def pdf_3d(epos):
#plot in 3D
# doesn't support log scale on axes, need a workaround
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.set_title('Synthetic Model Populations')
ax.set_xlabel('Orbital Period [days]')
ax.set_zlabel('Planets/bin')
if epos.RV: ax.set_ylabel(r'Planet Mass [M$_\bigoplus$]')
else: ax.set_ylabel(r'Planet Radius [R$_\bigoplus$]')
ax.set_xlim(np.log10(epos.xtrim))
ax.set_ylim(np.log10(epos.ytrim))
xplane, yplane= np.log10(epos.xtrim[0]), np.log10(epos.ytrim[-1])
ax.set_zlim(0,2000)
#ax.set_xscale('log')
#ax.set_yscale('log')
sim=epos.synthetic_survey
# PDF, individual contributions
# if epos.populationtype is 'model':
# for k, sg in enumerate(epos.groups):
# subset= sim['i sg']==k
# P= sim['P'][subset]
# R= sim['Y'][subset]
# ax.plot(np.log10(P),np.log10(R), zs=0,zdir='z',
# ls='',marker='.',mew=0,ms=5.0,color=clrs[k % 4])
#
# xgrid= np.logspace(*np.log10(epos.xtrim))
# pdf= regression.sliding_window_log(P, None, xgrid) #, width=2. )
# ax.plot(np.log10(xgrid), pdf, zs=yplane,zdir='y',
# ls='-', marker='', color=clrs[k % 4],
# label='{} x{:.3f}'.format(sg['name'], sg['weight']))
#
# ygrid= np.logspace(*np.log10(epos.ytrim))
# pdf= regression.sliding_window_log(R, None, ygrid) #, width=2. )
# ax.plot(np.log10(ygrid), pdf, zs=xplane, zdir='x',
# ls='-', marker='', color=clrs[k % 4])
# else:
# top left panel (P,R)
ax.plot(np.log10(sim['P']), np.log10(sim['Y']),zs=0,zdir='z',
ls='', marker='.', mew=0, ms=5.0, color='k')
# PDF, all combined, 2 panels
xgrid= np.logspace(*np.log10(epos.xtrim))
pdf= regression.sliding_window_log(sim['P'], None, xgrid) #, width=2. )
weight= np.sum([sg['weight'] for sg in epos.groups]) if epos.populationtype is 'model' else epos.fitpars.get('pps',Init=True)
ax.plot(np.log10(xgrid), pdf, zs=yplane,zdir='y',
ls='-', marker='', color='k',label='combined x {:.3f}'.format(weight))
ygrid= np.logspace(*np.log10(epos.ytrim))
pdf= regression.sliding_window_log(sim['Y'], None, ygrid) #, width=2. )
ax.plot(np.log10(ygrid), pdf, zs=xplane,zdir='x', ls='-', marker='', color='k')
# observations
if epos.Observation and epos.DetectionEfficiency:
ax.plot(np.log10(epos.obs_xvar), np.log10(epos.obs_yvar), zs=-10,zdir='z',
ls='', marker='.', mew=0, ms=5.0, color='0.7',zorder=0)
pdf= regression.sliding_window_log(epos.obs_xvar, None, xgrid)
ax.plot(np.log10(xgrid), pdf, zs=yplane,zdir='y', ls=':', marker='', color='k', label='Kepler')
pdf= regression.sliding_window_log(epos.obs_yvar, None, ygrid)
ax.plot(np.log10(ygrid), pdf, zs=xplane,zdir='x', ls=':', marker='', color='k')
# xmax= ax.get_ylim()[-1]
# ymax= ax.get_zlim()[-1]
# ax.errorbar(xmax/1.5, ymax*0.9, yerr=(xmax/1.5*(1.-np.sqrt(1./2.)) ),
# zs=0,zdir='y', color='k')
#
# xmax= ax.get_xlim()[-1]
# ymax= ax.get_zlim()[-1]
# ax.errorbar(xmax/1.5, ymax*0.9, xerr=(xmax/1.5*(1.-np.sqrt(1./2.)) ),
# zs=0,zdir='x', color='k')
#ax2.tick_params(labelbottom='on') # does not re-enable axis
''' Legend instead of 4th plot'''
#ax.legend()
ax.legend(shadow=False, prop={'size':14},bbox_to_anchor=(0.7,0.0))
ax.view_init(elev=20., azim=-35)
helpers.save(plt, epos.plotdir+'output/pdf.3D.diag')
def main():
link = session.query(Link)\
.filter(Link.visited_at == None)\
.order_by(asc(Link.id))\
.first()
if link is None:
print('Nothing to visit right now')
try:
print('Trying to visit: {}'.format(link))
r = requests.get(link, timeout=5)
soup = BeautifulSoup(r.text, 'html.parser')
domain_on_redirect = get_domain(r.url)
if not domain_exists(session, domain_on_redirect):
print('Found new domain: {}'.format(domain_on_redirect))
save(session, Domain(url=domain_on_redirect))
print('Saved that new domain.')
for site_url in set([o.get('href') for o in soup.find_all('a')]):
if site_url is None:
continue
url = site_url
if not is_url(site_url):
url = urljoin(get_domain(link.url),
site_url)
print('Found: {}'.format(url))
l = session.query(Link)\
.filter(Link.url == url).first()
if l is not None:
continue
l = Link(url=url)
domain = get_domain(l.url)
domain_in_db = session.query(Domain)\
.filter(Domain.url == domain)\
.first()
if domain_in_db is None:
print('Found new domain: {}'.format(domain))
domain_in_db = Domain(url=domain)
save(session, domain_in_db)
l.domain = domain_in_db
save(session, l)
except Exception as e:
print('Something went wrong')
print(e)
finally:
link.visited_at = datetime.now()
save(session, link)
dmsh.Circle([0.0, +0.5], 1.0)])
pts = np.array([[0.0, -5.0], [0.0, 4.1]])
pts = geo.boundary_step(pts.T).T
ref = np.array([[0.0, -0.5], [0.0, 0.5]])
assert np.all(np.abs(pts - ref) < 1.0e-10)
pts = np.array([[0.0, -0.1], [0.0, 0.1]])
pts = geo.boundary_step(pts.T).T
ref = np.array([[0.0, -0.5], [0.0, 0.5]])
assert np.all(np.abs(pts - ref) < 1.0e-10)
def test_boundary_step2():
geo = dmsh.Intersection(
[dmsh.Circle([0.0, -0.5], 1.0),
dmsh.Circle([0.0, +0.5], 1.0)])
np.random.seed(0)
pts = np.random.uniform(-1.0, 1.0, (2, 100))
pts = geo.boundary_step(pts)
# geo.plot()
# import matplotlib.pyplot as plt
# plt.plot(pts[0], pts[1], "xk")
# plt.show()
assert np.all(np.abs(geo.dist(pts)) < 1.0e-7)
if __name__ == "__main__":
X, cells = test_intersection(show=True)
save("intersection.png", X, cells)
# test_boundary_step2()
def periodinner(epos, MC=False, N=False, Input=False, MCMC=False):
# plot multiplicity
f, ax = plt.subplots()
ax.set_title('Period of the Innermost Planet')
#ax.set_xlabel('Orbital Period [days]')
ax.set_ylabel('Planet Counts')
#ax.set_xscale('log')
#ax.set_xlim(epos.xtrim)
helpers.set_axis_distance(ax, epos, Trim=True)
#bins= np.geomspace(*epos.xtrim)
bins = epos.MC_xvar
#pdf= regression.sliding_window_log(P, None, xgrid) #, width=2. )
#ax3.plot(xgrid, pdf, ls='-', marker='', color=clrs[k % 4],label='{} x{:.3f}'.format(sg['name'], sg['weight']))
# MC data
if MC or MCMC:
ss = epos.synthetic_survey
if MCMC:
for ss in epos.ss_sample:
ax.hist(ss['multi']['Pinner'],
bins=bins,
color='b',
alpha=0.1,
histtype='step')
else:
ax.hist(ss['multi']['Pinner'],
bins=bins,
color='C0',
histtype='stepfilled',
label='Simulated') #epos.name)
# Solar system analologs (from dr25_solarsystem.py)
# Pcut=45
# Pcut=130
# print 'P_in > {} days:'.format(Pcut)
# print ' obs: {}'.format((epos.obs_zoom['multi']['Pinner']>Pcut).sum())
# print ' sim: {}'.format((ss['multi']['Pinner']>Pcut).sum())
# print ''
#if False:
if hasattr(epos, 'ss_extra'):
# advance color cycle
# ax._get_lines.get_next_color()
ax.plot([], [])
ax.plot([], [])
# plot extra epos runs
for ss in epos.ss_extra:
ax.hist(ss['multi']['Pinner'],
bins=bins,
histtype='step',
label=ss['name'])
if N:
# Innermost is nth planet
#ax.hist(ss['multi']['PN'][0], bins=bins,
# ec='k', histtype='step', label='actual inner planet')
# Not actual inner planet
ax.hist(np.concatenate(ss['multi']['PN'][1:]),
bins=bins,
color='r',
histtype='stepfilled',
label='Not Inner Planet')
else:
# observed all
ax.hist(epos.multi['Pinner'],
bins=bins,
color='0.7',
label='Kepler all')
''' Initial distribution or zoomed observations '''
if Input and epos.Parametric:
Pingrid = np.geomspace(*epos.xtrim)
_, _, pdf0_X, _ = draw_PR(epos,
Init=True,
ybin=epos.yzoom,
xgrid=Pingrid)
norm = 0.95 * ax.get_ylim()[1] / max(pdf0_X)
ax.plot(Pingrid,
pdf0_X * norm,
marker='',
ls='-',
color='r',
label='Intrinsic')
elif epos.Zoom and not N:
ax.hist(epos.obs_zoom['multi']['Pinner'],
bins=bins,
ec='C1',
histtype='step',
label='Kepler')
prefix = 'output' if MC else 'survey'
suffix = '.index' if N else ''
suffix = '.input' if Input else suffix
if MCMC: prefix = 'mcmc'
if MC:
ax.legend(loc='upper right',
shadow=False,
prop={'size': 14},
numpoints=1)
helpers.save(plt, '{}{}/innerperiod{}'.format(epos.plotdir, prefix,
suffix))
def polar(epos):
'''
Plot planet populations as a half circle (not quite working)
'''
# plot multiplicity
f, axlist = plt.subplots(2,
2,
subplot_kw=dict(projection='polar'),
figsize=(10, 8))
pop = epos.population
# ticks not showing on log plot
Bugged = True
for ax, key in zip([axlist[0, 0], axlist[0, 1], axlist[1, 0]],
['system', 'single', 'multi']):
ax.set_title(key)
ax.set_xlabel('fraction')
ax.set_ylabel('P [days]')
ax.set_xlim(-0.05, 1.05)
ax.set_xticks(np.pi * np.linspace(0, 1, 5))
try:
ax.set_thetamin(0)
ax.set_thetamax(180)
except AttributeError:
print 'Update pyplot'
raise
if Bugged:
ax.set_ylim(-1, 3)
#ax.set_yticks([0,1,2,3])
ax.set_yticks([1])
ax.set_yticklabels(['10'])
ax.plot(pop[key]['order'] * np.pi,
np.log10(pop[key]['P']),
ls='',
marker='.',
mew=0,
ms=3,
color='k')
else:
ax.set_ylim(0.1, 1000) # 7.
ax.set_yscale('log')
ax.set_yticks([1, 10, 100, 1000])
#ax.set_yticks([10])
#ax.set_rlim(0.1,1000)
#ax.set_rscale('log')
#ax.set_rgrids([1, 10, 100, 1000])
#ax.set_rticks([1, 10, 100, 1000])
ax.plot(pop[key]['order'] * np.pi,
pop[key]['P'],
ls='',
marker='.',
mew=0,
ms=3,
color='k')
# nope
#ax.set_yscale('log')
#ax.set_yticks([1, 10, 100, 1000])
ax4 = axlist[1, 1]
ax4.set_title('all')
ax4.set_xlabel('fraction')
ax4.set_ylabel('P [days]')
ax4.set_xlim(-0.05, 1.05)
ax4.set_ylim(0.1, 1000) # 7.
ax4.set_yscale('log')
try:
ax4.set_thetamin(0)
ax4.set_thetamax(180)
except AttributeError:
print 'Update pyplot'
raise
ax4.plot(pop['order'] * np.pi,
pop['P'],
ls='',
marker='.',
mew=0,
ms=3,
color='gray')
helpers.save(plt, '{}/population/polar_test'.format(epos.plotdir))
file_names = ['cowper.txt', 'derby.txt', 'butler.txt', 'shakespeare.txt']
buffer_size = 50000
batch_size = 64
take_size = 5000
embedding_size = 64
lstm_size = 64
labeled_datasets = makeLabledDatasets(file_names)
labeled_datasets = processData(labeled_datasets, buffer_size)
vocab_size, vocab_set = tokenizeDataset(labeled_datasets)
encoded_data = encodeDataset(labeled_datasets, vocab_set)
opts = (take_size, buffer_size, batch_size)
train_data, test_data = generateTrainingSets(encoded_data, opts)
model = Sequential()
model.add(Embedding(vocab_size, embedding_size))
model.add(Bidirectional(LSTM(lstm_size)))
model.add(Dense(64, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(4, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_data, epochs=10, validation_data=test_data)
save(model)
saveVocab(vocab_set)
plotHistory(model.history.history)
def periodradius(epos, Nth=False, MC=True):
f, (ax, axR, axP) = helpers.make_panels(plt)
if MC:
sim = epos.synthetic_survey
ID = sim['ID']
P = sim['P']
Y = sim['Y']
outdir = 'output'
title = 'Simulated Detections'
else:
ID = epos.obs_starID
P = epos.obs_xvar
Y = epos.obs_yvar
outdir = 'survey'
title = r'Planet Candidates (score$\geq$0.9)'
''' plot R(P), main panel'''
ax.set_title(title)
helpers.set_axes(ax, epos, Trim=True)
''' Period side panel '''
helpers.set_axis_distance(axP, epos, Trim=True)
#axP.set_yscale('log')
#axP.set_ylim([2e-3,5])
#axP.set_yticks([0.01,0.1,1])
#axP.set_yticklabels(['1%','10%','100%'])
axP.yaxis.tick_right()
axP.yaxis.set_ticks_position('both')
#axP.tick_params(axis='y', which='minor',left='off',right='off')
#axP.hist(sim['P'], bins=epos.MC_xvar, color='0.7')
''' Radius side panel'''
helpers.set_axis_size(axR, epos, Trim=True, In=epos.MassRadius)
#axR.set_xscale('log')
#axR.set_xlim([2e-3,5])
#axR.set_xticks([1,10,100,1000])
#axR.set_xticklabels(['1','10','100','1000'], rotation=70)
for tick in axR.get_xticklabels():
tick.set_rotation(70)
#axR.tick_params(axis='x', which='minor',top='off',bottom='off')
#axP.tick_params(axis='y', which='minor',left='off',right='off')
''' which multiplanets to color '''
# single, multi= EPOS.multi.indices(sim['ID'])
# for k, (label, subset) in enumerate(zip(['single','multi'],[single, multi])):
# ax.plot(sim['P'][subset], sim['Y'][subset], ls='', marker='.', mew=0, ms=5.0, \
# label=label)
if Nth:
single, multi, ksys, multis = EPOS.multi.nth_planet(ID, P)
suffix = '.nth'
label_single = 'single'
else:
single, multi, ksys, multis = EPOS.multi.indices(ID)
suffix = ''
label_single = '1'
ax.plot(P[single], Y[single], ls='', marker='.', \
color='0.7', label=label_single)
''' Multiplanets with colors'''
Stacked = True
plist = []
ylist = []
colors = []
CDF = False
#stacked histogram
for k, subset in zip(ksys, multis):
ht, = ax.plot(P[subset], Y[subset], ls='', marker='.', \
label=k)
if not CDF:
if Stacked:
plist.insert(0, P[subset])
ylist.insert(0, Y[subset])
colors.insert(0, ht.get_color())
# pdf
else:
axP.hist(P[subset],
bins=epos.MC_xvar,
color=ht.get_color(),
histtype='step')
if k == ksys[-1]:
axP.hist(P[single],
bins=epos.MC_xvar,
color='0.7',
histtype='step')
else:
# cumulative
Plist = np.sort(P[subset])
axP.step(Plist, np.arange(Plist.size, dtype=float) / Plist.size)
if Stacked:
plist.append(P[single])
ylist.append(Y[single])
colors.append('0.7')
axP.hist(plist, bins=epos.MC_xvar, color=colors, histtype='barstacked')
axR.hist(ylist,
bins=epos.MC_yvar,
orientation='horizontal',
color=colors,
histtype='barstacked')
else:
#axR.hist(Y,orientation='horizontal', bins=epos.MC_yvar, color='0.7')
axR.hist(Y,
orientation='horizontal',
bins=epos.MC_yvar,
color='k',
histtype='step')
axR.hist(Y[single],
orientation='horizontal',
bins=epos.MC_yvar,
color='0.7')
#ax.legend(loc='lower left', shadow=False, prop={'size':14}, numpoints=1)
ax.legend(bbox_to_anchor=(1.0, 1.0),
markerscale=2,
frameon=True,
borderpad=0.2,
handlelength=1,
handletextpad=0.2)
helpers.save(plt, '{}{}/PR.multi{}'.format(epos.plotdir, outdir, suffix))
student_data = []
for i, episode in enumerate(expert_data):
seed = episode['seed']
teacher_states = episode['states']
env.seed(seed)
states, actions, rewards = run_episode(env,
sess,
state_ph,
p_action,
sleep=0.01)
print("#{index}\tseed: {seed}\trewards: {rewards}".format(
index=i, seed=seed, rewards=len(rewards)))
student_data.append(
dict(seed=seed, states=states, actions=actions, rewards=rewards))
helpers.save('./imitation-data/cartpole_vpc_student.tau', student_data)
print('cartpole_vpc_student.tau data is saved.')
### Results:
#
# The performance quickly converges, giving very good behavior cloning.
# This is expected because the domain is very small and there are only 8 parameters.
#
# 0. 46 1. 42 2. 56 3. 73 4. 49
# 0. 132 1. 151 2. 175 3. 199 4. 140
# 0. 1277 1. 471 2. 176 3. 691 4. 382
# 0. 211 1. 2389 2. 3074 3. 88 4. 87
# 0. 4737 1. 1324 2. 4820 3. 4999 4. 227
# 0. 4999 1. 4999 2. 4999 3. 4999 4. 4999
# 0. 4999 1. 1176 2. 4999 3. 4999 4. 4999
# 0. 4936 1. 4999 2. 4999 3. 4999 4. 4999
import numpy
import pytest
import dmsh
from helpers import assert_norm_equality, save
@pytest.mark.parametrize(
"radius,ref_norms",
[(0.1, [327.95194, 14.263721, 1.0]), (0.4, [18.899253166, 3.70111746, 1.0])],
)
def test_circle(radius, ref_norms, show=False):
geo = dmsh.Circle([0.0, 0.0], 1.0)
X, cells = dmsh.generate(geo, radius, show=show)
# make sure the origin is part of the mesh
assert numpy.sum(numpy.einsum("ij,ij->i", X, X) < 1.0e-10) == 1
assert_norm_equality(X.flatten(), ref_norms, 1.0e-5)
return X, cells
if __name__ == "__main__":
X, cells = test_circle(0.1, [327.95194, 14.263721, 1.0], show=False)
save("circle.png", X, cells)
def completeness(epos, PlotBox=False, Transit=False, Vetting=True):
assert epos.DetectionEfficiency
f, (ax, axb) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [20, 1]})
f.subplots_adjust(wspace=0)
#f.set_size_inches(7, 5)
ax.set_title('Detection Efficiency' if Transit else 'Survey Completeness')
helpers.set_axes(ax, epos, Trim=False, Eff=True)
if Transit:
toplot = epos.eff_2D
if Vetting and hasattr(epos, 'vetting'): toplot *= epos.vetting
else:
toplot = epos.completeness if Vetting else epos.completeness_novet
with np.errstate(divide='ignore'):
log_completeness = np.log10(toplot)
''' color map and ticks'''
cmap = 'YlOrBr' if Transit else 'PuRd'
#cmap = 'rainbow' if Transit else 'PuRd'
vmin, vmax = -4, 0
if Transit: vmin = -3
ticks = np.linspace(vmin, vmax, (vmax - vmin) + 1)
levels = np.linspace(vmin, vmax, 256)
cs = ax.contourf(epos.eff_xvar,
epos.eff_yvar,
log_completeness.T,
cmap=cmap,
levels=levels,
vmin=vmin,
vmax=vmax)
cbar = f.colorbar(cs, cax=axb, ticks=ticks)
axb.set_yticklabels(100 * 10.**ticks)
axb.tick_params(axis='y', direction='out')
axb.set_title('%')
''' Plot the zoom box or a few black contours'''
if PlotBox:
fname = '.box'
assert epos.Range
ax.add_patch(
patches.Rectangle((epos.xtrim[0], epos.ytrim[0]),
epos.xtrim[1] - epos.xtrim[0],
epos.ytrim[1] - epos.ytrim[0],
fill=False,
zorder=1,
ls='--'))
if epos.Zoom:
ax.add_patch(
patches.Rectangle((epos.xzoom[0], epos.yzoom[0]),
epos.xzoom[1] - epos.xzoom[0],
epos.yzoom[1] - epos.yzoom[0],
fill=False,
zorder=1))
else:
if hasattr(epos, 'xtrim'):
ax.set_xlim(*epos.xtrim)
ax.set_ylim(*epos.ytrim)
if epos.RV:
levels = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
labels = ['10', '20', '30', '40', '50', '60', '70', '80', '90']
cs= ax.contour(epos.eff_xvar, epos.eff_yvar, toplot.T,\
levels= levels, colors = 'k', linewidths=2.)
else:
levels = [1e-3, 0.01, 0.1, 0.5, 0.9] if Transit else 10.**ticks
cs = ax.contour(epos.eff_xvar,
epos.eff_yvar,
toplot.T,
colors='k',
levels=levels)
fmt_percent = lambda x: '{:g} %'.format(100. * x)
plt.clabel(cs, cs.levels, inline=True, fmt=fmt_percent)
fname = ''
if not Vetting: fname += '.novet'
helpers.save(plt, epos.plotdir+'survey/'+('efficiency' if Transit else 'completeness')+ \
fname)
def observed(epos, PlotBox=True, PlotScore=False):
assert epos.Observation
if PlotScore:
f, (ax, axb) = plt.subplots(1,
2,
gridspec_kw={'width_ratios': [20, 1]})
f.subplots_adjust(wspace=0)
else:
f, ax = plt.subplots()
ax.set_title('Observed Population')
helpers.set_axes(ax, epos, Trim=epos.Range)
if PlotScore:
''' color scale? '''
cmap = 'plasma' # viridis, plasma, inferno, magma, spring, cool
vmin, vmax = 0, 1
#ticks=np.linspace(vmin, vmax, (vmax-vmin)+1)
clrs, norm = helpers.color_array(epos.obs_score,
vmin=vmin,
vmax=vmax,
cmap=cmap)
ax.scatter(epos.obs_xvar, epos.obs_yvar, color=clrs, s=3)
# colorbar?
cb1 = clrbar.ColorbarBase(axb,
cmap=cmap,
norm=norm,
orientation='vertical') # horizontal
axb.tick_params(axis='y', direction='out')
fname = '.score'
else:
ax.plot(epos.obs_xvar,
epos.obs_yvar,
ls='',
marker='.',
mew=0,
ms=5.0,
color='k')
fname = ''
# add multis?
if PlotBox:
fname = '.box'
assert epos.Range
ax.add_patch(
patches.Rectangle((epos.xtrim[0], epos.ytrim[0]),
epos.xtrim[1] - epos.xtrim[0],
epos.ytrim[1] - epos.ytrim[0],
fill=False,
zorder=1,
ls='--'))
if epos.Zoom:
ax.add_patch(
patches.Rectangle((epos.xzoom[0], epos.yzoom[0]),
epos.xzoom[1] - epos.xzoom[0],
epos.yzoom[1] - epos.yzoom[0],
fill=False,
zorder=1))
helpers.save(plt, epos.plotdir + 'survey/planets' + fname)
def panels(epos, MCMC=False):
f, (ax, axR, axP)= helpers.make_panels(plt)
sim=epos.synthetic_survey
clr_bf= 'g'
''' plot R(P), main panel'''
ax.set_title('Simulated Detections')
helpers.set_axes(ax, epos, Trim=True)
if epos.MonteCarlo:
ax.plot(sim['P'], sim['Y'], ls='', marker='.', color=clr_bf if MCMC else 'C0')
else:
levels= np.linspace(0,np.max(sim['pdf']))
ax.contourf(epos.MC_xvar, epos.MC_yvar, sim['pdf'].T, cmap='Blues', levels=levels)
''' Period side panel '''
helpers.set_axis_distance(axP, epos, Trim=True)
#axP.set_yscale('log')
#axP.set_ylim([2e-3,5])
#axP.set_yticks([0.01,0.1,1])
#axP.set_yticklabels(['1%','10%','100%'])
axP.yaxis.tick_right()
axP.yaxis.set_ticks_position('both')
#axP.tick_params(axis='y', which='minor',left='off',right='off')
''' Radius side panel'''
helpers.set_axis_size(axR, epos, Trim=True) #, In= epos.MassRadius)
#axR.set_xscale('log')
#axR.set_xlim([2e-3,5])
#axR.set_xticks([1,10,100,1000])
#axR.set_xticklabels(['1','10','100','1000'], rotation=70)
for tick in axR.get_xticklabels():
tick.set_rotation(70)
#axR.tick_params(axis='x', which='minor',top='off',bottom='off')
#axP.tick_params(axis='y', which='minor',left='off',right='off')
''' Histograms / posterior samples '''
try:
xbins= np.geomspace(*epos.xzoom, num=20)
ybins= np.geomspace(*epos.yzoom, num=10)
except:
xbins= np.logspace(*np.log10(epos.xzoom), num=20)
ybins= np.logspace(*np.log10(epos.yzoom), num=10)
xscale= np.log(xbins[1]/xbins[0])
yscale= np.log(ybins[1]/ybins[0])
if MCMC:
histkeys= {'color':'b', 'alpha':0.1}
for ss in epos.ss_sample:
if epos.MonteCarlo:
axP.hist(ss['P zoom'], bins=xbins, histtype='step', **histkeys)
axR.hist(ss['Y zoom'], bins=ybins, orientation='horizontal', \
histtype='step', **histkeys)
else:
axP.plot(ss['P zoom'], ss['P zoom pdf']*xscale,
marker='', ls='-', **histkeys)
axR.plot(ss['Y zoom pdf']*yscale, ss['Y zoom'],
marker='', ls='-', **histkeys)
histdict= {'histtype':'step', 'color':clr_bf}
else:
histdict={}
if epos.MonteCarlo:
axP.hist(sim['P zoom'], bins=xbins, **histdict)
axR.hist(sim['Y zoom'], bins=ybins, orientation='horizontal', **histdict)
else:
axP.plot(sim['P zoom'], sim['P zoom pdf']*xscale, marker='', ls='-')
axR.plot(sim['Y zoom pdf']*yscale, sim['Y zoom'], marker='', ls='-')
''' Observations'''
axP.hist(epos.obs_zoom['x'], bins=xbins,histtype='step', color='C1')
axR.hist(epos.obs_zoom['y'], bins=ybins, orientation='horizontal',histtype='step', color='C1')
''' Box/ lines'''
if epos.Zoom:
for zoom in epos.xzoom: axP.axvline(zoom, ls='--', color='k')
for zoom in epos.yzoom: axR.axhline(zoom, ls='--', color='k')
ax.add_patch(patches.Rectangle( (epos.xzoom[0],epos.yzoom[0]),
epos.xzoom[1]-epos.xzoom[0], epos.yzoom[1]-epos.yzoom[0],fill=False, zorder=1) )
#ax.legend(loc='lower left', shadow=False, prop={'size':14}, numpoints=1)
fdir= 'mcmc' if MCMC else 'output'
helpers.save(plt, '{}{}/pdf.zoom'.format(epos.plotdir, fdir))
def cdf(epos):
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
f.set_size_inches(9, 7) # default 7, 5
'''
top left: synthetic obsservation
'''
sim= epos.synthetic_survey
helpers.set_axes(ax1, epos, Trim=True)
ax1.set_title('Synthetic ({})'.format(sim['nobs']))
if epos.MonteCarlo:
ax1.plot(sim['P'], sim['Y'], ls='', marker='.', mew=0, ms=5.0, color='r', alpha=0.5)
else:
levels= np.linspace(0,np.max(sim['pdf']))
ax1.contourf(epos.MC_xvar, epos.MC_yvar, sim['pdf'].T, cmap='Reds', levels=levels)
if epos.Zoom:
ax1.add_patch(patches.Rectangle( (epos.xzoom[0],epos.yzoom[0]),
epos.xzoom[1]-epos.xzoom[0], epos.yzoom[1]-epos.yzoom[0],fill=False, zorder=1) )
'''
Top right: observed population
'''
ax2.set_title('Observed ({})'.format(epos.obs_zoom['x'].size))
helpers.set_axes(ax2, epos, Trim=True)
ax2.plot(epos.obs_xvar, epos.obs_yvar, ls='', marker='.', mew=0, ms=5.0, color='b')
if epos.Zoom:
ax2.add_patch(patches.Rectangle( (epos.xzoom[0],epos.yzoom[0]),
epos.xzoom[1]-epos.xzoom[0], epos.yzoom[1]-epos.yzoom[0],fill=False, zorder=1) )
'''
cdf orbital period
'''
if 'xvar' in epos.prob:
ax3.set_title('Period, p={:.3g}'.format(epos.prob['xvar']))
else:
ax3.set_title('Orbital Period')
ax3.set_xlabel('Orbital Period [days]')
ax3.set_ylabel('CDF')
ax3.set_xscale('log')
ax3.set_ylim([-0.05,1.05])
ax3.set_xlim(*epos.xzoom)
if epos.MonteCarlo:
#model histogram x
P= sim['P zoom']
ax3.plot(np.sort(P), np.arange(P.size, dtype=float)/P.size, ls='-', marker='', color='r')
else:
ax3.plot(sim['P zoom'], sim['P zoom cdf'], ls='-', marker='', color='r')
#obs histogram x
P= epos.obs_zoom['x']
ax3.plot(np.sort(P), np.arange(P.size, dtype=float)/P.size, ls='-', marker='', color='b')
'''
CDF planet radius
'''
if 'yvar' in epos.prob:
ax4.set_title('{}, p={:.3g}'.format('M sin i' if epos.RV else 'Radius', epos.prob['yvar']))
else:
ax4.set_title('{}'.format('M sin i' if epos.RV else 'Radius'))
if epos.RV: ax4.set_xlabel(r'Planet M sin i [M$_\bigoplus$]')
else: ax4.set_xlabel(r'Planet Radius [R$_\bigoplus$]')
ax4.set_ylabel('CDF')
ax4.set_xscale('log')
ax4.set_ylim([-0.05,1.05])
ax4.set_xlim(*epos.yzoom)
ax4.set_xticks(epos.yticks)
ax4.get_xaxis().set_major_formatter(tck.ScalarFormatter())
if epos.MonteCarlo:
#model histogram x
R= sim['Y zoom']
ax4.plot(np.sort(R), np.arange(R.size, dtype=float)/R.size, ls='-', marker='', color='r')
else:
ax4.plot(sim['Y zoom'], sim['Y zoom cdf'], ls='-', marker='', color='r')
#obs histogram x
R= epos.obs_zoom['y']
ax4.plot(np.sort(R), np.arange(R.size, dtype=float)/R.size, ls='-', marker='', color='b')
f.tight_layout()
helpers.save(plt, epos.plotdir+'output/cdf.diag')
def pdf(epos):
#assert epos.populationtype is 'model'
f, ((ax1,ax2),(ax3,ax4)) = plt.subplots(2, 2, sharex='col', sharey='row')
f.subplots_adjust(hspace=0, wspace=0)
# 1 2
# 3 4
ax1.set_title('Synthetic Model Populations',loc='left')
ax3.set_xlabel('Orbital Period [days]')
if epos.RV: ax1.set_ylabel(r'Planet Mass [M$_\bigoplus$]')
else: ax1.set_ylabel(r'Planet Radius [R$_\bigoplus$]')
ax2.set_xlabel('Planets/bin')
ax3.set_ylabel('Planets/bin')
ax1.set_xlim(epos.xtrim)
ax1.set_ylim(epos.ytrim)
ax1.set_xscale('log')
ax1.set_yscale('log')
sim=epos.synthetic_survey
# PDF, individual contributions
if epos.populationtype is 'model':
for k, sg in enumerate(epos.groups):
subset= sim['i sg']==k
P= sim['P'][subset]
R= sim['Y'][subset]
ax1.plot(P,R, ls='', marker='.', mew=0, ms=5.0, color=clrs[k % 4])
xgrid= np.logspace(*np.log10(epos.xtrim))
pdf= regression.sliding_window_log(P, None, xgrid) #, width=2. )
ax3.plot(xgrid, pdf, ls='-', marker='', color=clrs[k % 4],label='{} x{:.3f}'.format(sg['name'], sg['weight']))
ygrid= np.logspace(*np.log10(epos.ytrim))
pdf= regression.sliding_window_log(R, None, ygrid) #, width=2. )
ax2.plot(pdf, ygrid, ls='-', marker='', color=clrs[k % 4])
else:
# top left panel (P,R)
ax1.plot(sim['P'], sim['Y'], ls='', marker='.', mew=0, ms=5.0, color='k')
# PDF, all combined, 2 panels
xgrid= np.logspace(*np.log10(epos.xtrim))
pdf= regression.sliding_window_log(sim['P'], None, xgrid) #, width=2. )
weight= np.sum([sg['weight'] for sg in epos.groups]) if epos.populationtype is 'model' else epos.fitpars.get('pps',Init=True)
ax3.plot(xgrid, pdf, ls='-', marker='', color='k',label='combined x {:.3f}'.format(weight))
ygrid= np.logspace(*np.log10(epos.ytrim))
pdf= regression.sliding_window_log(sim['Y'], None, ygrid) #, width=2. )
ax2.plot(pdf, ygrid, ls='-', marker='', color='k')
# observations
if epos.Observation and epos.DetectionEfficiency:
pdf= regression.sliding_window_log(epos.obs_xvar, None, xgrid)
ax3.plot(xgrid, pdf, ls=':', marker='', color='k', label='Kepler')
pdf= regression.sliding_window_log(epos.obs_yvar, None, ygrid)
ax2.plot(pdf, ygrid, ls=':', marker='', color='k')
xmax= ax3.get_xlim()[-1]
ymax= ax3.get_ylim()[-1]
ax3.errorbar(xmax/1.5, ymax*0.9, xerr=(xmax/1.5*(1.-np.sqrt(1./2.)) ), color='k')
xmax= ax2.get_xlim()[-1]
ymin= ax2.get_ylim()[0]
ax2.errorbar(xmax*0.9, ymin*1.5, yerr=(ymin*1.5*(1.-np.sqrt(1./2.)) ), color='k')
#ax2.tick_params(labelbottom='on') # does not re-enable axis
''' Legend instead of 4th plot'''
ax3.legend(loc='center left', shadow=False, prop={'size':14}, numpoints=1,bbox_to_anchor=(1,0.5))
ax4.axis('off')
helpers.save(plt, epos.plotdir+'output/pdf.diag')
def period(epos,
Population=False,
Occurrence=False,
Observation=False,
Tag=False,
color='C1',
Zoom=False,
Rbin=[1., 4.]):
''' Model occurrence as function of orbital period'''
f, ax = plt.subplots()
helpers.set_axis_distance(ax, epos, Trim=True)
ax.set_xlim(0.5, 200)
ax.set_ylabel(
r'Planet Occurrence {:.2g}-{:.2g} $R_\bigoplus$'.format(*Rbin))
ax.set_yscale('log')
pfm = epos.pfm
eta = epos.modelpars.get('eta', Init=True)
if not 'R' in pfm:
pfm['R'], _ = epos.MR(pfm['M'])
if Tag:
# function that return a simulation subset based on the tag
subset = {
'Fe/H<=0': lambda tag: tag <= 0,
'Fe/H>0': lambda tag: tag > 0
}
''' Bins '''
#xbins= np.geomspace(1,1000,10)/(10.**(1./3.))
#xbins= np.geomspace(0.5,200,15)
dwP = 0.3 # bin width in ln space
if Zoom:
xbins = np.exp(
np.arange(np.log(epos.xzoom[0]),
np.log(epos.xzoom[-1]) + dwP, dwP))
else:
xbins = np.exp(
np.arange(np.log(epos.xtrim[0]),
np.log(epos.xtrim[-1]) + dwP, dwP))
''' Plot model occurrence or observed counts'''
weights = np.full(pfm['np'], eta / pfm['ns'])
if 'draw prob' in pfm and not Tag:
prob = pfm['draw prob'][pfm['ID']]
weights *= prob * pfm['ns'] # system weights sum up to 1
# histograms
if Tag:
for key, f in subset.iteritems():
toplot = f(pfm['tag']) #& (pfm['R']>1.)
weights = np.where(toplot, eta, 0.) / f(pfm['system tag']).sum()
ax.hist(pfm['P'],
bins=xbins,
weights=weights,
histtype='step',
label=key,
color='#88CCEE' if key == 'Fe/H<=0' else '#332288')
else:
ax.hist(pfm['P'],
bins=xbins,
weights=weights,
color=color,
histtype='step')
if Tag:
fname = '.tag'
ax.set_title(epos.name + ': Disk Fe/H')
ax.legend(fontsize='small', loc='lower right')
else:
fname = ''
if Occurrence:
cut = (Rbin[0] < epos.obs_yvar) & (epos.obs_yvar < Rbin[-1])
weights = epos.occurrence['planet']['occ'][cut]
ax.hist(epos.obs_xvar[cut],
bins=xbins,
weights=weights,
histtype='step',
color='k')
helpers.save(plt,
'{}occurrence/model.period{}'.format(epos.plotdir, fname))
# elif Observation: # counts
else:
helpers.save(plt, '{}model/input.period{}'.format(epos.plotdir, fname))
import numpy
import dmsh
from helpers import assert_norm_equality, save
def test_union(show=False):
angles = numpy.pi * numpy.array([3.0 / 6.0, 7.0 / 6.0, 11.0 / 6.0])
geo = dmsh.Intersection(
[
dmsh.Circle([numpy.cos(angles[0]), numpy.sin(angles[0])], 1.5),
dmsh.Circle([numpy.cos(angles[1]), numpy.sin(angles[1])], 1.5),
dmsh.Circle([numpy.cos(angles[2]), numpy.sin(angles[2])], 1.5),
]
)
X, cells = dmsh.generate(geo, 0.1, show=show, tol=1.0e-10)
ref_norms = [6.8247386668599034e01, 5.1256971008793917e00, 7.2474487138537913e-01]
assert_norm_equality(X.flatten(), ref_norms, 1.0e-12)
return X, cells
if __name__ == "__main__":
X, cells = test_union(show=False)
save("intersection_three_circles.png", X, cells)
def HansenMurray(epos, color='purple'):
''' figures not included '''
import matplotlib.image as mpimg
pfm = epos.pfm
print pfm.keys()
''' Hansen & Murray 2013 Figure 1 '''
f, ax = plt.subplots()
ax.set_xlabel('$N_p$')
ax.set_ylabel('counts')
ax.set_xlim([0.5, 10.5])
ax.set_ylim([0, 35])
fig1 = mpimg.imread('HM13figs/fig1_cut.png')
ax.imshow(fig1, aspect='auto', extent=[0.5, 10.5, 0, 35])
_, counts = np.unique(pfm['ID'], return_counts=True)
ax.hist(counts,
bins=np.arange(0, 11),
align='left',
color='purple',
alpha=0.7,
weights=np.full_like(counts, 2))
helpers.save(plt, '{}HM13/fig1'.format(epos.plotdir), dpi=300)
''' Hansen & Murray 2013 Figure 5 '''
f, ax = plt.subplots()
ax.set_xlabel('$P_2/P_1$')
ax.set_ylabel('counts')
ax.set_xlim([1, 4])
ax.set_ylim([0, 32])
#ax.set_xscale('log')
#ax.set_yscale('log')
fig1 = mpimg.imread('HM13figs/fig5_cut.png')
ax.imshow(fig1, aspect='auto', extent=[1, 4, 0, 32])
dP = pfm['dP'][pfm['dP'] > 1]
ax.hist(dP,
bins=np.linspace(1. + (1.5 / 37.), 4, 38),
align='mid',
color='purple',
alpha=0.7,
weights=np.full_like(dP, 2))
helpers.save(plt, '{}HM13/fig5'.format(epos.plotdir), dpi=300)
''' Hansen & Murray 2013 Figure 9 '''
f, ax = plt.subplots()
ax.set_xlabel('Eccentricity')
ax.set_ylabel('counts')
ax.set_xlim([0, 0.24])
ax.set_ylim([0, 35])
#ax.set_xscale('log')
#ax.set_yscale('log')
fig1 = mpimg.imread('HM13figs/fig9_cut.png')
ax.imshow(fig1, aspect='auto', extent=[0, 0.24, 0, 35])
ax.hist(pfm['ecc'],
bins=np.linspace(0.01, 0.25, 25),
align='left',
color='purple',
alpha=0.7,
weights=np.full_like(pfm['ecc'], 2))
helpers.save(plt, '{}HM13/fig9'.format(epos.plotdir), dpi=300)
''' Hansen & Murray 2013 Figure 10 '''
f, ax = plt.subplots()
ax.set_xlabel('sma [au]')
ax.set_ylabel('Inclination')
ax.set_xlim([0.04, 1.2])
ax.set_ylim([0, 33])
#ax.set_xscale('log')
fig10 = mpimg.imread('HM13figs/fig10_cut.png')
ax.imshow(fig10, aspect='auto', extent=[0.04, 1.2, 0, 33])
ax.get_xaxis().set_ticks([])
axlog = ax.twiny()
axlog.set_xlim([0.04, 1.2])
axlog.set_xscale('log')
axlog.plot(pfm['sma'],
pfm['inc'],
color='purple',
alpha=0.7,
marker='o',
ls='')
helpers.save(plt, '{}HM13/fig10'.format(epos.plotdir), dpi=300)
''' Hansen & Murray 2013 Figure 11 '''
f, (axa, axb) = plt.subplots(2)
fig11a = mpimg.imread('HM13figs/fig11a_cut.png')
fig11b = mpimg.imread('HM13figs/fig11b_cut.png')
#ax[0].set_xlabel('Inclination')
axa.set_ylabel('counts')
axa.set_xlim([0, 24.5])
axa.set_ylim([0, 20])
axb.set_xlabel('Inclination')
axb.set_ylabel('counts')
axb.set_xlim([0, 14.5])
axb.set_ylim([0, 35])
axa.imshow(fig11a, aspect='auto', extent=[0, 24.5, 0, 20])
axb.imshow(fig11b, aspect='auto', extent=[0, 14.5, 0, 35])
inner = pfm['inc'][pfm['sma'] < 0.1]
outer = pfm['inc'][(pfm['sma'] > 0.1) & (pfm['sma'] < 1.0)]
axa.hist(inner,
bins=np.linspace(0, 25, 26),
align='mid',
color='purple',
alpha=0.7,
weights=np.full_like(inner, 2))
axb.hist(outer,
bins=np.linspace(0, 15, 50),
align='mid',
color='purple',
alpha=0.7,
weights=np.full_like(outer, 2))
helpers.save(plt, '{}HM13/fig11'.format(epos.plotdir), dpi=300)
import dmsh
from helpers import assert_norm_equality, save
def test_pacman(show=False):
geo = dmsh.Difference(
dmsh.Circle([0.0, 0.0], 1.0),
dmsh.Polygon([[0.0, 0.0], [1.5, 0.4], [1.5, -0.4]]),
)
X, cells = dmsh.generate(geo, 0.1, show=show, tol=1.0e-10)
ref_norms = [3.0385105041432689e02, 1.3644964912810719e01, 1.0]
assert_norm_equality(X.flatten(), ref_norms, 1.0e-12)
return X, cells
if __name__ == "__main__":
X, cells = test_pacman(show=False)
save("pacman.png", X, cells)
def multiplicity(epos, MC=False, Planets=False, MCMC=False):
# plot multiplicity
f, ax = plt.subplots()
ax.set_title('Multi-Planet Frequency')
ax.set_xlabel('Planets per System')
ax.set_ylabel('Planet Counts' if Planets else 'System Counts')
ax.set_xlim(0.5, 7.5)
if not Planets:
ax.set_ylim(0.5, 1e4) # 7.
ax.set_yscale('log')
#ax.get_yaxis().set_major_formatter(tck.ScalarFormatter())
key = 'pl cnt' if Planets else 'count'
# MC data
if MC or MCMC:
ss = epos.synthetic_survey
if Planets:
ax.bar(ss['multi']['bin'],
ss['multi'][key],
color='C0',
label='Simulated',
width=1)
f_iso = epos.fitpars.get('f_iso')
if f_iso > 0:
fsingle = np.sum(ss['multi']['count']) * f_iso
ax.bar(1,
fsingle,
bottom=ss['multi'][key][0] - fsingle,
color='',
label='Single Planets',
width=1,
hatch='xx') #, ec='k')
#ax.plot(1, ss['multi'][key][0]-fsingle, marker='+', ms=10, ls='', color='k', label='no dichotomy')
elif MCMC:
for ss in epos.ss_sample:
ax.hlines(ss['multi'][key],
ss['multi']['bin'] - 0.5,
ss['multi']['bin'] + 0.5,
color='b',
alpha=0.1)
else:
#ax.step(ss['multi']['bin'], ss['multi'][key], color='C0',label=epos.name, where='mid')
ax.plot(ss['multi']['bin'],
ss['multi'][key],
ls='',
marker='+',
ms=10,
color='C0',
label=epos.name)
if hasattr(epos, 'ss_extra'):
# advance color cycle
# ax._get_lines.get_next_color()
ax.plot([], [])
ax.plot([], [])
# plot extra epos runs
for ss in epos.ss_extra:
ax.plot(ss['multi']['bin'],
ss['multi'][key],
ls='',
marker='+',
ms=10,
label=ss['name'])
# observations in same region
ax.step(epos.obs_zoom['multi']['bin'],
epos.obs_zoom['multi'][key],
where='mid',
color='C1',
label='Kepler')
else:
# observations
ax.plot(epos.multi['bin'],
epos.multi[key],
drawstyle='steps-mid',
ls='--',
marker='',
color='gray',
label='Kepler all')
ax.legend(loc='upper right', shadow=False, prop={'size': 14}, numpoints=1)
prefix = 'output' if MC else 'survey'
if MCMC: prefix = 'mcmc'
suffix = '.planets' if Planets else ''
helpers.save(plt, '{}{}/multiplicity{}'.format(epos.plotdir, prefix,
suffix))
import numpy as np
from helpers import assert_norm_equality, save
import dmsh
def test(show=False):
r = dmsh.Rectangle(-1.0, +1.0, -1.0, +1.0)
c = dmsh.Circle([0.0, 0.0], 0.3)
geo = dmsh.Difference(r, c)
X, cells = dmsh.generate(
geo,
lambda pts: np.abs(c.dist(pts)) / 5 + 0.05,
show=show,
tol=1.0e-10,
max_steps=100,
)
ref_norms = [2.48e02, 1.200e01, 1.0]
assert_norm_equality(X.flatten(), ref_norms, 1.0e-2)
return X, cells
if __name__ == "__main__":
X, cells = test(show=True)
save("square_hole_refined.png", X, cells)
def periodratio(epos, MC=False, N=False, Input=False, MCMC=False):
# plot multiplicity
f, ax = plt.subplots()
ax.set_title('Period Ratio of Adjacent Planets')
ax.set_xlabel(r'$\mathcal{P}$ = Period Outer/Inner')
ax.set_ylabel('Planet Counts')
ax.set_xlim(1, 10)
ax.set_xscale('log')
for s in [ax.set_xticks, ax.set_xticklabels]:
s([1, 2, 3, 4, 5, 7, 10])
ax.set_xticks([], minor=True) # minor ticks generate labels
bins = np.logspace(0, 1, 15) # 1,10
# MC data
if MC or MCMC:
ss = epos.synthetic_survey
if MCMC:
for ss in epos.ss_sample:
# bar?
ax.hist(ss['multi']['Pratio'],
bins=bins,
histtype='step',
color='b',
alpha=0.1)
#ax.hist(ss['multi']['Pratio'], bins=bins, color='b', alpha=1./len(epos.ss_sample))
else:
ax.hist(ss['multi']['Pratio'],
bins=bins,
color='C0',
histtype='stepfilled',
label='Simulated') #epos.name)
if N:
# planets inbetween?
#ax.hist(ss['multi']['dPN'][0], \
# bins=bins, ec='k', histtype='step', label='Adjacent planet')
ax.hist(np.concatenate(ss['multi']['dPN'][1:]),
bins=bins,
hatch='xx',
histtype='stepfilled',
label='Planet Inbetween') # ec, color
else:
#ax.axvline(np.median(ss['multi']['Pratio']), color='C0', ls='--')
pass
if hasattr(epos, 'ss_extra'):
# advance color cycle
# ax._get_lines.get_next_color()
ax.plot([], [])
ax.plot([], [])
ax.plot([], [])
# plot extra epos runs
for ss in epos.ss_extra:
ax.hist(ss['multi']['Pratio'],
bins=bins,
histtype='step',
label=ss['name'])
else:
# observed all
ax.hist(epos.multi['Pratio'],
bins=bins,
color='0.7',
label='Kepler all')
#ax.axvline(np.median(epos.multi['Pratio']), color='0.7', ls='--')
''' input distribution '''
if Input and epos.Parametric:
#Pgrid=bins
Pgrid = np.logspace(0, 1)
pdf, _ = draw_dP(epos, Pgrid=Pgrid)
pdf *= 0.95 * ax.get_ylim()[1] / max(pdf)
ax.plot(Pgrid, pdf, marker='', ls='-', color='r', label='Intrinsic')
elif epos.Zoom:
# Observed zoom
ax.hist(epos.obs_zoom['multi']['Pratio'], \
bins=bins, ec='C1', histtype='step', label='Kepler')
if not MCMC:
ax.axvline(np.median(epos.obs_zoom['multi']['Pratio']),
color='C1',
ls='--')
prefix = 'output' if MC else 'survey'
suffix = '.index' if N else ''
if Input: suffix += '.input'
if MCMC: prefix = 'mcmc'
if MC:
ax.legend(loc='upper right',
shadow=False,
prop={'size': 14},
numpoints=1)
helpers.save(plt, '{}{}/periodratio{}'.format(epos.plotdir, prefix,
suffix))
def panels_radius(epos,
Population=False,
Occurrence=False,
Observation=False,
Tag=False,
color='C0',
clr_obs='C3',
Shade=True,
Fancy=True,
Zoom=False):
f, (ax, axR, axP) = helpers.make_panels(plt, Fancy=Fancy)
pfm = epos.pfm
eta = epos.modelpars.get('eta', Init=True)
title = ''
if not 'R' in pfm:
pfm['R'], _ = epos.MR(pfm['M'])
if Tag:
# function that return a simulation subset based on the tag
subset = {
'Fe/H<=0': lambda tag: tag <= 0,
'Fe/H>0': lambda tag: tag > 0
}
''' Bins '''
dwR = 0.2 # bin width in ln space
dwP = 0.3
if Zoom:
xbins = np.exp(
np.arange(np.log(epos.xzoom[0]),
np.log(epos.xzoom[-1]) + dwP, dwP))
ybins = np.exp(
np.arange(np.log(epos.yzoom[0]),
np.log(epos.yzoom[-1]) + dwR, dwR))
else:
xbins = np.exp(
np.arange(np.log(epos.xtrim[0]),
np.log(epos.xtrim[-1]) + dwP, dwP))
ybins = np.exp(
np.arange(np.log(epos.ytrim[0]),
np.log(epos.ytrim[-1]) + dwR, dwR))
''' Plot model occurrence or observed counts'''
if Observation:
# plot model planets * completeness
weights = eta * epos.occurrence['model']['completeness'] / pfm['ns']
else:
weights = np.full(pfm['np'], eta / pfm['ns'])
if 'draw prob' in pfm and not Tag:
prob = pfm['draw prob'][pfm['ID']]
weights *= prob * pfm['ns'] # system weights sum up to 1
#nonzero= np.where(prob>0, 1., 0.)
#weights*= nonzero*(pfm['np']/nonzero.sum())
# histograms
if Tag:
for key, f in subset.iteritems():
toplot = f(pfm['tag'])
#weights= eta*epos.occurrence['model']['completeness'] \
# *np.where(toplot,1.,0.)/f(pfm['system tag']).sum()
weights = np.where(toplot, eta, 0.) / f(pfm['system tag']).sum()
axP.hist(pfm['P'],
bins=xbins,
weights=weights,
histtype='step',
label=key)
axR.hist(pfm['R'],
bins=ybins,
orientation='horizontal',
weights=weights,
histtype='step')
else:
# color have to be 1-element lists ??
axP.hist(pfm['P'], bins=xbins, weights=weights, color=[color])
axR.hist(pfm['R'],
bins=ybins,
orientation='horizontal',
weights=weights,
color=[color])
''' Overplot observations? '''
if Population:
assert hasattr(epos, 'func')
fname = '.pop' + ('.zoom' if Zoom else '')
title = epos.title
pps, pdf, pdf_X, pdf_Y = periodradius(epos, Init=True)
_, _, pdf_X, _ = periodradius(epos, Init=True, ybin=ybins)
_, _, _, pdf_Y = periodradius(epos, Init=True, xbin=xbins)
pps, _, _, _ = periodradius(epos, Init=True, xbin=xbins, ybin=ybins)
#pdf/= np.max(pdf)
#pdflog= np.log10(pdf) # in %
levels = np.linspace(0, np.max(pdf))
lines = np.array([0.1, 0.5]) * np.max(pdf)
if Shade:
ax.contourf(epos.X_in,
epos.Y_in,
pdf,
cmap='Purples',
levels=levels)
#ax.contour(epos.X_in, epos.Y_in, pdf, levels=lines)
# Side panels
#print 'pps model= {}'.format(eta)
axP.plot(epos.MC_xvar,
pdf_X * dwP,
marker='',
ls='-',
color='purple')
axR.plot(pdf_Y * dwR,
epos.in_yvar,
marker='',
ls='-',
color='purple')
else:
# renormalize
xnorm = axP.get_ylim()[1] / max(pdf_X)
ynorm = axR.get_xlim()[1] / max(pdf_Y)
axP.plot(epos.MC_xvar,
pdf_X * xnorm,
marker='',
ls='-',
color=clr_obs)
axR.plot(pdf_Y * ynorm,
epos.in_yvar,
marker='',
ls='-',
color=clr_obs)
elif Observation:
fname = '.obs' + ('.zoom' if Zoom else '')
title = epos.title + ': Counts'
ax.plot(epos.obs_xvar,
epos.obs_yvar,
ls='',
marker='.',
ms=5.0,
color='0.5')
weights = np.full(epos.obs_xvar.size, 1. / epos.nstars)
axP.hist(epos.obs_xvar,
bins=xbins,
weights=weights,
histtype='step',
color='0.5')
axR.hist(epos.obs_yvar,
bins=ybins,
weights=weights,
orientation='horizontal',
histtype='step',
color='0.5')
elif Occurrence:
fname = '.occ' + ('.zoom' if Zoom else '')
title = epos.title + r': Occurrence, $\eta={:.2g}$'.format(eta)
ax.plot(epos.obs_xvar,
epos.obs_yvar,
ls='',
marker='.',
ms=5.0,
color='0.5')
cut = epos.obs_yvar > 0.45
weights = 1. / (epos.occurrence['planet']['completeness'][cut] *
epos.nstars)
axP.hist(epos.obs_xvar[cut],
bins=xbins,
weights=weights,
histtype='step',
color='k')
axR.hist(epos.obs_yvar[cut],
bins=ybins,
weights=weights,
orientation='horizontal',
histtype='step',
color='k')
elif Tag:
fname = '.tag'
ax.set_title(epos.title + ': Tag')
axP.legend(frameon=False, fontsize='small')
# for k, tag in enumerate(subset):
# axP.text(0.98,0.95-0.05*k,tag,ha='right',va='top',color='C1',
# transform=axP.transAxes)
else:
fname = ''
if Fancy:
plt.suptitle(title, ha='center') #, x=0.05)
else:
ax.set_title(title)
''' plot main panel'''
#helpers.set_axes(ax, epos, Trim=True)
helpers.set_axes(ax, epos, Trim=True)
if Tag:
for key, f in subset.iteritems():
todraw = f(pfm['tag'])
ax.plot(pfm['P'][todraw], pfm['R'][todraw], **fmt_symbol)
elif 'draw prob' in pfm:
#fmt_symbol['alpha']= 0.6*pfm['draw prob'][pfm['ID']] # alpha can't be array
todraw = pfm['draw prob'][pfm['ID']] > 0
ax.plot(pfm['P'][todraw], pfm['R'][todraw], color=color, **fmt_symbol)
else:
ax.plot(pfm['P'], pfm['R'], color=color, **fmt_symbol)
''' Period side panel '''
#axP.yaxis.tick_right()
#axP.yaxis.set_ticks_position('both')
#axP.tick_params(axis='y', which='minor',left='off',right='off')
helpers.set_axis_distance(axP, epos, Trim=True)
''' Mass side panel'''
helpers.set_axis_size(axR, epos, Trim=True) #, In= epos.MassRadius)
helpers.save(plt, '{}model/input.radius{}'.format(epos.plotdir, fname))
def inner(epos):
'''
Plot inner planet
'''
# plot multiplicity
f, ax = plt.subplots()
pop = epos.population
ax.set_title('Simulated Systems')
ax.set_xlabel('Orbital Period (days)')
ax.set_ylabel('Fraction of stars') # 0-45 %
#ax.set_ylim(-0.05, 1.05)
ax.set_ylim(0, 1)
ax.set_xlim(0.2, 730)
ax.set_xscale('log')
x = np.geomspace(0.2, 730)
# ax.fill_between(x, 0.0, 0.159, color='g', alpha=0.2, lw=0)
# ax.fill_between(x, 0.159, 0.841, color='c', alpha=0.2, lw=0)
# ax.fill_between(x, 0.841, 1, color='g', alpha=0.2, lw=0)
ax.fill_between(x, 0.0, 0.023, color='g', alpha=0.2, lw=0)
ax.fill_between(x, 0.023, 0.159, color='c', alpha=0.2, lw=0)
ax.fill_between(x, 0.159, 0.841, color='y', alpha=0.2, lw=0)
ax.fill_between(x, 0.841, 0.977, color='c', alpha=0.2, lw=0)
ax.fill_between(x, 0.977, 1, color='g', alpha=0.2, lw=0)
ax.axhline(0.5, color='k', lw=1)
xtext = 0.21
ax.text(xtext, 0.023, '$2\sigma$', va='center', size=10)
ax.text(xtext, 0.159, '$1\sigma$', va='center', size=10)
#ax.text(xtext, 0.5, 'mean', va='center',size=8)
ax.text(xtext, 0.5 + 0.01, 'mean', size=10)
ax.text(xtext, 0.841, '$1\sigma$', va='center', size=10)
ax.text(xtext, 0.977, '$2\sigma$', va='center', size=10)
# BG all planets?
# ax.plot(pop['P'], pop['order'],
# ls='', marker='.', mew=0, ms=3, color='0.8')
# all multis
key = 'single'
ax.plot(pop[key]['P'],
pop[key]['order'],
ls='',
marker='.',
mew=0,
ms=3,
color='b')
key = 'multi'
ax.plot(pop[key]['P'],
pop[key]['order'],
ls='',
marker='.',
mew=0,
ms=3,
color='purple')
# for key in ['system','single','multi']:
# ax.plot(pop['P'], pop['order'],
# ls='', marker='.', mew=0, ms=3, color='gray')
# Solar System
#ax.plot([0.95]*4,[])
yss = 0.9
#yss= 0.1
ax.text(88, yss, 'M', ha='center', va='center', color='r', size=8)
ax.text(225, yss, 'V', ha='center', va='center', color='r', size=8)
ax.text(365, yss, 'E', ha='center', va='center', color='r', size=8)
ax.text(687, yss, 'M', ha='center', va='center', color='r', size=8)
helpers.save(plt, '{}/population/inner_test'.format(epos.plotdir))
def panels_mass(epos, Population=False, color='C1'):
f, (ax, axM, axP) = helpers.make_panels(plt)
pfm = epos.pfm
eta = epos.modelpars.get('eta', Init=True)
''' Bins '''
dw = 0.5 # bin width in ln space
xbins = np.exp(
np.arange(np.log(pfm['P limits'][0]),
np.log(pfm['P limits'][-1]) + dw, dw))
ybins = np.exp(
np.arange(np.log(pfm['M limits'][0]),
np.log(pfm['M limits'][-1]) + dw, dw))
''' Posterior '''
if Population:
assert hasattr(epos, 'func')
fname = '.pop'
# side panels marginalized over M and P limits
pps, pdf, pdf_X, pdf_Y = periodradius(epos, Init=True)
_, _, pdf_X, _ = periodradius(epos, Init=True, ybin=ybins)
_, _, _, pdf_Y = periodradius(epos, Init=True, xbin=xbins)
pps, _, _, _ = periodradius(epos, Init=True, xbin=xbins, ybin=ybins)
#pdf/= np.max(pdf)
#pdflog= np.log10(pdf) # in %
levels = np.linspace(0, np.max(pdf))
lines = np.array([0.1, 0.5]) * np.max(pdf)
ax.contourf(epos.X_in, epos.Y_in, pdf, cmap='Purples', levels=levels)
#ax.contour(epos.X_in, epos.Y_in, pdf, levels=lines)
# Side panels
#print 'pps model= {}'.format(eta)
scale = dw
axP.plot(epos.MC_xvar,
pdf_X * scale,
marker='',
ls='-',
color='purple')
axM.plot(pdf_Y * scale,
epos.in_yvar,
marker='',
ls='-',
color='purple')
else:
fname = ''
''' plot main panel'''
ax.set_title(epos.name)
#helpers.set_axes(ax, epos, Trim=True)
ax.set_xscale('log')
ax.set_yscale('log')
#ax.set_xlim(epos.mod_xlim)
#ax.set_ylim(epos.mod_ylim)
ax.plot(pfm['P'], pfm['M'], color=color, **fmt_symbol)
xlim = ax.get_xlim()
ylim = ax.get_ylim()
''' Period side panel '''
#axP.yaxis.tick_right()
#axP.yaxis.set_ticks_position('both')
#axP.tick_params(axis='y', which='minor',left='off',right='off')
axP.set_xscale('log')
axP.set_xlim(xlim)
#ax.set_xlabel('Semi-Major Axis [au]')
axP.set_xlabel('Orbital Period [days]')
axP.hist(pfm['P'],
color=color,
bins=xbins,
weights=np.full(pfm['np'], eta / pfm['np']))
''' Mass side panel'''
#helpers.set_axis_size(axR, epos, Trim=True) #, In= epos.MassRadius)
axM.set_ylabel(r'Planet Mass [M$_\bigoplus$]')
axM.set_yscale('log')
axM.set_ylim(ylim)
axM.hist(pfm['M'],
bins=ybins,
orientation='horizontal',
weights=np.full(pfm['np'], eta / pfm['np']),
color=color)
helpers.save(plt, '{}model/input.mass{}'.format(epos.plotdir, fname))
import numpy
import dmsh
from helpers import assert_norm_equality, save
def test(show=False):
geo = dmsh.Rotation(dmsh.Rectangle(-1.0, +2.0, -1.0, +1.0), 0.1 * numpy.pi)
X, cells = dmsh.generate(geo, 0.1, show=show, tol=1.0e-10)
ref_norms = [
9.5352192763033452e02, 3.1344318120314945e01, 2.2111300269652543e00
]
assert_norm_equality(X.flatten(), ref_norms, 1.0e-10)
return X, cells
if __name__ == "__main__":
X, cells = test(show=False)
save("rotation.png", X, cells)
def periodratio(epos, color='C0', clr_obs='C3', Simple=False, Fancy=True):
pfm = epos.pfm
if Fancy:
f, (ax, axR, axP) = helpers.make_panels(plt, Fancy=True)
f.suptitle('Multi-planets {}'.format(epos.title))
else:
f, (ax, axR, axP) = helpers.make_panels_right(plt)
ax.set_title('Input Multi-planets {}'.format(epos.title))
''' Inc-sma'''
ax.set_xlabel('Semi-Major Axis [au]')
ax.set_ylabel('Period ratio')
#ax.set_xlim(epos.mod_xlim)
ax.set_ylim(0.9, 10)
ax.set_xscale('log')
ax.set_yscale('log')
#ax.axhline(np.median(pfm['inc']), ls='--')
single = pfm['dP'] == np.nan
inner = pfm['dP'] == 1
nth = pfm['dP'] > 1
# print pfm['dP'][single]
# print pfm['dP'][nth]
# print pfm['dP'][inner]
# print pfm['dP'][nth].size, pfm['np'] # ok
ax.plot(pfm['sma'][single], pfm['dP'][single], color='0.7', **fmt_symbol)
ax.plot(pfm['sma'][nth], pfm['dP'][nth], color=color, **fmt_symbol)
ax.plot(pfm['sma'][inner], pfm['dP'][inner], color='C1', **fmt_symbol)
''' Histogram Period Ratio'''
axR.set_yscale('log')
axR.set_ylim(0.9, 10)
#dP= np.logspace(0,1,15)
dP = np.logspace(0, 1, 25)
axR.hist(pfm['dP'][nth], bins=dP, orientation='horizontal', color=color)
if not Simple:
ax.axhline(np.median(pfm['dP'][nth]), ls='--', color=color, zorder=-1)
#Model best-fit
dP = np.logspace(0, 1)
xmax = axR.get_xlim()[-1]
with np.errstate(divide='ignore'):
Dgrid = np.log10(2. * (dP**(2. / 3.) - 1.) / (dP**(2. / 3.) + 1.))
Dgrid[0] = -2
if Simple:
scale = -0.37
pdf = scipy.stats.norm(scale, 0.19).pdf(Dgrid)
pdf *= xmax / max(pdf)
axR.plot(pdf, dP, ls='-', color=clr_obs)
pscale = np.interp(scale, Dgrid, dP)
#ax.axhline(pscale, color=clr_obs, ls='--')
else:
for scale, ls in zip([-0.30, -0.37, -0.41], [':', '--', ':']):
pdf = scipy.stats.norm(scale, 0.19).pdf(Dgrid)
pdf *= xmax / max(pdf)
axR.plot(pdf, dP, ls=ls, color='purple')
pscale = np.interp(scale, Dgrid, dP)
ax.axhline(pscale, color='purple', ls=ls, zorder=-1)
''' Histogram Inner Planet'''
axP.set_xscale('log')
axP.set_xlim(ax.get_xlim())
sma = np.geomspace(*ax.get_xlim(), num=25)
axP.hist(pfm['sma'][inner], bins=sma, color='C1', label='Inner Planet')
ymax = axP.get_ylim()[-1]
P = np.geomspace(0.5, 730)
smaP = (P / 365.25)**(1. / 1.5)
pdf = brokenpowerlaw1D(P, 10, 1.5, -0.8)
pdf *= ymax / max(pdf)
#axP.legend(frameon=False)
if Fancy:
ax.text(0.02,
0.98,
'Inner Planet',
ha='left',
va='top',
color='C1',
transform=ax.transAxes)
ax.text(0.02,
0.98,
'\nOuter Planet(s)',
ha='left',
va='top',
color=color,
transform=ax.transAxes)
else:
axP.text(0.98,
0.95,
'Inner Planet',
ha='right',
va='top',
color='C1',
transform=axP.transAxes)
axP.plot(smaP, pdf, ls='-', color=clr_obs)
#helpers.set_axes(ax, epos, Trim=True)
#helpers.set_axis_distance(axP, epos, Trim=True)
#helpers.set_axis_size(axR, epos, Trim=True) #, In= epos.MassRadius)
helpers.save(plt, epos.plotdir + 'model/Pratio-sma')
from helpers import assert_norm_equality, save
import dmsh
def test(show=False):
geo = dmsh.Rectangle(0.0, 1.0, 0.0, 1.0)
# p0 = dmsh.Path([[0.0, 0.0]])
p1 = dmsh.Path([[0.4, 0.6], [0.6, 0.4]])
def edge_size(x):
return 0.03 + 0.1 * p1.dist(x)
X, cells = dmsh.generate(geo, edge_size, show=show, tol=1.0e-10, max_steps=100)
ref_norms = [3.7918105331047593e02, 1.5473837427489348e01, 1.0000000000000000e00]
assert_norm_equality(X.flatten(), ref_norms, 1.0e-3)
return X, cells
if __name__ == "__main__":
X, cells = test(show=False)
save("refinement_line.png", X, cells)
def periodratio_size(epos, color='C1'):
pfm = epos.pfm
f, (ax, axR, axP) = helpers.make_panels_right(plt)
''' Inc-sma'''
ax.set_title('Input Multi-planets {}'.format(epos.name))
axP.set_xlabel('Period ratio')
#ax.set_ylabel(r'Size [$R_\bigoplus$]')
ax.set_xlim(0.9, 10)
#ax.set_ylim(0.3,20)
ax.set_xscale('log')
#ax.set_yscale('log')
helpers.set_axis_size(ax, epos, Trim=True)
''' grids '''
dP = np.logspace(0, 1)
dP_bins = np.logspace(0, 1, 15)
# exoplanet data + hist
ax.plot(epos.multi['Pratio'],
epos.multi['Rpair'],
ls='',
marker='.',
ms=5.0,
color='0.5')
#ax.axhline(np.median(pfm['inc']), ls='--')
single = pfm['dP'] == np.nan
inner = pfm['dP'] == 1
nth = pfm['dP'] > 1
# print pfm['dP'][single]
# print pfm['dP'][nth]
# print pfm['dP'][inner]
# print pfm['dP'][nth].size, pfm['np'] # ok
ax.plot(pfm['dP'][single], pfm['R'][single], color='0.7', **fmt_symbol)
ax.plot(pfm['dP'][nth], pfm['R'][nth], color=color, **fmt_symbol)
ax.plot(pfm['dP'][inner], pfm['R'][inner], color='C1', **fmt_symbol)
''' Histogram Period Ratio'''
axP.set_xscale('log')
axP.set_xlim(0.9, 10)
axP.hist(pfm['dP'][nth], bins=dP_bins, color=color)
ax.axvline(np.median(pfm['dP'][nth]), ls='--', color=color)
''' Model best-fit '''
xmax = axP.get_ylim()[-1]
with np.errstate(divide='ignore'):
Dgrid = np.log10(2. * (dP**(2. / 3.) - 1.) / (dP**(2. / 3.) + 1.))
Dgrid[0] = -2
for scale, ls in zip([-0.30, -0.37, -0.41], [':', '--', ':']):
pdf = scipy.stats.norm(scale, 0.19).pdf(Dgrid)
pdf *= xmax / max(pdf)
axP.plot(dP, pdf, ls=ls, color='purple')
pscale = np.interp(scale, Dgrid, dP)
ax.axvline(pscale, color='purple', ls=ls)
''' Raw data'''
scale = 1. * pfm['dP'][nth].size / epos.multi['Pratio'].size
weights = np.full(epos.multi['Pratio'].size, scale)
axP.hist(epos.multi['Pratio'],
bins=dP_bins,
weights=weights,
histtype='step',
color='0.5',
zorder=1)
''' Histogram Planet Size'''
axR.set_yscale('log')
axR.set_ylim(ax.get_ylim())
radius = np.geomspace(*ax.get_ylim(), num=15)
axR.hist(pfm['R'][inner],
bins=radius,
orientation='horizontal',
color='C1',
label='Inner Planet')
#helpers.set_axes(ax, epos, Trim=True)
#helpers.set_axis_distance(axP, epos, Trim=True)
''' Linear regression '''
try:
# data
slope, intercept, r_value, p_value, std_err= \
scipy.stats.linregress(np.log(epos.multi['Pratio']), np.log(epos.multi['Rpair']))
ax.plot(dP,
np.exp(intercept + slope * np.log(dP)),
label='r={:.2f}'.format(r_value),
marker='',
ls='-',
color='0.5')
#print slope, intercept, r_value, p_value
slope, intercept, r_value, p_value, std_err= \
scipy.stats.linregress(np.log(pfm['dP'][nth]), np.log(pfm['R'][nth]))
ax.plot(dP,
np.exp(intercept + slope * np.log(dP)),
label='r={:.2f}'.format(r_value),
marker='',
ls='-',
color=color)
ax.legend(loc='lower right')
except Exception as e:
print(e)
helpers.save(plt, epos.plotdir + 'model/Pratio-size')
x0=chains['x0'][maxlike],
z=z,
mwebv=float(mwebv))
# the errors passed in here are errors in the measured parameters. Best-fit taken from maximum likelihood sample
sncosmo.plot_lc(emfit.data,
model=model,
errors=err,
fname='./plots/emcee/jla/%s.pdf' % (nickname),
color='black')
if args.noskew:
triangle_keys = ['mB', 'c', 't0', 'x1']
else:
triangle_keys = ['mB', 'c', 's', 't0', 'x1']
helpers.save(chains, './chains/%s.chains' % (nickname))
# triangle plots
emfit.plots(chains,
nickname,
triangle_keys,
outdir='./plots/emcee/jla/triangle')
except Exception as e:
# as of final release of the code no JLA SNe fail the try except:
# occasionally a simulated LC will fail, usually due to poor S/N
traceback.print_exc()
continue
# output lcfit file
outdir = os.path.abspath('./fit_results/emcee/JLA/%s' % (nickname))
if not os.path.exists(outdir):
import numpy
import dmsh
from helpers import assert_norm_equality, save
def test_halfspace(show=False):
geo = dmsh.Intersection([
dmsh.HalfSpace(numpy.sqrt(0.5) * numpy.array([1.0, 1.0]), 0.0),
dmsh.Circle([0.0, 0.0], 1.0),
])
X, cells = dmsh.generate(geo, 0.1, show=show)
ref_norms = [
1.6445971629723411e02, 1.0032823867864321e01, 9.9962000746451751e-01
]
assert_norm_equality(X.flatten(), ref_norms, 1.0e-12)
return X, cells
if __name__ == "__main__":
X, cells = test_halfspace(show=False)
save("halfspace.png", X, cells)
