diff --git a/docs/source/code.rst b/docs/source/code.rst
index 4a317056780cdfb70fb26dfd2dde5ffad991c57e..6e5bbb4860c9f318ecc70495810afa75a882ac0e 100644
--- a/docs/source/code.rst
+++ b/docs/source/code.rst
@@ -199,9 +199,9 @@ an overloaded :attr:`~operators.operator.Operator.apply` method, which can take:
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 point-wise application of functions like
-:class:`exp()`, :class:`log()`, :class:`sqrt()`, :class:`conjugate()`.
+multiplied, chained (via the :attr:`__call__` method or the `@` operator) and
+support point-wise application of functions like :class:`exp()`, :class:`log()`,
+:class:`sqrt()`, :class:`conjugate()`.
Advanced operators
@@ -210,35 +210,40 @@ Advanced operators
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 are
+.. 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 inverse-gamma distribution.
-- :class:`CorrelatedField`, which models a diffuse log-normal field. It takes an
+- :class:`~correlated_fields.CorrelatedField`, which models a diffuse log-normal 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:`operators.linear_operator.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:`operators.operator.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:
@@ -250,33 +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
@@ -284,12 +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:`operators.linear_operator.LinearOperator`
+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,
@@ -308,29 +327,32 @@ Minimization
Most problems in IFT are solved by (possibly nested) minimizations of
high-dimensional 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 floating-point 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
+floating-point 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 position-independent 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
+position-independent 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
@@ -353,50 +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
-
-:math:`D = \left(R^\dagger N^{-1} R + S^{-1}\right)^{-1}`,
+.. currentmodule:: nifty5
-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.
+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^{-1} = \left(R^\dagger N^{-1} R + S^{-1}\right)`.
+
+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`.