def close(self):
"""
Close and flush the dataset, plus calculate stats
"""
if self.calcStats:
calcstats.calcStats(self.ds, ignore=self.nullVal)
self.ds.FlushCache()
self.ds = None
def close(self):
"""
Calculate stats etc
"""
from rios import calcstats
if self.mode != basedriver.READ:
progress = self.controls.progress
ignore = self.userClass.rasterIgnore
calcstats.calcStats(self.ds, progress, ignore)
self.ds.FlushCache()
self.ds = None
def writeOutImg(inputArray, outfile, n, m, c, TLX, TLY, nulVal, proj, dType):
# Write the output DEM into an image file with GDAL
nBands = 1
drvr = gdal.GetDriverByName('HFA')
ds = drvr.Create(outfile, n, m, nBands, dType, ['COMPRESS=YES'])
band = ds.GetRasterBand(1)
band.WriteArray(inputArray)
ds.SetGeoTransform((TLX, c, 0, TLY, 0, -c))
ds.SetProjection(proj)
progress = cuiprogress.CUIProgressBar()
calcstats.calcStats(ds, progress, ignore=nulVal)
del ds
def mainRoutine():
"""
Main routine
"""
cmdargs = getCmdargs()
info = sen2meta.Sen2TileMeta(filename=cmdargs.infile)
ds = createOutfile(cmdargs.outfile, info)
nullValDN = 1000
# Get a sorted list of the Sentinel-2 band names. Note that sometimes this
# is an incomplete list of band names, which appears to be due to a bug in
# earlier versions of ESA's processing software. I suspect it relates to
# Anomaly number 11 in the following page.
# https://sentinel.esa.int/web/sentinel/news/-/article/new-processing-baseline-for-sentinel-2-products
bandNames = sorted(info.viewAzimuthDict.keys())
# Mean over all bands
satAzDeg = numpy.array([info.viewAzimuthDict[i] for i in bandNames])
satAzDegMeanOverBands = satAzDeg.mean(axis=0)
satZenDeg = numpy.array([info.viewZenithDict[i] for i in bandNames])
satZenDegMeanOverBands = satZenDeg.mean(axis=0)
sunAzDeg = info.sunAzimuthGrid
sunZenDeg = info.sunZenithGrid
stackDeg = numpy.array(
[satAzDegMeanOverBands, satZenDegMeanOverBands, sunAzDeg, sunZenDeg])
stackRadians = numpy.radians(stackDeg)
stackDN = numpy.round(stackRadians / SCALE_TO_RADIANS).astype(numpy.int16)
nullmask = numpy.isnan(stackDeg)
stackDN[nullmask] = nullValDN
lnames = ['SatelliteAzimuth', 'SatelliteZenith', 'SunAzimuth', 'SunZenith']
for i in range(ds.RasterCount):
b = ds.GetRasterBand(i + 1)
b.WriteArray(stackDN[i])
b.SetNoDataValue(nullValDN)
b.SetDescription(lnames[i])
calcstats.calcStats(ds,
ignore=nullValDN,
progress=cuiprogress.SilentProgress())
del ds
def main():
"""
Main routine for calling from command line.
"""
cmdargs = getCmdargs()
for filename in cmdargs.imgfile:
ds = gdal.Open(filename, gdal.GA_Update)
# If no ignore value given, check if one is already defined in the file
if cmdargs.ignore is not None:
ignore = cmdargs.ignore
else:
b1 = ds.GetRasterBand(1)
ignore = b1.GetNoDataValue()
calcstats.calcStats(ds, ignore=ignore, approx_ok=cmdargs.approx)
ds.FlushCache()
# so entry points return success at command line
return 0
def makeTestFile(imgfile, withRat=True):
# Make a test image with a simple RAT
nRows = 100
nCols = 1
ds = riostestutils.createTestFile(imgfile, numRows=nRows, numCols=nCols)
imgArray = numpy.ones((nRows, nCols), dtype=numpy.uint8)
imgArray[1:10, 0] = numpy.arange(1, 10)
imgArray[50:, 0] = 0
band = ds.GetRasterBand(1)
band.WriteArray(imgArray)
nullDN = 0
calcstats.calcStats(ds,
ignore=nullDN,
progress=cuiprogress.SilentProgress())
columnName = 'Value'
# Note that the RAT has a row for lots of values which have no corresponding pixel
ratValues = (numpy.mgrid[0:nRows] + 10).astype(numpy.int32)
ratValues[0] = 500
if withRat:
rat.writeColumnToBand(band, columnName, ratValues)
band.SetMetadataItem('LAYER_TYPE', 'thematic')
del ds
def run():
"""
Run a test of statistics calculation
"""
riostestutils.reportStart(TESTNAME)
nullVal = 0
# We repeat the basic test for a number of different GDAL datatypes, with different
# ranges of data. Each element of the following list is a tuple of
# (gdalDataType, numpyDataType, scalefactor)
# for which the test is run. The original data being scaled is in
# the range 25-100 (after clobbering half the array as nulls, to ensure that
# the nulls are enough to make a difference).
dataTypesList = [(gdal.GDT_Byte, numpy.uint8, 1),
(gdal.GDT_UInt16, numpy.uint16, 1),
(gdal.GDT_Int16, numpy.int16, 300),
(gdal.GDT_UInt16, numpy.uint16, 300),
(gdal.GDT_Int32, numpy.int32, 30000),
(gdal.GDT_UInt32, numpy.uint32, 30000),
(gdal.GDT_Float32, numpy.float32, 1),
(gdal.GDT_Float32, numpy.float32, 100),
(gdal.GDT_Float32, numpy.float32, 0.01)]
# We repeat these tests on a number of different drivers, if they are available,
# as some stats-related things may work fine on some drivers but not on others.
driverTestList = [
('HFA', ['COMPRESS=YES'], 'img'),
('GTiff', ['COMPRESS=LZW', 'TILED=YES', 'INTERLEAVE=BAND'], 'tif'),
('KEA', [], 'kea')
]
# Remove any which current GDAL not suporting
driverTestList = [(drvrName, options, ext)
for (drvrName, options, ext) in driverTestList
if gdal.GetDriverByName(drvrName) is not None]
# Loop over all drivers
for (driverName, creationOptions, ext) in driverTestList:
# Loop over all datatype tuples in the list
for (fileDtype, arrDtype, scalefactor) in dataTypesList:
imgfile = 'test.' + ext
ds = riostestutils.createTestFile(imgfile,
dtype=fileDtype,
driverName=driverName,
creationOptions=creationOptions)
rampArr = riostestutils.genRampArray().astype(
arrDtype) * scalefactor
(nRows, nCols) = rampArr.shape
# Set half of it to null
rampArr[:, :nCols // 2] = nullVal
band = ds.GetRasterBand(1)
band.WriteArray(rampArr)
del ds
# Calculate the stats on the file
ds = gdal.Open(imgfile, gdal.GA_Update)
calcstats.calcStats(ds,
progress=cuiprogress.SilentProgress(),
ignore=nullVal)
del ds
# Read back the data as a numpy array
ds = gdal.Open(imgfile)
band = ds.GetRasterBand(1)
rampArr = band.ReadAsArray()
# Get stats from file, and from array, and compare
stats1 = getStatsFromBand(band)
stats2 = getStatsFromArray(rampArr, nullVal)
iterationName = "%s %s scale=%s" % (
driverName, gdal.GetDataTypeName(fileDtype), scalefactor)
# This relative tolerance is used for comparing the median and mode,
# because those are approximate only, and the likely error depends on the
# size of the numbers in question (thus it depends on the scalefactor).
# Please do not make it any larger unless you have a really solid reason.
relativeTolerance = 0.1 * scalefactor
ok = compareStats(stats1, stats2, iterationName, relativeTolerance)
del ds
if os.path.exists(imgfile):
os.remove(imgfile)
if ok:
riostestutils.report(TESTNAME, "Passed")
else:
riostestutils.report(
TESTNAME,
("Note that the mode and median tests will fail in GDAL < 2.0, " +
"unless the GDAL fixes suggested in tickets " +
"http://trac.osgeo.org/gdal/ticket/4750 and " +
"http://trac.osgeo.org/gdal/ticket/5289 are applied"))
return ok
def run():
"""
Run a test of RAT statistics calculation
"""
riostestutils.reportStart(TESTNAME)
allOK = True
imgfile = 'test.img'
nRows = 100
nCols = 1
ds = riostestutils.createTestFile(imgfile, numRows=nRows, numCols=nCols)
imgArray = numpy.ones((nRows, nCols), dtype=numpy.uint8)
imgArray[1:10, 0] = numpy.arange(1, 10)
imgArray[50:, 0] = 0
band = ds.GetRasterBand(1)
band.WriteArray(imgArray)
nullDN = 0
calcstats.calcStats(ds,
ignore=nullDN,
progress=cuiprogress.SilentProgress())
columnName = 'Value'
# Note that the RAT has a row for lots of values which have no corresponding pixel
ratValues = (numpy.mgrid[0:nRows]**2).astype(numpy.float64)
ratValues[0] = 500
rat.writeColumnToBand(band, columnName, ratValues)
band.SetMetadataItem('LAYER_TYPE', 'thematic')
del ds
ratStats = fileinfo.RatStats(imgfile, columnlist=[columnName])
# Construct an image of the values, by translating pixel values into RAT values
ratValImg = numpy.zeros(imgArray.shape, dtype=numpy.float64)
for dn in numpy.unique(imgArray):
if dn != 0:
mask = (imgArray == dn)
ratValImg[mask] = ratValues[dn]
ratValImgNonNull = ratValImg[imgArray != 0]
# Now find the "true" values of the various stats for this image (i.e. this is the
# histogramweighted=True case, which I think will be the most common one)
trueMean = ratValImgNonNull.mean()
trueStddev = ratValImgNonNull.std()
trueMin = ratValImgNonNull.min()
trueMax = ratValImgNonNull.max()
tolerance = 0.000001
if not equalTol(ratStats.Value.mean, trueMean, tolerance):
riostestutils.report(
TESTNAME, "Mismatched means: %s, %s" %
(repr(ratStats.Value.mean), repr(trueMean)))
allOK = False
if not equalTol(ratStats.Value.stddev, trueStddev, tolerance):
riostestutils.report(
TESTNAME, "Mismatched stddevs: %s, %s" %
(repr(ratStats.Value.stddev), repr(trueStddev)))
allOK = False
if not equalTol(ratStats.Value.min, trueMin, tolerance):
riostestutils.report(
TESTNAME, "Mismatched mins: %s, %s" %
(repr(ratStats.Value.min), repr(trueMin)))
allOK = False
if not equalTol(ratStats.Value.max, trueMax, tolerance):
riostestutils.report(
TESTNAME, "Mismatched maxes: %s, %s" %
(repr(ratStats.Value.max), repr(trueMax)))
allOK = False
if allOK:
riostestutils.report(TESTNAME, "Passed")
return allOK