Merge pull request #391 from pyt-team/frantzen/0.1-preparations

Preparations for 0.1 release

.github/workflows/docs.yml

@@ -49,6 +49,7 @@ jobs:
           repository-name: pyt-team/pyt-team.github.io
           target-folder: toponetx
           clean: true
+
   numpydoc-validation:
     runs-on: ${{matrix.os}}
     strategy:
@@ -70,6 +71,4 @@ jobs:
           pip install -e .[doc]
       - name: Checking NumpyDoc Validation for files
         run: |
-          pip install pre-commit
-          pre-commit install
-          pre-commit run --all-files numpydoc-validation
+          numpydoc lint test/**/*.py toponetx/**/*.py

README.md

@@ -3,7 +3,7 @@
 </h2>
 
 <h3 align="center">
-    Computing with Relational Data abstracted as Topological Domains
+   Computing with Relational Data Abstracted as Topological Domains
 </h3>
 
 <p align="center">
@@ -81,29 +81,10 @@ TNX is developed by the [pyt-team](https://github.com/pyt-team)
 
 # 🤖 Installing TopoNetX
 
-1. Clone a copy of `TopoNetX` from source:
-
-```bash
-git clone https://github.com/pyt-team/TopoNetX
-cd TopoNetX
-```
-
-2. If you have already cloned `TopoNetX` from source, update it:
+`TopoNetX` is available on PyPI and can be installed using `pip`:
 
 ```bash
-git pull
-```
-
-3. Install `TopoNetX` in editable mode (requires pip ≥ 21.3 for [PEP 660](https://peps.python.org/pep-0610/) support):
-
-```bash
-pip install -e '.[all]'
-```
-
-4. Install pre-commit hooks:
-
-```bash
-pre-commit install
+pip install toponetx
 ```
 
 # 🦾 Getting Started
@@ -161,6 +142,35 @@ B02 = cc.incidence_matrix(0, 2)
 B03 = cc.incidence_matrix(0, 3)
 ```
 
+## 🧑‍💻 Install from source
+
+To install the latest version from source, follow these steps:
+
+1. Clone a copy of `TopoNetX` from source:
+
+```bash
+git clone https://github.com/pyt-team/TopoNetX
+cd TopoNetX
+```
+
+2. If you have already cloned `TopoNetX` from source, update it:
+
+```bash
+git pull
+```
+
+3. Install `TopoNetX` in editable mode (requires `pip` ≥ 21.3 for [PEP 660](https://peps.python.org/pep-0610/) support):
+
+```bash
+pip install -e '.[all]'
+```
+
+4. Install pre-commit hooks:
+
+```bash
+pre-commit install
+```
+
 ## 🔍 References
 
 To learn more about topological domains, and how they can be used in deep learning:

docs/api/index.rst

@@ -5,21 +5,20 @@ API Reference
 The API reference gives an overview of ``TopoNetX``, which consists of several modules:
 
 - ``classes`` implements the topological domains: simplicial complexes, cellular complexes, combinatorial complexes.
-- ``algorithms`` implements signal processing techniques on topological domains, such as the eigendecomposition of a laplacian.
 - ``datasets`` implements utilities to load small datasets on topological domains.
 - ``transform`` implements functions to transform the topological domain that supports a dataset, effectively "lifting" the dataset onto another domain.
+- ``algorithms`` implements signal processing techniques on topological domains, such as the eigendecomposition of a laplacian.
 - ``generators`` implements functions to generate random topological domains.
-- ``transform`` implements functions to convert a graph to a topological domain: simplicial complexes etc.
+- ``readwrite`` implements functions to read and write topological domains from and to disk.
 
 
 .. toctree::
    :maxdepth: 2
    :caption: Packages & Modules
 
-   datasets
    classes
+   datasets
    transform
    algorithms
    generators
-   transform
    readwrite

docs/api/readwrite.rst

@@ -1,6 +1,6 @@
-*********
+**********
 Read-Write
-*********
+**********
 
 .. automodule:: toponetx.readwrite.atomlist
     :members:

docs/index.rst

@@ -59,6 +59,15 @@ TNX was developed by the pyt-team.
 
 6. Package dependencies are kept to a minimum, to facilitate easy installation and to reduce future installation issues arising from such dependencies.
 
+🤖 Installing TopoNetX
+----------------------
+
+``TopoNetX`` is available on PyPI and can be installed using ``pip``:
+
+.. code-block:: bash
+
+   pip install toponetx
+
 🔍 References
 -------------
 
@@ -103,3 +112,14 @@ Want to try it yourself? Check out our tutorials to get started.
    api/index
    tutorials/index
    contributing/index
+
+⭐ Funding
+----------
+
+.. image:: https://raw.githubusercontent.com/pyt-team/TopoNetX/main/resources/erc_logo.png
+   :align: right
+   :width: 200
+
+Partially funded by the European Union (ERC, HIGH-HOPeS, 101039827). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
+
+Partially funded by the National Science Foundation (DMS-2134231, DMS-2134241).

pyproject.toml

@@ -45,7 +45,7 @@ doc = [
     "jupyter",
     "nbsphinx",
     "nbsphinx_link",
-    "numpydoc",
+    "numpydoc >= 1.8.0",
     "sphinx",
     "sphinx_gallery",
     "pydata-sphinx-theme"

test/classes/test_cell_complex.py

@@ -824,21 +824,6 @@ def test_cell_equivalence_class(self):
 
         assert len(equivalence_classes) == 4
 
-    def test__remove_equivalent_cells(self):
-        """Test the remove equivalent cells method."""
-        CC = CellComplex()
-        CC.add_cell((1, 2, 3, 4), rank=2)
-        CC.add_cell((2, 3, 4, 1), rank=2)
-        CC.add_cell((1, 2, 3, 4), rank=2)
-        CC.add_cell((1, 2, 3, 6), rank=2)
-        CC.add_cell((3, 4, 1, 2), rank=2)
-        CC.add_cell((4, 3, 2, 1), rank=2)
-        CC.add_cell((1, 2, 7, 3), rank=2)
-        c1 = Cell((1, 2, 3, 4, 5))
-        CC.add_cell(c1, rank=2)
-        CC._remove_equivalent_cells()
-        assert len(CC.cells) == 4
-
     def test_get_cell_attributes2(self):
         """Test the remove equivalent cells method."""
         import networkx as nx

toponetx/classes/cell_complex.py

@@ -463,37 +463,6 @@ def _cell_equivalence_class(self) -> dict[Cell, set[Cell]]:
                         all_inserted_cells.add(j)
         return equiv_classes
 
-    def _remove_equivalent_cells(self) -> None:
-        """Remove homotopic cells from the cell complex.
-
-        Examples
-        --------
-        >>> CC = tnx.CellComplex()
-        >>> CC.add_cell((1, 2, 3, 4), rank=2)
-        >>> CC.add_cell((2, 3, 4, 1), rank=2)
-        >>> CC.add_cell((1, 2, 3, 4), rank=2)
-        >>> CC.add_cell((1, 2, 3, 6), rank=2)
-        >>> CC.add_cell((3, 4, 1, 2), rank=2)
-        >>> CC.add_cell((4, 3, 2, 1), rank=2)
-        >>> CC.add_cell((1, 2, 7, 3), rank=2)
-        >>> c1 = tnx.Cell((1, 2, 3, 4, 5))
-        >>> CC.add_cell(c1, rank=2)
-        >>> CC._remove_equivalent_cells()
-        >>> CC
-        """
-        equiv_classes = self._cell_equivalence_class()
-        for c in list(self.cells):
-            if c not in equiv_classes:
-                d = self._cells._cells[c.elements]
-                if len(d) == 1:
-                    self._delete_cell(c)
-                else:
-                    d_c = dict(d)
-                    for k in d_c:
-                        if len(d) == 1:
-                            break
-                        self._delete_cell(c, k)
-
     def degree(self, node: Hashable, rank: int = 1) -> int:
         """Compute the number of cells of certain rank that contain node.
 
@@ -1134,7 +1103,7 @@ def set_cell_attributes(
         ...     (1, 2, 3, 4): {"color": "red", "attr2": 1},
         ...     (1, 2, 4): {"color": "blue", "attr2": 3},
         ... }
-        >>> CC.set_cell_attributes(d)
+        >>> CC.set_cell_attributes(d, rank=2)
         >>> CC.cells[(1, 2, 3, 4)][0]["color"]
         'red'
         """
@@ -1359,9 +1328,19 @@ def remove_equivalent_cells(self) -> None:
         >>> CC.add_cell([3, 4, 8], rank=2)
         >>> print(CC.cells)
         >>> CC.remove_equivalent_cells()
-        >>> print(CC.cells)  # observe homotopic cells have been removed
         """
-        self._remove_equivalent_cells()
+        equiv_classes = self._cell_equivalence_class()
+        for c in list(self.cells):
+            if c not in equiv_classes:
+                d = self._cells._cells[c.elements]
+                if len(d) == 1:
+                    self._delete_cell(c)
+                else:
+                    d_c = dict(d)
+                    for k in d_c:
+                        if len(d) == 1:
+                            break
+                        self._delete_cell(c, k)
 
     def is_insertable_cycle(
         self,
@@ -1833,8 +1812,6 @@ def up_laplacian_matrix(
         >>> CC.add_cell([1, 2, 3], rank=2)
         >>> CC.add_cell([3, 4, 5], rank=2)
         >>> index, L1_up = CC.up_laplacian_matrix(1, index=True)
-        >>> print(index)
-        >>> print(L1_up)
         """
         if weight is not None:
             raise ValueError("Weighted Laplacian is not supported in this version.")

toponetx/classes/colored_hypergraph.py

@@ -1228,8 +1228,6 @@ def node_to_all_cell_adjacnecy_matrix(self, index: bool = False, s: int = 1):
     def coadjacency_matrix(self, rank, via_rank, s: int = 1, index: bool = False):
         """Compute the coadjacency matrix.
 
-        The sparse weighted :term:`s-coadjacency matrix`.
-
         Parameters
         ----------
         rank : int

toponetx/classes/combinatorial_complex.py

@@ -840,7 +840,7 @@ def incidence_matrix(
         )
 
     def adjacency_matrix(self, rank, via_rank, s: int = 1, index: bool = False):
-        """Sparse weighted :term:`s-adjacency matrix`.
+        """Sparse weighted `s-adjacency matrix`.
 
         Parameters
         ----------
@@ -877,8 +877,6 @@ def adjacency_matrix(self, rank, via_rank, s: int = 1, index: bool = False):
     def coadjacency_matrix(self, rank, via_rank, s: int = 1, index: bool = False):
         """Compute the coadjacency matrix of self.
 
-        The sparse weighted :term:`s-coadjacency matrix`
-
         Parameters
         ----------
         rank, via_rank : int