def period(epos,
Population=False,
Occurrence=False,
Observation=False,
Tag=False,
color='C1'):
''' 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 1-4 $R_\bigoplus$')
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)
''' 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)
if Tag:
fname = '.tag'
ax.set_title(epos.name + ': Disk Fe/H')
ax.legend(fontsize='small', loc='lower right')
else:
fname = ''
helpers.save(plt, '{}model/input.period{}'.format(epos.plotdir, fname))
def periodinner(epos, MC=False, N=False, Input=False, MCMC=False, color='C1'):
# 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=color,
histtype='stepfilled',
label='Simulated') #epos.name)
if hasattr(epos, 'pfm'):
ax.hist(epos.pfm['Pin'],
bins=bins,
color='C7',
histtype='step',
label='Input',
ls=':') #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:
ax.hist(epos.obs_zoom['multi']['Pinner'],
bins=bins,
ec='C3',
histtype='step',
label='Kepler',
ls='--')
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')
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 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 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))
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 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 oneD_x(epos, PlotZoom=False, MCMC=False, Occ=False, Log=True):
if Occ:
fname = 'occurrence/posterior' if MCMC else 'occurrence/input'
ybin = epos.yzoom
unit = r'$M_\bigoplus$' if epos.RV else r'$R_\bigoplus$'
title = r'Planet Occurrence ({1:.1f}-{2:.0f} {0})'.format(
unit, *epos.yzoom)
else:
fname = 'mcmc/posterior' if MCMC else 'input/parametric_initial'
ybin = None
title = r'Marginalized Distribution ({:.1f}-{:.0f} $R_\bigoplus$)'.format(
*epos.ytrim)
if not Log:
fname += '.linear'
# initial guess
pps, _, pdf0_X, _ = periodradius(epos, Init=True, ybin=ybin)
if MCMC:
# best-fit parameters
pps, _, pdf_X, _ = periodradius(epos, ybin=ybin)
''' construct the posterior parameters '''
if MCMC:
plotsample = epos.samples[np.random.randint(len(epos.samples),
size=100)]
''' Orbital Period '''
f, ax = plt.subplots()
ax.set_title(title)
ax.set_ylabel('Occurrence / {}P'.format(epos.plotpars['area']))
#ax.set_xlabel('Orbital Period [days]')
#ax.set_xscale('log')
#ax.set_xlim(epos.xtrim)
helpers.set_axis_distance(ax, epos, Trim=True)
if Log:
ax.set_yscale('log')
if 'occrange' in epos.plotpars:
ax.set_ylim(epos.plotpars['occrange'])
else:
ax.set_ylim([1e-3, 1e1])
else:
ax.set_ylim([0, 0.45])
if MCMC:
for fpara in plotsample:
_, _, xpdf, _ = periodradius(epos, fpara=fpara, ybin=ybin)
ax.plot(epos.MC_xvar, xpdf, color='b', alpha=0.1)
ax.plot(epos.MC_xvar,
pdf0_X,
marker='',
ls=':',
color='k',
label='Starting Guess')
ax.plot(epos.MC_xvar,
pdf_X,
marker='',
ls='-',
color='k',
label='Best Fit')
else:
if 'P break' in epos.fitpars.keys2d:
ax.axvline(epos.fitpars.get('P break', Init=True),
ls='-',
color='gray')
ax.plot(epos.MC_xvar, pdf0_X, marker='', ls='-', color='k')
# plot posterior excluding low detection regions (arbitrary 2000 planets assumed)
if not (epos.RV or epos.MassRadius):
cens = np.where(epos.f_det < 1. / 3000., 0, 1.)
_, _, cens_pdf_X, _ = periodradius(epos, ybin=ybin, fdet=cens)
ax.plot(epos.MC_xvar,
cens_pdf_X,
marker='',
ls='-',
color='green',
label='biased')
if epos.Zoom:
for zoom in epos.xzoom:
ax.axvline(zoom, ls='--', color='k')
if Occ:
occbin = epos.occurrence['yzoom']
ax.errorbar(occbin['xc'],
occbin['occ'] / occbin['dlnx'],
yerr=occbin['err'] / occbin['dlnx'],
color='r',
marker='_',
ls='',
capsize=3) #,capthick=2
# lognormal inner disk edge?
#from scipy.stats import norm
#gauss= 2.*norm(loc=1., scale=0.4).pdf(np.log10(epos.MC_xvar))
#ax.plot(epos.MC_xvar, gauss, ls='-', marker='', color='r')
if not Log:
if 'Pinner all' in epos.plotpars:
xx = np.geomspace(20, 400)
a, b, c = epos.plotpars['Pinner all']
ax.plot(xx,
a * (xx / b)**c,
marker='',
ls='-',
color='g',
label='All Planets')
if 'Pinner ylim' in epos.plotpars:
ax.set_ylim(epos.plotpars['Pinner ylim'])
if MCMC:
ax.legend(loc='upper left')
helpers.save(plt, epos.plotdir + fname + '_x')
