xoa.plot.plot_ts

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xoa.plot.plot_ts#

xoa.plot.plot_ts(temp, sal, dens=True, ref_dens=0, pres=None, potential=None, absolute=None, axes=None, scatter_kwargs=None, contour_kwargs=None, clabel=True, clabel_kwargs=None, colorbar=None, colorbar_kwargs=None, **kwargs)[source]#

Plot a TS diagram

A TS diagram is a scatter plot with salinity (practical or absolute) as X axis and potential temperature as Y axis. The density is generally added as background contours.

Parameters:

  • temp (xarray.DataArray) – Temperature. If not potential, it will be converted into potential if potential=None or potential=False. Note that if temp is not potential and contains a depth coordinate, depth values must be negative (to compute pres with gsw.p_from_z if necessary)

  • sal (xarray.DataArray) – Salinity (practical or absolute). If not absolute, it will be converted into absolute salinity if the potential temperature needs to be computed.

  • dens (bool) – Add contours of density. The density is by default computed with function gsw.density.sigma0() (ref_dens=0).

  • ref_dens (integer, 0) – choice of reference for density calculation (between 0 and 4) ref_dens=0 will consider func:gsw.density.sigma0, etc..

  • pres (xarray.DataArray, None) – Pressure to compute potential temperature and absolute salinity.

  • potential (bool, None) – Is the temperature potential? If None, infer from attributes.

  • absolute (bool, None) – Is the salinity absolute? If None, infer from attributes.

  • clabel (bool) – Add labels to density contours

  • clabel_kwargs (dict, None) – Parameters that are passed to clabel().

  • colorbar (bool, None) – Should we add the colorbar? If None, check if scatter plot color is a data array.

  • colorbar_kwargs (dict, None) – Parameters that are passed to colorbar().

  • contour_kwargs (dict, None) – Parameters that are passed to contour().

  • axes (None) – Matplotlib axes instance

  • kwargs (dict) – Extra parameters are filtered by xoa.misc.dict_filter() and passed to the plot functions.

See also

gsw.density, gsw.conversions

Returns:

dict – With the following keys, depending on what is plotted: axes, scatter, colorbar, contour, clabel.

Example

# Register the main xoa accessor
In [1]: xoa.register_accessors()

# Load the Mercator data
In [2]: file_name = xoa.get_data_sample("MODELS/CMEMS-IBI/ibi-argo-7900573.nc")

In [3]: ds = xr.open_dataset(file_name)

In [4]: temp = ds.thetao

In [5]: sal = ds.so

In [6]: depth = ds.depth.broadcast_like(temp)

# Plot
In [7]: plot_ts(temp, sal, potential=True, scatter_c=depth, contour_linewidths=0.2, clabel_fontsize=8, cmap="cmo.deep_r")
Out[7]: 
{'axes': <Axes: xlabel='Salinity', ylabel='Temperature [degrees_C]'>,
 'scatter': <matplotlib.collections.PathCollection at 0x7b38c91b5f90>,
 'colorbar': <matplotlib.colorbar.Colorbar at 0x7b38c9b4f380>,
 'contour': <matplotlib.contour.QuadContourSet at 0x7b38e48579d0>,
 'clabel': <a list of 6 text.Text objects>}
../_images/api.plot.plot_ts.png
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