def computeDistances(self, field, subscriber=1, percentage=0):
xGrid,yGrid = numpy.meshgrid(field.dimensions[-1].data,field.dimensions[-2].data)
x = numpy.extract(numpy.logical_not(numpy.isnan(field.data)),xGrid)
xCon = DataContainer.FieldContainer(x,unit=field.dimensions[-1].unit,
longname=field.dimensions[-1].longname,
shortname=field.dimensions[-1].shortname)
y = numpy.extract(numpy.logical_not(numpy.isnan(field.data)),field.data)
yCon = DataContainer.FieldContainer(y,longname=field.longname,
shortname=field.shortname,
unit=field.unit)
xOff, xStep, xInd = OA.grid2Index(x, self.paramExtentX.value)
yOff, yStep, yInd = OA.grid2Index(y, self.paramExtentY.value)
xMax = xInd.maxV
yMax = yInd.maxV
xDim = DataContainer.FieldContainer( numpy.linspace(xInd.minV,xInd.maxV,xInd.stepCount), xCon.unit,
longname = xCon.longname, shortname = xCon.shortname )
yDim = DataContainer.FieldContainer( numpy.linspace(yInd.minV,yInd.maxV,yInd.stepCount), yCon.unit,
longname = yCon.longname, shortname = yCon.shortname )
functional = numpy.ones((xInd.stepCount, yInd.stepCount), dtype='float')
distances = numpy.zeros(x.shape,'f')
ni = functional.shape[0]
nj = functional.shape[1]
increment = 50.0/(ni*nj)
for i in xrange(ni):
for j in xrange(nj):
for k in xrange(len(x)):
distances[k] = numpy.sqrt((x[k]-xDim.data[i])**2+
(y[k]-yDim.data[j])**2)
functional[i,j]=distances.min()
percentage += increment
subscriber %= percentage
result = DataContainer.FieldContainer(functional.transpose(),
dimensions=[yDim, xDim],
longname = 'functional of %s'%field.longname,
shortname= 'F_{%s}'%field.shortname
)
return result
def testSignificantNoise(self):
"""Tests the conservation of abscissae data in case of significant deviation."""
worker = OA.OscAbsorptionCalculator()
worker.paramClipping.value = 0
self.sampleC['I'].dimensions[-1].data += numpy.random.randn(self.n)
self.sampleC.seal()
result = worker.calcAbsorption(self.sampleC)
expectedDim = copy.deepcopy(self.sampleC['I'].dimensions)
expectedResult = DC.FieldContainer(numpy.ones(
(self.m, self.n), 'float') - self.I.data,
dimensions=expectedDim,
longname=u'absorption',
shortname=ur'\tilde{A}')
self.assertEqual(result, expectedResult)
def testCalculation(self):
"""Tests the correct calculation of absorption without clipping.
It is assumed, that the dark reference intensity is equal to zero,
while white reference intensity is equal to one."""
worker = OA.OscAbsorptionCalculator()
worker.paramClipping.value = 0
self.sampleC.seal()
result = worker.calcAbsorption(self.sampleC)
expectedDim = [
DC.generateIndex(1, self.m),
DC.FieldContainer(self.x,
longname='position',
shortname='x',
unit='1m')
]
expectedResult = DC.FieldContainer(numpy.ones(
(self.m, self.n), 'float') - self.I.data,
dimensions=expectedDim,
longname=u'absorption',
shortname=ur'\tilde{A}')
self.assertEqual(result, expectedResult)
def testNegligibleNoise(self):
"""Tests the merging of abscissae data in case of negliglible deviation."""
worker = OA.OscAbsorptionCalculator()
worker.paramClipping.value = 0
self.sampleC['I'].dimensions[-1].data += 1e-8 * numpy.random.randn(
self.n)
self.sampleC.seal()
result = worker.calcAbsorption(self.sampleC)
expectedDim = [
DC.generateIndex(1, self.m),
DC.FieldContainer(self.x,
longname='position',
shortname='x',
unit='1m')
]
expectedResult = DC.FieldContainer(numpy.ones(
(self.m, self.n), 'float') - self.I.data,
dimensions=expectedDim,
longname=u'absorption',
shortname=ur'\tilde{A}')
self.assertEqual(result, expectedResult)