def rawspectrum(run,name,mins=(183,227),maxs=(234,302)):
p = PelFile(basedir+"%04i/" % run + name+".pel")
mon = MonFile(basedir+"%04i/" % run + name+".pel.txt",False)
spectrum_total = np.sum(mon.spec)
#get the data spectrum
val = p.make1d(mins,maxs)
val = rebin(val)
err = np.sqrt(val)
# get the background spectrum
bval = p.make1d((412,412),(512,512))
bval = rebin(bval)
berr = np.sqrt(bval)
#normalize background to beam size
bval *= (maxs[0]-mins[0])/100*(maxs[1]-mins[1])/100
berr *= (maxs[0]-mins[0])/100*(maxs[1]-mins[1])/100
val -= bval
err = np.sqrt(err**2+berr**2)
val /= spectrum_total
err = np.sqrt((err/spectrum_total)**2+
(err*np.sqrt(spectrum_total)/spectrum_total**2)**2)
return (val,err)
def makePel(self):
"""Create a blank Pel object for loading detector data"""
data = PelFile()
def statusfunc(x):
self.progress.SetValue(x)
self.Yield()
data.statusfunc = statusfunc
return data
def raw(run,name,start=50,end=100,size=512):
p = PelFile(basedir+"%04i/" % run + name+".pel")
mon = MonFile(basedir+"%04i/" % run + name+
".pel.txt",False)
val = np.sum(p.make3d(size)[:,:,start:end],axis=2)
print val.shape
spectrum_total = np.sum(mon.spec)
return val/spectrum_total,np.sqrt(val)/spectrum_total
def simple_spectrum(run,mins=(0,0),maxs=(16,128),mask=None):
p = PelFile(basedir+"SESAME_%i/SESAME_%i_neutron_event.dat"%(run,run))
mon = MonFile(basedir+"SESAME_%i/SESAME_%i_bmon_histo.dat"%(run,run),False)
data = p.make1d(mins,maxs,mask)
err = np.sqrt(data)/np.sum(mon.spec)
data /= np.sum(mon.spec)
return (data,err)
def fr(run,name,mins=(183,227),maxs=(234,302)):
p = PelFile(basedir+"%04i/" % run + name+"up.pel")
mon = MonFile(basedir+"%04i/" % run + name+"up.pel.txt",False)
up = p.make1d(mins,maxs)/np.sum(mon.spec)
p = PelFile(basedir+"%04i/" % run + name+"down.pel")
mon = MonFile(basedir+"%04i/" % run + name+"down.pel.txt",False)
down = p.make1d(mins,maxs)/np.sum(mon.spec)
return np.sum(up[50:100])/np.sum(down[50:100])
def spectrum(run,name,mins=(183,227),maxs=(234,302)):
p = PelFile(basedir+"%04i/" % run + name+"up.pel")
mon = MonFile(basedir+"%04i/" % run + name+"up.pel.txt",False)
up = p.make1d(mins,maxs)/np.sum(mon.spec)
p = PelFile(basedir+"%04i/" % run + name+"down.pel")
mon = MonFile(basedir+"%04i/" % run + name+"down.pel.txt",False)
down = p.make1d(mins,maxs)/np.sum(mon.spec)
return (up-down)/(up+down)
def spectrum(run,name,mins=(0,0),maxs=(16,128),mask=None):
name = normalize_name(name)
p = PelFile(basedir+"SESAME_%i/" % run + name+"up_neutron_event.dat")
mon = MonFile(basedir+"SESAME_%i/" % run + name+"up_bmon_histo.dat",False)
up = p.make1d(mins,maxs,mask)
uperr = np.sqrt(up)/np.sum(mon.spec)
up /= np.sum(mon.spec)
p = PelFile(basedir+"SESAME_%i/" % run + name+"down_neutron_event.dat")
mon = MonFile(basedir+"SESAME_%i/" % run + name+"down_bmon_histo.dat",False)
down = p.make1d(mins,maxs,mask)
downerr = np.sqrt(down)/np.sum(mon.spec)
down /= np.sum(mon.spec)
return (up-down)/(up+down)
def getIntegratedSpectra(run,name,mins,maxs,mask):
name = normalize_name(name)
p = PelFile(basedir+"SESAME_%i/" % run + name+"up_neutron_event.dat")
mon = MonFile(basedir+"SESAME_%i/" % run + name+"up_bmon_histo.dat",False)
up = np.sum(p.make1d(mins,maxs,mask)[40:90])
uperr = np.sqrt(up)/np.sum(mon.spec)
up /= np.sum(mon.spec)
p = PelFile(basedir+"SESAME_%i/" % run + name+"down_neutron_event.dat")
mon = MonFile(basedir+"SESAME_%i/" % run + name+"down_bmon_histo.dat",False)
down = np.sum(p.make1d(mins,maxs,mask)[40:90])
downerr = np.sqrt(down)/np.sum(mon.spec)
down /= np.sum(mon.spec)
return (up,uperr,down,downerr)
def spectrum(run,mins=(0,0),maxs=(16,128),mask=None):
eventfile = basedir+"SESAME_%i/SESAME_%i" % (run,run) + "_neutron_event.dat"
if not os.path.isfile(eventfile):
return None
p = PelFile(eventfile)
mon = MonFile(basedir+"SESAME_%i/SESAME_%i" % (run,run) +"_bmon_histo.dat",False)
up = p.make1d(mins,maxs,mask)
manifestFile = basedir+"SESAME_%i/SESAME_%i" % (run,run) + "_runinfo.xml"
text = ET.parse(manifestFile).getroot().find(".//"+XMLNS+"Notes").text
text = HTMLParser.HTMLParser().unescape(text)
manifest = json.loads(text)
return up,np.sum(mon.spec),manifest
def spectrum(up,down,mins=(183,227),maxs=(234,302)):
p = PelFile(up)
mon = MonFile(up+".txt",False)
up = p.make1d(mins,maxs)
uperr = np.sqrt(up)
up /= np.sum(mon.spec)
uperr /= np.sum(mon.spec)
p = PelFile(down)
mon = MonFile(down+".txt",False)
down = p.make1d(mins,maxs)
downerr = np.sqrt(down)
down /= np.sum(mon.spec)
downerr /= np.sum(mon.spec)
del p
p = (up-down)/(up+down)
e = 2 * up /(up**2-down**2)*np.sqrt(uperr**2+downerr**2)
return (p,e)
def get_2d_int(run,name):
p = PelFile(basedir+"SESAME_%i/" % run + name+"_neutron_event.dat")
mon = MonFile(basedir+"SESAME_%i/" % run + name+"_bmon_histo.dat",False)
return np.sum(p.make3d(),axis=2) / np.sum(mon.spec)
from reader import PelFile
from optparse import OptionParser
import numpy as np
basedir = "C:/Documents and Settings/sesaadmin/My Documents/Neutron Data/"
if __name__=='__main__':
parser = OptionParser()
(options,runs) = parser.parse_args()
p = PelFile(basedir + "%04i/0001.pel"%int(runs[0]))
x = p.data & 0x7FF
y = p.data & 0x3FF800 >> 11
t = p.convertTime(p.data >> 32 & 0x7FFFFFFF)
for i in range(len(x)):
print "%i\t%i\t%f"%(x[i],y[i],t[i])
import __future__
from reader import PelFile
import glob
import numpy as np
import sys
files = glob.glob(sys.argv[1])
print(files)
files = [x for x in files if x[-3:]=="pel"]
for file in files:
data = PelFile(file)
hist = data.make3d()
np.save(file+".npy",hist)
print(file)
print(b)
print("Orthonormed")
return b
def project(data,basis):
co = [] #coefficients
for x in basis:
co.append(np.inner(data,x))
return co
if __name__ == "__main__":
dir = "C:/Documents and Settings/adlwashi/My Documents/Variable Gains"
files = ["11122009n.pel","11122009r.pel","11122009t.pel","11122009u.pel",
"11122009y.pel","11132009c.pel","11132009g.pel"]
files = [dir+"/"+file for file in files]
data = PelFile()
ws = [data.getgains(data.peakheader(file)) for file in files]
alphas = []
for file in files[:2]:
data = PelFile()
data.readfileimage(file)
alphas.append(data.make2d(0,0x7FFFFFFF).reshape(256*256))
print(alphas)
betas = orthonorm(alphas)
perfect = np.ones(shape=(256*256),dtype=np.double)
# perfect=perfect/np.linalg.norm(perfect)
# print(perfect)
co = project(perfect,betas)
print(co)
a = np.zeros(shape=(256*256),dtype=np.double)
