Docstring fixups

docs/source/code.rst

@@ -158,7 +158,7 @@ be extracted first, then changed, and a new field has to be created from the
 result.
 
 Fields defined on a MultiDomain
-------------------------------
+-------------------------------
 
 The :class:`MultiField` class can be seen as a dictionary of individual
 :class:`Field` s, each identified by a name, which is defined on a
@@ -300,7 +300,7 @@ As an example one may consider the following combination of ``x``, which is an o
 
 
 Basic operators
-------------
+---------------
 # FIXME All this is outdated!
 
 Basic operator classes provided by NIFTy are

docs/source/ift.rst

@@ -104,6 +104,7 @@ with :math:`{R}` the measurement response, which maps the continous signal field
 
 This is called a free theory, as the information Hamiltonian
 associate professor
+
 .. math::
 
     \mathcal{H}(d,s)= -\log \mathcal{P}(d,s)= \frac{1}{2} s^\dagger S^{-1} s + \frac{1}{2} (d-R\,s)^\dagger N^{-1} (d-R\,s) + \mathrm{const}
@@ -179,23 +180,22 @@ NIFTy takes advantage of this formulation in several ways:
 
 The reconstruction of a non-Gaussian signal with unknown covarinance from a non-trivial (tomographic) response is demonstrated in demos/getting_started_3.py. Here, the uncertainty of the field and the power spectrum of its generating process are probed via posterior samples provided by the MGVI algorithm.
 
-+-------------------------------------------------+
-| .. image:: images/getting_started_3_setup.png   |
-|     :width:  30 %                               |
-+-------------------------------------------------+
-| .. image:: images/getting_started_3_results.png |
-|     :width:  30 %                               |
-+-------------------------------------------------+
-| Output of tomography demo getting_started_3.py. |
-| **Top row:** Non-Gaussian signal field,         |
-| data backprojected into the image domain, power |
-| spectrum of underlying Gausssian process.       |
-| **Bottom row:** Posterior mean field signal     |
-| reconstruction, its uncertainty, and the power  |
-| spectrum of the process for different posterior |
-| samples in comparison to the correct one (thick |
-| orange line).                                   |
-+-------------------------------------------------+
-
-
-
++----------------------------------------------------+
+| **Output of tomography demo getting_started_3.py** |
++----------------------------------------------------+
+| .. image:: images/getting_started_3_setup.png      |
+|                                                    |
++----------------------------------------------------+
+| Non-Gaussian signal field,                         |
+| data backprojected into the image domain, power    |
+| spectrum of underlying Gausssian process.          |
++----------------------------------------------------+
+| .. image:: images/getting_started_3_results.png    |
+|                                                    |
++----------------------------------------------------+
+| Posterior mean field signal                        |
+| reconstruction, its uncertainty, and the power     |
+| spectrum of the process for different posterior    |
+| samples in comparison to the correct one (thick    |
+| orange line).                                      |
++----------------------------------------------------+

nifty5/library/amplitude_operator.py

@@ -111,23 +111,27 @@ def _SlopePowerSpectrum(logk_space, sm, sv, im, iv):
 
 def AmplitudeOperator(s_space, Npixdof, ceps_a, ceps_k, sm, sv, im, iv,
                       keys=['tau', 'phi'], zero_mode=True):
-    '''
+    ''' Operator for parametrizing smooth power spectra.
+
     Computes a smooth power spectrum.
     Output is defined on a PowerSpace.
 
     Parameters
     ----------
-
-    Npixdof : #pix in dof_space
-
-    ceps_a, ceps_k0 : Smoothness parameters in ceps_kernel
-                        eg. ceps_kernel(k) = (a/(1+(k/k0)**2))**2
-                        a = ceps_a,  k0 = ceps_k0
-
-    sm, sv : slope_mean = expected exponent of power law (e.g. -4),
-                slope_variance (default=1)
-
-    im, iv : y-intercept_mean, y-intercept_variance  of power_slope
+    Npixdof : int
+        #pix in dof_space
+    ceps_a : float
+        Smoothness parameters in ceps_kernel eg. ceps_kernel(k) = (a/(1+(k/k0)**2))**2 a = ceps_a,  k0 = ceps_k0
+    ceps_k0 : float
+        Smoothness parameters in ceps_kernel eg. ceps_kernel(k) = (a/(1+(k/k0)**2))**2 a = ceps_a,  k0 = ceps_k0
+    sm : float
+        slope_mean = expected exponent of power law (e.g. -4)
+    sv : float
+        slope_variance (default=1)
+    im : float
+        y-intercept_mean
+    iv : float
+        y-intercept_variance  of power_slope
     '''
 
     from ..operators.exp_transform import ExpTransform

nifty5/operators/domain_tuple_field_inserter.py

@@ -23,18 +23,18 @@ from .linear_operator import LinearOperator
 
 
 class DomainTupleFieldInserter(LinearOperator):
-    def __init__(self, domain, new_space, index, position):
-        '''Writes the content of a field into one slice of a DomainTuple.
+    '''Writes the content of a field into one slice of a DomainTuple.
 
-        Parameters
-        ----------
-        domain : Domain, tuple of Domain or DomainTuple
-        new_space : Domain, tuple of Domain or DomainTuple
-        index : Integer
-            Index at which new_space shall be added to domain.
-        position : tuple
-            Slice in new_space in which the input field shall be written into.
-        '''
+    Parameters
+    ----------
+    domain : Domain, tuple of Domain or DomainTuple
+    new_space : Domain, tuple of Domain or DomainTuple
+    index : Integer
+        Index at which new_space shall be added to domain.
+    position : tuple
+        Slice in new_space in which the input field shall be written into.
+    '''
+    def __init__(self, domain, new_space, index, position):
         self._domain = DomainTuple.make(domain)
         tgt = list(self.domain)
         tgt.insert(index, new_space)