more doc work

nifty4/domains/power_space.py

@@ -24,14 +24,14 @@ from .. import dobj
 class PowerSpace(StructuredDomain):
     """NIFTy class for spaces of power spectra.
 
-    A power space is the result of a projection of a harmonic space where
+    A power space is the result of a projection of a harmonic domain where
     k-modes of equal length get mapped to one power index.
 
     Parameters
     ----------
-    harmonic_partner : Space
-        The harmonic Space of which this is the power space.
-    binbounds: None, or tuple/array/list of float
+    harmonic_partner : StructuredDomain
+        The harmonic dmain of which this is the power space.
+    binbounds : None, or tuple of float
         if None:
             There will be as many bins as there are distinct k-vector lengths
             in the harmonic partner space.
@@ -54,9 +54,9 @@ class PowerSpace(StructuredDomain):
         binbounds[0]=first_bound and binbounds[-1]=last_bound and the remaining
         values equidistantly spaced (in linear scale) between these two.
 
-        nbin: integer
+        nbin : int
             the number of bins
-        first_bound, last_bound: float
+        first_bound, last_bound : float
             the k values for the right boundary of the first bin and the left
             boundary of the last bin, respectively. They are given in length
             units of the harmonic partner space.
@@ -74,9 +74,9 @@ class PowerSpace(StructuredDomain):
         values equidistantly spaced (in natural logarithmic scale)
         between these two.
 
-        nbin: integer
+        nbin : int
             the number of bins
-        first_bound, last_bound: float
+        first_bound, last_bound : float
             the k values for the right boundary of the first bin and the left
             boundary of the last bin, respectively. They are given in length
             units of the harmonic partner space.

nifty4/library/critical_power_energy.py

@@ -34,33 +34,34 @@ class CriticalPowerEnergy(Energy):
 
     Parameters
     ----------
-    position : Field,
+    position : Field
         The current position of this energy. (Logarithm of power spectrum)
-    m : Field,
+    m : Field
         The map whose power spectrum is inferred. Needs to live in harmonic
         signal space.
-    D : EndomorphicOperator,
+    D : EndomorphicOperator, optional
         The curvature of the Gaussian encoding the posterior covariance.
         If not specified, the map is assumed to be no reconstruction.
         default : None
-    alpha : float
+    alpha : float, optional
         The spectral prior of the inverse gamma distribution. 1.0 corresponds
         to non-informative.
         default : 1.0
-    q : float
+    q : float, optional
         The cutoff parameter of the inverse gamma distribution. 0.0 corresponds
         to non-informative.
         default : 0.0
-    smoothness_prior : float
+    smoothness_prior : float, optional
         Controls the strength of the smoothness prior
         default : 0.0
-    logarithmic : boolean
+    logarithmic : bool, optional
         Whether smoothness acts on linear or logarithmic scale.
-    samples : integer
+        default : True
+    samples : int, optional
         Number of samples used for the estimation of the uncertainty
         corrections.
         default : 3
-    w : Field
+    w : Field, optional
         The contribution from the map with or without uncertainty. It is used
         to pass on the result of the costly sampling during the minimization.
         default : None

nifty4/library/nonlinear_power_energy.py

@@ -34,18 +34,18 @@ class NonlinearPowerEnergy(Energy):
 
     Parameters
     ----------
-    position : Field,
+    position : Field
         The current position of this energy.
-    m : Field,
+    m : Field
         The map whichs power spectrum has to be inferred
-    D : EndomorphicOperator,
+    D : EndomorphicOperator
         The curvature of the Gaussian encoding the posterior covariance.
         If not specified, the map is assumed to be no reconstruction.
         default : None
     sigma : float
         The parameter of the smoothness prior.
         default : ??? None? ???????
-    samples : integer
+    samples : int
         Number of samples used for the estimation of the uncertainty
         corrections.
         default : 3

nifty4/minimization/conjugate_gradient.py

@@ -56,9 +56,9 @@ class ConjugateGradient(Minimizer):
 
         Returns
         -------
-        energy : QuadraticEnergy
+        QuadraticEnergy
             state at last point of the iteration
-        status : integer
+        int
             Can be controller.CONVERGED or controller.ERROR
         """
         controller = self._controller

nifty4/minimization/descent_minimizer.py

@@ -50,15 +50,15 @@ class DescentMinimizer(Minimizer):
 
         Parameters
         ----------
-        energy : Energy object
+        nergy : Energy
            Energy object which provides value, gradient and curvature at a
            specific position in parameter space.
 
         Returns
         -------
-        energy : Energy object
+        Energy
             Latest `energy` of the minimization.
-        status : integer
+        int
             Can be controller.CONVERGED or controller.ERROR
 
         Notes

nifty4/minimization/line_search_strong_wolfe.py

@@ -41,13 +41,13 @@ class LineSearchStrongWolfe(LineSearch):
         Parameter for curvature condition rule. (Default: 0.9)
     max_step_size : float
         Maximum step allowed in to be made in the descent direction.
-        (Default: 50)
-    max_iterations : integer
+        (Default: 1000000000)
+    max_iterations : int, optional
         Maximum number of iterations performed by the line search algorithm.
-        (Default: 10)
-    max_zoom_iterations : integer
+        (Default: 100)
+    max_zoom_iterations : int, optional
         Maximum number of iterations performed by the zoom algorithm.
-        (Default: 10)
+        (Default: 100)
     """
 
     def __init__(self, c1=1e-4, c2=0.9,
@@ -71,18 +71,18 @@ class LineSearchStrongWolfe(LineSearch):
 
         Parameters
         ----------
-        energy : Energy object
+        energy : Energy
             Energy object from which we will calculate the energy and the
             gradient at a specific point.
         pk : Field
             Vector pointing into the search direction.
-        f_k_minus_1 : float
+        f_k_minus_1 : float, optional
             Value of the fuction (which is being minimized) at the k-1
             iteration of the line search procedure. (Default: None)
 
         Returns
         -------
-        energy_star : Energy object
+        Energy
             The new Energy object on the new position.
         """
         le_0 = LineEnergy(0., energy, pk, 0.)
@@ -188,7 +188,7 @@ class LineSearchStrongWolfe(LineSearch):
 
         Returns
         -------
-        energy_star : Energy object
+        Energy
             The new Energy object on the new position.
         """
         cubic_delta = 0.2  # cubic interpolant checks

nifty4/minimization/minimizer.py

@@ -39,8 +39,9 @@ class Minimizer(with_metaclass(NiftyMeta, type('NewBase', (object,), {}))):
 
         Returns
         -------
-        energy : Energy object
+        Energy
             Latest `energy` of the minimization.
-        status : integer
+        int
+            exit status of the minimization
         """
         raise NotImplementedError

nifty4/minimization/nonlinear_cg.py

@@ -51,9 +51,9 @@ class NonlinearCG(Minimizer):
 
         Returns
         -------
-        energy :
+        Energy
             state at last point of the iteration
-        status : integer
+        int
             Can be controller.CONVERGED or controller.ERROR
         """
         controller = self._controller

nifty4/minimization/vl_bfgs.py

@@ -87,7 +87,7 @@ class InformationStore(object):
 
     Parameters
     ----------
-    max_history_length : integer
+    max_history_length : int
         Maximum number of stored past updates.
     x0 : Field
         Initial position in variable space.
@@ -96,7 +96,7 @@ class InformationStore(object):
 
     Attributes
     ----------
-    max_history_length : integer
+    max_history_length : int
         Maximum number of stored past updates.
     s : List
         Circular buffer of past position differences, which are Fields.
@@ -106,7 +106,7 @@ class InformationStore(object):
         Latest position in variable space.
     last_gradient : Field
         Gradient at latest position.
-    k : integer
+    k : int
         Number of updates that have taken place
     ss : numpy.ndarray
         2D circular buffer of scalar products between different elements of s.
@@ -139,7 +139,7 @@ class InformationStore(object):
 
         Returns
         -------
-        result : List
+        List
             List of new basis vectors.
         """
         result = []
@@ -165,7 +165,7 @@ class InformationStore(object):
 
         Returns
         -------
-        result : numpy.ndarray
+        numpy.ndarray
             Scalar matrix.
         """
         m = self.history_length
@@ -207,7 +207,7 @@ class InformationStore(object):
 
         Returns
         -------
-        delta : List
+        List
             List of the new scalar coefficients (deltas).
         """
         m = self.history_length

nifty4/operators/laplace_operator.py

@@ -35,7 +35,7 @@ class LaplaceOperator(EndomorphicOperator):
 
     Parameters
     ----------
-    logarithmic : boolean,
+    logarithmic : bool, optional
         Whether smoothness is calculated on a logarithmic scale or linear scale
         default : True
     space : int

nifty4/operators/linear_operator.py

@@ -57,7 +57,7 @@ class LinearOperator(with_metaclass(
     @abc.abstractproperty
     def domain(self):
         """
-        domain : DomainTuple
+        DomainTuple
             The domain on which the Operator's input Field lives.
             Every Operator which inherits from the abstract LinearOperator
             base class must have this attribute.
@@ -67,7 +67,7 @@ class LinearOperator(with_metaclass(
     @abc.abstractproperty
     def target(self):
         """
-        target : DomainTuple
+        DomainTuple
             The domain on which the Operator's output Field lives.
             Every Operator which inherits from the abstract LinearOperator
             base class must have this attribute.
@@ -77,7 +77,7 @@ class LinearOperator(with_metaclass(
     @property
     def inverse(self):
         """
-        inverse : LinearOperator
+        LinearOperator
             Returns a LinearOperator object which behaves as if it were
             the inverse of this operator.
         """
@@ -87,7 +87,7 @@ class LinearOperator(with_metaclass(
     @property
     def adjoint(self):
         """
-        adjoint : LinearOperator
+        LinearOperator
             Returns a LinearOperator object which behaves as if it were
             the adjoint of this operator.
         """
@@ -141,7 +141,7 @@ class LinearOperator(with_metaclass(
 
         Returns
         -------
-        out : integer
+        int
             This is any subset of LinearOperator.{TIMES, ADJOINT_TIMES,
             INVERSE_TIMES, ADJOINT_INVERSE_TIMES, INVERSE_ADJOINT_TIMES},
             joined together by the "|" operator.
@@ -158,7 +158,7 @@ class LinearOperator(with_metaclass(
             The input Field, living on the Operator's domain or target,
             depending on mode.
 
-        mode : integer
+        mode : int
             LinearOperator.TIMES: normal application
             LinearOperator.ADJOINT_TIMES: adjoint application
             LinearOperator.INVERSE_TIMES: inverse application
@@ -168,7 +168,7 @@ class LinearOperator(with_metaclass(
 
         Returns
         -------
-        out : Field
+        Field
             The processed Field living on the Operator's target or domain,
             depending on mode.
         """
@@ -188,7 +188,7 @@ class LinearOperator(with_metaclass(
 
         Returns
         -------
-        out : Field
+        Field
             The processed Field living on the Operator's target domain.
         """
         return self.apply(x, self.TIMES)
@@ -203,7 +203,7 @@ class LinearOperator(with_metaclass(
 
         Returns
         -------
-        out : Field
+        Field
             The processed Field living on the Operator's domain.
         """
         return self.apply(x, self.INVERSE_TIMES)
@@ -218,7 +218,7 @@ class LinearOperator(with_metaclass(
 
         Returns
         -------
-        out : Field
+        Field
             The processed Field living on the Operator's domain.
         """
         return self.apply(x, self.ADJOINT_TIMES)
@@ -233,7 +233,7 @@ class LinearOperator(with_metaclass(
 
         Returns
         -------
-        out : Field
+        Field
             The processed Field living on the Operator's target domain.
 
         Notes
@@ -267,7 +267,7 @@ class LinearOperator(with_metaclass(
 
         Returns
         -------
-        sample : Field
-            Returns the a sample from the Gaussian of given covariance.
+        Field
+            A sample from the Gaussian of given covariance.
         """
         raise NotImplementedError

nifty4/operators/smoothness_operator.py

@@ -36,9 +36,9 @@ def SmoothnessOperator(domain, strength=1., logarithmic=True, space=None):
 
     Parameters
     ----------
-    strength: nonnegative float
+    strength : nonnegative float
         Specifies the strength of the SmoothnessOperator
-    logarithmic : boolean
+    logarithmic : bool, optional
         Whether smoothness is calculated on a logarithmic scale or linear scale
         default : True
     """