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)
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)
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
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
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
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
