def testGradient(self):
        print "Gradient testing"
        x = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)
        g = np.gradient(x)
        self.checkitems([1.0, 1.5, 2.5, 3.5, 4.5, 5.0], g)
        g = np.gradient(x, 2)
        self.checkitems([0.5, 0.75, 1.25, 1.75, 2.25, 2.5], g)
        a = np.arange(6, dtype=np.float) * 2
        g = np.gradient(x, a)
        self.checkitems([0.5, 0.75, 1.25, 1.75, 2.25, 2.5], g)

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))
        self.checkitems([[2.0, 2.0, -1.0], [2.0, 2.0, -1.0]], g[0])
        self.checkitems([[1.0, 2.5, 4.0], [1.0, 1.0, 1.0]], g[1])

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), 2)
        self.checkitems([[1.0, 1.0, -0.5], [1.0, 1.0, -0.5]], g[0])
        self.checkitems([[0.5, 1.25, 2.0], [0.5, 0.5, 0.5]], g[1])

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), 2, 1)
        self.checkitems([[1.0, 1.0, -0.5], [1.0, 1.0, -0.5]], g[0])
        self.checkitems([[1.0, 2.5, 4.0], [1.0, 1.0, 1.0]], g[1])

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), 1, np.array([1.0, 2.0, 5.0]))
        self.checkitems([[2.0, 2.0, -1.0], [2.0, 2.0, -1.0]], g[0])
        self.checkitems([[1.0, 1.25, 4.0 / 3], [1.0, 0.5, 1.0 / 3]], g[1])

        # test slice views
        x = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)
        g = np.gradient(x[2:])
        self.checkitems([3, 3.5, 4.5, 5.0], g)
Exemple #2
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    def testGradient(self):
        print 'Gradient testing'
        x = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)
        g = np.gradient(x)
        self.checkitems([1., 1.5, 2.5, 3.5, 4.5, 5.], g)
        g = np.gradient(x, 2)
        self.checkitems([0.5, 0.75, 1.25, 1.75, 2.25, 2.5], g)
        a = np.arange(6, dtype=np.float) * 2
        g = np.gradient(x, a)
        self.checkitems([0.5, 0.75, 1.25, 1.75, 2.25, 2.5], g)

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))
        self.checkitems([[2., 2., -1.], [2., 2., -1.]], g[0])
        self.checkitems([[1., 2.5, 4.], [1., 1., 1.]], g[1])

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), 2)
        self.checkitems([[1., 1., -0.5], [1., 1., -0.5]], g[0])
        self.checkitems([[0.5, 1.25, 2.], [0.5, 0.5, 0.5]], g[1])

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), 2, 1)
        self.checkitems([[1., 1., -0.5], [1., 1., -0.5]], g[0])
        self.checkitems([[1., 2.5, 4.], [1., 1., 1.]], g[1])

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), 1,
                        np.array([1., 2., 5.]))
        self.checkitems([[2., 2., -1.], [2., 2., -1.]], g[0])
        self.checkitems([[1., 1.25, 4. / 3], [1., 0.5, 1. / 3]], g[1])

        # test slice views
        x = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)
        g = np.gradient(x[2:])
        self.checkitems([3, 3.5, 4.5, 5.], g)
Exemple #3
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 def testGradient(self):
     print 'Gradient testing'
     z = np.arange(200.)
     dz = np.gradient(z)
     self.assertEquals(1, len(dz.shape))
     self.assertEquals(200, dz.size)
     self.assertEquals(1, dz[0])
 def testGradient(self):
     print "Gradient testing"
     z = np.arange(200.0)
     dz = np.gradient(z)
     self.assertEquals(1, len(dz.shape))
     self.assertEquals(200, dz.size)
     self.assertEquals(1, dz[0])
	def baseline(self,xdataset, ydataset, smoothness):
		'''find the baseline y value for a peak in y dataset'''
		ymaxindex=ydataset.argMax()
		if smoothness > 1:
			wnd = dnp.ones(smoothness, dtype=dnp.float64)/smoothness
			ydataset = dnp.convolve(ydataset, wnd, 'same')
 		result=dnp.gradient(ydataset, xdataset)
		leftresult=result[:ymaxindex]
		rightresult=result[ymaxindex+1:]
		leftminderivativeindex=dnp.abs(leftresult).argmin()
		rightminderivativeindex=dnp.abs(rightresult).argmin()
		leftbasey=ydataset.getElementDoubleAbs(leftminderivativeindex)
		rightbasey=ydataset.getElementDoubleAbs(rightminderivativeindex+1+leftresult.shape[0])
		basey=(leftbasey+rightbasey)/2
		return basey
Exemple #6
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 def baseline(self, xdataset, ydataset, smoothness):
     '''find the baseline y value for a peak in y dataset'''
     ymaxindex = ydataset.argMax()
     if smoothness > 1:
         wnd = dnp.ones(smoothness, dtype=dnp.float64) / smoothness
         ydataset = dnp.convolve(ydataset, wnd, 'same')
     result = dnp.gradient(ydataset, xdataset)
     leftresult = result[:ymaxindex]
     rightresult = result[ymaxindex + 1:]
     leftminderivativeindex = dnp.abs(leftresult).argmin()
     rightminderivativeindex = dnp.abs(rightresult).argmin()
     leftbasey = ydataset.getElementDoubleAbs(leftminderivativeindex)
     rightbasey = ydataset.getElementDoubleAbs(rightminderivativeindex + 1 +
                                               leftresult.shape[0])
     basey = (leftbasey + rightbasey) / 2
     return basey
 def baseline(self,xdataset, ydataset, smoothness):
     '''find the baseline y value for a peak in y dataset'''
     xdataset = dnp.asarray(xdataset)
     ydataset = dnp.asarray(ydataset)
     ymaxindex=ydataset.argmax()
     #TODO
     result=dnp.gradient(ydataset,xdataset)
     #derivative(xdataset, ydataset, smoothness)
     leftresult=result[:ymaxindex]
     rightresult=result[ymaxindex+1:]
     leftminderivativeindex=dnp.abs(leftresult).argmin()
     rightminderivativeindex=dnp.abs(rightresult).argmin()
     leftbasey=ydataset[leftminderivativeindex]
     rightbasey=ydataset[rightminderivativeindex+1+leftresult.shape[0]]
     basey=(leftbasey+rightbasey)/2
     return basey
 def baseline(self, xdataset, ydataset, smoothness):
     '''find the baseline y value for a peak in y dataset'''
     xdataset = dnp.asarray(xdataset)
     ydataset = dnp.asarray(ydataset)
     ymaxindex = ydataset.argmax()
     #TODO
     result = dnp.gradient(ydataset, xdataset)
     #derivative(xdataset, ydataset, smoothness)
     leftresult = result[:ymaxindex]
     rightresult = result[ymaxindex + 1:]
     leftminderivativeindex = dnp.abs(leftresult).argmin()
     rightminderivativeindex = dnp.abs(rightresult).argmin()
     leftbasey = ydataset[leftminderivativeindex]
     rightbasey = ydataset[rightminderivativeindex + 1 +
                           leftresult.shape[0]]
     basey = (leftbasey + rightbasey) / 2
     return basey
Exemple #9
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    def testGradient(self):
        print('Gradient testing')
        z = np.arange(200.)
        dz = np.gradient(z)
        self.assertEqual(1, len(dz.shape))
        self.assertEqual(200, dz.size)
        self.assertEqual(1, dz[0])

        x = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)
        g = np.gradient(x)
        self.checkitems([1., 1.5, 2.5, 3.5, 4.5, 5.], g)
        g = np.gradient(x, 2)
        self.checkitems([0.5, 0.75, 1.25, 1.75, 2.25, 2.5], g)
        a = np.arange(6, dtype=np.float) * 2
        g = np.gradient(x, a)
        self.checkitems([0.5, 0.75, 1.25, 1.75, 2.25, 2.5], g)

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))
        self.checkitems([[2., 2., -1.], [2., 2., -1.]], g[0])
        self.checkitems([[1., 2.5, 4.], [1., 1., 1.]], g[1])

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), 2)
        self.checkitems([[1., 1., -0.5], [1., 1., -0.5]], g[0])
        self.checkitems([[0.5, 1.25, 2.], [0.5, 0.5, 0.5]], g[1])

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), 2, 1)
        self.checkitems([[1., 1., -0.5], [1., 1., -0.5]], g[0])
        self.checkitems([[1., 2.5, 4.], [1., 1., 1.]], g[1])

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), 1,
                        np.array([1., 2., 5.]))
        self.checkitems([[2., 2., -1.], [2., 2., -1.]], g[0])
        self.checkitems([[1., 1.25, 4. / 3], [1., 0.5, 1. / 3]], g[1])

        # test slice views
        x = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)
        g = np.gradient(x[2:])
        self.checkitems([3, 3.5, 4.5, 5.], g)
    def testGradient(self):
        print 'Gradient testing'
        z = np.arange(200.)
        dz = np.gradient(z)
        self.assertEquals(1, len(dz.shape))
        self.assertEquals(200, dz.size)
        self.assertEquals(1, dz[0])

        x = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)
        g = np.gradient(x)
        self.checkitems([1., 1.5, 2.5, 3.5, 4.5, 5.], g)
        g = np.gradient(x, 2)
        self.checkitems([0.5, 0.75, 1.25, 1.75, 2.25, 2.5], g)
        a = np.arange(6, dtype=np.float)*2
        g = np.gradient(x, a)
        self.checkitems([0.5, 0.75, 1.25, 1.75, 2.25, 2.5], g)

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))
        self.checkitems([[2., 2., -1.], [2., 2., -1.]], g[0])
        self.checkitems([[1., 2.5, 4.], [1., 1., 1.]], g[1])

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), 2)
        self.checkitems([[1., 1., -0.5], [1., 1., -0.5]], g[0])
        self.checkitems([[0.5, 1.25, 2.], [0.5, 0.5, 0.5]], g[1])

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), 2, 1)
        self.checkitems([[1., 1., -0.5], [1., 1., -0.5]], g[0])
        self.checkitems([[1., 2.5, 4.], [1., 1., 1.]], g[1])

        g = np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), 1, np.array([1.,2.,5.]))
        self.checkitems([[2., 2., -1.], [2., 2., -1.]], g[0])
        self.checkitems([[1., 1.25, 4./3], [1., 0.5, 1./3]], g[1])

        # test slice views
        x = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)
        g = np.gradient(x[2:])
        self.checkitems([3, 3.5, 4.5, 5.], g)
	def findBases(self, xdataset, ydataset, delta, smoothness):
		bases=[]
		peaks=self.findPeaksAndTroughs(ydataset, delta)[0]
		yslices=[]
		xslices=[]
		startindex=0
		for index,value in peaks: #@UnusedVariable
			yslices.append(ydataset[startindex:index])
			xslices.append(xdataset[startindex:index])
			startindex=index+1
		if smoothness > 1:
			wnd = dnp.ones(smoothness, dtype=dnp.float64)/smoothness
		for xset, yset in xslices, yslices:
			if smoothness > 1:
				yset = dnp.convolve(yset, wnd, 'same')
	 		result=dnp.gradient(yset, xset)
			minimumderivativeindex=dnp.abs(result).argmin()
			bases.append((xset[minimumderivativeindex],yset[minimumderivativeindex]))
		return bases
Exemple #12
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 def findBases(self, xdataset, ydataset, delta, smoothness):
     bases = []
     peaks = self.findPeaksAndTroughs(ydataset, delta)[0]
     yslices = []
     xslices = []
     startindex = 0
     for index, value in peaks:  #@UnusedVariable
         yslices.append(ydataset[startindex:index])
         xslices.append(xdataset[startindex:index])
         startindex = index + 1
     if smoothness > 1:
         wnd = dnp.ones(smoothness, dtype=dnp.float64) / smoothness
     for xset, yset in xslices, yslices:
         if smoothness > 1:
             yset = dnp.convolve(yset, wnd, 'same')
         result = dnp.gradient(yset, xset)
         minimumderivativeindex = dnp.abs(result).argmin()
         bases.append(
             (xset[minimumderivativeindex], yset[minimumderivativeindex]))
     return bases
    def findBasePoints(self, xdataset, ydataset, delta, smoothness):
        xdataset = dnp.asarray(xdataset)
        ydataset = dnp.asarray(ydataset)
        peaks=self.findPeaksAndTroughs(ydataset, delta)[0]
        #print peaks
        yslices=[]
        xslices=[]
        startindex=0
        for index,value in peaks: #@UnusedVariable
            yslices.append(ydataset[startindex:index])
            xslices.append(xdataset[startindex:index])
            startindex=index+1
        yslices.append(ydataset[startindex:])
        xslices.append(xdataset[startindex:])

        bases=[]
        for xset, yset in zip(xslices, yslices):
            result=dnp.gradient(yset, xset)
            minimumderivativeindex=dnp.abs(result).argmin()
            bases.append((xset[minimumderivativeindex],yset[minimumderivativeindex]))
        #print "Base Points (position, value)   : ", bases
        return bases
    def findBasePoints(self, xdataset, ydataset, delta, smoothness):
        xdataset = dnp.asarray(xdataset)
        ydataset = dnp.asarray(ydataset)
        peaks = self.findPeaksAndTroughs(ydataset, delta)[0]
        #print peaks
        yslices = []
        xslices = []
        startindex = 0
        for index, value in peaks:  #@UnusedVariable
            yslices.append(ydataset[startindex:index])
            xslices.append(xdataset[startindex:index])
            startindex = index + 1
        yslices.append(ydataset[startindex:])
        xslices.append(xdataset[startindex:])

        bases = []
        for xset, yset in zip(xslices, yslices):
            result = dnp.gradient(yset, xset)
            minimumderivativeindex = dnp.abs(result).argmin()
            bases.append(
                (xset[minimumderivativeindex], yset[minimumderivativeindex]))
        #print "Base Points (position, value)   : ", bases
        return bases