def evaluate(individual):
individual = individual[0]
prod_MWsel = sum(x * y for x, y in zip(prod, individual))
#check if the total production is witin boundaries, if not return a penalty vector
if up_targ >= prod_MWsel >= low_targ:
# goal 1
mean_enerdsel = sum(x * y for x, y in zip(enerd, individual)) / sum(individual)
# goal 2
count_WTsel = sum(individual)
# goal 3 (subset the input points by the WT_IDs which are in the ini pop (=1)
WT_pop = np.column_stack((id, individual))
WT_sel = WT_pop[WT_pop[:, [1]] == 1]
WT_sel = WT_sel.astype(int)
qry = '"WT_ID" IN ' + str(tuple(WT_sel))
subset = arcpy.MakeFeatureLayer_management(all_pts, "tmp", qry)
nn_output = arcpy.AverageNearestNeighbor_stats(subset, "EUCLIDEAN_DISTANCE", "NO_REPORT", "41290790000")
clus = float(nn_output.getOutput(0))
res = (clus, count_WTsel, mean_enerdsel)
## delete the feature tmp since otherwise it will not work in a loop
arcpy.Delete_management("tmp")
arcpy.Delete_management("subset")
else:
res = (10e20, 10e20, 0)
return res
#def feasible(individual):
individual = individual[0]
prod_MWsel = sum(x * y for x, y in zip(prod, individual))
if (prod_MWsel <= up_targ and prod_MWsel >= low_targ):
return True
return False
def NearestNeighbor(fc, i, locationType):
distanceTable = 'nnDistance'
#Create table for output
tableExists = arcpy.ListTables(distanceTable)
#Check for existing table before creating
if tableExists == []:
arcpy.CreateTable_management(workspacePath, distanceTable)
arcpy.AddField_management(distanceTable, 'StateName', 'TEXT')
arcpy.AddField_management(distanceTable, 'NNdistance', 'FLOAT')
#Location stats
arcpy.AddField_management(distanceTable, 'LOCATION_COUNT',
'SHORT')
arcpy.AddField_management(distanceTable, 'LOCATION_DENSITY',
'FLOAT')
#Table variables
fields = ['OBJECTID', 'StateName', 'NNdistance']
#Open cursor to distanceTable for inserting
cursor = arcpy.da.InsertCursor(distanceTable, fields)
#Calculate average nearest neighbor stats
nn_output = arcpy.AverageNearestNeighbor_stats(
fc, "EUCLIDEAN_DISTANCE", "NO_REPORT")
#Grab observed distance and convert from meters to miles
obsDistance = round((float(nn_output[4]) * 0.000621371), 2)
#Add space back to name for those states that should have one (required for joining purposes); Index of -1 returned for those not found
if fc.find('New') != -1:
fc = fc.replace('New', 'New ')
elif fc.find('North') != -1:
fc = fc.replace('North', 'North ')
elif fc.find('South') != -1:
fc = fc.replace('South', 'South ')
elif fc.find('West') != -1:
fc = fc.replace('West', 'West ')
elif fc.find('Rhode') != -1:
fc = fc.replace('Rhode', 'Rhode ')
#Remove activity from stateName
stateName = fc.replace(locationType + 'Filtered', '')
#Add values to table
row = [i, stateName, obsDistance]
cursor.insertRow(row)
#i+=1
del cursor
def Nearest_Neighbour_Index_Analysis(facilityName):
'''
To conduct Nearest Neighbour Index Analysis
:param facilityName: feature class name to be analyzed
:return:
'''
try:
nn_output = arcpy.AverageNearestNeighbor_stats(facilityName, "EUCLIDEAN_DISTANCE", "GENERATE_REPORT", "#")
# Create list of Average Nearest Neighbor output values by splitting the result object
# print("The nearest neighbor index is: " + nn_output[0])
# print("The z-score of the nearest neighbor index is: " + nn_output[1])
# print("The p-value of the nearest neighbor index is: " + nn_output[2])
# print("The expected mean distance is: " + nn_output[3])
# print("The observed mean distance is: " + nn_output[4])
# print("The path of the HTML report: " + nn_output[5])
print "Complete Nearest Neighbour Analysis"
except:
print(arcpy.GetMessages())
def evaluate(individual):
individual = individual[0]
#first check if the production of the seleced WT's is in the range between 4.31 and 4.29 TWH
# goal 1
mean_enerdsel = sum(x * y
for x, y in zip(enerd, individual)) / sum(individual)
# goal 2
count_WTsel = sum(individual)
# goal 3 (subset the input points by the WT_IDs which are in the ini pop (=1)
WT_pop = np.column_stack((id, individual))
WT_sel = WT_pop[WT_pop[:, [1]] == 1]
WT_sel = WT_sel.astype(int)
qry = '"WT_ID" IN ' + str(tuple(WT_sel))
subset = arcpy.MakeFeatureLayer_management(all_pts, "tmp", qry)
nn_output = arcpy.AverageNearestNeighbor_stats(subset,
"EUCLIDEAN_DISTANCE",
"NO_REPORT", "41290790000")
clus = float(nn_output.getOutput(0))
res = (clus, count_WTsel, mean_enerdsel)
## delete the feature tmp since otherwise it will not work in a loop
arcpy.Delete_management("tmp")
arcpy.Delete_management("subset")
return (res)
# print("The nearest neighbor index is: " + nn_output[0])
# print("The z-score of the nearest neighbor index is: " + nn_output[1])
# print("The p-value of the nearest neighbor index is: " + nn_output[2])
# print("The expected mean distance is: " + nn_output[3])
# print("The observed mean distance is: " + nn_output[4])
# print("The path of the HTML report: " + nn_output[5])
workPath = r"C:\Users\chenpipy\Desktop\xian"
try:
# Set the current workspace (to avoid having to specify the full path to the feature classes each time)
arcpy.env.workspace = workPath
files = arcpy.ListFeatureClasses()
for file in files:
try:
nn_output = arcpy.AverageNearestNeighbor_stats(
file, "EUCLIDEAN_DISTANCE", "NO_REPORT", "#")
rows = arcpy.UpdateCursor(file)
for row in rows:
row.setValue("ave_dis", nn_output[4])
row.setValue("exp_dis", nn_output[3])
row.setValue("index", nn_output[0])
row.setValue("p", nn_output[2])
row.setValue("z", nn_output[1])
rows.updateRow(row)
except arcpy.ExecuteError:
print("跳过")
except arcpy.ExecuteError:
# If an error occurred when running the tool, print out the error message.
print(arcpy.GetMessages())
# JSON library lets us convert strings to and from json format
import json
# Declare the bounding rectangle area as float variable
given_bounding_rect = 1760000000.0
# From the resulting bounding rectangle when performing Nearest Neighbor Analysis in ArcMap
calc_bounding_rect = 747896332.677655
"""
USING ARCPY
"""
data = "INSERT PATH TO FILE"
# Prints the five average nearest neighbor analysis values
# from the given bounding rectangle area
arcpy.AverageNearestNeighbor_stats(data, "EUCLIDEAN_DISTANCE", "NO_REPORT",
given_bounding_rect)
# if no area is provided, ArcPy will calculate its own bounding rectangle area
arcpy.AverageNearestNeighbor_stats(data, "EUCLIDEAN_DISTANCE")
"""
USING PLAIN PYTHON
"""
def get_distance(p, p_list):
"""
:param p: reference point
:param p_list: list of points
:return: list of distances from that point to all other points in the list
"""
d_list = [
# --------------------------------------------------------------------------- # Cluster.py # Created on: 2017-09-14 04:49:18.00000 # (generated by ArcGIS/ModelBuilder) # Usage: Cluster <Input_Feature_Class> <Output_Standard_Distance_Feature_Class> # Description: # --------------------------------------------------------------------------- # Import arcpy module import arcpy # Script arguments Input_Feature_Class = arcpy.GetParameterAsText(0) Output_Standard_Distance_Feature_Class = arcpy.GetParameterAsText(1) # Local variables: NNRatio = "0" NNZScore = "0" PValue = "0" NNExpected = "0" NNObserved = "0" Report_File = "" # Process: Average Nearest Neighbor arcpy.AverageNearestNeighbor_stats(Input_Feature_Class, "EUCLIDEAN_DISTANCE", "GENERATE_REPORT", "") # Process: Standard Distance arcpy.StandardDistance_stats(Input_Feature_Class, Output_Standard_Distance_Feature_Class, "1_STANDARD_DEVIATION", "", "")
def wildFireAnalysis():
try:
work = raw_input(r"Enter the full path of WildlandFires.gdb: ")
if arcpy.Exists(work):
y = raw_input ("WARNING! This file exists! Overwrite? (Y or N):")
if y == 'N' or y!= 'Y':
raise overwriteError('Program Ended... No Feature Class Created')
else:
raise existsError('Path does not exist!')
arcpy.env.workspace = work # Set the workspace to the geodatabase
iFile = raw_input(r"Enter the full path of wildfire text file: ")
if not os.path.exists(iFile):
raise existsError('Path does not exist!')
f = open(iFile, 'r')
line = f.readline()
newConfid= line.split(',')
confidName = newConfid[2]
setConfidVal = confidName.strip("\n")
lstFires = f.readlines()
f.close()
featName = raw_input("Enter the name of the output feature class: ")
if arcpy.Exists(featName):
y = raw_input ("WARNING! This file exists! Overwrite? (Y or N):")
if y == 'N' or y!= 'Y':
raise overwriteError('Program Ended... No Feature Class Created')
int_a = input("Specify the minimum confidence threshold (0-100):")
arcpy.CreateFeatureclass_management(work, featName, "Point")
arcpy.AddField_management(featName, setConfidVal, "LONG", 4)
fields = ["SHAPE@", setConfidVal]
cur = arcpy.da.InsertCursor(featName, fields)
cntr = 0
counter = 0
while True:
int_a = int(int_a)
if (0 <= int_a <= 100):
# Correct number, break the while loop
break
# Wrong number
int_a = input("Warning...Threshold must be between 0 and 100...Re-enter:")
# Here int_a is in the correct range
fc = 'C:\Users\Thoma\Desktop\Lab5GEOG656\Lab 8 Data\WildlandFires.gdb\NationalParks'
counter = 0
with arcpy.da.SearchCursor(fc, ['Name','SHAPE@']) as cursor:
for row in cursor:
name = row[0]
geom = row[1]
for fire in lstFires:
if 'Latitude' in fire: # Skip the header
continue
pnt = arcpy.Point()
pointGeom = fire.split(',')
lstValues = pointGeom
latitude = pointGeom[0]
longitude = pointGeom[1]
confidence = pointGeom[2]
confid = int(confidence)
pnt.X = float(longitude)
pnt.Y = float(latitude)
if confid > int_a:
row = [pnt, confid]
cur.insertRow(row)
cntr = cntr + 1
print "Record # " + str(cntr) + " written to feature class"
if pnt.within(geom) is True:
counter = counter + 1
print('There were {} fires in {}'.format(counter, name))
del cur
nn_output = arcpy.AverageNearestNeighbor_stats(featName, "EUCLIDEAN_DISTANCE", "NO_REPORT", "#")
zScore=float((nn_output.getOutput(1)))
roundZscore = (round(zScore, 2))
if roundZscore < -1.65:
print('The fire incidents are *significantly clustered* (z-score = {})'.format(roundZscore))
elif roundZscore > 1.65:
print('The fire incidents are *significantly dispersed* (z-score = {})'.format(roundZscore))
elif roundZscore > -1.65 and roundZscore < 1.65:
print('The fire incidents are * spatially random* (z-score = {})'.format(roundZscore))
except overwriteError as e:
print "Error: " + str(e) # Prints Python-related errors
except existsError as e:
print "Error: " + str(e) # Prints Python-related errors
longitude = listValues[1]
confid = listValues[2]
pnt.X = longitude
pnt.Y = latitude
#if the point is in the polygon, insert it into new feature class and increment record count and number in polygon count
row = [pnt, confid]
cur.insertRow(row)
cntr = cntr + 1
print "Record # " + str(cntr) + " written to feature class"
if pnt.within(polygon):
inNationalPark +=1
del cur, searchCurs #delete both cursors
print 'The were {num} fires within {name}'.format(num = inNationalPark, name = name)
#Analyze Clustering of Fire Points
nn_output = arcpy.AverageNearestNeighbor_stats(newOutputFeatureClass, "EUCLIDEAN_DISTANCE", "NO_REPORT",'#')
zscoreval = round(float(nn_output[1]),2)
if nn_output[1] < -1.65:
print "The fire incidents are *signifiantly clustered* (z-score= ({score})".format(score=zscoreval)
elif nn_output[1] > 1.65:
print "The fire incidents are *significantly disperesed* (z-score= {score})".format(score=zscoreval)
else:
print "The fire incidents are *sptially random* (z-score= {score})".format(score=zscoreval)
except overwriteError:
print "Program Ended... No Feature Class Created"
except existsError:
print "Program Ended... Path Does Not Exist"
except Exception as e:
print "Error: " + str(e) # Prints Python-related errors
print arcpy.GetMessages() # Prints ArcPy-related errors