Source code for libpysal.weights.user

"""
Convenience functions for the construction of spatial weights based on
contiguity and distance criteria.
"""

__author__ = "Sergio J. Rey <srey@asu.edu> "

import numpy as np

from .. import cg
from ..io.fileio import FileIO
from .util import get_points_array_from_shapefile, min_threshold_distance

__all__ = [
    "min_threshold_dist_from_shapefile",
    "build_lattice_shapefile",
    "spw_from_gal",
]


[docs] def spw_from_gal(galfile): """ Sparse scipy matrix for w from a gal file. Parameters ---------- galfile : string name of gal file including suffix Returns ------- spw : sparse_matrix scipy sparse matrix in CSR format ids : array identifiers for rows/cols of spw Examples -------- >>> import libpysal >>> spw = libpysal.weights.spw_from_gal(libpysal.examples.get_path("sids2.gal")) >>> spw.sparse.nnz 462 """ return FileIO(galfile, "r").read(sparse=True)
[docs] def min_threshold_dist_from_shapefile(shapefile, radius=None, p=2): """ Get the maximum nearest neighbor distance between observations in the shapefile. Parameters ---------- shapefile : string shapefile name with shp suffix. radius : float If supplied arc_distances will be calculated based on the given radius. p will be ignored. p : float Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance Returns ------- d : float Maximum nearest neighbor distance between the n observations. Examples -------- >>> import libpysal >>> md = libpysal.weights.min_threshold_dist_from_shapefile( ... libpysal.examples.get_path("columbus.shp") ... ) >>> md 0.6188641580768541 >>> libpysal.weights.min_threshold_dist_from_shapefile( ... libpysal.examples.get_path("stl_hom.shp"), ... libpysal.cg.sphere.RADIUS_EARTH_MILES ... ) 31.846942936393717 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. """ points = get_points_array_from_shapefile(shapefile) if radius is not None: kdt = cg.kdtree.Arc_KDTree(points, radius=radius) nn = kdt.query(kdt.data, k=2) nnd = nn[0].max(axis=0)[1] return nnd return min_threshold_distance(points, p)
[docs] def build_lattice_shapefile(nrows, ncols, outFileName): # noqa: N803 """ Build a lattice shapefile with nrows rows and ncols cols. Parameters ---------- nrows : int Number of rows ncols : int Number of cols outFileName : str shapefile name with shp suffix Returns ------- None """ if not outFileName.endswith(".shp"): raise ValueError("outFileName must end with .shp") o = FileIO(outFileName, "w") dbf_name = outFileName.split(".")[0] + ".dbf" d = FileIO(dbf_name, "w") d.header = ["ID"] d.field_spec = [("N", 8, 0)] c = 0 for i in range(ncols): for j in range(nrows): ll = i, j ul = i, j + 1 ur = i + 1, j + 1 lr = i + 1, j o.write(cg.Polygon([ll, ul, ur, lr, ll])) d.write([c]) c += 1 d.close() o.close()
def _test(): import doctest # the following line could be used to define an alternative to the '<BLANKLINE>' # flag doctest.BLANKLINE_MARKER = 'something better than <BLANKLINE>' start_suppress = np.get_printoptions()["suppress"] np.set_printoptions(suppress=True) doctest.testmod() np.set_printoptions(suppress=start_suppress) if __name__ == "__main__": _test()