def findoffBB(self, t, plane, MADTwiss, betalist):
print "METHOD => Starting to calculate off-momentum beta-beat"
self.bpms = intersect(t)
self.bpms = modelIntersect(self.bpms, MADTwiss)
MADTwiss.chrombeat()
self.slope = {}
self.slopeE = {}
self.slopeM = {}
dpplist = []
for i in range(len(betalist)): #### for sorting
dpplist.append(betalist[i]['DPP'])
dpplist.sort()
#if dpplist[0]>dpplist[1]
#dpplist.reverse()
for i in range(len(self.bpms)):
bn = upper(self.bpms[i][1])
check = 0
slopei = 0.0
slopeiM = 0.0
beta = []
k = 0
for k in range(len(dpplist)):
count = 0
for j in range(len(betalist)):
if dpplist[k] == betalist[j]['DPP']:
if count == 0:
beta.append(betalist[j][bn][0])
count = 1
[slope, offset, R, slope_error,
offset_error] = linreg.linreg(dpplist, beta)
a = FIT(dpplist, beta, 1, 1e-15)
#print ' list '+str(dpplist)
coef, error = a.linreg()
if plane == 'H':
slopeiM = MADTwiss.dbx[MADTwiss.indx[upper(bn)]]
else:
slopeiM = MADTwiss.dby[MADTwiss.indx[upper(bn)]]
#self.slope[bn]=coef[0]
self.slope[bn] = coef[0] / 100
#self.slopeE[bn]=error[0]
self.slopeE[bn] = error[0] / 100
self.slopeM[bn] = slopeiM
def startUp(self):
#generate dummy data to plot at start-up
X = np.arange(800, 2000, 2)
Y1 = FIT.lorents(X, 1800, 1358, 110)
Y2 = FIT.lorents(X, 1700, 1590, 50)
self.subplot.plot(X, (Y1+Y2)+20*np.random.randn(len(X)), 'o')
self.subplot.plot(X, Y1+Y2, 'r--', label = 'Sample data')
self.subplot.legend()
self.subplot.plot(X, Y1)
self.subplot.plot(X, Y2)
self.subplot.set_xlim(np.min(X), np.max(X))
self.plotAdjust()
def previewAll(self):
if not np.shape(self.data.X):
self.errorBox('There is no data', 'No data...')
return
self.subplot.clear()
self.subplot.plot(self.data.X, self.data.current, label = 'Experimental data')
cols = self.tableWidget.columnCount()
rows = self.tableWidget.rowCount()
cumulative = np.zeros(len(self.data.X))
intensity = [float(self.tableWidget.item(row, 5).text()) for row in range(rows) if self.tableWidget.item(row, 0).checkState()&Qt.Checked]
norm = max(self.data.current)/max(intensity)
for row in range(rows):
if self.tableWidget.item(row, 0).checkState() & Qt.Checked:
error = [self.itemCheck(self.tableWidget.item(row, col), silent=True) for col in range(1, cols-1)]
if True in error:
self.errorBox("Bad parameters", "Bad values")
return
params =[float(self.tableWidget.item(row, col).text()) for col in [5, 6, 2]]
params[0] *= norm
label = self.tableWidget.item(row, 1).text()
shape = self.tableWidget.cellWidget(row, 7).currentText().strip()[0]
if shape == 'V':
params = np.append(params, 0.5)
elif shape == 'B':
params = np.append(params, 0.02)
p = FIT.Peak(FIT, self.data.X, *params, shape = shape)
cumulative += p
self.subplot.plot(self.data.X, p, label = label)
self.subplot.plot(self.data.X, cumulative, 'r--', label = "Cumulative")
self.plotAdjust()
def previewBand(self, row):
if not np.shape(self.data.X):
self.errorBox('There is no data', 'No data...')
return
cols = self.tableWidget.columnCount()
params =[self.tableWidget.item(row, col).text() for col in range(1, cols-1)]
shape = self.tableWidget.cellWidget(row, cols-1).currentText().strip()[0]
label = params[0]
params = np.asarray([params[4], params[5], params[1]]).astype(float)
if shape == 'V':
params = np.append(params, 0.5)
elif shape == 'B':
params = np.append(params, 0.02)
self.Plot(self.data.X, self.data.current, "Experimental data")
self.subplot.plot(self.data.X, FIT.Peak(FIT, self.data.X, *params, shape = shape), label = label)
self.subplot.legend()
self.canvas.draw()
def bound(nr, parameters):
names = parameters['labels'][:nr]
shape = parameters['shape'][:nr]
fit = FIT(shape, names)
#load the initial guess
parguess =np.concatenate( [[parameters['intens'][i], parameters['width'][i],
parameters['freq'][i], parameters['voigt'][i]] if shape[i]=='V' else
[parameters['intens'][i], parameters['width'][i], parameters['freq'][i]]
for i in np.arange(0, len(names))])
#lower bound
lower = np.concatenate( [[parameters['intens_min'][i], parameters['width_min'][i],
parameters['freq_min'][i], parameters['voigt_min'][i]] if shape[i]=='V' else
[parameters['intens_min'][i], parameters['width_min'][i], parameters['freq_min'][i]]
for i in np.arange(0, len(names))])
#higher bound
upper = np.concatenate( [[parameters['intens_max'][i], parameters['width_max'][i],
parameters['freq_max'][i], parameters['voigt_max'][i]] if shape[i]=='V' else
[parameters['intens_max'][i], parameters['width_max'][i], parameters['freq_max'][i]]
for i in np.arange(0, len(names))])
return fit, parguess, [lower, upper]
def prepDataForDeconv(self, **kwargs):
#prepare the data for deconvolution
#the function first checks if the data is present and that the baseline was removed
#fitting parameters are then taken from the table and put in a list
batch = kwargs.get('batch', False)
if not np.shape(self.data.X) and not batch:
self.errorBox('There is no data', 'No data...')
return -1, -1, -1
if not np.shape(self.data.baseline) and not batch:
result = QMessageBox.question(self,
"Baseline...",
"The baseline is still present!\nWould you like to remove it first?",
QMessageBox.Yes| QMessageBox.No)
if result == QMessageBox.Yes:
res = self.Baseline()
if res == -1:
return -1, -1, -1
else:
return -1, -1, -1
rows = self.tableWidget.rowCount()
cols = self.tableWidget.columnCount()
params = pd.DataFrame(columns = ['lb', 'pos', 'pos_min', 'pos_max', 'int', 'width', 'shape'])
for row in range(rows):
if self.tableWidget.item(row, 0).checkState() & Qt.Checked:
error = [self.itemCheck(self.tableWidget.item(row, col), silent=True) for col in range(1, cols-1)]
if True in error:
self.errorBox("Bad parameters", "Bad values")
self.dockGuess.raise_()
return -1, -1, -1
d =[self.tableWidget.item(row, col).text() for col in range(1, cols-1)]
p_shape = self.tableWidget.cellWidget(row, cols-1).currentText().strip()[0]
params = params.append({'lb': d[0], 'pos' : d[1], 'pos_min' : d[2],
'pos_max' : d[3], 'int' : d[4], 'width' : d[5], 'shape' : p_shape}, ignore_index =True)
self.fit = FIT(params['shape'], params['lb'])
parguess = np.concatenate([[params['int'][i], params['width'][i], params['pos'][i]]
for i in range(len(params['lb'])) ]).astype(float)
lower = np.concatenate([[5, 5, params['pos_min'][i]] for i in range(len(params['lb'])) ]).astype(float)
upper = np.concatenate([[float('inf'), float('inf'), params['pos_max'][i]] for i in range(len(params['lb'])) ]).astype(float)
return parguess, lower, upper
def BatchWorker(*params):
f, names, shape, bsParams, crop, peakLimits, parguess, bounds, index, folder = params
dt = DATA()
result = pd.Series(name = os.path.basename(f), index = index, dtype = object)
try:
dt.loadData(f)
dt.crop(crop.min, crop.max)
dt.fitBaseline(bsParams, peakLimits, abs = True)
text = ''
for deg in np.arange(0, dt.bsDegree+1):
if dt.bsCoef[deg]>=0 and deg!=0:
text += '+'
text += '{:.4E}*x^{}'.format(dt.bsCoef[deg], dt.bsDegree-deg)
result.loc['baseline'] = text
except Exception as e:
print("Error {}".format(e))
try:
fit = FIT(shape, names)
fit.deconvolute(dt, parguess, bounds, batch=True)
for i, pars in enumerate(zip(names, shape)):
result.loc[pars[0]+'_'+'Position'] = fit.pars[int(np.sum(fit.args[:i])+2)]
result.loc[pars[0]+'_'+'Amplitude'] = fit.pars[int(np.sum(fit.args[:i]))]
result.loc[pars[0]+'_'+'FWHM'] = fit.fwhm[i]
result.loc[pars[0]+'_'+'Area'] = fit.area[i]
if pars[1] == 'V':
result.loc[pars[0]+'_'+'L/G'] = fit.pars[int(np.sum(fit.args[:i])+3)]
elif pars[1] == 'B':
result.loc[pars[0]+'_'+'1/q'] = fit.pars[int(np.sum(fit.args[:i])+3)]
# result.iloc[1::4] = fit.pars[2::3]
# result.iloc[2::4] = fit.pars[::3]
# result.iloc[3::4] = fit.fwhm
# result.iloc[4::4] = fit.area
except Exception as e:
print('Could not deconvolute the {} file'.format(f))
result.iloc[1:len(index)] = -1*np.ones(len(index)-1)
print(e)
path = folder+ '/' +os.path.basename(f)+'.png'
fig = plt.figure(figsize=(12,8))
fit.plot(figure = fig, path = path)
return result
def findoffPhase(self, t, plane, MADTwiss, phaselist):
print "METHOD => Start calculating off-momentum phase"
# t is collection of twiss files
dpplist = []
for i in range(len(phaselist)): #### for sorting
dpplist.append(phaselist[i]['DPP'])
dpplist.sort()
if len(t) < 2:
print " Not enough files \n ... I cannot work like this! => sys.exit()"
sys.exit()
self.bpms = intersect(t)
self.bpms = modelIntersect(self.bpms, MADTwiss)
self.slope = {}
self.slopeE = {}
self.slopeM = {}
print "The lenght of the bpms " + str(len(self.bpms))
for i in range(len(self.bpms)):
print i
if i == len(self.bpms) - 1:
bn = upper(self.bpms[i][1])
print bn
print "hereeeeeee"
bn2 = upper(self.bpms[0][1])
else:
bn = upper(self.bpms[i][1])
bn2 = upper(self.bpms[i + 1][1])
phase = []
if plane == 'H':
slopeMp = MADTwiss.DMUX[MADTwiss.indx[bn2]] - MADTwiss.DMUX[
MADTwiss.indx[bn]]
else:
slopeMp = MADTwiss.DMUY[MADTwiss.indx[bn2]] - MADTwiss.DMUY[
MADTwiss.indx[bn]]
for k in range(len(dpplist)):
count = 0
for j in range(len(phaselist)):
if dpplist[k] == phaselist[j]['DPP']:
if count == 0:
phase.append(phaselist[j][bn][0])
count = 1
a = FIT(dpplist, phase, 1, 1e-15)
coef, error = a.linreg()
self.slope[bn] = coef[0]
self.slopeE[bn] = error[0]
self.slopeM[bn] = slopeMp
def findoffPhase(self,t,plane,MADTwiss,phaselist):
print "METHOD => Start calculating off-momentum phase"
# t is collection of twiss files
dpplist=[]
for i in range(len(phaselist)): #### for sorting
dpplist.append(phaselist[i]['DPP'])
dpplist.sort()
if len(t)<2:
print " Not enough files \n ... I cannot work like this! => sys.exit()"
sys.exit()
self.bpms=intersect(t)
self.bpms=modelIntersect(self.bpms, MADTwiss)
self.slope={}
self.slopeE={}
self.slopeM={}
print "The lenght of the bpms "+str(len(self.bpms))
for i in range(len(self.bpms)):
print i
if i==len(self.bpms)-1:
bn=upper(self.bpms[i][1])
print bn
print "hereeeeeee"
bn2=upper(self.bpms[0][1])
else:
bn=upper(self.bpms[i][1])
bn2=upper(self.bpms[i+1][1])
phase=[]
if plane=='H':
slopeMp=MADTwiss.DMUX[MADTwiss.indx[bn2]]-MADTwiss.DMUX[MADTwiss.indx[bn]]
else:
slopeMp=MADTwiss.DMUY[MADTwiss.indx[bn2]]-MADTwiss.DMUY[MADTwiss.indx[bn]]
for k in range(len(dpplist)):
count=0
for j in range(len(phaselist)):
if dpplist[k]==phaselist[j]['DPP']:
if count==0:
phase.append(phaselist[j][bn][0])
count=1
a=FIT(dpplist,phase,1,1e-15)
coef,error=a.linreg()
self.slope[bn]=coef[0]
self.slopeE[bn]=error[0]
self.slopeM[bn]=slopeMp
def findoffBB(self,t,plane,MADTwiss,betalist):
print "METHOD => Starting to calculate off-momentum beta-beat"
self.bpms=intersect(t)
self.bpms=modelIntersect(self.bpms, MADTwiss)
MADTwiss.chrombeat()
self.slope={}
self.slopeE={}
self.slopeM={}
dpplist=[]
for i in range(len(betalist)): #### for sorting
dpplist.append(betalist[i]['DPP'])
dpplist.sort()
#if dpplist[0]>dpplist[1]
#dpplist.reverse()
for i in range(len(self.bpms)):
bn=upper(self.bpms[i][1])
check=0
slopei=0.0
slopeiM=0.0
beta=[]
k=0
for k in range(len(dpplist)):
count=0
for j in range(len(betalist)):
if dpplist[k]==betalist[j]['DPP']:
if count==0:
beta.append(betalist[j][bn][0])
count=1
[slope, offset, R, slope_error, offset_error]=linreg.linreg(dpplist,beta)
a=FIT(dpplist,beta,1,1e-15)
#print ' list '+str(dpplist)
coef,error=a.linreg()
if plane=='H':
slopeiM=MADTwiss.dbx[MADTwiss.indx[upper(bn)]]
else:
slopeiM=MADTwiss.dby[MADTwiss.indx[upper(bn)]]
#self.slope[bn]=coef[0]
self.slope[bn]=coef[0]/100
#self.slopeE[bn]=error[0]
self.slopeE[bn]=error[0]/100
self.slopeM[bn]=slopeiM
