xarray.Dataset.pad#
- Dataset.pad(pad_width=None, mode='constant', stat_length=None, constant_values=None, end_values=None, reflect_type=None, keep_attrs=None, **pad_width_kwargs)[source]#
Pad this dataset along one or more dimensions.
Warning
This function is experimental and its behaviour is likely to change especially regarding padding of dimension coordinates (or IndexVariables).
When using one of the modes (“edge”, “reflect”, “symmetric”, “wrap”), coordinates will be padded with the same mode, otherwise coordinates are padded using the “constant” mode with fill_value dtypes.NA.
- Parameters
pad_width (mapping of hashable to
tuple
ofint
) – Mapping with the form of {dim: (pad_before, pad_after)} describing the number of values padded along each dimension. {dim: pad} is a shortcut for pad_before = pad_after = padmode (
{"constant", "edge", "linear_ramp", "maximum", "mean", "median", "minimum", "reflect", "symmetric", "wrap"}
, default:"constant"
) – How to pad the DataArray (taken from numpy docs):“constant”: Pads with a constant value.
“edge”: Pads with the edge values of array.
“linear_ramp”: Pads with the linear ramp between end_value and the array edge value.
“maximum”: Pads with the maximum value of all or part of the vector along each axis.
“mean”: Pads with the mean value of all or part of the vector along each axis.
“median”: Pads with the median value of all or part of the vector along each axis.
“minimum”: Pads with the minimum value of all or part of the vector along each axis.
“reflect”: Pads with the reflection of the vector mirrored on the first and last values of the vector along each axis.
“symmetric”: Pads with the reflection of the vector mirrored along the edge of the array.
“wrap”: Pads with the wrap of the vector along the axis. The first values are used to pad the end and the end values are used to pad the beginning.
stat_length (
int
,tuple
or mapping of hashable totuple
, default:None
) – Used in ‘maximum’, ‘mean’, ‘median’, and ‘minimum’. Number of values at edge of each axis used to calculate the statistic value. {dim_1: (before_1, after_1), … dim_N: (before_N, after_N)} unique statistic lengths along each dimension. ((before, after),) yields same before and after statistic lengths for each dimension. (stat_length,) or int is a shortcut for before = after = statistic length for all axes. Default isNone
, to use the entire axis.constant_values (scalar,
tuple
, mapping of dimension name to scalar ortuple
, or mapping of variable name to scalar,tuple
orto mapping
of dimension name to scalar ortuple
, default:None
) – Used in ‘constant’. The values to set the padded values for each data variable / axis.{var_1: {dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)}, ... var_M: (before, after)}
unique pad constants per data variable.{dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)}
unique pad constants along each dimension.((before, after),)
yields same before and after constants for each dimension.(constant,)
orconstant
is a shortcut forbefore = after = constant
for all dimensions. Default isNone
, pads withnp.nan
.end_values (scalar,
tuple
or mapping of hashable totuple
, default:None
) – Used in ‘linear_ramp’. The values used for the ending value of the linear_ramp and that will form the edge of the padded array.{dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)}
unique end values along each dimension.((before, after),)
yields same before and after end values for each axis.(constant,)
orconstant
is a shortcut forbefore = after = constant
for all axes. Default is None.reflect_type (
{"even", "odd", None}
, optional) – Used in “reflect”, and “symmetric”. The “even” style is the default with an unaltered reflection around the edge value. For the “odd” style, the extended part of the array is created by subtracting the reflected values from two times the edge value.keep_attrs (
bool
orNone
, optional) – If True, the attributes (attrs
) will be copied from the original object to the new one. If False, the new object will be returned without attributes.**pad_width_kwargs – The keyword arguments form of
pad_width
. One ofpad_width
orpad_width_kwargs
must be provided.
- Returns
padded (
Dataset
) – Dataset with the padded coordinates and data.
See also
Dataset.shift
,Dataset.roll
,Dataset.bfill
,Dataset.ffill
,numpy.pad
,dask.array.pad
Notes
By default when
mode="constant"
andconstant_values=None
, integer types will be promoted tofloat
and padded withnp.nan
. To avoid type promotion specifyconstant_values=np.nan
Padding coordinates will drop their corresponding index (if any) and will reset default indexes for dimension coordinates.
Examples
>>> ds = xr.Dataset({"foo": ("x", range(5))}) >>> ds.pad(x=(1, 2)) <xarray.Dataset> Size: 64B Dimensions: (x: 8) Dimensions without coordinates: x Data variables: foo (x) float64 64B nan 0.0 1.0 2.0 3.0 4.0 nan nan