def buildWeights(self):
"""Performs Distance-based Weights Creation"""
ARCPY.SetProgressor("default", "Constructing spatial weights object...")
#### Shorthand Attributes ####
distanceType = self.distanceType
threshold = self.threshold
knnNum = self.knnNum
idField = self.idField
outputExt = self.outputExt
ssdo = self.ssdo
#### Create Distance-based WeightObj (0-based IDs) ####
dataArray = ssdo.xyCoords
if distanceType.upper() == DISTTYPE[0]:
weightObj = PYSAL.threshold_binaryW_from_array(dataArray, threshold)
elif distanceType.upper() == DISTTYPE[1]:
weightObj = PYSAL.knnW_from_array(dataArray, knnNum)
elif distanceType.upper() == DISTTYPE[2]:
alpha = -1 * self.inverseDist
weightObj = PYSAL.threshold_continuousW_from_array(\
dataArray, threshold, alpha=alpha)
#### Re-Create WeightObj for NOT 0-based idField ####
if idField:
if ssdo.master2Order.keys() != ssdo.master2Order.values():
o2M = ssdo.order2Master
neighborDict = {o2M[oid] : [o2M[nid] for nid in nbrs] \
for oid,nbrs in weightObj.neighbors.items()}
weightDict = {o2M[oid] : weights \
for oid, weights in weightObj.weights.items()}
weightObj = W(neighborDict, weightDict)
#### Save weightObj Class Object for Writing Result ####
self.weightObj = weightObj
def test_knnW_from_array(self):
import numpy as np
x, y = np.indices((5, 5))
x.shape = (25, 1)
y.shape = (25, 1)
data = np.hstack([x, y])
wnn2 = pysal.knnW_from_array(data, k=2)
wnn4 = pysal.knnW_from_array(data, k=4)
self.assertEquals(set(wnn4.neighbors[0]), set([1, 5, 6, 2]))
self.assertEquals(set(wnn4.neighbors[5]), set([0, 6, 10, 1]))
self.assertEquals(set(wnn2.neighbors[0]), set([1, 5]))
self.assertEquals(set(wnn2.neighbors[5]), set([0, 6]))
self.assertAlmostEquals(wnn2.pct_nonzero, 8.0)
self.assertAlmostEquals(wnn4.pct_nonzero, 16.0)
wnn4 = pysal.knnW_from_array(data, k=4)
self.assertEquals(set(wnn4.neighbors[0]), set([1, 5, 6, 2]))
'''
def test_knnW_from_array(self):
import numpy as np
x, y = np.indices((5, 5))
x.shape = (25, 1)
y.shape = (25, 1)
data = np.hstack([x, y])
wnn2 = pysal.knnW_from_array(data, k=2)
wnn4 = pysal.knnW_from_array(data, k=4)
self.assertEquals(set(wnn4.neighbors[0]), set([1, 5, 6, 2]))
self.assertEquals(set(wnn4.neighbors[5]), set([0, 6, 10, 1]))
self.assertEquals(set(wnn2.neighbors[0]), set([1, 5]))
self.assertEquals(set(wnn2.neighbors[5]), set([0, 6]))
self.assertAlmostEquals(wnn2.pct_nonzero, 8.0)
self.assertAlmostEquals(wnn4.pct_nonzero, 16.0)
wnn4 = pysal.knnW_from_array(data, k=4)
self.assertEquals(set(wnn4.neighbors[0]), set([1, 5, 6, 2]))
'''
def setUp(self):
sids = pysal.open(pysal.examples.get_path("sids2.shp"), "r")
self.sids = sids
self.d = np.array([i.centroid for i in sids])
self.w = knnW_from_array(self.d, k=5)
if not self.w.id_order_set:
self.w.id_order = self.w.id_order
sids_db = pysal.open(pysal.examples.get_path("sids2.dbf"), "r")
self.b, self.e = np.array(sids_db[:, 8]), np.array(sids_db[:, 9])
def setUp(self):
sids = pysal.open(pysal.examples.get_path('sids2.shp'), 'r')
self.sids = sids
self.d = np.array([i.centroid for i in sids])
self.w = knnW_from_array(self.d, k=5)
if not self.w.id_order_set:
self.w.id_order = self.w.id_order
sids_db = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
self.b, self.e = np.array(sids_db[:, 8]), np.array(sids_db[:, 9])
def test_knnW_from_array(self):
import numpy as np
x, y = np.indices((5, 5))
x.shape = (25, 1)
y.shape = (25, 1)
data = np.hstack([x, y])
wnn2 = pysal.knnW_from_array(data, k=2)
wnn4 = pysal.knnW_from_array(data, k=4)
self.assertEquals(wnn4.neighbors[0], [1, 5, 6, 2])
self.assertEquals(wnn4.neighbors[5], [0, 6, 10, 1])
self.assertEquals(wnn2.neighbors[0], [1, 5])
self.assertEquals(wnn2.neighbors[5], [0, 6])
self.assertAlmostEquals(wnn2.pct_nonzero, 0.080000000000000002)
self.assertAlmostEquals(wnn4.pct_nonzero, 0.16)
wnn4 = pysal.knnW_from_array(data, k=4)
self.assertEquals(wnn4.neighbors[0], [1, 5, 6, 2])
wnn3e = pysal.knnW(data, p=2, k=3)
self.assertEquals(wnn3e.neighbors[0], [1, 5, 6])
wnn3m = pysal.knnW(data, p=1, k=3)
self.assertEquals(wnn3m.neighbors[0], [1, 5, 2])
def test_Headbanging_Median_Rate(self):
sids_d = np.array([i.centroid for i in self.sids])
sids_w = pysal.knnW_from_array(sids_d, k=5)
if not sids_w.id_order_set:
sids_w.id_order = sids_w.id_order
s_ht = sm.Headbanging_Triples(sids_d, sids_w, k=5)
sids_db = pysal.open(pysal.examples.get_path("sids2.dbf"), "r")
s_e, s_b = np.array(sids_db[:, 9]), np.array(sids_db[:, 8])
sids_hb_r = sm.Headbanging_Median_Rate(s_e, s_b, s_ht)
sids_hb_rr5 = np.array([0.00075586, 0.0, 0.0008285, 0.0018315, 0.00498891])
np.testing.assert_array_almost_equal(sids_hb_rr5, sids_hb_r.r[:5])
sids_hb_r2 = sm.Headbanging_Median_Rate(s_e, s_b, s_ht, iteration=5)
sids_hb_r2r5 = np.array([0.0008285, 0.00084331, 0.00086896, 0.0018315, 0.00498891])
np.testing.assert_array_almost_equal(sids_hb_r2r5, sids_hb_r2.r[:5])
sids_hb_r3 = sm.Headbanging_Median_Rate(s_e, s_b, s_ht, aw=s_b)
sids_hb_r3r5 = np.array([0.00091659, 0.0, 0.00156838, 0.0018315, 0.00498891])
np.testing.assert_array_almost_equal(sids_hb_r3r5, sids_hb_r3.r[:5])
def calcWeightMatrix(self, get_wm=0):
wm = None
if self.geotype == 'Polygon':
parser = WKTParser()
shape_l = MyList([parser.fromWKT(s) for s in self.listWKT])
neighbour_data = ContiguityWeights(shape_l).w
wm = WeightMatrix(neighbour_data, id_order=self.ids)
elif self.geotype == 'Point':
parser = WKTParser()
shape_l = MyList([parser.fromWKT(s) for s in self.listWKT])
#wm = pysal.knnW_from_array(np.array(self.listWKT), k=5, ids=self.ids)
wm = pysal.knnW_from_array(np.array(shape_l), k=5, ids=self.ids)
if get_wm == 1:
return wm
warray = self.dict2array(wm, self.N)
return warray
def test_Headbanging_Median_Rate(self):
sids_d = np.array([i.centroid for i in self.sids])
sids_w = pysal.knnW_from_array(sids_d, k=5)
if not sids_w.id_order_set:
sids_w.id_order = sids_w.id_order
s_ht = sm.Headbanging_Triples(sids_d, sids_w, k=5)
sids_db = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
s_e, s_b = np.array(sids_db[:, 9]), np.array(sids_db[:, 8])
sids_hb_r = sm.Headbanging_Median_Rate(s_e, s_b, s_ht)
sids_hb_rr5 = np.array([0.00075586, 0.,
0.0008285, 0.0018315, 0.00498891])
np.testing.assert_array_almost_equal(sids_hb_rr5, sids_hb_r.r[:5])
sids_hb_r2 = sm.Headbanging_Median_Rate(s_e, s_b, s_ht, iteration=5)
sids_hb_r2r5 = np.array([0.0008285, 0.00084331,
0.00086896, 0.0018315, 0.00498891])
np.testing.assert_array_almost_equal(sids_hb_r2r5, sids_hb_r2.r[:5])
sids_hb_r3 = sm.Headbanging_Median_Rate(s_e, s_b, s_ht, aw=s_b)
sids_hb_r3r5 = np.array([0.00091659, 0.,
0.00156838, 0.0018315, 0.00498891])
np.testing.assert_array_almost_equal(sids_hb_r3r5, sids_hb_r3.r[:5])
def randomKnnW(n, k, p=2.):
'''
Builds a W object from random points based on knn
...
Arguments
---------
n : int
Number of observations for the W object
k : int
Number of neighbors per observation in the W object
p : float
Minkowski p-norm distance metric parameter
Returns
-------
w : W
PySAL weights object
'''
xy = np.random.random(n * 2)
xy = xy.reshape((n, 2))
return pysal.knnW_from_array(xy, k, p)
def setUp(self):
sids = pysal.open(pysal.examples.get_path('sids2.shp'), 'r')
self.sids = sids
self.d = np.array([i.centroid for i in sids])
self.w = knnW_from_array(self.d, k=5)
if not self.w.id_order_set:
self.w.id_order = self.w.id_order
sids_db = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
self.b, self.e = np.array(sids_db[:, 8]), np.array(sids_db[:, 9])
self.sids_hb_rr5 = np.array([0.00075586, 0.,
0.0008285, 0.0018315, 0.00498891])
self.sids_hb_r2r5 = np.array([0.0008285, 0.00084331,
0.00086896, 0.0018315, 0.00498891])
self.sids_hb_r3r5 = np.array([0.00091659, 0.,
0.00156838, 0.0018315, 0.00498891])
if not PANDAS_EXTINCT:
self.df = sids_db.to_df()
self.ename = 'SID74'
self.bname = 'BIR74'
self.enames = [self.ename, 'SID79']
self.bnames = [self.bname, 'BIR79']
self.sids79r = np.array([.000563, .001659, .001879,
.002410, .002720])
def setUp(self):
sids = pysal.open(pysal.examples.get_path('sids2.shp'), 'r')
self.sids = sids
self.d = np.array([i.centroid for i in sids])
self.w = knnW_from_array(self.d, k=5)
if not self.w.id_order_set:
self.w.id_order = self.w.id_order
sids_db = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
self.b, self.e = np.array(sids_db[:, 8]), np.array(sids_db[:, 9])
self.sids_hb_rr5 = np.array(
[0.00075586, 0., 0.0008285, 0.0018315, 0.00498891])
self.sids_hb_r2r5 = np.array(
[0.0008285, 0.00084331, 0.00086896, 0.0018315, 0.00498891])
self.sids_hb_r3r5 = np.array(
[0.00091659, 0., 0.00156838, 0.0018315, 0.00498891])
if not PANDAS_EXTINCT:
self.df = sids_db.to_df()
self.ename = 'SID74'
self.bname = 'BIR74'
self.enames = [self.ename, 'SID79']
self.bnames = [self.bname, 'BIR79']
self.sids79r = np.array(
[.000563, .001659, .001879, .002410, .002720])
def randomKnnW(n, k, p=2.):
'''
Builds a W object from random points based on knn
...
Arguments
---------
n : int
Number of observations for the W object
k : int
Number of neighbors per observation in the W object
p : float
Minkowski p-norm distance metric parameter
Returns
-------
w : W
PySAL weights object
'''
xy = np.random.random(n * 2)
xy = xy.reshape((n, 2))
return pysal.knnW_from_array(xy, k, p)
def buildWeights(self):
"""Performs Distance-based Weights Creation"""
ARCPY.SetProgressor("default",
"Constructing spatial weights object...")
#### Shorthand Attributes ####
distanceType = self.distanceType
threshold = self.threshold
knnNum = self.knnNum
idField = self.idField
outputExt = self.outputExt
ssdo = self.ssdo
#### Create Distance-based WeightObj (0-based IDs) ####
dataArray = ssdo.xyCoords
if distanceType.upper() == DISTTYPE[0]:
weightObj = PYSAL.threshold_binaryW_from_array(
dataArray, threshold)
elif distanceType.upper() == DISTTYPE[1]:
weightObj = PYSAL.knnW_from_array(dataArray, knnNum)
elif distanceType.upper() == DISTTYPE[2]:
alpha = -1 * self.inverseDist
weightObj = PYSAL.threshold_continuousW_from_array(\
dataArray, threshold, alpha=alpha)
#### Re-Create WeightObj for NOT 0-based idField ####
if idField:
if ssdo.master2Order.keys() != ssdo.master2Order.values():
o2M = ssdo.order2Master
neighborDict = {o2M[oid] : [o2M[nid] for nid in nbrs] \
for oid,nbrs in weightObj.neighbors.items()}
weightDict = {o2M[oid] : weights \
for oid, weights in weightObj.weights.items()}
weightObj = W(neighborDict, weightDict)
#### Save weightObj Class Object for Writing Result ####
self.weightObj = weightObj
YVar = 'StructurePrice'
XVarsdummy = ['LivingArea']
num = 0
for s in finalSet:
XVarsdummy.append(s)
lst[s] = lst[s].astype(float)
XVars = ['median_income', 'LivingArea', 'Age', 'num_trees']
yxs = lst.loc[:, XVars + [YVar]].dropna()
yxs_dummy = lst.loc[:, XVarsdummy + [YVar]].dropna()
y = lst[YVar]
w = pysal.knnW_from_array(lst.loc[\
yxs.index, \
['centroid_long', 'centroid_lat']\
].values, k=30)
w.transform = 'R'
yxs = yxs.assign(w_res=pysal.lag_spatial(w, yxs_dummy['residential'].values))
"""
yxs = yxs.assign(w_mixed=pysal.lag_spatial(w, yxs_dummy['mixed'].values))
yxs = yxs.assign(w_retail=pysal.lag_spatial(w, yxs_dummy['retail'].values))
yxs = yxs.assign(w_apt=pysal.lag_spatial(w, yxs_dummy['apt'].values))
yxs = yxs.assign(w_industrial=pysal.lag_spatial(w, yxs_dummy['industrial'].values))
yxs = yxs.assign(w_office=pysal.lag_spatial(w, yxs_dummy['office'].values))
yxs = yxs.assign(w_school=pysal.lag_spatial(w, yxs_dummy['school'].values))
yxs = yxs.assign(w_auto_shop=pysal.lag_spatial(w, yxs_dummy['auto_shop'].values))
yxs = yxs.assign(w_religious=pysal.lag_spatial(w, yxs_dummy['religious'].values))
yxs = yxs.assign(w_food=pysal.lag_spatial(w, yxs_dummy['food'].values))
yxs = yxs.assign(w_charitable=pysal.lag_spatial(w, yxs_dummy['charitable'].values))
