doc typos

ultranest/calibrator.py

@@ -52,11 +52,13 @@ class ReactiveNestedCalibrator():
     Usage is designed to be a drop-in replacement for ReactiveNestedSampler.
 
     If your code was::
+
         sampler = ReactiveNestedSampler(my_param_names, my_loglike, my_transform)
         sampler.stepsampler = SliceSampler(nsteps=10, generate_direction=region_oriented_direction)
         sampler.run(min_num_livepoints=400)
 
     You would change it to::
+
         sampler = ReactiveNestedCalibrator(my_param_names, my_loglike, my_transform)
         sampler.stepsampler = SliceSampler(nsteps=10, generate_direction=region_oriented_direction)
         sampler.run(min_num_livepoints=400)
@@ -77,19 +79,22 @@ def __init__(self,
         """Initialise nested sampler calibrator.
 
         Parameters
-        -----------
+        ----------
+
         param_names: list of str
             Names of the parameters.
             Length gives dimensionality of the sampling problem.
+
         loglike: function
             log-likelihood function.
+
         transform: function
             parameter transform from unit cube to physical parameters.
-        kwargs: dict
-            further arguments passed to ReactiveNestedSampler
 
-        if `log_dir` is set, then the suffix `-nsteps%d` is added for each
-        run where %d is replaced with the number of steps (2, 4, 8 etc).
+        kwargs: dict
+            further arguments passed to ReactiveNestedSampler.
+            if `log_dir` is set, then the suffix `-nsteps%d` is added for each
+            run, where %d is replaced with the number of steps (2, 4, 8 etc).
         """
         self.init_args = dict(param_names=param_names, loglike=loglike, transform=transform, **kwargs)
         self.stepsampler = None
@@ -107,12 +112,12 @@ def run(self, **kwargs):
         and 2) the consecutive log(Z) error bars must overlap.
 
         Parameters
-        -----------
+        ----------
         **kwargs: dict
             All arguments are passed to :py:meth:`ReactiveNestedSampler.run`.
 
         Yields
-        -------
+        ------
         nsteps: int
             number of steps for the current run
         result: dict