nifty7 -> nifty8

docs/source/conf.py

@@ -34,7 +34,7 @@ html_theme_options = {
     "icon_links": [
         {
             "name": "PyPI",
-            "url": "https://pypi.org/project/nifty7",
+            "url": "https://pypi.org/project/nifty8",
             "icon": "fas fa-box",
         }
     ],

docs/source/index.rst

@@ -7,5 +7,5 @@ Welcome to the nifty8 documentation!
    :maxdepth: 1
 
    User Guide <user/index>
-   API reference <mod/nifty7>
+   API reference <mod/nifty8>
    Development <dev/index>

docs/source/user/approximate_inference.rst

@@ -11,7 +11,7 @@ As a compromise between being optimal and being computationally affordable, the
     \int \mathcal{D}\xi \,\mathcal{Q}(\xi) \log \left( \frac{\mathcal{Q}(\xi)}{\mathcal{P}(\xi)} \right)
 
 NIFTy features two main alternatives for variational inference: Metric Gaussian Variational Inference (MGVI) and geometric Variational Inference (geoVI).
-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_7/demos/variational_inference_visualized.py>`_.
+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>`_.
 
 
 Metric Gaussian Variational Inference (MGVI)
@@ -45,7 +45,7 @@ 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:`~nifty7.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.
 
@@ -77,7 +77,7 @@ 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:`~nifty7.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`.
 
 
 Publications

docs/source/user/index.rst

@@ -3,7 +3,7 @@ NIFTy user guide
 ================
 
 This guide is an overview and explains the main idea behind nifty. More details
-are found in the `API reference <../mod/nifty7.html>`_.
+are found in the `API reference <../mod/nifty8.html>`_.
 
 
 .. toctree::

src/version.py

-__version__ = '7.0'
+__version__ = '8.0'