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Changes since NIFTy 5:

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Interface Change for non-linear Operators
=========================================

The method `Operator.apply()` takes a `Linearization` or a `Field` or a
`MultiField` as input. This has not changed. However, now each non-linear
operator assumes that the input `Linearization` comes with an identity operator
as jacobian. Also it is assumed that the `apply()` method returns a
`Linearization` with the jacobian of the operator itself. The user is not in
charge anymore of stacking together the jacobians of operator chains. Something
like `x.jac` should not appear in any self-written `apply()` methods. The method
`Operator._check_input` tests if this condition is met. The same goes for the
metric. There is no need anymore to call `SandwichOperator` in an `apply()`
method when implementing new energies. This change should not lead to unexpected
behaviour since both `Operator._check_input()` and
`extra.check_jacobian_consistency()` tests for the new conditions to be
fulfilled.


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Special functions for complete Field reduction operations
=========================================================

So far, reduction operations called on Fields (like `vdot`, `sum`, `integrate`,
`mean`, `var`, `std`, `prod` etc.) returned a scalar when the reduction was
carried out over all domains, and otherwise a `Field`.
Having the data type of the returned value depend on input parameters is
extremely confusing, so all of these reduction operations now always return a
Field. We also introduced another set of reduction operations which always
operate over all subdomains and therefore don't take a `spaces` argument; they
are named `s_vdot`, `s_sum` etc. and always return a scalar.

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Updates regarding correlated fields
===================================

The most commonly used model for homogeneous and isotropic correlated fields in
nifty5 has been `SLAmplitude` combined with `CorrelatedField`. This model
exhibits unintuitive couplings between its parameters and as been replaced
by `CorrelatedFieldMaker` in NIFTy 6. This model aims to conceptionally provide
the same functionality. However, internally it works quite differently. Therefore,
specific classes for `SLAmplitude` like `LogRGSpace`, `QHTOperator`, `ExpTransform`,
`SlopeOperator`, `SymmetrizingOperator`, `CepstrumOperator`, `CorrelatedField`
and `MfCorrelatedField` are not needed anymore and have been removed. In general,
`CorrelatedFieldMaker` feels to be better conditioned leading to faster convergence
but it is hard to make explicit tests since the two approaches cannot be mapped
onto each other exactly. We experienced that preconditioning in the `MetricGaussianKL`
via `napprox` breaks the inference scheme with the new model so `napprox` may not
be used here.
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Removal of the standard MPI parallelization scheme:
===================================================
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When several MPI tasks are present, NIFTy5 distributes every Field over these
tasks by splitting it along the first axis. This approach to parallelism is not
very efficient, and it has not been used by anyone for several years, so we
decided to remove it, which led to many simplifications within NIFTy.
User-visible changes:
- the methods `to_global_data`, `from_global_data`, `from_local_data` and
  the property `local_data` have been removed from `Field` and `MultiField`.
  Instead there are now the property `val` (returning a read-only numpy.ndarray
  for `Field` and a dictionary of read-only numpy.ndarrays for `MultiField`) and
  the method `val_rw()` (returning the same structures with writable copies of
  the arrays). Fields and MultiFields can be created from such structures using
  the static method `from_raw`
- the functions `from_global_data` and `from_local_data` in `sugar` have been
  replaced by a single function called `makeField`
- the property `local_shape` has been removed from `Domain` (and subclasses)
  and `DomainTuple`.
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Transfer of MPI parallelization into operators:
===============================================

As was already the case with the `MetricGaussianKL_MPI` in NIFTy5, MPI
parallelization in NIFTy6 is handled by specialized MPI-enabled operators.

They are accessible via the `nifty6.mpi` namespace, from which they can be
imported directly: `from nifty6.mpi import MPIenabledOperator`.