Defines whether, and how to treat missing values. This can occur in two circumstances when aggregating (setting a sample frequency): * missing values: if there are missing (or invalid) values stored for a given freq-interval, "interpolation" specifies how to compute these. * down-sampling: when the specified freq is smaller than the series’ actual frequency. "interpolation" specifies how to compute intermediate values.
| Name | Type | Description | Notes |
|---|---|---|---|
| method | InterpolationMethod | ||
| value | int | Optional parameter value for the interpolation method (see method description). | [optional] |
| order | int | Optional order parameter for the interpolation method (see method description). | [optional] |
from waylay.services.queries.models.interpolation_spec import InterpolationSpec
# TODO update the JSON string below
json = "{}"
# create an instance of InterpolationSpec from a JSON string
interpolation_spec_instance = InterpolationSpec.from_json(json)
# print the JSON string representation of the object
print InterpolationSpec.to_json()
# convert the object into a dict
interpolation_spec_dict = interpolation_spec_instance.to_dict()
# create an instance of InterpolationSpec from a dict
interpolation_spec_form_dict = interpolation_spec.from_dict(interpolation_spec_dict)