def compare_ttVSubv(mname, path, bands=('U', 'B', 'V', 'R', 'I'), t_cut=1., is_plot_time_points=False):
dic_results = {}
model = Stella(mname, path=path)
tt = model.get_tt().load()
# time cut days
mags = tt[tt['time'] > t_cut]
n1 = mags.dtype.names
def f(x):
if len(x) == 2 and x.startswith('M'):
return x.replace('M', '')
else:
return x
n2 = map(lambda x: f(x), n1)
mags.dtype.names = n2
dic_results['tt'] = mags
# ubv
serial_spec = model.read_series_spectrum(t_diff=1.05)
mags = serial_spec.mags_bands(bands)
dic_results['ubv'] = mags
plot_all(dic_results, bands, title=mname, is_time_points=is_plot_time_points)
def compare_ttVSubv(mname, path, bands=['U', 'B', 'V', 'R', 'I'], t_cut=1., is_plot_time_points=False):
dic_results = {}
model = Stella(mname, path=path)
tt = model.read_tt_data()
# time cut days
mags = tt[tt['time'] > t_cut]
n1 = mags.dtype.names
def f(x):
if len(x) == 2 and x.startswith('M'):
return x.replace('M', '')
else:
return x
n2 = map(lambda x: f(x), n1)
mags.dtype.names = n2
dic_results['tt'] = mags
# ubv
serial_spec = model.read_series_spectrum(t_diff=1.05)
mags = serial_spec.mags_bands(bands)
dic_results['ubv'] = mags
plot_all(dic_results, bands, title=mname, is_time_points=is_plot_time_points)
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 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
class TestStellaTt(unittest.TestCase):
def setUp(self):
# name = 'ccsn2007bi1dNi6smE23bRlDC'
name = 'cat_R500_M15_Ni006_E12'
# name = 'cat_R1000_M15_Ni007_E15'
path = join(dirname(abspath(__file__)), 'data', 'stella')
self.stella = Stella(name, path=path)
self.tt = self.stella.get_tt()
def test_info_parse(self):
tt_header = """
<===== HYDRODYNAMIC RUN OF MODEL cat_R1000_M15_Ni007_E15.prf =====>
MASS(SOLAR)=15.000 RADIUS(SOLAR)= 1000.000
EXPLOSION ENERGY(10**50 ERG)= 1.50000E+01
<===== =====>
INPUT PARAMETERS
EPS = 0.00300 Rce = 1.00000E-02 SOL.Rad.
HMIN = 1.00000E-11 S AMht = 1.00000E-02 SOL.MASS
HMAX = 5.00000E+04 S Tburst= 1.00000E-01 S
THRMAT= 1.00000E-30 Ebstht= 1.50000E+01 1e50 ergs
METH = 3 CONV = F
JSTART= 0 EDTM = T
MAXORD= 4 CHNCND= T
NSTA = -4500 FitTau= 3.00000E+02
NSTB = -1 TauTol= 1.30000E+00
NOUT = 100 IOUT = -1
TcurA = 0.00000 Rvis = 1.00000
TcurB = 200.00000 BQ = 1.00000E+00
NSTMAX= 360000 DRT = 1.00000E-01
XMNI = 7.00000E-02 SOL.MASS NRT = 1
XNifor= 1.16561E-01
MNicor= 1.16999E-01 SOL.MASS SCAT = T
"""
pattern = filter(lambda x: len(x) > 0, tt_header.splitlines())
pattern = map(str.strip, pattern)
p = r"(.*?)\s*=\s+([-+]?\d*\.\d+|\d+)"
for line in pattern:
res = re.findall(p, line)
if len(res) > 0:
for k, v in res:
print "key: %s v: %f " % (k, float(v))
def test_info_parse(self):
info = self.tt.Info.parse()
info.show()
tmp = 1000.
self.assertEquals(info.R, tmp, "Radius [%f] should be %f" % (info.R, tmp))
tmp = 15.
self.assertEquals(info.M, tmp, "Mass [%f] should be %f" % (info.M, tmp))
tmp = 15.
self.assertEquals(info.E, tmp, "Ebstht [%f] should be %f" % (info.E, tmp))
def test_tt_vs_ph(self):
curves_tt = self.tt.read_curves_tt()
bands = curves_tt.BandNames
serial_spec = self.stella.read_series_spectrum(t_diff=1.00001)
curves_ph = serial_spec.flux_to_curves(bands)
models_dic = {'tt': curves_tt, 'ph': curves_ph}
lc_types = {'tt': '--', 'ph': '-'}
plt.matplotlib.rcParams.update({'font.size': 14})
fig, ax = plt.subplots(1, 1)
lcf.plot_models_curves_fixed_bands(ax, models_dic, bands, lc_types=lc_types, ylim=(-10, -24))
# lcf.plot_models_curves(ax, models_dic, bands, lc_types=lc_types, ylim=(-10, -24), xlim=(0, 20))
plt.legend()
plt.show()
def test_gri_vs_ph(self):
curves_gri = self.tt.read_curves_gri()
bands = curves_gri.BandNames
# bands = ('J','H','K')
serial_spec = self.stella.read_series_spectrum(t_diff=1.)
curves_ph = serial_spec.flux_to_curves(bands)
models_dic = {'gri': curves_gri, 'ph': curves_ph}
lc_types = {'gri': '--', 'ph': ':'}
plt.matplotlib.rcParams.update({'font.size': 14})
fig, ax = plt.subplots(1, 1)
# lcf.plot_models_curves_fixed_bands(ax, models_dic, bands=('r','B','V'), lc_types=lc_types, ylim=(-13, -23), lw=3)
lcf.plot_models_curves(ax, models_dic, lc_types=lc_types, ylim=(-10, -23), lw=3)
plt.legend()
plt.show()
def test_tt_vs_gri_vs_ph(self):
curves_tt = self.tt.read_curves_tt()
bands_tt = curves_tt.BandNames
curves_gri = self.tt.read_curves_gri()
bands_gri = curves_gri.BandNames
bands = np.unique(np.array(bands_tt + bands_gri))
serial_spec = self.stella.read_series_spectrum(t_diff=1.00001)
curves_ph = serial_spec.flux_to_curves(bands)
models_dic = {'tt': curves_tt, 'gri': curves_gri, 'ph': curves_ph}
lc_types = {'tt': ':', 'gri': '--', 'ph': '-'}
plt.matplotlib.rcParams.update({'font.size': 14})
fig, ax = plt.subplots(1, 1)
lcf.plot_models_curves(ax, models_dic, lc_types=lc_types, ylim=(-10, -19), lw=3)
# lcf.plot_models_curves_fixed_bands(ax, models_dic, bands=('B', 'V'), lc_types=lc_types, ylim=(-13, -23), lw=3)
plt.legend()
plt.show()
