Viewing File: /home/ubuntu/combine_ai/combine/lib/python3.10/site-packages/skimage/_shared/tests/test_coord.py

import time

import numpy as np
import pytest
from scipy.spatial.distance import pdist, minkowski

from skimage._shared.coord import ensure_spacing


@pytest.mark.parametrize("p", [1, 2, np.inf])
@pytest.mark.parametrize("size", [30, 50, None])
def test_ensure_spacing_trivial(p, size):
    # --- Empty input
    assert ensure_spacing([], p_norm=p) == []

    # --- A unique point
    coord = np.random.randn(1, 2)
    assert np.array_equal(coord, ensure_spacing(coord, p_norm=p,
                                                min_split_size=size))

    # --- Verified spacing
    coord = np.random.randn(100, 2)

    # --- 0 spacing
    assert np.array_equal(coord, ensure_spacing(coord, spacing=0, p_norm=p,
                                                min_split_size=size))

    # Spacing is chosen to be half the minimum distance
    spacing = pdist(coord, metric=minkowski, p=p).min() * 0.5

    out = ensure_spacing(coord, spacing=spacing, p_norm=p,
                         min_split_size=size)

    assert np.array_equal(coord, out)


@pytest.mark.parametrize("ndim", [1, 2, 3, 4, 5])
@pytest.mark.parametrize("size", [2, 10, None])
def test_ensure_spacing_nD(ndim, size):
    coord = np.ones((5, ndim))

    expected = np.ones((1, ndim))

    assert np.array_equal(ensure_spacing(coord, min_split_size=size), expected)


@pytest.mark.parametrize("p", [1, 2, np.inf])
@pytest.mark.parametrize("size", [50, 100, None])
def test_ensure_spacing_batch_processing(p, size):
    coord = np.random.randn(100, 2)

    # --- Consider the average distance btween the point as spacing
    spacing = np.median(pdist(coord, metric=minkowski, p=p))

    expected = ensure_spacing(coord, spacing=spacing, p_norm=p)

    assert np.array_equal(ensure_spacing(coord, spacing=spacing, p_norm=p,
                                         min_split_size=size),
                          expected)


def test_max_batch_size():
    """Small batches are slow, large batches -> large allocations -> also slow.

    https://github.com/scikit-image/scikit-image/pull/6035#discussion_r751518691
    """
    coords = np.random.randint(low=0, high=1848, size=(40000, 2))
    tstart = time.time()
    ensure_spacing(coords, spacing=100, min_split_size=50,
                   max_split_size=2000)
    dur1 = time.time() - tstart

    tstart = time.time()
    ensure_spacing(coords, spacing=100, min_split_size=50,
                   max_split_size=20000)
    dur2 = time.time() - tstart

    # Originally checked dur1 < dur2 to assert that the default batch size was
    # faster than a much larger batch size. However, on rare occasion a CI test
    # case would fail with dur1 ~5% larger than dur2. To be more robust to
    # variable load or differences across architectures, we relax this here.
    assert dur1 < 1.33 * dur2


@pytest.mark.parametrize("p", [1, 2, np.inf])
@pytest.mark.parametrize("size", [30, 50, None])
def test_ensure_spacing_p_norm(p, size):
    coord = np.random.randn(100, 2)

    # --- Consider the average distance btween the point as spacing
    spacing = np.median(pdist(coord, metric=minkowski, p=p))
    out = ensure_spacing(coord, spacing=spacing, p_norm=p, min_split_size=size)

    assert pdist(out, metric=minkowski, p=p).min() > spacing