ASIRI13 CTD χpod#
This is redoing my analysis.
We only have 100m of data.
Need to find out the “injection scale” of the spice. The ‘mean’ scale needs to be larger than that.
%load_ext watermark
%matplotlib inline
import glob
import os
import cf_xarray as cfxr
import cf_xarray.units
import cmocean as cmo
import dcpy
import distributed
import flox
import gsw
import holoviews as hv
import hvplot.xarray
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pint_xarray
import scipy as sp
import tqdm
import xfilter
import xgcm
from cf_xarray.units import units
from IPython.display import Image
from scipy.io import loadmat
import eddydiff as ed
import xarray as xr
units.default_format = "~P"
xr.set_options(
keep_attrs=True, # use_flox=True
)
# cfxr.set_options(warn_on_missing_variables=False)
plt.rcParams["figure.dpi"] = 140
plt.rcParams["savefig.dpi"] = 200
plt.style.use("ggplot")
profile_hvplot_kwargs = dict(
by="cast",
y="pressure",
ylim=(2000, 0),
logx=True,
muted_alpha=0,
line_width=0.5,
aspect=1 / 2,
frame_width=250,
grid=True,
)
hv.extension("bokeh")
%watermark -iv
xr.DataArray([1.0])
The watermark extension is already loaded. To reload it, use:
%reload_ext watermark
distributed: 2022.4.0
cmocean : 2.0
holoviews : 1.14.8
gsw : 3.4.0
numpy : 1.22.3
scipy : 1.8.0
xarray : 0.20.3.dev137+g3f3a197c8
matplotlib : 3.5.1
cf_xarray : 0.7.1.dev16+g7305fd8
hvplot : 0.7.3
dcpy : 0.2.1.dev11+gebbbf87.d20220428
pandas : 1.4.2
xfilter : 0.2.1.dev6+g5fdc006
flox : 0.4.1
tqdm : 4.64.0
pint_xarray: 0.2.1
xgcm : 0.6.1
eddydiff : 0.1
<xarray.DataArray (dim_0: 1)> array([1.]) Dimensions without coordinates: dim_0
bob = xr.open_dataset("../datasets/ctd-chipod/asiri13/asiri13-ctd-chipod.nc").pipe(
ed.sections.add_ancillary_variables
)
bob
<xarray.Dataset>
Dimensions: (pressure: 110, cast: 86)
Coordinates:
* pressure (pressure) int64 1 3 5 7 9 11 ... 211 213 215 217 219
* cast (cast) int64 1 2 3 4 5 6 7 8 ... 79 80 81 82 83 84 85 86
datenum (cast) float64 nan nan nan nan nan ... nan nan nan nan
P (pressure, cast) float64 0.8012 0.5537 ... 218.9 219.0
lat (cast) float64 7.057 7.397 7.711 ... 5.772 5.464 5.153
lon (cast) float64 85.39 85.52 85.64 ... 85.1 85.01 84.88
reference_pressure int64 0
Data variables: (12/24)
chi (pressure, cast) float64 nan nan ... 2.576e-11 6.295e-10
eps (pressure, cast) float64 nan nan ... 7.806e-12 8.364e-11
KT (pressure, cast) float64 nan nan ... 4.847e-08 4.444e-07
Tz_orig (pressure, cast) float64 nan nan nan ... 0.01629 0.02483
fspd (pressure, cast) float64 0.009063 0.05923 ... -1.363
T (pressure, cast) float64 nan nan nan ... 13.08 12.92
... ...
chi_masked (pressure, cast) float64 nan nan ... 2.576e-11 6.295e-10
Krho (pressure, cast) float64 nan nan nan nan ... nan nan nan
KrhoTz (pressure, cast) float64 nan nan nan nan ... nan nan nan
eps_chi (pressure, cast) float64 nan nan nan nan ... nan nan nan
Kt (pressure, cast) float64 nan nan ... 1.208e-06 1.562e-07
KtTz (pressure, cast) float64 nan nan ... 3.944e-09 7.013e-09bob.τ0.cf.plot(x="profile_id", robust=True)
bob.chi.cf.plot(x="profile_id", robust=True, norm=mpl.colors.LogNorm())
<matplotlib.collections.QuadMesh at 0x17b0fb220>
Process MATLAB dataset#
mat = loadmat("../datasets/ctd-chipod/asiri13/ChiSumm.mat")
mat
{'__header__': b'MATLAB 5.0 MAT-file, Platform: MACI64, Created on: Fri Mar 21 12:23:26 2014',
'__version__': '1.0',
'__globals__': [],
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16.15563791, 16.47003896, 16.78752216, 17.09837189, 17.46479681,
17.83022281, 17.59908039, 17.36816304, 17.13520797, 16.90342733,
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...,
[ 0.84681022, nan, nan, ..., -1.13264438,
-0.96067722, -1.0973467 ],
[ 0.75395349, nan, nan, ..., -0.94718385,
-0.85660051, -1.47009174],
[ 0.90898768, nan, nan, ..., -0.83602077,
-0.94979052, -1.52141864]]), array([[ nan, nan, nan, ..., nan,
nan, nan],
[ nan, nan, nan, ..., nan,
nan, nan],
[28.89731925, nan, 28.94435947, ..., nan,
nan, 28.75513024],
...,
[10.58979103, nan, nan, ..., 10.76557085,
11.27666869, 11.34498873],
[10.57397315, nan, nan, ..., 10.73996087,
11.24883417, 11.33249424],
[10.5591593 , nan, nan, ..., 10.72189124,
11.22184993, 11.31915558]]), array([[ nan, nan, nan, ..., nan,
nan, nan],
[ nan, nan, nan, ..., nan,
nan, nan],
[33.60258219, nan, 33.07107264, ..., nan,
nan, 34.30401581],
...,
[35.0338067 , nan, nan, ..., 35.06122016,
35.11155136, 35.10204881],
[35.03267524, nan, nan, ..., 35.06061575,
35.11024841, 35.1030033 ],
[35.03301073, nan, nan, ..., 35.06005303,
35.10882919, 35.1056982 ]]), 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]]), 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]]), array([[3.66885707e-01, 5.33524311e-01, 2.70153859e-01, ...,
4.81453798e-03, nan, nan],
[7.58798404e-01, 1.54543361e-02, nan, ...,
4.92053855e-02, nan, nan],
[ nan, nan, nan, ...,
5.23934434e-02, nan, nan],
...,
[2.62730168e-04, nan, nan, ...,
2.64953153e-04, 1.74256004e-04, 9.30581394e-05],
[2.50632341e-04, nan, nan, ...,
3.41625918e-04, 1.89592936e-04, 1.41026750e-04],
[3.48070201e-04, nan, nan, ...,
8.11860334e-04, 1.54049216e-04, 4.06407789e-04]]), 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]]), array([[ 7.05703834, 7.39735945, 7.71116077, 8.02686046, 8.33850993,
8.64745864, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 12.71454231, 0. ,
13.34056439, 13.65197105, 13.96529315, 14.27826294, 0. ,
14.90361433, 15.21747316, 15.53077184, 15.84222023, 16.15565165,
16.15563467, 0. , 0. , 17.09837078, 17.46482334,
17.83021743, 17.59900882, 17.36814925, 17.13521177, 16.90342172,
16.6719217 , 16.4416541 , 16.21094 , 15.97880551, 15.74700689,
15.51441952, 15.28397552, 15.04848751, 14.81951134, 14.58818587,
14.3567625 , 14.12507391, 0. , 0. , 13.43371583,
13.20111682, 11.06325349, 0. , 0. , 10.38226802,
10.14973993, 9.91935172, 9.68685243, 9.68686483, 9.42051731,
9.23864495, 8.82259127, 8.63438682, 8.4466614 , 8.26014102,
8.07184252, 7.88523229, 7.88521763, 7.69760024, 7.69753767,
7.51098171, 7.32334292, 7.13593156, 6.94762871, 6.76062202,
6.42918785, 6.10287649, 0. , 5.7722026 , 5.46438158,
5.15255906]]), array([[85.39164 , 85.51998693, 85.63606553, 85.75485332, 85.87088057,
85.9846601 , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 87.5075082 , 0. ,
87.74253188, 87.86055155, 87.97878748, 88.09748617, 0. ,
88.33520352, 88.45489526, 88.57447157, 88.69260771, 88.81248282,
88.81246479, 0. , 0. , 89.17369947, 89.31609171,
89.47253315, 89.19951722, 88.92781776, 88.65462109, 88.38140056,
88.10947548, 87.83838974, 87.56736127, 87.2921591 , 87.01933011,
86.74914252, 86.47685066, 86.19487961, 85.93106322, 85.65757139,
85.38813623, 85.11544521, 0. , 0. , 84.29489615,
84.02577693, 86.37022647, 0. , 0. , 87.38849089,
87.73233207, 88.07168522, 88.41504337, 88.41503714, 88.81597622,
88.85897671, 88.56337973, 88.26682718, 87.96921979, 87.67287809,
87.37625531, 87.07204615, 87.07202872, 86.78316 , 86.78311331,
86.48564106, 86.19102371, 85.89465154, 85.59748279, 85.30051832,
85.2677321 , 85.19720608, 0. , 85.103513 , 85.0116706 ,
84.87871316]])) ]],
dtype=[('chi1', 'O'), ('eps1', 'O'), ('chi2', 'O'), ('eps2', 'O'), ('KT1', 'O'), ('KT2', 'O'), ('datenum', 'O'), ('P', 'O'), ('N2', 'O'), ('dTdz', 'O'), ('fspd', 'O'), ('T', 'O'), ('S', 'O'), ('theta', 'O'), ('sigma', 'O'), ('TP1var', 'O'), ('TP2var', 'O'), ('lat', 'O'), ('lon', 'O')])}
def _expand(ds):
return [
xr.concat([ds[0], xr.full_like(ds[0].isel(cast=slice(5)), np.nan)], dim="cast"),
xr.concat([ds[1], xr.full_like(ds[1].isel(cast=slice(5)), np.nan)], dim="cast"),
ds[2],
]
ds = xr.Dataset()
for name in mat["big"].dtype.names:
if "2" in name:
continue
data = mat["big"][name]
if data[0, 0].squeeze().ndim == 2:
dims = ("pressure", "cast")
else:
dims = ("cast",)
ds[name] = xr.concat(
_expand(
[
xr.DataArray(data=data[0, itrans].squeeze(), dims=dims)
for itrans in range(3)
]
),
dim="sensor",
)
ds = ds.where(ds.lat > 0)
# ds.coords["transect"] = ("cast", [1] * 81 + [2] * 81 + [3] * 86)
ds.coords["datenum"] = ds.datenum.isel(sensor=0, pressure=0)
ds["pressure"] = ("pressure", np.arange(1, 401, 2), {"axis": "Z", "positive": "down"})
ds["cast"] = ("cast", np.arange(ds.sizes["cast"]) + 1, {"cf_role": "profile_id"})
ds["sigma"] = dcpy.eos.pden(ds.S, ds.T, ds.P) - 1000
ds
<xarray.Dataset>
Dimensions: (sensor: 3, pressure: 200, cast: 86)
Coordinates:
datenum (cast) float64 nan nan nan nan nan ... nan nan nan nan
* pressure (pressure) int64 1 3 5 7 9 11 ... 391 393 395 397 399
* cast (cast) int64 1 2 3 4 5 6 7 8 ... 79 80 81 82 83 84 85 86
reference_pressure int64 0
Dimensions without coordinates: sensor
Data variables: (12/13)
chi1 (sensor, pressure, cast) float64 nan nan ... 2.196e-10
eps1 (sensor, pressure, cast) float64 nan nan ... 1.443e-10
KT1 (sensor, pressure, cast) float64 nan nan ... 1.673e-06
P (sensor, pressure, cast) float64 nan nan ... 398.8 399.4
dTdz (sensor, pressure, cast) float64 nan nan ... 0.008077
fspd (sensor, pressure, cast) float64 nan nan ... -1.521
... ...
S (sensor, pressure, cast) float64 nan nan ... 35.11 35.11
theta (sensor, pressure, cast) float64 nan nan nan ... nan nan
sigma (sensor, pressure, cast) float64 nan nan ... 26.83 26.81
TP1var (sensor, pressure, cast) float64 nan nan ... 0.0004064
lat (sensor, cast) float64 nan nan nan ... 5.772 5.464 5.153
lon (sensor, cast) float64 nan nan nan ... 85.1 85.01 84.88Combine “sensors”#
Here is Emily’s MATLAB Code:
nanmean(
cat(
3,
cat(2, big(1).chi1, nan(200, 5)),
cat(2, big(2).chi1, nan(200, 5)),
big(3).chi1,
),
3,
)
I think big(1) & big(2) are sensor1; big(3) is sensor 2.
The following plots confirm it
plt.figure()
ds.lat.plot(hue="sensor", marker="x", aspect=4, size=2)
turns = [36, 56, 65]
dcpy.plots.linex(turns)
plt.figure()
ds.lon.plot(hue="sensor", marker="x", aspect=4, size=2)
dcpy.plots.linex(turns)
print(np.abs(ds[["lat", "lon"]].diff("sensor").max()))
<xarray.Dataset>
Dimensions: ()
Data variables:
lat float64 0.0
lon float64 0.0
<Figure size 840x560 with 0 Axes>
<Figure size 840x560 with 0 Axes>
Final dataset#
# combine sensors
chi = (
ds.mean("sensor")
.cf.sel(Z=slice(220))
.set_coords(["lat", "lon", "datenum", "P"])
.rename_vars({"chi1": "chi", "eps1": "eps", "KT1": "KT"})
)
chi.S.attrs = {"standard_name": "sea_water_salinity"}
chi.T.attrs = {"standard_name": "sea_water_temperature", "units": "degC"}
chi.pressure.attrs.update({"standard_name": "sea_water_pressure", "units": "dbar"})
chi = chi.cf.guess_coord_axis()
chi.sigma.cf.plot(x="profile_id")
<matplotlib.collections.QuadMesh at 0x177466730>
