In [1]:
import numpy as np
import matplotlib.pyplot as plt
import matfile 
%matplotlib widget
import seawater.eos80 as sw
import seawater.constants as swcons
import xarray as xr
In [2]:
with xr.open_dataset('202110/CtdGrid.nc') as cgrid:

    print(cgrid.O2)
    #cgrid['lon'][3] = cgrid['lon'][3]+2.
    #cgrid['lon'][3] = -123.25
    print(cgrid['lon'])

    print (cgrid.alongx,cgrid.acrossx)

    print(cgrid.keys()) 

    print(cgrid.O2[:, -1])

<xarray.DataArray 'O2' (depths: 324, time: 14)>
array([[261.630315, 256.019916, 237.060751, ..., 191.900819, 188.830737,
        182.067497],
       [261.766968, 255.500965, 240.605567, ..., 191.48103 , 191.200878,
        182.292067],
       [260.712525, 254.179178, 244.157096, ..., 190.52348 , 190.400306,
        182.262137],
       ...,
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan]])
Coordinates:
  * depths   (depths) float64 0.5 1.5 2.5 3.5 4.5 ... 320.5 321.5 322.5 323.5
  * time     (time) datetime64[ns] 2021-10-05T12:10:00 ... 2021-10-04T12:55:00
Attributes:
    units:    mmol/kg
<xarray.DataArray 'lon' (time: 14)>
array([-123.499767, -123.472283, -123.457483, -123.457483, -123.3943  ,
       -123.365783, -123.3454  , -123.3262  , -123.303917, -123.2871  ,
       -123.258783, -123.244033, -123.241567, -123.237917])
Coordinates:
  * time     (time) datetime64[ns] 2021-10-05T12:10:00 ... 2021-10-04T12:55:00
Attributes:
    units:    deg W
<xarray.DataArray 'alongx' (time: 14)>
array([ 5.370537,  8.106811,  9.444944, 10.093009, 15.061506, 17.935794,
       19.507951, 21.038104, 23.252325, 25.154515, 27.950795, 29.186919,
       29.324932, 29.324932])
Coordinates:
  * time     (time) datetime64[ns] 2021-10-05T12:10:00 ... 2021-10-04T12:55:00
Attributes:
    units:    dist from S4 [km] <xarray.DataArray 'acrossx' (time: 14)>
array([0.213287, 0.08535 , 0.121079, 1.336894, 0.007913, 0.224482, 0.053387,
       0.035037, 0.040673, 0.121704, 0.164846, 0.381607, 0.405402, 0.815278])
Coordinates:
  * time     (time) datetime64[ns] 2021-10-05T12:10:00 ... 2021-10-04T12:55:00
Attributes:
    units:    dist from S4 [km]
KeysView(<xarray.Dataset>
Dimensions:  (depths: 324, time: 14)
Coordinates:
  * depths   (depths) float64 0.5 1.5 2.5 3.5 4.5 ... 320.5 321.5 322.5 323.5
  * time     (time) datetime64[ns] 2021-10-05T12:10:00 ... 2021-10-04T12:55:00
Data variables: (12/15)
    cond     (depths, time) float64 ...
    cond0    (depths, time) float64 ...
    temp     (depths, time) float64 ...
    pres     (depths, time) float64 ...
    O2       (depths, time) float64 261.6 256.0 237.1 222.6 ... nan nan nan nan
    O2sat    (depths, time) float64 ...
    ...       ...
    pden     (depths, time) float64 ...
    id       (time) object ...
    lat      (time) float64 ...
    lon      (time) float64 -123.5 -123.5 -123.5 -123.5 ... -123.2 -123.2 -123.2
    alongx   (time) float64 5.371 8.107 9.445 10.09 ... 27.95 29.19 29.32 29.32
    acrossx  (time) float64 0.2133 0.08535 0.1211 1.337 ... 0.3816 0.4054 0.8153)
<xarray.DataArray 'O2' (depths: 324)>
array([182.067497, 182.292067, 182.262137, ...,        nan,        nan,
              nan])
Coordinates:
  * depths   (depths) float64 0.5 1.5 2.5 3.5 4.5 ... 320.5 321.5 322.5 323.5
    time     datetime64[ns] 2021-10-04T12:55:00
Attributes:
    units:    mmol/kg
In [3]:
# plot topo

dat=matfile.loadmatbunch('../../topo/SouthVanIsle.mat')
topo = dat['VanIsleTopo']
#print topo.keys()
In [4]:
dat=[[-123.534430, 48.523672],
    [-123.543075, 48.536432],
    [-123.544309, 48.551016],
    [-123.509733, 48.560130],
    [-123.497384, 48.569245],
    [-123.496149, 48.585651],
    [-123.499854, 48.639427],
    [-123.498619, 48.679531],
    [-123.496149, 48.695937],
    [-123.456633, 48.714167],
    [-123.420822, 48.726927],
    [-123.393654, 48.743333],
    [-123.370192, 48.757005],
    [-123.344259, 48.767031],
    [-123.326971, 48.765208],
    [-123.286220, 48.738776],
    [-123.259052, 48.718724],
    [-123.245469, 48.712344],
    [-123.261522, 48.693203],
    [-123.223241, 48.584740]]
dat = np.array(dat)
In [5]:
with xr.open_dataset('202110/CtdGrid.nc') as cgrid:

    fig,ax = plt.subplots()
    ax.plot(cgrid.lon,cgrid.lat,'rd')
    for i in range(len(cgrid.lon)):
        ax.annotate(xy=(cgrid.lon[i],cgrid.lat[i]),s=cgrid['id'][i].values)
    #    ax.annotate()
    ax.get_xaxis().get_major_formatter().set_useOffset(False)
    ax.set_aspect(1./np.cos(48.7*np.pi/180.))
    ax.contour(topo['Lon'],topo['Lat'],topo['z'],[-1000,0],colors='k')
    pc=ax.pcolormesh(topo['Lon'],topo['Lat'],topo['z'],vmin=-125,vmax=-50,cmap=plt.get_cmap('ocean'),rasterized=True)
    ax.set_xlim([-123.59,-123.1])
    ax.set_ylim([48.51,48.86])
    plt.colorbar(pc,shrink=0.7,extend='both')
    ax.plot(dat[:,0], dat[:,1])
    ax.set_xlabel('Longitude $[^o E]$')
    ax.set_ylabel('Latitude $[^o N]$')
    fig.savefig('201909CruiseTrack.pdf')

/var/folders/vy/zxjl84j90ws6d1k09_rhst0w0000gp/T/ipykernel_11871/129806412.py:6: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.
  ax.annotate(xy=(cgrid.lon[i],cgrid.lat[i]),s=cgrid['id'][i].values)
/var/folders/vy/zxjl84j90ws6d1k09_rhst0w0000gp/T/ipykernel_11871/129806412.py:11: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.
  pc=ax.pcolormesh(topo['Lon'],topo['Lat'],topo['z'],vmin=-125,vmax=-50,cmap=plt.get_cmap('ocean'),rasterized=True)
In [6]:
with xr.open_dataset('202110/CtdGrid.nc') as cgrid:
    fig,axs=plt.subplots(1,4,figsize=(12,5))
    tind = np.argsort(cgrid['time'].values)
    axs[0].plot(cgrid['pden'].data-1000.,cgrid['depths'],'k')
    #axs[0].plot(cgrid['pden'][:,insaan]-1000.,cgrid['depths'],'c')
    axs[0].plot(cgrid['pden'][:,tind[-1]]-1000.,cgrid['depths'],'r')

    # axs[0].plot(cgrid['pden'][:,tind[4]]-1000.,cgrid['depths'],'r')

    axs[0].set_ylim([340,0])
    axs[0].set_xlabel('$\sigma_{\\theta}\ [kg\,m^{-3}]$')
    axs[0].set_ylabel('DEPTH [m]')
    axs[0].set_xticks(np.arange(22.,26.,1.))

    axs[1].plot(cgrid['sal'].data,cgrid['depths'],'k')
    #axs[1].plot(cgrid['sal'][:,insaan],cgrid['depths'],'c')
    axs[1].plot(cgrid['sal'][:,-1],cgrid['depths'],'r')
    axs[1].set_ylim([340,0])
    #axs[1].set_xticks(np.arange(29.,34.,1.))
    axs[1].set_xlabel('S [psu]')

    axs[2].plot(cgrid['temp'].data,cgrid['depths'],'k')
    #axs[2].plot(cgrid['t'][:,insaan],cgrid['depths'],'c')
    axs[2].plot(cgrid['temp'][:,-1],cgrid['depths'],'r')
    axs[2].set_ylim([340,0])
    axs[2].set_xlabel('$T\ [^o C]$')

    axs[3].plot(cgrid['O2'].data,cgrid['depths'],'k')
    #axs[3].plot(cgrid['O2'][:,insaan],cgrid['depths'],'c')
    axs[3].plot(cgrid['O2'][:,-1],cgrid['depths'],'r')
    axs[3].set_ylim([340,0])
    axs[3].set_xlabel('$O2 [ml/l]$')
    #fig.savefig('20161005Profiles.pdf')
In [7]:
print(cgrid.temp)

<xarray.DataArray 'temp' (depths: 324, time: 16)>
array([[13.508441,       nan, 12.227251, ...,       nan, 11.705227, 11.339487],
       [13.459556, 13.075575, 12.197464, ..., 11.247339, 11.351902, 11.304703],
       [13.523921, 12.988432, 12.215892, ..., 11.125693, 11.146022, 11.164524],
       ...,
       [      nan,       nan,       nan, ...,       nan,       nan,       nan],
       [      nan,       nan,       nan, ...,       nan,       nan,       nan],
       [      nan,       nan,       nan, ...,       nan,       nan,       nan]])
Coordinates:
  * depths   (depths) float64 0.5 1.5 2.5 3.5 4.5 ... 320.5 321.5 322.5 323.5
  * time     (time) datetime64[ns] 2020-09-30T12:53:00 ... 2020-09-30T08:30:00
Attributes:
    units:    deg C
In [8]:
import seawater
In [8]:
%load_ext autoreload
%autoreload 2
with xr.open_dataset('202110/CtdGrid.nc') as cgrid:
    print(cgrid)
    from PlotCrossSec import PlotCrossSec 
    ind = range(len(cgrid.time))
    fig,ax=PlotCrossSec(cgrid,ind, o2name='O2')
    fig.savefig('figs/CrossSec202110.pdf')

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload
<xarray.Dataset>
Dimensions:  (depths: 324, time: 14)
Coordinates:
  * depths   (depths) float64 0.5 1.5 2.5 3.5 4.5 ... 320.5 321.5 322.5 323.5
  * time     (time) datetime64[ns] 2021-10-05T12:10:00 ... 2021-10-04T12:55:00
Data variables: (12/15)
    cond     (depths, time) float64 ...
    cond0    (depths, time) float64 ...
    temp     (depths, time) float64 ...
    pres     (depths, time) float64 ...
    O2       (depths, time) float64 ...
    O2sat    (depths, time) float64 ...
    ...       ...
    pden     (depths, time) float64 ...
    id       (time) object ...
    lat      (time) float64 ...
    lon      (time) float64 ...
    alongx   (time) float64 ...
    acrossx  (time) float64 ...
S12 <xarray.DataArray 'alongx' ()>
array(5.370537)
Coordinates:
    time     datetime64[ns] 2021-10-05T12:10:00
Attributes:
    units:    dist from S4 [km]
S12.5 <xarray.DataArray 'alongx' ()>
array(8.106811)
Coordinates:
    time     datetime64[ns] 2021-10-05T11:51:00
Attributes:
    units:    dist from S4 [km]
S5 <xarray.DataArray 'alongx' ()>
array(9.444944)
Coordinates:
    time     datetime64[ns] 2021-10-05T11:10:00
Attributes:
    units:    dist from S4 [km]
S5.5 <xarray.DataArray 'alongx' ()>
array(10.093009)
Coordinates:
    time     datetime64[ns] 2021-10-05T10:38:00
Attributes:
    units:    dist from S4 [km]
S8 <xarray.DataArray 'alongx' ()>
array(15.061506)
Coordinates:
    time     datetime64[ns] 2021-10-05T10:00:00
Attributes:
    units:    dist from S4 [km]
A5 <xarray.DataArray 'alongx' ()>
array(17.935794)
Coordinates:
    time     datetime64[ns] 2021-10-05T09:33:00
Attributes:
    units:    dist from S4 [km]
A4 <xarray.DataArray 'alongx' ()>
array(19.507951)
Coordinates:
    time     datetime64[ns] 2021-10-05T08:55:00
Attributes:
    units:    dist from S4 [km]
A3 <xarray.DataArray 'alongx' ()>
array(21.038104)
Coordinates:
    time     datetime64[ns] 2021-10-05T08:37:00
Attributes:
    units:    dist from S4 [km]
S6 <xarray.DataArray 'alongx' ()>
array(23.252325)
Coordinates:
    time     datetime64[ns] 2021-10-04T14:08:00
Attributes:
    units:    dist from S4 [km]
A2 <xarray.DataArray 'alongx' ()>
array(25.154515)
Coordinates:
    time     datetime64[ns] 2021-10-04T13:48:00
Attributes:
    units:    dist from S4 [km]
A1.5 <xarray.DataArray 'alongx' ()>
array(27.950795)
Coordinates:
    time     datetime64[ns] 2021-10-04T13:25:00
Attributes:
    units:    dist from S4 [km]
H1/A1 <xarray.DataArray 'alongx' ()>
array(29.186919)
Coordinates:
    time     datetime64[ns] 2021-10-04T12:12:00
Attributes:
    units:    dist from S4 [km]
H1 <xarray.DataArray 'alongx' ()>
array(29.324932)
Coordinates:
    time     datetime64[ns] 2021-10-04T12:21:00
Attributes:
    units:    dist from S4 [km]
A1 <xarray.DataArray 'alongx' ()>
array(29.324932)
Coordinates:
    time     datetime64[ns] 2021-10-04T12:55:00
Attributes:
    units:    dist from S4 [km]

/Users/jklymak/Dropbox/Teaching/CruiseData314/CruiseData21/ctd/PlotCrossSec.py:22: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.
  pc=ax[0].pcolormesh(x,cgrid['depths'],cgrid['sal'][:,ind],rasterized=True,vmin=slim[0],vmax=slim[1])
/Users/jklymak/Dropbox/Teaching/CruiseData314/CruiseData21/ctd/PlotCrossSec.py:34: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.
  pc=ax[1].pcolormesh(x,cgrid['depths'],cgrid['temp'][:,ind],rasterized=True,vmin=tlim[0],vmax=tlim[1])
/Users/jklymak/Dropbox/Teaching/CruiseData314/CruiseData21/ctd/PlotCrossSec.py:41: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.
  pc=ax[2].pcolormesh(x,cgrid['depths'],np.ma.masked_invalid(cgrid[o2name][:,ind]),rasterized=True,vmin=olim[0],vmax=olim[1])
In [37]:
with xr.open_dataset('201809a/20180919_S12.nc') as ctd:
    print(ctd)
    fig,ax = plt.subplots()
    ax.plot(ctd.pres)

<xarray.Dataset>
Dimensions:  (scan: 2172)
Coordinates:
  * scan     (scan) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...
Data variables:
    cond     (scan) float64 -0.0001529 -0.0001529 -0.0001383 -0.0001058 ...
    cond0    (scan) float64 -0.0001529 -0.000104 -0.0001101 -0.0001071 ...
    temp     (scan) float64 12.1 12.1 12.1 12.1 12.1 12.1 12.1 12.1 12.1 ...
    pres     (scan) float64 -0.1877 -0.1877 -0.1939 -0.1878 -0.1878 -0.1847 ...
    O2       (scan) float64 nan nan nan nan nan nan nan nan nan nan nan nan ...
    O2sat    (scan) float64 nan nan nan nan nan nan nan nan nan nan nan nan ...
    Par      (scan) float64 3.462e+10 3.479e+10 3.5e+10 3.531e+10 3.579e+10 ...
    Flu      (scan) float64 2.237 2.239 2.248 2.239 2.237 2.236 2.232 2.213 ...
    sal      (scan) float64 nan nan nan nan nan nan nan nan nan nan nan nan ...
    pden     (scan) float64 nan nan nan nan nan nan nan nan nan nan nan nan ...
    time     datetime64[ns] 2018-09-19T20:30:00
Attributes:
    alongx:   5.44854485448545
    acrossx:  0.1881045570973546
    lat:      48.692
    lon:      -123.49931666666667
    id:       S12
    header:   * Sea-Bird SBE19plus Data File:\n\n* FileName = C:\Documents an...
In [17]:
fig,ax=plt.subplots()
ax.pcolormesh(cgrid['t'],rasterized='True')
Out[17]:
<matplotlib.collections.QuadMesh at 0x11b84d9e8>
In [38]:
dat=matfile.loadmatbunch('201609b/CtdGrid.mat',masked=False)
cgrid = dat['cgrid']
In [43]:
print cgrid['t'][:12,:12]

[[         nan          nan          nan          nan          nan
           nan          nan          nan          nan          nan
           nan]
 [         nan          nan          nan          nan          nan
           nan          nan          nan          nan          nan
           nan]
 [         nan          nan          nan          nan          nan
           nan          nan          nan          nan          nan
           nan]
 [ 14.41383114  14.47595532  14.08887655  13.39507031  13.71162718
   11.62144596  11.51680076  11.3956455   11.57512     10.91731054
   11.01188255]
 [ 14.11954182  14.44380998  13.83224798  13.23351409  13.59193541
   11.62283611  11.50751222  11.38328213  11.39732355  10.91574041
   11.00679611]
 [ 13.49762562  14.30637941  13.22787798  13.0819691   13.2335549
   11.60692048  11.48895184  11.35285984  11.27796817  10.91622431
   10.99364919]
 [ 13.11746399  13.95303933  12.87379891  13.16713538  12.71898052
   11.58979592  11.48496355  11.3247802   11.2172242   10.91967222
   10.98690665]
 [ 12.96144062  13.47958074  12.67091737  13.20105607  12.32438003
   11.5860588   11.48311825  11.30159974  11.20045682  10.91212076
   10.98930782]
 [ 12.90384434  13.17842495  12.57273912  12.81672119  12.23549807
   11.58291271  11.47413299  11.2948867   11.18650624  10.90639025
   10.99131987]
 [ 12.8081966   12.86730876  12.54327225  12.42982126  12.20098869
   11.57956493  11.45056426  11.28680079  11.15903631  10.90423819
   10.99278449]
 [ 12.72747968  12.63178476  12.52725464  12.36205496  12.18748315
   11.57514746  11.4322906   11.26274444  11.1030148   10.9031404
   10.99626814]
 [ 12.63805388  12.46385423  12.52254407  12.35675951  12.17769497
   11.58220541  11.35713026  11.23429051  11.06463174  10.89356906
   11.00302437]]
In [12]:
fig,ax=plt.subplots()
ax.pcolormesh(cgrid['alongx'],cgrid['depths'],cgrid['t'],vmin=6,vmax=16)
Out[12]:
<matplotlib.collections.QuadMesh at 0x11b98e898>
In [ ]:
w739871382