glidertools.mapping.grid_data

glidertools.mapping.grid_data(x, y, var, bins=None, how='mean', interp_lim=6, verbose=True, return_xarray=True)

Grids the input variable to bins for depth/dens (y) and time/dive (x). The bins can be specified to be non-uniform to adapt to variable sampling intervals of the profile. It is useful to use the gt.plot.bin_size function to identify the sampling intervals. The bins are averaged (mean) by default but can also be the median, std, count,

Parameters:

  • x (np.array, dtype=float, shape=[n, ]) – The horizontal values by which to bin need to be in a psudeo discrete format already. Dive number or time_average_per_dive are the standard inputs for this variable. Has p unique values.

  • y (np.array, dtype=float, shape=[n, ]) – The vertical values that will be binned; typically depth, but can also be density or any other variable.

  • bins (np.array, dtype=float; shape=[q, ], default=[0 : 1 : max_depth ]) – Define the bin edges for y with this function. If not defined, defaults to one meter bins.

  • how (str, defualt='mean') – the string form of a function that can be applied to pandas.Groupby objects. These include mean, median, std, count.

  • interp_lim (int, default=6) – sets the maximum extent to which NaNs will be filled.

Returns:

glider_section – A 2D section in the format specified by ax_xarray input.

Return type:

xarray.DataArray, shape=[p, q]

Raises:

Userwarning – Triggers when x does not have discrete values.