xarray.DataArray.qsel

DataArray.qsel(self, indexers=None, *, func='mean', **indexers_kwargs)[source]

Select and aggregate data along specified dimensions.

Parameters:

  • indexers (Mapping[Hashable, float | slice] | None, default: None) –

    Dictionary specifying the dimensions and their values or slices. Position along a dimension can be specified in three ways:

    • As a scalar value or a collection of scalar values: alpha=-1.2 or alpha=[-1.2, 0.0, 1.2]:

      If no width is specified, the data is selected along the nearest value for each element. It is equivalent to calling xarray.DataArray.sel() with method='nearest'. When a collection is provided, multiple selections are made and concatenated along the dimension. In this case, the width may also be provided as a collection with the same length.

    • As a value and width: alpha=5, alpha_width=0.5

      The data is aggregated over a slice of width alpha_width, centered at alpha. If alpha is a collection, the data is aggregated over multiple slices and concatenated along the dimension.

    • As a slice: alpha=slice(-10, 10)

      The data is selected over the specified slice. No averaging is performed. This is equivalent to calling xarray.DataArray.sel() with a slice.

    One of indexers or indexers_kwargs must be provided.

  • **indexers_kwargs – The keyword arguments form of indexers. One of indexers or indexers_kwargs must be provided.

  • func ({"mean", "min", "max", "sum"}, optional) – Function used when qsel aggregates over width-based selections. Must be one of "mean", "min", "max", or "sum". "mean" by default. This argument only affects reduced data; reduced numeric coordinates are still represented by their mean.

Returns:

DataArray – The selected and aggregated data.

Note

Unlike xarray.DataArray.sel(), this method treats all dimensions without coordinates as equivalent to having coordinates assigned from 0 to n-1, where n is the size of the dimension. For example:

da = xr.DataArray(np.random.rand(10), dims=("x",))

da.sel(x=slice(2, 3))  # This works

da.sel(x=slice(2.0, 3.0))  # This raises a TypeError

da.qsel(x=slice(2.0, 3.0))  # This works