def testParabelArray(self):
x = numpy.array([-2,-1,0,1,2],'float')
field = numpy.array([(x-0.2)**2-0.5,(x+0.1)**2])
inputField = DC.FieldContainer(field,
error=numpy.resize(numpy.repeat(0.1,len(field)),field.shape),
dimensions=[DC.FieldContainer(numpy.array([1,2]),
longname='type',shortname=r'\theta'),
DC.FieldContainer(x,longname='position',shortname='x')],
longname='parabel',
shortname='f')
def error(y):
error = inputField.error[0,0] / (y[1]-2*y[2]+y[3])**2
error *= numpy.abs(y[2]-y[3]) + numpy.abs(y[1]-y[3]) + numpy.abs(y[1]-y[2])
return error
expectedResult = DC.FieldContainer(numpy.array([[0.2,-0.1]]),
longname = 'position of the local minima of parabel',
shortname = 'x_0',
error = numpy.array([[error(field[0]),error(field[1])]]))
expectedResult.dimensions[-1] = DC.FieldContainer(numpy.array([1,2]),
longname='type',
shortname=r'\theta')
w = EF.ExtremumFinder(None)
w.paramExtremum.value=u'minima'
#Retrieve result from worker
result = w.locate(inputField)
DC.assertEqual(result,expectedResult)
def testDistanceMappingInvertedStringDyDx(self):
"""
Consider two features, which are divided by a vertical line.
The respective distances increase by dx for each column counted
from the centre line.
"""
self.dydx = 0.01
self.yDim = DataContainer.FieldContainer(numpy.linspace(
-self.dydx, self.dydx, self.dim),
unit='1m',
longname='height',
shortname='h')
referenceField = DataContainer.FieldContainer(
numpy.logical_not(stringFeature(self.dim, distanceToBorder=0)),
dimensions=[self.xDim, self.yDim],
unit='1',
longname='Inverted String Feature',
shortname='S')
referenceField.seal()
result = self.worker.calculateDistanceMap(referenceField)
#Compute result afoot
afoot = numpy.zeros(referenceField.data.shape, 'f')
dx = numpy.diff(referenceField.dimensions[0].data)[0]
for i in xrange(referenceField.data.shape[1]):
afoot[:, i] = dx * abs(5 - i)
afootC = DataContainer.FieldContainer(afoot,
unit=self.xDim.unit,
dimensions=map(
copy.deepcopy,
referenceField.dimensions))
DataContainer.assertEqual(afootC, result)
def testDistanceMappingInvertedStringDyDx(self):
"""
Consider two features, which are divided by a vertical line.
The respective distances increase by dx for each column counted
from the centre line.
"""
self.dydx = 0.01
self.yDim = DataContainer.FieldContainer(
numpy.linspace(-self.dydx,self.dydx,self.dim),
unit = '1m',
longname= 'height',
shortname='h')
referenceField = DataContainer.FieldContainer(
numpy.logical_not(stringFeature(self.dim,distanceToBorder=0)),
dimensions = [self.xDim, self.yDim],
unit = '1',
longname='Inverted String Feature',
shortname='S')
referenceField.seal()
result = self.worker.calculateDistanceMap(referenceField)
#Compute result afoot
afoot = numpy.zeros(referenceField.data.shape,'f')
dx = numpy.diff(referenceField.dimensions[0].data)[0]
for i in xrange(referenceField.data.shape[1]):
afoot[:,i]= dx * abs(5-i)
afootC=DataContainer.FieldContainer(afoot, unit=self.xDim.unit,
dimensions = map(copy.deepcopy,referenceField.dimensions))
DataContainer.assertEqual(afootC, result)
def testParabel(self):
x = numpy.array([-2, -1, 0, 1, 2], 'float') + 0.1
y = x**2
inputField = DC.FieldContainer(y,
error=numpy.repeat(0.1, len(y)),
dimensions=[
DC.FieldContainer(
x,
longname='abscissae',
shortname='x')
],
longname='parabel',
shortname='f')
error = inputField.error[slice(0, 1)] / (y[1] - 2 * y[2] + y[3])**2
error *= numpy.abs(y[2] - y[3]) + numpy.abs(y[1] -
y[3]) + numpy.abs(y[1] -
y[2])
expectedResult = DC.FieldContainer(
numpy.array([0.0]),
longname='position of the local minimum of parabel',
shortname='x_0',
error=error)
w = EF.ExtremumFinder(None)
w.paramExtremum.value = u'minima'
#Retrieve result from worker
result = w.locate(inputField)
DC.assertEqual(result, expectedResult)
def testMinima(self):
"""Test the correct computation of all local minima for a bistable potential."""
#Predict result
x0,curv,mask = fixedPoints(numpy.array([self.LAMBDA]),kappa1=self.kappa1)
expectedResult = DC.FieldContainer(numpy.extract(curv[0]>0,x0[0]),
unit = self.xField.unit,
longname = 'position of the local minima of electric potential',
shortname = 'x_0')
#Retrieve result from worker
w = EF.ExtremumFinder(None)
w.paramExtremum.value=u'minima'
result = w.locate(self.V)
#Testing
DC.assertEqual(result,expectedResult)
def testParabel(self):
x = numpy.array([-2,-1,0,1,2],'float')+0.1
y = x**2
inputField = DC.FieldContainer(y,
error=numpy.repeat(0.1,len(y)),
dimensions=[DC.FieldContainer(x,longname='abscissae',shortname='x')],
longname='parabel',
shortname='f')
error = inputField.error[slice(0,1)] / (y[1]-2*y[2]+y[3])**2
error *= numpy.abs(y[2]-y[3]) + numpy.abs(y[1]-y[3]) + numpy.abs(y[1]-y[2])
expectedResult = DC.FieldContainer(numpy.array([0.0]),
longname = 'position of the local minimum of parabel',
shortname = 'x_0',
error = error)
w = EF.ExtremumFinder(None)
w.paramExtremum.value=u'minima'
#Retrieve result from worker
result = w.locate(inputField)
DC.assertEqual(result,expectedResult)
def testParabelSymmetricallyBoxedMinimum(self):
x = numpy.array([-2,-1,0,1,2],'float')-0.5
y = x**2
inputField = DC.FieldContainer(y,
error=numpy.repeat(0.1,len(y)),
dimensions=[DC.FieldContainer(x,longname='abscissae',shortname='x')],
longname='parabel',
shortname='f'
)
expectedResult = DC.FieldContainer(numpy.array([0.0]),
longname = 'position of the local minimum of parabel',
shortname = 'x_0',
error = numpy.array([0.1])
)
w = EF.ExtremumFinder(None)
w.paramExtremum.value=u'minima'
#Retrieve result from worker
result = w.locate(inputField)
DC.assertEqual(result,expectedResult)
