def plot_dist(real_sample):
alphascale = 1 / real_sample['gdir_prad_err1']
alphascale /= np.max(alphascale)
real_sample['alpha'] = alphascale
for i, row in real_sample.iterrows():
p = row['koi_period']
r = row['gdir_prad']
e = row['gdir_prad_err1']
a = row['alpha']
pl.errorbar(p, r, yerr=e, alpha=a, fmt='k.')
# pl.errorbar(real_sample['koi_period'], real_sample['gdir_prad'],
# yerr=real_sample['gdir_prad_err1'], fmt='k.')
pl.loglog()
cksgaia.plot.config.logscale_rad(axis='y')
ax = pl.gca()
ax.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.xaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%0.1f'))
pl.xlim(0.3, 100)
pl.xlabel('Orbital Period [days]')
# pl.axhline(1.75, lw=3, color='r', linestyle='dashed')
plot_box(e_corners, 'b-', lw=2)
plot_box(sn_corners, 'b-', lw=2)
plot_box(gap_corners, 'g-', lw=2)
xt = [0.3, 1, 3, 10, 30, 100]
yt = [0.7, 1, 1.5, 2, 3, 4, 6]
pl.xticks(xt, xt)
pl.yticks(yt, yt)
pl.minorticks_off()
pl.grid(False)
def plot_setticks(x=True, y=True):
pl.minorticks_on()
ax = pl.gca()
if x:
ax.xaxis.set_major_locator(pl.AutoLocator())
x_major = ax.xaxis.get_majorticklocs()
dx_minor = (x_major[-1] - x_major[0]) / (len(x_major) - 1) / 5.0
ax.xaxis.set_minor_locator(pl.MultipleLocator(dx_minor))
else:
pl.minorticks_off()
if y:
ax.yaxis.set_major_locator(pl.AutoLocator())
y_major = ax.yaxis.get_majorticklocs()
dy_minor = (y_major[-1] - y_major[0]) / (len(y_major) - 1) / 5.0
ax.yaxis.set_minor_locator(pl.MultipleLocator(dy_minor))
else:
pl.minorticks_off()
def plot_setticks(x=True, y=True):
pl.minorticks_on()
ax = pl.gca()
if x:
ax.xaxis.set_major_locator(pl.AutoLocator())
x_major = ax.xaxis.get_majorticklocs()
dx_minor = (x_major[-1] - x_major[0]) / (len(x_major) - 1) / 5.
ax.xaxis.set_minor_locator(pl.MultipleLocator(dx_minor))
else:
pl.minorticks_off()
if y:
ax.yaxis.set_major_locator(pl.AutoLocator())
y_major = ax.yaxis.get_majorticklocs()
dy_minor = (y_major[-1] - y_major[0]) / (len(y_major) - 1) / 5.
ax.yaxis.set_minor_locator(pl.MultipleLocator(dy_minor))
else:
pl.minorticks_off()
def fig_per_prad():
fig = pl.figure(figsize=(4, 3.5))
pl.loglog()
df = cksgaia.io.load_table('cksgaia-planets')
df = df
#df = df.query('cks_fp==0 and ((gdir_prad /gdir_prad_err1) > 0.1 ) ')
df = df.query('cks_fp==0')
pl.plot(df.koi_period, df.gdir_prad, '.', color='RoyalBlue', ms=2)
yt = [0.2, 0.3, 0.5, 0.7, 1, 2, 3, 5, 7, 10, 20]
pl.yticks(yt, yt)
xt = [0.1, 0.3, 1, 3, 10, 30, 100, 300, 1000]
_xt, _xtl = pl.xticks(xt, xt)
pl.setp(_xtl, rotation=0)
pl.xlim(0.1, 1000)
pl.ylim(0.25, 20)
pl.xlabel('Period [days]')
pl.ylabel('Planet Size [Earth radii]')
pl.tight_layout(True)
pl.minorticks_off()
def fig_insol_prad():
fig = pl.figure(figsize=(4, 3.5))
pl.loglog()
df = cksgaia.io.load_table('cksgaia-planets')
df = df
df = df.query('cks_fp==0')
pl.plot(df.giso_insol, df.gdir_prad, '.', color='RoyalBlue', ms=2)
yt = [0.2, 0.3, 0.5, 0.7, 1, 2, 3, 5, 7, 10, 20]
pl.yticks(yt, yt)
#xt = [10000,3000,1000,300,100,30,10,3,1]
#_xt, _xtl = pl.xticks(xt,xt)
#pl.setp(_xtl, rotation=45)
pl.xlim(10000, 1)
pl.ylim(0.25, 20)
pl.xlabel('Stellar light intensity relative to Earth')
pl.ylabel('Planet Size [Earth radii]')
pl.tight_layout(True)
pl.minorticks_off()
def plot_fit(obs, obs_err, photbands, pars={}, constraints={}, grids=[], gridnames=[], result='best', plot_components=True, plot_colors=False, plot_constraints=False):
pars = pars.copy()
# use model from 'best' results or 'pc' results
resi = 0 if result == 'best' else 1
print(gridnames)
colors = np.array([filters.is_color(p) for p in photbands])
psystems = np.array([p.split('.')[0] for p in photbands])
waves = np.array([filters.eff_wave(p) for p in photbands[~colors]])
#-- def system colors
all_systems = set(psystems)
colorcycle = cm.rainbow(np.linspace(0, 1, len(all_systems)))
system_colors = {}
for p, c in zip(all_systems, colorcycle):
system_colors[p] = c
abs_xlim = (0.9 * np.min(waves), 1.1 * np.max(waves) )
if not len(list(pars.keys())) == 0:
ipars = pars.copy()
for key, value in list(pars.items()):
ipars[key] = [value[resi]]
pars[key] = value[resi]
_ = ipars.pop('d')
_ = pars.pop('d')
syn, Labs = model.get_itable(grid=grids, photbands=photbands, **ipars)
syn = syn[:,0]
scale = pars['scale']
# plot the fit of the absolute data
#====================================
if not plot_colors and not plot_constraints:
ax1 = pl.subplot2grid((3,3), (0, 0), colspan=3, rowspan=2)
else:
ax1 = pl.subplot2grid((3,3), (0, 0), colspan=2, rowspan=2)
if not len(list(pars.keys())) == 0:
#-- synthetic integrated fluxes
pl.plot(waves, scale*syn[~colors], 'xr')
#-- if possible, plot a non integrated model
# can only be done if provided gridnames are not paths to integrated files.
if len(gridnames) > 0 and not os.path.isfile(gridnames[0]):
#-- synthetic model
ebv = pars['ebv']
wave, flux = model.get_table(grid=gridnames, **pars)
flux = reddening.redden(flux, wave=wave, ebv=ebv, rtype='flux', law='fitzpatrick2004')
#ascii.write([wave, scale*flux], 'binary_model.txt', names=['wave', 'flux'], overwrite=True)
pl.plot(wave, scale*flux, '-r')
#-- plot components
if plot_components and 'teff2' in pars:
wave, flux = model.get_table(grid=gridnames[0], teff=pars['teff'], logg=pars['logg'], rad=pars['rad'], ebv=pars['ebv'])
flux = reddening.redden(flux, wave=wave, ebv=ebv, rtype='flux', law='fitzpatrick2004')
#ascii.write([wave, scale*flux], 'primary_model.txt', names=['wave', 'flux'], overwrite=True)
pl.plot(wave, scale*flux, '--g')
wave, flux = model.get_table(grid=gridnames[1], teff=pars['teff2'], logg=pars['logg2'], rad=pars['rad2'], ebv=pars['ebv'])
flux = reddening.redden(flux, wave=wave, ebv=ebv, rtype='flux', law='fitzpatrick2004')
#ascii.write([wave, scale*flux], 'secondary_model.txt', names=['wave', 'flux'], overwrite=True)
pl.plot(wave, scale*flux, '--b')
wave, flux, error, band = [], [], [], []
for system in all_systems:
s = np.where((psystems == system) & (~colors))
if len(obs[s]) == 0: continue
w = np.array([filters.eff_wave(p) for p in photbands[s]])
pl.errorbar(w, obs[s], yerr=obs_err[s], ls='', marker='o',
color=system_colors[system], label=system)
wave.extend(w)
flux.extend(obs[s])
error.extend(obs_err[s])
band.extend([system for i in w])
ascii.write([wave, flux, error, band], 'observations.txt', names=['wave', 'flux', 'error', 'band'], overwrite=True)
pl.xlim(abs_xlim)
pl.ylim([0.9*np.min(obs[~colors]), 1.1*np.max(obs[~colors])])
pl.legend(loc='best', prop={'size': 9}, numpoints=1)
ax1.set_xscale("log", nonposx='clip')
ax1.set_yscale("log", nonposy='clip')
pl.ylabel('Absolute Flux')
#-- add band names to top of axis
ax2 = ax1.twiny()
ax2.set_xscale("log", nonposx='clip')
ax2.set_xlim(abs_xlim)
waves = np.array([filters.eff_wave(p) for p in photbands[~colors]])
bandnames = np.array([b.split('.')[-1] for b in photbands[~colors]])
ax2.set_xticks(waves)
ax2.set_xticklabels(bandnames)
ax2.xaxis.tick_top()
pl.minorticks_off()
# plot O-C of the absolute measurements
#====================================
if not plot_colors and not plot_constraints:
ax = pl.subplot2grid((3,3), (2, 0), colspan=3, rowspan=1)
else:
ax = pl.subplot2grid((3,3), (2, 0), colspan=2, rowspan=1)
for system in all_systems:
s = np.where((psystems == system) & (~colors))
if len(obs[s]) == 0: continue
w = np.array([filters.eff_wave(p) for p in photbands[s]])
y = ((obs - syn*scale) / obs)[s]
yerr = (obs_err / obs)[s]
y = 2.5*np.log10(syn[s]*scale / obs[s])
yerr = 2.5 / np.log(10.0) * obs_err[s] / obs[s]
pl.errorbar(w, -y , yerr=yerr, ls='', marker='o', color=system_colors[system])
pl.figtext(0.96, 0.96, '$\chi^2$ = {:0.2f}'.format(pars['chi2']), ha='right')
pl.axhline(y=0, color='k', ls='--')
pl.xlim(abs_xlim)
pl.gca().set_xscale('log',nonposx='clip')
pl.ylabel('(O-C) (mag)')
pl.xlabel('Wavelength (AA)')
# plot O-C of the color fits
#====================================
if len(obs[colors]) > 0 and plot_colors:
ax = pl.subplot2grid((3,3), (0, 2), colspan=1, rowspan=2)
y = np.array(list(range(len(obs[colors]))))
x = obs[colors] - syn[colors]
x_err = obs_err[colors]
oc_sys = psystems[colors]
for system in all_systems:
s = np.where(oc_sys == system)
if len(x[s]) == 0: continue
pl.errorbar(x[s], y[s], xerr=x_err[s], ls='', marker='o',
color=system_colors[system])
pl.axvline(x=0, color='k', ls='--')
yticknames = [b.split('.')[1] for b in photbands[colors]]
pl.yticks(y, yticknames)
pl.ylim([-y[-1]*0.1, y[-1]*1.1])
ax.yaxis.tick_right()
ax.xaxis.tick_top()
ax.xaxis.set_label_position('top')
pl.xlabel('O-C')
# plot fractional O-C of the constraints
#=======================================
if plot_constraints:
ax = pl.subplot2grid((3,3), (2, 2), colspan=1, rowspan=1)
x, xticknames = [], []
if 'q' in constraints:
q, ql, qu = constraints['q']
ax.plot([1], pars['q'], 'k_', ms=15, mew=2)
ax.errorbar([1], [q], yerr = [[ql], [qu]], marker='o')
x.append(1)
xticknames.append('q')
if 'distance' in constraints:
ax2 = ax.twinx()
d = constraints['distance'][0] / 44365810.04823812
de = constraints['distance'][1] / 44365810.04823812
ax2.plot([2], pars['d'], 'k_', ms=15, mew=2)
ax2.errorbar([2], [d], yerr = de, marker='o')
x.append(2)
xticknames.append('distance')
pl.xlim([0.5, 2.5])
pl.xticks(x, xticknames)
pl.figtext(0.03, 0.96, 'model = {}'.format(result))
def contour_plot_kde(physmerge,
xcol,
ycol,
xlim,
ylim,
ylog=True,
pltxlim=None,
pltylim=None,
epos=[3000, 5],
cont=True,
nodata=False,
weighted=False,
nstars=36075.,
eaoff=(0, -30),
clabel=None,
vlims=(0.0, 0.05),
kwidth=None,
single=False,
cbar=True):
"""Plot contour plots
Make the contour plots associated with Figures 8-10 in Fulton et al. (2017)
Args:
physmerge (DataFrame): Pandas DataFrame with the input planet catalogue
xcol (string): name of x-axis column to plot
ycol (string): name of y-axis column to plot
xlim (tuple): x axis limits (min, max) for the wKDE calculation, should be slightly outside the pltxlim parameter
ylim (tuple): y axis limits (min, max) for the wKDE calculation, should be slightly outside the pltylim parameter
ylog (bool): (optional) log scale for y-axis?
pltxlim (tuple): (optional) x-axis limit to display on plot (if different from xlim)
pltylim (tuple): (optional) y-axis limit to display on plot (if different from ylim)
epos (tuple): (optional) position to plot the typical uncertainty error bar
cont (bool): (optional) plot the contours (default = True)
nodata (bool): (optional) omit the datapoints from the plot (default = False)
weighted (bool): (optional) use the 'weight' column to calculate a weighted KDE (default = False)
nstars (float): (optional) total number of stars observed in sample (default = 36075.0). Used to calculate
absolute occurrence scale
eaoff (tuple): (optional) text offset for label of the typical uncertainty error bar (default = (0, -70))
clabel (string): (optional) string to label color bar scale
vlims (tuple): (optional) colorscale limits (default = (0.0, 0.05))
kwidth (tuple): (optional) kernel width in units of (x,y)
cbar (bool): (optional) plot colorbar?
Returns:
tuple: (axes object, 10**(grid x values), 10**(grid y values), grid z values)
"""
# fig = pl.figure(figsize=(13, 8))
crop = physmerge[(physmerge[ycol] < ylim[1]) & (physmerge[ycol] > ylim[0])
& (physmerge[xcol] < xlim[1]) &
(physmerge[xcol] < xlim[1])]
nplanets = float(len(crop))
if weighted:
weights = crop['weight'].values
else:
weights = np.ones_like(crop[xcol].values)
xerr1 = crop[xcol + '_err1'].values
yerr1 = crop[ycol + '_err1'].values
if kwidth != None:
print(kwidth)
xerr1 = np.zeros_like(xerr1) + kwidth[0] * crop[xcol].values
yerr1 = np.zeros_like(yerr1) + kwidth[1] * crop[ycol].values
xi, yi, zi = fitting.wkde2D(crop[xcol].values,
crop[ycol].values,
xerr1,
yerr1,
weights,
xlim=xlim,
ylim=ylim,
nstars=nstars)
print(nplanets, (crop['gdir_prad_err1'] / crop['gdir_prad']).median())
# cmap = plt.cm.inferno_r
# cmap = plt.cm.gray_r
# cmap = plt.cm.bone_r
# cmap = plt.cm.gist_heat_r
# cmap = plt.cm.magma_r
cmap = plt.cm.afmhot_r
# cmap = plt.cm.Blues_r
vmin, vmax = vlims
levels = np.arange(vmin, vmax + 0.0001, 0.005)
if len(levels) <= 7:
levels = np.linspace(vmin, vmax + 1e-4, 11)
if single:
levels = [0, np.mean([vmin, vmax])]
if cont and not single:
CS = plt.contourf(10**xi,
10**yi,
zi,
levels=levels,
cmap=cmap,
vmin=vmin,
vmax=vmax)
for c in CS.collections:
c.set_edgecolor("face")
if single:
CS = plt.contour(10**xi,
10**yi,
zi,
levels=levels,
cmap=cmap,
vmin=vmin,
vmax=vmax,
lw=3)
for c in CS.collections:
c.set_edgecolor("face")
# pl.plot(big_sdist, big_pdist, 'k.', color='0.3', ms=1.0)
if not nodata:
pl.plot(crop[xcol],
crop[ycol],
'ko',
ms=3,
markeredgewidth=0.5,
markeredgecolor='w')
from .. import misc
xe, ye = epos
xerr1, xerr2 = misc.frac_err(physmerge, xe, xcol)
yerr1, yerr2 = misc.frac_err(physmerge, ye, ycol)
_, caps, _ = pl.errorbar(xe,
ye,
yerr=[[yerr1], [yerr2]],
xerr=[[xerr1], [xerr2]],
markeredgewidth=0,
lw=1,
capsize=2,
markeredgecolor='w',
fmt='k.',
ms=0.1)
for cap in caps:
cap.set_markeredgewidth(1)
pl.annotate("typical\nuncert.",
xy=(xe, ye),
xytext=eaoff,
xycoords="data",
textcoords="offset points",
horizontalalignment='center',
fontsize=12)
# pl.semilogx()
if ylog:
pl.loglog()
pl.xlim(pltxlim)
pl.ylim(pltylim)
else:
pl.semilogx()
pl.xlim(pltxlim)
pl.ylim(pltylim)
yticks = np.array([0.3, 1, 2, 4, 6, 10, 30])
xticks = np.array([0.3, 1, 3, 10, 30, 100, 300, 1000, 3000, 10000, 3e4])
yt = yticks[(yticks >= ylim[0]) & (yticks <= ylim[1])]
xt = xticks[(xticks >= xlim[0]) & (xticks <= xlim[1])]
ax = pl.gca()
if ylog:
# pl.yticks(yt)
pl.xticks(xt)
else:
pl.xticks(xt)
pl.xlim(pltxlim)
pl.ylim(pltylim)
if cbar:
if clabel is not None:
clabel = clabel
elif weighted:
clabel = 'Relative Occurrence'
else:
clabel = 'Relative Density of Planets'
ct = [0.0, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035, 0.04]
cmap = pl.colorbar(pad=0, ticks=ct, label=clabel)
ax.xaxis.grid(True)
pl.minorticks_off()
return (ax, 10**xi, 10**yi, zi)