rm CondaPkg environment

.CondaPkg/env/Lib/site-packages/skimage/_shared/coord.py

@@ -1,121 +0,0 @@
-import numpy as np
-from scipy.spatial import cKDTree, distance
-
-
-def _ensure_spacing(coord, spacing, p_norm, max_out):
-    """Returns a subset of coord where a minimum spacing is guaranteed.
-
-    Parameters
-    ----------
-    coord : ndarray
-        The coordinates of the considered points.
-    spacing : float
-        the maximum allowed spacing between the points.
-    p_norm : float
-        Which Minkowski p-norm to use. Should be in the range [1, inf].
-        A finite large p may cause a ValueError if overflow can occur.
-        ``inf`` corresponds to the Chebyshev distance and 2 to the
-        Euclidean distance.
-    max_out: int
-        If not None, at most the first ``max_out`` candidates are
-        returned.
-
-    Returns
-    -------
-    output : ndarray
-        A subset of coord where a minimum spacing is guaranteed.
-
-    """
-
-    # Use KDtree to find the peaks that are too close to each other
-    tree = cKDTree(coord)
-
-    indices = tree.query_ball_point(coord, r=spacing, p=p_norm)
-    rejected_peaks_indices = set()
-    naccepted = 0
-    for idx, candidates in enumerate(indices):
-        if idx not in rejected_peaks_indices:
-            # keep current point and the points at exactly spacing from it
-            candidates.remove(idx)
-            dist = distance.cdist([coord[idx]],
-                                  coord[candidates],
-                                  distance.minkowski,
-                                  p=p_norm).reshape(-1)
-            candidates = [c for c, d in zip(candidates, dist)
-                          if d < spacing]
-
-            # candidates.remove(keep)
-            rejected_peaks_indices.update(candidates)
-            naccepted += 1
-            if max_out is not None and naccepted >= max_out:
-                break
-
-    # Remove the peaks that are too close to each other
-    output = np.delete(coord, tuple(rejected_peaks_indices), axis=0)
-    if max_out is not None:
-        output = output[:max_out]
-
-    return output
-
-
-def ensure_spacing(coords, spacing=1, p_norm=np.inf, min_split_size=50,
-                   max_out=None, *, max_split_size=2000):
-    """Returns a subset of coord where a minimum spacing is guaranteed.
-
-    Parameters
-    ----------
-    coords : array_like
-        The coordinates of the considered points.
-    spacing : float
-        the maximum allowed spacing between the points.
-    p_norm : float
-        Which Minkowski p-norm to use. Should be in the range [1, inf].
-        A finite large p may cause a ValueError if overflow can occur.
-        ``inf`` corresponds to the Chebyshev distance and 2 to the
-        Euclidean distance.
-    min_split_size : int
-        Minimum split size used to process ``coords`` by batch to save
-        memory. If None, the memory saving strategy is not applied.
-    max_out : int
-        If not None, only the first ``max_out`` candidates are returned.
-    max_split_size : int
-        Maximum split size used to process ``coords`` by batch to save
-        memory. This number was decided by profiling with a large number
-        of points. Too small a number results in too much looping in
-        Python instead of C, slowing down the process, while too large
-        a number results in large memory allocations, slowdowns, and,
-        potentially, in the process being killed -- see gh-6010. See
-        benchmark results `here
-        <https://github.com/scikit-image/scikit-image/pull/6035#discussion_r751518691>`_.
-
-    Returns
-    -------
-    output : array_like
-        A subset of coord where a minimum spacing is guaranteed.
-
-    """
-
-    output = coords
-    if len(coords):
-
-        coords = np.atleast_2d(coords)
-        if min_split_size is None:
-            batch_list = [coords]
-        else:
-            coord_count = len(coords)
-            split_idx = [min_split_size]
-            split_size = min_split_size
-            while coord_count - split_idx[-1] > max_split_size:
-                split_size *= 2
-                split_idx.append(split_idx[-1] + min(split_size,
-                                                     max_split_size))
-            batch_list = np.array_split(coords, split_idx)
-
-        output = np.zeros((0, coords.shape[1]), dtype=coords.dtype)
-        for batch in batch_list:
-            output = _ensure_spacing(np.vstack([output, batch]),
-                                     spacing, p_norm, max_out)
-            if max_out is not None and len(output) >= max_out:
-                break
-
-    return output