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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Installation
Follow directions at the PySAL-ArcGIS-Toolbox Git Repository [https
Step1:  Example
Step2:  Use the PySAL-ArcGIS Utilities to Read in Spatial Weights Files
Step3:  Run the Auto Model Class and Export Your Data to an Output Feature Class
Step4:  Compare OLS and Spatial Lag Results
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Python Code:
import arcpy as ARCPY
import arcgisscripting as ARC
import SSDataObject as SSDO
import SSUtilities as UTILS
import WeightsUtilities as WU
import numpy as NUM
import scipy as SCIPY
import pysal as PYSAL
import os as OS
import pandas as PANDAS
Explanation: Installation
Follow directions at the PySAL-ArcGIS-Toolbox Git Repository [https://github.com/Esri/PySAL-ArcGIS-Toolbox]
Please make note of the section on Adding a Git Project to your ArcGIS Installation Python Path.
End of explanation
inputFC = r'../data/CA_Polygons.shp'
fullFC = OS.path.abspath(inputFC)
fullPath, fcName = OS.path.split(fullFC)
ssdo = SSDO.SSDataObject(inputFC)
uniqueIDField = "MYID"
fieldNames = ['GROWTH', 'LOGPCR69', 'PERCNOHS', 'POP1969']
ssdo.obtainData(uniqueIDField, fieldNames)
df = ssdo.getDataFrame()
print(df.head())
Explanation: Example: Testing the Income Convergence Hypothesis in California Counties (1969 - 2010)
Use the Auto-Model Spatial Econometric Tool to identify the appropriate model
Regressing the growth rate of incomes on the log of starting incomes 
a significant negative coefficient indicates convergence
The percentage of the population w/o a high school education and the population itself are the other exogenous factors.
Importing Your Data into a PANDAS DataFrame
End of explanation
import pysal2ArcUtils as PYSAL_UTILS
swmFile = OS.path.join(fullPath, "queen.swm")
W = PYSAL_UTILS.PAT_W(ssdo, swmFile)
w = W.w
kernelSWMFile = OS.path.join(fullPath, "knn8.swm")
KW = PYSAL_UTILS.PAT_W(ssdo, kernelSWMFile)
kw = KW.w
Explanation: Use the PySAL-ArcGIS Utilities to Read in Spatial Weights Files
End of explanation
import AutoModel as AUTO
auto = AUTO.AutoSpace_PySAL(ssdo, "GROWTH", ['LOGPCR69', 'PERCNOHS', 'POP1969'],
                            W, KW, pValue = 0.1, useCombo = True)
ARCPY.env.overwriteOutput = True
outputFC = r'../data/pysal_automodel.shp'
auto.createOutput(outputFC)
Explanation: Run the Auto Model Class and Export Your Data to an Output Feature Class
End of explanation
print(auto.olsModel.summary)
print(auto.finalModel.summary)
Explanation: Compare OLS and Spatial Lag Results
End of explanation
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