def resample_posterior_product(self,bv_arr,metallicity_arr,bv_errs=None,measure_err_arr=None,\
upperLim_arr=None,maxAge_arr = None, \
pdfPage=None,showPlot=False,showStars=False,title=None,givenAge=None,givenErr = None,
sample_num=None,numIter=4):
if (bv_errs is None):
bv_errs = [self.const.BV_UNCERTAINTY] * len(bv_arr)
if upperLim_arr is None:
upperLim_arr = [False] * len(metallicity_arr)
if maxAge_arr is None:
maxAge_arr = [None] * len(metallicity_arr)
if measure_err_arr is None:
measure_err_arr = [self.const.MEASURE_ERR] * len(metallicity_arr)
if sample_num is None: sample_num = 15 #len(bv_arr) - 5
ln_prob = np.zeros(len(self.const.AGE))
star_post = []
resample_args = (bv_arr, metallicity_arr, bv_errs, measure_err_arr,
upperLim_arr, maxAge_arr)
args = (pdfPage, showPlot, showStars, title, givenAge, givenErr)
post = prob.resample(self.posterior_product, resample_args, args,
sample_num, numIter)
prob.normalize(self.const.AGE, post)
p_struct = posterior()
p_struct.array = post
p_struct.stats = prob.stats(self.const.AGE, post)
if (showPlot or pdfPage):
title = title if title else 'Resampled Posterior Product Age Distribution'
my_plot.posterior(self.const.AGE, p_struct.array, p_struct.stats,title,\
pdfPage,showPlot,star_post,givenAge,givenErr=givenErr)
return p_struct
def posterior_product(self,bv_arr,metallicity_arr,bv_errs=None,measure_err_arr=None,\
upperLim_arr=None,maxAge_arr = None, \
pdfPage=None,showPlot=False,showStars=False,title=None,givenAge=None,givenErr = None):
if (bv_errs is None):
bv_errs = [self.const.BV_UNCERTAINTY] * len(bv_arr)
if upperLim_arr is None:
upperLim_arr = [False] * len(metallicity_arr)
if maxAge_arr is None:
maxAge_arr = [None] * len(metallicity_arr)
if measure_err_arr is None:
measure_err_arr = [self.const.MEASURE_ERR] * len(metallicity_arr)
ln_prob = np.zeros(len(self.const.AGE))
star_post = []
np.seterr(divide='ignore')
if self.metal == 'lithium' and np.mean(metallicity_arr) < 3:
metallicity_arr = np.power(10, metallicity_arr)
sec_per_star = 0.5
start = time.time()
for i in range(len(bv_arr)):
star_time = time.time()
y = self.likelihood(bv_arr[i],bv_errs[i],metallicity_arr[i],measure_err_arr[i],\
upperLim_arr[i]) * self.prior(maxAge_arr[i])
prob.normalize(self.const.AGE, y)
inds = np.nonzero(y)[0]
ln_prob += np.log(y)
if (showStars):
star_post.append(y)
utils.progress_bar(
float(i + 1) / len(bv_arr),
int((len(bv_arr) - (i + 1)) * sec_per_star))
#exp moving average
sec_per_star = sec_per_star + 0.1 * (
(time.time() - star_time) - sec_per_star)
#sec_per_star = (time.time() - start)/float(i+1)
print("Finished %d stars. Average time per star: %.2f seconds." \
% (len(bv_arr),(time.time() - start)/len(bv_arr)))
post = np.exp(ln_prob - np.max(ln_prob)) #prevent underflow
prob.normalize(self.const.AGE, post)
p_struct = posterior()
p_struct.array = post
p_struct.stats = prob.stats(self.const.AGE, post)
p_struct.stars_posteriors = star_post
if (showPlot or pdfPage):
my_plot.posterior(self.const.AGE, p_struct.array, p_struct.stats,title,\
pdfPage,showPlot,star_post,givenAge,givenErr=givenErr,\
bv_arr = bv_arr,metal=self.metal)
return p_struct
def robs_fomalhaut():
import astropy
from scipy.signal import savgol_filter
t = astropy.io.fits.open(join('data','Fom-age-pdf.fits'))
data = t[0].data
x = np.linspace(np.min(data)-10,np.max(data)+10,1000)
cdf = np.array([(data < n).sum() for n in x],dtype='float')/len(x)
cdf /= cdf[-1]
smoothed = savgol_filter(cdf, 51, 3)
pdf = np.gradient(smoothed)
pdf = savgol_filter(pdf,101,3)
prob.normalize(x,pdf)
#plt.plot(x,pdf,label='PDF')
#plt.hist(data,bins=100,density=True)
#plt.show()
pdf[0:2],pdf[-2:] = [0,0],[0,0]
return my_fits.piecewise(x,pdf)
def get_posterior(self,bv,metallicity,pdfPage=None,showPlot=False,\
bv_uncertainty=None,measure_err = None,upperLim=False,\
maxAge=None,givenAge=None,givenErr = None,mamajekAge=None,title=None,\
logPlot = False):
if bv is None and self.metal == 'calcium': bv = 0.65
assert self.const.BV_RANGE[0] <= bv <= self.const.BV_RANGE[1], \
"B-V of %.2f out of range. Valid range: " % bv + str(self.const.BV_RANGE)
if self.metal == 'calcium':
assert self.const.METAL_RANGE[0] <= metallicity <= self.const.METAL_RANGE[1], \
"Indicator value %.2f out of range. Valid range: " % metallicity + str(self.const.METAL_RANGE)
elif self.metal == 'lithium':
assert self.const.METAL_RANGE_LIN[0] <= metallicity <= self.const.METAL_RANGE_LIN[1], \
"Indicator value %.2f out of range. Valid range: " \
% metallicity + str(self.const.METAL_RANGE_LIN) + " mA"
if mamajekAge == True and self.const.METAL_RANGE[0] <= metallicity <= \
self.const.METAL_RANGE[1]:
mamajekAge = utils.getMamaAge(metallicity)
posterior_arr = self.likelihood(bv,bv_uncertainty,metallicity,measure_err,\
upperLim) * self.prior(maxAge)
if all(posterior_arr == 0):
print(
"Posterior not well defined. Area is zero so adding constant")
posterior_arr += 1
prob.normalize(self.const.AGE, posterior_arr)
p_struct = posterior()
p_struct.array = posterior_arr
p_struct.stats = prob.stats(self.const.AGE, posterior_arr, upperLim)
p_struct.upperLim = upperLim
if (showPlot or pdfPage):
if (title == None):
title = '(B-V)o = '+'%.2f' % bv \
+', ' + self.metal + ' = %.2f' % metallicity
my_plot.posterior(self.const.AGE, p_struct.array, p_struct.stats,title,pdfPage,\
showPlot,givenAge=givenAge,givenErr=givenErr, mamajekAge=mamajekAge,logPlot=logPlot)
return p_struct
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_age(bv=None,
rhk=None,
li=None,
bv_err=None,
li_err=None,
upperLim=False,
maxAge=None,
fileName='baffles',
pdfPage=None,
showPlots=True,
savePlots=False,
savePostAsText=False):
if (not rhk and not li):
raise RuntimeError(
"Must provide at least one of calcium logR'HK or lithium EW")
if (li and not bv):
raise RuntimeError("Must provide B-V value with lithium EW")
if (not pdfPage and savePlots):
pdfPage = PdfPages(fileName + '.pdf')
p = None
if (rhk):
baf = age_estimator('calcium')
p = baf.get_posterior(bv,
rhk,
pdfPage,
showPlots,
bv_err,
li_err,
upperLim=None,
maxAge=maxAge,
mamajekAge=utils.getMamaAge(rhk))
if (savePostAsText):
np.savetxt(fileName + "_calcium.csv",
list(zip(const.AGE, p.array)),
delimiter=",")
print("Ca Median Age: %.3g Myr, 68%% CI: %.3g - %.3g Myr, 95%% CI: %.3g - %.3g Myr" \
% (p.stats[2],p.stats[1],p.stats[3],p.stats[0],p.stats[4]))
p2 = None
if (li):
baf2 = age_estimator('lithium')
p2 = baf2.get_posterior(bv,
li,
pdfPage,
showPlots,
bv_err,
li_err,
upperLim=upperLim,
maxAge=maxAge)
if (savePostAsText):
np.savetxt(fileName + "_lithium.csv",
list(zip(const.AGE, p2.array)),
delimiter=",")
if p2.upperLim:
print("1 sig lower-lim: %.3g Myr, 2 sig lower-lim: \
%.3g Myr, 3 sig: %.3g Myr" %
(p2.stats[2], p2.stats[1], p2.stats[0]))
else:
print(
"Li Median Age: %.3g Myr, 68%% CI: %.3g - %.3g Myr, 95%% CI: \
%.3g - %.3g Myr" % (p2.stats[2], p2.stats[1], p2.stats[3], p2.stats[0],
p2.stats[4]))
p3 = None
if (p and p2):
title = 'Final Posterior'
y = p.array * p2.array
prob.normalize(const.AGE, y)
stats = prob.stats(const.AGE, y)
my_plot.posterior(const.AGE, y, prob.stats(const.AGE, y), title,
pdfPage, showPlots)
p3 = posterior()
p3.array, p3.stats = y, stats
print("Final Median Age: %.3g Myr, 68%% CI: %.3g - %.3g, 95%% CI: %.3g - %.3g" \
% (stats[2],stats[1],stats[3],stats[0],stats[4]))
if (savePostAsText):
np.savetxt(fileName + "_product.csv",
list(zip(const.AGE, y)),
delimiter=",")
if pdfPage:
pdfPage.close()
if p3 is not None: return p3
return p if p is not None else p2
def fit_histogram(metal, residual_arr=None, fromFile=True, saveToFile=False):
if fromFile:
[x, pdf, cdf] = np.load(join('grids', metal + '_likelihood_fit.npy'))
return piecewise(x, pdf), piecewise(x, cdf)
const = utils.init_constants(metal)
assert residual_arr is not None or fromFile, "Must provide residuals if not \
reading from file"
mu = np.mean(residual_arr)
sigma = np.std(residual_arr)
x = np.linspace(
np.min(residual_arr) - .5,
np.max(residual_arr) + .5, 1000) #1000 for linear?
lim = 2
if metal == 'calcium':
#lim = 5
lim = 1
x = np.linspace(
np.min(residual_arr) - .5,
np.max(residual_arr) + .1, 800)
before, after = np.linspace(-lim, min(x), 50), np.linspace(max(x), lim, 50)
x = np.concatenate((before, x, after))
cdf = np.array([(residual_arr < n).sum() for n in x], dtype='float')
cdf /= cdf[-1]
if metal == 'calcium':
smoothed = savgol_filter(cdf, 145, 3)
smoothed = savgol_filter(smoothed, 55, 3)
else:
smoothed = savgol_filter(cdf, 85, 3)
smoothed = savgol_filter(smoothed, 55, 3)
#smoothed = savgol_filter(cdf, 55, 3)
#smoothed = savgol_filter(smoothed, 25, 3)
#smoothed = savgol_filter(smoothed, 9, 3)
pdf = np.gradient(smoothed)
prob.normalize(x, pdf)
inds = np.nonzero(pdf > max(pdf) / 2)[0]
#inds = np.nonzero(pdf > 1.5)[0] if metal=='lithium' else \
# np.nonzero(pdf > max(pdf)/2)[0]
i, j = inds[0], inds[-1]
def exp_fit(x, a, b, c):
return a * np.exp(b * x + c)
popt, pcov = curve_fit(exp_fit, x[:i], pdf[:i], p0=[5, 5, -1])
pdf[:i] = exp_fit(x[:i], *popt)
popt, pcov = curve_fit(exp_fit, x[j:], pdf[j:], p0=[.5, -9, 2.5])
pdf[j:] = exp_fit(x[j:], *popt)
pdf = savgol_filter(pdf, 9, 3)
if metal == 'calcium':
m, n = np.nonzero(pdf >= 0.32)[0][0], np.nonzero(pdf >= 0.45)[0][0]
pdf[m:n] = piecewise([x[m], x[n]], [pdf[m], pdf[n]])(x[m:n])
pdf = savgol_filter(pdf, 21, 3)
pdf[:2] = [0, 0]
pdf[-2:] = [0, 0]
prob.normalize(x, pdf)
cdf = integrate.cumtrapz(pdf, x=x, initial=0)
cdf /= cdf[-1]
if saveToFile:
np.save(join('grids', metal + '_likelihood_fit'), [x, pdf, cdf])
return piecewise(x, pdf), piecewise(x, cdf)
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()