def desample(x, y, num):
import fitting as my_fits
ddy = np.abs(np.gradient(np.gradient(y)))
f_arr = cdf(x, ddy)
f_arr = f_arr * (x[-1] - x[0]) + x[0]
f = my_fits.piecewise(f_arr, x)
new_x = f(np.linspace(x[0], x[-1], num))
new_y = my_fits.piecewise(x, y)(new_x)
return new_x, new_y
def gaussian_cdf_space(mu, sig, num, sig_lim=5):
import fitting as my_fits
x = np.linspace(mu - sig_lim * sig, mu + sig_lim * sig, 300)
cdf = gaussian_cdf(x, mu, sig)
fit = my_fits.piecewise(cdf, x)
temp = np.linspace(0, 1, num + 1) #splitting into equal area sections
y = (temp[1:] + temp[:-1]) / 2 #making point the center of each section
arr = fit(y)
#plt.plot(x,gaussian(x,mu,sig))
#plt.scatter(arr,gaussian(arr,mu,sig))
#plt.show()
return arr
def shadeStats(age, y, stat, upperLim):
if upperLim:
plt.fill_between(age,y, where= (age >= stat[2]) & (age <= stat[3]),\
color='.3', label=r'1$\sigma$ lower lim: %.3g Myr' % stat[2])
plt.fill_between(age,y, where= (age >= stat[1]) & (age <= stat[2]),\
color='.6',alpha=1, label=r'2$\sigma$ lower lim: %.3g Myr' % stat[1])
plt.fill_between(age,y, where= (age >= stat[0]) & (age <= stat[1]),\
color='.9',alpha=1,label=r'3$\sigma$ lower lim: %.3g Myr' % stat[0])
return
age2 = np.linspace(stat[0], stat[-1], 500)
interp = my_fits.piecewise(age, y)
y2 = interp(age2)
plt.vlines(x=stat[2],ymin= 0,ymax= interp(stat[2]), \
label='BAFFLES median age: %.3g Myr' % stat[2] ,color = 'orange')
plt.fill_between(age2,y2, where= (age2 >= stat[1]) & (age2 <= stat[3]),color='.3', \
label='68%% CI: %.2g - %.2g' % (stat[1],stat[-2]))
plt.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]))
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 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 get_percentile(age, y, givenAge):
import fitting as my_fits
c = cdf(age, y)
fit = my_fits.piecewise(age, c)
return np.abs(fit(givenAge) - .5) * 2 * 100
def stats(age, y, upperLim=False):
import fitting as my_fits
c = cdf(age, y)
probs = UL_PROBS if upperLim else GAUSS_PROBS
fit = my_fits.piecewise(c, age)
return fit(probs)
def undo_picklable(fits):
const = utils.init_constants('lithium')
for c,i in [(c,i) for c in range(len(fits)) for i in range(2)]:
if type(fits[c][i]) != type(np.poly1d([1])):
fits[c][i] = my_fits.piecewise(const.BV,fits[c][i])
return fits