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 testMethods(self):
print('Methods testing')
print(np.arange(6, dtype=np.int32))
print(np.arange(6, dtype=np.float))
a = np.array([4, 3.])
print(type(a))
print(np.sort(a, None))
self.checkitems([3., 4], np.sort(a, None))
print(a.sort())
self.checkitems([3., 4], a)
a = np.array([4, 3.])
self.checkitems([1, 0], a.argsort())
self.checkitems([1, 0], np.argsort(a, None))
a = np.arange(6, dtype=np.float)
print(a.take([0, 2, 4]))
print(a.take([0, 2, 4], 0))
d = np.take(a, [0, 2, 4], 0)
print(type(d), d)
d = np.diag([0, 2, 3])
print(type(d), d)
a.shape = 2, 3
self.checkitems([1, 2], a.take([1, 2]))
self.checkitems([[1, 2], [4, 5]], a.take([1, 2], 1))
self.checkitems([0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5], a.repeat(2))
self.checkitems([[0, 1, 2], [0, 1, 2], [3, 4, 5], [3, 4, 5]],
a.repeat(2, axis=0))
self.checkitems([[0, 0, 1, 1, 2, 2], [3, 3, 4, 4, 5, 5]],
a.repeat(2, axis=1))
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 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)
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 testAmin(self):
a = dnp.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
self.assertEqual(dnp.amin(a, axis=(0, 1)).shape, (2, ))
self.assertEqual(
dnp.amin(a, axis=(0, 1), keepdims=True).shape, (1, 1, 2))
self.checkitems(dnp.amin(a, axis=(0, 1), keepdims=True),
dnp.array([[[1, 2]]]))
def tensor_product(self, other):
if isinstance(other, Vector):
other = dnp.array([[other.x], [other.y], [other.z]])
other = dnp.transpose(other)
vector = dnp.array([[self.x], [self.y], [self.z]])
M = dnp.dot(vector, other)
return M
def testHistogramdd(self):
print('Histogramdd testing')
h, (v, w) = np.histogramdd(np.column_stack(
(np.array([1, 2, 1, 0]), np.array([2, 3, 1, 3]))),
bins=[[0, 1, 2], [2, 3, 4]])
self.checkitems([[0, 1], [1, 1]], h)
self.checkitems([0, 1, 2], v)
self.checkitems([2, 3, 4], w)
h, (v, w) = np.histogramdd(np.array([[0, 1, 2, 1], [2, 3, 1, 3]]).T,
bins=2)
self.checkitems([[0, 1], [1, 2]], h)
self.checkitems([0, 1, 2], v)
self.checkitems([1, 2, 3], w)
h, (v, w) = np.histogramdd(np.array([[0, 1, 2, 1], [2, 3, 1, 3]]).T,
bins=2,
weights=np.full(4, 0.25))
self.checkitems([[0, 0.25], [0.25, 0.5]], h)
self.checkitems([0, 1, 2], v)
self.checkitems([1, 2, 3], w)
h, (v, w) = np.histogramdd(np.column_stack(
(np.arange(4), np.arange(1, 5))),
bins=4,
range=[[0, 4], [1, 5]])
self.checkitems([1, 1, 1, 1],
np.array([h[i, i] for i in range(len(h))]))
self.checkitems([0, 1, 2, 3, 4], v)
self.checkitems([1, 2, 3, 4, 5], w)
def testMethods(self):
print('Methods testing')
print(np.arange(6, dtype=np.int32))
print(np.arange(6, dtype=np.float))
a = np.array([4,3.])
print(type(a))
print(np.sort(a, None))
self.checkitems([3.,4], np.sort(a, None))
print(a.sort())
self.checkitems([3.,4], a)
a = np.array([4,3.])
self.checkitems([1,0], a.argsort())
self.checkitems([1,0], np.argsort(a, None))
a = np.arange(6, dtype=np.float)
print(a.take([0, 2, 4]))
print(a.take([0, 2, 4], 0))
d = np.take(a, [0, 2, 4], 0)
print(type(d), d)
d = np.diag([0, 2, 3])
print(type(d), d)
a.shape = 2,3
self.checkitems([1,2], a.take([1,2]))
self.checkitems([[1,2], [4,5]], a.take([1,2],1))
self.checkitems([0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5], a.repeat(2))
self.checkitems([[0, 1, 2], [0, 1, 2], [3, 4, 5], [3, 4, 5]], a.repeat(2, axis=0))
self.checkitems([[0, 0, 1, 1, 2, 2], [3, 3, 4, 4, 5, 5]], a.repeat(2, axis=1))
def getInnerAndDetDatasetForGivenOuterValue(self, outerValue): mush = [[o, i, d] for o,i,d in zip(self.outer, self.inner, self.det) if o==float(outerValue)] x = dnp.array([a[1] for a in mush]) y = dnp.array([a[2] for a in mush]) return x, y
def save(self, collectionNumber=None):
voltages=[]
electrometers=[]
if collectionNumber == None:
filename = self.filename+"_"+self.getName()+".dat"
print "concatenating data points to produce P-E data..."
for each in self.voltages:
voltages += each
voltages += []
for each in self.electrometers:
electrometers += each
electrometers += []
else:
filename = self.filename+"_"+self.getName()+"_"+str(collectionNumber)+".dat"
print "summing data points to produce P-E data..."
voltages = sum_datasets(self.voltages)
electrometers = sum_datasets(self.electrometers)
print "%s: saving data to %s" % (self.getName(), filename)
if len(voltages) != len(electrometers):
print "***Warning: voltage nord: %d, electrometer nord: %d" % (len(voltages),len(electrometers))
self.file=open(filename, "w")
for voltage, electrometer in zip(voltages, electrometers):
self.file.write("%f\t%f\n"%(voltage, electrometer))
self.file.close()
print "%s: save data to %s completed." % (self.getName(), filename)
voltagearray=scisoftpy.array(voltages)
electrometerarray=scisoftpy.array(electrometers)
vds=scisoftpy.toDS(voltagearray)
eds=scisoftpy.toDS(electrometerarray)
print "plotting PE-loop in 'DataPlot' panel..."
if self.firstData:
Plotter.plot("DataPlot", vds, eds)
self.firstData=False
else:
Plotter.plotOver("DataPlot", vds, eds)
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 demoRealDataFitting():
m6Data=dnp.io.load("/dls/i06-1/data/2012/si7816-1/68917.dat", formats=['srs'], asdict=True);
# m6Data=dnp.io.load("/dls/i06-1/data/2012/si7816-1/68918.dat", formats=['srs'], asdict=True);
x, y=m6Data['m6qg'], m6Data['ca62sr'];
# [mu, sigma, peak, gf] = fineGaussianFitting(y, x, "Plot 2");
[mu, sigma, peak, gf] = fineGaussianFitting(y, x);
# One vertical line on the peak point:
# xx, yy=dnp.array([mu-1, mu+1]), dnp.array([0, peak]);
xx, yy=dnp.array([mu-1, mu, mu+1]), dnp.array([0, peak, 0]);
xxx, yyy=dnp.array([mu]), dnp.array([peak]);
# To find the closest data point to the peak;
cPos=(dnp.abs(x-mu)).minpos()[0];
cX, cY=x[cPos], y[cPos];
print("To plot the fitted data." )
x1=dnp.linspace(x[0], x[x.size-1], 500);
y1=myGaussianFunc(mu, sigma, peak, [x1]);
#Line plot does not work,
#dnp.plot.line(x, [y, y1] ) # plot line of evaluated function
#dnp.plot.updateline(xx, yy)
dnp.plot.points(x, y, None, 5);
sleep(1);
dnp.plot.addpoints(x1, y1, None, 1)
sleep(1);
dnp.plot.addpoints(xxx, yyy, None, 10);
return [mu, sigma, peak, gf];
def getTrace(self, par):
# floating point with 8 bytes / 64 bits per number
#self.write("SYST:ERR:CLE")
#print self.query("*OPC?")
#print self.query("SYST:ERR?") #returns "No Error"
self.write(":CALC:PAR%1d:DATA:SDAT?" % par)
head = self.sock.recv(2) # header part 1
head = self.sock.recv(int(head[1])) # header part 2
MSGLEN = int(head) # extrace message len from header
chunks = []
bytes_recd = 0
while bytes_recd < MSGLEN:
chunk = self.sock.recv(min(MSGLEN - bytes_recd, 2048))
chunks.append(chunk)
bytes_recd = bytes_recd + len(chunk)
data = ''.join(chunks)
extra = self.sock.recv(2048)
#print len(extra)
#print chr(extra)
#print len(data), " bytes, ", len(data)/8, " numbers"
num = len(data) / 8
[
data,
] = struct.unpack('%dd' % num, data),
#return [np.array(data[::2]), np.array(data[1::2]) ]
return dnp.array(data[::2]) + dnp.array(data[1::2]) * 1j
def testDiv(self):
print("test div")
da = np.array(self.db, np.int)
dr = da // (da + 1)
self.checkitemsdiv(self.db, self.db, dr, convert=toInt)
dr = da.copy()
dr //= da + 1
self.checkitemsdiv(self.db, self.db, dr, convert=toInt)
dr = da / 1.2
self.checkitemsdivconst(self.db, 1.2, dr, convert=toInt)
# dr = da.copy()
# dr /= 1.2
# self.checkitemsdivconst2(self.db, 1.2, dr, convert=toInt)
da = np.array(self.db, np.float)
dr = da / (da + 1)
self.checkitemsdiv(self.db, self.db, dr)
dr = da.copy()
dr /= da + 1
self.checkitemsdiv(self.db, self.db, dr)
dr = da / 1.2
self.checkitemsdivconst(self.db, 1.2, dr)
dr = 1.2 / (da + 1)
self.checkitemsdivconst(1.2, self.db, dr)
dr = da.copy()
dr /= 1.2
self.checkitemsdivconst(self.db, 1.2, dr)
za = np.array(self.zb, np.complex)
zr = za / (za + 1)
self.checkitemsdiv(self.zb, self.zb, zr, iscomplex=True)
zr = za.copy()
zr /= za + 1
self.checkitemsdiv(self.zb, self.zb, zr, iscomplex=True)
zr = za / (1.3 + 0.2j)
self.checkitemsdivconst(self.zb, (1.3 + 0.2j), zr, iscomplex=True)
zr = za.copy()
zr /= (1.3 + 0.2j)
self.checkitemsdivconst(self.zb, (1.3 + 0.2j), zr, iscomplex=True)
a = np.arange(-4, 4, dtype=np.int8)
import sys
py3 = sys.hexversion >= 0x03000000
if py3:
self.checkitems([-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5], a / 2)
self.checkitems([2, 1.5, 1, 0.5, 0, -0.5, -1, -1.5], a / -2)
else:
self.checkitems([-2, -2, -1, -1, 0, 0, 1, 1], a / 2)
self.checkitems([2, 1, 1, 0, 0, -1, -1, -2], a / -2)
self.checkitems([-2, -2, -1, -1, 0, 0, 1, 1], a // 2)
self.checkitems([0, 1, 0, 1, 0, 1, 0, 1], a % 2)
self.checkitems([2, 1, 1, 0, 0, -1, -1, -2], a // -2)
self.checkitems([0, -1, 0, -1, 0, -1, 0, -1], a % -2)
self.checkitems([-1.6, -1.2, -0.8, -0.4, 0., 0.4, 0.8, 1.2], a / 2.5)
self.checkitems([-2., -2., -1., -1., 0., 0., 0., 1.], a // 2.5)
self.checkitems([1., 2., 0.5, 1.5, 0., 1., 2., 0.5], a % 2.5)
self.checkitems([1.6, 1.2, 0.8, 0.4, -0., -0.4, -0.8, -1.2], a / -2.5)
self.checkitems([1., 1., 0., 0., -0., -1., -1., -2.], a // -2.5)
self.checkitems([-1.5, -0.5, -2., -1., 0., -1.5, -0.5, -2.], a % -2.5)
def testDiv(self):
print("test div")
da = np.array(self.db, np.int)
dr = da // (da+1)
self.checkitemsdiv(self.db, self.db, dr, convert=toInt)
dr = da.copy()
dr //= da + 1
self.checkitemsdiv(self.db, self.db, dr, convert=toInt)
dr = da / 1.2
self.checkitemsdivconst(self.db, 1.2, dr, convert=toInt)
# dr = da.copy()
# dr /= 1.2
# self.checkitemsdivconst2(self.db, 1.2, dr, convert=toInt)
da = np.array(self.db, np.float)
dr = da / (da+1)
self.checkitemsdiv(self.db, self.db, dr)
dr = da.copy()
dr /= da + 1
self.checkitemsdiv(self.db, self.db, dr)
dr = da / 1.2
self.checkitemsdivconst(self.db, 1.2, dr)
dr = 1.2 / (da+1)
self.checkitemsdivconst(1.2, self.db, dr)
dr = da.copy()
dr /= 1.2
self.checkitemsdivconst(self.db, 1.2, dr)
za = np.array(self.zb, np.complex)
zr = za / (za + 1)
self.checkitemsdiv(self.zb, self.zb, zr, iscomplex=True)
zr = za.copy()
zr /= za + 1
self.checkitemsdiv(self.zb, self.zb, zr, iscomplex=True)
zr = za / (1.3 + 0.2j)
self.checkitemsdivconst(self.zb, (1.3 + 0.2j), zr, iscomplex=True)
zr = za.copy()
zr /= (1.3 + 0.2j)
self.checkitemsdivconst(self.zb, (1.3 + 0.2j), zr, iscomplex=True)
a = np.arange(-4, 4, dtype=np.int8)
import sys
py3 = sys.hexversion >= 0x03000000
if py3:
self.checkitems([-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5], a / 2)
self.checkitems([2, 1.5, 1, 0.5, 0, -0.5, -1, -1.5], a / -2)
else:
self.checkitems([-2, -2, -1, -1, 0, 0, 1, 1], a / 2)
self.checkitems([2, 1, 1, 0, 0, -1, -1, -2], a / -2)
self.checkitems([-2, -2, -1, -1, 0, 0, 1, 1], a // 2)
self.checkitems([0, 1, 0, 1, 0, 1, 0, 1], a % 2)
self.checkitems([2, 1, 1, 0, 0, -1, -1, -2], a // -2)
self.checkitems([0, -1, 0, -1, 0, -1, 0, -1], a % -2)
self.checkitems([-1.6, -1.2, -0.8, -0.4, 0. , 0.4, 0.8, 1.2], a / 2.5)
self.checkitems([-2., -2., -1., -1., 0., 0., 0., 1.], a // 2.5)
self.checkitems([1., 2., 0.5, 1.5, 0., 1., 2., 0.5], a % 2.5)
self.checkitems([1.6, 1.2, 0.8, 0.4, -0., -0.4, -0.8, -1.2], a / -2.5)
self.checkitems([1., 1., 0., 0., -0., -1., -1., -2.], a // -2.5)
self.checkitems([-1.5, -0.5, -2., -1., 0., -1.5, -0.5, -2.], a % -2.5)
def testSelect(self):
print 'test select'
tm = np.select([
np.array([[[False, True], [True, False]],
[[True, True], [False, False]]])
], [np.array(self.mm)], -2.3)
self.checkitems([[[-2.3, 2.], [6., -2.3]], [[20., 30.], [-2.3, -2.3]]],
tm)
def mgrid(x, y): y0=[y]*len(x) Y=dnp.array(y0); x0=[x]*len(y); X=dnp.array( zip(*x0) ) return X, Y;
def testInteger(self):
print 'test integer get and set'
tm = np.array(self.mm).flatten()
d = tm[np.array([2, 4, 7])]
self.checkitems([6., 20., 56.], d)
tm[np.array([3, 4, 5, 6, 7])] = -2.3
self.checkitems([0., 2., 6., -2.3, -2.3, -2.3, -2.3, -2.3], tm)
def testInteger(self):
print 'test integer get and set'
tm = np.array(self.mm).flatten()
d = tm[np.array([2, 4, 7])]
self.checkitems([ 6., 20., 56. ], d)
tm[np.array([3, 4, 5, 6, 7])] = -2.3
self.checkitems([ 0., 2., 6., -2.3, -2.3, -2.3, -2.3, -2.3 ], tm)
def project(self, energy, UB, pixels, gamma, delta, omega, alpha, nu):
# put the detector at the right position
dx, dy, dz = pixels
# convert angles to radians
gamma, delta, alpha, omega, nu = numpy.radians(
(gamma, delta, alpha, omega, nu))
RGam = numpy.matrix([[1, 0, 0], [0, cos(gamma), -sin(gamma)],
[0, sin(gamma), cos(gamma)]])
RDel = (numpy.matrix([[cos(delta), -sin(delta), 0],
[sin(delta), cos(delta), 0], [0, 0, 1]])).getI()
RNu = numpy.matrix([[cos(nu), 0, sin(nu)], [0, 1, 0],
[-sin(nu), 0, cos(nu)]])
# calculate Cartesian coordinates for each pixel using clever matrix stuff
M = numpy.mat(
numpy.concatenate(
(dx.flatten(0), dy.flatten(0),
dz.flatten(0))).reshape(3, dx.shape[0] * dx.shape[1]))
XYZp = RGam * RDel * RNu * M
xp = dnp.array(XYZp[0]).reshape(dx.shape)
yp = dnp.array(XYZp[1]).reshape(dy.shape)
zp = dnp.array(XYZp[2]).reshape(dz.shape)
# don't bother with the part about slits...
# Calculate effective gamma and delta for each pixel
d_ds = dnp.sqrt(xp**2 + yp**2 + zp**2)
Gam = dnp.arctan2(zp, yp)
Del = -1 * dnp.arcsin(-xp / d_ds)
# wavenumber
k = 2 * math.pi / 12.398 * energy
# Define the needed matrices. The notation follows the article by Bunk &
# Nielsen. J.Appl.Cryst. (2004) 37, 216-222.
M1 = k * numpy.matrix(
cos(omega) * sin(Del) - sin(omega) *
(cos(alpha) *
(cos(Gam) * cos(Del) - 1) + sin(alpha) * sin(Gam) * cos(Del)))
M2 = k * numpy.matrix(
sin(omega) * sin(Del) + cos(omega) *
(cos(alpha) *
(cos(Gam) * cos(Del) - 1) + sin(alpha) * sin(Gam) * cos(Del)))
M3 = k * numpy.matrix(-sin(alpha) * (cos(Gam) * cos(Del) - 1) +
cos(alpha) * sin(Gam) * cos(Del))
# invert UB matrix
UBi = numpy.matrix(UB).getI()
# calculate HKL
H = UBi[0, 0] * M1 + UBi[0, 1] * M2 + UBi[0, 2] * M3
K = UBi[1, 0] * M1 + UBi[1, 1] * M2 + UBi[1, 2] * M3
L = UBi[2, 0] * M1 + UBi[2, 1] * M2 + UBi[2, 2] * M3
return (H, K, L)
def display_parameters(self, parameter_number):
keys = self.results.keys()
keys.sort()
data = []
for key in keys:
result = self.results[key][2]
data.append(result[parameter_number])
y = dnp.array(data)
x = dnp.array(keys)
dnp.plot.line(x, y, name=self.calibration_window)
def display_parameters(self, parameter_number):
keys = self.results.keys()
keys.sort()
data = []
for key in keys:
result = self.results[key][2]
data.append(result[parameter_number])
y = dnp.array(data)
x = dnp.array(keys)
dnp.plot.line(x, y, name=self.calibration_window)
def testTwos(self): # make new datasets with twos
print("test twos")
dds = np.full(3, 2.)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(np.float64, dds.dtype)
self.assertEquals(1, dds.ndim)
self.assertEquals(3, dds.shape[0])
self.assertEquals(2., dds[0])
dds = np.full((2,3), 2.)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(np.float64, dds.dtype)
self.assertEquals(2, dds.ndim)
self.assertEquals(2, dds.shape[0])
self.assertEquals(3, dds.shape[1])
self.assertEquals(2., dds[0,0])
dds = np.full_like(dds, 2.)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(np.float64, dds.dtype)
self.assertEquals(2, dds.ndim)
self.assertEquals(2, dds.shape[0])
self.assertEquals(3, dds.shape[1])
self.assertEquals(2., dds[0,0])
dds = np.full(3, 2, dtype=np.float)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(np.float64, dds.dtype)
self.assertEquals(1, dds.ndim)
self.assertEquals(3, dds.shape[0])
self.assertEquals(2., dds[0])
dds = np.full((2,3), 2, dtype=np.float)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(np.float64, dds.dtype)
self.assertEquals(2, dds.ndim)
self.assertEquals(2, dds.shape[0])
self.assertEquals(3, dds.shape[1])
self.assertEquals(2., dds[0,0])
dds = np.full_like(dds, 2, dtype=np.float)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(np.float64, dds.dtype)
self.assertEquals(2, dds.ndim)
self.assertEquals(2, dds.shape[0])
self.assertEquals(3, dds.shape[1])
self.assertEquals(2., dds[0,0])
dds = np.full(np.array([1,2]), 2, dtype=np.float)
self.assertEqual((1,2), dds.shape)
self.assertEquals(2, dds[0,0])
dds = np.full_like(np.array([1,2]), 2, dtype=np.float)
self.assertEquals((2,), dds.shape)
self.assertEquals(2, dds[0])
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))
s = dr[:2,...,...,1].shape
print s
self.assertEquals(s, (2,3,5))
def testTwos(self): # make new datasets with twos
print("test twos")
dds = np.full(3, 2.)
if isjava:
self.assertEqual(1, dds.dtype.elements)
self.assertEqual(np.float64, dds.dtype)
self.assertEqual(1, dds.ndim)
self.assertEqual(3, dds.shape[0])
self.assertEqual(2., dds[0])
dds = np.full((2, 3), 2.)
if isjava:
self.assertEqual(1, dds.dtype.elements)
self.assertEqual(np.float64, dds.dtype)
self.assertEqual(2, dds.ndim)
self.assertEqual(2, dds.shape[0])
self.assertEqual(3, dds.shape[1])
self.assertEqual(2., dds[0, 0])
dds = np.full_like(dds, 2.)
if isjava:
self.assertEqual(1, dds.dtype.elements)
self.assertEqual(np.float64, dds.dtype)
self.assertEqual(2, dds.ndim)
self.assertEqual(2, dds.shape[0])
self.assertEqual(3, dds.shape[1])
self.assertEqual(2., dds[0, 0])
dds = np.full(3, 2, dtype=np.float)
if isjava:
self.assertEqual(1, dds.dtype.elements)
self.assertEqual(np.float64, dds.dtype)
self.assertEqual(1, dds.ndim)
self.assertEqual(3, dds.shape[0])
self.assertEqual(2., dds[0])
dds = np.full((2, 3), 2, dtype=np.float)
if isjava:
self.assertEqual(1, dds.dtype.elements)
self.assertEqual(np.float64, dds.dtype)
self.assertEqual(2, dds.ndim)
self.assertEqual(2, dds.shape[0])
self.assertEqual(3, dds.shape[1])
self.assertEqual(2., dds[0, 0])
dds = np.full_like(dds, 2, dtype=np.float)
if isjava:
self.assertEqual(1, dds.dtype.elements)
self.assertEqual(np.float64, dds.dtype)
self.assertEqual(2, dds.ndim)
self.assertEqual(2, dds.shape[0])
self.assertEqual(3, dds.shape[1])
self.assertEqual(2., dds[0, 0])
dds = np.full(np.array([1, 2]), 2, dtype=np.float)
self.assertEqual((1, 2), dds.shape)
self.assertEqual(2, dds[0, 0])
dds = np.full_like(np.array([1, 2]), 2, dtype=np.float)
self.assertEqual((2, ), dds.shape)
self.assertEqual(2, dds[0])
def plotPEdata(self, *args, **kwargs):
xarray=scisoftpy.array(kwargs[self.hv][args[0]])
yrarray=scisoftpy.array(kwargs[self.el][args[0]])
vds=scisoftpy.toDS(xarray)
eds=scisoftpy.toDS(yrarray)
print "plotting PE-loop in 'DataPlot' panel..."
if self.firstData:
Plotter.plot("DataPlot", vds, eds)
self.firstData=False
else:
Plotter.plotOver("DataPlot", vds, eds)
def testBincount(self):
print('Bincount testing')
a = np.array([2, 1, 2, 4, 6, 0])
c = np.bincount(a)
self.checkitems([1, 1, 2, 0, 1, 0, 1], c)
c = np.bincount(a, None, 10)
self.checkitems([1, 1, 2, 0, 1, 0, 1, 0, 0, 0], c)
w = np.array([1, 1, 2, 4, 1, 0])
c = np.bincount(a, w)
self.checkitems([0, 1, 3, 0, 4, 0, 1], c)
c = np.bincount(a, w * 0.5)
self.checkitems([0, 1, 3, 0, 4, 0, 1], 2 * c)
def testPut(self):
print 'test put'
ds = np.arange(6.)
ds.put([2, 5], [-2, -5.5])
self.checkitems([0, 1, -2, 3, 4, -5.5], ds)
ds.put(np.array([0, 4]), [-2, -5.5])
self.checkitems([-2, 1, -2, 3, -5.5, -5.5], ds)
ds = np.arange(6.).reshape(2,3)
ds.put([2, 5], [-2, -5.5])
self.checkitems([[0, 1, -2], [3, 4, -5.5]], ds)
ds.put(np.array([0, 4]), [-2, -5.5])
self.checkitems([[-2, 1, -2], [3, -5.5, -5.5]], ds)
def testPut(self):
print 'test put'
ds = np.arange(6.)
ds.put([2, 5], [-2, -5.5])
self.checkitems([0, 1, -2, 3, 4, -5.5], ds)
ds.put(np.array([0, 4]), [-2, -5.5])
self.checkitems([-2, 1, -2, 3, -5.5, -5.5], ds)
ds = np.arange(6.).reshape(2, 3)
ds.put([2, 5], [-2, -5.5])
self.checkitems([[0, 1, -2], [3, 4, -5.5]], ds)
ds.put(np.array([0, 4]), [-2, -5.5])
self.checkitems([[-2, 1, -2], [3, -5.5, -5.5]], ds)
def testBincount(self):
print('Bincount testing')
a = np.array([2, 1, 2, 4, 6, 0])
c = np.bincount(a)
self.checkitems([1, 1, 2, 0, 1, 0, 1], c)
c = np.bincount(a, None, 10)
self.checkitems([1, 1, 2, 0, 1, 0, 1, 0, 0, 0], c)
w = np.array([1, 1, 2, 4, 1, 0])
c = np.bincount(a, w)
self.checkitems([0, 1, 3, 0, 4, 0, 1], c)
c = np.bincount(a, w * 0.5)
self.checkitems([0, 1, 3, 0, 4, 0, 1], 2*c)
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 createMask(self, low, high, dataset=None):
if dataset is None:
if self.dataset is None:
print "No reference dataset to mask"
return
else:
dataset = self.dataset
maskArray = dnp.logical_and(
dnp.array(dataset) >= low,
dnp.array(dataset) <= high)
self.mask = maskArray._jdataset()
return self.mask
def testInteger(self):
print 'test integer get and set'
tm = np.array(self.mm).flatten()
# self.mm = [ [[0., 2.], [6., 12.]], [[20., 30.], [42., 56.]] ]
d = tm[np.array([2, 4, 7])]
self.checkitems([ 6., 20., 56. ], d)
# d = tm[np.array([2, 4, 7])]
# self.checkitems([ 6., 20., 56. ], d)
tm[np.array([3, 4, 5, 6, 7])] = -2.3
# self.checkitems([ [[0., 2.], [6., -2.3]], [[-2.3, -2.3], [-2.3, -2.3]] ], tm)
self.checkitems([ 0., 2., 6., -2.3, -2.3, -2.3, -2.3, -2.3 ], tm)
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 testCorrelate(self):
print 'test correlate'
da = np.array(self.da, np.float)
ada = np.correlate(da, axes=[0])
self.checkitems(None, ada)
a = np.array([1, 2, 3])
b = np.array([0, 1, 0.5])
self.checkitems([3.5], np.correlate(a, b))
self.checkitems([2., 3.5, 3.], np.correlate(a, b, "same"))
self.checkitems([0.5, 2., 3.5, 3., 0.], np.correlate(a, b, "full"))
self.checkitems([3.5], np.correlate(b, a))
self.checkitems([2., 3.5, 3.][::-1], np.correlate(b, a, "same"))
self.checkitems([0.5, 2., 3.5, 3., 0.][::-1], np.correlate(b, a, "full"))
def testConvolve(self):
print 'test convolve'
# da = np.array(self.da, np.float)
# ada = np.convolve(da, axes=[0])
# self.checkitems(None, ada)
a = np.array([1, 2, 3])
b = np.array([0, 1, 0.5])
self.checkitems([0., 1., 2.5, 4., 1.5], np.convolve(a, b))
self.checkitems([1., 2.5, 4.], np.convolve(a, b, 'same'))
self.checkitems([2.5], np.convolve(a, b, 'valid'))
self.checkitems([0., 1., 2.5, 4., 1.5], np.convolve(b, a))
self.checkitems([1., 2.5, 4.], np.convolve(b, a, 'same'))
self.checkitems([2.5], np.convolve(b, a, 'valid'))
def testAmin(self):
a = dnp.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
self.assertEqual(dnp.amin(a, axis=0).shape, (2,3))
self.checkitems(dnp.amin(a, axis=0), dnp.array([[1, 2, 3], [4, 5, 6]]))
self.assertEqual(dnp.amin(a, axis=2).shape, (2,2))
self.assertEqual(dnp.amin(a, axis=(0, 1)).shape, (3,))
self.checkitems(dnp.amin(a, axis=(0, 1)), dnp.array([1, 2, 3]))
self.assertEqual(dnp.amin(a, axis=(0, 1, 2)), 1)
self.assertEqual(dnp.amin(a, axis=0, keepdims=True).shape, (1, 2, 3))
self.assertEqual(dnp.amin(a, axis=2, keepdims=True).shape, (2, 2, 1))
self.assertEqual(dnp.amin(a, axis=(0, 1), keepdims=True).shape, (1, 1, 3))
self.checkitems(dnp.amin(a, axis=(0, 1), keepdims=True), dnp.array([[[1, 2, 3]]]))
self.assertEqual(dnp.amin(a, axis=(0, 2), keepdims=True).shape, (1, 2, 1))
self.checkitems(dnp.amin(a, axis=(0, 2), keepdims=True), dnp.array([[[1], [4]]]))
def test2dInput(self):
arr_input = dnp.array([[1, 2], [3, 4], [5, 6]])
list_input = [[1, 2], [3, 4], [5, 6]]
zeros_output = dnp.array([[0, 0], [0, 0], [0, 0]])
zeros_like_arr = dnp.zeros_like(arr_input)
zeros_like_list = dnp.zeros_like(list_input)
self.assertEqual(zeros_like_arr.shape, (3, 2))
self.checkitems(zeros_like_arr, zeros_output)
self.assertEqual(zeros_like_list.shape, (3, 2))
self.checkitems(zeros_like_list, zeros_output)
def testEmptyListInput(self):
array_input = dnp.array([])
list_input = []
zeros_output = dnp.array([])
zeros_like_array = dnp.zeros_like(list_input)
zeros_like_list = dnp.zeros_like(list_input)
self.assertEqual(zeros_like_array.shape, (0, ))
self.checkitems(zeros_like_array, zeros_output)
self.assertEqual(zeros_like_list.shape, (0, ))
self.checkitems(zeros_like_list, zeros_output)
def testEmptyListInput(self):
array_input = dnp.array([])
list_input = []
zeros_output = dnp.array([])
zeros_like_array = dnp.zeros_like(list_input)
zeros_like_list = dnp.zeros_like(list_input)
self.assertEquals(zeros_like_array.shape, (0,))
self.checkitems(zeros_like_array, zeros_output)
self.assertEquals(zeros_like_list.shape, (0,))
self.checkitems(zeros_like_list, zeros_output)
def test2dInput(self):
arr_input = dnp.array([[1, 2], [3, 4], [5, 6]])
list_input = [[1, 2], [3, 4], [5, 6]]
zeros_output = dnp.array([[0, 0], [0, 0], [0, 0]])
zeros_like_arr = dnp.zeros_like(arr_input)
zeros_like_list = dnp.zeros_like(list_input)
self.assertEquals(zeros_like_arr.shape, (3,2))
self.checkitems(zeros_like_arr, zeros_output)
self.assertEquals(zeros_like_list.shape, (3,2))
self.checkitems(zeros_like_list, zeros_output)
def testConvolve(self):
print 'test convolve'
# da = np.array(self.da, np.float)
# ada = np.convolve(da, axes=[0])
# self.checkitems(None, ada)
a = np.array([1, 2, 3])
b = np.array([0, 1, 0.5])
self.checkitems([0., 1., 2.5, 4., 1.5], np.convolve(a, b))
self.checkitems([1., 2.5, 4.], np.convolve(a, b, 'same'))
self.checkitems([2.5], np.convolve(a, b, 'valid'))
self.checkitems([0., 1., 2.5, 4., 1.5], np.convolve(b, a))
self.checkitems([1., 2.5, 4.], np.convolve(b, a, 'same'))
self.checkitems([2.5], np.convolve(b, a, 'valid'))
def testAmin(self):
a = dnp.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
self.assertEquals(dnp.amin(a, axis=0).shape, (2,3))
self.checkitems(dnp.amin(a, axis=0), dnp.array([[1, 2, 3], [4, 5, 6]]))
self.assertEquals(dnp.amin(a, axis=2).shape, (2,2))
self.assertEquals(dnp.amin(a, axis=(0, 1)).shape, (3,))
self.checkitems(dnp.amin(a, axis=(0, 1)), dnp.array([1, 2, 3]))
self.assertEquals(dnp.amin(a, axis=(0, 1, 2)), 1)
self.assertEquals(dnp.amin(a, axis=0, keepdims=True).shape, (1, 2, 3))
self.assertEquals(dnp.amin(a, axis=2, keepdims=True).shape, (2, 2, 1))
self.assertEquals(dnp.amin(a, axis=(0, 1), keepdims=True).shape, (1, 1, 3))
self.checkitems(dnp.amin(a, axis=(0, 1), keepdims=True), dnp.array([[[1, 2, 3]]]))
self.assertEquals(dnp.amin(a, axis=(0, 2), keepdims=True).shape, (1, 2, 1))
self.checkitems(dnp.amin(a, axis=(0, 2), keepdims=True), dnp.array([[[1], [4]]]))
def _process(self,xDataSet, yDataSet):
if yDataSet.max()-yDataSet.min() == 0:
raise ValueError("There is no peak")
xDataSet = np.Sciwrap(xDataSet)
yDataSet = np.Sciwrap(yDataSet) * self.scale
funcs = [ holefunc ]
initparams = [ xDataSet.mean(), (xDataSet.max() - xDataSet.min())* 0.90, yDataSet.min(), yDataSet.max()]
fr = np.fit.fit(funcs, xDataSet, yDataSet, initparams, ptol=1e-4, optimizer='local')
if self.plotPanel != None:
fr.plot(self.plotPanel)
initparams = [ p for p in fr.func.getParameterValues() ]
start = initparams[0] - 0.5 * initparams[1]
length = initparams[1]
truncx = []
truncy = []
for i in range(xDataSet.size):
val = xDataSet[i]
if val >= start and val <= start+length:
truncx.append(val)
truncy.append(yDataSet[i])
lala = Generic1DFitter.fitPeaks(np.toDS(np.array(truncx)), np.toDS(np.array(truncy)), Gaussian(1,1,1,1), NelderMead(self.accuracy), 2, self.gaussians, 0.05, True, False)
print lala
if lala == None or len(lala) == 0:
print "no peaks found"
return [0, []]
positions = []
cff = np.fit.fitcore.cfitfunc()
for f in lala:
print f.getParameter(1), f.getParameter(2)
if f.getParameter(1).getValue() <= self.maxfwhm and f.getParameter(2).getValue() >= self.minarea:
positions.append(f.getParameter(0).getValue())
cff.addFunction(f)
else:
print "removed one"
if len(positions) == 0:
print "too bad nothing found within threshold"
else:
print "plotting remaining found positions"
if self.plotPanel != None:
fire = np.fit.fitcore.fitresult(cff, [np.toDS(np.array(truncx))], np.array(truncy))
fire.plot(self.plotPanel)
return [len(positions), positions]
def readTestInputs(
filename="height.txt",
idealname="ideal.txt",
visit_directory="/dls/i12/data/2014/cm4963-2",
input_directory="tmp",
printres=False,
):
infilename = "%s/%s/%s" % (visit_directory, input_directory, filename)
idealfilename = "%s/%s/%s" % (visit_directory, input_directory, idealname)
inlist = []
ideallist = []
try:
infile = open(infilename, "r")
except:
print ("File %s not opened properly" % infilename)
return None
n = 0
for line in infile:
if printres:
print n, line
n = n + 1
inlist.append(float(line))
infile.close()
try:
infile = open(idealfilename, "r")
except:
print ("File %s not opened properly" % idealfilename)
return None
for line in infile:
if printres:
print n, line
n = n + 1
ideallist.append(float(line))
infile.close()
if printres:
print inlist
print ideallist
inarray = dnp.array(inlist)
idealarray = dnp.array(ideallist)
print len(inarray)
angarray = dnp.linspace(0, dnp.pi * 2.0, len(inarray), True)
horizarray = 2500.0 * dnp.cos(angarray)
ofile = open("%s/%s/testpoints.txt" % (visit_directory, input_directory), "w")
ofile.write("angle,X,idealY,noisyY\n")
for i in range(0, len(angarray)):
ofile.write("%g,%g,%g,%g\n" % (angarray[i], horizarray[i], idealarray[i], inarray[i]))
ofile.close()
return (horizarray, inarray, idealarray)
def testZeroRank(self):
print 'Zero rank arrays testing'
zi = np.array(1)
print zi
self.assertEquals(0, len(zi.shape))
self.assertEquals(1, zi[()])
self.assertEquals(np.array(1), zi[...])
zi[()] = -3
self.assertEquals(-3, zi[()])
zf = np.array(1.)
self.assertEquals(0, len(zf.shape))
self.assertEquals(1., zf[()])
self.assertEquals(np.array(1.), zf[...])
zf[()] = -3
self.assertEquals(-3, zf[()])
def calc_bins_vrs_q(self):
peaklist = self.get_peak_list_from_view()
peakpos = []
for peak in peaklist:
peakpos.append(peak.getPosition())
peakpos.sort()
x = dnp.array(self.standard_peaks)
y = dnp.array(peakpos)
max_size = min(x.shape[0], y.shape[0])
x = x[0:max_size]
y = y[0:max_size]
x.name = "Calibration Values"
y.name = "Peak positions"
fr = dnp.fit.polyfit(y, x, 2, full=True)
return (x, y, fr[1])
def testSavingBits(self):
d = dnp.arange(12*32).reshape((12,32))
b = dnp.abs(dnp.array(d, dnp.int8))
b[b < 0] = 0
print b.min(), b.max()
dnp.io.save(OutTestFolder+'uint.tiff', d, bits=32, signed=False)
dnp.io.save(OutTestFolder+'ushort.tiff', d, bits=16, signed=False)
dnp.io.save(OutTestFolder+'ubyte.tiff', b, bits=8, signed=False)
dnp.io.save(OutTestFolder+'int.tiff', d, bits=32)
dnp.io.save(OutTestFolder+'short.tiff', d, bits=16)
dnp.io.save(OutTestFolder+'byte.tiff', dnp.array(d, dnp.int8), bits=8)
dnp.io.save(OutTestFolder+'double.tiff', d, bits=33)
dnp.io.save(OutTestFolder+'float.tiff', d, bits=33)
dnp.io.save(OutTestFolder+'short.png', d, bits=16)
dnp.io.save(OutTestFolder+'byte.png', b, bits=8)
def calc_bins_vrs_q(self):
peaklist = self.get_peak_list_from_view()
peakpos = []
for peak in peaklist:
peakpos.append(peak.getPosition())
peakpos.sort()
x = dnp.array(self.standard_peaks)
y = dnp.array(peakpos)
max_size = min(x.shape[0], y.shape[0])
x = x[0:max_size]
y = y[0:max_size]
x.name = "Calibration Values"
y.name = "Peak positions"
fr = dnp.fit.polyfit(y, x, 2, full=True)
return (x, y, fr[1])
def testSavingBits(self):
d = dnp.arange(12 * 32).reshape((12, 32))
b = dnp.abs(dnp.array(d, dnp.int8))
b[b < 0] = 0
print(b.min(), b.max())
self.save('uint.tiff', d, bits=32, signed=False)
self.save('ushort.tiff', d, bits=16, signed=False)
self.save('ubyte.tiff', b, bits=8, signed=False)
self.save('int.tiff', d, bits=32)
self.save('short.tiff', d, bits=16)
self.save('byte.tiff', dnp.array(d, dnp.int8), bits=8)
self.save('double.tiff', d, bits=33)
self.save('float.tiff', d, bits=33)
self.save('short.png', d, bits=16)
self.save('byte.png', b, bits=8)
def testCorrelate(self):
print 'test correlate'
da = np.array(self.da, np.float)
ada = np.correlate(da, axes=[0])
self.checkitems(None, ada)
a = np.array([1, 2, 3])
b = np.array([0, 1, 0.5])
self.checkitems([3.5], np.correlate(a, b))
self.checkitems([2., 3.5, 3.], np.correlate(a, b, "same"))
self.checkitems([0.5, 2., 3.5, 3., 0.], np.correlate(a, b, "full"))
self.checkitems([3.5], np.correlate(b, a))
self.checkitems([2., 3.5, 3.][::-1], np.correlate(b, a, "same"))
self.checkitems([0.5, 2., 3.5, 3., 0.][::-1],
np.correlate(b, a, "full"))
def testZeroRank(self):
print 'Zero rank arrays testing'
zi = np.array(1)
print zi
self.assertEquals(0, len(zi.shape))
self.assertEquals(1, zi[()])
self.assertEquals(np.array(1), zi[...])
zi[()] = -3
self.assertEquals(-3, zi[()])
zf = np.array(1.)
self.assertEquals(0, len(zf.shape))
self.assertEquals(1., zf[()])
self.assertEquals(np.array(1.), zf[...])
zf[()] = -3
self.assertEquals(-3, zf[()])
def testSavingBits(self):
d = dnp.arange(12*32).reshape((12,32))
b = dnp.abs(dnp.array(d, dnp.int8))
b[b < 0] = 0
print b.min(), b.max()
dnp.io.save(OutTestFolder+'uint.tiff', d, bits=32, signed=False)
dnp.io.save(OutTestFolder+'ushort.tiff', d, bits=16, signed=False)
dnp.io.save(OutTestFolder+'ubyte.tiff', b, bits=8, signed=False)
dnp.io.save(OutTestFolder+'int.tiff', d, bits=32)
dnp.io.save(OutTestFolder+'short.tiff', d, bits=16)
dnp.io.save(OutTestFolder+'byte.tiff', dnp.array(d, dnp.int8), bits=8)
dnp.io.save(OutTestFolder+'double.tiff', d, bits=33)
dnp.io.save(OutTestFolder+'float.tiff', d, bits=33)
dnp.io.save(OutTestFolder+'short.png', d, bits=16)
dnp.io.save(OutTestFolder+'byte.png', b, bits=8)
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 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 normalised_number_of_reflections(grouped_reflections):
no_of_reflec_list = []
for group in grouped_reflections:
no_of_reflec_list.append(len(group))
max_no = max(no_of_reflec_list)
no_of_reflec_list = [(float(no)/float(max_no))*100.0 for no in no_of_reflec_list]
return dnp.array(no_of_reflec_list)
