def plot_Ownposteriors_histagram(self,
Traces,
labels,
sigfig_n=4,
xlim=None,
ylim=None):
n_traces = len(Traces)
self.FigConf(Figtype='Posteriors', n_colors=n_traces)
for i in range(len(Traces)):
Trace = Traces[i]
Axis_Location = int(str(n_traces) + '1' + str(i + 1))
Axis = plt.subplot(Axis_Location)
HDP_coords = [percentile(Trace, 16), percentile(Trace, 84)]
mean_value = median(Trace)
for HDP in HDP_coords:
Axis.axvline(
x=HDP,
label='Percentiles: 16th-84th: ' + round_sig(
HDP_coords[0], n=sigfig_n, scien_notation=False) +
' - ' +
round_sig(HDP_coords[1], n=sigfig_n, scien_notation=False),
color='grey',
linestyle='dashed')
Median_text_legend = r'Median value: $' + round_sig(
mean_value, n=sigfig_n,
scien_notation=False) + '_{-' + round_sig(
mean_value - HDP_coords[0],
n=sigfig_n,
scien_notation=False) + '}^{+' + round_sig(
HDP_coords[1] - mean_value,
n=sigfig_n,
scien_notation=False) + '}$'
Axis.axvline(x=mean_value,
label=Median_text_legend,
color='grey',
linestyle='solid')
Axis.hist(Trace,
histtype='stepfilled',
bins=35,
alpha=.7,
color=self.ColorVector[2][i],
normed=False)
Axis.set_ylabel(labels[i], fontsize=20)
self.legend_conf(Axis, loc='best', edgelabel=True)
# Axis.yaxis.set_ticks(arange(0, 250, 50))
if xlim != None:
Axis.set_xlim(xlim[0], xlim[1])
if ylim != None:
Axis.set_ylim(ylim[0], ylim[1])
return
def plot_Ownposteriors_histagram(self, Traces, labels, sigfig_n = 4, xlim = None, ylim = None):
n_traces = len(Traces)
self.FigConf(Figtype = 'Posteriors', n_colors = n_traces)
for i in range(len(Traces)):
Trace = Traces[i]
Axis_Location = int(str(n_traces) + '1' + str(i + 1))
Axis = plt.subplot(Axis_Location)
HDP_coords = [percentile(Trace, 16), percentile(Trace, 84)]
mean_value = median(Trace)
for HDP in HDP_coords:
Axis.axvline(x = HDP, label = 'Percentiles: 16th-84th: ' + round_sig(HDP_coords[0], n=sigfig_n, scien_notation=False) + ' - ' + round_sig(HDP_coords[1], n=sigfig_n, scien_notation=False), color='grey', linestyle = 'dashed')
Median_text_legend = r'Median value: $' + round_sig(mean_value, n=sigfig_n, scien_notation=False) + '_{-' + round_sig(mean_value - HDP_coords[0], n=sigfig_n, scien_notation=False) + '}^{+' + round_sig(HDP_coords[1] - mean_value, n=sigfig_n, scien_notation=False) + '}$'
Axis.axvline(x = mean_value, label = Median_text_legend, color='grey', linestyle = 'solid')
Axis.hist(Trace, histtype='stepfilled', bins=35, alpha=.7, color=self.ColorVector[2][i], normed=False)
Axis.set_ylabel(labels[i],fontsize=20)
self.legend_conf(Axis, loc='best', edgelabel=True)
# Axis.yaxis.set_ticks(arange(0, 250, 50))
if xlim != None:
Axis.set_xlim(xlim[0], xlim[1])
if ylim != None:
Axis.set_ylim(ylim[0], ylim[1])
return
def plot_posteriors_histagram(self, Traces, labels):
n_traces = len(Traces)
self.FigConf(Figtype='Posteriors', n_colors=n_traces)
for i in range(len(Traces)):
Trace = Traces[i]
Axis_Location = int(str(n_traces) + '1' + str(i + 1))
Axis = plt.subplot(Axis_Location)
HDP_coords = self.statistics_dict[Trace]['95% HPD interval']
for HDP in HDP_coords:
Axis.axvline(x=HDP,
label='HPD interval: ' +
round_sig(HDP_coords[0], 4) + ' - ' +
round_sig(HDP_coords[1], 4),
color='grey',
linestyle='dashed')
Axis.axvline(x=self.statistics_dict[Trace]['mean'],
label='Mean value: ' +
round_sig(self.statistics_dict[Trace]['mean'], 4),
color='grey',
linestyle='solid')
Axis.hist(self.Traces_dict[Trace][:],
histtype='stepfilled',
bins=35,
alpha=.7,
color=self.ColorVector[2][i],
normed=False)
Axis.set_ylabel(labels[i], fontsize=20)
self.legend_conf(Axis, loc='best', edgelabel=True)
def plot_chiSq_Behaviour(self, Traces, labels):
n_traces = len(Traces)
self.FigConf(Figtype='Grid',
n_colors=n_traces,
n_columns=4,
n_rows=2,
FigHeight=9,
FigWidth=16)
chisq_adapted = reshape(
self.pymc_database.trace('ChiSq')[:],
len(self.pymc_database.trace('ChiSq')[:])) * -2
y_lim = 30
min_chi_index = argmin(chisq_adapted)
for i in range(len(Traces)):
Trace = Traces[i]
label = labels[i]
if Trace != 'ChiSq':
self.Axis1[i].scatter(x=self.pymc_database.trace(Trace)[:],
y=chisq_adapted,
color=self.ColorVector[2][i])
x_min = npmin(self.pymc_database.trace(Trace)[:])
x_max = npmax(self.pymc_database.trace(Trace)[:])
self.Axis1[i].axvline(
x=self.statistics_dict[Trace]['mean'],
label='Inference value: ' +
round_sig(self.statistics_dict[Trace]['mean'],
4,
scien_notation=False),
color='grey',
linestyle='solid')
self.Axis1[i].scatter(
self.pymc_database.trace(Trace)[:][min_chi_index],
chisq_adapted[min_chi_index],
color='Black',
label=r'$\chi^{2}_{min}$ value: ' + round_sig(
self.pymc_database.trace(Trace)[:][min_chi_index],
4,
scien_notation=False))
self.Axis1[i].set_ylabel(r'$\chi^{2}$', fontsize=20)
self.Axis1[i].set_ylim(0, y_lim)
self.Axis1[i].set_xlim(x_min, x_max)
self.Axis1[i].set_title(label, fontsize=20)
legend_i = self.Axis1[i].legend(loc='best', fontsize='x-large')
legend_i.get_frame().set_facecolor('white')
def format_for_table(entry, rounddig=4, scientific_notation=False):
# Check None entry
if entry != None:
# Check string entry
if type(entry) == str:
formatted_entry = entry
# Case of Numerical entry
else:
# Case of an array
scalarVariable = True
if isinstance(entry, (Sequence, np.ndarray)):
# Confirm is not a single value array
if len(entry) == 1:
entry = entry[0]
# Case of an array
else:
scalarVariable = False
formatted_entry = "_".join(entry) # we just put all together in a "_' joined string
# Case single scalar
if scalarVariable:
# Case with error quantified
if isinstance(entry, UFloat):
formatted_entry = (
round_sig(nominal_values(entry), rounddig, scien_notation=scientific_notation)
+ r"$\pm$"
+ round_sig(std_devs(entry), rounddig - 2, scien_notation=scientific_notation)
)
# Case single float
else:
formatted_entry = round_sig(entry, rounddig, scien_notation=scientific_notation)
else:
# None entry is converted to None
formatted_entry = "None"
return formatted_entry
def plot_tracers(self, Traces, labels):
n_traces = len(Traces)
self.FigConf(Figtype = 'Posteriors', n_colors = n_traces)
for i in range(len(Traces)):
Trace = Traces[i]
Axis_Location = int(str(n_traces) + '1' + str(i + 1))
Axis = plt.subplot(Axis_Location)
Variable_value = self.statistics_dict[Trace]['mean']
Variable_error = self.statistics_dict[Trace]['standard deviation']
label = labels[i] + ': ' + round_sig(Variable_value, 4) + r'$\pm$' + round_sig(Variable_error,4)
Axis.plot(self.pymc_database.trace(Trace)[:], label = label, color=self.ColorVector[2][i])
Axis.axhline(y = self.statistics_dict[Trace]['mean'], color=self.ColorVector[2][i], linestyle = '--' )
Axis.set_ylabel(labels[i],fontsize=20)
self.legend_conf(Axis, loc=1, edgelabel=True)
plt.locator_params(axis = 'y', nbins = 5)
def format_for_table(entry, rounddig = 4, scientific_notation = False):
#Check None entry
if entry != None:
#Check string entry
if type(entry) == str:
formatted_entry = entry
#Case of Numerical entry
else:
#Case of an array
scalarVariable = True
if isinstance(entry, (Sequence, np.ndarray)):
#Confirm is not a single value array
if len(entry) == 1:
entry = entry[0]
#Case of an array
else:
scalarVariable = False
formatted_entry = '_'.join(entry) # we just put all together in a "_' joined string
#Case single scalar
if scalarVariable:
#Case with error quantified
if isinstance(entry, UFloat):
formatted_entry = round_sig(nominal_values(entry), rounddig, scien_notation = scientific_notation) + r'$\pm$' + round_sig(std_devs(entry), rounddig - 2, scien_notation = scientific_notation)
#Case single float
else:
formatted_entry = round_sig(entry, rounddig, scien_notation = scientific_notation)
else:
#None entry is converted to None
formatted_entry = 'None'
return formatted_entry
def plot_tracers(self, Traces, labels):
n_traces = len(Traces)
self.FigConf(Figtype='Posteriors', n_colors=n_traces)
for i in range(len(Traces)):
Trace = Traces[i]
Axis_Location = int(str(n_traces) + '1' + str(i + 1))
Axis = plt.subplot(Axis_Location)
Variable_value = self.statistics_dict[Trace]['mean']
Variable_error = self.statistics_dict[Trace]['standard deviation']
label = labels[i] + ': ' + round_sig(
Variable_value, 4) + r'$\pm$' + round_sig(Variable_error, 4)
Axis.plot(self.pymc_database.trace(Trace)[:],
label=label,
color=self.ColorVector[2][i])
Axis.axhline(y=self.statistics_dict[Trace]['mean'],
color=self.ColorVector[2][i],
linestyle='--')
Axis.set_ylabel(labels[i], fontsize=20)
self.legend_conf(Axis, loc=1, edgelabel=True)
plt.locator_params(axis='y', nbins=5)
def plot_posteriors_histagram(self, Traces, labels):
n_traces = len(Traces)
self.FigConf(Figtype = 'Posteriors', n_colors = n_traces)
for i in range(len(Traces)):
Trace = Traces[i]
Axis_Location = int(str(n_traces) + '1' + str(i + 1))
Axis = plt.subplot(Axis_Location)
HDP_coords = self.statistics_dict[Trace]['95% HPD interval']
for HDP in HDP_coords:
Axis.axvline(x = HDP, label = 'HPD interval: ' + round_sig(HDP_coords[0], 4) + ' - ' + round_sig(HDP_coords[1], 4), color='grey', linestyle = 'dashed')
Axis.axvline(x = self.statistics_dict[Trace]['mean'], label = 'Mean value: ' + round_sig(self.statistics_dict[Trace]['mean'], 4), color='grey', linestyle = 'solid')
Axis.hist(self.Traces_dict[Trace][:], histtype='stepfilled', bins=35, alpha=.7, color=self.ColorVector[2][i], normed=False)
Axis.set_ylabel(labels[i],fontsize=20)
self.legend_conf(Axis, loc='best', edgelabel=True)
def plot_chiSq_Behaviour(self, Traces, labels):
n_traces = len(Traces)
self.FigConf(Figtype = 'Grid', n_colors = n_traces, n_columns=4, n_rows=2, FigHeight=9, FigWidth=16)
chisq_adapted = reshape(self.pymc_database.trace('ChiSq')[:], len(self.pymc_database.trace('ChiSq')[:])) * -2
y_lim = 30
min_chi_index = argmin(chisq_adapted)
for i in range(len(Traces)):
Trace = Traces[i]
label = labels[i]
if Trace != 'ChiSq':
self.Axis1[i].scatter(x = self.pymc_database.trace(Trace)[:], y = chisq_adapted, color=self.ColorVector[2][i])
x_min = npmin(self.pymc_database.trace(Trace)[:])
x_max = npmax(self.pymc_database.trace(Trace)[:])
self.Axis1[i].axvline(x = self.statistics_dict[Trace]['mean'], label = 'Inference value: ' + round_sig(self.statistics_dict[Trace]['mean'], 4,scien_notation=False), color='grey', linestyle = 'solid')
self.Axis1[i].scatter(self.pymc_database.trace(Trace)[:][min_chi_index], chisq_adapted[min_chi_index], color='Black', label = r'$\chi^{2}_{min}$ value: ' + round_sig(self.pymc_database.trace(Trace)[:][min_chi_index],4,scien_notation=False))
self.Axis1[i].set_ylabel(r'$\chi^{2}$',fontsize=20)
self.Axis1[i].set_ylim(0, y_lim)
self.Axis1[i].set_xlim(x_min, x_max)
self.Axis1[i].set_title(label,fontsize=20)
legend_i = self.Axis1[i].legend(loc='best', fontsize='x-large')
legend_i.get_frame().set_facecolor('white')
value = str(Data_Dict[Galaxy][parameter])
table.add_hline()
table.add_row((Label, value))
doc.append(r'\newpage')
doc.append(r'\tiny')
with doc.create(Table('|c|c|c|c|c|c|c|c|')) as table:
# List_Headers = Lines_Dict[CodeName].keys() + [r'$F_{Nebular}$', r'$F_{Stellar}$']
List_Lines = Lines_Dict[CodeName]['Ion']
table.add_hline()
table.add_row(Labels_list + [r'$F_{Nebular}$', r'$F_{Stellar}$'])
for j in range(len(List_Lines)):
Column1 = Lines_Dict[CodeName]['Ion'][j].replace('_', '-')
Column2 = round_sig(Lines_Dict[CodeName]['TheoWavelength'][j], 5)
Column3 = round_sig(Lines_Dict[CodeName]['WaveBrute'][j], 5)
Column4 = round_sig(Lines_Dict[CodeName]['FluxBrute'][j], 4)
Column5 = round_sig(Lines_Dict[CodeName]['FluxGauss'][j], 3) + r'$\pm$' + round_sig(Lines_Dict[CodeName]['ErrorEL_MCMC'][j], 3)
Column6 = round_sig(Lines_Dict[CodeName]['Eqw'][j], 2, scien_notation=False) + r'$\pm$' + round_sig(Lines_Dict[CodeName]['ErrorEqw'][j], 3, scien_notation=False)
Column7 = round_sig(Lines_Dict[CodeName]['Flux Neb'][j], 3) + r'$\pm$' + round_sig(Lines_Dict[CodeName]['Error Neb'][j], 3)
Column8 = round_sig(Lines_Dict[CodeName]['Flux Stellar'][j], 3) + r'$\pm$' + round_sig(Lines_Dict[CodeName]['Error Neb'][j], 3)
table.add_hline()
table.add_row((Column1,Column2,Column3,Column4,Column5,Column6,Column7,Column8))
# with doc.create(Subsection(r'$\chi^{2}$ plots:')):
# with doc.create(Figure(position='h!')) as kitten_pic:
# kitten_pic.add_image('docs/static/kitten.jpg', width='120px')
# kitten_pic.add_caption('Look it\'s on its back')
table.add_hline()
table.add_row((Label, value))
doc.append(r'\newpage')
doc.append(r'\tiny')
with doc.create(Table('|c|c|c|c|c|c|c|c|')) as table:
# List_Headers = Lines_Dict[CodeName].keys() + [r'$F_{Nebular}$', r'$F_{Stellar}$']
List_Lines = Lines_Dict[CodeName]['Ion']
table.add_hline()
table.add_row(Labels_list +
[r'$F_{Nebular}$', r'$F_{Stellar}$'])
for j in range(len(List_Lines)):
Column1 = Lines_Dict[CodeName]['Ion'][j].replace('_', '-')
Column2 = round_sig(
Lines_Dict[CodeName]['TheoWavelength'][j], 5)
Column3 = round_sig(Lines_Dict[CodeName]['WaveBrute'][j],
5)
Column4 = round_sig(Lines_Dict[CodeName]['FluxBrute'][j],
4)
Column5 = round_sig(
Lines_Dict[CodeName]['FluxGauss'][j],
3) + r'$\pm$' + round_sig(
Lines_Dict[CodeName]['ErrorEL_MCMC'][j], 3)
Column6 = round_sig(
Lines_Dict[CodeName]['Eqw'][j],
2,
scien_notation=False) + r'$\pm$' + round_sig(
Lines_Dict[CodeName]['ErrorEqw'][j],
3,
scien_notation=False)
pv.SaveParameter_ObjLog('WHT_Catalogue_properties.txt', Catalogue_Dic['Data_Folder'], Parameter = 'Gradient_'+Element+'_16th_p', Magnitude = m_16th, Error = '-', Log_extension='')
pv.SaveParameter_ObjLog('WHT_Catalogue_properties.txt', Catalogue_Dic['Data_Folder'], Parameter = 'Gradient_'+Element+'_84th_p', Magnitude = m_84th, Error = '-', Log_extension='')
Obj_Elem, Metal_abun, Ymass_Metal = Abundances_dict[regression][0], Abundances_dict[regression][1], Abundances_dict[regression][2]
#Plotting the data
element_label = r'$Y_{{P}} = {n}_{{-{lowerlimit}}}^{{+{upperlimit}}}$'.format(n = round(n_Median,4), lowerlimit = round(n_Median-n_16th,4), upperlimit = round(n_84th-n_Median,4))
dz.data_plot(nominal_values(Metal_abun), nominal_values(Ymass_Metal), color = dz.ColorVector[2][j], label=element_label, markerstyle='o', x_error=std_devs(Metal_abun), y_error=std_devs(Ymass_Metal))
dz.data_plot(Y_WMAP_coord[0].nominal_value, Y_WMAP_coord[1].nominal_value, color = dz.ColorVector[2][len(Regression_list) + 1], label='WMAP prediction', markerstyle='o', x_error=Y_WMAP_coord[0].std_dev, y_error=Y_WMAP_coord[1].std_dev)
dz.text_plot(Obj_Elem, nominal_values(Metal_abun), nominal_values(Ymass_Metal), color = dz.ColorVector[1], x_pad = 0.95, y_pad = 1)
#Plot the linear trendlines
x_regression_range = linspace(0.0, max(Metal_matrix[:,0])*1.10, 20)
y_regression_range = m_Median * x_regression_range + n_Median
label_S = r'Median value: $' + round_sig(n_Median, n=4, scien_notation=False) + '_{-' + round_sig(n_Median - n_16th, n=4, scien_notation=False) + '}^{+' + round_sig(n_84th - n_Median, n=4, scien_notation=False) + '}$'
dz.data_plot(x_regression_range, y_regression_range, label = 'Linear regression', color = dz.ColorVector[2][j], linestyle = '--')
dz.FigWording(Titles_wording[regression][2], Titles_wording[regression][3], Titles_wording[regression][1],legend_loc='best')
#Set legends
# dz.Axis.set_xlim(0.0, x_regression_range[-1]*1.10)
print 'bien'
#Save the data
SavingName = regression + '_MC_pyneb'
dz.savefig(Catalogue_Dic['Data_Folder'] + SavingName, reset_fig = True)
# pv.SaveManager(SavingName = SavingName, SavingFolder = Catalogue_Dic['Data_Folder'], ForceSave=True, savevectorfile=False) #Save PlotVector
pv.ResetPlot()
#Dictionary properties
Data_Dict = OrderedDict()
Lines_Dict = OrderedDict()
Address_dict = OrderedDict()
#Generate list of objects (Dazer should have a method for this)
for i in range(len(FilesList)):
#--------------------Prepare scientific data--------------------------------
CodeName, FileName, FileFolder = pv.Analyze_Address(FilesList[i])
cHbeta_mag = pv.GetParameter_ObjLog(CodeName, FileFolder, Parameter = 'cHBeta_stellar', Assumption = 'cHbeta_min')
print 'Chbeta', cHbeta_mag
print round_sig(cHbeta_mag.std_dev, 3)
#Load lines data
lineslog_dict = pv.load_object_lines(FileFolder, CodeName, Log_extension = Log_extension)
lineslog_dered_dict = pv.load_object_lines(FileFolder, CodeName, Log_extension = Log_extension, chbeta_coef=cHbeta_mag)
#lineslog_neb_dict = pv.load_object_lines(FileFolder, CodeName, Log_extension = Log_extension_Neb)
#lineslog_stellar_dict = pv.load_object_lines(FileFolder, CodeName, Log_extension = Log_extension_Stellar)
# for i in range(len(lineslog_dered_dict['Line_Label'])):
# print lineslog_dered_dict['Line_Label'][i], lineslog_dered_dict['Eqw'][i], lineslog_dered_dict['f_lambda'][i]
#Get Hbeta values
Hbeta_Flux = pv.get_line_value(lineslog_dict, 'H1_4861A', variable_out = 'Flux')
Hbeta_Flux_dered = pv.get_line_value(lineslog_dered_dict, 'H1_4861A', variable_out = 'Flux')
#Hbeta_Flux_Neb = pv.get_line_value(lineslog_neb_dict, 'H1_4861A', variable_out = 'Flux')
Lines_Dict = OrderedDict()
Address_dict = OrderedDict()
#Generate list of objects (Dazer should have a method for this)
for i in range(len(FilesList)):
#--------------------Prepare scientific data--------------------------------
CodeName, FileName, FileFolder = pv.Analyze_Address(FilesList[i])
cHbeta_mag = pv.GetParameter_ObjLog(CodeName,
FileFolder,
Parameter='cHBeta_stellar',
Assumption='cHbeta_min')
print 'Chbeta', cHbeta_mag
print round_sig(cHbeta_mag.std_dev, 3)
#Load lines data
lineslog_dict = pv.load_object_lines(FileFolder,
CodeName,
Log_extension=Log_extension)
lineslog_dered_dict = pv.load_object_lines(FileFolder,
CodeName,
Log_extension=Log_extension,
chbeta_coef=cHbeta_mag)
#lineslog_neb_dict = pv.load_object_lines(FileFolder, CodeName, Log_extension = Log_extension_Neb)
#lineslog_stellar_dict = pv.load_object_lines(FileFolder, CodeName, Log_extension = Log_extension_Stellar)
# for i in range(len(lineslog_dered_dict['Line_Label'])):
# print lineslog_dered_dict['Line_Label'][i], lineslog_dered_dict['Eqw'][i], lineslog_dered_dict['f_lambda'][i]
