
    |h1                         d dl Zd ZefdZy)    Nc           	      f   t        j                  |       } t        |       }t        j                  t        j                  |             }t        j                  |       }t        t        j                  |             }||k  rt        d      f|z  S t        j                  |||z  d|z  z  d      }||k  j                         rht        |      D ]Z  }||   ||<   t        t        j                  ||dz   d             }||z  d||z
  dz
  z  z  ||dz   d ||k\  j                         sZ n |dz  j                  t              }t        j                  |      j                  t              }t        ||      D 	
cg c]  \  }	}
t        |	d|
       c}
}	t!        fd|D              S c c}
}	w )a  Find `n_points` regularly spaced along `ar_shape`.

    The returned points (as slices) should be as close to cubically-spaced as
    possible. Essentially, the points are spaced by the Nth root of the input
    array size, where N is the number of dimensions. However, if an array
    dimension cannot fit a full step size, it is "discarded", and the
    computation is done for only the remaining dimensions.

    Parameters
    ----------
    ar_shape : array-like of ints
        The shape of the space embedding the grid. ``len(ar_shape)`` is the
        number of dimensions.
    n_points : int
        The (approximate) number of points to embed in the space.

    Returns
    -------
    slices : tuple of slice objects
        A slice along each dimension of `ar_shape`, such that the intersection
        of all the slices give the coordinates of regularly spaced points.

        .. versionchanged:: 0.14.1
            In scikit-image 0.14.1 and 0.15, the return type was changed from a
            list to a tuple to ensure `compatibility with Numpy 1.15`_ and
            higher. If your code requires the returned result to be a list, you
            may convert the output of this function to a list with:

            >>> result = list(regular_grid(ar_shape=(3, 20, 40), n_points=8))

            .. _compatibility with NumPy 1.15: https://github.com/numpy/numpy/blob/master/doc/release/1.15.0-notes.rst#deprecations

    Examples
    --------
    >>> ar = np.zeros((20, 40))
    >>> g = regular_grid(ar.shape, 8)
    >>> g
    (slice(5, None, 10), slice(5, None, 10))
    >>> ar[g] = 1
    >>> ar.sum()
    8.0
    >>> ar = np.zeros((20, 40))
    >>> g = regular_grid(ar.shape, 32)
    >>> g
    (slice(2, None, 5), slice(2, None, 5))
    >>> ar[g] = 1
    >>> ar.sum()
    32.0
    >>> ar = np.zeros((3, 20, 40))
    >>> g = regular_grid(ar.shape, 8)
    >>> g
    (slice(1, None, 3), slice(5, None, 10), slice(5, None, 10))
    >>> ar[g] = 1
    >>> ar.sum()
    8.0
    Ng      ?float64dtype      c              3   (   K   | ]	  }|     y w)N ).0islicess     Y/var/www/html/test/engine/venv/lib/python3.12/site-packages/skimage/util/_regular_grid.py	<genexpr>zregular_grid.<locals>.<genexpr>O   s     66!96s   )np
asanyarraylenargsortsortfloatprodslicefullanyrangeallastypeintroundziptuple)ar_shapen_pointsndimunsort_dim_idxssorted_dims
space_size	stepsizesdimstartsstartstepr   s              @r   regular_gridr,      s   r }}X&Hx=DjjH!56O''(#Krwwx()JXd~$$zH4#*EYWIi$$&; 	C(-IcNrww{379'=>?J$.$9sdSjSTn?U#VIcAgi y(--/	 1n$$S)F#**3/I:=fi:PQ;5$eE4&QF6o66FM Rs   <F-c                     t        | |      }t        j                  | |      }dt        j                  t        j                  ||   j
                        ||   j                        z   ||<   |S )ae  Return an image with ~`n_points` regularly-spaced nonzero pixels.

    Parameters
    ----------
    ar_shape : tuple of int
        The shape of the desired output image.
    n_points : int
        The desired number of nonzero points.
    dtype : numpy data type, optional
        The desired data type of the output.

    Returns
    -------
    seed_img : array of int or bool
        The desired image.

    Examples
    --------
    >>> regular_seeds((5, 5), 4)
    array([[0, 0, 0, 0, 0],
           [0, 1, 0, 2, 0],
           [0, 0, 0, 0, 0],
           [0, 3, 0, 4, 0],
           [0, 0, 0, 0, 0]])
    r   r   )r,   r   zerosreshapearangesizeshape)r!   r"   r   gridseed_imgs        r   regular_seedsr5   S   sb    4 (+Dxx.H
		(4.%%&(<(< HTN O    )numpyr   r,   r   r5   r
   r6   r   <module>r8      s    L^ -0 r6   