def period_disk_class_histograms():
"""
What do the period distributions of disked vs nondisked stars look like?
"""
# UKvar sTrict Periodics
uk_tp = ukvar_spread.where((ukvar_spread.strict == 1) &
(ukvar_spread.periodic == 1))
# Disk criterion
excess = 1.714*uk_tp.hmk_median - uk_tp.jmh_median
p_sample = uk_tp.where(excess <= 0)
d_sample = uk_tp.where((excess > 0) & (excess <= 0.614))
e_sample = uk_tp.where(excess > 0.614)
print len(p_sample), len(d_sample), len(e_sample)
fig = plt.figure()
s1 = fig.add_subplot(3,1,1)
s2 = fig.add_subplot(3,1,2)
s3 = fig.add_subplot(3,1,3)
s1.hist(p_sample.Period, bins=50, range=(0,20))
s1.text(12,10, "Photosphere sample, n=%d" % len(p_sample))
s1.set_title("Histograms of periods for photosphere, disk, extreme samples")
s2.hist(d_sample.Period, bins=50, range=(0,20),color='r')
s2.text(12,10, "Disk sample, n=%d" % len(d_sample))
s3.hist(e_sample.Period, bins=50, range=(0,20),color='k')
s3.text(12,0.8, "Extreme sample, n=%d" % len(e_sample))
s3.set_xlabel("Period (days)")
plt.show()
fig2 = plt.figure()
plot_trajectory_vanilla(plt.gca())
plt.plot(p_sample.hmk_median, p_sample.jmh_median, 'bo')
plt.plot(d_sample.hmk_median, d_sample.jmh_median, 'ro')
plt.plot(e_sample.hmk_median, e_sample.jmh_median, 'ko')
plt.xlabel(r"median $H-K$")
plt.ylabel(r"median $J-H$")
plt.title("Color-color diagram of periodic stars")
plt.xlim(-0.1,2.5)
plt.ylim(-0.2, 2.8)
plt.show()
return
def f_cc_generic(spread, title="", cmap='cubehelix_r'):
"""
Given an input dataset, makes a color-color plot with cool
colors and stuff.
Parameters
----------
spread : atpy.Table
A spreadsheet with information on stars whose median
J-H and H-K colors want to be plotted.
title : str
Desired title for the plot, if any.
Returns
-------
fig : Figure object
Figure that we plotted onto.
"""
fig = plt.figure()
ax = plt.gca()
plot_trajectory_vanilla(ax)
# First group: only group! Because of how the data quality works.
plt.scatter(spread.hmk_median, spread.jmh_median, c=spread.k_median,
vmin=11, vmax=15, cmap=cmap)
plt.xlabel(r"median $H-K$")
plt.ylabel(r"median $J-H$")
plt.xlim(-0.1,2.5)
plt.ylim(-0.2, 2.8)
cbar = plt.colorbar()
cbar.ax.invert_yaxis()
cbar.set_label(r"Median $K$ magnitude")
if title:
plt.title(title)
plt.show()
return fig
def f_cc_cmd_and_map_by_megeath_class(sample='all', title=True):
"""
A color-color diagram where stars are colored by Megeath class.
Also a CMD and a map.
"""
megeath_class_column = make_megeath_class_column()
# Nonperiodic stars only
if 'non' in sample.lower():
sample_boolean_criterion = np.isnan(ukvar_periods)
sample_name = "Non-periodic"
elif 'per' in sample.lower():
sample_boolean_criterion = ~np.isnan(ukvar_periods)
sample_name = "Periodic"
elif 'strict' in sample.lower():
sample_boolean_criterion = (ukvar_spread.strict == 1)
sample_name = "Q=2"
else:
sample_boolean_criterion = (np.zeros_like(ukvar_periods) == 0) #all true
sample_name = "All"
# Now we want the rows in ukvar_spread that correspond to certain
# Megeath subsamples.
disk_indices = (
# its Class is 'D'
(megeath_class_column == 'D') &
# and it matches our 'periodic/nonperiodic/all' cut.
sample_boolean_criterion)
protostar_indices = (
(megeath_class_column == 'P') & sample_boolean_criterion)
nondisk_indices = (
(megeath_class_column == 'ND') & sample_boolean_criterion)
unknown_indices = (
(megeath_class_column == 'na') & sample_boolean_criterion)
fig = plt.figure()
ax = plt.gca()
plot_trajectory_vanilla(ax, a_k=3)
plt.plot(ukvar_spread.hmk_median[nondisk_indices],
ukvar_spread.jmh_median[nondisk_indices],
'o', color=color_dict['nondisk'], ms=4, label="Non-disks")
plt.plot(ukvar_spread.hmk_median[disk_indices],
ukvar_spread.jmh_median[disk_indices],
'o', color=color_dict['disk'], ms=4, label="Disks")
plt.plot(ukvar_spread.hmk_median[protostar_indices],
ukvar_spread.jmh_median[protostar_indices],
'*', color=color_dict['protostar'], ms=10, label="Protostars")
plt.xlabel(r"median $H-K$")
plt.ylabel(r"median $J-H$")
plt.xlim(-0.1,3.1)
plt.ylim(-0.2, 4.4)
if title:
plt.title(sample_name + " variables, colored by Megeath+2012 class")
plt.legend(loc='lower right')
fig3 = plt.figure()
ax3 = plt.gca()
plot_trajectory_vanilla(ax, a_k=3)
plt.plot(ukvar_spread.hmk_median[nondisk_indices],
ukvar_spread.k_median[nondisk_indices],
'o', color=color_dict['nondisk'], ms=4, label="Non-disks")
plt.plot(ukvar_spread.hmk_median[disk_indices],
ukvar_spread.k_median[disk_indices],
'o', color=color_dict['disk'], ms=4, label="Disks")
plt.plot(ukvar_spread.hmk_median[protostar_indices],
ukvar_spread.k_median[protostar_indices],
'*', color=color_dict['protostar'], ms=10, label="Protostars")
plt.xlabel(r"median $H-K$")
plt.ylabel(r"median $K$ mag")
plt.xlim(-0.1,3.1)
plt.ylim(17, 8)
plt.title(sample_name + " variables, colored by Megeath 2012 class")
plt.legend(loc='upper right')
fig2 = plt.figure()
plt.plot(np.degrees(ukvar_spread.RA)[nondisk_indices],
np.degrees(ukvar_spread.DEC)[nondisk_indices],
'o', color=color_dict['nondisk'], ms=4, label="Non-disks")
plt.plot(np.degrees(ukvar_spread.RA)[disk_indices],
np.degrees(ukvar_spread.DEC)[disk_indices],
'o', color=color_dict['disk'], ms=4, label="Disks")
plt.plot(np.degrees(ukvar_spread.RA)[protostar_indices],
np.degrees(ukvar_spread.DEC)[protostar_indices],
'*', color=color_dict['protostar'], ms=10, label="Protostars")
plt.legend()
plt.gca().invert_xaxis()
plt.show()
return (fig, fig2, fig3)