Module Import alias identifier ImportFrom alias ImportFrom alias ImportFrom alias alias alias alias ImportFrom alias alias alias Assign NameStore ListLoad Str Str Str Str Str Str Str Str Str Str Str Str Str Str Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam defaults Str Num NameLoad NameLoad body Expr Str If CompareNotEq AttributeLoad NameLoad attr AttributeLoad NameLoad attr body Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr If CompareNotEq AttributeLoad NameLoad attr AttributeLoad NameLoad attr body Raise Call NameLoad Str Assign NameStore Call NameLoad NameLoad NameLoad Str keyword NameLoad Expr Call NameLoad NameLoad NameLoad Str Return Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad SubscriptLoad NameLoad Index NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam defaults Str Num NameLoad body Expr Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign NameStore Call AttributeLoad NameLoad attr If CompareNotEq AttributeLoad NameLoad attr Num body Raise Call NameLoad Str Expr Call NameLoad NameLoad NameLoad Str If BoolOpAnd AttributeLoad AttributeLoad NameLoad attr attr CompareLt Call NameLoad NameLoad SubscriptLoad AttributeLoad NameLoad attr Index NameLoad body Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr Assign NameStore BinOpAdd ListComp NameLoad comprehension NameStore Call NameLoad AttributeLoad NameLoad attr CompareNotEq NameLoad NameLoad ListLoad NameLoad Assign NameStore ListComp Call AttributeLoad NameLoad attr NameLoad comprehension NameStore Call NameLoad AttributeLoad NameLoad attr Assign NameStore AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign NameStore AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr TupleLoad Num SubscriptLoad AttributeLoad NameLoad attr Index Num Assign NameStore Call NameLoad NameLoad NameLoad Str Expr Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad SubscriptLoad NameLoad Index NameLoad Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign NameStore Call AttributeLoad NameLoad attr NameLoad Return NameLoad orelse Assign NameStore BinOpMult ListLoad NameLoad AttributeLoad NameLoad attr Assign SubscriptStore NameLoad Index NameLoad Call NameLoad Num NameLoad Assign NameStore SubscriptLoad NameLoad Index Call NameLoad NameLoad Return Call NameLoad NameLoad NameLoad keyword NameLoad keyword NameLoad keyword NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam defaults NameLoad Str Num NameLoad NameLoad body Expr Str If CompareIs NameLoad NameLoad body Assign NameStore Call AttributeLoad NameLoad attr BinOpMult TupleLoad Num Call NameLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr orelse If CompareNotEq AttributeLoad NameLoad attr AttributeLoad NameLoad attr body Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr If CompareNotEq AttributeLoad NameLoad attr AttributeLoad NameLoad attr body Raise Call NameLoad Str Assign NameStore BinOpFloorDiv Call AttributeLoad NameLoad attr Num Assign NameStore Call NameLoad NameLoad NameLoad Str keyword NameLoad Expr Call NameLoad NameLoad NameLoad Str Return Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad Call NameLoad NameLoad SubscriptLoad NameLoad Index NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam defaults Str Num NameLoad body Expr Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad Assign NameStore Call NameLoad NameLoad NameLoad Str keyword AttributeLoad NameLoad attr Expr Call NameLoad NameLoad NameLoad Str Return Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad SubscriptLoad NameLoad Index NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Str Num NameLoad NameLoad body Expr Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad Assign NameStore Call NameLoad NameLoad NameLoad Str keyword NameLoad Expr Call NameLoad NameLoad NameLoad Str Return Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad NameLoad SubscriptLoad NameLoad Index NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam defaults Str Num NameLoad NameLoad body Expr Str Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr Assign NameStore Call NameLoad NameLoad NameLoad Str keyword NameLoad Expr Call NameLoad NameLoad NameLoad Str Return Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad SubscriptLoad NameLoad Index NameLoad Num decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str If CompareNotEq AttributeLoad NameLoad attr Num body Raise Call NameLoad Str Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr NameLoad Return Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Num Str Num NameLoad NameLoad body Expr Str Expr Call NameLoad NameLoad Str Assign NameStore Call NameLoad NameLoad Assign NameStore BinOpMult NameLoad NameLoad Assign NameStore Call NameLoad BinOpAdd BinOpMult Num NameLoad Num If CompareLtE NameLoad Num body Raise Call NameLoad Str Assign NameStore Call AttributeLoad NameLoad attr BinOpAdd BinOpMult Num NameLoad Num keyword NameLoad AugAssignSub NameStore NameLoad Assign NameStore Call AttributeLoad NameLoad attr BinOpDiv BinOpMult NameLoad NameLoad BinOpMult Num NameLoad AugAssignDiv NameStore Call AttributeLoad NameLoad attr NameLoad If CompareEq NameLoad Num body Pass orelse If CompareEq NameLoad Num body AugAssignMult NameStore BinOpDiv UnaryOpUSub NameLoad NameLoad orelse If CompareEq NameLoad Num body AugAssignMult NameStore BinOpDiv BinOpSub BinOpDiv BinOpMult NameLoad NameLoad NameLoad Num NameLoad orelse If CompareEq NameLoad Num body AugAssignMult NameStore BinOpDiv BinOpMult BinOpSub Num BinOpDiv BinOpMult NameLoad NameLoad NameLoad NameLoad BinOpMult NameLoad NameLoad orelse Raise Call NameLoad Str Return Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad keyword NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Str Num NameLoad NameLoad body Expr Str Assign NameStore Call NameLoad NameLoad Assign NameStore Call NameLoad NameLoad NameLoad Str keyword NameLoad Assign NameStore Call NameLoad NameLoad NameLoad Str Assign NameStore Call NameLoad NameLoad NameLoad Str Assign SubscriptStore NameLoad Ellipsis SubscriptLoad NameLoad Ellipsis Assign NameStore NameLoad For NameStore Call NameLoad AttributeLoad NameLoad attr body Assign NameStore SubscriptLoad NameLoad Index NameLoad Assign NameStore SubscriptLoad NameLoad Index NameLoad Assign NameStore Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Assign TupleStore NameStore NameStore TupleLoad NameLoad NameLoad Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Assign NameStore Call NameLoad NameLoad If CompareNotEq AttributeLoad NameLoad attr Num body Raise Call NameLoad BinOpMod Str NameLoad If UnaryOpNot NameLoad body Return Call AttributeLoad NameLoad attr Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam defaults Num NameLoad Num body For NameStore Call NameLoad Num BinOpAdd Num NameLoad body Assign NameStore BinOpPow Num BinOpAdd Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr NameLoad NameLoad Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign NameStore BinOpSub NameLoad NameLoad AugAssignFloorDiv NameStore Num If CompareLtE Call AttributeLoad NameLoad attr NameLoad NameLoad body Continue Assign NameStore ListLoad For TupleStore NameStore NameStore Call NameLoad NameLoad NameLoad body Expr Call AttributeLoad NameLoad attr Call NameLoad NameLoad BinOpAdd NameLoad NameLoad Return TupleLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam defaults Num NameLoad Num body Expr Str Assign TupleStore NameStore NameStore Call NameLoad AttributeLoad NameLoad attr NameLoad Assign NameStore Call AttributeLoad NameLoad attr NameLoad keyword NameLoad AugAssignAdd NameStore NameLoad Assign SubscriptStore NameLoad Index NameLoad NameLoad Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults Num body Expr Str Assign TupleStore NameStore NameStore Call NameLoad NameLoad NameLoad Return SubscriptLoad NameLoad Index NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults NameLoad NameLoad body Expr Str Assign NameStore Call NameLoad NameLoad NameLoad Str Expr Call AttributeLoad NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr If NameLoad body AugAssignDiv NameStore Num Return NameLoad decorator_list Assign NameStore ListComp BinOpMod Str NameLoad comprehension NameStore Call NameLoad Num Num Num FunctionDef arguments args NameParam defaults body TryExcept body Return Call AttributeLoad NameLoad attr NameLoad handlers ExceptHandler body Raise Call NameLoad Call AttributeLoad Str attr NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults NameLoad body Expr Str Assign NameStore Call NameLoad NameLoad NameLoad Str Assign NameStore Call NameLoad NameLoad Expr Call AttributeLoad NameLoad attr NameLoad NameLoad Expr Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr NameLoad Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults NameLoad body Expr Str Assign NameStore Call NameLoad NameLoad NameLoad Str Assign NameStore Call NameLoad NameLoad Expr Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr NameLoad NameLoad Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults NameLoad NameLoad body Expr Str Assign NameStore Call NameLoad NameLoad NameLoad Str Expr Call AttributeLoad NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr If NameLoad body AugAssignMult NameStore Num Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults Num body Expr Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad keyword AttributeLoad NameLoad attr If CompareNotEq AttributeLoad NameLoad attr Num body Raise Call NameLoad Str Assign NameStore Call NameLoad Num Call NameLoad NameLoad Num Assign NameStore BinOpDiv BinOpSub Num NameLoad BinOpAdd NameLoad Num Assign NameStore BinOpDiv NameLoad BinOpAdd NameLoad Num Assign NameStore BinOpDiv Num BinOpAdd NameLoad Num Assign NameStore Call AttributeLoad NameLoad attr ListLoad ListLoad NameLoad NameLoad NameLoad ListLoad NameLoad NameLoad NameLoad ListLoad NameLoad NameLoad NameLoad Assign NameStore Call NameLoad NameLoad NameLoad keyword Str Return NameLoad decorator_list
N/A N/A numpy np N/A _convolve N/A morph internal _get_output _normalize_sequence _verify_is_floatingpoint_type _as_floating_point_array _filters mode2int modes _check_mode N/A __all__ N/A convolve convolve1d daubechies idaubechies find haar ihaar median_filter rank_filter template_match gaussian_filter1d gaussian_filter wavelet_center wavelet_decenter laplacian_2D convolve N/A N/A f weights mode cval out output N/A reflect 0.0 None None N/A N/A 
    convolved = convolve(f, weights, mode='reflect', cval=0.0, out={new array})

    Convolution of `f` and `weights`

    Convolution is performed in `doubles` to avoid over/underflow, but the
    result is then cast to `f.dtype`.

    Parameters
    ----------
    f : ndarray
        input. Any dimension is supported
    weights : ndarray
        weight filter. If not of the same dtype as `f`, it is cast
    mode : {'reflect' [default], 'nearest', 'wrap', 'mirror', 'constant', 'ignore'}
        How to handle borders
    cval : double, optional
        If `mode` is constant, which constant to use (default: 0.0)
    out : ndarray, optional
        Output array. Must have same shape and dtype as `f` as well as be
        C-contiguous.

    Returns
    -------
    convolved : ndarray of same dtype as `f`
     N/A N/A N/A f dtype N/A weights dtype N/A N/A weights N/A N/A weights astype N/A f dtype N/A N/A N/A f ndim N/A weights ndim N/A N/A N/A ValueError mahotas.convolve: `f` and `weights` must have the same dimensions N/A output N/A _get_output f out convolve output output N/A N/A _check_mode mode cval convolve N/A N/A N/A _convolve convolve f weights output N/A mode2int N/A mode N/A convolve1d N/A N/A f weights axis mode cval out N/A reflect 0.0 None N/A N/A 
    convolved = convolve1d(f, weights, axis, mode='reflect', cval=0.0, out={new array})

    Convolution of `f` and `weights` along axis `axis`.

    Convolution is performed in `doubles` to avoid over/underflow, but the
    result is then cast to `f.dtype`.

    Parameters
    ----------
    f : ndarray
        input. Any dimension is supported
    weights : 1-D ndarray
        weight filter. If not of the same dtype as `f`, it is cast
    axis : int
        Axis along which to convolve
    mode : {'reflect' [default], 'nearest', 'wrap', 'mirror', 'constant', 'ignore'}
        How to handle borders
    cval : double, optional
        If `mode` is constant, which constant to use (default: 0.0)
    out : ndarray, optional
        Output array. Must have same shape and dtype as `f` as well as be
        C-contiguous.

    Returns
    -------
    convolved : ndarray of same dtype as `f`

    See Also
    --------
    convolve : function
        generic convolution
     N/A weights N/A N/A np asanyarray weights N/A weights N/A N/A weights squeeze N/A N/A N/A weights ndim 1 N/A N/A N/A ValueError mahotas.convolve1d: only 1-D sequences allowed N/A N/A _check_mode mode cval convolve1d N/A N/A N/A N/A f flags contiguous N/A N/A len weights N/A N/A f shape N/A axis N/A N/A weights N/A N/A weights astype N/A np double N/A indices N/A N/A a N/A a N/A range N/A f ndim N/A a axis N/A axis N/A rindices N/A N/A N/A indices index a N/A a N/A range N/A f ndim N/A oshape N/A f shape N/A f N/A N/A f transpose indices N/A tshape N/A f shape N/A f N/A N/A f reshape N/A -1 N/A N/A f shape N/A -1 N/A out N/A _get_output f out convolve1d N/A N/A N/A _convolve convolve1d f weights out N/A mode2int N/A mode N/A out N/A N/A out reshape tshape N/A out N/A N/A out transpose rindices N/A out N/A N/A out reshape oshape N/A out N/A N/A index N/A N/A None N/A f ndim N/A N/A index N/A axis N/A slice 0 None N/A weights N/A weights N/A N/A tuple index N/A N/A convolve f weights mode mode cval cval out out N/A median_filter N/A N/A f Bc mode cval out output N/A None reflect 0.0 None None N/A N/A 
    median = median_filter(f, Bc={square}, mode='reflect', cval=0.0, out={np.empty(f.shape, f.dtype})

    Median filter

    Parameters
    ----------
    f : ndarray
        input. Any dimension is supported
    Bc : ndarray or int, optional
        Defines the neighbourhood, default is a square of side 3.
    mode : {'reflect' [default], 'nearest', 'wrap', 'mirror', 'constant', 'ignore'}
        How to handle borders
    cval : double, optional
        If `mode` is constant, which constant to use (default: 0.0)
    out : ndarray, optional
        Output array. Must have same shape and dtype as `f` as well as be
        C-contiguous.

    Returns
    -------
    median : ndarray of same type and shape as ``f``
        median[i,j] is the median value of the points in f close to (i,j)
     N/A N/A Bc None N/A N/A Bc N/A N/A np ones N/A N/A 3 N/A len N/A f shape N/A f dtype N/A N/A N/A N/A f dtype N/A Bc dtype N/A N/A Bc N/A N/A Bc astype N/A f dtype N/A N/A N/A f ndim N/A Bc ndim N/A N/A N/A ValueError mahotas.median_filter: `f` and `Bc` must have the same number of dimensions N/A rank N/A N/A N/A Bc sum 2 N/A output N/A _get_output f out median_filter output output N/A N/A _check_mode mode cval median_filter N/A N/A N/A _convolve rank_filter f Bc output N/A int rank N/A mode2int N/A mode N/A mean_filter N/A N/A f Bc mode cval out N/A ignore 0.0 None N/A N/A mean = mean_filter(f, Bc, mode='ignore', cval=0.0, out=None)

    Mean filter. The value at ``mean[i,j]`` will be the mean of the values in
    the neighbourhood defined by ``Bc``.

    Parameters
    ----------
    f : ndarray
        input. Any dimension is supported
    Bc : ndarray
        Defines the neighbourhood. Must be explicitly passed, no default.
    mode : {'reflect', 'nearest', 'wrap', 'mirror', 'constant', 'ignore' [ignore]}
        How to handle borders. The default is to ignore points beyond the
        border, so that the means computed near the border include fewer elements.
    cval : double, optional
        If `mode` is constant, which constant to use (default: 0.0)
    out : ndarray, optional
        Output array. Must be a double array with the same shape as `f` as well
        as be C-contiguous.

    Returns
    -------
    mean : ndarray of type double and same shape as ``f``

    See Also
    --------
    median_filter : An alternative filtering method
     N/A Bc N/A N/A morph get_structuring_elem f Bc N/A out N/A _get_output f out mean_filter dtype N/A np float64 N/A N/A _check_mode mode cval mean_filter N/A N/A N/A _convolve mean_filter f Bc out N/A mode2int N/A mode cval N/A rank_filter N/A N/A f Bc rank mode cval out output N/A reflect 0.0 None None N/A N/A 
    ranked = rank_filter(f, Bc, rank, mode='reflect', cval=0.0, out=None)

    Rank filter. The value at ``ranked[i,j]`` will be the ``rank``th largest in
    the neighbourhood defined by ``Bc``.

    Parameters
    ----------
    f : ndarray
        input. Any dimension is supported
    Bc : ndarray
        Defines the neighbourhood. Must be explicitly passed, no default.
    rank : integer
    mode : {'reflect' [default], 'nearest', 'wrap', 'mirror', 'constant', 'ignore'}
        How to handle borders
    cval : double, optional
        If `mode` is constant, which constant to use (default: 0.0)
    out : ndarray, optional
        Output array. Must have same shape and dtype as `f` as well as be
        C-contiguous.

    Returns
    -------
    ranked : ndarray of same type and shape as ``f``
        ranked[i,j] is the ``rank``th value of the points in f close to (i,j)

    See Also
    --------
    median_filter : A special case of rank_filter
     N/A Bc N/A N/A morph get_structuring_elem f Bc N/A output N/A _get_output f out rank_filter output output N/A N/A _check_mode mode cval rank_filter N/A N/A N/A _convolve rank_filter f Bc output rank N/A mode2int N/A mode N/A template_match N/A N/A f template mode cval out output N/A reflect 0.0 None None N/A N/A Match template to image

    match = template_match(f, template, mode='reflect', cval=0., out={np.empty_like(f)})

    The value at ``match[i,j]`` will be the difference (in squared euclidean
    terms), between `template` and a same sized window on `f` centered on that
    point.

    Note that the computation is performed using the same dtype as ``f``. Thus
    is may overflow if the template is large.

    Parameters
    ----------
    f : ndarray
        input. Any dimension is supported
    template : ndarray
        Template to match. Must be explicitly passed, no default.
    mode : {'reflect' [default], 'nearest', 'wrap', 'mirror', 'constant', 'ignore'}
        How to handle borders
    cval : double, optional
        If `mode` is constant, which constant to use (default: 0.0)
    out : ndarray, optional
        Output array. Must have same shape and dtype as `f` as well as be
        C-contiguous.

    Returns
    -------
    match : ndarray of same type and shape as ``f``
        match[i,j] is the squared euclidean distance between
        ``f[i-s0:i+s0,j-s1:j+s1]`` and ``template`` (for appropriately defined
        ``s0`` and ``s1``).
     N/A template N/A N/A template astype N/A f dtype N/A output N/A _get_output f out template_match output output N/A N/A _check_mode mode cval template_match N/A N/A N/A _convolve template_match f template output N/A mode2int N/A mode 0 N/A find N/A N/A f template N/A N/A N/A Match template to image exactly

    coordinates = find(f, template)

    The output is in the same format as the ``np.where`` function.

    Parameters
    ----------
    f : ndarray
        input. Currently, only 2-dimensional images are supported.
    template : ndarray
        Template to match. Must be explicitly passed, no default.

    Returns
    -------
    match : np.array
    coordinates : np.array
        These are the coordinates of the match. The format is similar to the
        output of ``np.where``, but in an ndarray.

     N/A N/A N/A f ndim 2 N/A N/A N/A ValueError mahotas.find: Cannot handle multi-dimensional images N/A template N/A N/A template astype N/A f dtype N/A out N/A N/A np empty N/A f shape bool N/A N/A N/A _convolve find2d f template out N/A gaussian_filter1d N/A N/A array sigma axis order mode cval out output N/A -1 0 reflect 0.0 None None N/A N/A 
    filtered = gaussian_filter1d(array, sigma, axis=-1, order=0, mode='reflect', cval=0., out={np.empty_like(array)})

    One-dimensional Gaussian filter.

    Parameters
    ----------
    array : ndarray
        input array of a floating-point type

    sigma : float
        standard deviation for Gaussian kernel (in pixel units)
    axis : int, optional
        axis to operate on
    order : {0, 1, 2, 3}, optional
        An order of 0 corresponds to convolution with a Gaussian
        kernel. An order of 1, 2, or 3 corresponds to convolution with
        the first, second or third derivatives of a Gaussian. Higher
        order derivatives are not implemented
    mode : {'reflect' [default], 'nearest', 'wrap', 'mirror', 'constant', 'ignore'}
        How to handle borders
    cval : double, optional
        If `mode` is constant, which constant to use (default: 0.0)
    out : ndarray, optional
        Output array. Must have same shape and dtype as `array` as well as be
        C-contiguous.

    Returns
    -------
    filtered : ndarray
        Filtered version of `array`

     N/A N/A _verify_is_floatingpoint_type array gaussian_filter1d N/A sigma N/A float sigma N/A s2 N/A sigma sigma N/A lw N/A int N/A N/A 4.0 sigma 0.5 N/A N/A lw 0 N/A N/A N/A ValueError mahotas.gaussian_filter1d: sigma must be greater or equal to 0.125 [1/8] N/A x N/A N/A np arange N/A N/A 2 lw 1 dtype float N/A x lw N/A weights N/A N/A np exp N/A N/A x x N/A -2.0 s2 N/A weights N/A N/A np sum weights N/A N/A order 0 N/A N/A N/A N/A N/A order 1 N/A N/A weights N/A N/A x s2 N/A N/A N/A order 2 N/A N/A weights N/A N/A N/A N/A x x s2 1.0 s2 N/A N/A N/A order 3 N/A N/A weights N/A N/A N/A 3.0 N/A N/A x x s2 x N/A s2 s2 N/A N/A N/A ValueError mahotas.convolve.gaussian_filter1d: Order outside 0..3 not implemented N/A N/A convolve1d array weights axis mode cval out output N/A gaussian_filter N/A N/A array sigma order mode cval out output N/A 0 reflect 0.0 None None N/A N/A 
    filtered = gaussian_filter(array, sigma, order=0, mode='reflect', cval=0., out={np.empty_like(array)})

    Multi-dimensional Gaussian filter.

    Parameters
    ----------
    array : ndarray
        input array, any dimension is supported. If the array is an integer
        array, it will be converted to a double array.
    sigma : scalar or sequence of scalars
        standard deviation for Gaussian kernel. The standard
        deviations of the Gaussian filter are given for each axis as a
        sequence, or as a single number, in which case it is equal for
        all axes.
    order : {0, 1, 2, 3} or sequence from same set, optional
        The order of the filter along each axis is given as a sequence
        of integers, or as a single number.  An order of 0 corresponds
        to convolution with a Gaussian kernel. An order of 1, 2, or 3
        corresponds to convolution with the first, second or third
        derivatives of a Gaussian. Higher order derivatives are not
        implemented
    mode : {'reflect' [default], 'nearest', 'wrap', 'mirror', 'constant', 'ignore'}
        How to handle borders
    cval : double, optional
        If `mode` is constant, which constant to use (default: 0.0)
    out : ndarray, optional
        Output array. Must have same shape as `array` as well as be
        C-contiguous. If `array` is an integer array, this must be a double
        array; otherwise, it must have the same type as `array`.

    Returns
    -------
    filtered : ndarray
        Filtered version of `array`

    Notes
    -----
    The multi-dimensional filter is implemented as a sequence of
    one-dimensional convolution filters. The intermediate arrays are
    stored in the same data type as the output. Therefore, for output
    types with a limited precision, the results may be imprecise
    because intermediate results may be stored with insufficient
    precision.
     N/A array N/A _as_floating_point_array array N/A output N/A _get_output array out gaussian_filter output output N/A orders N/A _normalize_sequence array order gaussian_filter N/A sigmas N/A _normalize_sequence array sigma gaussian_filter N/A N/A output N/A N/A array N/A N/A noutput None N/A axis N/A range N/A array ndim N/A N/A sigma N/A sigmas N/A axis N/A order N/A orders N/A axis N/A noutput N/A gaussian_filter1d output sigma axis order mode cval noutput N/A N/A output noutput N/A noutput output N/A output N/A _wavelet_array N/A N/A f inline func N/A N/A N/A f N/A _as_floating_point_array f N/A N/A N/A f ndim 2 N/A N/A N/A ValueError N/A mahotas.convolve.%s: Only works for 2D images func N/A N/A inline N/A N/A N/A N/A f copy N/A f N/A _wavelet_center_compute N/A N/A oshape border dtype cval N/A 0 None 0.0 N/A N/A c N/A range 1 N/A 16 border N/A N/A nshape N/A 2 N/A N/A N/A np floor N/A N/A np log2 oshape c N/A nshape N/A N/A nshape astype int N/A delta N/A nshape oshape N/A delta 2 N/A N/A N/A N/A np min delta border N/A N/A N/A position N/A N/A N/A d e N/A zip delta oshape N/A N/A N/A N/A position append N/A slice d N/A d e N/A N/A nshape position N/A wavelet_center N/A N/A f border dtype cval N/A 0 float 0.0 N/A N/A 
    fc = wavelet_center(f, border=0, dtype=float, cval=0.0)

    ``fc`` is a centered version of ``f`` with a shape that is composed of
    powers of 2.

    Parameters
    ----------
    f : ndarray
        input image
    border : int, optional
        The border to use (default is no border)
    dtype : type, optional
        Type of ``fc``
    cval : float, optional
        Which value to fill the border with (default is 0)

    Returns
    -------
    fc : ndarray

    See Also
    --------
    wavelet_decenter : function
        Reverse function
     N/A N/A nshape position N/A _wavelet_center_compute N/A f shape border N/A nimage N/A N/A np zeros nshape dtype dtype N/A nimage cval N/A N/A nimage N/A position f N/A nimage N/A wavelet_decenter N/A N/A w oshape border N/A 0 N/A N/A 
    f = wavelet_decenter(w, oshape, border=0)

    Undoes the effect of ``wavelet_center``

    Parameters
    ----------
    w : ndarray
        Wavelet array
    oshape : tuple
        Desired shape
    border : int, optional
        The desired border. This **must** be the same value as was used for
        ``wavelet_center`` 

    Returns
    -------
    f : ndarray
        This will have shape ``oshape``

    See Also
    --------
    wavelet_center : function
        Forward function
     N/A N/A nshape position N/A _wavelet_center_compute oshape border N/A N/A w N/A position N/A haar N/A N/A f preserve_energy inline N/A True False N/A N/A 
    t = haar(f, preserve_energy=True, inline=False)

    Haar transform

    Parameters
    ----------
    f : 2-D ndarray
        Input image
    preserve_energy : bool, optional
        Whether to normalise the result so that energy is preserved (the
        default).
    inline : bool, optional
        Whether to write the results to the input image. By default, a new
        image is returned. Integer images are always converted to floating
        point and copied.

    See Also
    --------
    ihaar : function
        Reverse Haar transform
     N/A f N/A _wavelet_array f inline haar N/A N/A N/A _convolve haar f N/A N/A N/A _convolve haar N/A f T N/A preserve_energy N/A N/A f 2.0 N/A f N/A N/A _daubechies_codes N/A N/A D%s ci N/A ci N/A range 2 21 2 _daubechies_code N/A N/A c N/A N/A N/A N/A N/A N/A N/A _daubechies_codes index c N/A N/A N/A N/A N/A ValueError N/A N/A mahotas.convolve: Known daubechies codes are {0}. You passed in {1}. format _daubechies_codes c N/A daubechies N/A N/A f code inline N/A False N/A N/A 
    filtered = daubechies(f, code, inline=False)

    Daubechies wavelet transform

    This function works best if the image sizes are powers of 2!

    Parameters
    ----------
    f : ndarray
        2-D image
    code : str
        One of 'D2', 'D4', ... 'D20'
    inline : bool, optional
        Whether to write the results to the input image. By default, a new
        image is returned. Integer images are always converted to floating
        point and copied.

    See Also
    --------
    haar : function
        Haar transform (equivalent to D2)
     N/A f N/A _wavelet_array f inline daubechies N/A code N/A _daubechies_code code N/A N/A N/A _convolve daubechies f code N/A N/A N/A _convolve daubechies N/A f T code N/A f N/A idaubechies N/A N/A f code inline N/A False N/A N/A 
    rfiltered = idaubechies(f, code, inline=False)

    Daubechies wavelet inverse transform

    Parameters
    ----------
    f : ndarray
        2-D image
    code : str
        One of 'D2', 'D4', ... 'D20'
    inline : bool, optional
        Whether to write the results to the input image. By default, a new
        image is returned. Integer images are always converted to floating
        point and copied.

    See Also
    --------
    haar : function
        Haar transform (equivalent to D2)
     N/A f N/A _wavelet_array f inline idaubechies N/A code N/A _daubechies_code code N/A N/A N/A _convolve idaubechies N/A f T code N/A N/A N/A _convolve idaubechies f code N/A f N/A ihaar N/A N/A f preserve_energy inline N/A True False N/A N/A 
    t = ihaar(f, preserve_energy=True, inline=False)

    Reverse Haar transform

    ``ihaar(haar(f))`` is more or less equal to ``f`` (equal, except for
    possible rounding issues).

    Parameters
    ----------
    f : 2-D ndarray
        Input image. If it is an integer image, it is converted to floating
        point (double).
    preserve_energy : bool, optional
        Whether to normalise the result so that energy is preserved (the
        default).
    inline : bool, optional
        Whether to write the results to the input image. By default, a new
        image is returned. Integer images are always converted to floating
        point and copied.

    Returns
    -------
    f : ndarray

    See Also
    --------
    haar : function
        Forward Haar transform
     N/A f N/A _wavelet_array f inline ihaar N/A N/A N/A _convolve ihaar f N/A N/A N/A _convolve ihaar N/A f T N/A preserve_energy N/A N/A f 2.0 N/A f N/A laplacian_2D N/A N/A array alpha N/A 0.2 N/A N/A 
    filtered = laplacian_2D(array, alpha = 0.2)

    2D Laplacian filter.

    Parameters
    ----------
    array : ndarray
        input 2D array. If the array is an integer array, it will be converted 
        to a double array.
    alpha : scalar or sequence of scalars
        controls the shape of Laplacian operator. Must be 0-1. A larger values 
        makes the operator empahsize the diagonal direction.

    Returns
    -------
    filtered : ndarray
        Filtered version of `array`
     N/A array N/A N/A np array array dtype N/A np float N/A N/A N/A array ndim 2 N/A N/A N/A ValueError mahotas.laplacian_2D: Only available for 2-dimensional arrays N/A alpha N/A max 0 N/A min alpha 1 N/A ver_hor_weight N/A N/A 1.0 alpha N/A alpha 1.0 N/A diag_weight N/A alpha N/A alpha 1.0 N/A center N/A -4.0 N/A alpha 1.0 N/A weights N/A N/A np array N/A N/A diag_weight ver_hor_weight diag_weight N/A ver_hor_weight center ver_hor_weight N/A diag_weight ver_hor_weight diag_weight N/A output N/A convolve array weights mode nearest N/A output N/A