Update Python section and fix internal links

docs/software-packages/flacs.md

@@ -6,7 +6,7 @@ explosion modelling and one of the best validated tools for modeling
 flammable and toxic releases in a technical safety context.
 
 The Cirrus cluster is ideally suited to run multiple FLACS simulations
-simultaneously, via its [batch system](../../user-guide/batch/). Short
+simultaneously, via its [batch system](../user-guide/batch.md). Short
 lasting simulations (of typically up to a few hours computing time each)
 can be processed efficiently and you could get a few hundred done in a
 day or two. In contrast, the Cirrus cluster is not particularly suited
@@ -202,7 +202,7 @@ list only your jobs use:
 ### Submitting many FLACS jobs as a job array
 
 Running many related scenarios with the FLACS simulator is ideally
-suited for using [job arrays](../../user-guide/batch/#job-arrays), i.e.
+suited for using [job arrays](../user-guide/batch.md#job-arrays), i.e.
 running the simulations as part of a single job.
 
 Note you must determine ahead of time the number of scenarios involved.

docs/user-guide/data.md

@@ -472,5 +472,5 @@ Please note that “remote” is the name that you have chosen when running rclo
 
 The Cirrus `/work` filesystem, which is hosted on the e1000 fileserver, has a Globus Collection (formerly known as an endpoint) with the name `e1000-fs1 directories`   
 
-[Full step-by-step guide for using Globus](../globus) to transfer files to/from Cirrus `/work`
+[Full step-by-step guide for using Globus](globus.md) to transfer files to/from Cirrus `/work`
 

docs/user-guide/development.md

@@ -296,7 +296,7 @@ using the NVLink intra-node GPU comm links (and inter-node GPU comms are direct
 intead of passing through the host processor).
 
 Hence, the OpenMPI GPU modules allow the user to run GPU-aware MPI code as efficiently
-as possible, see [Compiling and using GPU-aware MPI](../gpu/#compiling-and-using-gpu-aware-mpi).
+as possible, see [Compiling and using GPU-aware MPI](gpu.md#compiling-and-using-gpu-aware-mpi).
 
 OpenMPI modules for use on the CPU nodes are also available, but these are not
 expected to provide any performance advantage over HPE MPT or Intel MPI.

docs/user-guide/introduction.md

@@ -31,10 +31,10 @@ meaning.
 CPUh  
 Cirrus CPU time is measured in CPUh. Each job you run on the service
 consumes CPUhs from your budget. You can find out more about CPUhs and
-how to track your usage in the [resource management section](../resource_management/)
+how to track your usage in the [resource management section.](resource_management.md)
 
 GPUh  
 Cirrus GPU time is measured in GPUh. Each job you run on the GPU nodes
 consumes GPUhs from your budget, and requires positive CPUh, even though
 these will not be consumed. You can find out more about GPUhs and how to
-track your usage in the [resource management section](../resource_management/)
+track your usage in the [resource management section.](resource_management.md)

docs/user-guide/python.md

@@ -555,7 +555,7 @@ you can start from a login node prompt.
 
 	If you have extended a central Python venv following the
 	instructions about for [Installing your own Python packages
-	(with pip)](#installing-your-own-python-packages-(with-pip)),
+	(with pip)](#installing-your-own-python-packages-with-pip),
 	Jupyter Lab will load the central ipython kernel, not the one
 	for your venv. To enable loading of the ipython kernel for your
 	venv from within Jupyter Lab, first install the ipykernel module
@@ -567,13 +567,17 @@ you can start from a login node prompt.
 	```
 	changing placeholder account and username as appropriate.
 	Thereafter, launch Jupyter Lab as above and select the `myvenv`
-	kernel.
+	kernel. It may be needed to load the following environment variables:
+        ```
+        export PYTHONUSERBASE=$(pwd)/.local
+        export PATH=$PYTHONUSERBASE/bin:$PATH
+        export HOME=$(pwd)
+        export JUPYTER_RUNTIME_DIR=$(pwd)
+        ```
 
 If you are on a compute node, the JupyterLab server will be available
 for the length of the interactive session you have requested.
 
 You can also run Jupyter sessions using the centrally-installed
-Miniconda3 modules available on Cirrus. For example, the following link
-provides instructions for how to setup a Jupyter server on a GPU node.
-
-<https://github.com/hpc-uk/build-instructions/tree/main/pyenvs/ipyparallel>
+Miniconda3 modules available on Cirrus. [This page provides instructions
+for how to setup a Jupyter server on a GPU node.](https://github.com/hpc-uk/build-instructions/tree/main/pyenvs/ipyparallel)

docs/user-guide/resource_management.md

@@ -14,7 +14,7 @@ Finally we cover some guidelines for I/O and data archiving on Cirrus.
 ## The Cirrus Administration Web Site (SAFE)
 
 All users have a login and password on the Cirrus Administration Web
-Site (also know as the 'SAFE'): [SAFE](https://safe.epcc.ed.ac.uk/).
+Site (also know as the [SAFE](https://safe.epcc.ed.ac.uk/).
 Once logged into this web site, users can find out much about their
 usage of the Cirrus system, including:
 
@@ -29,8 +29,7 @@ usage of the Cirrus system, including:
 
 ## Checking your CPU/GPU time allocations
 
-You can view these details by logging into the SAFE
-(<https://safe.epcc.ed.ac.uk>).
+You can view these details by logging into the [SAFE.](https://safe.epcc.ed.ac.uk)
 
 Use the *Login accounts* menu to select the user account that you wish
 to query. The page for the login account will summarise the resources

mkdocs.yml

@@ -36,7 +36,7 @@ markdown_extensions:
 
 nav:
   - "Overview": index.md
-  - "Cirrus migration to E1000 system": e1000-migration
+  - "Cirrus migration to E1000 system": e1000-migration/index.md
   - "User Guide":
     - "Introduction": user-guide/introduction.md
     - "Connecting to Cirrus": user-guide/connecting.md