def analyseData(dd):
#ss = dd.sum(0)
#dnp.plot.image(ss)
##ss is the flat field
print "Calculating median for the dataset ..."
med = dnp.median(dd, 0)
print "Median calculated"
if plotData: dnp.plot.image(med)
Sleep.sleep(2000)
#flatFieldCorrectedSum = ss/med
#dnp.plot.image(flatFieldCorrectedSum)
xVals = []
yVals = []
##
print "Calculating centroid ..."
centroids = []
for i in range(18):
#dnp.plot.image(dd[i, :, :] / med)
im = dd[i, :, :] / med
im = javaImage.medianFilter(im, [3, 3])
im = dnp.array(im)
threshold = 0.85
if plotData: dnp.plot.image(im)
if plotData: Sleep.sleep(1000)
if plotData: dnp.plot.image(im < threshold)
if plotData: Sleep.sleep(1000)
cent = dnp.centroid(im < threshold)
yVals.append(cent[0])
xVals.append(cent[1])
centroids.append(cent)
print "y:" + `yVals`
print('')
print "x:" + `xVals`
#help(dnp.fit.ellipsefit)
if plotData: dnp.plot.line(dnp.array(xVals), dnp.array(yVals))
ellipseFitValues = dnp.fit.ellipsefit(xVals, yVals)
print "major: " + `ellipseFitValues[0]`
print "minor semi-axis: " + `ellipseFitValues[1]`
print "major axis angle: " + `ellipseFitValues[2]`
print "centre co-ord 1: " + `ellipseFitValues[3]`
print "centre co-ord 2: " + `ellipseFitValues[4]`
print "centroids:" + `centroids`
xOffset = getXOffset(ellipseFitValues[1], ellipseFitValues[0])
zOffset = getZOffset(ellipseFitValues[2])
return xOffset, zOffset, centroids, ellipseFitValues
def analyseData(dd):
#ss = dd.sum(0)
#dnp.plot.image(ss)
##ss is the flat field
print "Calculating median for the dataset ..."
med = dnp.median(dd, 0)
print "Median calculated"
if plotData: dnp.plot.image(med)
sleep(2)
#flatFieldCorrectedSum = ss/med
#dnp.plot.image(flatFieldCorrectedSum)
xVals = []
yVals = []
##
print "Calculating centroid ..."
centroids = []
for i in range(18):
#dnp.plot.image(dd[i, :, :] / med)
im = dd[i, :, :] / med
im = javaImage.medianFilter(im, [3, 3])
im = dnp.array(im)
threshold = 0.85
if plotData: dnp.plot.image(im)
if plotData: sleep(1)
if plotData: dnp.plot.image(im < threshold)
if plotData: sleep(1)
cent = dnp.centroid(im < threshold)
yVals.append(cent[0])
xVals.append(cent[1])
centroids.append(cent)
print "y:" + ` yVals `
print('')
print "x:" + ` xVals `
#help(dnp.fit.ellipsefit)
if plotData: dnp.plot.line(dnp.array(xVals), dnp.array(yVals))
ellipseFitValues = dnp.fit.ellipsefit(xVals, yVals)
print "major: " + ` ellipseFitValues[0] `
print "minor semi-axis: " + ` ellipseFitValues[1] `
print "major axis angle: " + ` ellipseFitValues[2] `
print "centre co-ord 1: " + ` ellipseFitValues[3] `
print "centre co-ord 2: " + ` ellipseFitValues[4] `
print "centroids:" + ` centroids `
xOffset = getXOffset(ellipseFitValues[1], ellipseFitValues[0])
zOffset = getZOffset(ellipseFitValues[2])
return xOffset, zOffset, centroids, ellipseFitValues
def testQuantile(self):
print 'Quantile testing'
a = np.array([6., 47., 49., 15., 42., 41., 7., 39., 43., 40., 36., 21.])
ans = [19.5, 39.5, 42.25]
self.assertEquals(np.median(a), ans[1])
iqr = np.iqr(a)
self.assertEquals(iqr, ans[2] - ans[0])
qs = np.quantile(a, [0.25, 0.5, 0.75])
self.checkitems(ans, np.array(qs))
a.shape = (3,4)
qs = np.quantile(a, [0.25, 0.5, 0.75], axis=1)
self.checkitems([12.75, 31., 32.25], qs[0])
self.checkitems([31., 40., 38.], qs[1])
self.checkitems([47.5, 41.25, 40.75], qs[2])
iqr = np.iqr(a, axis=1)
print type(iqr)
self.assertEquals(-12.75 + 47.5, iqr[0])
self.assertEquals(-31. + 41.25, iqr[1])
self.assertEquals(-32.25 + 40.75, iqr[2])
def testQuantile(self):
print 'Quantile testing'
a = np.array([6., 47., 49., 15., 42., 41., 7., 39., 43., 40., 36., 21.])
ans = [19.5, 39.5, 42.25]
self.assertEquals(np.median(a), ans[1])
iqr = np.iqr(a)
self.assertEquals(iqr, ans[2] - ans[0])
qs = np.quantile(a, [0.25, 0.5, 0.75])
self.checkitems(ans, np.array(qs))
a.shape = (3,4)
qs = np.quantile(a, [0.25, 0.5, 0.75], axis=1)
self.checkitems([12.75, 31., 32.25], qs[0])
self.checkitems([31., 40., 38.], qs[1])
self.checkitems([47.5, 41.25, 40.75], qs[2])
iqr = np.iqr(a, axis=1)
print type(iqr)
self.assertEquals(-12.75 + 47.5, iqr[0])
self.assertEquals(-31. + 41.25, iqr[1])
self.assertEquals(-32.25 + 40.75, iqr[2])
