pyinterp.core.fill.loess_float32

pyinterp.core.fill.loess_float32(*args, **kwargs)

Overloaded function.

  1. loess_float32(grid: pyinterp.core.Grid2DFloat32, nx: int = 3, ny: int = 3, value_type: pyinterp.core.fill.ValueType = <ValueType.Undefined: 0>, num_threads: int = 0) -> numpy.ndarray[numpy.float32]

Fills undefined values using a locally weighted regression function or LOESS. The weight function used for LOESS is the tri-cube weight function, \(w(x)=(1-|d|^3)^3\).

Parameters:

  • grid – Grid function on a uniform 2-dimensional grid to be filled.

  • nx – Number of points of the half-window to be taken into account along the X-axis. Defaults to 3.

  • ny – Number of points of the half-window to be taken into account along the Y-axis. Defaults to 3.

  • value_type – Type of values processed by the filter

  • num_threads – The number of threads to use for the computation. If 0 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. Defaults to 0.

Returns:

The grid will have all the NaN filled with extrapolated values.

  1. loess_float32(grid: pyinterp.core.Grid3DFloat32, nx: int = 3, ny: int = 3, value_type: pyinterp.core.fill.ValueType = <ValueType.Undefined: 0>, num_threads: int = 0) -> numpy.ndarray[numpy.float32]

Fills undefined values using a locally weighted regression function or LOESS. The weight function used for LOESS is the tri-cube weight function, \(w(x)=(1-|d|^3)^3\).

Parameters:

  • grid – Grid containing the values to be filtered.

  • nx – Number of points of the half-window to be taken into account along the X-axis. Defaults to 3.

  • ny – Number of points of the half-window to be taken into account along the Y-axis. Defaults to 3.

  • value_type – Type of values processed by the filter

  • num_threads – The number of threads to use for the computation. If 0 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. Defaults to 0.

Returns:

The grid will have all the NaN filled with extrapolated values.

  1. loess_float32(grid: pyinterp.core.TemporalGrid3DFloat32, nx: int = 3, ny: int = 3, value_type: pyinterp.core.fill.ValueType = <ValueType.Undefined: 0>, num_threads: int = 0) -> numpy.ndarray[numpy.float32]

Fills undefined values using a locally weighted regression function or LOESS. The weight function used for LOESS is the tri-cube weight function, \(w(x)=(1-|d|^3)^3\).

Parameters:

  • grid – Grid containing the values to be filtered.

  • nx – Number of points of the half-window to be taken into account along the X-axis. Defaults to 3.

  • ny – Number of points of the half-window to be taken into account along the Y-axis. Defaults to 3.

  • value_type – Type of values processed by the filter

  • num_threads – The number of threads to use for the computation. If 0 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. Defaults to 0.

Returns:

The grid will have all the NaN filled with extrapolated values.

  1. loess_float32(grid: pyinterp.core.Grid4DFloat32, nx: int = 3, ny: int = 3, value_type: pyinterp.core.fill.ValueType = <ValueType.Undefined: 0>, num_threads: int = 0) -> numpy.ndarray[numpy.float32]

Fills undefined values using a locally weighted regression function or LOESS. The weight function used for LOESS is the tri-cube weight function, \(w(x)=(1-|d|^3)^3\).

Parameters:

  • grid – Grid containing the values to be filtered.

  • nx – Number of points of the half-window to be taken into account along the X-axis. Defaults to 3.

  • ny – Number of points of the half-window to be taken into account along the Y-axis. Defaults to 3.

  • value_type – Type of values processed by the filter

  • num_threads – The number of threads to use for the computation. If 0 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. Defaults to 0.

Returns:

The grid will have all the NaN filled with extrapolated values.

  1. loess_float32(grid: pyinterp.core.TemporalGrid4DFloat32, nx: int = 3, ny: int = 3, value_type: pyinterp.core.fill.ValueType = <ValueType.Undefined: 0>, num_threads: int = 0) -> numpy.ndarray[numpy.float32]

Fills undefined values using a locally weighted regression function or LOESS. The weight function used for LOESS is the tri-cube weight function, \(w(x)=(1-|d|^3)^3\).

Parameters:

  • grid – Grid containing the values to be filtered.

  • nx – Number of points of the half-window to be taken into account along the X-axis. Defaults to 3.

  • ny – Number of points of the half-window to be taken into account along the Y-axis. Defaults to 3.

  • value_type – Type of values processed by the filter

  • num_threads – The number of threads to use for the computation. If 0 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. Defaults to 0.

Returns:

The grid will have all the NaN filled with extrapolated values.