def classicals_qi(data):
# the top right panel i-q of Petit et al. only.
data['peri'] = data['a'] * (1. - data['e'])
data['peri_E'] = (data['a_E'] / data['a']) + (data['e_E'] / data['e'])
o3e45 = data[numpy.where(data['object'] == 'o3e45')]
classicals = data[numpy.where(data['p'] == 'm')]
ms = 11
capsize = 1 # error bar cap width
alpha = 0.3
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
ax.errorbar(classicals['i'], classicals['peri'],
xerr=classicals['i_E'],
yerr=classicals['peri_E'],
fmt='.', alpha=alpha, ecolor='0.1', capsize=capsize, ms=ms)
ax.errorbar(o3e45['i'], o3e45['peri'],
xerr=o3e45['i_E'],
yerr=o3e45['peri_E'],
fmt='*', alpha=0.7, ecolor='0.1', capsize=capsize, ms=ms, color='r')
ax.grid(True, alpha=0.4)
ax.set_xlim([0., 35.])
ax.set_xlabel('inclination (deg)')
ax.set_xticks(range(5, 40, 10))
ax.set_ylim([34., 48.])
ax.set_yticks(range(36, 50, 4))
ax.set_ylabel('perihelion (AU)')
plot_fanciness.remove_border(ax)
plt.draw()
outfile = 'OSSOS_v{}_classical_iq.pdf'.format(parameters.RELEASE_VERSION)
plt.savefig(outfile, transparent=True, bbox_inches='tight')
def delta_a_over_a(discoveries):
arclen = []
da_over_a = []
for orbit in discoveries:
if (orbit.da / orbit.a) < 0.01:
da_over_a.append((orbit.da / orbit.a) * 100)
arclen.append(orbit.arc_length)
plt.figure(figsize=(6, 6))
plt.scatter(arclen, da_over_a, marker='o', facecolor='b', edgecolor=src.ossos.core.ossos.planning.plotting.plot_fanciness.ALMOST_BLACK,
linewidth=0.15, alpha=0.5)
plt.xlabel('arc length of orbit (days)')
plt.ylabel('d(a)/a (percent)')
plot_fanciness.remove_border()
plt.draw()
outfile = 'delta_a_over_a_13AE_corrected'
plt.savefig(outfile + '.pdf', transparent=True)
print('objects plotted:', len(arclen))
def classicals_qi(data):
# the top right panel i-q of Petit et al. only.
data['peri'] = data['a'] * (1. - data['e'])
data['peri_E'] = (data['a_E'] / data['a']) + (data['e_E'] / data['e'])
o3e45 = data[numpy.where(data['object'] == 'o3e45')]
classicals = data[numpy.where(data['p'] == 'm')]
ms = 11
capsize = 1 # error bar cap width
alpha = 0.3
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
ax.errorbar(classicals['i'],
classicals['peri'],
xerr=classicals['i_E'],
yerr=classicals['peri_E'],
fmt='.',
alpha=alpha,
ecolor='0.1',
capsize=capsize,
ms=ms)
ax.errorbar(o3e45['i'],
o3e45['peri'],
xerr=o3e45['i_E'],
yerr=o3e45['peri_E'],
fmt='*',
alpha=0.7,
ecolor='0.1',
capsize=capsize,
ms=ms,
color='r')
ax.grid(True, alpha=0.4)
ax.set_xlim([0., 35.])
ax.set_xlabel('inclination (deg)')
ax.set_xticks(list(range(5, 40, 10)))
ax.set_ylim([34., 48.])
ax.set_yticks(list(range(36, 50, 4)))
ax.set_ylabel('perihelion (AU)')
plot_fanciness.remove_border(ax)
plt.draw()
outfile = 'OSSOS_v{}_classical_iq.pdf'.format(parameters.RELEASE_VERSION)
plt.savefig(outfile, transparent=True, bbox_inches='tight')
def argperi_a(directory):
# dir = '/Users/bannisterm/Dropbox/OSSOS/measure3/test_argperi_align/'
# directory = '/Users/bannisterm/Dropbox/OSSOS/measure3/2014B-H/track/'
tnos = parsers.ossos_discoveries(directory=directory, all_objects=True)
fig, ax = plt.subplots(
3, 1, sharex=True,
figsize=(7, 8)) # a4 is 1 x sqrt(2), so use those proportions
fig.subplots_adjust(hspace=0.25)
for obj in tnos:
if obj.orbit.arc_length < 60. * units.day:
print('Skipping', obj.name, obj.orbit.arc_length)
continue
alpha = 0.4
obj_r = obj.orbit.distance.value
obj_dr = obj.orbit.distance_uncertainty.value
obj_a = obj.orbit.a.value
obj_da = obj.orbit.da.value
obj_i = obj.orbit.inc.value
obj_di = obj.orbit.dinc.value
obj_e = obj.orbit.e.value
obj_de = obj.orbit.de.value
obj_peri = obj_a * (1. - obj_e)
obj_dperi = (obj_da / obj_a) + (obj_de / obj_e)
obj_argP = obj.orbit.om.value
if obj_argP > 180.:
obj_argP = obj_argP - 360.
obj_dargP = obj.orbit.dom.value
ax[0].errorbar(obj_r,
obj_i,
xerr=obj_dr,
yerr=obj_di,
fmt='.',
ms=10,
color='b',
alpha=alpha)
ax[1].errorbar(obj_a,
obj_i,
xerr=obj_da,
yerr=obj_di,
fmt='.',
ms=10,
color='b',
alpha=alpha)
ax[2].errorbar(obj_a,
obj_e,
xerr=obj_da,
yerr=obj_de,
fmt='.',
ms=10,
color='b',
alpha=alpha)
# ax[3].errorbar(obj_a, obj_argP,
# xerr=obj_dargP,
# yerr=obj_da,
# fmt='.', ms=10, color='b', alpha=alpha
# )
ymin = -0.001
imax = 50
emax = .85
xinner = 20
xouter = 80
xticker = 5
grid_alpha = 0.2
resonances(ax, imax, emax)
ax[0].set_ylim([ymin, imax])
ax[1].set_ylim([ymin, imax])
ax[2].set_ylim([ymin, emax])
# ax[3].set_ylim([-180, 180])
plt.xlim([xinner, xouter])
ax[0].set_xticks(list(range(xinner, xouter, xticker)))
ax[1].set_xticks(list(range(xinner, xouter, xticker)))
ax[2].set_xticks(list(range(xinner, xouter, xticker)))
plot_fanciness.remove_border(ax[0])
plot_fanciness.remove_border(ax[1])
plot_fanciness.remove_border(ax[2])
# plot_fanciness.remove_border(ax[3])
ax[0].set_ylabel('inclination (degrees)')
ax[0].grid(True, alpha=grid_alpha)
ax[0].set_xlabel('heliocentric distance (AU)')
ax[1].set_ylabel('inclination (degrees)')
ax[1].grid(True, alpha=grid_alpha)
ax[2].set_ylabel('eccentricity')
ax[2].grid(True, alpha=grid_alpha)
# ax[2].set_xlabel('semimajor axis (AU)')
plt.xlabel('semimajor axis (AU)')
# ax[3].grid(True, alpha=grid_alpha)
# ax[3].set_xlabel('perihelion (AU)')
# ax[3].set_ylabel('arg. peri. (degrees)')
plt.draw()
# ofile = 'OSSOS+CFEPS+NGVS_aei_argperi.pdf'
outfile = 'OSSOS_aei_{}.pdf'.format(parameters.RELEASE_VERSION)
plt.savefig(outfile, transparent=True, bbox_inches='tight')
def classicals_aeiq(data):
# designed to match the parameters and look of Petit et al. 2011
# layout: top left, a-q, top right, i-q, lower left, a-i, lower right, a-e.
data['peri'] = data['a'] * (1. - data['e'])
data['e_peri'] = (data['e_a'] / data['a']) + (data['e_e'] / data['e'])
# o3e45 = data[numpy.where(data['object'] == 'o3e45')]
classicals = data[numpy.where(data['p'] == 'm')]
outer = data[numpy.where(data['p'] == 'o')]
outer = outer[numpy.where(outer['i'] < 5.)]
print(outer)
ms = 7
capsize = 2 # error bar cap width
fmt = ['.', '*']
colour = ['b', 'r']
alpha = 0.4
fig, ax = plt.subplots(2, 2, figsize=(10, 10))
fig.subplots_adjust(hspace=0.07) # want to work on the v-space as well
for i, data in enumerate([classicals, outer]):
# top left: semimajor axis vs perihelion
ax[0][0].errorbar(
data['a'],
data['peri'],
xerr=data['e_a'],
# no yerr?
fmt=fmt[i],
alpha=alpha,
ecolor='0.1',
capsize=capsize,
ms=ms,
mfc=colour[i])
ax[0][0].grid(True, alpha=0.4)
# semimajor axis
ax[0][0].set_xlim([38., 49.])
# pericentre
ax[0][0].set_ylabel('pericenter (AU)')
ax[0][0].set_ylim([34., 47.])
ax[0][0].set_yticks(list(range(34, 47, 2)))
# top right: inclination vs perihelion
ax[0][1].errorbar(data['i'],
data['peri'],
xerr=data['e_i'],
fmt=fmt[i],
alpha=alpha,
ecolor='0.1',
capsize=capsize,
ms=ms,
mfc=colour[i])
ax[0][1].grid(True, alpha=0.4)
ax[0][1].set_xlabel('inclination (deg)')
ax[0][1].set_xlim([0., 35.])
ax[0][1].set_xticks(list(range(0, 35, 5)))
# pericentre
ax[0][1].set_ylim([34., 47.])
ax[0][1].set_yticks(list(range(34, 47, 2)))
# lower left: semimajor axis vs inclination
ax[1][0].errorbar(data['a'],
data['i'],
xerr=data['e_a'],
yerr=data['e_i'],
fmt=fmt[i],
alpha=alpha,
ecolor='0.1',
ms=ms,
capsize=capsize,
mfc=colour[i])
ax[1][0].set_xlabel('semimajor axis (AU)')
ax[1][0].set_xlim([38., 49.])
ax[1][0].set_xticks(list(range(38, 49, 2)))
ax[1][0].set_ylabel('inclination (deg)')
ax[1][0].set_ylim([0., 35.])
ax[1][0].set_yticks(list(range(0, 35, 5)))
ax[1][0].grid(True, which='both', alpha=0.4)
# lower right: semimajor axis vs eccentricity
ax[1][1].errorbar(data['a'],
data['e'],
xerr=data['e_a'],
yerr=data['e_e'],
fmt=fmt[i],
alpha=alpha,
ecolor='0.1',
ms=ms,
capsize=capsize,
mfc=colour[i])
ax[1][1].grid(True, alpha=0.4)
ax[1][1].set_ylabel('eccentricity')
ax[1][1].set_ylim([0., 0.25])
ax[1][1].set_xlabel('semimajor axis (AU)')
ax[1][1].set_xlim([38., 49.])
ax[1][1].set_xticks(list(range(38, 49, 2)))
plot_fanciness.remove_border(ax[0][0])
# ax[0][0].tick_params(labelbottom='off')
plot_fanciness.remove_border(ax[0][1])
plot_fanciness.remove_border(ax[1][0])
plot_fanciness.remove_border(ax[1][1], remove=['right', 'top'], keep=[])
# ax[1][1].tick_params(labelbottom='off', labelleft='off')
plt.draw()
outfile = 'OSSOS_v{}_classical_aeiq.pdf'.format(parameters.RELEASE_VERSION)
plt.savefig(outfile, transparent=True, bbox_inches='tight')
def full_aei(data_release, fov, icut=False, aiq=False):
fig, ax = plt.subplots(
3, 1, sharex=True,
figsize=(7, 8)) # a4 is 1 x sqrt(2), so use those proportions
fig.subplots_adjust(hspace=0.15)
ymin = -0.001
orbit_classes = ['cen', 'cla', 'res', 'sca', 'det', 'jc', 'xxx']
colmap = palettable.wesanderson.Zissou_5.mpl_colors
col = [colmap[4]] + colmap[0:4] + ['m'] + ['k']
if parameters.RELEASE_VERSION == '4':
# don't want uncharacterised for this plot
data = data_release[numpy.array(
[name.startswith("o") for name in data_release['object']])]
imax = 55
emax = 0.45 #.65
qmin = data['peri'].min() - 4
qmax = data['peri'].max() + 4
xinner = 25
xouter = 90
annotation = False
e45_annotation = False
if parameters.RELEASE_VERSION == '8':
imax = 35 #55
emax = 0.49 #.99
xinner = 36 #10
xouter = 56 #720
xstep = 2 #50
ms = 5
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array(
[nobs > 7 for nobs in data_release['nobs']])]
# data = data_release[numpy.array([name.startswith("o4h") for name in data_release['object']])]
plot_resonances = True
if parameters.RELEASE_VERSION == '11' and fov == 'medium':
imax = 55
emax = 0.86
xinner = 5
xouter = 105
xstep = 5
ms = 4
cold_alpha = 0.85 # when plotting blue only # 0.7
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([
name.__contains__("nt") is False for name in data_release['object']
])]
# data = data[numpy.array([n < 4 for n in data['e_a']])] # couple of big-uncertainty objects making things messy
plot_resonances = True
if parameters.RELEASE_VERSION == '11' and fov == 'classical':
imax = 45
emax = 0.53 #0.37
xinner = 38
xouter = 55
xstep = 5
ms = 3
cold_alpha = 0.65
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([
name.__contains__("nt") is False for name in data_release['object']
])]
# data = data[numpy.array([n < .15 for n in data['e_a']])] # couple of big-uncertainty objects making things messy
# data = data[numpy.array([n < .02 for n in data['e_e']])] # couple of big-uncertainty objects making things messy
plot_resonances = True
if parameters.RELEASE_VERSION == '11' and fov == 'thousand_au':
imax = 70
emax = 0.99
xinner = 0
xouter = 850
xstep = 50
ms = 6
cold_alpha = 0.85
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([
name.__contains__("nt") is False for name in data_release['object']
])]
plot_resonances = True
if parameters.RELEASE_VERSION == '10' and fov == 'medium':
imax = 55
emax = 0.86
xinner = 5
xouter = 105
xstep = 5
ms = 4
cold_alpha = 0.85 # when plotting blue only # 0.7
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([
name.__contains__("nt") is False for name in data_release['object']
])]
# data = data[numpy.array([n < 4 for n in data['e_a']])] # couple of big-uncertainty objects making things messy
plot_resonances = True
if parameters.RELEASE_VERSION == '9' and fov == 'medium':
imax = 47
emax = 0.8
xinner = 10
xouter = 85
xstep = 5
ms = 6
cold_alpha = 0.65 # when plotting blue only # 0.7
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([
name.__contains__("nt") is False for name in data_release['object']
])]
# data = data[numpy.array([n < 4 for n in data['e_a']])] # couple of big-uncertainty objects making things messy
plot_resonances = True
if parameters.RELEASE_VERSION == '9' and fov == 'classical':
imax = 45
emax = 0.53 #0.37
xinner = 30 #38
xouter = 60 #49
xstep = 5
ms = 5
cold_alpha = 0.65
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([
name.__contains__("nt") is False for name in data_release['object']
])]
data = data[numpy.array([
n < .15 for n in data['e_a']
])] # couple of big-uncertainty objects making things messy
data = data[numpy.array([
n < .02 for n in data['e_e']
])] # couple of big-uncertainty objects making things messy
plot_resonances = True
if parameters.RELEASE_VERSION == '9' and fov == 'thousand_au':
imax = 70
emax = 0.99
xinner = 0
xouter = 850
xstep = 50
ms = 6
cold_alpha = 0.85
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([
name.__contains__("nt") is False for name in data_release['object']
])]
plot_resonances = True
data['peri'] = data['a'] * (1. - data['e'])
data['e_peri'] = (data['e_a'] / data['a']) + (data['e_e'] / data['e'])
data.sort('cl')
coldcol = col
hotcol = col[2]
e45col = col #[palettable.wesanderson.Moonrise5_6.mpl_colors[4]]
# cold_alpha = 0.85 # when plotting blue only # 0.7
hot_alpha = 0.25
grid_alpha = 0.2
ebarcolor = 'k' # error bar colour: 0.1 is greyscale
capsize = 1 # error bar cap width
fmt = '.'
# sans_o3e45 = data[numpy.where(data['object'] != 'o3e45')]
# if icut:
# cold = sans_o3e45[numpy.where(sans_o3e45['i'] < 5.)]
# hot = sans_o3e45[numpy.where(sans_o3e45['i'] >= 5.)]
# else: # no splits whatsoever
# cold = sans_o3e45
cold = data
# want to show o3e45 with a different symbol.
# o3e45 = data[numpy.where(data['object'] == 'o3e45')]
ax = helio_i(ax,
cold,
fmt,
cold_alpha,
ebarcolor,
capsize,
ms,
grid_alpha,
coldcol,
1,
orbit_classes,
annotation=annotation)
if icut:
ax = helio_i(ax,
hot,
fmt,
hot_alpha,
ebarcolor,
capsize,
ms,
grid_alpha,
hotcol,
0,
orbit_classes,
annotation=annotation)
# ax = helio_i(ax, o3e45, '*', 0.7, ebarcolor, capsize, ms, grid_alpha, e45col, 2, ['cla'], annotation=e45_annotation)
ax[0].set_ylim([ymin, imax])
ax = a_i(ax,
cold,
fmt,
cold_alpha,
ebarcolor,
capsize,
ms,
grid_alpha,
coldcol,
1,
orbit_classes,
annotation=annotation)
if icut:
ax = a_i(ax,
hot,
fmt,
hot_alpha,
ebarcolor,
capsize,
ms,
grid_alpha,
hotcol,
0,
annotation=annotation)
# ax = a_i(ax, o3e45, '*', 0.7, ebarcolor, capsize, ms, grid_alpha, e45col, 2, ['cla'], annotation=e45_annotation)
ax[1].set_ylim([ymin, imax])
if aiq:
ax = a_q(ax,
cold,
fmt,
cold_alpha,
ebarcolor,
capsize,
ms,
grid_alpha,
coldcol,
'$i < 5^{\circ}$',
1,
annotation=annotation)
if icut:
ax = a_q(ax,
hot,
fmt,
hot_alpha,
ebarcolor,
capsize,
ms,
grid_alpha,
hotcol,
'$i \geq 5^{\circ}$',
0,
annotation=annotation)
ax = a_q(ax,
o3e45,
'*',
0.7,
ebarcolor,
capsize,
ms,
grid_alpha,
e45col,
'',
2,
annotation=e45_annotation)
ax[2].set_ylim([qmin, qmax])
else:
if not icut:
label = '$i < 5^{\circ}$'
ax = a_e(ax,
cold,
fmt,
cold_alpha,
ebarcolor,
capsize,
ms,
grid_alpha,
coldcol,
label,
1,
orbit_classes,
annotation=annotation)
if icut:
ax = a_e(ax,
hot,
fmt,
hot_alpha,
ebarcolor,
capsize,
ms,
grid_alpha,
hotcol,
'$i \geq 5^{\circ}$',
0,
orbit_classes,
annotation=annotation)
# ax = a_e(ax, o3e45, '*', 0.7, ebarcolor, capsize, ms, grid_alpha, e45col, '', 2, ['cla'], annotation=e45_annotation)
ax[2].set_ylim([ymin, emax])
plt.xlim([xinner, xouter])
ax[0].set_xticks(list(range(xinner, xouter, xstep)))
ax[1].set_xticks(list(range(xinner, xouter, xstep)))
ax[2].set_xticks(list(range(xinner, xouter, xstep)))
plot_fanciness.remove_border(ax[0])
plot_fanciness.remove_border(ax[1])
plot_fanciness.remove_border(ax[2])
if plot_resonances:
resonances(ax, imax, emax)
handles, labels = ax[2].get_legend_handles_labels()
# handles.append(mlines.Line2D([], [], marker='*', color=col[0], alpha=cold_alpha, linestyle=None))
labels = [
'centaurs', 'classical', 'resonant', 'scattering', 'detached',
'unclassified', 'Jupiter-coupled'
] #, 'o3e45']
#print len(handles)
#print len(labels)
#ax[0].legend(handles, labels, loc='upper right', numpoints=1, fontsize='small')
plt.xlabel('semimajor axis (AU)')
plt.draw()
if aiq:
type = 'aiq'
else:
type = 'aei'
outfile = 'OSSOS_v{}_discoveries_{}_{}-{}_ilabel.pdf'.format(
parameters.RELEASE_VERSION, type, xinner, xouter)
plt.savefig(outfile, transparent=True, bbox_inches='tight')
sys.stdout.write('{}\n'.format(outfile))
def full_aei(data_release, fov, icut=False, aiq=False):
fig, ax = plt.subplots(
2, 1, sharex=True,
figsize=(7, 8)) # a4 is 1 x sqrt(2), so use those proportions
fig.subplots_adjust(hspace=0.15)
ymin = -0.001
orbit_classes = ['cla', 'res', 'sca',
'det'] #, 'xxx'] # fewer unclassified objects now
colmap = palettable.wesanderson.Zissou_5.mpl_colors
col = colmap[0:4] #['m'] + [colmap[4]] +
data = data_release[numpy.where(data_release['data_arc'] > 365.)]
data = data[numpy.where(data['sigma_a'] < 100.)]
print(len(data))
if fov == 'medium':
imax = 55
emax = 0.86
xinner = 30
xouter = 160
xstep = 10
qmin = 30
qmax = 90
ms = 4
alpha = 0.7 # when plotting blue only # 0.7
annotation = False
plot_resonances = True
if parameters.RELEASE_VERSION == '9' and fov == 'classical':
imax = 45
emax = 0.53 #0.37
xinner = 30 #38
xouter = 60 #49
xstep = 5
ms = 5
alpha = 0.65
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([
name.__contains__("nt") is False for name in data_release['object']
])]
data = data[numpy.array([
n < .15 for n in data['e_a']
])] # couple of big-uncertainty objects making things messy
data = data[numpy.array([
n < .02 for n in data['e_e']
])] # couple of big-uncertainty objects making things messy
plot_resonances = True
if fov == 'thousand_au':
imax = 40
emax = 1.0
xinner = 0
xouter = 850
xstep = 50
ms = 9
alpha = 0.85
annotation = False
plot_resonances = True
coldcol = col
hotcol = col[2]
hot_alpha = 0.25
grid_alpha = 0.2
ebarcolor = 'k' # error bar colour: 0.1 is greyscale
capsize = 1 # error bar cap width
fmt = '.'
alpha = alpha
ax = a_i(ax,
data,
fmt,
alpha,
ebarcolor,
capsize,
ms,
grid_alpha,
coldcol,
1,
orbit_classes,
annotation=annotation)
ax[0].set_ylim([ymin, imax])
if aiq:
ax = a_q(ax,
data,
fmt,
alpha,
ebarcolor,
capsize,
ms,
grid_alpha,
coldcol,
'$i < 5^{\circ}$',
1,
annotation=annotation)
ax[1].set_ylim([qmin, qmax])
else:
ax = a_e(ax,
data,
fmt,
alpha,
ebarcolor,
capsize,
ms,
grid_alpha,
coldcol,
'placeholder',
1,
orbit_classes,
annotation=annotation)
# constant q line
e = [(1 - (40. / a)) for a in range(xinner + 1, xouter, 3)]
ax[1].plot(list(range(xinner + 1, xouter, 3)), e, '-')
ax[1].set_ylim([ymin, emax])
plt.xlim([xinner, xouter])
ax[0].set_xticks(list(range(xinner, xouter, xstep)))
ax[1].set_xticks(list(range(xinner, xouter, xstep)))
plot_fanciness.remove_border(ax[0])
plot_fanciness.remove_border(ax[1])
if plot_resonances:
resonances(ax, imax, emax)
handles, labels = ax[1].get_legend_handles_labels()
# handles.append(mlines.Line2D([], [], marker='*', color=col[0], alpha=cold_alpha, linestyle=None))
labels = ['q < 40 au', 'q >= 40 au']
ax[1].legend(handles,
labels,
loc='lower right',
numpoints=1,
fontsize='small')
plt.xlabel('semimajor axis (AU)')
plt.draw()
if aiq:
type = 'aiq'
else:
type = 'aei'
outfile = 'mpcTNOs_{}_{}-{}au.pdf'.format(type, xinner, xouter)
plt.savefig(outfile, transparent=True, bbox_inches='tight')
sys.stdout.write('{}\n'.format(outfile))
def argperi_a(directory):
# dir = '/Users/bannisterm/Dropbox/OSSOS/measure3/test_argperi_align/'
# directory = '/Users/bannisterm/Dropbox/OSSOS/measure3/2014B-H/track/'
tnos = parsers.ossos_discoveries(directory=directory, all_objects=True)
fig, ax = plt.subplots(3, 1, sharex=True, figsize=(7, 8)) # a4 is 1 x sqrt(2), so use those proportions
fig.subplots_adjust(hspace=0.25)
for obj in tnos:
if obj.orbit.arc_length < 60.*units.day:
print 'Skipping', obj.name, obj.orbit.arc_length
continue
alpha = 0.4
obj_r = obj.orbit.distance.value
obj_dr = obj.orbit.distance_uncertainty.value
obj_a = obj.orbit.a.value
obj_da = obj.orbit.da.value
obj_i = obj.orbit.inc.value
obj_di = obj.orbit.dinc.value
obj_e = obj.orbit.e.value
obj_de = obj.orbit.de.value
obj_peri = obj_a * (1. - obj_e)
obj_dperi = (obj_da/obj_a) + (obj_de/obj_e)
obj_argP = obj.orbit.om.value
if obj_argP > 180.:
obj_argP = obj_argP - 360.
obj_dargP = obj.orbit.dom.value
ax[0].errorbar(obj_r, obj_i,
xerr=obj_dr,
yerr=obj_di,
fmt='.', ms=10, color='b', alpha=alpha
)
ax[1].errorbar(obj_a, obj_i,
xerr=obj_da,
yerr=obj_di,
fmt='.', ms=10, color='b', alpha=alpha
)
ax[2].errorbar(obj_a, obj_e,
xerr=obj_da,
yerr=obj_de,
fmt='.', ms=10, color='b', alpha=alpha
)
# ax[3].errorbar(obj_a, obj_argP,
# xerr=obj_dargP,
# yerr=obj_da,
# fmt='.', ms=10, color='b', alpha=alpha
# )
ymin = -0.001
imax = 50
emax = .85
xinner = 20
xouter = 80
xticker = 5
grid_alpha = 0.2
resonances(ax, imax, emax)
ax[0].set_ylim([ymin, imax])
ax[1].set_ylim([ymin, imax])
ax[2].set_ylim([ymin, emax])
# ax[3].set_ylim([-180, 180])
plt.xlim([xinner, xouter])
ax[0].set_xticks(range(xinner, xouter, xticker))
ax[1].set_xticks(range(xinner, xouter, xticker))
ax[2].set_xticks(range(xinner, xouter, xticker))
plot_fanciness.remove_border(ax[0])
plot_fanciness.remove_border(ax[1])
plot_fanciness.remove_border(ax[2])
# plot_fanciness.remove_border(ax[3])
ax[0].set_ylabel('inclination (degrees)')
ax[0].grid(True, alpha=grid_alpha)
ax[0].set_xlabel('heliocentric distance (AU)')
ax[1].set_ylabel('inclination (degrees)')
ax[1].grid(True, alpha=grid_alpha)
ax[2].set_ylabel('eccentricity')
ax[2].grid(True, alpha=grid_alpha)
# ax[2].set_xlabel('semimajor axis (AU)')
plt.xlabel('semimajor axis (AU)')
# ax[3].grid(True, alpha=grid_alpha)
# ax[3].set_xlabel('perihelion (AU)')
# ax[3].set_ylabel('arg. peri. (degrees)')
plt.draw()
# ofile = 'OSSOS+CFEPS+NGVS_aei_argperi.pdf'
outfile = 'OSSOS_aei_{}.pdf'.format(parameters.RELEASE_VERSION)
plt.savefig(outfile, transparent=True, bbox_inches='tight')
def classicals_aeiq(data):
# designed to match the parameters and look of Petit et al. 2011
# layout: top left, a-q, top right, i-q, lower left, a-i, lower right, a-e.
data['peri'] = data['a'] * (1. - data['e'])
data['e_peri'] = (data['e_a'] / data['a']) + (data['e_e'] / data['e'])
# o3e45 = data[numpy.where(data['object'] == 'o3e45')]
classicals = data[numpy.where(data['p'] == 'm')]
outer = data[numpy.where(data['p'] == 'o')]
outer = outer[numpy.where(outer['i'] < 5.)]
print outer
ms = 7
capsize = 2 # error bar cap width
fmt = ['.', '*']
colour = ['b', 'r']
alpha = 0.4
fig, ax = plt.subplots(2, 2, figsize=(10, 10))
fig.subplots_adjust(hspace=0.07) # want to work on the v-space as well
for i, data in enumerate([classicals, outer]):
# top left: semimajor axis vs perihelion
ax[0][0].errorbar(data['a'], data['peri'],
xerr=data['e_a'],
# no yerr?
fmt=fmt[i], alpha=alpha, ecolor='0.1', capsize=capsize, ms=ms, mfc=colour[i])
ax[0][0].grid(True, alpha=0.4)
# semimajor axis
ax[0][0].set_xlim([38., 49.])
# pericentre
ax[0][0].set_ylabel('pericenter (AU)')
ax[0][0].set_ylim([34., 47.])
ax[0][0].set_yticks(range(34, 47, 2))
# top right: inclination vs perihelion
ax[0][1].errorbar(data['i'], data['peri'],
xerr=data['e_i'],
fmt=fmt[i], alpha=alpha, ecolor='0.1', capsize=capsize, ms=ms, mfc=colour[i])
ax[0][1].grid(True, alpha=0.4)
ax[0][1].set_xlabel('inclination (deg)')
ax[0][1].set_xlim([0., 35.])
ax[0][1].set_xticks(range(0, 35, 5))
# pericentre
ax[0][1].set_ylim([34., 47.])
ax[0][1].set_yticks(range(34, 47, 2))
# lower left: semimajor axis vs inclination
ax[1][0].errorbar(data['a'], data['i'],
xerr=data['e_a'], yerr=data['e_i'],
fmt=fmt[i], alpha=alpha, ecolor='0.1', ms=ms, capsize=capsize, mfc=colour[i])
ax[1][0].set_xlabel('semimajor axis (AU)')
ax[1][0].set_xlim([38., 49.])
ax[1][0].set_xticks(range(38, 49, 2))
ax[1][0].set_ylabel('inclination (deg)')
ax[1][0].set_ylim([0., 35.])
ax[1][0].set_yticks(range(0, 35, 5))
ax[1][0].grid(True, which='both', alpha=0.4)
# lower right: semimajor axis vs eccentricity
ax[1][1].errorbar(data['a'], data['e'],
xerr=data['e_a'], yerr=data['e_e'],
fmt=fmt[i], alpha=alpha, ecolor='0.1', ms=ms, capsize=capsize, mfc=colour[i])
ax[1][1].grid(True, alpha=0.4)
ax[1][1].set_ylabel('eccentricity')
ax[1][1].set_ylim([0., 0.25])
ax[1][1].set_xlabel('semimajor axis (AU)')
ax[1][1].set_xlim([38., 49.])
ax[1][1].set_xticks(range(38, 49, 2))
plot_fanciness.remove_border(ax[0][0])
# ax[0][0].tick_params(labelbottom='off')
plot_fanciness.remove_border(ax[0][1])
plot_fanciness.remove_border(ax[1][0])
plot_fanciness.remove_border(ax[1][1], remove=['right', 'top'], keep=[])
# ax[1][1].tick_params(labelbottom='off', labelleft='off')
plt.draw()
outfile = 'OSSOS_v{}_classical_aeiq.pdf'.format(parameters.RELEASE_VERSION)
plt.savefig(outfile, transparent=True, bbox_inches='tight')
def full_aei(data_release, fov, icut=False, aiq=False):
fig, ax = plt.subplots(3, 1, sharex=True, figsize=(7, 8)) # a4 is 1 x sqrt(2), so use those proportions
fig.subplots_adjust(hspace=0.15)
ymin = -0.001
orbit_classes = ['cen', 'cla', 'res', 'sca', 'det', 'jc', 'xxx']
colmap = palettable.wesanderson.Zissou_5.mpl_colors
col = [colmap[4]] + colmap[0:4] + ['m'] + ['k']
if parameters.RELEASE_VERSION == '4':
# don't want uncharacterised for this plot
data = data_release[numpy.array([name.startswith("o") for name in data_release['object']])]
imax = 55
emax = 0.45 #.65
qmin = data['peri'].min() - 4
qmax = data['peri'].max() + 4
xinner = 25
xouter = 90
annotation = False
e45_annotation = False
if parameters.RELEASE_VERSION == '8':
imax = 35 #55
emax = 0.49 #.99
xinner = 36 #10
xouter = 56 #720
xstep = 2 #50
ms = 5
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([nobs > 7 for nobs in data_release['nobs']])]
# data = data_release[numpy.array([name.startswith("o4h") for name in data_release['object']])]
plot_resonances = True
if parameters.RELEASE_VERSION == '11' and fov == 'medium':
imax = 55
emax = 0.86
xinner = 5
xouter = 105
xstep = 5
ms = 4
cold_alpha = 0.85 # when plotting blue only # 0.7
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([name.__contains__("nt") is False for name in data_release['object']])]
# data = data[numpy.array([n < 4 for n in data['e_a']])] # couple of big-uncertainty objects making things messy
plot_resonances = True
if parameters.RELEASE_VERSION == '11' and fov == 'classical':
imax = 45
emax = 0.53 #0.37
xinner = 38
xouter = 55
xstep = 5
ms = 3
cold_alpha = 0.65
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([name.__contains__("nt") is False for name in data_release['object']])]
# data = data[numpy.array([n < .15 for n in data['e_a']])] # couple of big-uncertainty objects making things messy
# data = data[numpy.array([n < .02 for n in data['e_e']])] # couple of big-uncertainty objects making things messy
plot_resonances = True
if parameters.RELEASE_VERSION == '11' and fov == 'thousand_au':
imax = 70
emax = 0.99
xinner = 0
xouter = 850
xstep = 50
ms = 6
cold_alpha = 0.85
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([name.__contains__("nt") is False for name in data_release['object']])]
plot_resonances = True
if parameters.RELEASE_VERSION == '10' and fov == 'medium':
imax = 55
emax = 0.86
xinner = 5
xouter = 105
xstep = 5
ms = 4
cold_alpha = 0.85 # when plotting blue only # 0.7
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([name.__contains__("nt") is False for name in data_release['object']])]
# data = data[numpy.array([n < 4 for n in data['e_a']])] # couple of big-uncertainty objects making things messy
plot_resonances = True
if parameters.RELEASE_VERSION == '9' and fov == 'medium':
imax = 47
emax = 0.8
xinner = 10
xouter = 85
xstep = 5
ms = 6
cold_alpha = 0.65 # when plotting blue only # 0.7
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([name.__contains__("nt") is False for name in data_release['object']])]
# data = data[numpy.array([n < 4 for n in data['e_a']])] # couple of big-uncertainty objects making things messy
plot_resonances = True
if parameters.RELEASE_VERSION == '9' and fov == 'classical':
imax = 45
emax = 0.53 #0.37
xinner = 30 #38
xouter = 60 #49
xstep = 5
ms = 5
cold_alpha = 0.65
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([name.__contains__("nt") is False for name in data_release['object']])]
data = data[numpy.array([n < .15 for n in data['e_a']])] # couple of big-uncertainty objects making things messy
data = data[numpy.array([n < .02 for n in data['e_e']])] # couple of big-uncertainty objects making things messy
plot_resonances = True
if parameters.RELEASE_VERSION == '9' and fov == 'thousand_au':
imax = 70
emax = 0.99
xinner = 0
xouter = 850
xstep = 50
ms = 6
cold_alpha = 0.85
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([name.__contains__("nt") is False for name in data_release['object']])]
plot_resonances = True
data['peri'] = data['a'] * (1. - data['e'])
data['e_peri'] = (data['e_a'] / data['a']) + (data['e_e'] / data['e'])
data.sort('cl')
coldcol = col
hotcol = col[2]
e45col = col #[palettable.wesanderson.Moonrise5_6.mpl_colors[4]]
# cold_alpha = 0.85 # when plotting blue only # 0.7
hot_alpha = 0.25
grid_alpha = 0.2
ebarcolor = 'k' # error bar colour: 0.1 is greyscale
capsize = 1 # error bar cap width
fmt = '.'
# sans_o3e45 = data[numpy.where(data['object'] != 'o3e45')]
# if icut:
# cold = sans_o3e45[numpy.where(sans_o3e45['i'] < 5.)]
# hot = sans_o3e45[numpy.where(sans_o3e45['i'] >= 5.)]
# else: # no splits whatsoever
# cold = sans_o3e45
cold = data
# want to show o3e45 with a different symbol.
# o3e45 = data[numpy.where(data['object'] == 'o3e45')]
ax = helio_i(ax, cold, fmt, cold_alpha, ebarcolor, capsize, ms, grid_alpha, coldcol, 1, orbit_classes, annotation=annotation)
if icut:
ax = helio_i(ax, hot, fmt, hot_alpha, ebarcolor, capsize, ms, grid_alpha, hotcol, 0, orbit_classes, annotation=annotation)
# ax = helio_i(ax, o3e45, '*', 0.7, ebarcolor, capsize, ms, grid_alpha, e45col, 2, ['cla'], annotation=e45_annotation)
ax[0].set_ylim([ymin, imax])
ax = a_i(ax, cold, fmt, cold_alpha, ebarcolor, capsize, ms, grid_alpha, coldcol, 1, orbit_classes, annotation=annotation)
if icut:
ax = a_i(ax, hot, fmt, hot_alpha, ebarcolor, capsize, ms, grid_alpha, hotcol, 0, annotation=annotation)
# ax = a_i(ax, o3e45, '*', 0.7, ebarcolor, capsize, ms, grid_alpha, e45col, 2, ['cla'], annotation=e45_annotation)
ax[1].set_ylim([ymin, imax])
if aiq:
ax = a_q(ax, cold, fmt, cold_alpha, ebarcolor, capsize, ms, grid_alpha, coldcol, '$i < 5^{\circ}$',
1, annotation=annotation)
if icut:
ax = a_q(ax, hot, fmt, hot_alpha, ebarcolor, capsize, ms, grid_alpha, hotcol, '$i \geq 5^{\circ}$',
0, annotation=annotation)
ax = a_q(ax, o3e45, '*', 0.7, ebarcolor, capsize, ms, grid_alpha, e45col, '', 2, annotation=e45_annotation)
ax[2].set_ylim([qmin, qmax])
else:
if not icut:
label = '$i < 5^{\circ}$'
ax = a_e(ax, cold, fmt, cold_alpha, ebarcolor, capsize, ms, grid_alpha, coldcol, label,
1, orbit_classes, annotation=annotation)
if icut:
ax = a_e(ax, hot, fmt, hot_alpha, ebarcolor, capsize, ms, grid_alpha, hotcol, '$i \geq 5^{\circ}$',
0, orbit_classes, annotation=annotation)
# ax = a_e(ax, o3e45, '*', 0.7, ebarcolor, capsize, ms, grid_alpha, e45col, '', 2, ['cla'], annotation=e45_annotation)
ax[2].set_ylim([ymin, emax])
plt.xlim([xinner, xouter])
ax[0].set_xticks(range(xinner, xouter, xstep))
ax[1].set_xticks(range(xinner, xouter, xstep))
ax[2].set_xticks(range(xinner, xouter, xstep))
plot_fanciness.remove_border(ax[0])
plot_fanciness.remove_border(ax[1])
plot_fanciness.remove_border(ax[2])
if plot_resonances:
resonances(ax, imax, emax)
handles, labels = ax[2].get_legend_handles_labels()
# handles.append(mlines.Line2D([], [], marker='*', color=col[0], alpha=cold_alpha, linestyle=None))
labels = ['centaurs', 'classical', 'resonant', 'scattering', 'detached', 'unclassified', 'Jupiter-coupled']#, 'o3e45']
#print len(handles)
#print len(labels)
#ax[0].legend(handles, labels, loc='upper right', numpoints=1, fontsize='small')
plt.xlabel('semimajor axis (AU)')
plt.draw()
if aiq:
type = 'aiq'
else:
type = 'aei'
outfile = 'OSSOS_v{}_discoveries_{}_{}-{}_ilabel.pdf'.format(parameters.RELEASE_VERSION, type, xinner, xouter)
plt.savefig(outfile, transparent=True, bbox_inches='tight')
sys.stdout.write('{}\n'.format(outfile))
def full_aei(data_release, fov, icut=False, aiq=False):
fig, ax = plt.subplots(2, 1, sharex=True, figsize=(7, 8)) # a4 is 1 x sqrt(2), so use those proportions
fig.subplots_adjust(hspace=0.15)
ymin = -0.001
orbit_classes = ['cla', 'res', 'sca', 'det']#, 'xxx'] # fewer unclassified objects now
colmap = palettable.wesanderson.Zissou_5.mpl_colors
col = colmap[0:4] #['m'] + [colmap[4]] +
data = data_release[numpy.where(data_release['data_arc'] > 365.)]
data = data[numpy.where(data['sigma_a'] < 100.)]
print len(data)
if fov == 'medium':
imax = 55
emax = 0.86
xinner = 30
xouter = 160
xstep = 10
qmin = 30
qmax = 90
ms = 4
alpha = 0.7 # when plotting blue only # 0.7
annotation = False
plot_resonances = True
if parameters.RELEASE_VERSION == '9' and fov == 'classical':
imax = 45
emax = 0.53 #0.37
xinner = 30 #38
xouter = 60 #49
xstep = 5
ms = 5
alpha = 0.65
annotation = False
e45_annotation = False
# let's trim off anything with super-large error bars as well
data = data_release[numpy.array([name.__contains__("nt") is False for name in data_release['object']])]
data = data[numpy.array([n < .15 for n in data['e_a']])] # couple of big-uncertainty objects making things messy
data = data[numpy.array([n < .02 for n in data['e_e']])] # couple of big-uncertainty objects making things messy
plot_resonances = True
if fov == 'thousand_au':
imax = 40
emax = 1.0
xinner = 0
xouter = 850
xstep = 50
ms = 9
alpha = 0.85
annotation = False
plot_resonances = True
coldcol = col
hotcol = col[2]
hot_alpha = 0.25
grid_alpha = 0.2
ebarcolor = 'k' # error bar colour: 0.1 is greyscale
capsize = 1 # error bar cap width
fmt = '.'
alpha = alpha
ax = a_i(ax, data, fmt, alpha, ebarcolor, capsize, ms, grid_alpha, coldcol, 1, orbit_classes, annotation=annotation)
ax[0].set_ylim([ymin, imax])
if aiq:
ax = a_q(ax, data, fmt, alpha, ebarcolor, capsize, ms, grid_alpha, coldcol, '$i < 5^{\circ}$',
1, annotation=annotation)
ax[1].set_ylim([qmin, qmax])
else:
ax = a_e(ax, data, fmt, alpha, ebarcolor, capsize, ms, grid_alpha, coldcol, 'placeholder', 1, orbit_classes,
annotation=annotation)
# constant q line
e = [(1-(40./a)) for a in range(xinner+1, xouter, 3)]
ax[1].plot(range(xinner+1, xouter, 3), e, '-')
ax[1].set_ylim([ymin, emax])
plt.xlim([xinner, xouter])
ax[0].set_xticks(range(xinner, xouter, xstep))
ax[1].set_xticks(range(xinner, xouter, xstep))
plot_fanciness.remove_border(ax[0])
plot_fanciness.remove_border(ax[1])
if plot_resonances:
resonances(ax, imax, emax)
handles, labels = ax[1].get_legend_handles_labels()
# handles.append(mlines.Line2D([], [], marker='*', color=col[0], alpha=cold_alpha, linestyle=None))
labels = ['q < 40 au', 'q >= 40 au']
ax[1].legend(handles, labels, loc='lower right', numpoints=1, fontsize='small')
plt.xlabel('semimajor axis (AU)')
plt.draw()
if aiq:
type = 'aiq'
else:
type = 'aei'
outfile = 'mpcTNOs_{}_{}-{}au.pdf'.format(type, xinner, xouter)
plt.savefig(outfile, transparent=True, bbox_inches='tight')
sys.stdout.write('{}\n'.format(outfile))