# Copyright (C) 2003-2005 Peter J. Verveer
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from __future__ import division, print_function, absolute_import

import numpy
import numpy as np
from . import _ni_support
from . import _nd_image
from . import morphology

__all__ = ['label', 'find_objects', 'labeled_comprehension', 'sum', 'mean',
           'variance', 'standard_deviation', 'minimum', 'maximum', 'median',
           'minimum_position', 'maximum_position', 'extrema', 'center_of_mass',
           'histogram', 'watershed_ift']


def label(input, structure=None, output=None):
    """
    Label features in an array.

    Parameters
    ----------
    input : array_like
        An array-like object to be labeled.  Any non-zero values in `input` are
        counted as features and zero values are considered the background.
    structure : array_like, optional
        A structuring element that defines feature connections.
        `structure` must be symmetric.  If no structuring element is provided,
        one is automatically generated with a squared connectivity equal to
        one.  That is, for a 2-D `input` array, the default structuring element
        is::

            [[0,1,0],
             [1,1,1],
             [0,1,0]]

    output : (None, data-type, array_like), optional
        If `output` is a data type, it specifies the type of the resulting
        labeled feature array
        If `output` is an array-like object, then `output` will be updated
        with the labeled features from this function

    Returns
    -------
    labeled_array : array_like
        An array-like object where each unique feature has a unique value
    num_features : int
        How many objects were found

    If `output` is None or a data type, this function returns a tuple,
    (`labeled_array`, `num_features`).

    If `output` is an array, then it will be updated with values in
    `labeled_array` and only `num_features` will be returned by this function.


    See Also
    --------
    find_objects : generate a list of slices for the labeled features (or
                   objects); useful for finding features' position or
                   dimensions

    Examples
    --------

    Create an image with some features, then label it using the default
    (cross-shaped) structuring element:

    >>> a = array([[0,0,1,1,0,0],
    ...            [0,0,0,1,0,0],
    ...            [1,1,0,0,1,0],
    ...            [0,0,0,1,0,0]])
    >>> labeled_array, num_features = label(a)

    Each of the 4 features are labeled with a different integer:

    >>> print num_features
    4
    >>> print labeled_array
    array([[0, 0, 1, 1, 0, 0],
           [0, 0, 0, 1, 0, 0],
           [2, 2, 0, 0, 3, 0],
           [0, 0, 0, 4, 0, 0]])

    Generate a structuring element that will consider features connected even
    if they touch diagonally:

    >>> s = generate_binary_structure(2,2)

    or,

    >>> s = [[1,1,1],
             [1,1,1],
             [1,1,1]]

    Label the image using the new structuring element:

    >>> labeled_array, num_features = label(a, structure=s)

    Show the 2 labeled features (note that features 1, 3, and 4 from above are
    now considered a single feature):

    >>> print num_features
    2
    >>> print labeled_array
    array([[0, 0, 1, 1, 0, 0],
           [0, 0, 0, 1, 0, 0],
           [2, 2, 0, 0, 1, 0],
           [0, 0, 0, 1, 0, 0]])

    """
    input = numpy.asarray(input)
    if numpy.iscomplexobj(input):
        raise TypeError('Complex type not supported')
    if structure is None:
        structure = morphology.generate_binary_structure(input.ndim, 1)
    structure = numpy.asarray(structure, dtype = bool)
    if structure.ndim != input.ndim:
        raise RuntimeError('structure and input must have equal rank')
    for ii in structure.shape:
        if ii != 3:
            raise  RuntimeError('structure dimensions must be equal to 3')
    if not structure.flags.contiguous:
        structure = structure.copy()
    requested_output = None
    requested_dtype = None
    if isinstance(output, numpy.ndarray):
        if output.dtype.type != numpy.int32:
            if output.shape != input.shape:
                raise RuntimeError("output shape not correct")
            # _ndimage.label() needs np.int32
            requested_output = output
            output = numpy.int32
        else:
            # output will be written directly
            pass
    else:
        requested_dtype = output
        output = numpy.int32
    output, return_value = _ni_support._get_output(output, input)
    max_label = _nd_image.label(input, structure, output)
    if return_value is None:
        # result was written in-place
        return max_label
    elif requested_output is not None:
        # original output was not int32
        requested_output[...] = output[...]
        return max_label
    else:
        if requested_dtype is not None:
            return_value = return_value.astype(requested_dtype)
        return return_value, max_label

def find_objects(input, max_label=0):
    """
    Find objects in a labeled array.

    Parameters
    ----------
    input : ndarray of ints
        Array containing objects defined by different labels.
    max_label : int, optional
        Maximum label to be searched for in `input`. If max_label is not
        given, the positions of all objects are returned.

    Returns
    -------
    object_slices : list of tuples
        A list of tuples, with each tuple containing N slices (with N the
        dimension of the input array).  Slices correspond to the minimal
        parallelepiped that contains the object. If a number is missing,
        None is returned instead of a slice.

    See Also
    --------
    label, center_of_mass

    Notes
    -----
    This function is very useful for isolating a volume of interest inside
    a 3-D array, that cannot be "seen through".

    Examples
    --------
    >>> a = np.zeros((6,6), dtype=np.int)
    >>> a[2:4, 2:4] = 1
    >>> a[4, 4] = 1
    >>> a[:2, :3] = 2
    >>> a[0, 5] = 3
    >>> a
    array([[2, 2, 2, 0, 0, 3],
           [2, 2, 2, 0, 0, 0],
           [0, 0, 1, 1, 0, 0],
           [0, 0, 1, 1, 0, 0],
           [0, 0, 0, 0, 1, 0],
           [0, 0, 0, 0, 0, 0]])
    >>> ndimage.find_objects(a)
    [(slice(2, 5, None), slice(2, 5, None)), (slice(0, 2, None), slice(0, 3, None)), (slice(0, 1, None), slice(5, 6, None))]
    >>> ndimage.find_objects(a, max_label=2)
    [(slice(2, 5, None), slice(2, 5, None)), (slice(0, 2, None), slice(0, 3, None))]
    >>> ndimage.find_objects(a == 1, max_label=2)
    [(slice(2, 5, None), slice(2, 5, None)), None]

    >>> loc = ndimage.find_objects(a)[0]
    >>> a[loc]
    array([[1, 1, 0]
           [1, 1, 0]
           [0, 0, 1]])

    """
    input = numpy.asarray(input)
    if numpy.iscomplexobj(input):
        raise TypeError('Complex type not supported')
    if max_label < 1:
        max_label = input.max()
    return _nd_image.find_objects(input, max_label)

def labeled_comprehension(input, labels, index, func, out_dtype, default, pass_positions=False):
    """
    Roughly equivalent to [func(input[labels == i]) for i in index].

    Sequentially applies an arbitrary function (that works on array_like input)
    to subsets of an n-D image array specified by `labels` and `index`.
    The option exists to provide the function with positional parameters as the
    second argument.

    Parameters
    ----------
    input : array_like
        Data from which to select `labels` to process.
    labels : array_like, or None
        Labels to objects in `input`.
        If not None, array must be same shape as `input`.
        If None, `func` is applied to raveled `input`.
    index : int, sequence of int, or None
        Subset of `labels` to which to apply `func`.
        If a scalar, a single value is returned.
        If None, `func` is applied to all non-zero values of `labels`.
    func : callable
        Python function to apply to `labels` from `input`.
    out_dtype : dtype
        Dtype to use for `result`.
    default : int, float, or None
        Default return value when a element of `index` does not exist
        in `labels`.
    pass_positions : bool, optional
        If True, pass linear indices to `func` as a second argument.
        Default is False.

    Returns
    -------
    result : ndarray
        Result of applying `func` to each of `labels` to `input` in `index`.

    Examples
    --------
    >>> a = np.array([[1, 2, 0, 0],
                      [5, 3, 0, 4],
                      [0, 0, 0, 7],
                      [9, 3, 0, 0]])
    >>> from scipy import ndimage
    >>> lbl, nlbl = ndimage.label(a)
    >>> lbls = np.arange(1, nlbl+1)
    >>> ndimage.labeled_comprehension(a, lbl, lbls, np.mean, float, 0)
    array([ 2.75,  5.5 ,  6.  ])

    Falling back to `default`:

    >>> lbls = np.arange(1, nlbl+2)
    >>> ndimage.labeled_comprehension(a, lbl, lbls, np.mean, float, -1)
    array([ 2.75,  5.5 ,  6.  , -1.  ])

    Passing positions:

    >>> def fn(val, pos):
    ...     print "fn says:", val, ":", pos
    ...     return (val.sum()) if (pos.sum() % 2 == 0) else (-val.sum())
    ...
    >>> ndimage.labeled_comprehension(a, lbl, lbls, fn, float, 0, True)
    fn says: [1 2 5 3] : [0 1 4 5]
    fn says: [4 7] : [7 11]
    fn says: [9 3] : [12 13]
    array([ 11.,  11., -12.])

    """

    as_scalar = numpy.isscalar(index)
    input = numpy.asarray(input)

    if pass_positions:
        positions = numpy.arange(input.size).reshape(input.shape)

    if labels is None:
        if index is not None:
            raise ValueError("index without defined labels")
        if not pass_positions:
            return func(input.ravel())
        else:
            return func(input.ravel(), positions.ravel())

    try:
        input, labels = numpy.broadcast_arrays(input, labels)
    except ValueError:
        raise ValueError("input and labels must have the same shape "
                            "(excepting dimensions with width 1)")

    if index is None:
        if not pass_positions:
            return func(input[labels > 0])
        else:
            return func(input[labels > 0], positions[labels > 0])

    index = numpy.atleast_1d(index)
    if np.any(index.astype(labels.dtype).astype(index.dtype) != index):
        raise ValueError("Cannot convert index values from <%s> to <%s> "
                            "(labels' type) without loss of precision" %
                            (index.dtype, labels.dtype))
    index = index.astype(labels.dtype)

    # optimization: find min/max in index, and select those parts of labels, input, and positions
    lo = index.min()
    hi = index.max()
    mask = (labels >= lo) & (labels <= hi)

    # this also ravels the arrays
    labels = labels[mask]
    input = input[mask]
    if pass_positions:
        positions = positions[mask]

    # sort everything by labels
    label_order = labels.argsort()
    labels = labels[label_order]
    input = input[label_order]
    if pass_positions:
        positions = positions[label_order]

    index_order = index.argsort()
    sorted_index = index[index_order]

    def do_map(inputs, output):
        '''labels must be sorted'''

        nlabels = labels.size
        nidx = sorted_index.size

        # Find boundaries for each stretch of constant labels
        # This could be faster, but we already paid N log N to sort labels.
        lo = numpy.searchsorted(labels, sorted_index, side='left')
        hi = numpy.searchsorted(labels, sorted_index, side='right')

        for i, l, h in zip(range(nidx), lo, hi):
            if l == h:
                continue
            idx = sorted_index[i]
            output[i] = func(*[inp[l:h] for inp in inputs])

    temp = numpy.empty(index.shape, out_dtype)
    temp[:] = default
    if not pass_positions:
        do_map([input], temp)
    else:
        do_map([input, positions], temp)
    output = numpy.zeros(index.shape, out_dtype)
    output[index_order] = temp

    if as_scalar:
        output = output[0]

    return output

def _safely_castable_to_int(dt):
    """Test whether the numpy data type `dt` can be safely cast to an int."""
    int_size = np.dtype(int).itemsize
    safe = ((np.issubdtype(dt, int) and dt.itemsize <= int_size) or
            (np.issubdtype(dt, np.unsignedinteger) and dt.itemsize < int_size))
    return safe

def _stats(input, labels=None, index=None, centered=False):
    """Count, sum, and optionally compute (sum - centre)^2 of input by label

    Parameters
    ----------
    input : array_like, n-dimensional
        The input data to be analyzed.
    labels : array_like (n-dimensional), optional
        The labels of the data in `input`.  This array must be broadcast
        compatible with `input`; typically it is the same shape as `input`.
        If `labels` is None, all nonzero values in `input` are treated as
        the single labeled group.
    index : label or sequence of labels, optional
        These are the labels of the groups for which the stats are computed.
        If `index` is None, the stats are computed for the single group where
        `labels` is greater than 0.
    centered : bool, optional
        If True, the centered sum of squares for each labeled group is
        also returned.  Default is False.

    Returns
    -------
    counts : int or ndarray of ints
        The number of elements in each labeled group.
    sums : scalar or ndarray of scalars
        The sums of the values in each labeled group.
    sums_c : scalar or ndarray of scalars, optional
        The sums of mean-centered squares of the values in each labeled group.
        This is only returned if `centered` is True.

    """
    def single_group(vals):
        if centered:
            vals_c = vals - vals.mean()
            return vals.size, vals.sum(), (vals_c * vals_c.conjugate()).sum()
        else:
            return vals.size, vals.sum()

    if labels is None:
        return single_group(input)

    # ensure input and labels match sizes
    input, labels = numpy.broadcast_arrays(input, labels)

    if index is None:
        return single_group(input[labels > 0])

    if numpy.isscalar(index):
        return single_group(input[labels == index])

    def _sum_centered(labels):
        # `labels` is expected to be an ndarray with the same shape as `input`.
        # It must contain the label indices (which are not necessarily the labels
        # themselves).
        means = sums / counts
        centered_input = input - means[labels]
        # bincount expects 1d inputs, so we ravel the arguments.
        bc = numpy.bincount(labels.ravel(),
                              weights=(centered_input * \
                                       centered_input.conjugate()).ravel())
        return bc

    # Remap labels to unique integers if necessary, or if the largest
    # label is larger than the number of values.

    if (not _safely_castable_to_int(labels.dtype) or
            labels.min() < 0 or labels.max() > labels.size):
        # Use numpy.unique to generate the label indices.  `new_labels` will
        # be 1-d, but it should be interpreted as the flattened n-d array of
        # label indices.
        unique_labels, new_labels = numpy.unique(labels, return_inverse=True)
        counts = numpy.bincount(new_labels)
        sums = numpy.bincount(new_labels, weights=input.ravel())
        if centered:
            # Compute the sum of the mean-centered squares.
            # We must reshape new_labels to the n-d shape of `input` before
            # passing it _sum_centered.
            sums_c = _sum_centered(new_labels.reshape(labels.shape))
        idxs = numpy.searchsorted(unique_labels, index)
        # make all of idxs valid
        idxs[idxs >= unique_labels.size] = 0
        found = (unique_labels[idxs] == index)
    else:
        # labels are an integer type allowed by bincount, and there aren't too
        # many, so call bincount directly.
        counts = numpy.bincount(labels.ravel())
        sums = numpy.bincount(labels.ravel(), weights=input.ravel())
        if centered:
            sums_c = _sum_centered(labels)
        # make sure all index values are valid
        idxs = numpy.asanyarray(index, numpy.int).copy()
        found = (idxs >= 0) & (idxs < counts.size)
        idxs[~found] = 0

    counts = counts[idxs]
    counts[~found] = 0
    sums = sums[idxs]
    sums[~found] = 0

    if not centered:
        return (counts, sums)
    else:
        sums_c = sums_c[idxs]
        sums_c[~found] = 0
        return (counts, sums, sums_c)


def sum(input, labels=None, index=None):
    """
    Calculate the sum of the values of the array.

    Parameters
    ----------
    input : array_like
        Values of `input` inside the regions defined by `labels`
        are summed together.
    labels : array_like of ints, optional
        Assign labels to the values of the array. Has to have the same shape as
        `input`.
    index : scalar or array_like, optional
        A single label number or a sequence of label numbers of
        the objects to be measured.

    Returns
    -------
    output : list
        A list of the sums of the values of `input` inside the regions
        defined by `labels`.

    See also
    --------
    mean, median

    Examples
    --------
    >>> input =  [0,1,2,3]
    >>> labels = [1,1,2,2]
    >>> sum(input, labels, index=[1,2])
    [1.0, 5.0]

    """
    count, sum = _stats(input, labels, index)
    return sum

def mean(input, labels=None, index=None):
    """
    Calculate the mean of the values of an array at labels.

    Parameters
    ----------
    input : array_like
        Array on which to compute the mean of elements over distinct
        regions.
    labels : array_like, optional
        Array of labels of same shape, or broadcastable to the same shape as
        `input`. All elements sharing the same label form one region over
        which the mean of the elements is computed.
    index : int or sequence of ints, optional
        Labels of the objects over which the mean is to be computed.
        Default is None, in which case the mean for all values where label is
        greater than 0 is calculated.

    Returns
    -------
    out : list
        Sequence of same length as `index`, with the mean of the different
        regions labeled by the labels in `index`.

    See also
    --------
    ndimage.variance, ndimage.standard_deviation, ndimage.minimum,
    ndimage.maximum, ndimage.sum
    ndimage.label

    Examples
    --------
    >>> a = np.arange(25).reshape((5,5))
    >>> labels = np.zeros_like(a)
    >>> labels[3:5,3:5] = 1
    >>> index = np.unique(labels)
    >>> labels
    array([[0, 0, 0, 0, 0],
           [0, 0, 0, 0, 0],
           [0, 0, 0, 0, 0],
           [0, 0, 0, 1, 1],
           [0, 0, 0, 1, 1]])
    >>> index
    array([0, 1])
    >>> ndimage.mean(a, labels=labels, index=index)
    [10.285714285714286, 21.0]

    """

    count, sum = _stats(input, labels, index)
    return sum / numpy.asanyarray(count).astype(numpy.float)

def variance(input, labels=None, index=None):
    """
    Calculate the variance of the values of an n-D image array, optionally at
    specified sub-regions.

    Parameters
    ----------
    input : array_like
        Nd-image data to process.
    labels : array_like, optional
        Labels defining sub-regions in `input`.
        If not None, must be same shape as `input`.
    index : int or sequence of ints, optional
        `labels` to include in output.  If None (default), all values where
        `labels` is non-zero are used.

    Returns
    -------
    vars : float or ndarray
        Values of variance, for each sub-region if `labels` and `index` are
        specified.

    See Also
    --------
    label, standard_deviation, maximum, minimum, extrema

    Examples
    --------
    >>> a = np.array([[1, 2, 0, 0],
                      [5, 3, 0, 4],
                      [0, 0, 0, 7],
                      [9, 3, 0, 0]])
    >>> from scipy import ndimage
    >>> ndimage.variance(a)
    7.609375

    Features to process can be specified using `labels` and `index`:

    >>> lbl, nlbl = ndimage.label(a)
    >>> ndimage.variance(a, lbl, index=np.arange(1, nlbl+1))
    array([ 2.1875,  2.25  ,  9.    ])

    If no index is given, all non-zero `labels` are processed:

    >>> ndimage.variance(a, lbl)
    6.1875

    """

    count, sum, sum_c_sq = _stats(input, labels, index, centered=True)

    return sum_c_sq / np.asanyarray(count).astype(float)

def standard_deviation(input, labels=None, index=None):
    """
    Calculate the standard deviation of the values of an n-D image array,
    optionally at specified sub-regions.

    Parameters
    ----------
    input : array_like
        Nd-image data to process.
    labels : array_like, optional
        Labels to identify sub-regions in `input`.
        If not None, must be same shape as `input`.
    index : int or sequence of ints, optional
        `labels` to include in output.  If None (default), all values where
        `labels` is non-zero are used.

    Returns
    -------
    std : float or ndarray
        Values of standard deviation, for each sub-region if `labels` and
        `index` are specified.

    See Also
    --------
    label, variance, maximum, minimum, extrema

    Examples
    --------
    >>> a = np.array([[1, 2, 0, 0],
                      [5, 3, 0, 4],
                      [0, 0, 0, 7],
                      [9, 3, 0, 0]])
    >>> from scipy import ndimage
    >>> ndimage.standard_deviation(a)
    2.7585095613392387

    Features to process can be specified using `labels` and `index`:

    >>> lbl, nlbl = ndimage.label(a)
    >>> ndimage.standard_deviation(a, lbl, index=np.arange(1, nlbl+1))
    array([ 1.479,  1.5  ,  3.   ])

    If no index is given, non-zero `labels` are processed:

    >>> ndimage.standard_deviation(a, lbl)
    2.4874685927665499

    """

    return numpy.sqrt(variance(input, labels, index))

def _select(input, labels=None, index=None, find_min=False, find_max=False,
            find_min_positions=False, find_max_positions=False,
            find_median=False):
    """Returns min, max, or both, plus their positions (if requested), and
    median."""

    input = numpy.asanyarray(input)

    find_positions = find_min_positions or find_max_positions
    positions = None
    if find_positions:
        positions = numpy.arange(input.size).reshape(input.shape)

    def single_group(vals, positions):
        result = []
        if find_min:
            result += [vals.min()]
        if find_min_positions:
            result += [positions[vals == vals.min()][0]]
        if find_max:
            result += [vals.max()]
        if find_max_positions:
            result += [positions[vals == vals.max()][0]]
        if find_median:
            result += [numpy.median(vals)]
        return result

    if labels is None:
        return single_group(input, positions)

    # ensure input and labels match sizes
    input, labels = numpy.broadcast_arrays(input, labels)

    if index is None:
        mask = (labels > 0)
        masked_positions = None
        if find_positions:
            masked_positions = positions[mask]
        return single_group(input[mask], masked_positions)

    if numpy.isscalar(index):
        mask = (labels == index)
        masked_positions = None
        if find_positions:
            masked_positions = positions[mask]
        return single_group(input[mask], masked_positions)

    # remap labels to unique integers if necessary, or if the largest
    # label is larger than the number of values.
    if (not _safely_castable_to_int(labels.dtype) or
            labels.min() < 0 or labels.max() > labels.size):
        # remap labels, and indexes
        unique_labels, labels = numpy.unique(labels, return_inverse=True)
        idxs = numpy.searchsorted(unique_labels, index)

        # make all of idxs valid
        idxs[idxs >= unique_labels.size] = 0
        found = (unique_labels[idxs] == index)
    else:
        # labels are an integer type, and there aren't too many.
        idxs = numpy.asanyarray(index, numpy.int).copy()
        found = (idxs >= 0) & (idxs <= labels.max())

    idxs[~ found] = labels.max() + 1

    if find_median:
        order = numpy.lexsort((input.ravel(), labels.ravel()))
    else:
        order = input.ravel().argsort()
    input = input.ravel()[order]
    labels = labels.ravel()[order]
    if find_positions:
        positions = positions.ravel()[order]

    result = []
    if find_min:
        mins = numpy.zeros(labels.max() + 2, input.dtype)
        mins[labels[::-1]] = input[::-1]
        result += [mins[idxs]]
    if find_min_positions:
        minpos = numpy.zeros(labels.max() + 2)
        minpos[labels[::-1]] = positions[::-1]
        result += [minpos[idxs]]
    if find_max:
        maxs = numpy.zeros(labels.max() + 2, input.dtype)
        maxs[labels] = input
        result += [maxs[idxs]]
    if find_max_positions:
        maxpos = numpy.zeros(labels.max() + 2)
        maxpos[labels] = positions
        result += [maxpos[idxs]]
    if find_median:
        locs = numpy.arange(len(labels))
        lo = numpy.zeros(labels.max() + 2, numpy.int)
        lo[labels[::-1]] = locs[::-1]
        hi = numpy.zeros(labels.max() + 2, numpy.int)
        hi[labels] = locs
        lo = lo[idxs]
        hi = hi[idxs]
        # lo is an index to the lowest value in input for each label,
        # hi is an index to the largest value.
        # move them to be either the same ((hi - lo) % 2 == 0) or next
        # to each other ((hi - lo) % 2 == 1), then average.
        step = (hi - lo) // 2
        lo += step
        hi -= step
        result += [(input[lo] + input[hi]) / 2.0]

    return result

def minimum(input, labels=None, index=None):
    """
    Calculate the minimum of the values of an array over labeled regions.

    Parameters
    ----------
    input : array_like
        Array_like of values. For each region specified by `labels`, the
        minimal values of `input` over the region is computed.
    labels : array_like, optional
        An array_like of integers marking different regions over which the
        minimum value of `input` is to be computed. `labels` must have the
        same shape as `input`. If `labels` is not specified, the minimum
        over the whole array is returned.
    index : array_like, optional
        A list of region labels that are taken into account for computing the
        minima. If index is None, the minimum over all elements where `labels`
        is non-zero is returned.

    Returns
    -------
    output : float or list of floats
        List of minima of `input` over the regions determined by `labels` and
        whose index is in `index`. If `index` or `labels` are not specified, a
        float is returned: the minimal value of `input` if `labels` is None,
        and the minimal value of elements where `labels` is greater than zero
        if `index` is None.

    See also
    --------
    label, maximum, median, minimum_position, extrema, sum, mean, variance,
    standard_deviation

    Notes
    -----
    The function returns a Python list and not a Numpy array, use
    `np.array` to convert the list to an array.

    Examples
    --------
    >>> a = np.array([[1, 2, 0, 0],
    ...               [5, 3, 0, 4],
    ...               [0, 0, 0, 7],
    ...               [9, 3, 0, 0]])
    >>> labels, labels_nb = ndimage.label(a)
    >>> labels
    array([[1, 1, 0, 0],
           [1, 1, 0, 2],
           [0, 0, 0, 2],
           [3, 3, 0, 0]])
    >>> ndimage.minimum(a, labels=labels, index=np.arange(1, labels_nb + 1))
    [1.0, 4.0, 3.0]
    >>> ndimage.minimum(a)
    0.0
    >>> ndimage.minimum(a, labels=labels)
    1.0

    """
    return _select(input, labels, index, find_min=True)[0]

def maximum(input, labels=None, index=None):
    """
    Calculate the maximum of the values of an array over labeled regions.

    Parameters
    ----------
    input : array_like
        Array_like of values. For each region specified by `labels`, the
        maximal values of `input` over the region is computed.
    labels : array_like, optional
        An array of integers marking different regions over which the
        maximum value of `input` is to be computed. `labels` must have the
        same shape as `input`. If `labels` is not specified, the maximum
        over the whole array is returned.
    index : array_like, optional
        A list of region labels that are taken into account for computing the
        maxima. If index is None, the maximum over all elements where `labels`
        is non-zero is returned.

    Returns
    -------
    output : float or list of floats
        List of maxima of `input` over the regions determined by `labels` and
        whose index is in `index`. If `index` or `labels` are not specified, a
        float is returned: the maximal value of `input` if `labels` is None,
        and the maximal value of elements where `labels` is greater than zero
        if `index` is None.

    See also
    --------
    label, minimum, median, maximum_position, extrema, sum, mean, variance,
    standard_deviation

    Notes
    -----
    The function returns a Python list and not a Numpy array, use
    `np.array` to convert the list to an array.

    Examples
    --------
    >>> a = np.arange(16).reshape((4,4))
    >>> a
    array([[ 0,  1,  2,  3],
           [ 4,  5,  6,  7],
           [ 8,  9, 10, 11],
           [12, 13, 14, 15]])
    >>> labels = np.zeros_like(a)
    >>> labels[:2,:2] = 1
    >>> labels[2:, 1:3] = 2
    >>> labels
    array([[1, 1, 0, 0],
           [1, 1, 0, 0],
           [0, 2, 2, 0],
           [0, 2, 2, 0]])
    >>> from scipy import ndimage
    >>> ndimage.maximum(a)
    15.0
    >>> ndimage.maximum(a, labels=labels, index=[1,2])
    [5.0, 14.0]
    >>> ndimage.maximum(a, labels=labels)
    14.0

    >>> b = np.array([[1, 2, 0, 0],
                      [5, 3, 0, 4],
                      [0, 0, 0, 7],
                      [9, 3, 0, 0]])
    >>> labels, labels_nb = ndimage.label(b)
    >>> labels
    array([[1, 1, 0, 0],
           [1, 1, 0, 2],
           [0, 0, 0, 2],
           [3, 3, 0, 0]])
    >>> ndimage.maximum(b, labels=labels, index=np.arange(1, labels_nb + 1))
    [5.0, 7.0, 9.0]

    """
    return _select(input, labels, index, find_max=True)[0]

def median(input, labels=None, index=None):
    """
    Calculate the median of the values of an array over labeled regions.

    Parameters
    ----------
    input : array_like
        Array_like of values. For each region specified by `labels`, the
        median value of `input` over the region is computed.
    labels : array_like, optional
        An array_like of integers marking different regions over which the
        median value of `input` is to be computed. `labels` must have the
        same shape as `input`. If `labels` is not specified, the median
        over the whole array is returned.
    index : array_like, optional
        A list of region labels that are taken into account for computing the
        medians. If index is None, the minimum over all elements where `labels`
        is non-zero is returned.

    Returns
    -------
    output : float or list of floats
        List of medians of `input` over the regions determined by `labels` and
        whose index is in `index`. If `index` or `labels` are not specified, a
        float is returned: the median value of `input` if `labels` is None,
        and the median value of elements where `labels` is greater than zero
        if `index` is None.

    See also
    --------
    label, minimum, maximum, extrema, sum, mean, variance, standard_deviation

    Notes
    -----
    The function returns a Python list and not a Numpy array, use
    `np.array` to convert the list to an array.

    Examples
    --------
    >>> a = np.array([[1, 2, 0, 1],
    ...               [5, 3, 0, 4],
    ...               [0, 0, 0, 7],
    ...               [9, 3, 0, 0]])
    >>> labels, labels_nb = ndimage.label(a)
    >>> labels
    array([[1, 1, 0, 2],
           [1, 1, 0, 2],
           [0, 0, 0, 2],
           [3, 3, 0, 0]])
    >>> ndimage.median(a, labels=labels, index=np.arange(1, labels_nb + 1))
    [2.5, 4.0, 6.0]
    >>> ndimage.median(a)
    1.0
    >>> ndimage.median(a, labels=labels)
    3.0

    """
    return _select(input, labels, index, find_median=True)[0]

def minimum_position(input, labels=None, index=None):
    """Find the positions of the minimums of the values of an array at labels.

    Labels must be None or an array of the same dimensions as the input.

    Index must be None, a single label or sequence of labels.  If
    none, all values where label is greater than zero are used.
    """

    dims = numpy.array(numpy.asarray(input).shape)
    # see numpy.unravel_index to understand this line.
    dim_prod = numpy.cumprod([1] + list(dims[:0:-1]))[::-1]

    result = _select(input, labels, index, find_min_positions=True)[0]

    if numpy.isscalar(result):
        return tuple((result // dim_prod) % dims)

    return [tuple(v) for v in (result.reshape(-1, 1) // dim_prod) % dims]

def maximum_position(input, labels=None, index=None):
    """Find the positions of the maximums of the values of an array at labels.

    Labels must be None or an array of the same dimensions as the input.

    Index must be None, a single label or sequence of labels.  If
    none, all values where label is greater than zero are used.
    """

    dims = numpy.array(numpy.asarray(input).shape)
    # see numpy.unravel_index to understand this line.
    dim_prod = numpy.cumprod([1] + list(dims[:0:-1]))[::-1]

    result = _select(input, labels, index, find_max_positions=True)[0]

    if numpy.isscalar(result):
        return tuple((result // dim_prod) % dims)

    return [tuple(v) for v in (result.reshape(-1, 1) // dim_prod) % dims]

def extrema(input, labels=None, index=None):
    """
    Calculate the minimums and maximums of the values of an array
    at labels, along with their positions.

    Parameters
    ----------
    input : ndarray
        Nd-image data to process.
    labels : ndarray, optional
        Labels of features in input.
        If not None, must be same shape as `input`.
    index : int or sequence of ints, optional
        Labels to include in output.  If None (default), all values where
        non-zero `labels` are used.

    Returns
    -------
    minimums, maximums : int or ndarray
        Values of minimums and maximums in each feature.
    min_positions, max_positions : tuple or list of tuples
        Each tuple gives the n-D coordinates of the corresponding minimum
        or maximum.

    See Also
    --------
    maximum, minimum, maximum_position, minimum_position, center_of_mass

    Examples
    --------
    >>> a = np.array([[1, 2, 0, 0],
                      [5, 3, 0, 4],
                      [0, 0, 0, 7],
                      [9, 3, 0, 0]])
    >>> from scipy import ndimage
    >>> ndimage.extrema(a)
    (0, 9, (0, 2), (3, 0))

    Features to process can be specified using `labels` and `index`:

    >>> lbl, nlbl = ndimage.label(a)
    >>> ndimage.extrema(a, lbl, index=np.arange(1, nlbl+1))
    (array([1, 4, 3]),
     array([5, 7, 9]),
     [(0.0, 0.0), (1.0, 3.0), (3.0, 1.0)],
     [(1.0, 0.0), (2.0, 3.0), (3.0, 0.0)])

    If no index is given, non-zero `labels` are processed:

    >>> ndimage.extrema(a, lbl)
    (1, 9, (0, 0), (3, 0))

    """

    dims = numpy.array(numpy.asarray(input).shape)
    # see numpy.unravel_index to understand this line.
    dim_prod = numpy.cumprod([1] + list(dims[:0:-1]))[::-1]

    minimums, min_positions, maximums, max_positions = _select(input, labels, index,
                                                               find_min=True, find_max=True,
                                                               find_min_positions=True, find_max_positions=True)


    if numpy.isscalar(minimums):
        return minimums, maximums, tuple((min_positions // dim_prod) % dims), tuple((max_positions // dim_prod) % dims)

    min_positions = [tuple(v) for v in (min_positions.reshape(-1, 1) // dim_prod) % dims]
    max_positions = [tuple(v) for v in (max_positions.reshape(-1, 1) // dim_prod) % dims]

    return minimums, maximums, min_positions, max_positions

def center_of_mass(input, labels=None, index=None):
    """
    Calculate the center of mass of the values of an array at labels.

    Parameters
    ----------
    input : ndarray
        Data from which to calculate center-of-mass.
    labels : ndarray, optional
        Labels for objects in `input`, as generated by ndimage.labels.
        Dimensions must be the same as `input`.
    index : int or sequence of ints, optional
        Labels for which to calculate centers-of-mass. If not specified,
        all labels greater than zero are used.

    Returns
    -------
    centerofmass : tuple, or list of tuples
        Co-ordinates of centers-of-masses.

    Examples
    --------
    >>> a = np.array(([0,0,0,0],
                      [0,1,1,0],
                      [0,1,1,0],
                      [0,1,1,0]))
    >>> from scipy import ndimage
    >>> ndimage.measurements.center_of_mass(a)
    (2.0, 1.5)

    Calculation of multiple objects in an image

    >>> b = np.array(([0,1,1,0],
                      [0,1,0,0],
                      [0,0,0,0],
                      [0,0,1,1],
                      [0,0,1,1]))
    >>> lbl = ndimage.label(b)[0]
    >>> ndimage.measurements.center_of_mass(b, lbl, [1,2])
    [(0.33333333333333331, 1.3333333333333333), (3.5, 2.5)]

    """

    normalizer = sum(input, labels, index)
    grids = numpy.ogrid[[slice(0, i) for i in input.shape]]

    results = [sum(input * grids[dir].astype(float), labels, index) / normalizer for dir in range(input.ndim)]

    if numpy.isscalar(results[0]):
        return tuple(results)

    return [tuple(v) for v in numpy.array(results).T]

def histogram(input, min, max, bins, labels=None, index=None):
    """
    Calculate the histogram of the values of an array, optionally at labels.

    Histogram calculates the frequency of values in an array within bins
    determined by `min`, `max`, and `bins`. `Labels` and `index` can limit
    the scope of the histogram to specified sub-regions within the array.

    Parameters
    ----------
    input : array_like
        Data for which to calculate histogram.
    min, max : int
        Minimum and maximum values of range of histogram bins.
    bins : int
        Number of bins.
    labels : array_like, optional
        Labels for objects in `input`.
        If not None, must be same shape as `input`.
    index : int or sequence of ints, optional
        Label or labels for which to calculate histogram. If None, all values
        where label is greater than zero are used

    Returns
    -------
    hist : ndarray
        Histogram counts.

    Examples
    --------
    >>> a = np.array([[ 0.    ,  0.2146,  0.5962,  0.    ],
                      [ 0.    ,  0.7778,  0.    ,  0.    ],
                      [ 0.    ,  0.    ,  0.    ,  0.    ],
                      [ 0.    ,  0.    ,  0.7181,  0.2787],
                      [ 0.    ,  0.    ,  0.6573,  0.3094]])
    >>> from scipy import ndimage
    >>> ndimage.measurements.histogram(a, 0, 1, 10)
    array([13,  0,  2,  1,  0,  1,  1,  2,  0,  0])

    With labels and no indices, non-zero elements are counted:

    >>> lbl, nlbl = ndimage.label(a)
    >>> ndimage.measurements.histogram(a, 0, 1, 10, lbl)
    array([0, 0, 2, 1, 0, 1, 1, 2, 0, 0])

    Indices can be used to count only certain objects:

    >>> ndimage.measurements.histogram(a, 0, 1, 10, lbl, 2)
    array([0, 0, 1, 1, 0, 0, 1, 1, 0, 0])

    """

    _bins = numpy.linspace(min, max, bins + 1)

    def _hist(vals):
        return numpy.histogram(vals, _bins)[0]

    return labeled_comprehension(input, labels, index, _hist, object, None, pass_positions=False)

def watershed_ift(input, markers, structure=None, output=None):
    """Apply watershed from markers using a iterative forest transform
    algorithm.

    Negative markers are considered background markers which are
    processed after the other markers. A structuring element defining
    the connectivity of the object can be provided. If none is
    provided, an element is generated with a squared connectivity equal
    to one. An output array can optionally be provided.
    """
    input = numpy.asarray(input)
    if input.dtype.type not in [numpy.uint8, numpy.uint16]:
        raise TypeError('only 8 and 16 unsigned inputs are supported')
    if structure is None:
        structure = morphology.generate_binary_structure(input.ndim, 1)
    structure = numpy.asarray(structure, dtype = bool)
    if structure.ndim != input.ndim:
        raise RuntimeError('structure and input must have equal rank')
    for ii in structure.shape:
        if ii != 3:
            raise  RuntimeError('structure dimensions must be equal to 3')
    if not structure.flags.contiguous:
        structure = structure.copy()
    markers = numpy.asarray(markers)
    if input.shape != markers.shape:
        raise RuntimeError('input and markers must have equal shape')

    integral_types = [numpy.int0,
                      numpy.int8,
                      numpy.int16,
                      numpy.int32,
                      numpy.int_,
                      numpy.int64,
                      numpy.intc,
                      numpy.intp]

    if markers.dtype.type not in integral_types:
        raise RuntimeError('marker should be of integer type')
    if isinstance(output, numpy.ndarray):
        if output.dtype.type not in integral_types:
            raise RuntimeError('output should be of integer type')
    else:
        output = markers.dtype
    output, return_value = _ni_support._get_output(output, input)
    _nd_image.watershed_ift(input, markers, structure, output)
    return return_value
