def __init__(self, data,
priors):
# TODO: autoriser l'utilisateur à ne pas faire de Gelman-Rubin
# TODO: Revoir les méthodes Temporary et showCorner de AISampler
# TODO: revoir la méthode showPDF
# TODO: dans le notebook, avant de faire tourner le MCMC, on doit présenter les méthodes liés à la visualisation des data, ...
# TODO: créer une méthode qui ajoute ou retire un point d'observation dans les data.
# TODO: les highly probable initial guess devrait se trouver dans un fichier texte lui-même sauvegardé dans PyAstrOFit/rsc.
""" The constructor of the class """
# -----------------------------
# Main part of the constructor
# -----------------------------
## Get the data and convert the time of observation in JD
self._ob, self._er = get_planet_data(data)
if self._ob is None and self._er is None:
try:
FileHandler.__init__(self,data,addContent=[])
(self._ob, self._er) = FileHandler.getInfo(self)
except:
raise Exception("Crash during the load of your data. Is the path correct ? --> {}".format(data))
if len(self._ob.values()[0]) == 0:
raise Exception("The file '{}' seems to be empty.".format(data))
self._ob['timePositionJD'] = [Time(self._ob['timePosition'][k],format='iso',scale='utc').jd for k in range(len(self._ob['timePosition']))]
self._l = len(self._ob['timePositionJD'])
self._data = data
self._ndim = 6
self._pKey = ['semiMajorAxis','eccentricity','inclinaison', 'longitudeAscendingNode', 'periastron', 'periastronTime']
## Check what contains the attribut priors and initialize the initial state self._theta0
self._priors, self._theta0 = AISampler.priors_check(self,priors)
## List class attributs
self._list = [s for s in dir(self) if s[0] == '_' and s[1] != '_' and s[1:12] != 'FileHandler' and s[1:4] != 'get']
def __init__(self, markovchain, data, priors, lnprobability=None, input_parameters=None, outputPath=""):
""" The constructor of the class """
### TODO: les attributs qui ne nécessitent pas d'appel via @property
### devraient être renommé self.attribut au lieu de self._attribut
# Priors and Input parameters
self._priors = priors
self._input_parameters = input_parameters
if input_parameters is not None:
self._synthetic = self._input_parameters["synthetic"]
else:
self._synthetic = False
# Markov chain
if self._synthetic:
self.markovchain_synthetic = markovchain
self.markovchain = np.zeros_like(markovchain)
for j, walker in enumerate(self.markovchain_synthetic):
self.markovchain[j, :, :] = np.array(
[
toKepler(
parameters,
which=self._priors["which"],
mass=self._priors["starMass"],
referenceTime=self._priors["referenceTime"],
)
for parameters in walker
]
)
else:
self.markovchain = markovchain
self.markovchain_synthetic = np.zeros_like(markovchain)
self._isamples = np.empty([0])
self._isamples_in_confidence = np.empty([0])
self._isamples_out_confidence = np.empty([0])
self._isamples_synthetic = np.empty([0])
# log-likelihood
self.lnprobability = lnprobability
self._ilnprobability = np.empty([0])
# chi2 and n_orbit
self._iorbit = None
self._n_iorbit = 0.0
self._ichi2_in_confidence = np.empty([0])
self._n_iorbit_in_confidence = 0.0
self._ichi2_out_confidence = np.empty([0])
self._n_iorbit_out_confidence = 0.0
# Load observations
self._ob, self._er = get_planet_data(data)
if self._ob is None and self._er is None:
try:
# FileHandler.__init__(self,self._dataFilePath,addContent=[])
st_temp = fh(data)
(self._ob, self._er) = st_temp.getInfo()
except:
raise Exception("Crash during the load of your data. Is the path correct ? --> {}".format(data))
if len(self._ob.values()[0]) == 0:
raise Exception("The file '{}' seems to be empty.".format(data))
self._nob = len(self._ob["timePosition"])
self._ob["timePositionJD"] = [
Time(self._ob["timePosition"][k], format="iso", scale="utc").jd for k in range(self._nob)
]
# Other arguments
self._outputpath = outputPath
self._pKey = [
"semiMajorAxis",
"eccentricity",
"inclinaison",
"longitudeAscendingNode",
"periastron",
"periastronTime",
]
self._nwalker, self._walker_length, self._nparameters = self.markovchain.shape
self._dof = self._nob * 2 - self._nparameters
self._length = -1
self._dim = self.markovchain.shape[2]
self._best = None
self.valMax = None
self.confidenceInterval = None
