This page was generated from notebooks/io.ipynb.
Interactive online version:
[1]:
import sys
import os
sys.path.append(os.path.abspath('..'))
import libpysal
[2]:
w = libpysal.weights.lat2W(5,5)
[3]:
w.n
[3]:
25
[4]:
w.pct_nonzero
[4]:
12.8
[5]:
w.neighbors[0]
[5]:
[5, 1]
[6]:
w.neighbors[5]
[6]:
[0, 10, 6]
[7]:
libpysal.examples.available()
[7]:
['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']
[8]:
libpysal.examples.explain('baltim')
[8]:
{'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.']}
[9]:
pth = libpysal.examples.get_path('baltim.shp')
[10]:
pth
[10]:
'/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/examples/baltim/baltim.shp'
[11]:
shp_file = libpysal.io.open(pth)
[12]:
shapes = [shp for shp in shp_file]
[13]:
shapes[0]
[13]:
(907.0, 534.0)
[14]:
w = libpysal.io.open(libpysal.examples.get_path('baltim_q.gal')).read()
[15]:
w.n
[15]:
211