pyKVFinder.depth

pyKVFinder.depth(cavities: ndarray, step: float | int = 0.6, selection: List[int] | List[str] | None = None, nthreads: int | None = None, verbose: bool = False) → Tuple[ndarray, Dict[str, float], Dict[str, float]][source]

Characterization of the depth of the detected cavities, including depth per cavity point and maximum and average depths of detected cavities.

Parameters:

cavities (numpy.ndarray) –
Cavity points in the 3D grid (cavities[nx][ny][nz]). Cavities array has integer labels in each position, that are:
- -1: bulk points;
- 0: biomolecule points;
- 1: empty space points;
- >=2: cavity points.
The empty space points are regions that do not meet the chosen volume cutoff to be considered a cavity.
step (Union[float, int], optional) – Grid spacing (A).
selection (Union[List[int], List[str]], optional) – A list of integer labels or a list of cavity names to be selected, by default None.
nthreads (int, optional) – Number of threads, by default None. If None, the number of threads is os.cpu_count() - 1.
verbose (bool, optional) – Print extra information to standard output, by default False.

Returns:

depths (numpy.ndarray) – A numpy.ndarray with depth of cavity points (depth[nx][ny][nz]).
max_depth (Dict[str, float]) – A dictionary with maximum depth of each detected cavity.
avg_depth (Dict[str, float]) – A dictionary with average depth of each detected cavity.

Raises:

TypeError – cavities must be a numpy.ndarray.
ValueError – cavities has the incorrect shape. It must be (nx, ny, nz).
TypeError – step must be a positive real number.
ValueError – step must be a positive real number.
TypeError – selection must be a list of strings (cavity names) or integers (cavity labels).
ValueError – Invalid selection: {selection}.
TypeError – nthreads must be a positive integer.
ValueError – nthreads must be a positive integer.
TypeError – verbose must be a boolean.

Note

The cavity nomenclature is based on the integer label. The cavity marked with 2, the first integer corresponding to a cavity, is KAA, the cavity marked with 3 is KAB, the cavity marked with 4 is KAC and so on.

See also

detect, export, write_results

Example

With the cavity points identified with detect, we can perform a depth characterization, that includes maximum depth, average depth and defining depth of cavity points:

>>> from pyKVFinder import depth
>>> depths, max_depth, avg_depth = depth(cavities)
>>> depths
array([[[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]],
      ...,
      [[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]]])
>>> max_depth
{'KAA': 3.79, 'KAB': 2.68, 'KAC': 2.62, 'KAD': 0.85, 'KAE': 3.0, 'KAF': 0.85, 'KAG': 0.6, 'KAH': 10.73, 'KAI': 0.0, 'KAJ': 2.24, 'KAK': 0.0, 'KAL': 3.0, 'KAM': 1.2, 'KAN': 0.0, 'KAO': 1.04, 'KAP': 2.08, 'KAQ': 0.85, 'KAR': 0.6}
>>> avg_depth
{'KAA': 1.35, 'KAB': 0.91, 'KAC': 0.68, 'KAD': 0.32, 'KAE': 0.99, 'KAF': 0.24, 'KAG': 0.1, 'KAH': 3.91, 'KAI': 0.0, 'KAJ': 0.96, 'KAK': 0.0, 'KAL': 1.07, 'KAM': 0.24, 'KAN': 0.0, 'KAO': 0.29, 'KAP': 0.7, 'KAQ': 0.22, 'KAR': 0.12}