def test_FitMCMC_best_curves_dt_SN1999em(self):
from pystella.rf import light_curve_func as lcf
from pystella.rf import light_curve_plot as lcp
# Get observations
D = 11.5e6 # pc
dm = -5. * np.log10(D) + 5
# dm = -30.4 # D = 12.e6 pc
curves_obs = sn1999em.read_curves()
curves_obs.set_mshift(dm)
# Get model
name = 'cat_R500_M15_Ni006_E12'
path = join(dirname(dirname(abspath(__file__))), 'data', 'stella')
curves_mdl = lcf.curves_compute(name,
path,
distance=10,
bands=curves_obs.BandNames)
# fit
is_debug = True # True
# fitter = FitLcMcmc()
fitter = ps.FitMCMC()
fitter.is_info = True
if is_debug:
fitter.is_debug = is_debug
fitter.nwalkers = 100 # number of MCMC walkers
fitter.nburn = 20 # "burn-in" period to let chains stabilize
fitter.nsteps = 200 # number of MCMC steps to take
threads = 1
# fitter.is_debug = False
fit_result, res, (th, e1,
e2), samples = fitter.best_curves(curves_mdl,
curves_obs,
dt0=0.,
threads=threads,
is_sampler=True)
fig = fitter.plot_corner(samples,
labels=('dt', ),
bnames=curves_obs.BandNames)
# print
# '{:10s} {:.4f} ^{:.4f}_{:.4f}\n'.format('lnf:', res['lnf'], res['lnfsig2'], res['lnfsig1']) + \
txt = '{:10s} {:.4f} ^{:.4f}_{:.4f} \n'.format('tshift:', th.dt, e1.dt, e2.dt) + \
'{:10s} chi2= {:.1f} BIC= {:.1f} AIC= {:.1f} dof= {} accept= {:.3f}\n'. \
format('stat:', res['chi2'], res['bic'], res['aic'], res['dof'], res['acceptance_fraction'])
print(txt)
# plot model
curves_obs.set_tshift(-th.dt)
ax = lcp.curves_plot(curves_mdl)
lt = {lc.Band.Name: 'o' for lc in curves_obs}
lcp.curves_plot(curves_obs, ax, lt=lt, xlim=(-10, 300), is_line=False)
ax.text(0.1, 0.1, txt, transform=ax.transAxes)
plt.show()
def test_fit_mpfit_curves_Stella_SN1999em(self):
from pystella.rf import light_curve_func as lcf
from pystella.rf import light_curve_plot as lcp
# matplotlib.rcParams['backend'] = "TkAgg"
# matplotlib.rcParams['backend'] = "Qt4Agg"
# from matplotlib import pyplot as plt
# Get observations
D = 11.5e6 # pc
dm = -5. * np.log10(D) + 5
# dm = -30.4 # D = 12.e6 pc
curves_obs = sn1999em.read_curves()
curves_obs.set_mshift(dm)
# Get model
name = 'cat_R500_M15_Ni006_E12'
path = join(dirname(dirname(abspath(__file__))), 'data', 'stella')
curves_mdl = lcf.curves_compute(name, path, curves_obs.BandNames)
# fit
# fitter = FitLcMcmc()
fitter = ps.FitMPFit()
fitter.is_debug = True
res = fitter.fit_curves(curves_obs, curves_mdl)
# print
# txt = '{0:10} {1:.4e} \n'.format('tshift:', res.tshift) + \
# '{0:10} {1:.4e} \n'.format('tsigma:', res.tsigma) + \
# '{0}\n'.format(res.comm)
txt = '{:10s} {:.4f}+-{:.4f} \n'.format('tshift:', res.tshift, res.tsigma) + \
'{:10s} {:.4f}+-{:.4f}\n'.format('msigma:', res.mshift, res.msigma) + \
'{0}\n'.format(res.comm)
print(txt)
# plot model
curves_obs.set_tshift(res.tshift)
# curves_mdl.set_tshift(0.)
ax = lcp.curves_plot(curves_mdl)
lt = {lc.Band.Name: 'o' for lc in curves_obs}
lcp.curves_plot(curves_obs, ax, lt=lt, xlim=(-10, 300), is_line=False)
ax.text(0.1, 0.1, txt, transform=ax.transAxes)
plt.show()
def test_FitMPFit_best_curves_gp_SN1999em(self):
from pystella.rf import light_curve_func as lcf
from pystella.rf import light_curve_plot as lcp
# matplotlib.rcParams['backend'] = "TkAgg"
# matplotlib.rcParams['backend'] = "Qt4Agg"
# from matplotlib import pyplot as plt
# Get observations
D = 11.5e6 # pc
ebv_sn = 0.1
dm = -5. * np.log10(D) + 5
# dm = -30.4 # D = 12.e6 pc
curves_obs = sn1999em.read_curves()
curves_obs.set_mshift(dm)
# Get model
name = 'cat_R500_M15_Ni006_E12'
path = join(dirname(dirname(abspath(__file__))), 'data', 'stella')
curves_mdl = lcf.curves_compute(name, path, curves_obs.BandNames)
# fit
# fitter = FitLcMcmc()
fitter = ps.FitMPFit()
fitter.is_info = True
fitter.is_debug = True
res = fitter.best_curves_gp(curves_mdl, curves_obs, dt0=0., dm0=0.)
# print
txt = '{0:10} {1:.4e} \n'.format('tshift:', res['dt']) + \
'{0:10} {1:.4e} \n'.format('tsigma:', res['dtsig'])
print(txt)
# plot model
curves_obs.set_tshift(res['dt'])
# curves_mdl.set_tshift(0.)
ax = lcp.curves_plot(curves_mdl)
lt = {lc.Band.Name: 'o' for lc in curves_obs}
lcp.curves_plot(curves_obs, ax, lt=lt, xlim=(-10, 300), is_line=False)
plt.show()
def plot_curves_vel(curves_o, vels_o, res_models, res_sorted, vels_m,
**kwargs):
from pystella.rf import light_curve_plot as lcp
from matplotlib import pyplot as plt
font_size = kwargs.get('font_size', 10)
linewidth = kwargs.get('linewidth', 2.0)
markersize = kwargs.get('markersize', 5)
xlim = kwargs.get('xlim', None)
ylim = None
num = len(res_sorted)
nrow = int(num / 2.1) + 1
ncol = 2
fig = plt.figure(figsize=(12, nrow * 4))
plt.matplotlib.rcParams.update({'font.size': font_size})
tshift_lc = 0.
if curves_o is not None:
tshift_lc = ps.first(curves_o).tshift
tshift_vel = 0.
if vels_o is not None:
tshift_vel = ps.first(vels_o).tshift
i = 0
for k, v in res_sorted.items():
i += 1
# Plot UBV
axUbv = fig.add_subplot(nrow, ncol, i)
tshift_best = v.tshift
axUbv.text(0.99,
0.94,
k,
horizontalalignment='right',
transform=axUbv.transAxes)
axUbv.text(0.98,
0.85,
"dt={:.2f}".format(tshift_best),
horizontalalignment='right',
transform=axUbv.transAxes)
axUbv.text(0.01,
0.05,
"$\chi^2: {:.2f}$".format(v.measure),
horizontalalignment='left',
transform=axUbv.transAxes,
bbox=dict(facecolor='green', alpha=0.3))
curves = res_models[k]
if curves is not None:
lcp.curves_plot(curves,
ax=axUbv,
figsize=(12, 8),
linewidth=1,
is_legend=False)
lt = {lc.Band.Name: 'o' for lc in curves_o}
curves_o.set_tshift(tshift_lc + tshift_best)
if xlim is None:
xlim = axUbv.get_xlim()
lcp.curves_plot(curves_o,
axUbv,
xlim=xlim,
lt=lt,
markersize=2,
is_legend=False,
is_line=False)
# legend
axUbv.legend(curves.BandNames,
loc='lower right',
frameon=False,
ncol=min(5, len(curves.BandNames)),
fontsize='small',
borderpad=1)
if i % ncol == 0:
axUbv.yaxis.tick_right()
axUbv.set_ylabel('')
axUbv.yaxis.set_ticks([])
else:
axUbv.yaxis.tick_left()
axUbv.yaxis.set_label_position("left")
axUbv.grid(linestyle=':')
# Plot Vel
axVel = axUbv.twinx()
axVel.set_ylim((0., 29))
axVel.set_ylabel('Velocity [1000 km/s]')
ts_vel = vels_m[k]
x = ts_vel.Time
y = ts_vel.V
axVel.plot(x,
y,
label='Vel %s' % k,
color='blue',
ls="-",
linewidth=linewidth)
# Obs. vel
markers_cycler = cycle(markers_style)
for vel_o in vels_o:
vel_o.tshift = tshift_vel + tshift_best
marker = next(markers_cycler)
if vel_o.IsErr:
yyerr = abs(vel_o.Err)
axVel.errorbar(vel_o.Time,
vel_o.V,
yerr=yyerr,
label='{0}'.format(vel_o.Name),
color="blue",
ls='',
markersize=markersize,
fmt=marker,
markeredgewidth=2,
markerfacecolor='none',
markeredgecolor='blue')
else:
axVel.plot(vel_o.Time,
vel_o.V,
label=vel_o.Name,
color='blue',
ls='',
marker=marker,
markersize=markersize)
fig.subplots_adjust(wspace=0, hspace=0)
return fig
def plot_curves(curves_o, res_models, res_sorted, **kwargs):
from pystella.rf import light_curve_plot as lcp
from matplotlib import pyplot as plt
import math
font_size = kwargs.get('font_size', 8)
xlim = kwargs.get('xlim', None)
ylim = kwargs.get('ylim', None)
num = len(res_sorted)
# nrow = int(num / 2.1) + 1
# ncol = 2 if num > 1 else 1
ncol = min(4, int(np.sqrt(num))) # 2 if num > 1 else 1
nrow = math.ceil(num / ncol)
# fig = plt.figure(figsize=(12, nrow * 4))
fig = plt.figure(figsize=(min(ncol, 2) * 5, max(nrow, 2) * 4))
plt.matplotlib.rcParams.update({'font.size': font_size})
# tshift0 = ps.first(curves_o).tshift
i = 0
for k, v in res_sorted.items():
i += 1
ax = fig.add_subplot(nrow, ncol, i)
tshift_best = v.tshift
curves = res_models[k]
curves.set_tshift(tshift_best)
lcp.curves_plot(curves,
ax=ax,
figsize=(12, 8),
linewidth=1,
is_legend=False)
if xlim is None or xlim[1] == float('inf'):
xlim = ax.get_xlim()
else:
ax.set_xlim(xlim)
lt = {lc.Band.Name: 'o' for lc in curves_o}
# curves_o.set_tshift(tshift0)
lcp.curves_plot(curves_o,
ax,
xlim=xlim,
lt=lt,
markersize=4,
is_legend=False,
is_line=False)
ax.text(0.99,
0.94,
k,
horizontalalignment='right',
transform=ax.transAxes)
ax.text(0.98,
0.85,
"{} dt={:.2f}".format(i, tshift_best),
horizontalalignment='right',
transform=ax.transAxes)
ax.text(0.01,
0.05,
r"$\chi^2: {:.2f}$".format(v.measure),
horizontalalignment='left',
transform=ax.transAxes,
bbox=dict(facecolor='green', alpha=0.3))
# ax.text(0.9, 0.9, "{:.2f}".format(v.measure), horizontalalignment='right', transform=ax.transAxes,
# bbox=dict(facecolor='green', alpha=0.3))
# fix axes
if ncol == 1:
ax.yaxis.tick_left()
ax.yaxis.set_label_position("left")
ax.set_ylabel('Magnitude')
elif i % ncol == 0:
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")
# ax.set_ylabel('')
# ax.set_yticklabels([])
# ax2.set_ylabel('Magnitude')
elif i % ncol == 1:
ax.yaxis.tick_left()
ax.yaxis.set_label_position("left")
ax.set_ylabel('Magnitude')
else:
ax.set_ylabel('')
ax.set_yticklabels([])
ax.yaxis.set_ticks_position('both')
# legend
ax.legend(curves.BandNames,
loc='lower right',
frameon=False,
ncol=min(5, len(curves.BandNames)),
fontsize='small',
borderpad=1)
# lc_colors = band.bands_colors()
fig.subplots_adjust(wspace=0, hspace=0)
return fig
def test_FitMCMC_best_curves_dtdm_SN1999em(self):
from pystella.rf import light_curve_func as lcf
from pystella.rf import light_curve_plot as lcp
# Get observations
D = 11.5e6 # pc
# MD0 = -5. * np.log10(D) + 5
ebv_sn = 0.1
# dm = -30.4 # D = 12.e6 pc
curves_obs = sn1999em.read_curves()
# curves_obs.set_mshift(MD0)
# Get model
name = 'cat_R500_M15_Ni006_E12'
path = join(dirname(dirname(abspath(__file__))), 'data', 'stella')
curves_mdl = ps.Stella(name, path=path).curves(curves_obs.BandNames,
distance=D,
ebv=ebv_sn)
# fit
is_debug = True # True
# fitter = FitLcMcmc()
fitter = ps.FitMCMC()
fitter.is_info = True
if is_debug:
fitter.is_debug = is_debug
fitter.nwalkers = 100 # number of MCMC walkers
fitter.nburn = 20 # "burn-in" period to let chains stabilize
fitter.nsteps = 200 # number of MCMC steps to take
threads = 3
# fitter.is_debug = False
# fit_result, res, samples \
fit_result, res, (th, ep,
em), sampler = fitter.best_curves(curves_mdl,
curves_obs,
dt0=0.,
dm0=0.,
threads=threads,
is_sampler=True)
nb = curves_obs.Length
dt, dm, sigs = fitter.theta2arr(th, nb)
ep_dt, ep_dm, ep_sigs = fitter.theta2arr(ep, nb)
em_dt, em_dm, em_sigs = fitter.theta2arr(em, nb)
samples = sampler.flatchain[fitter.nburn:, :]
fig = fitter.plot_corner(samples,
bnames=curves_obs.BandNames,
bins=55,
alpha=0.5,
verbose=fitter.is_info)
# print
txt = 'chi2= {:.1f} BIC= {:.1f} AIC= {:.1f} measure= {:.3f}\n'.\
format(res['chi2'], res['bic'], res['aic'], res['measure'])
txt += '\n dt= {:.1f} ^{{{:.1f}}}_{{{:.1f}}} '.format(dt, ep_dt, em_dt)
txt += '\n dm= {:.2f} ^{{{:.2f}}}_{{{:.2f}}} '.format(dm, ep_dm, em_dm)
for i, bname in enumerate(curves_obs.BandNames):
txt += '\n sigma_{{{:s}}} = {:.2f}^{{{:.2f}}}_{{{:.2f}}} '. \
format(bname, sigs[i], ep_sigs[i], em_sigs[i])
print(txt)
curves_mdl.set_tshift(dt)
curves_mdl.set_mshift(dm)
ax = None
errs = {}
for i, bn in enumerate(curves_mdl.BandNames):
errs[bn] = [
sigs[i]
] * curves_mdl[bn].Length # The error are the same for all points
curves_clone = curves_mdl.clone(err=errs)
ax = ps.curves_plot(curves_clone,
ax=ax,
is_legend=False,
is_fill=True,
alpha=0.2)
# Obs
lt = {lc.Band.Name: 'o' for lc in curves_obs}
lcp.curves_plot(curves_obs, ax, lt=lt, xlim=(-10, 300), is_line=False)
ax.text(0.05, 0.05, txt, transform=ax.transAxes)
plt.show()
def test_FitMPF_best_curves_SN1999em(self):
from pystella.rf import light_curve_func as lcf
from pystella.rf import light_curve_plot as lcp
# Get observations
D = 11.5e6 # pc
ebv_sn = 0.
# ebv_sn = 0.1
# dm = -5. * np.log10(D) + 5
# dm = -30.4 # D = 12.e6 pc
curves_obs = sn1999em.read_curves()
# Get model
name = 'cat_R500_M15_Ni006_E12'
path = join(dirname(dirname(abspath(__file__))), 'data', 'stella')
curves_mdl = ps.Stella(name, path=path).curves(curves_obs.BandNames,
distance=D,
ebv=ebv_sn)
# fit
fitter = ps.FitMPFit()
fitter.is_info = True
fitter.is_debug = True
fitter.is_quiet = True
fit_result, res, dum = fitter.best_curves(
curves_mdl,
curves_obs,
dt0=0.,
)
# fit_result, res, dum = fitter.best_curves(curves_mdl, curves_obs, dt0=0., dm0=0.)
dt, dtsig = res['dt'], res['dtsig']
dm, dmsig = res['dm'], res['dmsig']
# plot model
curves_mdl.set_tshift(dt)
ax = lcp.curves_plot(curves_mdl)
lt = {lc.Band.Name: 'o' for lc in curves_obs}
lcp.curves_plot(curves_obs, ax, lt=lt, xlim=(-10, 300), is_line=False)
# print
txt = '{:10} {:.2f} +- {:.4f}'.format('dt:', res['dt'], res['dtsig'])
txt += '\n{:10} {:.2f} +- {:.4f}'.format('dm:', res['dm'],
res['dmsig'])
print(txt)
title_font = {
'size': '12',
'color': 'black',
'weight': 'normal',
'verticalalignment': 'bottom'
}
ax.text(0.03,
0.95,
txt,
transform=ax.transAxes,
bbox={
'facecolor': 'none',
'alpha': 0.2,
'edgecolor': 'none'
},
**title_font)
plt.show()
