Python ma.empty方法代碼示例

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def test_prepare_array_maskedarrays():
    """Convert masked array data into a proper array."""
    # input is ma.masked_array
    data = ma.empty((1, 1, 1))
    # output ndarray
    output = prepare_array(data, masked=False)
    assert isinstance(output, np.ndarray)
    assert not isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # output masked array
    output = prepare_array(data)
    assert isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # input is ma.masked_array with full mask
    data = ma.masked_array(data=np.ones((1, 1, 1)), mask=np.ones((1, 1, 1)))
    # output ndarray
    output = prepare_array(data, masked=False)
    assert isinstance(output, np.ndarray)
    assert not isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # output masked array
    output = prepare_array(data)
    assert isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def comp_gradient(blob):  # compare g within sub blob, a component of intra_blob

    dert__ = ma.empty(shape=blob.dert__.shape, dtype=int)   # initialize dert__
    g__ = ma.array(blob.dert__[:, :, 3], mask=~blob.map)    # apply mask = ~map

    dy__ = g__[2:, 1:-1] - g__[:-2, 1:-1]  # vertical comp between rows -> dy, (1:-1): first and last column are discarded
    dx__ = g__[1:-1, 2:] - g__[1:-1, :-2]  # lateral comp between columns -> dx, (1:-1): first and last row are discarded
    gg__ = np.hypot(dy__, dx__) - ave      # deviation of gradient

    # pack all derts into dert__
    dert__[:, :, 0] = g__
    dert__[1:-1, 1:-1, 1] = dy__  # first row, last row, first column and last-column are discarded
    dert__[1:-1, 1:-1, 2] = dx__
    dert__[1:-1, 1:-1, 3] = gg__

    blob.new_dert__[0] = dert__  # pack dert__ into blob
    return 1  # comp rng 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def comp_angle(blob):  # compute and compare angles, a component of intra_blob
    # compute angles:

    dy__ = ma.array(blob.dert__[:, :, 1], mask=~blob.map)
    dx__ = ma.array(blob.dert__[:, :, 2], mask=~blob.map)
    a__ = np.arctan2(dy__, dx__) * angle_coef + 128

    # compare angles:
    dert__ = ma.empty(shape=blob.dert__.shape, dtype=int)  # initialize dert__

    day__ = correct_da(a__[2:, 1:-1] - a__[:-2, 1:-1])  # vertical comp between rows -> dy, (1:-1): first and last column are discarded
    dax__ = correct_da(a__[1:-1, 2:] - a__[1:-1, :-2])  # lateral comp between columns -> dx, (1:-1): first and last row are discarded
    ga__ = np.hypot(day__, dax__) - ave                 # deviation of gradient

    # pack all derts into dert__
    dert__[:, :, 0] = a__
    dert__[1:-1, 1:-1, 1] = day__  # first row, last row, first column and last-column are discarded
    dert__[1:-1, 1:-1, 2] = dax__
    dert__[1:-1, 1:-1, 3] = ga__

    blob.new_dert__[0] = dert__  # pack dert__ into blob
    return 1    # comp rng

    # ---------- comp_angle() end -------------------------------------------------------------------------------------- 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def argstoarray(*args):
    """
    Constructs a 2D array from a group of sequences.

    Sequences are filled with missing values to match the length of the longest
    sequence.

    Parameters
    ----------
    args : sequences
        Group of sequences.

    Returns
    -------
    argstoarray : MaskedArray
        A ( `m` x `n` ) masked array, where `m` is the number of arguments and
        `n` the length of the longest argument.

    Notes
    -----
    `numpy.ma.row_stack` has identical behavior, but is called with a sequence
    of sequences.

    """
    if len(args) == 1 and not isinstance(args[0], ndarray):
        output = ma.asarray(args[0])
        if output.ndim != 2:
            raise ValueError("The input should be 2D")
    else:
        n = len(args)
        m = max([len(k) for k in args])
        output = ma.array(np.empty((n,m), dtype=float), mask=True)
        for (k,v) in enumerate(args):
            output[k,:len(v)] = v

    output[np.logical_not(np.isfinite(output._data))] = masked
    return output 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def argstoarray(*args):
    """
    Constructs a 2D array from a group of sequences.

    Sequences are filled with missing values to match the length of the longest
    sequence.

    Parameters
    ----------
    args : sequences
        Group of sequences.

    Returns
    -------
    argstoarray : MaskedArray
        A ( `m` x `n` ) masked array, where `m` is the number of arguments and
        `n` the length of the longest argument.

    Notes
    -----
    numpy.ma.row_stack has identical behavior, but is called with a sequence of
    sequences.

    """
    if len(args) == 1 and not isinstance(args[0], ndarray):
        output = ma.asarray(args[0])
        if output.ndim != 2:
            raise ValueError("The input should be 2D")
    else:
        n = len(args)
        m = max([len(k) for k in args])
        output = ma.array(np.empty((n,m), dtype=float), mask=True)
        for (k,v) in enumerate(args):
            output[k,:len(v)] = v
    output[np.logical_not(np.isfinite(output._data))] = masked
    return output


#####--------------------------------------------------------------------------
#---- --- Ranking ---
#####-------------------------------------------------------------------------- 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def merge_structured_arrays(*arrays):
    """merge a set of structured numpy arrays
    
    Parameters
    ----------
    *arrays
        a set of masked structured arrays. all arrays must have the same number
        of rows.
    
    Returns
    -------
    x : MaskedArray
        the merged array
    """
    #concatenate all of the sub-arrays fields
    dtype = sum((array.dtype.descr for array in arrays), [])
    
    #empty array with all fields
    x = ma.empty(len(arrays[0]), dtype=dtype)
    x = SerializableMaskedArray(x)
    
    #assign each field
    for array in arrays:
        for name in array.dtype.names:
            x[name] = array[name]
    
    return x 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def test_prepare_array_iterables():
    """Convert iterable data into a proper array."""
    # input is iterable
    # iterable contains arrays
    data = [np.zeros((1, 1))]
    # output ndarray
    output = prepare_array(data, masked=False)
    assert isinstance(output, np.ndarray)
    assert not isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # output masked array
    output = prepare_array(data)
    assert isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # iterable contains masked arrays
    data = [ma.empty((1, 1))]
    output = prepare_array(data, masked=False)
    assert isinstance(output, np.ndarray)
    assert not isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # output masked array
    output = prepare_array(data)
    assert isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # iterable contains masked arrays with full mask
    data = [ma.masked_array(data=np.ones((1, 1)), mask=np.ones((1, 1)))]
    output = prepare_array(data, masked=False)
    assert isinstance(output, np.ndarray)
    assert not isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # output masked array
    output = prepare_array(data)
    assert isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def __getitem__(self, item):
        if isinstance(item, str):
            return self.columns[item]
        elif isinstance(item, (int, np.integer)):
            return self.Row(self, item)
        elif (isinstance(item, np.ndarray) and item.shape == () and item.dtype.kind == 'i'):
            return self.Row(self, item.item())
        elif self._is_list_or_tuple_of_str(item):
            out = self.__class__([self[x] for x in item],
                                 copy_indices=self._copy_indices)
            out._groups = groups.TableGroups(out, indices=self.groups._indices,
                                             keys=self.groups._keys)
            out.meta = self.meta.copy()  # Shallow copy for meta
            return out
        elif ((isinstance(item, np.ndarray) and item.size == 0) or
              (isinstance(item, (tuple, list)) and not item)):
            # If item is an empty array/list/tuple then return the table with no rows
            return self._new_from_slice([])
        elif (isinstance(item, slice) or
              isinstance(item, np.ndarray) or
              isinstance(item, list) or
              isinstance(item, tuple) and all(isinstance(x, np.ndarray)
                                              for x in item)):
            # here for the many ways to give a slice; a tuple of ndarray
            # is produced by np.where, as in t[np.where(t['a'] > 2)]
            # For all, a new table is constructed with slice of all columns
            return self._new_from_slice(item)
        else:
            raise ValueError('Illegal type {} for table item access'
                             .format(type(item))) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def rankdata(data, axis=None, use_missing=False):
    """Returns the rank (also known as order statistics) of each data point
    along the given axis.

    If some values are tied, their rank is averaged.
    If some values are masked, their rank is set to 0 if use_missing is False,
    or set to the average rank of the unmasked values if use_missing is True.

    Parameters
    ----------
    data : sequence
        Input data. The data is transformed to a masked array
    axis : {None,int}, optional
        Axis along which to perform the ranking.
        If None, the array is first flattened. An exception is raised if
        the axis is specified for arrays with a dimension larger than 2
    use_missing : bool, optional
        Whether the masked values have a rank of 0 (False) or equal to the
        average rank of the unmasked values (True).

    """
    def _rank1d(data, use_missing=False):
        n = data.count()
        rk = np.empty(data.size, dtype=float)
        idx = data.argsort()
        rk[idx[:n]] = np.arange(1,n+1)

        if use_missing:
            rk[idx[n:]] = (n+1)/2.
        else:
            rk[idx[n:]] = 0

        repeats = find_repeats(data.copy())
        for r in repeats[0]:
            condition = (data == r).filled(False)
            rk[condition] = rk[condition].mean()
        return rk

    data = ma.array(data, copy=False)
    if axis is None:
        if data.ndim > 1:
            return _rank1d(data.ravel(), use_missing).reshape(data.shape)
        else:
            return _rank1d(data, use_missing)
    else:
        return ma.apply_along_axis(_rank1d,axis,data,use_missing).view(ndarray) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def plotting_positions(data, alpha=0.4, beta=0.4):
    """
    Returns plotting positions (or empirical percentile points) for the data.

    Plotting positions are defined as ``(i-alpha)/(n+1-alpha-beta)``, where:
        - i is the rank order statistics
        - n is the number of unmasked values along the given axis
        - `alpha` and `beta` are two parameters.

    Typical values for `alpha` and `beta` are:
        - (0,1)    : ``p(k) = k/n``, linear interpolation of cdf (R, type 4)
        - (.5,.5)  : ``p(k) = (k-1/2.)/n``, piecewise linear function
          (R, type 5)
        - (0,0)    : ``p(k) = k/(n+1)``, Weibull (R type 6)
        - (1,1)    : ``p(k) = (k-1)/(n-1)``, in this case,
          ``p(k) = mode[F(x[k])]``. That's R default (R type 7)
        - (1/3,1/3): ``p(k) = (k-1/3)/(n+1/3)``, then
          ``p(k) ~ median[F(x[k])]``.
          The resulting quantile estimates are approximately median-unbiased
          regardless of the distribution of x. (R type 8)
        - (3/8,3/8): ``p(k) = (k-3/8)/(n+1/4)``, Blom.
          The resulting quantile estimates are approximately unbiased
          if x is normally distributed (R type 9)
        - (.4,.4)  : approximately quantile unbiased (Cunnane)
        - (.35,.35): APL, used with PWM
        - (.3175, .3175): used in scipy.stats.probplot

    Parameters
    ----------
    data : array_like
        Input data, as a sequence or array of dimension at most 2.
    alpha : float, optional
        Plotting positions parameter. Default is 0.4.
    beta : float, optional
        Plotting positions parameter. Default is 0.4.

    Returns
    -------
    positions : MaskedArray
        The calculated plotting positions.

    """
    data = ma.array(data, copy=False).reshape(1,-1)
    n = data.count()
    plpos = np.empty(data.size, dtype=float)
    plpos[n:] = 0
    plpos[data.argsort(axis=None)[:n]] = ((np.arange(1, n+1) - alpha) /
                                          (n + 1.0 - alpha - beta))
    return ma.array(plpos, mask=data._mask) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def rankdata(data, axis=None, use_missing=False):
    """Returns the rank (also known as order statistics) of each data point
    along the given axis.

    If some values are tied, their rank is averaged.
    If some values are masked, their rank is set to 0 if use_missing is False,
    or set to the average rank of the unmasked values if use_missing is True.

    Parameters
    ----------
        data : sequence
            Input data. The data is transformed to a masked array
        axis : {None,int}, optional
            Axis along which to perform the ranking.
            If None, the array is first flattened. An exception is raised if
            the axis is specified for arrays with a dimension larger than 2
        use_missing : {boolean}, optional
            Whether the masked values have a rank of 0 (False) or equal to the
            average rank of the unmasked values (True).
    """
    #
    def _rank1d(data, use_missing=False):
        n = data.count()
        rk = np.empty(data.size, dtype=float)
        idx = data.argsort()
        rk[idx[:n]] = np.arange(1,n+1)
        #
        if use_missing:
            rk[idx[n:]] = (n+1)/2.
        else:
            rk[idx[n:]] = 0
        #
        repeats = find_repeats(data.copy())
        for r in repeats[0]:
            condition = (data == r).filled(False)
            rk[condition] = rk[condition].mean()
        return rk
    #
    data = ma.array(data, copy=False)
    if axis is None:
        if data.ndim > 1:
            return _rank1d(data.ravel(), use_missing).reshape(data.shape)
        else:
            return _rank1d(data, use_missing)
    else:
        return ma.apply_along_axis(_rank1d,axis,data,use_missing).view(ndarray)


#####--------------------------------------------------------------------------
#---- --- Central tendency ---
#####-------------------------------------------------------------------------- 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def plotting_positions(data, alpha=0.4, beta=0.4):
    """
    Returns plotting positions (or empirical percentile points) for the data.

    Plotting positions are defined as ``(i-alpha)/(n+1-alpha-beta)``, where:
        - i is the rank order statistics
        - n is the number of unmasked values along the given axis
        - `alpha` and `beta` are two parameters.

    Typical values for `alpha` and `beta` are:
        - (0,1)    : ``p(k) = k/n``, linear interpolation of cdf (R, type 4)
        - (.5,.5)  : ``p(k) = (k-1/2.)/n``, piecewise linear function
          (R, type 5)
        - (0,0)    : ``p(k) = k/(n+1)``, Weibull (R type 6)
        - (1,1)    : ``p(k) = (k-1)/(n-1)``, in this case,
          ``p(k) = mode[F(x[k])]``. That's R default (R type 7)
        - (1/3,1/3): ``p(k) = (k-1/3)/(n+1/3)``, then
          ``p(k) ~ median[F(x[k])]``.
          The resulting quantile estimates are approximately median-unbiased
          regardless of the distribution of x. (R type 8)
        - (3/8,3/8): ``p(k) = (k-3/8)/(n+1/4)``, Blom.
          The resulting quantile estimates are approximately unbiased
          if x is normally distributed (R type 9)
        - (.4,.4)  : approximately quantile unbiased (Cunnane)
        - (.35,.35): APL, used with PWM
        - (.3175, .3175): used in scipy.stats.probplot

    Parameters
    ----------
    data : array_like
        Input data, as a sequence or array of dimension at most 2.
    alpha : float, optional
        Plotting positions parameter. Default is 0.4.
    beta : float, optional
        Plotting positions parameter. Default is 0.4.

    Returns
    -------
    positions : MaskedArray
        The calculated plotting positions.

    """
    data = ma.array(data, copy=False).reshape(1,-1)
    n = data.count()
    plpos = np.empty(data.size, dtype=float)
    plpos[n:] = 0
    plpos[data.argsort()[:n]] = (np.arange(1, n+1) - alpha) / \
                                (n + 1.0 - alpha - beta)
    return ma.array(plpos, mask=data._mask) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def plotting_positions(data, alpha=0.4, beta=0.4):
    """
    Returns plotting positions (or empirical percentile points) for the data.

    Plotting positions are defined as ``(i-alpha)/(n+1-alpha-beta)``, where:
        - i is the rank order statistics
        - n is the number of unmasked values along the given axis
        - `alpha` and `beta` are two parameters.

    Typical values for `alpha` and `beta` are:
        - (0,1)    : ``p(k) = k/n``, linear interpolation of cdf (R, type 4)
        - (.5,.5)  : ``p(k) = (k-1/2.)/n``, piecewise linear function
          (R, type 5)
        - (0,0)    : ``p(k) = k/(n+1)``, Weibull (R type 6)
        - (1,1)    : ``p(k) = (k-1)/(n-1)``, in this case,
          ``p(k) = mode[F(x[k])]``. That's R default (R type 7)
        - (1/3,1/3): ``p(k) = (k-1/3)/(n+1/3)``, then
          ``p(k) ~ median[F(x[k])]``.
          The resulting quantile estimates are approximately median-unbiased
          regardless of the distribution of x. (R type 8)
        - (3/8,3/8): ``p(k) = (k-3/8)/(n+1/4)``, Blom.
          The resulting quantile estimates are approximately unbiased
          if x is normally distributed (R type 9)
        - (.4,.4)  : approximately quantile unbiased (Cunnane)
        - (.35,.35): APL, used with PWM
        - (.3175, .3175): used in scipy.stats.probplot

    Parameters
    ----------
    data : array_like
        Input data, as a sequence or array of dimension at most 2.
    alpha : float, optional
        Plotting positions parameter. Default is 0.4.
    beta : float, optional
        Plotting positions parameter. Default is 0.4.

    Returns
    -------
    positions : MaskedArray
        The calculated plotting positions.

    """
    data = ma.array(data, copy=False).reshape(1,-1)
    n = data.count()
    plpos = np.empty(data.size, dtype=float)
    plpos[n:] = 0
    plpos[data.argsort()[:n]] = ((np.arange(1, n+1) - alpha) /
                                 (n + 1.0 - alpha - beta))
    return ma.array(plpos, mask=data._mask) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def test_reproject_geometry(landpoly):
    """Reproject geometry."""
    with fiona.open(landpoly, "r") as src:
        for feature in src:

            # WGS84 to Spherical Mercator
            out_geom = reproject_geometry(
                shape(feature["geometry"]), CRS(src.crs),
                CRS().from_epsg(3857))
            assert out_geom.is_valid

            # WGS84 to LAEA
            out_geom = reproject_geometry(
                shape(feature["geometry"]), CRS(src.crs),
                CRS().from_epsg(3035))
            assert out_geom.is_valid

            # WGS84 to WGS84
            out_geom = reproject_geometry(
                shape(feature["geometry"]), CRS(src.crs),
                CRS().from_epsg(4326))
            assert out_geom.is_valid

    # WGS84 bounds to Spherical Mercator
    big_box = box(-180, -90, 180, 90)
    reproject_geometry(big_box, CRS().from_epsg(4326), CRS().from_epsg(3857))

    # WGS84 bounds to Spherical Mercator raising clip error
    with pytest.raises(RuntimeError):
        reproject_geometry(
            big_box, CRS().from_epsg(4326), CRS().from_epsg(3857),
            error_on_clip=True
        )
    outside_box = box(-180, 87, 180, 90)
    assert reproject_geometry(
        outside_box, CRS().from_epsg(4326), CRS().from_epsg(3857),
    ).is_valid

    # empty geometry
    assert reproject_geometry(
        Polygon(), CRS().from_epsg(4326), CRS().from_epsg(3857)).is_empty
    assert reproject_geometry(
        Polygon(), CRS().from_epsg(4326), CRS().from_epsg(4326)).is_empty

    # CRS parameter
    big_box = box(-180, -90, 180, 90)
    assert reproject_geometry(
        big_box, 4326, 3857) == reproject_geometry(
        big_box, "4326", "3857")
    with pytest.raises(TypeError):
        reproject_geometry(big_box, 1.0, 1.0) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import empty [as 別名]
def comp_range(blob):   # compare rng-distant pixels within blob: a component of intra_blob

    rng = blob.rng + 1
    p__ = ma.array(blob.dert__[:, :, 0], mask=~blob.map)  # apply mask = ~map
    dy__ = ma.array(blob.dert__[:, :, 1], mask=~blob.map)
    dx__ = ma.array(blob.dert__[:, :, 2], mask=~blob.map)

    dert__ = ma.empty(shape=(height, width, 4), dtype=int)  # initialize new dert__
    comp_rng = rng * 2

    # vertical comp:
    d__ = p__[comp_rng:, rng:-rng] - p__[:-comp_rng, rng:-rng]  # bilateral comparison between p at coordinates (x, y + rng) and p at coordinates (x, y - rng)
    dy__[rng:-rng, rng:-rng] += d__                             # bilateral accumulation on dy (x, y)

    # horizontal comp:
    d__ = p__[rng:-rng, comp_rng:] - p__[rng:-rng, :-comp_rng]  # bilateral comparison between p at coordinates (x + rng, y) and p at coordinates (x - rng, y)
    dx__[rng:-rng, rng:-rng] += d__                             # bilateral accumulation on dy (x, y)

    # diagonal comparison:

    for xd in range(1, rng):
        yd = rng - xd             # half y and x distance between comparands
        bi_xd = xd * 2
        bi_yd = comp_rng - bi_xd  # y and x distance between comparands
        hyp = hypot(bi_yd, bi_xd)
        y_coef = bi_yd / hyp      # to decompose d into dy
        x_coef = bi_xd / hyp      # to decompose d into dx

        # top-left and bottom-right quadrants:

        d__ = p__[bi_yd:, bi_xd:] - p__[:-bi_yd, :-bi_xd]   # comparison between p (x - xd, y - yd) and p (x + xd, y + yd)
        # decompose d to dy, dx:
        temp_dy__ = d__ * y_coef                    # buffer for dy accumulation
        temp_dx__ = d__ * x_coef                    # buffer for dx accumulation
        # accumulate dy, dx:
        dy__[yd:-yd, xd:-xd] += temp_dy__.astype(int)   # bilateral accumulation on dy (x, y)
        dx__[yd:-yd, xd:-xd] += temp_dx__.astype(int)   # bilateral accumulation on dx (x, y)

        # top-right and bottom-left quadrants:

        d__ = p__[bi_yd:, :-bi_xd] - p__[:-bi_yd, bi_xd:]   # comparison between p (x + xd, y - yd) and p (x - xd, y + yd)
        # decompose d to dy, dx:
        temp_dy__ = d__ * y_coef                    # buffer for dy accumulation
        temp_dx__ = -(d__ * x_coef)                 # buffer for dx accumulation, sign inverted with comp direction
        # accumulate dy, dx:
        dy__[yd:-yd, xd:-xd] += temp_dy__.astype(int)   # bilateral accumulation on dy (x, y)
        dx__[yd:-yd, xd:-xd] += temp_dx__.astype(int)   # bilateral accumulation on dx (x, y)

    g__ = np.hypot(dy__, dx__) - ave * blob.ncomp  # compute g__

    # pack all derts into dert__

    dert__[:, :, 0] = p__
    dert__[:, :, 1] = dy__
    dert__[:, :, 2] = dx__
    dert__[:, :, 3] = g__

    blob.new_dert__[0] = dert__ # pack dert__ into blob
    return rng

    # ---------- inc_range() end ----------------------------------------------------------------------------------------