# PyTransit: fast and easy exoplanet transit modelling in Python.
# Copyright (C) 2010-2019 Hannu Parviainen
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import timeit
from typing import Tuple, Callable, Union, List, Optional
from numpy import ndarray, array, squeeze, atleast_2d, atleast_1d, zeros, asarray, linspace, sqrt, pi, ones, log, exp, \
tile, full, isscalar
from scipy.integrate import trapz
from .ldmodel import LDModel
from .numba.ldmodels import *
from .numba.rrmodel import rrmodel_direct_s, rrmodel_interpolated_s, \
rrmodel_direct_s_simple, rrmodel_direct_v, \
rrmodel_interpolated_v
from .numba.rrmodel import create_z_grid, calculate_weights_3d
from .transitmodel import TransitModel
__all__ = ['RoadRunnerModel']
[docs]class RoadRunnerModel(TransitModel):
ldmodels = {'uniform': (ld_uniform, ldi_uniform),
'linear': (ld_linear, ldi_linear),
'quadratic': (ld_quadratic, ldi_quadratic),
'quadratic-tri': (ld_quadratic_tri, ldi_quadratic_tri),
'nonlinear': ld_nonlinear,
'general': ld_general,
'square_root': ld_square_root,
'logarithmic': ld_logarithmic,
'exponential': ld_exponential,
'power-2': ld_power_2,
'power-2-pm': ld_power_2_pm}
[docs] def __init__(self, ldmodel: Union[str, Callable, Tuple[Callable, Callable]] = 'quadratic',
interpolate: bool = False, klims: tuple = (0.005, 0.5), nk: int = 256,
nzin: int = 20, nzlimb: int = 20, zcut=0.7, ng: int = 50, parallel: bool = False,
small_planet_limit: float = 0.05):
"""The RoadRunner transit model by Parviainen (2020).
Parameters
----------
interpolate : bool, optional
Use the interpolation method presented in Parviainen (2015) if true.
klims : tuple, optional
Radius ratio limits (kmin, kmax) for the interpolated model.
nk : int, optional
Radius ratio grid size for the interpolated model.
nz : int, optional
Normalized distance grid size for the interpolated model.
"""
super().__init__()
self.interpolate = interpolate
self.parallel = parallel
self.is_simple = False
self.splimit = small_planet_limit
# Set up the limb darkening model
# --------------------------------
if isinstance(ldmodel, str):
try:
if isinstance(self.ldmodels[ldmodel], tuple):
self.ldmodel = self.ldmodels[ldmodel][0]
self.ldmmean = self.ldmodels[ldmodel][1]
else:
self.ldmodel = self.ldmodels[ldmodel]
self.ldmmean = None
except KeyError:
print(
f"Unknown limb darkening model: {ldmodel}. Choose from [{', '.join(self.ldmodels.keys())}] or supply a callable function.")
raise
elif isinstance(ldmodel, LDModel):
self.ldmodel = ldmodel
self.ldmmean = ldmodel._integrate
elif callable(ldmodel):
self.ldmodel = ldmodel
self.ldmmean = None
elif isinstance(ldmodel, tuple) and callable(ldmodel[0]) and callable(ldmodel[1]):
self.ldmodel = ldmodel[0]
self.ldmmean = ldmodel[1]
else:
raise NotImplementedError
# Set the basic variable
# ----------------------
self.klims = klims
self.nk = nk
self.ng = ng
self.nzin = nzin
self.nzlimb = nzlimb
self.zcut = zcut
# Declare the basic arrays
# ------------------------
self.ze = None
self.zm = None
self.mu = None
self.dk = None
self.dg = None
self.weights = None
self._m_direct_s = None
self._m_direct_v = None
self._m_interp_s = None
self._m_interp_v = None
self._ldmu = linspace(1, 0, 200)
self._ldz = sqrt(1 - self._ldmu ** 2)
self.init_integration(nzin, nzlimb, zcut, ng, nk)
[docs] def set_data(self, time: Union[ndarray, List],
lcids: Optional[Union[ndarray, List]] = None,
pbids: Optional[Union[ndarray, List]] = None,
nsamples: Optional[Union[ndarray, List]] = None,
exptimes: Optional[Union[ndarray, List]] = None,
epids: Optional[Union[ndarray, List]] = None) -> None:
super().set_data(time, lcids, pbids, nsamples, exptimes, epids)
self.set_methods()
def set_methods(self):
if self.npb == 1 and all(self.nsamples == 1) and all(self.epids == 0):
self.is_simple = True
else:
self.is_simple = False
def init_integration(self, nzin, nzlimb, zcut, ng, nk=None):
self.nk = nk
self.ng = ng
self.nzin = nzin
self.nzlimb = nzlimb
self.zcut = zcut
self.ze, self.zm = create_z_grid(zcut, nzin, nzlimb)
self.mu = sqrt(1 - self.zm ** 2)
if self.interpolate:
self.dk, self.dg, self.weights = calculate_weights_3d(nk, self.klims[0], self.klims[1], self.ze, ng)
[docs] def evaluate(self, k: Union[float, ndarray], ldc: Union[ndarray, List], t0: Union[float, ndarray],
p: Union[float, ndarray],
a: Union[float, ndarray], i: Union[float, ndarray], e: Optional[Union[float, ndarray]] = None,
w: Optional[Union[float, ndarray]] = None, copy: bool = True) -> ndarray:
"""Evaluate the transit model for a set of scalar or vector parameters.
Parameters
----------
k
Radius ratio(s) either as a single float, 1D vector, or 2D array.
ldc
Limb darkening coefficients as a 1D or 2D array.
t0
Transit center(s) as a float or a 1D vector.
p
Orbital period(s) as a float or a 1D vector.
a
Orbital semi-major axis (axes) divided by the stellar radius as a float or a 1D vector.
i
Orbital inclination(s) as a float or a 1D vector.
e : optional
Orbital eccentricity as a float or a 1D vector.
w : optional
Argument of periastron as a float or a 1D vector.
Notes
-----
The model can be evaluated either for one set of parameters or for many sets of parameters simultaneously. In
the first case, the orbital parameters should all be given as floats. In the second case, the orbital parameters
should be given as a 1D array-like.
Returns
-------
ndarray
Modelled flux either as a 1D or 2D ndarray.
"""
# Scalar parameters branch
# ------------------------
if isscalar(p):
if e is None:
e, w = 0.0, 0.0
return self.evaluate_ps(k, ldc, t0, p, a, i, e, w, copy)
# Parameter population branch
# ---------------------------
else:
k, t0, p, a, i = asarray(k), asarray(t0), asarray(p), asarray(a), asarray(i)
if k.ndim == 1:
k = k.reshape((k.size, 1))
if t0.ndim == 1:
t0 = t0.reshape((t0.size, 1))
npv = p.size
if e is None:
e, w = zeros(npv), zeros(npv)
if isinstance(self.ldmodel, LDModel):
ldp, istar = self.ldmodel(self.mu, ldc)
else:
ldp = evaluate_ld(self.ldmodel, self.mu, ldc)
if self.ldmmean is not None:
istar = evaluate_ldi(self.ldmmean, ldc)
else:
istar = zeros((npv, self.npb))
ldpi = evaluate_ld(self.ldmodel, self._ldmu, ldc)
for ipv in range(npv):
for ipb in range(self.npb):
istar[ipv, ipb] = 2 * pi * trapz(self._ldz * ldpi[ipv, ipb], self._ldz)
if self.interpolate:
flux = rrmodel_interpolated_v(self.time, k, t0, p, a, i, e, w, ldp, istar, self.weights, self.dk,
self.klims[0], self.dg, self.lcids, self.pbids, self.epids, self.nsamples,
self.exptimes, self.npb, self.parallel)
else:
flux = rrmodel_direct_v(self.time, k, t0, p, a, i, e, w, ldp, istar, self.ze, self.ng,
self.lcids, self.pbids, self.epids, self.nsamples,
self.exptimes, self.npb, self.parallel)
return squeeze(flux)
[docs] def evaluate_ps(self, k: Union[float, ndarray], ldc: ndarray, t0: Union[float, ndarray], p: float, a: float, i: float,
e: float = 0.0, w: float = 0.0, copy: bool = True) -> ndarray:
"""Evaluate the transit model for a set of scalar parameters.
Parameters
----------
k : array-like
Radius ratio(s) either as a single float or an 1D array.
ldc : array-like
Limb darkening coefficients as a 1D array.
t0 : float
Transit center as a float.
p : float
Orbital period as a float.
a : float
Orbital semi-major axis divided by the stellar radius as a float.
i : float
Orbital inclination(s) as a float.
e : float, optional
Orbital eccentricity as a float.
w : float, optional
Argument of periastron as a float.
Notes
-----
This version of the `evaluate` method is optimized for calculating a single transit model (such as when using a
local optimizer). If you want to evaluate the model for a large number of parameters simultaneously, use either
`evaluate` or `evaluate_pv`.
Returns
-------
ndarray
Modelled flux as a 1D ndarray.
"""
k = asarray(k)
ldc = asarray(ldc)
t0 = asarray(t0)
if isinstance(self.ldmodel, LDModel):
ldp, istar = self.ldmodel(self.mu, ldc)
else:
ldp = evaluate_ld(self.ldmodel, self.mu, ldc)
if self.ldmmean is not None:
istar = evaluate_ldi(self.ldmmean, ldc)
else:
istar = zeros((1,self.npb))
if ldc.ndim == 1:
ldc = ldc.reshape((1, 1, -1))
elif ldc.ndim == 2:
ldc = ldc.reshape((1, ldc.shape[1], -1))
for ipb in range(self.npb):
istar[0,ipb] = 2 * pi * trapz(self._ldz * self.ldmodel(self._ldmu, ldc[0,ipb]), self._ldz)
if self.interpolate:
flux = rrmodel_interpolated_s(self.time, k, t0, p, a, i, e, w, ldp, istar, self.weights, self.zm,
self.dk, self.klims[0], self.dg, self.splimit,
self.lcids, self.pbids, self.epids, self.nsamples, self.exptimes, self.parallel)
else:
flux = rrmodel_direct_s(self.time, k, t0, p, a, i, e, w, ldp, istar, self.ze, self.zm, self.ng, self.splimit,
self.lcids, self.pbids, self.epids, self.nsamples, self.exptimes, self.parallel)
return squeeze(flux)