Source code for orbitize.plot

import numpy as np
import corner
import warnings
import itertools

import astropy.units as u
import astropy.constants as consts
from astropy.time import Time

import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import matplotlib.colors as colors

from erfa import ErfaWarning

import orbitize
import orbitize.kepler as kepler


# TODO: deprecatation warning for plots in results

# define modified color map for default use in orbit plots
cmap = mpl.cm.Purples_r
cmap = colors.LinearSegmentedColormap.from_list(
    'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=0.0, b=0.7),
    cmap(np.linspace(0.0, 0.7, 1000))
)

[docs]def plot_corner(results, param_list=None, **corner_kwargs): """ Make a corner plot of posterior on orbit fit from any sampler Args: param_list (list of strings): each entry is a name of a parameter to include. Valid strings:: sma1: semimajor axis ecc1: eccentricity inc1: inclination aop1: argument of periastron pan1: position angle of nodes tau1: epoch of periastron passage, expressed as fraction of orbital period per1: period K1: stellar radial velocity semi-amplitude [repeat for 2, 3, 4, etc if multiple objects] plx: parallax pm_ra: RA proper motion pm_dec: Dec proper motion alpha0: primary offset from reported Hipparcos RA @ alphadec0_epoch (generally 1991.25) delta0: primary offset from reported Hipparcos Dec @ alphadec0_epoch (generally 1991.25) gamma: rv offset sigma: rv jitter mi: mass of individual body i, for i = 0, 1, 2, ... (only if fit_secondary_mass == True) mtot: total mass (only if fit_secondary_mass == False) **corner_kwargs: any remaining keyword args are sent to ``corner.corner``. See `here <https://corner.readthedocs.io/>`_. Note: default axis labels used unless overwritten by user input. Return: ``matplotlib.pyplot.Figure``: corner plot .. Note:: **Example**: Use ``param_list = ['sma1,ecc1,inc1,sma2,ecc2,inc2']`` to only plot posteriors for semimajor axis, eccentricity and inclination of the first two companions Written: Henry Ngo, 2018 """ # Define array of default axis labels (overwritten if user specifies list) default_labels = { 'sma': '$a_{0}$ [au]', 'ecc': '$ecc_{0}$', 'inc': '$inc_{0}$ [$^\\circ$]', 'aop': '$\\omega_{0}$ [$^\\circ$]', 'pan': '$\\Omega_{0}$ [$^\\circ$]', 'tau': '$\\tau_{0}$', 'plx': '$\\pi$ [mas]', 'gam': '$\\gamma$ [km/s]', 'sig': '$\\sigma$ [km/s]', 'mtot': '$M_T$ [M$_{{\\odot}}$]', 'm0': '$M_0$ [M$_{{\\odot}}$]', 'm': '$M_{0}$ [M$_{{\\rm Jup}}$]', 'pm_ra': '$\\mu_{{\\alpha}}$ [mas/yr]', 'pm_dec': '$\\mu_{{\\delta}}$ [mas/yr]', 'alpha0': '$\\alpha^{{*}}_{{0}}$ [mas]', 'delta0': '$\\delta_0$ [mas]', 'm': '$M_{0}$ [M$_{{\\rm Jup}}$]', 'per' : '$P_{0}$ [yr]', 'K' : '$K_{0}$ [km/s]', 'x' : '$X_{0}$ [AU]', 'y' : '$Y_{0}$ [AU]', 'z' : '$Z_{0}$ [AU]', 'xdot' : '$xdot_{0}$ [km/s]', 'ydot' : '$ydot_{0}$ [km/s]', 'zdot' : '$zdot_{0}$ [km/s]' } if param_list is None: param_list = results.labels param_indices = [] angle_indices = [] secondary_mass_indices = [] for i, param in enumerate(param_list): index_num = results.param_idx[param] # only plot non-fixed parameters if np.std(results.post[:, index_num]) > 0: param_indices.append(index_num) label_key = param if label_key.startswith('aop') or label_key.startswith('pan') or label_key.startswith('inc'): angle_indices.append(i) if label_key.startswith('m') and label_key != 'm0' and label_key != 'mtot': secondary_mass_indices.append(i) samples = np.copy(results.post[:, param_indices]) # keep only chains for selected parameters samples[:, angle_indices] = np.degrees( samples[:, angle_indices]) # convert angles from rad to deg samples[:, secondary_mass_indices] *= u.solMass.to(u.jupiterMass) # convert to Jupiter masses for companions if 'labels' not in corner_kwargs: # use default labels if user didn't already supply them reduced_labels_list = [] for i in np.arange(len(param_indices)): label_key = param_list[i] if label_key.startswith("m") and label_key != 'm0' and label_key != 'mtot': body_num = label_key[1] label_key = "m" elif label_key == 'm0' or label_key == 'mtot' or label_key.startswith('plx'): body_num = "" # maintain original label key elif label_key in ['pm_ra', 'pm_dec', 'alpha0', 'delta0']: body_num = "" elif label_key.startswith("gamma") or label_key.startswith("sigma"): body_num = "" label_key = label_key[0:3] else: body_num = label_key[-1] label_key = label_key[0:-1] reduced_labels_list.append(default_labels[label_key].format(body_num)) corner_kwargs['labels'] = reduced_labels_list figure = corner.corner(samples, **corner_kwargs) return figure
[docs]def plot_orbits(results, object_to_plot=1, start_mjd=51544., num_orbits_to_plot=100, num_epochs_to_plot=100, square_plot=True, show_colorbar=True, cmap=cmap, sep_pa_color='lightgrey', sep_pa_end_year=2025.0, cbar_param='Epoch [year]', mod180=False, rv_time_series=False, plot_astrometry=True, plot_astrometry_insts=False, plot_errorbars=True, fig=None): """ Plots one orbital period for a select number of fitted orbits for a given object, with line segments colored according to time Args: object_to_plot (int): which object to plot (default: 1) start_mjd (float): MJD in which to start plotting orbits (default: 51544, the year 2000) num_orbits_to_plot (int): number of orbits to plot (default: 100) num_epochs_to_plot (int): number of points to plot per orbit (default: 100) square_plot (Boolean): Aspect ratio is always equal, but if square_plot is True (default), then the axes will be square, otherwise, white space padding is used show_colorbar (Boolean): Displays colorbar to the right of the plot [True] cmap (matplotlib.cm.ColorMap): color map to use for making orbit tracks (default: modified Purples_r) sep_pa_color (string): any valid matplotlib color string, used to set the color of the orbit tracks in the Sep/PA panels (default: 'lightgrey'). sep_pa_end_year (float): decimal year specifying when to stop plotting orbit tracks in the Sep/PA panels (default: 2025.0). cbar_param (string): options are the following: 'Epoch [year]', 'sma1', 'ecc1', 'inc1', 'aop1', 'pan1', 'tau1', 'plx. Number can be switched out. Default is Epoch [year]. mod180 (Bool): if True, PA will be plotted in range [180, 540]. Useful for plotting short arcs with PAs that cross 360 deg during observations (default: False) rv_time_series (Boolean): if fitting for secondary mass using MCMC for rv fitting and want to display time series, set to True. plot_astrometry (Boolean): set to True by default. Plots the astrometric data. plot_astrometry_insts (Boolean): set to False by default. Plots the astrometric data by instruments. plot_errorbars (Boolean): set to True by default. Plots error bars of measurements fig (matplotlib.pyplot.Figure): optionally include a predefined Figure object to plot the orbit on. Most users will not need this keyword. Return: ``matplotlib.pyplot.Figure``: the orbit plot if input is valid, ``None`` otherwise (written): Henry Ngo, Sarah Blunt, 2018 Additions by Malena Rice, 2019 """ if Time(start_mjd, format='mjd').decimalyear >= sep_pa_end_year: raise ValueError('start_mjd keyword date must be less than sep_pa_end_year keyword date.') if object_to_plot > results.num_secondary_bodies: raise ValueError("Only {0} secondary bodies being fit. Requested to plot body {1} which is out of range".format(results.num_secondary_bodies, object_to_plot)) if object_to_plot == 0: raise ValueError("Plotting the primary's orbit is currently unsupported. Stay tuned.") with warnings.catch_warnings(): warnings.simplefilter('ignore', ErfaWarning) data = results.data[results.data['object'] == object_to_plot] possible_cbar_params = [ 'sma', 'ecc', 'inc', 'aop' 'pan', 'tau', 'plx' ] if cbar_param == 'Epoch [year]': pass elif cbar_param[0:3] in possible_cbar_params: index = results.param_idx[cbar_param] else: raise Exception( "Invalid input; acceptable inputs include 'Epoch [year]', 'plx', 'sma1', 'ecc1', 'inc1', 'aop1', 'pan1', 'tau1', 'sma2', 'ecc2', ...)" ) # Select random indices for plotted orbit num_orbits = len(results.post[:, 0]) if num_orbits_to_plot > num_orbits: num_orbits_to_plot = num_orbits choose = np.random.randint(0, high=num_orbits, size=num_orbits_to_plot) # Get posteriors from random indices standard_post = [] if results.sampler_name == 'MCMC': # Convert the randomly chosen posteriors to standard keplerian set for i in np.arange(num_orbits_to_plot): orb_ind = choose[i] param_set = np.copy(results.post[orb_ind]) standard_post.append(results.basis.to_standard_basis(param_set)) else: # For OFTI, posteriors are already converted for i in np.arange(num_orbits_to_plot): orb_ind = choose[i] standard_post.append(results.post[orb_ind]) standard_post = np.array(standard_post) sma = standard_post[:, results.standard_param_idx['sma{}'.format(object_to_plot)]] ecc = standard_post[:, results.standard_param_idx['ecc{}'.format(object_to_plot)]] inc = standard_post[:, results.standard_param_idx['inc{}'.format(object_to_plot)]] aop = standard_post[:, results.standard_param_idx['aop{}'.format(object_to_plot)]] pan = standard_post[:, results.standard_param_idx['pan{}'.format(object_to_plot)]] tau = standard_post[:, results.standard_param_idx['tau{}'.format(object_to_plot)]] plx = standard_post[:, results.standard_param_idx['plx']] # Then, get the other parameters if 'mtot' in results.labels: mtot = standard_post[:, results.standard_param_idx['mtot']] elif 'm0' in results.labels: m0 = standard_post[:, results.standard_param_idx['m0']] m1 = standard_post[:, results.standard_param_idx['m{}'.format(object_to_plot)]] mtot = m0 + m1 raoff = np.zeros((num_orbits_to_plot, num_epochs_to_plot)) deoff = np.zeros((num_orbits_to_plot, num_epochs_to_plot)) vz_star = np.zeros((num_orbits_to_plot, num_epochs_to_plot)) epochs = np.zeros((num_orbits_to_plot, num_epochs_to_plot)) # Loop through each orbit to plot and calcualte ra/dec offsets for all points in orbit # Need this loops since epochs[] vary for each orbit, unless we want to just plot the same time period for all orbits for i in np.arange(num_orbits_to_plot): # Compute period (from Kepler's third law) period = np.sqrt(4*np.pi**2.0*(sma*u.AU)**3/(consts.G*(mtot*u.Msun))) period = period.to(u.day).value # Create an epochs array to plot num_epochs_to_plot points over one orbital period epochs[i, :] = np.linspace(start_mjd, float( start_mjd+period[i]), num_epochs_to_plot) # Calculate ra/dec offsets for all epochs of this orbit raoff0, deoff0, _ = kepler.calc_orbit( epochs[i, :], sma[i], ecc[i], inc[i], aop[i], pan[i], tau[i], plx[i], mtot[i], tau_ref_epoch=results.tau_ref_epoch ) raoff[i, :] = raoff0 deoff[i, :] = deoff0 # Create a linearly increasing colormap for our range of epochs if cbar_param != 'Epoch [year]': cbar_param_arr = results.post[:, index] norm = mpl.colors.Normalize(vmin=np.min(cbar_param_arr), vmax=np.max(cbar_param_arr)) norm_yr = mpl.colors.Normalize(vmin=np.min( cbar_param_arr), vmax=np.max(cbar_param_arr)) elif cbar_param == 'Epoch [year]': min_cbar_date = np.min(epochs) max_cbar_date = np.max(epochs[-1, :]) # if we're plotting orbital periods greater than 1,000 yrs, limit the colorbar dynamic range if max_cbar_date - min_cbar_date > 1000 * 365.25: max_cbar_date = min_cbar_date + 1000 * 365.25 norm = mpl.colors.Normalize(vmin=min_cbar_date, vmax=max_cbar_date) norm_yr = mpl.colors.Normalize( vmin=Time(min_cbar_date, format='mjd').decimalyear, vmax=Time(max_cbar_date, format='mjd').decimalyear ) # Before starting to plot rv data, make sure rv data exists: rv_indices = np.where(data['quant_type'] == 'rv') if rv_time_series and len(rv_indices) == 0: warnings.warn("Unable to plot radial velocity data.") rv_time_series = False # Create figure for orbit plots if fig is None: fig = plt.figure(figsize=(14, 6)) if rv_time_series: fig = plt.figure(figsize=(14, 9)) ax = plt.subplot2grid((3, 14), (0, 0), rowspan=2, colspan=6) else: fig = plt.figure(figsize=(14, 6)) ax = plt.subplot2grid((2, 14), (0, 0), rowspan=2, colspan=6) else: plt.set_current_figure(fig) if rv_time_series: ax = plt.subplot2grid((3, 14), (0, 0), rowspan=2, colspan=6) else: ax = plt.subplot2grid((2, 14), (0, 0), rowspan=2, colspan=6) astr_inds=np.where((~np.isnan(data['quant1'])) & (~np.isnan(data['quant2']))) astr_epochs=data['epoch'][astr_inds] radec_inds = np.where(data['quant_type'] == 'radec') seppa_inds = np.where(data['quant_type'] == 'seppa') # transform RA/Dec points to Sep/PA sep_data = np.copy(data['quant1']) sep_err = np.copy(data['quant1_err']) pa_data = np.copy(data['quant2']) pa_err = np.copy(data['quant2_err']) if len(radec_inds[0] > 0): sep_from_ra_data, pa_from_dec_data = orbitize.system.radec2seppa( data['quant1'][radec_inds], data['quant2'][radec_inds] ) num_radec_pts = len(radec_inds[0]) sep_err_from_ra_data = np.empty(num_radec_pts) pa_err_from_dec_data = np.empty(num_radec_pts) for j in np.arange(num_radec_pts): sep_err_from_ra_data[j], pa_err_from_dec_data[j], _ = orbitize.system.transform_errors( np.array(data['quant1'][radec_inds][j]), np.array(data['quant2'][radec_inds][j]), np.array(data['quant1_err'][radec_inds][j]), np.array(data['quant2_err'][radec_inds][j]), np.array(data['quant12_corr'][radec_inds][j]), orbitize.system.radec2seppa ) sep_data[radec_inds] = sep_from_ra_data sep_err[radec_inds] = sep_err_from_ra_data pa_data[radec_inds] = pa_from_dec_data pa_err[radec_inds] = pa_err_from_dec_data # Transform Sep/PA points to RA/Dec ra_data = np.copy(data['quant1']) ra_err = np.copy(data['quant1_err']) dec_data = np.copy(data['quant2']) dec_err = np.copy(data['quant2_err']) if len(seppa_inds[0] > 0): ra_from_seppa_data, dec_from_seppa_data = orbitize.system.seppa2radec( data['quant1'][seppa_inds], data['quant2'][seppa_inds] ) num_seppa_pts = len(seppa_inds[0]) ra_err_from_seppa_data = np.empty(num_seppa_pts) dec_err_from_seppa_data = np.empty(num_seppa_pts) for j in np.arange(num_seppa_pts): ra_err_from_seppa_data[j], dec_err_from_seppa_data[j], _ = orbitize.system.transform_errors( np.array(data['quant1'][seppa_inds][j]), np.array(data['quant2'][seppa_inds][j]), np.array(data['quant1_err'][seppa_inds][j]), np.array(data['quant2_err'][seppa_inds][j]), np.array(data['quant12_corr'][seppa_inds][j]), orbitize.system.seppa2radec ) ra_data[seppa_inds] = ra_from_seppa_data ra_err[seppa_inds] = ra_err_from_seppa_data dec_data[seppa_inds] = dec_from_seppa_data dec_err[seppa_inds] = dec_err_from_seppa_data # For plotting different astrometry instruments if plot_astrometry_insts: astr_colors = ('purple','#FF7F11', '#11FFE3', '#14FF11', '#7A11FF', '#FF1919') astr_symbols = ( 'o', '*', 'p', 's') ax_colors = itertools.cycle(astr_colors) ax_symbols = itertools.cycle(astr_symbols) astr_data = data[astr_inds] astr_insts = np.unique(data[astr_inds]['instrument']) # Indices corresponding to each instrument in datafile astr_inst_inds = {} for i in range(len(astr_insts)): astr_inst_inds[astr_insts[i]]=np.where(astr_data['instrument']==astr_insts[i].encode())[0] # Plot each orbit (each segment between two points coloured using colormap) for i in np.arange(num_orbits_to_plot): points = np.array([raoff[i, :], deoff[i, :]]).T.reshape(-1, 1, 2) segments = np.concatenate([points[:-1], points[1:]], axis=1) lc = LineCollection( segments, cmap=cmap, norm=norm, linewidth=1.0 ) if cbar_param != 'Epoch [year]': lc.set_array(np.ones(len(epochs[0]))*cbar_param_arr[i]) elif cbar_param == 'Epoch [year]': lc.set_array(epochs[i, :]) ax.add_collection(lc) # if plot_astrometry: # # Plot astrometry along with instruments # if plot_astrometry_insts: # for i in range(len(astr_insts)): # ra = ra_data[astr_inst_inds[astr_insts[i]]] # dec = dec_data[astr_inst_inds[astr_insts[i]]] # if plot_errorbars: # xerr = ra_err[astr_inst_inds[astr_insts[i]]] # yerr = dec_err[astr_inst_inds[astr_insts[i]]] # else: # xerr = None # yerr = None # ax.errorbar(ra, dec, xerr=xerr, yerr=yerr, marker=next(ax_symbols), c=next(ax_colors), zorder=10, label=astr_insts[i], linestyle='', ms=5, capsize=2) # else: # if plot_errorbars: # xerr = ra_err # yerr = dec_err # else: # xerr = None # yerr = None # ax.errorbar(ra_data, dec_data, xerr=xerr, yerr=yerr, marker='o', c='#FF7F11', zorder=10, linestyle='', capsize=2, ms=5) # modify the axes if square_plot: adjustable_param = 'datalim' else: adjustable_param = 'box' ax.set_aspect('equal', adjustable=adjustable_param) ax.set_xlabel('$\\Delta$RA [mas]') ax.set_ylabel('$\\Delta$Dec [mas]') ax.locator_params(axis='x', nbins=6) ax.locator_params(axis='y', nbins=6) ax.invert_xaxis() # To go to a left-handed coordinate system # plot sep/PA and/or rv zoom-in panels if rv_time_series: ax1 = plt.subplot2grid((3, 14), (0, 8), colspan=6) ax2 = plt.subplot2grid((3, 14), (1, 8), colspan=6) ax3 = plt.subplot2grid((3, 14), (2, 0), colspan=14, rowspan=1) ax2.set_ylabel('PA [$^{{\\circ}}$]') ax1.set_ylabel('$\\rho$ [mas]') ax3.set_ylabel('RV [km/s]') ax3.set_xlabel('Epoch') ax2.set_xlabel('Epoch') plt.subplots_adjust(hspace=0.3) else: ax1 = plt.subplot2grid((2, 14), (0, 9), colspan=6) ax2 = plt.subplot2grid((2, 14), (1, 9), colspan=6) ax2.set_ylabel('PA [$^{{\\circ}}$]') ax1.set_ylabel('$\\rho$ [mas]') ax2.set_xlabel('Epoch') if plot_astrometry_insts: ax1_colors = itertools.cycle(astr_colors) ax1_symbols = itertools.cycle(astr_symbols) ax2_colors = itertools.cycle(astr_colors) ax2_symbols = itertools.cycle(astr_symbols) epochs_seppa = np.zeros((num_orbits_to_plot, num_epochs_to_plot)) for i in np.arange(num_orbits_to_plot): epochs_seppa[i, :] = np.linspace( start_mjd, Time(sep_pa_end_year, format='decimalyear').mjd, num_epochs_to_plot ) # Calculate ra/dec offsets for all epochs of this orbit if rv_time_series: raoff0, deoff0, _ = kepler.calc_orbit( epochs_seppa[i, :], sma[i], ecc[i], inc[i], aop[i], pan[i], tau[i], plx[i], mtot[i], tau_ref_epoch=results.tau_ref_epoch, mass_for_Kamp=m0[i] ) raoff[i, :] = raoff0 deoff[i, :] = deoff0 else: raoff0, deoff0, _ = kepler.calc_orbit( epochs_seppa[i, :], sma[i], ecc[i], inc[i], aop[i], pan[i], tau[i], plx[i], mtot[i], tau_ref_epoch=results.tau_ref_epoch ) raoff[i, :] = raoff0 deoff[i, :] = deoff0 yr_epochs = Time(epochs_seppa[i, :], format='mjd').decimalyear seps, pas = orbitize.system.radec2seppa(raoff[i, :], deoff[i, :], mod180=mod180) plt.sca(ax1) plt.plot(yr_epochs, seps, color=sep_pa_color, zorder=1) plt.sca(ax2) plt.plot(yr_epochs, pas, color=sep_pa_color, zorder=1) # Plot sep/pa instruments if plot_astrometry_insts: for i in range(len(astr_insts)): sep = sep_data[astr_inst_inds[astr_insts[i]]] pa = pa_data[astr_inst_inds[astr_insts[i]]] epochs = astr_epochs[astr_inst_inds[astr_insts[i]]] if plot_errorbars: serr = sep_err[astr_inst_inds[astr_insts[i]]] perr = pa_err[astr_inst_inds[astr_insts[i]]] else: yerr = None perr = None plt.sca(ax1) plt.errorbar(Time(epochs,format='mjd').decimalyear,sep,yerr=serr,ms=5, linestyle='',marker=next(ax1_symbols),c=next(ax1_colors),zorder=10,label=astr_insts[i], capsize=2) plt.sca(ax2) plt.errorbar(Time(epochs,format='mjd').decimalyear,pa,yerr=perr,ms=5, linestyle='',marker=next(ax2_symbols),c=next(ax2_colors),zorder=10, capsize=2) plt.sca(ax1) plt.legend(title='Instruments', bbox_to_anchor=(1.3, 1), loc='upper right') else: if plot_errorbars: serr = sep_err perr = pa_err else: yerr = None perr = None plt.sca(ax1) plt.errorbar(Time(astr_epochs,format='mjd').decimalyear,sep_data,yerr=serr,ms=5, linestyle='',marker='o',c='purple',zorder=2, capsize=2) plt.sca(ax2) plt.errorbar(Time(astr_epochs,format='mjd').decimalyear,pa_data,yerr=perr,ms=5, linestyle='',marker='o',c='purple',zorder=2, capsize=2) if rv_time_series: rv_data = results.data[results.data['object'] == 0] rv_data = rv_data[rv_data['quant_type'] == 'rv'] # switch current axis to rv panel plt.sca(ax3) # get list of rv instruments insts = np.unique(rv_data['instrument']) if len(insts) == 0: insts = ['defrv'] # get gamma/sigma labels and corresponding positions in the posterior gams=['gamma_'+inst for inst in insts] sigs = ['sigma_'+inst for inst in insts] if isinstance(results.labels,list): labels=np.array(results.labels) else: labels=results.labels # get the indices corresponding to each gamma within results.labels gam_idx=[np.where(labels==inst_gamma)[0] for inst_gamma in gams] # indices corresponding to each instrument in the datafile inds={} for i in range(len(insts)): inds[insts[i]]=np.where(rv_data['instrument']==insts[i].encode())[0] # choose the orbit with the best log probability best_like=np.where(results.lnlike==np.amax(results.lnlike))[0][0] # Get the posteriors for this index and convert to standard basis best_post = results.basis.to_standard_basis(results.post[best_like].copy()) # Get the masses for the best posteriors: best_m0 = best_post[results.standard_param_idx['m0']] best_m1 = best_post[results.standard_param_idx['m{}'.format(object_to_plot)]] best_mtot = best_m0 + best_m1 # colour/shape scheme scheme for rv data points clrs=('purple', '#0496FF','#372554','#FF1053','#3A7CA5','#143109') symbols=('o','^','v','s') ax3_colors = itertools.cycle(clrs) ax3_symbols = itertools.cycle(symbols) # get rvs and plot them for i,name in enumerate(inds.keys()): inst_data=rv_data[inds[name]] rvs=inst_data['quant1'] epochs=inst_data['epoch'] epochs=Time(epochs, format='mjd').decimalyear rvs -= best_post[results.param_idx[gams[i]]] if plot_errorbars: yerr = inst_data['quant1_err'] yerr = np.sqrt(yerr**2 + best_post[results.param_idx[sigs[i]]]**2) plt.errorbar(epochs,rvs,yerr=yerr,ms=5, linestyle='',marker=next(ax3_symbols),c=next(ax3_colors),label=name,zorder=5,capsize=2) if len(inds.keys()) == 1 and 'defrv' in inds.keys(): pass else: plt.legend() # calculate the predicted rv trend using the best orbit _, _, vz = kepler.calc_orbit( epochs_seppa[0, :], best_post[results.standard_param_idx['sma{}'.format(object_to_plot)]], best_post[results.standard_param_idx['ecc{}'.format(object_to_plot)]], best_post[results.standard_param_idx['inc{}'.format(object_to_plot)]], best_post[results.standard_param_idx['aop{}'.format(object_to_plot)]], best_post[results.standard_param_idx['pan{}'.format(object_to_plot)]], best_post[results.standard_param_idx['tau{}'.format(object_to_plot)]], best_post[results.standard_param_idx['plx']], best_mtot, tau_ref_epoch=results.tau_ref_epoch, mass_for_Kamp=best_m0 ) vz=vz*-(best_m1)/np.median(best_m0) # plot rv trend plt.plot(Time(epochs_seppa[0, :],format='mjd').decimalyear, vz, color=sep_pa_color, zorder=1) # add colorbar if show_colorbar: if rv_time_series: # Create an axes for colorbar. The position of the axes is calculated based on the position of ax. # You can change x1.0.05 to adjust the distance between the main image and the colorbar. # You can change 0.02 to adjust the width of the colorbar. cbar_ax = fig.add_axes( [ax.get_position().x1+0.005, ax.get_position().y0, 0.02, ax.get_position().height]) cbar = mpl.colorbar.ColorbarBase( cbar_ax, cmap=cmap, norm=norm_yr, orientation='vertical', label=cbar_param) else: # xpos, ypos, width, height, in fraction of figure size cbar_ax = fig.add_axes([0.47, 0.15, 0.015, 0.7]) cbar = mpl.colorbar.ColorbarBase( cbar_ax, cmap=cmap, norm=norm_yr, orientation='vertical', label=cbar_param) ax1.locator_params(axis='x', nbins=6) ax1.locator_params(axis='y', nbins=6) ax2.locator_params(axis='x', nbins=6) ax2.locator_params(axis='y', nbins=6) return fig