# looping the code
for a in obs.shortlist:
#obsTime="20130829.1800"
#kongreyDSS.load() # reload
dss.unload()
obsTime = a.dataTime
pp.pipeline(dss=dss,
filteringAlgorithm = filters.gaussianFilter,
filteringAlgorithmArgs = {'sigma':5,
'stream_key': "obs" },
matchingAlgorithm = algorithms.nonstandardKernel,
matchingAlgorithmArgs = {'obsTime': obsTime, 'maxHourDiff':7,
'regions':regions,
'k' : 24, # steps of semi-lagrangian advections performed
'shiibaArgs':{'searchWindowWidth':11, 'searchWindowHeight':7, },
'outputFolder':outputFolder,
'volumeProportionWeight':volumeProportionWeight,
} ,
outputFolder=outputFolder,
toLoad=False,
#remarks= "Covariance used, rather than correlation: algorithms.py line 221: tempScore = a1.cov(w1)[0,1]",
remarks = "Correlation used"
)
print 'start time:', startTime
print 'total time spent:', time.time()-time0
for a in obs.shortlist:
# obsTime="20130829.1800"
# kongreyDSS.load() # reload
dss.unload()
obsTime = a.dataTime
pp.pipeline(
dss=dss,
filteringAlgorithm=filters.gaussianFilter,
filteringAlgorithmArgs={"sigma": 5, "stream_key": "all"},
matchingAlgorithm=algorithms.nonstandardKernel,
matchingAlgorithmArgs={
"obsTime": obsTime,
"maxHourDiff": 6,
"regions": regions,
"k": 24, # steps of semi-lagrangian advections performed
"shiibaArgs": {"searchWindowWidth": 15, "searchWindowHeight": 9},
"outputFolder": outputFolder,
"volumeProportionWeight": volumeProportionWeight,
},
outputFolder=outputFolder,
toLoad=False,
# remarks= "Covariance used, rather than correlation: algorithms.py line 221: tempScore = a1.cov(w1)[0,1]",
remarks="Correlation used",
)
print "start time:", startTime
print "total time spent:", time.time() - time0
for a in obs.shortlist:
#obsTime="20130829.1800"
#kongreyDSS.load() # reload
dss.unload()
obsTime = a.dataTime
pp.pipeline(dss=dss,
#filteringAlgorithm = filters.gaussianFilter,
#filteringAlgorithmArgs = {'sigma':5,
# 'stream_key': "obs" },
filteringAlgorithm = "",
matchingAlgorithm = algorithms.shiftedCorr,
matchingAlgorithmArgs = {'obsTime': obsTime, 'maxHourDiff':6,
'regions':regions,
'outputFolder':outputFolder,
'volumeProportionWeight':volumeProportionWeight,
'maxLatDiff': 16,
'maxLongDiff': 24,
'shiftStep': 4,
} ,
outputFolder=outputFolder,
toLoad=False,
#remarks= "Covariance used, rather than correlation: algorithms.py line 221: tempScore = a1.cov(w1)[0,1]",
remarks = "Correlation used"
)
print 'start time:', startTime
print 'total time spent:', time.time()-time0
hualien4 = misc.getFourCorners(dp.hualienCounty)
yilan4 = misc.getFourCorners(dp.yilanCounty)
kaohsiung4 = misc.getFourCorners(dp.kaohsiungCounty)
regions = [{'name': "hualien", 'points': hualien4, 'weight': 0.4},
{'name': "kaohsiung", 'points':kaohsiung4, 'weight':0.3},
{'name':"yilan", 'points':yilan4, 'weight':0.3},
]
### next up: work on the i/o so that i don't need to exit/re-enter ipython every time
# for loop added 18-03-2014
for a in kongreyDSS.obs:
kongreyDSS.load() # reload
pp.pipeline(dss=kongreyDSS,
filteringAlgorithm = filters.gaussianFilter,
filteringAlgorithmArgs = {'sigma':5},
matchingAlgorithm = algorithms.nonstandardKernel,
matchingAlgorithmArgs = {'obsTime': a.dataTime, 'maxHourDiff':7,
'regions':regions,
'k' : 48, # steps of semi-lagrangian advections performed
'shiibaArgs':{'searchWindowWidth':11, 'searchWindowHeight':11, },
} ,
outputFolder=dp.defaultRootFolder + "labReports/2014-03-07-filter-matching-scoring-pipeline/",
toLoad=False,
remarks= "Covariance used, rather than correlation: algorithms.py line 221: tempScore = a1.cov(w1)[0,1]",
)
print 'start time:', startTime
print 'time spent:', time.time()-time0
if not os.path.exists(outputFolder):
os.makedirs(outputFolder)
shutil.copyfile(dp.defaultRootFolder+"python/armor/validation/"+ scriptFileName, outputFolder+ scriptFileName)
shutil.copyfile(dp.defaultRootFolder+"python/armor/patternMatching/algorithms.py", outputFolder+ "algorithms.py")
shutil.copyfile(dp.defaultRootFolder+"python/armor/patternMatching/pipeline.py", outputFolder+ "pipeline.py")
################################################################################
# looping the code
for a in obs.shortlist:
#obsTime="20130829.1800"
#kongreyDSS.load() # reload
dss.unload()
obsTime = a.dataTime
pp.pipeline(dss=dss,
filteringAlgorithm = filteringAlgorithm,
filteringAlgorithmArgs = filteringAlgorithmArgs,
matchingAlgorithm = matchingAlgorithm,
matchingAlgorithmArgs = matchingAlgorithmArgs,
outputFolder=outputFolder,
toLoad=False,
#remarks= "Covariance used, rather than correlation: algorithms.py line 221: tempScore = a1.cov(w1)[0,1]",
remarks = "Correlation used"
)
print 'start time:', startTime
print 'total time spent:', time.time()-time0
