rm CondaPkg environment

.CondaPkg/env/Lib/site-packages/skimage/data/_binary_blobs.py

@@ -1,63 +0,0 @@
-import numpy as np
-
-from .._shared.filters import gaussian
-
-
-def binary_blobs(length=512, blob_size_fraction=0.1, n_dim=2,
-                 volume_fraction=0.5, seed=None):
-    """
-    Generate synthetic binary image with several rounded blob-like objects.
-
-    Parameters
-    ----------
-    length : int, optional
-        Linear size of output image.
-    blob_size_fraction : float, optional
-        Typical linear size of blob, as a fraction of ``length``, should be
-        smaller than 1.
-    n_dim : int, optional
-        Number of dimensions of output image.
-    volume_fraction : float, default 0.5
-        Fraction of image pixels covered by the blobs (where the output is 1).
-        Should be in [0, 1].
-    seed : {None, int, `numpy.random.Generator`}, optional
-        If `seed` is None the `numpy.random.Generator` singleton is used.
-        If `seed` is an int, a new ``Generator`` instance is used,
-        seeded with `seed`.
-        If `seed` is already a ``Generator`` instance then that instance is
-        used.
-
-    Returns
-    -------
-    blobs : ndarray of bools
-        Output binary image
-
-    Examples
-    --------
-    >>> from skimage import data
-    >>> data.binary_blobs(length=5, blob_size_fraction=0.2)  # doctest: +SKIP
-    array([[ True, False,  True,  True,  True],
-           [ True,  True,  True, False,  True],
-           [False,  True, False,  True,  True],
-           [ True, False, False,  True,  True],
-           [ True, False, False, False,  True]])
-    >>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.1)
-    >>> # Finer structures
-    >>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.05)
-    >>> # Blobs cover a smaller volume fraction of the image
-    >>> blobs = data.binary_blobs(length=256, volume_fraction=0.3)
-
-    """
-    # filters is quite an expensive import since it imports all of scipy.signal
-    # We lazy import here
-
-    rs = np.random.default_rng(seed)
-    shape = tuple([length] * n_dim)
-    mask = np.zeros(shape)
-    n_pts = max(int(1. / blob_size_fraction) ** n_dim, 1)
-    points = (length * rs.random((n_dim, n_pts))).astype(int)
-    mask[tuple(indices for indices in points)] = 1
-    mask = gaussian(mask, sigma=0.25 * length * blob_size_fraction,
-                    preserve_range=False)
-    threshold = np.percentile(mask, 100 * (1 - volume_fraction))
-    return np.logical_not(mask < threshold)