Cleaned up instructions on path variables.

README.rst

@@ -117,10 +117,10 @@ Microsoft Windows
 
 * Download and install `Microsoft Visual C++ 2015 Build Tools <http://download.microsoft.com/download/5/F/7/5F7ACAEB-8363-451F-9425-68A90F98B238/visualcppbuildtools_full.exe>`_ (currently you must use the 2015 version, not 2017). Use the custom installation option and deselect everything apart from the Windows SDK for your version of Windows.
 
-If you have problems with building gprMax you may need to add Microsoft Visual Studio tools to your path environment variable, usually :code:`C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin`
-
 Alternatively if you are using Windows 10 and feeling adventurous you can install the `Windows Subsystem for Linux <https://msdn.microsoft.com/en-gb/commandline/wsl/about>`_ and then follow the Linux install instructions for gprMax. Note however that currently WSL does not aim to support GUI desktops or applications, e.g. Gnome, KDE, etc....
 
+
+
 3. Build and install gprMax
 ---------------------------
 
@@ -135,6 +135,7 @@ Once you have installed the aforementioned tools follow these steps to build and
 
 **You are now ready to proceed to running gprMax.**
 
+If you have problems with building gprMax on Microsoft Windows, you may need to add :code:`C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin` to your path environment variable. 
 
 Running gprMax
 ==============

docs/source/gpu.rst

@@ -11,8 +11,9 @@ Extra installation steps for GPU usage
 
 The following steps provide guidance on how to install the extra components to allow gprMax to run on your NVIDIA GPU:
 
-1. Install the `NVIDIA CUDA Toolkit <https://developer.nvidia.com/cuda-toolkit>`_. You can follow the Installation Guides in the `NVIDIA CUDA Toolkit Documentation <http://docs.nvidia.com/cuda/index.html#installation-guides>`_ You must ensure the version of CUDA you install is compatible with the compiler you are using. This information can usually be found in a table in the CUDA Installation Guide under System Requirements. You may need to add the location of the CUDA compiler (:code:`nvcc`) to your user path environment variable, e.g. :code:`C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin` or :code:`/Developer/NVIDIA/CUDA-10.0/bin`.
-2. Install the pycuda Python module. Open a Terminal (Linux/macOS) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`conda activate gprMax`. Run :code:`pip install pycuda`
+1. Install the `NVIDIA CUDA Toolkit <https://developer.nvidia.com/cuda-toolkit>`_. You can follow the Installation Guides in the `NVIDIA CUDA Toolkit Documentation <http://docs.nvidia.com/cuda/index.html#installation-guides>`_ You must ensure the version of CUDA you install is compatible with the compiler you are using. This information can usually be found in a table in the CUDA Installation Guide under System Requirements.
+2. You may need to add the location of the CUDA compiler (:code:`nvcc`) to your user path environment variable, e.g. for Windows :code:`C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin` or Linux/macOS :code:`/Developer/NVIDIA/CUDA-10.0/bin`.
+3. Install the pycuda Python module. Open a Terminal (Linux/macOS) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`conda activate gprMax`. Run :code:`pip install pycuda`
 
 Running gprMax using GPU(s)
 ===========================