Reorganise likelihood and add GaussianLikelihood

• Moves the hyperPE likelihood to core
• Adds the GaussianLikelihood
• Update docs
• Update examples

docs/likelihood.txt

@@ -167,11 +167,55 @@ In the last example, we considered only cases with known noise (e.g., a
 prespecified standard deviation. We now present a general function which can
 handle unknown noise (in which case you need to specify a prior for
 :math:`\sigma`, or known noise (in which case you pass the known noise in when
-instatiating the likelihood
-
-.. literalinclude:: ../examples/other_examples/linear_regression_unknown_noise.py
-   :lines: 52-94
-
+instatiating the likelihood::
+
+  class GaussianLikelihood(tupak.Likelihood):
+      def __init__(self, x, y, function, sigma=None):
+          """
+          A general Gaussian likelihood for known or unknown noise - the model
+          parameters are inferred from the arguments of function
+
+          Parameters
+          ----------
+          x, y: array_like
+              The data to analyse
+          function:
+              The python function to fit to the data. Note, this must take the
+              dependent variable as its first argument. The other arguments are
+              will require a prior and will be sampled over (unless a fixed
+              value is given).
+          sigma: None, float, array_like
+              If None, the standard deviation of the noise is unknown and will be
+              estimated (note: this requires a prior to be given for sigma). If
+              not None, this defined the standard-deviation of the data points.
+              This can either be a single float, or an array with length equal
+              to that for `x` and `y`.
+          """
+          self.x = x
+          self.y = y
+          self.N = len(x)
+          self.sigma = sigma
+          self.function = function
+
+          # These lines of code infer the parameters from the provided function
+          parameters = inspect.getargspec(function).args
+          parameters.pop(0)
+          self.parameters = dict.fromkeys(parameters)
+          self.function_keys = self.parameters.keys()
+          if self.sigma is None:
+              self.parameters['sigma'] = None
+
+      def log_likelihood(self):
+          sigma = self.parameters.get('sigma', self.sigma)
+          model_parameters = {k: self.parameters[k] for k in self.function_keys}
+          res = self.y - self.function(self.x, **model_parameters)
+          return -0.5 * (np.sum((res / sigma)**2)
+                         + self.N*np.log(2*np.pi*sigma**2))
+
+We provide this general-purpose class as part of tupak:
+
+.. autoclass:: tupak.core.likelihood.GaussianLikelihood
+   :members:
 
 An example using this likelihood can be found `on this page <https://git.ligo.org/Monash/tupak/blob/master/examples/other_examples/linear_regression_unknown_noise.py>`_.