# pyinterp.core.fill.loess_float64¶

pyinterp.core.fill.loess_float64(*args, **kwargs)

1. loess_float64(grid: pyinterp.core.Grid2DFloat64, nx: int = 3, ny: int = 3, value_type: pyinterp.core.fill.ValueType = <ValueType.Undefined: 0>, num_threads: int = 0) -> numpy.ndarray[numpy.float64]

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 (pyinterp.core.Grid2DFloat64) – Grid function on a uniform 2-dimensional grid to be filled.

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

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

• value_type (pyinterp.core.fill.ValueType, optional) – Type of values processed by the filter

• num_threads (int, optional) – 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.

Return type

numpy.ndarray

1. loess_float64(grid: pyinterp.core.Grid3DFloat64, nx: int = 3, ny: int = 3, value_type: pyinterp.core.fill.ValueType = <ValueType.Undefined: 0>, num_threads: int = 0) -> numpy.ndarray[numpy.float64]

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 (pyinterp.core.Grid3DFloat64) – Grid containing the values to be filtered.

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

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

• value_type (pyinterp.core.fill.ValueType, optional) – Type of values processed by the filter

• num_threads (int, optional) – 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.

Return type

numpy.ndarray

1. loess_float64(grid: pyinterp.core.TemporalGrid3DFloat64, nx: int = 3, ny: int = 3, value_type: pyinterp.core.fill.ValueType = <ValueType.Undefined: 0>, num_threads: int = 0) -> numpy.ndarray[numpy.float64]

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 (pyinterp.core.TemporalGrid3DFloat64) – Grid containing the values to be filtered.

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

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

• value_type (pyinterp.core.fill.ValueType, optional) – Type of values processed by the filter

• num_threads (int, optional) – 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.

Return type

numpy.ndarray