@@ -77,7 +77,7 @@ The additional methods are specified in the abstract class
provide information about the domain's pixel volume(s) and its total volume.
- The property :attr:`~StructuredDomain.harmonic` specifies whether a domain
is harmonic (i.e. describes a frequency space) or not
- If the domain is harmonic, the methods
- If (and only if) the domain is harmonic, the methods
:meth:`~StructuredDomain.get_k_length_array`,
:meth:`~StructuredDomain.get_unique_k_lengths`, and
:meth:`~StructuredDomain.get_fft_smoothing_kernel_function` provide absolute
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@@ -90,7 +90,7 @@ NIFTy comes with several concrete subclasses of :class:`StructuredDomain`:
- :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:`~log_rg_space.LogRGSpace` implements a Cartesian grid wit logarithimcally
- :class:`~log_rg_space.LogRGSpace` implements a Cartesian grid with logarithmically
spaced bins and an arbitrary number of dimensions.
- :class:`~hp_space.HPSpace` and :class:`~gl_space.GLSpace` describe pixelisations of the
2-sphere; their counterpart in harmonic space is :class:`~lm_space.LMSpace`, which
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@@ -125,18 +125,13 @@ Some examples are:
.. currentmodule:: nifty5
Consequently, NIFTy defined a class called :class:`~domain_tuple.DomainTuple`
Consequently, NIFTy defines a class called :class:`~domain_tuple.DomainTuple`
holding a sequence of :class:`~domains.domain.Domain` objects. The full domain is
specified as the product of all elementary domains. Thus, an instance of
:class:`~domain_tuple.DomainTuple` would be suitable to describe the former two
:class:`~domain_tuple.DomainTuple` would be suitable to describe the first two
examples above. In principle, a :class:`~domain_tuple.DomainTuple`
can even be empty, which implies that the field living on it is a scalar.
.. 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.
A :class:`~domain_tuple.DomainTuple` supports iteration and indexing, and also
provides the properties :attr:`~domain_tuple.DomainTuple.shape` and
:attr:`~domain_tuple.DomainTuple.size` in analogy to the elementary
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@@ -147,7 +142,7 @@ identified by a name, is described by the :class:`~multi_domain.MultiDomain`
class. In contrast to a :class:`~domain_tuple.DomainTuple` a
:class:`~multi_domain.MultiDomain` is a collection and does not define the
product space of its elements. It would be the adequate space to use in the
latter of above's examples.
last of the above examples.
Fields
======
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@@ -167,10 +162,11 @@ be used with distributed memory processing.
Fields support a wide range of arithmetic operations, either involving
two fields of equal domains or a field and a scalar. Arithmetic operations are
performed point-wise, and the returned field has the same domain as the input field(s). Available operators are addition ("+"), subtraction ("-"),
performed point-wise, and the returned field has the same domain as the input field(s).
Available operators are addition ("+"), subtraction ("-"),
multiplication ("*"), division ("/"), floor division ("//") and
exponentiation ("**"). Inplace operators ("+=", etc.) are not supported.
Further, boolean operators, performing a pointwise comparison of a field with
Further, boolean operators, performing a point-wise comparison of a field with
either another field of equal domain or a scalar, are available as well. These
include equals ("=="), not equals ("!="), less ("<"), less or equal ("<="),
greater (">") and greater or equal (">=). The domain of the field returned equals
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@@ -375,16 +371,25 @@ 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).
For MGVI, NIFTy provides the :class:`~energy.Energy` subclass :class:`~minimization.metric_gaussian_kl.MetricGaussianKL`,
which computes the sampled estimated of the KL divergence, its gradient and the Fisher metric. The constructor
of :class:`~minimization.metric_gaussian_kl.MetricGaussianKL` requires an instance of
:class:`~operators.energy_operators.StandardHamiltonian`, an operator to compute the negative log-likelihood of the problem in standardized coordinates
at a given position in parameter space. Finally, the :class:`~operators.energy_operators.StandardHamiltonian` can be constructed from
the likelihood, represented by an :class:`~operators.energy_operators.EnergyOperator` instance. Several commonly used forms of the likelihoods are already provided in
NIFTy, such as :class:`~operators.energy_operators.GaussianEnergy`, :class:`~operators.energy_operators.PoissonianEnergy`,
:class:`~operators.energy_operators.InverseGammaLikelihood` or :class:`~operators.energy_operators.BernoulliEnergy`, but the user
is free to implement a likelihood customized to the problem at hand. The dome code `demos/getting_started_3.py` illustrates how to set up an energy functional
for MGVI and minimize it.
For MGVI, NIFTy provides the :class:`~energy.Energy` subclass
