Loading of simulation data
==========================

Mdevaulate provides a convenient function :func:`mdevaluate.load_simulation`
that loads a simulation more or less automatically.
It takes a path as input and looks for all files it needs in this directory.

For information about the topology either a `tpr` or `gro` a file is read,
where the former is the preferred choice.
Trajectory data will be read from a xtc file.
If the directory contains more than one file of any type, the desired file
has to be specified with the appropriate keyword argument.
For details see :func:`mdevaluate.open`.

The function will return a coordinates object, for the whole system.
A subset of the system may be obtained directly from the coordinates object by
calling its :func:`~mdevaluate.coordinates.Coordinates.subset` method.
This function accepts the same input as :func:`mdevaluate.atoms.AtomSubset.subset`.
A new feature that was introduced in the function is the possibility to chose
atoms with regular expressions.

Example
-------

The following code loads the example trajectory and selects a subset of all CW atoms.
Since there are two CW atoms in each molecule (CW1 and CW2) a regular expression is
used when selecting the subset.

::

  import mdevaluate as md

  trajectory = md.open('/data/niels/tutorial')
  CW_atoms = trajectory.subset(atom_name='CW.')

And that's it, now one can evaluate stuff for this subset of atoms.

Selecting a subset
------------------

As shown in the example above it is often necessary to select a subset of the system for analysis.
This can be a special group of atoms (e.g. all C atoms) or a whole residue for which the center of mass should be computed.
Subsets are selected with the :func:`~mdevaluate.Coordinates.subset` method of Coordinates objects.

This method accepts four keyword arguments, with which the atom name, residue name and residue id or atom indices can be specified.
The former two name arguments accept a regular expression which allows two include several different names in one subset.
Some examples:

- All carbon atoms (which are named CW1, CT1, CA, ...): ``tr.subset(atom_name='C.*')``
- Atoms NA1, NA2 and OW: ``tr.subset(atom_name='NA.|OW')``
- All oxygen atoms of residue EG: ``tr.subset(atom_name='O.*', residue_name='EG')``


Specifying data files
---------------------

The above example only works if the directory contains exactly one tpr file and
one xtc file.
If your data files are located in subdirectories or multiple files of these types exist,
they can be specified by the keywords ``topology`` and ``trajectory``.
Those filenames can be a relative path to the simulation directory and can also make
use of *shell globing*. For example::

  traj = md.open('/path/to/sim', topology='atoms.gro', trajectory='out/traj_*.xtc')

Note that the topology can be specified as a gro file, with the limitation that
only atom and residue names will be read from those files.
Information about atom masses and charges for example will only be read from tpr files,
therefore it is generally recommended to use the latter topologies.

The trajectory above is specified through a shell globing, meaning the ``*`` may be
expanded to any string (without containing a forward slash).
If more than one file exists which match this pattern an error will be raised,
since the trajectory can not be identified clearly.

Caching of frames
-----------------

One bottleneck in the analysis of MD data is the reading speed of the trajectory.
In many cases frames will be needed repeatedly and hence the amount of time spend reading
data from disk (or worse over the network) is huge.
Therefore the mdevaluate package implements a simple caching mechanism, which holds
on to a number of read frames.
The downside if this is increased memory usage which may slow down the computation too.

Caching is done on the level of the trajectory readers, so that all ``Coordinate`` and
``CoordinateMap`` objects working on the same trajectory will be sharing a cache.
Caching has to be activated when opening a trajectory::

  traj = md.open('/path/to/sim', cached=True)

The ``cached`` keyword takes either a boolean, a integer or None as input value.
The value of ``cached`` controls the size of the cache and thereby the additional memory usage.
Setting it to True will activate the caching with a maximum size of 128 frames,
with an integer any other maximum size may be set.
The special value ``None`` will set the cache size to infinite, so all frames will be cached.
This will prevent the frames from being loaded twice but can also consume a whole lot of memory,
since a single frame can easily take 1 MB of memory.

Clearing cached frames
++++++++++++++++++++++

In some scenarios it may be advisable to free cached frames which are no longer needed.
For this case the reader has a function ``clear_cache()``.
The current state of the cache can be displayed with the ``cache_info`` property::

  >>> traj.frames.cache_info
  CacheInfo(hits=12, misses=20, maxsize=128, currsize=20)
  >>> traj.frames.clear_cache()
  >>> traj.frames.cache_info
  CacheInfo(hits=0, misses=0, maxsize=128, currsize=0)

Clearing the cache when it is not needed anymore is advisable since this will help the
Python interpreter to reuse the memory.