Get Specific Months Data and annual mean

spec_moth_dat

get specific months’ data

Parameter:

• DaArray (xr.DataArray): shape = (time, *number) original Dataarray

• months (list): get data from specific month

Raises:

• ValueError: xr.DataArray’ input is required, not the %s

Returns:

• xr.DataArray: data in specific month

Example

Load Modules

import xarray as xr
import sacpy as scp

Load and Get Data

sst = scp.load_sst()['sst']
scp.spec_moth_dat(sst,[1,2])

<frozen importlib._bootstrap>:219: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility. Expected 16 from C header, got 96 from PyObject

<xarray.DataArray 'sst' (time: 62, lat: 13, lon: 57)>
[45942 values with dtype=float64]
Coordinates:
  * time     (time) datetime64[ns] 1991-01-16T12:00:00 ... 2021-02-16T12:00:00
  * lon      (lon) int64 20 25 30 35 40 45 50 55 ... 270 275 280 285 290 295 300
  * lat      (lat) int64 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30

xarray.DataArray

'sst'

• time: 62
• lat: 13
• lon: 57

• ...

  [45942 values with dtype=float64]

• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    1991-01-16T12:00:00 ... 2021-02-...

    array(['1991-01-16T12:00:00.000000000', '1991-02-15T00:00:00.000000000',
           '1992-01-16T12:00:00.000000000', '1992-02-15T00:00:00.000000000',
           '1993-01-16T12:00:00.000000000', '1993-02-15T00:00:00.000000000',
           '1994-01-16T12:00:00.000000000', '1994-02-15T00:00:00.000000000',
           '1995-01-16T12:00:00.000000000', '1995-02-15T00:00:00.000000000',
           '1996-01-16T12:00:00.000000000', '1996-02-15T00:00:00.000000000',
           '1997-01-16T12:00:00.000000000', '1997-02-15T00:00:00.000000000',
           '1998-01-16T12:00:00.000000000', '1998-02-15T00:00:00.000000000',
           '1999-01-16T12:00:00.000000000', '1999-02-15T00:00:00.000000000',
           '2000-01-16T12:00:00.000000000', '2000-02-15T00:00:00.000000000',
           '2001-01-16T12:00:00.000000000', '2001-02-15T00:00:00.000000000',
           '2002-01-16T12:00:00.000000000', '2002-02-15T00:00:00.000000000',
           '2003-01-16T12:00:00.000000000', '2003-02-15T00:00:00.000000000',
           '2004-01-16T12:00:00.000000000', '2004-02-15T00:00:00.000000000',
           '2005-01-16T12:00:00.000000000', '2005-02-15T00:00:00.000000000',
           '2006-01-16T12:00:00.000000000', '2006-02-15T00:00:00.000000000',
           '2007-01-16T12:00:00.000000000', '2007-02-15T00:00:00.000000000',
           '2008-01-16T12:00:00.000000000', '2008-02-16T12:00:00.000000000',
           '2009-01-16T12:00:00.000000000', '2009-02-16T12:00:00.000000000',
           '2010-01-16T12:00:00.000000000', '2010-02-16T12:00:00.000000000',
           '2011-01-16T12:00:00.000000000', '2011-02-16T12:00:00.000000000',
           '2012-01-16T12:00:00.000000000', '2012-02-16T12:00:00.000000000',
           '2013-01-16T12:00:00.000000000', '2013-02-16T12:00:00.000000000',
           '2014-01-16T12:00:00.000000000', '2014-02-16T12:00:00.000000000',
           '2015-01-16T12:00:00.000000000', '2015-02-16T12:00:00.000000000',
           '2016-01-16T12:00:00.000000000', '2016-02-16T12:00:00.000000000',
           '2017-01-16T12:00:00.000000000', '2017-02-16T12:00:00.000000000',
           '2018-01-16T12:00:00.000000000', '2018-02-16T12:00:00.000000000',
           '2019-01-16T12:00:00.000000000', '2019-02-16T12:00:00.000000000',
           '2020-01-16T12:00:00.000000000', '2020-02-16T12:00:00.000000000',
           '2021-01-16T12:00:00.000000000', '2021-02-16T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    int64
    20 25 30 35 40 ... 285 290 295 300

    array([ 20,  25,  30,  35,  40,  45,  50,  55,  60,  65,  70,  75,  80,  85,
            90,  95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155,
           160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225,
           230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 295,
           300])

  • lat
    (lat)
    int64
    -30 -25 -20 -15 -10 ... 15 20 25 30

    array([-30, -25, -20, -15, -10,  -5,   0,   5,  10,  15,  20,  25,  30])

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1991-01-16 12:00:00', '1991-02-15 00:00:00',
                   '1992-01-16 12:00:00', '1992-02-15 00:00:00',
                   '1993-01-16 12:00:00', '1993-02-15 00:00:00',
                   '1994-01-16 12:00:00', '1994-02-15 00:00:00',
                   '1995-01-16 12:00:00', '1995-02-15 00:00:00',
                   '1996-01-16 12:00:00', '1996-02-15 00:00:00',
                   '1997-01-16 12:00:00', '1997-02-15 00:00:00',
                   '1998-01-16 12:00:00', '1998-02-15 00:00:00',
                   '1999-01-16 12:00:00', '1999-02-15 00:00:00',
                   '2000-01-16 12:00:00', '2000-02-15 00:00:00',
                   '2001-01-16 12:00:00', '2001-02-15 00:00:00',
                   '2002-01-16 12:00:00', '2002-02-15 00:00:00',
                   '2003-01-16 12:00:00', '2003-02-15 00:00:00',
                   '2004-01-16 12:00:00', '2004-02-15 00:00:00',
                   '2005-01-16 12:00:00', '2005-02-15 00:00:00',
                   '2006-01-16 12:00:00', '2006-02-15 00:00:00',
                   '2007-01-16 12:00:00', '2007-02-15 00:00:00',
                   '2008-01-16 12:00:00', '2008-02-16 12:00:00',
                   '2009-01-16 12:00:00', '2009-02-16 12:00:00',
                   '2010-01-16 12:00:00', '2010-02-16 12:00:00',
                   '2011-01-16 12:00:00', '2011-02-16 12:00:00',
                   '2012-01-16 12:00:00', '2012-02-16 12:00:00',
                   '2013-01-16 12:00:00', '2013-02-16 12:00:00',
                   '2014-01-16 12:00:00', '2014-02-16 12:00:00',
                   '2015-01-16 12:00:00', '2015-02-16 12:00:00',
                   '2016-01-16 12:00:00', '2016-02-16 12:00:00',
                   '2017-01-16 12:00:00', '2017-02-16 12:00:00',
                   '2018-01-16 12:00:00', '2018-02-16 12:00:00',
                   '2019-01-16 12:00:00', '2019-02-16 12:00:00',
                   '2020-01-16 12:00:00', '2020-02-16 12:00:00',
                   '2021-01-16 12:00:00', '2021-02-16 12:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • lon
    PandasIndex

    PandasIndex(Int64Index([ 20,  25,  30,  35,  40,  45,  50,  55,  60,  65,  70,  75,  80,
                 85,  90,  95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145,
                150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210,
                215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275,
                280, 285, 290, 295, 300],
               dtype='int64', name='lon'))

  • lat
    PandasIndex

    PandasIndex(Int64Index([-30, -25, -20, -15, -10, -5, 0, 5, 10, 15, 20, 25, 30], dtype='int64', name='lat'))

• Attributes: (0)

spec_moth_yrmean

get specific month data and average them in each year

Parameter:

• DaArray (xr.DataArray): shape = (time, *number) original Dataarray

• months (list): get data from specific month

Returns:

• xr.DataArray: data in specific month and average them in each year

Example

sst = scp.load_sst()['sst']
scp.spec_moth_yrmean(sst,[12,1,2])

<xarray.DataArray 'sst' (time: 30, lat: 13, lon: 57)>
array([[[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [        nan,         nan,         nan, ...,         nan,
         25.82674297, 25.56359132],
        [        nan,         nan,         nan, ..., 24.08079465,
         23.95300039, 23.89301427],
        [        nan,         nan,         nan, ..., 21.20026779,
         21.11359294, 21.35712878]],

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
...
        [        nan,         nan,         nan, ...,         nan,
         27.00849136, 26.57378038],
        [        nan,         nan,         nan, ..., 25.40256437,
         25.14014912, 24.91551399],
        [        nan,         nan,         nan, ..., 22.52778212,
         22.33078369, 22.41735236]],

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [        nan,         nan,         nan, ...,         nan,
         26.71056239, 26.41532103],
        [        nan,         nan,         nan, ..., 25.12457975,
         24.86658351, 24.81672478],
        [        nan,         nan,         nan, ..., 22.16162841,
         22.10272376, 22.36174266]]])
Coordinates:
  * time     (time) int64 1991 1992 1993 1994 1995 ... 2016 2017 2018 2019 2020
  * lon      (lon) int64 20 25 30 35 40 45 50 55 ... 270 275 280 285 290 295 300
  * lat      (lat) int64 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30

xarray.DataArray

'sst'

• time: 30
• lat: 13
• lon: 57

• nan nan nan 25.06 24.79 24.26 23.64 ... nan nan 22.9 22.16 22.1 22.36

  array([[[        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          [        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          [        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          ...,
          [        nan,         nan,         nan, ...,         nan,
           25.82674297, 25.56359132],
          [        nan,         nan,         nan, ..., 24.08079465,
           23.95300039, 23.89301427],
          [        nan,         nan,         nan, ..., 21.20026779,
           21.11359294, 21.35712878]],
  
         [[        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          [        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          [        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
  ...
          [        nan,         nan,         nan, ...,         nan,
           27.00849136, 26.57378038],
          [        nan,         nan,         nan, ..., 25.40256437,
           25.14014912, 24.91551399],
          [        nan,         nan,         nan, ..., 22.52778212,
           22.33078369, 22.41735236]],
  
         [[        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          [        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          [        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          ...,
          [        nan,         nan,         nan, ...,         nan,
           26.71056239, 26.41532103],
          [        nan,         nan,         nan, ..., 25.12457975,
           24.86658351, 24.81672478],
          [        nan,         nan,         nan, ..., 22.16162841,
           22.10272376, 22.36174266]]])

• Coordinates: (3)
  • time
    (time)
    int64
    1991 1992 1993 ... 2018 2019 2020

    array([1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
           2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
           2015, 2016, 2017, 2018, 2019, 2020])

  • lon
    (lon)
    int64
    20 25 30 35 40 ... 285 290 295 300

    array([ 20,  25,  30,  35,  40,  45,  50,  55,  60,  65,  70,  75,  80,  85,
            90,  95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155,
           160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225,
           230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 295,
           300])

  • lat
    (lat)
    int64
    -30 -25 -20 -15 -10 ... 15 20 25 30

    array([-30, -25, -20, -15, -10,  -5,   0,   5,  10,  15,  20,  25,  30])

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(Int64Index([1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
                2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
                2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020],
               dtype='int64', name='time'))

  • lon
    PandasIndex

    PandasIndex(Int64Index([ 20,  25,  30,  35,  40,  45,  50,  55,  60,  65,  70,  75,  80,
                 85,  90,  95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145,
                150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210,
                215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275,
                280, 285, 290, 295, 300],
               dtype='int64', name='lon'))

  • lat
    PandasIndex

    PandasIndex(Int64Index([-30, -25, -20, -15, -10, -5, 0, 5, 10, 15, 20, 25, 30], dtype='int64', name='lat'))

• Attributes: (0)