Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/skimage/data/_binary_blobs.py

@@ -0,0 +1,57 @@
+import numpy as np
+
+
+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 : int, optional
+        Seed to initialize the random number generator.
+        If `None`, a random seed from the operating system 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, seed=1)
+    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
+    from ..filters import gaussian
+
+    rs = np.random.RandomState(seed)
+    shape = tuple([length] * n_dim)
+    mask = np.zeros(shape)
+    n_pts = max(int(1. / blob_size_fraction) ** n_dim, 1)
+    points = (length * rs.rand(n_dim, n_pts)).astype(np.int)
+    mask[tuple(indices for indices in points)] = 1
+    mask = gaussian(mask, sigma=0.25 * length * blob_size_fraction)
+    threshold = np.percentile(mask, 100 * (1 - volume_fraction))
+    return np.logical_not(mask < threshold)