Merge pull request #134 from geometric-kernels/slava/migrate-to-new-o…

…rganization

Replace all references to "gpflow" organization with "geometric-kernels" organization

CONTRIBUTING.md

@@ -12,19 +12,19 @@ Here is the list of original contributors to the project (in alphabetical order)
 
 ### Reporting a bug
 
-Finding and fixing bugs helps us provide robust functionality to all users. You can either submit a bug report or, if you know how to fix the bug yourself, you can submit a bug fix. We gladly welcome either, but a fix is likely to be released sooner, simply because others may not have time to quickly implement a fix themselves. If you're interested in implementing it, but would like help in doing so, you can send [the maintainers](#who-are-we) an email or open an [issue](https://github.com/GPflow/GeometricKernels/issues/new).
+Finding and fixing bugs helps us provide robust functionality to all users. You can either submit a bug report or, if you know how to fix the bug yourself, you can submit a bug fix. We gladly welcome either, but a fix is likely to be released sooner, simply because others may not have time to quickly implement a fix themselves. If you're interested in implementing it, but would like help in doing so, you can send [the maintainers](#who-are-we) an email or open an [issue](https://github.com/geometric-kernels/GeometricKernels/issues/new).
 
-We use GitHub issues for bug reports. You can use the [issue template](https://github.com/GPflow/GeometricKernels/issues/new) to start writing yours. Once you've submitted it, the maintainers will take a look as soon as possible, ideally within the week, and get back to you about how to proceed. If it's a small easy fix, they may implement it then and there. For fixes that are more involved, they will discuss with you about how urgent the fix is, with the aim of providing some timeline of when you can expect to see it.
+We use GitHub issues for bug reports. You can use the [issue template](https://github.com/geometric-kernels/GeometricKernels/issues/new) to start writing yours. Once you've submitted it, the maintainers will take a look as soon as possible, ideally within the week, and get back to you about how to proceed. If it's a small easy fix, they may implement it then and there. For fixes that are more involved, they will discuss with you about how urgent the fix is, with the aim of providing some timeline of when you can expect to see it.
 
-If you'd like to submit a bug fix, [open a pull request](https://github.com/GPflow/GeometricKernels/compare). We recommend you discuss your changes with the community before you begin working on them (e.g. via issues), so that questions and suggestions can be made early on.
+If you'd like to submit a bug fix, [open a pull request](https://github.com/geometric-kernels/GeometricKernels/compare). We recommend you discuss your changes with the community before you begin working on them (e.g. via issues), so that questions and suggestions can be made early on.
 
 ### Requesting a feature
 
 GeometricKernels is built on features added and improved by the community. You can submit a feature request either as an issue or, if you can implement the change yourself, as a pull request. We gladly welcome either, but a pull request is likely to be released sooner, simply because others may not have time to quickly implement it themselves.
 
-We use GitHub issues for feature requests. You can use the [issue template](https://github.com/GPflow/GeometricKernels/issues/new) to start writing yours. Once you've submitted it, the maintainers will take a look as soon as possible, ideally within the week, and get back to you about how to proceed. If it's a small easy feature that is backwards compatible, they may implement it then and there. For features that are more involved, they will discuss with you about a timeline for implementing it. Features that are not backwards compatible are likely to take longer to reach a release. It may become apparent during discussions that a feature doesn't lie within the scope of GeometricKernels, in which case we will discuss alternative options with you, such as adding it as a notebook or an external extension to GeometricKernels.
+We use GitHub issues for feature requests. You can use the [issue template](https://github.com/geometric-kernels/GeometricKernels/issues/new) to start writing yours. Once you've submitted it, the maintainers will take a look as soon as possible, ideally within the week, and get back to you about how to proceed. If it's a small easy feature that is backwards compatible, they may implement it then and there. For features that are more involved, they will discuss with you about a timeline for implementing it. Features that are not backwards compatible are likely to take longer to reach a release. It may become apparent during discussions that a feature doesn't lie within the scope of GeometricKernels, in which case we will discuss alternative options with you, such as adding it as a notebook or an external extension to GeometricKernels.
 
-If you'd like to submit a pull request, [open a pull request](https://github.com/GPflow/GeometricKernels/compare). We recommend you discuss your changes with the community before you begin working on them (e.g. via issues), so that questions and suggestions can be made early on.
+If you'd like to submit a pull request, [open a pull request](https://github.com/geometric-kernels/GeometricKernels/compare). We recommend you discuss your changes with the community before you begin working on them (e.g. via issues), so that questions and suggestions can be made early on.
 
 ### Pull request guidelines
 
@@ -71,7 +71,7 @@ $ make test
 
 #### Continuous integration
 
-[GitHub actions](https://github.com/GPflow/GeometricKernels/blob/main/.github/workflows/quality-checks.yaml) will automatically run the quality checks against pull requests to the develop branch. The GitHub repository is set up such that these need to pass in order to merge.
+[GitHub actions](https://github.com/geometric-kernels/GeometricKernels/blob/main/.github/workflows/quality-checks.yaml) will automatically run the quality checks against pull requests to the develop branch. The GitHub repository is set up such that these need to pass in order to merge.
 
 
 # License

README.md

@@ -1,7 +1,7 @@
 # GeometricKernels
 
-[![Quality checks and Tests](https://github.com/GPflow/GeometricKernels/actions/workflows/quality-checks.yaml/badge.svg)](https://github.com/GPflow/GeometricKernels/actions/workflows/quality-checks.yaml)
-[![Documentation](https://github.com/GPflow/GeometricKernels/actions/workflows/docs.yaml/badge.svg)](https://gpflow.github.io/GeometricKernels/index.html)
+[![Quality checks and Tests](https://github.com/geometric-kernels/GeometricKernels/actions/workflows/quality-checks.yaml/badge.svg)](https://github.com/geometric-kernels/GeometricKernels/actions/workflows/quality-checks.yaml)
+[![Documentation](https://github.com/geometric-kernels/GeometricKernels/actions/workflows/docs.yaml/badge.svg)](https://geometric-kernels.github.io/GeometricKernels/index.html)
 [![Landing Page](https://img.shields.io/badge/Landing_Page-informational)](https://geometric-kernels.github.io/)
 
 [![GeometricKernels](https://geometric-kernels.github.io/assets/title-sm.png)](https://geometric-kernels.github.io/)
@@ -36,7 +36,7 @@ This enables kernel methods — in particular Gaussian process models &mdash
     If you want to install specific GitHub branch called `[branch]`, run
 
     ```bash
-    pip install "git+https://github.com/GPflow/GeometricKernels@[branch]"
+    pip install "git+https://github.com/geometric-kernels/GeometricKernels@[branch]"
     ```
 
 2. Install a backend of your choice
@@ -85,7 +85,7 @@ This enables kernel methods — in particular Gaussian process models &mdash
 
 ## A basic example
 
-This example shows how to compute a 3x3 kernel matrix for the Matern52 kernel on the standard two-dimensional sphere. It relies on the numpy-based backend. Look up the information on how to use other backends in [the documentation](https://gpflow.github.io/GeometricKernels/index.html).
+This example shows how to compute a 3x3 kernel matrix for the Matern52 kernel on the standard two-dimensional sphere. It relies on the numpy-based backend. Look up the information on how to use other backends in [the documentation](https://geometric-kernels.github.io/GeometricKernels/index.html).
 
 ```python
 # Import a backend.
@@ -122,7 +122,7 @@ This should output
 
 ## Documentation
 
-The documentation for GeometricKernels is available on a [separate website](https://gpflow.github.io/GeometricKernels/index.html).
+The documentation for GeometricKernels is available on a [separate website](https://geometric-kernels.github.io/GeometricKernels/index.html).
 
 ## For development and running the tests
 
@@ -149,7 +149,7 @@ make test
 
 Post it in issues using the `"How do I do ..." and other issues` template and the "question" label.
 
-This [link](https://github.com/GPflow/GeometricKernels/issues/new?assignees=&labels=question&projects=&template=other-issue.md) chooses the right template and label for you.
+This [link](https://github.com/geometric-kernels/GeometricKernels/issues/new?assignees=&labels=question&projects=&template=other-issue.md) chooses the right template and label for you.
 
 ## Citation
 

docs/conf.py

@@ -114,7 +114,7 @@ def never_skip_init_or_new(app, what, name, obj, would_skip, options):
 # Theme-specific options. See theme docs for more info
 html_context = {
   'display_github': True,
-  'github_user': 'GPflow',
+  'github_user': 'geometric-kernels',
   'github_repo': 'GeometricKernels',
   'github_version': 'main/docs/'
 }

docs/index.rst

@@ -79,7 +79,7 @@ To install GeometricKernels, run
 
   ..  code-block:: bash
 
-      pip install "git+https://github.com/GPflow/GeometricKernels@[branch]"
+      pip install "git+https://github.com/geometric-kernels/GeometricKernels@[branch]"
 
 The kernels are compatible with several backends, namely
 
@@ -420,5 +420,5 @@ You can find the relevant references for any space in
    Theory <theory/index>
    API reference <autoapi/geometric_kernels/index>
    Bibliography <bibliography>
-   GitHub <https://github.com/GPflow/GeometricKernels>
+   GitHub <https://github.com/geometric-kernels/GeometricKernels>
 

notebooks/Graph.ipynb

@@ -13,7 +13,7 @@
     "\n",
     "# If you want to use a version of the library from a specific branch on GitHub,\n",
     "# say, from the \"devel\" branch, uncomment the line below instead\n",
-    "# !pip install \"git+https://github.com/GPflow/GeometricKernels@devel\""
+    "# !pip install \"git+https://github.com/geometric-kernels/GeometricKernels@devel\""
    ]
   },
   {
@@ -222,7 +222,7 @@
     "There is also an optional second parameter `num` which determines the order of approximation of the kernel (*number of levels*).\n",
     "There is a sensible default value for each of the spaces in the library, so change it only if you know what you are doing.\n",
     "\n",
-    "A brief account on theory behind the kernels on graphs can be found on this [documentation page](https://gpflow.github.io/GeometricKernels/theory/graphs.html)."
+    "A brief account on theory behind the kernels on graphs can be found on this [documentation page](https://geometric-kernels.github.io/GeometricKernels/theory/graphs.html)."
    ]
   },
   {
@@ -277,7 +277,7 @@
     "\n",
     "**Note:** consider a graph with the scaled adjacency $\\mathbf{A}' = \\alpha^2 \\mathbf{A}$, for some $\\alpha > 0$.\n",
     "Denote the kernel corresponding to $\\mathbf{A}$ by $k_{\\nu, \\kappa}$ and the kernel corresponding to $\\mathbf{A}'$ by $k_{\\nu, \\kappa}'$.\n",
-    "Then, as apparent from [the theory](https://gpflow.github.io/GeometricKernels/theory/graphs.html), for the normalized graph Laplacian we have $k_{\\nu, \\kappa}' (i, j) = k_{\\nu, \\kappa} (i, j)$.\n",
+    "Then, as apparent from [the theory](https://geometric-kernels.github.io/GeometricKernels/theory/graphs.html), for the normalized graph Laplacian we have $k_{\\nu, \\kappa}' (i, j) = k_{\\nu, \\kappa} (i, j)$.\n",
     "On the other hand, for the unnormalized graph Laplacian, we have $k_{\\nu, \\kappa}' (i, j) = k_{\\nu, \\alpha \\cdot \\kappa} (i, j)$, i.e. the lengthscale changes."
    ]
   },
@@ -608,7 +608,7 @@
     "This might be useful for speeding up computations.\n",
     "We showcase this below by showing how to efficiently sample the Gaussian process $\\mathrm{GP}(0, k)$.\n",
     "\n",
-    "For a brief theoretical introduction into feature maps, see this [documentation page](https://gpflow.github.io/GeometricKernels/theory/feature_maps.html)."
+    "For a brief theoretical introduction into feature maps, see this [documentation page](https://geometric-kernels.github.io/GeometricKernels/theory/feature_maps.html)."
    ]
   },
   {
@@ -705,7 +705,7 @@
     "### Efficient Sampling using Feature Maps\n",
     "\n",
     "GeometricKernels provides a simple tool to efficiently sample (without incurring cubic costs) the Gaussian process $f \\sim \\mathrm{GP}(0, k)$, based on an approximate finite-dimensional feature map $\\phi$.\n",
-    "The underlying machinery is briefly discussed in this [documentation page](https://gpflow.github.io/GeometricKernels/theory/feature_maps.html).\n",
+    "The underlying machinery is briefly discussed in this [documentation page](https://geometric-kernels.github.io/GeometricKernels/theory/feature_maps.html).\n",
     "\n",
     "The function `sampler` from `geometric_kernels.sampling` takes in a feature map and, optionally, the keyword argument `s` that specifies the number of samples to generate.\n",
     "It returns a function we name `sample_paths`.\n",

notebooks/Hyperbolic.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "# If you want to use a version of the library from a specific branch on GitHub,\n",
     "# say, from the \"devel\" branch, uncomment the line below instead\n",
-    "# !pip install \"git+https://github.com/GPflow/GeometricKernels@devel\""
+    "# !pip install \"git+https://github.com/geometric-kernels/GeometricKernels@devel\""
    ]
   },
   {
@@ -140,7 +140,7 @@
     "There is also an optional parameter `num` which determines the order of approximation of the kernel (*number of levels*).\n",
     "There is a sensible default value for each of the spaces in the library, so change it only if you know what you are doing.\n",
     "\n",
-    "A brief account on theory behind the kernels on non-compact symmetric spaces (which hyperbolic spaces are instances of) can be found on this [documentation page](https://gpflow.github.io/GeometricKernels/theory/symmetric.html)."
+    "A brief account on theory behind the kernels on non-compact symmetric spaces (which hyperbolic spaces are instances of) can be found on this [documentation page](https://geometric-kernels.github.io/GeometricKernels/theory/symmetric.html)."
    ]
   },
   {
@@ -508,7 +508,7 @@
     "This might be useful for spe?eding up computations.\n",
     "We showcase this below by showing how to efficiently sample the Gaussian process $\\mathrm{GP}(0, k)$.\n",
     "\n",
-    "For a brief theoretical introduction into feature maps, see this [documentation page](https://gpflow.github.io/GeometricKernels/theory/feature_maps.html).\n",
+    "For a brief theoretical introduction into feature maps, see this [documentation page](https://geometric-kernels.github.io/GeometricKernels/theory/feature_maps.html).\n",
     "**Note:** for non-compact symmetric spaces like the hyperbolic space, the kernel is always evaluated via a feature map under the hood."
    ]
   },
@@ -628,7 +628,7 @@
     "### Efficient Sampling using Feature Maps\n",
     "\n",
     "GeometricKernels provides a simple tool to efficiently sample (without incurring cubic costs) the Gaussian process $f \\sim \\mathrm{GP}(0, k)$, based on an approximate finite-dimensional feature map $\\phi$.\n",
-    "The underlying machinery is briefly discussed in this [documentation page](https://gpflow.github.io/GeometricKernels/theory/feature_maps.html).\n",
+    "The underlying machinery is briefly discussed in this [documentation page](https://geometric-kernels.github.io/GeometricKernels/theory/feature_maps.html).\n",
     "\n",
     "The function `sampler` from `geometric_kernels.sampling` takes in a feature map and, optionally, the keyword argument `s` that specifies the number of samples to generate.\n",
     "It returns a function we name `sample_paths`.\n",

notebooks/Hypersphere.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "# If you want to use a version of the library from a specific branch on GitHub,\n",
     "# say, from the \"devel\" branch, uncomment the line below instead\n",
-    "# !pip install \"git+https://github.com/GPflow/GeometricKernels@devel\""
+    "# !pip install \"git+https://github.com/geometric-kernels/GeometricKernels@devel\""
    ]
   },
   {
@@ -139,7 +139,7 @@
     "There is also an optional second parameter `num` which determines the order of approximation of the kernel.\n",
     "There is a sensible default value for each of the spaces in the library, so change it only if you know what you are doing.\n",
     "\n",
-    "A brief account on theory behind the kernels on compact manifolds like (which hyperspheres are examples of) can be found on these documentation pages: [one](https://gpflow.github.io/GeometricKernels/theory/compact.html), [two](https://gpflow.github.io/GeometricKernels/theory/addition_theorem.html)."
+    "A brief account on theory behind the kernels on compact manifolds like (which hyperspheres are examples of) can be found on these documentation pages: [one](https://geometric-kernels.github.io/GeometricKernels/theory/compact.html), [two](https://geometric-kernels.github.io/GeometricKernels/theory/addition_theorem.html)."
    ]
   },
   {
@@ -485,7 +485,7 @@
     "This might be useful for speeding up computations.\n",
     "We showcase this below by showing how to efficiently sample the Gaussian process $\\mathrm{GP}(0, k)$.\n",
     "\n",
-    "For a brief theoretical introduction into feature maps, see this [documentation page](https://gpflow.github.io/GeometricKernels/theory/feature_maps.html)."
+    "For a brief theoretical introduction into feature maps, see this [documentation page](https://geometric-kernels.github.io/GeometricKernels/theory/feature_maps.html)."
    ]
   },
   {
@@ -600,7 +600,7 @@
     "### Efficient Sampling using Feature Maps\n",
     "\n",
     "GeometricKernels provides a simple tool to efficiently sample (without incurring cubic costs) the Gaussian process $f \\sim \\mathrm{GP}(0, k)$, based on an approximate finite-dimensional feature map $\\phi$.\n",
-    "The underlying machinery is briefly discussed in this [documentation page](https://gpflow.github.io/GeometricKernels/theory/feature_maps.html).\n",
+    "The underlying machinery is briefly discussed in this [documentation page](https://geometric-kernels.github.io/GeometricKernels/theory/feature_maps.html).\n",
     "\n",
     "The function `sampler` from `geometric_kernels.sampling` takes in a feature map and, optionally, the keyword argument `s` that specifies the number of samples to generate.\n",
     "It returns a function we name `sample_paths`.\n",