def imp_spec(FP, xcal='nm', normalize=False, axis=(0, 1)):
"""importing spectrum, calibrating to angular frequency (if input is in nm)
return [angular frequency in 2*Pi*Hz, normalized field (sqrt(intensity))]"""
try:
if FP[-4:] == 'spec':
"""load akspec file"""
axis = (0, 4)
Sp = np.loadtxt(FP, skiprows=8)
ind0 = Sp < np.zeros(Sp.shape)
Sp[ind0] = 0
elif FP[-3:] == 'txt' or FP[
-3:] == 'dat': #standard case with a pure 2 colomn data
if open(FP, 'r').readline() == 'Wavelength\tIntensity\n':
"""file saved by a Wavescan (two column)"""
Sp = Wavescan(FP, xout='nm')
else:
L = np.fromstring(open(FP, 'r').readline(), sep='\t')
if len(L) == 2:
Sp = np.loadtxt(FP)
else:
raise ER.SL_exception(
'unknown fundamental spectrum file format')
elif FP[-11:] == 'IntSpectrum':
"""load Sfrogger spectrum"""
axis = (0, 1)
Sp = np.loadtxt(FP, skiprows=1)
xcal = 'PHz'
else:
raise ER.SL_exception('unknown fundamental spectrum file format')
except OSError as er:
raise ER.ReadError(er)
else:
if xcal == 'nm':
Sp1 = Sp[:, (axis[0], axis[1])]
Sp1[:, 0] = 2 * Pi * c / Sp1[:, 0] * 10**9
Sp1[:, 1] = np.abs(Sp1[:, 1])**0.5
Sp1 = Sp1[::-1]
elif xcal == 'THz':
Sp1 = Sp[:, (axis[0], axis[1])]
Sp1[:, 0] = 2 * Pi * Sp1[:, 0] * 10**12
Sp1[:, 1] = np.abs(Sp1[:, 1])**0.5
elif xcal == 'PHz':
Sp1 = Sp[:, (axis[0], axis[1])]
Sp1[:, 0] = 2 * Pi * Sp1[:, 0] * 10**15
Sp1[:, 1] = np.abs(Sp1[:, 1])**0.5
else:
print("unknown x calibration")
raise ER.CalibrationError(FP)
if normalize:
Sp1[:, 1] = Sp1[:, 1] / (Sp1[:, 1]).max()
return Sp1
def transpose_frog(self):
if not self.Args['frog_file'] == '':
if len(self.Results['frog_load']) == 0:
self.showerror(ER.SL_exception('no FROG loaded yet'))
else:
self.Results['frog_in'] = np.transpose(self.Results['frog_in'])
self.showFROGex()
def set_wavelength(self):
self.error_message.setPlaceholderText('') #clear errors
if self.checkmaxwavelength.isChecked(
) or self.checkminwavelength.isChecked():
if self.Args['frog_file'][-3:] == 'frg' or self.Args['frog_file'][
-4:] == 'frog':
self.error_message.setPlaceholderText(
'Not an error. Note that frg file is assumed to be proparly prepared, thus wavelength limits and delay correction are not applied to it. (though symmetrization and background substraction do work)'
)
else:
self.Args['MaxWavelength'] = self.maxwavelength.value()
self.Args['MinWavelength'] = self.minwavelength.value()
if not self.Args['frog_file'] == '':
if self.checkmaxwavelength.isChecked():
Wmin = np.max([
2 * Pi * c / self.Args['MaxWavelength'] * 10**9 *
10**-15, self.Results['W_load'][0]
])
else:
Wmin = self.Results['W_load'][0]
if self.checkminwavelength.isChecked():
Wmax = np.min([
2 * Pi * c / self.Args['MinWavelength'] * 10**9 *
10**-15, self.Results['W_load'][-1]
])
else:
Wmax = self.Results['W_load'][-1]
if Wmin >= Wmax:
self.showerror(
ER.SL_exception(
'max wavelenghth has to be smaller than min wavelength'
))
Wmin0 = Wmin
Wmin = Wmax
Wmax = Wmin0
ind = np.logical_and(self.Results['W_load'] >= Wmin,
self.Results['W_load'] <= Wmax)
self.Results['W'] = np.copy(self.Results['W_load'][ind])
self.Results['frog_in_0'] = self.Results['frog_load'][:,
ind]
self.remove_bkg()
self.showFROGex()
else:
if not self.Args['frog_file'] == '':
self.Results['W'] = np.copy(self.Results['W_load'])
self.Results['frog_in_0'] = self.Results['frog_load']
self.remove_bkg()
self.showFROGex()
self.Results['frog_in_processed?'] = False
def imp_time(FP, xcal='fs', normalize=True, axis=(0, 1)):
"""import temporal pulse profile"""
try:
if FP[-7:] == 'IntTime':
"""load Sfrogger data"""
axis = (0, 1)
Sp = np.loadtxt(FP, skiprows=1)
else:
raise ER.SL_exception('unknown temporal file format')
except OSError as er:
raise ER.ReadError(er)
else:
if xcal == 'fs':
return Sp
else:
print("unknown x calibration")
def readparam(self):
"""read parameters for the reconstruction"""
self.Args['G_goal'] = self.goal.value()
self.Args['ret_iterations'] = self.iternumber.value()
self.Args['fix_fund_spec'] = self.fix_fund_spec.isChecked()
self.Args['use_fund_spec'] = self.use_fund_spec.isChecked()
self.progressBar.setMaximum(self.Args['ret_iterations'])
self.progressBar.setMinimum(0)
self.Args['max size'] = int(self.size.value())
self.Args['parallel'] = False
self.Args['symmetrization'] = self.check_symmetry.isChecked()
self.Args['multi_grid'] = self.multi_grid.isChecked()
if self.check_flatPH.isChecked():
self.Args['init_phase'] = 'random'
elif self.check_flatPH.isChecked():
self.Args['init_phase'] = 'flat'
elif self.check_flatPH.isChecked():
self.Args['init_phase'] = 'GDD'
if self.Args['frog_file'] == '':
raise ER.SL_exception('no FROG file is loaded')
def width(X, Y, method='FWHM'):
"""computed the width (for example pulse duration of spectrum width) of a data set.
data are expected to be 1d np.array"""
if len(Y) > 0:
M = Y.max()
if method == 'FWHM':
"""in case of mutiple peaks, the maximum width will be returned"""
# NM=Y.argmax()
# N1=NM
# N2=NM
# for i in range(NM-1,-1,-1):
# if Y[i] > M/2: N1=i
# for i in range(NM+1,len(Y)):
# if Y[i] > M/2: N2=i
level = 0.5
ind = Y > M * level
indx = np.where(ind == True)
N1 = indx[0][0]
N2 = indx[0][-1]
# print(X[N1],X[N2])
# print(Y[N1]/M,Y[N2]/M)
# print(Y[N1-1]/M,Y[N2+1]/M)
elif method == 'e**-2':
level = np.exp(-2)
ind = Y > level * M
indx = np.where(ind == True)
N1 = indx[0][0]
N2 = indx[0][-1]
# print(X[N1],X[N2])
elif method == 'e**-1':
level = np.exp(-1)
ind = Y > level * M
indx = np.where(ind == True)
N1 = indx[0][0]
N2 = indx[0][-1]
# print(X[N1],X[N2])
elif method == '4sigma':
Xmean = np.sum(X * Y) / np.sum(Y)
sigmaX = (np.sum((X - Xmean)**2 * Y) / np.sum(Y))**0.5
Width = 4 * sigmaX
else:
raise ER.SL_exception('unknown method')
if method == '4sigma':
return Width
else:
if N1 - 1 > 0:
if Y[N1 - 1] == M * level:
X1 = X[N1 - 1]
else:
y1 = Y[N1 - 1]
y2 = Y[N1]
x1 = X[N1 - 1]
x2 = X[N1]
# print(y1)
# print((M*level-y1)/(y2-y1))
X1 = x1 + (M * level - y1) / (y2 - y1) * (
x2 - x1
) #linear interpolation for accuracy improvement
else:
X1 = X[0]
if N2 + 1 < len(Y) - 1:
if Y[N2 + 1] == M * level:
X2 = X[N2 + 1]
else:
y1 = Y[N2]
y2 = Y[N2 + 1]
x1 = X[N2]
x2 = X[N2 + 1]
X2 = x1 + (M * level - y1) / (y2 - y1) * (
x2 - x1
) #linear interpolation for accuracy improvement
else:
X2 = X[-1]
Width = np.abs(X2 - X1)
return Width
else:
raise ER.SL_exception('no data for width calculation')
def load_frog(file):
"""loads a FROG scan"""
if file[-3:]=='txt' or file[-3:]=='dat':
"""for program generated frogs (basically for test purposes)"""
try:
T0=open(file,'r').readline()
T=np.fromstring(T0,sep='\t')
Sp=np.genfromtxt(file,skip_header=1)
except OSError as er:
raise ER.ReadError(er)
else:
W=2*Pi*c/Sp[0]*10**9*10**-15
frog=Sp[1:]
frog=frog/frog.max()
return (T, W, frog)
elif file[-21:]=='akSpecScantransformed':
"""for files saved by akXFROG soft and preprocessed to akSpecScantransformed"""
try:
M0=open(file,'r').readline()
M=np.fromstring(M0,sep='\t') #motor positions
T=(M-M[0])/c*10**-9*10**15 #delays
Sp=np.genfromtxt(file,skip_header=1)
except OSError as er:
raise ER.ReadError(er)
else:
W=2*Pi*c/Sp[0]*10**9*10**-15 #frequencies
ind=W.argsort()
W=W[ind]
Sp1=Sp[1:]
frog=Sp1[:,ind] #frog data
frog=frog/frog.max()
return (T, W, frog)
elif file[-11:]=='txtSpecScan':
"""for files saved by akvlXFROG_txt soft directly"""
try:
M0=open(file,'r').readline()
T=np.fromstring(M0,sep='\t') #delays in fs
Sp=np.genfromtxt(file,skip_header=1)
except OSError as er:
raise ER.ReadError(er)
else:
W=2*Pi*c/Sp[0]*10**9*10**-15 #frequencies
ind=W.argsort()
W=W[ind]
Sp1=Sp[1:]
frog=Sp1[:,ind] #frog data
frog=frog/frog.max()
return (T, W, frog)
elif file[-4:]=='frog':
"""for files saved by Sfrogger"""
try:
M0=open(file,'r').readline()
T=np.fromstring(M0,sep='\t') #delays in fs
Sp=np.genfromtxt(file,skip_header=1)
except OSError as er:
raise ER.ReadError(er)
else:
W=2*Pi*Sp[0] #frequencies
frog=Sp[1:] #frog data
return (T, W, frog)
elif file[-3:]=='frg':
"""for files prepared as for Trebino frogger"""
try:
par=np.fromstring(open(file,'r').readline(),sep='\t')
Nbin=int(par[0])
dt=par[2]
dw=par[3]
W0=par[4]*2*Pi
Sp=np.genfromtxt(file,skip_header=1)
except OSError as er:
raise ER.ReadError(er)
else:
frog=np.transpose(Sp.reshape((Nbin,Nbin)))
frog=frog/frog.max()
#zero negative values
ind0=frog < np.zeros(frog.shape)
frog[ind0]=0
T=dt*np.array([i-Nbin/2 for i in range(Nbin)])
W=W0+dw*2*Pi*np.array([i-Nbin/2 for i in range(Nbin)])
return (T, W, frog)
elif file[-9:]=='frgav.npy':
"""for averaged frog traces"""
In=np.load(file)
W=In[0,1:]
T=In[1:,0]
frog=In[1:,1:]
return (T, W, frog)
else:
raise ER.SL_exception('unknown file format')