def mags_bands(self, bands, z=0., d=rf.pc_to_cm(10.), magnification=1.):
mags = dict((k, None) for k in bands)
for n in bands:
b = band.band_by_name(n)
mags[n] = self.flux_to_mags(b, z=z, d=d, magnification=magnification)
mags['time'] = self.Time * (1. + z)
return mags
def curves_compute(name, path, bands, z=0., distance=10., magnification=1.,
t_beg=0., t_end=None, is_show_info=False, is_save=False):
"""
Compute magnitude in bands for the 'name' model.
:param name: the name of a model and data files
:param path: the directory with data-files
:param bands: photometric bands
:param z: redshift, default 0
:param distance: distance to star in parsec, default 10 pc
:param magnification: gravitational lensing magnification
:param t_end:
:param t_beg:
:param is_show_info: flag to write some information, default True
:param is_save: flag to save result in file, default False
:return: dictionary with keys = bands, value = star's magnitudes
"""
if len(bands) == 0:
raise ValueError("You have not set any bands for model: " + str(name))
model = Stella(name, path=path)
if not model.is_ph_data:
model.show_info()
raise ValueError("Error: No spectral data for: " + str(model))
if is_show_info:
print ''
model.show_info()
# serial_spec = model.read_serial_spectrum(t_diff=0.)
serial_spec = model.read_series_spectrum(t_diff=1.05, t_beg=t_beg, t_end=t_end)
curves = serial_spec.flux_to_curves(bands, z=z, d=rf.pc_to_cm(distance), magnification=magnification)
# curves = SetLightCurve(name)
# for n in bands:
# b = band.band_by_name(n)
# lc = serial_spec.flux_to_curve(b, z=z, dl=rf.pc_to_cm(distance), magnification=magnification)
# # time = serial_spec.times * (1. + z)
# # lc = LightCurve(b, time, mags)
# curves.add(lc)
if is_save:
fname = os.path.join(path, name + '.ubv')
curves_save(curves, fname)
print "Magnitudes have been saved to " + fname
if is_show_info:
# print the time of maximum LC
tmin = 2.0
t = curves.TimeDef
for n in bands:
t_min = t[t > tmin][curves[n][t > tmin].argmin()]
print "t_max(%s) = %f" % (n, t_min)
return curves
def compute_mag(name, path, bands, ext=None, z=0., distance=10., magnification=1., t_diff=1.05, is_show_info=True,
is_save=False):
"""
Compute magnitude in bands for the 'name' model.
:param name: the name of a model and data files
:param path: the directory with data-files
:param bands: photometric bands
:param ext: extinction
:param z: redshift, default 0
:param distance: distance to star in parsec, default 10 pc
:param magnification: gravitational lensing magnification
:param t_diff: depression between time points
:param is_show_info: flag to write some information, default True
:param is_save: flag to save result in file, default False
:return: dictionary with keys = bands, value = star's magnitudes
"""
model = Stella(name, path=path)
if is_show_info:
print ''
model.show_info()
if not model.is_ph_data:
model.show_info()
print "Error: No data for: " + str(model)
return None
# serial_spec = model.read_serial_spectrum(t_diff=0.)
serial_spec = model.read_series_spectrum(t_diff=t_diff)
mags = serial_spec.mags_bands(bands, z=z, d=rf.pc_to_cm(distance), magnification=magnification)
if mags is not None:
fname = os.path.join(path, name + '.ubv')
if is_save:
mags_save(mags, bands, fname)
print "Magnitudes have been saved to " + fname
if is_show_info:
# print the time of maximum LC
tmin = 2.0
t = mags['time']
for n in bands:
t_min = t[t > tmin][mags[n][t > tmin].argmin()]
print "t_max(%s) = %f" % (n, t_min)
if ext is not None: # add extinction
for n in bands:
mags[n] = mags[n] + ext[n]
return mags
def flux_to_curves(self, bands, z=0., d=rf.pc_to_cm(10.), magnification=1.):
"""
:param bands:
:param z:
:param d:
:param magnification:
:return:
"""
curves = SetLightCurve(self.name)
for n in bands:
b = band.band_by_name(n)
lc = self.flux_to_curve(b, z=z, d=d, magnification=magnification)
curves.add(lc)
return curves
def compute_mag(name,
path,
bands,
z=0.,
distance=10.,
t_cut=0.,
t_up=400.,
tdiff=0.):
"""
Compute magnitude in bands for the 'name' model.
:param name: the name of a model and data files
:param path: the directory with data-files
:param bands: photometric bands
:param z: redshift, default 0
:param distance: distance to star in parsec, default 10 pc
:param t_cut:
:param t_up:
:param tdiff:
:return: dictionary with keys = bands, value = star's magnitudes
"""
model = Stella(name, path=path)
if not model.is_ph:
print("No data for: " + str(model))
return None
# serial_spec = model.read_serial_spectrum(t_diff=0.)
serial_spec = model.read_series_spectrum(t_diff=1.05)
mags = serial_spec.mags_bands(bands, z=z, d=rf.pc_to_cm(distance))
# t_cut
time = mags['time']
cut = (t_cut < time) & (time < t_up)
mags['time'] = time[cut]
for n in bands:
mags[n] = mags[n][cut]
time = mags['time']
if tdiff > 0.:
ts = np.arange(np.min(time), np.max(time), tdiff)
for n in bands:
tck = interpolate.splrep(time, mags[n])
mags[n] = interpolate.splev(ts, tck)
mags['time'] = ts
return mags
def compute_tcolor(name, path, bands, d=rf.pc_to_cm(10.0), z=0.0, t_cut=1.0):
model = Stella(name, path=path)
if not model.is_ph_data:
print "No ph-data for: " + str(model)
return None
if not model.is_tt_data:
print "No tt-data for: " + str(model)
return None
# serial_spec = model.read_series_spectrum(t_diff=1.)
# curves = serial_spec.flux_to_curves(bands, d=distance)
serial_spec = model.read_series_spectrum(t_diff=1.05, t_beg=t_cut)
# curves = serial_spec.
mags = serial_spec.mags_bands(bands, z=z, d=d)
# read R_ph
tt = model.read_tt_data()
tt = tt[tt["time"] > t_cut] # time cut days
# compute Tnu, W
Tnu, Teff, W = compute_Tnu_w(serial_spec, tt=tt)
# fit mags by B(T_col) and get \zeta\theta & T_col
Tcolors, zetaR, times = compute_Tcolor_zeta(mags, tt=tt, bands=bands, freq=serial_spec.Freq, d=d, z=z)
# show results
res = np.array(
np.zeros(len(Tcolors)),
dtype=np.dtype({"names": ["time", "Tcol", "zeta", "Tnu", "Teff", "W"], "formats": [np.float64] * 6}),
)
res["time"] = times
res["Tcol"] = Tcolors
res["zeta"] = zetaR
res["Tnu"] = Tnu
res["Teff"] = Teff
res["W"] = W
return res
def compute_mag(name, path, bands, z=0.0, distance=10.0, t_cut=0.0, t_up=400.0, tdiff=0.0):
"""
Compute magnitude in bands for the 'name' model.
:param name: the name of a model and data files
:param path: the directory with data-files
:param bands: photometric bands
:param z: redshift, default 0
:param distance: distance to star in parsec, default 10 pc
:return: dictionary with keys = bands, value = star's magnitudes
"""
model = Stella(name, path=path)
if not model.is_ph_data:
print "No data for: " + str(model)
return None
# serial_spec = model.read_serial_spectrum(t_diff=0.)
serial_spec = model.read_series_spectrum(t_diff=1.05)
mags = serial_spec.mags_bands(bands, z=z, d=rf.pc_to_cm(distance))
# t_cut
time = mags["time"]
cut = (t_cut < time) & (time < t_up)
mags["time"] = time[cut]
for n in bands:
mags[n] = mags[n][cut]
time = mags["time"]
if tdiff > 0.0:
ts = np.arange(np.min(time), np.max(time), tdiff)
for n in bands:
tck = interpolate.splrep(time, mags[n])
mags[n] = interpolate.splev(ts, tck)
mags["time"] = ts
return mags
def main(name="", path="./", is_force=False, is_save=False, is_plot_Tnu=False, is_plot_time_points=False):
is_info = False
is_fit = False
is_save_plot = False
is_fit_bakl = False
model_ext = ".tt"
theta_dic = None
distance = rf.pc_to_cm(10.0) # pc
z = 0.0
band.Band.load_settings()
try:
opts, args = getopt.getopt(sys.argv[1:], "fhswtb:d:e:i:p:o:z:")
except getopt.GetoptError as err:
print str(err) # will print something like "option -a not recognized"
usage()
sys.exit(2)
if len(args) > 0:
path, name = os.path.split(str(args[0]))
path = os.path.expanduser(path)
elif len(opts) == 0:
usage()
sys.exit(2)
if not name:
for opt, arg in opts:
if opt == "-i":
name = os.path.splitext(os.path.basename(str(arg)))[0]
break
set_bands = ["B-V", "B-V-I", "V-I", "J-H-K"]
# set_bands = ['B-V', 'B-V-I', 'V-I', 'J-H-K']
# set_bands = ['U-B-V-I', 'U-B-V-R-I', 'U-B', 'V-R', 'B-V-I', 'B-V', 'V-I']
for opt, arg in opts:
if opt == "-e":
model_ext = "." + arg
continue
if opt == "-b":
set_bands = str(arg).split("_")
for bset in set_bands:
for b in bset.split("-"):
if not band.band_is_exist(b):
print "No such band: " + b
sys.exit(2)
continue
if opt == "-z":
z = float(arg)
continue
if opt == "-d":
distance = rf.pc_to_cm(float(arg))
continue
if opt == "-s":
is_save_plot = True
continue
if opt == "-o":
ops = str(arg).split(":")
is_plot_Tnu = "Tnu" in ops
is_plot_time_points = "time" in ops
is_fit = "fit" in ops
is_fit_bakl = "fitb" in ops
continue
if opt == "-w":
is_save = True
continue
if opt == "-f":
is_force = True
is_save = True
continue
if opt == "-p":
path = os.path.expanduser(str(arg))
if not (os.path.isdir(path) and os.path.exists(path)):
print "No such directory: " + path
sys.exit(2)
continue
elif opt == "-h":
usage()
sys.exit(2)
names = []
if name != "":
names.append(name)
else: # run for all files in the path
names = get_model_names(path, model_ext)
if len(names) > 0:
results = {} # dict((k, None) for k in names)
ib = 0
for bset in set_bands:
ib += 1
im = 0
dic = {}
print "\nRun: %s [%d/%d], z=%.2f, d=%.2e" % (bset, ib, len(set_bands), z, distance)
for name in names:
res = None
im += 1
print "Run: %s [%d/%d]" % (name, im, len(names))
is_err = False
fname = cache_name(name, path, bset)
if not is_force and os.path.exists(fname):
res = cache_load(fname)
else:
res = compute_tcolor(name, path, bset.split("-"), d=distance, z=z, t_cut=0.9)
if is_save and res is not None:
print "Save Tcolor & Zeta for %s in %s" % (bset, fname)
cache_save(res, fname=fname)
dic[name] = res
# check for errors
idx = np.argmin(np.abs(res["Tcol"] - 1.0e4))
if abs(res["zeta"][idx] - 1.0) <= 1e-3:
is_err = True
if is_err:
print " ERROR for %s in %s" % (name, bset)
results[bset] = dic
print "Finish: %s" % name
if is_fit:
theta_dic = {}
im = 0
results_flter = {}
for bset in set_bands:
im += 1
models = results[bset]
tlim = (7.0, 80)
if tlim is not None:
models_zt_new = {}
for mname, tbl in models.iteritems():
models_zt_new[mname] = table_cut_by_col(tbl, tlim, "time")
models = models_zt_new
# templim = (4.e3, 12e3)
# if templim is not None:
# models_zt_new = {}
# for mname, tbl in models.iteritems():
# models_zt_new[mname] = table_cut_by_col(tbl, templim, 'Tcol')
# models = models_zt_new
print "\nFit: %s [%d/%d]" % (bset, im, len(set_bands))
theta = fit_bayesian(models, is_debug=True, is_info=is_info, title=bset)
results_flter[bset] = models
theta_dic[bset] = theta
print_coef(theta)
# fig = plot_zeta(results, set_bands, theta_dic, t_cut=1.9, is_fit=is_fit, is_fit_bakl=is_fit_bakl,
fig = plot_zeta(
results_flter,
set_bands,
theta_dic,
t_cut=1.9,
is_fit=is_fit,
is_fit_bakl=is_fit_bakl,
is_plot_Tnu=is_plot_Tnu,
is_time_points=is_plot_time_points,
)
if is_save_plot and len(results) > 0:
fsave = os.path.join(os.path.expanduser("~/"), "epm_" + "_".join(set_bands) + ".pdf")
print "Save plot in %s" % fsave
fig.savefig(fsave, bbox_inches="tight", format="pdf")
else:
print "There are no models in the directory: %s with extension: %s " % (path, model_ext)
