + + + + + + + + + + + + + + + + + + + +
    + Comm: tcp://127.0.0.1:34991 + + Total threads: 1 +
    + Dashboard: http://127.0.0.1:42955/status + + Memory: 3.90 GiB +
    + Nanny: tcp://127.0.0.1:39263 +
    + Local directory: /tmp/dask-scratch-space/worker-x7od4nkh +
    +
    +
    +
    + +
    +
    +
    +
    + +

    Worker: 3

    +     + + + + + + + + + + + + + + + @@ -1171,17 +1261,17 @@

    Worker: 1

    title: 4x daily NMC reanalysis (1948) description: Data is from NMC initialized reanalysis\n(4x/day). These a... platform: Model - references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    + Comm: tcp://127.0.0.1:34645 + + Total threads: 1 +
    + Dashboard: http://127.0.0.1:46629/status + + Memory: 3.90 GiB +
    + Nanny: tcp://127.0.0.1:36631 +
    + Local directory: /tmp/dask-scratch-space/worker-4hmabm2v
    + dtype='datetime64[ns]')
    • air
      (time, lat, lon)
      float32
      dask.array<chunksize=(100, 25, 53), meta=np.ndarray>
      long_name :
      4xDaily Air temperature at sigma level 995
      units :
      degK
      precision :
      2
      GRIB_id :
      11
      GRIB_name :
      TMP
      var_desc :
      Air temperature
      dataset :
      NMC Reanalysis
      level_desc :
      Surface
      statistic :
      Individual Obs
      parent_stat :
      Other
      actual_range :
      [185.16 322.1 ]
    • @@ -1295,16 +1385,16 @@

    Worker: 1

    -
    • lat
      PandasIndex
      PandasIndex(Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,
+
    • lat
      PandasIndex
      PandasIndex(Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,
        45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,
        15.0],
-      dtype='float32', name='lat'))
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
+      dtype='float32', name='lat'))
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
        225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,
        250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,
        275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,
        300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,
        325.0, 327.5, 330.0],
-      dtype='float32', name='lon'))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 06:00:00',
+      dtype='float32', name='lon'))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 06:00:00',
                '2013-01-01 12:00:00', '2013-01-01 18:00:00',
                '2013-01-02 00:00:00', '2013-01-02 06:00:00',
                '2013-01-02 12:00:00', '2013-01-02 18:00:00',
@@ -1315,7 +1405,7 @@ 
      Worker: 1
      
                '2014-12-30 12:00:00', '2014-12-30 18:00:00',
                '2014-12-31 00:00:00', '2014-12-31 06:00:00',
                '2014-12-31 12:00:00', '2014-12-31 18:00:00'],
-              dtype='datetime64[ns]', name='time', length=2920, freq=None))
  • Conventions :
    COARDS
    title :
    4x daily NMC reanalysis (1948)
    description :
    Data is from NMC initialized reanalysis + dtype='datetime64[ns]', name='time', length=2920, freq=None))

  • Conventions :
    COARDS
    title :
    4x daily NMC reanalysis (1948)
    description :
    Data is from NMC initialized reanalysis (4x/day). These are the 0.9950 sigma level values.
    platform :
    Model
    references :
    http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html

    • @@ -1700,20 +1790,20 @@

    Simple example @@ -1786,13 +1876,7 @@

    Simple example
    diff --git a/data_cleaning/ice_velocity.html b/data_cleaning/ice_velocity.html index f0770916..aefa605f 100644 --- a/data_cleaning/ice_velocity.html +++ b/data_cleaning/ice_velocity.html @@ -922,7 +922,7 @@

    Re-organize InSAR ice velocity data @@ -1325,7 +1325,7 @@

    Re-organize InSAR ice velocity data @@ -1779,19 +1779,19 @@

    Re-organize InSAR ice velocity datayaxis and xaxis and drop the nx and ny coordinates:

    @@ -2203,19 +2203,19 @@

    Re-organize InSAR ice velocity datadata_vars are really only 3 unique variables that exist along a time dimension (with a length of 10). We want to add a time dimension to the dataset and concatenate the data variables in each of the three groups together.

    @@ -2626,19 +2626,19 @@

    Re-organize InSAR ice velocity datada_vx_ls:

    @@ -3039,7 +3039,7 @@

    Re-organize InSAR ice velocity dataxr.DataArray in the list:

    @@ -3457,7 +3457,7 @@

    Re-organize InSAR ice velocity datada_vx_ls_test is the xr.DataArray containing the vx1996 variable, and the time coord is 0. In the second (1-place) element, the xr.DataArray is called vx2000 and the time coord is 1.

    Finally, we will rename the xr.DataArrays to reflect just the variable name, rather than the year, because that is now referenced in the time coordinate.

    @@ -3882,7 +3882,7 @@

    Re-organize InSAR ice velocity dataxr.DataArrays for the vx data variable where each xr.DataArray has a time dimension and coordinates along the time dimension. This list is ready to be concatenated along the time dimension.

    First, we will perform the same steps for the other two data variables (vy and err) before concatenating all three along the time dimension and merging into one xr.Dataset. For vy and err, we will combine the steps followed for vx into one operation. Note one other difference between the workflow for vx and the workflow for vy and err: rather than assigning coordinate values using the assign_coords() function, we do this within the expand_dims() function, where a time dimension is added as well as coordinate values for the dimension ([int(var[-4:])]).

    @@ -4313,9 +4313,9 @@

    Re-organize InSAR ice velocity data @@ -4462,7 +4462,7 @@

    Re-organize InSAR ice velocity data -
    <matplotlib.collections.QuadMesh at 0x7f26e5c2b4d0>
+
    <matplotlib.collections.QuadMesh at 0x7fefd34ba950>
 
    
 
    
 ../_images/d3d0cb7fecf8d46b0bf1f36aaed1df349455ea06bed53082834d453a96c4e0a1.png
@@ -4870,9 +4870,9 @@ 
    Re-organize InSAR ice velocity data
@@ -5052,7 +5052,7 @@ 
    Re-organize InSAR ice velocity data
-
    <matplotlib.collections.QuadMesh at 0x7f26e5920a50>
+
    <matplotlib.collections.QuadMesh at 0x7fefd3586910>
 
    
 
    
 ../_images/bc0de4a9fc38208dab7c967c796a340ed8b130c50b7a20d24a118da0fed47f69.png
diff --git a/fundamentals/01.1_creating_data_structures.html b/fundamentals/01.1_creating_data_structures.html
index 285112e1..c8ef8d55 100644
--- a/fundamentals/01.1_creating_data_structures.html
+++ b/fundamentals/01.1_creating_data_structures.html
@@ -926,17 +926,17 @@ 
    Creating Data Structures
@@ -1391,7 +1391,7 @@ 
    DataArray
 
    
@@ -1843,7 +1843,7 @@ 
    DataArraydim_0, dim_1, dim_2
    
 
    
@@ -2261,7 +2261,7 @@ 
    Dimension Names
@@ -3123,7 +3123,7 @@ 
    Coordinates
@@ -3553,7 +3553,7 @@ 
    Coordinates
@@ -3993,7 +3993,7 @@ 
    Attributes
@@ -4431,7 +4431,7 @@ 
    Attributes
@@ -4872,7 +4872,7 @@ 
    Non-dimension coordinates
@@ -5320,7 +5320,7 @@ 
    Exercises
@@ -5731,7 +5731,7 @@ 
    Exercises
@@ -6161,7 +6161,7 @@ 
    Exercises
 
    Data Variables#
    
@@ -6965,14 +6965,14 @@ 
    Data Variables
@@ -7445,14 +7445,14 @@ 
    Data Variables
@@ -7969,17 +7969,17 @@ 
    Coordinates
@@ -8462,14 +8462,14 @@ 
    Coordinates
 
    
@@ -8981,17 +8981,17 @@ 
    Coordinates
@@ -9521,17 +9521,17 @@ 
    Attributes
 
    
@@ -10046,7 +10046,7 @@ 
    Exercises
@@ -10466,7 +10466,7 @@ 
    Exercises
 
  • add metadata to coordinates and variables:
    • 
@@ -10920,7 +10920,7 @@ 
    Exercises
 
    
@@ -1348,7 +1348,7 @@ 
    NetCDFhttps://zarr.readthedocs.io/en/stable/_static/logo1.png
 
    
@@ -1367,7 +1367,7 @@ 
    Zarr#<
 
    
 
    
 
    
-
    <xarray.backends.zarr.ZarrStore at 0x7f1298cb9380>
+
    <xarray.backends.zarr.ZarrStore at 0x7ff00442cf20>
 
    
 
    
 
    
@@ -1751,7 +1751,7 @@ 
    Zarr#<
   * z        (z) int64 -3 -2 -1 0 1 2
 Data variables:
     a        (x, y) float64 ...
-    b        (z, x) float64 ...

    + b (z, x) float64 ...

    setting the chunks parameter to None avoids dask (more on that in a later session)

    @@ -1765,7 +1765,7 @@

    Zarr#<
    -
    <xarray.backends.zarr.ZarrStore at 0x7f1298cb8890>
+
    <xarray.backends.zarr.ZarrStore at 0x7ff00442c7b0>
    @@ -2164,10 +2164,10 @@

    Raster files using rioxarray
    diff --git a/fundamentals/01_datastructures.html b/fundamentals/01_datastructures.html index 31b27d90..927a846a 100644 --- a/fundamentals/01_datastructures.html +++ b/fundamentals/01_datastructures.html @@ -979,17 +979,17 @@

    Datasetmean).

    @@ -1981,7 +1981,7 @@

    Dataset

    What is all this anyway? (String representations)#

    @@ -2444,17 +2444,17 @@

    What is all this anyway? (String representations)

    The output consists of:

    @@ -2988,7 +2988,7 @@

    DataArray

    String representations#

    @@ -3492,7 +3492,7 @@

    String representations + dtype='datetime64[ns]', name='time', length=2920, freq=None))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • @@ -4229,7 +4229,7 @@

    To Pandas and back
    diff --git a/fundamentals/02.1_indexing_Basic.html b/fundamentals/02.1_indexing_Basic.html index 4bd6648f..5d4da772 100644 --- a/fundamentals/02.1_indexing_Basic.html +++ b/fundamentals/02.1_indexing_Basic.html @@ -983,17 +983,17 @@

    Quick Overview @@ -1496,10 +1496,10 @@

    NumPy style indexing with Xarray @@ -1984,7 +1984,7 @@

    Label-based Indexing + dtype='datetime64[ns]', name='time', length=2920, freq=None))

  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • @@ -2422,7 +2422,7 @@

    Label-based Indexing + dtype='datetime64[ns]', name='time', length=2920, freq=None))

  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]

    • Dropping using drop_sel#

    @@ -2858,7 +2858,7 @@

    Dropping using * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0 * lon (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0 * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00 -Attributes: (11) + dtype='datetime64[ns]', name='time', length=2920, freq=None))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]

    • So far, all the above will require us to specify exact coordinate values, but what if we don’t have the exact values? We can use nearest neighbor lookups to address this issue:

    @@ -3311,11 +3311,11 @@

    Nearest Neighbor Lookupstolerance argument limits the maximum distance for valid matches with an inexact lookup:

    @@ -3705,11 +3705,11 @@

    Nearest Neighbor Lookups

    Tip

    @@ -4105,11 +4105,11 @@

    Nearest Neighbor Lookupstime="2013-01-01" will return all timestamps for that day (4 of them here):

    @@ -4919,16 +4919,16 @@

    Selecting data based on single datetime @@ -6788,15 +6788,15 @@

    Indexing with a DatetimeIndex or date string list

    @@ -7230,17 +7230,17 @@

    Fancy indexing based on year, month, day, or other datetime components

    Or, if you wanted to select data from a specific day of each month, you could use:

    @@ -7682,14 +7682,14 @@

    Fancy indexing based on year, month, day, or other datetime components diff --git a/fundamentals/02.2_manipulating_dimensions.html b/fundamentals/02.2_manipulating_dimensions.html index 446013c3..c1a9c283 100644 --- a/fundamentals/02.2_manipulating_dimensions.html +++ b/fundamentals/02.2_manipulating_dimensions.html @@ -919,19 +919,19 @@

    Manipulating Dimensions (Data Resolution)

    Interpolation#

    @@ -1359,7 +1359,7 @@

    Interpolationnan.

    @@ -1859,7 +1859,7 @@

    Interpolation
    @@ -1311,7 +1311,7 @@

    Computing with Multiple Objectsarr1 is 2D; while arr2 is 1D along space and has values at two locations only.

    Now subtract the two.

    @@ -1689,7 +1689,7 @@

    Computing with Multiple Objects @@ -2096,7 +2096,7 @@

    Broadcasting: adjusting arrays to the same shape
    + * space (space) <U1 'a' 'b' 'c'

    @@ -2462,7 +2462,7 @@

    Broadcasting: adjusting arrays to the same shape @@ -2841,9 +2841,9 @@

    Broadcasting: adjusting arrays to the same shapearray1 is missing the dimension time and array2 is missing the dimension space. Xarray then broadcasts or “expands” both arrays to 2D with dimensions space, time. Here is an illustration:

    @@ -3244,9 +3244,9 @@

    Broadcasting: adjusting arrays to the same shape

    Broadcasting in numpy#

    @@ -3698,9 +3698,9 @@

    Alignment: putting data on the same grid
    @@ -4082,8 +4082,8 @@

    Alignment: putting data on the same gridarr1 and arr2 have the same dimensions (space, time) but have values at different locations in the (space, time) plane with some locations in common.

    @@ -4511,7 +4511,7 @@

    Controlling alignment

    + * time (time) int64 0 1 2 3
    @@ -4887,7 +4887,7 @@

    Controlling alignment

    + * time (time) int64 0 1 2 3

    Here’s a visual depiction of all the join options

    @@ -5331,10 +5331,10 @@

    Controlling the fill valuefill_value keyword argument

    @@ -5715,10 +5715,10 @@

    Controlling the fill value @@ -6160,10 +6160,10 @@

    Controlling automatic alignment @@ -1904,7 +1904,7 @@

    Applying functions @@ -2433,7 +2433,7 @@

    Applying Arbitrary Functions

    Tip

    @@ -2903,7 +2903,7 @@

    Reductionsaxis (as in numpy), we can instead perform them using dimension names. This turns out to be a huge convenience, particularly in @@ -3329,7 +3329,7 @@

    Reductions @@ -3751,7 +3751,7 @@

    Reductions @@ -4179,7 +4179,7 @@

    Reductionsmin, max, sum, std, etc.) are available on both Datasets and DataArrays.

    diff --git a/fundamentals/03.2_groupby_with_xarray.html b/fundamentals/03.2_groupby_with_xarray.html index 7ecee451..5d21cbbc 100644 --- a/fundamentals/03.2_groupby_with_xarray.html +++ b/fundamentals/03.2_groupby_with_xarray.html @@ -941,14 +941,14 @@

    Example Dataset @@ -1430,20 +1430,20 @@

    Groupby @@ -1469,7 +1469,7 @@

    Identifying groups -
    <xarray.core.accessor_dt.DatetimeAccessor at 0x7f3a02519910>
+
    <xarray.core.accessor_dt.DatetimeAccessor at 0x7f9f43b9c750>
    @@ -1848,17 +1848,17 @@

    Identifying groups
    @@ -2234,17 +2234,17 @@

    Identifying groups @@ -2681,14 +2681,14 @@

    Aggregations or Reductionsgb.mean()

    @@ -3682,14 +3682,14 @@

    Apply by iteration @@ -4229,14 +4229,14 @@

    Transformationstime dimension by passing time='5Y'. '5Y' is a special frequency string. Xarray uses pandas to convert such a frequency string to a groupby operation. See the pandas documentation for how to specify a different frequency.

    @@ -5634,14 +5634,14 @@

    Resample
    @@ -6175,7 +6175,7 @@

    GroupBy vs Resample
    + dtype='int64', name='year'))

  • long_name :
    Monthly Means of Sea Surface Temperature
    units :
    degC
    var_desc :
    Sea Surface Temperature
    level_desc :
    Surface
    statistic :
    Mean
    dataset :
    NOAA Extended Reconstructed SST V5
    parent_stat :
    Individual Values
    actual_range :
    [-1.8 42.32636]
    valid_range :
    [-1.8 45. ]

    • @@ -6561,7 +6561,7 @@

    GroupBy vs Resample
    diff --git a/fundamentals/03.3_windowed.html b/fundamentals/03.3_windowed.html index 14b0ce62..ef6611d2 100644 --- a/fundamentals/03.3_windowed.html +++ b/fundamentals/03.3_windowed.html @@ -940,14 +940,14 @@

    Windowed Computations + dtype='datetime64[ns]', name='time', length=624, freq=None))

  • climatology :
    Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.
    description :
    In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.
    keywords_vocabulary :
    NASA Global Change Master Directory (GCMD) Science Keywords
    keywords :
    Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature >
    instrument :
    Conventional thermometers
    source_comment :
    SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker
    geospatial_lon_min :
    -1.0
    geospatial_lon_max :
    359.0
    geospatial_laty_max :
    89.0
    geospatial_laty_min :
    -89.0
    geospatial_lat_max :
    89.0
    geospatial_lat_min :
    -89.0
    geospatial_lat_units :
    degrees_north
    geospatial_lon_units :
    degrees_east
    cdm_data_type :
    Grid
    project :
    NOAA Extended Reconstructed Sea Surface Temperature (ERSST)
    original_publisher_url :
    http://www.ncdc.noaa.gov
    References :
    https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 at NCEI and http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html
    source :
    In situ data: ICOADS R3.0 before 2015, NCEP in situ GTS from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015
    title :
    NOAA ERSSTv5 (in situ only)
    history :
    created 07/2017 by PSD data using NCEI's ERSST V5 NetCDF values
    institution :
    This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Centers for Environmental Information and time aggregated. Original Full Source: NOAA/NESDIS/NCEI/CCOG
    citation :
    Huang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1
    platform :
    Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS
    standard_name_vocabulary :
    CF Standard Name Table (v40, 25 January 2017)
    processing_level :
    NOAA Level 4
    Conventions :
    CF-1.6, ACDD-1.3
    metadata_link :
    :metadata_link = https://doi.org/10.7289/V5T72FNM (original format)
    creator_name :
    Boyin Huang (original)
    date_created :
    2017-06-30T12:18:00Z (original)
    product_version :
    Version 5
    creator_url_original :
    https://www.ncei.noaa.gov
    license :
    No constraints on data access or use
    comment :
    SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters
    summary :
    ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis
    dataset_title :
    NOAA Extended Reconstructed SST V5
    data_modified :
    2022-06-07

    • Rolling or moving windows#

    @@ -1434,17 +1434,17 @@

    Rolling or moving windows
    @@ -1517,7 +1517,7 @@

    Rolling or moving windows -
    <matplotlib.legend.Legend at 0x7f263502c350>
+
    <matplotlib.legend.Legend at 0x7fbe7acafe50>
 
    
 
    
 ../_images/0aa642b5bb67fe59c9396d5da47465306c960fde2688cfd15c025296d3317ad5.png
@@ -1921,7 +1921,7 @@ 
    Custom reductionsconstruct and provide a name for the new dimension: window
    
 
    
@@ -2299,11 +2299,11 @@ 
    Custom reductions
 
@@ -2796,18 +2796,18 @@ 
    Coarsening
 
    
@@ -3243,10 +3243,10 @@ 
    Custom reductionsyear of size 12.
    
 
    
@@ -3624,9 +3624,9 @@ 
    Custom reductions
 
diff --git a/fundamentals/03.4_weighted.html b/fundamentals/03.4_weighted.html
index 1215f58d..9daf9728 100644
--- a/fundamentals/03.4_weighted.html
+++ b/fundamentals/03.4_weighted.html
@@ -923,14 +923,14 @@ 
    Weighted Reductions
 
    
@@ -1028,7 +1028,7 @@ 
    Weighted Reductions
    ('lat',)
 
    
 
    
-
    [<matplotlib.lines.Line2D at 0x7fa4a6b1b950>]
+
    [<matplotlib.lines.Line2D at 0x7f591724f550>]
 
    
 
    
 ../_images/f90ce26d7973eb6ca21f62d80514e891742ce03a7e583d4a59bcc98a8b4148c1.png
diff --git a/fundamentals/04.1_basic_plotting.html b/fundamentals/04.1_basic_plotting.html
index 995f15f1..fde8bb49 100644
--- a/fundamentals/04.1_basic_plotting.html
+++ b/fundamentals/04.1_basic_plotting.html
@@ -937,26 +937,26 @@ 
    Load dataTair is air temperature and dTdx
@@ -1032,7 +1032,7 @@ 
    2D plots
-
    <matplotlib.collections.QuadMesh at 0x7f6133879710>
+
    <matplotlib.collections.QuadMesh at 0x7fa534317510>
 
    
 
    
 ../_images/20bf5366aefdfcd89972a36b2d98dc6b1634bfd43e085091940393e63bf5ea98.png
@@ -1046,7 +1046,7 @@ 
    2D plots
-
    <matplotlib.collections.QuadMesh at 0x7f6133727750>
+
    <matplotlib.collections.QuadMesh at 0x7fa534200c10>
 
    
 
    
 ../_images/20bf5366aefdfcd89972a36b2d98dc6b1634bfd43e085091940393e63bf5ea98.png
@@ -1078,7 +1078,7 @@ 
    2D plots
-
    <matplotlib.collections.QuadMesh at 0x7f613162aad0>
+
    <matplotlib.collections.QuadMesh at 0x7fa5320d8c10>
 
    
 
    
 ../_images/dfebfd4472bc86f0ff49ddc695a3e7c4606d769ac7ec9cc1869f21c6440c46b5.png
@@ -1109,7 +1109,7 @@ 
    Exercise
 
 
    
-
    <matplotlib.collections.QuadMesh at 0x7f61314f1290>
+
    <matplotlib.collections.QuadMesh at 0x7fa531fd0c10>
 
    
 
    
 ../_images/ee153f06d07947f56eb9420678290b72a7e4378661121dd8197dda1db5f9712a.png
@@ -1148,7 +1148,7 @@ 
    Exercise
 
 
    
-
    <matplotlib.contour.QuadContourSet at 0x7f61316b57d0>
+
    <matplotlib.contour.QuadContourSet at 0x7fa5321a1e90>
 
    
 
    
 ../_images/83387f2a2fce0b579b9e4f237970fafe7722c11042562b947fe266f0691815a3.png
@@ -1169,7 +1169,7 @@ 
    1D line plots
-
    [<matplotlib.lines.Line2D at 0x7f6131446850>]
+
    [<matplotlib.lines.Line2D at 0x7fa531d6ec90>]
 
    
 
    
 ../_images/aa43f46013753f1326438d7dbe0ca84df8b3cbfb841715ec7a2e9eefc197a674.png
@@ -1183,7 +1183,7 @@ 
    1D line plots
-
    [<matplotlib.lines.Line2D at 0x7f61312b8510>]
+
    [<matplotlib.lines.Line2D at 0x7fa531db51d0>]
 
    
 
    
 ../_images/aa43f46013753f1326438d7dbe0ca84df8b3cbfb841715ec7a2e9eefc197a674.png
@@ -1200,9 +1200,9 @@ 
    Multiple lines with 
-
    [<matplotlib.lines.Line2D at 0x7f613134de90>,
- <matplotlib.lines.Line2D at 0x7f61311711d0>,
- <matplotlib.lines.Line2D at 0x7f6131171550>]
+
    [<matplotlib.lines.Line2D at 0x7fa531de53d0>,
+ <matplotlib.lines.Line2D at 0x7fa531c79010>,
+ <matplotlib.lines.Line2D at 0x7fa531c79250>]
 
    
 
    
 ../_images/472e711340e5e672e04ce783728e850748166a619a2e2086f0adf438a66cc12d.png
diff --git a/fundamentals/04.2_faceting.html b/fundamentals/04.2_faceting.html
index 4b2ed3f9..06a97bb7 100644
--- a/fundamentals/04.2_faceting.html
+++ b/fundamentals/04.2_faceting.html
@@ -615,7 +615,7 @@ 
    Exercise
-
    <xarray.plot.facetgrid.FacetGrid at 0x7f8f0b975350>
+
    <xarray.plot.facetgrid.FacetGrid at 0x7fb9f7ab6850>
 
    
 
    
 
    
@@ -646,7 +646,7 @@ 
    Modifying all facets
 
    
 
    
-
    <xarray.plot.facetgrid.FacetGrid at 0x7f8f1420ebd0>
+
    <xarray.plot.facetgrid.FacetGrid at 0x7fb9f7ade3d0>
 
    
 
    
 ../_images/0d66896768bbcf2d8c3994281aa33a6bb49c27ef8888c71669b297c276102805.png
@@ -1082,7 +1082,7 @@ 
    Faceting multiple DataArrays
diff --git a/fundamentals/04.3_geographic_plotting.html b/fundamentals/04.3_geographic_plotting.html
index ab534c0d..b84f67e6 100644
--- a/fundamentals/04.3_geographic_plotting.html
+++ b/fundamentals/04.3_geographic_plotting.html
@@ -564,7 +564,7 @@ 
    Basic plot
-
    <cartopy.mpl.feature_artist.FeatureArtist at 0x7fb35d1b5a50>
+
    <cartopy.mpl.feature_artist.FeatureArtist at 0x7f2d28f4f550>
 
    
 
    
 
    /home/runner/micromamba/envs/xarray-tutorial/lib/python3.11/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip
@@ -597,7 +597,7 @@ 
    Faceting maps
-
    <xarray.plot.facetgrid.FacetGrid at 0x7fb35cfde850>
+
    <xarray.plot.facetgrid.FacetGrid at 0x7f2d28fe1910>
 
    
 
    
 ../_images/725bc21df49f8c6aa0d346a99867e37f73292268dac5cd7d3d184ddb4f72b2ee.png
diff --git a/intermediate/01-high-level-computation-patterns.html b/intermediate/01-high-level-computation-patterns.html
index bf100778..8dcf76cc 100644
--- a/intermediate/01-high-level-computation-patterns.html
+++ b/intermediate/01-high-level-computation-patterns.html
@@ -1028,7 +1028,7 @@ 
    Load example dataset
+              dtype='datetime64[ns]', name='time', length=2920, freq=None))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    •

    @@ -1569,7 +1569,7 @@

    Concept refresher: “index space” vs “label space”
    • lat
      (lat)
      float32
      75.0 72.5 70.0 ... 20.0 17.5 15.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,
+        296.6    ]], dtype=float32)
    • lat
      (lat)
      float32
      75.0 72.5 70.0 ... 20.0 17.5 15.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,
        45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,
-       15. ], dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
+       15. ], dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
        225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
        250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
        275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
        300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
-       325. , 327.5, 330. ], dtype=float32)
    • time
      ()
      datetime64[ns]
      2013-01-01
      standard_name :
      time
      long_name :
      Time
      array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
    • lat
      PandasIndex
      PandasIndex(Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,
+       325. , 327.5, 330. ], dtype=float32)
    • time
      ()
      datetime64[ns]
      2013-01-01
      standard_name :
      time
      long_name :
      Time
      array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
    • lat
      PandasIndex
      PandasIndex(Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,
        45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,
        15.0],
-      dtype='float32', name='lat'))
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
+      dtype='float32', name='lat'))
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
        225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,
        250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,
        275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,
        300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,
        325.0, 327.5, 330.0],
-      dtype='float32', name='lon'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • + dtype='float32', name='lon'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • @@ -1987,7 +1987,7 @@

    Concept refresher: “index space” vs “label space”

    • lat
      ()
      float32
      50.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array(50., dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
+      dtype=float32)
    • lat
      ()
      float32
      50.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array(50., dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
        225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
        250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
        275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
        300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
-       325. , 327.5, 330. ], dtype=float32)
    • time
      ()
      datetime64[ns]
      2013-01-01
      standard_name :
      time
      long_name :
      Time
      array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
+       325. , 327.5, 330. ], dtype=float32)
    • time
      ()
      datetime64[ns]
      2013-01-01
      standard_name :
      time
      long_name :
      Time
      array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
        225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,
        250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,
        275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,
        300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,
        325.0, 327.5, 330.0],
-      dtype='float32', name='lon'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • + dtype='float32', name='lon'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • @@ -2397,7 +2397,7 @@

    Concept refresher: “index space” vs “label space”

    • lat
      ()
      float32
      50.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array(50., dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
+      dtype=float32)
    • lat
      ()
      float32
      50.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array(50., dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
        225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
        250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
        275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
        300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
-       325. , 327.5, 330. ], dtype=float32)
    • time
      ()
      datetime64[ns]
      2013-01-01
      standard_name :
      time
      long_name :
      Time
      array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
+       325. , 327.5, 330. ], dtype=float32)
    • time
      ()
      datetime64[ns]
      2013-01-01
      standard_name :
      time
      long_name :
      Time
      array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
        225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,
        250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,
        275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,
        300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,
        325.0, 327.5, 330.0],
-      dtype='float32', name='lon'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • + dtype='float32', name='lon'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • @@ -2807,7 +2807,7 @@

    Concept refresher: “index space” vs “label space”

    • lat
      ()
      float32
      50.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array(50., dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
+      dtype=float32)
    • lat
      ()
      float32
      50.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array(50., dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
        225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
        250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
        275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
        300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
-       325. , 327.5, 330. ], dtype=float32)
    • time
      ()
      datetime64[ns]
      2013-01-01
      standard_name :
      time
      long_name :
      Time
      array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
+       325. , 327.5, 330. ], dtype=float32)
    • time
      ()
      datetime64[ns]
      2013-01-01
      standard_name :
      time
      long_name :
      Time
      array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
        225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,
        250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,
        275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,
        300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,
        325.0, 327.5, 330.0],
-      dtype='float32', name='lon'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • + dtype='float32', name='lon'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • @@ -3220,7 +3220,7 @@

    Concept refresher: “index space” vs “label space”

    • lat
      ()
      float32
      50.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array(50., dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
+      dtype=float32)
    • lat
      ()
      float32
      50.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array(50., dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
        225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
        250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
        275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
        300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
-       325. , 327.5, 330. ], dtype=float32)
    • time
      ()
      datetime64[ns]
      2013-01-01
      standard_name :
      time
      long_name :
      Time
      array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
+       325. , 327.5, 330. ], dtype=float32)
    • time
      ()
      datetime64[ns]
      2013-01-01
      standard_name :
      time
      long_name :
      Time
      array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
        225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,
        250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,
        275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,
        300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,
        325.0, 327.5, 330.0],
-      dtype='float32', name='lon'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • + dtype='float32', name='lon'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • @@ -3714,7 +3714,7 @@

    View the rollin 
    <xarray.DataArray (time: 10)>
 0 1 2 3 4 5 6 7 8 9
 Coordinates:
-  * time     (time) int64 0 1 2 3 4 5 6 7 8 9
    + * time (time) int64 0 1 2 3 4 5 6 7 8 9
    @@ -4091,7 +4091,7 @@

    View the rollin nan nan 0.0 1.0 2.0 nan 0.0 1.0 2.0 3.0 ... 7.0 8.0 9.0 nan 7.0 8.0 9.0 nan nan Coordinates: * time (time) int64 0 1 2 3 4 5 6 7 8 9 -Dimensions without coordinates: window + [ 7., 8., 9., nan, nan]])

    • time
      (time)
      int64
      0 1 2 3 4 5 6 7 8 9
      array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    • time
      PandasIndex
      PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64', name='time'))

    • Note

    @@ -4520,7 +4520,7 @@

    Advanced: Another + dtype='float32', name='lon'))

  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • @@ -5404,7 +5404,7 @@

    Block windows of fixed length: + 297.5628 , 297.21838, 298.0064 , 297.0692 ]], dtype=float32)

    • lat
      (lat)
      float32
      70.0 57.5 45.0 32.5 20.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array([70. , 57.5, 45. , 32.5, 20. ], dtype=float32)
    • lon
      (lon)
      float32
      205.0 217.5 230.0 ... 305.0 317.5
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([205. , 217.5, 230. , 242.5, 255. , 267.5, 280. , 292.5, 305. , 317.5],
+      dtype=float32)
    • time
      ()
      datetime64[ns]
      2013-01-01
      standard_name :
      time
      long_name :
      Time
      array('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')
    • lat
      PandasIndex
      PandasIndex(Index([70.0, 57.5, 45.0, 32.5, 20.0], dtype='float32', name='lat'))
    • lon
      PandasIndex
      PandasIndex(Index([205.0, 217.5, 230.0, 242.5, 255.0, 267.5, 280.0, 292.5, 305.0, 317.5], dtype='float32', name='lon'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • @@ -5423,7 +5423,7 @@

    Block windows of fixed length:

    -
    <matplotlib.collections.QuadMesh at 0x7f6638ed7c10>
+
    <matplotlib.collections.QuadMesh at 0x7fbe030a72d0>
 
    
 
    
 ../_images/6e24f02dbf4dcaa6cb7619432656380116000489224111f12f41caa702b15d74.png
@@ -5833,11 +5833,11 @@ 
    Coarsen supports 
 
    
@@ -6214,9 +6214,9 @@ 
    Coarsen supports 
@@ -6599,9 +6599,9 @@ 
    Coarsen supports side kwarg to coarsen. For side="right" we get more sensible output.
    
@@ -6981,9 +6981,9 @@ 
    Coarsen supports coarsen pads with NaNs. For more control over padding, use
 DataArray.pad explicitly.
    
@@ -7366,9 +7366,9 @@ 
    Coarsen supports 
 
    Note
    
@@ -7866,7 +7866,7 @@ 
    Deconstructing GroupBy
+              dtype='datetime64[ns]', name='time', length=2920, freq=None))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • @@ -8343,7 +8343,7 @@

    Deconstructing GroupBy + dtype='float32', name='lon'))

  • PandasIndex
    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]

    • Notice that since we have averaged over all the years for each month, our resulting DataArray no longer has a “year” coordinate.

    If we want to see how Xarray identifies “groups” for the monthly climatology computation, we can plot our input to groupby. GroupBy is clever enough to figure out how many values there are an thus how many groups to make.

    @@ -8848,13 +8848,13 @@

    @@ -9243,10 +9243,10 @@

    @@ -9632,10 +9632,10 @@

    @@ -10024,10 +10024,10 @@

    @@ -10463,10 +10463,10 @@
    Custom seasons with
    @@ -10493,7 +10493,7 @@
    Custom seasons with -
    <xarray.plot.facetgrid.FacetGrid at 0x7f6638e5db90>
+
    <xarray.plot.facetgrid.FacetGrid at 0x7fbe0304ba90>
 
    
 
    
 ../_images/a62fdb0ad3207886e59dba4859e6160b93bdc7943499cf7742b376116341f553.png
@@ -10879,13 +10879,13 @@ 
    floor
+              dtype='datetime64[ns]', name='time', length=2920, freq=None))
  • standard_name :
    time
    long_name :
    Time

    • @@ -11276,13 +11276,13 @@
    floor + dtype='datetime64[ns]', name='time', length=2920, freq=None))
  • standard_name :
    time
    long_name :
    Time
    • @@ -11673,11 +11673,11 @@
    strftime 
    <xarray.DataArray 'strftime' (time: 2920)>
 'Jan-01' 'Jan-01' 'Jan-01' 'Jan-01' ... 'Dec-31' 'Dec-31' 'Dec-31' 'Dec-31'
 Coordinates:
-  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
    + dtype='datetime64[ns]', name='time', length=2920, freq=None))
    • @@ -11703,7 +11703,7 @@

    Custom reductions with
    -
    <xarray.plot.facetgrid.FacetGrid at 0x7f6638ac2d50>
+
    <xarray.plot.facetgrid.FacetGrid at 0x7fbe02c17c10>
 
    
 
    
 ../_images/377de1f14e6b5ca157dabe6019ebf3362d105e4d0db1bbd9381c09022f5d9175.png
@@ -12133,7 +12133,7 @@ 
    Instead looping over groupby objects is possible
@@ -12643,7 +12643,7 @@ 
    In most cases, avoid a for loop using
    • lat
      (lat)
      float32
      75.0 72.5 70.0 ... 20.0 17.5 15.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,
+          1.43247986,  1.5       ]]])
    • lat
      (lat)
      float32
      75.0 72.5 70.0 ... 20.0 17.5 15.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,
        45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,
-       15. ], dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
+       15. ], dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
        225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
        250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
        275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
        300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
-       325. , 327.5, 330. ], dtype=float32)
    • month
      (month)
      int64
      1 2 3 4 5 6 7 8 9 10 11 12
      array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])
    • lat
      PandasIndex
      PandasIndex(Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,
+       325. , 327.5, 330. ], dtype=float32)
    • month
      (month)
      int64
      1 2 3 4 5 6 7 8 9 10 11 12
      array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])
    • lat
      PandasIndex
      PandasIndex(Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,
        45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,
        15.0],
-      dtype='float32', name='lat'))
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
+      dtype='float32', name='lat'))
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
        225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,
        250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,
        275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,
        300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,
        325.0, 327.5, 330.0],
-      dtype='float32', name='lon'))
    • month
      PandasIndex
      PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • + dtype='float32', name='lon'))
  • month
    PandasIndex
    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))
  • long_name :
    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • diff --git a/intermediate/cmip6-cloud.html b/intermediate/cmip6-cloud.html index 82bc0157..be3f8476 100644 --- a/intermediate/cmip6-cloud.html +++ b/intermediate/cmip6-cloud.html @@ -1331,7 +1331,7 @@

    Reading from the remote Zarr storage @@ -1386,7 +1386,7 @@

    Reading from the remote Zarr storage @@ -1464,7 +1464,7 @@

    Reading from the remote Zarr storage @@ -1519,7 +1519,7 @@

    Reading from the remote Zarr storage @@ -1597,14 +1597,14 @@

    Reading from the remote Zarr storage @@ -1659,8 +1659,8 @@

    Reading from the remote Zarr storage @@ -1770,7 +1770,7 @@

    Reading from the remote Zarr storage
    -
    <matplotlib.image.AxesImage at 0x7f6cf94d29d0>
+
    <matplotlib.image.AxesImage at 0x7f2c4cc86450>
 
    
 
    
 ../_images/d5daad6f9031d5bc813f2ca19c7d9534a1f3ba86fcf402310238afd516d5f791.png
diff --git a/intermediate/hvplot.html b/intermediate/hvplot.html
index 3088d8bc..dfd52a29 100644
--- a/intermediate/hvplot.html
+++ b/intermediate/hvplot.html
@@ -1082,11 +1082,11 @@ 
    Basics
 
    
-  
    
+