TAO Ri analysis

Analysis of bulk & gradient Ri with TAO data

%load_ext watermark

import dask
import dcpy
import distributed
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
import cf_xarray

import pump

dask.config.set({"array.slicing.split_large_chunks": True})
mpl.rcParams["figure.dpi"] = 140

%watermark -iv

The watermark extension is already loaded. To reload it, use:
  %reload_ext watermark
cf_xarray  : 0.4.1.dev21+gab9dc66
xarray     : 0.17.1.dev3+g48378c4b1
dask       : 2021.3.0
dcpy       : 0.1
numpy      : 1.20.1
distributed: 2021.3.0
pump       : 0.1
matplotlib : 3.3.4

Next steps

1. Make a merged velocity dataset

2. Add 147E to merged dataset

3. Try the Pham extrapolation

   • no since I just ignore the near-surface

4. Do something about eucmax in El-Nino years

5. eucmax = 25 before 1996?

6. eucmax at 156E, 165E

7. 165E doesn’t really have much data at all

8. deal with euc shoaling

9. Where is the high-res Ucur, Vcur for 2012-ish? It’s present in the daily data

10. I need to interpolate the density field to get a good dens_euc

I will need to

1. Recalculate bulk Ri stuff. This really shouldn’t change much?

2. Scatter median gradient Ri against the new bulk Ri

3. What the heck is going on at 195W. Marginal stability?

   • barrier & isothermal layers

Ri_g quantile analysis

Ri_q_old = xr.open_dataset("tao-hourly-Ri-seasonal-percentiles-before-salinity.nc")
Ri_q_old

<xarray.Dataset>
Dimensions:    (longitude: 5, quantile: 3, season: 4, zeuc: 59)
Coordinates:
  * season     (season) object 'DJF' 'MAM' 'JJA' 'SON'
  * longitude  (longitude) float64 -204.0 -195.0 -170.0 -140.0 -110.0
  * zeuc       (zeuc) float64 -47.5 -42.5 -37.5 -32.5 ... 232.5 237.5 242.5
  * quantile   (quantile) float64 0.25 0.5 0.75
    latitude   float32 0.0
    num_obs    (season, longitude, zeuc) int64 2956 3450 3685 4162 ... 0 0 0 0
Data variables:
    Rig        (season, longitude, zeuc, quantile) float64 0.8657 1.847 ... nan

Ri_q = xr.open_dataset("tao-hourly-Ri-seasonal-percentiles-2.nc")
Ri_q

Ri = xr.concat(
    [
        Ri_q_old.Rig,
        Ri_q.Rig_T,
        Ri_q.Ri,
    ],
    dim="kind",
)
counts = xr.concat(
    [
        Ri_q_old.num_obs.reset_coords(drop=True),
        Ri_q.num_Rig_T.reset_coords(drop=True),
        Ri_q.num_Rig.reset_coords(drop=True),
    ],
    dim="kind",
)
counts["kind"] = ["$Ri^T_{old}$", "$Ri_g^T$", "$Ri_g$"]
counts.name = "count"

merged = xr.Dataset()
merged["Rig"] = Ri
merged["counts"] = counts
merged = merged.reindex(season=["DJF", "MAM", "JJA", "SON"])
merged

<xarray.Dataset>
Dimensions:             (kind: 3, longitude: 5, quantile: 3, season: 4, zeuc: 59)
Coordinates:
  * season              (season) <U3 'DJF' 'MAM' 'JJA' 'SON'
  * kind                (kind) object '$Ri^T_{old}$' '$Ri_g^T$' '$Ri_g$'
    reference_pressure  int64 0
    num_Rig_T           (season, longitude, zeuc) int64 2949 3436 3665 ... 0 0 0
    num_Rig             (season, longitude, zeuc) int64 169 352 545 ... 0 0 0
  * longitude           (longitude) float64 -204.0 -195.0 -170.0 -140.0 -110.0
  * zeuc                (zeuc) float64 -47.5 -42.5 -37.5 ... 232.5 237.5 242.5
  * quantile            (quantile) float64 0.25 0.5 0.75
    latitude            float32 0.0
    num_obs             (season, longitude, zeuc) int64 2956 3450 3685 ... 0 0 0
Data variables:
    Rig                 (kind, season, longitude, zeuc, quantile) float64 0.8...
    counts              (kind, season, longitude, zeuc) int64 2956 3450 ... 0 0

merged.counts.plot.line(
    hue="season", row="kind", y="zeuc", col="longitude", xscale="linear"
)

<xarray.plot.facetgrid.FacetGrid at 0x7fe05af53eb0>

with mpl.rc_context(pump.plot.sm13_cycler):
    fg = (
        merged.Rig.isel(quantile=1)
        .where(merged.counts > 1000)
        .plot.line(hue="season", row="kind", y="zeuc", col="longitude", xlim=(0, 3))
    )

fg.map(lambda: dcpy.plots.linex(0.25))
fg.map(lambda: dcpy.plots.liney(0))

<xarray.plot.facetgrid.FacetGrid at 0x7fe05e262310>

Argo: How “important” is salinity to N²

argo = dcpy.oceans.read_argo_clim()
argo

<xarray.Dataset>
Dimensions:          (lat: 145, lon: 360, pres: 58, time: 180)
Coordinates:
  * lon              (lon) float32 20.5 21.5 22.5 23.5 ... 377.5 378.5 379.5
  * lat              (lat) float32 -64.5 -63.5 -62.5 -61.5 ... 77.5 78.5 79.5
  * pres             (pres) float32 2.5 10.0 20.0 ... 1.8e+03 1.9e+03 1.975e+03
  * time             (time) datetime64[ns] 2004-01-16 2004-02-15 ... 2018-09-29
Data variables:
    Tmean            (pres, lat, lon) float32 dask.array<chunksize=(58, 20, 60), meta=np.ndarray>
    Tanom            (time, pres, lat, lon) float32 dask.array<chunksize=(180, 58, 20, 60), meta=np.ndarray>
    BATHYMETRY_MASK  (pres, lat, lon) float32 dask.array<chunksize=(58, 20, 60), meta=np.ndarray>
    MAPPING_MASK     (pres, lat, lon) float32 dask.array<chunksize=(58, 20, 60), meta=np.ndarray>
    T                (time, pres, lat, lon) float32 dask.array<chunksize=(180, 58, 20, 60), meta=np.ndarray>
    Smean            (pres, lat, lon) float32 dask.array<chunksize=(58, 20, 60), meta=np.ndarray>
    Sanom            (time, pres, lat, lon) float32 dask.array<chunksize=(180, 58, 20, 60), meta=np.ndarray>
    S                (time, pres, lat, lon) float32 dask.array<chunksize=(180, 58, 20, 60), meta=np.ndarray>

argo0 = (
    argo.cf.interp(latitude=0, longitude=360 + np.array([-195, -170, -140, -110]))
    .assign_coords(lon=np.array([-195, -170, -140, -110]))
    .load()
)
argo0["dens"] = dcpy.eos.pden(argo0.S, argo0.T, 0)
N2 = 9.81 / 1025 * argo0.dens.cf.differentiate("Z")
argo0

<xarray.Dataset>
Dimensions:             (lon: 4, pres: 58, time: 180)
Coordinates:
  * pres                (pres) float32 2.5 10.0 20.0 ... 1.9e+03 1.975e+03
  * time                (time) datetime64[ns] 2004-01-16 ... 2018-09-29
  * lon                 (lon) int64 -195 -170 -140 -110
    lat                 int64 0
    reference_pressure  int64 0
Data variables:
    Tmean               (pres, lon) float64 29.55 28.08 26.04 ... 2.305 2.318
    Tanom               (time, pres, lon) float64 0.5328 0.2495 ... -0.03075
    BATHYMETRY_MASK     (pres, lon) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    MAPPING_MASK        (pres, lon) float64 2e+03 2e+03 2e+03 ... 2e+03 2e+03
    T                   (time, pres, lon) float64 30.09 28.33 ... 2.34 2.287
    Smean               (pres, lon) float64 34.77 35.2 35.11 ... 34.64 34.64
    Sanom               (time, pres, lon) float64 -0.3855 -0.1468 ... 0.00375
    S                   (time, pres, lon) float64 34.38 35.05 ... 34.64 34.64
    dens                (time, pres, lon) float64 1.021e+03 ... 1.028e+03

N2T = 9.81 / 1025 * dcpy.eos.pden(35, argo0.T, 0).differentiate("pres")
N2S = 9.81 / 1025 * dcpy.eos.pden(argo0.S, 27, 0).differentiate("pres")

f, ax = plt.subplots(1, 2, sharex=True, sharey=True, constrained_layout=True)
(N2T / N2).mean("time").cf.plot(hue="lon", ylim=(250, 0), ax=ax[0])
(N2S / N2).mean("time").cf.plot(hue="lon", ylim=(250, 0), ax=ax[1])

ax[0].set_xlabel("$N_S^2/N²$")
ax[1].set_xlabel("$N_T^2/N²$")

{'hue': 'lon', 'ylim': (250, 0), 'ax': <AxesSubplot:>, 'x': None, 'y': 'pres', 'yincrease': False}
{'hue': 'lon', 'ylim': (250, 0), 'ax': <AxesSubplot:>, 'x': None, 'y': 'pres', 'yincrease': False}

Text(0.5, 0, '$N_T^2/N²$')

(N2T / N2).mean("time").cf.plot(hue="lon", ylim=(250, 0))

{'hue': 'lon', 'ylim': (250, 0), 'x': None, 'y': 'pres', 'yincrease': False}

[<matplotlib.lines.Line2D at 0x7f12b6a01a00>,
 <matplotlib.lines.Line2D at 0x7f12b6a01d60>,
 <matplotlib.lines.Line2D at 0x7f12b6a01070>,
 <matplotlib.lines.Line2D at 0x7f12b6a01130>]

(N2S / N2).mean("time").cf.plot(hue="lon")

[<matplotlib.lines.Line2D at 0x7f12b4bd11c0>,
 <matplotlib.lines.Line2D at 0x7f12b4bd12b0>,
 <matplotlib.lines.Line2D at 0x7f12b4bd1370>,
 <matplotlib.lines.Line2D at 0x7f12b4bd1430>]

argo0.T.mean("time").cf.plot(hue="lon")

[<matplotlib.lines.Line2D at 0x7f12b667af70>,
 <matplotlib.lines.Line2D at 0x7f12b66060a0>,
 <matplotlib.lines.Line2D at 0x7f12b6606160>,
 <matplotlib.lines.Line2D at 0x7f12b6606220>]

N2.mean("time").cf["pres"]

<xarray.DataArray 'pres' (pres: 58)>
array([   2.5,   10. ,   20. ,   30. ,   40. ,   50. ,   60. ,   70. ,   80. ,
         90. ,  100. ,  110. ,  120. ,  130. ,  140. ,  150. ,  160. ,  170. ,
        182.5,  200. ,  220. ,  240. ,  260. ,  280. ,  300. ,  320. ,  340. ,
        360. ,  380. ,  400. ,  420. ,  440. ,  462.5,  500. ,  550. ,  600. ,
        650. ,  700. ,  750. ,  800. ,  850. ,  900. ,  950. , 1000. , 1050. ,
       1100. , 1150. , 1200. , 1250. , 1300. , 1350. , 1412.5, 1500. , 1600. ,
       1700. , 1800. , 1900. , 1975. ], dtype=float32)
Coordinates:
  * pres     (pres) float32 2.5 10.0 20.0 30.0 ... 1.8e+03 1.9e+03 1.975e+03
Attributes:
    units:          dbar
    positive:       down
    point_spacing:  uneven
    axis:           Z

N2.mean("time").cf.plot(hue="lon", y="Z", yincrease=False)

[<matplotlib.lines.Line2D at 0x7f12dc07d160>,
 <matplotlib.lines.Line2D at 0x7f12dc07d250>,
 <matplotlib.lines.Line2D at 0x7f12dc07d310>,
 <matplotlib.lines.Line2D at 0x7f12dc07d3d0>]

Sampling: how is tao resolving MI at 140W during MAM

tao = pump.obs.read_tao_zarr("ancillary")

from dask.distributed import Client

client = Client("tcp://127.0.0.1:44303")
client

Client Scheduler: tcp://127.0.0.1:44303 Dashboard: http://127.0.0.1:8787/status

Cluster Workers: 3 Cores: 6 Memory: 25.04 GB

tao140 = tao.sel(longitude=-140).persist()

Looks like there’s plenty of T data above the EUC max so no issues there? There’s a decade where EUCmax is deeper than 75m or so…

tao140.u.sel(depth=slice(-200, None), time=slice("2011", "2013")).plot(x="time")
tao140.eucmax.plot(lw=0.3)
tao140.eucmax.resample(time="W").mean().plot(color="k", lw=0.6)

[<matplotlib.lines.Line2D at 0x7fe0732db6a0>]

tao140.T.sel(depth=slice(-200, None), time=slice("1996", None)).plot(x="time")
tao140.eucmax.plot(lw=0.3)
tao140.eucmax.resample(time="W").mean().plot(color="k", lw=0.6)

[<matplotlib.lines.Line2D at 0x7fe05d3aa0d0>]

tao140.eucmax.to_netcdf("eucmax-140.nc")

calculate Rig

sub = tao.Rig.isel(longitude=2).compute().groupby("time.season").median("time")
sub.plot(
    hue="season",
    y="depth",
)

/gpfs/u/home/dcherian/python/xarray/xarray/core/common.py:664: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, the dimension order of these coordinates will be restored as well unless you specify restore_coord_dims=False.
  self, group, squeeze=squeeze, restore_coord_dims=restore_coord_dims

[<matplotlib.lines.Line2D at 0x2b192110b940>,
 <matplotlib.lines.Line2D at 0x2b191fe94da0>,
 <matplotlib.lines.Line2D at 0x2b191fe94b38>,
 <matplotlib.lines.Line2D at 0x2b191fe94128>]

median_Rig_z = tao.Rig.groupby("time.season").apply(
    lambda x: x.chunk({"time": -1}).quantile(q=0.5, dim="time")
)
median_Rig_z.load()

xarray.DataArray

'Rig'

• season: 4
• longitude: 5
• depth: 101

• nan nan nan nan nan nan nan nan ... 0.173 0.06759 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]]])

• Coordinates: (4)
  • quantile
    ()
    float64
    0.5

    array(0.5)

  • longitude
    (longitude)
    float64
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.])

  • depth
    (depth)
    float64
    -500.0 -495.0 -490.0 ... -5.0 0.0

    array([-500., -495., -490., -485., -480., -475., -470., -465., -460., -455.,
           -450., -445., -440., -435., -430., -425., -420., -415., -410., -405.,
           -400., -395., -390., -385., -380., -375., -370., -365., -360., -355.,
           -350., -345., -340., -335., -330., -325., -320., -315., -310., -305.,
           -300., -295., -290., -285., -280., -275., -270., -265., -260., -255.,
           -250., -245., -240., -235., -230., -225., -220., -215., -210., -205.,
           -200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
           -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
           -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.,
            -50.,  -45.,  -40.,  -35.,  -30.,  -25.,  -20.,  -15.,  -10.,   -5.,
              0.])

  • season
    (season)
    object
    'DJF' 'JJA' 'MAM' 'SON'

    array(['DJF', 'JJA', 'MAM', 'SON'], dtype=object)

• Attributes: (1)

  long_name :

  Ri

fg = median_Rig_z.squeeze().plot.line(
    col="longitude",
    hue="season",
    y="depth",
    ylim=[-150, 0],
    xlim=[0.1, 3.5],
    xscale="log",
)
fg.map(lambda: dcpy.plots.linex([0.25]))
plt.gcf().set_size_inches((8, 4))

bin in euc-relative coordinates

tao = pump.obs.read_tao_zarr("ancillary")
interped = xr.open_zarr("tao-zeuc.zarr")
tao

<xarray.Dataset>
Dimensions:             (depth: 61, longitude: 5, time: 287335)
Coordinates:
    deepest             (time, longitude) float64 dask.array<chunksize=(100000, 1), meta=np.ndarray>
  * depth               (depth) float64 -300.0 -295.0 -290.0 ... -10.0 -5.0 0.0
    eucmax              (time, longitude) float64 dask.array<chunksize=(287335, 4), meta=np.ndarray>
    latitude            float32 ...
  * longitude           (longitude) float64 -204.0 -195.0 -170.0 -140.0 -110.0
    mld                 (time, longitude) float64 dask.array<chunksize=(100000, 1), meta=np.ndarray>
    reference_pressure  int64 ...
    shallowest          (time, longitude) float64 dask.array<chunksize=(100000, 1), meta=np.ndarray>
  * time                (time) datetime64[ns] 1988-05-15T18:00:00 ... 2021-02-24
    zeuc                (depth, time, longitude) float64 dask.array<chunksize=(61, 287335, 4), meta=np.ndarray>
Data variables:
    N2                  (time, longitude, depth) float64 dask.array<chunksize=(100000, 1, 61), meta=np.ndarray>
    N2T                 (time, longitude, depth) float64 dask.array<chunksize=(100000, 1, 61), meta=np.ndarray>
    Ri                  (time, longitude, depth) float64 dask.array<chunksize=(100000, 1, 61), meta=np.ndarray>
    Rig_T               (time, longitude, depth) float64 dask.array<chunksize=(100000, 1, 61), meta=np.ndarray>
    S                   (time, longitude, depth) float64 dask.array<chunksize=(100000, 1, 61), meta=np.ndarray>
    S2                  (time, depth, longitude) float32 dask.array<chunksize=(100000, 61, 1), meta=np.ndarray>
    T                   (time, longitude, depth) float64 dask.array<chunksize=(100000, 1, 61), meta=np.ndarray>
    dens                (time, longitude, depth) float64 dask.array<chunksize=(100000, 1, 61), meta=np.ndarray>
    densT               (time, longitude, depth) float64 dask.array<chunksize=(100000, 1, 61), meta=np.ndarray>
    u                   (time, depth, longitude) float32 dask.array<chunksize=(100000, 61, 1), meta=np.ndarray>
    v                   (time, depth, longitude) float32 dask.array<chunksize=(100000, 61, 1), meta=np.ndarray>

interped.Rig_T.sel(longitude=-140, time="2008-10").plot(x="time", robust=True)

<matplotlib.collections.QuadMesh at 0x7fe0afbec430>

seasonal["median"].where(seasonal["count"] > 60 * 24).plot.line(
    hue="season", y="zeuc", col="longitude", xlim=(0, 2)
)

<xarray.plot.facetgrid.FacetGrid at 0x2ba8ae2a4a90>

subset = good_data.sel(longitude=-140, time="2002")
subset.Rig.plot(x="time", robust=True, vmin=0.1, vmax=2, norm=mpl.colors.LogNorm())
subset.mld.plot(x="time")

[<matplotlib.lines.Line2D at 0x2ba8c4687278>]

/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/matplotlib/colors.py:1110: RuntimeWarning: invalid value encountered in less_equal
  mask |= resdat <= 0

Gradient Ri quantiles

grouped = interped.groupby("time.season")
seasonal = grouped.mean().compute()

seasonal.v.plot(y="zeuc", col="longitude", hue="season")

<xarray.plot.facetgrid.FacetGrid at 0x2b3790fd3240>

%matplotlib inline

fg = Ri_q.where(Ri_q.num_obs > 90 * 24).plot.line(
    row="quantile",
    col="longitude",
    hue="season",
    y="zeuc",
    xlim=[1e-1, 2],
    ylim=[0, 250],
    xscale="log",
)

fg.map(lambda: dcpy.plots.linex(0.25, ax=plt.gca()))

/gpfs/u/home/dcherian/python/xarray/xarray/plot/plot.py:107: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  xplt = darray.transpose(ydim, huedim)

<xarray.plot.facetgrid.FacetGrid at 0x2ba8c3798710>

%matplotlib inline

fg = Ri_q.where(Ri_q.num_obs > 90).plot.line(
    row="quantile",
    col="longitude",
    hue="season",
    y="zeuc",
    xlim=[1e-1, 2],
    ylim=[0, 250],
    xscale="log",
)

fg.map(lambda: dcpy.plots.linex(0.25, ax=plt.gca()))

# fg.fig.suptitle("25,50,75 percentile gradient Ri from all available hourly TAO data.", y=1.0)
# fg.fig.savefig("images/tao-marginal-stability-hourly-euc-coordinates.png", dpi=200)

/gpfs/u/home/dcherian/python/xarray/xarray/plot/plot.py:107: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  xplt = darray.transpose(ydim, huedim)
findfont: Font family ['STIXGeneral'] not found. Falling back to DejaVu Sans.
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findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.

<xarray.plot.facetgrid.FacetGrid at 0x2ac0723c69b0>

Median in euc-relative coordinate

Ri_q = xr.load_dataarray("tao-hourly-Ri-seasonal-percentiles.nc")
Ri_q

xarray.DataArray

• season: 4
• longitude: 5
• zeuc: 59
• quantile: 3

• 0.8657 1.847 4.079 0.9686 2.076 4.698 ... nan nan nan nan nan nan

  array([[[[8.65716985e-01, 1.84699736e+00, 4.07929186e+00],
           [9.68607747e-01, 2.07586669e+00, 4.69831821e+00],
           [1.12714689e+00, 2.45510051e+00, 5.63565819e+00],
           ...,
           [8.25933339e-01, 1.21812980e+00, 2.22266901e+00],
           [3.31773526e-02, 2.15271859e-01, 6.91555336e-01],
           [6.30251313e-01, 9.59637420e-01, 1.28902353e+00]],
  
          [[6.99998537e-01, 1.29253461e+00, 2.86526357e+00],
           [7.26263211e-01, 1.40190939e+00, 3.03401490e+00],
           [8.14764327e-01, 1.58527315e+00, 3.47211588e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[1.04342082e+00, 1.80473934e+00, 3.48219512e+00],
           [1.10594958e+00, 1.94826060e+00, 3.84621979e+00],
           [1.18464712e+00, 2.10952571e+00, 4.29768611e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[5.99332633e-01, 1.01832679e+00, 2.02945611e+00],
           [6.36358415e-01, 1.09730253e+00, 2.22929311e+00],
           [6.69077456e-01, 1.17499171e+00, 2.47144998e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[3.93460173e-01, 6.78976245e-01, 1.35634816e+00],
           [3.96662435e-01, 6.72044675e-01, 1.30979069e+00],
           [3.89880826e-01, 6.65452753e-01, 1.31105090e+00],
           ...,
           [1.71923451e+00, 2.11843237e+00, 3.14030388e+00],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]]],
  
  
         [[[9.64841693e-01, 2.03641443e+00, 5.24673701e+00],
           [1.00937285e+00, 2.07883897e+00, 5.36984820e+00],
           [1.07954196e+00, 2.17156104e+00, 5.51939592e+00],
           ...,
           [9.45015072e-01, 1.23069288e+00, 3.89244182e+00],
           [3.19059242e-01, 4.46329690e-01, 1.42386922e+00],
           [           nan,            nan,            nan]],
  
          [[9.77766440e-01, 1.94110422e+00, 4.23775491e+00],
           [1.02809324e+00, 2.09342055e+00, 4.71047287e+00],
           [1.20866875e+00, 2.40437219e+00, 5.50768658e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[1.06829041e+00, 1.84754009e+00, 3.53931845e+00],
           [1.11732667e+00, 1.94676703e+00, 3.81204909e+00],
           [1.15610819e+00, 2.03169397e+00, 4.13639554e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[6.45954343e-01, 1.04209220e+00, 1.87051596e+00],
           [6.91774334e-01, 1.11851238e+00, 2.00851346e+00],
           [7.23639413e-01, 1.19311723e+00, 2.28014936e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[3.90188842e-01, 5.96989693e-01, 1.09097169e+00],
           [3.99915480e-01, 6.17991764e-01, 1.10944998e+00],
           [4.09988742e-01, 6.36251961e-01, 1.14362343e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]]],
  
  
         [[[8.05875978e-01, 1.64882518e+00, 3.74725616e+00],
           [8.49498749e-01, 1.81732453e+00, 4.24185284e+00],
           [9.22981317e-01, 1.94493706e+00, 4.58776057e+00],
           ...,
           [3.77534561e-01, 5.81013898e-01, 1.00414836e+00],
           [4.43140283e-01, 2.25869804e+00, 6.26462169e+00],
           [7.27405524e-01, 1.93333063e+00, 1.82996463e+01]],
  
          [[8.33461276e-01, 1.78540849e+00, 4.40883574e+00],
           [8.75326686e-01, 1.94566370e+00, 4.97448619e+00],
           [1.09038278e+00, 2.41704308e+00, 5.99210430e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[1.03154098e+00, 1.90779205e+00, 4.02230303e+00],
           [1.06865210e+00, 2.04293217e+00, 4.50922149e+00],
           [1.09868897e+00, 2.09941647e+00, 4.83581297e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[6.13561831e-01, 9.54200964e-01, 1.59989813e+00],
           [6.30494824e-01, 9.95023982e-01, 1.67218575e+00],
           [6.41170129e-01, 1.02841434e+00, 1.77831551e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[3.93044016e-01, 6.17636578e-01, 1.07574078e+00],
           [3.96811132e-01, 6.24261383e-01, 1.06323474e+00],
           [3.92496939e-01, 6.23224291e-01, 1.09776787e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]]],
  
  
         [[[7.20142171e-01, 1.55444359e+00, 3.42266777e+00],
           [7.95652503e-01, 1.63517308e+00, 3.68113071e+00],
           [8.57140530e-01, 1.82649115e+00, 4.21420888e+00],
           ...,
           [2.47228307e-01, 6.21093974e-01, 1.91750068e+00],
           [3.27682126e-01, 1.10605834e+00, 3.20444965e+00],
           [3.40164626e-01, 5.65530493e-01, 1.53144780e+00]],
  
          [[6.63675669e-01, 1.31916576e+00, 3.00179180e+00],
           [6.91861346e-01, 1.44553130e+00, 3.44077254e+00],
           [8.46168409e-01, 1.73035774e+00, 4.14209451e+00],
           ...,
           [6.55769401e-02, 3.88032611e-01, 8.03026828e-01],
           [6.08337896e+01, 6.08337896e+01, 6.08337896e+01],
           [           nan,            nan,            nan]],
  
          [[1.22505629e+00, 2.16464382e+00, 4.25647302e+00],
           [1.31365069e+00, 2.36794923e+00, 4.86517140e+00],
           [1.38017117e+00, 2.53467087e+00, 5.59876342e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[6.04536606e-01, 1.02415097e+00, 1.99978561e+00],
           [6.29322459e-01, 1.08261699e+00, 2.06070930e+00],
           [6.45054365e-01, 1.11824484e+00, 2.15339242e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[3.98786048e-01, 6.72431954e-01, 1.26091140e+00],
           [4.02719219e-01, 6.77482230e-01, 1.26239862e+00],
           [3.97279637e-01, 6.78105856e-01, 1.31576185e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]]]])

• Coordinates: (6)
  • season
    (season)
    object
    'DJF' 'MAM' 'JJA' 'SON'

    array(['DJF', 'MAM', 'JJA', 'SON'], dtype=object)

  • quantile
    (quantile)
    float64
    0.25 0.5 0.75

    array([0.25, 0.5 , 0.75])

  • zeuc
    (zeuc)
    float64
    -47.5 -42.5 -37.5 ... 237.5 242.5

    long_name :

    Depth relative to EUC max

    units :

    m

    array([-47.5, -42.5, -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,
             2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,
            52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,
           102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,
           152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,
           202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5])

  • longitude
    (longitude)
    float32
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.], dtype=float32)

  • latitude
    ()
    float32
    0.0

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array(0., dtype=float32)

  • num_obs
    (season, longitude, zeuc)
    int64
    2956 3450 3685 4162 ... 0 0 0 0

    array([[[ 2956,  3450,  3685, ...,    11,     4,     2],
            [13003, 15472, 16505, ...,     0,     0,     0],
            [25255, 25759, 26188, ...,     0,     0,     0],
            [32022, 32550, 32964, ...,     0,     0,     0],
            [22159, 22496, 22742, ...,     7,     0,     0]],
    
           [[ 3942,  4331,  4609, ...,    16,    10,     0],
            [17279, 19213, 19858, ...,     0,     0,     0],
            [24860, 25030, 25287, ...,     0,     0,     0],
            [31769, 32155, 32493, ...,     0,     0,     0],
            [23765, 23781, 23797, ...,     0,     0,     0]],
    
           [[ 3745,  3967,  4061, ...,    12,     4,     4],
            [20430, 22423, 23235, ...,     0,     0,     0],
            [25438, 25492, 25513, ...,     0,     0,     0],
            [30761, 31005, 31326, ...,     0,     0,     0],
            [26311, 26305, 26311, ...,     0,     0,     0]],
    
           [[ 1779,  2291,  2663, ...,   302,   179,   102],
            [17003, 19290, 20532, ...,     9,     1,     0],
            [23085, 23454, 23690, ...,     0,     0,     0],
            [35541, 35937, 36215, ...,     0,     0,     0],
            [22990, 23038, 23080, ...,     0,     0,     0]]])

• Attributes: (0)

median_Rig = Ri_q.sel(quantile=0.5, zeuc=slice(0, None))
median_Rig

xarray.DataArray

• season: 4
• longitude: 5
• zeuc: 49

• 12.14 5.204 3.505 3.307 3.33 3.44 2.921 ... nan nan nan nan nan nan

  array([[[1.21434122e+01, 5.20354724e+00, 3.50501919e+00, 3.30736311e+00,
           3.33031631e+00, 3.43964060e+00, 2.92148757e+00, 2.48129720e+00,
           2.11243240e+00, 1.83912229e+00, 1.69382899e+00, 1.61765319e+00,
           1.57829252e+00, 1.65197799e+00, 1.67660646e+00, 1.70361004e+00,
           1.75811871e+00, 1.77633452e+00, 1.78274006e+00, 1.77130445e+00,
           1.76453557e+00, 1.59982041e+00, 1.53944503e+00, 1.46579473e+00,
           1.38052897e+00, 1.36555182e+00, 1.22801891e+00, 1.11480848e+00,
           9.81393217e-01, 8.09430894e-01, 7.11033315e-01, 6.64250979e-01,
           6.77348794e-01, 6.59767973e-01, 6.68314872e-01, 6.45685835e-01,
           6.25755328e-01, 6.47728194e-01, 6.16354965e-01, 7.43610355e-01,
           7.47971748e-01, 7.51553840e-01, 6.64788050e-01, 6.37038637e-01,
           7.09365153e-01, 1.08899569e+00, 1.21812980e+00, 2.15271859e-01,
           9.59637420e-01],
          [1.13882227e+01, 4.77078332e+00, 3.17914688e+00, 2.61438944e+00,
           2.32290267e+00, 2.03380830e+00, 1.79832267e+00, 1.61383103e+00,
           1.51623494e+00, 1.46151757e+00, 1.53912420e+00, 1.50846894e+00,
           1.62729495e+00, 1.62687128e+00, 1.76641538e+00, 1.71702053e+00,
           1.82824524e+00, 1.68400249e+00, 1.69879042e+00, 1.47777091e+00,
           1.38645753e+00, 1.11946488e+00, 1.01298141e+00, 8.26534889e-01,
           6.92515550e-01, 6.09600706e-01, 5.75299815e-01, 5.40169666e-01,
           5.15796523e-01, 4.88624275e-01, 4.84976674e-01, 4.53798638e-01,
           4.52561612e-01, 4.34450104e-01, 4.29997400e-01, 3.87855213e-01,
           3.46594639e-01, 3.25506862e-01, 3.52597288e-01, 3.80018426e-01,
           3.84656549e-01, 4.43017123e-01, 4.07717999e-01, 5.74185724e-01,
           9.16682658e-01, 4.36350802e-01,            nan,            nan,
                      nan],
          [1.47684744e+01, 5.04312084e+00, 2.56775010e+00, 1.85025533e+00,
           1.51538755e+00, 1.21760241e+00, 9.54120770e-01, 7.90525610e-01,
           6.72324136e-01, 5.98292186e-01, 5.29106407e-01, 4.99256817e-01,
           4.65811035e-01, 4.33383038e-01, 4.10126868e-01, 3.87110152e-01,
           3.66788709e-01, 3.40755814e-01, 3.19803196e-01, 3.02440042e-01,
           2.89701995e-01, 2.82765766e-01, 2.75048658e-01, 2.70829288e-01,
           2.70248861e-01, 2.72051211e-01, 2.72058429e-01, 2.67041272e-01,
           2.58904990e-01, 2.60149478e-01, 2.65921342e-01, 2.79376712e-01,
           2.90498832e-01, 2.87567105e-01, 2.71350610e-01, 2.39308328e-01,
           2.84417943e-01, 2.52591608e-01, 2.24577574e-01, 1.48891455e-01,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan],
          [8.36970312e+00, 2.14714176e+00, 8.60081631e-01, 5.37678636e-01,
           4.56452975e-01, 4.08223936e-01, 3.65848689e-01, 3.36680169e-01,
           3.07557415e-01, 2.86030920e-01, 2.67174971e-01, 2.53727388e-01,
           2.47924187e-01, 2.42138913e-01, 2.37619971e-01, 2.32915351e-01,
           2.35271202e-01, 2.33308752e-01, 2.29976608e-01, 2.25829450e-01,
           2.19308704e-01, 2.04431004e-01, 2.03778743e-01, 1.90089052e-01,
           1.91867722e-01, 2.53620813e-01, 2.88059360e-01, 3.41855224e-01,
           3.33964955e-01, 2.12386931e-01, 1.75787371e-01, 1.36908322e-01,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan],
          [6.06131842e+00, 1.47315757e+00, 6.69238501e-01, 4.78770333e-01,
           4.38250354e-01, 4.14118137e-01, 3.88180626e-01, 3.71394866e-01,
           3.42725616e-01, 3.24029195e-01, 3.07485755e-01, 3.10154128e-01,
           3.08549184e-01, 3.35307815e-01, 3.69083744e-01, 4.11380242e-01,
           5.76389174e-01, 6.93941870e-01, 6.69804430e-01, 7.37061782e-01,
           7.07122624e-01, 1.13043653e+00, 1.73115935e+00, 2.02911056e+00,
           1.24368276e+00, 1.05902519e+00, 1.11146421e+00, 1.23294705e+00,
           2.33638675e+00, 2.93042190e+00, 3.58860533e+00, 5.00408848e+00,
           6.55130805e+00, 5.27558959e+00, 8.02832863e+00, 5.61414450e+00,
           4.23552049e+00, 3.11610086e+00, 3.76147376e+00, 2.42637315e+00,
           3.42179676e+00, 2.30791843e+00, 2.42310845e+00, 3.61083078e+00,
           3.51495200e+00, 2.22169454e+00, 2.11843237e+00,            nan,
                      nan]],
  
         [[1.18795150e+01, 5.73232095e+00, 4.17068039e+00, 3.66942172e+00,
           3.71244499e+00, 3.72028871e+00, 3.30464552e+00, 3.17999834e+00,
           2.75183906e+00, 2.49180164e+00, 2.22375740e+00, 2.11169120e+00,
           1.92279833e+00, 1.94631748e+00, 1.91476112e+00, 1.84819788e+00,
           1.80467154e+00, 1.64739303e+00, 1.54788270e+00, 1.33018077e+00,
           1.29655312e+00, 1.17936204e+00, 1.10972316e+00, 1.04965287e+00,
           9.83979900e-01, 1.01748364e+00, 9.67494278e-01, 9.48095799e-01,
           8.66171745e-01, 8.21134166e-01, 7.33456707e-01, 6.10038674e-01,
           5.60157638e-01, 4.31943839e-01, 4.10451759e-01, 3.38990636e-01,
           3.02373638e-01, 2.85209705e-01, 2.25864290e-01, 2.14437955e-01,
           1.87437119e-01, 1.78117461e-01, 1.86693252e-01, 2.06638961e-01,
           4.83587654e-01, 5.92063881e-01, 1.23069288e+00, 4.46329690e-01,
                      nan],
          [1.21266701e+01, 4.79648555e+00, 3.50769751e+00, 2.90230283e+00,
           2.65311451e+00, 2.12264266e+00, 1.87921132e+00, 1.59730303e+00,
           1.48343092e+00, 1.34714062e+00, 1.33025270e+00, 1.21568299e+00,
           1.26878140e+00, 1.23142890e+00, 1.28923710e+00, 1.16348267e+00,
           1.19842881e+00, 1.04129351e+00, 1.09257463e+00, 9.60674825e-01,
           9.38267437e-01, 7.76860458e-01, 7.30618795e-01, 6.16369855e-01,
           5.64380567e-01, 4.93782675e-01, 4.53639315e-01, 3.81601623e-01,
           3.70866313e-01, 3.21142329e-01, 3.14585744e-01, 2.84435444e-01,
           2.89148003e-01, 2.76887421e-01, 2.84614061e-01, 2.80558164e-01,
           2.86626233e-01, 2.88373412e-01, 3.06993896e-01, 3.12691570e-01,
           3.57301330e-01, 2.98035276e-01, 2.97355837e-01, 4.22352520e-01,
           4.40715975e-01, 3.81879096e-01,            nan,            nan,
                      nan],
          [1.32308680e+01, 4.60275099e+00, 2.38688532e+00, 1.77956782e+00,
           1.60767100e+00, 1.53063058e+00, 1.40887315e+00, 1.30373969e+00,
           1.09884649e+00, 9.16109021e-01, 7.61750976e-01, 6.66489983e-01,
           5.87283785e-01, 5.25661282e-01, 4.81864084e-01, 4.41365009e-01,
           4.19537926e-01, 3.90161175e-01, 3.63381473e-01, 3.50927044e-01,
           3.30290755e-01, 3.20888983e-01, 3.12251187e-01, 3.13585513e-01,
           3.27251221e-01, 3.35796922e-01, 3.71503610e-01, 3.73879739e-01,
           3.83201630e-01, 3.59528435e-01, 3.30063234e-01, 2.89632833e-01,
           2.70732746e-01, 2.91731611e-01, 3.06177473e-01, 4.33485338e-01,
           7.40472313e-01, 4.94800430e-01, 1.87327741e+00,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan],
          [9.38441536e+00, 2.66479397e+00, 1.15839296e+00, 7.39236742e-01,
           5.94381771e-01, 5.03965375e-01, 4.28284019e-01, 3.79675794e-01,
           3.37561653e-01, 3.12639645e-01, 2.94816835e-01, 2.75987607e-01,
           2.61016578e-01, 2.46401457e-01, 2.42315368e-01, 2.37416130e-01,
           2.28437781e-01, 2.22667753e-01, 2.03709351e-01, 1.95004949e-01,
           1.72384242e-01, 2.00197873e-01, 1.83429982e-01, 1.71063054e-01,
           1.68982952e-01, 1.93423097e-01, 2.50654072e-02,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan],
          [6.35667026e+00, 1.58289170e+00, 7.40185625e-01, 5.89306177e-01,
           6.00743499e-01, 6.02856712e-01, 6.05625307e-01, 5.60725962e-01,
           5.30575976e-01, 5.26772048e-01, 4.33881507e-01, 4.35793151e-01,
           4.99908041e-01, 4.51393154e-01, 4.15098246e-01, 2.65339357e-01,
           1.14163436e+00, 1.49539644e+00, 1.30468077e+00, 1.44661570e+01,
           2.76362223e+00, 2.06068540e+00, 4.82168482e+00, 4.49350238e+00,
           4.51277068e+00, 3.64989798e+00, 5.42045806e+00, 2.60856677e+00,
           3.04463197e+00, 1.40800983e+00, 1.38749771e+00, 1.54530999e+00,
           6.59299158e+00, 1.07311729e+01, 1.05502139e+01, 3.33085248e+01,
           3.20924688e+01, 2.13964199e+01, 1.51388879e+01, 4.20119909e+00,
           6.52112723e+00, 1.75267170e+01, 1.55344339e+01, 8.78469408e+00,
                      nan,            nan,            nan,            nan,
                      nan]],
  
         [[1.05714129e+01, 4.62359230e+00, 3.46139879e+00, 3.35240952e+00,
           3.58240908e+00, 3.60377978e+00, 3.51227250e+00, 3.45704319e+00,
           3.31288158e+00, 3.50207365e+00, 3.30392792e+00, 3.05101028e+00,
           2.90888777e+00, 2.71406160e+00, 2.51353778e+00, 2.45364021e+00,
           2.30567717e+00, 2.15569023e+00, 2.19715252e+00, 1.97989130e+00,
           1.94674416e+00, 1.84492966e+00, 1.94365311e+00, 1.95732762e+00,
           1.94741159e+00, 1.97090166e+00, 1.69769634e+00, 1.72398378e+00,
           1.51018620e+00, 1.47244655e+00, 1.41086089e+00, 1.14914169e+00,
           1.40162635e+00, 1.24632637e+00, 1.17904731e+00, 1.19666952e+00,
           7.49406766e-01, 6.86870323e-01, 5.43489970e-01, 5.20109112e-01,
           5.17017122e-01, 4.75424181e-01, 3.94350914e-01, 5.11334188e-01,
           6.07132291e-01, 6.30565797e-01, 5.81013898e-01, 2.25869804e+00,
           1.93333063e+00],
          [1.17940640e+01, 5.25544201e+00, 4.22967127e+00, 3.62164805e+00,
           3.88617101e+00, 3.37901211e+00, 3.28576976e+00, 2.63130394e+00,
           2.42744109e+00, 1.99966443e+00, 1.88839719e+00, 1.59958155e+00,
           1.62788966e+00, 1.47858501e+00, 1.53199188e+00, 1.35668051e+00,
           1.50458319e+00, 1.32140472e+00, 1.38419409e+00, 1.20549313e+00,
           1.23711492e+00, 1.05168104e+00, 1.04349843e+00, 9.17463818e-01,
           8.31690289e-01, 6.96096843e-01, 6.24577881e-01, 5.19898097e-01,
           5.11161347e-01, 4.45226097e-01, 4.50346637e-01, 3.91552356e-01,
           3.88578151e-01, 3.65311011e-01, 3.82296141e-01, 3.98548037e-01,
           4.05276644e-01, 4.08935421e-01, 4.25603088e-01, 4.87255405e-01,
           5.89694583e-01, 5.36924034e-01, 7.43590748e-01, 4.63893288e-01,
           2.94460035e-01, 1.82202586e-01,            nan,            nan,
                      nan],
          [1.07297006e+01, 3.82770691e+00, 2.01728290e+00, 1.44375591e+00,
           1.19391101e+00, 9.41738554e-01, 7.61547681e-01, 6.86062120e-01,
           6.13427458e-01, 5.64384458e-01, 5.04007329e-01, 4.62596793e-01,
           4.25500673e-01, 3.97350213e-01, 3.79143925e-01, 3.49461486e-01,
           3.25380721e-01, 3.11733912e-01, 3.01066981e-01, 3.00100976e-01,
           3.03328532e-01, 3.03769908e-01, 2.91578618e-01, 2.74011282e-01,
           2.50571836e-01, 2.43947408e-01, 2.29420991e-01, 2.12201145e-01,
           1.94873662e-01, 1.63965650e-01, 1.23270352e-01, 1.10001085e-01,
           9.16278115e-02, 7.36690614e-02, 8.54985489e-02,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan],
          [8.38778976e+00, 1.95349263e+00, 7.51629323e-01, 4.76176370e-01,
           4.08459571e-01, 3.64684456e-01, 3.27602914e-01, 2.98867151e-01,
           2.75016466e-01, 2.62129512e-01, 2.54552710e-01, 2.48601040e-01,
           2.46271453e-01, 2.36938130e-01, 2.39076504e-01, 2.30147425e-01,
           2.34933765e-01, 2.55580714e-01, 2.66980573e-01, 2.83303540e-01,
           2.79064475e-01, 2.64805858e-01, 2.48679408e-01, 2.63079630e-01,
           2.52527164e-01, 2.90270974e-01, 2.84629061e-01, 2.48436912e-01,
           2.43670789e-01, 2.35500689e-01,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan],
          [5.37563422e+00, 1.12358895e+00, 4.95643238e-01, 3.65101132e-01,
           3.51802985e-01, 3.52177576e-01, 3.52007361e-01, 3.51005701e-01,
           3.37515271e-01, 3.39513082e-01, 3.35223730e-01, 3.18289001e-01,
           3.06821750e-01, 2.90340441e-01, 2.98470763e-01, 2.79100185e-01,
           2.99315859e-01, 3.15053960e-01, 3.55265472e-01, 3.23207753e-01,
           2.97037879e-01, 3.19642411e-01,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan]],
  
         [[1.32084796e+01, 5.39319207e+00, 3.52229632e+00, 2.93341640e+00,
           2.72676167e+00, 2.49043560e+00, 2.21115447e+00, 2.14446298e+00,
           2.25307901e+00, 2.37758877e+00, 2.36786726e+00, 2.26457119e+00,
           2.05241769e+00, 2.02006020e+00, 1.96651541e+00, 1.97930909e+00,
           2.05072275e+00, 2.11200633e+00, 2.25526030e+00, 2.15241252e+00,
           2.24380199e+00, 2.08202988e+00, 1.99581743e+00, 1.89842029e+00,
           1.68641665e+00, 1.55177330e+00, 1.36028593e+00, 1.24278528e+00,
           1.12188757e+00, 1.02753915e+00, 9.51756111e-01, 8.79999922e-01,
           8.48478459e-01, 7.86434153e-01, 7.75960964e-01, 7.31223467e-01,
           6.94611082e-01, 6.61910228e-01, 6.69654245e-01, 6.46087328e-01,
           6.16348989e-01, 6.27899772e-01, 5.98733773e-01, 5.66040755e-01,
           5.54252448e-01, 5.32374004e-01, 6.21093974e-01, 1.10605834e+00,
           5.65530493e-01],
          [1.18595525e+01, 4.97959322e+00, 3.58755757e+00, 2.94108483e+00,
           2.85884400e+00, 2.54028601e+00, 2.54466762e+00, 2.27151768e+00,
           2.20081956e+00, 2.03409988e+00, 2.06725784e+00, 1.89290255e+00,
           1.98072942e+00, 1.86709794e+00, 1.86255919e+00, 1.60786293e+00,
           1.67623955e+00, 1.40131629e+00, 1.44908704e+00, 1.26560946e+00,
           1.35469045e+00, 1.16296249e+00, 1.15626968e+00, 1.00359287e+00,
           9.27385770e-01, 8.05813051e-01, 7.29405245e-01, 6.14712908e-01,
           5.74781919e-01, 5.05714310e-01, 4.88913616e-01, 4.44145253e-01,
           4.45800974e-01, 4.19765613e-01, 4.26248772e-01, 4.53539067e-01,
           4.69807422e-01, 4.60143069e-01, 4.79973706e-01, 4.44125125e-01,
           4.26681701e-01, 5.16226474e-01, 5.06248645e-01, 5.43729686e-01,
           4.03384597e-01, 3.22069622e-01, 3.88032611e-01, 6.08337896e+01,
                      nan],
          [1.20008153e+01, 4.52373447e+00, 2.53205436e+00, 1.95987145e+00,
           1.68561559e+00, 1.35823013e+00, 1.06112770e+00, 9.04148180e-01,
           7.73121226e-01, 7.06270473e-01, 6.38955325e-01, 5.88677049e-01,
           5.23072367e-01, 4.77237345e-01, 4.40651750e-01, 4.03559240e-01,
           3.77945334e-01, 3.61810002e-01, 3.48063565e-01, 3.40408987e-01,
           3.19275420e-01, 3.15722505e-01, 2.98753510e-01, 2.94470373e-01,
           2.87329178e-01, 2.84434984e-01, 2.89159681e-01, 2.96999130e-01,
           3.09476536e-01, 3.09896010e-01, 3.43075732e-01, 3.64153279e-01,
           3.37287147e-01, 2.83830864e-01, 2.65328327e-01, 3.10828094e-01,
           5.27707428e-01, 1.09958888e+00, 1.70428055e+00, 4.72042158e-01,
           1.73144303e+00,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan],
          [8.14140230e+00, 1.88084775e+00, 7.26223443e-01, 4.61641841e-01,
           4.01225449e-01, 3.77276792e-01, 3.61756498e-01, 3.51907089e-01,
           3.34929641e-01, 3.17450628e-01, 3.01135119e-01, 2.91249790e-01,
           2.82846238e-01, 2.72306145e-01, 2.69324223e-01, 2.60152814e-01,
           2.52086985e-01, 2.42525708e-01, 2.39314482e-01, 2.27459368e-01,
           2.29909146e-01, 2.33270130e-01, 2.32517123e-01, 2.57107429e-01,
           2.37520841e-01, 2.17734028e-01, 1.77322463e-01, 1.66670892e-01,
           2.06064679e-01, 1.81312113e-01, 3.56486993e-01, 4.76187062e-02,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan],
          [5.30451555e+00, 1.39890220e+00, 6.59641136e-01, 4.74013103e-01,
           4.34966521e-01, 4.14950727e-01, 3.92516821e-01, 3.73728739e-01,
           3.41558522e-01, 3.13010930e-01, 2.81422250e-01, 2.62277502e-01,
           2.44637364e-01, 2.39803077e-01, 2.42269302e-01, 2.32348871e-01,
           2.32636819e-01, 2.28041078e-01, 2.54482152e-01, 2.75752326e-01,
           3.44877263e-01, 3.81703585e-01, 3.86630974e-01, 6.74861080e-01,
           8.42115501e-01, 7.61087575e-01, 6.42034748e-01, 5.88438534e-01,
           5.59815524e-01, 4.67952854e-01, 3.81562168e-01, 6.05726756e-01,
           4.05398653e-01, 4.76605698e-01, 8.32539916e-01, 9.93984286e+00,
           4.42157658e-01,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan,            nan,            nan,            nan,
                      nan]]])

• Coordinates: (6)
  • season
    (season)
    object
    'DJF' 'MAM' 'JJA' 'SON'

    array(['DJF', 'MAM', 'JJA', 'SON'], dtype=object)

  • quantile
    ()
    float64
    0.5

    array(0.5)

  • zeuc
    (zeuc)
    float64
    2.5 7.5 12.5 ... 232.5 237.5 242.5

    long_name :

    Depth relative to EUC max

    units :

    m

    array([  2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,
            52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,
           102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,
           152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,
           202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5])

  • longitude
    (longitude)
    float32
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.], dtype=float32)

  • latitude
    ()
    float32
    0.0

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array(0., dtype=float32)

  • num_obs
    (season, longitude, zeuc)
    int64
    5189 5238 5231 5230 ... 0 0 0 0

    array([[[ 5189,  5238,  5231,  5230,  5231,  5225,  5232,  5235,  5245,
              5255,  5256,  5258,  5256,  5256,  5254,  5253,  5245,  5237,
              5230,  5198,  5124,  5047,  4937,  4771,  4597,  4395,  4119,
              3854,  3539,  3216,  2899,  2553,  2197,  1932,  1635,  1380,
              1083,   844,   702,   526,   454,   301,   199,   119,    45,
                26,    11,     4,     2],
            [23197, 23683, 23695, 23706, 23719, 23727, 23734, 23722, 23717,
             23667, 23602, 23526, 23432, 23362, 23202, 23034, 22656, 22223,
             21555, 20735, 19739, 18716, 17243, 16280, 14753, 13865, 12340,
             11586, 10615,  9855,  8655,  7623,  6302,  5236,  4054,  3232,
              2440,  1820,  1467,   972,   751,   356,   160,    93,    16,
                 8,     0,     0,     0],
            [26534, 26473, 26403, 26382, 26340, 26318, 26274, 26225, 26173,
             26086, 25955, 25785, 25533, 25196, 24769, 24191, 23587, 22905,
             22068, 21015, 19896, 18569, 17018, 15203, 13468, 11411,  9552,
              7627,  5617,  4331,  2854,  1914,  1174,   627,   347,   143,
                74,    26,    10,     2,     0,     0,     0,     0,     0,
                 0,     0,     0,     0],
            [34205, 34096, 34055, 33934, 33824, 33723, 33544, 33339, 32945,
             32262, 31174, 29605, 27190, 25027, 21073, 18218, 14241, 10909,
              8219,  5474,  3805,  2160,  1259,   590,   313,   134,    80,
                53,    35,    20,     7,     3,     0,     0,     0,     0,
                 0,     0,     0,     0,     0,     0,     0,     0,     0,
                 0,     0,     0,     0],
            [23051, 22789, 22550, 21664, 21035, 19304, 17604, 15190, 12834,
             10885,  8348,  6440,  4162,  2736,  1475,   835,   422,   220,
               148,    98,    85,    82,    82,    82,    82,    82,    82,
                82,    82,    82,    82,    82,    82,    82,    82,    82,
                82,    81,    78,    73,    65,    59,    45,    30,    21,
                10,     7,     0,     0]],
    
           [[ 5739,  5765,  5763,  5765,  5758,  5755,  5758,  5756,  5763,
              5766,  5767,  5767,  5758,  5752,  5735,  5708,  5678,  5634,
              5559,  5472,  5364,  5248,  5099,  4922,  4714,  4481,  4198,
              3929,  3594,  3275,  3001,  2650,  2237,  1935,  1470,  1232,
               934,   716,   555,   367,   310,   195,   135,    77,    49,
                28,    16,    10,     0],
            [26097, 26544, 26549, 26557, 26559, 26547, 26564, 26561, 26465,
             26429, 26132, 25988, 25700, 25512, 24895, 24687, 23994, 23646,
             23061, 22435, 21749, 21034, 20413, 19632, 18773, 17947, 16638,
             15595, 13809, 12704, 10636,  9382,  6859,  5818,  4328,  3571,
              2329,  1719,  1160,   655,   442,   216,   116,    59,    12,
                 9,     0,     0,     0],
            [25567, 25577, 25587, 25601, 25612, 25605, 25592, 25538, 25473,
             25368, 25165, 24964, 24562, 24182, 23459, 22776, 21907, 20900,
             20063, 18598, 17346, 15494, 13757, 11684,  9549,  7734,  5609,
              4306,  2990,  2327,  1591,  1168,   784,   372,   207,    89,
                34,    20,     6,     0,     0,     0,     0,     0,     0,
                 0,     0,     0,     0],
            [32480, 32302, 32149, 31903, 31739, 31260, 30523, 28792, 26380,
             22986, 19116, 15992, 11820,  9641,  6566,  4914,  3351,  2127,
              1464,   762,   460,   235,   104,    61,    25,    16,     5,
                 0,     0,     0,     0,     0,     0,     0,     0,     0,
                 0,     0,     0,     0,     0,     0,     0,     0,     0,
                 0,     0,     0,     0],
            [23211, 22039, 20939, 17940, 15815, 12032,  9155,  6190,  3938,
              2485,  1492,  1030,   544,   335,   144,    64,    27,    10,
                 6,     6,     6,     6,     6,     6,     5,     5,     4,
                 4,     4,     4,     4,     4,     4,     4,     4,     4,
                 4,     4,     4,     4,     4,     4,     3,     2,     0,
                 0,     0,     0,     0]],
    
           [[ 4480,  4500,  4499,  4497,  4495,  4496,  4498,  4496,  4497,
              4495,  4493,  4490,  4467,  4437,  4408,  4355,  4291,  4203,
              4126,  4083,  4023,  3933,  3862,  3720,  3623,  3499,  3372,
              3223,  3021,  2743,  2453,  2158,  1695,  1372,   993,   769,
               564,   440,   325,   225,   177,    86,    61,    47,    29,
                20,    12,     4,     4],
            [28543, 28727, 28740, 28727, 28740, 28726, 28730, 28692, 28682,
             28643, 28594, 28574, 28433, 28267, 27703, 27276, 26293, 25968,
             25412, 24898, 23886, 23121, 22107, 21230, 19737, 18648, 16673,
             15521, 13530, 12295, 10212,  8902,  6885,  5734,  4083,  3214,
              1968,  1358,   888,   453,   283,   115,    43,    27,     5,
                 4,     0,     0,     0],
            [25516, 25481, 25434, 25346, 25226, 25062, 24859, 24609, 24294,
             23897, 23304, 22553, 21292, 19975, 18100, 16357, 14305, 12092,
             10478,  8442,  7184,  5590,  4348,  2969,  2050,  1368,   823,
               563,   344,   230,   115,    48,    22,     2,     1,     0,
                 0,     0,     0,     0,     0,     0,     0,     0,     0,
                 0,     0,     0,     0],
            [32602, 32386, 32069, 31482, 31128, 30489, 30139, 29314, 28114,
             25690, 22304, 18818, 13925, 11099,  7246,  5532,  3778,  2802,
              2228,  1733,  1358,  1009,   730,   466,   328,   161,    84,
                48,    15,     9,     0,     0,     0,     0,     0,     0,
                 0,     0,     0,     0,     0,     0,     0,     0,     0,
                 0,     0,     0,     0],
            [26374, 26253, 26139, 25353, 24470, 22322, 19979, 16887, 13414,
             10584,  7419,  5781,  3810,  2785,  1692,  1068,   540,   230,
               122,    35,    15,     2,     0,     0,     0,     0,     0,
                 0,     0,     0,     0,     0,     0,     0,     0,     0,
                 0,     0,     0,     0,     0,     0,     0,     0,     0,
                 0,     0,     0,     0]],
    
           [[ 5864,  6282,  6275,  6263,  6262,  6266,  6267,  6288,  6295,
              6294,  6299,  6300,  6300,  6300,  6297,  6297,  6297,  6295,
              6293,  6287,  6273,  6248,  6223,  6179,  6126,  6065,  5986,
              5869,  5767,  5600,  5457,  5254,  4985,  4708,  4294,  3943,
              3439,  2991,  2589,  2130,  1857,  1400,  1131,   906,   640,
               499,   302,   179,   102],
            [28058, 28591, 28636, 28646, 28659, 28652, 28653, 28638, 28639,
             28616, 28569, 28546, 28508, 28488, 28400, 28338, 27852, 27630,
             27088, 26703, 25280, 24744, 23457, 22708, 21295, 20504, 19182,
             18447, 16783, 15947, 13857, 12707, 10354,  8909,  7020,  5680,
              4199,  3219,  2389,  1398,  1041,   526,   289,   186,    58,
                36,     9,     1,     0],
            [24198, 24225, 24193, 24096, 24055, 24004, 23967, 23948, 23914,
             23841, 23727, 23537, 23205, 22774, 22126, 21348, 20305, 18961,
             17572, 15528, 13984, 12186, 10555,  8836,  7424,  5913,  4563,
              3585,  2288,  1625,   857,   497,   283,   119,    64,    26,
                14,    13,    13,    10,     2,     0,     0,     0,     0,
                 0,     0,     0,     0],
            [36251, 36142, 36085, 35978, 35882, 35712, 35446, 35053, 34637,
             33850, 32712, 31096, 28191, 25721, 21310, 18219, 14005, 10939,
              8571,  6216,  4670,  3151,  2219,  1254,   770,   381,   200,
               109,    34,    17,     5,     3,     0,     0,     0,     0,
                 0,     0,     0,     0,     0,     0,     0,     0,     0,
                 0,     0,     0,     0],
            [23594, 23481, 23372, 23092, 22838, 22132, 21284, 19876, 18201,
             16345, 13776, 11721,  8513,  6616,  4616,  3212,  2297,  1447,
              1044,   543,   324,   120,    61,    27,    21,    18,    16,
                15,    15,    14,    10,    10,     5,     5,     3,     2,
                 1,     0,     0,     0,     0,     0,     0,     0,     0,
                 0,     0,     0,     0]]])

• Attributes: (0)

%matplotlib notebook

tao.u.isel(longitude=0).sel(time=slice("1992", "1993")).plot(x="time")
tao.eucmax.isel(longitude=0).sel(time=slice("1992", "1993")).plot(
    x="time", lw=0.5, xlim=["1992", "1993"]
)

[<matplotlib.lines.Line2D at 0x2b7aef5ee0f0>]

after binning instead of reinterpolating to euc depth coordinate

fg = median_Rig.where(median_Rig.num_obs > 60 * 24).plot.line(
    col="longitude",
    hue="season",
    y="zeuc",
    xlim=[1e-1, 10],
    ylim=[0, 250],
    xscale="log",
)

fg.map(lambda: dcpy.plots.linex(0.25, ax=plt.gca()))
fg.fig.set_size_inches((10, 4))

after gridding T,u to same grid

some minor differences especially at 110W

%matplotlib inline

fg = (
    Ri_q.where(Ri_q.num_obs > 60 * 24)
    .sel(quantile=50)
    .plot.line(
        col="longitude",
        hue="season",
        y="zeuc",
        xlim=[1e-1, 2],
        ylim=[0, 250],
        xscale="log",
    )
)

fg.map(lambda: dcpy.plots.linex(0.25, ax=plt.gca()))
fg.fig.set_size_inches((10, 4))

after filling NaNs with pchip

and properly filtering eucmax

fg = (
    Ri_q.where(Ri_q.num_obs > 90 * 24)
    .sel(quantile=50)
    .plot.line(
        col="longitude",
        hue="season",
        y="zeuc",
        xlim=[1e-1, 2],
        ylim=[0, 250],
        xscale="log",
    )
)

fg.map(lambda: dcpy.plots.linex(0.25, ax=plt.gca()))
fg.fig.set_size_inches((10, 4))

fg.fig.savefig("images/tao-marginal-stability-zeuc-Rig-median.png", dpi=200)

before filling NaNs with pchip

fg = (
    Ri_q.where(Ri_q.num_obs > 30 * 24)
    .sel(quantile=50)
    .plot.line(
        col="longitude",
        hue="season",
        y="zeuc",
        xlim=[1e-1, 2],
        ylim=[0, 250],
        xscale="log",
    )
)

fg.map(lambda: dcpy.plots.linex(0.25, ax=plt.gca()))
fg.fig.set_size_inches((10, 4))

fg.fig.savefig("images/tao-marginal-stability-zeuc-Rig-median.png", dpi=200)

# Ri_q.Ri.sel(quantile=50).plot(xscale="log", xlim=[0.1, 2], y="zeuc", hue="season", col="longitude", ylim=[0, 250])

Using number of observations > 90

This seems to work best.

There is an error here. Need 90*24

fg = (
    Ri_q.sel(quantile=50)
    .where(Ri_q.num_obs > 90)
    .plot.line(col="longitude", hue="season", y="zeuc", xlim=[0, 2], ylim=[0, 250])
)
[dcpy.plots.linex(0.25, ax=ax) for ax in fg.axes.flat]

[None, None, None, None, None]

after removing mld

fg = Ri_q.sel(quantile=50).plot.line(
    col="longitude", hue="season", y="zeuc", xlim=[0, 2], ylim=[0, 250]
)
[dcpy.plots.linex(0.25, ax=ax) for ax in fg.axes.flat]

[None, None, None, None, None]

before removing mld

fg = (
    Ri_q.sel(quantile=50)
    .isel(zeuc=slice(None, None, 2))
    .plot.line(col="longitude", hue="season", y="zeuc", xlim=[0, 2], ylim=[0, 250])
)
[dcpy.plots.linex(0.25, ax=ax) for ax in fg.axes.flat]

[None, None, None, None, None]

Before limiting eucmax to be deeper than 20m

Ri_q.sel(quantile=50).plot.line(
    col="longitude", hue="season", y="zeuc", xlim=[0, 2], ylim=[0, 200]
)

<xarray.plot.facetgrid.FacetGrid at 0x2b00a6c4a2e8>

Ri_q = (
    Ri.chunk({"time": -1, "depth": 25})
    .groupby("time.season")
    .apply(dask_quantile, dim="time", q=[25, 50, 75])
).reindex(season=["DJF", "MAM", "JJA", "SON"])
Ri_q

<xarray.DataArray 'Ri' (season: 4, depth: 135, longitude: 5, quantile: 3)>
dask.array<getitem, shape=(4, 135, 5, 3), dtype=float64, chunksize=(1, 25, 1, 3), chunktype=numpy.ndarray>
Coordinates:
  * season     (season) object 'DJF' 'MAM' 'JJA' 'SON'
    latitude   float32 0.0
  * quantile   (quantile) int64 25 50 75
  * depth      (depth) float32 -750.0 -500.0 -495.0 -490.0 ... -3.0 -1.0 -0.0
  * longitude  (longitude) float32 -204.0 -195.0 -170.0 -140.0 -110.0

Ri_q.sel(quantile=50).plot.line(
    y="depth", hue="season", col="longitude", xlim=[0, 2], ylim=[-100, 0]
)

<xarray.plot.facetgrid.FacetGrid at 0x2b79f92b9898>

Ri.Ri.groupby("time.season").quantile([25, 50, 75])

Ri.Ri.groupby("time.season").quantile

median = Ri.Ri.groupby("time.season").apply(lambda x: dask_median(x, "time"))

<xarray.DataArray 'Ri' (season: 4, depth: 135, longitude: 5)>
dask.array<concatenate, shape=(4, 135, 5), dtype=float64, chunksize=(1, 10, 1), chunktype=numpy.ndarray>
Coordinates:
    latitude   float32 0.0
  * depth      (depth) float32 -750.0 -500.0 -495.0 -490.0 ... -3.0 -1.0 -0.0
  * longitude  (longitude) float64 -204.0 -195.0 -170.0 -140.0 -110.0
  * season     (season) object 'DJF' 'JJA' 'MAM' 'SON'

seasonal = Ri.groupby("time.season")

for season, Ris in seasonal:
    Rigrouped = Ris.Ri.groupby_bins(Ris.zeuc, np.arange(0, 200, 10))
    for bin, group in Rigrouped:
        print(bin)

seasonal = (
    Ri.groupby("time.season")
    .apply(lambda x: dask_median(x, "time"))
    .reindex(season=["DJF", "MAM", "JJA", "SON"])
)

fg = seasonal.plot.line(
    col="longitude",
    hue="season",
    y="depth",
    ylim=[-150, 0],
    xlim=[0.1, 3.5],
    xscale="log",
)
fg.map(lambda: dcpy.plots.linex([0.25, 0.3]))
plt.gcf().suptitle("Seasonal median 5m Ri | Hourly mean TAO ADCP, T ", y=1.02)
plt.gcf().set_size_inches((8, 4))
plt.gcf().set_dpi(200)
# f, ax = plt.subplots(1, 1, constrained_layout=True)
# f.savefig('images/tao-marginal-stability-hourly.png')

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-90-95983074cd2a> in <module>
      2 
      3 for season, Ris in seasonal:
----> 4     Rigrouped = Ris.Ri.groupby_bins(Ris.zeuc, np.arange(0, 200, 10))
      5     for bin, group in Rigrouped:
      6         print(bin)

/gpfs/u/home/dcherian/python/xarray/xarray/core/common.py in groupby_bins(self, group, bins, right, labels, precision, include_lowest, squeeze, restore_coord_dims)
    734                 "labels": labels,
    735                 "precision": precision,
--> 736                 "include_lowest": include_lowest,
    737             },
    738         )

/gpfs/u/home/dcherian/python/xarray/xarray/core/groupby.py in __init__(self, obj, group, squeeze, grouper, bins, restore_coord_dims, cut_kwargs)
    321             raise ValueError("`group` must have a name")
    322 
--> 323         group, obj, stacked_dim, inserted_dims = _ensure_1d(group, obj)
    324         (group_dim,) = group.dims
    325 

/gpfs/u/home/dcherian/python/xarray/xarray/core/groupby.py in _ensure_1d(group, obj)
    192         # the copy is necessary here, otherwise read only array raises error
    193         # in pandas: https://github.com/pydata/pandas/issues/12813
--> 194         group = group.stack(**{stacked_dim: orig_dims}).copy()
    195         obj = obj.stack(**{stacked_dim: orig_dims})
    196     else:

/gpfs/u/home/dcherian/python/xarray/xarray/core/dataarray.py in stack(self, dimensions, **dimensions_kwargs)
   1723         DataArray.unstack
   1724         """
-> 1725         ds = self._to_temp_dataset().stack(dimensions, **dimensions_kwargs)
   1726         return self._from_temp_dataset(ds)
   1727 

/gpfs/u/home/dcherian/python/xarray/xarray/core/dataset.py in stack(self, dimensions, **dimensions_kwargs)
   3232         result = self
   3233         for new_dim, dims in dimensions.items():
-> 3234             result = result._stack_once(dims, new_dim)
   3235         return result
   3236 

/gpfs/u/home/dcherian/python/xarray/xarray/core/dataset.py in _stack_once(self, dims, new_dim)
   3188         # consider dropping levels that are unused?
   3189         levels = [self.get_index(dim) for dim in dims]
-> 3190         idx = utils.multiindex_from_product_levels(levels, names=dims)
   3191         variables[new_dim] = IndexVariable(new_dim, idx)
   3192 

KeyboardInterrupt: 

How good is bulk Ri vs gradient Ri

# del tao.time.encoding["_FillValue"]
tao.to_zarr("tao-gridded-ancillary.zarr", mode="w", consolidated=True)

<xarray.backends.zarr.ZarrStore at 0x2b3780b1e888>

tao = pump.obs.read_tao_zarr("gridded")
tao

xarray.Dataset

• Dimensions:
  • depth: 101
  • longitude: 5
  • time: 292267
• Coordinates: (8)
  • deepest
    (time, longitude)
    float64
    dask.array<chunksize=(10000, 1), meta=np.ndarray>

    description :

    Deepest depth with a valid observation

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     80.00 kB 
        
     Shape 
     (292267, 5) 
     
     (10000, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • depth
    (depth)
    float64
    -500.0 -495.0 -490.0 ... -5.0 0.0

    array([-500., -495., -490., -485., -480., -475., -470., -465., -460., -455.,
           -450., -445., -440., -435., -430., -425., -420., -415., -410., -405.,
           -400., -395., -390., -385., -380., -375., -370., -365., -360., -355.,
           -350., -345., -340., -335., -330., -325., -320., -315., -310., -305.,
           -300., -295., -290., -285., -280., -275., -270., -265., -260., -255.,
           -250., -245., -240., -235., -230., -225., -220., -215., -210., -205.,
           -200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
           -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
           -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.,
            -50.,  -45.,  -40.,  -35.,  -30.,  -25.,  -20.,  -15.,  -10.,   -5.,
              0.])

  • eucmax
    (time, longitude)
    float64
    dask.array<chunksize=(10000, 2), meta=np.ndarray>

    long_name :

    Depth of EUC max

    units :

    m

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     160.00 kB 
        
     Shape 
     (292267, 5) 
     
     (10000, 2) 
        
     Count 
     226 Tasks 
     90 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • latitude
    ()
    float32
    ...

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array(0., dtype=float32)

  • longitude
    (longitude)
    float32
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.], dtype=float32)

  • mld
    (time, longitude)
    float64
    dask.array<chunksize=(10000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     80.00 kB 
        
     Shape 
     (292267, 5) 
     
     (10000, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • time
    (time)
    datetime64[ns]
    1985-10-01T06:00:00 ... 2019-02-03

    FORTRAN_format :

    point_spacing :

    even

    type :

    EVEN

    array(['1985-10-01T06:00:00.000000000', '1985-10-01T07:00:00.000000000',
           '1985-10-01T08:00:00.000000000', ..., '2019-02-02T22:00:00.000000000',
           '2019-02-02T23:00:00.000000000', '2019-02-03T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • zeuc
    (depth, time, longitude)
    float64
    dask.array<chunksize=(101, 10000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.18 GB 
     
     8.08 MB 
        
     Shape 
     (101, 292267, 5) 
     
     (101, 10000, 1) 
        
     Count 
     976 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

• Data variables: (5)
  • Rig
    (time, longitude, depth)
    float64
    dask.array<chunksize=(10000, 1, 101), meta=np.ndarray>

    long_name :

    Ri

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.18 GB 
     
     8.08 MB 
        
     Shape 
     (292267, 5, 101) 
     
     (10000, 1, 101) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • T
    (time, longitude, depth)
    float64
    dask.array<chunksize=(10000, 1, 101), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.18 GB 
     
     8.08 MB 
        
     Shape 
     (292267, 5, 101) 
     
     (10000, 1, 101) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • dens
    (time, longitude, depth)
    float64
    dask.array<chunksize=(10000, 1, 101), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.18 GB 
     
     8.08 MB 
        
     Shape 
     (292267, 5, 101) 
     
     (10000, 1, 101) 
        
     Count 
     12603 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • u
    (time, depth, longitude)
    float32
    dask.array<chunksize=(10000, 101, 1), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    1205

    generic_name :

    u

    long_name :

    u

    name :

    u

    units :

    m/s

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     590.38 MB 
     
     4.04 MB 
        
     Shape 
     (292267, 101, 5) 
     
     (10000, 101, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • v
    (time, depth, longitude)
    float32
    dask.array<chunksize=(10000, 101, 1), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    1206

    generic_name :

    v

    long_name :

    v

    name :

    v

    units :

    m/s

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     590.38 MB 
     
     4.04 MB 
        
     Shape 
     (292267, 101, 5) 
     
     (10000, 101, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

• Attributes: (0)

tao_zeuc = xr.open_zarr("tao-zeuc.zarr", consolidated=True)
tao_zeuc

xarray.Dataset

• Dimensions:
  • longitude: 5
  • time: 292267
  • zeuc: 59
• Coordinates: (7)
  • deepest
    (time, longitude)
    float64
    dask.array<chunksize=(10000, 1), meta=np.ndarray>

    description :

    Deepest depth with a valid observation

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     80.00 kB 
        
     Shape 
     (292267, 5) 
     
     (10000, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • eucmax
    (time, longitude)
    float64
    dask.array<chunksize=(292267, 5), meta=np.ndarray>

    long_name :

    Depth of EUC max

    units :

    m

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     11.69 MB 
        
     Shape 
     (292267, 5) 
     
     (292267, 5) 
        
     Count 
     2 Tasks 
     1 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • latitude
    ()
    float32
    ...

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array(0., dtype=float32)

  • longitude
    (longitude)
    float32
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.], dtype=float32)

  • mld
    (time, longitude)
    float64
    dask.array<chunksize=(10000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     80.00 kB 
        
     Shape 
     (292267, 5) 
     
     (10000, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • time
    (time)
    datetime64[ns]
    1985-10-01T06:00:00 ... 2019-02-03

    FORTRAN_format :

    point_spacing :

    even

    type :

    EVEN

    array(['1985-10-01T06:00:00.000000000', '1985-10-01T07:00:00.000000000',
           '1985-10-01T08:00:00.000000000', ..., '2019-02-02T22:00:00.000000000',
           '2019-02-02T23:00:00.000000000', '2019-02-03T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • zeuc
    (zeuc)
    float64
    -47.5 -42.5 -37.5 ... 237.5 242.5

    array([-47.5, -42.5, -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,
             2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,
            52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,
           102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,
           152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,
           202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5])

• Data variables: (5)
  • Rig
    (time, longitude, zeuc)
    float64
    dask.array<chunksize=(10000, 1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     689.75 MB 
     
     4.72 MB 
        
     Shape 
     (292267, 5, 59) 
     
     (10000, 1, 59) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • T
    (time, longitude, zeuc)
    float64
    dask.array<chunksize=(10000, 1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     689.75 MB 
     
     4.72 MB 
        
     Shape 
     (292267, 5, 59) 
     
     (10000, 1, 59) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • dens
    (time, longitude, zeuc)
    float64
    dask.array<chunksize=(10000, 1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     689.75 MB 
     
     4.72 MB 
        
     Shape 
     (292267, 5, 59) 
     
     (10000, 1, 59) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • u
    (time, longitude, zeuc)
    float64
    dask.array<chunksize=(10000, 1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     689.75 MB 
     
     4.72 MB 
        
     Shape 
     (292267, 5, 59) 
     
     (10000, 1, 59) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • v
    (time, longitude, zeuc)
    float64
    dask.array<chunksize=(10000, 1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     689.75 MB 
     
     4.72 MB 
        
     Shape 
     (292267, 5, 59) 
     
     (10000, 1, 59) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

• Attributes: (0)

tao_clim["Rig"] = pump.calc.calc_tao_ri(
    tao_clim[["u", "v"]].expand_dims(time=1), tao_clim["T"].expand_dims(time=1)
)

tao_clim.Rib.plot(x="longitude")
# tao_clim.Rig.plot(x="longitude")

[<matplotlib.lines.Line2D at 0x2b37dfb427f0>]

interped.sel(zeuc=slice(0, None)).quantile(dim="zeuc", q=0.5).count("time").compute()

xarray.DataArray

• longitude: 5

• 21840 107780 102208 136161 97035

  array([ 21840, 107780, 102208, 136161,  97035])

• Coordinates: (2)
  • longitude
    (longitude)
    float64
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.])

  • quantile
    ()
    float64
    0.5

    array(0.5)

• Attributes: (0)

Ri_q = xr.load_dataarray("tao-hourly-Ri-seasonal-percentiles.nc")
Ri_q

xarray.DataArray

'Rig'

• season: 4
• longitude: 5
• zeuc: 59
• quantile: 3

• 0.8657 1.847 4.079 0.9686 2.076 4.698 ... nan nan nan nan nan nan

  array([[[[8.65716985e-01, 1.84699736e+00, 4.07929186e+00],
           [9.68607747e-01, 2.07586669e+00, 4.69831821e+00],
           [1.12714689e+00, 2.45510051e+00, 5.63565819e+00],
           ...,
           [8.25933339e-01, 1.21812980e+00, 2.22266901e+00],
           [3.31773526e-02, 2.15271859e-01, 6.91555336e-01],
           [6.30251313e-01, 9.59637420e-01, 1.28902353e+00]],
  
          [[6.99998537e-01, 1.29253461e+00, 2.86526357e+00],
           [7.26263211e-01, 1.40190939e+00, 3.03401490e+00],
           [8.14764327e-01, 1.58527315e+00, 3.47211588e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[1.04342082e+00, 1.80473934e+00, 3.48219512e+00],
           [1.10594958e+00, 1.94826060e+00, 3.84621979e+00],
           [1.18464712e+00, 2.10952571e+00, 4.29768611e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[5.99332633e-01, 1.01832679e+00, 2.02945611e+00],
           [6.36358415e-01, 1.09730253e+00, 2.22929311e+00],
           [6.69077456e-01, 1.17499171e+00, 2.47144998e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[3.93460173e-01, 6.78976245e-01, 1.35634816e+00],
           [3.96662435e-01, 6.72044675e-01, 1.30979069e+00],
           [3.89880826e-01, 6.65452753e-01, 1.31105090e+00],
           ...,
           [1.71923451e+00, 2.11843237e+00, 3.14030388e+00],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]]],
  
  
         [[[9.64841693e-01, 2.03641443e+00, 5.24673701e+00],
           [1.00937285e+00, 2.07883897e+00, 5.36984820e+00],
           [1.07954196e+00, 2.17156104e+00, 5.51939592e+00],
           ...,
           [9.45015072e-01, 1.23069288e+00, 3.89244182e+00],
           [3.19059242e-01, 4.46329690e-01, 1.42386922e+00],
           [           nan,            nan,            nan]],
  
          [[9.77766440e-01, 1.94110422e+00, 4.23775491e+00],
           [1.02809324e+00, 2.09342055e+00, 4.71047287e+00],
           [1.20866875e+00, 2.40437219e+00, 5.50768658e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[1.06829041e+00, 1.84754009e+00, 3.53931845e+00],
           [1.11732667e+00, 1.94676703e+00, 3.81204909e+00],
           [1.15610819e+00, 2.03169397e+00, 4.13639554e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[6.45954343e-01, 1.04209220e+00, 1.87051596e+00],
           [6.91774334e-01, 1.11851238e+00, 2.00851346e+00],
           [7.23639413e-01, 1.19311723e+00, 2.28014936e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[3.90188842e-01, 5.96989693e-01, 1.09097169e+00],
           [3.99915480e-01, 6.17991764e-01, 1.10944998e+00],
           [4.09988742e-01, 6.36251961e-01, 1.14362343e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]]],
  
  
         [[[8.05875978e-01, 1.64882518e+00, 3.74725616e+00],
           [8.49498749e-01, 1.81732453e+00, 4.24185284e+00],
           [9.22981317e-01, 1.94493706e+00, 4.58776057e+00],
           ...,
           [3.77534561e-01, 5.81013898e-01, 1.00414836e+00],
           [4.43140283e-01, 2.25869804e+00, 6.26462169e+00],
           [7.27405524e-01, 1.93333063e+00, 1.82996463e+01]],
  
          [[8.33461276e-01, 1.78540849e+00, 4.40883574e+00],
           [8.75326686e-01, 1.94566370e+00, 4.97448619e+00],
           [1.09038278e+00, 2.41704308e+00, 5.99210430e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[1.03154098e+00, 1.90779205e+00, 4.02230303e+00],
           [1.06865210e+00, 2.04293217e+00, 4.50922149e+00],
           [1.09868897e+00, 2.09941647e+00, 4.83581297e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[6.13561831e-01, 9.54200964e-01, 1.59989813e+00],
           [6.30494824e-01, 9.95023982e-01, 1.67218575e+00],
           [6.41170129e-01, 1.02841434e+00, 1.77831551e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[3.93044016e-01, 6.17636578e-01, 1.07574078e+00],
           [3.96811132e-01, 6.24261383e-01, 1.06323474e+00],
           [3.92496939e-01, 6.23224291e-01, 1.09776787e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]]],
  
  
         [[[7.20142171e-01, 1.55444359e+00, 3.42266777e+00],
           [7.95652503e-01, 1.63517308e+00, 3.68113071e+00],
           [8.57140530e-01, 1.82649115e+00, 4.21420888e+00],
           ...,
           [2.47228307e-01, 6.21093974e-01, 1.91750068e+00],
           [3.27682126e-01, 1.10605834e+00, 3.20444965e+00],
           [3.40164626e-01, 5.65530493e-01, 1.53144780e+00]],
  
          [[6.63675669e-01, 1.31916576e+00, 3.00179180e+00],
           [6.91861346e-01, 1.44553130e+00, 3.44077254e+00],
           [8.46168409e-01, 1.73035774e+00, 4.14209451e+00],
           ...,
           [6.55769401e-02, 3.88032611e-01, 8.03026828e-01],
           [6.08337896e+01, 6.08337896e+01, 6.08337896e+01],
           [           nan,            nan,            nan]],
  
          [[1.22505629e+00, 2.16464382e+00, 4.25647302e+00],
           [1.31365069e+00, 2.36794923e+00, 4.86517140e+00],
           [1.38017117e+00, 2.53467087e+00, 5.59876342e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[6.04536606e-01, 1.02415097e+00, 1.99978561e+00],
           [6.29322459e-01, 1.08261699e+00, 2.06070930e+00],
           [6.45054365e-01, 1.11824484e+00, 2.15339242e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]],
  
          [[3.98786048e-01, 6.72431954e-01, 1.26091140e+00],
           [4.02719219e-01, 6.77482230e-01, 1.26239862e+00],
           [3.97279637e-01, 6.78105856e-01, 1.31576185e+00],
           ...,
           [           nan,            nan,            nan],
           [           nan,            nan,            nan],
           [           nan,            nan,            nan]]]])

• Coordinates: (6)
  • season
    (season)
    object
    'DJF' 'MAM' 'JJA' 'SON'

    array(['DJF', 'MAM', 'JJA', 'SON'], dtype=object)

  • latitude
    ()
    float32
    0.0

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array(0., dtype=float32)

  • longitude
    (longitude)
    float32
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.], dtype=float32)

  • quantile
    (quantile)
    int64
    25 50 75

    array([25, 50, 75])

  • zeuc
    (zeuc)
    float64
    -47.5 -42.5 -37.5 ... 237.5 242.5

    long_name :

    Depth relative to EUC max

    units :

    m

    array([-47.5, -42.5, -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,
             2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,
            52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,
           102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,
           152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,
           202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5])

  • num_obs
    (season, longitude, zeuc)
    int64
    2956 3450 3685 4162 ... 0 0 0 0

    array([[[ 2956,  3450,  3685, ...,    11,     4,     2],
            [13003, 15472, 16505, ...,     0,     0,     0],
            [25255, 25759, 26188, ...,     0,     0,     0],
            [32022, 32550, 32964, ...,     0,     0,     0],
            [22159, 22496, 22742, ...,     7,     0,     0]],
    
           [[ 3942,  4331,  4609, ...,    16,    10,     0],
            [17279, 19213, 19858, ...,     0,     0,     0],
            [24860, 25030, 25287, ...,     0,     0,     0],
            [31769, 32155, 32493, ...,     0,     0,     0],
            [23765, 23781, 23797, ...,     0,     0,     0]],
    
           [[ 3745,  3967,  4061, ...,    12,     4,     4],
            [20430, 22423, 23235, ...,     0,     0,     0],
            [25438, 25492, 25513, ...,     0,     0,     0],
            [30761, 31005, 31326, ...,     0,     0,     0],
            [26311, 26305, 26311, ...,     0,     0,     0]],
    
           [[ 1779,  2291,  2663, ...,   302,   179,   102],
            [17003, 19290, 20532, ...,     9,     1,     0],
            [23085, 23454, 23690, ...,     0,     0,     0],
            [35541, 35937, 36215, ...,     0,     0,     0],
            [22990, 23038, 23080, ...,     0,     0,     0]]])

• Attributes: (0)

median_Rig = interped.sel(zeuc=slice(0, None)).quantile(dim="zeuc", q=0.5)
median_Rig

xarray.DataArray

• time: 292267
• longitude: 5

• dask.array<chunksize=(10000, 1), meta=np.ndarray>

  
  
  
  
    
  
      
   
   Array 
   Chunk 
    
    
  
      
   Bytes 
   11.69 MB 
   
   80.00 kB 
      
   Shape 
   (292267, 5) 
   
   (10000, 1) 
      
   Count 
   48664 Tasks 
   150 Chunks 
      
   Type 
   float64 
   numpy.ndarray 
    
  
  
  
  
  
  
  

• Coordinates: (6)
  • longitude
    (longitude)
    float64
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.])

  • time
    (time)
    datetime64[ns]
    1985-10-01T06:00:00 ... 2019-02-03

    FORTRAN_format :

    point_spacing :

    even

    type :

    EVEN

    array(['1985-10-01T06:00:00.000000000', '1985-10-01T07:00:00.000000000',
           '1985-10-01T08:00:00.000000000', ..., '2019-02-02T22:00:00.000000000',
           '2019-02-02T23:00:00.000000000', '2019-02-03T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • eucmax
    (time, longitude)
    float64
    dask.array<chunksize=(292267, 5), meta=np.ndarray>

    long_name :

    Depth of EUC max

    units :

    m

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     11.69 MB 
        
     Shape 
     (292267, 5) 
     
     (292267, 5) 
        
     Count 
     1 Tasks 
     1 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • mld
    (time, longitude)
    float64
    dask.array<chunksize=(10000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     80.00 kB 
        
     Shape 
     (292267, 5) 
     
     (10000, 1) 
        
     Count 
     3417 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • deepest
    (time, longitude)
    float64
    dask.array<chunksize=(10000, 1), meta=np.ndarray>

    description :

    Deepest depth with a valid observation

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     80.00 kB 
        
     Shape 
     (292267, 5) 
     
     (10000, 1) 
        
     Count 
     2517 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • quantile
    ()
    float64
    0.5

    array(0.5)

• Attributes: (0)

quick fix. Take median of bulk Ri

median_Rib = (
    bulk_Ri.groupby("time.season")
    .apply(lambda x: x.chunk({"time": -1}).quantile(q=0.5, dim="time"))
    .compute()
)

/gpfs/u/home/dcherian/python/xarray/xarray/core/common.py:664: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, the dimension order of these coordinates will be restored as well unless you specify restore_coord_dims=False.
  self, group, squeeze=squeeze, restore_coord_dims=restore_coord_dims

medians = xr.Dataset()
medians["Rib"] = median_Rib
medians["Rig"] = median_Rig.sel(zeuc=slice(0, None)).median("zeuc")

fg = medians.plot.scatter(x="Rib", y="Rig", hue="season", col="longitude")
fg.axes[0, 0].set_xscale("log")
fg.axes[0, 0].set_yscale("log")

tao_bulk = pump.calc.estimate_euc_depth_terms(tao, inplace=False)
bulk_Ri = tao_bulk["Rib"]

Ri = xr.Dataset({"bulk": bulk_Ri, "gradient": median_Rig})
Ri["season"] = Ri.time.dt.season
Ri.load()

/gpfs/u/home/dcherian/python/xarray/xarray/core/missing.py:393: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  ).transpose(*arr.dims)
/gpfs/u/home/dcherian/python/xarray/xarray/core/missing.py:393: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  ).transpose(*arr.dims)

xarray.Dataset

• Dimensions:
  • longitude: 5
  • time: 292267
• Coordinates: (7)
  • latitude
    ()
    float32
    0.0

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array(0., dtype=float32)

  • longitude
    (longitude)
    float64
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.])

  • time
    (time)
    datetime64[ns]
    1985-10-01T06:00:00 ... 2019-02-03

    FORTRAN_format :

    point_spacing :

    even

    type :

    EVEN

    array(['1985-10-01T06:00:00.000000000', '1985-10-01T07:00:00.000000000',
           '1985-10-01T08:00:00.000000000', ..., '2019-02-02T22:00:00.000000000',
           '2019-02-02T23:00:00.000000000', '2019-02-03T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • eucmax
    (time, longitude)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Depth of EUC max

    units :

    m

    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]])

  • mld
    (time, longitude)
    float64
    nan nan nan nan ... nan -5.0 -5.0

    array([[ nan,  nan,  nan,  nan,  -5.],
           [ nan,  nan,  nan,  nan, -10.],
           [ nan,  nan,  nan,  nan, -10.],
           ...,
           [ nan, -75.,  nan,  -5.,  -5.],
           [ nan, -70.,  nan,  -5.,  -5.],
           [ nan, -20.,  nan,  -5.,  -5.]])

  • deepest
    (time, longitude)
    float64
    nan nan nan ... nan -500.0 -500.0

    description :

    Deepest depth with a valid observation

    array([[  nan,   nan,   nan,   nan, -300.],
           [  nan,   nan,   nan,   nan, -300.],
           [  nan,   nan,   nan,   nan, -300.],
           ...,
           [  nan, -500.,   nan, -500., -500.],
           [  nan, -500.,   nan, -500., -500.],
           [  nan, -500.,   nan, -500., -500.]])

  • quantile
    ()
    float64
    0.5

    array(0.5)

• Data variables: (3)
  • bulk
    (time, longitude)
    float64
    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]])

  • gradient
    (time, longitude)
    float64
    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]])

  • season
    (time)
    <U3
    'SON' 'SON' 'SON' ... 'DJF' 'DJF'

    array(['SON', 'SON', 'SON', ..., 'DJF', 'DJF', 'DJF'], dtype='<U3')

• Attributes: (0)

# Ri.time.encoding.pop("_FillValue")
Ri.to_netcdf("rib-median-rig.nc")

tao.T.sel(longitude=-170).plot(x="time")

<matplotlib.collections.QuadMesh at 0x2b09a25efc18>

tao.u.sel(longitude=-170).plot(x="time")
tao.eucmax.sel(longitude=-170).plot(x="time")

[<matplotlib.lines.Line2D at 0x2b098b736208>]

tao.u.sel(longitude=-140, time=slice("1996-09-04", "1996-12-01")).plot(x="time")

<matplotlib.collections.QuadMesh at 0x2b34a38ac2e8>

tao.eucmax.plot.line(x="time", col="longitude")

<xarray.plot.facetgrid.FacetGrid at 0x2b0982c0de48>

fg = tao.ueuc.plot(x="time", col="longitude")
fg.map(lambda: plt.gca().axhline(0, color="k", ls="--"))

<xarray.plot.facetgrid.FacetGrid at 0x2b098b6678d0>

tao.dens_euc.compute().plot.line(x="time", col="longitude")

<xarray.plot.facetgrid.FacetGrid at 0x2b098f0a2cf8>

Ri.to_netcdf("rib-median-rig.nc")

Estimate and plot histograms

Ri = xr.load_dataset("rib-median-rig.nc")
Ri

xarray.Dataset

• Dimensions:
  • longitude: 5
  • time: 292267
• Coordinates: (7)
  • deepest
    (time, longitude)
    float64
    nan nan nan nan ... nan nan nan nan

    description :

    Deepest depth with a valid observation

    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]])

  • eucmax
    (time, longitude)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Depth of EUC max

    units :

    m

    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]])

  • latitude
    ()
    float64
    0.0

    array(0.)

  • longitude
    (longitude)
    float32
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.], dtype=float32)

  • mld
    (time, longitude)
    float64
    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]])

  • time
    (time)
    datetime64[ns]
    1985-10-01T06:00:00 ... 2019-02-03

    FORTRAN_format :

    point_spacing :

    even

    type :

    EVEN

    array(['1985-10-01T06:00:00.000000000', '1985-10-01T07:00:00.000000000',
           '1985-10-01T08:00:00.000000000', ..., '2019-02-02T22:00:00.000000000',
           '2019-02-02T23:00:00.000000000', '2019-02-03T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • quantile
    ()
    float64
    0.5

    array(0.5)

• Data variables: (3)
  • bulk
    (time, longitude)
    float64
    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]])

  • gradient
    (time, longitude)
    float64
    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]])

  • season
    (time)
    object
    'SON' 'SON' 'SON' ... 'DJF' 'DJF'

    array(['SON', 'SON', 'SON', ..., 'DJF', 'DJF', 'DJF'], dtype=object)

• Attributes: (0)

def plot_Ri_histograms(Ri_hist, **kwargs):
    if "season" in Ri_hist:
        kwargs.setdefault("row", "season")

    fg = Ri_hist.where(Ri_hist > 0).plot(
        col="longitude",
        x="bulk_bin",
        y="gradient_bin",
        xscale="log",
        yscale="log",
        cmap=mpl.cm.BuGn,
        cbar_kwargs={
            "orientation": "horizontal",
            # "label": "probability density",
            "aspect": 40,
            "shrink": 0.6,
            "pad": 0.1,
        },
        **kwargs
    )

    def mark_lines():
        lstyle = dict(zorder=10, lw=0.5)
        dcpy.plots.linex([0.25, 1], ax=plt.gca(), **lstyle)
        dcpy.plots.liney([0.25, 1], ax=plt.gca(), **lstyle)
        plt.gca().set_aspect(1)
        dcpy.plots.line45(ax=plt.gca(), **lstyle)

    fg.set_xlabels("$Ri_b$")
    fg.set_ylabels("$Ri_g$")
    fg.fig.set_size_inches((8, 8))
    fg.map(mark_lines)

    fg.map_dataarray(
        xr.plot.contour,
        x="bulk_bin",
        y="gradient_bin",
        colors="w",
        levels=np.linspace(*np.nanpercentile(Ri_hist, [99.1, 99.9]), 3),
        linewidths=0.3,
        add_colorbar=False,
    )

    return fg

At hourly timescales this is pretty noisy. Bulk Ri in the central Pac is O(10) when calculated from annual means. I have to show that Rib_b is a reasonable estimate of the median Ri_g over some long time scale?

?Ri.bulk.clip

lon = -204

Ri.bulk.clip(min=0, max=100).sel(longitude=lon).plot(x="time")
# Ri.bulk.clip(max=100).resample(time="M").mean().sel(longitude=lon).plot(x="time")

Ri.gradient.clip(max=100).sel(longitude=lon).plot(x="time")
Ri.gradient.clip(max=100).resample(time="M").mean().sel(longitude=lon).plot(x="time")

[<matplotlib.lines.Line2D at 0x2b37ecee7278>]

def wrap_hist(ds, density=True):
    from xhistogram.xarray import histogram

    hist_over = ("time",)

    xbins = xr.DataArray(np.logspace(-1, 2, 100), dims="bulk_bin")
    ybins = xr.DataArray(np.logspace(-1, 2, 100), dims="gradient_bin")
    counts = histogram(
        ds.bulk,
        ds.gradient,
        dim=hist_over,
        bins=[xbins.values, ybins.values],
    )

    if density:
        dens = (
            counts
            / xbins.diff("bulk_bin")
            / ybins.diff("gradient_bin")
            / counts.sum(set(counts.dims) - set(hist_over))
        )
        dens.name = "PDF"
    else:
        dens = counts
        dens.name = "counts"
    return dens


plot_Ri_histograms(
    wrap_hist(Ri, density=False),
    norm=mpl.colors.LogNorm(vmin=1, vmax=100),
)
plot_Ri_histograms(
    wrap_hist(Ri, density=True),
    norm=mpl.colors.LogNorm(vmin=1e-5, vmax=0.5),
)

/gpfs/u/home/dcherian/python/xarray/xarray/core/dataarray.py:695: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  return key in self.data
/gpfs/u/home/dcherian/python/xarray/xarray/plot/plot.py:966: UserWarning: No contour levels were found within the data range.
  primitive = ax.contour(x, y, z, **kwargs)

<xarray.plot.facetgrid.FacetGrid at 0x2b37e02c1908>

/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/matplotlib/colors.py:1110: RuntimeWarning: invalid value encountered in less_equal
  mask |= resdat <= 0

reduced_Ri = (
    Ri.drop("season").map(lambda x: x.clip(min=0, max=100)).resample(time="D").median()
)

plot_Ri_histograms(
    wrap_hist(reduced_Ri, density=False),
    norm=mpl.colors.LogNorm(vmin=1, vmax=10),
)
plot_Ri_histograms(
    wrap_hist(reduced_Ri, density=True),
    norm=mpl.colors.LogNorm(vmin=1e-5, vmax=0.5),
)

/gpfs/u/home/dcherian/python/xarray/xarray/core/dataarray.py:695: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  return key in self.data
/gpfs/u/home/dcherian/python/xarray/xarray/plot/plot.py:966: UserWarning: No contour levels were found within the data range.
  primitive = ax.contour(x, y, z, **kwargs)

<xarray.plot.facetgrid.FacetGrid at 0x2b3797906588>

/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/matplotlib/colors.py:1110: RuntimeWarning: invalid value encountered in less_equal
  mask |= resdat <= 0

seasonal Rib vs Rig

Ri_seasonal_hist = (
    Ri.groupby("season").apply(wrap_hist).reindex(season=["DJF", "MAM", "JJA", "SON"])
)
Ri_seasonal_hist

xarray.DataArray

'histogram_bulk_gradient'

• season: 4
• longitude: 5
• bulk_bin: 49
• gradient_bin: 49

• 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 1 1 1 0 1 1 2 2 0 1 0 1 1 0 0 0

  array([[[[ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0]],
  
          [[ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  2, ...,  0,  0,  0]],
  
          [[ 1,  3,  3, ...,  0,  0,  0],
           [12,  1,  3, ...,  0,  0,  0],
           [ 4,  4,  2, ...,  0,  0,  0],
           ...,
           [ 0,  1,  0, ...,  1,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0]],
  
          [[ 3,  6,  8, ...,  0,  0,  0],
           [ 7,  9,  9, ...,  0,  0,  0],
           [12, 20, 13, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  1, ...,  1,  0,  0]],
  
          [[ 2,  8,  2, ...,  0,  0,  0],
           [ 7,  4,  3, ...,  0,  0,  0],
           [ 6,  2,  4, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  1,  1,  0],
           [ 0,  0,  0, ...,  1,  1,  0],
           [ 0,  0,  0, ...,  2,  0,  1]]],
  
  
         [[[ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0]],
  
          [[ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  1,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0]],
  
          [[ 1,  0,  3, ...,  0,  0,  0],
           [ 0,  1,  2, ...,  0,  0,  0],
           [ 2,  1,  1, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  2,  1,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  1,  0]],
  
          [[ 0,  5,  5, ...,  0,  0,  0],
           [ 2,  4,  8, ...,  0,  0,  0],
           [ 3,  4,  3, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  1,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0]],
  
          [[ 2,  2,  7, ...,  0,  1,  0],
           [ 3,  6,  6, ...,  0,  0,  0],
           [ 3,  2, 11, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  1,  2,  1],
           [ 0,  0,  0, ...,  2,  1,  3],
           [ 0,  0,  0, ...,  4,  2,  1]]],
  
  
         [[[ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  1],
           [ 0,  0,  0, ...,  0,  0,  0]],
  
          [[ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  1,  0,  0],
           [ 0,  0,  0, ...,  0,  1,  0],
           [ 0,  0,  0, ...,  0,  0,  0]],
  
          [[ 1,  2,  4, ...,  0,  0,  0],
           [ 2,  3,  2, ...,  0,  0,  0],
           [ 5,  1,  5, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  1,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  1],
           [ 0,  0,  0, ...,  0,  0,  0]],
  
          [[ 2,  5,  5, ...,  0,  0,  0],
           [ 2,  7,  5, ...,  0,  0,  0],
           [ 4,  2, 10, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0]],
  
          [[ 4,  5,  5, ...,  0,  0,  0],
           [ 4,  5,  5, ...,  0,  0,  0],
           [ 3,  7,  4, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  1],
           [ 0,  0,  0, ...,  0,  0,  1]]],
  
  
         [[[ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0]],
  
          [[ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  1,  0],
           [ 0,  0,  0, ...,  0,  0,  0]],
  
          [[ 2,  3,  3, ...,  0,  0,  0],
           [ 1,  3,  7, ...,  0,  0,  0],
           [ 1,  2,  5, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0]],
  
          [[ 2,  5,  8, ...,  0,  0,  0],
           [ 4,  7,  5, ...,  0,  0,  0],
           [ 7,  6, 13, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0],
           [ 0,  0,  0, ...,  0,  0,  0]],
  
          [[ 6,  3,  6, ...,  0,  0,  0],
           [ 3,  8,  6, ...,  0,  0,  0],
           [ 7, 11, 15, ...,  0,  0,  0],
           ...,
           [ 0,  0,  0, ...,  0,  0,  2],
           [ 0,  0,  0, ...,  0,  0,  1],
           [ 0,  0,  0, ...,  0,  0,  0]]]])

• Coordinates: (4)
  • season
    (season)
    object
    'DJF' 'MAM' 'JJA' 'SON'

    array(['DJF', 'MAM', 'JJA', 'SON'], dtype=object)

  • gradient_bin
    (gradient_bin)
    float64
    0.1049 0.1153 ... 8.695 9.551

    array([0.104927, 0.115267, 0.126625, 0.139103, 0.15281 , 0.167868, 0.18441 ,
           0.202582, 0.222545, 0.244475, 0.268566, 0.295031, 0.324103, 0.356041,
           0.391125, 0.429667, 0.472007, 0.518519, 0.569615, 0.625745, 0.687407,
           0.755145, 0.829558, 0.911303, 1.001104, 1.099754, 1.208125, 1.327175,
           1.457957, 1.601625, 1.759451, 1.93283 , 2.123293, 2.332525, 2.562374,
           2.814874, 3.092255, 3.396969, 3.73171 , 4.099437, 4.5034  , 4.947171,
           5.43467 , 5.970209, 6.55852 , 7.204804, 7.914774, 8.694705, 9.551491])

  • bulk_bin
    (bulk_bin)
    float64
    0.1049 0.1153 ... 8.695 9.551

    array([0.104927, 0.115267, 0.126625, 0.139103, 0.15281 , 0.167868, 0.18441 ,
           0.202582, 0.222545, 0.244475, 0.268566, 0.295031, 0.324103, 0.356041,
           0.391125, 0.429667, 0.472007, 0.518519, 0.569615, 0.625745, 0.687407,
           0.755145, 0.829558, 0.911303, 1.001104, 1.099754, 1.208125, 1.327175,
           1.457957, 1.601625, 1.759451, 1.93283 , 2.123293, 2.332525, 2.562374,
           2.814874, 3.092255, 3.396969, 3.73171 , 4.099437, 4.5034  , 4.947171,
           5.43467 , 5.970209, 6.55852 , 7.204804, 7.914774, 8.694705, 9.551491])

  • longitude
    (longitude)
    float32
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.], dtype=float32)

• Attributes: (0)

fg = Ri_seasonal_hist.where(Ri_hist > 0).plot(
    row="season",
    col="longitude",
    x="bulk_bin",
    y="gradient_bin",
    robust=True,
    xscale="log",
    yscale="log",
    cmap=mpl.cm.BuGn,
    cbar_kwargs={
        "orientation": "horizontal",
        "label": "probability density",
        "aspect": 40,
        "shrink": 0.6,
        "pad": 0.1,
    },
)


def mark_lines():
    dcpy.plots.linex([0.25, 1], ax=plt.gca(), zorder=10, lw=0.5)
    dcpy.plots.liney([0.25, 1], ax=plt.gca(), zorder=10, lw=0.5)
    plt.gca().set_aspect(1)


fg.set_xlabels("$Ri_b$")
fg.set_ylabels("$Ri_g$")
fg.fig.set_size_inches((8, 8))
fg.map(mark_lines)

fg.map_dataarray(
    xr.plot.contour,
    x="bulk_bin",
    y="gradient_bin",
    colors="w",
    levels=np.linspace(*np.nanpercentile(Ri_hist, [99.1, 99.9]), 3),
    linewidths=0.3,
    add_colorbar=False,
)

fg.fig.savefig("images/gradient-bulk-ri-joint-pdf.png", dpi=200, bbox_inches="tight")

mask = (
    Ri.bulk.notnull()
    & Ri.gradient.notnull()
    & (Ri.gradient < 100)
    & (Ri.bulk < 100)
    & (Ri.bulk > 0)
    & (Ri.gradient > 0)
)
masked = Ri.where(mask, drop=True)
masked["season"] = masked.time.dt.season
fg = masked.plot.scatter(
    col="longitude",
    x="bulk",
    y="gradient",
    s=0.5,
    alpha=0.1,
)
plt.gca().set_xscale("log")
plt.gca().set_yscale("log")
plt.gca().set_xlim([0.1, 100])
plt.gca().set_ylim([0.1, 100])

# fg.fig.savefig("images/bulk-vs-gradient-ri.png", dpi=180)

(0.1, 100)

Datasets

johnson

johnson = pump.obs.read_johnson().sel(latitude=0)
johnson["eucmax"] = pump.get_euc_max(johnson.u)
johnson = pump.calc.estimate_euc_depth_terms(johnson)
johnson.attrs["name"] = "Johnson"
johnson.load()

xarray.Dataset

• Dimensions:
  • depth: 50
  • lon_edges: 11
  • longitude: 10
• Coordinates: (4)
  • longitude
    (longitude)
    float64
    -217.0 -204.0 ... -110.0 -95.0

    units :

    degrees_east

    point_spacing :

    uneven

    edges :

    XLONedges

    array([-217., -204., -195., -180., -170., -155., -140., -125., -110.,  -95.])

  • lon_edges
    (lon_edges)
    float64
    136.5 149.5 160.5 ... 257.5 272.5

    edges :

    array([136.5, 149.5, 160.5, 172.5, 185. , 197.5, 212.5, 227.5, 242.5, 257.5,
           272.5])

  • latitude
    ()
    float64
    0.0

    units :

    degrees_north

    point_spacing :

    even

    array(0.)

  • depth
    (depth)
    float64
    -5.0 -15.0 -25.0 ... -485.0 -495.0

    units :

    m

    positive :

    down

    point_spacing :

    even

    array([  -5.,  -15.,  -25.,  -35.,  -45.,  -55.,  -65.,  -75.,  -85.,  -95.,
           -105., -115., -125., -135., -145., -155., -165., -175., -185., -195.,
           -205., -215., -225., -235., -245., -255., -265., -275., -285., -295.,
           -305., -315., -325., -335., -345., -355., -365., -375., -385., -395.,
           -405., -415., -425., -435., -445., -455., -465., -475., -485., -495.])

• Data variables: (17)
  • temp
    (depth, longitude)
    float32
    29.32408 29.32196 ... 8.023529

    long_name :

    Potential Temperature (If > 16 obs)

    history :

    From meanfit2

    units :

    Degrees C

    array([[29.32408 , 29.32196 , 28.98892 , 27.90945 , 27.43092 , 26.43207 ,
            25.66783 , 24.77945 , 23.91235 , 24.17392 ],
           [29.32408 , 29.32196 , 28.98892 , 27.90945 , 27.43092 , 26.43207 ,
            25.39479 , 24.34854 , 23.30844 , 22.8154  ],
           [29.29031 , 29.10626 , 28.98892 , 27.90945 , 27.43092 , 26.32236 ,
            25.08089 , 23.83151 , 22.59741 , 21.35751 ],
           [29.22716 , 28.85828 , 29.0114  , 27.90945 , 27.40585 , 26.19342 ,
            24.70398 , 23.20563 , 21.73312 , 19.89951 ],
           [29.11751 , 28.58234 , 29.05516 , 27.87341 , 27.31975 , 26.02008 ,
            24.24197 , 22.44806 , 20.66937 , 18.54071 ],
           [28.94427 , 28.2827  , 29.0847  , 27.76042 , 27.1562  , 25.77721 ,
            23.67273 , 21.53607 , 19.36005 , 17.27875 ],
           [28.69038 , 27.96368 , 29.06453 , 27.56325 , 26.89882 , 25.43964 ,
            22.97412 , 20.47206 , 17.89484 , 16.1673  ],
           [28.33871 , 27.62956 , 28.95915 , 27.27461 , 26.5312  , 24.98222 ,
            22.12405 , 19.28279 , 16.3996  , 15.52618 ],
           [27.24928 , 27.28465 , 28.73305 , 26.88725 , 26.03697 , 24.37979 ,
            21.1004  , 18.04407 , 15.63684 , 15.06998 ],
           [25.69624 , 26.93323 , 28.35072 , 26.39389 , 25.39972 , 23.60722 ,
            19.84656 , 16.81937 , 15.05563 , 14.80109 ],
           [24.59415 , 26.48326 , 27.7767  , 25.78729 , 24.60304 , 22.45779 ,
            18.73473 , 15.87775 , 14.61612 , 14.55823 ],
           [23.90182 , 25.50484 , 26.97545 , 25.06018 , 23.35297 , 21.11325 ,
            17.78653 , 15.24286 , 14.258   , 14.27493 ],
           [23.15096 , 24.33902 , 25.91148 , 23.97722 , 21.85654 , 19.80803 ,
            16.86479 , 14.74571 , 13.99222 , 14.04681 ],
           [22.37863 , 23.50153 , 24.51822 , 22.65346 , 20.51752 , 18.60098 ,
            16.02727 , 14.30791 , 13.78873 , 13.86414 ],
           [21.68732 , 22.04544 , 23.06126 , 21.44217 , 19.28545 , 17.58766 ,
            15.38237 , 13.97093 , 13.57544 , 13.72467 ],
           [20.75703 , 20.77695 , 21.44151 , 20.42384 , 18.23784 , 16.74146 ,
            14.86705 , 13.68599 , 13.41086 , 13.5976  ],
           [19.96706 , 20.23109 , 20.21817 , 19.33298 , 17.41815 , 15.88654 ,
            14.37912 , 13.43445 , 13.27631 , 13.52286 ],
           [19.07982 , 19.43237 , 19.01668 , 18.2585  , 16.60391 , 15.13246 ,
            13.90611 , 13.26233 , 13.1532  , 13.40552 ],
           [18.10533 , 18.32791 , 17.97375 , 17.35115 , 15.82162 , 14.52766 ,
            13.51637 , 13.09442 , 13.06415 , 13.28369 ],
           [17.32513 , 17.43242 , 17.09192 , 16.40903 , 15.11705 , 14.05872 ,
            13.22348 , 12.94258 , 12.98453 , 13.18607 ],
           [16.56908 , 16.7054  , 16.05908 , 15.53229 , 14.43898 , 13.6187  ,
            12.98932 , 12.80449 , 12.91074 , 13.12445 ],
           [15.7403  , 15.8884  , 15.31093 , 14.62061 , 13.92845 , 13.23193 ,
            12.79079 , 12.67036 , 12.83368 , 13.08475 ],
           [14.88843 , 15.19087 , 14.61732 , 13.94189 , 13.51976 , 12.93842 ,
            12.62874 , 12.58163 , 12.74707 , 13.03943 ],
           [14.21449 , 14.37332 , 14.08969 , 13.49072 , 13.14166 , 12.73004 ,
            12.49225 , 12.49564 , 12.64725 , 12.99365 ],
           [13.67493 , 13.87909 , 13.63873 , 13.13522 , 12.8201  , 12.5076  ,
            12.36952 , 12.40393 , 12.54868 , 12.95062 ],
           [13.21628 , 13.4469  , 13.15767 , 12.78528 , 12.54713 , 12.32721 ,
            12.25945 , 12.30789 , 12.43127 , 12.8425  ],
           [12.69659 , 13.01869 , 12.89626 , 12.48763 , 12.30817 , 12.12895 ,
            12.14195 , 12.18086 , 12.31851 , 12.62442 ],
           [12.2379  , 12.68857 , 12.62312 , 12.22984 , 12.05876 , 11.98328 ,
            12.01073 , 12.00888 , 12.22034 , 12.25964 ],
           [11.88644 , 12.39337 , 12.36827 , 11.95863 , 11.81403 , 11.86166 ,
            11.87872 , 11.80838 , 12.06009 , 11.85861 ],
           [11.60667 , 12.02661 , 12.15102 , 11.71196 , 11.6461  , 11.72345 ,
            11.7348  , 11.63666 , 11.85245 , 11.64828 ],
           [11.36227 , 11.84581 , 11.97107 , 11.44998 , 11.45624 , 11.57515 ,
            11.54443 , 11.44118 , 11.63568 , 11.41647 ],
           [11.2014  , 11.64331 , 11.67618 , 11.19637 , 11.22504 , 11.36928 ,
            11.31738 , 11.21265 , 11.36841 , 11.18402 ],
           [10.98215 , 11.39815 , 11.41144 , 11.00513 , 11.03409 , 11.16341 ,
            11.06606 , 11.00195 , 11.13129 , 10.93347 ],
           [10.73012 , 11.02322 , 11.15036 , 10.82312 , 10.85234 , 10.93846 ,
            10.84474 , 10.73927 , 10.85544 , 10.70337 ],
           [10.46219 , 10.70462 , 10.90627 , 10.65529 , 10.66057 , 10.67241 ,
            10.60849 , 10.49425 , 10.62375 , 10.51364 ],
           [10.2482  , 10.43069 , 10.64708 , 10.50499 , 10.42839 , 10.39482 ,
            10.31615 , 10.28821 , 10.44104 , 10.25812 ],
           [10.13878 , 10.18817 , 10.40074 , 10.32144 , 10.21191 , 10.1647  ,
            10.15469 , 10.05888 , 10.26425 , 10.06909 ],
           [10.04207 ,  9.83345 , 10.17516 , 10.07573 ,  9.969681,  9.956329,
            10.00398 ,  9.862122, 10.07703 ,  9.853943],
           [ 9.937027,  9.620514,  9.944717,  9.900055,  9.722244,  9.762558,
             9.859375,  9.653275,  9.828979,  9.652252],
           [ 9.758362,  9.455246,  9.742355,  9.722519,  9.506882,  9.548325,
             9.669708,  9.449707,  9.597351,  9.48996 ],
           [ 9.57663 ,  9.334534,  9.592117,  9.520996,  9.327911,  9.41275 ,
             9.4944  ,  9.24881 ,  9.396362,  9.327209],
           [ 9.408295,  9.236465,  9.4104  ,  9.322113,  9.146378,  9.251678,
             9.306137,  9.105072,  9.218933,  9.13736 ],
           [ 9.237915,  9.127319,  9.243271,  9.092224,  8.990463,  9.085892,
             9.121628,  8.981293,  9.049103,  8.939606],
           [ 9.012558,  9.014236,  9.053009,  8.903809,  8.833206,  8.919189,
             8.962875,  8.840866,  8.874451,  8.749969],
           [ 8.838165,  8.924759,  8.859741,  8.731323,  8.693237,  8.758499,
             8.798279,  8.694916,  8.725861,  8.613098],
           [ 8.676407,  8.853699,  8.699463,  8.602127,  8.559341,  8.582352,
             8.635315,  8.54184 ,  8.593994,  8.476929],
           [ 8.531479,  8.777344,  8.537933,  8.480743,  8.441788,  8.45578 ,
             8.476379,  8.423401,  8.469025,  8.343597],
           [ 8.403397,  8.678635,  8.399918,  8.340393,  8.319824,  8.313049,
             8.313309,  8.319244,  8.350708,  8.234406],
           [ 8.264923,  8.54155 ,  8.266632,  8.211258,  8.189224,  8.205643,
             8.163498,  8.215576,  8.241669,  8.132355],
           [ 8.149963,  8.364044,  8.138885,  8.082443,  8.085785,  8.113434,
             8.038971,  8.120865,  8.131653,  8.023529]], dtype=float32)

  • salt
    (depth, longitude)
    float32
    34.19115 34.45227 ... 34.63364

    long_name :

    Salinity (If > 16 obs)

    history :

    From meanfit2

    array([[34.19115, 34.45227, 34.77197, 35.10362, 35.20035, 35.08017,
            34.91434, 34.88116, 34.77487, 34.42538],
           [34.19115, 34.45227, 34.77197, 35.10362, 35.20035, 35.08017,
            34.93756, 34.91861, 34.81915, 34.5513 ],
           [34.23563, 34.51073, 34.77197, 35.10362, 35.20035, 35.08202,
            34.96658, 34.96786, 34.87271, 34.7316 ],
           [34.29428, 34.58981, 34.78226, 35.10362, 35.20233, 35.08423,
            34.99948, 35.02048, 34.92984, 34.89874],
           [34.37   , 34.68227, 34.81055, 35.10739, 35.20792, 35.08775,
            35.03435, 35.06799, 34.98483, 34.98526],
           [34.46564, 34.78075, 34.853  , 35.11786, 35.21649, 35.09349,
            35.06926, 35.1019 , 35.03195, 35.00824],
           [34.58414, 34.87796, 34.90575, 35.13374, 35.22749, 35.1024 ,
            35.10231, 35.12231, 35.06473, 35.03207],
           [34.72836, 34.96657, 34.96497, 35.15375, 35.24034, 35.11539,
            35.13158, 35.134  , 35.05405, 35.03503],
           [34.9373 , 35.03931, 35.02682, 35.17657, 35.25444, 35.13339,
            35.15517, 35.15167, 35.03003, 35.00998],
           [35.07974, 35.08884, 35.08745, 35.20097, 35.26921, 35.15736,
            35.16934, 35.1373 , 35.00513, 34.99847],
           [35.1535 , 35.10938, 35.14301, 35.22563, 35.28406, 35.20461,
            35.16574, 35.09335, 34.99008, 34.99011],
           [35.18417, 35.15689, 35.18965, 35.24927, 35.31711, 35.24088,
            35.16296, 35.05212, 34.97742, 34.98337],
           [35.21426, 35.1582 , 35.22354, 35.27348, 35.30492, 35.24562,
            35.13309, 35.02576, 34.96542, 34.97742],
           [35.26941, 35.1566 , 35.24542, 35.30377, 35.32983, 35.2356 ,
            35.08498, 35.00104, 34.9577 , 34.96945],
           [35.29089, 35.1815 , 35.25694, 35.3414 , 35.3111 , 35.18716,
            35.04697, 34.97842, 34.94647, 34.96326],
           [35.28278, 35.23138, 35.25937, 35.33232, 35.2655 , 35.1498 ,
            35.02155, 34.95728, 34.93915, 34.95807],
           [35.27933, 35.26804, 35.26962, 35.31262, 35.23154, 35.11311,
            34.99522, 34.9451 , 34.93332, 34.95551],
           [35.27118, 35.25432, 35.2417 , 35.26328, 35.19061, 35.08121,
            34.96991, 34.93658, 34.92593, 34.95013],
           [35.25504, 35.20674, 35.21436, 35.21555, 35.14104, 35.03889,
            34.95471, 34.92856, 34.92044, 34.94034],
           [35.24103, 35.18182, 35.17174, 35.15184, 35.09428, 35.00682,
            34.93967, 34.91943, 34.91571, 34.93237],
           [35.21672, 35.17496, 35.11241, 35.07944, 35.04143, 34.97726,
            34.92383, 34.91055, 34.91052, 34.9274 ],
           [35.15515, 35.12094, 35.07687, 35.01402, 35.00288, 34.94701,
            34.90823, 34.90182, 34.90646, 34.92493],
           [35.0829 , 35.08543, 35.03171, 34.97716, 34.97368, 34.92241,
            34.89746, 34.8963 , 34.90237, 34.92242],
           [35.03551, 35.03549, 35.00185, 34.95581, 34.94153, 34.90765,
            34.88925, 34.89029, 34.89655, 34.91986],
           [34.99211, 35.02132, 34.96918, 34.93452, 34.91536, 34.89056,
            34.88123, 34.88339, 34.89011, 34.91721],
           [34.95396, 34.987  , 34.93456, 34.91089, 34.89389, 34.87691,
            34.87334, 34.87601, 34.88202, 34.91043],
           [34.92433, 34.93976, 34.92464, 34.88953, 34.87749, 34.86264,
            34.86488, 34.8663 , 34.87442, 34.89624],
           [34.89989, 34.91281, 34.90849, 34.87186, 34.86197, 34.8541 ,
            34.85587, 34.85406, 34.86801, 34.87308],
           [34.88025, 34.89453, 34.89005, 34.85168, 34.84573, 34.84709,
            34.84656, 34.84052, 34.85745, 34.84787],
           [34.85989, 34.87015, 34.87263, 34.8353 , 34.83545, 34.8385 ,
            34.83659, 34.82959, 34.84399, 34.83401],
           [34.83737, 34.85526, 34.86157, 34.8166 , 34.8219 , 34.82869,
            34.82416, 34.81876, 34.8302 , 34.81934],
           [34.82092, 34.83484, 34.83923, 34.79843, 34.80699, 34.81473,
            34.80975, 34.80536, 34.81485, 34.80557],
           [34.8009 , 34.81364, 34.81848, 34.78638, 34.79536, 34.80148,
            34.79367, 34.79214, 34.80072, 34.79089],
           [34.7773 , 34.78947, 34.79964, 34.7746 , 34.7836 , 34.78758,
            34.78032, 34.77603, 34.78345, 34.77646],
           [34.75635, 34.76877, 34.78355, 34.76422, 34.77106, 34.77013,
            34.76596, 34.76151, 34.76807, 34.7652 ],
           [34.74203, 34.75171, 34.76598, 34.75468, 34.75621, 34.75127,
            34.74908, 34.74905, 34.75647, 34.75134],
           [34.73523, 34.73727, 34.74875, 34.74382, 34.74304, 34.73714,
            34.73962, 34.73572, 34.74612, 34.74149],
           [34.72945, 34.71526, 34.7346 , 34.72931, 34.72842, 34.72542,
            34.73079, 34.72449, 34.73557, 34.72974],
           [34.72185, 34.70065, 34.7204 , 34.71838, 34.71239, 34.71416,
            34.72238, 34.71231, 34.72244, 34.71857],
           [34.70865, 34.689  , 34.7086 , 34.70758, 34.69878, 34.70198,
            34.71201, 34.70163, 34.71014, 34.70978],
           [34.69536, 34.68063, 34.70045, 34.6946 , 34.68877, 34.69434,
            34.70271, 34.69095, 34.69937, 34.70074],
           [34.68439, 34.67447, 34.68884, 34.68219, 34.67804, 34.68527,
            34.69257, 34.68298, 34.68958, 34.69043],
           [34.67361, 34.66776, 34.67722, 34.66872, 34.66901, 34.67603,
            34.68259, 34.67609, 34.68048, 34.67987],
           [34.65982, 34.66003, 34.6644 , 34.65845, 34.66011, 34.66681,
            34.67422, 34.66867, 34.67117, 34.6702 ],
           [34.65039, 34.65517, 34.65262, 34.64856, 34.65201, 34.65866,
            34.6658 , 34.66125, 34.66354, 34.66321],
           [34.64139, 34.6515 , 34.64371, 34.64093, 34.64473, 34.65001,
            34.65744, 34.65366, 34.65683, 34.6564 ],
           [34.6333 , 34.64731, 34.63506, 34.63457, 34.63876, 34.64362,
            34.64928, 34.64774, 34.65039, 34.64966],
           [34.62619, 34.64192, 34.62735, 34.6275 , 34.63257, 34.63666,
            34.64122, 34.64227, 34.64423, 34.64404],
           [34.61801, 34.63422, 34.61969, 34.62085, 34.62584, 34.63124,
            34.63382, 34.63693, 34.6387 , 34.63898],
           [34.61145, 34.62399, 34.6123 , 34.61426, 34.62076, 34.62677,
            34.62744, 34.63205, 34.63336, 34.63364]], dtype=float32)

  • dens
    (depth, longitude)
    float32
    1021.3508 1021.5474 ... 1026.9829

    long_name :

    Potential Density (If > 16 obs)

    history :

    From meanfit2

    units :

    kg/m3

    array([[1021.3508 , 1021.5474 , 1021.8989 , 1022.50433, 1022.73236,
            1022.9604 , 1023.0737 , 1023.32007, 1023.49854, 1023.1566 ],
           [1021.3508 , 1021.5474 , 1021.8989 , 1022.50433, 1022.73236,
            1022.9604 , 1023.1753 , 1023.4778 , 1023.70886, 1023.6479 ],
           [1021.39557, 1021.6636 , 1021.8989 , 1022.50433, 1022.73236,
            1022.9963 , 1023.2932 , 1023.6685 , 1023.9539 , 1024.1934 ],
           [1021.4607 , 1021.80566, 1021.8991 , 1022.50433, 1022.74194,
            1023.0384 , 1023.43225, 1023.8913 , 1024.2405 , 1024.7122 ],
           [1021.5542 , 1021.9666 , 1021.9057 , 1022.5189 , 1022.7739 ,
            1023.0952 , 1023.59717, 1024.1447 , 1024.5731 , 1025.1273 ],
           [1021.68384, 1022.13934, 1021.9277 , 1022.5636 , 1022.83295,
            1023.17505, 1023.792  , 1024.4259 , 1024.9543 , 1025.455  ],
           [1021.8571 , 1022.3169 , 1021.97406, 1022.63947, 1022.92346,
            1023.286  , 1024.0203 , 1024.7308 , 1025.3485 , 1025.7347 ],
           [1022.0816 , 1022.4922 , 1022.0537 , 1022.7476 , 1023.04987,
            1023.43567, 1024.2844 , 1025.0522 , 1025.6979 , 1025.8828 ],
           [1022.5928 , 1022.65826, 1022.1755 , 1022.8888 , 1023.2157 ,
            1023.631  , 1024.586  , 1025.3784 , 1025.8541 , 1025.9651 ],
           [1023.18976, 1022.80804, 1022.34753, 1023.0635 , 1023.4243 ,
            1023.878  , 1024.9326 , 1025.6635 , 1025.9645 , 1026.0151 ],
           [1023.58185, 1022.96625, 1022.5772 , 1023.2717 , 1023.678  ,
            1024.2456 , 1025.2164 , 1025.8483 , 1026.0488 , 1026.0613 ],
           [1023.8116 , 1023.3071 , 1022.87054, 1023.5131 , 1024.0734 ,
            1024.6478 , 1025.4504 , 1025.9592 , 1026.1157 , 1026.1167 ],
           [1024.0541 , 1023.6619 , 1023.2315 , 1023.8569 , 1024.4912 ,
            1025.0009 , 1025.6495 , 1026.0482 , 1026.1627 , 1026.1605 ],
           [1024.3173 , 1023.9084 , 1023.6742 , 1024.2651 , 1024.8768 ,
            1025.3037 , 1025.8076 , 1026.1234 , 1026.1993 , 1026.1927 ],
           [1024.5277 , 1024.3445 , 1024.1124 , 1024.634  , 1025.1869 ,
            1025.5176 , 1025.9243 , 1026.1772 , 1026.2349 , 1026.217  ],
           [1024.7765 , 1024.7319 , 1024.5718 , 1024.9038 , 1025.4175 ,
            1025.6914 , 1026.0184 , 1026.2203 , 1026.2631 , 1026.2394 ],
           [1024.9847 , 1024.9062 , 1024.9109 , 1025.1758 , 1025.5927 ,
            1025.8615 , 1026.1038 , 1026.2628 , 1026.286  , 1026.2527 ],
           [1025.2092 , 1025.1055 , 1025.2029 , 1025.4106 , 1025.7551 ,
            1026.0062 , 1026.1843 , 1026.2914 , 1026.3053 , 1026.2726 ],
           [1025.4424 , 1025.3501 , 1025.4438 , 1025.5966 , 1025.8977 ,
            1026.1055 , 1026.2534 , 1026.3192 , 1026.319  , 1026.2899 ],
           [1025.6224 , 1025.551  , 1025.6251 , 1025.7709 , 1026.0197 ,
            1026.1808 , 1026.3016 , 1026.3427 , 1026.3313 , 1026.3036 ],
           [1025.7833 , 1025.7192 , 1025.8214 , 1025.9158 , 1026.1266 ,
            1026.2498 , 1026.3367 , 1026.3633 , 1026.342  , 1026.3123 ],
           [1025.927  , 1025.8671 , 1025.9633 , 1026.0663 , 1026.2051 ,
            1026.3057 , 1026.3643 , 1026.3832 , 1026.3542 , 1026.3184 ],
           [1026.0612 , 1025.9965 , 1026.0807 , 1026.1824 , 1026.2675 ,
            1026.3457 , 1026.388  , 1026.3964 , 1026.3684 , 1026.3254 ],
           [1026.1699 , 1026.136  , 1026.1704 , 1026.2595 , 1026.3197 ,
            1026.3759 , 1026.4084 , 1026.4086 , 1026.3837 , 1026.3326 ],
           [1026.2496 , 1026.2297 , 1026.2394 , 1026.3156 , 1026.3639 ,
            1026.4064 , 1026.4261 , 1026.4211 , 1026.398  , 1026.3392 ],
           [1026.3142 , 1026.2927 , 1026.311  , 1026.3674 , 1026.4012 ,
            1026.431  , 1026.4414 , 1026.4341 , 1026.4147 , 1026.3556 ],
           [1026.3954 , 1026.3431 , 1026.356  , 1026.4095 , 1026.4352 ,
            1026.4583 , 1026.4575 , 1026.4512 , 1026.4308 , 1026.3878 ],
           [1026.4662 , 1026.3881 , 1026.3976 , 1026.4459 , 1026.4712 ,
            1026.4795 , 1026.4757 , 1026.4746 , 1026.4448 , 1026.4412 ],
           [1026.5183 , 1026.4318 , 1026.4332 , 1026.4823 , 1026.5052 ,
            1026.4972 , 1026.4935 , 1026.5022 , 1026.4674 , 1026.4984 ],
           [1026.5552 , 1026.4838 , 1026.4618 , 1026.5164 , 1026.5288 ,
            1026.5166 , 1026.5131 , 1026.526  , 1026.4966 , 1026.5272 ],
           [1026.5831 , 1026.5066 , 1026.4877 , 1026.5508 , 1026.5537 ,
            1026.5369 , 1026.5391 , 1026.5541 , 1026.5267 , 1026.5591 ],
           [1026.5999 , 1026.5288 , 1026.5261 , 1026.5833 , 1026.5847 ,
            1026.5642 , 1026.57   , 1026.5857 , 1026.5645 , 1026.5912 ],
           [1026.6241 , 1026.558  , 1026.5593 , 1026.6088 , 1026.6105 ,
            1026.5917 , 1026.6034 , 1026.6138 , 1026.5969 , 1026.6251 ],
           [1026.651  , 1026.6079 , 1026.5927 , 1026.6323 , 1026.634  ,
            1026.6217 , 1026.6329 , 1026.6484 , 1026.6334 , 1026.6552 ],
           [1026.6821 , 1026.6489 , 1026.6244 , 1026.6542 , 1026.6586 ,
            1026.6558 , 1026.6638 , 1026.6804 , 1026.6627 , 1026.6799 ],
           [1026.7083 , 1026.684  , 1026.657  , 1026.6733 , 1026.6879 ,
            1026.69   , 1026.7019 , 1026.7068 , 1026.6859 , 1026.7137 ],
           [1026.7218 , 1026.7148 , 1026.6869 , 1026.6969 , 1026.7153 ,
            1026.7189 , 1026.7225 , 1026.7358 , 1026.7086 , 1026.7386 ],
           [1026.7339 , 1026.7583 , 1026.7151 , 1026.728  , 1026.7455 ,
            1026.7454 , 1026.7415 , 1026.7606 , 1026.7327 , 1026.7661 ],
           [1026.7458 , 1026.7826 , 1026.7434 , 1026.7495 , 1026.7748 ,
            1026.7694 , 1026.7595 , 1026.7863 , 1026.7646 , 1026.7913 ],
           [1026.7657 , 1026.8009 , 1026.7684 , 1026.771  , 1026.8    ,
            1026.7957 , 1026.7833 , 1026.8118 , 1026.7938 , 1026.8114 ],
           [1026.7858 , 1026.8142 , 1026.7872 , 1026.7944 , 1026.8217 ,
            1026.8121 , 1026.8052 , 1026.8363 , 1026.8187 , 1026.8312 ],
           [1026.805  , 1026.8254 , 1026.8082 , 1026.8175 , 1026.8428 ,
            1026.8314 , 1026.8282 , 1026.8533 , 1026.8401 , 1026.854  ],
           [1026.8245 , 1026.8379 , 1026.8264 , 1026.8442 , 1026.8608 ,
            1026.851  , 1026.8503 , 1026.8679 , 1026.8604 , 1026.8774 ],
           [1026.8501 , 1026.85   , 1026.8472 , 1026.8663 , 1026.8789 ,
            1026.8705 , 1026.8693 , 1026.8844 , 1026.881  , 1026.8999 ],
           [1026.8705 , 1026.8605 , 1026.8688 , 1026.886  , 1026.8947 ,
            1026.8895 , 1026.8889 , 1026.9016 , 1026.8986 , 1026.9159 ],
           [1026.8889 , 1026.8689 , 1026.8871 , 1026.9001 , 1026.9098 ,
            1026.9104 , 1026.908  , 1026.9196 , 1026.914  , 1026.9318 ],
           [1026.9052 , 1026.8777 , 1026.9055 , 1026.9141 , 1026.9233 ,
            1026.925  , 1026.9263 , 1026.9332 , 1026.9282 , 1026.947  ],
           [1026.9194 , 1026.889  , 1026.9209 , 1026.93   , 1026.9371 ,
            1026.9414 , 1026.945  , 1026.945  , 1026.9417 , 1026.9592 ],
           [1026.9342 , 1026.9043 , 1026.9352 , 1026.9446 , 1026.9518 ,
            1026.9535 , 1026.9619 , 1026.9565 , 1026.954  , 1026.9707 ],
           [1026.9464 , 1026.9237 , 1026.9487 , 1026.9589 , 1026.9634 ,
            1026.964  , 1026.9757 , 1026.967  , 1026.9664 , 1026.9829 ]],
          dtype=float32)

  • u
    (depth, longitude)
    float32
    0.08695984 0.1127472 ... nan nan

    long_name :

    U component of velocity

    history :

    From meanfit2

    units :

    m/s

    array([[ 8.695984e-02,  1.127472e-01,  1.524353e-02, -1.189270e-01,
            -3.015137e-01, -3.043213e-01, -3.773346e-01, -1.208038e-01,
            -8.332825e-02, -1.861877e-01],
           [ 8.341980e-02,  1.353455e-01,  3.355408e-02, -1.002655e-01,
            -2.718811e-01, -2.579498e-01, -2.339020e-01,  3.550720e-02,
             6.764221e-02, -1.617432e-02],
           [ 4.620361e-02,  1.165466e-01,  4.588318e-02, -7.801819e-02,
            -2.297363e-01, -1.702881e-01, -8.813477e-02,  1.996918e-01,
             2.585754e-01,  2.067261e-01],
           [-1.550293e-02,  7.502747e-02,  5.908203e-02, -3.242493e-02,
            -1.621857e-01, -7.597351e-02,  5.973816e-02,  3.710785e-01,
             4.628906e-01,  4.254761e-01],
           [-8.711243e-02,  1.896667e-02,  7.255554e-02,  1.246643e-02,
            -8.317566e-02,  2.496338e-02,  2.094879e-01,  5.490570e-01,
             6.539917e-01,  5.830994e-01],
           [-1.540375e-01, -4.347229e-02,  8.641052e-02,  5.673218e-02,
             5.538940e-03,  1.325378e-01,  3.608398e-01,  7.329712e-01,
             8.052826e-01,  6.708374e-01],
           [-2.016754e-01, -1.040955e-01,  1.007233e-01,  1.006012e-01,
             1.021271e-01,  2.467041e-01,  5.135651e-01,  9.245758e-01,
             9.021301e-01,  7.123413e-01],
           [-2.154236e-01, -1.547241e-01,  1.156158e-01,  1.443329e-01,
             2.048187e-01,  3.674316e-01,  6.674347e-01,  1.063049e+00,
             9.161682e-01,  6.905518e-01],
           [-1.822052e-01, -1.871643e-01,  1.311493e-01,  1.881866e-01,
             3.118134e-01,  4.947357e-01,  8.222046e-01,  1.109756e+00,
             8.835602e-01,  6.234131e-01],
           [-1.288452e-01, -1.932678e-01,  1.474304e-01,  2.324066e-01,
             4.213257e-01,  6.285553e-01,  9.803314e-01,  1.084610e+00,
             8.134766e-01,  5.719604e-01],
           [-4.925537e-02, -1.495056e-01,  1.645355e-01,  2.772522e-01,
             5.315552e-01,  7.843628e-01,  1.051697e+00,  1.021378e+00,
             7.394714e-01,  5.158997e-01],
           [-1.098633e-03, -6.327820e-02,  1.825714e-01,  3.229675e-01,
             6.489563e-01,  8.942719e-01,  1.056854e+00,  9.377899e-01,
             6.436462e-01,  4.607849e-01],
           [ 5.458069e-02,  2.542114e-02,  2.016296e-01,  4.371948e-01,
             7.398376e-01,  9.527435e-01,  1.010208e+00,  8.368683e-01,
             5.527802e-01,  4.091492e-01],
           [ 1.172028e-01,  1.205750e-01,  2.369995e-01,  5.076141e-01,
             7.891235e-01,  9.493256e-01,  9.315186e-01,  7.351685e-01,
             4.722290e-01,  3.593445e-01],
           [ 1.567688e-01,  2.273102e-01,  2.850800e-01,  5.347595e-01,
             8.105927e-01,  9.132996e-01,  8.379517e-01,  6.443481e-01,
             3.985291e-01,  3.148499e-01],
           [ 2.116394e-01,  2.839203e-01,  3.376923e-01,  5.384521e-01,
             8.122864e-01,  8.595123e-01,  7.484131e-01,  5.569000e-01,
             3.392029e-01,  2.763977e-01],
           [ 2.561951e-01,  3.965759e-01,  3.799438e-01,  5.419769e-01,
             7.734985e-01,  7.872620e-01,  6.551361e-01,  4.700775e-01,
             2.832794e-01,  2.465820e-01],
           [ 2.909698e-01,  4.647217e-01,  4.096527e-01,  5.608826e-01,
             7.182617e-01,  6.921082e-01,  5.640564e-01,  3.982391e-01,
             2.414856e-01,  2.124329e-01],
           [ 3.351898e-01,  4.944916e-01,  4.251556e-01,  5.546722e-01,
             6.461182e-01,  6.045837e-01,  4.808960e-01,  3.305969e-01,
             2.084351e-01,  1.757812e-01],
           [ 3.651123e-01,  4.864502e-01,  4.245148e-01,  5.066528e-01,
             5.726471e-01,  5.227509e-01,  4.061127e-01,  2.690430e-01,
             1.748047e-01,  1.453857e-01],
           [ 3.980103e-01,  4.820404e-01,  4.007721e-01,  4.504395e-01,
             4.949188e-01,  4.479980e-01,  3.345795e-01,  2.157440e-01,
             1.431732e-01,  1.227722e-01],
           [ 4.239502e-01,  4.687500e-01,  3.651428e-01,  3.894348e-01,
             4.218292e-01,  3.804016e-01,  2.717896e-01,  1.761932e-01,
             1.075439e-01,  1.014709e-01],
           [          nan,  4.299316e-01,  3.158112e-01,  3.197327e-01,
             3.539734e-01,  3.157654e-01,  2.140656e-01,  1.539001e-01,
             7.321167e-02,  7.849121e-02],
           [          nan,  4.211578e-01,  2.572327e-01,  2.558136e-01,
             2.936707e-01,  2.563477e-01,  1.590424e-01,  1.320953e-01,
             4.878235e-02,  5.648804e-02],
           [          nan,  3.812561e-01,  2.008667e-01,  1.951752e-01,
             2.291412e-01,  1.961060e-01,  1.091309e-01,  1.091919e-01,
             3.186035e-02,  3.695679e-02],
           [          nan,  3.300781e-01,  1.551514e-01,  1.511841e-01,
             1.643066e-01,  1.421814e-01,  5.932617e-02,  8.364868e-02,
             1.728821e-02,  2.288818e-02],
           [          nan,  2.603912e-01,  1.189575e-01,  1.146240e-01,
             1.084595e-01,  9.318542e-02,  1.634216e-02,  5.674744e-02,
             3.082275e-03,  1.013184e-02],
           [          nan,  1.941986e-01,  7.278442e-02,  6.607056e-02,
             5.632019e-02,  4.957581e-02, -2.172852e-02,  4.266357e-02,
            -6.408691e-03,  9.429932e-03],
           [          nan,  1.157990e-01,  4.563904e-02,  3.280640e-03,
             1.614380e-02,  9.841919e-03, -5.218506e-02,  3.323364e-02,
            -9.414673e-03,  2.828979e-02],
           [          nan,  1.203613e-01,  1.246643e-02, -5.570984e-02,
            -2.716064e-02, -2.252197e-02, -7.606506e-02,  3.015137e-02,
            -8.316040e-03,  3.686523e-02],
           [          nan,  9.794617e-02, -4.412842e-02, -9.243774e-02,
            -6.040955e-02, -4.800415e-02, -8.935547e-02,  2.958679e-02,
            -6.530762e-03,  3.253174e-02],
           [          nan,  7.344055e-02, -7.162476e-02, -1.152039e-01,
            -9.327698e-02, -5.438232e-02, -7.911682e-02,  2.906799e-02,
            -7.827759e-03,  2.523804e-02],
           [          nan,  3.254700e-02, -9.220886e-02, -1.249847e-01,
            -1.132660e-01, -5.345154e-02, -7.229614e-02,  3.753662e-02,
            -8.239746e-03,  1.803589e-02],
           [          nan, -2.380371e-03, -1.158142e-01, -1.360931e-01,
            -1.222992e-01, -7.463074e-02, -5.857849e-02,  4.637146e-02,
            -3.097534e-03,  3.814697e-03],
           [          nan,           nan, -1.109467e-01, -1.327057e-01,
            -1.240997e-01, -5.921936e-02, -3.999329e-02,  5.001831e-02,
             9.918213e-04, -2.288818e-03],
           [          nan,           nan,           nan, -1.358490e-01,
            -1.091003e-01, -4.792786e-02, -2.346802e-02,  4.570007e-02,
             4.135132e-03, -3.631592e-03],
           [          nan,           nan,           nan, -1.330261e-01,
            -9.156799e-02, -3.633118e-02, -8.117676e-03,  4.237366e-02,
             5.706787e-03, -8.300781e-03],
           [          nan,           nan,           nan, -1.232758e-01,
            -7.325745e-02, -3.036499e-02, -9.216309e-03,  3.376770e-02,
             4.547119e-03, -1.171875e-02],
           [          nan,           nan,           nan, -1.109924e-01,
            -7.661438e-02, -4.739380e-02, -2.273560e-03,  2.806091e-02,
            -1.876831e-02, -1.608276e-02],
           [          nan,           nan,           nan, -1.244202e-01,
            -5.711365e-02, -3.259277e-02, -3.532410e-02, -1.571655e-03,
            -1.554871e-02, -2.319336e-03],
           [          nan,           nan,           nan, -9.547424e-02,
            -3.237915e-02, -3.512573e-02, -3.874207e-02,  1.718140e-02,
            -1.019287e-02,  2.380371e-03],
           [          nan,           nan,           nan, -3.152466e-02,
             1.884460e-02, -2.856445e-02, -4.353333e-02,  1.614380e-02,
            -7.949829e-03,  1.159668e-03],
           [          nan,           nan,           nan,  3.807068e-02,
            -1.484680e-02, -3.552246e-02, -5.863953e-02,  3.302002e-02,
             1.257324e-02,           nan],
           [          nan,           nan,           nan,           nan,
                      nan, -5.599976e-03, -2.841187e-02,  4.179382e-02,
            -9.516907e-02,           nan],
           [          nan,           nan,           nan,           nan,
                      nan,           nan, -4.182434e-02,           nan,
                      nan,           nan],
           [          nan,           nan,           nan,           nan,
                      nan,           nan,           nan,           nan,
                      nan,           nan],
           [          nan,           nan,           nan,           nan,
                      nan,           nan,           nan,           nan,
                      nan,           nan],
           [          nan,           nan,           nan,           nan,
                      nan,           nan,           nan,           nan,
                      nan,           nan],
           [          nan,           nan,           nan,           nan,
                      nan,           nan,           nan,           nan,
                      nan,           nan],
           [          nan,           nan,           nan,           nan,
                      nan,           nan,           nan,           nan,
                      nan,           nan]], dtype=float32)

  • TSPTS
    (depth, longitude)
    float32
    20.0 34.0 28.0 ... 47.0 38.0 24.0

    long_name :

    Number of CTD data pts used for T and S

    history :

    From meanfit2

    units :

    no unit

    array([[ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57., -53., -47., -38., -24.],
           [-20., -34.,  28.,  46.,  50., -57., -53., -47., -38., -24.],
           [-20., -34., -28.,  46., -50., -57., -53., -47., -38., -24.],
           [-20., -34., -28., -46., -50., -57., -53., -47., -38.,  24.],
           [-20., -34., -28., -46., -50., -57., -53.,  47.,  38.,  24.],
           [-20., -34., -28., -46., -50., -57., -53.,  47.,  38.,  24.],
           [ 20., -34., -28., -46., -50., -57., -53.,  47.,  38.,  24.],
           [ 20., -34., -28., -46., -50., -57.,  53.,  47.,  38.,  24.],
           [ 20.,  34., -28., -46., -50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34., -28., -46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34., -28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.],
           [ 20.,  34.,  28.,  46.,  50.,  57.,  53.,  47.,  38.,  24.]],
          dtype=float32)

  • UPTS
    (depth, longitude)
    float32
    20.0 34.0 27.0 ... nan 2.362488 nan

    long_name :

    Number of ADCP data pts used for U

    history :

    From meanfit2

    units :

    no unit

    array([[ 20.      ,  34.      ,  27.      ,  45.      ,  49.      ,
             57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 20.      ,  34.      ,  27.      ,  45.      ,  49.      ,
             57.      , -53.      , -47.      , -38.      , -24.      ],
           [-20.      , -34.      ,  27.      ,  45.      ,  49.      ,
            -57.      , -53.      , -47.      , -38.      , -24.      ],
           [-20.      , -34.      , -27.      ,  45.      , -49.      ,
            -57.      , -53.      , -47.      , -38.      , -24.      ],
           [-20.      , -34.      , -27.      , -45.      , -49.      ,
            -57.      , -53.      , -47.      , -38.      ,  24.      ],
           [-20.      , -34.      , -27.      , -45.      , -49.      ,
            -57.      , -53.      ,  47.      ,  38.      ,  24.      ],
           [-20.      , -34.      , -27.      , -45.      , -49.      ,
            -57.      , -53.      ,  47.      ,  38.      ,  24.      ],
           [ 20.      , -34.      , -27.      , -45.      , -49.      ,
            -57.      , -53.      ,  47.      ,  38.      ,  24.      ],
           [ 20.      , -34.      , -27.      , -45.      , -49.      ,
            -57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 20.      ,  34.      , -27.      , -45.      , -49.      ,
             57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 20.      ,  34.      , -27.      , -45.      ,  49.      ,
             57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 20.      ,  34.      , -27.      ,  45.      ,  49.      ,
             57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 20.      ,  34.      ,  27.      ,  45.      ,  49.      ,
             57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 20.      ,  34.      ,  27.      ,  45.      ,  49.      ,
             57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 20.      ,  34.      ,  27.      ,  45.      ,  49.      ,
             57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 19.      ,  33.      ,  27.      ,  45.      ,  49.      ,
             57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 19.      ,  33.      ,  27.      ,  45.      ,  49.      ,
             57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 19.      ,  33.      ,  27.      ,  45.      ,  49.      ,
             57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 18.      ,  32.      ,  27.      ,  45.      ,  49.      ,
             57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 17.      ,  31.      ,  27.      ,  44.      ,  49.      ,
             57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 17.      ,  30.      ,  26.      ,  44.      ,  49.      ,
             57.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 17.      ,  30.      ,  26.      ,  44.      ,  49.      ,
             56.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 16.      ,  29.      ,  26.      ,  44.      ,  49.      ,
             56.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 16.      ,  29.      ,  26.      ,  43.08949 ,  48.      ,
             56.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 16.      ,  29.      ,  26.      ,  42.      ,  47.63275 ,
             56.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 16.      ,  28.      ,  25.      ,  41.29543 ,  47.      ,
             56.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 16.      ,  28.      ,  25.      ,  40.06972 ,  47.      ,
             56.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 15.      ,  28.      ,  24.      ,  40.      ,  47.      ,
             56.      ,  53.      ,  47.      ,  38.      ,  24.      ],
           [ 15.      ,  27.      ,  23.      ,  38.58197 ,  45.      ,
             55.      ,  53.      ,  46.79071 ,  38.      ,  24.      ],
           [ 13.50247 ,  25.      ,  22.84517 ,  37.      ,  45.      ,
             54.      ,  52.      ,  46.      ,  38.      ,  24.      ],
           [ 11.71429 ,  25.      ,  21.25563 ,  34.      ,  42.64449 ,
             52.65231 ,  52.      ,  45.60422 ,  38.      ,  24.      ],
           [ 10.03615 ,  24.13306 ,  21.      ,  33.      ,  40.      ,
             50.18347 ,  48.04211 ,  44.      ,  37.      ,  24.      ],
           [ 10.      ,  22.21303 ,  20.      ,  30.28235 ,  35.      ,
             47.68831 ,  46.68185 ,  44.      ,  37.      ,  24.      ],
           [  8.      ,  19.      ,  20.      ,  29.11446 ,  33.38109 ,
             44.      ,  45.4581  ,  44.      ,  37.      ,  24.      ],
           [  5.808273,  15.1183  ,  19.      ,  24.      ,  28.15425 ,
             39.44559 ,  43.      ,  44.      ,  37.      ,  24.      ],
           [  5.      ,  12.61229 ,  15.      ,  24.      ,  26.22131 ,
             38.      ,  42.      ,  44.      ,  37.      ,  24.      ],
           [  5.      ,  12.      ,  14.32101 ,  23.      ,  26.      ,
             38.      ,  42.      ,  44.      ,  37.      ,  24.      ],
           [  4.240875,   9.18866 ,  14.      ,  23.      ,  26.      ,
             38.      ,  42.      ,  40.94077 ,  37.      ,  22.      ],
           [  3.      ,   9.      ,  14.      ,  23.      ,  25.      ,
             35.07103 ,  40.11191 ,  37.      ,  35.      ,  20.87506 ],
           [  3.      ,   8.83989 ,  13.      ,  23.      ,  24.00661 ,
             31.88373 ,  36.      ,  33.83786 ,  33.23816 ,  18.86536 ],
           [  3.      ,   7.      ,  13.      ,  22.57132 ,  22.84377 ,
             29.79913 ,  33.49008 ,  29.51288 ,  30.13312 ,  18.      ],
           [  2.510162,   6.471069,  12.18974 ,  20.26578 ,  20.72708 ,
             27.6134  ,  30.38028 ,  23.01508 ,  26.99951 ,  16.61554 ],
           [  1.56459 ,   4.448212,  11.      ,  16.58417 ,  18.      ,
             21.64183 ,  22.91777 ,  18.66138 ,  23.86865 ,  11.52292 ],
           [  1.      ,   4.      ,   9.      ,  15.      ,  15.35428 ,
             17.87775 ,  19.07364 ,  16.13197 ,  17.61755 ,   8.      ],
           [  1.      ,   4.      ,   9.      ,  11.24826 ,  11.63588 ,
             14.0504  ,  16.11916 ,  14.53514 ,  14.      ,   8.      ],
           [  1.      ,   4.      ,   6.587708,   9.979492,   9.022217,
              9.942642,  10.64244 ,  10.10248 ,  11.61432 ,   5.658813],
           [  1.      ,   4.      ,   4.903351,   7.603943,   6.341446,
              7.509903,   6.384354,   6.727173,   8.532501,   3.305084],
           [  1.      ,   3.104813,   2.922562,   4.993423,   4.285583,
              4.861572,   3.509277,   3.513107,   4.681305,   3.      ],
           [  1.      ,   2.571671,   1.485153,   2.552307,   2.825714,
              3.300873,   1.      ,   1.706116,   3.      ,   1.931   ],
           [       nan,   2.      ,   1.      ,   1.124817,   1.663269,
              1.606293,        nan,        nan,   2.362488,        nan]],
          dtype=float32)

  • eucmax
    (longitude)
    float64
    -215.0 -185.0 ... -75.0 -65.0

    units :

    m

    positive :

    down

    point_spacing :

    even

    long_name :

    Depth of EUC max

    array([-215., -185., -185., -175., -155., -125., -115.,  -85.,  -75.,
            -65.])

  • h
    (longitude)
    float64
    -190.0 -160.0 ... -50.0 -40.0

    units :

    m

    positive :

    down

    point_spacing :

    even

    long_name :

    $h$

    array([-190., -160., -160., -150., -130., -100.,  -90.,  -60.,  -50.,
            -40.])

  • us
    (longitude)
    float32
    0.04620361 0.1165466 ... 0.2067261

    long_name :

    U component of velocity

    history :

    From meanfit2

    units :

    m/s

    array([ 0.04620361,  0.1165466 ,  0.04588318, -0.07801819, -0.2297363 ,
           -0.1702881 , -0.08813477,  0.1996918 ,  0.2585754 ,  0.2067261 ],
          dtype=float32)

  • ueuc
    (longitude)
    float32
    0.4239502 0.4944916 ... 0.7123413

    long_name :

    U component of velocity

    history :

    From meanfit2

    units :

    m/s

    array([0.4239502, 0.4944916, 0.4251556, 0.5608826, 0.8122864, 0.9527435,
           1.056854 , 1.109756 , 0.9161682, 0.7123413], dtype=float32)

  • du
    (longitude)
    float32
    -0.37774658 ... -0.50561523

    long_name :

    $\Delta$u

    history :

    From meanfit2

    units :

    m/s

    array([-0.37774658, -0.377945  , -0.37927243, -0.6389008 , -1.0420227 ,
           -1.1230316 , -1.1449888 , -0.9100642 , -0.6575928 , -0.50561523],
          dtype=float32)

  • dens_euc
    (longitude)
    float64
    1.026e+03 1.025e+03 ... 1.026e+03

    long_name :

    Potential Density (If > 16 obs)

    history :

    From meanfit2

    units :

    kg/m3

    array([1025.92700195, 1025.35009766, 1025.44384766, 1025.41064453,
           1025.41748047, 1025.00085449, 1025.45043945, 1025.37841797,
           1025.69787598, 1025.73474121])

  • b
    (depth, longitude)
    float64
    -9.766 -9.774 ... -9.822 -9.822

    long_name :

    Potential Density (If > 16 obs)

    history :

    From meanfit2

    units :

    kg/m3

    array([[-9.76624275, -9.77361867, -9.77608781, -9.78219568, -9.78431232,
            -9.79047202, -9.78726336, -9.79030798, -9.78896585, -9.7853437 ],
           [-9.76624275, -9.77361867, -9.77608781, -9.78219568, -9.78431232,
            -9.79047202, -9.78823473, -9.79181657, -9.79097668, -9.79004213],
           [-9.76667015, -9.77473014, -9.77608781, -9.78219568, -9.78431232,
            -9.79081501, -9.78936324, -9.79364135, -9.79332074, -9.79525847],
           [-9.76729363, -9.77608925, -9.77608972, -9.78219568, -9.78440374,
            -9.79121802, -9.79069364, -9.79577281, -9.79606182, -9.80022062],
           [-9.76818745, -9.77762931, -9.77615257, -9.78233568, -9.7847104 ,
            -9.79176204, -9.79227069, -9.79819665, -9.79924277, -9.80419071],
           [-9.7694268 , -9.77928175, -9.77636304, -9.78276329, -9.7852742 ,
            -9.79252614, -9.79413439, -9.80088715, -9.8028893 , -9.8073249 ],
           [-9.77108403, -9.78097991, -9.77680682, -9.78348899, -9.78614084,
            -9.79358844, -9.79631902, -9.80380528, -9.80665865, -9.81000019],
           [-9.77323053, -9.78265712, -9.77756869, -9.78452325, -9.78734938,
            -9.79502042, -9.79884554, -9.80687961, -9.8100005 , -9.81141686],
           [-9.77811845, -9.78424671, -9.77873339, -9.7858737 , -9.788936  ,
            -9.7968897 , -9.80173109, -9.8100006 , -9.81149529, -9.81220326],
           [-9.78382689, -9.78567915, -9.78037902, -9.7875451 , -9.79093214,
            -9.79925345, -9.80504709, -9.81272729, -9.81255022, -9.81268215],
           [-9.7875754 , -9.78719349, -9.78257605, -9.78953744, -9.79335875,
            -9.80277193, -9.80776217, -9.81449589, -9.81335664, -9.81312295],
           [-9.78977235, -9.79045458, -9.78538257, -9.79184692, -9.7971415 ,
            -9.80662197, -9.81000013, -9.81555685, -9.81399645, -9.81365325],
           [-9.79209114, -9.79384899, -9.78883572, -9.79513543, -9.80113854,
            -9.8100004 , -9.81190478, -9.81640829, -9.81444584, -9.81407216],
           [-9.79460792, -9.79620718, -9.79307074, -9.79904107, -9.80482797,
            -9.81289864, -9.81341707, -9.8171283 , -9.81479621, -9.81438063],
           [-9.7966202 , -9.80037878, -9.7972629 , -9.80257052, -9.80779456,
            -9.81494609, -9.81453415, -9.81764259, -9.81513611, -9.81461293],
           [-9.79899895, -9.80408654, -9.80165791, -9.80515142, -9.81000021,
            -9.81660958, -9.8154341 , -9.81805498, -9.81540555, -9.81482714],
           [-9.80099029, -9.80575423, -9.80490156, -9.80775328, -9.81167636,
            -9.81823686, -9.81625024, -9.81846165, -9.81562548, -9.81495377],
           [-9.80313679, -9.80766002, -9.80769474, -9.81000085, -9.81323061,
            -9.81962215, -9.81702163, -9.81873498, -9.81581019, -9.81514418],
           [-9.8053661 , -9.81000011, -9.81000034, -9.81177891, -9.81459438,
            -9.82057203, -9.81768254, -9.81900165, -9.81594063, -9.81530984],
           [-9.80708805, -9.81192304, -9.81173453, -9.8134465 , -9.81576102,
            -9.82129326, -9.81814347, -9.81922547, -9.81605869, -9.81544122],
           [-9.8086263 , -9.81353263, -9.81361253, -9.81483314, -9.8167848 ,
            -9.82195351, -9.81847868, -9.81942261, -9.81616151, -9.81552405],
           [-9.81000082, -9.81494602, -9.8149696 , -9.81627311, -9.81753526,
            -9.822488  , -9.81874248, -9.81961309, -9.81627767, -9.81558213],
           [-9.811283  , -9.81618416, -9.8160924 , -9.81738357, -9.81813238,
            -9.82287196, -9.81896913, -9.81973976, -9.81641382, -9.81564972],
           [-9.81232341, -9.81751945, -9.81695141, -9.81812165, -9.81863142,
            -9.82316064, -9.81916531, -9.81985595, -9.81655949, -9.81571827],
           [-9.81308587, -9.81841663, -9.81761137, -9.81865783, -9.81905426,
            -9.82345218, -9.81933482, -9.81997595, -9.81669564, -9.81578206],
           [-9.81370269, -9.81901856, -9.8182961 , -9.81915401, -9.81941235,
            -9.82368846, -9.81948053, -9.82009976, -9.81685559, -9.8159382 ],
           [-9.81447943, -9.81950144, -9.81872655, -9.81955686, -9.81973615,
            -9.82394856, -9.81963481, -9.82026357, -9.81700983, -9.81624667],
           [-9.81515621, -9.81993098, -9.81912463, -9.81990447, -9.8200809 ,
            -9.82415244, -9.81980908, -9.82048834, -9.81714408, -9.81675697],
           [-9.815655  , -9.82034909, -9.81946556, -9.82025303, -9.82040661,
            -9.82432108, -9.81997955, -9.82075215, -9.8173602 , -9.81730441],
           [-9.8160072 , -9.8208472 , -9.81973888, -9.82057874, -9.82063232,
            -9.82450686, -9.8201662 , -9.82097977, -9.81763917, -9.81757955],
           [-9.81627468, -9.82106531, -9.81998649, -9.82090826, -9.82087041,
            -9.82470122, -9.82041476, -9.82124835, -9.81792765, -9.81788421],
           [-9.81643459, -9.8212777 , -9.82035409, -9.82121873, -9.82116754,
            -9.82496323, -9.82070998, -9.82155026, -9.8182885 , -9.81819173],
           [-9.81666685, -9.82155676, -9.82067217, -9.82146253, -9.82141325,
            -9.82522618, -9.82102996, -9.82181883, -9.81859888, -9.81851638],
           [-9.81692291, -9.82203487, -9.82099024, -9.82168824, -9.82163896,
            -9.82551296, -9.8213128 , -9.82215122, -9.8189483 , -9.81880295],
           [-9.81722085, -9.82242727, -9.82129404, -9.82189681, -9.82187324,
            -9.8258388 , -9.82160803, -9.82245693, -9.81922821, -9.81904002],
           [-9.81747119, -9.82276252, -9.8216064 , -9.82208062, -9.82215418,
            -9.82616654, -9.82197277, -9.82270932, -9.81945005, -9.81936372],
           [-9.81760065, -9.82305777, -9.8218921 , -9.82230633, -9.82241703,
            -9.82644284, -9.8221699 , -9.82298742, -9.81966713, -9.81960173],
           [-9.81771583, -9.82347302, -9.82216161, -9.82260346, -9.8227056 ,
            -9.82669627, -9.82235084, -9.82322456, -9.81989754, -9.8198645 ],
           [-9.81783005, -9.82370541, -9.82243303, -9.82280917, -9.82298559,
            -9.82692683, -9.82252416, -9.82346933, -9.82020316, -9.82010442],
           [-9.81802043, -9.82388066, -9.82267206, -9.82301488, -9.82322749,
            -9.82717835, -9.82275177, -9.82371314, -9.82048212, -9.82029769],
           [-9.81821176, -9.82400828, -9.82285205, -9.82323869, -9.82343415,
            -9.82733556, -9.82296033, -9.82394838, -9.82072015, -9.8204862 ],
           [-9.81839642, -9.82411591, -9.82305299, -9.82345964, -9.82363605,
            -9.82752039, -9.82318127, -9.82411029, -9.82092485, -9.82070517],
           [-9.81858204, -9.82423496, -9.82322727, -9.82371582, -9.82380842,
            -9.82770713, -9.82339268, -9.82425124, -9.82111812, -9.8209289 ],
           [-9.81882668, -9.8243502 , -9.82342536, -9.82392725, -9.82398175,
            -9.82789386, -9.82357362, -9.82440839, -9.82131616, -9.82114407],
           [-9.81902181, -9.82445116, -9.82363201, -9.82411486, -9.82413223,
            -9.82807679, -9.82376123, -9.82457315, -9.82148373, -9.8212964 ],
           [-9.81919791, -9.82453116, -9.82380724, -9.8242501 , -9.82427698,
            -9.82827591, -9.82394408, -9.82474458, -9.8216313 , -9.82144873],
           [-9.81935402, -9.82461593, -9.82398342, -9.82438343, -9.8244065 ,
            -9.82841597, -9.82411931, -9.82487601, -9.82176745, -9.8215944 ],
           [-9.81949014, -9.8247245 , -9.82413103, -9.82453676, -9.82453888,
            -9.82857317, -9.82429739, -9.82498744, -9.82189599, -9.8217115 ],
           [-9.81963102, -9.82487022, -9.82426722, -9.8246758 , -9.82467888,
            -9.82868845, -9.82446024, -9.82509887, -9.82201405, -9.82182099],
           [-9.8197481 , -9.82505594, -9.82439673, -9.82481199, -9.82478935,
            -9.82878944, -9.82459166, -9.82519887, -9.82213306, -9.82193809]])

  • bs
    (longitude)
    float64
    -9.767 -9.775 ... -9.793 -9.795

    long_name :

    Potential Density (If > 16 obs)

    history :

    From meanfit2

    units :

    kg/m3

    array([-9.76667015, -9.77473014, -9.77608781, -9.78219568, -9.78431232,
           -9.79081501, -9.78936324, -9.79364135, -9.79332074, -9.79525847])

  • beuc
    (longitude)
    float64
    -9.81 -9.81 -9.81 ... -9.81 -9.81

    long_name :

    Potential Density (If > 16 obs)

    history :

    From meanfit2

    units :

    kg/m3

    array([-9.81, -9.81, -9.81, -9.81, -9.81, -9.81, -9.81, -9.81, -9.81,
           -9.81])

  • db
    (longitude)
    float64
    0.04333 0.03527 ... 0.01668 0.01474

    long_name :

    $\Delta$b

    history :

    From meanfit2

    units :

    kg/m3

    array([0.04332985, 0.03526986, 0.03391219, 0.02780432, 0.02568768,
           0.01918499, 0.02063676, 0.01635865, 0.01667926, 0.01474153])

  • Rib
    (longitude)
    float64
    57.7 39.51 37.72 ... 1.929 2.307

    array([57.69520508, 39.50630823, 37.72013662, 10.21731178,  3.07548718,
            1.52116955,  1.41671203,  1.18509875,  1.92855819,  2.30654635])

• Attributes: (2)

  history :

  FERRET V5.41 1-Oct-02

  name :

  Johnson

TAO daily → full average

# need to fill to the surface
tao_adcp = pump.obs.read_tao_adcp().mean("time").bfill("depth")
tao_adcp["eucmax"] = pump.get_euc_max(tao_adcp.u)

tao_ctd_raw = (
    pump.obs.read_tao()
    .sel(latitude=0, longitude=tao_adcp.longitude)
    .sortby("depth")
    .chunk({"depth": -1})
    .pipe(dcpy.interpolate.pchip_fillna, dim="depth")
    .drop(["u", "v"])
    .mean("time")
    .compute()
)
tao_ctd = tao_ctd.ffill("depth")
tao_ctd["dens"] = pump.mdjwf.dens(np.array(35.0), tao_ctd.temp, tao_ctd.depth)
tao_ctd["eucmax"] = tao_adcp.eucmax

tao_daily = xr.merge(
    [
        pump.calc.estimate_euc_depth_terms(tao_adcp)[
            ["h", "us", "ueuc", "du", "eucmax"]
        ],
        pump.calc.estimate_euc_depth_terms(tao_ctd)[["bs", "beuc", "db"]],
    ]
)
tao_daily = pump.calc.estimate_Rib(tao_daily)
tao_daily.attrs["name"] = "TAO"
tao_daily.load()

/glade/u/home/dcherian/pump/pump/obs.py:190: FutureWarning: In xarray version 0.15 the default behaviour of `open_mfdataset`
will change. To retain the existing behavior, pass
combine='nested'. To use future default behavior, pass
combine='by_coords'. See
http://xarray.pydata.org/en/stable/combining.html#combining-multi

  chunks={"lat": 1, "lon": 1, "depth": 5},
/gpfs/u/home/dcherian/python/xarray/xarray/backends/api.py:933: FutureWarning: The datasets supplied have global dimension coordinates. You may want
to use the new `combine_by_coords` function (or the
`combine='by_coords'` option to `open_mfdataset`) to order the datasets
before concatenation. Alternatively, to continue concatenating based
on the order the datasets are supplied in future, please use the new
`combine_nested` function (or the `combine='nested'` option to
open_mfdataset).The datasets supplied require both concatenation and merging. From
xarray version 0.15 this will operation will require either using the
new `combine_nested` function (or the `combine='nested'` option to
open_mfdataset), with a nested list structure such that you can combine
along the dimensions None. Alternatively if your datasets have global
dimension coordinates then you can use the new `combine_by_coords`
function.
  from_openmfds=True,
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 15
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 15
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 15
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 15
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 15
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 15
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 15
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 11
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 10
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 11
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 10
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 11
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 10
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 11
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 11
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 11
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 11
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 11
  **blockwise_kwargs
/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.6/site-packages/dask/array/core.py:3855: PerformanceWarning: Increasing number of chunks by factor of 11
  **blockwise_kwargs

xarray.Dataset

• Dimensions:
  • longitude: 6
• Coordinates: (2)
  • longitude
    (longitude)
    float64
    -213.0 -204.0 ... -140.0 -110.0

    FORTRAN_format :

    units :

    degree_east

    type :

    UNEVEN

    epic_code :

    502

    array([-213., -204., -195., -170., -140., -110.])

  • latitude
    ()
    float64
    0.0

    FORTRAN_format :

    units :

    degree_north

    type :

    UNEVEN

    epic_code :

    500

    array(0.)

• Data variables: (9)
  • h
    (longitude)
    float32
    -200.0 -175.0 ... -80.0 -50.0

    FORTRAN_format :

    units :

    m

    type :

    UNEVEN

    epic_code :

    3

    positive :

    down

    standard_name :

    depth

    long_name :

    $h$

    array([-200., -175., -170., -130.,  -80.,  -50.], dtype=float32)

  • us
    (longitude)
    float32
    -0.06934669 ... 0.19134645

    name :

    U

    long_name :

    u

    generic_name :

    u

    FORTRAN_format :

    f10.2

    units :

    m/s

    epic_code :

    1205

    array([-0.06934669,  0.09375758,  0.0036    , -0.13630131, -0.04317025,
            0.19134645], dtype=float32)

  • ueuc
    (longitude)
    float32
    0.33551154 0.4491259 ... 0.9650307

    name :

    U

    long_name :

    u

    generic_name :

    u

    FORTRAN_format :

    f10.2

    units :

    m/s

    epic_code :

    1205

    array([0.33551154, 0.4491259 , 0.49320757, 0.6898067 , 1.0492624 ,
           0.9650307 ], dtype=float32)

  • du
    (longitude)
    float32
    -0.40485823 ... -0.77368426

    name :

    U

    long_name :

    $\Delta$u

    generic_name :

    u

    FORTRAN_format :

    f10.2

    units :

    m/s

    epic_code :

    1205

    array([-0.40485823, -0.35536832, -0.48960757, -0.826108  , -1.0924326 ,
           -0.77368426], dtype=float32)

  • eucmax
    (longitude)
    float32
    -225.0 -200.0 ... -105.0 -75.0

    FORTRAN_format :

    units :

    m

    type :

    UNEVEN

    epic_code :

    3

    positive :

    down

    standard_name :

    depth

    long_name :

    Depth of EUC max

    array([-225., -200., -195., -155., -105.,  -75.], dtype=float32)

  • bs
    (longitude)
    float64
    -9.782 -9.783 ... -9.8 -9.801

    array([-9.78216727, -9.78328942, -9.78441106, -9.79413096, -9.79995362,
           -9.80081719])

  • beuc
    (longitude)
    float64
    -9.81 -9.81 -9.81 -9.81 -9.81 -9.81

    array([-9.81, -9.81, -9.81, -9.81, -9.81, -9.81])

  • db
    (longitude)
    float64
    0.02783 0.02671 ... 0.009183

    long_name :

    $\Delta$b

    array([0.02783273, 0.02671058, 0.02558894, 0.01586904, 0.01004638,
           0.00918281])

  • Rib
    (longitude)
    float64
    33.96 37.01 18.15 ... 0.6735 0.767

    array([33.96095672, 37.01382707, 18.14700667,  3.02287714,  0.67345763,
            0.76704032])

• Attributes: (1)

  name :

  TAO

TAO hourly → average

tao_clim = tao.mean("time")
tao_clim["eucmax"] = pump.calc.get_euc_max(tao_clim.u)
tao_clim = pump.calc.estimate_euc_depth_terms(tao_clim)
tao_clim.attrs["name"] = "TAO"
tao_clim.load()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-8-f60f543c3aa6> in <module>
----> 1 tao_clim = tao.mean("time")
      2 tao_clim["eucmax"] = pump.calc.get_euc_max(tao_clim.u)
      3 tao_clim = pump.calc.estimate_euc_depth_terms(tao_clim)
      4 tao_clim.attrs["name"] = "TAO"
      5 tao_clim.load()

NameError: name 'tao' is not defined

seasonal = tao_zeuc.reset_coords().groupby("time.season")
seasonal_mean = (
    seasonal.mean()
    .where(seasonal.count() > 90 * 24)
    .reindex(season=["DJF", "MAM", "JJA", "SON"])
)
seasonal_mean.load()

xarray.Dataset

• Dimensions:
  • longitude: 5
  • season: 4
  • zeuc: 59
• Coordinates: (3)
  • season
    (season)
    object
    'DJF' 'MAM' 'JJA' 'SON'

    array(['DJF', 'MAM', 'JJA', 'SON'], dtype=object)

  • longitude
    (longitude)
    float32
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.], dtype=float32)

  • zeuc
    (zeuc)
    float64
    -47.5 -42.5 -37.5 ... 237.5 242.5

    array([-47.5, -42.5, -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,
             2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,
            52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,
           102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,
           152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,
           202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5])

• Data variables: (9)
  • Rig
    (season, longitude, zeuc)
    float64
    8.232 11.67 9.22 ... nan nan nan

    array([[[ 8.23188423, 11.66693534,  9.2199752 , ...,         nan,
                     nan,         nan],
            [ 5.39199295,  6.17876334,  9.64884943, ...,         nan,
                     nan,         nan],
            [ 7.42184197,  9.00185298,  8.46085992, ...,         nan,
                     nan,         nan],
            [ 3.16769126,  3.61574038,  4.74233788, ...,         nan,
                     nan,         nan],
            [ 4.13980394,  5.66898608,  3.42710602, ...,         nan,
                     nan,         nan]],
    
           [[16.09135028, 19.32650838, 12.35707371, ...,         nan,
                     nan,         nan],
            [ 9.27719826, 10.23221501, 13.70622544, ...,         nan,
                     nan,         nan],
            [ 5.86048199,  8.26245507, 19.05090364, ...,         nan,
                     nan,         nan],
            [ 3.2191422 ,  4.37541233,  7.60627121, ...,         nan,
                     nan,         nan],
            [ 3.01226798,  2.17111758,  3.77887843, ...,         nan,
                     nan,         nan]],
    
           [[ 8.15280434, 11.38067043, 13.40515286, ...,         nan,
                     nan,         nan],
            [16.7003277 , 12.681034  , 17.97187299, ...,         nan,
                     nan,         nan],
            [10.52417135, 14.17288604, 12.97217026, ...,         nan,
                     nan,         nan],
            [ 4.16430575,  3.7978495 ,  3.53947752, ...,         nan,
                     nan,         nan],
            [ 2.61932804,  2.72024998,  1.87112047, ...,         nan,
                     nan,         nan]],
    
           [[        nan,  7.55666118,  8.08289828, ...,         nan,
                     nan,         nan],
            [ 7.5945738 ,  8.45530595,  9.12148807, ...,         nan,
                     nan,         nan],
            [ 8.79392536, 10.20514784, 18.54320167, ...,         nan,
                     nan,         nan],
            [ 3.53384471,  3.13537092,  3.07702339, ...,         nan,
                     nan,         nan],
            [ 3.34040942,  2.04296605,  4.76050517, ...,         nan,
                     nan,         nan]]])

  • T
    (season, longitude, zeuc)
    float64
    13.6 13.82 14.05 ... nan nan nan

    array([[[13.60272677, 13.81765477, 14.04973421, ...,         nan,
                     nan,         nan],
            [14.08051562, 14.29276024, 14.52687136, ...,         nan,
                     nan,         nan],
            [15.2542054 , 15.61801237, 16.00534442, ...,         nan,
                     nan,         nan],
            [14.98105297, 15.26082331, 15.57572854, ...,         nan,
                     nan,         nan],
            [14.43238422, 14.63861674, 14.86944824, ...,         nan,
                     nan,         nan]],
    
           [[13.97481721, 14.19923595, 14.43999648, ...,         nan,
                     nan,         nan],
            [14.29960899, 14.58159836, 14.88873895, ...,         nan,
                     nan,         nan],
            [16.10947125, 16.53365954, 16.98064405, ...,         nan,
                     nan,         nan],
            [16.28010285, 16.68069373, 17.10365414, ...,         nan,
                     nan,         nan],
            [15.11588387, 15.38952578, 15.69146173, ...,         nan,
                     nan,         nan]],
    
           [[14.19124333, 14.43947054, 14.70428479, ...,         nan,
                     nan,         nan],
            [14.28163924, 14.56672618, 14.87818235, ...,         nan,
                     nan,         nan],
            [17.9633928 , 18.48842358, 19.02859569, ...,         nan,
                     nan,         nan],
            [16.15290704, 16.55158269, 16.97285118, ...,         nan,
                     nan,         nan],
            [14.51619274, 14.71310707, 14.92908578, ...,         nan,
                     nan,         nan]],
    
           [[12.6385191 , 12.78170333, 12.94037027, ...,         nan,
                     nan,         nan],
            [13.88075179, 14.10412266, 14.35778167, ...,         nan,
                     nan,         nan],
            [16.51622736, 16.95323681, 17.40966696, ...,         nan,
                     nan,         nan],
            [15.49465876, 15.81505731, 16.17081449, ...,         nan,
                     nan,         nan],
            [14.36372388, 14.55920276, 14.77800993, ...,         nan,
                     nan,         nan]]])

  • deepest
    (season, longitude)
    float64
    -488.7 -470.1 ... -382.0 -440.7

    description :

    Deepest depth with a valid observation

    array([[-488.66630489, -470.0749788 , -485.12395406, -394.45479812,
            -453.66072205],
           [-470.50151107, -482.30936133, -482.5794072 , -392.56670616,
            -436.78830981],
           [-475.17839168, -463.62734002, -482.56703611, -384.96835797,
            -434.83037643],
           [-485.60853278, -445.6635083 , -484.87393853, -381.97008146,
            -440.74396417]])

  • dens
    (season, longitude, zeuc)
    float64
    1.025e+03 1.025e+03 ... nan nan

    array([[[1025.1896303 , 1025.16768973, 1025.14167951, ...,
                       nan,           nan,           nan],
            [1025.08449148, 1025.06139572, 1025.03308078, ...,
                       nan,           nan,           nan],
            [1024.93674242, 1024.87750392, 1024.81162896, ...,
                       nan,           nan,           nan],
            [1025.24669046, 1025.2056233 , 1025.15589332, ...,
                       nan,           nan,           nan],
            [1025.53061446, 1025.5070724 , 1025.477278  , ...,
                       nan,           nan,           nan]],
    
           [[1025.1277713 , 1025.10257229, 1025.07330318, ...,
                       nan,           nan,           nan],
            [1025.05683349, 1025.01862818, 1024.97423401, ...,
                       nan,           nan,           nan],
            [1024.81368263, 1024.73644831, 1024.65230089, ...,
                       nan,           nan,           nan],
            [1025.0442282 , 1024.97098636, 1024.89089167, ...,
                       nan,           nan,           nan],
            [1025.45672745, 1025.41677536, 1025.36936394, ...,
                       nan,           nan,           nan]],
    
           [[1025.12342782, 1025.09184337, 1025.05607605, ...,
                       nan,           nan,           nan],
            [1025.08862056, 1025.04907768, 1025.00315553, ...,
                       nan,           nan,           nan],
            [1024.47042669, 1024.36165382, 1024.24694065, ...,
                       nan,           nan,           nan],
            [1025.04793835, 1024.97721026, 1024.89959033, ...,
                       nan,           nan,           nan],
            [1025.51613849, 1025.49529673, 1025.46968208, ...,
                       nan,           nan,           nan]],
    
           [[1025.2765685 , 1025.27099748, 1025.26213737, ...,
                       nan,           nan,           nan],
            [1025.12477972, 1025.09915943, 1025.06675559, ...,
                       nan,           nan,           nan],
            [1024.72226629, 1024.64149088, 1024.55441906, ...,
                       nan,           nan,           nan],
            [1025.12174882, 1025.07006663, 1025.00958682, ...,
                       nan,           nan,           nan],
            [1025.48958241, 1025.46909959, 1025.44283963, ...,
                       nan,           nan,           nan]]])

  • eucmax
    (season, longitude)
    float64
    3.264 4.126 9.016 ... 2.976 6.241

    long_name :

    Depth of EUC max

    units :

    m

    array([[3.26371055, 4.12596818, 9.01598885, 3.31033996, 2.98506559],
           [3.40345398, 7.1442146 , 4.77359422, 5.07933099, 7.94734066],
           [4.82000884, 2.81524375, 5.65678933, 4.5077701 , 3.09996866],
           [4.02397433, 2.98996519, 3.1331995 , 2.975802  , 6.24062509]])

  • latitude
    (season)
    float32
    nan nan nan nan

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array([nan, nan, nan, nan], dtype=float32)

  • mld
    (season, longitude)
    float64
    -34.32 -34.82 ... -16.38 -11.45

    array([[-34.32471144, -34.82282621, -29.48257394, -16.9969471 ,
             -8.9511756 ],
           [-25.61249285, -23.50614987, -17.57099212, -10.65304839,
             -6.99384531],
           [-28.7099877 , -24.32255226, -22.45587298, -15.00682753,
             -9.89072025],
           [-23.79958377, -24.32021827, -21.79484468, -16.38221639,
            -11.45114781]])

  • u
    (season, longitude, zeuc)
    float64
    0.3643 0.389 0.4046 ... nan nan nan

    array([[[0.36428954, 0.38895224, 0.4045924 , ...,        nan,
                    nan,        nan],
            [0.2196237 , 0.24984855, 0.28538917, ...,        nan,
                    nan,        nan],
            [0.48131255, 0.51611832, 0.55078659, ...,        nan,
                    nan,        nan],
            [0.67239874, 0.71879344, 0.76604187, ...,        nan,
                    nan,        nan],
            [0.5293649 , 0.57199532, 0.61529949, ...,        nan,
                    nan,        nan]],
    
           [[0.34119612, 0.3584247 , 0.37808899, ...,        nan,
                    nan,        nan],
            [0.39010054, 0.4151643 , 0.44226967, ...,        nan,
                    nan,        nan],
            [0.61339652, 0.64884028, 0.68614529, ...,        nan,
                    nan,        nan],
            [0.94794887, 1.00387485, 1.05941852, ...,        nan,
                    nan,        nan],
            [0.79196506, 0.84939121, 0.90940154, ...,        nan,
                    nan,        nan]],
    
           [[0.36428762, 0.38510103, 0.40287176, ...,        nan,
                    nan,        nan],
            [0.42344114, 0.44910889, 0.47371738, ...,        nan,
                    nan,        nan],
            [0.60324866, 0.64291183, 0.68139045, ...,        nan,
                    nan,        nan],
            [0.7708133 , 0.82905148, 0.88847513, ...,        nan,
                    nan,        nan],
            [0.59904537, 0.64696057, 0.69706435, ...,        nan,
                    nan,        nan]],
    
           [[0.21821556, 0.23751159, 0.25679138, ...,        nan,
                    nan,        nan],
            [0.27289732, 0.30022284, 0.3258028 , ...,        nan,
                    nan,        nan],
            [0.5091764 , 0.54306584, 0.57568048, ...,        nan,
                    nan,        nan],
            [0.6384854 , 0.68760622, 0.73845552, ...,        nan,
                    nan,        nan],
            [0.54480893, 0.58868616, 0.63496933, ...,        nan,
                    nan,        nan]]])

  • v
    (season, longitude, zeuc)
    float64
    -0.009484 -0.007725 ... nan nan

    array([[[-9.48372233e-03, -7.72504046e-03, -7.33564424e-03, ...,
                         nan,             nan,             nan],
            [-2.91934285e-03,  1.29491931e-03, -1.05588421e-04, ...,
                         nan,             nan,             nan],
            [ 3.80231364e-03,  4.25051604e-03,  5.62939756e-03, ...,
                         nan,             nan,             nan],
            [-7.27330658e-03, -9.28644226e-03, -1.10784658e-02, ...,
                         nan,             nan,             nan],
            [-9.57764338e-03, -1.12131392e-02, -1.30186725e-02, ...,
                         nan,             nan,             nan]],
    
           [[-3.27018915e-03, -5.24507046e-03, -6.80652298e-03, ...,
                         nan,             nan,             nan],
            [-2.62702513e-02, -2.91648296e-02, -3.20181966e-02, ...,
                         nan,             nan,             nan],
            [ 8.69698067e-03,  1.33157619e-02,  1.80003830e-02, ...,
                         nan,             nan,             nan],
            [-1.29115721e-02, -1.42213956e-02, -1.49717792e-02, ...,
                         nan,             nan,             nan],
            [-2.30604146e-03, -1.97987091e-03, -1.11602583e-03, ...,
                         nan,             nan,             nan]],
    
           [[-1.73465022e-02, -1.90855457e-02, -2.11728806e-02, ...,
                         nan,             nan,             nan],
            [-3.14836084e-02, -3.53821270e-02, -3.75011088e-02, ...,
                         nan,             nan,             nan],
            [-3.26231133e-02, -2.52453222e-02, -1.67267726e-02, ...,
                         nan,             nan,             nan],
            [-7.30159134e-04, -1.65244324e-03, -3.93613844e-03, ...,
                         nan,             nan,             nan],
            [ 1.02352609e-02,  1.04821181e-02,  1.02196271e-02, ...,
                         nan,             nan,             nan]],
    
           [[ 4.70000001e-02,  4.73242333e-02,  4.39763077e-02, ...,
                         nan,             nan,             nan],
            [ 3.20087828e-02,  2.77005340e-02,  2.16889121e-02, ...,
                         nan,             nan,             nan],
            [-4.60854216e-04, -5.65226211e-04, -9.14556185e-04, ...,
                         nan,             nan,             nan],
            [-8.19118300e-03, -8.81910597e-03, -8.73322434e-03, ...,
                         nan,             nan,             nan],
            [-1.04267577e-03, -6.81707984e-05,  1.63465325e-03, ...,
                         nan,             nan,             nan]]])

• Attributes: (0)

seasonal_mean["Rig_mean"] = seasonal_mean.Rig
seasonal_mean["depth"] = seasonal_mean.eucmax + seasonal_mean.zeuc
seasonal_mean = seasonal_mean.set_coords(["eucmax", "depth"])
seasonal_mean["Rig"] = pump.calc.calc_tao_ri(
    seasonal_mean[["u", "v"]], seasonal_mean["T"], dim="zeuc"
)

/gpfs/u/home/dcherian/python/xarray/xarray/core/missing.py:329: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  ).transpose(*self.dims)
/gpfs/u/home/dcherian/python/xarray/xarray/core/missing.py:329: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  ).transpose(*self.dims)
/gpfs/u/home/dcherian/python/xarray/xarray/core/missing.py:329: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  ).transpose(*self.dims)

seasonal_mean["Rig_median"] = (
    tao_zeuc.Rig.chunk({"time": -1})
    .groupby("time.season")
    .quantile(dim="time", q=0.5)
    .compute()
)

/gpfs/u/home/dcherian/python/xarray/xarray/core/common.py:664: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, the dimension order of these coordinates will be restored as well unless you specify restore_coord_dims=False.
  self, group, squeeze=squeeze, restore_coord_dims=restore_coord_dims

seasonal_mean["N2"] = -9.81 / 1025 * seasonal_mean.dens.differentiate("zeuc")
seasonal_mean["S2"] = (
    seasonal_mean.u.differentiate("zeuc") ** 2
    + seasonal_mean.u.differentiate("zeuc") ** 2
)

(seasonal_mean.N2 / seasonal_mean.S2).plot(
    col="longitude", hue="season", y="zeuc", xlim=[0.1, 100], xscale="log"
)

/gpfs/u/home/dcherian/python/xarray/xarray/plot/plot.py:107: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  xplt = darray.transpose(ydim, huedim)

<xarray.plot.facetgrid.FacetGrid at 0x2b1984516470>

(seasonal_mean.T).plot(col="longitude", hue="season", y="zeuc")
(seasonal_mean.u).plot(col="longitude", hue="season", y="zeuc")

<xarray.plot.facetgrid.FacetGrid at 0x2b197e930828>

\(N²\) isn’t changing that much moving eastward consistent with the bulk \(Δb\). Shear \(S²\) is changing significantly, again consistent

(seasonal_mean.N2).plot(col="longitude", hue="season", y="zeuc")
(seasonal_mean.S2).plot(col="longitude", hue="season", y="zeuc")
(seasonal_mean.Rig_median).plot(
    col="longitude", hue="season", y="zeuc", xlim=[0.1, 100], xscale="log"
)

/gpfs/u/home/dcherian/python/xarray/xarray/plot/plot.py:107: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  xplt = darray.transpose(ydim, huedim)

<xarray.plot.facetgrid.FacetGrid at 0x2b1a3a5aa320>

counts = tao_zeuc.count("time").load()

counts.u.where(counts.u > 90 * 24).plot(x="longitude")

<matplotlib.collections.QuadMesh at 0x2b7387f5ee10>

annual_mean = (
    tao_zeuc.groupby("time.year").mean().mean("year").load().where(counts > 90 * 24)
)

fg = (4 * -9.81 / 1025 * annual_mean.dens.differentiate("zeuc")).plot(
    col="longitude", y="zeuc"
)
S2 = annual_mean.u.differentiate("zeuc") ** 2 + annual_mean.v.differentiate("zeuc") ** 2
for loc, ax in zip(fg.name_dicts.flat, fg.axes.flat):
    ax.plot(S2.sel(loc), S2.zeuc)
    # ax.set_xscale("log")

ax.legend(["4N²", "S²"])

<matplotlib.legend.Legend at 0x2b739ed77860>

counts = tao_zeuc.count("time")
tao_zeuc = tao_zeuc.where(counts > 30 * 24)

[<matplotlib.lines.Line2D at 0x2b19052c4748>,
 <matplotlib.lines.Line2D at 0x2b192aa97cf8>,
 <matplotlib.lines.Line2D at 0x2b192aa97ac8>,
 <matplotlib.lines.Line2D at 0x2b1922eabb00>,
 <matplotlib.lines.Line2D at 0x2b192b8d5278>]

zeuc_clim = pump.calc.calc_tao_ri(dim="zeuc")

xarray.Dataset

• Dimensions:
  • longitude: 5
  • zeuc: 59
• Coordinates: (3)
  • latitude
    ()
    float32
    ...

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array(0., dtype=float32)

  • longitude
    (longitude)
    float32
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.], dtype=float32)

  • zeuc
    (zeuc)
    float64
    -47.5 -42.5 -37.5 ... 237.5 242.5

    array([-47.5, -42.5, -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,
             2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,
            52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,
           102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,
           152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,
           202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5])

• Data variables: (5)
  • Rig
    (longitude, zeuc)
    float64
    dask.array<chunksize=(1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     2.36 kB 
     
     472 B 
        
     Shape 
     (5, 59) 
     
     (1, 59) 
        
     Count 
     356 Tasks 
     5 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • T
    (longitude, zeuc)
    float64
    dask.array<chunksize=(1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     2.36 kB 
     
     472 B 
        
     Shape 
     (5, 59) 
     
     (1, 59) 
        
     Count 
     356 Tasks 
     5 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • dens
    (longitude, zeuc)
    float64
    dask.array<chunksize=(1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     2.36 kB 
     
     472 B 
        
     Shape 
     (5, 59) 
     
     (1, 59) 
        
     Count 
     356 Tasks 
     5 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • u
    (longitude, zeuc)
    float64
    dask.array<chunksize=(1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     2.36 kB 
     
     472 B 
        
     Shape 
     (5, 59) 
     
     (1, 59) 
        
     Count 
     356 Tasks 
     5 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • v
    (longitude, zeuc)
    float64
    dask.array<chunksize=(1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     2.36 kB 
     
     472 B 
        
     Shape 
     (5, 59) 
     
     (1, 59) 
        
     Count 
     356 Tasks 
     5 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

• Attributes: (0)

Figure 5

tao_zeuc = xr.open_zarr("tao-zeuc.zarr")
tao_zeuc

xarray.Dataset

• Dimensions:
  • longitude: 5
  • time: 292267
  • zeuc: 59
• Coordinates: (7)
  • deepest
    (time, longitude)
    float64
    dask.array<chunksize=(10000, 1), meta=np.ndarray>

    description :

    Deepest depth with a valid observation

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     80.00 kB 
        
     Shape 
     (292267, 5) 
     
     (10000, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • eucmax
    (time, longitude)
    float64
    dask.array<chunksize=(10000, 1), meta=np.ndarray>

    long_name :

    Depth of EUC max

    units :

    m

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     80.00 kB 
        
     Shape 
     (292267, 5) 
     
     (10000, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • latitude
    ()
    float32
    ...

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array(0., dtype=float32)

  • longitude
    (longitude)
    float64
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.])

  • mld
    (time, longitude)
    float64
    dask.array<chunksize=(10000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     80.00 kB 
        
     Shape 
     (292267, 5) 
     
     (10000, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • time
    (time)
    datetime64[ns]
    1985-10-01T06:00:00 ... 2019-02-03

    FORTRAN_format :

    point_spacing :

    even

    type :

    EVEN

    array(['1985-10-01T06:00:00.000000000', '1985-10-01T07:00:00.000000000',
           '1985-10-01T08:00:00.000000000', ..., '2019-02-02T22:00:00.000000000',
           '2019-02-02T23:00:00.000000000', '2019-02-03T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • zeuc
    (zeuc)
    float64
    -47.5 -42.5 -37.5 ... 237.5 242.5

    array([-47.5, -42.5, -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,
             2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,
            52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,
           102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,
           152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,
           202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5])

• Data variables: (5)
  • Rig
    (time, longitude, zeuc)
    float64
    dask.array<chunksize=(10000, 1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     689.75 MB 
     
     4.72 MB 
        
     Shape 
     (292267, 5, 59) 
     
     (10000, 1, 59) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • T
    (time, longitude, zeuc)
    float64
    dask.array<chunksize=(10000, 1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     689.75 MB 
     
     4.72 MB 
        
     Shape 
     (292267, 5, 59) 
     
     (10000, 1, 59) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • dens
    (time, longitude, zeuc)
    float64
    dask.array<chunksize=(10000, 1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     689.75 MB 
     
     4.72 MB 
        
     Shape 
     (292267, 5, 59) 
     
     (10000, 1, 59) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • u
    (time, longitude, zeuc)
    float64
    dask.array<chunksize=(10000, 1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     689.75 MB 
     
     4.72 MB 
        
     Shape 
     (292267, 5, 59) 
     
     (10000, 1, 59) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • v
    (time, longitude, zeuc)
    float64
    dask.array<chunksize=(10000, 1, 59), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     689.75 MB 
     
     4.72 MB 
        
     Shape 
     (292267, 5, 59) 
     
     (10000, 1, 59) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

• Attributes: (0)

ri = tao_zeuc.Rig.compute().median("time")

ri.plot(hue="longitude", y="zeuc", xscale="log", xlim=(0.1, 100))

[<matplotlib.lines.Line2D at 0x2b1926f59ef0>,
 <matplotlib.lines.Line2D at 0x2b192b8feb70>,
 <matplotlib.lines.Line2D at 0x2b1944ed5a20>,
 <matplotlib.lines.Line2D at 0x2b1944ed5d68>,
 <matplotlib.lines.Line2D at 0x2b1944ed5c18>]

tao_zeuc = tao_zeuc.where(tao_zeuc.count("time") > 90 * 24)
Ri_q_annual = (
    tao_zeuc.Rig.sel(zeuc=slice(0, None))
    .chunk({"time": -1, "zeuc": -1})
    .quantile(q=[0.25, 0.5, 0.75], dim=["time", "zeuc"])
)
Ri_q_annual.load()

xarray.DataArray

'Rig'

• quantile: 3
• longitude: 5

• 0.8152 0.587 0.2669 0.2228 0.2682 ... 5.459 4.627 2.615 1.184 1.593

  array([[0.81522617, 0.58695033, 0.26694281, 0.22278418, 0.26821389],
         [1.97553892, 1.58871084, 0.70549343, 0.41711624, 0.54069482],
         [5.45856488, 4.62699244, 2.61483129, 1.18364173, 1.5925003 ]])

• Coordinates: (2)
  • longitude
    (longitude)
    float64
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.])

  • quantile
    (quantile)
    float64
    0.25 0.5 0.75

    array([0.25, 0.5 , 0.75])

• Attributes: (0)

f, ax = pump.plot.plot_bulk_Ri_diagnosis(tao_clim, ls="none", marker="s")
pump.plot.plot_bulk_Ri_diagnosis(tao_daily, ls="none", marker="o", f=f, ax=ax)
pump.plot.plot_bulk_Ri_diagnosis(johnson, ls="none", marker="^", f=f, ax=ax)
# ri.sel(zeuc=slice(0,None)).mean("zeuc").plot(ax=ax["Rib"], color='k')

dccpy.plots.fill_between(
    Ri_q_annual.sel(quantile=[0.25, 0.75]),
    axis="y",
    y="quantile",
    x="longitude",
    alpha=0.1,
    ax=ax["Rib"],
    color="k",
)
Ri_q_annual.sel(quantile=0.5).plot(color="k", ax=ax["Rib"], _labels=False)
# pump.plot.plot_bulk_Ri_diagnosis(johnson, ls="none", marker= '^', f=f, ax=ax)

[<matplotlib.lines.Line2D at 0x2b194600e278>]

tao = pump.obs.read_tao_zarr("ancillary")
tao

xarray.Dataset

• Dimensions:
  • depth: 101
  • longitude: 5
  • time: 292267
• Coordinates: (8)
  • deepest
    (time, longitude)
    float64
    dask.array<chunksize=(10000, 1), meta=np.ndarray>

    description :

    Deepest depth with a valid observation

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     80.00 kB 
        
     Shape 
     (292267, 5) 
     
     (10000, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • depth
    (depth)
    float64
    -500.0 -495.0 -490.0 ... -5.0 0.0

    array([-500., -495., -490., -485., -480., -475., -470., -465., -460., -455.,
           -450., -445., -440., -435., -430., -425., -420., -415., -410., -405.,
           -400., -395., -390., -385., -380., -375., -370., -365., -360., -355.,
           -350., -345., -340., -335., -330., -325., -320., -315., -310., -305.,
           -300., -295., -290., -285., -280., -275., -270., -265., -260., -255.,
           -250., -245., -240., -235., -230., -225., -220., -215., -210., -205.,
           -200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
           -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
           -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.,
            -50.,  -45.,  -40.,  -35.,  -30.,  -25.,  -20.,  -15.,  -10.,   -5.,
              0.])

  • eucmax
    (time, longitude)
    float64
    dask.array<chunksize=(10000, 1), meta=np.ndarray>

    long_name :

    Depth of EUC max

    units :

    m

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     80.00 kB 
        
     Shape 
     (292267, 5) 
     
     (10000, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • latitude
    ()
    float32
    ...

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array(0., dtype=float32)

  • longitude
    (longitude)
    float64
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.])

  • mld
    (time, longitude)
    float64
    dask.array<chunksize=(10000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     11.69 MB 
     
     80.00 kB 
        
     Shape 
     (292267, 5) 
     
     (10000, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • time
    (time)
    datetime64[ns]
    1985-10-01T06:00:00 ... 2019-02-03

    FORTRAN_format :

    point_spacing :

    even

    type :

    EVEN

    array(['1985-10-01T06:00:00.000000000', '1985-10-01T07:00:00.000000000',
           '1985-10-01T08:00:00.000000000', ..., '2019-02-02T22:00:00.000000000',
           '2019-02-02T23:00:00.000000000', '2019-02-03T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • zeuc
    (depth, time, longitude)
    float64
    dask.array<chunksize=(101, 10000, 1), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.18 GB 
     
     8.08 MB 
        
     Shape 
     (101, 292267, 5) 
     
     (101, 10000, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

• Data variables: (5)
  • Rig
    (time, longitude, depth)
    float64
    dask.array<chunksize=(10000, 1, 101), meta=np.ndarray>

    long_name :

    Ri

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.18 GB 
     
     8.08 MB 
        
     Shape 
     (292267, 5, 101) 
     
     (10000, 1, 101) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • T
    (time, longitude, depth)
    float64
    dask.array<chunksize=(10000, 1, 101), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.18 GB 
     
     8.08 MB 
        
     Shape 
     (292267, 5, 101) 
     
     (10000, 1, 101) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • dens
    (time, longitude, depth)
    float64
    dask.array<chunksize=(10000, 1, 101), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.18 GB 
     
     8.08 MB 
        
     Shape 
     (292267, 5, 101) 
     
     (10000, 1, 101) 
        
     Count 
     12603 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • u
    (time, depth, longitude)
    float32
    dask.array<chunksize=(10000, 101, 1), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    1205

    generic_name :

    u

    long_name :

    u

    name :

    u

    units :

    m/s

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     590.38 MB 
     
     4.04 MB 
        
     Shape 
     (292267, 101, 5) 
     
     (10000, 101, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • v
    (time, depth, longitude)
    float32
    dask.array<chunksize=(10000, 101, 1), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    1206

    generic_name :

    v

    long_name :

    v

    name :

    v

    units :

    m/s

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     590.38 MB 
     
     4.04 MB 
        
     Shape 
     (292267, 101, 5) 
     
     (10000, 101, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

• Attributes: (0)

tao_bulked = pump.calc.estimate_euc_depth_terms(tao)
tao

/gpfs/u/home/dcherian/python/xarray/xarray/core/missing.py:393: FutureWarning: This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.
  ).transpose(*arr.dims)

xarray.Dataset

• Dimensions:
  • depth: 101
  • longitude: 5
  • time: 292267
• Coordinates: (8)
  • deepest
    (time, longitude)
    float64
    nan nan nan ... nan -500.0 -500.0

    description :

    Deepest depth with a valid observation

    array([[  nan,   nan,   nan,   nan, -300.],
           [  nan,   nan,   nan,   nan, -300.],
           [  nan,   nan,   nan,   nan, -300.],
           ...,
           [  nan, -500.,   nan, -500., -500.],
           [  nan, -500.,   nan, -500., -500.],
           [  nan, -500.,   nan, -500., -500.]])

  • depth
    (depth)
    float64
    -500.0 -495.0 -490.0 ... -5.0 0.0

    array([-500., -495., -490., -485., -480., -475., -470., -465., -460., -455.,
           -450., -445., -440., -435., -430., -425., -420., -415., -410., -405.,
           -400., -395., -390., -385., -380., -375., -370., -365., -360., -355.,
           -350., -345., -340., -335., -330., -325., -320., -315., -310., -305.,
           -300., -295., -290., -285., -280., -275., -270., -265., -260., -255.,
           -250., -245., -240., -235., -230., -225., -220., -215., -210., -205.,
           -200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
           -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
           -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.,
            -50.,  -45.,  -40.,  -35.,  -30.,  -25.,  -20.,  -15.,  -10.,   -5.,
              0.])

  • eucmax
    (time, longitude)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Depth of EUC max

    units :

    m

    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]])

  • latitude
    ()
    float32
    0.0

    FORTRAN_format :

    epic_code :

    500

    type :

    EVEN

    units :

    degree_north

    array(0., dtype=float32)

  • longitude
    (longitude)
    float64
    -204.0 -195.0 -170.0 -140.0 -110.0

    FORTRAN_format :

    epic_code :

    502

    type :

    EVEN

    units :

    degree_east

    array([-204., -195., -170., -140., -110.])

  • mld
    (time, longitude)
    float64
    nan nan nan nan ... nan -5.0 -5.0

    array([[ nan,  nan,  nan,  nan,  -5.],
           [ nan,  nan,  nan,  nan, -10.],
           [ nan,  nan,  nan,  nan, -10.],
           ...,
           [ nan, -75.,  nan,  -5.,  -5.],
           [ nan, -70.,  nan,  -5.,  -5.],
           [ nan, -20.,  nan,  -5.,  -5.]])

  • time
    (time)
    datetime64[ns]
    1985-10-01T06:00:00 ... 2019-02-03

    FORTRAN_format :

    point_spacing :

    even

    type :

    EVEN

    array(['1985-10-01T06:00:00.000000000', '1985-10-01T07:00:00.000000000',
           '1985-10-01T08:00:00.000000000', ..., '2019-02-02T22:00:00.000000000',
           '2019-02-02T23:00:00.000000000', '2019-02-03T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • zeuc
    (depth, time, longitude)
    float64
    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]]])

• Data variables: (15)
  • Rig
    (time, longitude, depth)
    float64
    dask.array<chunksize=(10000, 1, 101), meta=np.ndarray>

    long_name :

    Ri

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.18 GB 
     
     8.08 MB 
        
     Shape 
     (292267, 5, 101) 
     
     (10000, 1, 101) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • T
    (time, longitude, depth)
    float64
    dask.array<chunksize=(10000, 1, 101), meta=np.ndarray>

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     1.18 GB 
     
     8.08 MB 
        
     Shape 
     (292267, 5, 101) 
     
     (10000, 1, 101) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float64 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • dens
    (time, longitude, depth)
    float64
    nan nan nan ... 1.023e+03 1.023e+03

    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, ...,
             1024.33543851, 1024.33967453, 1024.35413187]],
    
           [[          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [          nan,           nan,           nan, ...,
             1024.3464451 , 1024.35640034, 1024.3734441 ]],
    
           [[          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [          nan,           nan,           nan, ...,
             1024.3244193 , 1024.33437141, 1024.35136962]],
    
           ...,
    
           [[          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [1024.94435893, 1024.95764404, 1024.97049226, ...,
             1021.64220484, 1021.6638762 , 1021.68548079],
            [          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [1024.94513327, 1024.95531438, 1024.96528946, ...,
             1022.99817359, 1023.00538323, 1023.01015802],
            [1024.98458   , 1024.99185912, 1024.99910105, ...,
             1023.33459766, 1023.28636123, 1023.19881184]],
    
           [[          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [1024.94503828, 1024.95895975, 1024.97242195, ...,
             1021.63853494, 1021.65993884, 1021.68108452],
            [          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [1024.95064659, 1024.96189193, 1024.97287058, ...,
             1022.99077391, 1022.99297478, 1022.99497685],
            [1024.98441046, 1024.99211456, 1024.9997781 , ...,
             1023.32363328, 1023.25076443, 1023.19209542]],
    
           [[          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [1024.94518178, 1024.95882619, 1024.9720516 , ...,
             1021.63156833, 1021.64964326, 1021.66928559],
            [          nan,           nan,           nan, ...,
                       nan,           nan,           nan],
            [1024.93238447, 1024.94365735, 1024.95468325, ...,
             1022.97905259, 1022.97606375, 1022.97611193],
            [1024.98412292, 1024.99102948, 1024.99790102, ...,
             1023.30729996, 1023.21705942, 1023.19130163]]])

  • u
    (time, depth, longitude)
    float32
    nan nan nan nan ... nan nan nan nan

    FORTRAN_format :

    epic_code :

    1205

    generic_name :

    u

    long_name :

    u

    name :

    u

    units :

    m/s

    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]]], dtype=float32)

  • v
    (time, depth, longitude)
    float32
    dask.array<chunksize=(10000, 101, 1), meta=np.ndarray>

    FORTRAN_format :

    epic_code :

    1206

    generic_name :

    v

    long_name :

    v

    name :

    v

    units :

    m/s

    
    
    
    
      
    
        
     
     Array 
     Chunk 
      
      
    
        
     Bytes 
     590.38 MB 
     
     4.04 MB 
        
     Shape 
     (292267, 101, 5) 
     
     (10000, 101, 1) 
        
     Count 
     151 Tasks 
     150 Chunks 
        
     Type 
     float32 
     numpy.ndarray 
      
    
    
    
    
    
    
    

  • h
    (time, longitude)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    $h$

    units :

    m

    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]])

  • us
    (time, longitude)
    float32
    nan nan nan nan ... nan nan nan nan

    FORTRAN_format :

    epic_code :

    1205

    generic_name :

    u

    long_name :

    u

    name :

    u

    units :

    m/s

    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]], dtype=float32)

  • ueuc
    (time, longitude)
    float32
    nan nan nan nan ... nan nan nan nan

    FORTRAN_format :

    epic_code :

    1205

    generic_name :

    u

    long_name :

    u

    name :

    u

    units :

    m/s

    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]], dtype=float32)

  • du
    (time, longitude)
    float32
    nan nan nan nan ... nan nan nan nan

    FORTRAN_format :

    epic_code :

    1205

    generic_name :

    u

    long_name :

    $\Delta$u

    name :

    u

    units :

    m/s

    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]], dtype=float32)

  • dens_euc
    (time, longitude)
    float64
    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]])

  • b
    (time, longitude, depth)
    float64
    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]]])

  • bs
    (time, longitude)
    float64
    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]])

  • beuc
    (time, longitude)
    float64
    -9.81 -9.81 -9.81 ... -9.81 -9.81

    array([[-9.81, -9.81, -9.81, -9.81, -9.81],
           [-9.81, -9.81, -9.81, -9.81, -9.81],
           [-9.81, -9.81, -9.81, -9.81, -9.81],
           ...,
           [-9.81, -9.81, -9.81, -9.81, -9.81],
           [-9.81, -9.81, -9.81, -9.81, -9.81],
           [-9.81, -9.81, -9.81, -9.81, -9.81]])

  • db
    (time, longitude)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    $\Delta$b

    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]])

  • Rib
    (time, longitude)
    float64
    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]])

• Attributes: (0)

tao.us.plot(x="time")

<matplotlib.collections.QuadMesh at 0x2b736a5ef668>