file_name_list_O = ['TGrid_Mass100000.0_age5.48_zStar-2.1_zGas0.008.ele_O', 'TGrid_Mass200000.0_age5.48_zStar-2.1_zGas0.008.ele_O'] z_list = ['100000', '200000'] ions_list_S = ['S+', 'S+2', 'S+3'] ions_labels_S = [r'$S^{+}$', r'$S^{2+}$', r'$S^{3+}$'] ions_list_O = ['O+', 'O+2'] ions_labels_O = [r'$O^{+}$', r'$O^{2+}$'] labels_coords_S = [[(1.60e18, 0.98), (2.35e18, 0.98)], [(1.0e18, 0.72), (1.77e18, 0.72)], [(0.75e18, 0.000005), (2.0e18, 0.015)]] labels_coords_O = [[(1.55e18, 0.5), (2.3e18, 0.5)], [(1.03e18, 0.6), (1.8e18, 0.6)]] # Generate the color map dz.gen_colorList(0, 5) # ions_colors_S = [dz.get_color(0), dz.get_color(1), dz.get_color(2)] # ions_colors_O = [dz.get_color(3), dz.get_color(4)] ions_colors_S = ['tab:orange', 'tab:red', 'tab:brown'] ions_colors_O = ['tab:blue', 'tab:green'] line_type = ['--', '-'] for i in range(len(file_name_list_S)): file_name = file_name_list_S[i] elemIon_df = pd.read_csv(folder_data + file_name, sep='\t') for j in range(len(ions_list_S)):
sheetname='Sheet1') #Define plot frame and colors size_dict = { 'figure.figsize': (18, 8), 'axes.labelsize': 28, 'legend.fontsize': 35, 'font.family': 'Times New Roman', 'mathtext.default': 'regular', 'xtick.labelsize': 28, 'ytick.labelsize': 28 } dz.FigConf(plotStyle='colorblind', plotSize=size_dict) # Generate the color map dz.gen_colorList(0, df.index.size) marker_dict = {'Peimbert': 's', 'Skillman': '^', 'Izotov': 'o'} #Loop through the lines for i in range(df.index.size): author, value, error, year, comments, upper_limit, group = df.iloc[ i].values marker_type = '_' if group not in marker_dict else marker_dict[group] if error < 0.236: if author == 'Planck collaboration' and year == 2015: label = r'Planck collaboration 2018: $Y_{P}=0.24672^{-(0.00012)0.00061}_{ +(0.00011)0.00061}$' dz.Axis.axhspan(value - error,
#Perform the linear regression--------------------------- from dazer_methods import Dazer from numpy import nanmean, nanstd, min as np_min, linspace, max as np_max, arange from uncertainties import ufloat from uncertainties import unumpy from lib.Math_Libraries.FittingTools import bces_regression #Generate dazer object dz = Dazer() # Generate the color map dz.gen_colorList(0, 4) #Define plot frame and colors size_dict = { 'axes.labelsize': 35, 'legend.fontsize': 24, 'font.family': 'Times New Roman', 'mathtext.default': 'regular', 'xtick.labelsize': 30, 'ytick.labelsize': 30 } dz.FigConf(plotSize=size_dict) #Load catalogue dataframe catalogue_dict = dz.import_catalogue() #Declare data for the analisis AbundancesFileExtension = '_' + catalogue_dict[ 'Datatype'] + '_linesLog_reduc.txt' catalogue_df = dz.load_excel_DF(