Added info on MSVS and nvcc paths.

README.rst

@@ -117,6 +117,8 @@ 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

docs/source/gpu.rst

@@ -11,7 +11,7 @@ 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 should add the location of the CUDA compiler (:code:`nvcc`) to your user path.
+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`
 
 Running gprMax using GPU(s)