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 obtainDataGA(self, masterField, fields = [], types = [0,1,2,3,5,6],
minNumObs = 0, warnNumObs = 0):
"""Takes a list of field names and returns it in a dictionary
structure.
INPUTS:
masterField (str): name of field being used as the master
fields {list, []}: name(s) of the field to be returned
types (list): types of data allowed to be returned (1)
minNumObs {int, 0}: minimum number of observations for error
warnNumObs {int, 0}: minimum number of observations for warning
ATTRIBUTES:
gaTable (structure): instance of the GA Table
fields (dict): fieldName = instance of FCField
master2Order (dict): masterID = order in lists
order2Master (dict): order in lists = masterID
masterField (str): field that serves as the master
badRecords (list): master IDs that could not be read
xyCoords (array, nunObs x 2): xy-coordinates for feature centroids
NOTES:
(1) No Text Fields; short [0], long [1], float [2], double[3]
"""
#### Validation of Master Field ####
verifyMaster = ERROR.checkField(self.allFields, masterField,
types = [0,1,5])
#### Set MasterIsOID Boolean ####
self.masterIsOID = masterField == self.oidName
#### Set Master and Data Indices ####
if self.masterIsOID:
self.masterColumnIndex = 0
self.dataColumnIndex = 2
fieldList = []
else:
self.masterColumnIndex = 2
self.dataColumnIndex = 3
fieldList = [masterField]
#### Validation and Initialization of Data Fields ####
numFields = len(fields)
for field in fields:
fType = ERROR.checkField(self.allFields, field, types = types)
fieldList.append(field)
self.fields[field] = self.allFields[field]
#### ZCoords Are Last ####
getZBool = self.hasZ and (not self.renderType)
if getZBool:
fieldList.append("SHAPE&Z")
#### Create GA Data Structure ####
cnt = UTILS.getCount(self.inputFC)
fieldList = tuple(fieldList)
gaTable, gaInfo = WU.gaTable(self.inputFC, fieldNames = fieldList,
spatRef = self.spatialRefString)
#### Check Whether the Number of Features is Appropriate ####
numObs = gaInfo[0]
ERROR.checkNumberOfObs(numObs, minNumObs = minNumObs,
warnNumObs = warnNumObs,
silentWarnings = self.silentWarnings)
#### Process any bad records encountered ####
numBadIDs = cnt - numObs
if numBadIDs:
badIDs = WU.parseGAWarnings(gaTable.warnings)
if not self.silentWarnings:
ERROR.reportBadRecords(cnt, numBadIDs, badIDs,
label = self.oidName)
else:
badIDs = []
#### Initialization of Centroids ####
xyCoords = NUM.empty((numObs, 2), float)
#### Z Coords ####
if self.hasZ:
zCoords = NUM.empty((numObs, ), float)
#### Create Empty Data Arrays ####
for fieldName, fieldObj in self.fields.iteritems():
fieldObj.createDataArray(numObs)
#### Populate SSDataObject ####
ARCPY.SetProgressor("step", ARCPY.GetIDMessage(84001), 0, numObs, 1)
for row in xrange(numObs):
rowInfo = gaTable[row]
x,y = rowInfo[1]
masterID = int(rowInfo[self.masterColumnIndex])
if self.master2Order.has_key(masterID):
ARCPY.AddIDMessage("ERROR", 644, masterField)
ARCPY.AddIDMessage("ERROR", 643)
raise SystemExit()
else:
self.master2Order[masterID] = row
self.order2Master[row] = masterID
xyCoords[row] = (x, y)
if numFields:
restFields = rowInfo[self.dataColumnIndex:]
for fieldInd, fieldName in enumerate(fields):
self.fields[fieldName].data[row] = restFields[fieldInd]
if self.hasZ:
if getZBool:
zCoords[row] = rowInfo[-1]
else:
zCoords[row] = NUM.nan
ARCPY.SetProgressorPosition()
#### Set the Hidden Fields (E.g. Not in Use) ####
self.setHiddenFields()
#### Reset Extent to Honor Env and Subsets ####
try:
self.extent = UTILS.resetExtent(xyCoords)
except:
pass
#### Reset Coordinates for Chordal ####
if self.useChordal:
#### Project to XY on Spheroid ####
self.spheroidCoords = ARC._ss.lonlat_to_xy(xyCoords,
self.spatialRef)
self.sliceInfo = UTILS.SpheroidSlice(self.extent,
self.spatialRef)
else:
self.spheroidCoords = None
self.sliceInfo = None
#### Set Further Attributes ####
self.badRecords = badIDs
self.xyCoords = xyCoords
self.masterField = masterField
self.gaTable = gaTable
self.numObs = numObs
if self.hasZ:
self.zCoords = zCoords
else:
self.zCoords = None
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 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 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 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
