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))
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 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')
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))
