Source code for pytransit.models.ma_uniform_cl
# 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/>.
"""OpenCL implementation of the transit over a uniform disk (Mandel & Agol, ApJ 580, L171-L175 2002).
"""
from importlib.resources import files
from typing import Union, Optional
import pyopencl as cl
from numpy import array, uint32, float32, asarray, zeros, ones, unique, atleast_1d, atleast_2d, squeeze, ndarray, empty, concatenate
from ._deprecation import deprecated_evaluation_method
from .opencltransitmodel import OpenCLTransitModel, build_program
from ..orbits.taylor_z import vajs_from_paiew_v
[docs]
class UniformModelCL(OpenCLTransitModel):
"""Exoplanet transit over a uniform disk (Mandel & Agol, ApJ 580, L171-L175 2002).
"""
[docs]
def __init__(self, cl_ctx=None, cl_queue=None) -> None:
super().__init__()
# Initialize the OpenCL context and queue
# ---------------------------------------
self.ctx = cl_ctx or cl.create_some_context()
self.queue = cl_queue or cl.CommandQueue(self.ctx)
# Declare stuff
# -------------
self.nptb: int = 0 # Number of datapoints
self.nlc: int = 0 # Number of light curves
self.npb: int = 0 # Number of passpands
# Declare the arrays
# ------------------
self.flux: ndarray = None # Model flux values
self.time: ndarray = None # Mid-exposure times
self.lcids: ndarray = None # Light curve indices
self.pbids: ndarray = None # Passband indices
self.nsamples: ndarray = None # Number of samples per light curve
self.exptimes: ndarray = None # Exposure times per light curve
self.vajs: ndarray = None
self.pv: ndarray = array([]) # Parameter vector array
# Declare the buffers
# --------------------
self._b_time = None # Buffer for the mid-exposure times
self._b_flux = None # Buffer for the model flux values
self._b_pv = None # Parameter vector buffer
self._b_vajs = None
# Build the program
# -----------------
opencl_pkg = files('pytransit.models.opencl')
orbits_src = opencl_pkg.joinpath('orbits.cl').read_text()
model_src = opencl_pkg.joinpath('ma_uniform.cl').read_text()
self.prg = build_program(self.ctx, orbits_src + model_src)
[docs]
def evaluate(self, k: Union[float, ndarray], 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.
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.
"""
npv = 1 if isinstance(t0, float) else len(t0)
k = asarray(k)
if k.size == 1:
nk = 1
elif npv == 1:
nk = k.size
else:
nk = k.shape[1]
if e is None:
e, w = 0.0, 0.0
pvp = empty((npv, nk + 6), dtype=float32)
pvp[:, :nk] = k
pvp[:, nk] = t0
pvp[:, nk + 1] = p
pvp[:, nk + 2] = a
pvp[:, nk + 3] = i
pvp[:, nk + 4] = e
pvp[:, nk + 5] = w
return self._evaluate_pv(pvp, copy)
[docs]
@deprecated_evaluation_method()
def evaluate_ps(self, k, t0, p, a, i, e=0., w=0., copy=True):
"""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.
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.
"""
if isinstance(k, float):
pv = array([[k, t0, p, a, i, e, w]], float32)
else:
pv = concatenate([k, [t0, p, a, i, e, w]]).astype(float32)
return self._evaluate_pv(pv, copy)
[docs]
@deprecated_evaluation_method()
def evaluate_pv(self, pvp, copy=True):
"""Evaluate the transit model for 2D parameter array.
Parameters
----------
pvp
Parameter array with a shape `(npv, npar)` where `npv` is the number of parameter vectors, and each row
contains a set of parameters `[k, t0, p, a, i, e, w]`. The radius ratios can also be given per passband,
in which case the row should be structured as `[k_0, k_1, k_2, ..., k_npb, t0, p, a, i, e, w]`.
ldc
Limb darkening coefficient array with shape `(npv, 2*npb)`, where `npv` is the number of parameter vectors
and `npb` is the number of passbands.
Notes
-----
This version of the `evaluate` method is optimized for calculating several models in parallel, such as when
using *emcee* for MCMC sampling.
Returns
-------
ndarray
Modelled flux either as a 1D or 2D ndarray.
"""
return self._evaluate_pv(pvp, copy)
def _evaluate_pv(self, pvp, copy=True):
# Implementation shared with the supported `evaluate` method, so that calling
# `evaluate` does not raise the deprecation warning.
pvp = atleast_2d(pvp)
self.npv = uint32(pvp.shape[0])
self.spv = uint32(pvp.shape[1])
if pvp.size != self.pv.size:
if self._b_flux is not None:
self._b_flux.release()
self._b_pv.release()
self._b_vajs.release()
self.pv = zeros(pvp.shape, float32)
self.flux = zeros((self.npv, self.nptb), float32)
self.vajs = zeros((self.npv, 9), float32)
mf = cl.mem_flags
self._b_flux = cl.Buffer(self.ctx, mf.WRITE_ONLY, self.time.nbytes * self.npv)
self._b_pv = cl.Buffer(self.ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=self.pv)
self._b_vajs = cl.Buffer(self.ctx, mf.READ_WRITE, float32().nbytes*self.npv*9)
self.pv[:] = pvp
cl.enqueue_copy(self.queue, self._b_pv, self.pv)
self.prg.vajs_from_paiew_v(self.queue, (self.npv, ), None, self.spv, self._b_pv, self._b_vajs)
self.prg.uniform_eccentric_pop(self.queue, (self.npv, self.nptb), None, self._b_time, self._b_lcids, self._b_pbids,
self._b_pv, self._b_nsamples, self._b_etimes, self._b_vajs,
self.spv, self.nlc, self.npb, self._b_flux)
if copy:
cl.enqueue_copy(self.queue, self.flux, self._b_flux)
return squeeze(self.flux)
else:
return None