
from argopy import DataFetcher as ArgoDataFetcher
import matplotlib.pyplot as plt
import cmocean as cm
from matplotlib.ticker import MaxNLocator
from matplotlib.lines import Line2D
import matplotlib.gridspec as gridspec
from descartes import PolygonPatch
from shapely.geometry import Point
import alphashape
import cartopy.crs as ccrs
plt.rcParams['axes.grid'] = True
import numpy as np
import gsw
import xarray as xr
import pandas as pd

/Users/gmaze/miniconda3/envs/rbr/lib/python3.8/site-packages/pyproj/__init__.py:89: UserWarning: pyproj unable to set database path.
  _pyproj_global_context_initialize()


wmos = [6903075,6903076]


ArgoSet = ArgoDataFetcher().float(wmos)
ds = ArgoSet.load().data


ArgoSet.plot('trajectory')

(<Figure size 900x540 with 1 Axes>,
 <GeoAxesSubplot:xlabel='longitude', ylabel='latitude'>)


# GET DATA FROM REPO DATA COPY (cache=False because new profiles are added to the repo)
ds = ArgoDataFetcher(cache=False, src='gdac', mode='expert').float(wmos).to_xarray()
# ds = ArgoDataFetcher(cache=False,src='localftp',local_ftp='/export/home/kbalem/Bureau/earise-rbr/data/floats/ftp',mode='expert').float(wmos).to_xarray()
# CALCULATE TEOS10 VARIABLE
ds.argo.teos10(['SA','SIG0','PTEMP'])
# TURN TO PROFILES
ds = ds.argo.point2profile()
ds0=ds.where(ds['PLATFORM_NUMBER']==wmos[0],drop=True) 
ds1=ds.where(ds['PLATFORM_NUMBER']==wmos[1],drop=True)

ds1

<xarray.Dataset>
Dimensions:                   (N_PROF: 64, N_LEVELS: 1027)
Coordinates:
  * N_PROF                    (N_PROF) int64 0 65 66 67 68 ... 124 125 126 127
  * N_LEVELS                  (N_LEVELS) int64 0 1 2 3 4 ... 1023 1024 1025 1026
    TIME                      (N_PROF) datetime64[ns] 2020-12-11T10:25:00 ......
    LATITUDE                  (N_PROF) float64 29.17 29.15 29.1 ... 28.75 28.05
    LONGITUDE                 (N_PROF) float64 -19.0 -19.02 ... -20.5 -20.3
Data variables: (12/40)
    CONFIG_MISSION_NUMBER     (N_PROF) float64 1.0 1.0 2.0 2.0 ... 3.0 3.0 3.0
    CYCLE_NUMBER              (N_PROF) float64 1.0 1.0 2.0 ... 61.0 62.0 63.0
    DATA_CENTRE               (N_PROF) object 'IF' 'IF' 'IF' ... 'IF' 'IF' 'IF'
    DATA_MODE                 (N_PROF) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A'
    DATA_STATE_INDICATOR      (N_PROF) object '2B  ' '2B  ' ... '2B  ' '2B  '
    DC_REFERENCE              (N_PROF) object '                              ...
    ...                        ...
    TEMP_ADJUSTED_QC          (N_PROF, N_LEVELS) float64 1.0 1.0 ... 1e+05 1e+05
    TEMP_QC                   (N_PROF, N_LEVELS) float64 1.0 1.0 ... 1e+05 1e+05
    TIME_LOCATION             (N_PROF) datetime64[ns] 2020-12-11T09:26:41 ......
    TIME_QC                   (N_PROF) float64 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0
    VERTICAL_SAMPLING_SCHEME  (N_PROF) object 'Primary sampling: averaged [10...
    WMO_INST_TYPE             (N_PROF) float64 878.0 878.0 878.0 ... 878.0 878.0
Attributes:
    DATA_ID:              ARGO
    DOI:                  http://doi.org/10.17882/42182
    Fetched_from:         https://data-argo.ifremer.fr
    Fetched_by:           gmaze
    Fetched_date:         2022/09/12
    Fetched_constraints:  phy;WMO6903075;WMO6903076
    Fetched_uri:          https://data-argo.ifremer.fr/dac/coriolis/6903076/6...
    history:              Transformed with point2profile

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        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105,
       106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,
       120, 121, 122, 123, 124, 125, 126, 127])

array([   0,    1,    2, ..., 1024, 1025, 1026])

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       '2022-05-06T05:35:20.000000000', '2022-05-16T05:30:20.000000000',
       '2022-05-26T05:34:20.000000000', '2022-06-05T05:32:20.000000256',
       '2022-06-15T05:34:20.000000000', '2022-06-25T05:46:20.000000000',
       '2022-07-05T05:44:20.000000000', '2022-07-15T10:28:20.000000000',
       '2022-07-25T15:36:20.000000000', '2022-08-04T20:38:20.000000000',
       '2022-08-15T01:32:20.000000000', '2022-08-25T06:36:20.000000000'],
      dtype='datetime64[ns]')

array([29.16698167, 29.14661667, 29.10236333, 29.09201167, 29.05589833,
       29.16621833, 29.403165  , 29.61847333, 29.87339   , 30.188525  ,
       30.25565833, 30.11950667, 29.839565  , 29.52730333, 29.397985  ,
       29.63578667, 30.05389167, 30.41144667, 30.56528833, 30.55954167,
       30.45690667, 30.21757667, 29.9417    , 29.65176667, 29.33656667,
       29.21946833, 29.186195  , 28.95237   , 28.59757333, 28.24692333,
       28.09303667, 28.097715  , 28.21472667, 28.40395   , 28.66226167,
       28.91261   , 29.00986833, 29.03082667, 29.10994667, 29.30665833,
       29.50685333, 29.95994167, 30.651455  , 30.59780333, 30.49375   ,
       30.37467667, 29.95113   , 29.41812833, 29.193095  , 29.12503833,
       28.852835  , 28.47131833, 28.159415  , 28.15557333, 28.45303333,
       28.92913   , 29.379835  , 29.63524667, 29.38138   , 29.01704333,
       28.895255  , 29.049465  , 28.74969   , 28.04573333])

array([-19.00256167, -19.01972333, -19.11748333, -19.19157167,
       -19.21113333, -19.24770667, -19.31360333, -19.471775  ,
       -19.623935  , -19.645155  , -19.76130833, -19.79596   ,
       -19.675635  , -19.46817667, -18.98621167, -18.52723167,
       -18.43598333, -18.54520833, -18.42694833, -18.29108833,
       -18.27578667, -18.26151   , -18.36090167, -18.60153833,
       -19.03620333, -19.64517833, -20.18890167, -20.73764   ,
       -21.18446833, -21.456935  , -21.59888333, -21.87491667,
       -22.18369167, -22.62811833, -23.178485  , -23.85328833,
       -24.53214833, -25.15235333, -25.78662667, -26.33153833,
       -26.84808   , -27.03521667, -26.46826   , -25.661555  ,
       -25.02310333, -24.50030667, -24.252195  , -24.07778333,
       -23.79680333, -23.398265  , -23.27451   , -23.16448   ,
       -22.81616667, -22.40678333, -22.31909167, -22.540385  ,
       -22.72738   , -22.50295167, -22.385315  , -22.21238667,
       -21.70805   , -20.93978833, -20.50431   , -20.29527667])

array([1., 1., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
       2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
       2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
       2., 2., 2., 2., 2., 2., 2., 2., 3., 3., 3., 3., 3.])

array([ 1.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,
       13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
       26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38.,
       39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51.,
       52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63.])

array(['IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF',
       'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF',
       'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF',
       'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF',
       'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF',
       'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF'], dtype=object)

array(['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A'],
      dtype=object)


def full_profiles(ds0=ds0,ds1=ds1,markersize=1):
    fig,ax = plt.subplots(len(wmos),2,figsize=(16,len(wmos)*8),sharey=True)
    ax[0,0].invert_yaxis();
    ax[0,0].plot(ds0['TEMP'],-gsw.z_from_p(ds0['PRES'],ds0['LATITUDE'].mean()),'.k',markersize=markersize)
    ax[0,1].plot(ds0['PSAL'],-gsw.z_from_p(ds0['PRES'],ds0['LATITUDE'].mean()),'.k',markersize=markersize)    
    ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                            label='RBR Floats',markerfacecolor='k', markersize=10),
                            ],loc='upper right', bbox_to_anchor=(1.3, 1))
    ax[0,0].set_ylabel('Depth',fontsize='large',fontweight='bold')
    a=ax[0,1].text(1.05,0.4,'float '+str(wmos[0]),rotation="vertical",transform = ax[0,1].transAxes, fontsize='large', fontweight='bold')
    ax[0,0].set_xlabel('Temperature',fontsize='large',fontweight='bold')
    ax[0,1].set_xlabel('Salinity',fontsize='large',fontweight='bold')
    ax[1,0].plot(ds1['TEMP'],-gsw.z_from_p(ds1['PRES'],ds1['LATITUDE'].mean()),'.k',markersize=markersize)
    ax[1,1].plot(ds1['PSAL'],-gsw.z_from_p(ds1['PRES'],ds1['LATITUDE'].mean()),'.k',markersize=markersize)        
    ax[1,0].set_ylabel('Depth',fontsize='large',fontweight='bold')
    a=ax[1,1].text(1.05,0.4,'float '+str(wmos[1]),rotation="vertical",transform = ax[1,1].transAxes, fontsize='large', fontweight='bold')
    ax[1,0].set_xlabel('Temperature',fontsize='large',fontweight='bold')
    ax[1,1].set_xlabel('Salinity',fontsize='large',fontweight='bold')       
    ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                            label='RBR Floats',markerfacecolor='k', markersize=10),
                            ],loc='upper right', bbox_to_anchor=(1.3, 1))
    return fig,ax
fig,ax = full_profiles(ds0=ds0,ds1=ds1)


# TS DIAGRAM
def full_ts(ds0=ds0,ds1=ds1,vmin=25.95,vmax=27.8,sigma=8):    
    mint=ds['PTEMP'].min()
    maxt=ds['PTEMP'].max()
    mins=ds['SA'].min()
    maxs=ds['SA'].max()
    tempL=np.linspace(mint-1,maxt+1,156)
    salL=np.linspace(mins-1,maxs+1,156)
    Tg, Sg = np.meshgrid(tempL,salL)
    sigma_theta = gsw.sigma0(Sg, Tg)

    fig,ax=plt.subplots(1,2,figsize=(25,10))

    cs = ax[0].contour(Sg, Tg, sigma_theta,sigma, colors='grey', zorder=1)
    cl=ax[0].clabel(cs,fontsize=10,inline=False,fmt='%.1f')
    sc=ax[0].scatter(ds0['SA'],ds0['PTEMP'],c=ds0['SIG0'],s=10,cmap=cm.cm.matter,vmin=vmin,vmax=vmax)

    ax[0].set_xlabel('Salinity ($‰$)')
    ax[0].set_ylabel('Temperature[$^\circ$C]')
    ax[0].set_title(str(wmos[0]),fontsize=14, fontweight='bold')
    ax[0].xaxis.set_major_locator(MaxNLocator(nbins=6))
    ax[0].yaxis.set_major_locator(MaxNLocator(nbins=8))
    ax[0].tick_params(direction='out')

    cs = ax[1].contour(Sg, Tg, sigma_theta, sigma, colors='grey', zorder=1)
    cl=ax[1].clabel(cs,fontsize=10,inline=False,fmt='%.1f')
    sc=ax[1].scatter(ds1['SA'],ds1['PTEMP'],c=ds1['SIG0'],s=10,cmap=cm.cm.matter,vmin=vmin,vmax=vmax)

    ax[1].set_xlabel('Salinity ($‰$)')
    ax[1].set_ylabel('Temperature[$^\circ$C]')
    ax[1].set_title(str(wmos[1]),fontsize=14, fontweight='bold')
    ax[1].xaxis.set_major_locator(MaxNLocator(nbins=6))
    ax[1].yaxis.set_major_locator(MaxNLocator(nbins=8))
    ax[1].tick_params(direction='out')

    cb=plt.colorbar(sc,ax=ax.ravel().tolist())
    cb.ax.tick_params(direction='out')
    cb.set_label('Density[kg m$^{-3}$]')

    ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10)],loc='lower right')
    ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10)],loc='lower right')
    return fig,ax
fig,ax = full_ts()

/var/folders/ww/psmkfjds7xsc4kjsz66ghldr000nsn/T/ipykernel_51050/648577324.py:36: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
  cb=plt.colorbar(sc,ax=ax.ravel().tolist())


# WOA OPENDAP URL FOR 2005-2017, GLOBAL 1/4°
woa_temp='https://www.ncei.noaa.gov/thredds-ocean/dodsC/ncei/woa/temperature/A5B7/0.25/woa18_A5B7_t13_04.nc'
woa_psal='https://www.ncei.noaa.gov/thredds-ocean/dodsC/ncei/woa/salinity/A5B7/0.25/woa18_A5B7_s13_04.nc'

# LOAD DATASETS, No need to decode times for a clim
woat = xr.open_dataset(woa_temp,decode_times=False)
woas = xr.open_dataset(woa_psal,decode_times=False)


# INTERPOLATE WOA T/S/SIG TO FLOAT POSITION
woat_float0 = woat['t_mn'].interp(lat=ds0['LATITUDE'],lon=ds0['LONGITUDE'],method='linear').squeeze().to_dataset()
woas_float0 = woas['s_mn'].interp(lat=ds0['LATITUDE'],lon=ds0['LONGITUDE'],method='linear').squeeze().to_dataset()
woa_float0 = xr.merge([woat_float0,woas_float0])

woat_float1 = woat['t_mn'].interp(lat=ds1['LATITUDE'],lon=ds1['LONGITUDE'],method='linear').squeeze().to_dataset()
woas_float1 = woas['s_mn'].interp(lat=ds1['LATITUDE'],lon=ds1['LONGITUDE'],method='linear').squeeze().to_dataset()
woa_float1 = xr.merge([woat_float1,woas_float1])

# Reduce depth range
woa_float0 = woa_float0.sel(depth=slice(0,2000))
woa_float1 = woa_float1.sel(depth=slice(0,2000))


fig,ax=full_profiles()
ax[0,0].plot(woa_float0['t_mn'],woa_float0['depth'],'.r',markersize=4)         
ax[0,1].plot(woa_float0['s_mn'],woa_float0['depth'],'.r',markersize=4)       
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='WOA ',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
ax[1,0].plot(woa_float1['t_mn'],woa_float1['depth'],'.r',linewidth=1)         
ax[1,1].plot(woa_float1['s_mn'],woa_float1['depth'],'.r')       
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='WOA ',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))

<matplotlib.legend.Legend at 0x16f44baf0>


# ZOOM
fig,ax=full_profiles(markersize=4)
ax[0,0].set_ylim(2000,1500)
ax[0,0].plot(woa_float0['t_mn'],woa_float0['depth'],'.r',markersize=8)         
ax[0,1].plot(woa_float0['s_mn'],woa_float0['depth'],'.r',markersize=8)       
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='WOA ',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
ax[1,0].plot(woa_float1['t_mn'],woa_float1['depth'],'.r',linewidth=1)         
ax[1,1].plot(woa_float1['s_mn'],woa_float1['depth'],'.r')       
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='WOA ',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))

ax[0,0].set_xlim(4,6.5); ax[1,0].set_xlim(4,6.5);
ax[0,1].set_xlim(35.,35.5); ax[1,1].set_xlim(35.,35.5);


# GSW 
pr0 = np.empty([len(woa_float0['depth']),len(woa_float0['N_PROF'])])
pr1 = np.empty([len(woa_float1['depth']),len(woa_float1['N_PROF'])])

for i in range(len(woa_float0['N_PROF'])):
    pr0[:,i]=gsw.p_from_z(-woa_float0['depth'],woa_float0['LATITUDE'].isel(N_PROF=i))
woa_float0['PRES']=xr.DataArray(pr0,dims=['depth','N_PROF'])   
woa_float0['SA']=xr.DataArray(gsw.SA_from_SP(woa_float0['s_mn'],woa_float0['PRES'],woa_float0['LONGITUDE'],woa_float0['LATITUDE']),dims=['depth','N_PROF'])
woa_float0['PTEMP']=xr.DataArray(gsw.pt0_from_t(woa_float0['SA'],woa_float0['t_mn'],woa_float0['PRES']),dims=['depth','N_PROF'])
woa_float0['SIG0']=xr.DataArray(gsw.sigma0(woa_float0['SA'], woa_float0['PTEMP']),dims=['depth','N_PROF'])

for i in range(len(woa_float1['N_PROF'])):
    pr0[:,i]=gsw.p_from_z(-woa_float1['depth'],woa_float1['LATITUDE'].isel(N_PROF=i))
woa_float1['PRES']=xr.DataArray(pr0,dims=['depth','N_PROF'])   
woa_float1['SA']=xr.DataArray(gsw.SA_from_SP(woa_float1['s_mn'],woa_float1['PRES'],woa_float1['LONGITUDE'],woa_float1['LATITUDE']),dims=['depth','N_PROF'])
woa_float1['PTEMP']=xr.DataArray(gsw.pt0_from_t(woa_float1['SA'],woa_float1['t_mn'],woa_float1['PRES']),dims=['depth','N_PROF'])
woa_float1['SIG0']=xr.DataArray(gsw.sigma0(woa_float1['SA'], woa_float1['PTEMP']),dims=['depth','N_PROF'])
woa_float1

<xarray.Dataset>
Dimensions:    (depth: 67, N_PROF: 64)
Coordinates:
  * depth      (depth) float32 0.0 5.0 10.0 15.0 ... 1.9e+03 1.95e+03 2e+03
  * N_PROF     (N_PROF) int64 0 65 66 67 68 69 70 ... 122 123 124 125 126 127
    time       float32 73.5
    lat        (N_PROF) float64 29.17 29.15 29.1 29.09 ... 29.05 28.75 28.05
    lon        (N_PROF) float64 -19.0 -19.02 -19.12 ... -20.94 -20.5 -20.3
    TIME       (N_PROF) datetime64[ns] 2020-12-11T10:25:00 ... 2022-08-25T06:...
    LATITUDE   (N_PROF) float64 29.17 29.15 29.1 29.09 ... 29.05 28.75 28.05
    LONGITUDE  (N_PROF) float64 -19.0 -19.02 -19.12 ... -20.94 -20.5 -20.3
Data variables:
    t_mn       (depth, N_PROF) float64 nan nan nan nan nan ... nan nan nan nan
    s_mn       (depth, N_PROF) float64 36.85 36.85 nan nan ... nan nan nan nan
    PRES       (depth, N_PROF) float64 0.0 0.0 0.0 ... 2.023e+03 2.023e+03
    SA         (depth, N_PROF) float64 37.02 37.02 nan nan ... nan nan nan nan
    PTEMP      (depth, N_PROF) float64 nan nan nan nan nan ... nan nan nan nan
    SIG0       (depth, N_PROF) float64 nan nan nan nan nan ... nan nan nan nan

array([   0.,    5.,   10.,   15.,   20.,   25.,   30.,   35.,   40.,   45.,
         50.,   55.,   60.,   65.,   70.,   75.,   80.,   85.,   90.,   95.,
        100.,  125.,  150.,  175.,  200.,  225.,  250.,  275.,  300.,  325.,
        350.,  375.,  400.,  425.,  450.,  475.,  500.,  550.,  600.,  650.,
        700.,  750.,  800.,  850.,  900.,  950., 1000., 1050., 1100., 1150.,
       1200., 1250., 1300., 1350., 1400., 1450., 1500., 1550., 1600., 1650.,
       1700., 1750., 1800., 1850., 1900., 1950., 2000.], dtype=float32)

array([  0,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,
        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105,
       106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,
       120, 121, 122, 123, 124, 125, 126, 127])

array(73.5, dtype=float32)

array([29.16698167, 29.14661667, 29.10236333, 29.09201167, 29.05589833,
       29.16621833, 29.403165  , 29.61847333, 29.87339   , 30.188525  ,
       30.25565833, 30.11950667, 29.839565  , 29.52730333, 29.397985  ,
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       28.09303667, 28.097715  , 28.21472667, 28.40395   , 28.66226167,
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       29.50685333, 29.95994167, 30.651455  , 30.59780333, 30.49375   ,
       30.37467667, 29.95113   , 29.41812833, 29.193095  , 29.12503833,
       28.852835  , 28.47131833, 28.159415  , 28.15557333, 28.45303333,
       28.92913   , 29.379835  , 29.63524667, 29.38138   , 29.01704333,
       28.895255  , 29.049465  , 28.74969   , 28.04573333])

array([-19.00256167, -19.01972333, -19.11748333, -19.19157167,
       -19.21113333, -19.24770667, -19.31360333, -19.471775  ,
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       -26.84808   , -27.03521667, -26.46826   , -25.661555  ,
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       -22.72738   , -22.50295167, -22.385315  , -22.21238667,
       -21.70805   , -20.93978833, -20.50431   , -20.29527667])

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       '2022-08-15T01:32:20.000000000', '2022-08-25T06:36:20.000000000'],
      dtype='datetime64[ns]')

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       29.16621833, 29.403165  , 29.61847333, 29.87339   , 30.188525  ,
       30.25565833, 30.11950667, 29.839565  , 29.52730333, 29.397985  ,
       29.63578667, 30.05389167, 30.41144667, 30.56528833, 30.55954167,
       30.45690667, 30.21757667, 29.9417    , 29.65176667, 29.33656667,
       29.21946833, 29.186195  , 28.95237   , 28.59757333, 28.24692333,
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       28.92913   , 29.379835  , 29.63524667, 29.38138   , 29.01704333,
       28.895255  , 29.049465  , 28.74969   , 28.04573333])

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       -19.21113333, -19.24770667, -19.31360333, -19.471775  ,
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       -22.72738   , -22.50295167, -22.385315  , -22.21238667,
       -21.70805   , -20.93978833, -20.50431   , -20.29527667])

array([[        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [19.90413882, 19.92035109,         nan, ...,         nan,
                nan, 20.00279741],
       ...,
       [ 4.61089416,  4.60302342,  4.59258237, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])


# TS DIAGRAM
fig,ax = full_ts()
sc_b=ax[0].scatter(woa_float0['SA'],woa_float0['PTEMP'],c=woa_float0['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k')
sc_b=ax[1].scatter(woa_float1['SA'],woa_float1['PTEMP'],c=woa_float1['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k')

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='WOA',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='WOA',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

/var/folders/ww/psmkfjds7xsc4kjsz66ghldr000nsn/T/ipykernel_51050/648577324.py:36: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
  cb=plt.colorbar(sc,ax=ax.ravel().tolist())

<matplotlib.legend.Legend at 0x178ef4d00>


# ZOOM
fig,ax = full_ts(vmin=27.8,vmax=27.9,sigma=40)

ax[0].set_ylim(4,7.5); ax[1].set_ylim(4,7.5)
ax[0].set_xlim(35.2,35.6); ax[1].set_xlim(35.2,35.6)
sc_b=ax[0].scatter(woa_float0['SA'],woa_float0['PTEMP'],c=woa_float0['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k')
sc_b=ax[1].scatter(woa_float1['SA'],woa_float1['PTEMP'],c=woa_float1['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k')

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='WOA',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='WOA',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

/var/folders/ww/psmkfjds7xsc4kjsz66ghldr000nsn/T/ipykernel_51050/648577324.py:36: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
  cb=plt.colorbar(sc,ax=ax.ravel().tolist())

<matplotlib.legend.Legend at 0x1705162e0>


points = list(zip(ds['LONGITUDE'].values,ds['LATITUDE'].values))
alpha_shape = alphashape.alphashape(points, 0.9)
#alpha_shape
fig, ax = plt.subplots()
ax.scatter(*zip(*points))
ax.add_patch(PolygonPatch(alpha_shape, alpha=0.3))
plt.show()

/Users/gmaze/miniconda3/envs/rbr/lib/python3.8/site-packages/descartes/patch.py:62: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.
  vertices = concatenate([


#GET NEARBY ARGO PROFILES
px = ArgoDataFetcher(cache=False,src='erddap',mode='standard').region([-28.0,-18.0,28.0,30.8,0.,2000.,'2020-01-01','2022-04-01']).load().data


# CALCULATE TEOS10 VARIABLE
px.argo.teos10(['SA','SIG0','PTEMP'])
# TURN TO PROFILES
px = px.argo.point2profile()
inalphashape = [alpha_shape.contains(Point(px['LONGITUDE'].values[i],px['LATITUDE'].values[i])) for i in range(len(px['N_PROF']))]
px = px.sel(N_PROF = px['N_PROF'][inalphashape])

px

<xarray.Dataset>
Dimensions:                (N_PROF: 175, N_LEVELS: 1018)
Coordinates:
  * N_PROF                 (N_PROF) int64 64 4 65 5 66 ... 291 175 292 244 176
  * N_LEVELS               (N_LEVELS) int64 0 1 2 3 4 ... 1014 1015 1016 1017
    LATITUDE               (N_PROF) float64 28.64 29.51 28.56 ... 29.28 28.35
    LONGITUDE              (N_PROF) float64 -20.47 -23.2 ... -24.53 -21.11
    TIME                   (N_PROF) datetime64[ns] 2020-01-08T05:11:00 ... 20...
Data variables: (12/16)
    CONFIG_MISSION_NUMBER  (N_PROF) int32 2 3 2 3 2 2 2 2 2 ... 2 4 2 2 4 2 2 4
    CYCLE_NUMBER           (N_PROF) int32 59 50 60 51 61 ... 47 183 48 48 184
    DATA_MODE              (N_PROF) <U1 'D' 'D' 'D' 'D' 'D' ... 'A' 'A' 'A' 'A'
    DIRECTION              (N_PROF) <U1 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    PLATFORM_NUMBER        (N_PROF) int32 6901258 3901989 ... 6903075 6902768
    POSITION_QC            (N_PROF) int32 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
    ...                     ...
    PTEMP                  (N_PROF, N_LEVELS) float64 20.23 20.23 ... nan nan
    SA                     (N_PROF, N_LEVELS) float64 37.07 37.07 ... nan nan
    SIG0                   (N_PROF, N_LEVELS) float64 26.15 26.15 ... nan nan
    TEMP                   (N_PROF, N_LEVELS) float32 20.23 20.24 ... nan nan
    TEMP_QC                (N_PROF) int32 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
    TIME_QC                (N_PROF) int32 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
Attributes:
    DATA_ID:              ARGO
    DOI:                  http://doi.org/10.17882/42182
    Fetched_from:         https://erddap.ifremer.fr/erddap
    Fetched_by:           gmaze
    Fetched_date:         2022/09/12
    Fetched_constraints:  [x=-28.00/-18.00; y=28.00/30.80; z=0.0/2000.0; t=20...
    Fetched_uri:          ['https://erddap.ifremer.fr/erddap/tabledap/ArgoFlo...
    history:              Variables filtered according to DATA_MODE; Variable...

array([ 64,   4,  65,   5,  66, 113,  67, 114,  68, 115,  69, 116,  70,  10,
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       174, 243, 291, 175, 292, 244, 176])

array([   0,    1,    2, ..., 1015, 1016, 1017])

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       29.186195  , 29.13533833, 28.68599833, 28.95237   , 28.59757333,
       28.26674667, 28.24692333, 28.24242667, 28.310665  , 28.54595   ,
       28.3904    , 28.21472667, 28.69619   , 28.51829167, 28.40395   ,
       28.85063333, 28.66226167, 28.69429   , 29.16683667, 28.91261   ,
       29.677225  , 29.48924667, 29.00986833, 30.28695167, 30.04518   ,
       30.49418833, 30.12594833, 30.15762   , 30.07124833, 29.68275167,
       30.14012   , 29.22325   , 30.25066   , 29.17473   , 30.32293833,
       29.250795  , 30.318655  , 29.30884667, 30.31286833, 29.45519   ,
       30.07324833, 29.387845  , 29.65611833, 29.04620333, 29.42893   ,
       29.41812833, 28.54999   , 29.193095  , 29.28195833, 28.34584167])

array([-20.473     , -23.19520333, -21.119     , -23.85655333,
       -21.373     , -22.641     , -21.383     , -23.069     ,
       -21.312     , -23.606     , -21.38      , -24.173     ,
       -21.615     , -26.29933   , -21.882     , -25.50323167,
       -22.055     , -24.99684333, -22.135     , -25.21722667,
       -18.917     , -21.879     , -19.45      , -21.732     ,
       -21.692     , -19.153     , -21.468     , -19.436     ,
       -21.401     , -21.42      , -19.673     , -21.649     ,
       -20.336     , -21.961     , -20.014     , -22.395     ,
       -19.997     , -20.16      , -24.185     , -24.187     ,
       -23.65      , -23.8       , -23.426     , -19.00882667,
       -19.00256167, -18.99707   , -19.01972333, -19.10077167,
       -19.10651167, -19.18482   , -19.188945  , -19.19157167,
       -19.20782   , -19.22271333, -24.492     , -19.24770667,
       -19.26173167, -19.22993833, -24.786     , -19.32334333,
       -19.31360333, -19.303685  , -24.734     , -19.46715667,
       -19.471775  , -19.47517   , -24.227     , -19.65580833,
       -19.63873833, -19.623935  , -23.957     , -19.75253833,
       -19.67018833, -19.645155  , -23.934     , -23.143     ,
       -19.74310833, -22.714     , -19.675635  , -19.64699167,
...
       -18.65334   , -21.889     , -25.08487167, -18.54520833,
       -18.48583667, -18.73503833, -21.888     , -24.94981   ,
       -18.53234333, -24.674845  , -18.327005  , -24.640915  ,
       -18.27578667, -21.397     , -24.968745  , -18.26151   ,
       -25.287265  , -18.47118833, -18.36090167, -25.354315  ,
       -18.88236333, -18.60153833, -25.29192667, -19.03620333,
       -19.40489   , -25.29271167, -19.64517833, -19.854245  ,
       -25.27628333, -20.18890167, -20.31366667, -20.77144   ,
       -20.73764   , -21.18446833, -21.04191833, -21.456935  ,
       -21.55450167, -22.20778667, -24.23683833, -22.79457833,
       -22.18369167, -24.383825  , -23.42555333, -22.62811833,
       -24.624035  , -23.178485  , -24.057535  , -25.03307167,
       -23.85328833, -25.41726167, -25.46753167, -24.53214833,
       -25.49630833, -25.45932667, -25.2021    , -25.419815  ,
       -24.82531833, -25.64472833, -24.21916833, -26.036895  ,
       -23.9381    , -26.175015  , -23.61015333, -25.96314833,
       -23.093815  , -25.423125  , -22.545385  , -24.94171   ,
       -22.10296667, -24.595065  , -21.81835167, -24.51697167,
       -21.67842333, -24.61492333, -24.07778333, -21.45290667,
       -23.79680333, -24.531215  , -21.10793167])

array(['2020-01-08T05:11:00.000000000', '2020-01-13T20:42:20.000000000',
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       '2020-02-07T05:30:00.000000000', '2020-02-14T20:01:00.000000000',
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       '2020-02-27T05:25:00.000000000', '2020-03-05T19:56:00.000000000',
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       '2020-04-15T05:18:00.000000000', '2020-04-17T05:40:00.000000000',
       '2020-04-25T05:37:00.000000000', '2020-04-27T05:17:00.000000000',
       '2020-05-07T05:32:00.000000000', '2020-05-15T05:22:00.000000000',
       '2020-05-17T05:34:00.000000000', '2020-05-25T05:29:00.000000000',
       '2020-05-27T05:28:00.000000000', '2020-06-06T05:32:00.000000000',
       '2020-06-14T05:17:00.000000000', '2020-06-16T05:20:00.000000000',
       '2020-06-24T05:20:00.000000000', '2020-06-26T05:32:00.000000000',
       '2020-07-04T05:11:00.000000000', '2020-07-06T05:02:00.000000000',
       '2020-07-14T05:22:00.000000000', '2020-07-24T05:00:00.000000000',
       '2020-10-04T05:35:00.000000000', '2020-10-09T05:42:00.000000000',
...
       '2021-10-18T05:37:20.000000000', '2021-10-22T05:23:30.000000000',
       '2021-10-28T05:33:20.000000000', '2021-10-28T05:35:20.000000000',
       '2021-11-01T05:34:30.000000000', '2021-11-07T05:26:20.000000000',
       '2021-11-07T05:37:20.000000000', '2021-11-11T05:37:30.000000000',
       '2021-11-17T05:33:20.000000000', '2021-11-21T05:35:30.000000000',
       '2021-11-27T05:33:20.000000000', '2021-11-27T05:33:20.000000000',
       '2021-12-01T05:43:30.000000000', '2021-12-07T05:33:20.000000000',
       '2021-12-11T05:43:30.000000000', '2021-12-17T05:38:20.000000000',
       '2021-12-21T05:38:30.000000000', '2021-12-27T05:24:20.000000000',
       '2021-12-31T05:50:30.000000000', '2022-01-06T05:20:20.000000000',
       '2022-01-10T05:46:30.000000000', '2022-01-16T05:33:20.000000000',
       '2022-01-20T05:50:30.000000000', '2022-01-26T05:32:20.000000000',
       '2022-01-30T05:40:30.000000000', '2022-02-05T05:33:20.000000000',
       '2022-02-09T06:03:30.000000000', '2022-02-15T05:27:20.000000000',
       '2022-02-19T05:51:30.000000000', '2022-02-25T05:29:20.000000000',
       '2022-03-01T05:31:30.000000000', '2022-03-07T05:24:20.000000000',
       '2022-03-11T05:52:30.000000000', '2022-03-17T05:27:20.000000000',
       '2022-03-17T05:30:20.000000000', '2022-03-21T05:46:30.000000000',
       '2022-03-27T05:23:20.000000000', '2022-03-27T05:35:20.000000000',
       '2022-03-31T05:59:30.000000000'], dtype='datetime64[ns]')

array([2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 3, 2, 3, 3, 2,
       3, 2, 2, 3, 2, 3, 2, 2, 3, 2, 3, 2, 3, 2, 3, 3, 2, 3, 2, 3, 2, 1,
       1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2,
       2, 2, 4, 2, 2, 2, 4, 2, 2, 2, 4, 2, 2, 2, 2, 4, 2, 4, 2, 4, 2, 2,
       4, 2, 4, 2, 2, 4, 2, 2, 4, 2, 2, 4, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 4, 2, 2, 4, 2, 2, 4, 2, 2, 4, 2, 4, 2, 2, 4, 2, 4, 2, 4, 2,
       4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2, 4, 2, 2, 4],
      dtype=int32)

array([ 59,  50,  60,  51,  61, 162,  62, 163,  63, 164,  64, 165,  65,
        56,  66,  57,  67,  58,  68,  59,  58,  69,  59,  70,  71,  61,
        72,  62,  73,  74,  64,  75,  65,  76,  66,  77,  67,  68,  86,
        79,  87,  80,  88,   1,   1,   1,   1,   2,   2,   3,   3,   3,
         4,   4,  96,   5,   5,   5,  97,   6,   6,   6,  98,   7,   7,
         7,  99,   8,   8,   8, 100,   9,   9,   9, 101, 102,  11, 103,
        12,  12,  12, 104,  13,  13,  13, 149,  14,  14,  14, 106, 150,
        15,  15, 107, 151,  16,  16, 108, 152,  17,  17,  17, 109, 153,
        18, 154,  19, 155,  20, 112, 156,  21, 157,  22,  22, 158,  23,
        23, 159,  24,  24, 160,  25,  25, 161,  26,  26,  27,  27,  28,
        28,  29,  30,  31, 167,  32,  32, 168,  33,  33, 169,  34,  34,
       170,  35, 171,  36,  36, 172,  37, 173,  38, 174,  39, 175,  40,
       176,  41, 177,  42, 178,  43, 179,  44, 180,  45, 181,  46, 182,
        47,  47, 183,  48,  48, 184], dtype=int32)

array(['D', 'D', 'D', 'D', 'D', 'A', 'D', 'A', 'D', 'A', 'D', 'A', 'D',
       'D', 'D', 'D', 'D', 'D', 'D', 'D', 'R', 'D', 'R', 'D', 'D', 'R',
       'D', 'R', 'D', 'D', 'R', 'D', 'R', 'D', 'R', 'D', 'R', 'R', 'D',
       'R', 'D', 'R', 'D', 'R', 'A', 'A', 'A', 'R', 'A', 'R', 'A', 'A',
       'A', 'R', 'D', 'A', 'R', 'A', 'D', 'R', 'A', 'A', 'D', 'R', 'A',
       'A', 'D', 'R', 'A', 'A', 'D', 'R', 'A', 'A', 'D', 'D', 'A', 'R',
       'A', 'A', 'R', 'R', 'A', 'R', 'A', 'A', 'A', 'R', 'A', 'R', 'A',
       'A', 'R', 'R', 'A', 'A', 'R', 'R', 'A', 'A', 'A', 'R', 'R', 'A',
       'A', 'A', 'A', 'A', 'A', 'R', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A'], dtype='<U1')

array(['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'D', 'D', 'D', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
       'A', 'A', 'A', 'A', 'A', 'A'], dtype='<U1')


plt.figure()
# FETCH PROFILES META IN OUR REGION
plt.plot(px['LONGITUDE'],px['LATITUDE'],'k.',markersize=10)
# RBR PROFILES
plt.plot(ds['LONGITUDE'],ds['LATITUDE'],'r*',markersize=10)

[<matplotlib.lines.Line2D at 0x1771d0070>]


ds0b=ds0 #.isel(N_PROF=[0,1,2,3,4,5,6,7,8])
ds1b=ds1 #.isel(N_PROF=[0,1,2,3,4,5,6,7,8])

fig,ax = full_profiles(ds0=ds0b,ds1=ds1b)

ax[0,0].plot(px['TEMP'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=0.5,zorder=0)
ax[0,1].plot(px['PSAL'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=0.5,zorder=0)
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Nearby profiles',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
 
ax[1,0].plot(px['TEMP'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=0.5,zorder=0)   
ax[1,1].plot(px['PSAL'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=0.5,zorder=0)
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Nearby profiles',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))

<matplotlib.legend.Legend at 0x17c1fd6a0>


fig,ax = full_profiles(ds0=ds0b,ds1=ds1b,markersize=4)
ax[0,0].set_ylim(2000,1500)

ax[0,0].plot(px['TEMP'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=4,zorder=0)
ax[0,1].plot(px['PSAL'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=4,zorder=0)
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Nearby profiles',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
 
ax[1,0].plot(px['TEMP'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=4,zorder=0)   
ax[1,1].plot(px['PSAL'],-gsw.z_from_p(px['PRES'],px['LATITUDE'].mean()),'.r',markersize=4,zorder=0)
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='RBR Floats',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Nearby profiles',markerfacecolor='r', markersize=10),
                        ],loc='upper right', bbox_to_anchor=(1.3, 1))
   
ax[0,0].set_xlim(4,6.5); ax[1,0].set_xlim(4,6.5);
ax[0,1].set_xlim(35.,35.5); ax[1,1].set_xlim(35.,35.5);


# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b)

sc_b=ax[0].scatter(px['SA'],px['PTEMP'],c=px['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',zorder=0)
sc_b=ax[1].scatter(px['SA'],px['PTEMP'],c=px['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',zorder=0)

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Nearby profiles',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Nearby profiles',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

/var/folders/ww/psmkfjds7xsc4kjsz66ghldr000nsn/T/ipykernel_51050/648577324.py:36: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
  cb=plt.colorbar(sc,ax=ax.ravel().tolist())

<matplotlib.legend.Legend at 0x1834e8dc0>


# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b,vmin=27.8,vmax=27.9,sigma=40)

ax[0].set_ylim(4,7.5); ax[1].set_ylim(4,7.5)
ax[0].set_xlim(35.2,35.6); ax[1].set_xlim(35.2,35.6)
sc_b=ax[0].scatter(px['SA'],px['PTEMP'],c=px['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',zorder=0)
sc_b=ax[1].scatter(px['SA'],px['PTEMP'],c=px['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',zorder=0)
ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Nearby profiles',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Nearby profiles',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

/var/folders/ww/psmkfjds7xsc4kjsz66ghldr000nsn/T/ipykernel_51050/648577324.py:36: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
  cb=plt.colorbar(sc,ax=ax.ravel().tolist())

<matplotlib.legend.Legend at 0x186ef6310>


st25=pd.read_csv('../data/ref/RAPROCAN2012/Station25.csv',header=4,sep=';',names=['PRES','PSAL','TEMP'])
mt25={'latitude':29.17,'longitude':-19.00,'time':np.datetime64('2020-12-10T17:41:05')}

# st25['SA'] = gsw.SA_from_SP(st25['PSAL'],st25['PRES'],mt25['longitude'],mt25['latitude'])
st25['SA'] = gsw.SA_from_SP(st25['PSAL'].values, st25['PRES'].values, np.full_like(st25['PSAL'], mt25['longitude']), np.full_like(st25['PSAL'], mt25['latitude']))
st25['PTEMP'] = gsw.pt0_from_t(st25['SA'],st25['TEMP'],st25['PRES'])
st25['SIG0'] = gsw.sigma0(st25['SA'], st25['PTEMP'])
st25.head()


# LET's KEEP ONLY THE 1st PROFILES
ds0b=ds0.isel(N_PROF=[0,1,2,3])
ds1b=ds1.isel(N_PROF=[0,1,2,3])

fig,ax = full_profiles(ds0=ds0b,ds1=ds1b)
ax[0,0].set_ylim([2000,0]); 
ax[0,0].plot(st25['TEMP'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=1)
ax[0,1].plot(st25['PSAL'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=1)    
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[0])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Station 25 - RAPROCAN2012 ',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
ax[1,0].plot(st25['TEMP'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=1)
ax[1,1].plot(st25['PSAL'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=1)
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[1])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Station 25 - RAPROCAN2012 ',markerfacecolor='r', markersize=10),
                        ],loc='lower right')

<matplotlib.legend.Legend at 0x182abafa0>


fig,ax = full_profiles(ds0=ds0b,ds1=ds1b,markersize=4)

ax[0,0].set_ylim([2000,1500]); 
ax[0,0].plot(st25['TEMP'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=4)
ax[0,1].plot(st25['PSAL'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=4)    
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[0])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Station 25 - RAPROCAN2012 ',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
ax[1,0].plot(st25['TEMP'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=4)
ax[1,1].plot(st25['PSAL'],-gsw.z_from_p(st25['PRES'],mt25['latitude']),'.r',markersize=4)
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[1])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='Station 25 - RAPROCAN2012 ',markerfacecolor='r', markersize=10),
                        ],loc='lower right')

ax[0,0].set_xlim(4,6.5); ax[1,0].set_xlim(4,6.5);
ax[0,1].set_xlim(35.,35.5); ax[1,1].set_xlim(35.,35.5);


# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b)
sc_b=ax[0].scatter(st25['SA'],st25['PTEMP'],c=st25['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',linewidths=0.5)
sc_b=ax[1].scatter(st25['SA'],st25['PTEMP'],c=st25['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',linewidths=0.5)
ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Station 25 - RAPROCAN2012',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Station 25 - RAPROCAN2012',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

/var/folders/ww/psmkfjds7xsc4kjsz66ghldr000nsn/T/ipykernel_51050/648577324.py:36: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
  cb=plt.colorbar(sc,ax=ax.ravel().tolist())

<matplotlib.legend.Legend at 0x18eea9a60>


# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b,vmin=27.8,vmax=27.9,sigma=40)

ax[0].set_ylim(4,7.5); ax[1].set_ylim(4,7.5)
ax[0].set_xlim(35.2,35.6); ax[1].set_xlim(35.2,35.6)
sc_b=ax[0].scatter(st25['SA'],st25['PTEMP'],c=st25['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',linewidths=0.5)
sc_b=ax[1].scatter(st25['SA'],st25['PTEMP'],c=st25['SIG0'],s=20,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',linewidths=0.5)
ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Station 25 - RAPROCAN2012',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='Station 25 - RAPROCAN2012',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

/var/folders/ww/psmkfjds7xsc4kjsz66ghldr000nsn/T/ipykernel_51050/648577324.py:36: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
  cb=plt.colorbar(sc,ax=ax.ravel().tolist())

<matplotlib.legend.Legend at 0x18e0beac0>


# GET DATA FROM REPO DATA COPY (cache=False because new profiles are added to the repo)
dsbe = ArgoDataFetcher(cache=False,src='gdac',mode='expert').float(6903010).to_xarray()
# dsbe = ArgoDataFetcher(cache=False,src='localftp',local_ftp='/export/home/kbalem/Bureau/earise-rbr/data/floats/ftp',mode='expert').float(6903010).to_xarray()
# CALCULATE TEOS10 VARIABLE
dsbe.argo.teos10(['SA','SIG0','PTEMP'])
# TURN TO PROFILES
dsbe = dsbe.argo.point2profile()
dsbe

<xarray.Dataset>
Dimensions:                   (N_PROF: 64, N_LEVELS: 1022)
Coordinates:
  * N_PROF                    (N_PROF) int64 0 1 2 3 4 5 6 ... 58 59 60 61 62 63
  * N_LEVELS                  (N_LEVELS) int64 0 1 2 3 4 ... 1018 1019 1020 1021
    TIME                      (N_PROF) datetime64[ns] 2020-12-10T18:26:00 ......
    LATITUDE                  (N_PROF) float64 29.17 29.15 29.13 ... 25.96 25.93
    LONGITUDE                 (N_PROF) float64 -19.01 -19.0 ... -17.52 -17.59
Data variables: (12/40)
    CONFIG_MISSION_NUMBER     (N_PROF) int32 1 1 2 2 2 2 2 2 ... 2 2 2 3 3 3 3 3
    CYCLE_NUMBER              (N_PROF) int32 1 1 2 3 4 5 6 ... 58 59 60 61 62 63
    DATA_CENTRE               (N_PROF) <U2 'IF' 'IF' 'IF' ... 'IF' 'IF' 'IF'
    DATA_MODE                 (N_PROF) <U1 'R' 'R' 'R' 'R' ... 'R' 'R' 'R' 'R'
    DATA_STATE_INDICATOR      (N_PROF) <U4 '2B  ' '2B  ' ... '2B  ' '2B  '
    DC_REFERENCE              (N_PROF) <U32 '                                ...
    ...                        ...
    TEMP_ADJUSTED_QC          (N_PROF) int32 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0
    TEMP_QC                   (N_PROF, N_LEVELS) int32 1 1 1 ... 99999 99999
    TIME_LOCATION             (N_PROF) datetime64[ns] 2020-12-10T17:27:19 ......
    TIME_QC                   (N_PROF) int32 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
    VERTICAL_SAMPLING_SCHEME  (N_PROF) <U256 'Primary sampling: averaged [10 ...
    WMO_INST_TYPE             (N_PROF) int32 844 844 844 844 ... 844 844 844 844
Attributes:
    DATA_ID:              ARGO
    DOI:                  http://doi.org/10.17882/42182
    Fetched_from:         https://data-argo.ifremer.fr
    Fetched_by:           gmaze
    Fetched_date:         2022/09/12
    Fetched_constraints:  phy;WMO6903010
    Fetched_uri:          https://data-argo.ifremer.fr/dac/coriolis/6903010/6...
    history:              Transformed with point2profile

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
       54, 55, 56, 57, 58, 59, 60, 61, 62, 63])

array([   0,    1,    2, ..., 1019, 1020, 1021])

array(['2020-12-10T18:26:00.000000000', '2020-12-12T05:26:30.000000000',
       '2020-12-22T05:27:30.000000000', '2021-01-01T05:33:29.999999744',
       '2021-01-11T05:37:30.000000000', '2021-01-21T05:34:30.000000000',
       '2021-01-31T05:26:30.000000000', '2021-02-10T05:37:30.000000000',
       '2021-02-20T05:30:30.000000000', '2021-03-02T05:38:30.000000000',
       '2021-03-12T05:33:29.999999744', '2021-03-22T05:25:29.999999744',
       '2021-04-01T05:35:30.000000000', '2021-04-11T05:38:30.000000000',
       '2021-04-21T05:42:30.000000000', '2021-05-01T05:46:30.000000000',
       '2021-05-11T05:45:30.000000000', '2021-05-21T05:47:30.000000000',
       '2021-05-31T05:37:30.000000000', '2021-06-10T05:27:30.000000000',
       '2021-06-20T05:35:30.000000000', '2021-06-30T05:30:30.000000000',
       '2021-07-10T05:23:30.000000000', '2021-07-20T05:42:30.000000000',
       '2021-07-30T05:39:30.000000000', '2021-08-09T05:32:30.000000000',
       '2021-08-19T05:31:30.000000000', '2021-08-29T05:37:30.000000000',
       '2021-09-08T05:25:29.999999744', '2021-09-18T05:43:30.000000000',
       '2021-09-28T05:35:30.000000000', '2021-10-08T05:47:30.000000000',
       '2021-10-18T05:35:30.000000000', '2021-10-28T05:28:30.000000000',
       '2021-11-07T05:36:30.000000000', '2021-11-17T05:45:30.000000000',
       '2021-11-27T05:30:30.000000000', '2021-12-07T05:26:30.000000000',
       '2021-12-17T05:42:30.000000000', '2021-12-27T05:45:30.000000000',
       '2022-01-06T05:31:30.000000000', '2022-01-16T05:35:30.000000000',
       '2022-01-26T05:33:29.999999744', '2022-02-05T05:33:29.999999744',
       '2022-02-15T05:44:30.000000000', '2022-02-25T05:37:30.000000000',
       '2022-03-07T05:37:30.000000000', '2022-03-17T05:35:30.000000000',
       '2022-03-27T05:27:30.000000000', '2022-04-06T05:31:30.000000000',
       '2022-04-16T05:36:30.000000000', '2022-04-26T05:35:30.000000000',
       '2022-05-06T05:41:30.000000256', '2022-05-16T05:38:30.000000000',
       '2022-05-26T05:34:30.000000000', '2022-06-05T05:30:30.000000000',
       '2022-06-15T05:26:30.000000000', '2022-06-25T05:35:30.000000000',
       '2022-07-05T05:18:30.000000000', '2022-07-15T10:31:30.000000000',
       '2022-07-25T15:46:30.000000000', '2022-08-04T20:36:30.000000000',
       '2022-08-15T01:43:30.000000000', '2022-08-25T06:40:30.000000000'],
      dtype='datetime64[ns]')

array([29.165725  , 29.150605  , 29.13182833, 29.12126333, 29.09465167,
       29.18438   , 29.42158667, 29.65736833, 29.91031   , 30.14338667,
       30.22611333, 30.13541333, 29.86356   , 29.670635  , 29.50829   ,
       29.58076333, 29.95490167, 30.46821667, 30.79612167, 30.96628167,
       30.95473   , 30.76391833, 30.44196833, 30.174425  , 30.02790833,
       29.932595  , 29.88792667, 29.89399833, 29.879855  , 29.87762833,
       29.88723167, 29.83161833, 29.88819833, 29.94088333, 29.77318333,
       29.64949333, 29.345785  , 28.90281333, 28.73977333, 28.83973   ,
       29.09568667, 29.46604   , 29.611665  , 29.46062833, 29.30627333,
       29.11368667, 28.98193   , 28.83269667, 28.63208333, 28.56212167,
       28.05960167, 27.54124667, 27.61649   , 27.720445  , 27.672445  ,
       27.73055333, 27.77198833, 27.62168167, 27.10830667, 26.78609833,
       26.48216667, 26.12109333, 25.96002667, 25.93246833])

array([-19.00882667, -19.00287833, -19.10077167, -19.18482   ,
       -19.22271333, -19.26173167, -19.32334333, -19.46715667,
       -19.65580833, -19.75253833, -19.77647   , -19.868995  ,
       -19.82508833, -19.75431167, -19.47863   , -18.96217   ,
       -18.65334   , -18.73503833, -18.81796667, -18.73460167,
       -18.569095  , -18.371235  , -18.13372   , -17.84223833,
       -17.55822833, -17.48345333, -17.47025167, -17.43512833,
       -17.33888667, -17.30047   , -17.37579833, -17.560875  ,
       -17.68797667, -17.71702   , -17.80341333, -17.87926167,
       -17.79152667, -17.61216167, -17.14219   , -16.63092167,
       -16.11089667, -15.69873333, -15.47601   , -15.20295167,
       -14.992665  , -14.830845  , -14.88409333, -15.26937667,
       -15.62799333, -15.890435  , -16.36251167, -16.46766167,
       -16.366995  , -16.20666333, -16.06546333, -16.01651333,
       -16.14397667, -16.659885  , -16.64754333, -16.717165  ,
       -16.99061   , -17.25732667, -17.5163    , -17.58851667])

array([1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3],
      dtype=int32)

array([ 1,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63], dtype=int32)

array(['IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF',
       'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF',
       'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF',
       'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF',
       'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF',
       'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF', 'IF'], dtype='<U2')

array(['R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R',
       'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R',
       'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R',
       'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R',
       'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R'],
      dtype='<U1')


fig=plt.figure(figsize=(10,6))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
plt.plot(dsbe['LONGITUDE'],dsbe['LATITUDE'],'ro-',label='SBE 6903010')
plt.plot(ds0['LONGITUDE'],ds0['LATITUDE'],'bo-',label='RBR 6903075')
plt.plot(ds1['LONGITUDE'],ds1['LATITUDE'],'go-',label='RBR 6903076')
#plt.xlim([-19.3,-18.9])
#plt.ylim([29,29.2])
plt.legend()

<matplotlib.legend.Legend at 0x19935bc40>


ds0b=ds0.isel(N_PROF=[0,1,2,3,4,5,6,7,8,9])
ds1b=ds1.isel(N_PROF=[0,1,2,3,4,5,6,7,8,9])
dsbe0=dsbe.isel(N_PROF=[0,1,2,3,4,5,6,7,8,9])

fig,ax = full_profiles(ds0=ds0b,ds1=ds1b)
ax[0,0].plot(dsbe0['TEMP'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=0.5)
ax[0,1].plot(dsbe0['PSAL'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=0.5)
    
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[0])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='SBE Float 6903010',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
    
ax[1,0].plot(dsbe0['TEMP'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=0.5)
ax[1,1].plot(dsbe0['PSAL'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=0.5)
    
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[1])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='SBE Float 6903010',markerfacecolor='r', markersize=10),
                        ],loc='lower right')

<matplotlib.legend.Legend at 0x1911dc0d0>


# ZOOM
fig,ax = full_profiles(ds0=ds0b,ds1=ds1b,markersize=4)

ax[0,0].set_ylim([2000,1500]); 
ax[0,0].plot(dsbe0['TEMP'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=4)
ax[0,1].plot(dsbe0['PSAL'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=4)
    
ax[0,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[0])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='SBE Float 6903010',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
    
ax[1,0].plot(dsbe0['TEMP'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=4)
ax[1,1].plot(dsbe0['PSAL'],-gsw.z_from_p(dsbe0['PRES'],dsbe0['LATITUDE'].mean()),'.r',markersize=4)
    
ax[1,1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label=str(wmos[1])+' Float',markerfacecolor='k', markersize=10),
                        Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                        label='SBE Float 6903010',markerfacecolor='r', markersize=10),
                        ],loc='lower right')
    
ax[0,0].set_xlim(4,6.5); ax[1,0].set_xlim(4,6.5);
ax[0,1].set_xlim(35.,35.5); ax[1,1].set_xlim(35.,35.5);


# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b)
sc_b=ax[0].scatter(dsbe0['SA'],dsbe0['PTEMP'],c=dsbe0['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',linewidths=0.5)
sc_b=ax[1].scatter(dsbe0['SA'],dsbe0['PTEMP'],c=dsbe0['SIG0'],s=20,cmap=cm.cm.matter,marker='o',edgecolor='k',linewidths=0.5)

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='SBE Float 6903010',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='SBE Float 6903010',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

/var/folders/ww/psmkfjds7xsc4kjsz66ghldr000nsn/T/ipykernel_51050/648577324.py:36: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
  cb=plt.colorbar(sc,ax=ax.ravel().tolist())

<matplotlib.legend.Legend at 0x192cb20d0>


# TS DIAGRAM
fig,ax = full_ts(ds0=ds0b,ds1=ds1b,vmin=27.8,vmax=27.9,sigma=40)

ax[0].set_ylim(4,7.5); ax[1].set_ylim(4,7.5)
ax[0].set_xlim(35.2,35.6); ax[1].set_xlim(35.2,35.6)

sc_b=ax[0].scatter(ds1b['SA'],ds1b['PTEMP'],c=ds1b['SIG0'],s=10,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',linewidths=0.5)
sc_b=ax[1].scatter(ds1b['SA'],ds1b['PTEMP'],c=ds1b['SIG0'],s=10,cmap=cm.cm.matter,vmin=27.8,vmax=27.9,marker='o',edgecolor='k',linewidths=0.5)

ax[0].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[0])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='SBE Float 6903010',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')
ax[1].legend(handles=[Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=0, 
                   label=str(wmos[1])+' Float',markerfacecolor='orange', markersize=10),
                   Line2D([0], [0], marker='o', linewidth=0, markeredgewidth=1, color='k', 
                   label='SBE Float 6903010',markerfacecolor='orange', markersize=10),
                   ],loc='lower right')

/var/folders/ww/psmkfjds7xsc4kjsz66ghldr000nsn/T/ipykernel_51050/648577324.py:36: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
  cb=plt.colorbar(sc,ax=ax.ravel().tolist())

<matplotlib.legend.Legend at 0x198667970>


from wrangling import interp_climatology

ref_sal = st25['SA'].values
ref_theta = st25['PTEMP'].values
ref_pres = st25['PRES'].values

#Since the ow function interp_climatology uses multiple profiles as input, we just *2 our profile
#INTERP 6903075 ON ST25 THETA LEVELS 
SA_i0, PR_i0 = interp_climatology(np.array([ds0.isel(N_PROF=1)['SA'].values,]*2).transpose(), 
                                  np.array([ds0.isel(N_PROF=1)['PTEMP'].values,]*2).transpose(), 
                                  np.array([ds0.isel(N_PROF=1)['PRES'].values,]*2).transpose(), 
                                  ref_sal, ref_theta, ref_pres)
SA_i0 = SA_i0[:,0]
PR_i0 = PR_i0[:,0]

#INTERP 6903076 ON ST25 THETA LEVELS  
SA_i1, PR_i1 = interp_climatology(np.array([ds1.isel(N_PROF=1)['SA'].values,]*2).transpose(), 
                                  np.array([ds1.isel(N_PROF=1)['PTEMP'].values,]*2).transpose(), 
                                  np.array([ds1.isel(N_PROF=1)['PRES'].values,]*2).transpose(),
                                  ref_sal, ref_theta, ref_pres)
SA_i1 = SA_i1[:,0]
PR_i1 = PR_i1[:,0]

#INTERP 6903010 ON ST25 THETA LEVELS  
SA_i2, PR_i2 = interp_climatology(np.array([dsbe.isel(N_PROF=1)['SA'].values,]*2).transpose(), 
                                  np.array([dsbe.isel(N_PROF=1)['PTEMP'].values,]*2).transpose(), 
                                  np.array([dsbe.isel(N_PROF=1)['PRES'].values,]*2).transpose(),
                                  ref_sal, ref_theta, ref_pres)
SA_i2 = SA_i2[:,0]
PR_i2 = PR_i2[:,0]


cl = ['#008744','#ffa700','#d62d20']
fig,ax = plt.subplots(2,2,figsize=(18,15),gridspec_kw={'height_ratios': [14, 1]})

ax[0,0].invert_yaxis();
ax[0,0].plot(SA_i0 - ref_sal, ref_pres,'-',linewidth=1.5,label='6903075',color=cl[0])
ax[0,0].plot(SA_i1 - ref_sal, ref_pres,'-',linewidth=1.5,label='6903076',color=cl[1])
ax[0,0].plot(SA_i2 - ref_sal, ref_pres,'-',linewidth=1.5,label='6903010',color=cl[2])
ax[0,0].legend(fontsize='large')
ax[0,0].set_xlabel('$\Delta$ Salinity [psu] with St25',fontsize='large',fontweight='bold')
ax[0,0].set_ylabel('PRES [dba]',fontsize='large',fontweight='bold')


ax[0,1].invert_yaxis();
ax[0,1].plot(PR_i0 - ref_pres, ref_pres,'-',linewidth=1.5,label='6903075',color=cl[0])
ax[0,1].plot(PR_i1 - ref_pres, ref_pres,'-',linewidth=1.5,label='6903076',color=cl[1])
ax[0,1].plot(PR_i2 - ref_pres, ref_pres,'-',linewidth=1.5,label='6903010',color=cl[2])
ax[0,1].legend(fontsize='large')
ax[0,1].set_xlabel('$\Delta$ Pressure [dba] with St25',fontsize='large',fontweight='bold')

ax[1,0].plot(np.nanmean((SA_i0 - ref_sal)),0,'s',markersize=15,markerfacecolor=cl[0],markeredgewidth=1,markeredgecolor='k')
ax[1,0].plot(np.nanmean((SA_i0 - ref_sal)[np.argwhere(ref_pres>1200)]),0,'v',markersize=15,markerfacecolor=cl[0],markeredgewidth=1,markeredgecolor='k')
ax[1,0].plot(np.nanmean((SA_i1 - ref_sal)),1,'s',markersize=15,markerfacecolor=cl[1],markeredgewidth=1,markeredgecolor='k')
ax[1,0].plot(np.nanmean((SA_i1 - ref_sal)[np.argwhere(ref_pres>1200)]),1,'v',markersize=15,markerfacecolor=cl[1],markeredgewidth=1,markeredgecolor='k')
ax[1,0].plot(np.nanmean((SA_i2 - ref_sal)),2,'s',markersize=15,markerfacecolor=cl[2],markeredgewidth=1,markeredgecolor='k')
ax[1,0].plot(np.nanmean((SA_i2 - ref_sal)[np.argwhere(ref_pres>1200)]),2,'v',markersize=15,markerfacecolor=cl[2],markeredgewidth=1,markeredgecolor='k')
ax[1,0].set_ylim([-0.5,2.5])

ax[1,0].legend(handles=[Line2D([0], [0], marker='s', linewidth=0, markeredgewidth=0,label='Profile',markerfacecolor='k', markersize=12),
                        Line2D([0], [0], marker='v', linewidth=0, markeredgewidth=0,label='>1200',markerfacecolor='k', markersize=12)],
              bbox_to_anchor=(-0.02, 0.9),fontsize='large')

ax[1,1].plot(np.nanmean((PR_i0 - ref_pres)),0,'s',markersize=15,markerfacecolor=cl[0],markeredgewidth=1,markeredgecolor='k')
ax[1,1].plot(np.nanmean((PR_i0 - ref_pres)[np.argwhere(ref_pres>1200)]),0,'v',markersize=15,markerfacecolor=cl[0],markeredgewidth=1,markeredgecolor='k')
ax[1,1].plot(np.nanmean((PR_i1 - ref_pres)),1,'s',markersize=15,markerfacecolor=cl[1],markeredgewidth=1,markeredgecolor='k')
ax[1,1].plot(np.nanmean((PR_i1 - ref_pres)[np.argwhere(ref_pres>1200)]),1,'v',markersize=15,markerfacecolor=cl[1],markeredgewidth=1,markeredgecolor='k')
ax[1,1].plot(np.nanmean((PR_i2 - ref_pres)),2,'s',markersize=15,markerfacecolor=cl[2],markeredgewidth=1,markeredgecolor='k')
ax[1,1].plot(np.nanmean((PR_i2 - ref_pres)[np.argwhere(ref_pres>1200)]),2,'v',markersize=15,markerfacecolor=cl[2],markeredgewidth=1,markeredgecolor='k')
ax[1,1].set_ylim([-0.5,2.5])

ax[1,0].set_xlabel('Mean($\Delta$) [psu]',fontsize='large',fontweight='bold')
ax[1,1].set_xlabel('Mean($\Delta$) [dba]',fontsize='large',fontweight='bold')

ax[1,0].set_yticklabels([])
ax[1,1].set_yticklabels([])
plt.tight_layout()


def along_isot(isotherm):

    fig,ax = plt.subplots(2,1,figsize=(18,12),sharex=True)
    ds0_ai = ds0.where((ds0['PTEMP']<isotherm+.1)&(ds0['PTEMP']>isotherm-.1),drop=True).mean('N_LEVELS')
    ds1_ai = ds1.where((ds1['PTEMP']<isotherm+.1)&(ds1['PTEMP']>isotherm-.1),drop=True).mean('N_LEVELS')
    dsbe_ai = dsbe.where((dsbe['PTEMP']<isotherm+.1)&(dsbe['PTEMP']>isotherm-.1),drop=True).mean('N_LEVELS')
    ax[0].plot(ds0_ai['CYCLE_NUMBER'],ds0_ai['PRES'],'o-',label=str(wmos[0]))
    ax[0].plot(ds1_ai['CYCLE_NUMBER'],ds1_ai['PRES'],'o-',label=str(wmos[1]))
    ax[0].plot(dsbe_ai['CYCLE_NUMBER'],dsbe_ai['PRES'],'o-',label='sbe 6903010')
    #ax[0].set_xlabel('CYCLE')
    ax[0].set_ylabel('PRES')
    ax[0].legend()
    ax[0].set_title('Pressure along isotherm '+str(isotherm)+'°C')

    ax[1].plot(ds0_ai['CYCLE_NUMBER'],ds0_ai['SA'],'o-',label=str(wmos[0]))
    ax[1].plot(ds1_ai['CYCLE_NUMBER'],ds1_ai['SA'],'o-',label=str(wmos[1]))
    ax[1].plot(dsbe_ai['CYCLE_NUMBER'],dsbe_ai['SA'],'o-',label='sbe 6903010')
    ax[1].set_xlabel('CYCLE')
    ax[1].set_ylabel('Salinity')
    ax[1].legend()
    ax[1].set_title('Salinity along isotherm '+str(isotherm)+'°C')
    return fig,ax


for i in [10.,5.]:
    fig,ax = along_isot(i)

	PRES	PSAL	TEMP	SA	PTEMP	SIG0
0	8.0	36.9540	21.3449	37.128475	21.343339	25.870281
1	10.0	36.9541	21.3478	37.128578	21.345849	25.869658
2	12.0	36.9539	21.3508	37.128373	21.348458	25.868777
3	14.0	36.9539	21.3484	37.128369	21.345668	25.869552
4	16.0	36.9543	21.3442	37.128767	21.341078	25.871130

This is a notebook to analyse french arvor-rbr floats first data¶

Float trajectories¶

Get data & plot profiles¶

Let's plot TS diagram for all profiles¶

Comparison with woa¶

Comparison with argo profiles nearby¶

Comparison of 1sts profiles with RAPROCEAN2012 CTD Cast¶

SBE 6903010 float comparison¶

Difference profile between 1st profile and reference station [on theta levels]¶

Evolution of pres/salinity along an isotherm¶