diff git a/docs/source/code.rst b/docs/source/code.rst
index d01d01e18026f99e059d494fac6528639fc5a0d6..3fe2b01687937bb71f4d959c4be40afcae0edfd9 100644
 a/docs/source/code.rst
+++ b/docs/source/code.rst
@@ 89,16 +89,17 @@ NIFTy comes with several concrete subclasses of :class:`StructuredDomain`:
.. currentmodule:: nifty5.domains
 :class:`rg_space.RGSpace` represents a regular Cartesian grid with an arbitrary
+ :class:`~rg_space.RGSpace` represents a regular Cartesian grid with an arbitrary
number of dimensions, which is supposed to be periodic in each dimension.
 :class:`hp_space.HPSpace` and :class:`gl_space.GLSpace` describe pixelisations of the
 2sphere; their counterpart in harmonic space is :class:`lm_space.LMSpace`, which
+ :class:`~hp_space.HPSpace` and :class:`~gl_space.GLSpace` describe pixelisations of the
+ 2sphere; their counterpart in harmonic space is :class:`~lm_space.LMSpace`, which
contains spherical harmonic coefficients.
 :class:`power_space.PowerSpace` is used to describe onedimensional power spectra.
+ :class:`~power_space.PowerSpace` is used to describe onedimensional power spectra.
Among these, :class:`rg_space.RGSpace` can be harmonic or not (depending on constructor arguments), :class:`gl_space.GLSpace`, :class:`hp_space.HPSpace`, and :class:`power_space.PowerSpace` are
pure position domains (i.e. nonharmonic), and :class:`lm_space.LMSpace` is always
harmonic.
+Among these, :class:`~rg_space.RGSpace` can be harmonic or not (depending on
+constructor arguments), :class:`~gl_space.GLSpace`, :class:`~hp_space.HPSpace`,
+and :class:`~power_space.PowerSpace` are pure position domains (i.e.
+nonharmonic), and :class:`~lm_space.LMSpace` is always harmonic.
Combinations of domains
@@ 110,27 +111,31 @@ field on a product of elementary domains instead of a single one.
More sophisticated operators also require a set of several such fields.
Some examples are:
 sky emission depending on location and energy. This could be represented by
 a product of an :class:`HPSpace` (for location) with an :class:`RGSpace`
 (for energy).
+ sky emission depending on location and energy. This could be represented by a
+ product of an :class:`~hp_space.HPSpace` (for location) with an
+ :class:`~rg_space.RGSpace` (for energy).
 a polarized field, which could be modeled as a product of any structured
domain (representing location) with a fourelement
 :class:`UnstructuredDomain` holding Stokes I, Q, U and V components.
+ :class:`~unstructured_domain.UnstructuredDomain` holding Stokes I, Q, U and V components.
 a model for the sky emission, which holds both the current realization
(on a harmonic domain) and a few inferred model parameters (e.g. on an
unstructured grid).
Consequently, NIFTy defines a class called :class:`DomainTuple` holding
a sequence of :class:`Domain` objects, which is used to specify full field
domains. In principle, a :class:`DomainTuple` can even be empty, which implies
that the field living on it is a scalar.
+.. currentmodule:: nifty5
A :class:`DomainTuple` supports iteration and indexing, and also provides the
properties :attr:`~DomainTuple.shape`, :attr:`~DomainTuple.size` in analogy to
the elementary :class:`Domain`.
+Consequently, NIFTy defines a class called :class:`~domain_tuple.DomainTuple`
+holding a sequence of :class:`~domains.domain.Domain` objects, which is used to
+specify full field domains. In principle, a :class:`~domain_tuple.DomainTuple`
+can even be empty, which implies that the field living on it is a scalar.
An aggregation of several :class:`DomainTuple` s, each member identified by a
name, is described by the :class:`MultiDomain` class.
+A :class:`~domain_tuple.DomainTuple` supports iteration and indexing, and also
+provides the properties :attr:`~domain_tuple.DomainTuple.shape`,
+:attr:`~domain_tuple.DomainTuple.size` in analogy to the elementary
+:class:`~domains.domain.Domain`.
+
+An aggregation of several :class:`~domain_tuple.DomainTuple` s, each member
+identified by a name, is described by the :class:`~multi_domain.MultiDomain`
+class.
Fields
======
@@ 138,9 +143,9 @@ Fields
Fields on a single DomainTuple

A :class:`Field` object consists of the following components:
+A :class:`~field.Field` object consists of the following components:
 a domain in form of a :class:`DomainTuple` object
+ a domain in form of a :class:`~domain_tuple.DomainTuple` object
 a data type (e.g. numpy.float64)
 an array containing the actual values
@@ 168,67 +173,77 @@ 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
:class:`MultiDomain`.
+The :class:`~multi_field.MultiField` class can be seen as a dictionary of
+individual :class:`~field.Field` s, each identified by a name, which is defined
+on a :class:`~multi_domain.MultiDomain`.
Operators
=========
All transformations between different NIFTy fields are expressed (explicitly
or implicitly) in the form of :class:`Operator` objects. The interface of this
class is very minimalistic: it has a property called :class:`domain` which returns
a :class:`DomainTuple` or :class:`MultiDomain` object specifying the structure of the
:class:`Field` s or :class:`MultiField` s it expects as input, another property :class:`target`
describing its output, and finally an overloaded `apply` method, which can
take

 a :class:`Field`/:class:`MultiField` object, in which case it returns the transformed
 :class:`Field`/:class:`MultiField`
 a :class:`Linearization` object, in which case it returns the transformed
 :class:`Linearization`

This is the interface that all objects derived from :class:`Operator` must implement.
In addition, :class:`Operator` objects can be added/subtracted, multiplied, chained
(via the :class:`__call__` method and the `@` operator) and support pointwise
application of functions like
:class:`exp()`, :class:`log()`, :class:`sqrt()`, :class:`conjugate()` etc.
+All transformations between different NIFTy fields are expressed in the form of
+:class:`~operators.operator.Operator` objects. The interface of this class is
+rather minimalistic: it has a property called
+:attr:`~operators.operator.Operator.domain` which returns a
+:class:`~domain_tuple.DomainTuple` or :class:`~multi_domain.MultiDomain` object
+specifying the structure of the :class:`~field.Field` or
+:class:`~multi_field.MultiField` it expects as input, another property
+:attr:`~operators.operator.Operator.target` describing its output, and finally
+an overloaded :attr:`~operators.operator.Operator.apply` method, which can take:
+
+ a :class:`~field.Field`/:class:`~multi_field.MultiField` object, in which case
+ it returns the transformed :class:`~field.Field`/:class:`~multi_field.MultiField`.
+ a :class:`~linearization.Linearization` object, in which case it returns the
+ transformed :class:`~linearization.Linearization`.
+
+This is the interface that all objects derived from
+:class:`~operators.operator.Operator` must implement. In addition,
+:class:`~operators.operator.Operator` objects can be added/subtracted,
+multiplied, chained (via the :attr:`__call__` method or the `@` operator) and
+support pointwise application of functions like :class:`exp()`, :class:`log()`,
+:class:`sqrt()`, :class:`conjugate()`.
Advanced operators

NIFTy provides a library of more sophisticated operators which are used for more
+NIFTy provides a library of commonly employed operators which can be used for
specific inference problems. Currently these are:
 :class:`AmplitudeOperator`, which returns a smooth power spectrum.
 :class:`InverseGammaOperator`, which models point sources which follow a inverse gamma distribution.
 :class:`CorrelatedField`, which models a diffuse lognormal field. It takes an amplitude operator to specify the correlation structure of the field.
+.. currentmodule:: nifty5.library
+
+ :class:`~amplitude_operator.AmplitudeOperator`, which returns a smooth power spectrum.
+ :class:`~inverse_gamma_operator.InverseGammaOperator`, which models point sources which are
+ distributed according to a inversegamma distribution.
+ :class:`~correlated_fields.CorrelatedField`, which models a diffuse lognormal field. It takes an
+ amplitude operator to specify the correlation structure of the field.
Linear Operators
================
A linear operator (represented by NIFTy5's abstract :class:`LinearOperator`
class) is derived from `Operator` and can be interpreted as an
(implicitly defined) matrix. Since its operation is linear, it can provide some
additional functionality which is not available for the more generic :class:`Operator`
class.
+.. currentmodule:: nifty5.operators
+
+A linear operator (represented by NIFTy5's abstract
+:class:`~linear_operator.LinearOperator` class) is derived from
+:class:`~operator.Operator` and can be interpreted as an (implicitly defined)
+matrix. Since its operation is linear, it can provide some additional
+functionality which is not available for the more generic
+:class:`~operator.Operator` class.
Linear Operator basics

There are four basic ways of applying an operator :math:`A` to a field :math:`f`:
+There are four basic ways of applying an operator :math:`A` to a field :math:`s`:
 direct application: :math:`A\cdot f`
 adjoint application: :math:`A^\dagger \cdot f`
 inverse application: :math:`A^{1}\cdot f`
 adjoint inverse application: :math:`(A^\dagger)^{1}\cdot f`
+ direct application: :math:`A(s)`
+ adjoint application: :math:`A^\dagger (s)`
+ inverse application: :math:`A^{1} (s)`
+ adjoint inverse application: :math:`(A^\dagger)^{1} (s)`
(Because of the linearity, inverse adjoint and adjoint inverse application
are equivalent.)
+Note: The inverse of the adjoint of a linear map and the adjoint of the inverse
+of a linear map (if all those exist) are the same.
These different actions of a linear operator ``Op`` on a field ``f`` can be
invoked in various ways:
@@ 240,33 +255,45 @@ invoked in various ways:
Operator classes defined in NIFTy may implement an arbitrary subset of these
four operations. This subset can be queried using the
:attr:`~LinearOperator.capability` property.
+:attr:`~linear_operator.LinearOperator.capability` property.
If needed, the set of supported operations can be enhanced by iterative
inversion methods;
for example, an operator defining direct and adjoint multiplication could be
enhanced by this approach to support the complete set. This functionality is
provided by NIFTy's :class:`InversionEnabler` class, which is itself a linear
+inversion methods; for example, an operator defining direct and adjoint
+multiplication could be enhanced by this approach to support the complete set.
+This functionality is provided by NIFTy's
+:class:`~inversion_enabler.InversionEnabler` class, which is itself a linear
operator.
+.. currentmodule:: nifty5.operators.linear_operator
+
Direct multiplication and adjoint inverse multiplication transform a field
living on the operator's :attr:`~LinearOperator.domain` to one living on the operator's :attr:`~LinearOperator.target`, whereas adjoint multiplication
and inverse multiplication transform from :attr:`~LinearOperator.target` to :attr:`~LinearOperator.domain`.
+defined on the operator's :attr:`~LinearOperator.domain` to one defined on the
+operator's :attr:`~LinearOperator.target`, whereas adjoint multiplication and
+inverse multiplication transform from :attr:`~LinearOperator.target` to
+:attr:`~LinearOperator.domain`.
+
+.. currentmodule:: nifty5.operators
Operators with identical domain and target can be derived from
:class:`EndomorphicOperator`; typical examples for this category are the :class:`ScalingOperator`, which simply multiplies its input by a scalar
value, and :class:`DiagonalOperator`, which multiplies every value of its input
field with potentially different values.
+:class:`~endomorphic_operator.EndomorphicOperator`. Typical examples for this
+category are the :class:`~scaling_operator.ScalingOperator`, which simply
+multiplies its input by a scalar value, and
+:class:`~diagonal_operator.DiagonalOperator`, which multiplies every value of
+its input field with potentially different values.
+
+.. currentmodule:: nifty5
Further operator classes provided by NIFTy are
 :class:`HarmonicTransformOperator` for transforms from a harmonic domain to
 its counterpart in position space, and their adjoint
 :class:`PowerDistributor` for transforms from a :class:`PowerSpace` to
 an associated harmonic domain, and their adjoint
 :class:`GeometryRemover`, which transforms from structured domains to
 unstructured ones. This is typically needed when building instrument response
 operators.
+ :class:`~operators.harmonic_operators.HarmonicTransformOperator` for
+ transforms from a harmonic domain to its counterpart in position space, and
+ their adjoint
+ :class:`~operators.distributors.PowerDistributor` for transforms from a
+ :class:`~domains.power_space.PowerSpace` to an associated harmonic domain, and
+ their adjoint.
+ :class:`~operators.simple_linear_operators.GeometryRemover`, which transforms
+ from structured domains to unstructured ones. This is typically needed when
+ building instrument response operators.
Syntactic sugar
@@ 274,12 +301,14 @@ Syntactic sugar
Nifty5 allows simple and intuitive construction of altered and combined
operators.
As an example, if ``A``, ``B`` and ``C`` are of type :class:`LinearOperator`
and ``f1`` and ``f2`` are of type :class:`Field`, writing::
+As an example, if ``A``, ``B`` and ``C`` are of type :class:`~operators.linear_operator.LinearOperator`
+and ``f1`` and ``f2`` are of type :class:`~field.Field`, writing::
X = A(B.inverse(A.adjoint)) + C
f2 = X(f1)
+.. currentmodule:: nifty5.operators.linear_operator
+
will perform the operation suggested intuitively by the notation, checking
domain compatibility while building the composed operator.
The combined operator infers its domain and target from its constituents,
@@ 289,62 +318,6 @@ The properties :attr:`~LinearOperator.adjoint` and
were the original operator's adjoint or inverse, respectively.
Operators
=========

Operator classes (represented by NIFTy5's abstract :class:`Operator` class) are used to construct
the equations of a specific inference problem.
Most operators are defined via a position, which is a :class:`MultiField` object,
their value at this position, which is again a :class:`MultiField` object and a Jacobian derivative,
which is a :class:`LinearOperator` and is needed for the minimization procedure.

Using the existing basic operator classes one can construct more complicated operators, as
NIFTy allows for easy and selfconsinstent combination via pointwise multiplication,
addition and subtraction. The operator resulting from these operations then automatically
contains the correct Jacobians, positions and values.
Notably, :class:`Constant` and :class:`Variable` allow for an easy way to turn
inference of specific quantities on and off.

The basic operator classes also allow for more complex operations on operators such as
the application of :class:`LinearOperators` or local nonlinearities.
As an example one may consider the following combination of ``x``, which is an operator of type
:class:`Variable` and ``y``, which is an operator of type :class:`Constant`::

 z = x*x + y

``z`` will then be an operator with the following properties::

 z.value = x.value*x.value + y.value
 z.position = Union(x.position, y.position)
 z.jacobian = 2*makeOp(x.value)


Basic operators

# FIXME All this is outdated!

Basic operator classes provided by NIFTy are

 :class:`Constant` contains a constant value and has a zero valued Jacobian.
 Like other operators, it has a position, but its value does not depend on it.
 :class:`Variable` returns the position as its value, its derivative is one.
 :class:`LinearModel` applies a :class:`LinearOperator` on the model.
 :class:`LocalModel` applies a nonlinearity locally on the model.
 value and Jacobian are combined into corresponding :class:`MultiFields` and operators.


Advanced operators


NIFTy also provides a library of more sophisticated operators which are used for more
specific inference problems. Currently these are:

 :class:`AmplitudeOperator`, which returns a smooth power spectrum.
 :class:`InverseGammaOperator`, which models point sources which follow a inverse gamma distribution.
 :class:`CorrelatedField`, which models a diffuse lognormal field. It takes an amplitude operator
 to specify the correlation structure of the field.


.. _minimization:
@@ 354,29 +327,32 @@ Minimization
Most problems in IFT are solved by (possibly nested) minimizations of
highdimensional functions, which are often nonlinear.
+.. currentmodule:: nifty5.minimization
Energy functionals

In NIFTy5 such functions are represented by objects of type :class:`Energy`.
These hold the prescription how to calculate the function's
:attr:`~Energy.value`, :attr:`~Energy.gradient` and
(optionally) :attr:`~Energy.metric` at any given :attr:`~Energy.position`
in parameter space.
Function values are floatingpoint scalars, gradients have the form of fields
living on the energy's position domain, and metrics are represented by
linear operator objects.
+In NIFTy5 such functions are represented by objects of type
+:class:`~energy.Energy`. These hold the prescription how to calculate the
+function's :attr:`~energy.Energy.value`, :attr:`~energy.Energy.gradient` and
+(optionally) :attr:`~energy.Energy.metric` at any given
+:attr:`~energy.Energy.position` in parameter space. Function values are
+floatingpoint scalars, gradients have the form of fields living on the energy's
+position domain, and metrics are represented by linear operator objects.
+
+.. currentmodule:: nifty5
Energies are classes that typically have to be provided by the user when
tackling new IFT problems.
Some examples of concrete energy classes delivered with NIFTy5 are
:class:`QuadraticEnergy` (with positionindependent metric, mainly used with
conjugate gradient minimization) and :class:`~nifty5.library.WienerFilterEnergy`.
+tackling new IFT problems. An example of concrete energy classes delivered with
+NIFTy5 is :class:`~minimization.quadratic_energy.QuadraticEnergy` (with
+positionindependent metric, mainly used with conjugate gradient minimization).
Iteration control

+.. currentmodule:: nifty5.minimization.iteration_controllers
+
Iterative minimization of an energy reqires some means of
checking the quality of the current solution estimate and stopping once
it is sufficiently accurate. In case of numerical problems, the iteration needs
@@ 399,50 +375,61 @@ for their specific applications.
Minimization algorithms

+.. currentmodule:: nifty5.minimization
+
All minimization algorithms in NIFTy inherit from the abstract
:class:`Minimizer` class, which presents a minimalistic interface consisting
only of a :meth:`~Minimizer.__call__` method taking an :class:`Energy` object
and optionally a preconditioning operator, and returning the energy at the
discovered minimum and a status code.
+:class:`~minimizer.Minimizer` class, which presents a minimalistic interface
+consisting only of a :meth:`~minimizer.Minimizer.__call__()` method taking an
+:class:`~energy.Energy` object and optionally a preconditioning operator, and
+returning the energy at the discovered minimum and a status code.
For energies with a quadratic form (i.e. which
can be expressed by means of a :class:`QuadraticEnergy` object), an obvious
choice of algorithm is the :class:`ConjugateGradient` minimizer.
+For energies with a quadratic form (i.e. which can be expressed by means of a
+:class:`~quadratic_energy.QuadraticEnergy` object), an obvious choice of
+algorithm is the :class:`~conjugate_gradient.ConjugateGradient` minimizer.
A similar algorithm suited for nonlinear problems is provided by
:class:`NonlinearCG`.
+:class:`~nonlinear_cg.NonlinearCG`.
Many minimizers for nonlinear problems can be characterized as
 first deciding on a direction for the next step
 then finding a suitable step length along this direction, resulting in the
+ First deciding on a direction for the next step.
+ Then finding a suitable step length along this direction, resulting in the
next energy estimate.
This family of algorithms is encapsulated in NIFTy's :class:`DescentMinimizer`
class, which currently has three concrete implementations:
:class:`SteepestDescent`, :class:`VL_BFGS`, and :class:`RelaxedNewton`.
Of these algorithms, only :class:`RelaxedNewton` requires the energy object to
provide a :attr:`~Energy.metric` property, the others only need energy
values and gradients.

The flexibility of NIFTy's design allows using externally provided
minimizers. With only small effort, adapters for two SciPy minimizers were
written; they are available under the names :class:`NewtonCG` and
+This family of algorithms is encapsulated in NIFTy's
+:class:`~descent_minimizers.DescentMinimizer` class, which currently has three
+concrete implementations: :class:`~descent_minimizers.SteepestDescent`,
+:class:`~descent_minimizers.RelaxedNewton`,
+:class:`~descent_minimizers.NewtonCG`, :class:`~descent_minimizers.L_BFGS` and
+:class:`~descent_minimizers.VL_BFGS`. Of these algorithms, only
+:class:`~descent_minimizers.RelaxedNewton` requires the energy object to provide
+a :attr:`~energy.Energy.metric` property, the others only need energy values and
+gradients.
+
+The flexibility of NIFTy's design allows using externally provided minimizers.
+With only small effort, adapters for two SciPy minimizers were written; they are
+available under the names :class:`~scipy_minimizer.ScipyCG` and
:class:`L_BFGS_B`.
Application to operator inversion

It is important to realize that the machinery presented here cannot only be
used for minimizing IFT Hamiltonians, but also for the numerical inversion of
linear operators, if the desired application mode is not directly available.
A classical example is the information propagator
+.. currentmodule:: nifty5
+
+The machinery presented here cannot only be used for minimizing functionals
+derived from IFT, but also for the numerical inversion of linear operators, if
+the desired application mode is not directly available. A classical example is
+the information propagator whose inverse is defined as:
:math:`D = \left(R^\dagger N^{1} R + S^{1}\right)^{1}`,
+:math:`D^{1} = \left(R^\dagger N^{1} R + S^{1}\right).
which must be applied when calculating a Wiener filter. Only its inverse
application is straightforward; to use it in forward direction, we make use
of NIFTy's :class:`InversionEnabler` class, which internally performs a
minimization of a :class:`QuadraticEnergy` by means of the
:class:`ConjugateGradient` algorithm.
+It needs to be applied in forward direction in order to calculate the Wiener
+filter solution. Only its inverse application is straightforward; to use it in
+forward direction, we make use of NIFTy's
+:class:`~operators.inversion_enabler.InversionEnabler` class, which internally
+performs a minimization of a
+:class:`~minimization.quadratic_energy.QuadraticEnergy` by means of the
+:class:`~minimization.conjugate_gradient.ConjugateGradient` algorithm. An
+example is provided in
+:func:`~ļibrary.wiener_filter_curvature.WienerFilterCurvature`.