Post 2: Python Scripts

Introduction: Python Script 5
This exercise created a script that produced a weighted impact map. This map combines 5 factors that were used to create another map. In this script a streams feature class was weighted 1.5 that of the other factors. The other rasters were then added to the weighted raster to create a final raster.

Introduction: Python Script 4
This exercise created a script to find all historic major attractions within the maritime climate zone using field delimiters, building SQL statements, creating feature layers using those SQL statements and using the MakeFeatureLayer_management function.

import system modules
import arcpy

#set environments
from arcpy import env
env.workspace = "Q:\StudentCoursework\CHupy\GEOG.337.001.2175\KLEINSAS\DemoLab\SanDiego.gdb"
arcpy.env.overwriteOutput = True

fc1= "Climate"
fc2= "MajorAttractions"

#Add field delimiter for the TYPE and ESTAB
field1=arcpy.AddFieldDelimiters(fc1,"TYPE")
field2=arcpy.AddFieldDelimiters(fc2,"ESTAB")

#Build an SQL Statement for the Field TYPE=Maritime
maritimeSQLExp=field1+"="+"'Maritime'"

#Build an SQL Statement for the Field ESTAB<1956
historicSQLExp=field2+">0and"+field2+"<1956"

#Pass SQL Statement to make a New Feature Layer for Maritime
arcpy.MakeFeatureLayer_management(fc1,"MaritimeLyr", maritimeSQLExp)

#Pass SQL Statement to make a New Feature Layer for Historic
arcpy.MakeFeatureLayer_management(fc2, "HistoricLyr", historicSQLExp)

#Select features from the historic layers that are completely within the maritime layer and make a new selection
arcpy.SelectLayerByLocation_management("HistoricLyr", "COMPLETELY_WITHIN","MaritimeLyr","","NEW_SELECTION")

featCount=arcpy.GetCount_management("HistoricLyr")
print"Number of historic features selected:{}".format(featCount)

arcpy.CopyFeatures_management("HistoricLyr", "HistoricMaritime")

Introduction: Python Script 3
The goal of this exercise was to gain experience describing features and using FOR/IN loops.

#import system modules
import arcpy

#set environments
from arcpy import env
env.workspace = "Q:\StudentCoursework\CHupy\GEOG.337.001.2175\KLEINSAS\Python\Demo3.gdb"
arcpy.env.overwriteOutput = True


#set variables for the parameters and buffer the random points by 10 miles.
infc = "Mi_random"
outfc = "Mi_random_buff"
dist = "10 miles"
sidetype = "Full"
endtype = "Round"

#describe the type of dataset and the spatial reference of Mi_random
desc = arcpy.Describe (infc)
sr = desc.spatialReference
print"Dataset Type: " + desc.datasetType
print"Spatial Reference: " + sr.name


arcpy.Buffer_analysis (infc, outfc, dist, sidetype, endtype, "#", "#")

#set variables for the parameters and clip the buffer by the state of Michigan.

clipfc = "MI"
outclipfc = "buff_clip"
arcpy.Clip_analysis(outfc, clipfc, outclipfc)

fc_list = arcpy.ListFeatureClasses()
for name in fc_list:
    desc = arcpy.Describe(name)
    featCount = arcpy.GetCount_management(name)
    print "Name: {} Shape: {} SR: {} Count: {}".format(desc.name, desc.shapeType, desc.spatialReference.name, featCount)


print"the print is complete"

Introduction: Python Script 2
The goal of this exercise was to gain more experience using Pyscripter. In this script the buffer and clip tool were used in Pyscripter.

#import system modules
import arcpy

#set environments
from arcpy import env
env.workspace = "Q:\StudentCoursework\CHupy\GEOG.337.001.2175\KLEINSAS\Python\Demo2.gdb"
arcpy.env.overwriteOutput = True

#set up the buffer tool

arcpy.Buffer_analysis("Mi_random", "Mi_random_buff", "10 MILES", "FULL", "ROUND", "#", "#")


print("The script has completed")


arcpy.Clip_analysis("Mi_random_buff", "MI", "Mi_random_buff_clip")

Introduction: Python Script 1
Python is a script that is used throughout much of the computer science field. This language is also applicable to geographic information systems. This post presents the first python script used in this class to clip, edit and establish a coordinate system for multiple feature classes at once. The software used to create this script was PyScripter for Python 2.7.