import sys
import os
sys.path.append(os.path.abspath('..'))
import libpysal
w = libpysal.weights.lat2W(5,5)
w.n
25
w.pct_nonzero
12.8
w.neighbors[0]
[5, 1]
w.neighbors[5]
[0, 10, 6]
libpysal.examples.available()
['georgia', '__pycache__', 'tests', 'newHaven', 'Polygon_Holes', 'nat', 'Polygon', '10740', 'berlin', 'rio_grande_do_sul', 'sids2', 'sacramento2', 'burkitt', 'arcgis', 'calemp', 'stl', 'virginia', 'geodanet', 'desmith', 'book', 'nyc_bikes', 'Line', 'south', 'snow_maps', 'Point', 'street_net_pts', 'guerry', '__pycache__', 'baltim', 'networks', 'us_income', 'taz', 'columbus', 'tokyo', 'mexico', '__pycache__', 'chicago', 'wmat', 'juvenile', 'clearwater']
libpysal.examples.explain('baltim')
{'name': 'baltim', 'description': 'Baltimore house sales prices and hedonics 1978', 'explanation': ['* baltim.dbf: attribute data. (k=17)', '* baltim.shp: Point shapefile. (n=211)', '* baltim.shx: spatial index.', '* baltim.tri.k12.kwt: kernel weights using a triangular kernel with 12 nearest neighbors in KWT format.', '* baltim_k4.gwt: nearest neighbor weights (4nn) in GWT format.', '* baltim_q.gal: queen contiguity weights in GAL format.', '* baltimore.geojson: spatial weights in geojson format.']}
pth = libpysal.examples.get_path('baltim.shp')
pth
'/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/examples/baltim/baltim.shp'
shp_file = libpysal.io.open(pth)
shapes = [shp for shp in shp_file]
shapes[0]
(907.0, 534.0)
w = libpysal.io.open(libpysal.examples.get_path('baltim_q.gal')).read()
w.n
211