def __init__(self,cwd):
print('method __init__ in K_class runs...')
self.gw = get_raw.Get_raw(cwd)
self.alph=alpha.Alpha(cwd)
self.common_xaxis_12, self.k_12 = self.k1_eq12()
self.common_xaxis_15, self.k_15 = self.k1_eq15()
def __init__(self, cwd):
print('method __init__ in Vary_igp runs...')
self.gw = get_raw.Get_raw(cwd)
self.gmd = get_m_d.Gmd(cwd)
self.ign_pts, self.m_start_min, self.d_dispersion_max, self.d_dispersion_min, self.d_mean, self.dispersion_rel = self.gmd.get_md_igpo(
)
def run(self):
try:
if self.sender == 'Raw data':
from methods import get_raw
my_arg = get_raw.Get_raw(self.cwd)
elif self.sender == 'Tmin and Tmax':
from methods import get_Tmax_Tmin
my_arg = get_Tmax_Tmin.Get_Tmax_Tmin(self.cwd)
elif self.sender == 'Std.Dev. in d':
from methods import get_vary_igp
my_arg = get_vary_igp.Vary_igp(self.cwd)
elif self.sender == 'Index n':
from methods import get_m_d
my_arg = get_m_d.Gmd(self.cwd)
elif self.sender == 'Absorption alpha':
from methods import alpha
my_arg = alpha.Alpha(self.cwd)
elif self.sender == 'Wavenumber k':
from methods import k
my_arg = k.K_class(self.cwd)
self.signals.pass_plots.emit([my_arg, self.sender])
except Exception as inst:
self.signals.critical.emit(''.join([
"Critical message returned from the local method ",
self.sender, ":\n\n",
str(inst)
]))
self.signals.finished.emit()
def __init__(self, cwd):
print('method __init__ in Gmd runs...')
self.gtt = get_Tmax_Tmin.Get_Tmax_Tmin(cwd)
self.gw = get_raw.Get_raw(cwd)
_, _, com_axisTminTmax_final, _ = self.gtt.get_T_alpha()
self.common_xaxis_eV, self.Tmin, self.Tmax = com_axisTminTmax_final
_, self.Ts = self.gtt.fit_Ts_to_data(self.common_xaxis_eV)
# convert to wavelength in nm
self.common_xaxis_nm = 1239.84187 / self.common_xaxis_eV
_, self.nn1 = self.n1()
if self.gw.ignore_data_pts == 0:
_, self.m_start_min, self.nn2, self.d2 = self.n2(
self.common_xaxis_nm, self.nn1)
else:
_, self.m_start_min, self.nn2, self.d2 = self.n2(
self.common_xaxis_nm[:-self.gw.ignore_data_pts],
self.nn1[:-self.gw.ignore_data_pts])
_, self.n_trans = self.n_trans()
def __init__(self, cwd):
print('method __init__ in Alpha runs...')
self.gw = get_raw.Get_raw(cwd)
self.gmd = get_m_d.Gmd(cwd)
self.gtt = get_Tmax_Tmin.Get_Tmax_Tmin(cwd)
_, self.Ts = self.gtt.fit_Ts_to_data(self.gw.x_Tsubfilm)
_, _, com_axisTminTmax_final, all_extremas = self.gtt.get_T_alpha()
_, extremas_final, _ = all_extremas
x_min_, y_min_, x_max_, y_max_ = [
extremas_final[:][0], extremas_final[:][1], extremas_final[:][2],
extremas_final[:][3]
]
indcs = numpy.where((self.gw.x_Tsubfilm > x_min_[-1])
& (self.gw.x_Tsubfilm > x_max_[-1]))
self.x_all = self.gw.x_Tsubfilm[indcs]
self.y_all = self.gw.y_Tsubfilm[indcs]
self.Ts = self.Ts[indcs]
self.xaxis, self.Tmin, self.Tmax = com_axisTminTmax_final
_, self.Ts_min_max = self.gtt.fit_Ts_to_data(self.xaxis)
###############################################################
if self.gw.fit_poly_ranges_check == False:
self.coef = P.polyfit(self.xaxis, self.gmd.nn2,
self.gw.fit_poly_order)
self.curve_all = numpy.poly1d(self.coef[::-1])(self.gw.x_Tsubfilm)
elif self.gw.fit_poly_ranges_check == True:
self.acc_min_max = numpy.array([])
self.acc_n = numpy.array([])
self.rej_min_max = numpy.array([])
self.rej_n = numpy.array([])
# add first and last index element from the min_max list
ranges = [self.xaxis[0]
] + self.gw.fit_poly_ranges + [self.xaxis[-1]]
for i in range(len(ranges) - 1):
indx = numpy.where((self.xaxis >= ranges[i])
& (self.xaxis < ranges[i + 1]))
if i % 2 == 0:
self.rej_min_max = numpy.append(self.rej_min_max,
self.xaxis[indx])
self.rej_n = numpy.append(self.rej_n, self.gmd.nn2[indx])
else:
self.acc_min_max = numpy.append(self.acc_min_max,
self.xaxis[indx])
self.acc_n = numpy.append(self.acc_n, self.gmd.nn2[indx])
self.coef = P.polyfit(self.acc_min_max, self.acc_n,
self.gw.fit_poly_order)
self.curve_all = numpy.poly1d(self.coef[::-1])(self.gw.x_Tsubfilm)
self.xaxis_12, self.alpha_12 = self.alpha_eq12()
self.xaxis_15, self.alpha_15 = self.alpha_eq15()
self.xaxis_A3, self.alpha_A3 = self.alpha_eqA3()
self.min_max2, self.nn2, self.curve_2 = self.show_n_fit()