def testCopyItems(self):
if isjava:
import jarray
print "test copy items"
da = np.array(self.db, np.float)
jda = da._jdataset()
ta = np.zeros(da.shape)
jda.copyItemsFromAxes(None, None, ta._jdataset())
print ta.data
ta = np.array(self.m)
jda.setItemsOnAxes(None, None, ta.data)
print da.data
print '2'
da = np.array(self.db, np.float)
jda = da._jdataset()
ta = np.zeros((2,))
axes = [ True, False ]
jda.copyItemsFromAxes(None, axes, ta._jdataset())
print ta.data
ta = jarray.array([ -2, -3.4 ], 'd')
jda.setItemsOnAxes(None, axes, ta)
print da.data
print '3'
da = np.array(self.db, np.float)
jda = da._jdataset()
ta = np.zeros((2,))
axes = [ False, True ]
jda.copyItemsFromAxes(None, axes, ta._jdataset())
print ta.data
ta = jarray.array([ -2.5, -3.4 ], 'd')
jda.setItemsOnAxes(None, axes, ta)
print da.data
def testZeros(self): # make new datasets with zeros
print("test zeros")
dds = np.zeros(3, dtype=np.float)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(1, dds.ndim)
self.assertEquals(3, dds.shape[0])
self.assertEquals(0, dds[0])
dds = np.zeros((2,3), dtype=np.float)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(2, dds.ndim)
self.assertEquals(2, dds.shape[0])
self.assertEquals(3, dds.shape[1])
self.assertEquals(0, dds[0,0])
dds = np.zeros_like(dds)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(2, dds.ndim)
self.assertEquals(2, dds.shape[0])
self.assertEquals(3, dds.shape[1])
self.assertEquals(0, dds[0,0])
dds = np.zeros(np.array([1,2]), dtype=np.float)
self.assertEqual((1,2), dds.shape)
self.assertEquals(0, dds[0,0])
dds = np.zeros_like(np.array([1,2]), dtype=np.float)
self.assertEquals((2,), dds.shape)
self.assertEquals(0, dds[0])
def testCopyItems(self):
if isjava:
import jarray
print "test copy items"
da = np.array(self.db, np.float)
jda = da._jdataset()
ta = np.zeros(da.shape)
jda.copyItemsFromAxes(None, None, ta._jdataset())
print ta.data
ta = np.array(self.m)
jda.setItemsOnAxes(None, None, ta.data)
print da.data
print '2'
da = np.array(self.db, np.float)
jda = da._jdataset()
ta = np.zeros((2, ))
axes = [True, False]
jda.copyItemsFromAxes(None, axes, ta._jdataset())
print ta.data
ta = jarray.array([-2, -3.4], 'd')
jda.setItemsOnAxes(None, axes, ta)
print da.data
print '3'
da = np.array(self.db, np.float)
jda = da._jdataset()
ta = np.zeros((2, ))
axes = [False, True]
jda.copyItemsFromAxes(None, axes, ta._jdataset())
print ta.data
ta = jarray.array([-2.5, -3.4], 'd')
jda.setItemsOnAxes(None, axes, ta)
print da.data
def testCopyItems(self):
if isjava:
import jarray
print "test copy items"
da = np.array(self.db, np.float)
ta = np.zeros(da.getShape())
da.copyItemsFromAxes(None, None, ta)
print ta.getBuffer()
ta = np.array(self.m)
da.setItemsOnAxes(None, None, ta.getBuffer())
print da.getBuffer()
print '2'
da = np.array(self.db, np.float)
ta = np.zeros((2,))
axes = [ True, False ]
da.copyItemsFromAxes(None, axes, ta)
print ta.getBuffer()
ta = jarray.array([ -2, -3.4 ], 'd')
da.setItemsOnAxes(None, axes, ta)
print da.getBuffer()
print '3'
da = np.array(self.db, np.float)
ta = np.zeros((2,))
axes = [ False, True ]
da.copyItemsFromAxes(None, axes, ta)
print ta.getBuffer()
ta = jarray.array([ -2.5, -3.4 ], 'd')
da.setItemsOnAxes(None, axes, ta)
print da.getBuffer()
def testZeros(self): # make new datasets with zeros
print("test zeros")
dds = np.zeros(3, dtype=np.float)
if isjava:
self.assertEqual(1, dds.dtype.elements)
self.assertEqual(1, dds.ndim)
self.assertEqual(3, dds.shape[0])
self.assertEqual(0, dds[0])
dds = np.zeros((2, 3), dtype=np.float)
if isjava:
self.assertEqual(1, dds.dtype.elements)
self.assertEqual(2, dds.ndim)
self.assertEqual(2, dds.shape[0])
self.assertEqual(3, dds.shape[1])
self.assertEqual(0, dds[0, 0])
dds = np.zeros_like(dds)
if isjava:
self.assertEqual(1, dds.dtype.elements)
self.assertEqual(2, dds.ndim)
self.assertEqual(2, dds.shape[0])
self.assertEqual(3, dds.shape[1])
self.assertEqual(0, dds[0, 0])
dds = np.zeros(np.array([1, 2]), dtype=np.float)
self.assertEqual((1, 2), dds.shape)
self.assertEqual(0, dds[0, 0])
dds = np.zeros_like(np.array([1, 2]), dtype=np.float)
self.assertEqual((2, ), dds.shape)
self.assertEqual(0, dds[0])
def testSlicing(self):
a = np.array([], dtype=np.float_)
self.assertEquals(len(a), 0)
a = np.zeros((2,))
self.assertEquals(len(a), 2)
self.checkitems([0,0], a[:])
self.assertEquals(a[1], 0)
a[:] = 0.5
self.checkitems([0.5,0.5], a[:])
a = np.zeros((2,3))
self.assertEquals(len(a), 2)
self.checkitems([0,0,0], a[0])
a[1] = 0.2
self.checkitems([0.2,0.2,0.2], a[1])
a = np.zeros((2,3,4))
self.assertEquals(len(a), 2)
a = np.arange(10).reshape((5,2))
a[3,:] = np.array([0,0])
self.checkitems([0,0], a[3])
a[3,:] = np.array([1,1]).reshape(1,2)
self.checkitems([1,1], a[3])
a[:2,1] = np.array([2,2])
self.checkitems([2,2], a[:2,1])
a = np.arange(7)
c = range(7)
self.checkitems(c[::2], a[::2])
self.checkitems(c[0::2], a[0::2])
self.checkitems(c[3::2], a[3::2])
self.checkitems(c[6::2], a[6::2])
self.checkitems(c[6:6:2], a[6:6:2])
self.checkitems(c[7::2], a[7::2])
self.checkitems(c[-1::2], a[-1::2])
self.checkitems(c[-2::2], a[-2::2])
self.checkitems(c[::-2], a[::-2])
self.checkitems(c[0::-2], a[0::-2])
self.checkitems(c[3::-2], a[3::-2])
self.checkitems(c[6::-2], a[6::-2])
self.checkitems(c[6:6:-2], a[6:6:-2])
self.checkitems(c[7::-2], a[7::-2])
self.checkitems(c[-1::-2], a[-1::-2])
self.checkitems(c[-2::-2], a[-2::-2])
self.checkitems(a[::-2], a[:-8:-2])
a = np.arange(24).reshape(6,4)
self.assertEqual((6,4), a[:].shape)
self.assertEqual((6,4), a[:,].shape)
self.assertEqual((6,3), a[:,:3].shape)
self.assertEqual((0,3), a[4:2,:3].shape)
self.assertEqual((6,), a[:,3].shape)
self.assertEqual((0,), a[4:2,3].shape)
def testSlicing(self):
a = np.array([], dtype=np.float_)
self.assertEquals(len(a), 0)
a = np.zeros((2, ))
self.assertEquals(len(a), 2)
self.checkitems([0, 0], a[:])
self.assertEquals(a[1], 0)
a[:] = 0.5
self.checkitems([0.5, 0.5], a[:])
a = np.zeros((2, 3))
self.assertEquals(len(a), 2)
self.checkitems([0, 0, 0], a[0])
a[1] = 0.2
self.checkitems([0.2, 0.2, 0.2], a[1])
a = np.zeros((2, 3, 4))
self.assertEquals(len(a), 2)
a = np.arange(10).reshape((5, 2))
a[3, :] = np.array([0, 0])
self.checkitems([0, 0], a[3])
a[3, :] = np.array([1, 1]).reshape(1, 2)
self.checkitems([1, 1], a[3])
a[:2, 1] = np.array([2, 2])
self.checkitems([2, 2], a[:2, 1])
a = np.arange(7)
c = range(7)
self.checkitems(c[::2], a[::2])
self.checkitems(c[0::2], a[0::2])
self.checkitems(c[3::2], a[3::2])
self.checkitems(c[6::2], a[6::2])
self.checkitems(c[6:6:2], a[6:6:2])
self.checkitems(c[7::2], a[7::2])
self.checkitems(c[-1::2], a[-1::2])
self.checkitems(c[-2::2], a[-2::2])
self.checkitems(c[::-2], a[::-2])
self.checkitems(c[0::-2], a[0::-2])
self.checkitems(c[3::-2], a[3::-2])
self.checkitems(c[6::-2], a[6::-2])
self.checkitems(c[6:6:-2], a[6:6:-2])
self.checkitems(c[7::-2], a[7::-2])
self.checkitems(c[-1::-2], a[-1::-2])
self.checkitems(c[-2::-2], a[-2::-2])
self.checkitems(a[::-2], a[:-8:-2])
a = np.arange(24).reshape(6, 4)
self.assertEqual((6, 4), a[:].shape)
self.assertEqual((6, 4), a[:, ].shape)
self.assertEqual((6, 3), a[:, :3].shape)
self.assertEqual((0, 3), a[4:2, :3].shape)
self.assertEqual((6, ), a[:, 3].shape)
self.assertEqual((0, ), a[4:2, 3].shape)
def plot_results(plotName, function):
d = function.coords
d1 = dnp.zeros(d[0].shape[0]+1)
d1[1:] = d[0]
d2 = dnp.toList(d1)
data = function.data
data2 = dnp.zeros(data.shape[0]+1)
data2[0] = function.func.getParameterValue(1)
data2[1:] = data
func = dnp.fit.fitcore.fitresult(function.func, d2, data2)
func.plot(plotName)
def testCorrelate2D(self):
print 'test correlate2'
da = np.zeros([8,8], np.float)
da[3,3] = 20
db = da.copy()
ada = sig.correlate(da,db)
print ada
da += rnd.poisson(2, size=da.shape)
db = np.zeros([8,8], np.float)
db[5,6] = 20
db += rnd.poisson(2, size=db.shape)
ada = sig.correlate(da,db)
print ada
def tryImageShift(self, dx=2, dy=3, cx=23, cy=13):
da = np.zeros([40,40])
da[cy,cx] = 50
da += rnd.poisson(2, da.shape)
db = np.zeros([40,40])
db[cy+dy,cx+dx] = 65
db += rnd.poisson(2, db.shape)
sx = max(dx,5)
sy = max(dy,5)
r = roi.rectangle(point=[cx-2*sx,cy-2*sy],lengths=[4*sx,4*sy],angle=0)
shift = img.findshift(da, db, r)
print shift
self.assertAlmostEquals(-dy, shift[0], places=0)
self.assertAlmostEquals(-dx, shift[1], places=0)
def tryImageShiftGaussian(self, dx=2, dy=3, cx=23, cy=13):
da = np.zeros([40,40])
da[cy,cx] = 50
da += rnd.poisson(2, da.shape)
db = np.zeros([40,40])
db[cy+dy,cx+dx] = 65
db += rnd.poisson(2, db.shape)
sx = max(dx,10)
sy = max(dy,10)
r = roi.rect(cx-2*sx,cy-2*sy,4*sx,4*sy,0)
shift = img.findshift(da, db, r)
print shift
self.assertAlmostEquals(-dy, shift[0], places=0)
self.assertAlmostEquals(-dx, shift[1], places=0)
def testCorrelate2(self):
print 'test correlate2'
da = np.zeros([10], np.float)
da[3] = 1
db = da.copy()
ada = sig.correlate(da,db)
self.checkitems(None, ada)
db = np.zeros([10], np.float)
db[5] = 1
ada = sig.correlate(da,db)
self.checkitems(None, ada)
ada = sig.phasecorrelate(da,db)
self.checkitems(None, ada)
def __init__(self):
self.ccWaveFormHead = CAClient(pvWaveFormHead)
self.ccSubArraySize = CAClient(pvSubArraySize)
self.ccSubArrayIndex = CAClient(pvSubArrayIndex)
self.ccUpdate = CAClient(pvUpdate)
self.ccCh01 = CAClient(pvCh01)
self.ccCh02 = CAClient(pvCh02)
self.ccCh03 = CAClient(pvCh03)
self.ccCh04 = CAClient(pvCh04)
self.ccCh05 = CAClient(pvCh05)
self.ccCh06 = CAClient(pvCh06)
self.ccWaveFormHead.configure()
self.ccSubArraySize.configure()
self.ccSubArrayIndex.configure()
self.ccUpdate.configure()
self.ccCh01.configure()
self.ccCh02.configure()
self.ccCh03.configure()
self.ccCh04.configure()
self.ccCh05.configure()
self.ccCh06.configure()
self.length = 0
self.head = 0
self.dataSetCh01 = dnp.zeros([maxLength])
self.dataSetCh01.setName("Channel 1")
self.js = Finder.find("command_server")
def testCompound(self): # make new datasets with ones
print "test compound"
dds = np.ndarrayCB(3, (3,4))
self.assertEquals(3, dds.itemsize, msg="itemsize incorrect")
dds = np.ndarrayRGB((3,5))
print type(dds), dds.dtype, dds.shape
self.assertEquals(6, dds.itemsize, msg="itemsize incorrect")
dds = dds.get_red()
print type(dds), dds.dtype, dds.shape
self.assertEquals(2, dds.itemsize, msg="itemsize incorrect")
dds = np.zeros((3,4), np.cint32, 2)
print type(dds), dds.dtype, dds.shape
self.assertEquals(8, dds.itemsize, msg="itemsize incorrect")
dds = np.zeros((3,4), np.cint32(2))
print type(dds), dds.dtype, dds.shape
self.assertEquals(8, dds.itemsize, msg="itemsize incorrect")
def scan_start_temp():
temps = dnp.arange(10, 200, 10)
p0 = dnp.zeros(temps.shape)
p1 = dnp.zeros(temps.shape)
p2 = dnp.zeros(temps.shape)
count = 0
for temp in temps:
(fwhm, values) = scan_fwhm(0, 0.2, 0.01, temp)
res = dnp.fit.polyfit(values, fwhm, 2)
p0[count] = res[0]
p1[count] = res[1]
p2[count] = res[2]
count += 1
dnp.plot.line(temps, [p0, p1, p2], name="Plot 3")
return (dnp.fit.polyfit(temps, p0, 2), dnp.fit.polyfit(temps, p1, 2),
dnp.fit.polyfit(temps, p2, 2))
def testCompound(self): # make new datasets with ones
print "test compound"
# dds = np.ndarrayCB(3, (3,4))
# self.assertEquals(3, dds.itemsize, msg="itemsize incorrect")
dds = np.zeros((3,5), np.rgb)
print type(dds), dds.dtype, dds.shape
self.assertEquals(6, dds.itemsize, msg="itemsize incorrect")
dds = dds.red
print type(dds), dds.dtype, dds.shape
self.assertEquals(2, dds.itemsize, msg="itemsize incorrect")
dds = np.zeros((3,4), np.cint32, elements=2)
print type(dds), dds.dtype, dds.shape
self.assertEquals(8, dds.itemsize, msg="itemsize incorrect")
dds = np.zeros((3,4), np.cint32(2))
print type(dds), dds.dtype, dds.shape
self.assertEquals(8, dds.itemsize, msg="itemsize incorrect")
def testCompound(self): # make new datasets with ones
print "test compound"
# dds = np.ndarrayCB(3, (3,4))
# self.assertEquals(3, dds.itemsize, msg="itemsize incorrect")
dds = np.zeros((3, 5), np.rgb)
print type(dds), dds.dtype, dds.shape
self.assertEquals(6, dds.itemsize, msg="itemsize incorrect")
dds = dds.red
print type(dds), dds.dtype, dds.shape
self.assertEquals(2, dds.itemsize, msg="itemsize incorrect")
dds = np.zeros((3, 4), np.cint32, elements=2)
print type(dds), dds.dtype, dds.shape
self.assertEquals(8, dds.itemsize, msg="itemsize incorrect")
dds = np.zeros((3, 4), np.cint32(2))
print type(dds), dds.dtype, dds.shape
self.assertEquals(8, dds.itemsize, msg="itemsize incorrect")
def tryImageShiftGaussian(self, dx=2, dy=3, cx=23, cy=13):
da = np.zeros([40, 40])
da[cy, cx] = 50
da += rnd.poisson(2, da.shape)
db = np.zeros([40, 40])
db[cy + dy, cx + dx] = 65
db += rnd.poisson(2, db.shape)
sx = max(dx, 10)
sy = max(dy, 10)
r = roi.rectangle(point=[cx - 2 * sx, cy - 2 * sy],
lengths=[4 * sx, 4 * sy],
angle=0)
shift = img.findshift(da, db, r)
print shift
self.assertAlmostEquals(-dy, shift[0], places=0)
self.assertAlmostEquals(-dx, shift[1], places=0)
def scan_start_temp(): temps = dnp.arange(10,200,10) p0 = dnp.zeros(temps.shape) p1 = dnp.zeros(temps.shape) p2 = dnp.zeros(temps.shape) count = 0 for temp in temps: (fwhm,values) = scan_fwhm(0,0.2,0.01,temp) res = dnp.fit.polyfit(values,fwhm,2) p0[count] = res[0] p1[count] = res[1] p2[count] = res[2] count+=1 dnp.plot.line(temps,[p0,p1,p2],name="Plot 3") return(dnp.fit.polyfit(temps,p0,2), dnp.fit.polyfit(temps,p1,2), dnp.fit.polyfit(temps,p2,2))
def testCorrelate2D(self):
import os
if os.name != 'java':
return
print 'test correlate2'
da = np.zeros([8, 8], np.float)
da[3, 3] = 20
db = da.copy()
ada = np.correlate(da, db)
print ada
da += rnd.poisson(2, size=da.shape)
db = np.zeros([8, 8], np.float)
db[5, 6] = 20
db += rnd.poisson(2, size=db.shape)
ada = np.correlate(da, db)
print ada
def testCorrelate2(self):
print 'test correlate2'
da = np.zeros([10], np.float)
da[3] = 1
db = da.copy()
ada = np.correlate(da, db)
self.checkitems(None, ada)
db = np.zeros([10], np.float)
db[5] = 1
ada = np.correlate(da, db)
self.checkitems(None, ada)
import os
if os.name == 'java':
ada = sig.phasecorrelate(da, db)
self.checkitems(None, ada)
def testCorrelate2D(self):
import os
if os.name != 'java':
return
print 'test correlate2'
da = np.zeros([8,8], np.float)
da[3,3] = 20
db = da.copy()
ada = np.correlate(da,db)
print ada
da += rnd.poisson(2, size=da.shape)
db = np.zeros([8,8], np.float)
db[5,6] = 20
db += rnd.poisson(2, size=db.shape)
ada = np.correlate(da,db)
print ada
def testCorrelate2(self):
print('test correlate2')
da = np.zeros([10], np.float)
da[3] = 1
db = da.copy()
ada = np.correlate(da,db)
self.checkitems(None, ada)
db = np.zeros([10], np.float)
db[5] = 1
ada = np.correlate(da,db)
self.checkitems(None, ada)
import os
if os.name == 'java':
ada = sig.phasecorrelate(da,db)
self.checkitems(None, ada)
def scan_fwhm(start, stop, step, t_start, t_stop=500, t_step=1):
fwhms = dnp.arange(start, stop, step)
result = dnp.zeros(fwhms.shape)
count = 0
for fwhm in fwhms:
result[count] = scan_temps(t_start, t_stop, t_step, fwhm)
count += 1
dnp.plot.line(result, fwhms, name="Plot 2")
return (fwhms, result)
def scan_fwhm(start,stop,step,t_start, t_stop=500, t_step=1): fwhms = dnp.arange(start,stop,step) result = dnp.zeros(fwhms.shape) count = 0 for fwhm in fwhms: result[count] = scan_temps(t_start, t_stop, t_step, fwhm) count+=1 dnp.plot.line(result,fwhms,name="Plot 2") return (fwhms,result)
def testZeros(self): # make new datasets with zeros
print "test zeros"
dds = np.zeros(3, dtype=np.float)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(1, dds.ndim)
self.assertEquals(3, dds.shape[0])
self.assertEquals(0, dds[0])
dds = np.zeros((2, 3), dtype=np.float)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(2, dds.ndim)
self.assertEquals(2, dds.shape[0])
self.assertEquals(3, dds.shape[1])
self.assertEquals(0, dds[0, 0])
dds = np.zeros_like(dds)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(2, dds.ndim)
self.assertEquals(2, dds.shape[0])
self.assertEquals(3, dds.shape[1])
self.assertEquals(0, dds[0, 0])
def scan_temps(start, stop, step, fwhm):
fg.setParameterValues(0.1, start, 0, 1, 0, fwhm)
test = fg.calculateValues([x])
temps = dnp.arange(start, stop, step)
result = dnp.zeros(temps.shape)
count = 0
for temp in temps:
fg.setParameterValues(0.1, temp, 0, 1, 0, 0.0)
comp = fg.calculateValues([x])
result[count] = dnp.sum(dnp.square(dnp.array(test) - dnp.array(comp)))
count += 1
dnp.plot.line(temps, result)
return temps[result.minPos().tolist()]
def scan_temps(start,stop,step,fwhm): fg.setParameterValues(0.1,start,0,1,0,fwhm) test = fg.calculateValues([x]) temps = dnp.arange(start,stop,step) result = dnp.zeros(temps.shape) count = 0 for temp in temps: fg.setParameterValues(0.1,temp,0,1,0,0.0) comp = fg.calculateValues([x]) result[count] = dnp.sum(dnp.square(dnp.array(test)-dnp.array(comp))) count+=1 dnp.plot.line(temps,result) return temps[result.minPos().tolist()]
def testSlicing(self):
a = np.array([], dtype=np.float_)
self.assertEquals(len(a), 0)
a = np.zeros((2,))
self.assertEquals(len(a), 2)
self.checkitems([0,0], a[:])
self.assertEquals(a[1], 0)
a[:] = 0.5
self.checkitems([0.5,0.5], a[:])
a = np.zeros((2,3))
self.assertEquals(len(a), 2)
self.checkitems([0,0,0], a[0])
a[1] = 0.2
self.checkitems([0.2,0.2,0.2], a[1])
a = np.zeros((2,3,4))
self.assertEquals(len(a), 2)
a = np.arange(10).reshape((5,2))
a[3,:] = np.array([0,0])
self.checkitems([0,0], a[3])
a[3,:] = np.array([1,1]).reshape(1,2)
self.checkitems([1,1], a[3])
a[:2,1] = np.array([2,2])
self.checkitems([2,2], a[:2,1])
def plotData(self):
newHead = self.getDataNumbers()
if self.arrayHead >= newHead:
print "No new data added for plotting"
return
self.arrayHead = newHead
#to get new data
arrayEnergyPGM = self.energyPGM.cagetArrayDouble()
arrayEnergyIDGAP = self.energyIDGAP.cagetArrayDouble()
arrayChannel01 = self.channel01.cagetArrayDouble()
arrayChannel02 = self.channel02.cagetArrayDouble()
arrayChannel03 = self.channel03.cagetArrayDouble()
arrayChannel04 = self.channel04.cagetArrayDouble()
dataSetEnergyPGM = dnp.zeros([newHead])
dataSetEnergyPGM.setName("PGM Energy")
dataSetEnergyIDGAP = dnp.zeros([newHead])
dataSetEnergyIDGAP.setName("ID Gap Energy")
dataSetChannel01 = dnp.zeros([newHead])
dataSetChannel01.setName("Channel 1")
dataSetChannel02 = dnp.zeros([newHead])
dataSetChannel02.setName("Channel 2")
dataSetChannel03 = dnp.zeros([newHead])
dataSetChannel03.setName("Channel 3")
dataSetChannel04 = dnp.zeros([newHead])
dataSetChannel04.setName("Channel 4")
for i in range(0, newHead):
#print i, arrayEnergyPGM[i], arrayEnergyIDGAP[i], arrayChannel01[i], arrayChannel02[i], arrayChannel03[i], arrayChannel04[i];
dataSetEnergyPGM[i] = arrayEnergyPGM[i]
dataSetEnergyIDGAP[i] = arrayEnergyIDGAP[i]
dataSetChannel01[i] = arrayChannel01[i]
dataSetChannel02[i] = arrayChannel02[i]
dataSetChannel03[i] = arrayChannel03[i]
dataSetChannel04[i] = arrayChannel04[i]
#print i, arrayEnergyPGM[i], arrayEnergyIDGAP[i], arrayChannel01[i], arrayChannel02[i], arrayChannel03[i], arrayChannel04[i];
dvp = Plotter()
indexDataSet = dataSetEnergyPGM.getIndexDataSet()
#dvp.plot("Data Vector", indexDataSet, [dataSetChannel01, dataSetChannel02, dataSetChannel03, dataSetChannel04]);
dvp.plot("Fast Scan Panel", dataSetEnergyPGM, [
dataSetChannel01, dataSetChannel02, dataSetChannel03,
dataSetChannel04
])
def testDatasetSlice(self):
print('test slicing with start and stop')
ds = np.arange(16)
dr = ds[2:10]
self.checkitems(list(range(2,10)), dr)
print('test slicing with steps')
dr = ds[::2]
self.checkitems(list(range(0,16,2)), dr)
print('test slicing 3D')
dr = self.dda[1:,::2,1::2]
self.checkitems(self.t, dr)
print('test putting in a 3D slice')
dr = self.dda.copy()
dr[:,1::2,::2] = 7.
self.checkitems(self.u, dr)
print('test putting a list in a 3D slice')
dr = self.dda.copy()
print(dr[:,1::2,::2].shape)
dr[:,1::2,::2] = np.array([7., 7]).reshape(2,1,1)
self.checkitems(self.u, dr)
print('test slicing with ellipse')
dr = ds[...]
self.checkitems(list(range(16)), dr)
print('test slicing 3D with ellipse')
dr = self.dda[...,1::2]
self.checkitems(self.t, dr[1:, ::2])
print('test putting in a 3D slice with ellipsis')
dr = self.dda.copy()
dr[...,1::2,::2] = 7.
self.checkitems(self.u, dr)
print('test putting a list in a 3D slice with ellipsis')
dr = self.dda.copy()
print(dr[...,1::2,::2].shape)
dr[...,1::2,::2] = np.array([7., 7]).reshape(2,1,1)
self.checkitems(self.u, dr)
print('test slice shape reduction')
dr = np.arange(120).reshape(4,3,5,2)
s = dr[:2,...,1:].shape
print(s)
self.assertEquals(s, (2,3,5,1))
s = dr[:2,...,1].shape
print(s)
self.assertEquals(s, (2,3,5))
try:
s = dr[:2,...,...,1].shape
raise AssertionError('Should have raised an error')
except:
pass
print('test newaxis')
dr = np.arange(15).reshape(3,5)
s = dr[np.newaxis,:,1:].shape
print(s)
self.assertEquals(s, (1,3,4))
s = dr[np.newaxis,...].shape
print(s)
self.assertEquals(s, (1,3,5))
s = dr[np.newaxis,...,np.newaxis].shape
print(s)
self.assertEquals(s, (1,3,5,1))
s = dr[...,np.newaxis].shape
print(s)
self.assertEquals(s, (3,5,1))
s = dr[:,np.newaxis,...,np.newaxis].shape
print(s)
self.assertEquals(s, (3,1,5,1))
a = np.zeros((5,4))
b = np.arange(5.)
a[:,2] = b
self.checkitems(b, a[:,2])
a.fill(0)
try:
b.shape = (5,1)
a[:,2] = b
raise AssertionError('Should have raised an error')
except:
pass
try:
b.shape = (1,5,1)
a[:,2] = b
raise AssertionError('Should have raised an error')
except:
pass
def refl(runfiles,
pathtofiles,
outputpath,
scalar,
beamheight,
footprint,
angularfudgefactor,
wl,
back=()):
# scalar - scale factor to divide the data by
# beamheight FWHM in microns
# footprint in mm
# angular offset correction in degrees
# wavelength in wl
# back is an optional variable to subtract a background, set back=1 to do a background subtraction
qq = []
RR = []
dR = []
ii = -1
for filename in runfiles:
data = dnp.io.load(pathtofiles + "/" + str(filename) + ".dat")
ii += 1
theta = data.alpha
# work out the q vector
qqtemp = 4 * dnp.pi * dnp.sin(
(theta + angularfudgefactor) * dnp.pi / 180) / wl
#qqtemp = data.qdcd
# this section is to allow users to set limits on the q range used from each file
if not 'qmin' + str(ii) in refl.__dict__:
qmin = qqtemp.min()
else:
print "USER SET",
qmin = refl.__getattribute__('qmin' + str(ii))
print 'refl.qmin' + str(ii) + " = " + str(qmin) + " ;",
if not 'qmax' + str(ii) in refl.__dict__:
qmax = qqtemp.max()
else:
print "USER SET",
qmax = refl.__getattribute__('qmax' + str(ii))
print 'refl.qmax' + str(ii) + " = " + str(qmax) + " ;",
roi1_sum = data.roi1_sum
roi1_sum = roi1_sum[dnp.where((qqtemp >= qmin) & (qqtemp <= qmax))]
roi1dr = dnp.sqrt(roi1_sum)
theta = theta[dnp.where((qqtemp >= qmin) & (qqtemp <= qmax))]
qqtemp = qqtemp[dnp.where((qqtemp >= qmin) & (qqtemp <= qmax))]
bg_sum = dnp.zeros(len(roi1_sum))
bg_dr = dnp.zeros(len(roi1dr))
# if background ROI number given as int, convert to a single-item tuple
if type(back) == int:
back = (back, )
# subtract any background ROIs from the data
if len(back) > 0 and back[0] > 0:
if ii == 0:
print "Using background from " + str(
len(back)) + " ROIs: " + str(back)
for bg in back:
if ('roi' + str(bg) + '_sum' in data.keys()):
bg_cur = data[data.keys().index('roi' + str(bg) + '_sum')]
dr_cur = dnp.sqrt(bg_cur)
(bg_sum, bg_dr) = ep.EPadd(bg_sum, bg_dr, bg_cur, dr_cur)
(bg_sum, bg_dr) = ep.EPmulk(bg_sum, bg_dr, 1.0 / len(back))
else:
if ii == 0:
print "Not subtracting a background"
(RRtemp, drtemp) = ep.EPsub(roi1_sum, roi1dr, bg_sum, bg_dr)
# do a footprint correction.
# assumes that the beam is gaussian in profile, with a FWHM of "beamheight".
# footprint of sample is measured in mm.
areamultiplier = 2 * (norm.cdf(
footprint * dnp.sin(
(theta + angularfudgefactor) / 180 * dnp.pi) / 2, 0,
1e-3 * beamheight / (2 * dnp.sqrt(2 * dnp.log(2)))) - 0.5)
RRtemp /= areamultiplier
drtemp /= areamultiplier
# for the 2nd, 3rd, 4th q ranges have to splice the data on the end of the preexisting data
if (ii > 0):
# splice
(scalingfactor,
sferror) = nsplice.getScalingInOverlap(qq, RR, dR, qqtemp, RRtemp,
drtemp)
RRtemp *= scalingfactor
drtemp *= scalingfactor
print "Error in scaling factor: %2.3f %%" % (sferror /
scalingfactor * 100)
# now concatenate the data.
qq = dnp.concatenate((qq, qqtemp))
RR = dnp.concatenate((RR, RRtemp))
dR = dnp.concatenate((dR, drtemp))
# end of per-file loop
RR /= scalar
dR /= scalar
RR = RR[np.argsort(qq)]
dR = dR[np.argsort(qq)]
qq = np.sort(qq)
# write out the data.
np.savetxt(
outputpath + "/" + str(runfiles[0]) + "_refl.dat",
dnp.concatenate((qq, RR, dR)).reshape(3, qq.shape[0]).transpose())
def testDatasetSlice(self):
print 'test slicing with start and stop'
ds = np.arange(16)
dr = ds[2:10]
self.checkitems(range(2, 10), dr)
print 'test slicing with steps'
dr = ds[::2]
self.checkitems(range(0, 16, 2), dr)
print 'test slicing 3D'
dr = self.dda[1:, ::2, 1::2]
self.checkitems(self.t, dr)
print 'test putting in a 3D slice'
dr = self.dda.copy()
dr[:, 1::2, ::2] = 7.
self.checkitems(self.u, dr)
print 'test putting a list in a 3D slice'
dr = self.dda.copy()
print dr[:, 1::2, ::2].shape
dr[:, 1::2, ::2] = np.array([7., 7]).reshape(2, 1, 1)
self.checkitems(self.u, dr)
print 'test slicing with ellipse'
dr = ds[...]
self.checkitems(range(16), dr)
print 'test slicing 3D with ellipse'
dr = self.dda[..., 1::2]
self.checkitems(self.t, dr[1:, ::2])
print 'test putting in a 3D slice with ellipsis'
dr = self.dda.copy()
dr[..., 1::2, ::2] = 7.
self.checkitems(self.u, dr)
print 'test putting a list in a 3D slice with ellipsis'
dr = self.dda.copy()
print dr[..., 1::2, ::2].shape
dr[..., 1::2, ::2] = np.array([7., 7]).reshape(2, 1, 1)
self.checkitems(self.u, dr)
print 'test slice shape reduction'
dr = np.arange(120).reshape(4, 3, 5, 2)
s = dr[:2, ..., 1:].shape
print s
self.assertEquals(s, (2, 3, 5, 1))
s = dr[:2, ..., 1].shape
print s
self.assertEquals(s, (2, 3, 5))
try:
s = dr[:2, ..., ..., 1].shape
raise AssertionError, 'Should have raised an error'
except:
pass
print 'test newaxis'
dr = np.arange(15).reshape(3, 5)
s = dr[np.newaxis, :, 1:].shape
print s
self.assertEquals(s, (1, 3, 4))
s = dr[np.newaxis, ...].shape
print s
self.assertEquals(s, (1, 3, 5))
s = dr[np.newaxis, ..., np.newaxis].shape
print s
self.assertEquals(s, (1, 3, 5, 1))
s = dr[..., np.newaxis].shape
print s
self.assertEquals(s, (3, 5, 1))
s = dr[:, np.newaxis, ..., np.newaxis].shape
print s
self.assertEquals(s, (3, 1, 5, 1))
a = np.zeros((5, 4))
b = np.arange(5.)
a[:, 2] = b
self.checkitems(b, a[:, 2])
a.fill(0)
try:
b.shape = (5, 1)
a[:, 2] = b
raise AssertionError, 'Should have raised an error'
except:
pass
try:
b.shape = (1, 5, 1)
a[:, 2] = b
raise AssertionError, 'Should have raised an error'
except:
pass
def refl(runfiles, pathtofiles, outputpath, scalar, beamheight, footprint, angularfudgefactor, wl, back=()):
# scalar - scale factor to divide the data by
# beamheight FWHM in microns
# footprint in mm
# angular offset correction in degrees
# wavelength in wl
# back is an optional variable to subtract a background, set back=1 to do a background subtraction
qq = []
RR = []
dR = []
ii = -1
for filename in runfiles:
data = dnp.io.load(pathtofiles + "/" + str(filename) + ".dat")
ii += 1
theta = data.alpha
# work out the q vector
qqtemp = 4 * dnp.pi * dnp.sin((theta + angularfudgefactor) * dnp.pi /180) / wl
#qqtemp = data.qdcd
# this section is to allow users to set limits on the q range used from each file
if not 'qmin' + str(ii) in refl.__dict__:
qmin = qqtemp.min()
else:
print "USER SET",
qmin = refl.__getattribute__('qmin' + str(ii))
print 'refl.qmin' + str(ii) + " = " + str(qmin) + " ;",
if not 'qmax' + str(ii) in refl.__dict__:
qmax = qqtemp.max()
else:
print "USER SET",
qmax = refl.__getattribute__('qmax' + str(ii))
print 'refl.qmax' + str(ii) + " = " + str(qmax) + " ;",
roi1_sum = data.roi1_sum
roi1_sum = roi1_sum[dnp.where((qqtemp >= qmin) & (qqtemp <= qmax))]
roi1dr = dnp.sqrt(roi1_sum)
theta = theta[dnp.where((qqtemp >= qmin) & (qqtemp <= qmax))]
qqtemp = qqtemp[dnp.where((qqtemp >= qmin) & (qqtemp <= qmax))]
bg_sum = dnp.zeros(len(roi1_sum))
bg_dr = dnp.zeros(len(roi1dr))
# if background ROI number given as int, convert to a single-item tuple
if type(back) == int:
back = (back,)
# subtract any background ROIs from the data
if len(back) > 0 and back[0] > 0:
if ii==0:
print "Using background from " + str(len(back)) + " ROIs: " + str(back)
for bg in back:
if ('roi' + str(bg) + '_sum' in data.keys()):
bg_cur = data[data.keys().index('roi' +str(bg) + '_sum')]
dr_cur = dnp.sqrt(bg_cur)
(bg_sum, bg_dr) = ep.EPadd(bg_sum, bg_dr, bg_cur, dr_cur)
(bg_sum, bg_dr) = ep.EPmulk(bg_sum, bg_dr, 1.0/len(back))
else:
if ii==0:
print "Not subtracting a background"
(RRtemp, drtemp) = ep.EPsub(roi1_sum, roi1dr, bg_sum, bg_dr)
# do a footprint correction.
# assumes that the beam is gaussian in profile, with a FWHM of "beamheight".
# footprint of sample is measured in mm.
areamultiplier = 2*(norm.cdf(footprint * dnp.sin((theta + angularfudgefactor) / 180 * dnp.pi) / 2, 0, 1e-3 * beamheight/ (2*dnp.sqrt(2*dnp.log(2)))) - 0.5)
RRtemp /= areamultiplier
drtemp /= areamultiplier
# for the 2nd, 3rd, 4th q ranges have to splice the data on the end of the preexisting data
if(ii > 0):
# splice
(scalingfactor, sferror) = nsplice.getScalingInOverlap(qq, RR, dR, qqtemp, RRtemp, drtemp)
RRtemp *= scalingfactor
drtemp *= scalingfactor
print "Error in scaling factor: %2.3f %%" % (sferror/scalingfactor*100)
# now concatenate the data.
qq = dnp.concatenate((qq, qqtemp))
RR = dnp.concatenate((RR, RRtemp))
dR = dnp.concatenate((dR, drtemp))
# end of per-file loop
RR /= scalar
dR /= scalar
RR = RR[np.argsort(qq)]
dR = dR[np.argsort(qq)]
qq = np.sort(qq)
# write out the data.
np.savetxt(outputpath+"/"+str(runfiles[0])+"_refl.dat",dnp.concatenate((qq,RR,dR)).reshape(3,qq.shape[0]).transpose())
def process_peem(prefix=None,
displayname=IMAGEDPYNAME,
explorerview=IMAGEEXPNAME):
'''Process PEEM images selection from ImageExplorer view after defining rectangular regions of interest
process_peem(prefix=None, displayname="PEEM Image", explorerview="Image Explorer")
Results are returned as the CD map, positive and negative images and a list of averaged images.
They are also saved as prefix+ave-[0-3].png and prefix+pmap.png. The shifts are saved in prefix+shift.dat
'''
start = time.time()
files = get_peem_files_from_selection(explorerview)
if files == None:
print "No images selected"
if _PEEMDIR:
print "Using images in ", _PEEMDIR
import glob, os.path
names = glob.glob(os.path.join(_PEEMDIR, '*.png'))
omit = glob.glob(os.path.join(_PEEMDIR, '*ave-[0-3].png'))
omit += glob.glob(os.path.join(_PEEMDIR, '*pmap.png'))
files = [n for n in names if not n in omit]
else:
return None
num = len(files)
nsets = num // NPEEM # number of sets of images
print "%d sets of PEEM images" % nsets
if (num % NPEEM) != 0:
print "Need to select multiples of %d images" % NPEEM
return
# divide up images to (s+,la), (s+,lb), (s-,la) and (s-,lb)
ifiles = []
ifiles.append(files[0:nsets])
ifiles.append(files[nsets:2 * nsets])
ifiles.append(files[2 * nsets:3 * nsets])
ifiles.append(files[3 * nsets:4 * nsets])
crs = get_peem_rrois(displayname)
if crs == None:
print "WARNING, no rectangular region of interest selected"
aimages = []
da = peem_load_dataset(ifiles[0][0])
shape = da.shape
for n in range(NPEEM):
aimage = np.zeros(shape) #@UndefinedVariable
# images are organised to groups of nsets
lfiles = ifiles[n]
for s, im in enumerate(lfiles):
print " %s" % basename(im)
db = peem_load_dataset(im) #@UndefinedVariable
aimage += db
aimage /= nsets
aimage.name = "average %d" % n
peem_save_dataset(prefix + "ave-%d" % n + ".png", aimage)
aimages.append(aimage)
else:
rois = []
if len(crs) == 1:
rois.append(crs[0])
rois.append(crs[0])
rois.append(crs[0])
rois.append(crs[0])
elif len(crs) < 4:
rois.append(crs[0])
rois.append(crs[0])
rois.append(crs[1])
rois.append(crs[1])
elif len(crs) >= 4:
rois.append(crs[0])
rois.append(crs[1])
rois.append(crs[2])
rois.append(crs[3])
shifts = []
fromtop = False
for n in range(NPEEM):
shift, da = align_peem_images(ifiles[0], rois[0], fromtop)
shifts.append(shift)
fromtop = not fromtop
save_peem_shifts(prefix + "shifts.dat", shifts)
shape = da.shape
# work out average (save averaged set)
print "working out average"
aimages = []
for n in range(NPEEM):
aimage = np.zeros(shape) #@UndefinedVariable
lfiles = ifiles[n]
shift = shifts[n]
print "average %d" % n
for s, im in enumerate(lfiles):
print " %s" % basename(im)
db = peem_load_dataset(im) #@UndefinedVariable
sim = img.shiftimage(db, shift[s])
aimage += sim
aimage /= nsets
aimage.name = "average %d" % n
peem_save_dataset(prefix + "ave-%d" % n + ".png", aimage)
aimages.append(aimage)
# calculate degree of polarization map
pmap, pos, neg = xmcd(aimages)
pmap.name = "poln map"
peem_save_dataset(prefix + "pmap.png", pmap, scaled=True)
# pl.image(pmap)
print 'Aligned and processed images: %.2fs' % (time.time() - start)
return pmap, pos, neg, aimages
