larray.wrap_elementwise_array_func¶

larray.wrap_elementwise_array_func(func)[source]¶

Wrap a function using numpy arrays to work with LArray arrays instead.

Parameters:	func : function A function taking numpy arrays as arguments and returning numpy arrays of the same shape. If the function takes several arguments, this wrapping code assumes the result will have the combination of all axes present. In numpy talk, arguments will be broadcasted to each other.
Returns:	function A function taking LArray arrays arguments and returning LArray arrays.

Examples

For example, if we want to apply the Hodrick-Prescott filter from statsmodels we can use this:

>>> from statsmodels.tsa.filters.hp_filter import hpfilter         # doctest: +SKIP
>>> hpfilter = wrap_elementwise_array_func(hpfilter)               # doctest: +SKIP

hpfilter is now a function taking a one dimensional Array as input and returning a one dimensional Array as output

Now let us suppose we have a ND array such as:

>>> from larray.random import normal
>>> arr = normal(axes="sex=M,F;year=2016..2018")                   # doctest: +SKIP
>>> arr                                                            # doctest: +SKIP
sex\year   2016   2017   2018
       M  -1.15   0.56  -1.06
       F  -0.48  -0.39  -0.98

We can apply an Hodrick-Prescott filter to it by using:

>>> # 6.25 is the recommended smoothing value for annual data
>>> cycle, trend = arr.apply(hpfilter, 6.25, axes="year")          # doctest: +SKIP
>>> trend                                                          # doctest: +SKIP
sex\year   2016   2017   2018
       M  -0.61  -0.52  -0.52
       F  -0.37  -0.61  -0.87