Merge pull request #14 from KennethQNgo/main

Text Improvements: Typos and Grammar Fixes in Chapters 1-8

README.md

@@ -51,6 +51,7 @@ expertise in these fields.
 #### Contributors
 
 -  [Jake Anderson](https://github.com/jaketanderson) 
+-  [Kenneth Ngo](https://github.com/KennethQNgo)
 
 ### Acknowledgments and license
 

docs/source/chapters/chapter1.rst

@@ -325,8 +325,8 @@ any *AssertionError*:
 
 .. code-block:: bw
 
-    Utilities does not inherit from MonteCarlo, as expected
     MonteCarlo correctly inherits from Utilities
+    Utilities does not inherit from MonteCarlo, as expected
 
 Alternatively, this test can also be launched using *Pytest* by typing in a terminal:
 

docs/source/chapters/chapter2.rst

@@ -4,9 +4,9 @@ Setting Up the Simulation
 ==========================
 
 To streamline the simulation process, all user-specified parameters will be
-non-dimensionalized. By removing units from the calculations, we simplify
-complex operations like force evaluation, making the code more efficient and
-easier to manage. This non-dimensionalization is handled within the *Prepare*
+nondimensionalized. By removing units from the calculations, we simplify
+complex operations like force evaluation, making the code more efficient, and
+easier to manage. This nondimensionalization is handled within the *Prepare*
 class.
 
 While this step may not be the most thrilling aspect of the simulation, it is
@@ -80,7 +80,7 @@ Four atom parameters are provided to the *Prepare* class:
 - the LJ parameters :math:`\sigma` and :math:`\epsilon`,
 - and the number of atoms.
 
-All these quantities must be supplied as lists. This will be useful later when
+All these quantities must be provided as lists of values. This will be useful later when
 we want to mix atoms of different types within the same simulation box.
 
 Modify the *Prepare* class as follows:  

docs/source/chapters/chapter4.rst

@@ -82,13 +82,13 @@ following *run()* method to the *MinimizeEnergy* class:
         self.displacement = 0.01 # pick a random initial displacement (dimentionless)
         # *step* loops for 0 to *maximum_steps*+1
         for self.step in range(0, self.maximum_steps+1):
-            # First, meevaluate the initial energy and max force
+            # First, evaluate the initial energy and max force
             self.update_neighbor_lists() # Rebuild neighbor list, if necessary
             self.update_cross_coefficients() # Recalculate the cross coefficients, if necessary
             # Compute Epot/MaxF/force
-            if hasattr(self, 'Epot') is False: # If self.Epot does not exists yet, calculate it
+            if hasattr(self, 'Epot') is False: # If self.Epot does not exist yet, calculate it
                 self.Epot = self.compute_potential()
-            if hasattr(self, 'MaxF') is False: # If self.MaxF does not exists yet, calculate it
+            if hasattr(self, 'MaxF') is False: # If self.MaxF does not exist yet, calculate it
                 forces = self.compute_force()
                 self.MaxF = np.max(np.abs(forces))
             init_Epot = self.Epot

docs/source/chapters/chapter5.rst

@@ -12,11 +12,11 @@ VMD :cite:`humphrey1996vmd` or Ovito :cite:`stukowski2009visualization`.
 Create logger
 -------------
 
-Let us create a function named *log_simulation_data()* to a file named *logger.py*.
-With the logger, some output are being printed in a file, as well as in the terminal.
+Let us create a function named *log_simulation_data()* in a file named *logger.py*.
+With the logger, various outputs are being printed to a file, as well as to the terminal.
 The period of printing, which is a multiple of the simulation steps, will be set
 by a new parameter named *thermo_period* (integer). All quantities are 
-converted into *real* units before getting outputed (see :ref:`chapter2-label`).
+converted into *real* units before getting outputted (see :ref:`chapter2-label`).
 
 .. label:: start_logger_class
 
@@ -56,7 +56,7 @@ converted into *real* units before getting outputed (see :ref:`chapter2-label`).
         return logger
 
     def log_simulation_data(code):
-        # TOFIX: ceurrently, MaxF is returned dimensionless
+        # TOFIX: currently, MaxF is returned dimensionless
 
         # Setup the logger with the folder name, overwriting the log if code.step is 0
         logger = setup_logger(code.data_folder, overwrite=(code.step == 0))
@@ -87,10 +87,10 @@ converted into *real* units before getting outputed (see :ref:`chapter2-label`).
 
 .. label:: end_logger_class
 
-For simplicify, the *logger* uses the |logging| library, which provides a
+For simplicity, the *logger* uses the |logging| library, which provides a
 flexible framework for emitting log messages from Python programs. Depending on
-the value of *thermo_outputs*, different informations are printed, such as
-*step*, *Epot*, or/and *MaxF*.
+the value of *thermo_outputs*, different information are printed, such as
+*step*, *Epot*, and/or *MaxF*.
 
 .. |logging| raw:: html
 
@@ -185,7 +185,7 @@ Add the same lines at the top of the *MinimizeEnergy.py* file:
 .. label:: end_MinimizeEnergy_class
 
 Finally, let us make sure that *thermo_period*, *dumping_period*, and *thermo_outputs*
-parameters are passed the InitializeSimulation method:
+parameters are passed to the InitializeSimulation method:
 
 .. label:: start_InitializeSimulation_class
 
@@ -244,7 +244,7 @@ during energy minimization.
 Test the code
 -------------
 
-One can use a test similar as the previous ones. Let us ask our code to print
+One can use a test similar to the previous ones. Let us ask our code to print
 information in the *dump* and the *log* files, and then let us assert that the
 files were indeed created without the *Outputs/* folder:
 
@@ -316,10 +316,10 @@ simulation. The content of the *simulation.log* file should resemble:
     75 -1.22 3.77
     100 -2.10 1.28
 
-The data from the *simulation.log* can be used to generate plots using softwares
-line XmGrace, GnuPlot, or Python/Pyplot. For the later, one can use a simple data
+The data from the *simulation.log* can be used to generate plots using software
+like XmGrace, GnuPlot, or Python/Pyplot. For the latter, one can use a simple data
 reader to import the data from *simulation.log* into Python. Copy the
-following lines in a new file named *reader.py*:
+following lines into a new file named *reader.py*:
 
 .. label:: start_reader_class
 

docs/source/chapters/chapter6.rst

@@ -51,11 +51,11 @@ Let us add a method named *monte_carlo_move* to the *MonteCarlo* class:
             # When needed, recalculate neighbor/coeff lists
             self.update_neighbor_lists()
             self.update_cross_coefficients()
-            # If self.Epot does not exists yet, calculate it
+            # If self.Epot does not exist yet, calculate it
             # It should only be necessary when step = 0
             if hasattr(self, 'Epot') is False:
                 self.Epot = self.compute_potential()
-            # Make a copy of the initial atoms positions and initial energy
+            # Make a copy of the initial atom positions and initial energy
             initial_Epot = self.Epot
             initial_positions = copy.deepcopy(self.atoms_positions)
             # Pick an atom id randomly

docs/source/chapters/chapter7.rst

@@ -85,7 +85,7 @@ To evaluate all the vectors between all the particles, let us also add the
 Test the code
 -------------
 
-Let us test the outputed pressure. 
+Let us test the outputted pressure. 
 
 .. label:: start_test_7a_class
 

docs/source/chapters/chapter8.rst

@@ -5,13 +5,13 @@ Monte Carlo insert
 
 Here, a *Monte Carlo* simulation is implemented in the Grand Canonical ensemble,
 where the number of atoms in the system fluctuates. The principle of the
-simulation resemble the Monte Carlo move from :ref:`chapter6-label`:
+simulation resembles the Monte Carlo move from :ref:`chapter6-label`:
 
-- 1) We start from a given intial configuration, and measure the potential
+- 1) We start from a given initial configuration, and measure the potential
   energy, :math:`E_\text{pot}^\text{initial}`.
 - 2) A random number is chosen, and depending on its value, either a new particle
-  will tried to be inserted, or an existing particle will tried to be deleted.
-- 3) The energy of the system after the insersion/deletion,
+  will try to be inserted, or an existing particle will try to be deleted.
+- 3) The energy of the system after the insertion/deletion,
   :math:`E_\text{pot}^\text{trial}`, is measured.
 - 4) We then decide to keep or reject the move by calculating
   the difference in energy between the trial and the initial configurations:
@@ -97,7 +97,7 @@ Then, let us write the *monte_carlo_insert()* method:
         trial_Epot = self.compute_potential()
         Lambda = self.calculate_Lambda(self.atom_mass[self.inserted_type])
         beta =  1/self.desired_temperature
-        Nat = np.sum(self.number_atoms) # NUmber atoms, should it relly be N? of N (type) ?
+        Nat = np.sum(self.number_atoms) # Number atoms, should it really be N? of N (type) ?
         Vol = np.prod(self.box_size[:3]) # box volume
         # dimension of 3 is enforced in the power of the Lambda
         self.acceptation_probability = np.min([1, Vol/(Lambda**3*Nat) \
@@ -129,7 +129,7 @@ Let us add the very similar *monte_carlo_delete()* method:
             trial_Epot = self.compute_potential()
             Lambda = self.calculate_Lambda(self.atom_mass[self.inserted_type])
             beta =  1/self.desired_temperature
-            Nat = np.sum(self.number_atoms) # NUmber atoms, should it relly be N? of N (type) ?
+            Nat = np.sum(self.number_atoms) # Number atoms, should it really be N? of N (type) ?
             Vol = np.prod(self.box_size[:3]) # box volume
             # dimension of 3 is enforced in the power of the Lambda
             self.acceptation_probability = np.min([1, (Lambda**3 *(Nat-1)/Vol) \
@@ -145,7 +145,7 @@ Complete the *__init__* method as follows:
 
 .. code-block:: python
 
-    class MonteCarlo(Outputs):
+    class MonteCarlo(Measurements):
         def __init__(self,
                     (...)
                     displace_mc = None,
@@ -160,7 +160,7 @@ and
 
 .. code-block:: python
 
-    class MonteCarlo(Outputs):
+    class MonteCarlo(Measurements):
         def __init__(self,
             (...)
             self.displace_mc = displace_mc
@@ -169,7 +169,7 @@ and
 
 .. label:: end_MonteCarlo_class
 
-Let us also normalised the "desired_mu":
+Let us also normalize the "desired_mu":
 
 .. label:: start_MonteCarlo_class
 
@@ -300,4 +300,4 @@ potential *desired_mu*:
 .. label:: end_test_9a_class
 
 The evolution of the potential energy as a function of the
-number of steps are written in the *Outputs/Epot.dat*.
+number of steps is written in the *Outputs/Epot.dat*.

docs/source/projects/project1.rst

@@ -98,7 +98,7 @@ Within the Python script, write:
 
 .. code-block:: python
 
-    epsilon = (119.76*ureg.kelvin*cst.Boltzmann*cst.N_A).to(ureg.kcal/ureg.mol)
+    epsilon = (119.76*ureg.kelvin*kB*Na).to(ureg.kcal/ureg.mol)
     r_star = 3.822*ureg.angstrom 
     sigma = r_star / 2**(1/6) 
     m_argon = 39.948*ureg.gram/ureg.mol
@@ -161,17 +161,17 @@ statistical mechanics that describes how the state variables of a system, such a
 pressure :math:`p`, volume :math:`V`, and temperature :math:`T`, are interrelated.
 Here, let us extract the pressure of the fluid for different density values.
 
-To easily launch multiple simulation in parallel, let us create a
+To easily launch multiple simulations in parallel, let us create a
 function called *launch_MC_code* that will be used to call
 our Monte Carlo script with a chosen value of :math:`\tau = v / v^*`.
 
-Create a new function, so that the current script ressemble the following:
+Create a new function so that the current script resembles the following:
 
 .. code-block:: python
 
     def launch_MC_code(tau):
 
-        epsilon = (119.76*ureg.kelvin*cst.Boltzmann*cst.N_A).to(ureg.kcal/ureg.mol)
+        epsilon = (119.76*ureg.kelvin*kB*Na).to(ureg.kcal/ureg.mol)
         r_star = 3.822*ureg.angstrom 
         sigma = r_star / 2**(1/6) 
         m_argon = 39.948*ureg.gram/ureg.mol
@@ -217,7 +217,7 @@ simulation with too much overlap between the atoms:
         )
         em.run()
 
-Then, let us start the Monte carlo simulation. As initial positions for the atoms,
+Then, let us start the Monte Carlo simulation. For the initial positions of the atoms,
 let us use the last positions from the *em* run,
 i.e. *initial_positions = em.atoms_positions*em.reference_distance*:
 
@@ -256,12 +256,12 @@ i.e. *initial_positions = em.atoms_positions*em.reference_distance*:
         mc.run()
 
 Finally, it is possible to call the *launch_MC_code* function using
-*multiprocessing*, and perform the simulation for multiple value of :math:`\tau`
+*multiprocessing*, and perform the simulation for multiple values of :math:`\tau`
 at the same time (if your computer has enough CPU core, if not, perform these
 calculations in serial):
 
 .. code-block:: python
-
+A
     if __name__ == "__main__":
         tau_values = np.round(np.logspace(-0.126, 0.882, 10),2)
         pool = multiprocessing.Pool()
@@ -287,7 +287,7 @@ with those of Ref. :cite:`woodMonteCarloEquation1957`:
     :class: only-dark
 
 Figure: Equation of state of the argon fluid as calculated using the Monte
-carlo code (disks), and compared with the results from Ref. :cite:`woodMonteCarloEquation1957`.
-Normalised pressure, :math:`p V / RT` as a function of the normalised volume,
+Carlo code (disks), and compared with the results from Ref. :cite:`woodMonteCarloEquation1957`.
+Normalized pressure, :math:`p V / RT` as a function of the normalized volume,
 :math:`V / V^*`, where :math:`V^*` is the molar volume. For benchmark purposes,
 the data obtained using LAMMPS were also added.