update code

.github/workflows/ci.yml

@@ -0,0 +1,66 @@
+name: CI
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+
+permissions:
+  actions: write
+  contents: read
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+      # Step 1: Check out the repository
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      # Step 2: Set up Julia
+      - name: Set up Julia
+        uses: julia-actions/setup-julia@latest
+        with:
+          version: '1.9' # Specify the Julia version you need
+
+      # Step 3: Set up cache for dependencies
+      - name: Setup cache
+        uses: julia-actions/cache@v2
+
+      # Step 4: Install dependencies (registered and unregistered)
+      - name: Install Dependencies
+        run: |
+          julia -e '
+            import Pkg;
+            # Add unregistered dependency from GitHub
+            Pkg.develop(Pkg.PackageSpec(url="https://github.com/JuliaSNN/SpikingNeuralNetworks.jl"));
+            # Install all dependencies listed in Project.toml
+            Pkg.update();
+            Pkg.resolve();
+            Pkg.instantiate();  
+          '
+
+      # Step 5: Run tests
+      # Step 5: Run tests with force_latest_compatible_version=true
+      - name: Run tests
+        run: |
+          julia -e '
+            import Pkg;
+            # Add unregistered dependency from GitHub
+            Pkg.develop(Pkg.PackageSpec(url="https://github.com/JuliaSNN/SpikingNeuralNetworks.jl"));
+            # Install all dependencies listed in Project.toml
+            Pkg.update();
+            Pkg.resolve();
+            Pkg.instantiate();
+            Pkg.activate(".");
+            Pkg.test()
+          '
+      # Step 6: Upload test results on failure
+      - name: Upload test results
+        if: failure()
+        uses: actions/upload-artifact@v3
+        with:
+          name: test-results
+          path: test/

Project.toml

@@ -1 +1,23 @@
+name = "SNNExamples"
+uuid = "276b8b60-e50f-4651-bb99-b251f196bd69"
+
 [deps]
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+
+[extras]
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
+Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
+
+[targets]
+test = ["Test"]
+
+[compat]
+SpikingNeuralNetworks = "0.1.0"
+MLJ = "0.20.7"
+Reexport = "1.2.2"

README.md

@@ -1,2 +1,3 @@
 # SNNExamples
+![CI](https://github.com/russelljjarvis/SNNExamples/actions/workflows/ci.yml/badge.svg)
 Collection of examples from the JuliaSNN organization

experiments/Mongillo2008_WorkingMemory/STP_WM_Mongillo2008.jl

@@ -9,8 +9,8 @@ using StatsBase
 using Distributions
 using LaTeXStrings
 
-##
-include("../../parameters/Mongillo_WM2008.jl")
+
+include("../../parameters/mongillo_WM2008.jl")
 model, assemblies = Mongillo2008(n_assemblies=2)
 
 peak_rate = 2kHz