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
