def reg():
case_num = request.args.get('case_num', None)
fileDict = dao.getFileDict(case_num)
GSA_file_CSV = fileDict.get('GSA_Input_CSV')
GSA_file_SHP = fileDict.get('GSA_Input_SHP')
gsa_meta = fileDict.get('GSA_meta')
svgNaming = fileDict.get('GSA_data')[0]
with open('out/gsa/mymap.svg', 'r') as myfile:
mymap = myfile.read()
mymap = mymap.replace('"', "'")
observations = weights.extractObservations(GSA_file_CSV, "ALL",
gsa_meta[3])
w = weights.generateWeightsUsingShapefile(GSA_file_SHP,
idVariable=gsa_meta[2])
regions = regionalization.generateRegions(w=w,
observations=observations)[0]
regions = regionalization.getNamesFromRegions(regions)
nameMapping = util.getNameMapping('out/gsa/mymap.svg', gsa_meta[0],
gsa_meta[1])
nameMapping = {
key: value.replace("'", "APOSTROPHE")
for key, value in nameMapping.items()
}
numRegs = len(set(regions.values()))
return render_template("regionalization.html",
case_num=case_num,
mymap=json.dumps(mymap),
regions=json.dumps(regions),
numRegs=numRegs,
svgNaming=svgNaming,
nameMapping=json.dumps(str(nameMapping)))
def runGSA(case_num, autocorrelationRows, autocorrelationCols, sdRows, sdCols, idVariable):
fileDict = dao.getFileDict(case_num)
observations = weights.extractObservations(fileDict['GSA_Input_CSV'], autocorrelationRows, autocorrelationCols)
w = weights.generateWeightsUsingShapefile(fileDict['GSA_Input_SHP'], idVariable=idVariable)
globalAutoCorrelation = autocorrelation.globalAutocorrelation(observations, w)
localAutoCorrelation = autocorrelation.localAutocorrelation(observations, w)
observations = weights.extractObservations(fileDict['GSA_Input_CSV'], sdRows, sdCols)
spatialDynamics = spatial_dynamics.markov(observations, w, method="spatial")
return localAutoCorrelation, globalAutoCorrelation, spatialDynamics