use consistent pygments language tags

doc/src/Howto_pylammps.rst

@@ -152,14 +152,14 @@ Creating a new instance of PyLammps
 To create a PyLammps object you need to first import the class from the lammps
 module. By using the default constructor, a new *lammps* instance is created.
 
-.. code-block:: Python
+.. code-block:: python
 
    from lammps import PyLammps
    L = PyLammps()
 
 You can also initialize PyLammps on top of this existing *lammps* object:
 
-.. code-block:: Python
+.. code-block:: python
 
    from lammps import lammps, PyLammps
    lmp = lammps()
@@ -180,14 +180,14 @@ For instance, let's take the following LAMMPS command:
 In the original interface this command can be executed with the following
 Python code if *L* was a lammps instance:
 
-.. code-block:: Python
+.. code-block:: python
 
    L.command("region box block 0 10 0 5 -0.5 0.5")
 
 With the PyLammps interface, any command can be split up into arbitrary parts
 separated by white-space, passed as individual arguments to a region method.
 
-.. code-block:: Python
+.. code-block:: python
 
    L.region("box block", 0, 10, 0, 5, -0.5, 0.5)
 
@@ -199,14 +199,14 @@ The benefit of this approach is avoiding redundant command calls and easier
 parameterization. In the original interface parameterization needed to be done
 manually by creating formatted strings.
 
-.. code-block:: Python
+.. code-block:: python
 
    L.command("region box block %f %f %f %f %f %f" % (xlo, xhi, ylo, yhi, zlo, zhi))
 
 In contrast, methods of PyLammps accept parameters directly and will convert
 them automatically to a final command string.
 
-.. code-block:: Python
+.. code-block:: python
 
    L.region("box block", xlo, xhi, ylo, yhi, zlo, zhi)
 
@@ -256,7 +256,7 @@ LAMMPS variables can be both defined and accessed via the PyLammps interface.
 
 To define a variable you can use the :doc:`variable <variable>` command:
 
-.. code-block:: Python
+.. code-block:: python
 
    L.variable("a index 2")
 
@@ -265,14 +265,14 @@ A dictionary of all variables is returned by L.variables
 you can access an individual variable by retrieving a variable object from the
 L.variables dictionary by name
 
-.. code-block:: Python
+.. code-block:: python
 
    a = L.variables['a']
 
 The variable value can then be easily read and written by accessing the value
 property of this object.
 
-.. code-block:: Python
+.. code-block:: python
 
    print(a.value)
    a.value = 4
@@ -284,7 +284,7 @@ LAMMPS expressions can be immediately evaluated by using the eval method. The
 passed string parameter can be any expression containing global thermo values,
 variables, compute or fix data.
 
-.. code-block:: Python
+.. code-block:: python
 
    result = L.eval("ke") # kinetic energy
    result = L.eval("pe") # potential energy
@@ -298,7 +298,7 @@ All atoms in the current simulation can be accessed by using the L.atoms list.
 Each element of this list is an object which exposes its properties (id, type,
 position, velocity, force, etc.).
 
-.. code-block:: Python
+.. code-block:: python
 
    # access first atom
    L.atoms[0].id
@@ -311,7 +311,7 @@ position, velocity, force, etc.).
 
 Some properties can also be used to set:
 
-.. code-block:: Python
+.. code-block:: python
 
    # set position in 2D simulation
    L.atoms[0].position = (1.0, 0.0)
@@ -328,7 +328,7 @@ after a run via the L.runs list. This list contains a growing list of run data.
 The first element is the output of the first run, the second element that of
 the second run.
 
-.. code-block:: Python
+.. code-block:: python
 
    L.run(1000)
    L.runs[0] # data of first 1000 time steps
@@ -339,14 +339,14 @@ the second run.
 Each run contains a dictionary of all trajectories. Each trajectory is
 accessible through its thermo name:
 
-.. code-block:: Python
+.. code-block:: python
 
    L.runs[0].thermo.Step # list of time steps in first run
    L.runs[0].thermo.Ke   # list of kinetic energy values in first run
 
 Together with matplotlib plotting data out of LAMMPS becomes simple:
 
-.. code-block:: Python
+.. code-block:: python
 
    import matplotlib.plot as plt
    steps = L.runs[0].thermo.Step
@@ -406,7 +406,7 @@ Four atoms are placed in the simulation and the dihedral potential is applied on
 them using a datafile. Then one of the atoms is rotated along the central axis by
 setting its position from Python, which changes the dihedral angle.
 
-.. code-block:: Python
+.. code-block:: python
 
    phi = [d \* math.pi / 180 for d in range(360)]
 
@@ -439,7 +439,7 @@ Initially, a 2D system is created in a state with minimal energy.
 
 It is then disordered by moving each atom by a random delta.
 
-.. code-block:: Python
+.. code-block:: python
 
    random.seed(27848)
    deltaperturb = 0.2
@@ -458,7 +458,7 @@ It is then disordered by moving each atom by a random delta.
 Finally, the Monte Carlo algorithm is implemented in Python. It continuously
 moves random atoms by a random delta and only accepts certain moves.
 
-.. code-block:: Python
+.. code-block:: python
 
    estart = L.eval("pe")
    elast = estart
@@ -517,7 +517,7 @@ PyLammps can be run in parallel using mpi4py. This python package can be install
 The following is a short example which reads in an existing LAMMPS input file and
 executes it in parallel.  You can find in.melt in the examples/melt folder.
 
-.. code-block:: Python
+.. code-block:: python
 
    from mpi4py import MPI
    from lammps import PyLammps