Merge pull request #214 from galenseilis/master

Minor documentation changes.

docs/Background/mechanisms.rst

@@ -8,7 +8,7 @@ General
 ~~~~~~~
 
 Ciw uses the *event scheduling* approach [SW14]_ , similar to the three phase approach.
-In the event scheduling approach, three types of event take place: **A Events** move the clock forward, **B Events** are pre scheduled events, and **C Events** are events that arise because a **B Event** has happened.
+In the event scheduling approach, three types of event take place: **A Events** move the clock forward, **B Events** are prescheduled events, and **C Events** are events that arise because a **B Event** has happened.
 
 Here **A-events** correspond to moving the clock forward to the next **B-event**.
 **B-events** correspond to either an external arrival, a customer finishing service, or a server shift change.

docs/Background/other.rst

@@ -7,11 +7,11 @@ Other Computer Simulation Options
 Ciw is just one way in which simulations of queueing networks can be carried out.
 Here is a list of other options that may be of interest, both commercial and open source:
 
-- `SimPy <https://simpy.readthedocs.org/en/latest/>`_
-- `SIMUL8 <http://www.simul8.com/>`_
 - `AnyLogic <http://www.anylogic.com>`_
 - `Khronos <http://khronos-des.sourceforge.net/>`_
 - `NetSim <http://www.boson.com/netsim-cisco-network-simulator>`_
-- `Arena <https://www.arenasimulation.com/>`_
+- `Queueing-tool <https://queueing-tool.readthedocs.io/en/latest/>`_
+- `SimPy <https://simpy.readthedocs.org/en/latest/>`_
+- `SIMUL8 <http://www.simul8.com/>`_
 
-For a comparison of Ciw with other simulation tools, in terms of reproducibility and performance, see `Ciw: An open-source discrete event simulation library <https://www.tandfonline.com/doi/full/10.1080/17477778.2018.1473909>`_ in the Journal of Simulation.
+For a comparison of Ciw with other simulation tools, in terms of reproducibility and performance, see `Ciw: An open-source discrete event simulation library <https://www.tandfonline.com/doi/full/10.1080/17477778.2018.1473909>`_ in the Journal of Simulation.

docs/Tutorial-I/tutorial_iii.rst

@@ -40,7 +40,7 @@ This returns a list of named tuples. Each named tuple contains the following inf
 
 More information on each of these is given in :ref:`refs-results`.
 
-Using list comprehension, we can get lists on whichever statistic we like::
+Using a list comprehension, we can get lists on whichever statistic we like::
 
     >>> # A list of service times
     >>> servicetimes = [r.service_time for r in recs]
@@ -85,4 +85,4 @@ This is the average utilisation of each server, which is the amount of time a se
 
 Thus in our bank, on average the servers were busy 75.3% of the time.
 
-The next tutorial will show how to use Ciw to get trustworthy results, and finally find out the average waiting time at the bank.
+The next tutorial will show how to use Ciw to get trustworthy results, and finally find out the average waiting time at the bank.

docs/Tutorial-I/tutorial_iv.rst

@@ -41,7 +41,7 @@ Therefore each trial we will run for 1 day + 200 minutes (1640 minutes)::
     ...     mean_wait = sum(waits) / len(waits)
     ...     average_waits.append(mean_wait)
 
-The list :code:`average_waits` will now contain ten numbers, the mean waiting time from each of the trials.
+The list :code:`average_waits` will now contain ten numbers and the mean waiting time from each of the trials.
 Notice that we set a different seed every time, so each trial will yield different results::
 
     >>> average_waits