def model(value = y_data, x_values = x_data, m = m_coef, n = n_coef, sigma = sigma): value_theo = m*x_values + n chi_sq = np.sum( np.square(value - value_theo) / np.square(sigma)) L_prob = - chi_sq / 2.0 return L_prob # #---Chi Square Moodel @pymc.deterministic() def Chi(value = y_data, x_values = x_data, m = m_coef, n = n_coef, sigma = sigma): value_theo = m*x_values + n ChiSq = np.sum( np.square(value - value_theo) / np.square(sigma)) return ChiSq return locals() dz = bayes_plotter() Fig = plt.figure(figsize = (16, 9)) Axis1 = Fig.add_subplot(111) dz.Import_FigConf(Fig, Axis1) #Generating some data for the model_difference y = m * x + n m_true, n_true = 3, 2 sigma_true = 2 x_true = 25 * (np.random.random(50) - 0.5) y_true = m_true * x_true + n_true #Adding some scatter x_data, y_data = x_true, y_true # x_data, y_data = np.random.normal(x_true, 2), np.random.normal(y_true, 2)
#Add the HeIII component if observed if HeIII_HII != None: HeI_HI = HeII_HII_Inference + HeIII_HII else: HeI_HI = HeII_HII_Inference Y_mass_InferenceS = (4 * HeI_HI * (1 - 20 * ch_an.OI_SI * SI_HI)) / (1 + 4 * HeI_HI) else: Y_mass_InferenceS = None return Y_mass_InferenceO, Y_mass_InferenceS pv = myPickle() bp = bayes_plotter() ch_an = Chemical_Analysis() #Define data type and location Catalogue_Dic = DataToTreat() Pattern = Catalogue_Dic['Datatype'] + '.fits' #Databases format AbundancesFileExtension = '_WHT_LinesLog_v3.txt' database_extension = '_extandar_30000_5000_10_Revision3' globalfile_extension = '_global_30000_5000_10_Revision3.csv' #Variables to plot Traces_code = ['He_abud', 'T_e', 'n_e', 'c_Hbeta', 'Tau', 'Xi', 'ChiSq'] #['He_abud', 'T_e', 'n_e', 'abs_H', 'abs_He', 'c_Hbeta', 'Tau', 'Xi', 'ChiSq'] Traces_labels = [r'$y^{+}$', r'$T_{e}$', r'$n_{e}$', r'$c(H\beta)$', r'$\tau$', r'$\xi$', r'$\chi^{2}$'] #[r'$y^{+}$', r'$T_{e}$', r'$n_{e}$', r'$a_{H}$', r'$a_{He}$' r'$c(H\beta)$', r'$\tau$', r'$\xi$', r'$\chi^{2}$']
import corner from uncertainties import ufloat from CodeTools.PlottingManager import myPickle from ManageFlow import DataToTreat from Plotting_Libraries.bayesian_data import bayes_plotter from uncertainties.umath import log10 as uma_log10, pow as uma_pow from Astro_Libraries.Abundances_Class import Chemical_Analysis from numpy import array, mean, median, percentile, std import matplotlib.pyplot as plt pv = myPickle() bp = bayes_plotter() ch_an = Chemical_Analysis() #Define data type and location Pattern_low_ne = 'he_Abundance__30000_5000_10_NoNuissanceParameters_8_Model4_continuous' Pattern_High_ne = 'he_Abundance__30000_5000_10_NoNuissanceParameters_EqwHbetaPrior5_Model3_VeryGood_4649sec' Folder = '/home/vital/workspace/X_Data/' #Variables to plot # Traces_code = ['He_abud', 'T_e', 'n_e', 'abs_H', 'abs_He', 'c_Hbeta', 'Tau', 'Xi'] #['He_abud', 'T_e', 'n_e', 'c_Hbeta', 'Tau', 'Xi', 'ChiSq'] #['He_abud', 'T_e', 'n_e', 'abs_H', 'abs_He', 'c_Hbeta', 'Tau', 'Xi', 'ChiSq'] # Traces_labels = [r'$y^{+}$', r'$T_{e}\,(K)$', r'$n_{e}\,(cm^{-3})$', r'$a_{H}$', r'$a_{He}$', r'$c(H\beta)$', r'$\tau$', r'$\xi$']#[r'$y^{+}$', r'$T_{e}$', r'$n_{e}$', r'$c(H\beta)$', r'$\tau$', r'$\xi$', r'$\chi^{2}$'] #[r'$y^{+}$', r'$T_{e}$', r'$n_{e}$', r'$a_{H}$', r'$a_{He}$' r'$c(H\beta)$', r'$\tau$', r'$\xi$', r'$\chi^{2}$'] # True_LowDen = [0.08, 18000.0, 100.0, 1.0, 1.0, 0.1, 0.2, 1.0] # True_HighDen = [0.085, 16000.0, 500.0, 1.0, 0.5, 0.1, 1.0, 1.0] # pattern = Pattern_High_ne # true_values = True_HighDen # Name = 'Test_highDensity' # article_folder = '/home/vital/Dropbox/Astrophysics/Papers/Elemental_RegressionsSulfur/Images/'