using for loop to install conda package

.CondaPkg/env/Lib/site-packages/skimage/segmentation/_expand_labels.py

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+import numpy as np
+from scipy.ndimage import distance_transform_edt
+
+
+def expand_labels(label_image, distance=1):
+    """Expand labels in label image by ``distance`` pixels without overlapping.
+
+    Given a label image, ``expand_labels`` grows label regions (connected components)
+    outwards by up to ``distance`` pixels without overflowing into neighboring regions.
+    More specifically, each background pixel that is within Euclidean distance
+    of <= ``distance`` pixels of a connected component is assigned the label of that
+    connected component.
+    Where multiple connected components are within ``distance`` pixels of a background
+    pixel, the label value of the closest connected component will be assigned (see
+    Notes for the case of multiple labels at equal distance).
+
+    Parameters
+    ----------
+    label_image : ndarray of dtype int
+        label image
+    distance : float
+        Euclidean distance in pixels by which to grow the labels. Default is one.
+
+    Returns
+    -------
+    enlarged_labels : ndarray of dtype int
+        Labeled array, where all connected regions have been enlarged
+
+    Notes
+    -----
+    Where labels are spaced more than ``distance`` pixels are apart, this is
+    equivalent to a morphological dilation with a disc or hyperball of radius ``distance``.
+    However, in contrast to a morphological dilation, ``expand_labels`` will
+    not expand a label region into a neighboring region.
+
+    This implementation of ``expand_labels`` is derived from CellProfiler [1]_, where
+    it is known as module "IdentifySecondaryObjects (Distance-N)" [2]_.
+
+    There is an important edge case when a pixel has the same distance to
+    multiple regions, as it is not defined which region expands into that
+    space. Here, the exact behavior depends on the upstream implementation
+    of ``scipy.ndimage.distance_transform_edt``.
+
+    See Also
+    --------
+    :func:`skimage.measure.label`, :func:`skimage.segmentation.watershed`, :func:`skimage.morphology.dilation`
+
+    References
+    ----------
+    .. [1] https://cellprofiler.org
+    .. [2] https://github.com/CellProfiler/CellProfiler/blob/082930ea95add7b72243a4fa3d39ae5145995e9c/cellprofiler/modules/identifysecondaryobjects.py#L559
+
+    Examples
+    --------
+    >>> labels = np.array([0, 1, 0, 0, 0, 0, 2])
+    >>> expand_labels(labels, distance=1)
+    array([1, 1, 1, 0, 0, 2, 2])
+
+    Labels will not overwrite each other:
+
+    >>> expand_labels(labels, distance=3)
+    array([1, 1, 1, 1, 2, 2, 2])
+
+    In case of ties, behavior is undefined, but currently resolves to the
+    label closest to ``(0,) * ndim`` in lexicographical order.
+
+    >>> labels_tied = np.array([0, 1, 0, 2, 0])
+    >>> expand_labels(labels_tied, 1)
+    array([1, 1, 1, 2, 2])
+    >>> labels2d = np.array(
+    ...     [[0, 1, 0, 0],
+    ...      [2, 0, 0, 0],
+    ...      [0, 3, 0, 0]]
+    ... )
+    >>> expand_labels(labels2d, 1)
+    array([[2, 1, 1, 0],
+           [2, 2, 0, 0],
+           [2, 3, 3, 0]])
+    """
+
+    distances, nearest_label_coords = distance_transform_edt(
+        label_image == 0, return_indices=True
+    )
+    labels_out = np.zeros_like(label_image)
+    dilate_mask = distances <= distance
+    # build the coordinates to find nearest labels,
+    # in contrast to [1] this implementation supports label arrays
+    # of any dimension
+    masked_nearest_label_coords = [
+        dimension_indices[dilate_mask]
+        for dimension_indices in nearest_label_coords
+    ]
+    nearest_labels = label_image[tuple(masked_nearest_label_coords)]
+    labels_out[dilate_mask] = nearest_labels
+    return labels_out