5->6

91f480e9 · Martin Reinecke · 14052dd3 · 91f480e9 · 91f480e9 · 91f480e9
Commit 91f480e9 authored Nov 26, 2019 by Martin Reinecke
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -37,7 +37,7 @@ build_docker_from_cache:
 test_serial:
  stage: test
  script:
-    - pytest-3 -q --cov=nifty5 test
+    - pytest-3 -q --cov=nifty6 test
    - >
      python3 -m coverage report --omit "*plot*,*distributed_do*" | tee coverage.txt
    - >
@@ -59,7 +59,7 @@ pages:
    paths:
    - public
  only:
-  - NIFTy_5
+  - NIFTy_6


 before_script:

--- a/README.md
+++ b/README.md
 NIFTy - Numerical Information Field Theory
 ==========================================
-[![build status](https://gitlab.mpcdf.mpg.de/ift/NIFTy/badges/NIFTy_5/build.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/NIFTy_5)
-[![coverage report](https://gitlab.mpcdf.mpg.de/ift/NIFTy/badges/NIFTy_5/coverage.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/NIFTy_5)
+[![build status](https://gitlab.mpcdf.mpg.de/ift/NIFTy/badges/NIFTy_6/build.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/NIFTy_6)
+[![coverage report](https://gitlab.mpcdf.mpg.de/ift/NIFTy/badges/NIFTy_6/coverage.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/NIFTy_6)

 **NIFTy** project homepage:
 [http://ift.pages.mpcdf.de/nifty](http://ift.pages.mpcdf.de/nifty)
@@ -59,8 +59,8 @@ Optional dependencies:

 ### Sources

-The current version of Nifty5 can be obtained by cloning the repository and
-switching to the NIFTy_5 branch:
+The current version of NIFTy6 can be obtained by cloning the repository and
+switching to the NIFTy_6 branch:

    git clone https://gitlab.mpcdf.mpg.de/ift/nifty.git

@@ -69,10 +69,10 @@ switching to the NIFTy_5 branch:
 In the following, we assume a Debian-based distribution. For other
 distributions, the "apt" lines will need slight changes.

-NIFTy5 and its mandatory dependencies can be installed via:
+NIFTy6 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_5
+    pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/nifty.git@NIFTy_6
    pip3 install --user git+https://gitlab.mpcdf.mpg.de/mtr/pypocketfft

 Plotting support is added via:
@@ -100,7 +100,7 @@ To run the tests, additional packages are required:
 Afterwards the tests (including a coverage report) can be run using the
 following command in the repository root:

-    pytest-3 --cov=nifty5 test
+    pytest-3 --cov=nifty6 test


 ### First Steps

--- a/demos/Wiener_Filter.ipynb
+++ b/demos/Wiener_Filter.ipynb
@@ -81,11 +81,11 @@
 %% Cell type:code id: tags:

 ``` python
 import numpy as np
 np.random.seed(40)
-import nifty5 as ift
+import nifty6 as ift
 import matplotlib.pyplot as plt
 %matplotlib inline
 ```

 %% Cell type:markdown id: tags:

--- a/demos/bench_gridder.py
+++ b/demos/bench_gridder.py
@@ -3,7 +3,7 @@ from time import time
 import matplotlib.pyplot as plt
 import numpy as np

-import nifty5 as ift
+import nifty6 as ift

 np.random.seed(40)


--- a/demos/bernoulli_demo.py
+++ b/demos/bernoulli_demo.py
@@ -24,7 +24,7 @@

 import numpy as np

-import nifty5 as ift
+import nifty6 as ift

 if __name__ == '__main__':
    np.random.seed(41)

--- a/demos/find_amplitude_parameters.py
+++ b/demos/find_amplitude_parameters.py
@@ -18,7 +18,7 @@

 import numpy as np

-import nifty5 as ift
+import nifty6 as ift
 import matplotlib.pyplot as plt



--- a/demos/getting_started_1.py
+++ b/demos/getting_started_1.py
@@ -25,7 +25,7 @@ import sys

 import numpy as np

-import nifty5 as ift
+import nifty6 as ift


 def make_checkerboard_mask(position_space):

--- a/demos/getting_started_2.py
+++ b/demos/getting_started_2.py
@@ -25,7 +25,7 @@ import sys

 import numpy as np

-import nifty5 as ift
+import nifty6 as ift


 def exposure_2d():

--- a/demos/getting_started_3.py
+++ b/demos/getting_started_3.py
@@ -29,7 +29,7 @@ import sys

 import numpy as np

-import nifty5 as ift
+import nifty6 as ift


 def random_los(n_los):

--- a/demos/getting_started_mf.py
+++ b/demos/getting_started_mf.py
@@ -29,7 +29,7 @@ import sys

 import numpy as np

-import nifty5 as ift
+import nifty6 as ift


 class SingleDomain(ift.LinearOperator):

--- a/demos/misc/convolution.py
+++ b/demos/misc/convolution.py
 import numpy as np
-import nifty5 as ift
+import nifty6 as ift


 def convtest(test_signal, delta, func):

--- a/demos/multi_amplitudes_consistency.py
+++ b/demos/multi_amplitudes_consistency.py
-import nifty5 as ift
+import nifty6 as ift
 import numpy as np



--- a/demos/newamplitudes.py
+++ b/demos/newamplitudes.py
-import nifty5 as ift
+import nifty6 as ift
 import numpy as np
 np.random.seed(42)


--- a/demos/polynomial_fit.py
+++ b/demos/polynomial_fit.py
@@ -18,7 +18,7 @@
 import matplotlib.pyplot as plt
 import numpy as np

-import nifty5 as ift
+import nifty6 as ift
 np.random.seed(12)



--- a/docs/generate.sh
+++ b/docs/generate.sh
 rm -rf docs/build docs/source/mod
-sphinx-apidoc -e -o docs/source/mod nifty5
+sphinx-apidoc -e -o docs/source/mod nifty6
 sphinx-build -b html docs/source/ docs/build/
--- a/docs/source/citations.rst
+++ b/docs/source/citations.rst
@@ -2,8 +2,8 @@ NIFTy-related publications
 ==========================

 ::
-    @article{asclnifty5,
-      title={NIFTy5: Numerical Information Field Theory v5},
+    @article{asclnifty6,
+      title={NIFTy6: Numerical Information Field Theory v5},
      author={Arras, Philipp and Baltac, Mihai and Ensslin, Torsten A and Frank, Philipp and Hutschenreuter, Sebastian and Knollmueller, Jakob and Leike, Reimar and Newrzella, Max-Niklas and Platz, Lukas and Reinecke, Martin and others},
      journal={Astrophysics Source Code Library},
      year={2019}
@@ -13,7 +13,7 @@ NIFTy-related publications
      author = {{Martin Reinecke, Theo Steininger, Marco Selig}},
      title = {NIFTy -- Numerical Information Field TheorY},
      url = {https://gitlab.mpcdf.mpg.de/ift/NIFTy},
-      version = {nifty5},
+      version = {nifty6},
      date = {2018-04-05},
    }


--- a/docs/source/code.rst
+++ b/docs/source/code.rst
@@ -36,9 +36,9 @@ Domains
 Abstract base class
 -------------------

-.. currentmodule:: nifty5.domains.domain
+.. currentmodule:: nifty6.domains.domain

-One of the fundamental building blocks of the NIFTy5 framework is the *domain*.
+One of the fundamental building blocks of the NIFTy6 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:

@@ -52,7 +52,7 @@ A domain must be able to answer the following queries:
 Unstructured domains
 --------------------

-.. currentmodule:: nifty5.domains.unstructured_domain
+.. currentmodule:: nifty6.domains.unstructured_domain

 Domains can be either *structured* (i.e. there is geometrical information
 associated with them, like position in space and volume factors),
@@ -65,7 +65,7 @@ Unstructured domains can be described by instances of NIFTy's
 Structured domains
 ------------------

-.. currentmodule:: nifty5.domains.structured_domain
+.. currentmodule:: nifty6.domains.structured_domain

 In contrast to unstructured domains, these domains have an assigned geometry.
 NIFTy requires them to provide the volume elements of their grid cells.
@@ -86,7 +86,7 @@ The additional methods are specified in the abstract class

 NIFTy comes with several concrete subclasses of :class:`StructuredDomain`:

-.. currentmodule:: nifty5.domains
+.. currentmodule:: nifty6.domains

 - :class:`~rg_space.RGSpace` represents a regular Cartesian grid with an arbitrary
  number of dimensions, which is supposed to be periodic in each dimension.
@@ -123,7 +123,7 @@ Some examples are:
  (on a harmonic domain) and a few inferred model parameters (e.g. on an
  unstructured grid).

-.. currentmodule:: nifty5
+.. currentmodule:: nifty6

 Consequently, NIFTy defines a class called :class:`~domain_tuple.DomainTuple`
 holding a sequence of :class:`~domains.domain.Domain` objects. The full domain is
@@ -228,7 +228,7 @@ Advanced operators
 NIFTy provides a library of commonly employed operators which can be used for
 specific inference problems. Currently these are:

-.. currentmodule:: nifty5.library
+.. currentmodule:: nifty6.library

 - :class:`~smooth_linear_amplitude.SLAmplitude`, which returns a smooth power spectrum.
 - :class:`~inverse_gamma_operator.InverseGammaOperator`, which models point sources which are
@@ -240,9 +240,9 @@ specific inference problems. Currently these are:
 Linear Operators
 ================

-.. currentmodule:: nifty5.operators
+.. currentmodule:: nifty6.operators

-A linear operator (represented by NIFTy5's abstract
+A linear operator (represented by NIFTy6'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
@@ -282,7 +282,7 @@ This functionality is provided by NIFTy's
 :class:`~inversion_enabler.InversionEnabler` class, which is itself a linear
 operator.

-.. currentmodule:: nifty5.operators.operator
+.. currentmodule:: nifty6.operators.operator

 Direct multiplication and adjoint inverse multiplication transform a field
 defined on the operator's :attr:`~Operator.domain` to one defined on the
@@ -290,7 +290,7 @@ operator's :attr:`~Operator.target`, whereas adjoint multiplication and inverse
 multiplication transform from :attr:`~Operator.target` to
 :attr:`~Operator.domain`.

-.. currentmodule:: nifty5.operators
+.. currentmodule:: nifty6.operators

 Operators with identical domain and target can be derived from
 :class:`~endomorphic_operator.EndomorphicOperator`. Typical examples for this
@@ -299,7 +299,7 @@ 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
+.. currentmodule:: nifty6

 Further operator classes provided by NIFTy are

@@ -317,7 +317,7 @@ Further operator classes provided by NIFTy are
 Syntactic sugar
 ---------------

-Nifty5 allows simple and intuitive construction of altered and combined
+NIFTy 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`
 and ``f1`` and ``f2`` are of type :class:`~field.Field`, writing::
@@ -325,7 +325,7 @@ 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
+.. currentmodule:: nifty6.operators.linear_operator

 will perform the operation suggested intuitively by the notation, checking
 domain compatibility while building the composed operator.
@@ -349,12 +349,12 @@ Minimization
 Most problems in IFT are solved by (possibly nested) minimizations of
 high-dimensional functions, which are often nonlinear.

-.. currentmodule:: nifty5.minimization
+.. currentmodule:: nifty6.minimization

 Energy functionals
 ------------------

-In NIFTy5 such functions are represented by objects of type
+In NIFTy6 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
@@ -362,11 +362,11 @@ function's :attr:`~energy.Energy.value`, :attr:`~energy.Energy.gradient` and
 floating-point scalars, gradients have the form of fields defined on the energy's
 position domain, and metrics are represented by linear operator objects.

-.. currentmodule:: nifty5
+.. currentmodule:: nifty6

 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
-NIFTy5 is :class:`~minimization.quadratic_energy.QuadraticEnergy` (with
+NIFTy6 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
@@ -394,14 +394,14 @@ functional for MGVI and minimize it.
 Iteration control
 -----------------

-.. currentmodule:: nifty5.minimization.iteration_controllers
+.. currentmodule:: nifty6.minimization.iteration_controllers

 Iterative minimization of an energy requires 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
 to be terminated as well, returning a suitable error description.

-In NIFTy5, this functionality is encapsulated in the abstract
+In NIFTy6, 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
@@ -418,7 +418,7 @@ in NIFTy can be found below, but users have complete freedom to implement custom
 Minimization algorithms
 -----------------------

-.. currentmodule:: nifty5.minimization
+.. currentmodule:: nifty6.minimization

 All minimization algorithms in NIFTy inherit from the abstract
 :class:`~minimizer.Minimizer` class, which presents a minimalistic interface
@@ -459,7 +459,7 @@ available under the names :class:`~scipy_minimizer.ScipyCG` and
 Application to operator inversion
 ---------------------------------

-.. currentmodule:: nifty5
+.. currentmodule:: nifty6

 The machinery presented here cannot only be used for minimizing functionals
 derived from IFT, but also for the numerical inversion of linear operators, if

--- a/docs/source/conf.py
+++ b/docs/source/conf.py
-import nifty5
+import nifty6

 extensions = [
    'sphinx.ext.napoleon',  # Support for NumPy and Google style docstrings
@@ -15,11 +15,11 @@ napoleon_use_admonition_for_examples = True
 napoleon_use_admonition_for_references = True
 napoleon_include_special_with_doc = True

-project = u'NIFTy5'
+project = u'NIFTy6'
 copyright = u'2013-2019, Max-Planck-Society'
 author = u'Martin Reinecke'

-release = nifty5.version.__version__
+release = nifty6.version.__version__
 version = release[:-2]

 language = None
@@ -30,5 +30,5 @@ html_theme = "sphinx_rtd_theme"
 html_logo = 'nifty_logo_black.png'

 exclude_patterns = [
-    'mod/modules.rst', 'mod/nifty5.git_version.rst', 'mod/nifty5.logger.rst'
+    'mod/modules.rst', 'mod/nifty6.git_version.rst', 'mod/nifty6.logger.rst'
 ]
--- a/docs/source/ift.rst
+++ b/docs/source/ift.rst
@@ -174,7 +174,7 @@ It only requires minimizing the information Hamiltonian, e.g. by a gradient desc

    \frac{\partial \mathcal{H}(d,\xi)}{\partial \xi} = 0.

-NIFTy5 automatically calculates the necessary gradient from a generative model of the signal and the data and uses this to minimize the Hamiltonian.
+NIFTy6 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 hirachical 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 in the class :class:`~minimization.metric_gaussian_kl.MetricGaussianKL` within NIFTy5.
+This KL-divergence for MGVI is implemented in the class :class:`~minimization.metric_gaussian_kl.MetricGaussianKL` within NIFTy6.


 The demo `getting_started_3.py` for example not only infers a field this way, but also the power spectrum of the process that has generated the field.

--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -31,4 +31,4 @@ Contents
   installation
   code
   citations
-   Package Documentation <mod/nifty5>
+   Package Documentation <mod/nifty6>