def generateXmlFromCommandLineArgs(args):
"""
Main routine used to generate the :samp:`'<install_prefix>/share/pymango.xml'` XML file
which describes main-driver filters and data-types. XML description are generated
for filter classes which are registered with the :obj:`mango.application.main_driver` module.
The :samp:`{args}` argument is required to have the following attributes:
:samp:`{args.loggingLevel}` (:obj:`str`)
The logging level string, one of :samp:`'INFO', 'DEBUG', 'WARN', 'ERROR', 'CRITICAL', 'NONE'`.
:samp:`{args.xmlPart1}` (:obj:`str`)
XML string pre-pended to individual filter XML descriptions.
:samp:`{args.xmlPart2}` (:obj:`str`)
XML string appended after the individual filter XML descriptions.
:samp:`{args.outputFileName}` (:obj:`str`)
Name of the file in which the generated XML string is written.
:type args: object
:param args: Parameter object, see above.
"""
logLevel = getattr(logging, args.loggingLevel)
mpi.initialiseLoggers([
__name__, "mango.core", "mango.image", "mango.io", "mango.application"
],
logLevel=getattr(logging, args.loggingLevel))
xmlPartsFileNames = [args.xmlPart1, args.xmlPart2]
xmlPartsStrings = [
None,
] * len(xmlPartsFileNames)
for i in range(0, len(xmlPartsFileNames)):
xmlPartFileName = xmlPartsFileNames[i]
if (xmlPartFileName != None):
xmlPartsStrings[i] = open(xmlPartFileName, 'r').read()
xmlString = generatePyMangoFilterDescriptionXml(xmlPartsStrings[0],
xmlPartsStrings[1])
if (args.outputFileName != None):
if (mpi.rank == 0):
rootLogger.info("Writing XML to file %s..." % args.outputFileName)
open(args.outputFileName, 'w').write(xmlString)
rootLogger.info("Done writing XML to file %s." %
args.outputFileName)
else:
print(xmlString)
def generateXmlFromCommandLineArgs(args):
"""
Main routine used to generate the :samp:`'<install_prefix>/share/pymango.xml'` XML file
which describes main-driver filters and data-types. XML description are generated
for filter classes which are registered with the :obj:`mango.application.main_driver` module.
The :samp:`{args}` argument is required to have the following attributes:
:samp:`{args.loggingLevel}` (:obj:`str`)
The logging level string, one of :samp:`'INFO', 'DEBUG', 'WARN', 'ERROR', 'CRITICAL', 'NONE'`.
:samp:`{args.xmlPart1}` (:obj:`str`)
XML string pre-pended to individual filter XML descriptions.
:samp:`{args.xmlPart2}` (:obj:`str`)
XML string appended after the individual filter XML descriptions.
:samp:`{args.outputFileName}` (:obj:`str`)
Name of the file in which the generated XML string is written.
:type args: object
:param args: Parameter object, see above.
"""
logLevel=getattr(logging, args.loggingLevel)
mpi.initialiseLoggers(
[__name__, "mango.core", "mango.image", "mango.io", "mango.application"],
logLevel=getattr(logging, args.loggingLevel)
)
xmlPartsFileNames = [args.xmlPart1, args.xmlPart2]
xmlPartsStrings = [None,]*len(xmlPartsFileNames)
for i in range(0, len(xmlPartsFileNames)):
xmlPartFileName= xmlPartsFileNames[i]
if (xmlPartFileName != None):
xmlPartsStrings[i] = open(xmlPartFileName, 'r').read()
xmlString = generatePyMangoFilterDescriptionXml(xmlPartsStrings[0], xmlPartsStrings[1])
if (args.outputFileName != None):
if (mpi.rank == 0):
rootLogger.info("Writing XML to file %s..." % args.outputFileName)
open(args.outputFileName, 'w').write(xmlString)
rootLogger.info("Done writing XML to file %s." % args.outputFileName)
else:
print(xmlString)
from matplotlib import pyplot as plt
plt.ioff()
hd = HistData()
hd.edges = [sp.arange(0, 104, 1), sp.arange(0, 104, 1)]
hd.x = (hd.edges[0][1:] + hd.edges[0][0:-1])*0.5
hd.y = (hd.edges[1][1:] + hd.edges[1][0:-1])*0.5
xx,yy = np.meshgrid(hd.x, hd.y)
hd.hist2dData = mockHistData(xx,yy)
hd.hist1dData0 = sp.sum(hd.hist2dData, axis=0)
hd.hist1dData1 = sp.sum(hd.hist2dData, axis=1)
plt.pcolor(xx,yy,hd.hist2dData)
self.histData = hd
def testTernaryPlot(self):
"""
Tests ternary plotting of 2D histogram data.
"""
figList = mango.application.shale.generateShaleTernaryPlot(self.histData)
self.assertLess(2, len(figList))
# plt.show()
if __name__ == "__main__":
mpi.initialiseLoggers(
[__name__, "mango.application"],
logLevel=logging.DEBUG
)
unittest.main()
else:
parser = optparse.OptionParser(description=descStr)
for arg in argList:
addOptionDict = dict(arg)
del addOptionDict['cmdLine']
parser.add_option(*arg['cmdLine'], **addOptionDict)
return parser
if (__name__ == "__main__"):
argParser = getArgumentParser()
if haveArgParse:
args = argParser.parse_args()
else:
(args, argv) = argParser.parse_args()
mpi.initialiseLoggers([__name__, "mango.io", "mango.application"],
logLevel=getattr(logging, args.loggingLevel))
bdsp = \
BatchDownsampler(
mdssSrcRootDir=args.mdssSrc,
mdssDstRootDir=args.mdssDst,
mdssPrj=args.mdssPrj,
loclSrcRootDir=args.loclSrc,
loclDstRootDir=args.loclDst,
voxelSizeTuples=(parseVoxelSizes(args.voxelSizes.split(",")))
)
bdsp()
return outputArr
class ConvolutionFilterTest(NeighbourhoodFilterTest,_NeighbourhoodFilterTestImpl):
def setUp(self):
NeighbourhoodFilterTest.setUp(self)
self.diffTol = 1
def getWeights(self, se):
shape = se.toFootprint().shape
return mango.image.discrete_gaussian_kernel(sigma=np.min(shape)/(2*3.25))
def callMangoFilter(self, input, se, stride=(1,1,1), boffset=(0,0,0), eoffset=(0,0,0)):
output = mango.image.convolve(input, self.getWeights(se), mode=self.mode, stride=stride, boffset=boffset, eoffset=eoffset)
return output
def callScipyFilter(self, inDds, se):
kernel = self.getWeights(se)
rootLogger.info("Num kernel elements = %s" % sp.sum(sp.where(kernel, 1, 0)))
rootLogger.info("kernel.shape = %s" % (kernel.shape,))
outputArr = sp.ndimage.convolve(inDds.asarray(), weights=kernel, mode=self.mode)
return outputArr
if __name__ == "__main__":
mpi.initialiseLoggers(
[__name__, "mango.mpi", "mango.image", "mango.imageTest"],
logLevel=logging.INFO
)
unittest.main()
# Calculate the sp.array slicing for the non-halo (exclusive)
# part of the subdomain.
slc = []
for d in range(len(haloSz)):
slc.append(
slice(haloSz[d],
slcDds.asarray().shape[d] - haloSz[d]))
slc = tuple(slc)
self.assertEqual(np.min(slcDds.asarray()[slc]),
slcGlbIndex)
self.assertEqual(np.max(slcDds.asarray()[slc]),
slcGlbIndex)
self.assertTrue(
sp.all(slcDds.asarray()[slc] == slcGlbIndex))
def testGatherSliceWithHalo0(self):
self.doTestGatherSliceWithHalo(0)
def testGatherSliceWithHalo2(self):
self.doTestGatherSliceWithHalo(4)
if __name__ == "__main__":
mango.setLoggingVerbosityLevel("high")
mpi.initialiseLoggers(
[__name__, "mango.mpi", "mango.image", "mango.imageTest"],
logLevel=logging.INFO)
unittest.main()
input0 = mnDds,
input1 = sdDds,
centre=imgDds.shape*0.5,
radius=np.min(imgDds.shape)*0.40,
axislen = imgDds.shape[0]+2,
bins=(bins0, bins1)
)
if (mpi.rank == 0):
import matplotlib
import matplotlib.pyplot as plt
y = (bins0[1:]+bins0[0:-1])*0.5
x = (bins1[1:]+bins1[0:-1])*0.5
plt.plot(y, sp.sum(H, axis=1), linewidth=7)
plt.plot(y, H01, linewidth=3)
plt.plot(y, H02, linewidth=3)
#plt.show()
if __name__ == "__main__":
#mango.setLoggingVerbosityLevel("high")
mpi.initialiseLoggers(
[__name__, "mango.unittest", "mango.mpi", "mango.fmm", "mango.fmmTest"],
logLevel=logging.DEBUG
)
random.seed((mpi.rank+1)*23456243)
numpy.random.seed((mpi.rank+1)*23456134)
mango.unittest.main()
input0 = mnDds,
input1 = sdDds,
centre=imgDds.shape*0.5,
radius=np.min(imgDds.shape)*0.40,
axislen = imgDds.shape[0]+2,
bins=(bins0, bins1)
)
if (mpi.rank == 0):
import matplotlib
import matplotlib.pyplot as plt
y = (bins0[1:] + bins0[0:-1]) * 0.5
x = (bins1[1:] + bins1[0:-1]) * 0.5
plt.plot(y, sp.sum(H, axis=1), linewidth=7)
plt.plot(y, H01, linewidth=3)
plt.plot(y, H02, linewidth=3)
#plt.show()
if __name__ == "__main__":
#mango.setLoggingVerbosityLevel("high")
mpi.initialiseLoggers([
__name__, "mango.unittest", "mango.mpi", "mango.fmm", "mango.fmmTest"
],
logLevel=logging.DEBUG)
random.seed((mpi.rank + 1) * 23456243)
numpy.random.seed((mpi.rank + 1) * 23456134)
mango.unittest.main()
writehistogram=False)
def testDdsWriteAndRead(self):
outDir = self.createTmpDir("testDdsWriteAndRead")
dds = mango.data.gaussian_noise(shape=self.shape,
mtype="float64",
mean=0.0,
stdd=20000.)
outFileName = mango.io.writeDds(
os.path.join(outDir, "float64Noise.nc"), dds)
readDds = mango.io.readDds(outFileName, mpidims=dds.mpi.shape)
self.assertTrue(sp.all(dds.asarray() == readDds.asarray()))
if (mango.haveFloat16):
dds = mango.data.gaussian_noise(shape=self.shape,
mtype="float16",
mean=0.0,
stdd=2000.)
outFileName = mango.io.writeDds(os.path.join(
outDir, "float16Noise.nc"),
dds,
writehistogram=True)
readDds = mango.io.readDds(outFileName, mpidims=dds.mpi.shape)
self.assertTrue(sp.all(dds.asarray() == readDds.asarray()))
if __name__ == "__main__":
mango.setLoggingVerbosityLevel("high")
mpi.initialiseLoggers([__name__, "mango.io"], logLevel=logging.DEBUG)
mango.unittest.main()
if (failed):
self.fail("Did not throw exception for mango.io.splitpath('%s')" % path)
def testWriteDdsWithHistogram(self):
outDir = self.createTmpDir("testWriteDdsWithHistogram")
dds = mango.data.gaussian_noise(shape=self.shape, mtype="tomo_float", mean=0.0, stdd=0.02)
mango.io.writeDds(os.path.join(outDir, "tomo_floatWithHistogram.nc"), dds, writehistogram=True)
mango.io.writeDds(os.path.join(outDir, "tomo_floatWithOutHistogram.nc"), dds, writehistogram=False)
def testDdsWriteAndRead(self):
outDir = self.createTmpDir("testDdsWriteAndRead")
dds = mango.data.gaussian_noise(shape=self.shape, mtype="float64", mean=0.0, stdd=20000.)
outFileName = mango.io.writeDds(os.path.join(outDir, "float64Noise.nc"), dds)
readDds = mango.io.readDds(outFileName, mpidims=dds.mpi.shape)
self.assertTrue(sp.all(dds.asarray() == readDds.asarray()))
if (mango.haveFloat16):
dds = mango.data.gaussian_noise(shape=self.shape, mtype="float16", mean=0.0, stdd=2000.)
outFileName = mango.io.writeDds(os.path.join(outDir, "float16Noise.nc"), dds, writehistogram=True)
readDds = mango.io.readDds(outFileName, mpidims=dds.mpi.shape)
self.assertTrue(sp.all(dds.asarray() == readDds.asarray()))
if __name__ == "__main__":
mango.setLoggingVerbosityLevel("high")
mpi.initialiseLoggers(
[__name__, "mango.io"],
logLevel=logging.DEBUG
)
mango.unittest.main()
coordsys="abs",
unit=img.md.getVoxelSizeUnit())
img.subd.asarray()[...] += mango.data.gaussian_noise_like(
img, mean=0, stdd=300).subd.asarray()
mango.io.writeDds(os.path.join(dir, "tomoCyl3.nc"), img)
rootLogger.info("Done generating synthetic image.")
fit, cylImg = \
cylinder_fit_multi_res(
img,
3,
resolutions=[64, 128, 512],
retcylimg=True,
metricThreshRelTol=0.50
)
mango.io.writeDds(os.path.join(dir, "segmentedCyl3AnnularMsk.nc"),
cylImg)
rootLogger.info("Voxel size = %s %s" %
(img.md.getVoxelSize(), img.md.getVoxelSizeUnit()))
for i in range(len(cList)):
rootLogger.info("c%s=%s, r%s=%s, a%s=%s" %
(i, cList[i], i, rList[i], i, aList[i]))
rootLogger.info("Fit Results:\n%s" % ("\n".join(map(str, fit)), ))
if __name__ == "__main__":
mpi.initialiseLoggers([__name__, "mango.application"],
logLevel=logging.INFO)
mango.setLoggingVerbosityLevel("high")
mango.unittest.main()
for arg in argList:
addOptionDict = dict(arg)
del addOptionDict['cmdLine']
parser.add_option(*arg['cmdLine'], **addOptionDict)
return parser
if (__name__ == "__main__"):
argParser = getArgumentParser()
if haveArgParse:
args = argParser.parse_args()
else:
(args, argv) = argParser.parse_args()
mpi.initialiseLoggers(
[__name__, "mango.io", "mango.application"],
logLevel=getattr(logging, args.loggingLevel)
)
bdsp = \
BatchDownsampler(
mdssSrcRootDir=args.mdssSrc,
mdssDstRootDir=args.mdssDst,
mdssPrj=args.mdssPrj,
loclSrcRootDir=args.loclSrc,
loclDstRootDir=args.loclDst,
voxelSizeTuples=(parseVoxelSizes(args.voxelSizes.split(",")))
)
bdsp()
mvmvDirName = os.path.join(mvDstDir, os.path.split(mvDirName)[1])
mvmvFileName = os.path.join(mvDstDir, os.path.split(mvFileName)[1])
mdss.mv([mvFileName, mvDirName], mvDstDir)
self.assertFalse(mdss.exists(mvDirName))
self.assertFalse(mdss.exists(mvFileName))
self.assertTrue(mdss.exists(mvmvDirName))
self.assertTrue(mdss.isdir(mvmvDirName))
self.assertTrue(mdss.exists(mvmvFileName))
self.assertTrue(mdss.isfile(mvmvFileName))
fileList = mdss.listdir(mvDstDir)
self.assertEqual(2, len(fileList))
self.assertTrue(os.path.split(mvmvFileName)[1] in fileList)
self.assertTrue(os.path.split(mvmvDirName)[1] in fileList)
duList = mdss.du(os.path.join(mvDstDir, "*"))
rootLogger.info("du %s = %s" % (mvDstDir, duList))
self.assertEqual(2, len(duList))
if (mpi.haveMpi4py):
mpi.world.barrier()
if __name__ == "__main__":
mpi.initialiseLoggers(
[__name__, "mango.application.mdss"],
logLevel=logging.DEBUG
)
unittest.main()
