Extensions

absolute_minimum_deviation(*args, **kwargs)

Overloaded function.

1. absolute_minimum_deviation(coordinates: numpy.ndarray[numpy.float64], output: numpy.ndarray[numpy.float64]) -> None

Compute pairwise absolute minimum deviation for a set of coordinates (float64).

2. absolute_minimum_deviation(coordinates: numpy.ndarray[numpy.float32], output: numpy.ndarray[numpy.float32]) -> None

Compute pairwise absolute minimum deviation for a set of coordinates (float32).

3. absolute_minimum_deviation(coordinates: numpy.ndarray[numpy.int64], output: numpy.ndarray[numpy.int64]) -> None

Compute pairwise absolute minimum deviation for a set of coordinates (int64).

4. absolute_minimum_deviation(coordinates: numpy.ndarray[numpy.int32], output: numpy.ndarray[numpy.int32]) -> None

Compute pairwise absolute minimum deviation for a set of coordinates (int32).

find_candidate_coordinates(*args, **kwargs)

Overloaded function.

1. find_candidate_coordinates(coordinates: numpy.ndarray[numpy.float64], min_distance: float) -> numpy.ndarray[numpy.float64]

Finds candidate coordinates with minimum distance (float64).

2. find_candidate_coordinates(coordinates: numpy.ndarray[numpy.float32], min_distance: float) -> numpy.ndarray[numpy.float32]

Finds candidate coordinates with minimum distance (float32).

3. find_candidate_coordinates(coordinates: numpy.ndarray[numpy.int64], min_distance: int) -> numpy.ndarray[numpy.int64]

Finds candidate coordinates with minimum distance (int64).

4. find_candidate_coordinates(coordinates: numpy.ndarray[numpy.int32], min_distance: int) -> numpy.ndarray[numpy.int32]

Finds candidate coordinates with minimum distance (int32).

find_candidate_indices(*args, **kwargs)

Overloaded function.

1. find_candidate_indices(coordinates: numpy.ndarray[numpy.float64], min_distance: float) -> numpy.ndarray[numpy.int32]

Finds candidate indices with minimum distance (float64).

2. find_candidate_indices(coordinates: numpy.ndarray[numpy.float32], min_distance: float) -> numpy.ndarray[numpy.int32]

Finds candidate indices with minimum distance (float32).

3. find_candidate_indices(coordinates: numpy.ndarray[numpy.int64], min_distance: int) -> numpy.ndarray[numpy.int32]

Finds candidate indices with minimum distance (int64).

4. find_candidate_indices(coordinates: numpy.ndarray[numpy.int32], min_distance: int) -> numpy.ndarray[numpy.int32]

Finds candidate indices with minimum distance (int32).

max_euclidean_distance(*args, **kwargs)

Overloaded function.

1. max_euclidean_distance(coordinates: numpy.ndarray[numpy.float64]) -> tuple[float, tuple[int, int]]

Identify pair of points with maximal euclidean distance (float64).

2. max_euclidean_distance(coordinates: numpy.ndarray[numpy.float32]) -> tuple[float, tuple[int, int]]

Identify pair of points with maximal euclidean distance (float32).

3. max_euclidean_distance(coordinates: numpy.ndarray[numpy.int64]) -> tuple[float, tuple[int, int]]

Identify pair of points with maximal euclidean distance (int64).

4. max_euclidean_distance(coordinates: numpy.ndarray[numpy.int32]) -> tuple[float, tuple[int, int]]

Identify pair of points with maximal euclidean distance (int32).

max_index_by_label(*args, **kwargs)

Overloaded function.

1. max_index_by_label(labels: numpy.ndarray[numpy.float64], scores: numpy.ndarray[numpy.float64]) -> dict

Maximum value by label

2. max_index_by_label(labels: numpy.ndarray[numpy.float64], scores: numpy.ndarray[numpy.float32]) -> dict

Maximum value by label

3. max_index_by_label(labels: numpy.ndarray[numpy.float64], scores: numpy.ndarray[numpy.int64]) -> dict

Maximum value by label

4. max_index_by_label(labels: numpy.ndarray[numpy.float64], scores: numpy.ndarray[numpy.int32]) -> dict

Maximum value by label

5. max_index_by_label(labels: numpy.ndarray[numpy.float32], scores: numpy.ndarray[numpy.float64]) -> dict

Maximum value by label

6. max_index_by_label(labels: numpy.ndarray[numpy.float32], scores: numpy.ndarray[numpy.float32]) -> dict

Maximum value by label

7. max_index_by_label(labels: numpy.ndarray[numpy.float32], scores: numpy.ndarray[numpy.int64]) -> dict

Maximum value by label

8. max_index_by_label(labels: numpy.ndarray[numpy.float32], scores: numpy.ndarray[numpy.int32]) -> dict

Maximum value by label

9. max_index_by_label(labels: numpy.ndarray[numpy.int64], scores: numpy.ndarray[numpy.float64]) -> dict

Maximum value by label

10. max_index_by_label(labels: numpy.ndarray[numpy.int64], scores: numpy.ndarray[numpy.float32]) -> dict

Maximum value by label

11. max_index_by_label(labels: numpy.ndarray[numpy.int64], scores: numpy.ndarray[numpy.int64]) -> dict

Maximum value by label

12. max_index_by_label(labels: numpy.ndarray[numpy.int64], scores: numpy.ndarray[numpy.int32]) -> dict

Maximum value by label

13. max_index_by_label(labels: numpy.ndarray[numpy.int32], scores: numpy.ndarray[numpy.float64]) -> dict

Maximum value by label

14. max_index_by_label(labels: numpy.ndarray[numpy.int32], scores: numpy.ndarray[numpy.float32]) -> dict

Maximum value by label

15. max_index_by_label(labels: numpy.ndarray[numpy.int32], scores: numpy.ndarray[numpy.int64]) -> dict

Maximum value by label

16. max_index_by_label(labels: numpy.ndarray[numpy.int32], scores: numpy.ndarray[numpy.int32]) -> dict

Maximum value by label

online_statistics(*args, **kwargs)

Overloaded function.

1. online_statistics(arr: numpy.ndarray[numpy.float64], n: int = 0, rmean: float = 0, ssqd: float = 0, reference: float = 0) -> tuple

Compute running online statistics on a numpy array.

2. online_statistics(arr: numpy.ndarray[numpy.float32], n: int = 0, rmean: float = 0, ssqd: float = 0, reference: float = 0) -> tuple

Compute running online statistics on a numpy array.

3. online_statistics(arr: numpy.ndarray[numpy.int64], n: int = 0, rmean: float = 0, ssqd: float = 0, reference: int = 0) -> tuple

Compute running online statistics on a numpy array.

4. online_statistics(arr: numpy.ndarray[numpy.int32], n: int = 0, rmean: float = 0, ssqd: float = 0, reference: int = 0) -> tuple

Compute running online statistics on a numpy array.