Merge pull request #158 from pnnl/master

Merging Master into Develop

.cz.toml

@@ -1,6 +1,6 @@
 [tool.commitizen]
 name = "cz_conventional_commits"
-version = "2.3.3"
+version = "2.3.5"
 version_provider = "poetry"
 version_files = [
     "pyproject.toml",

.github/workflows/publish-to-dockerhub.yml

@@ -57,12 +57,6 @@ jobs:
         id: push
         uses: docker/build-push-action@v5
         with:
+          platforms: linux/amd64,linux/arm64
           tags: hypernetx/hypernetx:latest
           push: true
-
-      - name: Generate artifact attestation
-        uses: actions/attest-build-provenance@v1
-        with:
-          subject-name: hypernetx/hypernetx
-          subject-digest: ${{ steps.push.outputs.digest }}
-          push-to-registry: true

LONG_DESCRIPTION.rst

@@ -1,7 +1,7 @@
 .. _long_description:
 
 HyperNetX
-=================
+=========
 
 The HyperNetX library provides classes and methods for the analysis
 and visualization of complex network data modeled as hypergraphs.
@@ -25,7 +25,7 @@ Documentation is available at: https://pnnl.github.io/HyperNetX
 For questions and comments contact the developers directly at: [email protected]
 
 HyperNetX 2.3
-=============
+~~~~~~~~~~~~~
 
 HyperNetX 2.3. is the latest, stable release. The core library has been refactored to take better advantage
 of Pandas Dataframes, improve readability and maintainability, address bugs, and make it easier to change.

Makefile

@@ -66,7 +66,7 @@ lint: pylint flake8
 
 .PHONY: pylint
 pylint:
-	@$(PYTHON3) -m pylint --recursive=y --persistent=n --verbose hypernetx
+	@$(PYTHON3) -m pylint --recursive=y --persistent=n --exit-zero --verbose hypernetx
 
 # Todo: fix flake8 errors and remove --exit-zero
 .PHONY: flake8

README.md

@@ -235,63 +235,96 @@ After the container has started, access the HyperNetX Jupyter Notebooks by openi
 [http://localhost:8888/tree](http://localhost:8888/tree)
 
 
-Development
-===========
+# Development
 
-Install an editable version
-```
-pip install -e .
-```
+As a developer, set up your environment using either the standard `pip` tool or [`Poetry`](https://python-poetry.org/).
+
+## Using Pip
 
-Install additional dependencies to support testing and jupyter notebooks:
+### Setup virtual environment and install HNX
+
+Create a virtual environement. Then install an editable version of HNX and also install additional dependencies to support testing and jupyter notebooks:
 ```
+python -m venv venv-hnx
+source venv-hnx/bin/activate
+pip install -e .
 pip install -r requirements.txt
 ```
 
-You can also install all these requirements in one Make target:
+As an alternative, you can also install all these requirements in one Make target:
 
 ```
+make venv
+source venv-hnx/bin/activate
 make install
 ```
 
-Poetry
-======
+### Setup pre-commit
+
+Use the [pre-commit framework](https://pre-commit.com/) to automatically point out issues and resolve those issues before code review.
+It is highly recommended to install pre-commit in your development environment so that issues with your code can be found before you submit a
+pull request. More importantly, using pre-commit will automatically format your code changes so that they pass the CI build. For example, pre-commit will
+automatically run the formatter Black on your code changes.
+
+```shell
+# Once installed, pre-commit will be triggered every time you make a commit in your environment
+pre-commit install
+```
+
+
+## Using Poetry
 
 This library uses [Poetry](https://python-poetry.org/docs/) to manage dependencies and packaging. Poetry can also be
 used to manage your environment for development.
 
-Prerequisites
--------------
+### Prerequisites
 
 * [Install Poetry](https://python-poetry.org/docs/#installation)
 
 
-Configure Poetry
-----------------
+### Configure Poetry
+
+[Configure your Poetry](https://python-poetry.org/docs/configuration/) to ensure that the virtual environment gets created in your project directory (this is not necessary but recommended for convenience):
 
-[Configure your Poetry](https://python-poetry.org/docs/configuration/) to create the virtual environment in your project directory:
 ```
 poetry config virtualenvs.in-project true
 
 # check the poetry configuration
 poetry config --list
 ```
 
-Set up virtual environment
-----------------------------
+### Setup virtual environment and install HNX
 
 Create and activate a virtual environment.
+
 ```
 poetry shell
 ```
 
-Install required dependencies and HyperNetX in editable mode.
+Install HyperNetX in editable mode, the library's core/required dependencies, and the optional dependencies to support development.
+
 ```
-poetry install
+poetry install --with test,lint,docs,release,tutorials
 ```
 
-Install support for testing
------------------------------
+Details about these dependencies are defined in [pyproject.toml](pyproject.toml).
+
+### Setup Pre-commit
+
+Use the [pre-commit framework](https://pre-commit.com/) to automatically point out issues and resolve those issues before code review.
+It is highly recommended to install pre-commit in your development environment so that issues with your code can be found before you submit a
+pull request. More importantly, using pre-commit will automatically format your code changes so that they pass the CI build. For example, pre-commit will
+automatically run the formatter Black on your code changes.
+
+```shell
+# Once installed, pre-commit will be triggered every time you make a commit in your environment
+pre-commit install
+```
+
+### Details about optional dependencies
+
+#### Install support for testing
+
 
 > ℹ️ **NOTE:** This project has pytest configuration contained in pyproject.toml. By default, pytest will use those configuration settings to run tests.
 
@@ -312,8 +345,7 @@ coverage html
 open htmlcov/index.html
 ```
 
-Install support for tutorials
------------------------------
+#### Install support for tutorials
 
 ```shell
 poetry install --with tutorials
@@ -325,8 +357,8 @@ poetry shell
 make tutorials
 ```
 
-Code Quality
-------------
+#### Code Quality: Pylint, Black
+
 HyperNetX uses a number of tools to maintain code quality:
 
 * Pylint
@@ -341,8 +373,6 @@ poetry install --with lint
 poetry shell
 ```
 
-Pylint
-------
 
 [Pylint](https://pylint.pycqa.org/en/latest/index.html) is a static code analyzer for Python-based projects. From the [Pylint docs](https://pylint.pycqa.org/en/latest/index.html#what-is-pylint):
 
@@ -362,30 +392,14 @@ pylint hypernetx --output=pylint-results.txt
 
 For more information on configuration, see https://pylint.pycqa.org/en/latest/user_guide/configuration/index.html
 
-Black
------
 [Black](https://black.readthedocs.io/en/stable/) is a PEP 8 compliant formatter for Python-based project. This tool is highly opinionated about how Python should be formatted and will automagically reformat your code.
 
 
 ```shell
 black hypernetx
 ```
 
-Pre-commit
----------
-
-Use the [pre-commit framework](https://pre-commit.com/) to automatically point out issues and resolve those issues before code review.
-It is highly recommended to install pre-commit in your development environment so that issues with your code can be found before you submit a
-pull request. More importantly, using pre-commit will automatically format your code changes so that they pass the CI build. For example, pre-commit will
-automatically run the formatter Black on your code changes.
-
-```shell
-# Once installed, pre-commit will be triggered every time you make a commit in your environment
-pre-commit install
-```
-
-Documentation
--------------
+### Documentation
 
 Build and view documentation locally:
 
@@ -422,13 +436,14 @@ The HTML pages are in docs/html.
 Click on [http://127.0.0.1:8000/install.html](http://127.0.0.1:8000/install.html) to open the docs on your browser. Since this will auto-rebuild, every time
 you change a document file, it will automatically render on your browser, allowing you to verify your document changes.
 
-Testing the Docker Image
-------------------------
+
+## Developing and Testing the Docker Image
 
 If you want to test the Docker image after making any source code changes, follow this workflow:
 
 1. Make a change in the HNX codebase
-2. Build image: `docker build --rm --tag hypernetx/hypernetx:latest`
+2. Build image for multi-platforms (i.e.ARM64, x86): `docker build --platform linux/amd64,linux/arm64 --rm --tag hypernetx/hypernetx:latest .`
+   3. If you're having issues building, see https://docs.docker.com/desktop/containerd/
 3. Test image: `docker run -it --rm -p 8888:8888 -v "${PWD}":/home/jovyan/work hypernetx/hypernetx:latest`
 4. Open a browser to [http://localhost:8888/tree](http://localhost:8888/tree). Check that tutorials still work and/or open a notebook and test the changes that you made.
 5. Once finished testing, kill the container using Ctrl-C

docs/source/conf.py

@@ -19,7 +19,7 @@
 import os
 
 
-__version__ = "2.3.3"
+__version__ = "2.3.5"
 
 
 # If extensions (or modules to document with autodoc) are in another directory,

hypernetx/__init__.py

@@ -11,4 +11,4 @@
 from hypernetx.utils import *
 from hypernetx.utils.toys import *
 
-__version__ = "2.3.3"
+__version__ = "2.3.5"

hypernetx/algorithms/hypergraph_modularity.py

@@ -183,7 +183,7 @@ def modularity(HG, A, wdc=linear):
         _df = pd.DataFrame(zip(_keys, _vals), columns=["key", "val"])
         _df = _df.groupby(by="key").sum()
         EC = sum(
-            [wdc(k[1], k[0]) * v[0] for (k, v) in _df.iterrows() if k[0] > k[1] / 2]
+            [wdc(k[1], k[0]) * v.iloc[0] for (k, v) in _df.iterrows() if k[0] > k[1] / 2]
         )
 
     ## Degree Tax
@@ -292,6 +292,12 @@ def two_section(HG):
                 w = 1 / (len(E) - 1)
             s.extend([(k[0], k[1], w) for k in itertools.combinations(E, 2)])
     G = ig.Graph.TupleList(s, weights=True).simplify(combine_edges="sum")
+
+    ## add isolates if any
+    isolates = list(set([v for v in HG.nodes]) - set(G.vs['name']))
+    if len(isolates)>0:
+        G.add_vertices(isolates)
+
     return G
 
 
@@ -392,17 +398,17 @@ def _last_step_weighted(H, A, wdc, delta=0.01, verbose=False):
         n_moves = 0
         for v in list(np.random.permutation(list(H.nodes))):
             dct_A_v = dct_A[v]
-            H_id = [H.incidence_dict[x] for x in H.nodes[v].memberships]
+            H_id = [H.incidence_dict[x] for x in H.nodes[v]]
             L = [[dct_A[i] for i in x] for x in H_id]
 
             ## ec portion before move
             _keys = [(Counter(l).most_common(1)[0][1], len(l)) for l in L]
-            _vals = [H.edges[x].weight for x in H.nodes[v].memberships]
+            _vals = [H.edges[x].weight for x in H.nodes[v]]
             _df = pd.DataFrame(zip(_keys, _vals), columns=["key", "val"])
             _df = _df.groupby(by="key").sum()
             ec = sum(
                 [
-                    wdc(k[1], k[0]) * val[0]
+                    wdc(k[1], k[0]) * val.iloc[0]
                     for (k, val) in _df.iterrows()
                     if k[0] > k[1] / 2
                 ]
@@ -425,12 +431,12 @@ def _last_step_weighted(H, A, wdc, delta=0.01, verbose=False):
                 L = [[dct_A[i] for i in x] for x in H_id]
                 ## EC
                 _keys = [(Counter(l).most_common(1)[0][1], len(l)) for l in L]
-                _vals = [H.edges[x].weight for x in H.nodes[v].memberships]
+                _vals = [H.edges[x].weight for x in H.nodes[v]]
                 _df = pd.DataFrame(zip(_keys, _vals), columns=["key", "val"])
                 _df = _df.groupby(by="key").sum()
                 ecp = sum(
                     [
-                        wdc(k[1], k[0]) * val[0]
+                        wdc(k[1], k[0]) * val.iloc[0]
                         for (k, val) in _df.iterrows()
                         if k[0] > k[1] / 2
                     ]
@@ -491,7 +497,7 @@ def _last_step_unweighted(H, A, wdc, delta=0.01, verbose=False):
         n_moves = 0
         for v in list(np.random.permutation(list(H.nodes))):
             dct_A_v = dct_A[v]
-            H_id = [H.incidence_dict[x] for x in H.nodes[v].memberships]
+            H_id = [H.incidence_dict[x] for x in H.nodes[v]]
             L = [[dct_A[i] for i in x] for x in H_id]
             deg_v = H.degree(v)
 

hypernetx/algorithms/s_centrality_measures.py

@@ -77,7 +77,7 @@ def _s_centrality(func, H, s=1, edges=True, f=None, return_singletons=True, **kw
             vertices = h.nodes
 
         if h.shape[edges * 1] == 1:
-            stats = {v: 0 for v in vertices}
+            stats.update({v: 0 for v in vertices})
         else:
             g = h.get_linegraph(s=s, edges=edges)
             stats.update({k: v for k, v in func(g, **kwargs).items()})

pylintrc

@@ -1,7 +1,7 @@
 [MAIN]
 
 # Specify a score threshold to be exceeded before program exits with error.
-fail-under=7.00
+# fail-under=
 
 # A comma-separated list of package or module names from where C extensions may
 # be loaded. Extensions are loading into the active Python interpreter and may

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "hypernetx"
-version = "2.3.3"
+version = "2.3.5"
 description = "HyperNetX is a Python library for the creation and study of hypergraphs."
 authors = ["Brenda Praggastis <[email protected]>", "Dustin Arendt <[email protected]>",
     "Sinan Aksoy <[email protected]>", "Emilie Purvine <[email protected]>",

tox.ini

@@ -44,7 +44,7 @@ basepython = python3.11
 commands_pre =
     poetry install --with lint
 commands =
-    poetry run pylint --recursive=y --persistent=n hypernetx
+    poetry run pylint --recursive=y --exit-zero --persistent=n hypernetx
     poetry run flake8 hypernetx --exit-zero
 	poetry run pre-commit install
     poetry run pre-commit run --all-files