def test_fnpickling_class(tmpdir):
"""
Tests the `fnpickle` and `fnupickle` functions' ability to pickle
and unpickle custom classes.
"""
fn = str(tmpdir.join('test2.pickle'))
obj1 = 'astring'
obj2 = ToBePickled(obj1)
fnpickle(obj2, fn)
res = fnunpickle(fn)
assert res == obj2
def test_fnpickling_class(tmpdir):
"""
Tests the `fnpickle` and `fnupickle` functions' ability to pickle
and unpickle custom classes.
"""
fn = str(tmpdir.join('test2.pickle'))
obj1 = 'astring'
obj2 = ToBePickled(obj1)
fnpickle(obj2, fn)
res = fnunpickle(fn)
assert res == obj2
def bkg_info(input_file):
"""
Obtain the sky backgound for each science image.
More in: http://photutils.readthedocs.io/en/latest/api/photutils.background.Background.html#photutils.background.Background
WARNING:
This routine only need to be run one time for the same set of images.
___
INPUT:
For obtain this parameters, use the input_info function.
data_path: string, path where are the images data.
save_path: string, path where will save all reduced images.
input_file: dict, with information describe in the YAML file.
"""
#set the original directory
original_path = os.getcwd()
save_path = input_file['save_path']
os.chdir(save_path)
planet = input_file['exoplanet']
tempo = time.time()
print 'Obtain background data for each image ... \n'
if not os.path.exists('background'): #if background does not exist, create!
os.makedirs('background')
images = sorted(glob.glob('AB'+planet+'*.fits'))
#if background exist, check if files bkg_data_image_name_.pik exist
#if not exist, then create, else: get out of here! XD
if os.path.exists('background') == True :
value = []
for i in images:
value.append(os.path.isfile('./background/'+'bkg_data_'+i+'_.pik'))
if (False in value) == True:
print 'Does not exist all files to all images in the sample.'
print 'Calculating ...'
print 'This will take some time... go drink some coffe'
print ' while you wait for the routine finish \n'
for i in images:
im = fits.getdata(i,header=False)
im = np.array(im,dtype='Float64')
bkg = Background(im,tuple(input_file['skysection'])) #estimating the background using a boxpixel
fnpickle(bkg,'./background/'+'bkg_data_'+i+'_.pik')
use.update_progress((float(images.index(i))+1.)/len(images))
else:
print 'The sky background files *.pik exist. \n'
print 'Sky backgound obtained.'
print 'Total time = ',abs(time.time()-tempo)/60.,' minutes'
os.chdir(original_path)
return
def test_fnpickling_protocol(tmpdir):
"""
Tests the `fnpickle` and `fnupickle` functions' ability to pickle
and unpickle pickle files from all protcols.
"""
import pickle
obj1 = 'astring'
obj2 = ToBePickled(obj1)
for p in range(pickle.HIGHEST_PROTOCOL + 1):
fn = str(tmpdir.join('testp{}.pickle'.format(p)))
fnpickle(obj2, fn, protocol=p)
res = fnunpickle(fn)
assert res == obj2
def test_fnpickling_protocol(tmpdir):
"""
Tests the `fnpickle` and `fnupickle` functions' ability to pickle
and unpickle pickle files from all protcols.
"""
import pickle
obj1 = 'astring'
obj2 = ToBePickled(obj1)
for p in range(pickle.HIGHEST_PROTOCOL + 1):
fn = str(tmpdir.join('testp{}.pickle'.format(p)))
fnpickle(obj2, fn, protocol=p)
res = fnunpickle(fn)
assert res == obj2
def test_fnpickling_simple(tmpdir):
"""
Tests the `fnpickle` and `fnupickle` functions' basic operation by
pickling and unpickling a string, using both a filename and a
file.
"""
fn = str(tmpdir.join('test1.pickle'))
obj1 = 'astring'
fnpickle(obj1, fn)
res = fnunpickle(fn, 0)
assert obj1 == res
# now try with a file-like object instead of a string
with open(fn, 'wb') as f:
fnpickle(obj1, f)
with open(fn, 'rb') as f:
res = fnunpickle(f)
assert obj1 == res
def test_fnpickling_many(tmpdir):
"""
Tests the `fnpickle` and `fnupickle` functions' ability to pickle
and unpickle multiple objects from a single file.
"""
fn = str(tmpdir.join('test3.pickle'))
# now try multiples
obj3 = 328.3432
obj4 = 'blahblahfoo'
fnpickle(obj3, fn)
fnpickle(obj4, fn, append=True)
res = fnunpickle(fn, number=-1)
assert len(res) == 2
assert res[0] == obj3
assert res[1] == obj4
fnpickle(obj4, fn, append=True)
res = fnunpickle(fn, number=2)
assert len(res) == 2
with pytest.raises(EOFError):
fnunpickle(fn, number=5)
def test_fnpickling_many(tmpdir):
"""
Tests the `fnpickle` and `fnupickle` functions' ability to pickle
and unpickle multiple objects from a single file.
"""
fn = str(tmpdir.join('test3.pickle'))
# now try multiples
obj3 = 328.3432
obj4 = 'blahblahfoo'
fnpickle(obj3, fn)
fnpickle(obj4, fn, append=True)
res = fnunpickle(fn, number=-1)
assert len(res) == 2
assert res[0] == obj3
assert res[1] == obj4
fnpickle(obj4, fn, append=True)
res = fnunpickle(fn, number=2)
assert len(res) == 2
with pytest.raises(EOFError):
fnunpickle(fn, number=5)
def test_fnpickling_simple(tmpdir):
"""
Tests the `fnpickle` and `fnupickle` functions' basic operation by
pickling and unpickling a string, using both a filename and a
file.
"""
fn = str(tmpdir.join('test1.pickle'))
obj1 = 'astring'
fnpickle(obj1, fn)
res = fnunpickle(fn, 0)
assert obj1 == res
# now try with a file-like object instead of a string
with open(fn, 'wb') as f:
fnpickle(obj1, f)
with open(fn, 'rb') as f:
res = fnunpickle(f)
assert obj1 == res
with catch_warnings(AstropyDeprecationWarning):
fnunpickle(fn, 0, True)
def sgr(overwrite=False, seed=42):
np.random.seed(seed)
lm10_cache = os.path.join(project_root, "data", "spitzer_targets",
"lm10_cache.pickle")
if os.path.exists(lm10_cache) and overwrite:
os.remove(lm10_cache)
if not os.path.exists(lm10_cache):
# select particle data from the LM10 simulation
lm10 = particle_table(N=0, expr="(Pcol>-1) & (Pcol<8) & "\
"(abs(Lmflag)==1) & (dist<100)")
fnpickle(np.array(lm10), lm10_cache)
else:
lm10 = Table(fnunpickle(lm10_cache))
# read in the Catalina RR Lyrae data
spatial_data = ascii.read(os.path.join(project_root,
"data/catalog/Catalina_all_RRLyr.txt"))
velocity_data = ascii.read(os.path.join(project_root,
"data/catalog/Catalina_vgsr_RRLyr.txt"))
catalina = join(spatial_data, velocity_data, join_type='outer', keys="ID")
catalina.rename_column("RAdeg", "ra")
catalina.rename_column("DEdeg", "dec")
catalina.rename_column("dh", "dist")
# add Sgr coordinates to the Catalina data
catalina = add_sgr_coordinates(catalina)
# add Galactocentric distance to the Catalina and LM10 data
cat_gc_dist = tbl_to_gc_dist(catalina)
lm10_gc_dist = tbl_to_gc_dist(lm10)
catalina.add_column(Column(cat_gc_dist, name="gc_dist"))
lm10.add_column(Column(lm10_gc_dist, name="gc_dist"))
# 1) Select stars < 20 kpc from the orbital plane of Sgr
sgr_catalina = catalina[np.fabs(catalina["Z_sgr"]) < 20.]
x,y,z = tbl_to_xyz(sgr_catalina)
# 2) Stars with D > 15 kpc from the Galactic center
sgr_catalina = sgr_catalina[sgr_catalina["gc_dist"] > 15]
sgr_catalina_rv = sgr_catalina[~sgr_catalina["Vgsr"].mask]
print("{0} CSS RRLs have radial velocities.".format(len(sgr_catalina_rv)))
# plot X-Z plane, data and lm10 particles
fig,axes = plt.subplots(1,3,figsize=(15,6), sharex=True, sharey=True)
x,y,z = tbl_to_xyz(catalina)
axes[0].set_title("All RRL", fontsize=20)
axes[0].plot(x, z, marker='.', alpha=0.2, linestyle='none')
axes[0].set_xlabel("$X_{gc}$ kpc")
axes[0].set_ylabel("$Z_{gc}$ kpc")
x,y,z = tbl_to_xyz(sgr_catalina)
axes[1].set_title(r"RRL $|Z-Z_{sgr}|$ $<$ $20$ kpc", fontsize=20)
axes[1].plot(x, z, marker='.', alpha=0.2, linestyle='none')
axes[2].plot(lm10["x"], lm10["z"], marker='.',
alpha=0.2, linestyle='none')
axes[2].set_title("LM10", fontsize=20)
axes[2].set_xlim(-60, 40)
axes[2].set_ylim(-60, 60)
fig.tight_layout()
fig.savefig(os.path.join(notes_path, "catalina_all.pdf"))
# plot Lambda-dist plane, data and lm10 particles
fig,ax = plt.subplots(1,1,figsize=(6,6),
subplot_kw=dict(projection="polar"))
ax.set_theta_direction(-1)
ax.plot(np.radians(lm10["Lambda"]), lm10["dist"],
marker='.', alpha=0.2, linestyle='none')
ax.plot(np.radians(sgr_catalina["Lambda"]), sgr_catalina["dist"],
marker='.', alpha=0.75, linestyle='none', ms=6)
fig.tight_layout()
fig.savefig(os.path.join(notes_path, "rrl_over_lm10.pdf"))
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Select on radial velocities
rv_selection_cache = os.path.join(project_root, "data",
"spitzer_targets", "rv.pickle")
if os.path.exists(rv_selection_cache) and overwrite:
os.remove(rv_selection_cache)
if not os.path.exists(rv_selection_cache):
lmflag_rv_idx = sgr_rv(sgr_catalina_rv, lm10, Nbins=40, sigma_cut=3.)
fnpickle(lmflag_rv_idx, rv_selection_cache)
else:
lmflag_rv_idx = fnunpickle(rv_selection_cache)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Select on distance
_selection_cache = os.path.join(project_root, "data",
"spitzer_targets", "dist.pickle")
if os.path.exists(_selection_cache) and overwrite:
os.remove(_selection_cache)
if not os.path.exists(_selection_cache):
lmflag_dist_idx = sgr_dist(sgr_catalina_rv, lm10,
Nbins=40, sigma_cut=3.)
fnpickle(lmflag_dist_idx, _selection_cache)
else:
lmflag_dist_idx = fnunpickle(_selection_cache)
################################################################
# Make X-Z plot
fig,ax = plt.subplots(1,1,figsize=(6,6))
x,y,z = tbl_to_xyz(lm10)
ax.plot(x, z, marker=',', alpha=0.2,
linestyle='none')
for lmflag in [1,-1]:
ix = lmflag_dist_idx[lmflag] & lmflag_rv_idx[lmflag]
x,y,z = tbl_to_xyz(sgr_catalina_rv[ix])
ax.plot(x, z, marker='.', alpha=0.75,
linestyle='none', ms=6, label="Lmflag={0}".format(lmflag))
ax.legend(loc='lower right',\
prop={'size':12})
ax.set_title("RV-selected CSS RRLs", fontsize=20)
ax.set_xlabel(r"$X_{\rm gc}$ kpc")
ax.set_ylabel(r"$Z_{\rm gc}$ kpc")
fig.tight_layout()
fig.savefig(os.path.join(notes_path, "selected_xz.pdf"))
##############################################################
# Finalize two samples:
# - 1 with 10 stars in the nearby trailing wrap
# - 1 without these stars
L = sgr_catalina_rv["Lambda"]
B = sgr_catalina_rv["Beta"]
D = sgr_catalina_rv["dist"]
X,Y = D*np.cos(np.radians(L)),D*np.sin(np.radians(L))
lead_ix = lmflag_dist_idx[1] & lmflag_rv_idx[1] & (np.fabs(B) < 40)
trail_ix = lmflag_dist_idx[-1] & lmflag_rv_idx[-1] & (np.fabs(B) < 40)
trail_ix[L < 180] &= B[L < 180] > -5
trail_ix = trail_ix & ( ((L > 230) & (L < 315)) | (L < 180) )
#trail_ix &= np.logical_not((L > 50) & (L < 100) & (sgr_catalina_rv["dist"] < 40))
# draw a box around some possible bifurcation members
bif_ix = (L > 200) & (L < 230) & (B < 2) & (B > -10) & lead_ix
# deselect stars possibly associated with the bifurcation
no_bif = (L > 180) & (L < 225) & (B < 20) & (B > 2)
no_bif |= (L > 225) & (L < 360) & (B < 17) & (B > 2)
no_bif |= ((L <= 180) & (B < 15) & (B > -8) & (L > 50))
lead_ix &= no_bif
print(sum(bif_ix), "bifurcation stars")
print(sum(lead_ix), "leading arm stars")
print(sum(trail_ix), "trailing arm stars ")
# select 3 clumps in the leading arm
Nclump = 11
ix_lead_clumps = np.zeros_like(lead_ix).astype(bool)
for clump in [(215,17), (245,25), (260,40)]:
l,d = clump
x,y = d*np.cos(np.radians(l)),d*np.sin(np.radians(l))
# find N stars closest to the clump
clump_dist = np.sqrt((X-x)**2 + (Y-y)**2)
xxx = np.sort(clump_dist[lead_ix])[Nclump]
this_ix = lead_ix & (clump_dist <= xxx)
print("lead",sum(this_ix))
ix_lead_clumps |= this_ix #select_only(this_ix, 10)
ix_lead_clumps &= lead_ix
# all southern leading
lll = ((L > 45) & (L < 180) & lead_ix)
print("southern leading", sum(lll))
ix_lead_clumps |= lll
ix_trail_clumps = np.zeros_like(trail_ix).astype(bool)
for clump in [(260,19)]:
l,d = clump
x,y = d*np.cos(np.radians(l)),d*np.sin(np.radians(l))
# find N stars closest to the clump
clump_dist = np.sqrt((X-x)**2 + (Y-y)**2)
xxx = np.sort(clump_dist[trail_ix])[10]
this_ix = trail_ix & (clump_dist <= xxx)
#bonus_trail_ix = trail_ix & (clump_dist > xxx)
ix_trail_clumps |= this_ix #select_only(this_ix, 10)
ix_trail_clumps &= trail_ix
# all trailing southern
ttt = (L > 45) & (L < 180) & (trail_ix)
print("southern trailing", sum(ttt))
ix_trail_clumps |= ttt
i1 = integration_time(sgr_catalina_rv[bif_ix]["dist"])
i2 = integration_time(sgr_catalina_rv[ix_lead_clumps]["dist"])
i3 = integration_time(sgr_catalina_rv[ix_trail_clumps]["dist"])
print("final num bifurcation", len(sgr_catalina_rv[bif_ix]))
print("final num leading arm", len(sgr_catalina_rv[ix_lead_clumps]))
print("final num trailing arm",len(sgr_catalina_rv[ix_trail_clumps]))
print("final total", sum(ix_trail_clumps) + sum(ix_lead_clumps) + sum(bif_ix))
print("final total", sum(ix_trail_clumps | ix_lead_clumps | bif_ix))
targets = sgr_catalina_rv[ix_trail_clumps | ix_lead_clumps | bif_ix]
bonus_targets = sgr_catalina_rv[(lead_ix | trail_ix) & \
~(ix_trail_clumps | ix_lead_clumps | bif_ix)]
# print()
# print("bifurcation", np.sum(i1))
# print("leading",np.sum(i2))
# print("trailing",np.sum(i3))
# print("Total:",np.sum(integration_time(targets["dist"])))
tot_time = 0.
for t in targets:
Vmag = t["<Vmag>"]
if Vmag < 16.6:
tot_time += 1.286
elif 16.6 < Vmag < 16.8:
tot_time += 1.31
elif 16.8 < Vmag < 17.2:
tot_time += 1.83
elif 17.2 < Vmag < 17.8:
tot_time += 2.52
elif Vmag > 17.8:
tot_time += 4.34
print ("total time: ", tot_time)
output_file = "sgr.txt"
output = targets.copy()
output.rename_column("Eta", "hjd0")
output.rename_column("<Vmag>", "VMagAvg")
output.keep_columns(["ID", "ra", "dec", "VMagAvg", "Period", "hjd0"])
ascii.write(output,
os.path.join(project_root, "data", "spitzer_targets", output_file), Writer=ascii.Basic)
output_file = "sgr_bonus.txt"
output = bonus_targets.copy()
output.rename_column("Eta", "hjd0")
output.rename_column("<Vmag>", "VMagAvg")
output.keep_columns(["ID", "ra", "dec", "VMagAvg", "Period", "hjd0"])
ascii.write(output,
os.path.join(project_root, "data", "spitzer_targets", output_file), Writer=ascii.Basic)
# ----------------------------------
# Make Lambda-D plot
fig,ax = plt.subplots(1,1,figsize=(6,6),
subplot_kw=dict(projection="polar"))
ax.set_theta_direction(-1)
ax.plot(np.radians(lm10["Lambda"]), lm10["dist"],
marker=',', alpha=0.2, linestyle='none')
d = sgr_catalina_rv[lead_ix]
ax.plot(np.radians(d["Lambda"]), d["dist"], marker='.',
alpha=0.75, linestyle='none', ms=8, c="#CA0020", label="leading")
d = sgr_catalina_rv[trail_ix]
ax.plot(np.radians(d["Lambda"]), d["dist"], marker='.',
alpha=0.75, linestyle='none', ms=8, c="#5E3C99", label="trailing")
ax.plot(np.radians(targets["Lambda"]), targets["dist"], marker='.',
alpha=0.7, linestyle='none', ms=10, c="#31A354", label="targets",
mfc='none', mec='k', mew=1.5)
ax.legend(loc="lower right")
plt.setp(ax.get_legend().get_texts(), fontsize='12')
ax.set_ylim(0,65)
fig.tight_layout()
fig.savefig(os.path.join(notes_path, "xz.pdf"))
# ---------------------
# Make Lambda-Beta plot
fig,ax = plt.subplots(1,1,figsize=(12,5))
ax.plot(lm10["Lambda"], lm10["Beta"], marker=',', alpha=0.2,
linestyle='none')
dd_bif = sgr_catalina_rv[bif_ix]
ax.plot(dd_bif["Lambda"], dd_bif["Beta"], marker='.', alpha=0.75,
linestyle='none', ms=6, c="#31A354", label="bifurcation")
d = sgr_catalina_rv[lead_ix]
ax.plot(d["Lambda"], d["Beta"], marker='.',
alpha=0.75, linestyle='none', ms=8, c="#CA0020", label="leading")
d = sgr_catalina_rv[trail_ix]
ax.plot(d["Lambda"], d["Beta"], marker='.',
alpha=0.75, linestyle='none', ms=8, c="#5E3C99", label="trailing")
ax.plot(targets["Lambda"], targets["Beta"], marker='.',
alpha=0.7, linestyle='none', ms=10, c="#31A354", label="targets",
mfc='none', mec='k', mew=1.5)
ax.legend(loc="lower right")
plt.setp(ax.get_legend().get_texts(), fontsize='12')
ax.set_title("RV-selected CSS RRLs", fontsize=20)
ax.set_xlabel(r"$\Lambda$ [deg]")
ax.set_ylabel(r"$B$ [deg]")
ax.set_xlim(360, 0)
ax.set_ylim(-45, 45)
fig.tight_layout()
fig.savefig(os.path.join(notes_path, "LB.pdf"))
def test_pickle(self):
particles = self.sgr.particles(n=25, expr="tub==0")
fnpickle(particles, os.path.join(plot_path, "test.pickle"))
p = particles.to_frame(heliocentric)
fnpickle(p, os.path.join(plot_path, "test2.pickle"))
def test_pickle(self):
particles = self.sgr.particles(n=25, expr="tub==0")
fnpickle(particles, os.path.join(plot_path, "test.pickle"))
p = particles.to_frame(heliocentric)
fnpickle(p, os.path.join(plot_path, "test2.pickle"))
for k,v in kwargs.items():
params[k] = v
# send request, use feedparser to parse the ATOM feed response
resp = requests.get(url=api_url, params=params)
feed = feedparser.parse(resp.content)
# for each entry, extract ID, abstract, title, authors
for entry in feed['entries']:
id = os.path.basename(entry.id) # http://arxiv.org/abs/1305.1919v1
abstract = entry.summary_detail['value']
parsed_entries[id] = dict(title=entry.title,
authors=entry.authors,
abstract=abstract)
return parsed_entries
num = 10000
max_results = 1000
parsed_entries = dict()
for start in range(0, num+max_results, max_results):
entries = retrieve_abstracts(search_query="abs:galaxy",
start=start,
max_results=max_results)
parsed_entries = dict(parsed_entries.items() + entries.items())
time.sleep(3)
fnpickle(parsed_entries, "arxiv.pickle")
def sgr(overwrite=False, seed=42):
np.random.seed(seed)
lm10_cache = os.path.join(project_root, "data", "spitzer_targets",
"lm10_cache.pickle")
if os.path.exists(lm10_cache) and overwrite:
os.remove(lm10_cache)
if not os.path.exists(lm10_cache):
# select particle data from the LM10 simulation
lm10 = particle_table(N=0, expr="(Pcol>-1) & (Pcol<8) & "\
"(abs(Lmflag)==1) & (dist<100)")
fnpickle(np.array(lm10), lm10_cache)
else:
lm10 = Table(fnunpickle(lm10_cache))
# read in the Catalina RR Lyrae data
spatial_data = ascii.read(
os.path.join(project_root, "data/catalog/Catalina_all_RRLyr.txt"))
velocity_data = ascii.read(
os.path.join(project_root, "data/catalog/Catalina_vgsr_RRLyr.txt"))
catalina = join(spatial_data, velocity_data, join_type='outer', keys="ID")
catalina.rename_column("RAdeg", "ra")
catalina.rename_column("DEdeg", "dec")
catalina.rename_column("dh", "dist")
# add Sgr coordinates to the Catalina data
catalina = add_sgr_coordinates(catalina)
# add Galactocentric distance to the Catalina and LM10 data
cat_gc_dist = tbl_to_gc_dist(catalina)
lm10_gc_dist = tbl_to_gc_dist(lm10)
catalina.add_column(Column(cat_gc_dist, name="gc_dist"))
lm10.add_column(Column(lm10_gc_dist, name="gc_dist"))
# 1) Select stars < 20 kpc from the orbital plane of Sgr
sgr_catalina = catalina[np.fabs(catalina["Z_sgr"]) < 20.]
x, y, z = tbl_to_xyz(sgr_catalina)
# 2) Stars with D > 15 kpc from the Galactic center
sgr_catalina = sgr_catalina[sgr_catalina["gc_dist"] > 15]
sgr_catalina_rv = sgr_catalina[~sgr_catalina["Vgsr"].mask]
print("{0} CSS RRLs have radial velocities.".format(len(sgr_catalina_rv)))
# plot X-Z plane, data and lm10 particles
fig, axes = plt.subplots(1, 3, figsize=(15, 6), sharex=True, sharey=True)
x, y, z = tbl_to_xyz(catalina)
axes[0].set_title("All RRL", fontsize=20)
axes[0].plot(x, z, marker='.', alpha=0.2, linestyle='none')
axes[0].set_xlabel("$X_{gc}$ kpc")
axes[0].set_ylabel("$Z_{gc}$ kpc")
x, y, z = tbl_to_xyz(sgr_catalina)
axes[1].set_title(r"RRL $|Z-Z_{sgr}|$ $<$ $20$ kpc", fontsize=20)
axes[1].plot(x, z, marker='.', alpha=0.2, linestyle='none')
axes[2].plot(lm10["x"], lm10["z"], marker='.', alpha=0.2, linestyle='none')
axes[2].set_title("LM10", fontsize=20)
axes[2].set_xlim(-60, 40)
axes[2].set_ylim(-60, 60)
fig.tight_layout()
fig.savefig(os.path.join(notes_path, "catalina_all.pdf"))
# plot Lambda-dist plane, data and lm10 particles
fig, ax = plt.subplots(1,
1,
figsize=(6, 6),
subplot_kw=dict(projection="polar"))
ax.set_theta_direction(-1)
ax.plot(np.radians(lm10["Lambda"]),
lm10["dist"],
marker='.',
alpha=0.2,
linestyle='none')
ax.plot(np.radians(sgr_catalina["Lambda"]),
sgr_catalina["dist"],
marker='.',
alpha=0.75,
linestyle='none',
ms=6)
fig.tight_layout()
fig.savefig(os.path.join(notes_path, "rrl_over_lm10.pdf"))
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Select on radial velocities
rv_selection_cache = os.path.join(project_root, "data", "spitzer_targets",
"rv.pickle")
if os.path.exists(rv_selection_cache) and overwrite:
os.remove(rv_selection_cache)
if not os.path.exists(rv_selection_cache):
lmflag_rv_idx = sgr_rv(sgr_catalina_rv, lm10, Nbins=40, sigma_cut=3.)
fnpickle(lmflag_rv_idx, rv_selection_cache)
else:
lmflag_rv_idx = fnunpickle(rv_selection_cache)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Select on distance
_selection_cache = os.path.join(project_root, "data", "spitzer_targets",
"dist.pickle")
if os.path.exists(_selection_cache) and overwrite:
os.remove(_selection_cache)
if not os.path.exists(_selection_cache):
lmflag_dist_idx = sgr_dist(sgr_catalina_rv,
lm10,
Nbins=40,
sigma_cut=3.)
fnpickle(lmflag_dist_idx, _selection_cache)
else:
lmflag_dist_idx = fnunpickle(_selection_cache)
################################################################
# Make X-Z plot
fig, ax = plt.subplots(1, 1, figsize=(6, 6))
x, y, z = tbl_to_xyz(lm10)
ax.plot(x, z, marker=',', alpha=0.2, linestyle='none')
for lmflag in [1, -1]:
ix = lmflag_dist_idx[lmflag] & lmflag_rv_idx[lmflag]
x, y, z = tbl_to_xyz(sgr_catalina_rv[ix])
ax.plot(x,
z,
marker='.',
alpha=0.75,
linestyle='none',
ms=6,
label="Lmflag={0}".format(lmflag))
ax.legend(loc='lower right',\
prop={'size':12})
ax.set_title("RV-selected CSS RRLs", fontsize=20)
ax.set_xlabel(r"$X_{\rm gc}$ kpc")
ax.set_ylabel(r"$Z_{\rm gc}$ kpc")
fig.tight_layout()
fig.savefig(os.path.join(notes_path, "selected_xz.pdf"))
##############################################################
# Finalize two samples:
# - 1 with 10 stars in the nearby trailing wrap
# - 1 without these stars
L = sgr_catalina_rv["Lambda"]
B = sgr_catalina_rv["Beta"]
D = sgr_catalina_rv["dist"]
X, Y = D * np.cos(np.radians(L)), D * np.sin(np.radians(L))
lead_ix = lmflag_dist_idx[1] & lmflag_rv_idx[1] & (np.fabs(B) < 40)
trail_ix = lmflag_dist_idx[-1] & lmflag_rv_idx[-1] & (np.fabs(B) < 40)
trail_ix[L < 180] &= B[L < 180] > -5
trail_ix = trail_ix & (((L > 230) & (L < 315)) | (L < 180))
#trail_ix &= np.logical_not((L > 50) & (L < 100) & (sgr_catalina_rv["dist"] < 40))
# draw a box around some possible bifurcation members
bif_ix = (L > 200) & (L < 230) & (B < 2) & (B > -10) & lead_ix
# deselect stars possibly associated with the bifurcation
no_bif = (L > 180) & (L < 225) & (B < 20) & (B > 2)
no_bif |= (L > 225) & (L < 360) & (B < 17) & (B > 2)
no_bif |= ((L <= 180) & (B < 15) & (B > -8) & (L > 50))
lead_ix &= no_bif
print(sum(bif_ix), "bifurcation stars")
print(sum(lead_ix), "leading arm stars")
print(sum(trail_ix), "trailing arm stars ")
# select 3 clumps in the leading arm
Nclump = 11
ix_lead_clumps = np.zeros_like(lead_ix).astype(bool)
for clump in [(215, 17), (245, 25), (260, 40)]:
l, d = clump
x, y = d * np.cos(np.radians(l)), d * np.sin(np.radians(l))
# find N stars closest to the clump
clump_dist = np.sqrt((X - x)**2 + (Y - y)**2)
xxx = np.sort(clump_dist[lead_ix])[Nclump]
this_ix = lead_ix & (clump_dist <= xxx)
print("lead", sum(this_ix))
ix_lead_clumps |= this_ix #select_only(this_ix, 10)
ix_lead_clumps &= lead_ix
# all southern leading
lll = ((L > 45) & (L < 180) & lead_ix)
print("southern leading", sum(lll))
ix_lead_clumps |= lll
ix_trail_clumps = np.zeros_like(trail_ix).astype(bool)
for clump in [(260, 19)]:
l, d = clump
x, y = d * np.cos(np.radians(l)), d * np.sin(np.radians(l))
# find N stars closest to the clump
clump_dist = np.sqrt((X - x)**2 + (Y - y)**2)
xxx = np.sort(clump_dist[trail_ix])[10]
this_ix = trail_ix & (clump_dist <= xxx)
#bonus_trail_ix = trail_ix & (clump_dist > xxx)
ix_trail_clumps |= this_ix #select_only(this_ix, 10)
ix_trail_clumps &= trail_ix
# all trailing southern
ttt = (L > 45) & (L < 180) & (trail_ix)
print("southern trailing", sum(ttt))
ix_trail_clumps |= ttt
i1 = integration_time(sgr_catalina_rv[bif_ix]["dist"])
i2 = integration_time(sgr_catalina_rv[ix_lead_clumps]["dist"])
i3 = integration_time(sgr_catalina_rv[ix_trail_clumps]["dist"])
print("final num bifurcation", len(sgr_catalina_rv[bif_ix]))
print("final num leading arm", len(sgr_catalina_rv[ix_lead_clumps]))
print("final num trailing arm", len(sgr_catalina_rv[ix_trail_clumps]))
print("final total",
sum(ix_trail_clumps) + sum(ix_lead_clumps) + sum(bif_ix))
print("final total", sum(ix_trail_clumps | ix_lead_clumps | bif_ix))
targets = sgr_catalina_rv[ix_trail_clumps | ix_lead_clumps | bif_ix]
bonus_targets = sgr_catalina_rv[(lead_ix | trail_ix) & \
~(ix_trail_clumps | ix_lead_clumps | bif_ix)]
# print()
# print("bifurcation", np.sum(i1))
# print("leading",np.sum(i2))
# print("trailing",np.sum(i3))
# print("Total:",np.sum(integration_time(targets["dist"])))
tot_time = 0.
for t in targets:
Vmag = t["<Vmag>"]
if Vmag < 16.6:
tot_time += 1.286
elif 16.6 < Vmag < 16.8:
tot_time += 1.31
elif 16.8 < Vmag < 17.2:
tot_time += 1.83
elif 17.2 < Vmag < 17.8:
tot_time += 2.52
elif Vmag > 17.8:
tot_time += 4.34
print("total time: ", tot_time)
output_file = "sgr.txt"
output = targets.copy()
output.rename_column("Eta", "hjd0")
output.rename_column("<Vmag>", "VMagAvg")
output.keep_columns(["ID", "ra", "dec", "VMagAvg", "Period", "hjd0"])
ascii.write(output,
os.path.join(project_root, "data", "spitzer_targets",
output_file),
Writer=ascii.Basic)
output_file = "sgr_bonus.txt"
output = bonus_targets.copy()
output.rename_column("Eta", "hjd0")
output.rename_column("<Vmag>", "VMagAvg")
output.keep_columns(["ID", "ra", "dec", "VMagAvg", "Period", "hjd0"])
ascii.write(output,
os.path.join(project_root, "data", "spitzer_targets",
output_file),
Writer=ascii.Basic)
# ----------------------------------
# Make Lambda-D plot
fig, ax = plt.subplots(1,
1,
figsize=(6, 6),
subplot_kw=dict(projection="polar"))
ax.set_theta_direction(-1)
ax.plot(np.radians(lm10["Lambda"]),
lm10["dist"],
marker=',',
alpha=0.2,
linestyle='none')
d = sgr_catalina_rv[lead_ix]
ax.plot(np.radians(d["Lambda"]),
d["dist"],
marker='.',
alpha=0.75,
linestyle='none',
ms=8,
c="#CA0020",
label="leading")
d = sgr_catalina_rv[trail_ix]
ax.plot(np.radians(d["Lambda"]),
d["dist"],
marker='.',
alpha=0.75,
linestyle='none',
ms=8,
c="#5E3C99",
label="trailing")
ax.plot(np.radians(targets["Lambda"]),
targets["dist"],
marker='.',
alpha=0.7,
linestyle='none',
ms=10,
c="#31A354",
label="targets",
mfc='none',
mec='k',
mew=1.5)
ax.legend(loc="lower right")
plt.setp(ax.get_legend().get_texts(), fontsize='12')
ax.set_ylim(0, 65)
fig.tight_layout()
fig.savefig(os.path.join(notes_path, "xz.pdf"))
# ---------------------
# Make Lambda-Beta plot
fig, ax = plt.subplots(1, 1, figsize=(12, 5))
ax.plot(lm10["Lambda"],
lm10["Beta"],
marker=',',
alpha=0.2,
linestyle='none')
dd_bif = sgr_catalina_rv[bif_ix]
ax.plot(dd_bif["Lambda"],
dd_bif["Beta"],
marker='.',
alpha=0.75,
linestyle='none',
ms=6,
c="#31A354",
label="bifurcation")
d = sgr_catalina_rv[lead_ix]
ax.plot(d["Lambda"],
d["Beta"],
marker='.',
alpha=0.75,
linestyle='none',
ms=8,
c="#CA0020",
label="leading")
d = sgr_catalina_rv[trail_ix]
ax.plot(d["Lambda"],
d["Beta"],
marker='.',
alpha=0.75,
linestyle='none',
ms=8,
c="#5E3C99",
label="trailing")
ax.plot(targets["Lambda"],
targets["Beta"],
marker='.',
alpha=0.7,
linestyle='none',
ms=10,
c="#31A354",
label="targets",
mfc='none',
mec='k',
mew=1.5)
ax.legend(loc="lower right")
plt.setp(ax.get_legend().get_texts(), fontsize='12')
ax.set_title("RV-selected CSS RRLs", fontsize=20)
ax.set_xlabel(r"$\Lambda$ [deg]")
ax.set_ylabel(r"$B$ [deg]")
ax.set_xlim(360, 0)
ax.set_ylim(-45, 45)
fig.tight_layout()
fig.savefig(os.path.join(notes_path, "LB.pdf"))
def save(fits_file):
save_directory = "fitted_files/"
fnpickle(fits_file, save_directory + fits_file.get_file_name() + ".pkl")
t0off = m['model'].get('t0') - m['model'].mintime() # t0 offset from
# mintime
t0min = dtmin - 30. + t0off
t0max = dtmax - 30. + t0off
m['bounds']['t0'] = (t0min, t0max)
# Set mwebv of model.
m['model'].set(mwebv=mwebv)
print name, m['type']
res = _nest_lc(data, m['model'], m['param_names'],
bounds=m['bounds'], tied=m['tied'], nobj=50,
verbose=True)
# Set parameters to those of the model (presumably these are
# high-likelihood values), but we're mainly doing this to record
# the non-varied model parameters (such as mwebv)
res.param_dict = dict(zip(m['model'].param_names,
m['model'].parameters))
# record prior and type of the model.
res.mprior = m['mprior']
res.type = m['type']
allresults[name] = res
fnpickle(allresults, pikname)
f.close()