def nearest_stars_hist():
t = np.genfromtxt(join("data", "MH08_table13.txt"),
delimiter=';',
dtype=str,
skip_header=75)
bv, rhk = t[:, 6].astype(np.float), t[:, 8].astype(np.float)
#mask = (const.BV_RANGE[0] <= bv) & (bv <= const.BV_RANGE[1]) & \
mask = (.8 <= bv) & (bv <= .9) & \
(const.METAL_RANGE[0] <= rhk) & (rhk <= const.METAL_RANGE[1])
bv, rhk = bv[mask], rhk[mask]
baf = baffles.age_estimator('calcium')
for i in range(1):
ages = [] #,array = [],np.zeros(1000)
for b, r in zip(bv, rhk):
p = baf.get_posterior(b, r)
#chance = p.array/np.sum(p.array)
a = p.stats[2] #np.random.choice(const.AGE,p=chance)
ages.append(a)
#array += p.array
#plt.hist(ages)
#plt.xlabel("Age (Myr)")
#plt.ylabel("Frequency")
#plt.show()
plt.hist(ages)
plt.xlabel("Age (Myr)")
plt.ylabel("Frequency")
plt.title(".8 <= B-V <= .9")
plt.show()
def moving_group():
name = join('plots','moving_group_age.pdf')
pp = PdfPages(name)
baf = baffles.age_estimator('lithium')
abdor = readData.abdor()
fit = my_fits.poly_fit(abdor[0],abdor[1],2,scatter=True)
plt.title("AB Dor")
#my_plot.plot_fits([abdor],[fit],METAL,pdfPage=pp,showPlots=False)
p = baf.posterior_product(abdor[0],abdor[1],None,abdor[2],pdfPage=pp,showPlot=False,
showStars=True,title='AB Dor',givenAge=149,givenErr=[-19,51])
tuc = readData.tuchor()
fit = my_fits.poly_fit(tuc[0],tuc[1],2,scatter=True)
plt.title("Tuc/Hor")
#my_plot.plot_fits([tuc],[fit],METAL,pdfPage=pp,showPlots=False)
p2 = baf.posterior_product(tuc[0],tuc[1],None,tuc[2],pdfPage=pp,showPlot=False,
showStars=True,title='Tuc/Hor',givenAge=45,givenErr=4)
print("We derive ages for AB Dor: $%d_{%d}^{+%d}$ Myr, and Tuc/Hor: $%d_{%d}^{+%d}$ Myr" \
% (p.stats[2],p.stats[1]-p.stats[2],p.stats[3]-p.stats[2],
p2.stats[2],p2.stats[1]-p2.stats[2],p2.stats[3]-p2.stats[2]))
pp.close()
printName(name)
def get_CI_hyades_no_ul():
const = utils.init_constants(METAL)
baf_default = baffles.age_estimator(METAL)
mask = (~np.array(upper_lim[8])) | (bv_m[8][0] > 0.55)
print("Total stars:",len(bv_m[8][0]))
print("Stars selsected=:",np.sum(mask))
b,l,ul = bv_m[8][0][mask],bv_m[8][1][mask],np.array(upper_lim[8])[mask]
baf_default.posterior_product(b,l,upperLim_arr=ul,showPlot=True,showStars=True,title='Hyades B-V > .55',givenAge=700)
def combined_validation():
#Omitting each cluster
name = join('plots',METAL + '_combined_validation.pdf')
pp=PdfPages(name)
baf_default = baffles.age_estimator(METAL)
for i in range(len(bv_m)):
print(const.CLUSTER_NAMES[i])
baf = baffles.age_estimator(METAL,default_grids=False)
baf.make_grids(bv_m,fits,omit_cluster=i)
p_val = baf.posterior_product(bv_m[i][0],bv_m[i][1],upperLim_arr=upper_lim[i])
plt.plot(const.AGE,p_val.array,linewidth=2,linestyle='--',label='Posterior with removal')
p = baf_default.posterior_product(bv_m[i][0],bv_m[i][1],upperLim_arr=upper_lim[i],\
pdfPage=pp,showPlot=False,\
showStars=True,givenAge=const.CLUSTER_AGES[i],\
title= const.CLUSTER_NAMES[i])
printName(name)
pp.close()
def get_CI():
const = utils.init_constants(METAL)
baf_default = baffles.age_estimator(METAL)
for index,i in enumerate([1,2,7,9]):
print("\n", const.CLUSTER_NAMES[i])
bv_arr = bv_m[i][0]
p = baf_default.posterior_product(bv_m[i][0],bv_m[i][1],upperLim_arr=upper_lim[i],showStars=True)
age,y = const.AGE,p.array
givenAge=const.CLUSTER_AGES[i]
print('Isochronal age exists within %f %% CI' % prob.get_percentile(age,y,givenAge))
def main():
date = datetime.datetime.now().strftime("%m%d%y")
bv_m,fits = readData.read_calcium(fromFile=False,saveToFile=True)
baf = baffles.age_estimator('calcium',default_grids=False,load_pdf_fit=False)
baf.make_grids(bv_m,fits,medianSavefile=join('grids','median_rhk_'+date),\
setAsDefaults=True)
_,res_arr = my_fits.get_fit_residuals(bv_m,fits,'calcium',None,li_range=None,
linSpace=False,scale_by_std= False,vs_age_fit=True,zero_center=True)
my_fits.fit_histogram('calcium',residual_arr=res_arr,fromFile=False,saveToFile=True)
const = utils.init_constants('lithium')
bv_m, upper_lim, fits = readData.read_lithium(fromFile=False,saveToFile=True)
baf2 = baffles.age_estimator('lithium',default_grids=False,load_pdf_fit=False)
baf2.make_grids(bv_m,fits,upper_lim,join('grids','median_li_'+date),setAsDefaults=True)
my_fits.MIST_primordial_li(ngc2264_fit=fits[const.CLUSTER_NAMES.index('NGC2264')][0],
fromFile=False, saveToFile=True)
_,res_arr= my_fits.get_fit_residuals(bv_m,fits,'lithium',upper_lim,li_range=None,linSpace=False,
vs_age_fit=True,zero_center=True)
my_fits.fit_histogram('lithium',residual_arr=res_arr,fromFile=False,saveToFile=True)
def omitting(validation=False):
#Omitting each cluster
#name = join('plots',METAL + '_self_validation.pdf')
name = join('plots',METAL + '_omit_clusters.pdf') if not validation else \
join('plots',METAL + '_self_validation.pdf')
pp=PdfPages(name)
baf = baffles.age_estimator(METAL,default_grids=False)
for i in range(len(bv_m)):
print(const.CLUSTER_NAMES[i])
if not validation: baf.make_grids(bv_m,fits,omit_cluster=i)
p = baf.posterior_product(bv_m[i][0],bv_m[i][1],pdfPage=pp,showPlot=False,\
showStars=True,givenAge=const.CLUSTER_AGES[i],\
title= const.CLUSTER_NAMES[i])
printName(name)
pp.close()
def posteriors():
#Making posteriors
name = join('plots', METAL + '_posteriors.pdf')
pp = PdfPages(name)
baf = baffles.age_estimator(METAL, default_grids=False)
baf.make_grids(bv_m, fits, upper_lim) #,omit_cluster=0)
for bv in [0.65]:
for li in np.linspace(-3.8, -5, 5):
p = baf.get_posterior(bv,
li,
pdfPage=pp,
showPlot=True,
logPlot=False,
upperLim=False,
mamajekAge=True)
printName(name)
pp.close()
def combined_validation_subplots():
const = utils.init_constants(METAL)
#Omitting each cluster
name = join('plots', METAL + '_combined_validation_subplots.pdf')
pp = PdfPages(name)
baf_default = baffles.age_estimator(METAL)
fig, ax = plt.subplots(3, 2, figsize=(14, 15))
cmap = plt.cm.get_cmap('RdYlBu_r')
norm = mpl.colors.Normalize(vmin=const.BV_RANGE[0], vmax=const.BV_RANGE[1])
sc = plt.scatter([], [], c=[], norm=norm, cmap=cmap)
fig.tight_layout(pad=.4, w_pad=1, h_pad=2)
fig.subplots_adjust(left=0.06)
fig.subplots_adjust(bottom=0.06)
fig.subplots_adjust(top=.95)
fig.subplots_adjust(right=0.9)
#cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
cbar_ax = fig.add_axes([0.92, 0.25, 0.02, 0.5])
fig.colorbar(sc, cax=cbar_ax)
for index, i in enumerate([1, 3, 5, 6, 7,
8]): #[0,4,5,6,7,8] for submission 2!
print(const.CLUSTER_NAMES[i])
baf = baffles.age_estimator(METAL, default_grids=False)
baf.make_grids(bv_m, fits, omit_cluster=i)
p_val = baf.posterior_product(bv_m[i][0], bv_m[i][1])
pl = ax[int(index / 2), index % 2]
pl.plot(const.AGE,
p_val.array,
linewidth=2,
linestyle='--',
label='Posterior with removal')
pl.set_title(const.CLUSTER_NAMES[i], size=my_plot.TITLE_SIZE)
bv_arr = bv_m[i][0]
p = baf_default.posterior_product(bv_m[i][0],
bv_m[i][1],
upperLim_arr=None,
showStars=True)
#print(p.stars_posteriors)
age, y = const.AGE, p.array
givenAge = const.CLUSTER_AGES[i]
for star, post in enumerate(p.stars_posteriors):
color = cmap(norm(bv_arr[star]))
prob.scale_to_height(post, np.max(y))
pl.plot(const.AGE,
post,
alpha=1,
linewidth=1,
color=color,
zorder=0)
if (givenAge):
print('Isochronal age exists within %f %% CI' %
prob.get_percentile(age, y, givenAge))
pl.axvline(x=givenAge,
color='r',
label='Isochronal age: %d Myr' % givenAge)
#if (givenErr):
# if (type(givenErr) == float or type(givenErr) == int):
# givenErr = [-1*givenErr,givenErr]
# pl.axvspan(givenAge+givenErr[0], givenAge+givenErr[1], alpha=0.2, color='r',zorder=0)
#if (mamajekAge):
# plt.axvline(x=mamajekAge,color='C2',label='MH08 age: %d' % mamajekAge)
pl.plot(age, y, color='C0', linewidth=2)
stat = p.stats
age2 = np.linspace(stat[0], stat[-1], 500)
interp = my_fits.piecewise(age, y)
y2 = interp(age2)
pl.vlines(x=stat[2],ymin= 0,ymax= interp(stat[2]), \
label='BAFFLES median age: %.3g Myr' % stat[2] ,color = 'orange')
pl.fill_between(age2,y2, where= (age2 >= stat[1]) & (age2 <= stat[3]),color='.3', \
label='68%% CI: %.2g - %.2g' % (stat[1],stat[-2]))
pl.fill_between(age2,y2, where= (age2 >= stat[0]) & (age2 <= stat[-1]),color='.6',\
alpha=0.5, label='95%% CI: %.2g - %.2g' % (stat[0],stat[-1]))
pl.set_ylim([0, np.max(y) * 1.5])
r = my_plot.getAgeRange(p.stats, p.stars_posteriors, givenAge)
pl.set_xlim(r)
pl.legend()
pl.minorticks_on()
pl.tick_params(axis='both', which='both', right=True, top=True)
# Set common labels
fig.text(0.5,
0.02,
'Age (Myr)',
size=my_plot.AXIS_LABEL_SIZE,
ha='center',
va='center')
fig.text(0.01,
0.5,
'Probability Density (Myr^-1)',
size=my_plot.AXIS_LABEL_SIZE,
ha='center',
va='center',
rotation='vertical')
fig.text(0.99,
0.5,
'B-V',
size=my_plot.AXIS_LABEL_SIZE,
ha='center',
va='center',
rotation='vertical')
pp.savefig()
plt.close()
printName(name)
pp.close()
def notable_stars():
li_const = utils.init_constants('lithium')
name = join('plots','notable_stars.pdf')
names = ["HR 2562","HD 206893","TW PsA"]
bv = [.45,.44,1.1]
bv_err = [np.sqrt(.014**2+.01**2),np.sqrt(.02**2+.01**2),.03] # .03 b-v error for TW PsA?
rhk = [-4.55,-4.466,None]
mamaAge = [utils.getMamaAge(rhk[0]),utils.getMamaAge(rhk[1]),None]
li = [21,28.5,33]
li_err = [5,7,2]
markers = ['*','*','*']
markerSize = 25
colors = ['gold','green','darkmagenta']
age = [None,None,440]
age_range = [[300,900],[200,2100],[400,480]]
pp=PdfPages(name)
my_plot.metal_vs_bv(bv_ca,ca_fits,'calcium',None,False,specific_clusters=[0,5,7,8],
legend=False,textlabels=True)
plt.plot(bv,rhk,marker='s',markersize=markerSize,color='w',linestyle='None',zorder=9)
for i in [0,1]:
plt.plot(bv[i],rhk[i],marker=markers[i],markersize=markerSize,color=colors[i],
linestyle='None',zorder=10,label=names[i])
plt.legend()
plt.xlim([.42,.9])
pp.savefig()
#plt.show()
plt.close()
my_plot.metal_vs_bv(bv_m,fits,'lithium',None,False,upper_lim=upper_lim,specific_clusters=[0,1,4,6,8,9])
plt.plot(bv,li,marker='s',markersize=markerSize,color='w',linestyle='None',zorder=9)
for i in [0,1,2]:
plt.plot(bv[i],np.log10(li[i]),marker=markers[i],markersize=markerSize,color=colors[i],
linestyle='None',zorder=10,label=names[i])
plt.legend()
pp.savefig()
#plt.show()
plt.close()
baf_ca = baffles.age_estimator('calcium')
baf_li = baffles.age_estimator('lithium')
for i in [0,1]:
print(names[i])
plt.plot([],[],'C0',linewidth=2,label='Final Age Posterior')
p_li = baf_li.get_posterior(bv[i],li[i],bv_uncertainty=bv_err[i],measure_err=li_err[i],upperLim=False)
p_ca = baf_ca.get_posterior(None,rhk[i])
product = prob.normalize(const.AGE,p_ca.array*p_li.array)
prod_stats=prob.stats(const.AGE,product)
my_plot.posterior(const.AGE, product, prod_stats,names[i],logPlot=False)
plt.plot(const.AGE, p_ca.array,color='C3',label="Calcium Posterior")
plt.plot(const.AGE, p_li.array,color='C2',label="Lithium Posterior")
plt.axvspan(age_range[i][0],age_range[i][1], alpha=0.2, color='r',
label=r'Literature age: %d - %d Myr' % tuple(age_range[i]),zorder=0)
plt.axvline(x=mamaAge[i],color='C5',linestyle='--',label='MH08 age: %d' % mamaAge[i])
#plt.xlim([0,490])
plt.legend()
pp.savefig()
#plt.show()
plt.close()
print("%d Myr (68\\%%CI: %d - %d Myr)" % (utils.round_sigs(p_ca.stats[2],2),
utils.round_sigs(p_ca.stats[1],2),utils.round_sigs(p_ca.stats[3],2)))
print("%.1f Gyr (68\\%%CI: %.1f - %.1f Gyr)" % (p_li.stats[2]/1000,p_li.stats[1]/1000,p_li.stats[3]/1000))
print("%d Myr, with a 68\\%% confidence interval between %d Myr - %d Myr" % (utils.round_sigs(prod_stats[2],2),
utils.round_sigs(prod_stats[1],2),utils.round_sigs(prod_stats[3],2)))
print("TW PsA")
plt.axvspan(age_range[-1][0],age_range[-1][1], alpha=0.2, color='r',zorder = 0)
plt.axvspan(360-140,360+140, alpha=0.2, color='b',zorder=0)
p_li = baf_li.get_posterior(bv[2],li[2],bv_uncertainty=bv_err[2],measure_err=li_err[2],upperLim=False)
print("we report an age of %d Myr with a 68\\%% confidence interval between %d Myr - %d Myr\
(third panel of Fig. \\ref{fig:notable_stars}), consistent with Mamajek's lithium age,\
but a factor of $\sim$%.1f too young for his final adopted age." % (p_li.stats[2],p_li.stats[1],p_li.stats[3],
440/p_li.stats[2]))
my_plot.posterior(const.AGE, p_li.array, p_li.stats,names[2],None,False, logPlot=False)
plt.axvline(x=age[-1],color='r',label=r'Literature age: %d $\pm$ 40 Myr' % age[-1])
plt.axvline(x=360,color='b',label=r"M'12 Li age: 360 $\pm$ 140 Myr")
plt.xlim([-30,510])
plt.legend()
pp.savefig()
#plt.show()
plt.close()
plt.axvline(x=age[-1],color='r',label=r'Literature age: %d $\pm$ 40 Myr' % age[-1])
plt.axvspan(age_range[-1][0],age_range[-1][1], alpha=0.2, color='r',zorder = 0)
robs_f = robs_fomalhaut()
plt.plot(const.AGE,robs_f(const.AGE),'k--',label='Nielsen 2019 Fomalhaut PDF')
plt.plot(const.AGE,p_li.array,'g',label='BAFFLES Li posterior')
y = prob.normalize(const.AGE,p_li.array*robs_f(const.AGE))
plt.plot(const.AGE,y,color = 'C0',linewidth=2,label='Final Age')
stat = prob.stats(const.AGE,y)
print("to get a final age for the system, $%d^{+%d}_{%d}$ Myr." % (stat[2],stat[3]-stat[2],stat[1]-stat[2]))
plt.vlines(x=stat[2],ymin= 0,ymax= y[bisect.bisect_left(const.AGE,stat[2])], \
label='Final median age: %.3g Myr' % stat[2] ,color = 'orange')
plt.fill_between(const.AGE,y, where= (const.AGE >= stat[1]) & (const.AGE <= stat[3]),color='.4', \
label='68%% CI: %.2g - %.2g' % (stat[1],stat[-2]))
plt.title(names[2],size=my_plot.TITLE_SIZE)
plt.xlim([0,1200])
plt.legend()
plt.ylabel('Probability Density (Myr^-1)',size=my_plot.AXIS_LABEL_SIZE)
plt.xlabel("Age (Myr)",size=my_plot.AXIS_LABEL_SIZE)
plt.tight_layout()
plt.minorticks_on()
plt.tick_params(axis='both',which='both',right=True,top=True)
pp.savefig()
#plt.show()
plt.close()
printName(name)
pp.close()
def baffles_vs_mamajek(bv_rhk,
fits,
i,
pdfPage=None,
showPlots=False,
title=None,
mamaProduct=False):
import ca_constants as const
baf = baffles.age_estimator('calcium')
#baf.make_grids(bv_rhk,fits,omit_cluster=i)
my_ages = []
my_error = []
mamajek_ages = []
post_prod = 0
for j in range(len(bv_rhk[i][0])):
b, r = bv_rhk[i][0][j], bv_rhk[i][1][j]
mamajek_ages.append(utils.getMamaAge(r))
post = baf.get_posterior(b, r)
post_prod += np.log(post.array)
stat = post.stats
my_ages.append(stat[2])
my_error.append((stat[2] - stat[1], stat[3] - stat[2]))
post_prod = prob.normalize(const.AGE, np.exp(post_prod))
baffles_age = prob.stats(const.AGE, post_prod)[2]
plt.Line2D([0], [0],
color='C%d' % i,
marker=const.MARKERS[i],
label=const.CLUSTER_NAMES[i])
plt.axis([.4, 14000, .4, 14000])
plt.title(const.CLUSTER_NAMES[i], size=TITLE_SIZE)
plt.xlabel(r'Age derived from M & H (2008)', size=AXIS_LABEL_SIZE)
plt.ylabel(u'BAFFLES Age (Myr)', size=AXIS_LABEL_SIZE)
for j in range(len(my_ages)):
if (j == 0):
plt.errorbar(mamajek_ages[j],
my_ages[j],
np.array([my_error[j]]).T,
color=const.COLORS[i],
marker=const.MARKERS[i])
else:
plt.errorbar(mamajek_ages[j],
my_ages[j],
np.array([my_error[j]]).T,
color=const.COLORS[i],
marker=const.MARKERS[i])
plt.hlines(y=baffles_age,xmin= 0,xmax= baffles_age, \
label='BAFFLES cluster age: %.3g Myr' % baffles_age ,linestyle='--',color = 'C0')
if mamaProduct:
age = utils.getMamaProductAge(bv_rhk[i][1])
plt.vlines(x=age,ymin= 0,ymax= age, \
label='M & H (2008): %.3g Myr' % age ,linestyle='--',color = 'C2')
plt.plot([0, 10000], [0, 10000], dashes=[2, 2], color='k')
plt.plot(const.CLUSTER_AGES[i],
const.CLUSTER_AGES[i],
marker='*',
markersize=18,
color='C1',
linestyle='None',
label='Isochronal cluster age: %d Myr' % const.CLUSTER_AGES[i],
alpha=1,
zorder=10)
ax = plt.gca()
ax.set_yscale('log')
ax.set_xscale('log')
plt.legend(loc=2)
plt.tight_layout()
plt.minorticks_on()
plt.tick_params(axis='both', which='both', right=True, top=True)
if (pdfPage):
pdfPage.savefig()
if (showPlots):
plt.show()
if pdfPage:
plt.close()
def make_table(MR=False):
ca_const = utils.init_constants('calcium')
li_const = utils.init_constants('lithium')
empty = ''
"""
table = [] #[Object,RA,Dec,Sp Type,B-V,R'HK,Li EW,Source]
#first read in all the 4 tables and create a single big table, which then I sort and merge
t = np.genfromtxt('data/nielsen_2010_table2.csv',delimiter=',',dtype=str,skip_header=1)
for row in t:
if not utils.isFloat(row[1]) or not (.45 <= float(row[1]) <= 1.9): continue
arr = []
arr.append(row[21].strip())
ra,dec = ra_dec(row[22])
arr.append(ra)
arr.append(dec)
arr.append(row[4].strip())
arr.append(row[1])
arr.append(row[13])
arr.append(row[7])
arr.append("1")
if arr[0] == '' or not (utils.isFloat(arr[5]) or utils.isFloat(arr[6])):
continue
table.append(arr)
bv_to_teff = my_fits.magic_table_convert('bv','teff')
t = np.genfromtxt('data/brandt_2014_table.csv',delimiter=',',dtype=str,skip_header=2)
for row in t:
bv = None
if utils.isFloat(row[2]) and utils.isFloat(row[3]):
bv = float(row[2]) - float(row[3])
if bv is None or not (.45 <= bv <= 1.9): continue
arr = []
arr.append(row[14].strip())
ra,dec = ra_dec(row[15])
arr.append(ra)
arr.append(dec)
arr.append(row[4].strip())
arr.append("%f" % bv)
arr.append(row[7])
if row[9].find('A') != -1:
nli = float(row[9].split()[-1])
teff = bv_to_teff(bv)
ew = 10** my_fits.teff_nli_to_li([teff],[nli])[0]
arr.append("%d" % ew)
elif utils.isFloat(row[9]):
arr.append(row[9])
else:
arr.append(empty)
arr.append("2")
if arr[0] == '' or not (utils.isFloat(arr[5]) or utils.isFloat(arr[6])):
continue
table.append(arr)
t = np.genfromtxt("data/nearbyStars_Boro_Saikia_2018.txt",delimiter='\t',dtype=str,skip_header=58)
for row in t:
if not utils.isFloat(row[5]) or not (.45 <= float(row[5]) <= 1.9): continue
arr = []
arr.append(row[16].strip())
ra,dec = ra_dec(row[17])
arr.append(ra)
arr.append(dec)
arr.append(row[18].strip())
arr.append(row[5])
arr.append(row[10])
arr.append(empty)
arr.append("3")
if arr[0] == '' or not (utils.isFloat(arr[5]) or utils.isFloat(arr[6])):
continue
table.append(arr)
t = np.genfromtxt("data/guillot_2009_li_survey.txt",delimiter='\t',dtype=str,skip_header=77)
for row in t:
if not utils.isFloat(row[7]) or not (.45 <= float(row[7]) <= 1.9): continue
arr = []
arr.append(row[22].strip())
ra,dec = ra_dec(row[23])
arr.append(ra)
arr.append(dec)
arr.append(row[24].strip())
arr.append(row[7])
arr.append(empty)
arr.append(row[16])
arr.append("4")
if arr[0] == '' or not (utils.isFloat(arr[5]) or utils.isFloat(arr[6])):
continue
table.append(arr)
table = np.array(table)
name_sorted = table[table[:,0].argsort()]
thinned = [] #averaging b-v,measurements, sources as 1,4
for name in set(name_sorted[:,0]):
subset = name_sorted[name_sorted[:,0]==name]
if len(subset) == 1:
thinned.append(subset[0])
else:
arr = copy.deepcopy(subset[0])
arr[4] = average(subset[:,4])
arr[5] = average(subset[:,5])
arr[6] = average(subset[:,6])
x = list(set(subset[:,7]))
x.sort()
arr[7] = ','.join(x)
thinned.append(arr)
thinned = np.array(thinned)
final_table = thinned[thinned[:,1].argsort()]
np.save("final_table",final_table)
exit()
"""
final_table = np.load("data/merged_nielsen_brandt_saikia_guillot.npy")
delimiterMR = ','
baf_li = baffles.age_estimator('lithium')
baf_ca = baffles.age_estimator('calcium')
#[Object,RA,Dec,Sp Type,B-V,R'HK,Li EW,Source]
f = open("baffles_table2_latex.txt", 'w+')
fMR = open("baffles_table2.csv", 'w+')
cdf = ['2.5%', '16%', '50%', '84%', '97.5%']
column_head = [
'Name', 'RA', 'Dec', 'Sp. Type', 'B-V', "logR'HK", 'Li EW', 'Ref.'
]
column_head += ["R'HK Age at CDF=" + x for x in cdf]
column_head += ["Li EW Age at CDF=" + x for x in cdf]
column_head += ["Final Age at CDF=" + x for x in cdf]
units = [
'', 'h m s', 'h m s', '', 'mags', " ", 'mA', '', '', '', '', '', '',
'', '', '', '', '', '', '', '', '', ''
]
fMR.write(delimiterMR.join(column_head))
fMR.write('\n')
fMR.write(delimiterMR.join(units))
fMR.write('\n')
for row in final_table:
arr = []
arrMR = []
arr += [x.replace('V* ', '').replace('_', '-') for x in row[0:4]]
arrMR += [x.replace('$', '').replace('V* ', '') for x in row[0:4]]
bv = float(row[4])
arr.append("%.2f" % bv)
arrMR.append("%.3g" % bv)
p_ca, p_li = None, None
if utils.isFloat(row[5]):
rhk = float(row[5])
arr.append('$%.2f$' % rhk)
arrMR.append('%.3f' % rhk)
if ca_const.inRange(bv, rhk):
p_ca = baf_ca.get_posterior(bv, rhk, showPlot=False)
else:
arr.append(empty)
arrMR.append(empty)
ew = None
if utils.isFloat(row[6]):
ew = float(row[6])
arr.append('%d' % ew)
arrMR.append('%g' % ew)
else:
arr.append(empty)
arrMR.append(empty)
arr.append(row[7])
arrMR.append(row[7].replace(',', ';'))
if bv is not None and ew is not None and ew > 0 and li_const.inRange(
bv, np.log10(ew)):
p_li = baf_li.get_posterior(bv, ew, showPlot=False)
if p_ca is not None:
arr += printStats(p_ca.stats)
arrMR += printStats(p_ca.stats, MR=True)
else:
arr += [empty] * 5
arrMR += [empty] * 5
if p_li is not None:
arr += printStats(p_li.stats)
arrMR += printStats(p_li.stats, MR=True)
else:
arr += [empty] * 5
arrMR += [empty] * 5
if p_ca is None and p_li is None:
continue
if p_ca is not None and p_li is not None:
prod = p_ca.array * p_li.array
prob.normalize(ca_const.AGE, prod)
stats = prob.stats(ca_const.AGE, prod)
arr += printStats(stats)
arrMR += printStats(stats, MR=True)
elif p_ca is not None:
arr += printStats(p_ca.stats)
arrMR += printStats(p_ca.stats, MR=True)
elif p_li is not None:
arr += printStats(p_li.stats)
arrMR += printStats(p_li.stats, MR=True)
else:
arr += [empty] * 5
arrMR += [empty] * 5
f.write(' & '.join(arr) + " \\\\")
f.write('\n')
fMR.write(delimiterMR.join(arrMR))
fMR.write('\n')
f.close()
fMR.close()