def __init__(self, *args):
unittest.TestCase.__init__(self, *args)
self.x = dnp.linspace(0, 5, 20)
dnp.random.seed(12347)
self.n = dnp.random.randn(20) * 0.1
self.y = 3.2 * self.x + 0.35 + self.n
self.z = 3.2 * self.x * self.x - 12.2 * self.x + 0.35 + self.n
def __init__(self, *args):
unittest.TestCase.__init__(self, *args)
self.x = dnp.linspace(0, 5, 20)
dnp.random.seed(12347)
self.n = dnp.random.randn(20)*0.1
self.y = 3.2*self.x + 0.35 + self.n
self.z = 3.2*self.x*self.x - 12.2*self.x + 0.35 + self.n
def testSpace(self):
print "test space"
dds = np.linspace(0, 11, 12)
self.checkitems(range(12), dds)
dds = np.linspace(0, 11, 12, dtype=np.int32)
self.checkitems(range(12), dds, toInt)
dds = np.linspace(0, 5, 11)
self.checkitems([0.5*i for i in range(11)], dds)
dds = np.linspace(0, 5, 11, dtype=np.int32)
self.checkitems([i/2 for i in range(11)], dds, toInt)
import math
dds = np.logspace(0,2,5)
self.checkitems([math.pow(10.,0.5*i) for i in range(5)], dds)
dds = np.logspace(0,2,5, dtype=np.int32)
self.checkitems([1, 3, 10, 31, 100], dds, toInt)
def get_chi_steps(two_theta, starting_chi_value=100.0, final_chi_value=0.0):
theta = dnp.radians(two_theta / 2.0) # converts to radians and halfs to theta
radius = LENGTH * float(dnp.sin(theta))
delta_chi = float(dnp.rad2deg((WIDTH / radius)))
number_of_steps = dnp.abs(final_chi_value-starting_chi_value)/(delta_chi*0.5)
number_of_steps = int(number_of_steps)+1
return dnp.linspace(starting_chi_value, final_chi_value, number_of_steps)
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 testSpace(self):
print "test space"
dds = np.linspace(0, 11, 12)
self.checkitems(range(12), dds)
dds = np.linspace(0, 11, 12, dtype=np.int32)
self.checkitems(range(12), dds, toInt)
dds = np.linspace(0, 5, 11)
self.checkitems([0.5 * i for i in range(11)], dds)
dds = np.linspace(0, 5, 11, dtype=np.int32)
self.checkitems([i / 2 for i in range(11)], dds, toInt)
import math
dds = np.logspace(0, 2, 5)
self.checkitems([math.pow(10., 0.5 * i) for i in range(5)], dds)
dds = np.logspace(0, 2, 5, dtype=np.int32)
self.checkitems([1, 3, 10, 31, 100], dds, toInt)
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 m6qgmax():
print("Move exit slit s6y to -6.5")
s6y.moveTo(-6.5)
q0 = m6qg.getPosition()
print("Current m6qg position: " + str(q0))
print("Scan m6qg ...")
m6Data = m6qgscan(q0)
# m6Data=GuassianScan();
x, y = m6Data['m6qg'], m6Data['ca62sr']
# x, y=m6Data['x'], m6Data['y'];
# [mu, sigma, peak, gf] = fineGaussianFitting(y, x, "Plot 1");
[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]
# data according to the model
x1 = dnp.linspace(x[0], x[x.size - 1], 500)
y1 = myGaussianFunc(mu, sigma, peak, [x1])
print("To plot the fitted data.")
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)
print("The peak position is: (%g, %g)" % (mu, peak))
ans = queryYesNo(
"Do you want to move m6qg to the estimated peak position?", "no")
if ans is 'yes':
m6qg.moveTo(mu)
print "m6qg:" + str(m6qg.getPosition())
return
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 dofourier(xarray, yarray, printres=False):
nvals = len(yarray)
avht = yarray.mean()
angles = dnp.linspace(0, 2 * dnp.pi, len(yarray))
# print angles
ycarray = yarray - avht
cosines = dnp.cos(angles)
sines = dnp.sin(angles)
cosint = cosines * ycarray
sinint = sines * ycarray
if printres:
print "Yarray:", yarray
print "Ycarray:", ycarray
print "Sinint:", sinint
print "Cosint:", cosint
cosum = cosint[:-1].sum()
sinsum = sinint[:-1].sum()
cosfactor = cosum / float(nvals - 1)
sinfactor = sinsum / float(nvals - 1)
if printres:
print "sinfactor, cosfactor: %.4f %.4f" % (sinfactor, cosfactor)
ratio = sinfactor / cosfactor
delta = math.atan(ratio)
deltadeg = math.degrees(delta)
if printres:
print "Delta degrees %06.4f" % deltadeg
halfmag = math.sqrt(sinfactor ** 2 + cosfactor ** 2)
mag = 2.0 * halfmag
if printres:
print "Magnitude: %06.4f" % mag
print "vertical centre %7.4f" % avht
calcresult = avht + mag * dnp.cos(angles - delta)
if printres:
print "Calcresult:", calcresult
for idx in range(0, len(yarray)):
print yarray[idx], calcresult[idx]
return (avht, mag, delta, calcresult)
def dofourier(xarray,yarray,printres=False):
nvals=len(yarray)
avht=yarray.mean()
angles=dnp.linspace(0,2*dnp.pi,len(yarray))
#print angles
ycarray=yarray-avht
cosines=dnp.cos(angles)
sines=dnp.sin(angles)
cosint=cosines*ycarray
sinint=sines*ycarray
if printres:
print "Yarray:",yarray
print "Ycarray:",ycarray
print "Sinint:",sinint
print "Cosint:",cosint
cosum=cosint[:-1].sum()
sinsum=sinint[:-1].sum()
cosfactor=cosum/float(nvals-1)
sinfactor=sinsum/float(nvals-1)
if printres:
print "sinfactor, cosfactor: %.4f %.4f"%(sinfactor,cosfactor)
ratio=sinfactor/cosfactor
delta=math.atan(ratio)
deltadeg=math.degrees(delta)
if printres:
print"Delta degrees %06.4f"%deltadeg
halfmag=math.sqrt(sinfactor**2+cosfactor**2)
mag=2.0*halfmag
if printres:
print"Magnitude: %06.4f"%mag
print "vertical centre %7.4f"%avht
calcresult=avht+mag*dnp.cos(angles-delta)
if printres:
print"Calcresult:",calcresult
for idx in range(0,len(yarray)):
print yarray[idx],calcresult[idx]
return(avht,mag,delta,calcresult)
def testMyGaussianFunc(): t = dnp.linspace(0, 99, 100)# dataset of 40 coordinate points between 0 and 5 dnp.plot.line(t, myGaussianFunc(10, 3, 10, [t]) ) # plot line of evaluated function
n=x.size;
a=(x[n-1] - x[0])/(n-1); b=x[0];
mu=a*mu0+b;
sigma=a*sigma0;
#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[n-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);
########################