def extractProductForCompare(diff_tar,tarStorage,tiffsStorage,fmaskShellCall,quadsFolder,outRasterFolder):
print "call to extractProductForCompare with: "+ diff_tar
try:
# call download_landsat_data_by_sceneid.php using the downloadScene function
# in the event of a DownloadError, retry downloadScene up to 5 times, with a 5 second delay btw calls
retry(1, 5, DownloadError,downloadScene, diff_tar)
# now extract the downloaded file accordingly
extractedPath = checkExisting(os.path.join(LSF.tarStorage, diff_tar+".tar.gz"), tiffsStorage)
#do the other pre-processing stuff
rasterAnalysis_GDAL.runFmask(extractedPath,LSF.fmaskShellCall)
# get DN min number from each band in the scene and write to database
dnminExists = checkForDNminExist(extractedPath) # May not be needed in final design, used during testing
if dnminExists == False:
dnMinDict = rasterAnalysis_GDAL.getDNmin(extractedPath)
# fix bug introduced by me in commit de5df07c4ca3ff71ae4d7da27b6018fe1bc2df04
writeDNminToDB(dnMinDict,extractedPath)
# create quads from the input scene
quadPaths = rasterAnalysis_GDAL.cropToQuad(extractedPath,outRasterFolder,quadsFolder)
writeQuadToDB(quadPaths)
# get cloud cover percentage for each quad
quadCCDict = getQuadCCpercent(quadPaths)
# =========================================================================
# write input scene quads cloud cover percentage to the landsat_metadata table in the database
writeQuadsCCtoDB(quadCCDict,extractedPath)
print os.getcwd()
except Exception as e:
print "Error in extractProductForCompare"
# make sure we've returned to the original directory before raising this exception
raise
def extractedTar(quadsceneID):
sceneID=quadsceneID[:-2]
# guarantee that level 1 product tar file for sceneID now exists in /lsfdata/eros_data/extractedTars
tar(sceneID)
# if /lsfdata/extractedTars/sceneID doesn't contain the level 1 product band files, extract them from the tar file
#
if not(re.search(sceneID, ' '.join(glob.glob(os.path.join(LSF.tiffsStorage, '*'))))):
# make sure that the existing tar is valid
existingTar=os.path.join(LSF.tarStorage, sceneID+".tar.gz")
err=localLib.validTar(existingTar)
if err:
raise Exception(err)
# for now use checkExisting. change to use tarHandling class when completed
extractedPath = landsatFactTools_GDAL.checkExisting(existingTar, LSF.tiffsStorage)
rasterAnalysis_GDAL.runFmask(extractedPath,LSF.fmaskShellCall)
# get DN min number from each band in the scene and write to database
dnminExists = landsatFactTools_GDAL.checkForDNminExist(extractedPath) # May not be needed in final design, used during testing
if dnminExists == False:
dnMinDict = rasterAnalysis_GDAL.getDNmin(extractedPath)
# fix bug introduced by me in commit de5df07c4ca3ff71ae4d7da27b6018fe1bc2df04
landsatFactTools_GDAL.writeDNminToDB(dnMinDict,extractedPath)
# check to see if the tar file has been extracted, if not extract the files
print "Checking for: "+tar
extractedPath = landsatFactTools_GDAL.checkExisting(inNewSceneTar, tiffsStorage)
# run Fmask
# jdm 4/22/15: after spending a couple of days trying to get FMASK installed on cloud4
# I have not been able to get it to work. Therefore, for now I am commenting this out
# rasterAnalysis_GDAL.runFmask(extractedPath,Fmaskexe) #BM's original
print extractedPath
runFmaskBool = rasterAnalysis_GDAL.runFmask(extractedPath,fmaskShellCall)
#print "Fmask Boolean: "+runFmaskBool
if (runFmaskBool == True):
# get DN min number from each band in the scene and write to database
wrs2Name=tar[3:9]
dnminExists = landsatFactTools_GDAL.checkForDNminExist(extractedPath) # May not be needed in final design, used during testing
if dnminExists == False:
dnMinDict = rasterAnalysis_GDAL.getDNmin(extractedPath)
landsatFactTools_GDAL.writeDNminToDB(dnMinDict,extractedPath)
# create quads from the input scene
quadPaths = rasterAnalysis_GDAL.cropToQuad(extractedPath,projectStorage,quadsFolder)
landsatFactTools_GDAL.writeQuadToDB(quadPaths)
# get cloud cover percentage for each quad
# write input scene quads cloud cover percentage to the landsat_metadata table in the database
quadCCDict=landsatFactTools_GDAL.readAndWriteQuadCC(quadPaths, extractedPath)
# for each quad this finds the closest scene that passes the cloud cover threshold for processing
quadTiffList2Process = landsatFactTools_GDAL.getNextBestQuad(quadCCDict,cloudCoverThreshold)
# =========================================================================
#jdm: Need to make sure the next best quads we are going to be comparing to are actually extracted
#and pre-processed to a level appropriate for differencing.
print "quadTiffList2Process: ", quadTiffList2Process
#TO-DO: create/call a function in landsatFactTools_GDAL called extractProductForCompare()
#loop through quadTiffList2Process and get unique list of data to download