def unweightedCalc(self):
"""Performs unweighted k-function."""
#### Attribute Shortcuts ####
ssdo = self.ssdo
reduce = self.reduce
simulate = self.simulate
ripley = self.ripley
if reduce:
studyArea2Use = self.reduceArea
else:
studyArea2Use = self.studyArea
self.ld = COLL.defaultdict(float)
if self.permutations:
self.ldMin = COLL.defaultdict(float)
self.ldMax = COLL.defaultdict(float)
for order in self.cutoffOrder:
self.ldMin[order] = 99999999999.
permsPlus = self.permutations + 1
for perm in xrange(0, permsPlus):
#### Permutation Progressor ####
pmsg = ARCPY.GetIDMessage(84184)
progressMessage = pmsg.format(perm, permsPlus)
ARCPY.SetProgressor("default", progressMessage)
#### Permutate the XY ####
if perm != 0:
self.permutateTable()
gaSearch = GAPY.ga_nsearch(self.kTable)
gaSearch.init_nearest(self.stepMax, 0, "euclidean")
N = len(self.kTable)
numIDs = len(self.ids)
kij = COLL.defaultdict(float)
for i in xrange(N):
row = self.kTable[i]
id0 = row[0]
if id0 in self.ids:
x0,y0 = row[1]
gaSearch.search_by_idx(i)
for nh in gaSearch:
neighInfo = self.kTable[nh.idx]
nhID = neighInfo[0]
x1,y1 = neighInfo[1]
dist = WU.euclideanDistance(x0,x1,y0,y1)
if ripley:
value = self.returnRipley(id0, dist)
else:
value = 1.0
for order in self.reverseOrder:
cutoff = self.cutoffs[order]
if dist > cutoff:
break
kij[order] += value
ARCPY.SetProgressorPosition()
#### Calculate Stats USing Dictionaries ####
weightSumVal = numIDs * (numIDs - 1.0)
denom = NUM.pi * weightSumVal
for order in self.cutoffOrder:
res = kij[order]
numer = res * studyArea2Use
permResult = NUM.sqrt( (numer/denom) )
if perm:
self.ldMin[order] = min(self.ldMin[order],
permResult)
self.ldMax[order] = max(self.ldMax[order],
permResult)
else:
self.ld[order] = permResult
def weightedCalc(self):
"""Performs weighted k-function."""
#### Attribute Shortcuts ####
ssdo = self.ssdo
reduce = self.reduce
simulate = self.simulate
ripley = self.ripley
numIDs = len(self.ids)
if reduce:
studyArea2Use = self.reduceArea
else:
studyArea2Use = self.studyArea
if simulate:
simOrder = []
for simKey, origID in self.simDict.iteritems():
simOrder.append(self.weightDict[origID])
self.ld = COLL.defaultdict(float)
if self.permutations:
self.ldMin = COLL.defaultdict(float)
self.ldMax = COLL.defaultdict(float)
for order in self.cutoffOrder:
self.ldMin[order] = 99999999999.
permsPlus = self.permutations + 1
for perm in xrange(0, permsPlus):
#### Permutation Progressor ####
pmsg = ARCPY.GetIDMessage(84184)
progressMessage = pmsg.format(perm, permsPlus)
ARCPY.SetProgressor("default", progressMessage)
gaSearch = GAPY.ga_nsearch(self.kTable)
gaSearch.init_nearest(self.stepMax, 0, "euclidean")
N = len(self.kTable)
#### Permutate Weights ####
if perm:
weights = RAND.permutation(weights)
else:
weights = self.weightVals
if simulate:
simWeights = NUM.take(self.weightVals, simOrder)
#### Set Statistic Variables ####
weightSumVal = 0.0
kij = COLL.defaultdict(float)
start = 0
#### Loop Over Entire Table ####
for i in xrange(N):
row = self.kTable[i]
id0 = row[0]
#### Calculate For Inside IDs ####
if id0 in self.ids:
x0,y0 = row[1]
weightInd0 = self.weightDict[id0]
w0 = weights[weightInd0]
#### Weight Sum Resolution ####
weightSumVal += (NUM.sum(w0 * weights)) - w0**2.0
if simulate:
weightSumVal += (w0 * simWeights).sum()
#### Neighbors Within Largest Distance ####
gaSearch.search_by_idx(i)
for nh in gaSearch:
neighInfo = self.kTable[nh.idx]
id1 = neighInfo[0]
x1,y1 = neighInfo[1]
#### Input or Simulated Point ####
try:
weightInd1 = self.weightDict[id1]
except:
origID = self.simDict[id1]
weightInd1 = self.weightDict[origID]
#### Process Neighbor Pair ####
w1 = weights[weightInd1]
dist = WU.euclideanDistance(x0,x1,y0,y1)
if ripley:
value = self.returnRipley(id0, dist)
else:
value = 1.0
value = w0 * (w1 * value)
#### Add To Cutoffs ####
for order in self.reverseOrder:
cutoff = self.cutoffs[order]
if dist > cutoff:
break
kij[order] += value
ARCPY.SetProgressorPosition()
#### Calculate Stats USing Dictionaries ####
denom = NUM.pi * weightSumVal
for order in self.cutoffOrder:
res = kij[order]
numer = res * studyArea2Use
permResult = NUM.sqrt( (numer/denom) )
if perm:
self.ldMin[order] = min(self.ldMin[order],
permResult)
self.ldMax[order] = max(self.ldMax[order],
permResult)
else:
self.ld[order] = permResult
