def plot_fit(data, model):
p = FormatParams(model.params, data) #FIXME
sns.set_palette("muted")
palette = sns.color_palette("muted", data.nvisit)
#ind = model.phase > 0.5
#model.phase[ind] -= 1.
#calculate a range of times at higher resolution to make model look nice
phase_hr = np.linspace(model.phase.min() - 0.05,
model.phase.max() + 0.05, 1000)
print "per", p.per
t_hr = phase_hr * p.per[0] + p.t0[0]
#plot data
plt.subplot(211)
#plot best fit model from first visit
plt.plot(phase_hr, calc_astro(t_hr, model.params, data, model.myfuncs, 0))
#plot systematics removed data
for i in range(data.nvisit):
ind = data.vis_num == i
plt.plot(model.phase[ind],
model.data_nosys[ind],
color='b',
marker='o',
markersize=3,
linestyle="none")
#add labels/set axes
#xlo, xhi = np.min(model.phase)*0.9, np.max(model.phase)*1.1
xlo, xhi = 0.38, 0.62
plt.xlim(xlo, xhi)
plt.ylabel("Relative Flux")
#annotate plot with fit diagnostics
ax = plt.gca()
ax.text(0.85,
0.29,
'$\chi^2_\\nu$: ' + '{0:0.2f}'.format(model.chi2red) + '\n' +
'obs. rms: ' + '{0:0d}'.format(int(model.rms)) + '\n' +
'exp. rms: ' + '{0:0d}'.format(int(model.rms_predicted)),
verticalalignment='top',
horizontalalignment='left',
transform=ax.transAxes,
fontsize=12)
#plot fit residuals
plt.subplot(212)
plt.axhline(0, zorder=1, color='0.2', linestyle='dashed')
for i in range(data.nvisit):
ind = data.vis_num == i
plt.plot(model.phase[ind],
1.0e6 * model.norm_resid[ind],
color='b',
marker='o',
markersize=3,
linestyle="none")
#add labels/set axes
plt.xlim(xlo, xhi)
plt.ylabel("Residuals (ppm)")
plt.xlabel("Orbital phase")
plt.show()
def plot_fit(data, model):
p = FormatParams(model.params, data) #FIXME
sns.set_palette("muted")
palette = sns.color_palette("muted", data.nvisit)
#ind = model.phase > 0.5
#model.phase[ind] -= 1.
#calculate a range of times at higher resolution to make model look nice
phase_hr = np.linspace(model.phase.min() - 0.05,
model.phase.max() + 0.05, 1000)
t_hr = phase_hr * p.per[0] + p.t0[0]
#plot data
plt.subplot(211)
#plt.title("Best fit white light curve, no eclipse")
plt.title("Best fit white light curve, with eclipse")
#plot best fit model from first visit
#plt.plot(phase_hr, calc_astro(t_hr, model.params, data, model.myfuncs, 0))
"""data.time = np.linspace(0, 1., 1000)
t0 = 2454163.22367
phase = (data.time - t0)/per - np.floor((data.time - t0)/per)"""
data.time = t_hr
idx = phase_hr > -10000.
plt.plot(phase_hr, calc_astro(idx, model.params, data, model.myfuncs, 0))
#idx = data.vis_num == 0
#plt.plot(model.phase[idx], calc_astro(idx, model.params, data, model.myfuncs, 0))
colors = ['blue', 'red']
#plot systematics removed data
for i in range(data.nvisit):
ind = data.vis_num == i
plt.plot(model.phase[ind],
model.data_nosys[ind],
color=colors[i],
marker='o',
markersize=3,
linestyle="none")
#add labels/set axes
#xlo, xhi = np.min(model.phase)*0.9, np.max(model.phase)*1.1
xlo, xhi = 0.35, 0.65
plt.xlim(xlo, xhi)
plt.ylabel("Relative Flux")
#annotate plot with fit diagnostics
"""ax = plt.gca()
ax.text( 0.85, 0.29,
'$\chi^2_\\nu$: ' + '{0:0.2f}'.format(model.chi2red) + '\n'
+ 'obs. rms: ' + '{0:0d}'.format(int(model.rms)) + '\n'
+ 'exp. rms: ' + '{0:0d}'.format(int(model.rms_predicted)),
verticalalignment='top',horizontalalignment='left',
transform=ax.transAxes, fontsize = 12
)"""
#plot fit residuals
plt.subplot(212)
plt.axhline(0, zorder=1, color='0.2', linestyle='dashed')
for i in range(data.nvisit):
ind = data.vis_num == i
plt.plot(model.phase[ind],
1.0e6 * model.norm_resid[ind],
color=colors[i],
marker='o',
markersize=3,
linestyle="none")
#add labels/set axes
plt.xlim(xlo, xhi)
plt.ylabel("Residuals (ppm)")
plt.xlabel("Orbital phase")
plt.tight_layout()
plt.savefig("white_light.pdf")
plt.show()