def rebinfiles(self):
Eo = float(eval(self.Edgep.get()))
predg = float(eval(self.Prep.get()))
postedg = float(eval(self.Postp.get()))
xstep = float(eval(self.Edges.get()))
pstep = float(eval(self.Pres.get()))
kstep = float(eval(self.Posts.get()))
pstep = float(eval(self.Pres.get()))
kstep = float(eval(self.Posts.get()))
trunbef = float(eval(self.before.get())) / 1000
trunaft = float(eval(self.after.get())) / 1000
self.spectra = self.filesel.spectra
for item in self.spectra:
print "pippo1"
rebdata = bt.dat_Truncate(item.data, trunbef, trunaft)
rebdata = bt.rebin(rebdata,
0,
Eo,
predg,
postedg,
pstep,
xstep,
kstep,
file='',
msgStream=None)
writefile = item.fullfilename + ".reb"
item.bm29write(writefile, rebdata)
def plot(self):
#print self.Edgep
Eo = float(eval(self.Edgep.get()))
predg = float(eval(self.Prep.get()))
postedg = float(eval(self.Postp.get()))
xstep = float(eval(self.Edges.get()))
pstep = float(eval(self.Pres.get()))
kstep = float(eval(self.Posts.get()))
pstep = float(eval(self.Pres.get()))
kstep = float(eval(self.Posts.get()))
trunbef = float(eval(self.before.get())) / 1000
trunaft = float(eval(self.after.get())) / 1000
item = self.filesel.spectra[0]
rebdata = bt.dat_Truncate(item.data, trunbef, trunaft)
try:
rebdata = bt.rebin(rebdata,
0,
Eo,
predg,
postedg,
pstep,
xstep,
kstep,
file='',
msgStream=None)
except bt.RebinError, e:
self.graph = utility.Graph(e.parameter)
self.graph.plot([item.E, rebdata[:, 0] * 1000],
[item.Mu, rebdata[:, 1]],
["original", "Truncated"],
e.parameter + "\n only truncated data")
return
def Perform(self):
global linear_comb
global sel # define the range of spectra used
global x_array
sel=ut.string_range(self._sel.get())
x_array=self.file_sel.x_array[0]
start, end = self.retr_ranges()
Lista_Standard=LinComb.standard_list()
def float2(x):
try:
return float(x)
except:
return None
for item in self.Standard_list:
if item._active.get():
if item._label.get() in Lista_Standard.keys():
item._label.set(item._label.get()[:-1]+str(self.Standard_list.index(item)))
Lista_Standard.add(LinComb.standard(label=item._label.get(),
x=item.file_sel.spectra[0].x,
y=item.file_sel.spectra[0].y,
x0=x_array,
value=float2(item._start_value.get()),
fix=float2(item._fix.get()),
mini=float2(item._mini.get()),
maxi=float2(item._maxi.get())
))
linear_comb=list()
trunc=lambda x : bt.dat_Truncate([x_array,self.file_sel.spectra[x].y], start, end)
#for the column in selected spectra
for column in sel:
x,y=trunc(column)
linear_comb.append(LinComb.LinComb(
x,y, Lista_Standard))
self.Coeff=list()
self.Coeff_error=list()
self.residual=list()
self.total=list()
self.total_error=list()
self.chisq=list()
# non so perche??
linear_comb[0].solve()
for item in linear_comb:
item.solve()
value=[par.param.value for par in item.standards_list.itervalues()]
error=[par.stderr for par in item.standards_list.Standard_Parameters.itervalues()]
self.Coeff.append(value)
self.Coeff_error.append(error)
self.total.append(sum(value))
self.total_error.append(sum(error))
self.residual.append(item.result.residual)
self.chisq.append(item.result.chisqr)
#print self.Coeff
self.Coeff=numpy.transpose(numpy.array(self.Coeff))
self.Coeff_error=numpy.transpose(numpy.array(self.Coeff_error))
print "#"*20,"\nFit Done"
def calc(self):
"""calculate derivative and interpoolated x"""
if self.interpolation !=0:
self.x_deriv=numpy.arange(self.lim1,self.lim2,self.interpolation)
else:
self.x_deriv=bt.dat_Truncate(PPset.spectra[0].energy, self.lim1, self.lim2)
if len(PPset.spectra)!=0:
PPset.spectra[self.num_der].bm29derE(sampling=self.x_deriv, L1=self.lim1,
L2=self.lim2, s=self.smoot/100)
PPset.spectra[self.num_der].deriv=PPset.spectra[self.num_der].E_MuFp
PPset.spectra[self.num_der].x_int=self.x_deriv
def calc(self):
"""calculate derivative and interpoolated x"""
if self.interpolation != 0:
self.x_deriv = numpy.arange(self.lim1, self.lim2,
self.interpolation)
else:
self.x_deriv = bt.dat_Truncate(PPset.spectra[0].energy, self.lim1,
self.lim2)
if len(PPset.spectra) != 0:
PPset.spectra[self.num_der].bm29derE(sampling=self.x_deriv,
L1=self.lim1,
L2=self.lim2,
s=self.smoot / 100)
PPset.spectra[self.num_der].deriv = PPset.spectra[
self.num_der].E_MuFp
PPset.spectra[self.num_der].x_int = self.x_deriv
def Perform(self):
global spectra
global spectra_tot
global x_array
global x_array_t
sel=ut.string_range(self._sel.get())
x_array=self.file_sel.spectra[0].x
start, end = self.retr_ranges()
spectra_tot=ut.datalize(self.file_sel.spectra[0].y)
for i in sel[1:]:
spectra_tot =ut.datalize(spectra_tot,self.file_sel.spectra[i].y)
spectra=ut.datalize(x_array, spectra_tot)
spectra = bt.dat_Truncate(spectra, start, end)
x_array_t=spectra[:,0]
spectra =spectra[:,1:]
spectra=spectra.astype("double")
def rebinfiles(self):
Eo = float(eval(self.Edgep.get()))
predg= float(eval(self.Prep.get()))
postedg = float(eval(self.Postp.get()))
xstep = float(eval(self.Edges.get()))
pstep = float(eval(self.Pres.get()))
kstep = float(eval(self.Posts.get()))
pstep = float(eval(self.Pres.get()))
kstep = float(eval(self.Posts.get()))
trunbef= float(eval(self.before.get()))/1000
trunaft= float(eval(self.after.get()))/1000
self.spectra = self.filesel.spectra
for item in self.spectra:
print "pippo1"
rebdata=bt.dat_Truncate(item.data, trunbef, trunaft)
rebdata = bt.rebin(rebdata, 0, Eo, predg, postedg, pstep, xstep, kstep, file='', msgStream=None)
writefile = item.fullfilename +".reb"
item.bm29write(writefile,rebdata)
def plot(self):
#print self.Edgep
Eo = float(eval(self.Edgep.get()))
predg= float(eval(self.Prep.get()))
postedg = float(eval(self.Postp.get()))
xstep = float(eval(self.Edges.get()))
pstep = float(eval(self.Pres.get()))
kstep = float(eval(self.Posts.get()))
pstep = float(eval(self.Pres.get()))
kstep = float(eval(self.Posts.get()))
trunbef= float(eval(self.before.get()))/1000
trunaft= float(eval(self.after.get()))/1000
item = self.filesel.spectra[0]
rebdata = bt.dat_Truncate(item.data, trunbef, trunaft)
try:
rebdata=bt.rebin(rebdata, 0, Eo, predg, postedg, pstep, xstep, kstep, file='', msgStream=None)
except bt.RebinError, e:
self.graph = utility.Graph(e.parameter)
self.graph.plot([item.E,rebdata[:,0]*1000], [item.Mu,rebdata[:,1]],["original","Truncated"],e.parameter+"\n only truncated data")
return
def Perform(self):
global linear_comb
global sel # define the range of spectra used
global x_array
sel = ut.string_range(self._sel.get())
start, end = self.retr_ranges()
x_all = self.file_sel.x_array[0]
x_array = bt.dat_Truncate(x_all, start, end)
Lista_Standard = LinComb.standard_list()
def float2(x):
'''test if a value is a float'''
try:
return float(x)
except:
return None
# define the list of standart to be used in the refinement
for item in self.Standard_list:
try:
if item._active.get():
if item._label.get() in Lista_Standard.keys():
# change already used label
labelx = '%s%d' % (item._label.get()[:-1],
self.Standard_list.index(item))
item._label.set(labelx)
Lista_Standard.add(
LinComb.standard(label=item._label.get(),
x=item.file_sel.spectra[0].x,
y=item.file_sel.spectra[0].y,
x0=x_array,
value=float2(item._start_value.get()),
fix=float2(item._fix.get()),
mini=float2(item._mini.get()),
maxi=float2(item._maxi.get())))
except ValueError as err:
if 'Found x value not in the domain' in err:
a = 'Found x value outside %s ranges,' % item._label.get()
a = '%s\nfitting range will be decreased' % a
messager = a = '%s\nPlease Fit again\n' % a
newmin = item.file_sel.spectra[0].x.min()
newmin = newmin if newmin > x_array.min() else x_array.min(
)
self._fromf.set(str(round(newmin + 0.5)))
newmax = item.file_sel.spectra[0].x.max()
newmax = newmax if newmax < x_array.max() else x_array.max(
)
self._tof.set(str(round(newmax - 0.5)))
print messager
tkMessageBox.showinfo("Please fit again\n", messager)
raise ValueError('Found x value not in the domain')
else:
raise ValueError(err)
linear_comb = list()
trunc = lambda x: bt.dat_Truncate([x_all, self.file_sel.spectra[x].y],
start, end)
#for the column in selected spectra
for column in sel:
#pylab.plot(x_array,self.file_sel.spectra[column].y, 'o')
x, y = trunc(column)
linear_comb.append(LinComb.LinComb(x, y, Lista_Standard))
self.Coeff = list()
self.Coeff_error = list()
self.residual = list()
self.total = list()
self.total_error = list()
self.chisq = list()
# non so perche??
linear_comb[0].solve()
## fine test
pb = ttk.Progressbar(self.Quadro_Par4,
orient='horizontal',
mode='determinate',
maximum=len(linear_comb))
pb.pack(side=LEFT, anchor=W, expand=1, fill=X)
for j, item in enumerate(linear_comb):
if j != 0:
item.standards_list.Standard_Parameters = linear_comb[
j - 1].result.params
item.solve()
value = [par.value for par in item.result.params.itervalues()]
error = [par.stderr for par in item.result.params.itervalues()]
self.Coeff.append(value)
self.Coeff_error.append(error)
self.total.append(sum(value))
self.total_error.append(sum(error))
self.residual.append(item.result.residual)
self.chisq.append(item.result.chisqr)
pb.step()
pb.update_idletasks()
pb.destroy()
self.Coeff = numpy.transpose(numpy.array(self.Coeff))
self.Coeff_error = numpy.transpose(numpy.array(self.Coeff_error))
print "#" * 20, "\nFit Done"
def Perform(self):
param = PPset.spectra.call_pe
#-----------------Max derivate ---------------------
if self._check_deriv.get():
header = list(PPset.spectra.header)
print "\n ---Derivative Calculation---", num_deriv
if num_deriv:
for item in PPset.spectra:
if __verbose__: print "Xanes derivative"
item.bm29Num_der(window_len=self.smoot,
step=self.interpolation,
L1=float(self._deriv_start.get()),
L2=float(self._deriv_end.get()),
repeat=self.smoot_repeat)
self.derivate_PlSa_But.x_array = [
item.NumDer.x_int for item in PPset.spectra
]
self.derivate_PlSa_But.y_array = [
item.NumDer.deriv for item in PPset.spectra
]
self.derivate_PlSa_But.comments = [
header for item in PPset.spectra
]
else:
if self.interpolation != 0:
self.x_deriv=numpy.arange(float(self._deriv_start.get()), \
float(self._deriv_end.get()), \
self.interpolation)
else:
self.x_deriv=bt.dat_Truncate(PPset.spectra[0].energy, \
float(self._deriv_start.get()), \
float(self._deriv_end.get()))
for item in PPset.spectra:
if __verbose__: print "Xanes derivative"
item.bm29derE(sampling=self.x_deriv, L1=float(self._deriv_start.get()),\
L2=float(self._deriv_end.get()), s=self.smoot/100)
self.derivate_PlSa_But.x_array = [
self.x_deriv for item in PPset.spectra
]
self.derivate_PlSa_But.y_array = [
item.E_MuFp for item in PPset.spectra
]
self.derivate_PlSa_But.comments = [
header for item in PPset.spectra
]
c1 ="# Derivative calc. between "+ self._deriv_start.get() + \
" and "+ self._deriv_end.get()+"\n"
#item.append(c1)
if num_deriv:
c1 = "# Num. derivative calc. with smoot= %1.8f ," \
"interpolation= %1.4f, repeated= %2d\n" %(
self.smoot, self.interpolation ,self.smoot_repeat)
else:
c1 = "# Spl derivative calc. with smoot= %1.8f ,"\
"interpolation= %1.4f\n" %(
self.smoot, self.interpolation)
self.derivate_PlSa_But.comments[0].append(c1)
self.derivate_PlSa_But.comments[0].append("#L E Derivate_smot" +
str(self.smoot) + "\n")
self.Max_PlSa_But.x_array = [PPset.x]
self.Max_PlSa_But.comments = []
self.Max_PlSa_But.comments.append(
list(self.derivate_PlSa_But.comments[0]))
self.Max_PlSa_But.comments[0][-1] = "# index Derivate_Max\n"
for item in PPset.spectra:
self.Max_PlSa_But.y_array = [[
item.NumDer.x_int[numpy.argmax(item.NumDer.deriv)]
for item in PPset.spectra
]]
#-----------------XANES norm ---------------------
if self._check_xan.get():
print "\n ---XANES Normalization---"
header = list(PPset.spectra.header)
pb = ttk.Progressbar(self.quadro_perform,
orient='horizontal',
mode='determinate',
maximum=len(PPset.spectra))
pb.pack(side=LEFT, anchor=W, expand=1, fill=X)
for item in PPset.spectra:
if __verbose__: print "Xanes norm"
pb.step()
pb.update_idletasks()
item.XANES_Norm(**param)
if __verbose__: print "Xanes norm done"
pb.destroy()
#self._check_xan.set(0)
#put some reasonable value in the other case
self._deriv_start.set(round(PPset.spectra[0].e0 - 50, 2))
self._deriv_end.set(round(PPset.spectra[0].e0 + 80, 2))
self._INTxan_start.set(round(PPset.spectra[0].e0 - 50, 2))
self._INTxan_end.set(round(PPset.spectra[0].e0 + 80, 2))
self.Xanes_PlSa_But.x_array = [
item.energy for item in PPset.spectra
]
if self._xflatten.get() and True or False:
self.Xanes_PlSa_But.y_array = [
item.flat for item in PPset.spectra
]
else:
self.Xanes_PlSa_But.y_array = [
item.norm for item in PPset.spectra
]
self.Xanes_PlSa_But.comments = [header for item in PPset.spectra]
##comment how calculated XANES norm
c1 = "# Xanes normalization calc. with "
for i in ['e0', 'pre1', 'pre2']:
c1 += "%s = %s, " % (i, str(PPset.spectra.call_pe[i]))
self.Xanes_PlSa_But.comments[0].append(c1 + '\n')
c2 = "# Xanes normalization calc. with"
for i in ['norm1', 'norm2']:
c1 += "%s = %s, " % (i, str(PPset.spectra.call_pe[i]))
c2 += ", flatted= " + str(self._xflatten.get() and True or False)
self.Xanes_PlSa_But.comments[0].append(c2 + '\n')
#add nuovo E Norm for all comments
self.Xanes_PlSa_But.comments[0].append("# E Nor\n")
#-----------------iff e0---------------------
self.Eo_PlSa_But.x_array = [PPset.x]
self.Eo_PlSa_But.comments = []
self.Eo_PlSa_But.comments.append(list(header[:-1]))
##comment
self.Eo_PlSa_But.comments[0].append("# index iff_Eo\n")
self.Eo_PlSa_But.y_array = [[item.e0 for item in PPset.spectra]]
#-----------------iff ej---------------------
self.Ej_PlSa_But.x_array = [PPset.x]
self.Ej_PlSa_But.comments = []
self.Ej_PlSa_But.comments.append(list(header[:-1]))
self.Ej_PlSa_But.comments[0].append("# index iff_Ej\n")
self.Ej_PlSa_But.y_array = [[
item.edge_step for item in PPset.spectra
]]
#-----------------Integral---------------------
if self._check_INT.get():
self.INTxan_PlSa_But.x_array = [PPset.x]
self.INTxan_PlSa_But.comments = []
self.INTxan_PlSa_But.comments.append(
list(self.Xanes_PlSa_But.comments[0]))
self.INTxan_PlSa_But.comments[0].append(
"# Int Eo cAlculated with parameter %s , %s%s" %
(str(self._INTxan_start.get()), str(
self._INTxan_end.get()), "\n"))
self.INTxan_PlSa_But.comments[0].append("#L INDEX Int\n")
x1 = float(eval(self._INTxan_start.get()))
x2 = float(eval(self._INTxan_end.get()))
if self._xflatten.get() and True or False:
for i in PPset.spectra:
i.INTxan = i.bm29int(L1=x1, L2=x2, attribute='flat')
else:
for i in PPset.spectra:
i.INTxan = i.bm29int(L1=x1, L2=x2, attribute='nor')
self.INTxan_PlSa_But.y_array = [[
item.INTxan for item in PPset.spectra
]]
print "\n---module XANES done\n"
def Perform(self):
param=PPset.spectra.call_pe
#-----------------Max derivate ---------------------
if self._check_deriv.get():
header=list(PPset.spectra.header)
print "\n ---Derivative Calculation---" , num_deriv
if num_deriv:
for item in PPset.spectra:
if __verbose__: print "Xanes derivative"
item.bm29Num_der(window_len=self.smoot, step=self.interpolation,
L1=float(self._deriv_start.get()),
L2=float(self._deriv_end.get()),
repeat=self.smoot_repeat)
self.derivate_PlSa_But.x_array= [item.NumDer.x_int for item in PPset.spectra]
self.derivate_PlSa_But.y_array= [item.NumDer.deriv for item in PPset.spectra]
self.derivate_PlSa_But.comments= [header for item in PPset.spectra]
else:
if self.interpolation !=0:
self.x_deriv=numpy.arange(float(self._deriv_start.get()), \
float(self._deriv_end.get()), \
self.interpolation)
else:
self.x_deriv=bt.dat_Truncate(PPset.spectra[0].energy, \
float(self._deriv_start.get()), \
float(self._deriv_end.get()))
for item in PPset.spectra:
if __verbose__: print "Xanes derivative"
item.bm29derE(sampling=self.x_deriv, L1=float(self._deriv_start.get()),\
L2=float(self._deriv_end.get()), s=self.smoot/100)
self.derivate_PlSa_But.x_array= [self.x_deriv for item in PPset.spectra]
self.derivate_PlSa_But.y_array= [item.E_MuFp for item in PPset.spectra]
self.derivate_PlSa_But.comments= [header for item in PPset.spectra]
for item in self.derivate_PlSa_But.comments:
c1 ="# Derivative calc. between "+ self._deriv_start.get() + \
" and "+ self._deriv_end.get()+"\n"
#item.append(c1)
if num_deriv:
c1 = "# Num. derivative calc. with smoot= %1.8f ," \
"interpolation= %1.4f, repeated444= %2d\n" %(
self.smoot, self.interpolation ,self.smoot_repeat)
else:
c1 = "# Spl derivative calc. with smoot= %1.8f ,"\
"interpolation= %1.4f\n" %(
self.smoot, self.interpolation)
item.append(c1)
item.append("#L E Derivate_smot"+str(self.smoot)+"\n")
self.Max_PlSa_But.x_array= [PPset.x]
self.Max_PlSa_But.comments=[]
self.Max_PlSa_But.comments.append(header)
c1 ="# Max derivative calc. between "+ self._deriv_start.get()+" and "+ self._deriv_end.get()+"\n"
self.Max_PlSa_But.comments[0].append(c1)
if num_deriv:
c1 = "# Max derivative calc. with smoot= %1.8f ,interpolation= %1.4f, repeated= %1.4f\n" %(
self.smoot, self.interpolation,self.smoot_repeat)
self.Max_PlSa_But.comments[0].append(c1)
self.Max_PlSa_But.comments[0].append(PPset.spectra.header)
self.Max_PlSa_But.comments[0].append( "#L index Derivate_Max\n")
for item in PPset.spectra:
self.Max_PlSa_But.y_array= [[item.NumDer.x_int[numpy.argmax(item.NumDer.deriv)] for item in PPset.spectra]]
#-----------------XANES norm ---------------------
if self._check_xan.get():
print "\n ---XANES Normalization---"
header=list(PPset.spectra.header)
pb = ttk.Progressbar(self.quadro_perform, orient='horizontal',
mode='determinate',
maximum=len(PPset.spectra))
pb.pack(side = LEFT,anchor = W, expand = 1, fill = X)
for item in PPset.spectra:
if __verbose__: print "Xanes norm"
pb.step()
pb.update_idletasks()
item.XANES_Norm(**param)
if __verbose__: print "Xanes norm done"
pb.destroy()
#self._check_xan.set(0)
#put some reasonable value in the other case
self._deriv_start.set(round(PPset.spectra[0].e0 -50, 2))
self._deriv_end.set(round(PPset.spectra[0].e0 +80, 2))
self._INTxan_start.set(round(PPset.spectra[0].e0 -50, 2))
self._INTxan_end.set(round(PPset.spectra[0].e0 +80, 2))
self.Xanes_PlSa_But.x_array= [item.energy for item in PPset.spectra]
if self._xflatten.get() and True or False:
self.Xanes_PlSa_But.y_array= [item.flat for item in PPset.spectra]
else:
self.Xanes_PlSa_But.y_array= [item.norm for item in PPset.spectra]
self.Xanes_PlSa_But.comments= [header for item in PPset.spectra]
##comment how calculated XANES norm
c1 ="# Xanes normalization calc. with "
for i in ['e0','pre1','pre2']: c1+="%s = %s, " %(i, str(PPset.spectra.call_pe[i]))
self.Xanes_PlSa_But.comments[0].append(c1+'\n')
c2 = "# Xanes normalization calc. with"
for i in ['norm1', 'norm2']: c1+="%s = %s, " %(i, str(PPset.spectra.call_pe[i]))
c2+=", flatted= "+str(self._xflatten.get() and True or False)
self.Xanes_PlSa_But.comments[0].append(c2+'\n')
#add nuovo E Norm for all comments
self.Xanes_PlSa_But.comments[0].append("# E Nor\n")
#-----------------iff e0---------------------
self.Eo_PlSa_But.x_array= [PPset.x]
self.Eo_PlSa_But.comments=[]
self.Eo_PlSa_But.comments.append(list(header[:-1]))
##comment
self.Eo_PlSa_But.comments[0].append( "# index iff_Eo\n")
self.Eo_PlSa_But.y_array= [[item.e0 for item in PPset.spectra]]
#-----------------iff ej---------------------
self.Ej_PlSa_But.x_array= [PPset.x]
self.Ej_PlSa_But.comments=[]
self.Ej_PlSa_But.comments.append(list(header[:-1]))
self.Ej_PlSa_But.comments[0].append( "# index iff_Ej\n")
self.Ej_PlSa_But.y_array= [[item.edge_step for item in PPset.spectra]]
#-----------------Integral---------------------
if self._check_INT.get():
self.INTxan_PlSa_But.x_array= [PPset.x]
self.INTxan_PlSa_But.comments=[]
self.INTxan_PlSa_But.comments.append(list(self.Xanes_PlSa_But.comments[0]))
self.INTxan_PlSa_But.comments[0].append(
"# Int Eo cAlculated with parameter %s , %s%s"
%(str(self._INTxan_start.get()),
str(self._INTxan_end.get()),"\n"))
self.INTxan_PlSa_But.comments[0].append( "#L INDEX Int\n")
x1=float(eval(self._INTxan_start.get()))
x2=float(eval(self._INTxan_end.get()))
if self._xflatten.get() and True or False:
for i in PPset.spectra:i.INTxan= i.bm29int(L1=x1, L2=x2, attribute='flat')
else :
for i in PPset.spectra:i.INTxan= i.bm29int(L1=x1, L2=x2, attribute='nor')
self.INTxan_PlSa_But.y_array= [[item.INTxan for item in PPset.spectra]]
print "\n---module XANES done\n"
def Perform(self):
PPset.spectra = PPset.listum()
PP_spec = PPset.spectra
PP_spec.header = list(PPfs_spec.header)
PP_spec.other_pro = dict()
#print type(PPset.spectra.other_pro)
aver = self._aver.get()
#filesel define the index to keep
filesel = ut.string_range(self._from_tof.get())
Eo = float(eval(self.Edgep.get()))
predg = float(eval(self.Prep.get()))
postedg = float(eval(self.Postp.get()))
xstep = float(eval(self.Edges.get()))
pstep = float(eval(self.Pres.get()))
kstep = float(eval(self.Posts.get()))
trunbef = float(eval(self.before.get()))
trunaft = float(eval(self.after.get()))
#print aver , start
#------------------Averafges------------------------------
if self._c_aver.get() and (aver > 1):
#progress bar
for line in PP_spec.header:
if '# averaged each' in line: PP_spec.header.remove(line)
PP_spec.header.append(
"# averaged each {0:4d} spectra,\n".format(aver))
#self.lista_iter define the number of index of filesel to average
for item in range(0, len(filesel), aver):
list_ave = [PPfs_spec[ii] for ii in filesel[item:item + aver]]
E, Mu = bt.spectra_average([[i.energy, i.mu]
for i in list_ave])
PP_spec.append(bm29.bm29file([E, Mu]))
#average properties
part = len(filesel) % aver
full = len(filesel) - part
for option in PPfs_spec.other_pro:
ar_fl = PPfs_spec.other_pro[option][filesel]
print ar_fl.shape, ar_fl[:full].shape
ar_fl_full = numpy.mean(ar_fl[:full].reshape(-1, aver), axis=1)
ar_fl_part = ar_fl[full + 1:].mean() if part > 0 else []
PP_spec.other_pro[option] = numpy.append(
ar_fl_full, ar_fl_part)
else:
for item in [PPfs_spec[i] for i in filesel]:
data = numpy.transpose(numpy.vstack((item.E, item.Mu)))
PP_spec.append(bm29.bm29file(data))
PP_spec.other_pro = dict(PPfs_spec.other_pro)
#------------------Truncate------------------------------
if self._c_Trun.get():
PP_spec.header = [
item for item in PP_spec.header if not ("# truncate" in item)
]
PP_spec.header.append("# truncate between {0:5.9f}" +\
"and{1:5.9f} ".format(trunbef, trunaft))
for i, item in enumerate(PP_spec):
data = numpy.transpose(numpy.vstack((item.E, item.Mu)))
data = bt.dat_Truncate(data, trunbef, trunaft)
PP_spec[i].energy, PP_spec[i].mu = map(numpy.ravel,
numpy.hsplit(data, 2))
#spectra[i]=bm29.bm29file(data)
#------------------Rebin------------------------------
if self._c_Rebin.get():
PP_spec.header = [
item for item in PP_spec.header if not ('# rebin' in item)
]
PP_spec.header.append("# rebin with param. {0:5.7f},{1:5.7f},"\
"{2:5.7f},{3:5.7f},{4:5.7f},{5:5.7f}\n".format(
Eo, predg, postedg, pstep, xstep, kstep))
pb = ttk.Progressbar(self.quadro_buttonp1,
orient='horizontal',
mode='determinate',
maximum=len(PP_spec))
pb.pack(side=LEFT, anchor=W, expand=1, fill=X)
for i, item in enumerate(PP_spec):
data = numpy.transpose(numpy.vstack((item.energy, item.mu)))
pb.step()
pb.update_idletasks()
try:
data = bt.rebin(data,
0,
Eo,
predg,
postedg,
pstep,
xstep,
kstep,
file='',
msgStream=None)
PP_spec[i] = (bm29.bm29file(data))
except bt.RebinError, self.rebinerror:
top = Toplevel()
Label(top,
text="Rebin not performed " +
self.rebinerror.parameter).pack(expand=YES)
break
pb.destroy()
def Perform(self):
global linear_comb
global sel # define the range of spectra used
global x_array
sel = ut.string_range(self._sel.get())
x_array = self.file_sel.x_array[0]
start, end = self.retr_ranges()
Lista_Standard = LinComb.standard_list()
def float2(x):
try:
return float(x)
except:
return None
for item in self.Standard_list:
if item._active.get():
if item._label.get() in Lista_Standard.keys():
item._label.set(item._label.get()[:-1] +
str(self.Standard_list.index(item)))
Lista_Standard.add(
LinComb.standard(label=item._label.get(),
x=item.file_sel.spectra[0].x,
y=item.file_sel.spectra[0].y,
x0=x_array,
value=float2(item._start_value.get()),
fix=float2(item._fix.get()),
mini=float2(item._mini.get()),
maxi=float2(item._maxi.get())))
linear_comb = list()
trunc = lambda x: bt.dat_Truncate(
[x_array, self.file_sel.spectra[x].y], start, end)
#for the column in selected spectra
for column in sel:
x, y = trunc(column)
linear_comb.append(LinComb.LinComb(x, y, Lista_Standard))
self.Coeff = list()
self.Coeff_error = list()
self.residual = list()
self.total = list()
self.total_error = list()
self.chisq = list()
# non so perche??
linear_comb[0].solve()
for item in linear_comb:
item.solve()
value = [
par.param.value for par in item.standards_list.itervalues()
]
error = [
par.stderr for par in
item.standards_list.Standard_Parameters.itervalues()
]
self.Coeff.append(value)
self.Coeff_error.append(error)
self.total.append(sum(value))
self.total_error.append(sum(error))
self.residual.append(item.result.residual)
self.chisq.append(item.result.chisqr)
#print self.Coeff
self.Coeff = numpy.transpose(numpy.array(self.Coeff))
self.Coeff_error = numpy.transpose(numpy.array(self.Coeff_error))
print "#" * 20, "\nFit Done"
def Perform(self):
PPset.spectra=PPset.listum()
PP_spec=PPset.spectra
PP_spec.header=list(PPfs_spec.header)
PP_spec.other_pro=dict()
#print type(PPset.spectra.other_pro)
aver = self._aver.get()
#filesel define the index to keep
filesel = ut.string_range(self._from_tof.get())
Eo = float(eval(self.Edgep.get()))
predg= float(eval(self.Prep.get()))
postedg = float(eval(self.Postp.get()))
xstep = float(eval(self.Edges.get()))
pstep = float(eval(self.Pres.get()))
kstep = float(eval(self.Posts.get()))
trunbef= float(eval(self.before.get()))
trunaft= float(eval(self.after.get()))
#print aver , start
#------------------Averafges------------------------------
if self._c_aver.get() and (aver>1):
#progress bar
for line in PP_spec.header:
if '# averaged each' in line:PP_spec.header.remove(line)
PP_spec.header.append("# averaged each {0:4d} spectra,\n".format(aver))
#self.lista_iter define the number of index of filesel to average
for item in range(0,len(filesel),aver):
list_ave=[PPfs_spec[ii] for ii in filesel[item:item+aver]]
E, Mu = bt.spectra_average([[i.energy,i.mu] for i in list_ave])
PP_spec.append(bm29.bm29file([E,Mu]))
#average properties
part=len(filesel)%aver
full=len(filesel)-part
for option in PPfs_spec.other_pro:
ar_fl=PPfs_spec.other_pro[option][filesel]
print ar_fl.shape, ar_fl[:full].shape
ar_fl_full=numpy.mean(ar_fl[:full].reshape(-1, aver), axis=1)
ar_fl_part=ar_fl[full+1:].mean() if part>0 else []
PP_spec.other_pro[option]=numpy.append(ar_fl_full,ar_fl_part)
else:
for item in [PPfs_spec[i] for i in filesel]:
data = numpy.transpose(numpy.vstack((item.E,item.Mu)))
PP_spec.append(bm29.bm29file(data))
PP_spec.other_pro=dict(PPfs_spec.other_pro)
#------------------Truncate------------------------------
if self._c_Trun.get():
PP_spec.header=[item for item in PP_spec.header if not("# truncate" in item)]
PP_spec.header.append("# truncate between {0:5.9f}" +\
"and{1:5.9f} ".format(trunbef, trunaft))
for i,item in enumerate(PP_spec):
data = numpy.transpose(numpy.vstack((item.E,item.Mu)))
data = bt.dat_Truncate(data, trunbef, trunaft)
PP_spec[i].energy, PP_spec[i].mu = map(numpy.ravel,
numpy.hsplit(data,2))
#spectra[i]=bm29.bm29file(data)
#------------------Rebin------------------------------
if self._c_Rebin.get():
PP_spec.header=[item for item in PP_spec.header if not('# rebin' in item)]
PP_spec.header.append("# rebin with param. {0:5.7f},{1:5.7f},"\
"{2:5.7f},{3:5.7f},{4:5.7f},{5:5.7f}\n".format(
Eo, predg, postedg, pstep, xstep, kstep))
pb = ttk.Progressbar(self.quadro_buttonp1, orient='horizontal',
mode='determinate',
maximum=len(PP_spec))
pb.pack(side = LEFT,anchor = W, expand = 1, fill = X)
for i,item in enumerate(PP_spec):
data = numpy.transpose(numpy.vstack((item.energy,item.mu)))
pb.step() ;pb.update_idletasks()
try:
data=bt.rebin(data, 0, Eo, predg, postedg, pstep, xstep,
kstep, file='', msgStream=None)
PP_spec[i]=(bm29.bm29file(data))
except bt.RebinError, self.rebinerror:
top = Toplevel()
Label(top, text = "Rebin not performed " +
self.rebinerror.parameter).pack(expand = YES)
break
pb.destroy()
