def calculateAreas(inputFC, outputFC):
"""Creates a new feature class from the input polygon feature class
and adds a field that includes the area of the polygons.
INPUTS:
inputFC (str): path to the input feature class
outputFC (str): path to the output feature class
"""
#### Validate Output Workspace ####
ERROR.checkOutputPath(outputFC)
outPath, outName = OS.path.split(outputFC)
#### Create SSDataObject ####
ssdo = SSDO.SSDataObject(inputFC, templateFC = outputFC,
useChordal = False)
#### Assure Polygon FC ####
if ssdo.shapeType.lower() != "polygon":
ARCPY.AddIDMessage("ERROR", 931)
raise SystemExit()
#### Check Number of Observations ####
cnt = UTILS.getCount(inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs = 1)
#### Copy Features ####
try:
clearCopy = UTILS.clearExtent(DM.CopyFeatures)
clearCopy(inputFC, outputFC)
except:
ARCPY.AddIDMessage("ERROR", 210, outputFC)
raise SystemExit()
#### Add Area Field ####
areaFieldNameOut = ARCPY.ValidateFieldName(areaFieldName, outPath)
if not ssdo.allFields.has_key(areaFieldNameOut):
UTILS.addEmptyField(outputFC, areaFieldNameOut, "DOUBLE")
#### Calculate Field ####
clearCalc = UTILS.clearExtent(DM.CalculateField)
clearCalc(outputFC, areaFieldNameOut, "!shape.area!", "PYTHON_9.3")
def __init__(self,
inputFC,
outputFC=None,
caseField=None,
orientationOnly=False):
#### Create SSDataObject ####
ssdo = SSDO.SSDataObject(inputFC,
templateFC=outputFC,
useChordal=False)
cnt = UTILS.getCount(inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs=1)
fieldList = [ssdo.oidName, "SHAPE@"]
caseIsString = False
if caseField:
fieldList.append(caseField)
caseType = ssdo.allFields[caseField].type.upper()
caseIsString = caseType == "STRING"
#### Initialize Accounting Structures ####
xyLenVals = {}
sinCosVals = {}
#### Open Search Cursor ####
try:
rows = DA.SearchCursor(inputFC, fieldList, "",
ssdo.spatialRefString)
except:
ARCPY.AddIDMessage("ERROR", 204)
raise SystemExit()
#### Keep track of Invalid Fields ####
badIDs = []
badLengths = []
badRecord = False
negativeWeights = False
#### Create Progressor ####
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84001), 0, cnt, 1)
for row in rows:
OID = row[0]
shapeInfo = row[1]
badRow = row.count(None)
try:
centroidInfo = shapeInfo.trueCentroid
xVal = centroidInfo.X
yVal = centroidInfo.Y
length = float(shapeInfo.length)
firstPoint = shapeInfo.firstPoint
lastPoint = shapeInfo.lastPoint
if firstPoint == lastPoint:
badLengths.append(OID)
badRow = True
else:
firstX = float(firstPoint.X)
firstY = float(firstPoint.Y)
lastX = float(lastPoint.X)
lastY = float(lastPoint.Y)
except:
badRow = True
#### Process Good Records ####
if not badRow:
#### Case Field ####
caseVal = "ALL"
if caseField:
caseVal = UTILS.caseValue2Print(row[2], caseIsString)
#### Get Angle ####
numer = lastX - firstX
denom = lastY - firstY
angle = UTILS.getAngle(numer, denom)
#### Adjust for Orientation Only ####
if orientationOnly:
angle2Degree = UTILS.convert2Degree(angle)
if angle2Degree < 180:
numer = firstX - lastX
denom = firstY - lastY
angle = UTILS.getAngle(numer, denom)
sinVal = NUM.sin(angle)
cosVal = NUM.cos(angle)
xyLenVal = (xVal, yVal, length)
sinCosVal = (sinVal, cosVal)
try:
xyLenVals[caseVal].append(xyLenVal)
sinCosVals[caseVal].append(sinCosVal)
except:
xyLenVals[caseVal] = [xyLenVal]
sinCosVals[caseVal] = [sinCosVal]
else:
#### Bad Record ####
badRecord = True
badIDs.append(OID)
ARCPY.SetProgressorPosition()
del rows
#### Get Set of Bad IDs ####
badIDs = list(set(badIDs))
badIDs.sort()
badIDs = [str(i) for i in badIDs]
#### Process any bad records encountered ####
bn = len(badIDs)
if badRecord:
err = ERROR.reportBadRecords(cnt, bn, badIDs, label=ssdo.oidName)
#### Error For Not Enough Observations ####
goodRecs = cnt - bn
ERROR.errorNumberOfObs(goodRecs, minNumObs=1)
#### Report Features With No Length ####
badLengths = list(set(badLengths))
badLengths.sort()
badLengths = [str(i) for i in badLengths]
numBadLengths = len(badLengths)
if numBadLengths > 0:
ERROR.reportBadLengths(cnt,
numBadLengths,
badLengths,
label=ssdo.oidName)
#### Set up for Bad Cases ####
badCases = []
cases = xyLenVals.keys()
meanCenter = {}
dm = {}
#### Calculate Mean Center and Standard Distance ####
for case in cases:
xyLens = xyLenVals[case]
numFeatures = len(xyLens)
if numFeatures > 0:
#### Mean Centers and Lengths ####
xyLens = NUM.array(xyLens)
meanX, meanY, meanL = NUM.mean(xyLens, 0)
#### Sum Sin and Cos ####
scVals = NUM.array(sinCosVals[case])
sumSin, sumCos = NUM.sum(scVals, 0)
#### Calculate Angle ####
radianAngle = UTILS.getAngle(sumSin, sumCos)
degreeAngle = UTILS.convert2Degree(radianAngle)
#### Get Start and End Points ####
halfMeanLen = meanL / 2.0
endX = (halfMeanLen * NUM.sin(radianAngle)) + meanX
startX = (2.0 * meanX) - endX
endY = (halfMeanLen * NUM.cos(radianAngle)) + meanY
startY = (2.0 * meanY) - endY
unstandardized = NUM.sqrt(sumSin**2.0 + sumCos**2.0)
circVar = 1.0 - (unstandardized / (numFeatures * 1.0))
#### Re-adjust Angle Back towards North ####
if orientationOnly:
degreeAngle = degreeAngle - 180.0
radianAngle = UTILS.convert2Radians(degreeAngle)
#### Populate Results Structure ####
meanCenter[case] = (meanX, meanY)
dm[case] = [(startX, startY), (endX, endY), meanL, radianAngle,
degreeAngle, circVar]
#### Sorted Case List ####
caseKeys = dm.keys()
caseKeys.sort()
self.caseKeys = caseKeys
#### Set Attributes ####
self.ssdo = ssdo
self.meanCenter = meanCenter
self.dm = dm
self.badCases = badCases
self.inputFC = inputFC
self.outputFC = outputFC
self.caseField = caseField
self.orientationOnly = orientationOnly
self.caseIsString = caseIsString
def spaceTime2SWM(inputFC, swmFile, masterField, concept = "EUCLIDEAN",
threshold = None, rowStandard = True,
timeField = None, timeType = None,
timeValue = None):
"""
inputFC (str): path to the input feature class
swmFile (str): path to the SWM file.
masterField (str): field in table that serves as the mapping.
concept: {str, EUCLIDEAN}: EUCLIDEAN or MANHATTAN
threshold {float, None}: distance threshold
rowStandard {bool, True}: row standardize weights?
timeField {str, None}: name of the date-time field
timeType {str, None}: ESRI enumeration of date-time intervals
timeValue {float, None}: value forward and backward in time
"""
#### Assure Temporal Parameters are Set ####
if not timeField:
ARCPY.AddIDMessage("ERROR", 1320)
raise SystemExit()
if not timeType:
ARCPY.AddIDMessage("ERROR", 1321)
raise SystemExit()
if not timeValue or timeValue <= 0:
ARCPY.AddIDMessage("ERROR", 1322)
raise SystemExit()
#### Create SSDataObject ####
ssdo = SSDO.SSDataObject(inputFC, templateFC = inputFC,
useChordal = True)
cnt = UTILS.getCount(inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs = 2)
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84001), 0, cnt, 1)
#### Validation of Master Field ####
verifyMaster = ERROR.checkField(ssdo.allFields, masterField, types = [0,1])
badIDs = []
#### Create Temporal Hash ####
timeInfo = {}
xyCoords = NUM.empty((cnt, 2), float)
#### Process Field Values ####
fieldList = [masterField, "SHAPE@XY", timeField]
try:
rows = DA.SearchCursor(ssdo.catPath, fieldList, "",
ssdo.spatialRefString)
except:
ARCPY.AddIDMessage("ERROR", 204)
raise SystemExit()
#### Add Data to GATable and Time Dictionary ####
c = 0
for row in rows:
badRow = False
#### Assure Masterfield is Valid ####
masterID = row[0]
if masterID == None or masterID == "":
badRow = True
#### Assure Date/Time is Valid ####
timeStamp = row[-1]
if timeStamp == None or timeStamp == "":
badRow = True
#### Assure Centroid is Valid ####
badXY = row[1].count(None)
if not badXY:
x,y = row[1]
xyCoords[c] = (x,y)
else:
badRow = True
#### Process Data ####
if not badRow:
if timeInfo.has_key(masterID):
#### Assure Uniqueness ####
ARCPY.AddIDMessage("Error", 644, masterField)
ARCPY.AddIDMessage("Error", 643)
raise SystemExit()
else:
#### Fill Date/Time Dict ####
startDT, endDT = TUTILS.calculateTimeWindow(timeStamp,
timeValue,
timeType)
timeInfo[masterID] = (timeStamp, startDT, endDT)
else:
badIDs.append(masterID)
#### Set Progress ####
c += 1
ARCPY.SetProgressorPosition()
#### Clean Up ####
del rows
#### Get Set of Bad IDs ####
numBadObs = len(badIDs)
badIDs = list(set(badIDs))
badIDs.sort()
badIDs = [ str(i) for i in badIDs ]
#### Process any bad records encountered ####
if numBadObs:
ERROR.reportBadRecords(cnt, numBadObs, badIDs, label = masterField)
#### Load Neighbor Table ####
gaTable, gaInfo = WU.gaTable(ssdo.inputFC,
fieldNames = [masterField, timeField],
spatRef = ssdo.spatialRefString)
numObs = len(gaTable)
xyCoords = xyCoords[0:numObs]
#### Set the Distance Threshold ####
concept, gaConcept = WU.validateDistanceMethod(concept, ssdo.spatialRef)
if threshold == None:
#### Set Progressor for Search ####
ARCPY.SetProgressor("default", ARCPY.GetIDMessage(84144))
#### Create k-Nearest Neighbor Search Type ####
gaSearch = GAPY.ga_nsearch(gaTable)
gaSearch.init_nearest(0.0, 1, gaConcept)
neighDist = ARC._ss.NeighborDistances(gaTable, gaSearch)
N = len(neighDist)
threshold = 0.0
sumDist = 0.0
#### Find Maximum Nearest Neighbor Distance ####
for row in xrange(N):
dij = neighDist[row][-1][0]
if dij > threshold:
threshold = dij
sumDist += dij
ARCPY.SetProgressorPosition()
#### Increase For Rounding Error ####
threshold = threshold * 1.0001
avgDist = sumDist / (N * 1.0)
#### Add Linear/Angular Units ####
thresholdStr = ssdo.distanceInfo.printDistance(threshold)
ARCPY.AddIDMessage("Warning", 853, thresholdStr)
#### Chordal Default Check ####
if ssdo.useChordal:
hardMaxExtent = ARC._ss.get_max_gcs_distance(ssdo.spatialRef)
if threshold > hardMaxExtent:
ARCPY.AddIDMessage("ERROR", 1609)
raise SystemExit()
#### Clean Up ####
del gaSearch
#### Create Missing SSDO Info ####
extent = UTILS.resetExtent(xyCoords)
#### Reset Coordinates for Chordal ####
if ssdo.useChordal:
sliceInfo = UTILS.SpheroidSlice(extent, ssdo.spatialRef)
maxExtent = sliceInfo.maxExtent
else:
env = UTILS.Envelope(extent)
maxExtent = env.maxExtent
threshold = checkDistanceThresholdSWM(ssdo, threshold, maxExtent)
#### Set Default Progressor for Neigborhood Structure ####
ARCPY.SetProgressor("default", ARCPY.GetIDMessage(84143))
#### Create Distance Neighbor Search Type ####
gaSearch = GAPY.ga_nsearch(gaTable)
gaSearch.init_nearest(threshold, 0, gaConcept)
neighSearch = ARC._ss.NeighborSearch(gaTable, gaSearch)
#### Set Progressor for Weights Writing ####
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84127), 0, numObs, 1)
#### Initialize Spatial Weights Matrix File ####
swmWriter = WU.SWMWriter(swmFile, masterField, ssdo.spatialRefName,
numObs, rowStandard, inputFC = inputFC,
wType = 9, distanceMethod = concept,
threshold = threshold, timeField = timeField,
timeType = timeType, timeValue = timeValue)
for row in xrange(numObs):
masterID = gaTable[row][2]
#### Get Date/Time Info ####
dt0, startDT0, endDT0 = timeInfo[masterID]
nhs = neighSearch[row]
neighs = []
weights = []
for nh in nhs:
#### Search Through Spatial Neighbors ####
neighID = gaTable[nh][2]
#### Get Date/Time Info ####
dt1, startDT1, endDT1 = timeInfo[neighID]
#### Filter Based on Date/Time ####
insideTimeWindow = TUTILS.isTimeNeighbor(startDT0, endDT0, dt1)
if insideTimeWindow:
neighs.append(neighID)
weights.append(1.0)
#### Add Spatial Weights Matrix Entry ####
swmWriter.swm.writeEntry(masterID, neighs, weights)
#### Set Progress ####
ARCPY.SetProgressorPosition()
swmWriter.close()
del gaTable
#### Report Warning/Max Neighbors ####
swmWriter.reportNeighInfo()
#### Report Spatial Weights Summary ####
swmWriter.report()
#### Report SWM File is Large ####
swmWriter.reportLargeSWM()
def kNearest2SWM(inputFC, swmFile, masterField, concept = "EUCLIDEAN",
kNeighs = 1, rowStandard = True):
"""Creates a sparse spatial weights matrix (SWM) based on k-nearest
neighbors.
INPUTS:
inputFC (str): path to the input feature class
swmFile (str): path to the SWM file.
masterField (str): field in table that serves as the mapping.
concept: {str, EUCLIDEAN}: EUCLIDEAN or MANHATTAN
kNeighs {int, 1}: number of neighbors to return
rowStandard {bool, True}: row standardize weights?
"""
#### Assure that kNeighs is Non-Zero ####
if kNeighs <= 0:
ARCPY.AddIDMessage("ERROR", 976)
raise SystemExit()
#### Set Default Progressor for Neigborhood Structure ####
ARCPY.SetProgressor("default", ARCPY.GetIDMessage(84143))
#### Create SSDataObject ####
ssdo = SSDO.SSDataObject(inputFC, templateFC = inputFC,
useChordal = True)
cnt = UTILS.getCount(inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs = 2)
#### Validation of Master Field ####
verifyMaster = ERROR.checkField(ssdo.allFields, masterField, types = [0,1])
#### Create GA Data Structure ####
gaTable, gaInfo = WU.gaTable(ssdo.catPath, [masterField],
spatRef = ssdo.spatialRefString)
#### Assure Enough Observations ####
N = gaInfo[0]
ERROR.errorNumberOfObs(N, minNumObs = 2)
#### Process any bad records encountered ####
numBadRecs = cnt - N
if numBadRecs:
badRecs = WU.parseGAWarnings(gaTable.warnings)
err = ERROR.reportBadRecords(cnt, numBadRecs, badRecs,
label = ssdo.oidName)
#### Assure k-Nearest is Less Than Number of Features ####
if kNeighs >= N:
ARCPY.AddIDMessage("ERROR", 975)
raise SystemExit()
#### Create k-Nearest Neighbor Search Type ####
gaSearch = GAPY.ga_nsearch(gaTable)
concept, gaConcept = WU.validateDistanceMethod(concept, ssdo.spatialRef)
gaSearch.init_nearest(0.0, kNeighs, gaConcept)
neighWeights = ARC._ss.NeighborWeights(gaTable, gaSearch,
weight_type = 1,
row_standard = False)
#### Set Progressor for Weights Writing ####
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84127), 0, N, 1)
#### Initialize Spatial Weights Matrix File ####
swmWriter = WU.SWMWriter(swmFile, masterField, ssdo.spatialRefName,
N, rowStandard, inputFC = inputFC,
wType = 2, distanceMethod = concept,
numNeighs = kNeighs)
#### Unique Master ID Dictionary ####
masterSet = set([])
for row in xrange(N):
masterID = int(gaTable[row][2])
if masterID in masterSet:
ARCPY.AddIDMessage("Error", 644, masterField)
ARCPY.AddIDMessage("Error", 643)
raise SystemExit()
else:
masterSet.add(masterID)
neighs, weights = neighWeights[row]
neighs = [ gaTable[nh][2] for nh in neighs ]
#### Add Spatial Weights Matrix Entry ####
swmWriter.swm.writeEntry(masterID, neighs, weights)
#### Set Progress ####
ARCPY.SetProgressorPosition()
swmWriter.close()
del gaTable
#### Report Warning/Max Neighbors ####
swmWriter.reportNeighInfo()
#### Report Spatial Weights Summary ####
swmWriter.report()
#### Report SWM File is Large ####
swmWriter.reportLargeSWM()
def delaunay2SWM(inputFC, swmFile, masterField, rowStandard = True):
"""Creates a sparse spatial weights matrix (SWM) based on Delaunay
Triangulation.
INPUTS:
inputFC (str): path to the input feature class
swmFile (str): path to the SWM file.
masterField (str): field in table that serves as the mapping.
rowStandard {bool, True}: row standardize weights?
"""
#### Set Default Progressor for Neigborhood Structure ####
ARCPY.SetProgressor("default", ARCPY.GetIDMessage(84143))
#### Create SSDataObject ####
ssdo = SSDO.SSDataObject(inputFC, templateFC = inputFC,
useChordal = True)
cnt = UTILS.getCount(inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs = 2)
#### Validation of Master Field ####
verifyMaster = ERROR.checkField(ssdo.allFields, masterField, types = [0,1])
#### Create GA Data Structure ####
gaTable, gaInfo = WU.gaTable(ssdo.catPath, [masterField],
spatRef = ssdo.spatialRefString)
#### Assure Enough Observations ####
N = gaInfo[0]
ERROR.errorNumberOfObs(N, minNumObs = 2)
#### Process any bad records encountered ####
numBadRecs = cnt - N
if numBadRecs:
badRecs = WU.parseGAWarnings(gaTable.warnings)
err = ERROR.reportBadRecords(cnt, numBadRecs, badRecs,
label = ssdo.oidName)
#### Create Delaunay Neighbor Search Type ####
gaSearch = GAPY.ga_nsearch(gaTable)
gaSearch.init_delaunay()
neighWeights = ARC._ss.NeighborWeights(gaTable, gaSearch,
weight_type = 1,
row_standard = False)
#### Set Progressor for Weights Writing ####
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84127), 0, N, 1)
#### Initialize Spatial Weights Matrix File ####
swmWriter = WU.SWMWriter(swmFile, masterField, ssdo.spatialRefName,
N, rowStandard, inputFC = inputFC,
wType = 3)
#### Unique Master ID Dictionary ####
masterSet = set([])
for row in xrange(N):
masterID = int(gaTable[row][2])
if masterID in masterSet:
ARCPY.AddIDMessage("Error", 644, masterField)
ARCPY.AddIDMessage("Error", 643)
raise SystemExit()
else:
masterSet.add(masterID)
neighs, weights = neighWeights[row]
neighs = [ gaTable[nh][2] for nh in neighs ]
#### Add Spatial Weights Matrix Entry ####
swmWriter.swm.writeEntry(masterID, neighs, weights)
#### Set Progress ####
ARCPY.SetProgressorPosition()
#### Clean Up ####
swmWriter.close()
del gaTable
#### Report if Any Features Have No Neighbors ####
swmWriter.reportNoNeighbors()
#### Report Spatial Weights Summary ####
swmWriter.report()
#### Report SWM File is Large ####
swmWriter.reportLargeSWM()
def polygon2SWM(inputFC, swmFile, masterField,
concept = "EUCLIDEAN", kNeighs = 0,
rowStandard = True, contiguityType = "ROOK"):
"""Creates a sparse spatial weights matrix (SWM) based on polygon
contiguity.
INPUTS:
inputFC (str): path to the input feature class
swmFile (str): path to the SWM file.
masterField (str): field in table that serves as the mapping.
concept: {str, EUCLIDEAN}: EUCLIDEAN or MANHATTAN
kNeighs {int, 0}: number of neighbors to return (1)
rowStandard {bool, True}: row standardize weights?
contiguityType {str, Rook}: {Rook = Edges Only, Queen = Edges/Vertices}
NOTES:
(1) kNeighs is used if polygon is not contiguous. E.g. Islands
"""
#### Set Default Progressor for Neigborhood Structure ####
ARCPY.SetProgressor("default", ARCPY.GetIDMessage(84143))
#### Create SSDataObject ####
ssdo = SSDO.SSDataObject(inputFC, templateFC = inputFC,
useChordal = True)
cnt = UTILS.getCount(inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs = 2)
#### Validation of Master Field ####
verifyMaster = ERROR.checkField(ssdo.allFields, masterField,
types = [0,1])
#### Create GA Data Structure ####
gaTable, gaInfo = WU.gaTable(ssdo.catPath, [masterField],
spatRef = ssdo.spatialRefString)
#### Assure Enough Observations ####
N = gaInfo[0]
ERROR.errorNumberOfObs(N, minNumObs = 2)
#### Assure k-Nearest is Less Than Number of Features ####
if kNeighs >= N:
ARCPY.AddIDMessage("ERROR", 975)
raise SystemExit()
#### Create Nearest Neighbor Search Type For Islands ####
if kNeighs > 0:
gaSearch = GAPY.ga_nsearch(gaTable)
concept, gaConcept = WU.validateDistanceMethod(concept, ssdo.spatialRef)
gaSearch.init_nearest(0.0, kNeighs, gaConcept)
forceNeighbor = True
neighWeights = ARC._ss.NeighborWeights(gaTable, gaSearch,
weight_type = 1,
row_standard = False)
else:
forceNeighbor = False
neighSearch = None
#### Create Polygon Neighbors ####
polyNeighborDict = WU.polygonNeighborDict(inputFC, masterField,
contiguityType = contiguityType)
#### Write Poly Neighbor List (Dict) ####
#### Set Progressor for SWM Writing ####
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84127), 0, N, 1)
#### Initialize Spatial Weights Matrix File ####
if contiguityType == "ROOK":
wType = 4
else:
wType = 5
swmWriter = WU.SWMWriter(swmFile, masterField, ssdo.spatialRefName,
N, rowStandard, inputFC = inputFC,
wType = wType, distanceMethod = concept,
numNeighs = kNeighs)
#### Keep Track of Polygons w/o Neighbors ####
islandPolys = []
#### Write Polygon Contiguity to SWM File ####
for row in xrange(N):
rowInfo = gaTable[row]
oid = rowInfo[0]
masterID = rowInfo[2]
neighs = polyNeighborDict[masterID]
nn = len(neighs)
if forceNeighbor:
if nn < kNeighs:
#### Only Force KNN If Specified & Contiguity is Less ####
islandPolys.append(oid)
flag = True
knnNeighs, knnWeights = neighWeights[row]
c = 0
while flag:
try:
neighID = gaTable[knnNeighs[c]][2]
if neighID not in neighs:
neighs.append(neighID)
nn += 1
if nn == kNeighs:
flag = False
c += 1
except:
flag = False
weights = NUM.ones(nn)
#### Add Weights Entry ####
swmWriter.swm.writeEntry(masterID, neighs, weights)
#### Set Progress ####
ARCPY.SetProgressorPosition()
#### Report on Features with No Neighbors ####
countIslands = len(islandPolys)
if countIslands:
islandPolys.sort()
if countIslands > 30:
islandPolys = islandPolys[0:30]
ERROR.warningNoNeighbors(N, countIslands, islandPolys, ssdo.oidName,
forceNeighbor = forceNeighbor,
contiguity = True)
#### Clean Up ####
swmWriter.close()
del gaTable
#### Report Spatial Weights Summary ####
swmWriter.report()
#### Report SWM File is Large ####
swmWriter.reportLargeSWM()
del polyNeighborDict
def collectEvents(ssdo, outputFC):
"""This utility converts event data into weighted point data by
dissolving all coincident points into unique points with a new count
field that contains the number of original features at that
location.
INPUTS:
inputFC (str): path to the input feature class
outputFC (str): path to the input feature class
"""
#### Set Default Progressor for Neigborhood Structure ####
ARCPY.SetProgressor("default", ARCPY.GetIDMessage(84143))
#### Validate Output Workspace ####
ERROR.checkOutputPath(outputFC)
#### True Centroid Warning For Non-Point FCs ####
if ssdo.shapeType.upper() != "POINT":
ARCPY.AddIDMessage("WARNING", 1021)
#### Create GA Data Structure ####
gaTable, gaInfo = WU.gaTable(ssdo.inputFC, spatRef = ssdo.spatialRefString)
#### Assure Enough Observations ####
cnt = UTILS.getCount(ssdo.inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs = 4)
N = gaInfo[0]
ERROR.errorNumberOfObs(N, minNumObs = 4)
#### Process Any Bad Records Encountered ####
numBadRecs = cnt - N
if numBadRecs:
badRecs = WU.parseGAWarnings(gaTable.warnings)
if not ssdo.silentWarnings:
ERROR.reportBadRecords(cnt, numBadRecs, badRecs,
label = ssdo.oidName)
#### Create k-Nearest Neighbor Search Type ####
gaSearch = GAPY.ga_nsearch(gaTable)
gaSearch.init_nearest(0.0, 0, "euclidean")
#### Create Output Feature Class ####
outPath, outName = OS.path.split(outputFC)
try:
DM.CreateFeatureclass(outPath, outName, "POINT", "", ssdo.mFlag,
ssdo.zFlag, ssdo.spatialRefString)
except:
ARCPY.AddIDMessage("ERROR", 210, outputFC)
raise SystemExit()
#### Add Count Field ####
countFieldNameOut = ARCPY.ValidateFieldName(countFieldName, outPath)
UTILS.addEmptyField(outputFC, countFieldNameOut, "LONG")
fieldList = ["SHAPE@", countFieldNameOut]
#### Set Insert Cursor ####
rowsOut = DA.InsertCursor(outputFC, fieldList)
#### Set Progressor for Calculation ####
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84007), 0, N, 1)
#### ID List to Search ####
rowsIN = range(N)
maxCount = 0
numUnique = 0
for row in rowsIN:
#### Get Row Coords ####
rowInfo = gaTable[row]
x0, y0 = rowInfo[1]
count = 1
#### Search For Exact Coord Match ####
gaSearch.search_by_idx(row)
for nh in gaSearch:
count += 1
rowsIN.remove(nh.idx)
ARCPY.SetProgressorPosition()
#### Keep Track of Max Count ####
maxCount = max([count, maxCount])
#### Create Output Point ####
pnt = (x0, y0, ssdo.defaultZ)
#### Create and Populate New Feature ####
rowResult = [pnt, count]
rowsOut.insertRow(rowResult)
numUnique += 1
ARCPY.SetProgressorPosition()
#### Clean Up ####
del rowsOut, gaTable
return countFieldNameOut, maxCount, N, numUnique
def collectEvents(ssdo, outputFC):
"""This utility converts event data into weighted point data by
dissolving all coincident points into unique points with a new count
field that contains the number of original features at that
location.
INPUTS:
inputFC (str): path to the input feature class
outputFC (str): path to the input feature class
"""
#### Set Default Progressor for Neigborhood Structure ####
ARCPY.SetProgressor("default", ARCPY.GetIDMessage(84143))
#### Validate Output Workspace ####
ERROR.checkOutputPath(outputFC)
#### True Centroid Warning For Non-Point FCs ####
if ssdo.shapeType.upper() != "POINT":
ARCPY.AddIDMessage("WARNING", 1021)
#### Create GA Data Structure ####
gaTable, gaInfo = WU.gaTable(ssdo.inputFC, spatRef=ssdo.spatialRefString)
#### Assure Enough Observations ####
cnt = UTILS.getCount(ssdo.inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs=4)
N = gaInfo[0]
ERROR.errorNumberOfObs(N, minNumObs=4)
#### Process Any Bad Records Encountered ####
numBadRecs = cnt - N
if numBadRecs:
badRecs = WU.parseGAWarnings(gaTable.warnings)
if not ssdo.silentWarnings:
ERROR.reportBadRecords(cnt,
numBadRecs,
badRecs,
label=ssdo.oidName)
#### Create k-Nearest Neighbor Search Type ####
gaSearch = GAPY.ga_nsearch(gaTable)
gaSearch.init_nearest(0.0, 0, "euclidean")
#### Create Output Feature Class ####
outPath, outName = OS.path.split(outputFC)
try:
DM.CreateFeatureclass(outPath, outName, "POINT", "", ssdo.mFlag,
ssdo.zFlag, ssdo.spatialRefString)
except:
ARCPY.AddIDMessage("ERROR", 210, outputFC)
raise SystemExit()
#### Add Count Field ####
countFieldNameOut = ARCPY.ValidateFieldName(countFieldName, outPath)
UTILS.addEmptyField(outputFC, countFieldNameOut, "LONG")
fieldList = ["SHAPE@", countFieldNameOut]
#### Set Insert Cursor ####
rowsOut = DA.InsertCursor(outputFC, fieldList)
#### Set Progressor for Calculation ####
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84007), 0, N, 1)
#### ID List to Search ####
rowsIN = range(N)
maxCount = 0
numUnique = 0
for row in rowsIN:
#### Get Row Coords ####
rowInfo = gaTable[row]
x0, y0 = rowInfo[1]
count = 1
#### Search For Exact Coord Match ####
gaSearch.search_by_idx(row)
for nh in gaSearch:
count += 1
rowsIN.remove(nh.idx)
ARCPY.SetProgressorPosition()
#### Keep Track of Max Count ####
maxCount = max([count, maxCount])
#### Create Output Point ####
pnt = (x0, y0, ssdo.defaultZ)
#### Create and Populate New Feature ####
rowResult = [pnt, count]
rowsOut.insertRow(rowResult)
numUnique += 1
ARCPY.SetProgressorPosition()
#### Clean Up ####
del rowsOut, gaTable
return countFieldNameOut, maxCount, N, numUnique
def calculateDistanceBand(inputFC, kNeighs, concept="EUCLIDEAN"):
"""Provides the minimum, maximum and average distance from a
set of features based on a given neighbor count.
INPUTS:
inputFC (str): path to the input feature class
kNeighs (int): number of neighbors to return
concept {str, EUCLIDEAN}: EUCLIDEAN or MANHATTAN distance
"""
#### Assure that kNeighs is Non-Zero ####
if kNeighs <= 0:
ARCPY.AddIDMessage("ERROR", 976)
raise SystemExit()
#### Set Default Progressor for Neigborhood Structure ####
ARCPY.SetProgressor("default", ARCPY.GetIDMessage(84143))
#### Create SSDataObject ####
ssdo = SSDO.SSDataObject(inputFC, useChordal=True)
cnt = UTILS.getCount(inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs=2)
#### Create GA Data Structure ####
gaTable, gaInfo = WU.gaTable(inputFC, spatRef=ssdo.spatialRefString)
#### Assure Enough Observations ####
N = gaInfo[0]
ERROR.errorNumberOfObs(N, minNumObs=2)
#### Process Any Bad Records Encountered ####
numBadRecs = cnt - N
if numBadRecs:
badRecs = WU.parseGAWarnings(gaTable.warnings)
err = ERROR.reportBadRecords(cnt,
numBadRecs,
badRecs,
label=ssdo.oidName)
#### Assure k-Nearest is Less Than Number of Features ####
if kNeighs >= N:
ARCPY.AddIDMessage("ERROR", 975)
raise SystemExit()
#### Create k-Nearest Neighbor Search Type ####
gaSearch = GAPY.ga_nsearch(gaTable)
gaConcept = concept.lower()
gaSearch.init_nearest(0.0, kNeighs, gaConcept)
neighDist = ARC._ss.NeighborDistances(gaTable, gaSearch)
#### Set Progressor for Weights Writing ####
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84007), 0, N, 1)
distances = NUM.empty((N, ), float)
for row in xrange(N):
distances[row] = neighDist[row][-1].max()
ARCPY.SetProgressorPosition()
#### Calculate and Report ####
minDist = distances.min()
avgDist = distances.mean()
maxDist = distances.max()
if ssdo.useChordal:
hardMaxExtent = ARC._ss.get_max_gcs_distance(ssdo.spatialRef)
if maxDist > hardMaxExtent:
ARCPY.AddIDMessage("ERROR", 1609)
raise SystemExit()
minDistOut = LOCALE.format("%0.6f", minDist)
avgDistOut = LOCALE.format("%0.6f", avgDist)
maxDistOut = LOCALE.format("%0.6f", maxDist)
#### Create Output Text Table ####
header = ARCPY.GetIDMessage(84171)
row1 = [ARCPY.GetIDMessage(84165).format(kNeighs), minDistOut]
row2 = [ARCPY.GetIDMessage(84166).format(kNeighs), avgDistOut]
row3 = [ARCPY.GetIDMessage(84167).format(kNeighs), maxDistOut]
total = [row1, row2, row3]
tableOut = UTILS.outputTextTable(total, header=header, pad=1)
#### Add Linear/Angular Unit ####
distanceOut = ssdo.distanceInfo.outputString
distanceMeasuredStr = ARCPY.GetIDMessage(84344).format(distanceOut)
tableOut += "\n%s\n" % distanceMeasuredStr
#### Report Text Output ####
ARCPY.AddMessage(tableOut)
#### Set Derived Output ####
ARCPY.SetParameterAsText(3, minDist)
ARCPY.SetParameterAsText(4, avgDist)
ARCPY.SetParameterAsText(5, maxDist)
#### Clean Up ####
del gaTable
def spaceTime2SWM(inputFC, swmFile, masterField, concept = "EUCLIDEAN",
threshold = None, rowStandard = True,
timeField = None, timeType = None,
timeValue = None):
"""
inputFC (str): path to the input feature class
swmFile (str): path to the SWM file.
masterField (str): field in table that serves as the mapping.
concept: {str, EUCLIDEAN}: EUCLIDEAN or MANHATTAN
threshold {float, None}: distance threshold
rowStandard {bool, True}: row standardize weights?
timeField {str, None}: name of the date-time field
timeType {str, None}: ESRI enumeration of date-time intervals
timeValue {float, None}: value forward and backward in time
"""
#### Assure Temporal Parameters are Set ####
if not timeField:
ARCPY.AddIDMessage("ERROR", 1320)
raise SystemExit()
if not timeType:
ARCPY.AddIDMessage("ERROR", 1321)
raise SystemExit()
if not timeValue or timeValue <= 0:
ARCPY.AddIDMessage("ERROR", 1322)
raise SystemExit()
#### Create SSDataObject ####
ssdo = SSDO.SSDataObject(inputFC, templateFC = inputFC,
useChordal = True)
cnt = UTILS.getCount(inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs = 2)
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84001), 0, cnt, 1)
#### Validation of Master Field ####
verifyMaster = ERROR.checkField(ssdo.allFields, masterField, types = [0,1])
badIDs = []
#### Create Temporal Hash ####
timeInfo = {}
xyCoords = NUM.empty((cnt, 2), float)
#### Process Field Values ####
fieldList = [masterField, "SHAPE@XY", timeField]
try:
rows = DA.SearchCursor(ssdo.catPath, fieldList, "",
ssdo.spatialRefString)
except:
ARCPY.AddIDMessage("ERROR", 204)
raise SystemExit()
#### Add Data to GATable and Time Dictionary ####
c = 0
for row in rows:
badRow = False
#### Assure Masterfield is Valid ####
masterID = row[0]
if masterID == None or masterID == "":
badRow = True
#### Assure Date/Time is Valid ####
timeStamp = row[-1]
if timeStamp == None or timeStamp == "":
badRow = True
#### Assure Centroid is Valid ####
badXY = row[1].count(None)
if not badXY:
x,y = row[1]
xyCoords[c] = (x,y)
else:
badRow = True
#### Process Data ####
if not badRow:
if timeInfo.has_key(masterID):
#### Assure Uniqueness ####
ARCPY.AddIDMessage("Error", 644, masterField)
ARCPY.AddIDMessage("Error", 643)
raise SystemExit()
else:
#### Fill Date/Time Dict ####
startDT, endDT = TUTILS.calculateTimeWindow(timeStamp,
timeValue,
timeType)
timeInfo[masterID] = (timeStamp, startDT, endDT)
else:
badIDs.append(masterID)
#### Set Progress ####
c += 1
ARCPY.SetProgressorPosition()
#### Clean Up ####
del rows
#### Get Set of Bad IDs ####
numBadObs = len(badIDs)
badIDs = list(set(badIDs))
badIDs.sort()
badIDs = [ str(i) for i in badIDs ]
#### Process any bad records encountered ####
if numBadObs:
ERROR.reportBadRecords(cnt, numBadObs, badIDs, label = masterField)
#### Load Neighbor Table ####
gaTable, gaInfo = WU.gaTable(ssdo.inputFC,
fieldNames = [masterField, timeField],
spatRef = ssdo.spatialRefString)
numObs = len(gaTable)
xyCoords = xyCoords[0:numObs]
#### Set the Distance Threshold ####
concept, gaConcept = WU.validateDistanceMethod(concept, ssdo.spatialRef)
if threshold == None:
#### Set Progressor for Search ####
ARCPY.SetProgressor("default", ARCPY.GetIDMessage(84144))
#### Create k-Nearest Neighbor Search Type ####
gaSearch = GAPY.ga_nsearch(gaTable)
gaSearch.init_nearest(0.0, 1, gaConcept)
neighDist = ARC._ss.NeighborDistances(gaTable, gaSearch)
N = len(neighDist)
threshold = 0.0
sumDist = 0.0
#### Find Maximum Nearest Neighbor Distance ####
for row in xrange(N):
dij = neighDist[row][-1][0]
if dij > threshold:
threshold = dij
sumDist += dij
ARCPY.SetProgressorPosition()
#### Increase For Rounding Error ####
threshold = threshold * 1.0001
avgDist = sumDist / (N * 1.0)
#### Add Linear/Angular Units ####
thresholdStr = ssdo.distanceInfo.printDistance(threshold)
ARCPY.AddIDMessage("Warning", 853, thresholdStr)
#### Chordal Default Check ####
if ssdo.useChordal:
hardMaxExtent = ARC._ss.get_max_gcs_distance(ssdo.spatialRef)
if threshold > hardMaxExtent:
ARCPY.AddIDMessage("ERROR", 1609)
raise SystemExit()
#### Clean Up ####
del gaSearch
#### Create Missing SSDO Info ####
extent = UTILS.resetExtent(xyCoords)
#### Reset Coordinates for Chordal ####
if ssdo.useChordal:
sliceInfo = UTILS.SpheroidSlice(extent, ssdo.spatialRef)
maxExtent = sliceInfo.maxExtent
else:
env = UTILS.Envelope(extent)
maxExtent = env.maxExtent
threshold = checkDistanceThresholdSWM(ssdo, threshold, maxExtent)
#### Set Default Progressor for Neigborhood Structure ####
ARCPY.SetProgressor("default", ARCPY.GetIDMessage(84143))
#### Create Distance Neighbor Search Type ####
gaSearch = GAPY.ga_nsearch(gaTable)
gaSearch.init_nearest(threshold, 0, gaConcept)
neighSearch = ARC._ss.NeighborSearch(gaTable, gaSearch)
#### Set Progressor for Weights Writing ####
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84127), 0, numObs, 1)
#### Initialize Spatial Weights Matrix File ####
swmWriter = WU.SWMWriter(swmFile, masterField, ssdo.spatialRefName,
numObs, rowStandard, inputFC = inputFC,
wType = 9, distanceMethod = concept,
threshold = threshold, timeField = timeField,
timeType = timeType, timeValue = timeValue)
for row in xrange(numObs):
masterID = gaTable[row][2]
#### Get Date/Time Info ####
dt0, startDT0, endDT0 = timeInfo[masterID]
nhs = neighSearch[row]
neighs = []
weights = []
for nh in nhs:
#### Search Through Spatial Neighbors ####
neighID = gaTable[nh][2]
#### Get Date/Time Info ####
dt1, startDT1, endDT1 = timeInfo[neighID]
#### Filter Based on Date/Time ####
insideTimeWindow = TUTILS.isTimeNeighbor(startDT0, endDT0, dt1)
if insideTimeWindow:
neighs.append(neighID)
weights.append(1.0)
#### Add Spatial Weights Matrix Entry ####
swmWriter.swm.writeEntry(masterID, neighs, weights)
#### Set Progress ####
ARCPY.SetProgressorPosition()
swmWriter.close()
del gaTable
#### Report Warning/Max Neighbors ####
swmWriter.reportNeighInfo()
#### Report Spatial Weights Summary ####
swmWriter.report()
#### Report SWM File is Large ####
swmWriter.reportLargeSWM()
def kNearest2SWM(inputFC, swmFile, masterField, concept = "EUCLIDEAN",
kNeighs = 1, rowStandard = True):
"""Creates a sparse spatial weights matrix (SWM) based on k-nearest
neighbors.
INPUTS:
inputFC (str): path to the input feature class
swmFile (str): path to the SWM file.
masterField (str): field in table that serves as the mapping.
concept: {str, EUCLIDEAN}: EUCLIDEAN or MANHATTAN
kNeighs {int, 1}: number of neighbors to return
rowStandard {bool, True}: row standardize weights?
"""
#### Assure that kNeighs is Non-Zero ####
if kNeighs <= 0:
ARCPY.AddIDMessage("ERROR", 976)
raise SystemExit()
#### Set Default Progressor for Neigborhood Structure ####
ARCPY.SetProgressor("default", ARCPY.GetIDMessage(84143))
#### Create SSDataObject ####
ssdo = SSDO.SSDataObject(inputFC, templateFC = inputFC,
useChordal = True)
cnt = UTILS.getCount(inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs = 2)
#### Validation of Master Field ####
verifyMaster = ERROR.checkField(ssdo.allFields, masterField, types = [0,1])
#### Create GA Data Structure ####
gaTable, gaInfo = WU.gaTable(ssdo.catPath, [masterField],
spatRef = ssdo.spatialRefString)
#### Assure Enough Observations ####
N = gaInfo[0]
ERROR.errorNumberOfObs(N, minNumObs = 2)
#### Process any bad records encountered ####
numBadRecs = cnt - N
if numBadRecs:
badRecs = WU.parseGAWarnings(gaTable.warnings)
err = ERROR.reportBadRecords(cnt, numBadRecs, badRecs,
label = ssdo.oidName)
#### Assure k-Nearest is Less Than Number of Features ####
if kNeighs >= N:
ARCPY.AddIDMessage("ERROR", 975)
raise SystemExit()
#### Create k-Nearest Neighbor Search Type ####
gaSearch = GAPY.ga_nsearch(gaTable)
concept, gaConcept = WU.validateDistanceMethod(concept, ssdo.spatialRef)
gaSearch.init_nearest(0.0, kNeighs, gaConcept)
neighWeights = ARC._ss.NeighborWeights(gaTable, gaSearch,
weight_type = 1,
row_standard = False)
#### Set Progressor for Weights Writing ####
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84127), 0, N, 1)
#### Initialize Spatial Weights Matrix File ####
swmWriter = WU.SWMWriter(swmFile, masterField, ssdo.spatialRefName,
N, rowStandard, inputFC = inputFC,
wType = 2, distanceMethod = concept,
numNeighs = kNeighs)
#### Unique Master ID Dictionary ####
masterSet = set([])
for row in xrange(N):
masterID = int(gaTable[row][2])
if masterID in masterSet:
ARCPY.AddIDMessage("Error", 644, masterField)
ARCPY.AddIDMessage("Error", 643)
raise SystemExit()
else:
masterSet.add(masterID)
neighs, weights = neighWeights[row]
neighs = [ gaTable[nh][2] for nh in neighs ]
#### Add Spatial Weights Matrix Entry ####
swmWriter.swm.writeEntry(masterID, neighs, weights)
#### Set Progress ####
ARCPY.SetProgressorPosition()
swmWriter.close()
del gaTable
#### Report Warning/Max Neighbors ####
swmWriter.reportNeighInfo()
#### Report Spatial Weights Summary ####
swmWriter.report()
#### Report SWM File is Large ####
swmWriter.reportLargeSWM()
def delaunay2SWM(inputFC, swmFile, masterField, rowStandard = True):
"""Creates a sparse spatial weights matrix (SWM) based on Delaunay
Triangulation.
INPUTS:
inputFC (str): path to the input feature class
swmFile (str): path to the SWM file.
masterField (str): field in table that serves as the mapping.
rowStandard {bool, True}: row standardize weights?
"""
#### Set Default Progressor for Neigborhood Structure ####
ARCPY.SetProgressor("default", ARCPY.GetIDMessage(84143))
#### Create SSDataObject ####
ssdo = SSDO.SSDataObject(inputFC, templateFC = inputFC,
useChordal = True)
cnt = UTILS.getCount(inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs = 2)
#### Validation of Master Field ####
verifyMaster = ERROR.checkField(ssdo.allFields, masterField, types = [0,1])
#### Create GA Data Structure ####
gaTable, gaInfo = WU.gaTable(ssdo.catPath, [masterField],
spatRef = ssdo.spatialRefString)
#### Assure Enough Observations ####
N = gaInfo[0]
ERROR.errorNumberOfObs(N, minNumObs = 2)
#### Process any bad records encountered ####
numBadRecs = cnt - N
if numBadRecs:
badRecs = WU.parseGAWarnings(gaTable.warnings)
err = ERROR.reportBadRecords(cnt, numBadRecs, badRecs,
label = ssdo.oidName)
#### Create Delaunay Neighbor Search Type ####
gaSearch = GAPY.ga_nsearch(gaTable)
gaSearch.init_delaunay()
neighWeights = ARC._ss.NeighborWeights(gaTable, gaSearch,
weight_type = 1,
row_standard = False)
#### Set Progressor for Weights Writing ####
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84127), 0, N, 1)
#### Initialize Spatial Weights Matrix File ####
swmWriter = WU.SWMWriter(swmFile, masterField, ssdo.spatialRefName,
N, rowStandard, inputFC = inputFC,
wType = 3)
#### Unique Master ID Dictionary ####
masterSet = set([])
for row in xrange(N):
masterID = int(gaTable[row][2])
if masterID in masterSet:
ARCPY.AddIDMessage("Error", 644, masterField)
ARCPY.AddIDMessage("Error", 643)
raise SystemExit()
else:
masterSet.add(masterID)
neighs, weights = neighWeights[row]
neighs = [ gaTable[nh][2] for nh in neighs ]
#### Add Spatial Weights Matrix Entry ####
swmWriter.swm.writeEntry(masterID, neighs, weights)
#### Set Progress ####
ARCPY.SetProgressorPosition()
#### Clean Up ####
swmWriter.close()
del gaTable
#### Report if Any Features Have No Neighbors ####
swmWriter.reportNoNeighbors()
#### Report Spatial Weights Summary ####
swmWriter.report()
#### Report SWM File is Large ####
swmWriter.reportLargeSWM()
def polygon2SWM(inputFC, swmFile, masterField,
concept = "EUCLIDEAN", kNeighs = 0,
rowStandard = True, contiguityType = "ROOK"):
"""Creates a sparse spatial weights matrix (SWM) based on polygon
contiguity.
INPUTS:
inputFC (str): path to the input feature class
swmFile (str): path to the SWM file.
masterField (str): field in table that serves as the mapping.
concept: {str, EUCLIDEAN}: EUCLIDEAN or MANHATTAN
kNeighs {int, 0}: number of neighbors to return (1)
rowStandard {bool, True}: row standardize weights?
contiguityType {str, Rook}: {Rook = Edges Only, Queen = Edges/Vertices}
NOTES:
(1) kNeighs is used if polygon is not contiguous. E.g. Islands
"""
#### Set Default Progressor for Neigborhood Structure ####
ARCPY.SetProgressor("default", ARCPY.GetIDMessage(84143))
#### Create SSDataObject ####
ssdo = SSDO.SSDataObject(inputFC, templateFC = inputFC,
useChordal = True)
cnt = UTILS.getCount(inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs = 2)
#### Validation of Master Field ####
verifyMaster = ERROR.checkField(ssdo.allFields, masterField,
types = [0,1])
#### Create GA Data Structure ####
gaTable, gaInfo = WU.gaTable(ssdo.catPath, [masterField],
spatRef = ssdo.spatialRefString)
#### Assure Enough Observations ####
N = gaInfo[0]
ERROR.errorNumberOfObs(N, minNumObs = 2)
#### Assure k-Nearest is Less Than Number of Features ####
if kNeighs >= N:
ARCPY.AddIDMessage("ERROR", 975)
raise SystemExit()
#### Create Nearest Neighbor Search Type For Islands ####
gaSearch = GAPY.ga_nsearch(gaTable)
concept, gaConcept = WU.validateDistanceMethod(concept, ssdo.spatialRef)
gaSearch.init_nearest(0.0, kNeighs, gaConcept)
if kNeighs > 0:
forceNeighbor = True
neighWeights = ARC._ss.NeighborWeights(gaTable, gaSearch,
weight_type = 1,
row_standard = False)
else:
forceNeighbor = False
neighSearch = None
#### Create Polygon Neighbors ####
polyNeighborDict = WU.polygonNeighborDict(inputFC, masterField,
contiguityType = contiguityType)
#### Write Poly Neighbor List (Dict) ####
#### Set Progressor for SWM Writing ####
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84127), 0, N, 1)
#### Initialize Spatial Weights Matrix File ####
if contiguityType == "ROOK":
wType = 4
else:
wType = 5
swmWriter = WU.SWMWriter(swmFile, masterField, ssdo.spatialRefName,
N, rowStandard, inputFC = inputFC,
wType = wType, distanceMethod = concept,
numNeighs = kNeighs)
#### Keep Track of Polygons w/o Neighbors ####
islandPolys = []
#### Write Polygon Contiguity to SWM File ####
for row in xrange(N):
rowInfo = gaTable[row]
oid = rowInfo[0]
masterID = rowInfo[2]
neighs = polyNeighborDict[masterID]
if neighs:
weights = [ 1. for nh in neighs ]
isIsland = False
else:
isIsland = True
islandPolys.append(oid)
weights = []
#### Get Nearest Neighbor Based On Centroid Distance ####
if isIsland and forceNeighbor:
neighs, weights = neighWeights[row]
neighs = [ gaTable[nh][2] for nh in neighs ]
#### Add Weights Entry ####
swmWriter.swm.writeEntry(masterID, neighs, weights)
#### Set Progress ####
ARCPY.SetProgressorPosition()
#### Report on Features with No Neighbors ####
countIslands = len(islandPolys)
if countIslands:
islandPolys.sort()
if countIslands > 30:
islandPolys = islandPolys[0:30]
ERROR.warningNoNeighbors(N, countIslands, islandPolys, ssdo.oidName,
forceNeighbor = forceNeighbor,
contiguity = True)
#### Clean Up ####
swmWriter.close()
del gaTable
#### Report Spatial Weights Summary ####
swmWriter.report()
#### Report SWM File is Large ####
swmWriter.reportLargeSWM()
del polyNeighborDict
def stCollectByKNN(ssdo, timeField, outputFC, inSpan, inDistance):
"""
This method applied Jacquez Space-Time K-NN to convert event data into weighted
point data by dissolving all coincident points in space and time into unique
points with a new count field that contains the number of original features
at that location and time span.
INPUTS:
ssdo (obj): SSDataObject from input
timeField (str): Date/Time field name in input feature
outputFC (str): path to the output feature class
inSpan (int): value of temporal units within the same time bin
inDistance (int): value of spatial units considered as spatial neighbors
OUTPUTS:
Create new collected point feature
"""
#### Read raw time data ####
timeData = ssdo.fields[timeField].data
#### Convert temporal unit ####
time = NUM.array(timeData, dtype='datetime64[s]').astype('datetime64[D]')
#### Find Start Time ####
startTime = time.min()
#### Create Bin for Space and Time ####
timeBin = (time - startTime) / inSpan
numObs = ssdo.numObs
#### Create Sudo-fid to Find K-NN in Space and Time
fid = [i for i in xrange(numObs)]
#### Validate Output Workspace ####
ERROR.checkOutputPath(outputFC)
#### True Centroid Warning For Non-Point FCs ####
if ssdo.shapeType.upper() != "POINT":
ARCPY / AddIDMessage("WARNING", 1021)
#### Create GA Data Structure ####
gaTable, gaInfo = WU.gaTable(ssdo.inputFC, spatRef=ssdo.spatialRefString)
#### Assure Enough Observations ####
cnt = UTILS.getCount(ssdo.inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs=4)
N = gaInfo[0]
ERROR.errorNumberOfObs(N, minNumObs=4)
#### Process Any Bad Records Encountered ####
numBadRecs = cnt - N
if numBadRecs:
badRecs = WU.parseGAWarnings(gaTable.warnings)
if not ssdo.silentWarnings:
ERROR.reportBadRecords(cnt,
numBadRecs,
badRecs,
label=ssdo.oidName)
#### Create Output Feature Class ####
outPath, outName = OS.path.split(outputFC)
try:
DM.CreateFeatureclass(outPath, outName, "POINT", "", ssdo.mFlag,
ssdo.zFlag, ssdo.spatialRefString)
except:
ARCPY.AddIDMessage("ERROR", 210, outputFC)
raise SystemExit()
#### Create k-Nearest Neighbor Search Type ####
gaSearch = GAPY.ga_nsearch(gaTable)
gaSearch.init_nearest(inDistance, 0, "euclidean")
#### Add Count Field ####
countFieldNameOut = ARCPY.ValidateFieldName(countFieldName, outPath)
timeFieldNameOut = ARCPY.ValidateFieldName(timeFieldName, outPath)
UTILS.addEmptyField(outputFC, countFieldNameOut, "LONG")
UTILS.addEmptyField(outputFC, timeFieldNameOut, "DATE")
fieldList = ["SHAPE@", countFieldNameOut, timeFieldNameOut]
#### Set Insert Cursor ####
rowsOut = DA.InsertCursor(outputFC, fieldList)
#### Detect S-T K-NN by Space and Time Bin ####
duplicateList = []
for record in fid:
kNNList = [record]
if record not in duplicateList:
for pair in fid:
if pair != record:
gaSearch.search_by_idx(record)
for nh in gaSearch:
if timeBin[record] == timeBin[pair]:
kNNList.append(nh.idx)
duplicateList.append(nh.idx)
#### Create and Populate New Feature ####
kNNList = list(set(kNNList))
count = len(kNNList)
dt = time[record]
x0 = ssdo.xyCoords[kNNList, 0].mean()
y0 = ssdo.xyCoords[kNNList, 1].mean()
pnt = (x0, y0, ssdo.defaultZ)
rowResult = [pnt, count, dt]
rowsOut.insertRow(rowResult)
ARCPY.SetProgressorPosition()
#### Clean Up ####
del rowsOut, timeBin, kNNList, duplicateList
return countFieldNameOut
def stCollectByKNN(ssdo, timeField, outputFC, inSpan, inDistance):
"""
This method applied Jacquez Space-Time K-NN to convert event data into weighted
point data by dissolving all coincident points in space and time into unique
points with a new count field that contains the number of original features
at that location and time span.
INPUTS:
ssdo (obj): SSDataObject from input
timeField (str): Date/Time field name in input feature
outputFC (str): path to the output feature class
inSpan (int): value of temporal units within the same time bin
inDistance (int): value of spatial units considered as spatial neighbors
OUTPUTS:
Create new collected point feature
"""
#### Read raw time data ####
timeData = ssdo.fields[timeField].data
#### Convert temporal unit ####
time = NUM.array(timeData, dtype = 'datetime64[s]').astype('datetime64[D]')
#### Find Start Time ####
startTime = time.min()
#### Create Bin for Space and Time ####
timeBin = (time - startTime) / inSpan
numObs = ssdo.numObs
#### Create Sudo-fid to Find K-NN in Space and Time
fid = [i for i in xrange(numObs)]
#### Validate Output Workspace ####
ERROR.checkOutputPath(outputFC)
#### True Centroid Warning For Non-Point FCs ####
if ssdo.shapeType.upper() != "POINT":
ARCPY/AddIDMessage("WARNING", 1021)
#### Create GA Data Structure ####
gaTable, gaInfo = WU.gaTable(ssdo.inputFC, spatRef = ssdo.spatialRefString)
#### Assure Enough Observations ####
cnt = UTILS.getCount(ssdo.inputFC)
ERROR.errorNumberOfObs(cnt, minNumObs = 4)
N = gaInfo[0]
ERROR.errorNumberOfObs(N, minNumObs = 4)
#### Process Any Bad Records Encountered ####
numBadRecs = cnt -N
if numBadRecs:
badRecs = WU.parseGAWarnings(gaTable.warnings)
if not ssdo.silentWarnings:
ERROR.reportBadRecords(cnt, numBadRecs, badRecs, label = ssdo.oidName)
#### Create Output Feature Class ####
outPath, outName = OS.path.split(outputFC)
try:
DM.CreateFeatureclass(outPath, outName, "POINT", "", ssdo.mFlag,
ssdo.zFlag, ssdo.spatialRefString)
except:
ARCPY.AddIDMessage("ERROR", 210, outputFC)
raise SystemExit()
#### Create k-Nearest Neighbor Search Type ####
gaSearch = GAPY.ga_nsearch(gaTable)
gaSearch.init_nearest(inDistance, 0, "euclidean")
#### Add Count Field ####
countFieldNameOut = ARCPY.ValidateFieldName(countFieldName, outPath)
timeFieldNameOut = ARCPY.ValidateFieldName(timeFieldName, outPath)
UTILS.addEmptyField(outputFC, countFieldNameOut, "LONG")
UTILS.addEmptyField(outputFC, timeFieldNameOut, "DATE")
fieldList = ["SHAPE@", countFieldNameOut, timeFieldNameOut]
#### Set Insert Cursor ####
rowsOut = DA.InsertCursor(outputFC, fieldList)
#### Detect S-T K-NN by Space and Time Bin ####
duplicateList = []
for record in fid:
kNNList = [record]
if record not in duplicateList:
for pair in fid:
if pair != record :
gaSearch.search_by_idx(record)
for nh in gaSearch:
if timeBin[record] == timeBin[pair]:
kNNList.append(nh.idx)
duplicateList.append(nh.idx)
#### Create and Populate New Feature ####
kNNList = list(set(kNNList))
count = len(kNNList)
dt = time[record]
x0 = ssdo.xyCoords[kNNList, 0].mean()
y0 = ssdo.xyCoords[kNNList, 1].mean()
pnt =(x0, y0, ssdo.defaultZ)
rowResult = [pnt, count, dt]
rowsOut.insertRow(rowResult)
ARCPY.SetProgressorPosition()
#### Clean Up ####
del rowsOut, timeBin, kNNList, duplicateList
return countFieldNameOut
