def testConstantRegion(self):
"""A constant region does not have any local extrema and NaN values should be returned."""
y = numpy.array([2,2,2],'f')
result = EF.estimateExtremumPosition(y,self.x)
self.assertEqual(result,(numpy.NaN,numpy.NaN,numpy.NaN))
result = EF.estimateExtremumPosition(y,self.x,sigmaY=self.zeros)
self.assertEqual(result,(numpy.NaN,numpy.NaN,numpy.NaN))
def testExactGeneric(self):
result = EF.estimateExtremumPosition(self.y,self.x)
self.assertEqual(result,(2.5+1/1.5,numpy.NaN,1.0))
result = EF.estimateExtremumPosition(self.y,self.x,sigmaY=self.zeros)
self.assertEqual(result,(2.5+1/1.5,0.0,1.0))
result = EF.estimateExtremumPosition(self.y,self.x,sigmaY=self.error)
numpy.testing.assert_almost_equal(numpy.array(result),
numpy.array((2.5+1/1.5,0.02/1.5**2,1.0)))
def testSymmetricMaximum(self):
y = numpy.array([2,2,1],'f')
result = EF.estimateExtremumPosition(y,self.x)
self.assertEqual(result,(2.5,numpy.NaN,-1.0))
result = EF.estimateExtremumPosition(y,self.x,sigmaY=self.zeros)
self.assertEqual(result,(2.5,0.0,-1.0))
result = EF.estimateExtremumPosition(y,self.x,sigmaY=self.error)
numpy.testing.assert_almost_equal(numpy.array(result),
numpy.array((2.5,0.02,-1.0)))
def testExactMaximum(self):
y = numpy.array([2,3,2],'f')
result = EF.estimateExtremumPosition(y,self.x)
self.assertEqual(result,(3.0,numpy.NaN,-1.0))
result = EF.estimateExtremumPosition(y,self.x,sigmaY=self.zeros)
self.assertEqual(result,(3.0,0.0,-1.0))
result = EF.estimateExtremumPosition(y,self.x,sigmaY=self.error)
numpy.testing.assert_almost_equal(numpy.array(result),
numpy.array((3.0,0.005,-1.0)))
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 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 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)
def testRoots(self):
"""Test the correct computation of all local extrema for a bistable potential."""
#Prepare dimensions
self.prepareDimensions()
lambField = DC.FieldContainer(self.lambDim,
unit='1 V / m**3',
longname='parameter',
shortname='\lambda')
xField = DC.FieldContainer(self.xDim[0],
unit='1 m',
longname='position',
shortname='x')
#Prepare potential
V = []
for i in xrange(len(lambField.data)):
u = xField.data
V.append(-lambField.data[i] / 2 * u**2 + u**4 / 4 -
u * self.kappa1)
self.V = DC.FieldContainer(numpy.array(V),
unit='1 V',
dimensions=[lambField, xField],
longname='electric potential',
shortname=r'\varphi')
#Predict result
x0, curv, mask = fixedPoints(lambField.data, kappa1=self.kappa1)
error = 1.0 / curv.transpose()
error[:] = numpy.nan
data = x0.transpose()
expectedResult = DC.FieldContainer(
data,
unit=xField.unit,
mask=numpy.isnan(data),
dimensions=[DC.generateIndex(0, 3), lambField],
longname='position of the local extrema of electric potential',
shortname='x_0')
#Configure worker
w = EF.ExtremumFinder(None)
w.paramExtremum.value = u'both'
#Retrieve result from worker
result = w.locate(self.V)
self.test(result, expectedResult)
