NIFTy7 -> NIFTy8

README.md

@@ -66,7 +66,7 @@ switching to the NIFTy_8 branch:
 In the following, we assume a Debian-based distribution. For other
 distributions, the "apt" lines will need slight changes.
 
-NIFTy7 and its mandatory dependencies can be installed via:
+NIFTy8 and its mandatory dependencies can be installed via:
 
     sudo apt-get install git python3 python3-pip python3-dev
     pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/nifty.git@NIFTy_8

docs/source/code.rst

@@ -38,7 +38,7 @@ Abstract base class
 
 .. currentmodule:: nifty8.domains.domain
 
-One of the fundamental building blocks of the NIFTy7 framework is the *domain*.
+One of the fundamental building blocks of the NIFTy8 framework is the *domain*.
 Its required capabilities are expressed by the abstract :py:class:`Domain` class.
 A domain must be able to answer the following queries:
 
@@ -242,7 +242,7 @@ Linear Operators
 
 .. currentmodule:: nifty8.operators
 
-A linear operator (represented by NIFTy7's abstract
+A linear operator (represented by NIFTy8'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
@@ -354,7 +354,7 @@ high-dimensional functions, which are often nonlinear.
 Energy functionals
 ------------------
 
-In NIFTy7 such functions are represented by objects of type
+In NIFTy8 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
@@ -366,7 +366,7 @@ position domain, and metrics are represented by linear operator objects.
 
 Energies are classes that typically have to be provided by the user when
 tackling new IFT problems. An example of concrete energy classes delivered with
-NIFTy7 is :class:`~minimization.quadratic_energy.QuadraticEnergy` (with
+NIFTy8 is :class:`~minimization.quadratic_energy.QuadraticEnergy` (with
 position-independent metric, mainly used with conjugate gradient minimization).
 
 For MGVI and GeoVI, NIFTy provides :func:`~minimization.kl_energies.MetricGaussianKL`
@@ -400,7 +400,7 @@ checking the quality of the current solution estimate and stopping once
 it is sufficiently accurate. In case of numerical problems, the iteration needs
 to be terminated as well, returning a suitable error description.
 
-In NIFTy7, this functionality is encapsulated in the abstract
+In NIFTy8, this functionality is encapsulated in the abstract
 :class:`IterationController` class, which is provided with the initial energy
 object before starting the minimization, and is updated with the improved
 energy after every iteration. Based on this information, it can either continue

docs/source/conf.py

@@ -15,7 +15,7 @@ napoleon_use_admonition_for_examples = True
 napoleon_use_admonition_for_references = True
 napoleon_include_special_with_doc = True
 
-project = u'NIFTy7'
+project = u'NIFTy8'
 copyright = u'2013-2021, Max-Planck-Society'
 author = u'Martin Reinecke'
 

docs/source/ift.rst

@@ -173,7 +173,7 @@ It only requires minimizing the information Hamiltonian, e.g. by a gradient desc
 
     \frac{\partial \mathcal{H}(d,\xi)}{\partial \xi} = 0.
 
-NIFTy7 automatically calculates the necessary gradient from a generative model of the signal and the data and uses this to minimize the Hamiltonian.
+NIFTy8 automatically calculates the necessary gradient from a generative model of the signal and the data and uses this to minimize the Hamiltonian.
 
 However, MAP often provides unsatisfactory results in cases of deep hierarchical Bayesian networks.
 The reason for this is that MAP ignores the volume factors in parameter space, which are not to be neglected in deciding whether a solution is reasonable or not.
@@ -224,7 +224,7 @@ Thus, only the gradient of the KL is needed with respect to this, which can be e
 We stochastically estimate the KL-divergence and gradients with a set of samples drawn from the approximate posterior distribution.
 The particular structure of the covariance allows us to draw independent samples solving a certain system of equations.
 This KL-divergence for MGVI is implemented by
-:func:`~nifty8.minimization.kl_energies.MetricGaussianKL` within NIFTy7.
+:func:`~nifty8.minimization.kl_energies.MetricGaussianKL` within NIFTy8.
 
 Note that MGVI typically provides only a lower bound on the variance.
 
@@ -254,7 +254,7 @@ where :math:`\delta` denotes the Kronecker-delta.
 
 GeoVI obtains the optimal expansion point :math:`\bar{\xi}` such that :math:`\mathcal{Q}_{\bar{\xi}}` matches the posterior as good as possible.
 Analogous to the MGVI algorithm, :math:`\bar{\xi}` is obtained by minimization of the KL-divergence between :math:`\mathcal{P}` and :math:`\mathcal{Q}_{\bar{\xi}}` w.r.t. :math:`\bar{\xi}`.
-Furthermore the KL is represented as a stochastic estimate using a set of samples drawn from :math:`\mathcal{Q}_{\bar{\xi}}` which is implemented in NIFTy7 via :func:`~nifty8.minimization.kl_energies.GeoMetricKL`.
+Furthermore the KL is represented as a stochastic estimate using a set of samples drawn from :math:`\mathcal{Q}_{\bar{\xi}}` which is implemented in NIFTy8 via :func:`~nifty8.minimization.kl_energies.GeoMetricKL`.
 
 A visual comparison of the MGVI and GeoVI algorithm can be found in `variational_inference_visualized.py <https://gitlab.mpcdf.mpg.de/ift/nifty/-/blob/NIFTy_8/demos/variational_inference_visualized.py>`_.
 

docs/source/installation.rst

@@ -5,7 +5,7 @@ Installation
 In the following, we assume a Debian-based Linux distribution. For other
 distributions, the "apt" lines will need slight changes.
 
-NIFTy7 and its mandatory dependencies can be installed via::
+NIFTy8 and its mandatory dependencies can be installed via::
 
     sudo apt-get install git python3 python3-pip python3-dev
     pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/nifty.git@NIFTy_8

src/logger.py

@@ -18,7 +18,7 @@
 
 def _logger_init():
     import logging
-    res = logging.getLogger('NIFTy7')
+    res = logging.getLogger('NIFTy8')
     res.setLevel(logging.DEBUG)
     res.propagate = False
     ch = logging.StreamHandler()