From f827f94ff4ee31df238508e9b68f75d5fbf63322 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Thu, 19 May 2016 09:44:07 +0100
Subject: [PATCH 01/38] Geometry for Jupyter example.

---
 .../cylinder_half_space_geo.png                 | Bin 0 -> 25401 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 tools/Jupyter notebooks/cylinder_half_space_geo.png

diff --git a/tools/Jupyter notebooks/cylinder_half_space_geo.png b/tools/Jupyter notebooks/cylinder_half_space_geo.png
new file mode 100644
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From faaf053948983cbd67c175545726d4200cafa207 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Thu, 19 May 2016 13:11:50 +0100
Subject: [PATCH 02/38] Jupyter notebook for plotting builtin waveforms.

---
 .../Jupyter notebooks/plot_builtin_wave.ipynb | 156 ++++++++++++++++++
 1 file changed, 156 insertions(+)
 create mode 100644 tools/Jupyter notebooks/plot_builtin_wave.ipynb

diff --git a/tools/Jupyter notebooks/plot_builtin_wave.ipynb b/tools/Jupyter notebooks/plot_builtin_wave.ipynb
new file mode 100644
index 00000000..a2da8161
--- /dev/null
+++ b/tools/Jupyter notebooks/plot_builtin_wave.ipynb	
@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting a builtin waveform\n",
+    "\n",
+    "In the ``tools`` sub-package is a module called ``plot_builtin_wave`` which can be used to plot any of the builtin waveforms in time and frequency domains. The module takes the following arguments:\n",
+    "\n",
+    "* ``type`` is the type of the waveform, e.g. ricker\n",
+    "* ``amp`` is the amplitude of the waveform\n",
+    "* ``freq`` is the centre frequency of the waveform\n",
+    "* ``timewindow`` is the time window used to view the waveform, i.e. the time window of the proposed simulation\n",
+    "* ``dt`` is the time step used to view the waveform, i.e. the time step of the proposed simulation\n",
+    "\n",
+    "There is an optional argument:\n",
+    "\n",
+    "* ``-fft`` a switch to turn on the FFT plotting for a single field component or current\n",
+    "\n",
+    "For example (to use the module outside this notebook) to plot a Ricker waveform (and FFT) with an amplitude of 1, centre frequency of 1.5GHz and with a time window of 3ns and time step of 1.926ps:\n",
+    "\n",
+    "    python -m tools.plot_builtin_wave ricker 1 1.5e9 3e-9 1.926e-12 -fft"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use the following code to experiment (in this notebook) with plotting different waveforms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Waveform characteristics...\n",
+      "Type: ricker\n",
+      "Amplitude: 1\n",
+      "Centre frequency: 2.5e+07 Hz\n",
+      "Time to centre of pulse: 4e-08 s\n",
+      "Time window: 3e-07 s (3742 iterations)\n",
+      "Time step: 8.019e-11 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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iPs6IT3TEyBnxQaqtWbNG69ev19KlS7V06VI9+uijFa/dcccdWrt2rVavXq2+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\n6aab1KJFC02fPj2uSSIA9va3I44ovhqQK+y/VNrb90LC1RiFEPFxRnx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VFi9OaDiKTmFG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\n0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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1060cb0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from gprMax.waveforms import Waveform\n",
+    "from tools.plot_builtin_wave import check_timewindow, mpl_plot\n",
+    "\n",
+    "w = Waveform()\n",
+    "w.type = 'ricker'\n",
+    "w.amp = 1\n",
+    "w.freq = 25e6\n",
+    "timewindow = 300e-9\n",
+    "dt = 8.019e-11\n",
+    "\n",
+    "timewindow, iterations = check_timewindow(timewindow, dt)\n",
+    "plt = mpl_plot(w, timewindow, dt, iterations, fft=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use the following code as a guide to determining a spatial resolution for a simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minimum wavelength = 0.416378m\n",
+      "Maximum spatial resolution = 0.0416378m\n",
+      "Time step for 3D cubic cell = 8.01875e-11s\n",
+      "Time step for 2D cubic cell = 9.82093e-11s\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math import sqrt\n",
+    "\n",
+    "# Speed of light in vacuum (m/s)\n",
+    "c = 299792458\n",
+    "\n",
+    "# Highest relative permittivity present in model\n",
+    "er = 81\n",
+    "\n",
+    "# Maximum frequency present in model\n",
+    "fmax = 80e6\n",
+    "\n",
+    "# Minimum wavelength\n",
+    "wmin = c / (fmax * sqrt(er))\n",
+    "\n",
+    "# Maximum spatial resolution (allowing 10 cells per wavelength)\n",
+    "dmin = wmin / 10\n",
+    "\n",
+    "# Time steps at CFL limits for cubic cells\n",
+    "dt3D = dmin / (sqrt(3) * c)\n",
+    "dt2D = dmin / (sqrt(2) * c)\n",
+    "\n",
+    "print('Minimum wavelength = {:g}m'.format(wmin))\n",
+    "print('Maximum spatial resolution = {:g}m'.format(dmin))\n",
+    "print('Time step for 3D cubic cell = {:g}s'.format(dt3D))\n",
+    "print('Time step for 2D cubic cell = {:g}s'.format(dt2D))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From e7c487a100c05534c8451dd1f29e366b023e2a78 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Thu, 19 May 2016 16:14:24 +0100
Subject: [PATCH 03/38] Added printed info on initial ranges.

---
 user_libs/optimisation_taguchi/plot_results.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/user_libs/optimisation_taguchi/plot_results.py b/user_libs/optimisation_taguchi/plot_results.py
index 5b4829be..7cf37028 100644
--- a/user_libs/optimisation_taguchi/plot_results.py
+++ b/user_libs/optimisation_taguchi/plot_results.py
@@ -24,9 +24,12 @@ optparamsinit = pickle.load(f)
 print('Optimisations summary for: {}'.format(os.path.split(args.picklefile)[1]))
 print('Number of iterations: {:g}'.format(len(fitnessvalueshist)))
 print('History of fitness values: {}'.format(fitnessvalueshist))
+print('Initial parameter values:')
+for item in optparamsinit:
+    print('\t{}: {}'.format(item[0], item[1]))
 print('History of parameter values:')
 for key, value in optparamshist.items():
-    print(key, value)
+    print('\t{}: {}'.format(key, value))
       
     
 # Plot the history of fitness values and each optimised parameter values for the optimisation

From 85d6c62327d2e4063d3b91ecb86bac486652d3ec Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Thu, 19 May 2016 17:07:05 +0100
Subject: [PATCH 04/38] Formatting updates.

---
 tools/Jupyter notebooks/example_Ascan_2D.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/Jupyter notebooks/example_Ascan_2D.ipynb b/tools/Jupyter notebooks/example_Ascan_2D.ipynb
index 996e8e0e..88c45c68 100644
--- a/tools/Jupyter notebooks/example_Ascan_2D.ipynb	
+++ b/tools/Jupyter notebooks/example_Ascan_2D.ipynb	
@@ -146,8 +146,8 @@
     "# Maximum spatial resolution\n",
     "dmin = wmin / 10\n",
     "\n",
-    "print('Minimum wavelength = {:g}m'.format(wmin))\n",
-    "print('Maximum spatial resolution = {:g}m'.format(dmin))"
+    "print('Minimum wavelength: {:g} m'.format(wmin))\n",
+    "print('Maximum spatial resolution: {:g} m'.format(dmin))"
    ]
   },
   {

From 9ab01ebc225ebce1ec0094b166f4040b927896a7 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Thu, 19 May 2016 17:07:34 +0100
Subject: [PATCH 05/38] Formatting updates.

---
 .../Jupyter notebooks/plot_builtin_wave.ipynb  | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/tools/Jupyter notebooks/plot_builtin_wave.ipynb b/tools/Jupyter notebooks/plot_builtin_wave.ipynb
index a2da8161..1050b93a 100644
--- a/tools/Jupyter notebooks/plot_builtin_wave.ipynb	
+++ b/tools/Jupyter notebooks/plot_builtin_wave.ipynb	
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -96,10 +96,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Minimum wavelength = 0.416378m\n",
-      "Maximum spatial resolution = 0.0416378m\n",
-      "Time step for 3D cubic cell = 8.01875e-11s\n",
-      "Time step for 2D cubic cell = 9.82093e-11s\n"
+      "Minimum wavelength: 0.416378 m\n",
+      "Maximum spatial resolution: 0.0416378 m\n",
+      "Time step for 3D cubic cell: 8.01875e-11 s\n",
+      "Time step for 2D cubic cell: 9.82093e-11 s\n"
      ]
     }
    ],
@@ -125,10 +125,10 @@
     "dt3D = dmin / (sqrt(3) * c)\n",
     "dt2D = dmin / (sqrt(2) * c)\n",
     "\n",
-    "print('Minimum wavelength = {:g}m'.format(wmin))\n",
-    "print('Maximum spatial resolution = {:g}m'.format(dmin))\n",
-    "print('Time step for 3D cubic cell = {:g}s'.format(dt3D))\n",
-    "print('Time step for 2D cubic cell = {:g}s'.format(dt2D))"
+    "print('Minimum wavelength: {:g} m'.format(wmin))\n",
+    "print('Maximum spatial resolution: {:g} m'.format(dmin))\n",
+    "print('Time step for 3D cubic cell: {:g} s'.format(dt3D))\n",
+    "print('Time step for 2D cubic cell: {:g} s'.format(dt2D))"
    ]
   }
  ],

From 9d2945614e81c68d697e9cdfb10073c25f1c17c3 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Thu, 19 May 2016 17:15:16 +0100
Subject: [PATCH 06/38] Corrected URL to developer installation notes.

---
 README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 3ad8fea9..47e807c1 100644
--- a/README.rst
+++ b/README.rst
@@ -55,7 +55,7 @@ Package overview
 Installation
 ============
 
-You should use the following guidance to install gprMax if you are an end-user (i.e. you don't intend to develop or contribute to the software). Developers (or those intending to use gprMax in a HPC environment) should follow the Installation for developers section (http://docs.gprmax.com/en/latest/includereadme.html#installation-for-developers).
+You should use the following guidance to install gprMax if you are an end-user (i.e. you don't intend to develop or contribute to the software). Developers (or those intending to use gprMax in a HPC environment) should follow the Installation for developers section (http://docs.gprmax.com/en/latest/readme_install_devs.html#installation-for-developers).
 
 The steps are:
 

From a1c61a8106fd0453c4fba7acc45e853876b14730 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Thu, 19 May 2016 17:35:36 +0100
Subject: [PATCH 07/38] Removed pyfiglet dependancy.

---
 conda_env.yml       |  3 +--
 gprMax/utilities.py | 15 +++++++++++----
 2 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/conda_env.yml b/conda_env.yml
index 4f3e7fe6..1b854fd7 100644
--- a/conda_env.yml
+++ b/conda_env.yml
@@ -14,5 +14,4 @@ dependencies:
 - lxml
 
 - pip:
-  - psutil
-  - pyfiglet
\ No newline at end of file
+  - psutil
\ No newline at end of file
diff --git a/gprMax/utilities.py b/gprMax/utilities.py
index c069e2bc..e56a4123 100644
--- a/gprMax/utilities.py
+++ b/gprMax/utilities.py
@@ -19,8 +19,6 @@
 import sys
 import decimal as d
 
-from pyfiglet import Figlet
-
 
 class ListStream(object):
     """A list can be streamed into. Required when temporarily redirecting stdio to capture output from users Python code blocks."""
@@ -56,12 +54,21 @@ GNU General Public License for more details.
 You should have received a copy of the GNU General Public License
 along with gprMax.  If not, see <http://www.gnu.org/licenses/>."""
     
+    gprMaxlogo = """
+                                 __  __
+                 __ _ _ __  _ __|  \/  | __ ___  __
+                / _` | '_ \| '__| |\/| |/ _` \ \/ /
+               | (_| | |_) | |  | |  | | (_| |>  <
+                \__, | .__/|_|  |_|  |_|\__,_/_/\_\\
+                |___/|_|
+        """
+    
     width = 65
     url = 'www.gprmax.com'
+    
     print('\nElectromagnetic modelling software based on the Finite-Difference \nTime-Domain (FDTD) method')
     print('\n{} {} {}'.format('*'*round((width - len(url))/2), url, '*'*round((width - len(url))/2)))
-    gprMaxlogo = Figlet(font='standard', width=width, justify='center')
-    print('{}'.format(gprMaxlogo.renderText('gprMax')))
+    print('{}'.format(gprMaxlogo))
     print('{} v{} {}'.format('*'*round((width - len(version))/2), (version), '*'*round((width - len(version))/2)))
     print(licenseinfo)
 

From 9624c60bd768c64044702ed3431232dd8ed34834 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Thu, 19 May 2016 17:50:15 +0100
Subject: [PATCH 08/38] psutil now seems OK to use via conda (instead of pip)
 on Mac OS X El Capitan.

---
 conda_env.yml | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/conda_env.yml b/conda_env.yml
index 1b854fd7..7e486585 100644
--- a/conda_env.yml
+++ b/conda_env.yml
@@ -10,8 +10,6 @@ dependencies:
 - matplotlib
 - mpi4py
 - numpy
+- psutil
 - scipy
-- lxml
-
-- pip:
-  - psutil
\ No newline at end of file
+- lxml
\ No newline at end of file

From c5da3fd547cae1d5e5eedc9507fe7caadfbfb0d3 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Thu, 19 May 2016 17:52:11 +0100
Subject: [PATCH 09/38] Added more info on command line args/flags.

---
 gprMax/gprMax.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/gprMax/gprMax.py b/gprMax/gprMax.py
index 5747f66a..17987f1d 100644
--- a/gprMax/gprMax.py
+++ b/gprMax/gprMax.py
@@ -55,12 +55,12 @@ def main():
     parser = argparse.ArgumentParser(prog='gprMax', description='Electromagnetic modelling software based on the Finite-Difference Time-Domain (FDTD) method')
     parser.add_argument('inputfile', help='path to and name of inputfile')
     parser.add_argument('-n', default=1, type=int, help='number of times to run the input file')
-    parser.add_argument('-mpi', action='store_true', default=False, help='switch on MPI task farm')
-    parser.add_argument('-benchmark', action='store_true', default=False, help='switch on benchmarking mode')
-    parser.add_argument('--geometry-only', action='store_true', default=False, help='only build model and produce geometry file(s)')
-    parser.add_argument('--geometry-fixed', action='store_true', default=False, help='do not reprocess model geometry for multiple model runs')
-    parser.add_argument('--write-processed', action='store_true', default=False, help='write an input file after any Python code and include commands in the original input file have been processed')
-    parser.add_argument('--opt-taguchi', action='store_true', default=False, help='optimise parameters using the Taguchi optimisation method')
+    parser.add_argument('-mpi', action='store_true', default=False, help='flag to switch on MPI task farm')
+    parser.add_argument('-benchmark', action='store_true', default=False, help='flag to switch on benchmarking mode')
+    parser.add_argument('--geometry-only', action='store_true', default=False, help='flag to only build model and produce geometry file(s)')
+    parser.add_argument('--geometry-fixed', action='store_true', default=False, help='flag to not reprocess model geometry for multiple model runs')
+    parser.add_argument('--write-processed', action='store_true', default=False, help='flag to write an input file after any Python code and include commands in the original input file have been processed')
+    parser.add_argument('--opt-taguchi', action='store_true', default=False, help='flag to optimise parameters using the Taguchi optimisation method')
     args = parser.parse_args()
 
     run_main(args)

From 3dc9dffcadf6bab0cd3b3f4df899ae77a10dc78b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=C3=98ystein=20Bj=C3=B8rndal?= <obtitus@gmail.com>
Date: Fri, 20 May 2016 15:48:55 +0200
Subject: [PATCH 10/38] added some additional input_cmd_funcs, added Coordinate
 tuple and modified some of the existing functions so that they return a
 Coordinate instead of a plain tuple. Added example of usage as
 user_models/cylinder_2D_py.in

---
 gprMax/input_cmd_funcs.py     | 150 ++++++++++++++++++++++++++++++++--
 user_models/cylinder_2D_py.in |  53 ++++++++++++
 2 files changed, 194 insertions(+), 9 deletions(-)
 create mode 100644 user_models/cylinder_2D_py.in

diff --git a/gprMax/input_cmd_funcs.py b/gprMax/input_cmd_funcs.py
index 2ace6390..727d910d 100644
--- a/gprMax/input_cmd_funcs.py
+++ b/gprMax/input_cmd_funcs.py
@@ -17,8 +17,53 @@
 # along with gprMax.  If not, see <http://www.gnu.org/licenses/>.
 
 
-"""This module contains functional forms of some of the most commonly used gprMax commands. It can be useful to use these within Python scripting in an input file."""
+"""This module contains functional forms of some of the most commonly used gprMax commands. It can be useful to use these within Python scripting in an input file.
+For convenience, x, y, z coordinates are lumped in a namedtuple Coordinate:
+>>> c = Coordinate(0.1, 0.2, 0.3)
+>>> c
+Coordinate(x=0.1, y=0.2, z=0.3)
+>>> str(c)
+'0.1 0.2 0.3'
+# which can be accessed as a normal tuple:
+>>> print c[0], c[1], c[2]
+0.1 0.2 0.3
+# or more explicitly
+>>> print c.x, c.y, c.z
+0.1 0.2 0.3
+"""
+import sys
+from collections import namedtuple
+Coordinate_tuple = namedtuple('Coordinate', ['x', 'y', 'z'])
 
+class Coordinate(Coordinate_tuple):
+    """Subclass of a namedtuple where __str__ outputs 'x y z'"""
+    def __str__(self):
+        return '{:g} {:g} {:g}'.format(self.x, self.y, self.z)
+
+def command(cmd, *parameters):
+    """Helper function. Prints the gprMax #<cmd>: <parameters>. None is ignored in the output.
+
+    Args:
+        cmd (str): the gprMax cmd string to be printed
+        *parameters: one or more strings as arguments, any None values are ignored
+
+    Returns:
+        s (str): the printed string
+    """
+    # remove Nones
+    parameters = filter(None, parameters)
+    # convert to str
+    parameters = map(str, parameters)
+    # convert to list
+    parameters = list(parameters)
+    try:
+        s = '#{}: {}'.format(cmd, " ".join(parameters))
+    except TypeError as e:
+        if not e.args: e.args=('', )
+        e.args = e.args + ("Creating cmd = #%s with parameters %s failed" % (cmd, parameters),)
+        raise e
+    print(s)
+    return s
 
 def domain(x, y, z):
     """Prints the gprMax #domain command.
@@ -27,11 +72,11 @@ def domain(x, y, z):
         x, y, z (float): Extent of the domain in the x, y, and z directions.
         
     Returns:
-        domain (float): Tuple of the extent of the domain.
+        domain (Coordinate): Namedtuple of the extent of the domain.
     """
     
-    domain = (x, y, z)
-    print('#domain: {:g} {:g} {:g}'.format(domain[0], domain[1], domain[2]))
+    domain = Coordinate(x, y, z)
+    print('#domain: {}'.format(domain))
           
     return domain
 
@@ -46,8 +91,8 @@ def dx_dy_dz(x, y, z):
         dx_dy_dz (float): Tuple of the spatial resolutions.
     """
     
-    dx_dy_dz = (x, y, z)
-    print('#dx_dy_dz: {:g} {:g} {:g}'.format(dx_dy_dz[0], dx_dy_dz[1], dx_dy_dz[2]))
+    dx_dy_dz = Coordinate(x, y, z)
+    print('#dx_dy_dz: {}'.format(dx_dy_dz))
           
     return dx_dy_dz
           
@@ -153,9 +198,16 @@ def box(xs, ys, zs, xf, yf, zf, material, averaging=''):
         xs, ys, zs, xf, yf, zf (float): Start and finish coordinates.
         material (str): Material identifier(s).
         averaging (str): Turn averaging on or off.
+
+    Returns:
+        s, f (tuple): 2 namedtuple Coordinate for the start and finish coordinates
+
     """
     
-    print('#box: {:g} {:g} {:g} {:g} {:g} {:g} {} {}'.format(xs, ys, zs, xf, yf, zf, material, averaging))
+    s = Coordinate(xs, ys, zs)
+    f = Coordinate(xf, yf, zf)
+    print('#box: {} {} {} {}'.format(s, f, material, averaging))
+    return s, f
           
           
 def sphere(x, y, z, radius, material, averaging=''):
@@ -201,6 +253,86 @@ def cylindrical_sector(axis, ctr1, ctr2, t1, t2, radius, startingangle, sweptang
     print('#cylindrical_sector: {} {:g} {:g} {:g} {:g} {:g} {:g} {:g} {} {}'.format(axis, ctr1, ctr2, t1, t2, radius, startingangle, sweptangle, material, averaging))
           
           
-          
-    
+def excitation_file(file1):
+    """Prints the #excitation_file: <file1> command.
 
+    Args:
+        file1 (str): filename
+    Returns:
+        file1 (str): filename
+    """
+    command('excitation_file', file1)
+    return file1
+
+def waveform(shape, amplitude, frequency, identifier):
+    """Prints the #waveform: shape amplitude frequency identifier
+
+    Args:
+        shape (str): is the type of waveform
+        amplitude (float): is the amplitude of the waveform.
+        frequency (float): is the frequency of the waveform in Hertz.
+        identifier (str): is an identifier for the waveform used to assign it to a source.
+    Returns:
+        identifier (str): is an identifier for the waveform used to assign it to a source.
+    """
+    command('waveform', shape, amplitude, frequency, identifier)
+    return identifier
+
+def hertzian_dipole(polarization, f1, f2, f3, identifier, t0=None, t_remove=None):
+    """Prints the #hertzian_dipole: polarization, f1, f2, f3, identifier, [t0, t_remove]
+
+    Args:
+        polarization (str):  is the polarisation of the source and can be 'x', 'y', or 'z'.
+        f1 f2 f3 (float): are the coordinates (x,y,z) of the source in the model.
+        identifier (str): is the identifier of the waveform that should be used with the source.
+        t0 (float): is an optinal argument for the time delay in starting the source.
+        t_remove (float): is a time to remove the source.
+    Returns:
+        coordinates (tuple): namedtuple Coordinate of the source location
+    """
+
+    c = Coordinate(f1, f2, f3)
+    # since command ignores None, this is safe:
+    command('hertzian_dipole', polarization, str(c), identifier, t0, t_remove)
+    return c
+
+def rx(x, y, z, identifier=None, to_save=None):
+    """Prints the #rx: x, y, z, [identifier, to_save] command.
+
+    Args:
+        x, y, z (float): are the coordinates (x,y,z) of the receiver in the model.
+        identifier (str): is the optional identifier of the receiver
+        to_save (list):  is a list of outputs with this receiver. It can be any selection from 'Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz', 'Ix', 'Iy', or 'Iz'.
+    Returns:
+        coordinates (tuple): namedtuple Coordinate of the receiver location
+    """
+
+    c = Coordinate(x, y, z)
+    command('rx', str(c), identifier, to_save)
+    return c
+
+def src_steps(dx=0, dy=0, dz=0):
+    """Prints the #src_steps: dx, dy, dz command.
+
+    Args:
+        dx, dy, dz (float): are the increments in (x, y, z) to move all simple sources or all receivers.
+    Returns:
+        coordinates (tuple): namedtuple Coordinate of the increments
+    """
+
+    c = Coordinate(dx, dy, dz)
+    command('src_steps', str(c))
+    return c
+
+def rx_steps(dx=0, dy=0, dz=0):
+    """Prints the #rx_steps: dx, dy, dz command.
+
+    Args:
+        dx, dy, dz (float): are the increments in (x, y, z) to move all simple sources or all receivers.
+    Returns:
+        coordinates (tuple): namedtuple Coordinate of the increments
+    """
+
+    c = Coordinate(dx, dy, dz)
+    command('rx_steps', str(c))
+    return c
diff --git a/user_models/cylinder_2D_py.in b/user_models/cylinder_2D_py.in
new file mode 100644
index 00000000..f4afb52d
--- /dev/null
+++ b/user_models/cylinder_2D_py.in
@@ -0,0 +1,53 @@
+#python:
+from gprMax.input_cmd_funcs import *
+command('title', 'A or B scan from a metal cylinder buried in a dielectric half-space')
+z_dim = 0.002
+resolution = 0.002
+tsim = 3e-9
+B_scan = False
+
+domain = domain(x=64e-2, y=30e-2, z=z_dim)
+dx = dx_dy_dz(resolution, resolution, z_dim)
+time_window(tsim)
+
+material(permittivity=6, conductivity=0,
+         permeability=1, magconductivity=0, name='half_space')
+
+identifier = waveform('ricker', amplitude=1, frequency=1.5e9,
+                      identifier='my_ricker')
+
+if B_scan:
+    x_ant = 8e-2
+else:
+    x_ant = domain.x/2 - 1e-2 # in the middle of the x-axis
+
+tx = hertzian_dipole('z',
+                     x_ant, domain.y - 4e-2, 0, # minus 4 cm in y-direction
+                     identifier)
+rx(tx.x + 2e-2, tx.y, tx.z)     # 2 cm away in x-direction from tx
+
+if B_scan:
+    src_steps(dx=0.8e-2/4)
+    rx_steps(dx=0.8e-2/4)
+
+b0, b1 = box(0, 0, 0,
+             domain.x, domain.y - 4e-2, z_dim, # same as domain, minus 4 cm in y-direction
+             'half_space')
+
+c_x, c_y = (domain.x/2, b1.y - 5e-2) # in the middle of the x-axis and 5 cm down from the half_space
+cylinder(c_x, c_y, 0,
+         c_x, c_y, z_dim,
+         radius=1e-2, material='pec')
+
+# Outputs, geometry and snapshots
+geometry_view(0, 0, 0,
+              domain.x, domain.y, domain.z,
+              dx.x, dx.y, dx.z,
+              'cylinder', 'n')
+
+N = 32
+for i in range(1, N+1):
+    snapshot(0, 0, 0,
+             domain.x, domain.y, domain.z,
+             dx.x, dx.y, dx.z, i*(tsim/N), 'snapshot' + str(i))
+#end_python:

From 589613761a1924f355c4a0950d566e52fb8a4870 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Fri, 20 May 2016 16:18:31 +0100
Subject: [PATCH 11/38] Added flag to make Windows compiles do static linking.

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index c33006b3..a318eb0c 100644
--- a/setup.py
+++ b/setup.py
@@ -105,7 +105,7 @@ if 'cleanall' in sys.argv:
 # Set compiler options
 # Windows
 if sys.platform == 'win32':
-    compile_args = ['/O2', '/openmp', '/w']
+    compile_args = ['/O2', '/openmp', '/w', '/MT'] # Remove /MT flag for dynamic linking
     linker_args = []
     extra_objects = []
 # Mac OS X - needs gcc (usually via HomeBrew) because the default compiler LLVM (clang) does not support OpenMP

From 7ed026c5c9f8dd4f7bfd68068831fadc88028ebe Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=C3=98ystein=20Bj=C3=B8rndal?= <obtitus@gmail.com>
Date: Fri, 20 May 2016 19:15:54 +0200
Subject: [PATCH 12/38] converted a few print() calls to command() calls.
 Should be identical in behavior.

---
 gprMax/input_cmd_funcs.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/gprMax/input_cmd_funcs.py b/gprMax/input_cmd_funcs.py
index 727d910d..4f702aef 100644
--- a/gprMax/input_cmd_funcs.py
+++ b/gprMax/input_cmd_funcs.py
@@ -59,12 +59,16 @@ def command(cmd, *parameters):
     try:
         s = '#{}: {}'.format(cmd, " ".join(parameters))
     except TypeError as e:
+        # append info about cmd and parameters to the exception:
         if not e.args: e.args=('', )
-        e.args = e.args + ("Creating cmd = #%s with parameters %s failed" % (cmd, parameters),)
+        additional_info = "Creating cmd = #{} with parameters {} failed".format(cmd, parameters)
+        e.args = e.args + (additional_info,)
         raise e
+    # and now we can print it:
     print(s)
     return s
 
+
 def domain(x, y, z):
     """Prints the gprMax #domain command.
         
@@ -76,8 +80,8 @@ def domain(x, y, z):
     """
     
     domain = Coordinate(x, y, z)
-    print('#domain: {}'.format(domain))
-          
+    command('domain', domain)
+    
     return domain
 
 
@@ -92,7 +96,7 @@ def dx_dy_dz(x, y, z):
     """
     
     dx_dy_dz = Coordinate(x, y, z)
-    print('#dx_dy_dz: {}'.format(dx_dy_dz))
+    command('dx_dy_dz', dx_dy_dz)
           
     return dx_dy_dz
           
@@ -106,8 +110,8 @@ def time_window(time_window):
     Returns:
         time_window (float): Duration of simulation.
     """
-    
-    print('#time_window: {:g}'.format(time_window))
+
+    command('time_window', time_window)
           
     return time_window
 

From 393d5e38e9fc2f045fb3a549c4b36cd4bb1f9859 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=C3=98ystein=20Bj=C3=B8rndal?= <obtitus@gmail.com>
Date: Fri, 20 May 2016 19:39:25 +0200
Subject: [PATCH 13/38] bugfix, the filter command removed the float/int 0.
 Added command call for material(), NOTE: may modify rounding as str(x)
 outputs more digits than {:g}

---
 gprMax/input_cmd_funcs.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/gprMax/input_cmd_funcs.py b/gprMax/input_cmd_funcs.py
index 4f702aef..dbb96810 100644
--- a/gprMax/input_cmd_funcs.py
+++ b/gprMax/input_cmd_funcs.py
@@ -51,17 +51,17 @@ def command(cmd, *parameters):
         s (str): the printed string
     """
     # remove Nones
-    parameters = filter(None, parameters)
+    filtered = filter(lambda x: x is not None, parameters)
     # convert to str
-    parameters = map(str, parameters)
+    filtered_str = map(str, filtered)
     # convert to list
-    parameters = list(parameters)
+    filtered_list = list(filtered_str)
     try:
-        s = '#{}: {}'.format(cmd, " ".join(parameters))
+        s = '#{}: {}'.format(cmd, " ".join(filtered_list))
     except TypeError as e:
         # append info about cmd and parameters to the exception:
         if not e.args: e.args=('', )
-        additional_info = "Creating cmd = #{} with parameters {} failed".format(cmd, parameters)
+        additional_info = "Creating cmd = #{} with parameters {} -> {} failed".format(cmd, parameters, filtered_list)
         e.args = e.args + (additional_info,)
         raise e
     # and now we can print it:
@@ -127,7 +127,7 @@ def material(permittivity, conductivity, permeability, magconductivity, name):
         name (str): Material identifier.
     """
     
-    print('#material: {:g} {:g} {:g} {:g} {}'.format(permittivity, conductivity, permeability, magconductivity, name))
+    command('material', permittivity, conductivity, permeability, magconductivity, name)
 
 
 def geometry_view(xs, ys, zs, xf, yf, zf, dx, dy, dz, filename, type='n'):

From 3128fbc0d45b8190b9055e407f4bb09cc0191f03 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=C3=98ystein=20Bj=C3=B8rndal?= <obtitus@gmail.com>
Date: Fri, 20 May 2016 22:06:35 +0200
Subject: [PATCH 14/38] modifed the remaining functions to call command and
 return Coordinate(s)

---
 gprMax/input_cmd_funcs.py | 91 +++++++++++++++++++++++++++------------
 1 file changed, 64 insertions(+), 27 deletions(-)

diff --git a/gprMax/input_cmd_funcs.py b/gprMax/input_cmd_funcs.py
index dbb96810..45247a09 100644
--- a/gprMax/input_cmd_funcs.py
+++ b/gprMax/input_cmd_funcs.py
@@ -112,7 +112,7 @@ def time_window(time_window):
     """
 
     command('time_window', time_window)
-          
+    
     return time_window
 
 
@@ -138,9 +138,15 @@ def geometry_view(xs, ys, zs, xf, yf, zf, dx, dy, dz, filename, type='n'):
         dx, dy, dz (float): Spatial discretisation of geometry view.
         filename (str): Filename where geometry file information will be stored.
         type (str): Can be either n (normal) or f (fine) which specifies whether to output the geometry information on a per-cell basis (n) or a per-cell-edge basis (f).
+    Returns:
+        s, f, d (tuple): 3 namedtuple Coordinate for the start, finish coordinates and spatial discretisation
     """
-    
-    print('#geometry_view: {:g} {:g} {:g} {:g} {:g} {:g} {:g} {:g} {:g} {} {}'.format(xs, ys, zs, xf, yf, zf, dx, dy, dz, filename, type))
+    s = Coordinate(xs, ys, zs)
+    f = Coordinate(xf, yf, zf)
+    d = Coordinate(dx, dy, dz)
+    command('geometry_view', *s, *f, *d, filename, type)
+
+    return s, f, d
 
 
 def snapshot(xs, ys, zs, xf, yf, zf, dx, dy, dz, time, filename):
@@ -151,15 +157,20 @@ def snapshot(xs, ys, zs, xf, yf, zf, dx, dy, dz, time, filename):
         dx, dy, dz (float): Spatial discretisation of geometry view.
         time (float): Time in seconds (float) or the iteration number (integer) which denote the point in time at which the snapshot will be taken.
         filename (str): Filename where geometry file information will be stored.
+    Returns:
+        s, f, d (tuple): 3 namedtuple Coordinate for the start, finish coordinates and spatial discretisation
     """
+    s = Coordinate(xs, ys, zs)
+    f = Coordinate(xf, yf, zf)
+    d = Coordinate(dx, dy, dz)
     
     if  '.' in str(time) or 'e' in str(time):
-        time = float(time)
-        print('#snapshot: {:g} {:g} {:g} {:g} {:g} {:g} {:g} {:g} {:g} {:g} {}'.format(xs, ys, zs, xf, yf, zf, dx, dy, dz, time, filename))
+        time = '{:g}'.format(float(time))
     else:
-        time = int(time)
-        print('#snapshot: {:g} {:g} {:g} {:g} {:g} {:g} {:g} {:g} {:g} {:d} {}'.format(xs, ys, zs, xf, yf, zf, dx, dy, dz, time, filename))
+        time = '{:d}'.format(int(time))
 
+    command('snapshot', *s, *f, *d, time, filename)
+    return s, f, d
           
 def edge(xs, ys, zs, xf, yf, zf, material):
     """Prints the gprMax #edge command.
@@ -167,10 +178,15 @@ def edge(xs, ys, zs, xf, yf, zf, material):
     Args:
         xs, ys, zs, xf, yf, zf (float): Start and finish coordinates.
         material (str): Material identifier.
+    Returns:
+        s, f (tuple): 2 namedtuple Coordinate for the start and finish coordinates
     """
-    
-    print('#edge: {:g} {:g} {:g} {:g} {:g} {:g} {}'.format(xs, ys, zs, xf, yf, zf, material))
-          
+    s = Coordinate(xs, ys, zs)
+    f = Coordinate(xf, yf, zf)
+
+    command('edge', *s, *f, material)
+    return s, f
+
           
 def plate(xs, ys, zs, xf, yf, zf, material):
     """Prints the gprMax #plate command.
@@ -178,10 +194,14 @@ def plate(xs, ys, zs, xf, yf, zf, material):
     Args:
         xs, ys, zs, xf, yf, zf (float): Start and finish coordinates.
         material (str): Material identifier(s).
+    Returns:
+        s, f (tuple): 2 namedtuple Coordinate for the start and finish coordinates
     """
-    
-    print('#plate: {:g} {:g} {:g} {:g} {:g} {:g} {}'.format(xs, ys, zs, xf, yf, zf, material))
-          
+    s = Coordinate(xs, ys, zs)
+    f = Coordinate(xf, yf, zf)
+
+    command('plate', *s, *f, material)
+    return s, f
           
 def triangle(x1, y1, z1, x2, y2, z2, x3, y3, z3, thickness, material):
     """Prints the gprMax #triangle command.
@@ -190,10 +210,16 @@ def triangle(x1, y1, z1, x2, y2, z2, x3, y3, z3, thickness, material):
         x1, y1, z1, x2, y2, z2, x3, y3, z3 (float): Coordinates of the vertices.
         thickness (float): Thickness for a triangular prism, or zero for a triangular patch.
         material (str): Material identifier(s).
+    Returns:
+        v1, v2, v3 (tuple): 3 namedtuple Coordinate for the vertices
     """
-    
-    print('#triangle: {:g} {:g} {:g} {:g} {:g} {:g} {:g} {:g} {:g} {:g} {}'.format(x1, y1, z1, x2, y2, z2, x3, y3, z3, thickness, material))
-          
+    v1 = Coordinate(x1, y1, z1)
+    v2 = Coordinate(x2, y2, z2)
+    v3 = Coordinate(x3, y3, z3)
+
+    command('triangle', *v1, *v2, *v3, thickness, material)
+    return v1, v2, v3
+
           
 def box(xs, ys, zs, xf, yf, zf, material, averaging=''):
     """Prints the gprMax #box command.
@@ -205,14 +231,14 @@ def box(xs, ys, zs, xf, yf, zf, material, averaging=''):
 
     Returns:
         s, f (tuple): 2 namedtuple Coordinate for the start and finish coordinates
-
     """
     
     s = Coordinate(xs, ys, zs)
     f = Coordinate(xf, yf, zf)
-    print('#box: {} {} {} {}'.format(s, f, material, averaging))
+    
+    command('box', *s, *f, material, averaging)
     return s, f
-          
+
           
 def sphere(x, y, z, radius, material, averaging=''):
     """Prints the gprMax #sphere command.
@@ -222,11 +248,16 @@ def sphere(x, y, z, radius, material, averaging=''):
         radius (float): Radius.
         material (str): Material identifier(s).
         averaging (str): Turn averaging on or off.
+
+    Returns:
+        c (tuple): namedtuple Coordinate for the center of the sphere
     """
+    c = Coordinate(x, y, z)
     
-    print('#sphere: {:g} {:g} {:g} {:g} {} {}'.format(x, y, z, radius, material, averaging))
-          
-          
+    command('sphere', *c, radius, material, averaging)
+    return c
+
+
 def cylinder(x1, y1, z1, x2, y2, z2, radius, material, averaging=''):
     """Prints the gprMax #cylinder command.
         
@@ -235,10 +266,16 @@ def cylinder(x1, y1, z1, x2, y2, z2, radius, material, averaging=''):
         radius (float): Radius.
         material (str): Material identifier(s).
         averaging (str): Turn averaging on or off.
-    """
     
-    print('#cylinder: {:g} {:g} {:g} {:g} {:g} {:g} {:g} {} {}'.format(x1, y1, z1, x2, y2, z2, radius, material, averaging))
-          
+    Returns:
+        c1, c2 (tuple): 2 namedtuple Coordinate for the centres of the two faces of the cylinder.
+    """
+    c1 = Coordinate(x1, y1, z1)
+    c2 = Coordinate(x2, y2, z2)
+
+    command('cylinder', *c1, *c2, radius, material, averaging)
+    return c1, c2
+
           
 def cylindrical_sector(axis, ctr1, ctr2, t1, t2, radius, startingangle, sweptangle, material, averaging=''):
     """Prints the gprMax #cylindrical_sector command.
@@ -254,8 +291,8 @@ def cylindrical_sector(axis, ctr1, ctr2, t1, t2, radius, startingangle, sweptang
         averaging (str): Turn averaging on or off.
     """
     
-    print('#cylindrical_sector: {} {:g} {:g} {:g} {:g} {:g} {:g} {:g} {} {}'.format(axis, ctr1, ctr2, t1, t2, radius, startingangle, sweptangle, material, averaging))
-          
+    command('cylindrical_sector', axis, ctr1, ctr2, t1, t2, radius, startingangle, sweptangle, material, averaging)
+    
           
 def excitation_file(file1):
     """Prints the #excitation_file: <file1> command.

From 6a9ccc59bbc79c78ade47d1aac3aa680c3881867 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=C3=98ystein=20Bj=C3=B8rndal?= <obtitus@gmail.com>
Date: Fri, 20 May 2016 22:19:19 +0200
Subject: [PATCH 15/38] just some newline, trying to be consistent...

---
 gprMax/input_cmd_funcs.py | 99 +++++++++++++++++++++------------------
 1 file changed, 54 insertions(+), 45 deletions(-)

diff --git a/gprMax/input_cmd_funcs.py b/gprMax/input_cmd_funcs.py
index 45247a09..7010bc93 100644
--- a/gprMax/input_cmd_funcs.py
+++ b/gprMax/input_cmd_funcs.py
@@ -32,6 +32,7 @@ Coordinate(x=0.1, y=0.2, z=0.3)
 0.1 0.2 0.3
 """
 import sys
+
 from collections import namedtuple
 Coordinate_tuple = namedtuple('Coordinate', ['x', 'y', 'z'])
 
@@ -40,6 +41,7 @@ class Coordinate(Coordinate_tuple):
     def __str__(self):
         return '{:g} {:g} {:g}'.format(self.x, self.y, self.z)
 
+
 def command(cmd, *parameters):
     """Helper function. Prints the gprMax #<cmd>: <parameters>. None is ignored in the output.
 
@@ -71,54 +73,51 @@ def command(cmd, *parameters):
 
 def domain(x, y, z):
     """Prints the gprMax #domain command.
-        
+
     Args:
         x, y, z (float): Extent of the domain in the x, y, and z directions.
-        
+
     Returns:
         domain (Coordinate): Namedtuple of the extent of the domain.
     """
-    
     domain = Coordinate(x, y, z)
     command('domain', domain)
-    
+
     return domain
 
 
 def dx_dy_dz(x, y, z):
     """Prints the gprMax #dx_dy_dz command.
-        
+
     Args:
         x, y, z (float): Spatial resolution in the x, y, and z directions.
-        
+
     Returns:
         dx_dy_dz (float): Tuple of the spatial resolutions.
     """
-    
     dx_dy_dz = Coordinate(x, y, z)
     command('dx_dy_dz', dx_dy_dz)
-          
+
     return dx_dy_dz
-          
-    
+
+
 def time_window(time_window):
     """Prints the gprMax #time_window command.
-        
+
     Args:
         time_window (float): Duration of simulation.
-        
+
     Returns:
         time_window (float): Duration of simulation.
     """
-
     command('time_window', time_window)
-    
+
     return time_window
 
 
 def material(permittivity, conductivity, permeability, magconductivity, name):
     """Prints the gprMax #material command.
-        
+
     Args:
         permittivity (float): Relative permittivity of the material.
         conductivity (float): Conductivity of the material.
@@ -126,13 +125,12 @@ def material(permittivity, conductivity, permeability, magconductivity, name):
         magconductivity (float): Magnetic loss of the material.
         name (str): Material identifier.
     """
-    
     command('material', permittivity, conductivity, permeability, magconductivity, name)
 
 
 def geometry_view(xs, ys, zs, xf, yf, zf, dx, dy, dz, filename, type='n'):
     """Prints the gprMax #geometry_view command.
-        
+
     Args:
         xs, ys, zs, xf, yf, zf (float): Start and finish coordinates.
         dx, dy, dz (float): Spatial discretisation of geometry view.
@@ -151,7 +149,7 @@ def geometry_view(xs, ys, zs, xf, yf, zf, dx, dy, dz, filename, type='n'):
 
 def snapshot(xs, ys, zs, xf, yf, zf, dx, dy, dz, time, filename):
     """Prints the gprMax #snapshot command.
-        
+
     Args:
         xs, ys, zs, xf, yf, zf (float): Start and finish coordinates.
         dx, dy, dz (float): Spatial discretisation of geometry view.
@@ -163,18 +161,20 @@ def snapshot(xs, ys, zs, xf, yf, zf, dx, dy, dz, time, filename):
     s = Coordinate(xs, ys, zs)
     f = Coordinate(xf, yf, zf)
     d = Coordinate(dx, dy, dz)
-    
+
     if  '.' in str(time) or 'e' in str(time):
         time = '{:g}'.format(float(time))
     else:
         time = '{:d}'.format(int(time))
 
     command('snapshot', *s, *f, *d, time, filename)
+
     return s, f, d
-          
+
+
 def edge(xs, ys, zs, xf, yf, zf, material):
     """Prints the gprMax #edge command.
-        
+
     Args:
         xs, ys, zs, xf, yf, zf (float): Start and finish coordinates.
         material (str): Material identifier.
@@ -183,14 +183,14 @@ def edge(xs, ys, zs, xf, yf, zf, material):
     """
     s = Coordinate(xs, ys, zs)
     f = Coordinate(xf, yf, zf)
-
     command('edge', *s, *f, material)
+
     return s, f
 
-          
+
 def plate(xs, ys, zs, xf, yf, zf, material):
     """Prints the gprMax #plate command.
-        
+
     Args:
         xs, ys, zs, xf, yf, zf (float): Start and finish coordinates.
         material (str): Material identifier(s).
@@ -199,13 +199,14 @@ def plate(xs, ys, zs, xf, yf, zf, material):
     """
     s = Coordinate(xs, ys, zs)
     f = Coordinate(xf, yf, zf)
-
     command('plate', *s, *f, material)
+
     return s, f
-          
+
+
 def triangle(x1, y1, z1, x2, y2, z2, x3, y3, z3, thickness, material):
     """Prints the gprMax #triangle command.
-        
+
     Args:
         x1, y1, z1, x2, y2, z2, x3, y3, z3 (float): Coordinates of the vertices.
         thickness (float): Thickness for a triangular prism, or zero for a triangular patch.
@@ -216,14 +217,14 @@ def triangle(x1, y1, z1, x2, y2, z2, x3, y3, z3, thickness, material):
     v1 = Coordinate(x1, y1, z1)
     v2 = Coordinate(x2, y2, z2)
     v3 = Coordinate(x3, y3, z3)
-
     command('triangle', *v1, *v2, *v3, thickness, material)
+
     return v1, v2, v3
 
-          
+
 def box(xs, ys, zs, xf, yf, zf, material, averaging=''):
     """Prints the gprMax #box command.
-        
+
     Args:
         xs, ys, zs, xf, yf, zf (float): Start and finish coordinates.
         material (str): Material identifier(s).
@@ -232,17 +233,16 @@ def box(xs, ys, zs, xf, yf, zf, material, averaging=''):
     Returns:
         s, f (tuple): 2 namedtuple Coordinate for the start and finish coordinates
     """
-    
     s = Coordinate(xs, ys, zs)
     f = Coordinate(xf, yf, zf)
-    
     command('box', *s, *f, material, averaging)
+
     return s, f
 
-          
+
 def sphere(x, y, z, radius, material, averaging=''):
     """Prints the gprMax #sphere command.
-        
+
     Args:
         x, y, z (float): Coordinates of the centre of the sphere.
         radius (float): Radius.
@@ -253,33 +253,33 @@ def sphere(x, y, z, radius, material, averaging=''):
         c (tuple): namedtuple Coordinate for the center of the sphere
     """
     c = Coordinate(x, y, z)
-    
     command('sphere', *c, radius, material, averaging)
+
     return c
 
 
 def cylinder(x1, y1, z1, x2, y2, z2, radius, material, averaging=''):
     """Prints the gprMax #cylinder command.
-        
+
     Args:
         x1, y1, z1, x2, y2, z2 (float): Coordinates of the centres of the two faces of the cylinder.
         radius (float): Radius.
         material (str): Material identifier(s).
         averaging (str): Turn averaging on or off.
-    
+
     Returns:
         c1, c2 (tuple): 2 namedtuple Coordinate for the centres of the two faces of the cylinder.
     """
     c1 = Coordinate(x1, y1, z1)
     c2 = Coordinate(x2, y2, z2)
-
     command('cylinder', *c1, *c2, radius, material, averaging)
+
     return c1, c2
 
-          
+
 def cylindrical_sector(axis, ctr1, ctr2, t1, t2, radius, startingangle, sweptangle, material, averaging=''):
     """Prints the gprMax #cylindrical_sector command.
-        
+
     Args:
         axis (str): Axis of the cylinder from which the sector is defined and can be x, y, or z.
         ctr1, ctr2 (float): Coordinates of the centre of the cylindrical sector.
@@ -290,10 +290,9 @@ def cylindrical_sector(axis, ctr1, ctr2, t1, t2, radius, startingangle, sweptang
         material (str): Material identifier(s).
         averaging (str): Turn averaging on or off.
     """
-    
     command('cylindrical_sector', axis, ctr1, ctr2, t1, t2, radius, startingangle, sweptangle, material, averaging)
-    
-          
+
+
 def excitation_file(file1):
     """Prints the #excitation_file: <file1> command.
 
@@ -303,8 +302,10 @@ def excitation_file(file1):
         file1 (str): filename
     """
     command('excitation_file', file1)
+
     return file1
 
+
 def waveform(shape, amplitude, frequency, identifier):
     """Prints the #waveform: shape amplitude frequency identifier
 
@@ -317,8 +318,10 @@ def waveform(shape, amplitude, frequency, identifier):
         identifier (str): is an identifier for the waveform used to assign it to a source.
     """
     command('waveform', shape, amplitude, frequency, identifier)
+
     return identifier
 
+
 def hertzian_dipole(polarization, f1, f2, f3, identifier, t0=None, t_remove=None):
     """Prints the #hertzian_dipole: polarization, f1, f2, f3, identifier, [t0, t_remove]
 
@@ -331,12 +334,13 @@ def hertzian_dipole(polarization, f1, f2, f3, identifier, t0=None, t_remove=None
     Returns:
         coordinates (tuple): namedtuple Coordinate of the source location
     """
-
     c = Coordinate(f1, f2, f3)
     # since command ignores None, this is safe:
     command('hertzian_dipole', polarization, str(c), identifier, t0, t_remove)
+
     return c
 
+
 def rx(x, y, z, identifier=None, to_save=None):
     """Prints the #rx: x, y, z, [identifier, to_save] command.
 
@@ -347,11 +351,12 @@ def rx(x, y, z, identifier=None, to_save=None):
     Returns:
         coordinates (tuple): namedtuple Coordinate of the receiver location
     """
-
     c = Coordinate(x, y, z)
     command('rx', str(c), identifier, to_save)
+
     return c
 
+
 def src_steps(dx=0, dy=0, dz=0):
     """Prints the #src_steps: dx, dy, dz command.
 
@@ -363,8 +368,10 @@ def src_steps(dx=0, dy=0, dz=0):
 
     c = Coordinate(dx, dy, dz)
     command('src_steps', str(c))
+
     return c
 
+
 def rx_steps(dx=0, dy=0, dz=0):
     """Prints the #rx_steps: dx, dy, dz command.
 
@@ -377,3 +384,5 @@ def rx_steps(dx=0, dy=0, dz=0):
     c = Coordinate(dx, dy, dz)
     command('rx_steps', str(c))
     return c
+
+

From a8408a93a34337eea5ed7603ac4984d83f356f3a Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Tue, 24 May 2016 16:05:02 +0100
Subject: [PATCH 16/38] No static linking on Windows as there is no static
 version of the OpenMP library.

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index a318eb0c..09a9a82b 100644
--- a/setup.py
+++ b/setup.py
@@ -105,7 +105,7 @@ if 'cleanall' in sys.argv:
 # Set compiler options
 # Windows
 if sys.platform == 'win32':
-    compile_args = ['/O2', '/openmp', '/w', '/MT'] # Remove /MT flag for dynamic linking
+    compile_args = ['/O2', '/openmp', '/w'] # No static linking as no static version of OpenMP library.
     linker_args = []
     extra_objects = []
 # Mac OS X - needs gcc (usually via HomeBrew) because the default compiler LLVM (clang) does not support OpenMP

From e793af12b8cfb10b70f97c45f4263a0267d4546a Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Wed, 25 May 2016 09:38:00 +0100
Subject: [PATCH 17/38] Updated name of argument for receiver component.

---
 tools/plot_Bscan.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/plot_Bscan.py b/tools/plot_Bscan.py
index 5772d227..514130bb 100644
--- a/tools/plot_Bscan.py
+++ b/tools/plot_Bscan.py
@@ -101,7 +101,7 @@ if __name__ == "__main__":
     # Parse command line arguments
     parser = argparse.ArgumentParser(description='Plots a B-scan image.', usage='cd gprMax; python -m tools.plot_Bscan outputfile output')
     parser.add_argument('outputfile', help='name of output file including path')
-    parser.add_argument('rx_component', help='name of output component to be plotted', choices=['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz', 'Ix', 'Iy', 'Iz'])
+    parser.add_argument('rx-component', help='name of output component to be plotted', choices=['Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz', 'Ix', 'Iy', 'Iz'])
     args = parser.parse_args()
 
     # Open output file and read number of outputs (receivers)

From bdae110c0a6cdb05354c9e6adf2f5e867e0d65cd Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Wed, 25 May 2016 09:38:49 +0100
Subject: [PATCH 18/38] Updated info of plotting tools to match rationalised
 plotting functions.

---
 docs/source/plotting.rst | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/docs/source/plotting.rst b/docs/source/plotting.rst
index afb86940..1de29990 100644
--- a/docs/source/plotting.rst
+++ b/docs/source/plotting.rst
@@ -40,12 +40,12 @@ gprMax produces a separate output file for each trace (A-scan) in the B-scan. Th
 
 .. code-block:: none
 
-    python -m tools.plot_Bscan outputfile output
+    python -m tools.plot_Bscan outputfile rx-component
 
 where:
 
 * ``outputfile`` is the name of output file including the path
-* ``output`` is the name of output component (``Ex``, ``Ey``, ``Ez``, ``Hx``, ``Hy``, ``Hz``, ``Ix``, ``Iy`` or ``Iz``) to plot
+* ``rx-component`` is the name of the receiver output component (``Ex``, ``Ey``, ``Ez``, ``Hx``, ``Hy``, ``Hz``, ``Ix``, ``Iy`` or ``Iz``) to plot
 
 
 Antenna parameters
@@ -58,12 +58,17 @@ This module uses matplotlib to plot the input impedance (resistance and reactanc
 
 .. code-block:: none
 
-    python -m tools.plot_antenna_params outputfile --tln transmissionlinenumber
+    python -m tools.plot_antenna_params outputfile
 
-where:
+where ``outputfile`` is the name of output file including the path.
 
-* ``outputfile`` is the name of output file including the path
-* ``--tln`` is the number of the transmission line (default is one). Transmission lines are numbered (starting at one) in the order they appear in the input file.
+There are optional command line arguments:
+
+* ``--tl-num`` is the number of the transmission line (default is one). Transmission lines are numbered (starting at one) in the order they appear in the input file.
+* ``--rx-num`` is the number of the receiver output (default is None) required if calculating a s21 parameter. Receivers are numbered (starting at one) in the order they appear in the input file.
+* ``--rx-component`` is the electric field component of the receiver output required if calculating a s21 parameter (``Ex``, ``Ey`` or ``Ez``).
+
+receiver antenna - output number
 
 
 .. _waveforms:

From 50d60dc5f9c16399913d08a57378545147906174 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Wed, 25 May 2016 10:19:51 +0100
Subject: [PATCH 19/38] Removed extra text block.

---
 docs/source/plotting.rst | 2 --
 1 file changed, 2 deletions(-)

diff --git a/docs/source/plotting.rst b/docs/source/plotting.rst
index 1de29990..58238309 100644
--- a/docs/source/plotting.rst
+++ b/docs/source/plotting.rst
@@ -68,8 +68,6 @@ There are optional command line arguments:
 * ``--rx-num`` is the number of the receiver output (default is None) required if calculating a s21 parameter. Receivers are numbered (starting at one) in the order they appear in the input file.
 * ``--rx-component`` is the electric field component of the receiver output required if calculating a s21 parameter (``Ex``, ``Ey`` or ``Ez``).
 
-receiver antenna - output number
-
 
 .. _waveforms:
 

From 4cf2fb86acc43d2117a3207032f677b2c3d0a0b5 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Wed, 25 May 2016 10:23:42 +0100
Subject: [PATCH 20/38] Updated info on s21 parameter plotting.

---
 docs/source/plotting.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/plotting.rst b/docs/source/plotting.rst
index 58238309..b8ab0b53 100644
--- a/docs/source/plotting.rst
+++ b/docs/source/plotting.rst
@@ -54,7 +54,7 @@ Antenna parameters
 plot_antenna_params.py
 ----------------------
 
-This module uses matplotlib to plot the input impedance (resistance and reactance) and s11 parameter from an antenna model fed using a transmission line. It also plots the time history of the incident and reflected voltages in the transmission line and their frequency spectra. Usage (from the top-level gprMax directory) is:
+This module uses matplotlib to plot the input impedance (resistance and reactance) and s11 parameter from an antenna model fed using a transmission line. It also plots the time history of the incident and reflected voltages in the transmission line and their frequency spectra. The module can optionally plot the s21 parameter if a receiver (``#rx``) is placed on the receiving antenna. Usage (from the top-level gprMax directory) is:
 
 .. code-block:: none
 

From f89f8e1d8f4bc178e87f8899dc97df99a4a3b954 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Wed, 25 May 2016 11:52:18 +0100
Subject: [PATCH 21/38] Alphabetical order.

---
 conda_env.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/conda_env.yml b/conda_env.yml
index 7e486585..97206466 100644
--- a/conda_env.yml
+++ b/conda_env.yml
@@ -7,9 +7,9 @@ dependencies:
 - cython
 - h5py
 - jupyter
+- lxml
 - matplotlib
 - mpi4py
 - numpy
 - psutil
-- scipy
-- lxml
\ No newline at end of file
+- scipy
\ No newline at end of file

From 570640fb496bc36c7e8c1250923bdf30218bba40 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Thu, 26 May 2016 10:48:53 +0100
Subject: [PATCH 22/38] Revised antenna parameter plotting script to handle s21
 calculation and plotting when either a transmission line or receiver is used
 on the receiver antenna in the model.

---
 docs/source/plotting.rst     |  9 +++++----
 tools/plot_antenna_params.py | 38 +++++++++++++++++++++---------------
 2 files changed, 27 insertions(+), 20 deletions(-)

diff --git a/docs/source/plotting.rst b/docs/source/plotting.rst
index b8ab0b53..a9e0867e 100644
--- a/docs/source/plotting.rst
+++ b/docs/source/plotting.rst
@@ -54,7 +54,7 @@ Antenna parameters
 plot_antenna_params.py
 ----------------------
 
-This module uses matplotlib to plot the input impedance (resistance and reactance) and s11 parameter from an antenna model fed using a transmission line. It also plots the time history of the incident and reflected voltages in the transmission line and their frequency spectra. The module can optionally plot the s21 parameter if a receiver (``#rx``) is placed on the receiving antenna. Usage (from the top-level gprMax directory) is:
+This module uses matplotlib to plot the input impedance (resistance and reactance) and s11 parameter from an antenna model fed using a transmission line. It also plots the time history of the incident and reflected voltages in the transmission line and their frequency spectra. The module can optionally plot the s21 parameter if another transmission line or a receiver output (``#rx``) is used on the receiver antenna. Usage (from the top-level gprMax directory) is:
 
 .. code-block:: none
 
@@ -64,9 +64,10 @@ where ``outputfile`` is the name of output file including the path.
 
 There are optional command line arguments:
 
-* ``--tl-num`` is the number of the transmission line (default is one). Transmission lines are numbered (starting at one) in the order they appear in the input file.
-* ``--rx-num`` is the number of the receiver output (default is None) required if calculating a s21 parameter. Receivers are numbered (starting at one) in the order they appear in the input file.
-* ``--rx-component`` is the electric field component of the receiver output required if calculating a s21 parameter (``Ex``, ``Ey`` or ``Ez``).
+* ``--tltx-num`` is the number of the transmission line (default is one) for the transmitter antenna. Transmission lines are numbered (starting at one) in the order they appear in the input file.
+* ``--tlrx-num`` is the number of the transmission line (default is None) for the receiver antenna (for a s21 parameter). Transmission lines are numbered (starting at one) in the order they appear in the input file.
+* ``--rx-num`` is the number of the receiver output (default is None) for the receiver antenna (for a s21 parameter). Receivers are numbered (starting at one) in the order they appear in the input file.
+* ``--rx-component`` is the electric field component (``Ex``, ``Ey`` or ``Ez``) of the receiver output for the receiver antenna (for a s21 parameter).
 
 
 .. _waveforms:
diff --git a/tools/plot_antenna_params.py b/tools/plot_antenna_params.py
index a81f1776..d99c710d 100644
--- a/tools/plot_antenna_params.py
+++ b/tools/plot_antenna_params.py
@@ -25,12 +25,13 @@ import matplotlib.gridspec as gridspec
 from gprMax.exceptions import CmdInputError
 
 
-def calculate_antenna_params(filename, tlnumber=1, rxnumber=None, rxcomponent=None):
+def calculate_antenna_params(filename, tltxnumber=1, tlrxnumber=None, rxnumber=None, rxcomponent=None):
     """Calculates antenna parameters - incident, reflected and total volatges and currents; s11, (s21) and input impedance.
             
     Args:
         filename (string): Filename (including path) of output file.
-        tlnumber (int): Transmitting antenna - transmission line number
+        tltxnumber (int): Transmitter antenna - transmission line number
+        tlrxnumber (int): Receiver antenna - transmission line number
         rxnumber (int): Receiver antenna - output number
         rxcomponent (str): Receiver antenna - output electric field component
         
@@ -53,23 +54,27 @@ def calculate_antenna_params(filename, tlnumber=1, rxnumber=None, rxcomponent=No
     print('Time step: {:g} s'.format(dt))
     print('Frequency bin spacing: {:g} Hz'.format(df))
 
-    # Read/calculate voltages and currents
-    tlpath = '/tls/tl' + str(tlnumber) + '/'
+    # Read/calculate voltages and currents from transmitter antenna
+    tltxpath = '/tls/tl' + str(tltxnumber) + '/'
 
     # Incident voltages/currents
-    Vinc = f[tlpath + 'Vinc'][:]
-    Iinc = f[tlpath + 'Iinc'][:]
+    Vinc = f[tltxpath + 'Vinc'][:]
+    Iinc = f[tltxpath + 'Iinc'][:]
 
     # Total (incident + reflected) voltages/currents
-    Vtotal = f[tlpath +'Vtotal'][:]
-    Itotal = f[tlpath +'Itotal'][:]
+    Vtotal = f[tltxpath +'Vtotal'][:]
+    Itotal = f[tltxpath +'Itotal'][:]
 
     # Reflected voltages/currents
     Vref = Vtotal - Vinc
     Iref = Itotal - Iinc
 
-    # If a receiver number for a receiever antenna is given can get received voltage for s21
-    if rxnumber:       
+    # If a receiver antenna is used (with a transmission line or receiver), get received voltage for s21
+    if tlrxnumber:
+        tlrxpath = '/tls/tl' + str(tlrxnumber) + '/'
+        Vrec = f[tlrxpath +'Vtotal'][:]
+
+    elif rxnumber:
         rxpath = '/rxs/rx' + str(rxnumber) + '/'
         availableoutputs = list(f[rxpath].keys())
         
@@ -95,7 +100,7 @@ def calculate_antenna_params(filename, tlnumber=1, rxnumber=None, rxcomponent=No
 
     # Calculate s11 and (optionally) s21
     s11 = np.abs(np.fft.fft(Vref) * delaycorrection) / np.abs(np.fft.fft(Vinc) * delaycorrection)
-    if rxnumber:
+    if tlrxnumber or rxnumber:
         s21 = np.abs(np.fft.fft(Vrec)) / np.abs(np.fft.fft(Vinc) * delaycorrection)
 
     # Calculate input impedance
@@ -118,7 +123,7 @@ def calculate_antenna_params(filename, tlnumber=1, rxnumber=None, rxcomponent=No
                     'Vref': Vref, 'Vrefp': Vrefp, 'Iref': Iref, 'Irefp': Irefp,
                     'Vtotal': Vtotal, 'Vtotalp': Vtotalp, 'Itotal': Itotal, 'Itotalp': Itotalp,
                     's11': s11, 'zin': zin, 'yin': yin}
-    if rxnumber:
+    if tlrxnumber or rxnumber:
         s21 = 20 * np.log10(s21)
         antennaparams['s21'] = s21
 
@@ -159,7 +164,7 @@ def mpl_plot(time, freqs, Vinc, Vincp, Iinc, Iincp, Vref, Vrefp, Iref, Irefp, Vt
 
     # Figure 1
     # Plot incident voltage
-    fig1, ax = plt.subplots(num='Transmission line parameters', figsize=(20, 12), facecolor='w', edgecolor='w')
+    fig1, ax = plt.subplots(num='Transmitter transmission line parameters', figsize=(20, 12), facecolor='w', edgecolor='w')
     gs1 = gridspec.GridSpec(4, 2, hspace=0.7)
     ax = plt.subplot(gs1[0, 0])
     ax.plot(time, Vinc, 'r', lw=2, label='Vinc')
@@ -304,7 +309,7 @@ def mpl_plot(time, freqs, Vinc, Vincp, Iinc, Iincp, Vref, Vrefp, Iref, Irefp, Vt
     ax.grid()
 
     # Plot frequency spectra of s21
-    if s21:
+    if 's21' in locals() or globals():
         ax = plt.subplot(gs2[0, 1])
         markerline, stemlines, baseline = ax.stem(freqs[pltrange], s21[pltrange], '-.')
         plt.setp(baseline, 'linewidth', 0)
@@ -389,12 +394,13 @@ if __name__ == "__main__":
     # Parse command line arguments
     parser = argparse.ArgumentParser(description='Plots antenna parameters (s11, s21 parameters and input impedance) from an output file containing a transmission line source.', usage='cd gprMax; python -m tools.plot_antenna_params outputfile')
     parser.add_argument('outputfile', help='name of output file including path')
-    parser.add_argument('--tl-num', default=1, type=int, help='transmitting antenna - transmission line number')
+    parser.add_argument('--tltx-num', default=1, type=int, help='transmitter antenna - transmission line number')
+    parser.add_argument('--tlrx-num', type=int, help='receiver antenna - transmission line number')
     parser.add_argument('--rx-num', type=int, help='receiver antenna - output number')
     parser.add_argument('--rx-component', type=str, help='receiver antenna - output electric field component', choices=['Ex', 'Ey', 'Ez'])
     args = parser.parse_args()
 
-    antennaparams = calculate_antenna_params(args.outputfile, args.tl_num, args.rx_num, args.rx_component)
+    antennaparams = calculate_antenna_params(args.outputfile, args.tltx_num, args.tlrx_num, args.rx_num, args.rx_component)
     plt = mpl_plot(**antennaparams)
     plt.show()
 

From 188542f2e68c660f46b16e8cff4cb9c4b5aa339f Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Thu, 26 May 2016 10:55:33 +0100
Subject: [PATCH 23/38] Added an example of using the module to plot antenna
 parameters for s21.

---
 docs/source/plotting.rst | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/docs/source/plotting.rst b/docs/source/plotting.rst
index a9e0867e..45ff51a5 100644
--- a/docs/source/plotting.rst
+++ b/docs/source/plotting.rst
@@ -69,6 +69,12 @@ There are optional command line arguments:
 * ``--rx-num`` is the number of the receiver output (default is None) for the receiver antenna (for a s21 parameter). Receivers are numbered (starting at one) in the order they appear in the input file.
 * ``--rx-component`` is the electric field component (``Ex``, ``Ey`` or ``Ez``) of the receiver output for the receiver antenna (for a s21 parameter).
 
+For example to plot the input impedance, s11 and s21 parameters from a simulation with transmitter and receiver antennas that are attached to transmission lines (the transmission line feeding the transmitter appears first in the input file, and the transmission line attached to the receiver antenna appears after it).
+
+.. code-block:: none
+
+    python -m tools.plot_antenna_params outputfile --tltx-num 1 --tlrx-num 2
+
 
 .. _waveforms:
 

From 17c6839e3bb89a5632938628e6913a61cc4528ad Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Thu, 26 May 2016 12:16:32 +0100
Subject: [PATCH 24/38] Better handling of when s21 is None.

---
 tools/plot_antenna_params.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/plot_antenna_params.py b/tools/plot_antenna_params.py
index d99c710d..431c66ef 100644
--- a/tools/plot_antenna_params.py
+++ b/tools/plot_antenna_params.py
@@ -309,7 +309,7 @@ def mpl_plot(time, freqs, Vinc, Vincp, Iinc, Iincp, Vref, Vrefp, Iref, Irefp, Vt
     ax.grid()
 
     # Plot frequency spectra of s21
-    if 's21' in locals() or globals():
+    if s21 is not None:
         ax = plt.subplot(gs2[0, 1])
         markerline, stemlines, baseline = ax.stem(freqs[pltrange], s21[pltrange], '-.')
         plt.setp(baseline, 'linewidth', 0)

From 98556e5dea6ac85ce4ef2940f0b5b72594e7fdb0 Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Thu, 26 May 2016 12:18:05 +0100
Subject: [PATCH 25/38] Updated to reflect revised antenna plotting.

---
 .../plot_antenna_params.ipynb                 | 29 ++++++++++---------
 1 file changed, 15 insertions(+), 14 deletions(-)

diff --git a/tools/Jupyter notebooks/plot_antenna_params.ipynb b/tools/Jupyter notebooks/plot_antenna_params.ipynb
index b40a8f80..ba4ceb8c 100644
--- a/tools/Jupyter notebooks/plot_antenna_params.ipynb	
+++ b/tools/Jupyter notebooks/plot_antenna_params.ipynb	
@@ -8,11 +8,12 @@
     "\n",
     "In the ``tools`` sub-package is a module called ``plot_antenna_params`` which can be used to plot parameters from a model containing an antenna. These include s11 and, optionally, s21 as well as the input impedance. The module will also plot incident, reflected, and total voltages and currents at the antenna terminals. \n",
     "\n",
-    "The antenna in the model must be fed using the ``#transmission_line`` command and can, optionally, include a receiver antenna with a ``#rx`` command (for s21 parameter calculation). The module takes an argument which is the name of the output file and several optional arguments:\n",
+    "The transmitter antenna in the model must be fed using the ``#transmission_line`` command. Optionally, the model can include a receiver antenna which has a ``#rx`` command or attached ``#transmission_line`` command for s21 parameter calculation. The module takes an argument which is the name of the output file and several optional arguments:\n",
     "\n",
-    "* ``--tl-num`` is the transmission line number of the transmitting antenna (default=1)\n",
-    "* ``--rx-num`` is the receiver number for the receiver antenna (default=None)\n",
-    "* ``--rx-component`` is the electric field component to analyse for the receiver antenna (default=None)\n",
+    "* ``--tltx-num`` is the transmission line number of the transmitter antenna (default=1)\n",
+    "* ``--tlrx-num`` is the transmission line number of the receiver antenna (default=None)\n",
+    "* ``--rx-num`` is the number of the receiver output (default=None) for the receiver antenna (for a s21 parameter)\n",
+    "* ``--rx-component`` is the electric field component (``Ex``, ``Ey`` or ``Ez``) of the receiver output for the receiver antenna (for a s21 parameter)\n",
     "\n",
     "For example (to use the module outside this notebook) to plot the antenna parameters from the wire dipole antenna:\n",
     "\n",
@@ -28,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -37,19 +38,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time window: 6.00013e-08 s (31156 iterations)\n",
+      "Time window: 2.00017e-08 s (10386 iterations)\n",
       "Time step: 1.92583e-12 s\n",
-      "Frequency bin spacing: 1.66663e+07 Hz\n",
-      "s11 minimum: -40.1383 dB at 9.4998e+08 Hz\n",
-      "At 9.4998e+08 Hz...\n",
-      "Input impedance: 72.8+1.0j Ohms\n"
+      "Frequency bin spacing: 4.99957e+07 Hz\n",
+      "s11 minimum: -40.5174 dB at 9.49919e+08 Hz\n",
+      "At 9.49919e+08 Hz...\n",
+      "Input impedance: 72.8+0.9j Ohms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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jjfurV6/GJ598ov1+P3DgACS+3LF4n376KebOnQsAiImJ0f5c8zVziYqKutsm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VB2/ZVcd0Ce+muGB548+aiXlYND8ZFQ+tqzRwpSAVKQCpB6W1UPdXVlXKvpNUdVyQeav/76CyEh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6dCj69euHQYMGlTttLy8vjBkzBkuXLi13nKUdm5SUhPT0dDx8+LBYY0lX/urXr48ffvgBQUFB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\nEf4P/qzAzVQjRgIAAAAASUVORK5CYII=\n",
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a0Ss8BFN10vbqkrZXl4zecb45c+bQuHFj/vOf/6DX601T/2JjY9m/f7/F+Sq3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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c2a29e8>"
+       "<matplotlib.figure.Figure at 0x111f58668>"
       ]
      },
      "metadata": {},
@@ -57,9 +58,9 @@
     },
     {
      "data": {
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QmZap8lvKNeEHExT33enn+n7Xh5y95PV59fra1/XJ7k9OLn/jVf7r+S3OggQA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\nsI5WzXi68sorddlll+mPf/y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1Zs0aNWvWTD179tTChQvV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xvL0KFD7dusVitpaWkMHDiQ9PR0Tp8+bb9t3rx59O/fn8GDB7N27Vr79uLiYoYOHcqAAQOYPXu2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\nXDnn7DlcaLrAzXE3txr7zzP/ZMbrM7hl7S0UVxW3e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48ERRYbPZ8NLvX0LD2g3x0baP8PaPb0NzaD7X97XM33vmrJtTo/c4K5wYumAo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\nfwz8wkShnAh9AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111536588>"
+       "<matplotlib.figure.Figure at 0x1118ee080>"
       ]
      },
      "metadata": {},

From 438669959bf8916e72b003d9f8078592e7a1407f Mon Sep 17 00:00:00 2001
From: Craig Warren <craig_warren@me.com>
Date: Fri, 27 May 2016 15:15:24 +0100
Subject: [PATCH 26/38] Added filename argument to assist with saving figures
 from module.

---
 tools/Jupyter notebooks/plot_antenna_params.ipynb | 2 +-
 tools/plot_Ascan.py                               | 4 ++--
 tools/plot_antenna_params.py                      | 5 +++--
 3 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/tools/Jupyter notebooks/plot_antenna_params.ipynb b/tools/Jupyter notebooks/plot_antenna_params.ipynb
index ba4ceb8c..c6b62785 100644
--- a/tools/Jupyter notebooks/plot_antenna_params.ipynb	
+++ b/tools/Jupyter notebooks/plot_antenna_params.ipynb	
@@ -74,7 +74,7 @@
     "\n",
     "filename = os.path.join(os.pardir, os.pardir, 'user_models', 'antenna_wire_dipole_fs.out')\n",
     "antennaparams = calculate_antenna_params(filename)\n",
-    "plt = mpl_plot(**antennaparams)"
+    "plt = mpl_plot(filename, **antennaparams)"
    ]
   }
  ],
diff --git a/tools/plot_Ascan.py b/tools/plot_Ascan.py
index 6ab393ea..9324c839 100644
--- a/tools/plot_Ascan.py
+++ b/tools/plot_Ascan.py
@@ -210,8 +210,8 @@ def mpl_plot(filename, outputs=Rx.availableoutputs, fft=False):
                 ax.grid()
 
         # Save a PDF/PNG of the figure
-        #fig.savefig(os.path.splitext(os.path.abspath(file))[0] + '_rx' + str(rx) + '.pdf', dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1)
-        #fig.savefig(os.path.splitext(os.path.abspath(file))[0] + '_rx' + str(rx) + '.png', dpi=150, format='png', bbox_inches='tight', pad_inches=0.1)
+        #fig.savefig(os.path.splitext(os.path.abspath(filename))[0] + '_rx' + str(rx) + '.pdf', dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1)
+        #fig.savefig(os.path.splitext(os.path.abspath(filename))[0] + '_rx' + str(rx) + '.png', dpi=150, format='png', bbox_inches='tight', pad_inches=0.1)
 
     return plt
 
diff --git a/tools/plot_antenna_params.py b/tools/plot_antenna_params.py
index 431c66ef..3868f80f 100644
--- a/tools/plot_antenna_params.py
+++ b/tools/plot_antenna_params.py
@@ -130,10 +130,11 @@ def calculate_antenna_params(filename, tltxnumber=1, tlrxnumber=None, rxnumber=N
     return antennaparams
 
 
-def mpl_plot(time, freqs, Vinc, Vincp, Iinc, Iincp, Vref, Vrefp, Iref, Irefp, Vtotal, Vtotalp, Itotal, Itotalp, s11, zin, yin, s21=None):
+def mpl_plot(filename, time, freqs, Vinc, Vincp, Iinc, Iincp, Vref, Vrefp, Iref, Irefp, Vtotal, Vtotalp, Itotal, Itotalp, s11, zin, yin, s21=None):
     """Plots antenna parameters - incident, reflected and total volatges and currents; s11, (s21) and input impedance.
             
     Args:
+        filename (string): Filename (including path) of output file.
         time (array): Simulation time.
         freq (array): Frequencies for FFTs.
         Vinc, Vincp, Iinc, Iincp (array): Time and frequency domain representations of incident voltage and current.
@@ -401,6 +402,6 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     antennaparams = calculate_antenna_params(args.outputfile, args.tltx_num, args.tlrx_num, args.rx_num, args.rx_component)
-    plt = mpl_plot(**antennaparams)
+    plt = mpl_plot(args.outputfile, **antennaparams)
     plt.show()
 

From 1e2dbff92b0e2fcfd52738089100c95d41ba9b3b Mon Sep 17 00:00:00 2001
From: craig-warren <craig_warren@me.com>
Date: Mon, 30 May 2016 14:24:23 +0100
Subject: [PATCH 27/38] Revised installation procedure. Now the same for user
 and devs. No more hosting pre-built binary extensions, users build
 themselves.

---
 README.rst | 86 ++++++++++++++++++++++++++++++++++++++----------------
 1 file changed, 61 insertions(+), 25 deletions(-)

diff --git a/README.rst b/README.rst
index 47e807c1..c797b2ee 100644
--- a/README.rst
+++ b/README.rst
@@ -55,46 +55,82 @@ Package overview
 Installation
 ============
 
-You should use the following guidance to install gprMax if you are an end-user (i.e. you don't intend to develop or contribute to the software). Developers (or those intending to use gprMax in a HPC environment) should follow the Installation for developers section (http://docs.gprmax.com/en/latest/readme_install_devs.html#installation-for-developers).
+The following steps provide guidance on how to install gprMax:
 
-The steps are:
+1. Install Python and required Python packages, and get the gprMax source code from GitHub
+2. Install a C compiler which supports OpenMP
+3. Build and install gprMax
 
-1. Get the code
-2. Install Python and required Python packages
-3. (*Microsoft Windows only*) Install C libraries
-
-1. Get the code
----------------
-
-* Download the code from https://github.com/gprMax/gprMax
-
-    * Click on **Releases** from the top header and choose the release you want (latest is at the top).
-    * Download zip files of the **source code** and **binary extensions** for your platform (``windows-32bit`` for 32-bit or ``windows-64bit`` for 64-bit versions of Microsoft Windows, ``linux-64bit`` for 64-bit versions of Linux, or ``macosx-64bit`` for 64-bit versions of Mac OS X).
-    * Expand both zip files.
-    * Copy the contents (binary extensions) from the ``windows-32bit``, ``windows-64bit``, ``linux-64bit`` or ``macosx-64bit`` directory into the ``gprMax-v.X.Y.Z/gprMax`` directory.
-
-2. Install Python and required Python packages
+1. Install Python and required Python packages
 ----------------------------------------------
 
 We recommend using Miniconda to install Python and the required Python packages for gprMax in a self-contained Python environment. Miniconda is a mini version of Anaconda which is a completely free Python distribution (including for commercial use and redistribution). It includes more than 300 of the most popular Python packages for science, math, engineering, and data analysis.
 
-* Install the Python 3.5 version of Miniconda for your platform from http://conda.pydata.org/miniconda.html (You can get help with installing Miniconda from http://conda.pydata.org/docs/install/quick.html)
-* Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows) and navigate into the top-level gprMax directory.
-* Update conda :code:`conda update conda`
-* Create an environment (using the supplied ``conda_env.yml`` environment file) for gprMax with all the necessary Python packages :code:`conda env create -f conda_env.yml`
-* Activate the new environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows).
+* Install the Python 3.5 version of Miniconda for your platform from http://conda.pydata.org/miniconda.html (help is at http://conda.pydata.org/docs/install/quick.html)
+* Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows) and run the following commands:
+    * :code:`conda update conda` (to make sure conda is up-to-date)
+    * :code:`conda install git`
+    * :code:`git clone https://github.com/gprMax/gprMax.git` (to get the latest gprMax source code from GitHub)
+    * Go to the top-level gprMax directory and execute :code:`conda env create -f conda_env.yml` (this will create an environment for gprMax with all the necessary Python packages)
+    * Activate the new environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows).
 
 .. note::
     * When you are finished using gprMax the Miniconda environment can be deactivated using :code:`source deactivate` (Linux/Mac OS X)  or :code:`deactivate` (Windows).
     * If you want to install Python and the required Python packages manually, i.e. without using Anaconda/Miniconda, look in the ``conda_env.yml`` file for a list of the requirements.
 
-3. (*Microsoft Windows only*) Install C libraries
--------------------------------------------------
+2. Install a C compiler which supports OpenMP
+---------------------------------------------
 
-* Install the Microsoft Visual Studio 2015 C++ Redistributable (``vc_redist.x86.exe`` for 32-bit or ``vc_redist.x64.exe`` for 64-bit) from https://www.microsoft.com/en-us/download/details.aspx?id=48145.
+Linux
+^^^^^
+
+* gcc (https://gcc.gnu.org) should be already installed, so no action is required.
+
+
+Mac OS X
+^^^^^^^^
+
+* gcc (https://gcc.gnu.org) is easily installed using the Homebrew package manager (http://brew.sh) :code:`brew install gcc --without-multilib`.
+
+.. note::
+
+    Installations of Xcode on Mac OS X come with the LLVM (clang) compiler, but it does not currently support OpenMP, so you must install gcc.
+
+
+Microsoft Windows
+^^^^^^^^^^^^^^^^^
+
+* Download and install Microsoft Visual C++ Build Tools 2015 directly from http://go.microsoft.com/fwlink/?LinkId=691126 or by going to https://www.visualstudio.com/en-us/downloads/download-visual-studio-vs.aspx and choosing Visual Studio Downloads -> Tools for Visual Studio 2015 -> Microsoft Visual C++ Build Tools 2015. Install using the default options.
+
+3. Build and install gprMax
+---------------------------
+
+Once you have installed the aforementioned tools follow these steps to build and install gprMax:
+
+* Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and run the following commands:
+    * If it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows)
+    * :code:`python setup.py build`
+    * :code:`python setup.py install`
+
+.. note::
+
+    * You should see a set of :code:`.c` source files and a set of :code:`.so` (Linux/Mac OS X) or :code:`.pyd` (Windows) compiled module files inside the gprMax directory.
+    * If you want to remove/clean the built modules, e.g. before updating gprMax, you can use :code:`python setup.py cleanall`.
 
 **You are now ready to proceed to running gprMax.**
 
+
+Updating gprMax
+===============
+
+* Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and run the following commands:
+    * If it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows)
+    * :code:`git pull` (this will pull the most recent source code from GitHub)
+    * :code:`python setup.py cleanall`
+    * :code:`python setup.py build`
+    * :code:`python setup.py install`
+
+
 Running gprMax
 ==============
 

From a104bb914d340515d12f19c4aeb29c6dd77c405b Mon Sep 17 00:00:00 2001
From: craig-warren <craig_warren@me.com>
Date: Mon, 30 May 2016 14:25:52 +0100
Subject: [PATCH 28/38] User and dev installation now the same.

---
 docs/source/include_readme.rst      |  1 +
 docs/source/index.rst               |  2 +-
 docs/source/readme_install_devs.rst | 78 -----------------------------
 3 files changed, 2 insertions(+), 79 deletions(-)
 create mode 100644 docs/source/include_readme.rst
 delete mode 100644 docs/source/readme_install_devs.rst

diff --git a/docs/source/include_readme.rst b/docs/source/include_readme.rst
new file mode 100644
index 00000000..38ba8043
--- /dev/null
+++ b/docs/source/include_readme.rst
@@ -0,0 +1 @@
+.. include:: ../../README.rst
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 00a0cc31..43b040c7 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -6,7 +6,7 @@ gprMax User Guide
     :maxdepth: 2
     :caption: Introduction
 
-    readme_install_devs
+    include_readme
     features
     gprmodelling
 
diff --git a/docs/source/readme_install_devs.rst b/docs/source/readme_install_devs.rst
deleted file mode 100644
index 18dab16a..00000000
--- a/docs/source/readme_install_devs.rst
+++ /dev/null
@@ -1,78 +0,0 @@
-.. include:: ../../README.rst
-
-.. _install-dev:
-
-Installation for developers
-===========================
-
-Those that are interested in developing or contributing to gprMax should use the following installation guidance:
-
-1. Get the code
-2. Install Python and required Python packages
-3. Install a C compiler which supports OpenMP
-4. Compile the Cython extensions and install packages
-
-1. Get the code
----------------
-
-* Use **Git** (https://git-scm.com) and clone the master branch of the repository: :code:`git clone https://github.com/gprMax/gprMax.git`
-
-2. Install Python and required Python packages
-----------------------------------------------
-
-We recommend using Miniconda to install Python and the required Python packages for gprMax in a self-contained Python environment. Miniconda is a mini version of Anaconda which is a completely free Python distribution (including for commercial use and redistribution). It includes more than 300 of the most popular Python packages for science, math, engineering, and data analysis.
-
-* Install the Python 3.5 version of Miniconda for your platform from http://conda.pydata.org/miniconda.html (You can get help with installing Miniconda from http://conda.pydata.org/docs/install/quick.html)
-* Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows) and navigate into the top-level gprMax directory.
-* Update conda :code:`conda update conda`
-* Create an environment (using the supplied ``conda_env.yml`` environment file) for gprMax with all the necessary Python packages :code:`conda env create -f conda_env.yml`
-* Activate the new environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows).
-
-.. note::
-    * When you are finished using gprMax the Miniconda environment can be deactivated using :code:`source deactivate` (Linux/Mac OS X)  or :code:`deactivate` (Windows).
-    * If you want to install Python and the required Python packages manually, i.e. without using Anaconda/Miniconda, look in the ``conda_env.yml`` file for a list of the requirements.
-
-
-3. Install a C compiler which supports OpenMP
----------------------------------------------
-
-Linux
-^^^^^
-
-* gcc (https://gcc.gnu.org) should be already installed, so no action is required.
-
-
-Mac OS X
-^^^^^^^^
-
-* gcc (https://gcc.gnu.org) is easily installed using the Homebrew package manager (http://brew.sh) :code:`brew install gcc --without-multilib`.
-
-.. note::
-
-    Installations of Xcode on Mac OS X come with the LLVM (clang) compiler, but it does not currently support OpenMP, so you must install gcc.
-
-
-Microsoft Windows
-^^^^^^^^^^^^^^^^^
-
-* Download and install Microsoft Visual Studio 2015 Community (https://www.visualstudio.com/downloads/download-visual-studio-vs), which is free. Do a custom install and make sure 'Programming languages|Visual C++|Common Tools for Visual C++ 2015' is selected, no other options are required.
-
-.. figure:: images/MSVS2015_install.png
-    :width: 400 px
-
-    Screenshot of Microsoft Visual Studio 2015 Community custom install settings.
-
-
-4. Compile the Cython extensions and install packages
------------------------------------------------------
-
-Once you have installed the aforementioned tools follow these steps to build the Cython extension modules for gprMax:
-
-a) Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows) and navigate into the top-level gprMax directory.
-b) Compile the Cython extension modules and install using: :code:`python setup.py install`. You should see a set of :code:`.c` source files and a set of :code:`.so` (Linux/Mac OS X) or :code:`.pyd` (Windows) compiled module files inside the gprMax directory.
-
-.. note::
-
-    If you want to remove/clean Cython generated files, e.g. before rebuilding the Cython extensions, you can use :code:`python setup.py cleanall`.
-
-**You are now ready to proceed to running gprMax.**
\ No newline at end of file

From 1ff4deaa28ce158f9a02f3027ecf91f7f2eca7b2 Mon Sep 17 00:00:00 2001
From: craig-warren <craig_warren@me.com>
Date: Mon, 30 May 2016 14:37:16 +0100
Subject: [PATCH 29/38] Tidying and simplifying text.

---
 README.rst | 45 +++++++++++++++++++++++----------------------
 1 file changed, 23 insertions(+), 22 deletions(-)

diff --git a/README.rst b/README.rst
index c797b2ee..e7fd293a 100644
--- a/README.rst
+++ b/README.rst
@@ -57,7 +57,7 @@ Installation
 
 The following steps provide guidance on how to install gprMax:
 
-1. Install Python and required Python packages, and get the gprMax source code from GitHub
+1. Install Python, required Python packages, and get the gprMax source code from GitHub
 2. Install a C compiler which supports OpenMP
 3. Build and install gprMax
 
@@ -66,17 +66,17 @@ The following steps provide guidance on how to install gprMax:
 
 We recommend using Miniconda to install Python and the required Python packages for gprMax in a self-contained Python environment. Miniconda is a mini version of Anaconda which is a completely free Python distribution (including for commercial use and redistribution). It includes more than 300 of the most popular Python packages for science, math, engineering, and data analysis.
 
-* Install the Python 3.5 version of Miniconda for your platform from http://conda.pydata.org/miniconda.html (help is at http://conda.pydata.org/docs/install/quick.html)
+* Install Miniconda (Python 3.5 version) from http://conda.pydata.org/miniconda.html (help with Miniconda installation from http://conda.pydata.org/docs/install/quick.html)
 * Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows) and run the following commands:
-    * :code:`conda update conda` (to make sure conda is up-to-date)
-    * :code:`conda install git`
-    * :code:`git clone https://github.com/gprMax/gprMax.git` (to get the latest gprMax source code from GitHub)
-    * Go to the top-level gprMax directory and execute :code:`conda env create -f conda_env.yml` (this will create an environment for gprMax with all the necessary Python packages)
-    * Activate the new environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows).
+
+    * :code:`conda update conda` to make sure conda is up-to-date
+    * :code:`conda install git` so we can use Git to connect to GitHub
+    * :code:`git clone https://github.com/gprMax/gprMax.git` to get the latest gprMax source code from GitHub
+    * Go to the top-level gprMax directory and run :code:`conda env create -f conda_env.yml` to create an environment for gprMax with all the necessary Python packages
 
 .. note::
-    * When you are finished using gprMax the Miniconda environment can be deactivated using :code:`source deactivate` (Linux/Mac OS X)  or :code:`deactivate` (Windows).
-    * If you want to install Python and the required Python packages manually, i.e. without using Anaconda/Miniconda, look in the ``conda_env.yml`` file for a list of the requirements.
+
+    * If you prefer to install Python and the required Python packages manually, i.e. without using Anaconda/Miniconda, look in the ``conda_env.yml`` file for a list of the requirements.
 
 2. Install a C compiler which supports OpenMP
 ---------------------------------------------
@@ -90,7 +90,7 @@ Linux
 Mac OS X
 ^^^^^^^^
 
-* gcc (https://gcc.gnu.org) is easily installed using the Homebrew package manager (http://brew.sh) :code:`brew install gcc --without-multilib`.
+* gcc (https://gcc.gnu.org) is easily installed using the Homebrew package manager (http://brew.sh) :code:`brew install gcc-6 --without-multilib`.
 
 .. note::
 
@@ -100,22 +100,21 @@ Mac OS X
 Microsoft Windows
 ^^^^^^^^^^^^^^^^^
 
-* Download and install Microsoft Visual C++ Build Tools 2015 directly from http://go.microsoft.com/fwlink/?LinkId=691126 or by going to https://www.visualstudio.com/en-us/downloads/download-visual-studio-vs.aspx and choosing Visual Studio Downloads -> Tools for Visual Studio 2015 -> Microsoft Visual C++ Build Tools 2015. Install using the default options.
+* Download and install Microsoft Visual C++ Build Tools 2015 directly from http://go.microsoft.com/fwlink/?LinkId=691126 or by going to https://www.visualstudio.com/en-us/downloads/download-visual-studio-vs.aspx and choosing Visual Studio Downloads -> Tools for Visual Studio 2015 -> Microsoft Visual C++ Build Tools 2015. Use the default installation options.
 
 3. Build and install gprMax
 ---------------------------
 
 Once you have installed the aforementioned tools follow these steps to build and install gprMax:
 
-* Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and run the following commands:
-    * If it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows)
+* Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows). Run the following commands:
+
     * :code:`python setup.py build`
     * :code:`python setup.py install`
 
 .. note::
 
-    * You should see a set of :code:`.c` source files and a set of :code:`.so` (Linux/Mac OS X) or :code:`.pyd` (Windows) compiled module files inside the gprMax directory.
-    * If you want to remove/clean the built modules, e.g. before updating gprMax, you can use :code:`python setup.py cleanall`.
+    * When you are finished using gprMax, the conda environment can be deactivated using :code:`source deactivate` (Linux/Mac OS X)  or :code:`deactivate` (Windows).
 
 **You are now ready to proceed to running gprMax.**
 
@@ -123,10 +122,10 @@ Once you have installed the aforementioned tools follow these steps to build and
 Updating gprMax
 ===============
 
-* Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and run the following commands:
-    * If it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows)
-    * :code:`git pull` (this will pull the most recent source code from GitHub)
-    * :code:`python setup.py cleanall`
+* Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows). Run the following commands:
+
+    * :code:`git pull` to pull the most recent source code from GitHub
+    * :code:`python setup.py cleanall` to remove/clean previously built modules
     * :code:`python setup.py build`
     * :code:`python setup.py install`
 
@@ -134,9 +133,11 @@ Updating gprMax
 Running gprMax
 ==============
 
-* Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows) and navigate into the top-level gprMax directory.
-* If it is not already active, activate the gprMax Miniconda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows)
-* gprMax in designed as a Python package, i.e. a namespace which can contain multiple packages and modules, much like a directory. Basic usage is:
+gprMax in designed as a Python package, i.e. a namespace which can contain multiple packages and modules, much like a directory.
+
+Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows)
+
+Basic usage of gprMax is:
 
 .. code-block:: none
 

From 4f95357bfc9486bf883584052c26952f85212e7f Mon Sep 17 00:00:00 2001
From: craig-warren <craig_warren@me.com>
Date: Mon, 30 May 2016 14:47:59 +0100
Subject: [PATCH 30/38] Cleaned up formatting.

---
 README.rst | 44 +++++++++++++++++++++++---------------------
 1 file changed, 23 insertions(+), 21 deletions(-)

diff --git a/README.rst b/README.rst
index e7fd293a..5f702850 100644
--- a/README.rst
+++ b/README.rst
@@ -69,14 +69,17 @@ We recommend using Miniconda to install Python and the required Python packages
 * Install Miniconda (Python 3.5 version) from http://conda.pydata.org/miniconda.html (help with Miniconda installation from http://conda.pydata.org/docs/install/quick.html)
 * Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows) and run the following commands:
 
-    * :code:`conda update conda` to make sure conda is up-to-date
-    * :code:`conda install git` so we can use Git to connect to GitHub
-    * :code:`git clone https://github.com/gprMax/gprMax.git` to get the latest gprMax source code from GitHub
-    * Go to the top-level gprMax directory and run :code:`conda env create -f conda_env.yml` to create an environment for gprMax with all the necessary Python packages
+.. code-block:: none
 
-.. note::
+    conda update conda
+    conda install git
+    git clone https://github.com/gprMax/gprMax.git
+    cd gprMax
+    conda env create -f conda_env.yml
 
-    * If you prefer to install Python and the required Python packages manually, i.e. without using Anaconda/Miniconda, look in the ``conda_env.yml`` file for a list of the requirements.
+This will make sure conda is up-to-date, install Git, get the latest gprMax source code from GitHub, and create an environment for gprMax with all the necessary Python packages.
+
+If you prefer to install Python and the required Python packages manually, i.e. without using Anaconda/Miniconda, look in the ``conda_env.yml`` file for a list of the requirements.
 
 2. Install a C compiler which supports OpenMP
 ---------------------------------------------
@@ -90,12 +93,7 @@ Linux
 Mac OS X
 ^^^^^^^^
 
-* gcc (https://gcc.gnu.org) is easily installed using the Homebrew package manager (http://brew.sh) :code:`brew install gcc-6 --without-multilib`.
-
-.. note::
-
-    Installations of Xcode on Mac OS X come with the LLVM (clang) compiler, but it does not currently support OpenMP, so you must install gcc.
-
+* Installations of Xcode on Mac OS X come with the LLVM (clang) compiler, but it does not currently support OpenMP, so you must install gcc (https://gcc.gnu.org). This is easily done by installing the Homebrew package manager (http://brew.sh) and running :code:`brew install gcc-6 --without-multilib`.
 
 Microsoft Windows
 ^^^^^^^^^^^^^^^^^
@@ -109,12 +107,10 @@ Once you have installed the aforementioned tools follow these steps to build and
 
 * Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows). Run the following commands:
 
-    * :code:`python setup.py build`
-    * :code:`python setup.py install`
+.. code-block:: none
 
-.. note::
-
-    * When you are finished using gprMax, the conda environment can be deactivated using :code:`source deactivate` (Linux/Mac OS X)  or :code:`deactivate` (Windows).
+    python setup.py build
+    python setup.py install
 
 **You are now ready to proceed to running gprMax.**
 
@@ -124,10 +120,14 @@ Updating gprMax
 
 * Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows). Run the following commands:
 
-    * :code:`git pull` to pull the most recent source code from GitHub
-    * :code:`python setup.py cleanall` to remove/clean previously built modules
-    * :code:`python setup.py build`
-    * :code:`python setup.py install`
+.. code-block:: none
+
+    git pull
+    python setup.py cleanall
+    python setup.py build
+    python setup.py install
+
+This will pull the most recentr gprMax source code form GitHub, remove/clean previously built modules, and then build and install the latest version of gprMax.
 
 
 Running gprMax
@@ -157,6 +157,8 @@ When the simulation is complete you can plot the A-scan using:
 
 Your results should like those from the A-scan from a metal cylinder example in introductory/basic 2D models section (http://docs.gprmax.com/en/latest/examples_simple_2D.html#view-the-results).
 
+When you are finished using gprMax, the conda environment can be deactivated using :code:`source deactivate` (Linux/Mac OS X)  or :code:`deactivate` (Windows).
+
 Optional command line arguments
 -------------------------------
 

From 0490a3311abaebec94cb527417c6671d57359eff Mon Sep 17 00:00:00 2001
From: craig-warren <craig_warren@me.com>
Date: Mon, 30 May 2016 15:10:48 +0100
Subject: [PATCH 31/38] Formatting updates.

---
 README.rst | 52 ++++++++++++++++++++++++++++------------------------
 1 file changed, 28 insertions(+), 24 deletions(-)

diff --git a/README.rst b/README.rst
index 5f702850..2ad95b68 100644
--- a/README.rst
+++ b/README.rst
@@ -71,11 +71,11 @@ We recommend using Miniconda to install Python and the required Python packages
 
 .. code-block:: none
 
-    conda update conda
-    conda install git
-    git clone https://github.com/gprMax/gprMax.git
-    cd gprMax
-    conda env create -f conda_env.yml
+    $ conda update conda
+    $ conda install git
+    $ git clone https://github.com/gprMax/gprMax.git
+    $ cd gprMax
+    $ conda env create -f conda_env.yml
 
 This will make sure conda is up-to-date, install Git, get the latest gprMax source code from GitHub, and create an environment for gprMax with all the necessary Python packages.
 
@@ -93,7 +93,11 @@ Linux
 Mac OS X
 ^^^^^^^^
 
-* Installations of Xcode on Mac OS X come with the LLVM (clang) compiler, but it does not currently support OpenMP, so you must install gcc (https://gcc.gnu.org). This is easily done by installing the Homebrew package manager (http://brew.sh) and running :code:`brew install gcc-6 --without-multilib`.
+* Installations of Xcode on Mac OS X come with the LLVM (clang) compiler, but it does not currently support OpenMP, so you must install gcc (https://gcc.gnu.org). This is easily done by installing the Homebrew package manager (http://brew.sh) and running:
+
+.. code-block:: bash
+
+    $ brew install gcc-6 --without-multilib
 
 Microsoft Windows
 ^^^^^^^^^^^^^^^^^
@@ -107,10 +111,10 @@ Once you have installed the aforementioned tools follow these steps to build and
 
 * Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows). Run the following commands:
 
-.. code-block:: none
+.. code-block:: bash
 
-    python setup.py build
-    python setup.py install
+    (gprMax)$ python setup.py build
+    (gprMax)$ python setup.py install
 
 **You are now ready to proceed to running gprMax.**
 
@@ -120,12 +124,12 @@ Updating gprMax
 
 * Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows). Run the following commands:
 
-.. code-block:: none
+.. code-block:: bash
 
-    git pull
-    python setup.py cleanall
-    python setup.py build
-    python setup.py install
+    (gprMax)$ git pull
+    (gprMax)$ python setup.py cleanall
+    (gprMax)$ python setup.py build
+    (gprMax)$ python setup.py install
 
 This will pull the most recentr gprMax source code form GitHub, remove/clean previously built modules, and then build and install the latest version of gprMax.
 
@@ -139,21 +143,21 @@ Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the
 
 Basic usage of gprMax is:
 
-.. code-block:: none
+.. code-block:: bash
 
-    python -m gprMax path_to/name_of_input_file
+    (gprMax)$ python -m gprMax path_to/name_of_input_file
 
 For example to run one of the test models:
 
-.. code-block:: none
+.. code-block:: bash
 
-    python -m gprMax user_models/cylinder_Ascan_2D.in
+    (gprMax)$ python -m gprMax user_models/cylinder_Ascan_2D.in
 
 When the simulation is complete you can plot the A-scan using:
 
-.. code-block:: none
+.. code-block:: bash
 
-    python -m tools.plot_Ascan user_models/cylinder_Ascan_2D.out
+    (gprMax)$ python -m tools.plot_Ascan user_models/cylinder_Ascan_2D.out
 
 Your results should like those from the A-scan from a metal cylinder example in introductory/basic 2D models section (http://docs.gprmax.com/en/latest/examples_simple_2D.html#view-the-results).
 
@@ -175,15 +179,15 @@ There are optional command line arguments for gprMax:
 
 For example, to check the geometry of a model:
 
-.. code-block:: none
+.. code-block:: bash
 
-    python -m gprMax user_models/heterogeneous_soil.in --geometry-only
+    (gprMax)$ python -m gprMax user_models/heterogeneous_soil.in --geometry-only
 
 For example, to run a B-scan with 60 traces:
 
-.. code-block:: none
+.. code-block:: bash
 
-    python -m gprMax user_models/cylinder_Bscan_2D.in -n 60
+    (gprMax)$ python -m gprMax user_models/cylinder_Bscan_2D.in -n 60
 
 
 

From 493e13c340f4e55eaf2893f6d5929ececf4acaf7 Mon Sep 17 00:00:00 2001
From: craig-warren <craig_warren@me.com>
Date: Mon, 30 May 2016 15:14:29 +0100
Subject: [PATCH 32/38] Further formatting tweaks.

---
 README.rst | 41 +++++++++++++++++++----------------------
 1 file changed, 19 insertions(+), 22 deletions(-)

diff --git a/README.rst b/README.rst
index 2ad95b68..c762a509 100644
--- a/README.rst
+++ b/README.rst
@@ -95,7 +95,7 @@ Mac OS X
 
 * Installations of Xcode on Mac OS X come with the LLVM (clang) compiler, but it does not currently support OpenMP, so you must install gcc (https://gcc.gnu.org). This is easily done by installing the Homebrew package manager (http://brew.sh) and running:
 
-.. code-block:: bash
+.. code-block:: none
 
     $ brew install gcc-6 --without-multilib
 
@@ -111,7 +111,7 @@ Once you have installed the aforementioned tools follow these steps to build and
 
 * Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows). Run the following commands:
 
-.. code-block:: bash
+.. code-block:: none
 
     (gprMax)$ python setup.py build
     (gprMax)$ python setup.py install
@@ -119,21 +119,6 @@ Once you have installed the aforementioned tools follow these steps to build and
 **You are now ready to proceed to running gprMax.**
 
 
-Updating gprMax
-===============
-
-* Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows). Run the following commands:
-
-.. code-block:: bash
-
-    (gprMax)$ git pull
-    (gprMax)$ python setup.py cleanall
-    (gprMax)$ python setup.py build
-    (gprMax)$ python setup.py install
-
-This will pull the most recentr gprMax source code form GitHub, remove/clean previously built modules, and then build and install the latest version of gprMax.
-
-
 Running gprMax
 ==============
 
@@ -143,19 +128,19 @@ Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the
 
 Basic usage of gprMax is:
 
-.. code-block:: bash
+.. code-block:: none
 
     (gprMax)$ python -m gprMax path_to/name_of_input_file
 
 For example to run one of the test models:
 
-.. code-block:: bash
+.. code-block:: none
 
     (gprMax)$ python -m gprMax user_models/cylinder_Ascan_2D.in
 
 When the simulation is complete you can plot the A-scan using:
 
-.. code-block:: bash
+.. code-block:: none
 
     (gprMax)$ python -m tools.plot_Ascan user_models/cylinder_Ascan_2D.out
 
@@ -179,16 +164,28 @@ There are optional command line arguments for gprMax:
 
 For example, to check the geometry of a model:
 
-.. code-block:: bash
+.. code-block:: none
 
     (gprMax)$ python -m gprMax user_models/heterogeneous_soil.in --geometry-only
 
 For example, to run a B-scan with 60 traces:
 
-.. code-block:: bash
+.. code-block:: none
 
     (gprMax)$ python -m gprMax user_models/cylinder_Bscan_2D.in -n 60
 
 
+Updating gprMax
+===============
 
+* Open a Terminal (Linux/Mac OS X) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`source activate gprMax` (Linux/Mac OS X) or :code:`activate gprMax` (Windows). Run the following commands:
+
+.. code-block:: none
+
+    (gprMax)$ git pull
+    (gprMax)$ python setup.py cleanall
+    (gprMax)$ python setup.py build
+    (gprMax)$ python setup.py install
+
+This will pull the most recentr gprMax source code form GitHub, remove/clean previously built modules, and then build and install the latest version of gprMax.
 

From 2e990fdad42b4d8a1ab3084eaeec7cf2c8d18815 Mon Sep 17 00:00:00 2001
From: craig-warren <craig_warren@me.com>
Date: Mon, 30 May 2016 15:18:16 +0100
Subject: [PATCH 33/38] More formatting updates.

---
 README.rst | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index c762a509..1867f1d8 100644
--- a/README.rst
+++ b/README.rst
@@ -102,7 +102,9 @@ Mac OS X
 Microsoft Windows
 ^^^^^^^^^^^^^^^^^
 
-* Download and install Microsoft Visual C++ Build Tools 2015 directly from http://go.microsoft.com/fwlink/?LinkId=691126 or by going to https://www.visualstudio.com/en-us/downloads/download-visual-studio-vs.aspx and choosing Visual Studio Downloads -> Tools for Visual Studio 2015 -> Microsoft Visual C++ Build Tools 2015. Use the default installation options.
+* Download and install Microsoft Visual C++ Build Tools 2015 directly from http://go.microsoft.com/fwlink/?LinkId=691126. Use the default installation options.
+
+You can also download Microsoft Visual C++ Build Tools 2015 by going to https://www.visualstudio.com/en-us/downloads/download-visual-studio-vs.aspx and choosing Visual Studio Downloads -> Tools for Visual Studio 2015 -> Microsoft Visual C++ Build Tools 2015.
 
 3. Build and install gprMax
 ---------------------------

From 51099cd919c6dc80ed7b6e2ddc7b3126185b1e76 Mon Sep 17 00:00:00 2001
From: craig-warren <craig_warren@me.com>
Date: Mon, 30 May 2016 15:21:02 +0100
Subject: [PATCH 34/38] Now at v3 beta 30.

---
 gprMax/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gprMax/_version.py b/gprMax/_version.py
index 3dc51822..7f58591b 100644
--- a/gprMax/_version.py
+++ b/gprMax/_version.py
@@ -1,4 +1,4 @@
 # This is where the version number is set and read by setup.py and conf.py (for the docs)
 
-__version__ = '3.0.0b29'
+__version__ = '3.0.0b30'
 

From 2488b418dab9804b6dcc48eae4c4b5f5c45bec2b Mon Sep 17 00:00:00 2001
From: craig-warren <craig_warren@me.com>
Date: Mon, 30 May 2016 15:38:10 +0100
Subject: [PATCH 35/38] Noted installation screencast need updating.

---
 docs/source/screencasts.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/screencasts.rst b/docs/source/screencasts.rst
index ac222db3..af2f9adf 100644
--- a/docs/source/screencasts.rst
+++ b/docs/source/screencasts.rst
@@ -9,4 +9,4 @@ This section provides links to screencasts that explain how to install gprMax, d
 Installation
 ------------
 
-* `How to install gprMax for end users on Microsoft Windows <https://youtu.be/dvC0_7qFVbE>`_
\ No newline at end of file
+* `How to install gprMax for end users on Microsoft Windows <https://youtu.be/dvC0_7qFVbE>`_ -- Needs updating for new unified installation procedure (30/05/2016)
\ No newline at end of file

From c787983282cd396410e439577e37b9c668216a53 Mon Sep 17 00:00:00 2001
From: craig-warren <craig_warren@me.com>
Date: Mon, 30 May 2016 15:39:27 +0100
Subject: [PATCH 36/38] Remove unused image.

---
 docs/source/images/MSVS2015_install.png | Bin 62780 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 docs/source/images/MSVS2015_install.png

diff --git a/docs/source/images/MSVS2015_install.png b/docs/source/images/MSVS2015_install.png
deleted file mode 100644
index 77e427187833e9a284115f463fff3a222b8c0539..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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From 3823026dfc30daaf4a050fd3cb91bb794ec888b0 Mon Sep 17 00:00:00 2001
From: craig-warren <craig_warren@me.com>
Date: Mon, 30 May 2016 15:44:16 +0100
Subject: [PATCH 37/38] Clarified print message on build/build_ext.

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 09a9a82b..cc6e721f 100644
--- a/setup.py
+++ b/setup.py
@@ -52,7 +52,7 @@ if sys.version_info[:2] < (3, 4):
 
 # Process 'build' command line argument
 if 'build' in sys.argv:
-    print("'build' is not required for this package, running 'build_ext --inplace' instead.")
+    print("Running 'build_ext --inplace'")
     sys.argv.remove('build')
     sys.argv.append('build_ext')
     sys.argv.append('--inplace')

From 07c3b58b8f649c35631f2d71233d09e3c98c4e6f Mon Sep 17 00:00:00 2001
From: craig-warren <craig_warren@me.com>
Date: Mon, 30 May 2016 15:44:38 +0100
Subject: [PATCH 38/38] Fixed strange indent.

---
 tests/test_compare_numerical.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/tests/test_compare_numerical.py b/tests/test_compare_numerical.py
index cafb65fa..338d9073 100644
--- a/tests/test_compare_numerical.py
+++ b/tests/test_compare_numerical.py
@@ -77,12 +77,12 @@ for ID, name in fields.items():
 
 # Plot new
 fig1, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(nrows=3, ncols=2, sharex=False, sharey='col', subplot_kw=dict(xlabel='Time [ns]'), num=newfile + ' versus ' + oldfile, figsize=(20, 10), facecolor='w', edgecolor='w')
-    ax1.plot(timenew, new[:,0],'r', lw=2, label='Ex')
-    ax3.plot(timenew, new[:,2],'r', lw=2, label='Ey')
-    ax5.plot(timenew, new[:,4],'r', lw=2, label='Ez')
-    ax2.plot(timenew, new[:,1],'b', lw=2, label='Hx')
-    ax4.plot(timenew, new[:,3],'b', lw=2, label='Hy')
-    ax6.plot(timenew, new[:,5],'b', lw=2, label='Hz')
+ax1.plot(timenew, new[:,0],'r', lw=2, label='Ex')
+ax3.plot(timenew, new[:,2],'r', lw=2, label='Ey')
+ax5.plot(timenew, new[:,4],'r', lw=2, label='Ez')
+ax2.plot(timenew, new[:,1],'b', lw=2, label='Hx')
+ax4.plot(timenew, new[:,3],'b', lw=2, label='Hy')
+ax6.plot(timenew, new[:,5],'b', lw=2, label='Hz')
     
 # Set ylabels
 ylabels = ['$E_x$, field strength [V/m]', '$H_x$, field strength [A/m]', '$E_y$, field strength [V/m]', '$H_y$, field strength [A/m]', '$E_z$, field strength [V/m]', '$H_z$, field strength [A/m]']
@@ -103,12 +103,12 @@ for index, ax in enumerate(fig1.axes):
 
 # Plots of differences
 fig2, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(nrows=3, ncols=2, sharex=False, sharey='col', subplot_kw=dict(xlabel='Time [ns]'), num='Deltas: ' + newfile + ' versus ' + oldfile, figsize=(20, 10), facecolor='w', edgecolor='w')
-    ax1.plot(timenew[:timesmallest], diffs[:,0],'r', lw=2, label='Ex')
-    ax3.plot(timenew[:timesmallest], diffs[:,2],'r', lw=2, label='Ey')
-    ax5.plot(timenew[:timesmallest], diffs[:,4],'r', lw=2, label='Ez')
-    ax2.plot(timenew[:timesmallest], diffs[:,1],'b', lw=2, label='Hx')
-    ax4.plot(timenew[:timesmallest], diffs[:,3],'b', lw=2, label='Hy')
-    ax6.plot(timenew[:timesmallest], diffs[:,5],'b', lw=2, label='Hz')
+ax1.plot(timenew[:timesmallest], diffs[:,0],'r', lw=2, label='Ex')
+ax3.plot(timenew[:timesmallest], diffs[:,2],'r', lw=2, label='Ey')
+ax5.plot(timenew[:timesmallest], diffs[:,4],'r', lw=2, label='Ez')
+ax2.plot(timenew[:timesmallest], diffs[:,1],'b', lw=2, label='Hx')
+ax4.plot(timenew[:timesmallest], diffs[:,3],'b', lw=2, label='Hy')
+ax6.plot(timenew[:timesmallest], diffs[:,5],'b', lw=2, label='Hz')
 
 # Set ylabels
 ylabels = ['$E_x$', '$H_x$', '$E_y$', '$H_y$', '$E_z$', '$H_z$']