2.4. ECTResult

ECTResult class provides a fast way to manipulate ECT matrices while also maintaining properties of being a numpy array.

class ect.results.ECTResult(matrix, directions, thresholds)

A numpy ndarray subclass that carries ECT metadata and plotting capabilities Acts like a regular matrix but with added visualization methods and metadata about directions and thresholds

property has_csr

classmethod from_csr(row_ptr, col_idx, data, directions, thresholds, dtype=<class 'numpy.int32'>)

to_dense()

save_npz(path)

classmethod load_npz(path, directions)

plot(ax=None)

Plot ECT matrix with proper handling for both 2D and 3D

smooth()

Calculate the Smooth Euler Characteristic Transform

dist(other, metric='cityblock', **kwargs)

Compute distance to another ECTResult or list of ECTResults.

Parameters:

• other – Another ECTResult object or list of ECTResult objects

• metric –

  Distance metric to use. Can be: - String: any metric supported by scipy.spatial.distance

  (e.g., ‘euclidean’, ‘cityblock’, ‘chebyshev’, ‘cosine’, etc.)

  • Callable: a custom distance function that takes two 1D arrays and returns a scalar distance. The function should have signature: func(u, v) -> float

• **kwargs – Additional keyword arguments passed to the metric function (e.g., p=3 for minkowski distance, w=weights for weighted metrics)

Returns:

Single distance if other is an ECTResult,

array of distances if other is a list

Return type:

float or np.ndarray

Raises:

ValueError – If the shapes of the ECTResults don’t match

Examples

>>> # Built-in metrics
>>> dist1 = ect1.dist(ect2, metric='euclidean')
>>> dist2 = ect1.dist(ect2, metric='minkowski', p=3)
>>>
>>> # Custom distance function
>>> def my_distance(u, v):
...     return np.sum(np.abs(u - v) ** 0.5)
>>> dist3 = ect1.dist(ect2, metric=my_distance)
>>>
>>> # Batch distances with custom function
>>> dists = ect1.dist([ect2, ect3, ect4], metric=my_distance)