def match_xmm_to_ukirt():
"""
A function that performs an "inverse" cross-match between
X-ray selected sources from XMM, and our UKIRT stars.
"""
# Produces a table of cross-match IDs and indices.
mated_xmm = tablemater(XMM_north, ukirt_list)
mated_c1 = tablemater(XMM_north_c1, ukirt_list)
mated_c2 = tablemater(XMM_north_c2, ukirt_list)
mated_c3 = tablemater(XMM_north_c3, ukirt_list)
mated_list = [mated_xmm, mated_c1, mated_c2, mated_c3]
# What kinds of plots do we want?
# Histogram of S, for each xray category (incl. "all")
fig = plt.figure()
uks_i = 'UKIRT_autocan_strict_allstars_index'
uks_d = autocan_strict
uka_i = 'UKIRT_autocan_true_allstars_index'
uka_d = autocan_true
s1 = plt.subplot(4,1,1)
s2 = plt.subplot(4,1,2)
s3 = plt.subplot(4,1,3)
s4 = plt.subplot(4,1,4)
subplot_list = [s1, s2, s3, s4]
name_list = ["all XMM sources",
"Class 1 XMM sources",
"Class 2 XMM sources",
"Class 3 XMM sources"]
for s, m, name in zip(subplot_list, mated_list, name_list):
# in approximate english: "the stats table, where you take the row
# handed to you by the mated table, but only where there's a match"
try:
# AUTO
s.hist(
uka_d.Stetson[
m.where(m[uka_i] != -1)[uka_i]
],
range=[0,5], bins=20, color='0.7', label="1-band pristine"
)
except: pass
try:
# STRICT
s.hist(
uks_d.Stetson[
m.where(m[uks_i] != -1)[uks_i]
],
range=[0,5], bins=20, color='0.2', label="3-band pristine"
)
except: pass
# annotate each subplot so they're readable
if s == s1:
s.text(0.35, 0.75, name, transform=s.transAxes)
else:
s.text(0.65, 0.75, name, transform=s.transAxes)
s1.set_title("Histogram: Stetson indices of X-ray-selected stars in ONC")
s1.legend()
s4.set_xlabel("Stetson Index")
plt.show()
# Now, I want to print out some stats relevant to what we want.
# Think of how "official_star_counter" works.
# For each group, print the stuff.
for s, m, name in zip(subplot_list, mated_list, name_list):
# Median S and MAD?
auto_stetson = uka_d.Stetson[m.where(m[uka_i] != -1)[uka_i]]
strict_stetson = uks_d.Stetson[m.where(m[uks_i] != -1)[uks_i]]
print ("%s Median Stetson for Q=1+2: %.3f +- %.2f" %
(name, np.median(auto_stetson), rb.mad(auto_stetson)))
print "N = %d" % len(auto_stetson)
print ("%s Median Stetson for Q=2: %.3f +- %.2f" %
(name, np.median(strict_stetson), rb.mad(strict_stetson)))
print "N = %d" % len(strict_stetson)
for s, m, name in zip(subplot_list, mated_list, name_list):
# Median Delta Mag (and MAD)?
# auto_delta = uka_d.[m.where(m[uka_i] != -1)[uka_i]]# doesn't quite work
strict_delta = uks_d.k_range[m.where(m[uks_i] != -1)[uks_i]]
print ("%s Median delta-K (max-min) for strict-stars: %.3f +- %.2f" %
(name, np.median(strict_delta), rb.mad(strict_delta)))
print "N = %d" % len(strict_delta)
# What fraction have good periods? Not answerable right now.
return mated_xmm, mated_c1, mated_c2, mated_c3
def f_magnitude_hists_by_class(threepanels=True, onepanels=False):
"""
Makes a series of multipanel histograms of variability.
Uses "strict" sources only for these.
"""
megeath_class_column = make_megeath_class_column()
strict_protostars = ukvar_spread.where(
(ukvar_spread.strict == 1) & (megeath_class_column == 'P'))
strict_disks = ukvar_spread.where(
(ukvar_spread.strict == 1) & (megeath_class_column == 'D'))
strict_nondisks = ukvar_spread.where(
(ukvar_spread.strict == 1) & (megeath_class_column == 'ND'))
print "Protostars: %d, Disks: %d, Nondisks: %d" % (
len(strict_protostars), len(strict_disks), len(strict_nondisks) )
# Let's test the J mag aspect of this, and then define some dicts or forloops to iterate through all "5" bands.
names = ['J mag', 'H mag', 'K mag', '(J-H) color', '(H-K) color']
bands = ['j', 'h', 'k', 'jmh', 'hmk']
text_xposition = [0.375, 0.375, 0.375, 0.275, 0.275]
figs = []
hist_kwargs = {'range':(0,2), 'bins':20}
if threepanels:
for b, n, x in zip(bands, names, text_xposition):
j_fig = plt.figure(figsize=(5,6))
figs.append(j_fig)
jsub1 = plt.subplot(3,1,1)
jsub1.hist(strict_protostars['%s_ranger' % b], color=color_dict['protostar'],
**hist_kwargs)
jsub1.text(x, 0.65, "protostars \n"
r"median $\Delta %s: $%.2f \pm %.2f$" % (
n.replace(' ', '$ '),
np.median(strict_protostars['%s_ranger' % b]),
rb.mad(strict_protostars['%s_ranger' % b])),
transform = jsub1.transAxes)
jsub2 = plt.subplot(3,1,2, sharex=jsub1)
jsub2.hist(strict_disks['%s_ranger' % b], color=color_dict['disk'], **hist_kwargs)
jsub2.text(x, 0.65, "disks \n"
r"median $\Delta %s: $%.2f \pm %.2f$" % (
n.replace(' ', '$ '),
np.median(strict_disks['%s_ranger' % b]),
rb.mad(strict_disks['%s_ranger' % b])),
transform = jsub2.transAxes)
jsub3 = plt.subplot(3,1,3, sharex=jsub1)
jsub3.hist(strict_nondisks['%s_ranger' % b], color=color_dict['nondisk'],
**hist_kwargs)
jsub3.text(x, 0.65, "non-disks \n"
r"median $\Delta %s: $%.2f \pm %.2f$" % (
n.replace(' ', '$ '),
np.median(strict_nondisks['%s_ranger' % b]),
rb.mad(strict_nondisks['%s_ranger' % b])),
transform = jsub3.transAxes)
jsub1.set_title("%s range for $Q=2$ variables"%n)
jsub3.set_xlabel(r"$\Delta %s (outlier-proof)" %
n.replace(' ', '$ '))
if onepanels:
fig = plt.figure()
figs.append(fig)
plt.hist(strict_nondisks['k_ranger'],
color=color_dict['nondisk'], hatch='/', label='Megeath Non-disks',
**hist_kwargs)
plt.hist(strict_disks['k_ranger'],
color=color_dict['disk'], alpha=0.5, hatch='\\', label='Megeath Disks',
**hist_kwargs)
plt.hist(strict_protostars['k_ranger'],
color=color_dict['protostar'], hatch='--', label='Megeath Protostars',
**hist_kwargs)
plt.title("K magnitude range (robust) for pristine-data variables")
plt.xlabel(r"$\Delta K$ magnitude (outlier-proof)")
plt.legend()
plt.show()
return figs
def match_spitzer_to_ukirt():
"""
A function that performs an "inverse" cross-match between
mid-IR selected sources from Spitzer/Megeath2012, and our UKIRT stars.
"""
# Produces a table of cross-match IDs and indices.
mated_spitzer = tablemater(Megeath2012, ukirt_list)
# Clearly, we need to update this to use slices of the Megeath table
# filtered on Class.
mated_P = tablemater(Megeath_P, ukirt_list)
mated_D = tablemater(Megeath_D, ukirt_list)
mated_ND = tablemater(Megeath_ND, ukirt_list)
mated_list = [mated_P, mated_D, mated_ND]
# What kinds of plots do we want?
# Histogram of S, for each IR category
fig = plt.figure()
uks_i = 'UKIRT_autocan_strict_allstars_index'
uks_d = autocan_strict
uka_i = 'UKIRT_autocan_true_allstars_index'
uka_d = autocan_true
s1 = plt.subplot(3,1,1)
s2 = plt.subplot(3,1,2)
s3 = plt.subplot(3,1,3)
subplot_list = [s1, s2, s3]
name_list = ["Spitzer Protostars",
"Spitzer Disked sources",
"Spitzer Non-Disked sources"]
for s, m, name in zip(subplot_list, mated_list, name_list):
# in approximate english: "the stats table, where you take the row
# handed to you by the mated table, but only where there's a match"
try:
# AUTO
s.hist(
uka_d.Stetson[
m.where(m[uka_i] != -1)[uka_i]
],
range=[0,5], bins=20, color='0.7', label="Q=1"
)
except: pass
array_in_question = uka_d.Stetson[
m.where(m[uka_i] != -1)[uka_i]
]
print len(array_in_question[array_in_question>5])
try:
# STRICT
s.hist(
uks_d.Stetson[
m.where(m[uks_i] != -1)[uks_i]
],
range=[0,5], bins=20, color='0.2', label="Q=2"
)
except: pass
# Calculate medians.
auto_stetson = uka_d.Stetson[m.where(m[uka_i] != -1)[uka_i]]
strict_stetson = uks_d.Stetson[m.where(m[uks_i] != -1)[uks_i]]
print ("%s Median Stetson for Q=1+2: %.3f +- %.2f" %
(name, np.median(auto_stetson), rb.mad(auto_stetson)))
print "N = %d" % len(auto_stetson)
print ("%s Median Stetson for Q=2: %.3f +- %.2f" %
(name, np.median(strict_stetson), rb.mad(strict_stetson)))
print "N = %d" % len(strict_stetson)
# annotate each subplot so they're readable
if s == s1:
xtext = 0.65
else:
xtext = 0.5
s.text(xtext, 0.75,
"%s\nMedian S: %.2f$\pm$%.2f" % (name, np.median(auto_stetson),
rb.mad(auto_stetson)),
transform=s.transAxes)
s1.set_title("Histogram: Stetson indices of Spitzer-selected stars in ONC")
s1.legend(loc="upper left")
s3.set_xlabel("Stetson Index")
plt.show()
# Now, I want to print out some stats relevant to what we want.
# Think of how "official_star_counter" works.
# For each group, print the stuff.
print ""
for s, m, name in zip(subplot_list, mated_list, name_list):
# Mean Delta Mag (and sigmas)?
auto_delta = uka_d.k_ranger[m.where(m[uka_i] != -1)[uka_i]]# doesn't quite work
strict_delta = uks_d.k_ranger[m.where(m[uks_i] != -1)[uks_i]]
print ("%s Median delta-K (max-min) for Q=2: %.3f +- %.2f" %
(name, np.median(strict_delta), rb.mad(strict_delta)))
print ("%s Median delta-K (max-min) for Q=1+2: %.3f +- %.2f" %
(name, np.median(auto_delta), rb.mad(auto_delta)))
# What fraction have good periods? Not answerable right now.
# Finally, let's make a plot of S vs alpha_irac.
fig2 = plt.figure()
# define the Stetson column and the alpha_irac column
m = mated_spitzer
meg_i = 'Megeath2012_index'
meg_d = Megeath2012.data
auto_alpha_irac = meg_d.alpha[m.where(
m[uka_i] != -1)[meg_i] ]
strict_alpha_irac = meg_d.alpha[m.where(
m[uks_i] != -1)[meg_i] ]
auto_stetson = uka_d.Stetson[m.where(m[uka_i] != -1)[uka_i]]
strict_stetson = uks_d.Stetson[m.where(m[uks_i] != -1)[uks_i]]
plt.plot( auto_alpha_irac, auto_stetson, 'o', color='0.7', label="Q=1")
plt.plot( strict_alpha_irac, strict_stetson, 'o', color='0.2', label="Q=2")
plt.legend()
plt.xlabel(r"Spectral index $\alpha_{IRAC}$", fontsize=18)
plt.ylabel("Stetson Index", fontsize=18)
plt.title(r"Stetson vs $\alpha$ for Spitzer-selected sources")
plt.semilogy()
plt.show()
return mated_spitzer, mated_P, mated_D, fig, fig2
