示例#1
0
文件: user.py 项目: tra6sdc/libpysal
def threshold_binaryW_from_shapefile(shapefile,
                                     threshold,
                                     p=2,
                                     idVariable=None,
                                     radius=None):
    """
    Threshold distance based binary weights from a shapefile.

    Parameters
    ----------

    shapefile  : string
                 shapefile name with shp suffix
    threshold  : float
                 distance band
    p          : float
                 Minkowski p-norm distance metric parameter:
                 1<=p<=infinity
                 2: Euclidean distance
                 1: Manhattan distance
    idVariable : string
                 name of a column in the shapefile's DBF to use for ids
    radius     : float
                 If supplied arc_distances will be calculated
                 based on the given radius. p will be ignored.

    Returns
    -------

    w         : W
                instance
                Weights object with binary weights

    Examples
    --------
    >>> import libpysal.api as ps
    >>> import libpysal
    >>> w = ps.threshold_binaryW_from_shapefile(libpysal.examples.get_path("columbus.shp"),0.62,idVariable="POLYID")
    >>> w.weights[1]
    [1.0, 1.0]

    Notes
    -----
    Supports polygon or point shapefiles. For polygon shapefiles, distance is
    based on polygon centroids. Distances are defined using coordinates in
    shapefile which are assumed to be projected and not geographical
    coordinates.

    """

    data = get_points_array_from_shapefile(shapefile)
    if radius is not None:
        data = cg.KDTree(data, distance_metric='Arc', radius=radius)
    if idVariable:
        ids = get_ids(shapefile, idVariable)
        w = DistanceBand(data, threshold=threshold, p=p)
        w.remap_ids(ids)
        return w
    return threshold_binaryW_from_array(data, threshold, p=p)
示例#2
0
文件: user.py 项目: ds2010/pysal
def threshold_binaryW_from_shapefile(shapefile, threshold, p=2, idVariable=None, radius=None):
    """
    Threshold distance based binary weights from a shapefile.

    Parameters
    ----------

    shapefile  : string
                 shapefile name with shp suffix
    threshold  : float
                 distance band
    p          : float
                 Minkowski p-norm distance metric parameter:
                 1<=p<=infinity
                 2: Euclidean distance
                 1: Manhattan distance
    idVariable : string
                 name of a column in the shapefile's DBF to use for ids
    radius     : float
                 If supplied arc_distances will be calculated
                 based on the given radius. p will be ignored.

    Returns
    -------

    w         : W
                instance
                Weights object with binary weights

    Examples
    --------
    >>> w = threshold_binaryW_from_shapefile(pysal.examples.get_path("columbus.shp"),0.62,idVariable="POLYID")
    >>> w.weights[1]
    [1, 1]

    Notes
    -----
    Supports polygon or point shapefiles. For polygon shapefiles, distance is
    based on polygon centroids. Distances are defined using coordinates in
    shapefile which are assumed to be projected and not geographical
    coordinates.

    """

    data = get_points_array_from_shapefile(shapefile)
    if radius is not None:
        data = pysal.cg.KDTree(data, distance_metric='Arc', radius=radius)
    if idVariable:
        ids = get_ids(shapefile, idVariable)
        w = DistanceBand(data, threshold=threshold, p=p)
        w.remap_ids(ids)
        return w
    return threshold_binaryW_from_array(data, threshold, p=p)
示例#3
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def threshold_continuousW_from_array(array, threshold, p=2,
                                     alpha=-1, radius=None):

    """
    Continuous weights based on a distance threshold.

    Parameters
    ----------

    array      : array
                 (n,m)
                 attribute data, n observations on m attributes
    threshold  : float
                 distance band
    p          : float
                 Minkowski p-norm distance metric parameter:
                 1<=p<=infinity
                 2: Euclidean distance
                 1: Manhattan distance
    alpha      : float
                 distance decay parameter for weight (default -1.0)
                 if alpha is positive the weights will not decline with
                 distance.
    radius     : If supplied arc_distances will be calculated
                 based on the given radius. p will be ignored.

    Returns
    -------

    w         : W
                instance; Weights object with continuous weights.

    Examples
    --------

    inverse distance weights

    >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
    >>> wid=threshold_continuousW_from_array(points,11.2)
    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> wid.weights[0]
    [0.10000000000000001, 0.089442719099991588]

    gravity weights

    >>> wid2=threshold_continuousW_from_array(points,11.2,alpha=-2.0)
    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> wid2.weights[0]
    [0.01, 0.0079999999999999984]

    """

    if radius is not None:
        array = pysal.cg.KDTree(array, distance_metric='Arc', radius=radius)
    w = DistanceBand(
        array, threshold=threshold, p=p, alpha=alpha, binary=False)
    return w
示例#4
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def threshold_continuousW_from_shapefile(shapefile, threshold, p=2,
                                         alpha=-1, idVariable=None, radius=None):
    """
    Threshold distance based continuous weights from a shapefile.

    Parameters
    ----------

    shapefile  : string
                 shapefile name with shp suffix
    threshold  : float
                 distance band
    p          : float
                 Minkowski p-norm distance metric parameter:
                 1<=p<=infinity
                 2: Euclidean distance
                 1: Manhattan distance
    alpha      : float
                 distance decay parameter for weight (default -1.0)
                 if alpha is positive the weights will not decline with
                 distance.
    idVariable : string
                 name of a column in the shapefile's DBF to use for ids
    radius     : float
                 If supplied arc_distances will be calculated
                 based on the given radius. p will be ignored.

    Returns
    -------

    w         : W
                instance; Weights object with continuous weights.

    Examples
    --------
    >>> w = threshold_continuousW_from_shapefile(pysal.examples.get_path("columbus.shp"),0.62,idVariable="POLYID")
    >>> w.weights[1]
    [1.6702346893743334, 1.7250729841938093]

    Notes
    -----
    Supports polygon or point shapefiles. For polygon shapefiles, distance is
    based on polygon centroids. Distances are defined using coordinates in
    shapefile which are assumed to be projected and not geographical
    coordinates.

    """

    data = get_points_array_from_shapefile(shapefile)
    if radius is not None:
        data = pysal.cg.KDTree(data, distance_metric='Arc', radius=radius)
    if idVariable:
        ids = get_ids(shapefile, idVariable)
        w = DistanceBand(data, threshold=threshold, p=p, alpha=alpha, binary=False)
        w.remap_ids(ids)
    else:
        w =  threshold_continuousW_from_array(data, threshold, p=p, alpha=alpha)
    w.set_shapefile(shapefile,idVariable)
    return w
示例#5
0
文件: user.py 项目: tra6sdc/libpysal
def threshold_binaryW_from_array(array, threshold, p=2, radius=None):
    """
    Binary weights based on a distance threshold.

    Parameters
    ----------

    array      : array
                 (n,m)
                 attribute data, n observations on m attributes
    threshold  : float
                 distance band
    p          : float
                 Minkowski p-norm distance metric parameter:
                 1<=p<=infinity
                 2: Euclidean distance
                 1: Manhattan distance
    radius     : float
                 If supplied arc_distances will be calculated
                 based on the given radius. p will be ignored.

    Returns
    -------

    w          : W
                 instance
                 Weights object with binary weights

    Examples
    --------
    >>> import libpysal.api as ps
    >>> import libpysal
    >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
    >>> wcheck = ps.W({0: [1, 3], 1: [0, 3, ], 2: [], 3: [1, 0], 4: [5], 5: [4]})

    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> w=ps.threshold_binaryW_from_array(points,threshold=11.2)

    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> libpysal.weights.util.neighbor_equality(w, wcheck)
    True

    >>>
    """
    if radius is not None:
        array = cg.KDTree(array, distance_metric='Arc', radius=radius)
    return DistanceBand(array, threshold=threshold, p=p)
示例#6
0
def threshold_binaryW_from_array(array, threshold, p=2, radius=None):
    """
    Binary weights based on a distance threshold

    Parameters
    ----------

    array       : array (n,m)
                 attribute data, n observations on m attributes
    threshold  : float
                 distance band
    p          : float
                 Minkowski p-norm distance metric parameter:
                 1<=p<=infinity
                 2: Euclidean distance
                 1: Manhattan distance
    radius     : If supplied arc_distances will be calculated
                 based on the given radius. p will be ignored.

    Returns
    -------

    w         : W instance
                Weights object with binary weights

    Examples
    --------
    >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
    >>> w=threshold_binaryW_from_array(points,threshold=11.2)
    >>> w.weights
    {0: [1, 1], 1: [1, 1], 2: [], 3: [1, 1], 4: [1], 5: [1]}
    >>> w.neighbors
    {0: [1, 3], 1: [0, 3], 2: [], 3: [0, 1], 4: [5], 5: [4]}
    >>>
    """
    if radius is not None:
        array = pysal.cg.KDTree(array, distance_metric='Arc', radius=radius)
    return DistanceBand(array, threshold=threshold, p=p)