def view_eep_fit(self, mass, feh, plot_fit=True, order=5, p0=None, plot_p0=False):
import holoviews as hv
hv.extension('bokeh')
subdf = self.df.xs((mass, feh), level=('initial_mass', 'initial_feh'))
ds = hv.Dataset(subdf)
pts = hv.Points(ds, kdims=['age', 'eep'], vdims=['phase', 'interpolated']).options(tools=['hover'], width=800, height=400, marker='+')
primary_eeps = self.primary_eeps
primary_ages = [subdf.loc[e].age for e in primary_eeps if e < subdf.eep.max()]
from isochrones.eep import eep_fn, eep_jac, eep_fn_p0
from scipy.optimize import curve_fit
if p0 is None:
p0 = eep_fn_p0(subdf.age.values, subdf.eep.values, order=order)
m = subdf.eep < 808
if plot_fit:
pfit, _ = curve_fit(partial(eep_fn, order=order), subdf.age.values[m], subdf.eep.values[m], p0, jac=partial(eep_jac, order=order))
fit = hv.Points([(a, eep_fn(a, *pfit)) for a in subdf.age])
if plot_p0:
p0_fit = hv.Points([(a, eep_fn(a, *p0)) for a in subdf.age])
olay = pts * hv.Points([(a, e) for a, e in zip(primary_ages, primary_eeps)]).options(size=8)
if plot_fit:
olay = olay * fit
if plot_p0:
olay = olay * p0_fit
return olay
def fit_approx_eep(self, max_fit_eep=808):
fehs = self.df.index.levels[0]
ms = self.df.index.levels[1]
columns = ['p5', 'p4', 'p3', 'p2', 'p1', 'p0', 'A', 'x0', 'tau']
par_df = pd.DataFrame(index=pd.MultiIndex.from_product((fehs, ms)),
columns=columns)
for feh, m in tqdm(itertools.product(fehs, ms),
total=len(fehs) * len(ms),
desc='Fitting approximate eep(age) function'):
subdf = self.df.xs((feh, m), level=('initial_feh', 'initial_mass'))
p0 = eep_fn_p0(subdf.age, subdf.eep)
last_pfit = p0
mask = subdf.eep < max_fit_eep
try:
if subdf.eep.max() < 500:
raise RuntimeError
pfit, _ = curve_fit(eep_fn,
subdf.age.values[mask],
subdf.eep.values[mask],
p0,
jac=eep_jac)
except RuntimeError: # if the full fit barfs, just use the polynomial by setting A to zero, and the rest same as previous.
pfit = list(
np.polyfit(subdf.age.values[mask], subdf.eep.values[mask],
5)) + last_pfit[-3:]
pfit[-3] = 0
last_pfit = pfit
par_df.loc[(feh, m), :] = pfit
return par_df.astype(float)
def view_eep_fit(self,
mass,
feh,
plot_fit=True,
order=5,
p0=None,
plot_p0=False):
import holoviews as hv
hv.extension('bokeh')
subdf = self.df.xs((mass, feh), level=('initial_mass', 'initial_feh'))
ds = hv.Dataset(subdf)
pts = hv.Points(ds,
kdims=['age', 'eep'],
vdims=['phase',
'interpolated']).options(tools=['hover'],
width=800,
height=400,
marker='+')
primary_eeps = self.primary_eeps
primary_ages = [
subdf.loc[e].age for e in primary_eeps if e < subdf.eep.max()
]
from isochrones.eep import eep_fn, eep_jac, eep_fn_p0
from scipy.optimize import curve_fit
if p0 is None:
p0 = eep_fn_p0(subdf.age.values, subdf.eep.values, order=order)
m = subdf.eep < 808
if plot_fit:
pfit, _ = curve_fit(partial(eep_fn, order=order),
subdf.age.values[m],
subdf.eep.values[m],
p0,
jac=partial(eep_jac, order=order))
fit = hv.Points([(a, eep_fn(a, *pfit)) for a in subdf.age])
if plot_p0:
p0_fit = hv.Points([(a, eep_fn(a, *p0)) for a in subdf.age])
olay = pts * hv.Points([
(a, e) for a, e in zip(primary_ages, primary_eeps)
]).options(size=8)
if plot_fit:
olay = olay * fit
if plot_p0:
olay = olay * p0_fit
return olay
def fit_approx_eep(self, max_fit_eep=808):
fehs = self.df.index.levels[0]
ms = self.df.index.levels[1]
columns = ['p5', 'p4', 'p3', 'p2', 'p1', 'p0', 'A', 'x0', 'tau']
par_df = pd.DataFrame(index=pd.MultiIndex.from_product((fehs, ms)), columns=columns)
for feh, m in tqdm(itertools.product(fehs, ms),
total=len(fehs)*len(ms),
desc='Fitting approximate eep(age) function'):
subdf = self.df.xs((feh, m), level=('initial_feh', 'initial_mass'))
p0 = eep_fn_p0(subdf.age, subdf.eep)
last_pfit = p0
mask = subdf.eep < max_fit_eep
try:
if subdf.eep.max() < 500:
raise RuntimeError
pfit, _ = curve_fit(eep_fn, subdf.age.values[mask], subdf.eep.values[mask], p0, jac=eep_jac)
except RuntimeError: # if the full fit barfs, just use the polynomial by setting A to zero, and the rest same as previous.
pfit = list(np.polyfit(subdf.age.values[mask], subdf.eep.values[mask], 5)) + last_pfit[-3:]
pfit[-3] = 0
last_pfit = pfit
par_df.loc[(feh, m), :] = pfit
return par_df.astype(float)