ValueBasedSearchDefinition

trendminer_interface.objects.search.value_based_search.ValueBasedSearchDefinition

Bases: BaseSearchDefinition

Definition for a value based search

Attributes:

Name	Type	Description
queries	list[ValueBasedSearchQuery]	Search query tuples.
duration	Timedelta	Duration of the search.
operator	{'and', 'or'}	How the queries are combined ("and": all must match; "or": any may match).

queries instance-attribute

queries: list[ValueBasedSearchQuery] = queries

duration instance-attribute

duration: Timedelta = duration

operator instance-attribute

operator: ValueBasedSearchMode = mode

calculations instance-attribute

calculations: dict[str, SearchCalculation] = calculations

get_results

get_results(
    target: Interval[Timestamp] | DataFrame,
    drop: bool = True,
    page: int = 0,
    size: int = 10000,
) -> PagedDataFrame

Perform a search based on the definition

Parameters:

Name	Type	Description	Default
target	Interval or DataFrame	The input intervals to search for. Can be either a single pandas Interval or a DataFrame with IntervalIndex containing non-overlapping intervals. The closed property of the target will be retained in the search results.	required
drop	bool	Whether to drop the original interval columns from the resulting DataFrame, by default True. If False, the original interval in which each search result was found will be kept as a column in the resulting DataFrame. For a pd.Interval input, the name of this column will be search_interval. For a DataFrame input, the name of the original IntervalIndex will be kept.	True
page	int	The page number of results to retrieve. Default is 0 (the first page).	0
size	int	The number of results to include in each page. Default is 10000.	10000

Returns:

Type	Description
PagedDataFrame	Page containing DataFrame with IntervalIndex and columns corresponding to the calculations specified.

Raises:

Type	Description
ValueError	If target is neither a pandas Interval nor a pandas DataFrame, or if the input intervals overlap.

Notes

Any columns already present in an input DataFrame will be kept. Their values will be mapped to the search results based on the intervals in which the search results were found.