def __init__(self):
#Variables included in the series
self.fitting_parameters = ['idx0', 'idx1', 'idx2', 'idx3', 'idx4', 'idx5']
self.fitting_parameters += ['area_intg', 'area_intg_er', 'flux_gauss', 'flux_gauss_er', 'flux_intg', 'flux_intg_er'] #Additionally there is (A, mu, sigma, Eqw) + idx + _norm + _norm_er
self.fitting_parameters += ['m_zerolev', 'n_zerolev', 'zerolev_mean', 'zerolev_std', 'zerolev_linear', 'zerolev_width', 'continuum_width']
self.fitting_parameters += ['fit_routine', 'MC_iterations', 'blended_check', 'start_treatment', 'line_number', 'add_wide_component', 'wide_component']
self.fitting_parameters += ['params_lmfit', 'params_lmfit_wide', 'parameters_list', 'fit_output']
self.fitting_parameters += ['Wave1', 'Wave2', 'Wave3', 'Wave4', 'Wave5', 'Wave6']
self.fitting_parameters += ['group_label', 'blended_lambdas', 'blended_labels', 'blended_ions']
self.fitting_parameters += ['maxLambdas', 'maxPeaks', 'x_scaler', 'y_scaler', 'x_n', 'y_n', 'zerolev_n', 'sigZerolev_n']
self.GHcoeffs = {}
self.GHcoeffs['c0'] = sqrt(6.0) / 4.0
self.GHcoeffs['c1'] = -sqrt(3.0)
self.GHcoeffs['c2'] = -sqrt(6.0)
self.GHcoeffs['c3'] = 2.0 * sqrt(3.0) / 3.0
self.GHcoeffs['c4'] = sqrt(6.0) / 3.0
self.skeness_limit = {'fixed':(False)}
self.kutorsis_limit = {'fixed':(False)}
self.skeness_Glimit = {'fixed':(True)}
self.kutorsis_Glimit = {'fixed':(True)}
N2 = Atom('N', 2)
N2_6548A = N2.getEmissivity(tem=10000, den=100, wave=6548)
N2_6584A = N2.getEmissivity(tem=10000, den=100, wave=6584)
self.N2_Ratio = N2_6584A / N2_6548A
self.sqrt2pi = sqrt(2*pi)
def Load_lmfit_parameters_blended(N_comps, Initial_guesses_dic, wide_component = False, mu_precission = 1):
N2 = Atom('N', 2)
N2_6548A = N2.getEmissivity(tem=10000, den=100, wave=6548)
N2_6584A = N2.getEmissivity(tem=10000, den=100, wave=6584)
print 'Radio', N2_6584A / N2_6548A
params = Parameters()
for i in range(N_comps):
index = str(i)
params.add('A' + index, value = Initial_guesses_dic['A'][i])
params.add('mu' + index, value = Initial_guesses_dic['mu'][i], min = Initial_guesses_dic['mu'][i] - mu_precission, max = Initial_guesses_dic['mu'][i] + mu_precission) #One angstrom tolerance for min and max value of mu
params.add('sigma' + index, value = Initial_guesses_dic['sigma'][i], min = Initial_guesses_dic['min_sigma'][i], max = 5.0)
params.add('fwhm' + index, expr = '2.354820045 * {sigma}'.format(sigma = 'sigma' + index))
params.add('FWHM' + index, expr = '({fwhm}/{mu}) * 2.99792458e5'.format(fwhm = 'fwhm' + index, mu = 'mu' + index))
params.add('Flux' + index, expr = '({A}*{fwhm})/(2.35*0.3989)'.format(A = 'A' + index, fwhm = 'fwhm' + index))
#Set all sigmas to the same value
for i in range(1, N_comps):
index = str(i)
params['sigma' + index].expr='sigma0'
#
#Set flux in N2_6548A as 1/3 N2_6584A
params.add('Flux0', expr = 'Flux2 / {ratio}'.format(ratio = N2_6584A / N2_6548A))
if wide_component:
index = str(N_comps)
params.add('A' + index, value = Initial_guesses_dic['A'][N_comps])
params.add('mu' + index, expr = 'mu1')
params.add('sigma' + index, value = Initial_guesses_dic['sigma'][N_comps], min = Initial_guesses_dic['min_sigma'][N_comps], max = 8.0)
params.add('fwhm' + index, expr = '2.354820045 * {sigma}'.format(sigma = 'sigma' + index))
params.add('FWHM' + index, expr = '({fwhm}/{mu}) * 2.99792458e5'.format(fwhm = 'fwhm' + index, mu = 'mu' + index))
params.add('Flux' + index, expr = '({A}*{fwhm})/(2.35*0.3989)'.format(A = 'A' + index, fwhm = 'fwhm' + index))
# if wide_component:
# index = str(N_comps)
# print 'Y la europea', 'A' + index
# params.add('A' + index, value = Initial_guesses_dic['A'][N_comps], min=0.04748900 * 0.95, max = 0.04748900 * 1.05)
# params.add('mu' + index, value = Initial_guesses_dic['mu'][N_comps], min = Initial_guesses_dic['mu'][N_comps] - 2, max = Initial_guesses_dic['mu'][N_comps] + 2)
# params.add('sigma' + index, value = Initial_guesses_dic['sigma'][N_comps], min = Initial_guesses_dic['min_sigma'][N_comps], max = 8.0)
# params.add('fwhm' + index, expr = '2.354820045 * {sigma}'.format(sigma = 'sigma' + index))
# params.add('FWHM' + index, expr = '({fwhm}/{mu}) * 2.99792458e5'.format(fwhm = 'fwhm' + index, mu = 'mu' + index))
# params.add('Flux' + index, expr = '({A}*{fwhm})/(2.35*0.3989)'.format(A = 'A' + index, fwhm = 'fwhm' + index))
# if wide_component:
# index = str(N_comps + 1)
# params.add('sigm_tol', value = 2.5, min = 2, max = 4)
# params.add('A' + index, value = Initial_guesses_dic['A'][N_comps])
# params.add('mu' + index, expr = 'mu2')
# params.add('sigma' + index, expr = 'sigm_tol*sigma2')
# params.add('sigma' + index, value = Initial_guesses_dic['sigma'][N_comps], min = Initial_guesses_dic['min_sigma'][N_comps], max = 8.0)
# params.add('fwhm' + index, expr = '2.354820045 * {sigma}'.format(sigma = 'sigma' + index))
# params.add('FWHM' + index, expr = '({fwhm}/{mu}) * 2.99792458e5'.format(fwhm = 'fwhm' + index, mu = 'mu' + index))
# params.add('Flux' + index, expr = '({A}*{fwhm})/(2.35*0.3989)'.format(A = 'A' + index, fwhm = 'fwhm' + index))
return params
class Chemical_Analysis_pyneb():
def __init__(self):
self.MC_array_len = 1000
self.MC_warning_limit = self.MC_array_len * 0.1
self.Hbeta_label = 'H1_4861A'
def load_elements(self):
#Set atomic data
#atomicData.setDataFile('he_i_rec_Pal12-Pal13.fits')
atomicData.setDataFile('s_iii_coll_HRS12.dat')
#Default: 's_iii_atom_PKW09.dat'
'S3: All energy and A values: Podobedova, Kelleher, and Wiese 2009, J. Phys. Chem. Ref. Data, Vol.'
'S3: collision strengths: Tayal & Gupta 1999, ApJ, 526, 544'
#New Atomic data s_iii_coll_HRS12.dat
'S3: All energy and A values: Podobedova, Kelleher, and Wiese 2009, J. Phys. Chem. Ref. Data, Vol.'
'S3: collision strengths: Hudson, Ramsbottom & Scott 2012, ApJ, 750, 65'
#Declare ions
self.S2_atom = Atom('S', 2)
self.S3_atom = Atom('S', 3)
self.Ar3_atom = Atom('Ar', 3)
self.Ar4_atom = Atom('Ar', 4)
self.N2_atom = Atom('N', 2)
self.O2_atom = Atom('O', 2)
self.O3_atom = Atom('O', 3)
self.H1_atom = RecAtom('H', 1)
self.He1_atom = RecAtom('He', 1)
self.He2_atom = RecAtom('He', 2)
#Pyneb objects
self.diags = Diagnostics()
#Ohrs 2016 relation for the OI_SI gradient
self.logSI_OI_Gradient = random.normal(
-1.53, 0.05, size=self.MC_array_len
) # random.normal(-1.78, 0.03, size = self.MC_array_len)
self.OI_SI = power(10, -self.logSI_OI_Gradient)
#Theoretical ratios
self.S3_ratio = self.S3_atom.getEmissivity(
10000, 100, wave=9531) / self.S3_atom.getEmissivity(
10000, 100, wave=9069)
self.S3_9000_ratio = random.normal(
self.S3_atom.getEmissivity(10000, 100, wave=9531) /
self.S3_atom.getEmissivity(10000, 100, wave=9069),
0.01,
size=self.MC_array_len)
self.N2_6000_ratio = self.N2_atom.getEmissivity(
10000, 100, wave=6584) / self.N2_atom.getEmissivity(
10000, 100, wave=6548)
self.O3_5000_ratio = self.O3_atom.getEmissivity(
10000, 100, wave=5007) / self.O3_atom.getEmissivity(
10000, 100, wave=4959)
#Factors to speed calculations
self.lines_factors = {}
self.lines_factors['S3_9069A'] = 1 + self.S3_ratio
self.lines_factors['S3_9531A'] = 1 + 1 / self.S3_ratio
#Cloudy models for the SIV contribution
self.m_SIV_correction = random.normal(1.1628,
0.00559,
size=self.MC_array_len)
self.n_SIV_correction = random.normal(0.0470,
0.0097,
size=self.MC_array_len)
#self.m_SIV_correction = random.normal(1.109, 0.01, size = self.MC_array_len)
#self.n_SIV_correction = random.normal(0.135, 0.0173, size = self.MC_array_len)
#CHAOS relation TNII-TSIII
#T[SIII] = 1.312(+-0.075)T[NII]-0.313(+-0.058)
#TNII = (0.762+-0.044)*TSIII + 0.239+-0.046
self.m_TNII_correction = random.normal(0.762,
0.044,
size=self.MC_array_len)
self.n_TNII_correction = random.normal(0.239,
0.046,
size=self.MC_array_len)
#Truncated gaussian for the density
lower_trunc, upper_trunc = (1.0 - 50.0) / 25.0, (100 - 50) / 25.0
self.Truncated_gaussian = truncnorm(lower_trunc,
upper_trunc,
loc=50,
scale=25)
print '-Elements loaded\n'
return
def declare_object(self, lines_log_frame):
#List of all parameters
# lineRatios = ['R_SII', 'R_SII_prime', 'R_SIII', 'R_NII', 'R_OII', 'R_OII_prime', 'R_OIII']
# elecProperties = ['neSII', 'neOII', 'TeOII', 'TeSII', 'TeNII', 'TeOIII', 'TeSIII', 'TeOII_from_TeOIII', 'TeNII_from_TeOIII', 'TeSIII_from_TeOIII', 'TeOIII_from_TeSIII']
# ionicAbund = ['SII_HII', 'SIII_HII', 'SIV_HII', 'OII_HII', 'OII_HII_3279A', 'OII_HII_7319A', 'NII_HII', 'ArIII_HII', 'ArIV_HII', 'HeII_HII_from_O',
# 'HeIII_HII_from_O', 'HeII_HII_from_S', 'HeIII_HII_from_S']
# elemAbund = ['SI_HI', 'OI_HI', 'NI_OI', 'NI_HI', 'HeI_HI_from_O', 'HeI_HI_from_S', 'Ymass_O', 'Ymass_S']
self.abunData = Series()
self.Hbeta_flux = random.normal(
lines_log_frame.loc['H1_4861A', 'line_Int'].nominal_value,
lines_log_frame.loc['H1_4861A', 'line_Int'].std_dev,
size=self.MC_array_len)
self.low_density_dist = self.Truncated_gaussian.rvs(self.MC_array_len)
#Generate a dictionary to store the random array for all lines
self.lines_dict = OrderedDict()
#Dictionary with lines which my need special treatements
Blended_lines = {}
Blended_lines['O2_3726A'] = ('O2_3726A', 'O2_3729A')
Blended_lines['O2_7319A'] = ('O2_7319A', 'O2_7330A')
NoError_lines = {}
NoError_lines['N2_6548A'] = ('N2_6548A')
#Generate
for line in lines_log_frame.index.values:
#Start with the particular cases: Blended lines
if line in Blended_lines:
blended_lines = Blended_lines[line]
if set(lines_log_frame.index) >= set(blended_lines):
label_line = line + '+'
#Lines are blended we use integrated flux else we add the individual integrated
if lines_log_frame.loc[blended_lines[0],
'flux_intg'] == lines_log_frame.loc[
blended_lines[1]]['flux_intg']:
flux_line = lines_log_frame.loc[
blended_lines[0],
'line_IntBrute_dered'].nominal_value
error_line = lines_log_frame.loc[
blended_lines[0], 'line_IntBrute_dered'].std_dev
else:
line_sum = lines_log_frame.loc[
blended_lines[0],
'line_IntBrute_dered'] + lines_log_frame.loc[
blended_lines[1], 'line_IntBrute_dered']
flux_line = line_sum.nominal_value
error_line = line_sum.std_dev
#Case only one of the lines was measured
else:
label_line = line
flux_line = lines_log_frame.loc[line,
'line_Int'].nominal_value
error_line = lines_log_frame.loc[line, 'line_Int'].std_dev
#Lines with not error
elif (line in NoError_lines) and (
lines_log_frame.loc[line, 'line_Int'].std_dev == 0.0):
label_line = line
flux_line = lines_log_frame.loc[line, 'line_Int'].nominal_value
error_line = lines_log_frame.loc[
'N2_6584A', 'line_Int'].std_dev / self.N2_6000_ratio
#None-issue lines
else:
label_line = line
flux_line = lines_log_frame.loc[line, 'line_Int'].nominal_value
error_line = lines_log_frame.loc[line, 'line_Int'].std_dev
#Generate line gaussian shaped array
self.lines_dict[label_line] = random.normal(flux_line,
error_line,
size=self.MC_array_len)
return
def den_temp_diagnostic_pair(self,
diagProperties,
den_distribution=None,
atom_temp=None):
#Check if all necessary lines are there
if self.lines_dict.viewkeys() >= set(
diagProperties['required_denlines'] +
diagProperties['required_temlines']):
if den_distribution is None:
den_ratio = numexpr.evaluate(diagProperties['den_ratio'],
self.lines_dict)
tem_ratio = numexpr.evaluate(diagProperties['tem_ratio'],
self.lines_dict)
Te, ne = self.diags.getCrossTemDen(
diag_tem=diagProperties['diag_tem'],
diag_den=diagProperties['diag_den'],
value_tem=tem_ratio,
value_den=den_ratio)
else:
tem_ratio = numexpr.evaluate(diagProperties['tem_ratio'],
self.lines_dict)
Te = atom_temp.getTemDen(
tem_ratio,
den=den_distribution,
to_eval=diagProperties['atom_temdiag'])
ne = den_distribution
#else empty (nan) arrays
else:
Te, ne = empty(self.MC_array_len), empty(self.MC_array_len)
Te[:], ne[:] = np_nan, np_nan
return Te, ne
def determine_electron_parameters(self, objectData):
#----------To start make sure we are not in the very low density regimes,
low_density_dist = None
if self.lines_dict.viewkeys() >= {'S2_6716A', 'S2_6731A'}:
S2_ratio = mean(self.lines_dict['S2_6716A']) / mean(
self.lines_dict['S2_6731A'])
if S2_ratio > 1.35:
print '--Low density object'
lower, upper, mu, sigma = 1.0, 100.0, 50.0, 25.0
X_func = truncnorm((lower - mu) / sigma, (upper - mu) / sigma,
loc=mu,
scale=sigma)
low_density_dist = X_func.rvs(self.MC_array_len)
self.abunData['neSII'] = low_density_dist
if low_density_dist is None:
print 'WARNING: QUE PASA!!!!!!'
print lower, upper, mu, sigma
print xrange
#-----------Sulfur
diagProperties = {}
diagProperties['required_denlines'] = ['S2_6716A', 'S2_6731A']
diagProperties['required_temlines'] = [
'S3_9069A', 'S3_9531A', 'S3_6312A'
] if objectData.SIII_lines == 'BOTH' else [objectData.SIII_lines
] + ['S3_6312A']
diagProperties['diag_den'] = '[SII] 6731/6716'
diagProperties['diag_tem'] = '[SIII] 6312/9200+'
diagProperties['atom_temdiag'] = 'L(6312)/(L(9069)+L(9531))'
diagProperties['den_ratio'] = 'S2_6731A/S2_6716A'
diagProperties[
'tem_ratio'] = 'S3_6312A/(S3_9069A+S3_9531A)' if objectData.SIII_lines == 'BOTH' else 'S3_6312A/({valid_line} * {line_factor})'.format(
valid_line=objectData.SIII_lines,
line_factor=self.lines_factors[objectData.SIII_lines])
if '*' in diagProperties['tem_ratio']:
print '-- Using factor', diagProperties['tem_ratio']
S3_lines = [
'S3_9069A', 'S3_9531A', 'S3_6312A'
] if objectData.SIII_lines == 'BOTH' else [objectData.SIII_lines
] + ['S3_6312A']
#--Calculate NeSII and TeSIII
self.abunData['TeSIII'], neSII_TSIII = self.den_temp_diagnostic_pair(
diagProperties, low_density_dist, atom_temp=self.S3_atom)
#--Determine empirical TOIII from TSIII #Epm & Diaz 2005
self.abunData['TeOIII_from_TeSIII'] = (
0.95 * self.abunData.TeSIII / 10000 + 0.076) * 10000
diagProperties = {}
diagProperties['required_denlines'] = ['S2_6716A', 'S2_6731A']
diagProperties['required_temlines'] = ['S2_4069A', 'S2_4076A']
diagProperties['diag_den'] = '[SII] 6731/6716'
diagProperties['diag_tem'] = '[SII] 4069/4076'
diagProperties['atom_temdiag'] = 'L(4069)/L(4076)'
diagProperties['den_ratio'] = 'S2_6731A/S2_6716A'
diagProperties['tem_ratio'] = 'S2_4069A/S2_4076A'
# #--Calculate NeSII and TeSII
self.abunData['TeSII'], neSII_TSII = self.den_temp_diagnostic_pair(
diagProperties, low_density_dist, atom_temp=self.S2_atom)
#-----------Oxygen
diagProperties = {}
diagProperties['required_denlines'] = ['S2_6716A', 'S2_6731A']
diagProperties['required_temlines'] = [
'O3_4363A', 'O3_4959A', 'O3_5007A'
]
diagProperties['diag_den'] = '[SII] 6731/6716'
diagProperties['diag_tem'] = '[OIII] 4363/5007+'
diagProperties['atom_temdiag'] = 'L(4363)/(L(5007)+L(4959))'
diagProperties['den_ratio'] = 'S2_6731A/S2_6716A'
diagProperties['tem_ratio'] = 'O3_4363A/(O3_4959A+O3_5007A)'
#--Calculate NeSII and TeOIII
self.abunData['TeOIII'], neSII_OIII = self.den_temp_diagnostic_pair(
diagProperties, low_density_dist, atom_temp=self.O3_atom)
#--Determine empirical TOIII from TSIII #Epm & Diaz 2005
self.abunData['TeSIII_from_TeOIII'] = (
1.05 * self.abunData.TeOIII / 10000 - 0.08) * 10000
#--Determine empirical TOII from TOIII #Dors Jr 2006
self.abunData['TeOII_from_TeOIII'] = (1.397 / (
(1 / (self.abunData.TeOIII / 10000)) + 0.385)) * 10000
#--Determine empirical TNII from TOIII #Epm 2014
self.abunData['TeNII_from_TeOIII'] = (1.452 / (
(1 / (self.abunData.TeOIII / 10000)) + 0.479)) * 10000
#-----------Nitrogen
diagProperties = {}
diagProperties['required_denlines'] = ['S2_6716A', 'S2_6731A']
diagProperties['required_temlines'] = [
'N2_5755A', 'N2_6548A', 'N2_6584A'
]
diagProperties['diag_den'] = '[SII] 6731/6716'
diagProperties['diag_tem'] = '[NII] 5755/6584+'
diagProperties['atom_temdiag'] = '(L(6584) + L(6548)) / L(5755)'
diagProperties['den_ratio'] = 'S2_6731A/S2_6716A'
diagProperties['tem_ratio'] = '(N2_6548A+N2_6584A)/N2_5755A'
#--Calculate Ne_SII and Te_NII
self.abunData['TeNII'], neSII_TNII = self.den_temp_diagnostic_pair(
diagProperties, low_density_dist, atom_temp=self.N2_atom)
#Assign object density from lines or from the low density distribution
#--This code favors the neSII calculated from SIII-SII line pai+
if 'neSII' not in self.abunData:
if np_sum(isnan(neSII_TSIII)) < self.MC_array_len:
self.abunData['neSII'] = neSII_TSIII
elif np_sum(isnan(neSII_OIII)) < self.MC_array_len:
self.abunData['neSII'] = neSII_OIII
else:
self.abunData['neSII'] = neSII_TSIII
#--Check if some results contain nan entries
nanCount = OrderedDict()
for electron_parameter in self.abunData.index:
variable_array = self.abunData[electron_parameter]
nan_count = np_sum(isnan(variable_array))
if nan_count > self.MC_array_len * 0.90:
self.abunData.drop(electron_parameter, inplace=True)
elif nan_count > 0:
mag, error = nanmean(
self.abunData[electron_parameter]), nanstd(
self.abunData[electron_parameter])
self.abunData[electron_parameter] = random.normal(
mag, error, size=self.MC_array_len)
if nan_count > self.MC_warning_limit:
nanCount[electron_parameter] = nan_count
#Display calculations with issues
if len(nanCount) > 0:
print '-Issues calculating:'
for element in nanCount:
print '--', element, nanCount[element]
return
def determine_ionic_abundance(self, abund_code, atom, diagnos_eval,
diagnos_mag, tem, den):
try:
hbeta_flux = self.Hbeta_flux
except AttributeError:
hbeta_flux = self.H1_atom.getEmissivity(tem=tem,
den=den,
label='4_2',
product=False)
print '--Warning using theoretical Hbeta emissivity'
#Ionic abundance calculation using pyneb
ionic_abund = atom.getIonAbundance(int_ratio=diagnos_mag,
tem=tem,
den=den,
to_eval=diagnos_eval,
Hbeta=hbeta_flux)
#Evaluate the nan array
nan_idcs = isnan(ionic_abund)
nan_count = np_sum(nan_idcs)
#Directly save if not nan
if nan_count == 0:
self.abunData[abund_code] = ionic_abund
#Remove the nan entries performing a normal distribution
elif nan_count < 0.90 * self.MC_array_len:
mag, error = nanmean(ionic_abund), nanstd(ionic_abund)
#Generate truncated array to store the data
a, b = (0 - mag) / error, (1000 * mag - mag) / error
new_samples = truncnorm(a, b, loc=mag,
scale=error).rvs(size=nan_count)
#Replace nan entries
ionic_abund[nan_idcs] = new_samples
self.abunData[abund_code] = ionic_abund
if nan_count > self.MC_warning_limit:
print '-- {} calculated with {}'.format(abund_code, nan_count)
return
def check_obsLines(self, lines_list, just_one_line=False):
#WARNING it would be better something that reads a standard preference over some.
eval_lines = map(
lambda x: 'L({})'.format(x[x.find('_') + 1:len(x) - 1]),
lines_list) #Right format for pyneb eval: Ar3_7751A -> L(7751)
diagnos_eval = None
#Case all lines are there
if self.lines_dict.viewkeys() >= set(lines_list):
diagnos_mag = zeros(self.MC_array_len)
for i in range(len(lines_list)):
diagnos_mag += self.lines_dict[lines_list[i]]
diagnos_eval = '+'.join(eval_lines)
#Case we can use any line: #WARNING last line is favoured
elif just_one_line:
diagnos_mag = zeros(self.MC_array_len)
for i in range(len(lines_list)):
if lines_list[i] in self.lines_dict:
diagnos_mag = self.lines_dict[lines_list[i]]
diagnos_eval = eval_lines[i]
#Case none of the lines
if diagnos_eval is None:
diagnos_mag = self.generate_nan_array()
diagnos_eval = '+'.join(eval_lines)
return diagnos_eval, diagnos_mag
def argon_abundance_scheme(self, Tlow, Thigh, ne):
#Calculate the Ar_+2 abundance according to the lines observed
Ar3_lines = ['Ar3_7136A', 'Ar3_7751A']
diagnos_eval, diagnos_mag = self.check_obsLines(Ar3_lines,
just_one_line=True)
self.determine_ionic_abundance('ArIII_HII', self.Ar3_atom,
diagnos_eval, diagnos_mag, Tlow, ne)
#Calculate the Ar_+3 abundance according to the lines observed
Ar4_lines = ['Ar4_4740A', 'Ar4_4711A']
diagnos_eval, diagnos_mag = self.check_obsLines(Ar4_lines,
just_one_line=True)
self.determine_ionic_abundance('ArIV_HII', self.Ar4_atom, diagnos_eval,
diagnos_mag, Thigh, ne)
def oxygen_abundance_scheme(self, Tlow, Thigh, ne):
#Calculate the O_+1 abundances from 3200+ lines
O2_lines = ['O2_3726A+']
diagnos_eval, diagnos_mag = self.check_obsLines(O2_lines)
diagnos_eval = 'L(3726)+L(3729)'
self.determine_ionic_abundance('OII_HII_3279A', self.O2_atom,
diagnos_eval, diagnos_mag, Tlow, ne)
#Calculate the O_+1 abundances from 7300+ lines
O2_lines = ['O2_7319A+']
diagnos_eval, diagnos_mag = self.check_obsLines(O2_lines)
diagnos_eval = 'L(7319)+L(7330)'
self.determine_ionic_abundance('OII_HII_7319A', self.O2_atom,
diagnos_eval, diagnos_mag, Tlow, ne)
#--Correction for recombination contribution Liu2000
if 'OII_HII_7319A' in self.abunData:
try:
hbeta_flux = self.Hbeta_flux
except AttributeError:
hbeta_flux = self.H1_atom.getEmissivity(tem=Tlow,
den=ne,
label='4_2',
product=False)
print '--Warning using theoretical Hbeta emissivity'
Lines_Correction = (9.36 * power((Tlow / 10000), 0.44) *
self.abunData.OII_HII_7319A) * hbeta_flux
ratio = self.lines_dict['O2_7319A+'] - Lines_Correction
self.determine_ionic_abundance('OII_HII_7319A', self.O2_atom,
diagnos_eval, ratio, Tlow, ne)
#Get the ratios for empirical relation between OII lines
if 'O2_3726A+' in self.lines_dict:
self.abunData[
'O_R3200'] = self.lines_dict['O2_3726A+'] / self.Hbeta_flux
print 'O_R3200', mean(self.abunData['O_R3200'])
print 'OII_HII_3279A', mean(self.abunData['OII_HII_3279A'])
print 'Original flux', mean(self.lines_dict['O2_3726A+'])
if 'O2_7319A+' in self.lines_dict:
self.abunData[
'O_R7300'] = self.lines_dict['O2_7319A+'] / self.Hbeta_flux
print 'OII_HII_7319A', mean(self.abunData['OII_HII_7319A'])
if self.lines_dict.viewkeys() >= set(['O3_5007A']):
self.abunData[
'O_R3'] = self.lines_dict['O3_5007A'] / self.Hbeta_flux
#Calculate the abundance from the empirical O_R3200_ffO2
if set(self.abunData.index) >= {'O_R7300', 'O_R3'}:
logRO2 = 1.913 + log10(self.abunData['O_R7300']) - 0.374 * log10(
self.abunData['O_R3']) / 0.806
print 'logRO2', mean(logRO2)
RO2 = power(10, logRO2)
self.abunData['O_R3200_ffO2'] = RO2
print 'O_R3200_ffO2', mean(self.abunData['O_R3200_ffO2'])
print 'RO2*Hbeta', mean(RO2 * self.Hbeta_flux)
diagnos_eval = 'L(3726)+L(3729)'
self.determine_ionic_abundance('OII_HII_ffO2', self.O2_atom,
diagnos_eval, RO2 * self.Hbeta_flux,
Tlow, ne)
print 'OII_HII_ffO2', mean(self.abunData['OII_HII_ffO2'])
#Calculate the O_+2 abundance
O3_lines = ['O3_4959A', 'O3_5007A']
diagnos_eval, diagnos_mag = self.check_obsLines(O3_lines)
self.determine_ionic_abundance('OIII_HII', self.O3_atom, diagnos_eval,
diagnos_mag, Thigh, ne)
#Determine the O/H abundance (favoring the value from OII_HII
if set(self.abunData.index) >= {'OII_HII_3279A', 'OIII_HII'}:
self.abunData['OII_HII'] = self.abunData['OII_HII_3279A']
self.abunData['OI_HI'] = self.abunData[
'OII_HII_3279A'] + self.abunData['OIII_HII']
elif set(self.abunData.index) >= {'OII_HII_7319A', 'OIII_HII'}:
self.abunData['OII_HII'] = self.abunData['OII_HII_7319A']
self.abunData['OI_HI'] = self.abunData[
'OII_HII_7319A'] + self.abunData['OIII_HII']
if set(self.abunData.index) >= {'OII_HII_ffO2', 'OIII_HII'}:
if set(self.abunData.index) >= {'OII_HII_3279A'}:
self.abunData['OI_HI_ff02'] = self.abunData[
'OII_HII_3279A'] + self.abunData['OIII_HII']
else:
self.abunData['OI_HI_ff02'] = self.abunData[
'OII_HII_ffO2'] + self.abunData['OIII_HII']
return
def nitrogen_abundance_scheme(self, Tlow, ne):
#Calculate TNII temperature from the CHAOS relation
T_NII = Tlow #self.m_TNII_correction * Tlow + self.n_TNII_correction
#Calculate the N+1 abundance
N2_lines = ['N2_6548A', 'N2_6584A']
diagnos_eval, diagnos_mag = self.check_obsLines(N2_lines)
self.determine_ionic_abundance('NII_HII', self.N2_atom, diagnos_eval,
diagnos_mag, T_NII, ne)
#Calculate NI_HI using the OI_HI
if set(self.abunData.index) >= {'NII_HII', 'OI_HI'}:
#Compute NI_OI
self.abunData[
'NI_OI'] = self.abunData['NII_HII'] / self.abunData['OII_HII']
self.abunData[
'NI_HI'] = self.abunData['NI_OI'] * self.abunData['OI_HI']
# #Repeat calculation if 5755 line was observed to include the recombination contribution
# if self.lines_dict.viewkeys() >= {'N2_5755A'}:
#
# NIII_HI = self.abunData.NI_HI - self.abunData['NII_HII']
# Lines_Correction = 3.19 * power((Thigh/10000), 0.30) * NIII_HI * self.Hbeta_flux
# self.abunData['TNII'], nSII = self.diags.getCrossTemDen(diag_tem = '[NII] 5755/6584+',
# diag_den = '[SII] 6731/6716',
# value_tem = (self.lines_dict['N2_5755A'] - Lines_Correction)/(self.lines_dict['N2_6548A'] + self.lines_dict['N2_6584A']),
# value_den = self.lines_dict['S2_6731A']/self.lines_dict['S2_6716A'])
#
# Ratio = self.lines_dict['N2_6548A'] + self.lines_dict['N2_6584A']
# self.determine_ionic_abundance('NII_HII', self.N2_atom, Ratio, diagnos_mag, self.abunData['TNII'], ne)
#
# self.abunData['NI_OI'] = self.abunData['NII_HII'] / self.abunData['OII_HII']
# self.abunData['NI_HI'] = self.abunData['NI_OI'] * self.abunData['OI_HI']
return
def sulfur_abundance_scheme(self, Tlow, ne, SIII_lines_to_use):
print 'Metiendo esto', SIII_lines_to_use
#Calculate the S+1 abundance
S2_lines = ['S2_6716A', 'S2_6731A']
diagnos_eval, diagnos_mag = self.check_obsLines(S2_lines)
self.determine_ionic_abundance('SII_HII', self.S2_atom, diagnos_eval,
diagnos_mag, Tlow, ne)
#Calculate the S+2 abundance
S3_lines = ['S3_9069A', 'S3_9531A'
] if SIII_lines_to_use == 'BOTH' else [SIII_lines_to_use]
diagnos_eval, diagnos_mag = self.check_obsLines(S3_lines)
if set(S3_lines) != set(['S3_9069A', 'S3_9531A']):
print '-- Using SIII lines', diagnos_eval
self.determine_ionic_abundance('SIII_HII', self.S3_atom, diagnos_eval,
diagnos_mag, Tlow, ne)
#Calculate the total sulfur abundance
if set(self.abunData.index) >= {'SII_HII', 'SIII_HII'}:
self.abunData[
'SI_HI'] = self.abunData['SII_HII'] + self.abunData['SIII_HII']
#Add the S+3 component if the argon correction is found
if set(self.abunData.index) >= {'ArIII_HII', 'ArIV_HII'}:
logAr2Ar3 = log10(self.abunData['ArIII_HII'] /
self.abunData['ArIV_HII'])
logSIV = log10(self.abunData['SIII_HII']) - (
logAr2Ar3 - self.n_SIV_correction) / self.m_SIV_correction
SIV_HII = power(10, logSIV)
# Evaluate the nan array
nan_idcs = isnan(SIV_HII)
nan_count = np_sum(nan_idcs)
# Directly save if not nan
if nan_count == 0:
self.abunData['SIV_HII'] = SIV_HII
# Remove the nan entries performing a normal distribution
elif nan_count < 0.90 * self.MC_array_len:
mag, error = nanmean(SIV_HII), nanstd(SIV_HII)
# Generate truncated array to store the data
a, b = (0 - mag) / error, (1000 * mag - mag) / error
new_samples = truncnorm(a, b, loc=mag,
scale=error).rvs(size=nan_count)
# Replace nan entries
SIV_HII[nan_idcs] = new_samples
self.abunData['SIV_HII'] = SIV_HII
if nan_count > self.MC_warning_limit:
print '-- {} calculated with {}'.format(
'SIV_HII', nan_count)
self.abunData[
'SI_HI'] = self.abunData['SII_HII'] + self.abunData[
'SIII_HII'] + self.abunData['SIV_HII']
self.abunData['ICF_SIV'] = self.abunData['SI_HI'] / (
self.abunData['SII_HII'] + self.abunData['SIII_HII'])
return
def helium_abundance_elementalScheme(self,
Te,
ne,
lineslog_frame,
metal_ext=''):
#Check temperatures are not nan before starting the treatment
if (not isinstance(Te, float)) and (not isinstance(ne, float)):
#HeI_indices = (lineslog_frame.Ion.str.contains('HeI_')) & (lineslog_frame.index != 'He1_8446A') & (lineslog_frame.index != 'He1_7818A') & (lineslog_frame.index != 'He1_5016A')
HeI_indices = (lineslog_frame.Ion.str.contains('HeI_')) & (
lineslog_frame.index.isin(
['He1_4472A', 'He1_5876A', 'He1_6678A']))
HeI_labels = lineslog_frame.loc[HeI_indices].index.values
HeI_ions = lineslog_frame.loc[HeI_indices].Ion.values
Emis_Hbeta = self.H1_atom.getEmissivity(tem=Te,
den=ne,
label='4_2',
product=False)
#--Generating matrices with fluxes and emissivities
for i in range(len(HeI_labels)):
pyneb_code = float(HeI_ions[i][HeI_ions[i].find('_') +
1:len(HeI_ions[i])])
line_relative_Flux = self.lines_dict[
HeI_labels[i]] / self.Hbeta_flux
line_relative_emissivity = self.He1_atom.getEmissivity(
tem=Te, den=ne, wave=pyneb_code,
product=False) / Emis_Hbeta
line_relative_emissivity = self.check_nan_entries(
line_relative_emissivity)
if i == 0:
matrix_HeI_fluxes = copy(line_relative_Flux)
matrix_HeI_emis = copy(line_relative_emissivity)
else:
matrix_HeI_fluxes = vstack(
(matrix_HeI_fluxes, line_relative_Flux))
matrix_HeI_emis = vstack(
(matrix_HeI_emis, line_relative_emissivity))
matrix_HeI_fluxes = transpose(matrix_HeI_fluxes)
matrix_HeI_emis = transpose(matrix_HeI_emis)
#Perform the fit
params = Parameters()
params.add('Y', value=0.01)
HeII_HII_array = zeros(len(matrix_HeI_fluxes))
HeII_HII_error = zeros(len(matrix_HeI_fluxes))
for i in range(len(matrix_HeI_fluxes)):
fit_Output = lmfit_minimmize(residual_Y_v3,
params,
args=(matrix_HeI_emis[i],
matrix_HeI_fluxes[i]))
HeII_HII_array[i] = fit_Output.params['Y'].value
HeII_HII_error[i] = fit_Output.params['Y'].stderr
#NO SUMANDO LOS ERRORES CORRECTOS?
#self.abunData['HeII_HII_from_' + metal_ext] = random.normal(mean(HeII_HII_array), mean(HeII_HII_error), size = self.MC_array_len)
ionic_abund = random.normal(mean(HeII_HII_array),
mean(HeII_HII_error),
size=self.MC_array_len)
#Evaluate the nan array
nan_count = np_sum(isnan(ionic_abund))
if nan_count == 0:
self.abunData['HeII_HII_from_' + metal_ext] = ionic_abund
#Remove the nan entries performing a normal distribution
elif nan_count < 0.90 * self.MC_array_len:
mag, error = nanmean(ionic_abund), nanstd(ionic_abund)
self.abunData['HeII_HII_from_' + metal_ext] = random.normal(
mag, error, size=self.MC_array_len)
if nan_count > self.MC_warning_limit:
print '-- {} calculated with {}'.format(
'HeII_HII_from_' + metal_ext, nan_count)
#Calculate the He+2 abundance
if self.lines_dict.viewkeys() >= {'He2_4686A'}:
#self.abunData['HeIII_HII_from_' + metal_ext] = self.He2_atom.getIonAbundance(int_ratio = self.lines_dict['He2_4686A'], tem=Te, den=ne, wave = 4685.6, Hbeta = self.Hbeta_flux)
self.determine_ionic_abundance('HeIII_HII_from_' + metal_ext,
self.He2_atom, 'L(4685)',
self.lines_dict['He2_4686A'],
Te, ne)
#Calculate elemental abundance
Helium_element_keys = [
'HeII_HII_from_' + metal_ext, 'HeIII_HII_from_' + metal_ext
]
if set(self.abunData.index) >= set(Helium_element_keys):
self.abunData['HeI_HI_from_' +
metal_ext] = self.abunData[Helium_element_keys[
0]] + self.abunData[Helium_element_keys[1]]
else:
self.abunData['HeI_HI_from_' + metal_ext] = self.abunData[
Helium_element_keys[0]]
#Proceed to get the Helium mass fraction Y
Element_abund = metal_ext + 'I_HI'
Y_fraction, Helium_abund = 'Ymass_' + metal_ext, 'HeI_HI_from_' + metal_ext
if set(self.abunData.index) >= {Helium_abund, Element_abund}:
self.abunData[Y_fraction] = (
4 * self.abunData[Helium_abund] *
(1 - 20 * self.abunData[Element_abund])) / (
1 + 4 * self.abunData[Helium_abund])
def store_abundances_excel(self, objCode, catalogue_df, extension=''):
#Store the values using the mean and the std from the array
for parameter in self.abunData.index:
mean_value, std_value = mean(self.abunData[parameter]), std(
self.abunData[parameter])
if (~isnan(mean_value)) & (~isnan(std_value)):
catalogue_df.loc[objCode, parameter + extension] = ufloat(
mean_value, std_value)
else:
catalogue_df.loc[objCode, parameter + extension] = np_nan
print parameter, mean_value, std_value
return
def generate_nan_array(self):
nan_array = empty(self.MC_array_len)
nan_array[:] = np_nan
return nan_array
def check_nan_entries(self, input_array):
nan_count = np_sum(isnan(input_array))
if nan_count > 0:
mag, error = nanmean(input_array), nanstd(input_array)
new_distr = random.normal(mag, error, size=self.MC_array_len)
if nan_count > 0.1 * self.MC_array_len:
print '--Helium issue with {} nans'.format(nan_count)
else:
new_distr = input_array
return new_distr
def load_elements(self):
#Set atomic data
#atomicData.setDataFile('he_i_rec_Pal12-Pal13.fits')
atomicData.setDataFile('s_iii_coll_HRS12.dat')
#Default: 's_iii_atom_PKW09.dat'
'S3: All energy and A values: Podobedova, Kelleher, and Wiese 2009, J. Phys. Chem. Ref. Data, Vol.'
'S3: collision strengths: Tayal & Gupta 1999, ApJ, 526, 544'
#New Atomic data s_iii_coll_HRS12.dat
'S3: All energy and A values: Podobedova, Kelleher, and Wiese 2009, J. Phys. Chem. Ref. Data, Vol.'
'S3: collision strengths: Hudson, Ramsbottom & Scott 2012, ApJ, 750, 65'
#Declare ions
self.S2_atom = Atom('S', 2)
self.S3_atom = Atom('S', 3)
self.Ar3_atom = Atom('Ar', 3)
self.Ar4_atom = Atom('Ar', 4)
self.N2_atom = Atom('N', 2)
self.O2_atom = Atom('O', 2)
self.O3_atom = Atom('O', 3)
self.H1_atom = RecAtom('H', 1)
self.He1_atom = RecAtom('He', 1)
self.He2_atom = RecAtom('He', 2)
#Pyneb objects
self.diags = Diagnostics()
#Ohrs 2016 relation for the OI_SI gradient
self.logSI_OI_Gradient = random.normal(
-1.53, 0.05, size=self.MC_array_len
) # random.normal(-1.78, 0.03, size = self.MC_array_len)
self.OI_SI = power(10, -self.logSI_OI_Gradient)
#Theoretical ratios
self.S3_ratio = self.S3_atom.getEmissivity(
10000, 100, wave=9531) / self.S3_atom.getEmissivity(
10000, 100, wave=9069)
self.S3_9000_ratio = random.normal(
self.S3_atom.getEmissivity(10000, 100, wave=9531) /
self.S3_atom.getEmissivity(10000, 100, wave=9069),
0.01,
size=self.MC_array_len)
self.N2_6000_ratio = self.N2_atom.getEmissivity(
10000, 100, wave=6584) / self.N2_atom.getEmissivity(
10000, 100, wave=6548)
self.O3_5000_ratio = self.O3_atom.getEmissivity(
10000, 100, wave=5007) / self.O3_atom.getEmissivity(
10000, 100, wave=4959)
#Factors to speed calculations
self.lines_factors = {}
self.lines_factors['S3_9069A'] = 1 + self.S3_ratio
self.lines_factors['S3_9531A'] = 1 + 1 / self.S3_ratio
#Cloudy models for the SIV contribution
self.m_SIV_correction = random.normal(1.1628,
0.00559,
size=self.MC_array_len)
self.n_SIV_correction = random.normal(0.0470,
0.0097,
size=self.MC_array_len)
#self.m_SIV_correction = random.normal(1.109, 0.01, size = self.MC_array_len)
#self.n_SIV_correction = random.normal(0.135, 0.0173, size = self.MC_array_len)
#CHAOS relation TNII-TSIII
#T[SIII] = 1.312(+-0.075)T[NII]-0.313(+-0.058)
#TNII = (0.762+-0.044)*TSIII + 0.239+-0.046
self.m_TNII_correction = random.normal(0.762,
0.044,
size=self.MC_array_len)
self.n_TNII_correction = random.normal(0.239,
0.046,
size=self.MC_array_len)
#Truncated gaussian for the density
lower_trunc, upper_trunc = (1.0 - 50.0) / 25.0, (100 - 50) / 25.0
self.Truncated_gaussian = truncnorm(lower_trunc,
upper_trunc,
loc=50,
scale=25)
print '-Elements loaded\n'
return
def __init__(self):
self.Combined = None
self.MontecarloCheck = True
self.MC_Iterations = 10
self.NComps = 0
self.GaussianSampling = 101
self.Fitting_dict = OrderedDict()
self.Fitting_dict['Deblend check'] = False #This logic is true when a blended group is observed
self.Fitting_dict['Fitting method'] = None
self.Fitting_dict['start treatment'] = False #This logic is true to force the deblending process
self.Fitting_dict['line group'] = None #This integer provides the index for the group of lines describes the blended lines
self.Fitting_dict['line number'] = None
self.Fitting_dict['line label'] = None #This string describes the label for the current blended line we are mesuring: H1_6563A
self.Fitting_dict['blended label'] = None #This string should be used for the blended label for the lines log
self.Fitting_dict['blended number'] = None #This integer describes the number of blended lines. Currently it must be equal to the number in the blended list elements.
self.Fitting_dict['blended wavelengths']= None #This list contains the wavelengths of the blended lines
self.Fitting_dict['blended index'] = None #This integer provides the index of the current line in a blended group
self.Fitting_dict['kmpfit_dict'] = None #This dictionaries containes the parameters for kmpfit
self.Fitting_dict['Wide component'] = False #This keyword informs if there is a wide component on the emission line
self.Fitting_dict['line type'] = None #Spectral feature type string: 'Absorption', 'Emission'
self.Fitting_dict['peak_waves'] = None #This list contains the wavelenghts of all the peaks
self.Fitting_dict['y_scaler'] = None #This is the magnitude used to scale the y flux (normaly the line peak or line higher peak)
self.Fitting_dict['x_scaler'] = None #This is the magnitude used to scale the wavelength values (normaly the line middle wavelength)
self.Fitting_dict['x_resample'] = None #x Gaussian resampling of the line required for plotting
self.Fitting_dict['y_resample'] = None #y Gaussian resampling of the line required for plotting
self.Fitting_dict['y_resample_total'] = None #y Gaussian resampling of the blended group line required for plotting
self.Fitting_dict['ContinuumFlux'] = None #Continuum intensity across the whole plotting region
self.Fitting_dict['m_Continuum'] = None #Assuming a line this is the gradient (m)
self.Fitting_dict['n_Continuum'] = None #Assuming a line this is the y axis interception point (n)
self.Fitting_dict['zerolev_resample'] = None #Continuum level resampling at the line
self.Fitting_dict['zerolev_median'] = None #Mean level at the line center
self.Fitting_dict['zerolev_sigma'] = None #Continuum dispersion assuming linear shape
self.Fitting_dict['ContinuumWidth'] = None #This is the number of pixels manually selected
self.Fitting_dict['x_norm'] = None
self.Fitting_dict['y_norm'] = None
self.Fitting_dict['zerolev_norm'] = None
self.Fitting_dict['sig_zerolev_norm'] = None
self.Fitting_dict['lmfit_params'] = None #lmfit parameters dict
self.Fitting_dict['lmfit_params_wide'] = None #lmfit parameters dict
self.Fitting_dict['MC_iteration'] = None #Number of iterations for calculation (1 for normal 1000 for MC)
self.Fitting_dict['lmfit_output'] = None
self.Fitting_dict['FluxI_N_vector'] = None #This vectors holds all the fluxes calculated for the case of a gaussian fit
self.Fitting_dict['parameters_list'] = None #This list contains all the parameters which are fitted for a line: A1, mu1, sigma1, A2, mu2, sigma2 ...
self.Fitting_dict['FluxI'] = None
self.Fitting_dict['Add_wideComponent'] = None #Extra step to procced to the wide component calculation
self.Fitting_dict['WC_theowavelength'] = None #Gaussian_Coefficients coefficients
self.Fitting_dict['wide mask'] = None
self.GHcoeffs = {}
self.GHcoeffs['c0'] = sqrt(6.0) / 4.0
self.GHcoeffs['c1'] = -sqrt(3.0)
self.GHcoeffs['c2'] = -sqrt(6.0)
self.GHcoeffs['c3'] = 2.0 * sqrt(3.0) / 3.0
self.GHcoeffs['c4'] = sqrt(6.0) / 3.0
self.skeness_limit = {'fixed':(False)}
self.kutorsis_limit = {'fixed':(False)}
self.skeness_Glimit = {'fixed':(True)}
self.kutorsis_Glimit = {'fixed':(True)}
N2 = Atom('N', 2)
N2_6548A = N2.getEmissivity(tem=10000, den=100, wave=6548)
N2_6584A = N2.getEmissivity(tem=10000, den=100, wave=6584)
self.N2_Ratio = N2_6584A / N2_6548A
print self.N2_Ratio
self.s2pi = sqrt(2*pi)