Merge pull request #3 from marcus-k:fix-dependencies

Fix-dependencies

.github/workflows/python-package.yml

@@ -16,7 +16,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9"]
+        python-version: ["3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v2

.gitignore

@@ -127,3 +127,5 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+dev/

README.md

@@ -1,8 +1,10 @@
 # Random NWNs [![Tests](https://github.com/marcus-k/Random-NWNs/actions/workflows/python-package.yml/badge.svg)](https://github.com/marcus-k/Random-NWNs/actions/workflows/python-package.yml)
 
-Python package for modelling and analyzing random nanowire networks. This package was a summer research project lasting from May 2021 to August 2021 under the supervision of Dr. Claudia Gomes da Rocha.
+Python package for modelling and analyzing random nanowire networks. This package was a summer research project lasting from May 2021 to August 2021 under the supervision of Dr. Claudia Gomes da Rocha. 
 
-For future additions, feel free to fork the repository, but please give credit where it's due, either here or to Marcus Kasdorf.
+**Update:** This project will now be continuing as of May 2024. If you are using this project, please note there will be **active development** on it and the functionality may change.
+
+For future additions, feel free to fork the repository. Please cite Marcus Kasdorf if you wish to extend the project.
 
 # Table of Contents
 * [Installation](#installation)

environment.yml

@@ -1,14 +1,15 @@
-name: randomnwn
+name: nwn
 channels:
   - conda-forge
 dependencies:
-  - python=3.8
-  - numpy=1.24.3
-  - networkx=3.1
-  - scipy=1.10.1
-  - Shapely=1.7.1
-  - matplotlib=3.7.1
   - ipykernel
-  - line_profiler
   - joblib
+  - line_profiler
+  - matplotlib=3.8.4
+  - networkx=3.1
+  - numpy=1.26.4
   - pip
+  - pytest
+  - python=3.10
+  - scipy=1.13.0
+  - Shapely=2.0.4

randomnwn/plotting.py

@@ -77,13 +77,13 @@ def plot_NWN(
     # Defaults to blue and pink lines, else random colors are used.
     if rnd_color:
         for i in range(NWN.graph["wire_num"]):
-            ax.plot(*np.array(NWN.graph["lines"][i]).T)
+            ax.plot(*np.asarray(NWN.graph["lines"][i].coords).T)
     else:
         for i in range(NWN.graph["wire_num"]):
             if (i,) in NWN.graph["electrode_list"]:
-                ax.plot(*np.array(NWN.graph["lines"][i]).T, c="xkcd:light blue")
+                ax.plot(*np.asarray(NWN.graph["lines"][i].coords).T, c="xkcd:light blue")
             else:
-                ax.plot(*np.array(NWN.graph["lines"][i]).T, c="pink")
+                ax.plot(*np.asarray(NWN.graph["lines"][i].coords).T, c="pink")
 
     # Scale axes according to the characteristic values
     if scaled:

requirements.txt

@@ -1,5 +1,5 @@
-numpy==1.24.3
+matplotlib==3.8.4
 networkx==3.1
-scipy==1.10.1
-Shapely==1.7.1
-matplotlib==3.7.1
+numpy==1.26.4
+scipy==1.13.0
+Shapely==2.0.4