import numpy as np
import pandas as pd
from matplotlib import rcParams, gridspec
from matplotlib import pyplot as pl
from matplotlib.ticker import MaxNLocator
from astropy.time import Time
import copy
import radvel
from radvel import plot
from radvel.plot import mcmc_plots
from radvel.utils import t_to_phase, fastbin, sigfig
[docs]class MultipanelPlot(object):
"""
Class to handle the creation of RV multipanel plots.
Args:
post (radvel.Posterior): radvel.Posterior object. The model
plotted will be generated from `post.params`
epoch (int, optional): epoch to subtract off of all time measurements
yscale_auto (bool, optional): Use matplotlib auto y-axis
scaling (default: False)
yscale_sigma (float, optional): Scale y-axis limits for all panels to be +/-
yscale_sigma*(RMS of data plotted) if yscale_auto==False
phase_nrows (int, optional): number of columns in the phase
folded plots. Default is nplanets.
phase_ncols (int, optional): number of columns in the phase
folded plots. Default is 1.
uparams (dict, optional): parameter uncertainties, must
contain 'per', 'k', and 'e' keys.
telfmts (dict, optional): dictionary of dictionaries mapping
instrument suffix to plotting format code. Example:
telfmts = {
'hires': dict(fmt='o',label='HIRES'),
'harps-n' dict(fmt='s')
}
legend (bool, optional): include legend on plot? Default: True.
phase_limits (list, optional): two element list specifying
pyplot.xlim bounds for phase-folded plots. Useful for
partial orbits.
nobin (bool, optional): If True do not show binned data on
phase plots. Will default to True if total number of
measurements is less then 20.
phasetext_size (string, optional): fontsize for text in phase plots.
Choice of {'xx-small', 'x-small', 'small', 'medium', 'large',
'x-large', 'xx-large'}. Default: 'x-small'.
rv_phase_space (float, optional): amount of space to leave between orbit/residual plot
and phase plots.
figwidth (float, optional): width of the figures to be produced.
Default: 7.5 (spans a page with 0.5 in margins)
fit_linewidth (float, optional): linewidth to use for orbit model lines in phase-folded
plots and residuals plots.
set_xlim (list of float): limits to use for x-axes of the timeseries and residuals plots, in
JD - `epoch`. Ex: [7000., 70005.]
text_size (int): set matplotlib.rcParams['font.size'] (default: 9)
highlight_last (bool): make the most recent measurement much larger in all panels
show_rms (bool): show RMS of the residuals by instrument in the legend
legend_kwargs (dict): dict of options to pass to legend (plotted in top panel)
status (ConfigParser): (optional) result of radvel.driver.load_status on the .stat status file
"""
def __init__(self, post, saveplot=None, epoch=2450000, yscale_auto=False, yscale_sigma=3.0,
phase_nrows=None, phase_ncols=None, uparams=None, telfmts={}, legend=True,
phase_limits=[], nobin=False, phasetext_size='large', rv_phase_space=0.08,
figwidth=7.5, fit_linewidth=2.0, set_xlim=None, text_size=9, highlight_last=False,
show_rms=False, legend_kwargs=dict(loc='best'), status=None):
self.post = copy.deepcopy(post)
self.saveplot = saveplot
self.epoch = epoch
self.yscale_auto = yscale_auto
self.yscale_sigma = yscale_sigma
if phase_ncols is None:
self.phase_ncols = 1
else:
self.phase_ncols = phase_ncols
if phase_nrows is None:
self.phase_nrows = self.post.likelihood.model.num_planets
else:
self.phase_nrows = phase_nrows
self.uparams = uparams
self.rv_phase_space = rv_phase_space
self.telfmts = telfmts
self.legend = legend
self.phase_limits = phase_limits
self.nobin = nobin
self.phasetext_size = phasetext_size
self.figwidth = figwidth
self.fit_linewidth = fit_linewidth
self.set_xlim = set_xlim
self.highlight_last = highlight_last
self.show_rms = show_rms
self.legend_kwargs = legend_kwargs
rcParams['font.size'] = text_size
if status is not None:
self.status = status
if isinstance(self.post.likelihood, radvel.likelihood.CompositeLikelihood):
self.like_list = self.post.likelihood.like_list
else:
self.like_list = [self.post.likelihood]
# FIGURE PROVISIONING
self.ax_rv_height = self.figwidth * 0.6
self.ax_phase_height = self.ax_rv_height / 1.4
# convert params to synth basis
synthparams = self.post.params.basis.to_synth(self.post.params)
self.post.params = synthparams
self.post.vector.dict_to_vector()
self.model = self.post.likelihood.model
self.rvtimes = self.post.likelihood.x
self.rverr = self.post.likelihood.errorbars()
self.num_planets = self.model.num_planets
self.rawresid = self.post.likelihood.residuals()
self.resid = (
self.rawresid + self.post.params['dvdt'].value*(self.rvtimes-self.model.time_base)
+ self.post.params['curv'].value*(self.rvtimes-self.model.time_base)**2
)
if self.saveplot is not None:
resolution = 10000
else:
resolution = 2000
periods = []
for i in range(self.num_planets):
periods.append(synthparams['per%d' % (i+1)].value)
if len(periods) > 0:
longp = max(periods)
else:
longp = max(self.post.likelihood.x) - min(self.post.likelihood.x)
if self.set_xlim is not None:
self.dt = self.set_xlim[1] - self.set_xlim[0]
self.rvmodt = np.linspace(
(self.set_xlim[0]+self.epoch) - 0.05 * self.dt, (self.set_xlim[1]+self.epoch) + 0.05 * self.dt + longp,
int(resolution)
)
else:
self.dt = max(self.rvtimes) - min(self.rvtimes)
self.rvmodt = np.linspace(
min(self.rvtimes) - 0.05 * self.dt, max(self.rvtimes) + 0.05 * self.dt + longp,
int(resolution)
)
self.orbit_model = self.model(self.rvmodt)
self.rvmod = self.model(self.rvtimes)
if ((self.rvtimes - self.epoch) < -2.4e6).any():
self.plttimes = self.rvtimes
self.mplttimes = self.rvmodt
elif self.epoch == 0:
self.epoch = 2450000
self.plttimes = self.rvtimes - self.epoch
self.mplttimes = self.rvmodt - self.epoch
else:
self.plttimes = self.rvtimes - self.epoch
self.mplttimes = self.rvmodt - self.epoch
self.slope = (
self.post.params['dvdt'].value * (self.rvmodt-self.model.time_base)
+ self.post.params['curv'].value * (self.rvmodt-self.model.time_base)**2
)
self.slope_low = (
self.post.params['dvdt'].value * (self.rvtimes-self.model.time_base)
+ self.post.params['curv'].value * (self.rvtimes-self.model.time_base)**2
)
# list for Axes objects
self.ax_list = []
[docs] def plot_timeseries(self):
"""
Make a plot of the RV data and model in the current Axes.
"""
ax = pl.gca()
ax.axhline(0, color='0.5', linestyle='--')
if self.show_rms:
rms_values = dict()
for like in self.like_list:
inst = like.suffix
rms = np.std(like.residuals())
rms_values[inst] = rms
else:
rms_values = False
# plot orbit model
ax.plot(self.mplttimes, self.orbit_model, 'b-', rasterized=False, lw=self.fit_linewidth)
# plot data
vels = self.rawresid+self.rvmod
plot.mtelplot(
# data = residuals + model
self.plttimes, vels, self.rverr, self.post.likelihood.telvec, ax, telfmts=self.telfmts,
rms_values=rms_values
)
if self.set_xlim is not None:
ax.set_xlim(self.set_xlim)
else:
ax.set_xlim(min(self.plttimes)-0.01*self.dt, max(self.plttimes)+0.01*self.dt)
pl.setp(ax.get_xticklabels(), visible=False)
if self.highlight_last:
ind = np.argmax(self.plttimes)
pl.plot(self.plttimes[ind], vels[ind], **plot.highlight_format)
# legend
if self.legend:
ax.legend(numpoints=1, **self.legend_kwargs)
# years on upper axis
axyrs = ax.twiny()
xl = np.array(list(ax.get_xlim())) + self.epoch
decimalyear = Time(xl, format='jd', scale='utc').decimalyear
# axyrs.plot(decimalyear, decimalyear)
axyrs.get_xaxis().get_major_formatter().set_useOffset(False)
axyrs.set_xlim(*decimalyear)
axyrs.set_xlabel('Year', fontweight='bold')
pl.locator_params(axis='x', nbins=5)
if not self.yscale_auto:
scale = np.std(self.rawresid+self.rvmod)
ax.set_ylim(-self.yscale_sigma * scale, self.yscale_sigma * scale)
ax.set_ylabel('RV [{ms:}]'.format(**plot.latex), weight='bold')
ticks = ax.yaxis.get_majorticklocs()
ax.yaxis.set_ticks(ticks[1:])
[docs] def plot_residuals(self):
"""
Make a plot of residuals in the current Axes.
"""
ax = pl.gca()
ax.plot(self.mplttimes, self.slope-self.slope, 'b-', lw=self.fit_linewidth)
plot.mtelplot(self.plttimes, self.rawresid, self.rverr, self.post.likelihood.telvec, ax, telfmts=self.telfmts)
if not self.yscale_auto:
scale = np.std(self.rawresid)
ax.set_ylim(-self.yscale_sigma * scale, self.yscale_sigma * scale)
if self.highlight_last:
ind = np.argmax(self.plttimes)
pl.plot(self.plttimes[ind], self.rawresid[ind], **plot.highlight_format)
if self.set_xlim is not None:
ax.set_xlim(self.set_xlim)
else:
ax.set_xlim(min(self.plttimes)-0.01*self.dt, max(self.plttimes)+0.01*self.dt)
ticks = ax.yaxis.get_majorticklocs()
ax.yaxis.set_ticks([ticks[0], 0.0, ticks[-1]])
pl.xlabel('JD - {:d}'.format(int(np.round(self.epoch))), weight='bold')
ax.set_ylabel('Residuals', weight='bold')
ax.yaxis.set_major_locator(MaxNLocator(5, prune='both'))
[docs] def plot_phasefold(self, pltletter, pnum):
"""
Plot phased orbit plots for each planet in the fit.
Args:
pltletter (int): integer representation of
letter to be printed in the corner of the first
phase plot.
Ex: ord("a") gives 97, so the input should be 97.
pnum (int): the number of the planet to be plotted. Must be
the same as the number used to define a planet's
Parameter objects (e.g. 'per1' is for planet #1)
"""
ax = pl.gca()
if len(self.post.likelihood.x) < 20:
self.nobin = True
bin_fac = 1.75
bin_markersize = bin_fac * rcParams['lines.markersize']
bin_markeredgewidth = bin_fac * rcParams['lines.markeredgewidth']
rvmod2 = self.model(self.rvmodt, planet_num=pnum) - self.slope
modph = t_to_phase(self.post.params, self.rvmodt, pnum, cat=True) - 1
rvdat = self.rawresid + self.model(self.rvtimes, planet_num=pnum) - self.slope_low
phase = t_to_phase(self.post.params, self.rvtimes, pnum, cat=True) - 1
rvdatcat = np.concatenate((rvdat, rvdat))
rverrcat = np.concatenate((self.rverr, self.rverr))
rvmod2cat = np.concatenate((rvmod2, rvmod2))
bint, bindat, binerr = fastbin(phase+1, rvdatcat, nbins=25)
bint -= 1.0
ax.axhline(0, color='0.5', linestyle='--', )
ax.plot(sorted(modph), rvmod2cat[np.argsort(modph)], 'b-', linewidth=self.fit_linewidth)
plot.labelfig(pltletter)
telcat = np.concatenate((self.post.likelihood.telvec, self.post.likelihood.telvec))
if self.highlight_last:
ind = np.argmax(self.rvtimes)
hphase = t_to_phase(self.post.params, self.rvtimes[ind], pnum, cat=False)
if hphase > 0.5:
hphase -= 1
pl.plot(hphase, rvdatcat[ind], **plot.highlight_format)
plot.mtelplot(phase, rvdatcat, rverrcat, telcat, ax, telfmts=self.telfmts)
if not self.nobin and len(rvdat) > 10:
ax.errorbar(
bint, bindat, yerr=binerr, fmt='ro', mec='w', ms=bin_markersize,
mew=bin_markeredgewidth
)
if self.phase_limits:
ax.set_xlim(self.phase_limits[0], self.phase_limits[1])
else:
ax.set_xlim(-0.5, 0.5)
if not self.yscale_auto:
scale = np.std(rvdatcat)
ax.set_ylim(-self.yscale_sigma*scale, self.yscale_sigma*scale)
keys = [p+str(pnum) for p in ['per', 'k', 'e']]
labels = [self.post.params.tex_labels().get(k, k) for k in keys]
if pnum < self.num_planets:
ticks = ax.yaxis.get_majorticklocs()
ax.yaxis.set_ticks(ticks[1:-1])
ax.set_ylabel('RV [{ms:}]'.format(**plot.latex), weight='bold')
ax.set_xlabel('Phase', weight='bold')
print_params = ['per', 'k', 'e']
units = {'per': 'days', 'k': plot.latex['ms'], 'e': ''}
anotext = []
for l, p in enumerate(print_params):
val = self.post.params["%s%d" % (print_params[l], pnum)].value
if self.uparams is None:
_anotext = r'$\mathregular{%s}$ = %4.2f %s' % (labels[l].replace("$", ""), val, units[p])
else:
if hasattr(self.post, 'medparams'):
val = self.post.medparams["%s%d" % (print_params[l], pnum)]
else:
print("WARNING: medparams attribute not found in " +
"posterior object will annotate with " +
"max-likelihood values and reported uncertainties " +
"may not be appropriate.")
err = self.uparams["%s%d" % (print_params[l], pnum)]
if err > 1e-15:
val, err, errlow = sigfig(val, err)
_anotext = r'$\mathregular{%s}$ = %s $\mathregular{\pm}$ %s %s' \
% (labels[l].replace("$", ""), val, err, units[p])
else:
_anotext = r'$\mathregular{%s}$ = %4.2f %s' % (labels[l].replace("$", ""), val, units[p])
anotext += [_anotext]
if hasattr(self.post, 'derived'):
chains = pd.read_csv(self.status['derive']['chainfile'])
self.post.nplanets = self.num_planets
dp = mcmc_plots.DerivedPlot(chains, self.post)
labels = dp.labels
texlabels = dp.texlabels
units = dp.units
derived_params = ['mpsini']
for l, par in enumerate(derived_params):
par_label = par + str(pnum)
if par_label in self.post.derived.columns:
index = np.where(np.array(labels) == par_label)[0][0]
unit = units[index]
if unit == "M$_{\\rm Jup}$":
conversion_fac = 0.00315
elif unit == "M$_{\\odot}$":
conversion_fac = 0.000954265748
else:
conversion_fac = 1
val = self.post.derived["%s%d" % (derived_params[l], pnum)].loc[0.500] * conversion_fac
low = self.post.derived["%s%d" % (derived_params[l], pnum)].loc[0.159] * conversion_fac
high = self.post.derived["%s%d" % (derived_params[l], pnum)].loc[0.841] * conversion_fac
err_low = val - low
err_high = high - val
err = np.mean([err_low, err_high])
err = radvel.utils.round_sig(err)
if err > 1e-15:
val, err, errlow = sigfig(val, err)
_anotext = r'$\mathregular{%s}$ = %s $\mathregular{\pm}$ %s %s' \
% (texlabels[index].replace("$", ""), val, err, units[index])
else:
_anotext = r'$\mathregular{%s}$ = %4.2f %s' % (texlabels[index].replace("$", ""), val, units[index])
anotext += [_anotext]
anotext = '\n'.join(anotext)
plot.add_anchored(
anotext, loc=1, frameon=True, prop=dict(size=self.phasetext_size, weight='bold'),
bbox=dict(ec='none', fc='w', alpha=0.8)
)
[docs] def plot_multipanel(self, nophase=False, letter_labels=True):
"""
Provision and plot an RV multipanel plot
Args:
nophase (bool, optional): if True, don't
include phase plots. Default: False.
letter_labels (bool, optional): if True, include
letter labels on orbit and residual plots.
Default: True.
Returns:
tuple containing:
- current matplotlib Figure object
- list of Axes objects
"""
if nophase:
scalefactor = 1
else:
scalefactor = self.phase_nrows
figheight = self.ax_rv_height + self.ax_phase_height * scalefactor
# provision figure
fig = pl.figure(figsize=(self.figwidth, figheight))
fig.subplots_adjust(left=0.12, right=0.95)
gs_rv = gridspec.GridSpec(2, 1, height_ratios=[1., 0.5])
divide = 1 - self.ax_rv_height / figheight
gs_rv.update(left=0.12, right=0.93, top=0.93,
bottom=divide+self.rv_phase_space*0.5, hspace=0.)
# orbit plot
ax_rv = pl.subplot(gs_rv[0, 0])
self.ax_list += [ax_rv]
pl.sca(ax_rv)
self.plot_timeseries()
if letter_labels:
pltletter = ord('a')
plot.labelfig(pltletter)
pltletter += 1
# residuals
ax_resid = pl.subplot(gs_rv[1, 0])
self.ax_list += [ax_resid]
pl.sca(ax_resid)
self.plot_residuals()
if letter_labels:
plot.labelfig(pltletter)
pltletter += 1
# phase-folded plots
if not nophase:
gs_phase = gridspec.GridSpec(max([1,self.phase_nrows]), max([1,self.phase_ncols]))
if self.phase_ncols == 1:
gs_phase.update(left=0.12, right=0.93,
top=divide - self.rv_phase_space * 0.5,
bottom=0.07, hspace=0.003)
else:
gs_phase.update(left=0.12, right=0.93,
top=divide - self.rv_phase_space * 0.5,
bottom=0.07, hspace=0.25, wspace=0.25)
for i in range(self.num_planets):
i_row = int(i / self.phase_ncols)
i_col = int(i - i_row * self.phase_ncols)
ax_phase = pl.subplot(gs_phase[i_row, i_col])
self.ax_list += [ax_phase]
pl.sca(ax_phase)
self.plot_phasefold(pltletter, i+1)
pltletter += 1
if self.saveplot is not None:
pl.savefig(self.saveplot, dpi=150)
print("RV multi-panel plot saved to %s" % self.saveplot)
return fig, self.ax_list
[docs]class GPMultipanelPlot(MultipanelPlot):
"""
Class to handle the creation of RV multipanel plots for posteriors fitted
using Gaussian Processes.
Takes the same args as MultipanelPlot, with a few additional bells and whistles...
Args:
subtract_gp_mean_model (bool, optional): if True, subtract the Gaussian
process mean max likelihood model from the data and the
model when plotting the results. Default: False.
plot_likelihoods_separately (bool, optional): if True, plot a separate
panel for each Likelihood object. Default: False
subtract_orbit_model (bool, optional): if True, subtract the best-fit
orbit model from the data and the model when plotting
the results. Useful for seeing the structure of correlated
noise in the data. Default: False.
status (ConfigParser): (optional) result of radvel.driver.load_status on the .stat status file
"""
def __init__(self, post, saveplot=None, epoch=2450000, yscale_auto=False, yscale_sigma=3.0,
phase_nrows=None, phase_ncols=None, uparams=None, rv_phase_space=0.08, telfmts={},
legend=True,
phase_limits=[], nobin=False, phasetext_size='large', figwidth=7.5, fit_linewidth=2.0,
set_xlim=None, text_size=9, legend_kwargs=dict(loc='best'), subtract_gp_mean_model=False,
plot_likelihoods_separately=False, subtract_orbit_model=False, status=None, separate_orbit_gp=False):
super(GPMultipanelPlot, self).__init__(
post, saveplot=saveplot, epoch=epoch, yscale_auto=yscale_auto,
yscale_sigma=yscale_sigma, phase_nrows=phase_nrows, phase_ncols=phase_ncols,
uparams=uparams, rv_phase_space=rv_phase_space, telfmts=telfmts, legend=legend,
phase_limits=phase_limits, nobin=nobin, phasetext_size=phasetext_size,
figwidth=figwidth, fit_linewidth=fit_linewidth, set_xlim=set_xlim, text_size=text_size,
legend_kwargs=legend_kwargs
)
self.subtract_gp_mean_model = subtract_gp_mean_model
self.plot_likelihoods_separately = plot_likelihoods_separately
self.subtract_orbit_model = subtract_orbit_model
self.separate_orbit_gp = separate_orbit_gp
if status is not None:
self.status = status
is_gp = False
for like in self.like_list:
if isinstance(like, radvel.likelihood.GPLikelihood):
is_gp = True
break
else:
pass
assert is_gp, "This class requires at least one GPLikelihood object in the Posterior."
[docs] def plot_gp_like(self, like, orbit_model4data, ci):
"""
Plot a single Gaussian Process Likleihood object in the current Axes,
including Gaussian Process uncertainty bands.
Args:
like (radvel.GPLikelihood): radvel.GPLikelihood object. The model
plotted will be generated from `like.params`.
orbit_model4data (numpy array):
ci (int): index to use when choosing a color to plot from
radvel.plot.default_colors. This is only used if the
Likelihood object being plotted is not in the list of defaults.
Increments by 1 if it is used.
Returns: current (possibly changed) value of the input `ci`
"""
ax = pl.gca()
if isinstance(like, radvel.likelihood.GPLikelihood):
if self.set_xlim is not None:
xpred = np.linspace(self.set_xlim[0]+self.epoch, self.set_xlim[1]+self.epoch, num=int(3e3))
else:
xpred = np.linspace(np.min(like.x), np.max(like.x), num=int(3e3))
gpmu, stddev = like.predict(xpred)
if self.subtract_orbit_model:
gp_orbit_model = np.zeros(xpred.shape)
else:
gp_orbit_model = self.model(xpred)
if ((xpred - self.epoch) < -2.4e6).any():
pass
elif self.epoch == 0:
self.epoch = 2450000
xpred = xpred - self.epoch
else:
xpred = xpred - self.epoch
if self.subtract_gp_mean_model:
gpmu = 0.
else:
gp_mean4data, _ = like.predict(like.x)
indx = np.where(self.post.likelihood.telvec == like.suffix)
orbit_model4data[indx] += gp_mean4data
if like.suffix not in self.telfmts and like.suffix in plot.telfmts_default:
color = plot.telfmts_default[like.suffix]['color']
if like.suffix in self.telfmts:
color = self.telfmts[like.suffix]['color']
if like.suffix not in self.telfmts and like.suffix not in plot.telfmts_default:
color = plot.default_colors[ci]
ci += 1
ax.fill_between(xpred, gpmu+gp_orbit_model-stddev, gpmu+gp_orbit_model+stddev,
color=color, alpha=0.5, lw=0
)
if self.separate_orbit_gp:
ax.plot(xpred, gpmu, '-', color='orange', rasterized=False, lw=0.2, label='GP')
ax.plot(xpred, gp_orbit_model, 'g-', rasterized=False, lw=0.2, label="Orbit")
ax.plot(xpred, gpmu+gp_orbit_model, 'b-', rasterized=False, lw=0.4, label="Orbit+GP")
else:
ax.plot(xpred, gpmu+gp_orbit_model, 'b-', rasterized=False, lw=0.4)
else:
# plot orbit model
ax.plot(self.mplttimes, self.orbit_model, 'b-', rasterized=False, lw=0.1)
if not self.yscale_auto:
scale = np.std(self.rawresid+self.rvmod)
ax.set_ylim(-self.yscale_sigma * scale, self.yscale_sigma * scale)
ax.set_ylabel('RV [{ms:}]'.format(**plot.latex), weight='bold')
ticks = ax.yaxis.get_majorticklocs()
ax.yaxis.set_ticks(ticks[1:])
ax.xaxis.set_ticks([])
return ci
[docs] def plot_timeseries(self):
"""
Make a plot of the RV data and Gaussian Process + orbit model in the current Axes.
"""
ax = pl.gca()
ax.axhline(0, color='0.5', linestyle='--')
if self.subtract_orbit_model:
orbit_model4data = np.zeros(self.rvmod.shape)
else:
orbit_model4data = self.rvmod
ci = 0
for like in self.like_list:
ci = self.plot_gp_like(like, orbit_model4data, ci)
# plot data
plot.mtelplot(
# data = residuals + model
self.plttimes, self.rawresid+orbit_model4data, self.rverr,
self.post.likelihood.telvec, ax, telfmts=self.telfmts
)
if self.set_xlim is not None:
ax.set_xlim(self.set_xlim)
else:
ax.set_xlim(min(self.plttimes)-0.01*self.dt, max(self.plttimes)+0.01*self.dt)
pl.setp(ax.get_xticklabels(), visible=False)
# legend
if self.legend:
ax.legend(numpoints=1, **self.legend_kwargs)
# years on upper axis
axyrs = ax.twiny()
xl = np.array(list(ax.get_xlim())) + self.epoch
decimalyear = Time(xl, format='jd', scale='utc').decimalyear
axyrs.plot(decimalyear, decimalyear)
axyrs.get_xaxis().get_major_formatter().set_useOffset(False)
axyrs.set_xlim(*decimalyear)
pl.locator_params(axis='x', nbins=5)
axyrs.set_xlabel('Year', fontweight='bold')
[docs] def plot_multipanel(self, nophase=False):
"""
Provision and plot an RV multipanel plot for a Posterior object containing
one or more Gaussian Process Likelihood objects.
Args:
nophase (bool, optional): if True, don't
include phase plots. Default: False.
Returns:
tuple containing:
- current matplotlib Figure object
- list of Axes objects
"""
if not self.plot_likelihoods_separately:
super(GPMultipanelPlot, self).plot_multipanel()
else:
if nophase:
scalefactor = 1
else:
scalefactor = self.phase_nrows
n_likes = len(self.like_list)
figheight = self.ax_rv_height*(n_likes//self.phase_ncols+1.5) + self.ax_phase_height * scalefactor
# provision figure
fig = pl.figure(figsize=(self.figwidth, figheight))
fig.subplots_adjust(left=0.12, right=0.95)
hrs = np.zeros(n_likes+1) + 1.
hrs[-1] = 0.5
gs_rv = gridspec.GridSpec(n_likes+1, 1, height_ratios=hrs)
divide = 1 - self.ax_rv_height*len(self.like_list) / figheight
gs_rv.update(left=0.12, right=0.93, top=0.93,
bottom=divide+self.rv_phase_space*0.5, hspace=0.0)
# orbit plot for each likelihood
pltletter = ord('a')
i = 0
ci = 0
for like in self.like_list:
ax = pl.subplot(gs_rv[i, 0])
i += 1
self.ax_list += [ax]
pl.sca(ax)
ax.axhline(0, color='0.5', linestyle='--')
if self.subtract_orbit_model:
orbit_model4data = np.zeros(self.rvmod.shape)
else:
orbit_model4data = self.rvmod
self.plot_gp_like(like, orbit_model4data, ci)
# plot data
plot.mtelplot(
# data = residuals + model
self.plttimes, self.rawresid+orbit_model4data, self.rverr,
self.post.likelihood.telvec, ax, telfmts=self.telfmts
)
ax.set_xlim(min(self.plttimes)-0.01*self.dt, max(self.plttimes)+0.01*self.dt)
pl.setp(ax.get_xticklabels(), visible=False)
# legend
if self.legend and i == 1:
ax.legend(numpoints=1, **self.legend_kwargs)
# years on upper axis
if i == 1:
axyrs = ax.twiny()
xl = np.array(list(ax.get_xlim())) + self.epoch
decimalyear = Time(xl, format='jd', scale='utc').decimalyear
axyrs.plot(decimalyear, decimalyear)
axyrs.get_xaxis().get_major_formatter().set_useOffset(False)
axyrs.set_xlim(*decimalyear)
axyrs.set_xlabel('Year', fontweight='bold')
plot.labelfig(pltletter)
pltletter += 1
# residuals
ax_resid = pl.subplot(gs_rv[-1, 0])
self.ax_list += [ax_resid]
pl.sca(ax_resid)
self.plot_residuals()
plot.labelfig(pltletter)
pltletter += 1
# phase-folded plots
if not nophase:
gs_phase = gridspec.GridSpec(self.phase_nrows, self.phase_ncols)
if self.phase_ncols == 1:
gs_phase.update(left=0.12, right=0.93,
top=divide - self.rv_phase_space * 0.5,
bottom=0.07, hspace=0.003)
else:
gs_phase.update(left=0.12, right=0.93,
top=divide - self.rv_phase_space * 0.5,
bottom=0.07, hspace=0.25, wspace=0.25)
for i in range(self.num_planets):
i_row = int(i / self.phase_ncols)
i_col = int(i - i_row * self.phase_ncols)
ax_phase = pl.subplot(gs_phase[i_row, i_col])
self.ax_list += [ax_phase]
pl.sca(ax_phase)
self.plot_phasefold(pltletter, i+1)
pltletter += 1
if self.saveplot is not None:
pl.savefig(self.saveplot, dpi=150)
print("RV multi-panel plot saved to %s" % self.saveplot)
return fig, self.ax_list