def vars_match(primary_table=WUVARS_allvars_f):
"""
A function that matches our variables table to all the other tables!
Takes about 15 seconds (2/13/13).
"""
return tablemate_core.tablemater(primary_table, tables)
def vars_match():
"""
A function that matches our variables table to all the other tables!
Takes about 15 seconds (2/13/13).
"""
return tablemate_core.tablemater( Rice_2013_vars, tables)
def vars_match(primary_table=WUVARS_allvars_f):
"""
A function that matches our variables table to all the other tables!
Takes about 15 seconds (2/13/13).
"""
return tablemate_core.tablemater( primary_table, tables)
def match_chandra_to_ukirt():
"""
A function that performs an "inverse" cross-match between
X-ray selected sources from Chandra/Getman2002, and our UKIRT stars.
"""
# Produces a table of cross-match IDs and indices.
mated_chandra = tablemater(Getman_2002, q2_wfcam_stars)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# 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"
# Q2
ax.hist(q2_stars.Stetson[mated_chandra.where(
mated_chandra['WFCAM_q2_stars_index'] != -1)['WFCAM_q2_stars_index']],
range=[0, 20],
bins=60,
color='b',
label="Q=2")
ax.set_xlabel("Stetson Index")
ax.set_ylabel(
"Number of stars which are in both UKIRT(Q=2) and Chandra/ACIS")
ax.set_title("Variability of X-ray sources from Getman et al. 2002")
fig_range = plt.figure()
ax_range = fig_range.add_subplot(1, 1, 1)
# 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"
# Q2
ax_range.hist(q2_stars.k_ranger[mated_chandra.where(
mated_chandra['WFCAM_q2_stars_index'] != -1)['WFCAM_q2_stars_index']],
range=[0, 2],
bins=20,
color='b',
label="Q=2")
ax_range.set_xlabel(r"$\Delta K$ (mag)")
ax_range.set_ylabel(
"Number of stars which are in both UKIRT(Q=2) and Chandra/ACIS")
ax_range.set_title("Variability of YSOs from Getman et al. 2002")
return mated_chandra
def match_chandra_to_ukirt():
"""
A function that performs an "inverse" cross-match between
X-ray selected sources from Chandra/Getman2002, and our UKIRT stars.
"""
# Produces a table of cross-match IDs and indices.
mated_chandra = tablemater(Getman_2002, q2_wfcam_stars)
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
# 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"
# Q2
ax.hist(
q2_stars.Stetson[
mated_chandra.where(mated_chandra['WFCAM_q2_stars_index'] != -1)['WFCAM_q2_stars_index']
],
range=[0,20], bins=60, color='b', label="Q=2"
)
ax.set_xlabel("Stetson Index")
ax.set_ylabel("Number of stars which are in both UKIRT(Q=2) and Chandra/ACIS")
ax.set_title("Variability of X-ray sources from Getman et al. 2002")
fig_range = plt.figure()
ax_range = fig_range.add_subplot(1,1,1)
# 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"
# Q2
ax_range.hist(
q2_stars.k_ranger[
mated_chandra.where(mated_chandra['WFCAM_q2_stars_index'] != -1)['WFCAM_q2_stars_index']
],
range=[0,2], bins=20, color='b', label="Q=2"
)
ax_range.set_xlabel(r"$\Delta K$ (mag)")
ax_range.set_ylabel("Number of stars which are in both UKIRT(Q=2) and Chandra/ACIS")
ax_range.set_title("Variability of YSOs from Getman et al. 2002")
return mated_chandra
def test():
wov = tablemate_core.osc.autovars_strict
wov_avs = TableParameters(
#data
wov,
#alias
"WFCAM Orion",
#full name
"Strict automatic variables found in the WFCAM Orion monitoring survey. From 'High Amplitude and Periodic Near-Infrared Variables in the Orion Nebula Cluster' by Rice, Thomas S.; Reipurth, Bo; et al.",
#ra_cols, dec_cols
['RA'], ['DEC'],
#radec_fmt
'decimal radians',
#name_col
'SOURCEID')
return tablemate_core.tablemater(wov_avs, [
Herbst2002, YSOVAR_NoExcess, YSOVAR_YSOs, DaRio_2010,
DaRio_2009, Carpenter_2001, COUP_Getman2005, eso_ha])
def UKvars_match():
return tablemate_core.tablemater( Rice_UKvars, tables)
dropbox_bo = os.path.expanduser("~/Dropbox/Bo_Tom/")
output_directory = dropbox_bo+"paper/publication_tables/"
# Let's grab IRAC colors from Megeath.
megeath2012_by_ukvar = index_secondary_by_primary(mated_ukvar,
Megeath2012)
megeath2012_full_by_ukvar = index_secondary_by_primary(mated_ukvar,
Megeath_Full)
megeath2012_all_by_ukvar = index_secondary_by_primary(mated_ukvar,
Megeath_Allgoodsources)
# XMM catalog gets matched too.
XMM_north_by_ukvar = index_secondary_by_primary(
tablemater(Rice_UKvars, [XMM_north]), XMM_north)
# And let's make some color slope references that we like.
jhk_slope_reference = filter_color_slopes(autovars_strict, 'jhk',
slope_confidence=0.5)
jh_slope_reference = filter_color_slopes(autovars_true, 'jh',
slope_confidence=0.5)
hk_slope_reference = filter_color_slopes(autovars_true, 'hk',
slope_confidence=0.5)
def clobber_table_write(table, filename, **kwargs):
""" Writes a table, even if it has to clobber an older one. """
try:
table.write(filename, **kwargs)
except Exception, e:
def test():
return tablemate_core.tablemater(
WUVARS_maxvars_p,
[Twomass, Gutermuth_2008, Scholz_2009, Wilking_2004, Getman_2002])
def test():
return tablemate_core.tablemater(WUVARS_maxvars_p, [Twomass, Gutermuth_2008, Scholz_2009, Wilking_2004, Getman_2002])
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 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