A “spell” system

This notebook demonstrates x4c’s “spell” system: a concise string-based syntax for requesting diagnostics from CESM outputs. A “spell” encodes the variable, operation, and region (for example, ‘TS:ann:gm’ means global-mean annual timeseries). Use the examples below to compute, inspect, and plot diagnostics generated by x4c.

%load_ext autoreload
%autoreload 2

import os
import x4c
import datetime

os.chdir('/glade/u/home/fengzhu/Github/x4c/docsrc/notebooks')
print(x4c.__version__)
print(f'Last Update: {datetime.date.today()}')

2026.3.9
Last Update: 2026-03-09

dirpath = './b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005'
case = x4c.Timeseries(dirpath, grid_dict={'atm': 'ne16np4'}, cesm_ver=1)

>>> case.root_dir: /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005
>>> case.path_pattern: comp/proc/tseries/*/casename.hstr.vn.timespan.nc
>>> case.grid_dict: {'atm': 'ne16np4', 'ocn': 'g16', 'lnd': 'ne16np4', 'rof': 'ne16np4', 'ice': 'g16'}
>>> case.casename: b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005
>>> case.paths["atm"]["cam.h0"] created
>>> case.paths["ocn"]["pop.h"] created
>>> case.paths["lnd"]["clm2.h0"] created
>>> case.paths["rof"]["rtm.h0"] created
>>> case.paths["ice"]["cice.h"] created
>>> case.vns["atm"]["cam.h0"] created
>>> case.vns["ocn"]["pop.h"] created
>>> case.vns["lnd"]["clm2.h0"] created
>>> case.vns["rof"]["rtm.h0"] created
>>> case.vns["ice"]["cice.h"] created

A 1st order variable that exists under the CESM Postprocessing Timeseries directory

spell = 'TS'
case.calc(spell)
case.diags[spell]

>>> case.ds["TS"] created
>>> case.diags["TS"] created

<xarray.DataArray 'TS' (time: 600, ncol: 13826)> Size: 33MB
array([[30.723846, 30.15387 , 29.340027, ..., 20.610352, 17.8833  ,
        18.761658],
       [31.421326, 31.069   , 30.561584, ..., 19.588074, 17.21759 ,
        18.28897 ],
       [31.425201, 31.07251 , 30.54779 , ..., 19.613098, 18.46637 ,
        19.39978 ],
       ...,
       [25.155579, 24.095459, 22.823944, ..., 29.183075, 27.860046,
        28.439423],
       [27.179352, 26.204254, 24.977203, ..., 25.29721 , 22.786072,
        23.526276],
       [29.186981, 28.433838, 27.424072, ..., 21.56839 , 19.447754,
        20.136566]], shape=(600, 13826), dtype=float32)
Coordinates:
  * time     (time) object 5kB 8951-01-01 00:00:00 ... 9000-12-01 00:00:00
    lat      (ncol) float64 111kB -35.26 -35.98 -37.07 ... 37.91 37.91 36.74
    lon      (ncol) float64 111kB 315.0 316.6 319.1 320.6 ... 132.5 137.5 135.0
Dimensions without coordinates: ncol
Attributes:
    units:         °C
    long_name:     Surface temperature (radiative)
    cell_methods:  time: mean
    path:          /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1...
    gw:            <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB\narray...
    lat:           <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB\n[13826...
    lon:           <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB\n[13826...
    comp:          atm
    grid:          ne16np4

xarray.DataArray

'TS'

• time: 600
• ncol: 13826

• 30.72 30.15 29.34 28.77 31.63 30.99 ... 21.87 20.14 21.57 19.45 20.14

  array([[30.723846, 30.15387 , 29.340027, ..., 20.610352, 17.8833  ,
          18.761658],
         [31.421326, 31.069   , 30.561584, ..., 19.588074, 17.21759 ,
          18.28897 ],
         [31.425201, 31.07251 , 30.54779 , ..., 19.613098, 18.46637 ,
          19.39978 ],
         ...,
         [25.155579, 24.095459, 22.823944, ..., 29.183075, 27.860046,
          28.439423],
         [27.179352, 26.204254, 24.977203, ..., 25.29721 , 22.786072,
          23.526276],
         [29.186981, 28.433838, 27.424072, ..., 21.56839 , 19.447754,
          20.136566]], shape=(600, 13826), dtype=float32)

• Coordinates: (3)
  • time
    (time)
    object
    8951-01-01 00:00:00 ... 9000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(8951, 1, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8951, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8951, 3, 1, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(9000, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 12, 1, 0, 0, 0, 0, has_year_zero=True)],
          shape=(600,), dtype=object)

  • lat
    (ncol)
    float64
    -35.26 -35.98 ... 37.91 36.74

    long_name :

    latitude

    units :

    degrees_north

    array([-35.26438968, -35.98082588, -37.07155175, ...,  37.90897632,
            37.90897632,  36.74360278], shape=(13826,))

  • lon
    (ncol)
    float64
    315.0 316.6 319.1 ... 137.5 135.0

    long_name :

    longitude

    units :

    degrees_east

    array([315.        , 316.55471176, 319.07028824, ..., 132.47470023,
           137.52529977, 135.        ], shape=(13826,))

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([8951-01-01 00:00:00, 8951-02-01 00:00:00, 8951-03-01 00:00:00,
                 8951-04-01 00:00:00, 8951-05-01 00:00:00, 8951-06-01 00:00:00,
                 8951-07-01 00:00:00, 8951-08-01 00:00:00, 8951-09-01 00:00:00,
                 8951-10-01 00:00:00,
                 ...
                 9000-03-01 00:00:00, 9000-04-01 00:00:00, 9000-05-01 00:00:00,
                 9000-06-01 00:00:00, 9000-07-01 00:00:00, 9000-08-01 00:00:00,
                 9000-09-01 00:00:00, 9000-10-01 00:00:00, 9000-11-01 00:00:00,
                 9000-12-01 00:00:00],
                dtype='object', length=600, calendar='noleap', freq='MS'))

• Attributes: (9)

  units :

  °C

  long_name :

  Surface temperature (radiative)

  cell_methods :

  time: mean

  path :

  /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cam.h0.TS.895101-900012.nc

  gw :

  <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB array([0.00015457, 0.00051501, 0.00051372, ..., 0.00128989, 0.00128989, 0.00128935], shape=(13826,)) Dimensions without coordinates: ncol

  lat :

  <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: latitude units: degrees_north

  lon :

  <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: longitude units: degrees_east

  comp :

  atm

  grid :

  ne16np4

A deduced variable based on 1st order variables

spell = 'LST'
case.calc(spell)
case.diags[spell]

>>> LST is a supported derived variable.
>>> case.ds["TS"] already loaded; to reload, run case.load("TS", ..., reload=True).
>>> case.ds["LANDFRAC"] created
>>> case.ds["LST"] created
>>> case.diags["LST"] created

<xarray.DataArray 'LST' (time: 600, ncol: 13826)> Size: 33MB
array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       ...,
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]],
      shape=(600, 13826), dtype=float32)
Coordinates:
  * time     (time) object 5kB 8951-01-01 00:00:00 ... 9000-12-01 00:00:00
    lat      (ncol) float64 111kB -35.26 -35.98 -37.07 ... 37.91 37.91 36.74
    lon      (ncol) float64 111kB 315.0 316.6 319.1 320.6 ... 132.5 137.5 135.0
Dimensions without coordinates: ncol
Attributes:
    units:         °C
    long_name:     Land Surface Temperature
    cell_methods:  time: mean
    path:          /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1...
    gw:            <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB\narray...
    lat:           <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB\n[13826...
    lon:           <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB\n[13826...
    comp:          atm
    grid:          ne16np4
    vn:            LST

xarray.DataArray

'LST'

• time: 600
• ncol: 13826

• nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan

  array([[nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan],
         ...,
         [nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan]],
        shape=(600, 13826), dtype=float32)

• Coordinates: (3)
  • time
    (time)
    object
    8951-01-01 00:00:00 ... 9000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(8951, 1, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8951, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8951, 3, 1, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(9000, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 12, 1, 0, 0, 0, 0, has_year_zero=True)],
          shape=(600,), dtype=object)

  • lat
    (ncol)
    float64
    -35.26 -35.98 ... 37.91 36.74

    long_name :

    latitude

    units :

    degrees_north

    array([-35.26438968, -35.98082588, -37.07155175, ...,  37.90897632,
            37.90897632,  36.74360278], shape=(13826,))

  • lon
    (ncol)
    float64
    315.0 316.6 319.1 ... 137.5 135.0

    long_name :

    longitude

    units :

    degrees_east

    array([315.        , 316.55471176, 319.07028824, ..., 132.47470023,
           137.52529977, 135.        ], shape=(13826,))

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([8951-01-01 00:00:00, 8951-02-01 00:00:00, 8951-03-01 00:00:00,
                 8951-04-01 00:00:00, 8951-05-01 00:00:00, 8951-06-01 00:00:00,
                 8951-07-01 00:00:00, 8951-08-01 00:00:00, 8951-09-01 00:00:00,
                 8951-10-01 00:00:00,
                 ...
                 9000-03-01 00:00:00, 9000-04-01 00:00:00, 9000-05-01 00:00:00,
                 9000-06-01 00:00:00, 9000-07-01 00:00:00, 9000-08-01 00:00:00,
                 9000-09-01 00:00:00, 9000-10-01 00:00:00, 9000-11-01 00:00:00,
                 9000-12-01 00:00:00],
                dtype='object', length=600, calendar='noleap', freq='MS'))

• Attributes: (10)

  units :

  °C

  long_name :

  Land Surface Temperature

  cell_methods :

  time: mean

  path :

  /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cam.h0.TS.895101-900012.nc

  gw :

  <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB array([0.00015457, 0.00051501, 0.00051372, ..., 0.00128989, 0.00128989, 0.00128935], shape=(13826,)) Dimensions without coordinates: ncol

  lat :

  <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: latitude units: degrees_north

  lon :

  <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: longitude units: degrees_east

  comp :

  atm

  grid :

  ne16np4

  vn :

  LST

spell = 'd18Op'
case.calc(spell)
case.diags[spell]

>>> d18Op is a supported derived variable.
>>> case.ds["PRECRC_H216Or"] created
>>> case.ds["PRECSC_H216Os"] created
>>> case.ds["PRECRL_H216OR"] created
>>> case.ds["PRECSL_H216OS"] created
>>> case.ds["PRECRC_H218Or"] created
>>> case.ds["PRECSC_H218Os"] created
>>> case.ds["PRECRL_H218OR"] created
>>> case.ds["PRECSL_H218OS"] created
>>> case.ds["d18Op"] created
>>> case.diags["d18Op"] created

<xarray.DataArray 'd18Op' (time: 600, ncol: 13826)> Size: 33MB
array([[-3.9242506, -4.0639043, -4.1208863, ..., -9.632945 , -8.070588 ,
        -8.230329 ],
       [-5.675137 , -5.547166 , -5.905688 , ..., -8.86035  , -8.010567 ,
        -7.663846 ],
       [-4.2343736, -4.4196844, -4.8832297, ..., -7.989228 , -7.1674585,
        -6.616235 ],
       ...,
       [-4.006505 , -4.380405 , -5.3446293, ..., -5.651295 , -5.3805113,
        -5.291581 ],
       [-3.416419 , -4.7590733, -4.9126744, ..., -8.602322 , -6.9465637,
        -6.7592263],
       [-3.531158 , -3.5579205, -3.6706924, ..., -8.005201 , -7.1768165,
        -6.878078 ]], shape=(600, 13826), dtype=float32)
Coordinates:
  * time     (time) object 5kB 8951-01-01 00:00:00 ... 9000-12-01 00:00:00
    lat      (ncol) float64 111kB -35.26 -35.98 -37.07 ... 37.91 37.91 36.74
    lon      (ncol) float64 111kB 315.0 316.6 319.1 320.6 ... 132.5 137.5 135.0
Dimensions without coordinates: ncol
Attributes:
    units:         permil
    long_name:     Precipitation d18O
    cell_methods:  time: mean
    path:          /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1...
    gw:            <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB\narray...
    lat:           <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB\n[13826...
    lon:           <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB\n[13826...
    comp:          atm
    grid:          ne16np4
    vn:            d18Op

xarray.DataArray

'd18Op'

• time: 600
• ncol: 13826

• -3.924 -4.064 -4.121 -4.052 -3.643 ... -8.267 -8.005 -7.177 -6.878

  array([[-3.9242506, -4.0639043, -4.1208863, ..., -9.632945 , -8.070588 ,
          -8.230329 ],
         [-5.675137 , -5.547166 , -5.905688 , ..., -8.86035  , -8.010567 ,
          -7.663846 ],
         [-4.2343736, -4.4196844, -4.8832297, ..., -7.989228 , -7.1674585,
          -6.616235 ],
         ...,
         [-4.006505 , -4.380405 , -5.3446293, ..., -5.651295 , -5.3805113,
          -5.291581 ],
         [-3.416419 , -4.7590733, -4.9126744, ..., -8.602322 , -6.9465637,
          -6.7592263],
         [-3.531158 , -3.5579205, -3.6706924, ..., -8.005201 , -7.1768165,
          -6.878078 ]], shape=(600, 13826), dtype=float32)

• Coordinates: (3)
  • time
    (time)
    object
    8951-01-01 00:00:00 ... 9000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(8951, 1, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8951, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8951, 3, 1, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(9000, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 12, 1, 0, 0, 0, 0, has_year_zero=True)],
          shape=(600,), dtype=object)

  • lat
    (ncol)
    float64
    -35.26 -35.98 ... 37.91 36.74

    long_name :

    latitude

    units :

    degrees_north

    array([-35.26438968, -35.98082588, -37.07155175, ...,  37.90897632,
            37.90897632,  36.74360278], shape=(13826,))

  • lon
    (ncol)
    float64
    315.0 316.6 319.1 ... 137.5 135.0

    long_name :

    longitude

    units :

    degrees_east

    array([315.        , 316.55471176, 319.07028824, ..., 132.47470023,
           137.52529977, 135.        ], shape=(13826,))

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([8951-01-01 00:00:00, 8951-02-01 00:00:00, 8951-03-01 00:00:00,
                 8951-04-01 00:00:00, 8951-05-01 00:00:00, 8951-06-01 00:00:00,
                 8951-07-01 00:00:00, 8951-08-01 00:00:00, 8951-09-01 00:00:00,
                 8951-10-01 00:00:00,
                 ...
                 9000-03-01 00:00:00, 9000-04-01 00:00:00, 9000-05-01 00:00:00,
                 9000-06-01 00:00:00, 9000-07-01 00:00:00, 9000-08-01 00:00:00,
                 9000-09-01 00:00:00, 9000-10-01 00:00:00, 9000-11-01 00:00:00,
                 9000-12-01 00:00:00],
                dtype='object', length=600, calendar='noleap', freq='MS'))

• Attributes: (10)

  units :

  permil

  long_name :

  Precipitation d18O

  cell_methods :

  time: mean

  path :

  /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cam.h0.PRECRC_H218Or.895101-900012.nc

  gw :

  <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB array([0.00015457, 0.00051501, 0.00051372, ..., 0.00128989, 0.00128989, 0.00128935], shape=(13826,)) Dimensions without coordinates: ncol

  lat :

  <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: latitude units: degrees_north

  lon :

  <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: longitude units: degrees_east

  comp :

  atm

  grid :

  ne16np4

  vn :

  d18Op

Climatology

spell = 'TS:climo:gm'
case.calc(spell)
case.diags[spell]

>>> Variable `TS` is already calculated and the calculation is skipped.
>>> Timespan: [8951-01-01 00:00:00, 9000-12-01 00:00:00]
>>> case.diags["TS:climo:gm"] created

<xarray.DataArray 'TS' (time: 12)> Size: 96B
array([22.95040876, 23.45636378, 23.9469648 , 24.34329028, 24.78539403,
       25.38467318, 25.82864283, 25.74886232, 25.08311286, 24.10669313,
       23.1864649 , 22.72951153])
Coordinates:
  * time     (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12
Attributes:
    units:         °C
    long_name:     Global Mean Surface temperature (radiative) (Climatology)
    cell_methods:  time: mean
    path:          /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1...
    gw:            <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB\narray...
    lat:           <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB\n[13826...
    lon:           <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB\n[13826...
    comp:          atm
    grid:          ne16np4
    climo_period:  (np.int64(8951), np.int64(9000))

xarray.DataArray

'TS'

• time: 12

• 22.95 23.46 23.95 24.34 24.79 25.38 ... 25.75 25.08 24.11 23.19 22.73

  array([22.95040876, 23.45636378, 23.9469648 , 24.34329028, 24.78539403,
         25.38467318, 25.82864283, 25.74886232, 25.08311286, 24.10669313,
         23.1864649 , 22.72951153])

• Coordinates: (1)
  • time
    (time)
    int64
    1 2 3 4 5 6 7 8 9 10 11 12

    long_name :

    Model Year

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='time'))

• Attributes: (10)

  units :

  °C

  long_name :

  Global Mean Surface temperature (radiative) (Climatology)

  cell_methods :

  time: mean

  path :

  /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cam.h0.TS.895101-900012.nc

  gw :

  <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB array([0.00015457, 0.00051501, 0.00051372, ..., 0.00128989, 0.00128989, 0.00128935], shape=(13826,)) Dimensions without coordinates: ncol

  lat :

  <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: latitude units: degrees_north

  lon :

  <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: longitude units: degrees_east

  comp :

  atm

  grid :

  ne16np4

  climo_period :

  (np.int64(8951), np.int64(9000))

x4c.set_style('web', font_scale=1)
fig, ax = case.plot(spell)

A horizontal 2D map

spell = 'TS:ann'
case.calc(spell)
case.diags[spell]

>>> Variable `TS` is already calculated and the calculation is skipped.
>>> Timespan: [8951-01-01 00:00:00, 9000-12-01 00:00:00]
>>> case.diags["TS:ann"] created

<xarray.DataArray 'TS' (time: 50, ncol: 13826)> Size: 3MB
array([[27.095312, 26.468687, 25.667841, ..., 24.843073, 23.622728,
        24.450674],
       [26.876898, 26.190992, 25.369055, ..., 24.968979, 23.625565,
        24.622602],
       [27.404486, 26.787392, 25.993246, ..., 24.89312 , 23.81645 ,
        24.575073],
       ...,
       [27.478502, 26.83081 , 26.000784, ..., 24.397955, 23.188248,
        24.005974],
       [27.604712, 26.873049, 25.939466, ..., 24.391893, 23.050173,
        24.072235],
       [27.74832 , 27.028776, 26.09156 , ..., 25.60959 , 24.387312,
        25.152489]], shape=(50, 13826), dtype=float32)
Coordinates:
    lat      (ncol) float64 111kB -35.26 -35.98 -37.07 ... 37.91 37.91 36.74
    lon      (ncol) float64 111kB 315.0 316.6 319.1 320.6 ... 132.5 137.5 135.0
  * time     (time) object 400B 8951-12-31 00:00:00 ... 9000-12-31 00:00:00
Dimensions without coordinates: ncol
Attributes:
    units:         °C
    long_name:     Surface temperature (radiative) (Annual)
    cell_methods:  time: mean
    path:          /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1...
    gw:            <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB\narray...
    lat:           <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB\n[13826...
    lon:           <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB\n[13826...
    comp:          atm
    grid:          ne16np4

xarray.DataArray

'TS'

• time: 50
• ncol: 13826

• 27.1 26.47 25.67 25.22 27.96 27.24 ... 25.22 24.45 25.61 24.39 25.15

  array([[27.095312, 26.468687, 25.667841, ..., 24.843073, 23.622728,
          24.450674],
         [26.876898, 26.190992, 25.369055, ..., 24.968979, 23.625565,
          24.622602],
         [27.404486, 26.787392, 25.993246, ..., 24.89312 , 23.81645 ,
          24.575073],
         ...,
         [27.478502, 26.83081 , 26.000784, ..., 24.397955, 23.188248,
          24.005974],
         [27.604712, 26.873049, 25.939466, ..., 24.391893, 23.050173,
          24.072235],
         [27.74832 , 27.028776, 26.09156 , ..., 25.60959 , 24.387312,
          25.152489]], shape=(50, 13826), dtype=float32)

• Coordinates: (3)
  • lat
    (ncol)
    float64
    -35.26 -35.98 ... 37.91 36.74

    long_name :

    latitude

    units :

    degrees_north

    array([-35.26438968, -35.98082588, -37.07155175, ...,  37.90897632,
            37.90897632,  36.74360278], shape=(13826,))

  • lon
    (ncol)
    float64
    315.0 316.6 319.1 ... 137.5 135.0

    long_name :

    longitude

    units :

    degrees_east

    array([315.        , 316.55471176, 319.07028824, ..., 132.47470023,
           137.52529977, 135.        ], shape=(13826,))

  • time
    (time)
    object
    8951-12-31 00:00:00 ... 9000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(8951, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8952, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8953, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8954, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8955, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8956, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8957, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8958, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8959, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8960, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8961, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8962, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8963, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8964, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8965, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8966, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8967, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8968, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8969, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8970, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8971, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8972, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8973, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8974, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8975, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8976, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8977, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8978, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8979, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8980, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8981, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8982, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8983, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8984, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8985, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8986, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8987, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8988, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8989, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8990, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8991, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8992, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8993, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8994, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8995, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8996, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8997, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8998, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8999, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([8951-12-31 00:00:00, 8952-12-31 00:00:00, 8953-12-31 00:00:00,
                 8954-12-31 00:00:00, 8955-12-31 00:00:00, 8956-12-31 00:00:00,
                 8957-12-31 00:00:00, 8958-12-31 00:00:00, 8959-12-31 00:00:00,
                 8960-12-31 00:00:00, 8961-12-31 00:00:00, 8962-12-31 00:00:00,
                 8963-12-31 00:00:00, 8964-12-31 00:00:00, 8965-12-31 00:00:00,
                 8966-12-31 00:00:00, 8967-12-31 00:00:00, 8968-12-31 00:00:00,
                 8969-12-31 00:00:00, 8970-12-31 00:00:00, 8971-12-31 00:00:00,
                 8972-12-31 00:00:00, 8973-12-31 00:00:00, 8974-12-31 00:00:00,
                 8975-12-31 00:00:00, 8976-12-31 00:00:00, 8977-12-31 00:00:00,
                 8978-12-31 00:00:00, 8979-12-31 00:00:00, 8980-12-31 00:00:00,
                 8981-12-31 00:00:00, 8982-12-31 00:00:00, 8983-12-31 00:00:00,
                 8984-12-31 00:00:00, 8985-12-31 00:00:00, 8986-12-31 00:00:00,
                 8987-12-31 00:00:00, 8988-12-31 00:00:00, 8989-12-31 00:00:00,
                 8990-12-31 00:00:00, 8991-12-31 00:00:00, 8992-12-31 00:00:00,
                 8993-12-31 00:00:00, 8994-12-31 00:00:00, 8995-12-31 00:00:00,
                 8996-12-31 00:00:00, 8997-12-31 00:00:00, 8998-12-31 00:00:00,
                 8999-12-31 00:00:00, 9000-12-31 00:00:00],
                dtype='object', length=50, calendar='noleap', freq='YE-DEC'))

• Attributes: (9)

  units :

  °C

  long_name :

  Surface temperature (radiative) (Annual)

  cell_methods :

  time: mean

  path :

  /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cam.h0.TS.895101-900012.nc

  gw :

  <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB array([0.00015457, 0.00051501, 0.00051372, ..., 0.00128989, 0.00128989, 0.00128935], shape=(13826,)) Dimensions without coordinates: ncol

  lat :

  <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: latitude units: degrees_north

  lon :

  <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: longitude units: degrees_east

  comp :

  atm

  grid :

  ne16np4

x4c.set_style('journal_spines', font_scale=1.2)
fig, ax = case.plot(spell)

>>> case.ds["SSH"] created

spell = 'SST:-12,1,2'
case.calc(spell)
case.diags[spell]

>>> SST is a supported derived variable.
>>> case.ds["TEMP"] created
>>> case.ds["SST"] created
>>> Timespan: [8951-01-01 00:00:00, 9000-12-01 00:00:00]
>>> case.diags["SST:-12,1,2"] created

<xarray.DataArray 'SST' (time: 51, nlat: 384, nlon: 320)> Size: 25MB
array([[[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
...
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]]],
      shape=(51, 384, 320), dtype=float32)
Coordinates:
    z_t      float32 4B 500.0
    ULONG    (nlat, nlon) float64 983kB 343.5 344.8 346.1 ... 326.4 326.7 327.0
    ULAT     (nlat, nlon) float64 983kB -87.53 -87.52 -87.5 ... 72.64 72.64
    TLONG    (nlat, nlon) float64 983kB 341.6 342.9 344.2 ... 326.2 326.5 326.8
    TLAT     (nlat, nlon) float64 983kB -87.73 -87.72 -87.7 ... 72.52 72.52
  * time     (time) object 408B 8951-02-28 00:00:00 ... 9001-02-28 00:00:00
Dimensions without coordinates: nlat, nlon
Attributes:
    long_name:     Sea Surface Temperature (DJF)
    units:         °C
    grid_loc:      3111
    cell_methods:  time: mean
    path:          /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1...
    gw:            <xarray.DataArray 'TAREA' (nlat: 384, nlon: 320)> Size: 98...
    lat:           <xarray.DataArray 'TLAT' (nlat: 384, nlon: 320)> Size: 983...
    lon:           <xarray.DataArray 'TLONG' (nlat: 384, nlon: 320)> Size: 98...
    dz:            <xarray.DataArray 'dz' (z_t: 60)> Size: 240B\n[60 values w...
    comp:          ocn
    grid:          g16
    vn:            SST

xarray.DataArray

'SST'

• time: 51
• nlat: 384
• nlon: 320

• nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan

  array([[[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
  ...
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]]],
        shape=(51, 384, 320), dtype=float32)

• Coordinates: (6)
  • z_t
    ()
    float32
    500.0

    long_name :

    depth from surface to midpoint of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    500.0

    valid_max :

    537500.0

    array(500., dtype=float32)

  • ULONG
    (nlat, nlon)
    float64
    343.5 344.8 346.1 ... 326.7 327.0

    long_name :

    array of u-grid longitudes

    units :

    degrees_east

    array([[343.54038726, 344.8268904 , 346.1039482 , ..., 339.62326414,
            340.93866105, 342.24434105],
           [341.21162274, 342.48690786, 343.75439307, ..., 337.33852291,
            338.63746813, 339.92848993],
           [339.43939522, 340.702884  , 341.95981255, ..., 335.60940096,
            336.89266458, 338.1693267 ],
           ...,
           [327.34240762, 327.68443505, 328.02570331, ..., 326.31556495,
            326.65759238, 327.        ],
           [327.31926434, 327.63815582, 327.95630284, ..., 326.36184418,
            326.68073566, 327.        ],
           [327.29502978, 327.58969684, 327.88363973, ..., 326.41030316,
            326.70497022, 327.        ]], shape=(384, 320))

  • ULAT
    (nlat, nlon)
    float64
    -87.53 -87.52 -87.5 ... 72.64 72.64

    long_name :

    array of u-grid latitudes

    units :

    degrees_north

    array([[-87.53219215, -87.51509609, -87.49781204, ..., -87.58225575,
            -87.56578173, -87.5490906 ],
           [-87.15096924, -87.13425409, -87.11733774, ..., -87.19981607,
            -87.18375942, -87.16747408],
           [-86.76642971, -86.75003397, -86.7334281 , ..., -86.81426926,
            -86.79855605, -86.78260663],
           ...,
           [ 72.16065859,  72.16526223,  72.17292267, ...,  72.16526223,
             72.16065859,  72.15912282],
           [ 72.39844829,  72.40278283,  72.40999509, ...,  72.40278283,
             72.39844829,  72.39700224],
           [ 72.63861007,  72.64265531,  72.6493858 , ...,  72.64265531,
             72.63861007,  72.6372605 ]], shape=(384, 320))

  • TLONG
    (nlat, nlon)
    float64
    341.6 342.9 344.2 ... 326.5 326.8

    long_name :

    array of t-grid longitudes

    units :

    degrees_east

    array([[341.64972662, 342.93514694, 344.21196801, ..., 337.74124774,
            339.05283196, 340.35564121],
           [341.64972662, 342.93514694, 344.21196801, ..., 337.74124774,
            339.05283196, 340.35564121],
           [339.63298627, 340.90594116, 342.17174082, ..., 335.77093448,
            337.0655021 , 338.35284741],
           ...,
           [327.17676287, 327.53009726, 327.88285816, ..., 326.11714184,
            326.46990274, 326.82323713],
           [327.16544883, 327.49615817, 327.82630323, ..., 326.17369677,
            326.50384183, 326.83455117],
           [327.1536079 , 327.46063974, 327.76712034, ..., 326.23287966,
            326.53936026, 326.8463921 ]], shape=(384, 320))

  • TLAT
    (nlat, nlon)
    float64
    -87.73 -87.72 -87.7 ... 72.52 72.52

    long_name :

    array of t-grid latitudes

    units :

    degrees_north

    array([[-87.73386371, -87.71671455, -87.69937608, ..., -87.78407581,
            -87.76755438, -87.75081365],
           [-87.3506397 , -87.33384401, -87.31684841, ..., -87.39973296,
            -87.38359367, -87.36722608],
           [-86.9674157 , -86.95097347, -86.93432074, ..., -87.01539011,
            -86.99963296, -86.98363851],
           ...,
           [ 72.04218407,  72.04533769,  72.0516375 , ...,  72.0516375 ,
             72.04533769,  72.04218407],
           [ 72.27887742,  72.28185793,  72.28781167, ...,  72.28781167,
             72.28185793,  72.27887742],
           [ 72.51788946,  72.5206839 ,  72.5262657 , ...,  72.5262657 ,
             72.5206839 ,  72.51788946]], shape=(384, 320))

  • time
    (time)
    object
    8951-02-28 00:00:00 ... 9001-02-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(8951, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8952, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8953, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8954, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8955, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8956, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8957, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8958, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8959, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8960, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8961, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8962, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8963, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8964, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8965, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8966, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8967, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8968, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8969, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8970, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8971, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8972, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8973, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8974, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8975, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8976, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8977, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8978, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8979, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8980, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8981, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8982, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8983, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8984, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8985, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8986, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8987, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8988, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8989, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8990, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8991, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8992, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8993, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8994, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8995, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8996, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8997, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8998, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8999, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9001, 2, 28, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([8951-02-28 00:00:00, 8952-02-28 00:00:00, 8953-02-28 00:00:00,
                 8954-02-28 00:00:00, 8955-02-28 00:00:00, 8956-02-28 00:00:00,
                 8957-02-28 00:00:00, 8958-02-28 00:00:00, 8959-02-28 00:00:00,
                 8960-02-28 00:00:00, 8961-02-28 00:00:00, 8962-02-28 00:00:00,
                 8963-02-28 00:00:00, 8964-02-28 00:00:00, 8965-02-28 00:00:00,
                 8966-02-28 00:00:00, 8967-02-28 00:00:00, 8968-02-28 00:00:00,
                 8969-02-28 00:00:00, 8970-02-28 00:00:00, 8971-02-28 00:00:00,
                 8972-02-28 00:00:00, 8973-02-28 00:00:00, 8974-02-28 00:00:00,
                 8975-02-28 00:00:00, 8976-02-28 00:00:00, 8977-02-28 00:00:00,
                 8978-02-28 00:00:00, 8979-02-28 00:00:00, 8980-02-28 00:00:00,
                 8981-02-28 00:00:00, 8982-02-28 00:00:00, 8983-02-28 00:00:00,
                 8984-02-28 00:00:00, 8985-02-28 00:00:00, 8986-02-28 00:00:00,
                 8987-02-28 00:00:00, 8988-02-28 00:00:00, 8989-02-28 00:00:00,
                 8990-02-28 00:00:00, 8991-02-28 00:00:00, 8992-02-28 00:00:00,
                 8993-02-28 00:00:00, 8994-02-28 00:00:00, 8995-02-28 00:00:00,
                 8996-02-28 00:00:00, 8997-02-28 00:00:00, 8998-02-28 00:00:00,
                 8999-02-28 00:00:00, 9000-02-28 00:00:00, 9001-02-28 00:00:00],
                dtype='object', length=51, calendar='noleap', freq='YE-FEB'))

• Attributes: (12)

  long_name :

  Sea Surface Temperature (DJF)

  units :

  °C

  grid_loc :

  3111

  cell_methods :

  time: mean

  path :

  /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ocn/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.pop.h.TEMP.895101-900012.nc

  gw :

  <xarray.DataArray 'TAREA' (nlat: 384, nlon: 320)> Size: 983kB array([[3.58324052e+12, 3.58275618e+12, 3.58133194e+12, ..., 3.57903907e+12, 3.58138059e+12, 3.58278201e+12], [3.03286780e+12, 3.03419089e+12, 3.03553795e+12, ..., 3.02904677e+12, 3.03029530e+12, 3.03156912e+12], [3.40099516e+12, 3.40241807e+12, 3.40386668e+12, ..., 3.39688554e+12, 3.39822843e+12, 3.39959846e+12], ..., [3.17630203e+12, 3.17539484e+12, 3.17358204e+12, ..., 3.17358204e+12, 3.17539484e+12, 3.17630203e+12], [2.96328714e+12, 2.96239074e+12, 2.96059964e+12, ..., 2.96059964e+12, 2.96239074e+12, 2.96328714e+12], [2.74242741e+12, 2.74154913e+12, 2.73979435e+12, ..., 2.73979435e+12, 2.74154913e+12, 2.74242741e+12]], shape=(384, 320)) Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: area of T cells units: centimeter^2

  lat :

  <xarray.DataArray 'TLAT' (nlat: 384, nlon: 320)> Size: 983kB [122880 values with dtype=float64] Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: array of t-grid latitudes units: degrees_north

  lon :

  <xarray.DataArray 'TLONG' (nlat: 384, nlon: 320)> Size: 983kB [122880 values with dtype=float64] Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: array of t-grid longitudes units: degrees_east

  dz :

  <xarray.DataArray 'dz' (z_t: 60)> Size: 240B [60 values with dtype=float32] Coordinates: * z_t (z_t) float32 240B 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05 Attributes: long_name: thickness of layer k units: centimeters

  comp :

  ocn

  grid :

  g16

  vn :

  SST

fig, ax = case.plot(spell)

case.calc('MLD:3')
case.calc('MLD:9')

mask = case.diags['MLD:3'].lat > 0
case.diags['MLD:mix'] = case.diags['MLD:3'].mean('time').where(mask, other=case.diags['MLD:9'].mean('time'))

>>> MLD is a supported derived variable.
>>> case.ds["XMXL"] created
>>> case.ds["MLD"] created
>>> Timespan: [8951-01-01 00:00:00, 9000-12-01 00:00:00]
>>> case.diags["MLD:3"] created
>>> MLD is a supported derived variable.
>>> case.ds["XMXL"] already loaded; to reload, run case.load("XMXL", ..., reload=True).
>>> case.ds["MLD"] created
>>> Timespan: [8951-01-01 00:00:00, 9000-12-01 00:00:00]
>>> case.diags["MLD:9"] created

case.diags['MLD:mix']

<xarray.DataArray 'MLD' (nlat: 384, nlon: 320)> Size: 492kB
array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       ...,
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]],
      shape=(384, 320), dtype=float32)
Coordinates:
    ULONG    (nlat, nlon) float64 983kB 343.5 344.8 346.1 ... 326.4 326.7 327.0
    ULAT     (nlat, nlon) float64 983kB -87.53 -87.52 -87.5 ... 72.64 72.64
    TLONG    (nlat, nlon) float64 983kB 341.6 342.9 344.2 ... 326.2 326.5 326.8
    TLAT     (nlat, nlon) float64 983kB -87.73 -87.72 -87.7 ... 72.52 72.52
Dimensions without coordinates: nlat, nlon
Attributes:
    long_name:     Maximum Mixed-Layer Depth (M)
    units:         m
    grid_loc:      2110
    cell_methods:  time: maximum
    path:          /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1...
    gw:            <xarray.DataArray 'TAREA' (nlat: 384, nlon: 320)> Size: 98...
    lat:           <xarray.DataArray 'TLAT' (nlat: 384, nlon: 320)> Size: 983...
    lon:           <xarray.DataArray 'TLONG' (nlat: 384, nlon: 320)> Size: 98...
    dz:            <xarray.DataArray 'dz' (z_t: 60)> Size: 240B\n[60 values w...
    comp:          ocn
    grid:          g16
    vn:            MLD

xarray.DataArray

'MLD'

• nlat: 384
• nlon: 320

• nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan

  array([[nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan],
         ...,
         [nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan]],
        shape=(384, 320), dtype=float32)

• Coordinates: (4)
  • ULONG
    (nlat, nlon)
    float64
    343.5 344.8 346.1 ... 326.7 327.0

    long_name :

    array of u-grid longitudes

    units :

    degrees_east

    array([[343.54038726, 344.8268904 , 346.1039482 , ..., 339.62326414,
            340.93866105, 342.24434105],
           [341.21162274, 342.48690786, 343.75439307, ..., 337.33852291,
            338.63746813, 339.92848993],
           [339.43939522, 340.702884  , 341.95981255, ..., 335.60940096,
            336.89266458, 338.1693267 ],
           ...,
           [327.34240762, 327.68443505, 328.02570331, ..., 326.31556495,
            326.65759238, 327.        ],
           [327.31926434, 327.63815582, 327.95630284, ..., 326.36184418,
            326.68073566, 327.        ],
           [327.29502978, 327.58969684, 327.88363973, ..., 326.41030316,
            326.70497022, 327.        ]], shape=(384, 320))

  • ULAT
    (nlat, nlon)
    float64
    -87.53 -87.52 -87.5 ... 72.64 72.64

    long_name :

    array of u-grid latitudes

    units :

    degrees_north

    array([[-87.53219215, -87.51509609, -87.49781204, ..., -87.58225575,
            -87.56578173, -87.5490906 ],
           [-87.15096924, -87.13425409, -87.11733774, ..., -87.19981607,
            -87.18375942, -87.16747408],
           [-86.76642971, -86.75003397, -86.7334281 , ..., -86.81426926,
            -86.79855605, -86.78260663],
           ...,
           [ 72.16065859,  72.16526223,  72.17292267, ...,  72.16526223,
             72.16065859,  72.15912282],
           [ 72.39844829,  72.40278283,  72.40999509, ...,  72.40278283,
             72.39844829,  72.39700224],
           [ 72.63861007,  72.64265531,  72.6493858 , ...,  72.64265531,
             72.63861007,  72.6372605 ]], shape=(384, 320))

  • TLONG
    (nlat, nlon)
    float64
    341.6 342.9 344.2 ... 326.5 326.8

    long_name :

    array of t-grid longitudes

    units :

    degrees_east

    array([[341.64972662, 342.93514694, 344.21196801, ..., 337.74124774,
            339.05283196, 340.35564121],
           [341.64972662, 342.93514694, 344.21196801, ..., 337.74124774,
            339.05283196, 340.35564121],
           [339.63298627, 340.90594116, 342.17174082, ..., 335.77093448,
            337.0655021 , 338.35284741],
           ...,
           [327.17676287, 327.53009726, 327.88285816, ..., 326.11714184,
            326.46990274, 326.82323713],
           [327.16544883, 327.49615817, 327.82630323, ..., 326.17369677,
            326.50384183, 326.83455117],
           [327.1536079 , 327.46063974, 327.76712034, ..., 326.23287966,
            326.53936026, 326.8463921 ]], shape=(384, 320))

  • TLAT
    (nlat, nlon)
    float64
    -87.73 -87.72 -87.7 ... 72.52 72.52

    long_name :

    array of t-grid latitudes

    units :

    degrees_north

    array([[-87.73386371, -87.71671455, -87.69937608, ..., -87.78407581,
            -87.76755438, -87.75081365],
           [-87.3506397 , -87.33384401, -87.31684841, ..., -87.39973296,
            -87.38359367, -87.36722608],
           [-86.9674157 , -86.95097347, -86.93432074, ..., -87.01539011,
            -86.99963296, -86.98363851],
           ...,
           [ 72.04218407,  72.04533769,  72.0516375 , ...,  72.0516375 ,
             72.04533769,  72.04218407],
           [ 72.27887742,  72.28185793,  72.28781167, ...,  72.28781167,
             72.28185793,  72.27887742],
           [ 72.51788946,  72.5206839 ,  72.5262657 , ...,  72.5262657 ,
             72.5206839 ,  72.51788946]], shape=(384, 320))

• Indexes: (0)
• Attributes: (12)

  long_name :

  Maximum Mixed-Layer Depth (M)

  units :

  m

  grid_loc :

  2110

  cell_methods :

  time: maximum

  path :

  /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ocn/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.pop.h.XMXL.895101-900012.nc

  gw :

  <xarray.DataArray 'TAREA' (nlat: 384, nlon: 320)> Size: 983kB array([[3.58324052e+12, 3.58275618e+12, 3.58133194e+12, ..., 3.57903907e+12, 3.58138059e+12, 3.58278201e+12], [3.03286780e+12, 3.03419089e+12, 3.03553795e+12, ..., 3.02904677e+12, 3.03029530e+12, 3.03156912e+12], [3.40099516e+12, 3.40241807e+12, 3.40386668e+12, ..., 3.39688554e+12, 3.39822843e+12, 3.39959846e+12], ..., [3.17630203e+12, 3.17539484e+12, 3.17358204e+12, ..., 3.17358204e+12, 3.17539484e+12, 3.17630203e+12], [2.96328714e+12, 2.96239074e+12, 2.96059964e+12, ..., 2.96059964e+12, 2.96239074e+12, 2.96328714e+12], [2.74242741e+12, 2.74154913e+12, 2.73979435e+12, ..., 2.73979435e+12, 2.74154913e+12, 2.74242741e+12]], shape=(384, 320)) Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: area of T cells units: centimeter^2

  lat :

  <xarray.DataArray 'TLAT' (nlat: 384, nlon: 320)> Size: 983kB array([[-87.733864, -87.716715, -87.699376, ..., -87.784076, -87.767554, -87.750814], [-87.35064 , -87.333844, -87.316848, ..., -87.399733, -87.383594, -87.367226], [-86.967416, -86.950973, -86.934321, ..., -87.01539 , -86.999633, -86.983639], ..., [ 72.042184, 72.045338, 72.051637, ..., 72.051637, 72.045338, 72.042184], [ 72.278877, 72.281858, 72.287812, ..., 72.287812, 72.281858, 72.278877], [ 72.517889, 72.520684, 72.526266, ..., 72.526266, 72.520684, 72.517889]], shape=(384, 320)) Coordinates: ULONG (nlat, nlon) float64 983kB 343.5 344.8 346.1 ... 326.4 326.7 327.0 ULAT (nlat, nlon) float64 983kB -87.53 -87.52 -87.5 ... 72.64 72.64 TLONG (nlat, nlon) float64 983kB 341.6 342.9 344.2 ... 326.2 326.5 326.8 TLAT (nlat, nlon) float64 983kB -87.73 -87.72 -87.7 ... 72.52 72.52 Dimensions without coordinates: nlat, nlon Attributes: long_name: array of t-grid latitudes units: degrees_north

  lon :

  <xarray.DataArray 'TLONG' (nlat: 384, nlon: 320)> Size: 983kB array([[341.649727, 342.935147, 344.211968, ..., 337.741248, 339.052832, 340.355641], [341.649727, 342.935147, 344.211968, ..., 337.741248, 339.052832, 340.355641], [339.632986, 340.905941, 342.171741, ..., 335.770934, 337.065502, 338.352847], ..., [327.176763, 327.530097, 327.882858, ..., 326.117142, 326.469903, 326.823237], [327.165449, 327.496158, 327.826303, ..., 326.173697, 326.503842, 326.834551], [327.153608, 327.46064 , 327.76712 , ..., 326.23288 , 326.53936 , 326.846392]], shape=(384, 320)) Coordinates: ULONG (nlat, nlon) float64 983kB 343.5 344.8 346.1 ... 326.4 326.7 327.0 ULAT (nlat, nlon) float64 983kB -87.53 -87.52 -87.5 ... 72.64 72.64 TLONG (nlat, nlon) float64 983kB 341.6 342.9 344.2 ... 326.2 326.5 326.8 TLAT (nlat, nlon) float64 983kB -87.73 -87.72 -87.7 ... 72.52 72.52 Dimensions without coordinates: nlat, nlon Attributes: long_name: array of t-grid longitudes units: degrees_east

  dz :

  <xarray.DataArray 'dz' (z_t: 60)> Size: 240B [60 values with dtype=float32] Coordinates: * z_t (z_t) float32 240B 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05 Attributes: long_name: thickness of layer k units: centimeters

  comp :

  ocn

  grid :

  g16

  vn :

  MLD

fig, ax = case.plot(
    'MLD:mix',
    figsize=(6, 6),
    cbar_kwargs={'orientation': 'horizontal', 'aspect': 20, 'pad': 0.05},
)
ax.set_title('Mixed Layer Depth (NH: Mar; SH: Sept)', weight='bold')
x4c.showfig(fig)
x4c.savefig(fig, './figs/map_MLD_mix.pdf')

Figure saved at: "figs/map_MLD_mix.pdf"

A timeseries

spell = 'TS:ann:gm'
case.calc(spell)
case.diags[spell]

>>> Variable `TS` is already calculated and the calculation is skipped.
>>> Timespan: [8951-01-01 00:00:00, 9000-12-01 00:00:00]
>>> case.diags["TS:ann:gm"] created

<xarray.DataArray 'TS' (time: 50)> Size: 400B
array([24.4128289 , 24.52198967, 24.13382052, 24.33224356, 24.4598987 ,
       24.31180457, 24.33586058, 24.1110387 , 24.07948485, 24.22708159,
       24.1367307 , 24.17390508, 24.18800733, 24.09141946, 24.18525948,
       24.49602257, 24.1977737 , 23.97936985, 24.17802306, 24.35568247,
       24.51118634, 24.2515905 , 24.13155237, 24.1675983 , 24.33082636,
       24.2813164 , 24.31966906, 24.433534  , 24.32294183, 24.27283101,
       24.53654344, 24.28373572, 24.09457417, 24.27189317, 24.56742109,
       24.36937101, 24.23502009, 24.3549681 , 24.600416  , 24.54777983,
       24.28243932, 24.28388092, 24.30570442, 24.35778457, 24.37612048,
       24.43264161, 24.24372558, 24.21401955, 24.38051205, 24.12341764])
Coordinates:
  * time     (time) object 400B 8951-12-31 00:00:00 ... 9000-12-31 00:00:00
Attributes:
    units:         °C
    long_name:     Global Mean Surface temperature (radiative) (Annual)
    cell_methods:  time: mean
    path:          /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1...
    gw:            <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB\narray...
    lat:           <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB\n[13826...
    lon:           <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB\n[13826...
    comp:          atm
    grid:          ne16np4

xarray.DataArray

'TS'

• time: 50

• 24.41 24.52 24.13 24.33 24.46 24.31 ... 24.43 24.24 24.21 24.38 24.12

  array([24.4128289 , 24.52198967, 24.13382052, 24.33224356, 24.4598987 ,
         24.31180457, 24.33586058, 24.1110387 , 24.07948485, 24.22708159,
         24.1367307 , 24.17390508, 24.18800733, 24.09141946, 24.18525948,
         24.49602257, 24.1977737 , 23.97936985, 24.17802306, 24.35568247,
         24.51118634, 24.2515905 , 24.13155237, 24.1675983 , 24.33082636,
         24.2813164 , 24.31966906, 24.433534  , 24.32294183, 24.27283101,
         24.53654344, 24.28373572, 24.09457417, 24.27189317, 24.56742109,
         24.36937101, 24.23502009, 24.3549681 , 24.600416  , 24.54777983,
         24.28243932, 24.28388092, 24.30570442, 24.35778457, 24.37612048,
         24.43264161, 24.24372558, 24.21401955, 24.38051205, 24.12341764])

• Coordinates: (1)
  • time
    (time)
    object
    8951-12-31 00:00:00 ... 9000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(8951, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8952, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8953, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8954, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8955, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8956, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8957, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8958, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8959, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8960, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8961, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8962, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8963, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8964, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8965, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8966, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8967, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8968, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8969, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8970, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8971, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8972, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8973, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8974, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8975, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8976, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8977, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8978, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8979, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8980, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8981, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8982, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8983, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8984, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8985, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8986, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8987, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8988, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8989, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8990, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8991, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8992, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8993, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8994, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8995, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8996, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8997, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8998, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8999, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([8951-12-31 00:00:00, 8952-12-31 00:00:00, 8953-12-31 00:00:00,
                 8954-12-31 00:00:00, 8955-12-31 00:00:00, 8956-12-31 00:00:00,
                 8957-12-31 00:00:00, 8958-12-31 00:00:00, 8959-12-31 00:00:00,
                 8960-12-31 00:00:00, 8961-12-31 00:00:00, 8962-12-31 00:00:00,
                 8963-12-31 00:00:00, 8964-12-31 00:00:00, 8965-12-31 00:00:00,
                 8966-12-31 00:00:00, 8967-12-31 00:00:00, 8968-12-31 00:00:00,
                 8969-12-31 00:00:00, 8970-12-31 00:00:00, 8971-12-31 00:00:00,
                 8972-12-31 00:00:00, 8973-12-31 00:00:00, 8974-12-31 00:00:00,
                 8975-12-31 00:00:00, 8976-12-31 00:00:00, 8977-12-31 00:00:00,
                 8978-12-31 00:00:00, 8979-12-31 00:00:00, 8980-12-31 00:00:00,
                 8981-12-31 00:00:00, 8982-12-31 00:00:00, 8983-12-31 00:00:00,
                 8984-12-31 00:00:00, 8985-12-31 00:00:00, 8986-12-31 00:00:00,
                 8987-12-31 00:00:00, 8988-12-31 00:00:00, 8989-12-31 00:00:00,
                 8990-12-31 00:00:00, 8991-12-31 00:00:00, 8992-12-31 00:00:00,
                 8993-12-31 00:00:00, 8994-12-31 00:00:00, 8995-12-31 00:00:00,
                 8996-12-31 00:00:00, 8997-12-31 00:00:00, 8998-12-31 00:00:00,
                 8999-12-31 00:00:00, 9000-12-31 00:00:00],
                dtype='object', length=50, calendar='noleap', freq='YE-DEC'))

• Attributes: (9)

  units :

  °C

  long_name :

  Global Mean Surface temperature (radiative) (Annual)

  cell_methods :

  time: mean

  path :

  /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cam.h0.TS.895101-900012.nc

  gw :

  <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB array([0.00015457, 0.00051501, 0.00051372, ..., 0.00128989, 0.00128989, 0.00128935], shape=(13826,)) Dimensions without coordinates: ncol

  lat :

  <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: latitude units: degrees_north

  lon :

  <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: longitude units: degrees_east

  comp :

  atm

  grid :

  ne16np4

x4c.set_style('web', font_scale=1.2)
fig, ax = case.plot(spell)

A zonal mean plot

spell = 'LST:ann:zm'
case.calc(spell)
case.diags[spell]

>>> Variable `LST` is already calculated and the calculation is skipped.
>>> Timespan: [8951-01-01 00:00:00, 9000-12-01 00:00:00]
>>> case.diags["LST:ann:zm"] created

<xarray.DataArray 'LST' (time: 50, lat: 180)> Size: 36kB
array([[-17.580034, -18.036827, -17.992954, ...,        nan,        nan,
               nan],
       [-17.275988, -18.02568 , -18.102137, ...,        nan,        nan,
               nan],
       [-18.18247 , -18.830654, -18.876236, ...,        nan,        nan,
               nan],
       ...,
       [-17.469307, -18.135544, -18.18202 , ...,        nan,        nan,
               nan],
       [-16.71525 , -17.500294, -17.598314, ...,        nan,        nan,
               nan],
       [-16.856697, -17.553476, -17.634754, ...,        nan,        nan,
               nan]], shape=(50, 180), dtype=float32)
Coordinates:
  * time     (time) object 400B 8951-12-31 00:00:00 ... 9000-12-31 00:00:00
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
Attributes:
    units:         °C
    long_name:     Zonal Mean Land Surface Temperature (Annual)
    cell_methods:  time: mean
    path:          /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1...
    gw:            <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB\narray...
    lat:           <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB\n[13826...
    lon:           <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB\n[13826...
    comp:          atm
    grid:          ne16np4
    vn:            LST

xarray.DataArray

'LST'

• time: 50
• lat: 180

• -17.58 -18.04 -17.99 -17.79 -16.86 -14.28 ... nan nan nan nan nan nan

  array([[-17.580034, -18.036827, -17.992954, ...,        nan,        nan,
                 nan],
         [-17.275988, -18.02568 , -18.102137, ...,        nan,        nan,
                 nan],
         [-18.18247 , -18.830654, -18.876236, ...,        nan,        nan,
                 nan],
         ...,
         [-17.469307, -18.135544, -18.18202 , ...,        nan,        nan,
                 nan],
         [-16.71525 , -17.500294, -17.598314, ...,        nan,        nan,
                 nan],
         [-16.856697, -17.553476, -17.634754, ...,        nan,        nan,
                 nan]], shape=(50, 180), dtype=float32)

• Coordinates: (2)
  • time
    (time)
    object
    8951-12-31 00:00:00 ... 9000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(8951, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8952, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8953, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8954, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8955, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8956, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8957, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8958, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8959, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8960, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8961, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8962, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8963, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8964, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8965, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8966, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8967, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8968, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8969, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8970, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8971, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8972, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8973, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8974, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8975, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8976, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8977, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8978, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8979, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8980, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8981, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8982, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8983, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8984, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8985, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8986, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8987, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8988, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8989, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8990, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8991, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8992, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8993, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8994, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8995, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8996, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8997, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8998, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8999, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([8951-12-31 00:00:00, 8952-12-31 00:00:00, 8953-12-31 00:00:00,
                 8954-12-31 00:00:00, 8955-12-31 00:00:00, 8956-12-31 00:00:00,
                 8957-12-31 00:00:00, 8958-12-31 00:00:00, 8959-12-31 00:00:00,
                 8960-12-31 00:00:00, 8961-12-31 00:00:00, 8962-12-31 00:00:00,
                 8963-12-31 00:00:00, 8964-12-31 00:00:00, 8965-12-31 00:00:00,
                 8966-12-31 00:00:00, 8967-12-31 00:00:00, 8968-12-31 00:00:00,
                 8969-12-31 00:00:00, 8970-12-31 00:00:00, 8971-12-31 00:00:00,
                 8972-12-31 00:00:00, 8973-12-31 00:00:00, 8974-12-31 00:00:00,
                 8975-12-31 00:00:00, 8976-12-31 00:00:00, 8977-12-31 00:00:00,
                 8978-12-31 00:00:00, 8979-12-31 00:00:00, 8980-12-31 00:00:00,
                 8981-12-31 00:00:00, 8982-12-31 00:00:00, 8983-12-31 00:00:00,
                 8984-12-31 00:00:00, 8985-12-31 00:00:00, 8986-12-31 00:00:00,
                 8987-12-31 00:00:00, 8988-12-31 00:00:00, 8989-12-31 00:00:00,
                 8990-12-31 00:00:00, 8991-12-31 00:00:00, 8992-12-31 00:00:00,
                 8993-12-31 00:00:00, 8994-12-31 00:00:00, 8995-12-31 00:00:00,
                 8996-12-31 00:00:00, 8997-12-31 00:00:00, 8998-12-31 00:00:00,
                 8999-12-31 00:00:00, 9000-12-31 00:00:00],
                dtype='object', length=50, calendar='noleap', freq='YE-DEC'))

  • 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))

• Attributes: (10)

  units :

  °C

  long_name :

  Zonal Mean Land Surface Temperature (Annual)

  cell_methods :

  time: mean

  path :

  /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cam.h0.TS.895101-900012.nc

  gw :

  <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB array([0.00015457, 0.00051501, 0.00051372, ..., 0.00128989, 0.00128989, 0.00128935], shape=(13826,)) Dimensions without coordinates: ncol

  lat :

  <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: latitude units: degrees_north

  lon :

  <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: longitude units: degrees_east

  comp :

  atm

  grid :

  ne16np4

  vn :

  LST

fig, ax = case.plot(spell)

A vertical 2D plot

spell = 'MOC:ann:yz'
case.calc(spell)
case.diags[spell]

>>> MOC is a supported derived variable.
>>> case.ds["MOC"] created
>>> Timespan: [8951-01-01 00:00:00, 9000-12-01 00:00:00]
>>> case.diags["MOC:ann:yz"] created

<xarray.DataArray 'MOC' (time: 50, z_t: 61, lat: 91)> Size: 1MB
array([[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
         -1.94341654e-03, -3.89124529e-04,  1.73436900e-04],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          1.72229052e-01,  9.17188302e-02,  5.70570379e-02],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          2.36894444e-01,  1.23025537e-01,  7.05491826e-02],
        ...,
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          8.16434749e-07,  8.16434749e-07,  8.16434749e-07],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          2.74996466e-07,  2.74996466e-07,  2.74996466e-07],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],

       [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
         -4.55033849e-04, -2.44729948e-04, -7.91880884e-05],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          1.11171342e-01,  6.28634840e-02,  4.01812606e-02],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          1.63042381e-01,  1.01493865e-01,  6.58850148e-02],
...
          7.12094504e-07,  7.12094504e-07,  7.12094504e-07],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
         -5.03204035e-07, -5.03204035e-07, -5.03204035e-07],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],

       [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          1.90572022e-03,  4.49017592e-04, -7.26856306e-05],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          4.07577157e-02,  5.96384890e-02,  6.74766377e-02],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          2.71115452e-02,  7.92309120e-02,  9.72099304e-02],
        ...,
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
         -1.04821856e-06, -1.04821856e-06, -1.04821856e-06],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
         -9.27436304e-07, -9.27436304e-07, -9.27436304e-07],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]],
      shape=(50, 61, 91), dtype=float32)
Coordinates:
    moc_components  |S256 256B b'Eulerian Mean'
  * lat             (lat) float32 364B -90.0 -88.0 -86.0 ... 86.0 88.0 90.0
  * z_t             (z_t) float32 244B 0.0 0.01 0.02 0.03 ... 4.75 5.0 5.25 5.5
  * time            (time) object 400B 8951-12-31 00:00:00 ... 9000-12-31 00:...
Attributes:
    long_name:     Meridional Overturning Circulation (Annual)
    units:         Sverdrups
    cell_methods:  time: mean
    path:          /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1...
    gw:            <xarray.DataArray 'TAREA' (nlat: 384, nlon: 320)> Size: 98...
    lat:           <xarray.DataArray 'TLAT' (nlat: 384, nlon: 320)> Size: 983...
    lon:           <xarray.DataArray 'TLONG' (nlat: 384, nlon: 320)> Size: 98...
    dz:            <xarray.DataArray 'dz' (z_t: 60)> Size: 240B\n[60 values w...
    comp:          ocn
    grid:          g16
    lon_name:      Meridional Ocean Circulation
    vn:            MOC

xarray.DataArray

'MOC'

• time: 50
• z_t: 61
• lat: 91

• 0.0 0.0 0.0 5.424e-06 0.0002847 0.001025 ... 0.0 0.0 0.0 0.0 0.0 0.0

  array([[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
           -1.94341654e-03, -3.89124529e-04,  1.73436900e-04],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            1.72229052e-01,  9.17188302e-02,  5.70570379e-02],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            2.36894444e-01,  1.23025537e-01,  7.05491826e-02],
          ...,
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            8.16434749e-07,  8.16434749e-07,  8.16434749e-07],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            2.74996466e-07,  2.74996466e-07,  2.74996466e-07],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],
  
         [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
           -4.55033849e-04, -2.44729948e-04, -7.91880884e-05],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            1.11171342e-01,  6.28634840e-02,  4.01812606e-02],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            1.63042381e-01,  1.01493865e-01,  6.58850148e-02],
  ...
            7.12094504e-07,  7.12094504e-07,  7.12094504e-07],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
           -5.03204035e-07, -5.03204035e-07, -5.03204035e-07],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],
  
         [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            1.90572022e-03,  4.49017592e-04, -7.26856306e-05],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            4.07577157e-02,  5.96384890e-02,  6.74766377e-02],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            2.71115452e-02,  7.92309120e-02,  9.72099304e-02],
          ...,
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
           -1.04821856e-06, -1.04821856e-06, -1.04821856e-06],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
           -9.27436304e-07, -9.27436304e-07, -9.27436304e-07],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]],
        shape=(50, 61, 91), dtype=float32)

• Coordinates: (4)
  • moc_components
    ()
    |S256
    b'Eulerian Mean'

    long_name :

    MOC component names

    units :

    array(b'Eulerian Mean', dtype='|S256')

  • lat
    (lat)
    float32
    -90.0 -88.0 -86.0 ... 88.0 90.0

    long_name :

    latitude grid for transport diagnostics

    units :

    degrees_north

    valid_min :

    -90.0

    valid_max :

    90.0

    array([-90., -88., -86., -84., -82., -80., -78., -76., -74., -72., -70., -68.,
           -66., -64., -62., -60., -58., -56., -54., -52., -50., -48., -46., -44.,
           -42., -40., -38., -36., -34., -32., -30., -28., -26., -24., -22., -20.,
           -18., -16., -14., -12., -10.,  -8.,  -6.,  -4.,  -2.,   0.,   2.,   4.,
             6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,  24.,  26.,  28.,
            30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,  48.,  50.,  52.,
            54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,  72.,  74.,  76.,
            78.,  80.,  82.,  84.,  86.,  88.,  90.], dtype=float32)

  • z_t
    (z_t)
    float32
    0.0 0.01 0.02 0.03 ... 5.0 5.25 5.5

    long_name :

    depth from surface to top of layer

    units :

    km

    positive :

    down

    valid_min :

    0.0

    valid_max :

    549999.06

    array([0.      , 0.01    , 0.02    , 0.03    , 0.04    , 0.05    , 0.06    ,
           0.07    , 0.08    , 0.09    , 0.1     , 0.11    , 0.12    , 0.13    ,
           0.14    , 0.15    , 0.16    , 0.170197, 0.180761, 0.191821, 0.203499,
           0.215923, 0.229233, 0.243585, 0.259156, 0.276153, 0.294815, 0.315424,
           0.338312, 0.363875, 0.39258 , 0.424989, 0.461767, 0.503707, 0.551749,
           0.606997, 0.670729, 0.744398, 0.829607, 0.928044, 1.041368, 1.17104 ,
           1.318094, 1.482901, 1.664992, 1.863014, 2.074874, 2.298039, 2.529904,
           2.768098, 3.010671, 3.256138, 3.503449, 3.751892, 4.001011, 4.250525,
           4.500261, 4.75012 , 5.000047, 5.25001 , 5.49999 ], dtype=float32)

  • time
    (time)
    object
    8951-12-31 00:00:00 ... 9000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(8951, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8952, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8953, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8954, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8955, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8956, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8957, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8958, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8959, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8960, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8961, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8962, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8963, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8964, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8965, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8966, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8967, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8968, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8969, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8970, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8971, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8972, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8973, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8974, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8975, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8976, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8977, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8978, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8979, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8980, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8981, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8982, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8983, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8984, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8985, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8986, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8987, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8988, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8989, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8990, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8991, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8992, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8993, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8994, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8995, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8996, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8997, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8998, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8999, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([-90.0, -88.0, -86.0, -84.0, -82.0, -80.0, -78.0, -76.0, -74.0, -72.0,
           -70.0, -68.0, -66.0, -64.0, -62.0, -60.0, -58.0, -56.0, -54.0, -52.0,
           -50.0, -48.0, -46.0, -44.0, -42.0, -40.0, -38.0, -36.0, -34.0, -32.0,
           -30.0, -28.0, -26.0, -24.0, -22.0, -20.0, -18.0, -16.0, -14.0, -12.0,
           -10.0,  -8.0,  -6.0,  -4.0,  -2.0,   0.0,   2.0,   4.0,   6.0,   8.0,
            10.0,  12.0,  14.0,  16.0,  18.0,  20.0,  22.0,  24.0,  26.0,  28.0,
            30.0,  32.0,  34.0,  36.0,  38.0,  40.0,  42.0,  44.0,  46.0,  48.0,
            50.0,  52.0,  54.0,  56.0,  58.0,  60.0,  62.0,  64.0,  66.0,  68.0,
            70.0,  72.0,  74.0,  76.0,  78.0,  80.0,  82.0,  84.0,  86.0,  88.0,
            90.0],
          dtype='float32', name='lat'))

  • z_t
    PandasIndex

    PandasIndex(Index([                 0.0, 0.009999999776482582, 0.019999999552965164,
           0.029999999329447746,  0.03999999910593033,  0.05000000074505806,
            0.05999999865889549,  0.07000000029802322,  0.07999999821186066,
            0.09000000357627869,  0.10000000149011612,  0.10999999940395355,
            0.11999999731779099,  0.12999999523162842,  0.14000000059604645,
            0.15000000596046448,   0.1599999964237213,  0.17019681632518768,
            0.18076129257678986,  0.19182124733924866,  0.20349931716918945,
            0.21592344343662262,  0.22923313081264496,   0.2435845285654068,
              0.259155809879303,   0.2761526107788086,  0.29481470584869385,
            0.31542372703552246,   0.3383122682571411,  0.36387473344802856,
            0.39258047938346863,  0.42498886585235596,   0.4617665708065033,
             0.5037068724632263,   0.5517491102218628,   0.6069966554641724,
             0.6707285642623901,   0.7443980574607849,   0.8296069502830505,
               0.92804354429245,   1.0413682460784912,   1.1710401773452759,
             1.3180935382843018,   1.4829007387161255,   1.6649919748306274,
             1.8630143404006958,    2.074873924255371,    2.298038959503174,
             2.5299041271209717,   2.7680983543395996,   3.0106706619262695,
              3.256138324737549,    3.503448724746704,    3.751891851425171,
              4.001011371612549,    4.250524520874023,    4.500260829925537,
              4.750120162963867,    5.000046730041504,    5.250009536743164,
              5.499990463256836],
          dtype='float32', name='z_t'))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([8951-12-31 00:00:00, 8952-12-31 00:00:00, 8953-12-31 00:00:00,
                 8954-12-31 00:00:00, 8955-12-31 00:00:00, 8956-12-31 00:00:00,
                 8957-12-31 00:00:00, 8958-12-31 00:00:00, 8959-12-31 00:00:00,
                 8960-12-31 00:00:00, 8961-12-31 00:00:00, 8962-12-31 00:00:00,
                 8963-12-31 00:00:00, 8964-12-31 00:00:00, 8965-12-31 00:00:00,
                 8966-12-31 00:00:00, 8967-12-31 00:00:00, 8968-12-31 00:00:00,
                 8969-12-31 00:00:00, 8970-12-31 00:00:00, 8971-12-31 00:00:00,
                 8972-12-31 00:00:00, 8973-12-31 00:00:00, 8974-12-31 00:00:00,
                 8975-12-31 00:00:00, 8976-12-31 00:00:00, 8977-12-31 00:00:00,
                 8978-12-31 00:00:00, 8979-12-31 00:00:00, 8980-12-31 00:00:00,
                 8981-12-31 00:00:00, 8982-12-31 00:00:00, 8983-12-31 00:00:00,
                 8984-12-31 00:00:00, 8985-12-31 00:00:00, 8986-12-31 00:00:00,
                 8987-12-31 00:00:00, 8988-12-31 00:00:00, 8989-12-31 00:00:00,
                 8990-12-31 00:00:00, 8991-12-31 00:00:00, 8992-12-31 00:00:00,
                 8993-12-31 00:00:00, 8994-12-31 00:00:00, 8995-12-31 00:00:00,
                 8996-12-31 00:00:00, 8997-12-31 00:00:00, 8998-12-31 00:00:00,
                 8999-12-31 00:00:00, 9000-12-31 00:00:00],
                dtype='object', length=50, calendar='noleap', freq='YE-DEC'))

• Attributes: (12)

  long_name :

  Meridional Overturning Circulation (Annual)

  units :

  Sverdrups

  cell_methods :

  time: mean

  path :

  /glade/u/home/fengzhu/Github/x4c/docsrc/notebooks/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ocn/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.pop.h.MOC.895101-900012.nc

  gw :

  <xarray.DataArray 'TAREA' (nlat: 384, nlon: 320)> Size: 983kB array([[3.58324052e+12, 3.58275618e+12, 3.58133194e+12, ..., 3.57903907e+12, 3.58138059e+12, 3.58278201e+12], [3.03286780e+12, 3.03419089e+12, 3.03553795e+12, ..., 3.02904677e+12, 3.03029530e+12, 3.03156912e+12], [3.40099516e+12, 3.40241807e+12, 3.40386668e+12, ..., 3.39688554e+12, 3.39822843e+12, 3.39959846e+12], ..., [3.17630203e+12, 3.17539484e+12, 3.17358204e+12, ..., 3.17358204e+12, 3.17539484e+12, 3.17630203e+12], [2.96328714e+12, 2.96239074e+12, 2.96059964e+12, ..., 2.96059964e+12, 2.96239074e+12, 2.96328714e+12], [2.74242741e+12, 2.74154913e+12, 2.73979435e+12, ..., 2.73979435e+12, 2.74154913e+12, 2.74242741e+12]], shape=(384, 320)) Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: area of T cells units: centimeter^2

  lat :

  <xarray.DataArray 'TLAT' (nlat: 384, nlon: 320)> Size: 983kB [122880 values with dtype=float64] Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: array of t-grid latitudes units: degrees_north

  lon :

  <xarray.DataArray 'TLONG' (nlat: 384, nlon: 320)> Size: 983kB [122880 values with dtype=float64] Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: array of t-grid longitudes units: degrees_east

  dz :

  <xarray.DataArray 'dz' (z_t: 60)> Size: 240B [60 values with dtype=float32] Coordinates: * z_t (z_t) float32 240B 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05 Attributes: long_name: thickness of layer k units: centimeters

  comp :

  ocn

  grid :

  g16

  lon_name :

  Meridional Ocean Circulation

  vn :

  MOC

x4c.set_style('journal_spines', font_scale=1.2)
fig, ax = case.plot(spell)