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/'
