def __str__(self): try: tex1 = model.Parameters(9).tex_labels( param_list=[self.param1])[self.param1] tex2 = model.Parameters(9).tex_labels( param_list=[self.param2])[self.param2] s = "{} constrained to be less than {}".format(tex1, tex2) except KeyError: s = self.__repr__() return s
def __str__(self): try: tex = model.Parameters(9).tex_labels(param_list=[self.param])[self.param] s = "Gaussian prior on {}: ${} \\pm {}$ \\\\".format(tex, self. mu, self.sigma) except KeyError: s = self.__repr__() return s
def __str__(self): tex = model.Parameters(9, basis='per tc e w k').tex_labels() msg = "" for i, num_planet in enumerate(self.planet_list): par = "e{}".format(num_planet) label = tex[par] msg += "{} constrained to be $<{}$ \\\\\\\\\n".format(label, self.upperlims[i]) return msg[:-5]
def __str__(self): try: tex = model.Parameters(9).tex_labels( param_list=[self.param])[self.param] s = "Modified Jeffrey's prior: knee = {}; ${} < {} < {}$".format( self.kneeval, self.minval, tex.replace('$', ''), self.maxval) except KeyError: s = self.__repr__() return s
def __str__(self): try: tex = model.Parameters(9).tex_labels(param_list=[self.param])[self.param] s = "Bounded prior: ${} < {} < {}$".format(self.minval, tex.replace('$', ''), self.maxval) except KeyError: s = self.__repr__() return s
def __str__(self): try: tex = model.Parameters(9).tex_labels(param_list=self.param_list) t = [tex[key] for key in tex.keys()] if len(self.param_list) == 1: str2print = '{0}'.format(*t) elif len(self.param_list) == 2: str2print = '{} and {}'.format(*t) else: str2print = '' for el in np.arange(len(self.param_list) - 1): str2print += '{}, '.format(t[el]) str2print += 'and {}'.format(t[el + 1]) s = "Numerical prior on " + str2print + \ ", defined using Gaussian kernel density estimation." except KeyError: s = self.__repr__() return s