ratios_dict['all_x'], ratios_dict['all_y'])
trendline_all = cHbeta_all_MagEr * ratios_dict['all_x'] + n_all_MagEr
cHbeta_in_MagEr, n_in_MagEr = LinfitLinearRegression(
ratios_dict['in_x'], ratios_dict['in_y'])
trendline_in = cHbeta_in_MagEr * ratios_dict['in_x'] + n_in_MagEr
#--Blue points
if len(ratios_dict['blue_x']) > 0:
dz.data_plot(unumpy.nominal_values(ratios_dict['blue_x']),
unumpy.nominal_values(ratios_dict['blue_y']),
'blue arm emissions',
markerstyle='o',
color='#0072B2',
y_error=unumpy.std_devs(ratios_dict['blue_y']))
dz.plot_text(unumpy.nominal_values(ratios_dict['blue_x']),
unumpy.nominal_values(ratios_dict['blue_y']),
ratios_dict['blue_ions'],
color='#0072B2')
#--Red points
if len(ratios_dict['red_x']) > 0:
dz.data_plot(unumpy.nominal_values(ratios_dict['red_x']),
unumpy.nominal_values(ratios_dict['red_y']),
'red arm emissions',
markerstyle='o',
color='#D55E00',
y_error=unumpy.std_devs(ratios_dict['red_y']))
dz.plot_text(unumpy.nominal_values(ratios_dict['red_x']),
unumpy.nominal_values(ratios_dict['red_y']),
ratios_dict['red_ions'],
color='#D55E00')
dtype=types_list,
unpack=True)
objects = loadtxt(table_address, dtype=str, usecols=(0), unpack=True)
O_mean = O_mean * 1e-4
O_err = O_err * 1e-4
print objects
print Y_mean
print Y_sys
print Y_random
print O_mean
print O_err
dz = Dazer()
dz.FigConf()
dz.data_plot(O_mean,
Y_mean,
label='Peimbert et al (2016)',
markerstyle='o',
x_error=O_err,
y_error=Y_sys)
dz.plot_text(O_mean, Y_mean, text=objects)
dz.FigWording(xlabel='Oxygen abundance',
ylabel='Helium mass fraction',
title=r'$Y_{P}$ oxygen regression, Peimbert et al (2016)')
dz.display_fig()
idcs = (pd.notnull(catalogue_df.OI_HI_emis2nd)) & (pd.notnull(
catalogue_df.NI_HI_emis2nd)) & (
~catalogue_df.index.isin(['0564', '3', '70', '51991-224']))
#Prepare data
O_values = catalogue_df.loc[idcs].OI_HI_emis2nd.values
N_values = catalogue_df.loc[idcs].NI_HI_emis2nd.values
objects = catalogue_df.loc[idcs].index.values
print objects
N_O_ratio = N_values / O_values
for i in range(len(N_O_ratio)):
print objects[i], O_values[i], N_values[i], N_O_ratio[i]
dz.data_plot(unumpy.nominal_values(O_values) * 1e5,
unumpy.nominal_values(N_O_ratio),
label='Abundances from our sample',
markerstyle='o',
x_error=unumpy.std_devs(O_values) * 1e5,
y_error=unumpy.std_devs(N_O_ratio))
dz.plot_text(unumpy.nominal_values(O_values) * 1e5,
unumpy.nominal_values(N_O_ratio),
text=objects)
dz.FigWording(r'$O/H$ $(10^5)$', r'$N/O$',
r'N/O relation for HII galaxy sample')
dz.display_fig()
unumpy.nominal_values(TeSIII_array),
unumpy.std_devs(TeOII_array),
unumpy.std_devs(TeSIII_array))
# for i in range(len(regr_dict['m'])):
reg_code = 0
y_fit = regr_dict['m'][reg_code] * x_regression + regr_dict['n'][reg_code]
dz.data_plot(x_regression,
y_fit,
'Sample fit ({})'.format(regr_dict['methodology'][reg_code]),
linestyle='--')
dz.data_plot(unumpy.nominal_values(TeOII_array),
unumpy.nominal_values(TeSIII_array),
'HII galaxies',
markerstyle='o',
x_error=unumpy.std_devs(TeOII_array),
y_error=unumpy.std_devs(TeSIII_array))
dz.plot_text(unumpy.nominal_values(TeOII_array),
unumpy.nominal_values(TeSIII_array), objects)
dz.data_plot(x_regression, y_regression_One, '', color='black', linestyle='--')
#dz.data_plot(x_regression, y_regression_Epm2014, r'[$P\'erez$ montero 2014] models', linestyle = '--')
Title = r'Sulfur TSIII versus Oxygen TOII temperature comparison'
y_Title = r'$T_{e}[SIII]\,(K)$'
x_Title = r'$T_{e}[OII]\,(K)$'
dz.FigWording(x_Title, y_Title, Title)
dz.display_fig()
#Prepare data
O_values = catalogue_df.loc[idcs].OI_HI_emis2nd.values
N_values = catalogue_df.loc[idcs].NI_HI_emis2nd.values
HeII_HI = catalogue_df.loc[idcs].HeII_HII_from_O_emis2nd.values
HeIII_HI = catalogue_df.loc[idcs].HeIII_HII_from_O_emis2nd.values
objects = catalogue_df.loc[idcs].index.values
He_ratio = HeII_HI / HeIII_HI
print objects
N_O_ratio = N_values / O_values
for i in range(len(N_O_ratio)):
print objects[i], O_values[i], N_values[i], N_O_ratio[i]
dz.data_plot(unumpy.nominal_values(HeII_HI),
unumpy.nominal_values(N_O_ratio),
label='Abundances from our sample',
markerstyle='o',
x_error=unumpy.std_devs(HeII_HI),
y_error=unumpy.std_devs(N_O_ratio))
dz.plot_text(unumpy.nominal_values(HeII_HI),
unumpy.nominal_values(N_O_ratio),
text=objects)
dz.FigWording(r'$HeII/HeIII$', r'$N/O$', r'N/O relation for HII galaxy sample')
dz.display_fig()
size_dict = {'axes.labelsize':20, 'legend.framealpha':None, 'font.family':'Times New Roman', 'mathtext.default':'regular', 'xtick.labelsize':18, 'ytick.labelsize':18}
dz.FigConf(plotSize = size_dict)
#Load catalogue dataframe
catalogue_df = dz.load_excel_DF('/home/vital/Dropbox/Astrophysics/Data/WHT_observations/WHT_Galaxies_properties.xlsx')
dz.quick_indexing(catalogue_df)
idcs = ~catalogue_df.OII_HII_emis2nd.isnull() & ~catalogue_df.OIII_HII_emis2nd.isnull() & ~catalogue_df.SII_HII_emis2nd.isnull() & ~catalogue_df.SIII_HII_emis2nd.isnull() & (catalogue_df.quick_index.notnull())
TeOIII_array = catalogue_df.loc[idcs].TeOIII_emis.values
TeSIII_array = catalogue_df.loc[idcs].TeSIII_emis.values
#Axis values
objects = catalogue_df.loc[idcs].index.values
OII_HII_abundances = catalogue_df.loc[idcs].OII_HII_emis2nd.values
OIII_HII_abundances = catalogue_df.loc[idcs].OIII_HII_emis2nd.values
SII_HII_abundances = catalogue_df.loc[idcs].SII_HII_emis2nd.values
SIII_HII_abundances = catalogue_df.loc[idcs].SIII_HII_emis2nd.values
quick_reference = catalogue_df.loc[idcs].quick_index.values
oxygen_ratio = OII_HII_abundances / OIII_HII_abundances
sulfur_ratio = SII_HII_abundances / SIII_HII_abundances
dz.data_plot(unumpy.nominal_values(sulfur_ratio), unumpy.nominal_values(oxygen_ratio), 'HII galaxies', markerstyle='o', x_error=unumpy.std_devs(sulfur_ratio), y_error=unumpy.std_devs(oxygen_ratio))
dz.plot_text(unumpy.nominal_values(sulfur_ratio), unumpy.nominal_values(oxygen_ratio), quick_reference)
Title = r'Oxygen versus Sulfur ionization fractions'
y_Title = r'$\frac{O^{+}}{O^{2+}}$'
x_Title = r'$\frac{S^{+}}{S^{2+}}$'
dz.FigWording(x_Title, y_Title, Title) #, XLabelPad = 20
dz.savefig('/home/vital/Dropbox/Astrophysics/Papers/Yp_AlternativeMethods/Images/ionizationFraction')
'all_x'] + n_all_MagEr2
cHbeta_in_MagEr2, n_in_MagEr2 = LinfitLinearRegression(
ratios_dict2['in_x'], ratios_dict2['in_y'])
trendline_in2 = cHbeta_in_MagEr2 * ratios_dict2['in_x'] + n_in_MagEr2
#--Blue points
if len(ratios_dict['blue_x']) > 0:
dz.data_plot(unumpy.nominal_values(ratios_dict['blue_x'][2:]),
unumpy.nominal_values(ratios_dict['blue_y'][2:]),
'ISIS blue arm recombination ratios',
markerstyle='o',
color='#0072B2',
y_error=unumpy.std_devs(ratios_dict['blue_y'][2:]))
dz.plot_text(unumpy.nominal_values(ratios_dict['blue_x'][2:]),
unumpy.nominal_values(ratios_dict['blue_y'][2:]),
ratios_dict['blue_ions'][2:],
color='#0072B2',
fontsize=18)
#--Red points
if len(ratios_dict['red_x']) > 0:
dz.data_plot(unumpy.nominal_values(ratios_dict['red_x']),
unumpy.nominal_values(ratios_dict['red_y']),
'ISIS red arm emissions',
markerstyle='o',
color='#D55E00',
y_error=unumpy.std_devs(ratios_dict['red_y']))
dz.plot_text(unumpy.nominal_values(ratios_dict['red_x']),
unumpy.nominal_values(ratios_dict['red_y']),
ratios_dict['red_ions'],
color='#D55E00',
#Run the fits
for k in range(MC_iterations):
x_i = metal_matrix[:,k]
y_i = Y_matrix[:,k]
m, n, r_value, p_value, std_err = stats.linregress(x_i, y_i)
m_vector[k], n_vector[k] = m, n
#Get fit mean values
n_Median, n_16th, n_84th = median(n_vector), percentile(n_vector,16), percentile(n_vector,84)
m_Median, m_16th, m_84th = median(m_vector), percentile(m_vector,16), percentile(m_vector,84)
#Linear data
x_regression_range = linspace(0.0, max(nominal_values(x)) * 1.10, 20)
y_regression_range = m_Median * x_regression_range + n_Median
#Plotting the data
dz.data_plot(nominal_values(x), nominal_values(y), color = Regresions_dict['Colors'][i], label=regression, markerstyle='o', x_error=std_devs(x), y_error=std_devs(y))
dz.data_plot(WMAP_coordinates[0].nominal_value, WMAP_coordinates[1].nominal_value, color = dz.colorVector['pink'], label='WMAP prediction', markerstyle='o', x_error=WMAP_coordinates[0].std_dev, y_error=WMAP_coordinates[1].std_dev)
dz.data_plot(x_regression_range, y_regression_range, label = 'Linear regression', color = Regresions_dict['Colors'][i], linestyle = '--')
dz.plot_text(nominal_values(x), nominal_values(y), objects)
plotTitle = r'{title}: $Y_{{P}} = {n}_{{-{lowerlimit}}}^{{+{upperlimit}}}$'.format(title = regression, n = round_sig(n_Median,4, scien_notation=False), lowerlimit = round_sig(n_Median-n_16th,4, scien_notation=False), upperlimit = round_sig(n_84th-n_Median,4, scien_notation=False))
dz.FigWording(Regresions_dict['x label'][i], Regresions_dict['y label'][i], plotTitle, loc='lower right')
output_pickle = '{objFolder}{element}_regression'.format(objFolder=catalogue_dict['Data_Folder'], element = element)
dz.save_manager(output_pickle, save_pickle = True)
print 'New methods'
print m[0], m[1], m[2], m[3]
print m_err[0], m_err[1], m_err[2], m_err[3]
print n[0], n[1], n[2], n[3]
print n_err[0], n_err[1], n_err[2], n_err[3]
#Get fit mean values
n_Median, n_16th, n_84th = median(n_vector), percentile(n_vector,16), percentile(n_vector,84)
m_Median, m_16th, m_84th = median(m_vector), percentile(m_vector,16), percentile(m_vector,84)
print 'Classical'
print n_Median, round_sig(n_Median-n_16th,2, scien_notation=False), round_sig(n_84th-n_Median,2, scien_notation=False)
#Linear data
x_regression_range = linspace(0.0, max(nominal_values(x)) * 1.10, 20)
y_regression_range = m_Median * x_regression_range + n_Median
#Plotting the data,
dz.data_plot(nominal_values(x), nominal_values(y), color = Regresions_dict['Colors'][i], label=regression, markerstyle='o', x_error=std_devs(x), y_error=std_devs(y))
dz.data_plot(WMAP_coordinates[0].nominal_value, WMAP_coordinates[1].nominal_value, color = dz.colorVector['pink'], label=r'WMAP prediction: $Y = 0.24709\pm0.00025$', markerstyle='o', x_error=WMAP_coordinates[0].std_dev, y_error=WMAP_coordinates[1].std_dev)
dz.data_plot(x_regression_range, y_regression_range, label = 'Linear regression', color = Regresions_dict['Colors'][i], linestyle = '--')
dz.plot_text(nominal_values(x), nominal_values(y), objects)
plotTitle = r'{title}: $Y_{{P}} = {n}_{{-{lowerlimit}}}^{{+{upperlimit}}}$'.format(title = Regresions_dict['title'][i], n = round_sig(n_Median,4, scien_notation=False), lowerlimit = round_sig(n_Median-n_16th,2, scien_notation=False), upperlimit = round_sig(n_84th-n_Median,2, scien_notation=False))
dz.FigWording(Regresions_dict['x label'][i], Regresions_dict['y label'][i], plotTitle, loc='lower right')
output_pickle = '{objFolder}{element}_regression'.format(objFolder=catalogue_dict['Data_Folder'], element = element)
dz.save_manager(output_pickle, save_pickle = True)
obj = objects[i]
temp_label = catalogue_df.loc[obj, 'T_low']
T_low_array[i], T_low_err_array[i] = catalogue_df.loc[
obj, temp_label].nominal_value, catalogue_df.loc[obj,
temp_label].std_dev
print objects[i], x_values[i], y_values[i]
dz.data_plot(T_low_array,
unumpy.nominal_values(y_values),
label=r'Argon $ICF(S^{3+})$',
markerstyle='o',
x_error=T_low_err_array,
y_error=unumpy.std_devs(y_values))
dz.plot_text(unumpy.nominal_values(T_low_array),
unumpy.nominal_values(y_values),
text=quick_reference,
x_pad=1.005,
y_pad=1.005,
fontsize=18)
dz.data_plot(unumpy.nominal_values(x_IR_values),
unumpy.nominal_values(y_IR_values),
color=dz.colorVector['orangish'],
label=r'$ICF(S^{3+})$ from IR (Dors 2016)',
markerstyle='x',
x_error=unumpy.std_devs(x_IR_values),
y_error=unumpy.std_devs(y_IR_values))
dz.plot_text(unumpy.nominal_values(x_IR_values),
unumpy.nominal_values(y_IR_values),
text=objectsIR,
fontsize=18)
ratios_dict['in_x'], ratios_dict['in_y'])
trendline_in = cHbeta_in_MagEr * ratios_dict['in_x'] + n_in_MagEr
if j < 1:
#--Blue points
if len(ratios_dict['blue_x']) > 0:
dz.data_plot(unumpy.nominal_values(ratios_dict['blue_x']),
unumpy.nominal_values(ratios_dict['blue_y']),
'blue arm emissions',
markerstyle='o',
color='tab:cyan',
y_error=unumpy.std_devs(
ratios_dict['blue_y']))
dz.plot_text(unumpy.nominal_values(ratios_dict['blue_x']),
unumpy.nominal_values(ratios_dict['blue_y']),
ratios_dict['blue_ions'],
color='tab:cyan')
#--Red points
if len(ratios_dict['red_x']) > 0:
dz.data_plot(unumpy.nominal_values(ratios_dict['red_x']),
unumpy.nominal_values(ratios_dict['red_y']),
'red arm emissions',
markerstyle='o',
color='tab:red',
y_error=unumpy.std_devs(ratios_dict['red_y']))
dz.plot_text(unumpy.nominal_values(ratios_dict['red_x']),
unumpy.nominal_values(ratios_dict['red_y']),
ratios_dict['red_ions'],
color='tab:red')
#Plotting the data,
label_regression = r'Plank prediction: $Y = 0.24709\pm0.00025$'
dz.data_plot(nominal_values(x),
nominal_values(y),
color=Regresions_dict['Colors'][i],
label='HII galaxies included',
markerstyle='o',
x_error=std_devs(x),
y_error=std_devs(y))
dz.data_plot(x_regression_range,
y_regression_range,
label=label_regr,
color=Regresions_dict['Colors'][i],
linestyle='--')
dz.plot_text(nominal_values(x), nominal_values(y), quick_ref)
#Plotting NO objects
dz.data_plot(nominal_values(x_NO),
nominal_values(y_NO),
color=Regresions_dict['Colors'][i],
label='HII galaxies excluded',
markerstyle='x',
x_error=std_devs(x_NO),
y_error=std_devs(y_NO),
e_style=':')
dz.plot_text(nominal_values(x_NO), nominal_values(y_NO), quickref_NO)
#Plot WMAP prediction
dz.data_plot(WMAP_coordinates[0].nominal_value,
WMAP_coordinates[1].nominal_value,
unumpy.std_devs(TeSIII_array))
# for i in range(len(regr_dict['m'])):
reg_code = 3
y_fit = regr_dict['m'][reg_code] * x_regression + regr_dict['n'][reg_code]
dz.data_plot(x_regression, y_fit, 'Linear fit', linestyle='-')
dz.data_plot(unumpy.nominal_values(TeOIII_array),
unumpy.nominal_values(TeSIII_array),
'HII galaxies',
markerstyle='o',
x_error=unumpy.std_devs(TeOIII_array),
y_error=unumpy.std_devs(TeSIII_array),
color='tab:blue')
dz.plot_text(unumpy.nominal_values(TeOIII_array),
unumpy.nominal_values(TeSIII_array),
objects,
fontsize=17)
dz.data_plot(x_regression,
y_regression_Garnet92,
'Garnett (1992)',
linestyle=':')
dz.data_plot(x_regression,
y_regression_EpmDiaz05,
r'$P\'erez$-Montero et al (2005)',
linestyle='--')
dz.data_plot(x_regression,
y_regression_Epm2014,
r'$P\'erez$-Montero (2014)',
linestyle='-.')
#Load reddening curve for the lines
lines_wavelengths = lineslog_frame.lambda_theo.values
lineslog_frame['line_f'] = dz.Reddening_curve(lines_wavelengths, 'Cardelli1989') #WARNING ESTO NO APUNTA AL REDDENING CLASS SI NO A UNA AQUI
#Determine recombination coefficients for several combinations
ratios_dict = dz.compare_RecombCoeffs(object_data, lineslog_frame)
cHbeta_all_MagEr, n_all_MagEr = LinfitLinearRegression(ratios_dict['all_x'], ratios_dict['all_y'])
trendline_all = cHbeta_all_MagEr * ratios_dict['all_x'] + n_all_MagEr
cHbeta_in_MagEr, n_in_MagEr = LinfitLinearRegression(ratios_dict['in_x'], ratios_dict['in_y'])
trendline_in = cHbeta_in_MagEr * ratios_dict['in_x'] + n_in_MagEr
#--Blue points
if len(ratios_dict['blue_x']) > 0:
dz.data_plot(unumpy.nominal_values(ratios_dict['blue_x']), unumpy.nominal_values(ratios_dict['blue_y']), 'blue arm emissions', markerstyle='o', color = '#0072B2', y_error=unumpy.std_devs(ratios_dict['blue_y']))
dz.plot_text(unumpy.nominal_values(ratios_dict['blue_x']), unumpy.nominal_values(ratios_dict['blue_y']), ratios_dict['blue_ions'], color = '#0072B2')
#--Red points
if len(ratios_dict['red_x']) > 0:
dz.data_plot(unumpy.nominal_values(ratios_dict['red_x']), unumpy.nominal_values(ratios_dict['red_y']), 'red arm emissions', markerstyle='o', color = '#D55E00', y_error=unumpy.std_devs(ratios_dict['red_y']))
dz.plot_text(unumpy.nominal_values(ratios_dict['red_x']), unumpy.nominal_values(ratios_dict['red_y']), ratios_dict['red_ions'], color = '#D55E00')
#--Outside points
if ratios_dict['out_x'] is not None:
dz.data_plot(unumpy.nominal_values(ratios_dict['out_x']), unumpy.nominal_values(ratios_dict['out_y']), 'Invalid emissions', markerstyle='o', color = '#009E73', y_error=unumpy.std_devs(ratios_dict['out_y']))
dz.plot_text(unumpy.nominal_values(ratios_dict['out_x']), unumpy.nominal_values(ratios_dict['out_y']), ratios_dict['out_ions'], color = '#009E73')
#--Trendline
dz.data_plot(unumpy.nominal_values(ratios_dict['in_x']), unumpy.nominal_values(trendline_in), 'Valid points regression', linestyle='--', color = 'black')
#Store reddening coefficient
y_err = df_dict[type].y_error
objCodes = np.arange(1, len(df_dict[type].index) + 1).astype(str)
NO = (unumpy.uarray(df_dict[type]['Nitrogen'].values,
df_dict[type]['Nitrogen_error'].values) *
1e-6) / (unumpy.uarray(df_dict[type]['Oxygen'].values,
df_dict[type]['Oxygen_error'].values) * 1e-5)
dz.data_plot(y,
unumpy.nominal_values(NO),
label=conf_dict['legend_label'],
color=conf_dict[element + '_color'],
markerstyle='o',
x_error=y_err,
y_error=unumpy.std_devs(NO))
dz.plot_text(y, unumpy.nominal_values(NO), objCodes)
dz.FigWording(conf_dict[element + '_xlabel'], 'N/O', conf_dict['title'])
dz.display_fig()
# import numpy as np
# from uncertainties import ufloat, unumpy
# from dazer_methods import Dazer
#
# #Generate dazer object
# dz = Dazer()
#
# #Set figure format
# dz.FigConf()
#
# file_tradional_reduc = '/home/vital/Dropbox/Astrophysics/Data/Fabian_Catalogue/data/Traditional_Abundances.xlsx'
# Plotting the data,
label_regression = r'Plank prediction: $Y = 0.24709\pm0.00025$'
dz.data_plot(x,
y,
color=Regresions_dict['Colors'][i],
label='HII galaxies included',
markerstyle='o',
x_error=x_er,
y_error=y_er)
dz.data_plot(x_regression_range,
y_regression_range,
label=label_regr,
color=Regresions_dict['Colors'][i],
linestyle='--')
dz.plot_text(nominal_values(x), nominal_values(y), quick_ref)
# Plot WMAP prediction
dz.data_plot(WMAP_coordinates[0].nominal_value,
WMAP_coordinates[1].nominal_value,
color=dz.colorVector['pink'],
label='Planck prediction',
markerstyle='o',
x_error=WMAP_coordinates[0].std_dev,
y_error=WMAP_coordinates[1].std_dev)
# plotTitle = r'{title}: $Y_{{P}} = {n}_{{-{lowerlimit}}}^{{+{upperlimit}}}$'.format(title = Regresions_dict['title'][i], n = round_sig(n_Median,4, scien_notation=False), lowerlimit = round_sig(n_Median-n_16th,2, scien_notation=False), upperlimit = round_sig(n_84th-n_Median,2, scien_notation=False))
dz.Axis.set_ylim(0.1, 0.4)
dz.FigWording(Regresions_dict['x label'][i],
Regresions_dict['y label'][i],
'',
for j in range(len(ions_list_S)):
ion = ions_list_S[j]
radious = elemIon_df['#depth'].values
ion_frac = elemIon_df[ion].values
label = r'{0:1.1e} $M_\odot$'.format(float(z_list[i]))
dz.data_plot(radious / 1e19,
ion_frac,
color=ions_colors_S[j],
linestyle=line_type[i],
label=r'Cluster mass {}'.format(label),
linewidth=2)
dz.plot_text(labels_coords_S[j][i][0] / 1e19,
labels_coords_S[j][i][1],
text=ions_labels_S[j],
color=ions_colors_S[j],
fontsize=20,
axis_plot=None)
file_name = file_name_list_O[i]
elemIon_df = pd.read_csv(folder_data + file_name, sep='\t')
for j in range(len(ions_list_O)):
ion = ions_list_O[j]
radious = elemIon_df['#depth'].values
ion_frac = elemIon_df[ion].values
label = r'{0:1.1e} $M_\odot$'.format(float(z_list[i]))
dz.data_plot(radious / 1e19,
ion_frac,
# for i in range(len(regr_dict['m'])):
reg_code = 3
y_fit = regr_dict['m'][reg_code] * x_regression + regr_dict['n'][reg_code]
dz.data_plot(x_regression, y_fit, 'Linear fit', linestyle='-')
dz.data_plot(unumpy.nominal_values(TeOIII_array),
unumpy.nominal_values(TeSIII_array),
'HII galaxies',
markerstyle='o',
x_error=unumpy.std_devs(TeOIII_array),
y_error=unumpy.std_devs(TeSIII_array),
color=dz.get_color(0))
dz.plot_text(unumpy.nominal_values(TeOIII_array),
unumpy.nominal_values(TeSIII_array),
objects,
fontsize=17,
color=dz.get_color(0))
dz.data_plot(x_regression,
y_regression_Garnet92,
'Garnett (1992)',
linestyle=':',
color=dz.get_color(1))
dz.data_plot(x_regression,
y_regression_EpmDiaz05,
r'$P\'erez$-Montero et al (2005)',
linestyle='--',
color=dz.get_color(2))
dz.data_plot(x_regression,
y_regression_Epm2014,
