Merge remote-tracking branch 'origin/main' into remove_conda

# Conflicts:
#	.ci_support/environment-mpich.yml
#	.ci_support/environment-openmpi.yml
#	.ci_support/environment-win.yml
#	pyproject.toml

Author: jan-janssen

.ci_support/environment-mpich.yml

@@ -11,5 +11,5 @@ dependencies:
 - h5io =0.2.4
 - matplotlib =3.9.2
 - networkx =3.3
-- pygraphviz =1.13
+- pygraphviz =1.14
 - ipython =8.27.0

.ci_support/environment-openmpi.yml

@@ -11,5 +11,5 @@ dependencies:
 - h5io =0.2.4
 - matplotlib =3.9.2
 - networkx =3.3
-- pygraphviz =1.13
+- pygraphviz =1.14
 - ipython =8.27.0

.ci_support/environment-win.yml

@@ -11,5 +11,5 @@ dependencies:
 - h5io =0.2.4
 - matplotlib =3.9.2
 - networkx =3.3
-- pygraphviz =1.13
+- pygraphviz =1.14
 - ipython =8.27.0

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.6.4
+    rev: v0.6.8
     hooks:
       - id: ruff
         name: ruff lint

docs/_config.yml

@@ -1,6 +1,5 @@
 title: executorlib
 author: Jan Janssen
-logo: images/pyiron-logo.png
 
 execute:
   execute_notebooks           : off

docs/_toc.yml

@@ -4,4 +4,5 @@ chapters:
 - file: installation.md
 - file: examples.ipynb
 - file: development.md
-- file: api.rst
+- file: trouble_shooting.md
+- file: api.rst

docs/trouble_shooting.md

@@ -0,0 +1,103 @@
+# Trouble shooting
+
+## When `flux` fails:
+
+### Step-by-Step Guide to Create a Custom Jupyter Kernel for Flux
+
+#### Step 1: Create a New Kernel Specification
+
+1. Install [`flux-core`](https://anaconda.org/conda-forge/flux-core) in your Jupyter environment:
+
+   ```bash
+   conda install -c conda-forge flux-core
+   ```
+
+2. **Find the Jupyter Kernel Directory**:
+
+   Open your terminal or command prompt and run:
+
+   ```bash
+   jupyter --paths
+   ```
+
+   This command will display the paths where Jupyter looks for kernels. You'll usually find a directory named `kernels` under the `jupyter` data directory. You will create a new directory for the Flux kernel in the `kernels` directory.
+
+3. **Create the Kernel Directory**:
+
+   Navigate to the kernels directory (e.g., `~/.local/share/jupyter/kernels` on Linux or macOS) and create a new directory called `flux`.
+
+   ```bash
+   mkdir -p ~/.local/share/jupyter/kernels/flux
+   ```
+
+   If you're using Windows, the path will be different, such as `C:\Users\<YourUsername>\AppData\Roaming\jupyter\kernels`.
+
+4. **Create the `kernel.json` File**:
+
+   Inside the new `flux` directory, create a file named `kernel.json`:
+
+   ```bash
+   nano ~/.local/share/jupyter/kernels/flux/kernel.json
+   ```
+
+   Paste the following content into the file:
+
+   ```json
+   {
+     "argv": [
+       "flux",
+       "start",
+       "/srv/conda/envs/notebook/bin/python",
+       "-m",
+       "ipykernel_launcher",
+       "-f",
+       "{connection_file}"
+     ],
+     "display_name": "Flux",
+     "language": "python",
+     "metadata": {
+       "debugger": true
+     }
+   }
+   ```
+
+   - **`argv`**: This array specifies the command to start the Jupyter kernel. It uses `flux start` to launch Python in the Flux environment.
+   - **`display_name`**: The name displayed in Jupyter when selecting the kernel.
+   - **`language`**: The programming language (`python`).
+
+   **Note**:
+
+   - Make sure to replace `"/srv/conda/envs/notebook/bin/python"` with the correct path to your Python executable. You can find this by running `which python` or `where python` in your terminal.
+   - If you installed `flux` in a specific environment, you have to write the absolute path to `flux` in the `argv` array.
+
+#### Step 2: Restart Jupyter Notebook
+
+1. **Restart the Jupyter Notebook Server**:
+
+   Close the current Jupyter Notebook server and restart it:
+
+   ```bash
+   jupyter notebook
+   ```
+
+   ```bash
+   jupyter lab
+   ```
+
+    Or simply restart your server.
+
+2. **Select the Flux Kernel**:
+
+   When creating a new notebook or changing the kernel of an existing one, you should see an option for "Flux" in the list of available kernels. Select it to run your code with the Flux environment.
+
+#### Step 3: Run Your Code with `FluxExecutor`
+
+Now, your Jupyter environment is set up to use `flux-core`. You can run your code like this:
+
+```python
+import flux.job
+
+# Use FluxExecutor within the Flux kernel
+with flux.job.FluxExecutor() as flux_exe:
+    print("FluxExecutor is running within the Jupyter Notebook")
+```

pyproject.toml

@@ -41,7 +41,7 @@ hdf = [
     "h5io==0.2.4",
 ]
 graph = [
-    "pygraphviz==1.13",
+    "pygraphviz==1.14",
     "matplotlib==3.9.2",
     "networkx==3.3",
     "ipython==8.27.0",