class Simulation(object):
"""Class that represents one simulation.
Analysis capabilities are added via plugins.
1. Set the *active plugin* with the :meth:`Simulation.set_plugin` method.
2. Analyze the trajectory with the active plugin by calling the
:meth:`Simulation.run` method.
3. Analyze the output from :meth:`run` with :meth:`Simulation.analyze`; results are stored
in the plugin's :attr:`~Worker.results` dictionary.
4. Plot results with :meth:`Simulation.plot`.
"""
# NOTE: not suitable for multiple inheritance
def __init__(self, **kwargs):
"""Set up a Simulation object.
:Keywords:
*sim*
Any object that contains the attributes *tpr*, *xtc*,
and optionally *ndx*
(e.g. :class:`gromacs.cbook.Transformer`). The individual keywords such
as *xtc* override the values in *sim*.
*tpr*
Gromacs tpr file (**required**)
*xtc*
Gromacs trajectory, can also be a trr (**required**)
*edr*
Gromacs energy file (only required for some plugins)
*ndx*
Gromacs index file
*absolute*
``True``: Turn file names into absolute paths (typically required
for most plugins); ``False`` keep a they are [``True``]
*strict*
``True``: missing required file keyword raises a :exc:`TypeError`
and missing the file itself raises a :exc:`IOError`. ``False``:
missing required files only give a warning. [``True``]
*analysisdir*
directory under which derived data are stored;
defaults to the directory containing the tpr [None]
*plugins* : list
plugin instances or tuples (*plugin class*, *kwarg dict*) or tuples
(*plugin_class_name*, *kwarg dict*) to be used; more can be
added later with :meth:`Simulation.add_plugin`.
"""
logger.info("Loading simulation data")
sim = kwargs.pop('sim', None)
strict = kwargs.pop('strict', True)
def getpop(attr, required=False, strict=strict):
"""Return attribute from from kwargs or sim or None"""
val = kwargs.pop(attr, None) # must pop from kwargs to clean it
if val is not None:
return val
try:
return sim.__getattribute__(attr)
except AttributeError:
if required:
errmsg = "Required attribute {0!r} not found in kwargs or sim".format(
attr)
if strict:
logger.fatal(errmsg)
raise TypeError(errmsg)
else:
logger.warn(errmsg +
"... continuing because of strict=False")
warnings.warn(errmsg)
return None
make_absolute = kwargs.pop('absolute', True)
def canonical(*args):
"""Join *args* and get the :func:`os.path.realpath`."""
if None in args:
return None
if not make_absolute:
return os.path.join(*args)
return os.path.realpath(os.path.join(*args))
# required files
self.tpr = canonical(getpop('tpr', required=True))
self.xtc = canonical(getpop('xtc', required=True))
# optional files
self.ndx = canonical(getpop('ndx'))
self.edr = canonical(getpop('edr'))
# check existence of required files
resolve = "exception"
if not strict:
resolve = "warn"
for v in ('tpr', 'xtc'):
self.check_file(v, self.__getattribute__(v), resolve=resolve)
self.analysis_dir = kwargs.pop('analysisdir',
os.path.dirname(self.tpr))
#: Registry for plugins: This dict is central.
self.plugins = AttributeDict()
#: Use this plugin if none is explicitly specified. Typically set with
#: :meth:`~Simulation.set_plugin`.
self.default_plugin_name = None
# XXX: Or should we simply add instances and then re-register
# all instances using register() ?
# XXX: ... this API should be cleaned up. It seems to be connected
# back and forth in vicious circles. -- OB 2009-07-10
plugins = kwargs.pop('plugins', [])
# list of tuples (plugin, kwargs) or just (plugin,) if no kwords
# required (eg if plugin is an instance)
for x in plugins:
try:
P, kwargs = asiterable(
x) # make sure to wrap strings, especially 2-letter ones!
except ValueError:
P = x
kwargs = {}
self.add_plugin(P, **kwargs)
# convenience: if only a single plugin was registered we default to that one
if len(self.plugins) == 1:
self.set_plugin(self.plugins.keys()[0])
# Is this needed? If done properly, kwargs should be empty by now BUT
# because the same list is re-used for all plugins I cannot pop them in
# the plugins. I don't think multiple inheritance would work with this
# setup so let's not pretend it does: hence comment out the super-init
# call:
## super(Simulation, self).__init__(**kwargs)
logger.info("Simulation instance initialised:")
logger.info(str(self))
def add_plugin(self, plugin, **kwargs):
"""Add a plugin to the registry.
- If *plugin* is a :class:`Plugin` instance then the
instance is directly registered and any keyword arguments
are ignored.
- If *plugin* is a :class:`Plugin` class object or a
string that can be found in :mod:`gromacs.analysis.plugins`
then first an instance is created with the given keyword
arguments and then registered.
:Arguments:
*plugin* : class or string, or instance
If the parameter is a class then it should have been derived
from :class:`Plugin`. If it is a string then it is taken as a
plugin name in :mod:`gromacs.analysis.plugins` and the
corresponding class is added. In both cases any parameters for
initizlization should be provided.
If *plugin* is already a :class:`Plugin` instance then the kwargs
will be ignored.
*kwargs*
The kwargs are specific for the plugin and should be
described in its documentation.
"""
# simulation=self must be provided so that plugin knows who owns it
try:
plugin.register(simulation=self)
except (TypeError, AttributeError):
# NOTE: this except clause can mask bugs in the plugin code!!
if type(plugin) is str:
import plugins # We should be able to import this safely now...
plugin = plugins.__plugin_classes__[plugin]
# plugin registers itself in self.plugins
plugin(simulation=self, **kwargs) # simulation=self is REQUIRED!
def topdir(self, *args):
"""Returns a path under self.analysis_dir, which is guaranteed to exist.
.. Note:: Parent dirs are created if necessary."""
p = os.path.join(self.analysis_dir, *args)
parent = os.path.dirname(p)
try:
os.makedirs(parent)
except OSError, err:
if err.errno != errno.EEXIST:
raise
return p
class _COM(Worker):
"""COM worker class."""
def __init__(self, **kwargs):
"""Set up COM analysis.
:Keywords:
*group_names*
list of index group names
*ndx*
index file if groups are not in the default index
*offset*
add the *offset* to the residue numbers [0]
*name*
plugin name [COM]
*simulation*
The :class:`gromacs.analysis.Simulation` instance that
owns the plugin [None]
"""
group_names = asiterable(kwargs.pop('group_names', []))
ndx = kwargs.pop('ndx', None)
offset = kwargs.pop('offset', 0)
super(_COM, self).__init__(**kwargs)
self.parameters.group_names = group_names
self.parameters.offset = offset
self.ndx = ndx
if self.simulation is not None:
self._register_hook()
def _register_hook(self, **kwargs):
"""Run when registering; requires simulation."""
super(_COM, self)._register_hook(**kwargs)
assert self.simulation is not None
if self.ndx is None:
self.ndx = self.simulation.ndx
self.parameters.filenames = { # result xvg files
'com': self.plugindir('com.xvg'),
}
# default filename for the plots -- not used
self.parameters.fignames = {
'com': self.figdir('com'),
}
def run(self, force=None, **gmxargs):
"""Analyze trajectory and write COM file.
All three components of the COM coordinate are written.
:Arguments:
- *force*: ``True`` does analysis and overwrites existing files
- *gmxargs*: additional keyword arguments for :func:`gromacs.g_bundle`
"""
gmxargs['com'] = True
gmxargs['mol'] = False
gmxargs['ng'] = len(self.parameters.group_names)
gmxargs['x'] = True
gmxargs['y'] = True
gmxargs['z'] = True
if gmxargs['ng'] == 0:
errmsg = "No index group name(s) provided. Use group_name with the constructor."
logger.error(errmsg)
raise ValueError(errmsg)
if self.check_file_exists(self.parameters.filenames['com'],
resolve='warning',
force=force):
return
logger.info("Analyzing COM ...")
f = self.parameters.filenames
gromacs.g_traj(s=self.simulation.tpr,
f=self.simulation.xtc,
n=self.ndx,
ox=f['com'],
input=self.parameters.group_names,
**gmxargs)
def analyze(self, **kwargs):
"""Collect output xvg files as :class:`gromacs.formats.XVG` objects.
- Make COM as a function of time available as XVG files and
objects.
- Compute RMSD of the COM of each group (from average
position, "rmsd").
- Compute distance whic encompasses 50% of observations ("median")
- Compute drift of COM, i.e. length of the vector between
initial and final position. Initial and final position are
computed as averages over *nframesavg* frames ("drift").
RMSD, median, and drift are columns in an xvg file. The rows correspond
to the groups in :attr:`gromacs.analysis.plugins.com.Worker.results.group_names`.
:Keywords:
*nframesavg*
number of initial and final frames that are averaged in
order to compute the drift of the COM of each group
[5000]
*refgroup*
group name whose com is taken as the reference and subtracted from
all other coms for the distance calculations. If supplied,
additional result 'com_relative_*refgroup*' is created.
:Returns: a dictionary of the results and also sets
:attr:`gromacs.analysis.plugins.com.Worker.results`.
"""
from gromacs.formats import XVG
logger.info("Preparing COM graphs as XVG objects.")
self.results = AttributeDict(
(k, XVG(fn)) for k, fn in self.parameters.filenames.items())
# compute RMSD of COM and shift of COM (drift) between avg pos
# over first/last 5,000 frames
nframesavg = kwargs.pop('nframesavg', 5000)
ngroups = len(self.parameters.group_names)
xcom = self.results['com'].array
refgroup = kwargs.pop('refgroup', None)
if refgroup is not None:
if not refgroup in self.parameters.group_names:
errmsg = "refgroup={0!s} must be one of {1!r}".format(
refgroup, self.parameters.group_names)
logger.error(errmsg)
raise ValueError(errmsg)
nreference = 1 + 3 * self.parameters.group_names.index(
refgroup) # 1-based !!
reference_com = xcom[nreference:nreference + 3]
xcom[1:] -= numpy.vstack(ngroups *
[reference_com]) # can't use broadcast
logger.debug("distances computed with refgroup %r", refgroup)
self.store_xvg('com_relative_{0!s}'.format(refgroup),
xcom,
names=['time'] + self.parameters.group_names)
def vlength(v):
return numpy.sqrt(numpy.sum(v**2,
axis=0)) # distances over time step
logger.debug(
"drift calculated between %d-frame averages at beginning and end",
nframesavg)
records = []
for i in xrange(1, 3 * ngroups + 1, 3):
x = xcom[i:i + 3]
r = vlength(
x -
x.mean(axis=1)[:, numpy.newaxis]) # distances over time step
#r0 = vlength(r - r[:,0][:,numpy.newaxis]) # distances over time step from r(t=0)
#h,edges = numpy.histogram(r, bins=kwargs.get('bins', 100), normed=True)
#m = 0.5*(edges[1:]+edges[:-1])
#c = h.cumsum(dtype=float) # integral
#c /= c[-1] # normalized (0 to 1)
#median = m[c < 0.5][-1]
#g = h/(4*numpy.pi*m**2)
#import scipy.integrate
#radint = lambda y: 4*numpy.pi*scipy.integrate.simps(m**2*y, x=m)
#g /= radint(g) # properly normalized radial distribution function
rmsd = numpy.sqrt(numpy.mean(
r**2)) # radial spread sqrt(radint(m**2 * g))
median = numpy.median(
r) # radius that contains 50% of the observations
dx = x[:, :nframesavg].mean(axis=1) - x[:,
-nframesavg:].mean(axis=1)
drift = vlength(dx)
records.append((rmsd, median, drift))
self.store_xvg('distance',
numpy.transpose(records),
names="rmsd,median,drift")
return self.results
def plot(self, **kwargs):
"""Plot all results in one graph, labelled by the result keys.
:Keywords:
observables
select one or more of the stored results. Can be a list
or a string (a key into the results dict). ``None``
plots everything [``None``]
figure
- ``True``: save figures in the given formats
- "name.ext": save figure under this filename (``ext`` -> format)
- ``False``: only show on screen [``False``]
formats : sequence
sequence of all formats that should be saved [('png', 'pdf')]
plotargs
keyword arguments for pylab.plot()
"""
import pylab
figure = kwargs.pop('figure', False)
observables = asiterable(kwargs.pop('observables',
self.results.keys()))
extensions = kwargs.pop('formats', ('pdf', 'png'))
for name in observables:
result = self.results[name]
try:
result.plot(
**kwargs
) # This requires result classes with a plot() method!!
except AttributeError:
warnings.warn(
"Sorry, plotting of result {name!r} is not implemented".
format(**vars()),
category=UserWarning)
# quick labels -- relies on the proper ordering
labels = [
str(n) + " " + dim for n in self.parameters.group_names
for dim in 'xyz'
]
if kwargs.get('columns') is not None:
# select labels according to columns; only makes sense
# if plotting against the time (col 0)
if kwargs['columns'][0] == 0:
labels = numpy.array([None] + labels)[kwargs['columns'][1:]]
else:
labels = ()
pylab.legend(labels, loc='best')
if figure is True:
for ext in extensions:
self.savefig(ext=ext)
elif figure:
self.savefig(filename=figure)
class Simulation(object):
"""Class that represents one simulation.
Analysis capabilities are added via plugins.
1. Set the *active plugin* with the :meth:`Simulation.set_plugin` method.
2. Analyze the trajectory with the active plugin by calling the
:meth:`Simulation.run` method.
3. Analyze the output from :meth:`run` with :meth:`Simulation.analyze`; results are stored
in the plugin's :attr:`~Worker.results` dictionary.
4. Plot results with :meth:`Simulation.plot`.
"""
# NOTE: not suitable for multiple inheritance
def __init__(self, **kwargs):
"""Set up a Simulation object.
:Keywords:
*sim*
Any object that contains the attributes *tpr*, *xtc*,
and optionally *ndx*
(e.g. :class:`gromacs.cbook.Transformer`). The individual keywords such
as *xtc* override the values in *sim*.
*tpr*
Gromacs tpr file (**required**)
*xtc*
Gromacs trajectory, can also be a trr (**required**)
*edr*
Gromacs energy file (only required for some plugins)
*ndx*
Gromacs index file
*absolute*
``True``: Turn file names into absolute paths (typically required
for most plugins); ``False`` keep a they are [``True``]
*strict*
``True``: missing required file keyword raises a :exc:`TypeError`
and missing the file itself raises a :exc:`IOError`. ``False``:
missing required files only give a warning. [``True``]
*analysisdir*
directory under which derived data are stored;
defaults to the directory containing the tpr [None]
*plugins* : list
plugin instances or tuples (*plugin class*, *kwarg dict*) or tuples
(*plugin_class_name*, *kwarg dict*) to be used; more can be
added later with :meth:`Simulation.add_plugin`.
"""
logger.info("Loading simulation data")
sim = kwargs.pop('sim', None)
strict = kwargs.pop('strict', True)
def getpop(attr, required=False, strict=strict):
"""Return attribute from from kwargs or sim or None"""
val = kwargs.pop(attr, None) # must pop from kwargs to clean it
if not val is None:
return val
try:
return sim.__getattribute__(attr)
except AttributeError:
if required:
errmsg = "Required attribute %r not found in kwargs or sim" % attr
if strict:
logger.fatal(errmsg)
raise TypeError(errmsg)
else:
logger.warn(errmsg+"... continuing because of strict=False")
warnings.warn(errmsg)
return None
make_absolute = kwargs.pop('absolute', True)
def canonical(*args):
"""Join *args* and get the :func:`os.path.realpath`."""
if None in args:
return None
if not make_absolute:
return os.path.join(*args)
return os.path.realpath(os.path.join(*args))
# required files
self.tpr = canonical(getpop('tpr', required=True))
self.xtc = canonical(getpop('xtc', required=True))
# optional files
self.ndx = canonical(getpop('ndx'))
self.edr = canonical(getpop('edr'))
# check existence of required files
resolve = "exception"
if not strict:
resolve = "warn"
for v in ('tpr', 'xtc'):
self.check_file(v, self.__getattribute__(v), resolve=resolve)
self.analysis_dir = kwargs.pop('analysisdir', os.path.dirname(self.tpr))
#: Registry for plugins: This dict is central.
self.plugins = AttributeDict()
#: Use this plugin if none is explicitly specified. Typically set with
#: :meth:`~Simulation.set_plugin`.
self.default_plugin_name = None
# XXX: Or should we simply add instances and then re-register
# all instances using register() ?
# XXX: ... this API should be cleaned up. It seems to be connected
# back and forth in vicious circles. -- OB 2009-07-10
plugins = kwargs.pop('plugins', [])
# list of tuples (plugin, kwargs) or just (plugin,) if no kwords
# required (eg if plugin is an instance)
for x in plugins:
try:
P, kwargs = asiterable(x) # make sure to wrap strings, especially 2-letter ones!
except ValueError:
P = x
kwargs = {}
self.add_plugin(P, **kwargs)
# convenience: if only a single plugin was registered we default to that one
if len(self.plugins) == 1:
self.set_plugin(self.plugins.keys()[0])
# Is this needed? If done properly, kwargs should be empty by now BUT
# because the same list is re-used for all plugins I cannot pop them in
# the plugins. I don't think multiple inheritance would work with this
# setup so let's not pretend it does: hence comment out the super-init
# call:
## super(Simulation, self).__init__(**kwargs)
logger.info("Simulation instance initialised:")
logger.info(str(self))
def add_plugin(self, plugin, **kwargs):
"""Add a plugin to the registry.
- If *plugin* is a :class:`Plugin` instance then the
instance is directly registered and any keyword arguments
are ignored.
- If *plugin* is a :class:`Plugin` class object or a
string that can be found in :mod:`gromacs.analysis.plugins`
then first an instance is created with the given keyword
arguments and then registered.
:Arguments:
*plugin* : class or string, or instance
If the parameter is a class then it should have been derived
from :class:`Plugin`. If it is a string then it is taken as a
plugin name in :mod:`gromacs.analysis.plugins` and the
corresponding class is added. In both cases any parameters for
initizlization should be provided.
If *plugin* is already a :class:`Plugin` instance then the kwargs
will be ignored.
*kwargs*
The kwargs are specific for the plugin and should be
described in its documentation.
"""
# simulation=self must be provided so that plugin knows who owns it
try:
plugin.register(simulation=self)
except (TypeError, AttributeError):
# NOTE: this except clause can mask bugs in the plugin code!!
if type(plugin) is str:
import plugins # We should be able to import this safely now...
plugin = plugins.__plugin_classes__[plugin]
# plugin registers itself in self.plugins
plugin(simulation=self, **kwargs) # simulation=self is REQUIRED!
def topdir(self,*args):
"""Returns a path under self.analysis_dir, which is guaranteed to exist.
.. Note:: Parent dirs are created if necessary."""
p = os.path.join(self.analysis_dir, *args)
parent = os.path.dirname(p)
try:
os.makedirs(parent)
except OSError,err:
if err.errno != errno.EEXIST:
raise
return p
class _COM(Worker):
"""COM worker class."""
def __init__(self,**kwargs):
"""Set up COM analysis.
:Keywords:
*group_names*
list of index group names
*ndx*
index file if groups are not in the default index
*offset*
add the *offset* to the residue numbers [0]
*name*
plugin name [COM]
*simulation*
The :class:`gromacs.analysis.Simulation` instance that
owns the plugin [None]
"""
group_names = asiterable(kwargs.pop('group_names', []))
ndx = kwargs.pop('ndx', None)
offset = kwargs.pop('offset', 0)
super(_COM, self).__init__(**kwargs)
self.parameters.group_names = group_names
self.parameters.offset = offset
self.ndx = ndx
if not self.simulation is None:
self._register_hook()
def _register_hook(self, **kwargs):
"""Run when registering; requires simulation."""
super(_COM, self)._register_hook(**kwargs)
assert not self.simulation is None
if self.ndx is None:
self.ndx = self.simulation.ndx
self.parameters.filenames = { # result xvg files
'com': self.plugindir('com.xvg'),
}
# default filename for the plots -- not used
self.parameters.fignames = {
'com': self.figdir('com'),
}
def run(self, force=None, **gmxargs):
"""Analyze trajectory and write COM file.
All three components of the COM coordinate are written.
:Arguments:
- *force*: ``True`` does analysis and overwrites existing files
- *gmxargs*: additional keyword arguments for :func:`gromacs.g_bundle`
"""
gmxargs['com'] = True
gmxargs['mol'] = False
gmxargs['ng'] = len(self.parameters.group_names)
gmxargs['x'] = True
gmxargs['y'] = True
gmxargs['z'] = True
if gmxargs['ng'] == 0:
errmsg = "No index group name(s) provided. Use group_name with the constructor."
logger.error(errmsg)
raise ValueError(errmsg)
if self.check_file_exists(self.parameters.filenames['com'], resolve='warning', force=force):
return
logger.info("Analyzing COM ...")
f = self.parameters.filenames
gromacs.g_traj(s=self.simulation.tpr, f=self.simulation.xtc, n=self.ndx,
ox=f['com'], input=self.parameters.group_names, **gmxargs)
def analyze(self,**kwargs):
"""Collect output xvg files as :class:`gromacs.formats.XVG` objects.
- Make COM as a function of time available as XVG files and
objects.
- Compute RMSD of the COM of each group (from average
position, "rmsd").
- Compute distance whic encompasses 50% of observations ("median")
- Compute drift of COM, i.e. length of the vector between
initial and final position. Initial and final position are
computed as averages over *nframesavg* frames ("drift").
RMSD, median, and drift are columns in an xvg file. The rows correspond
to the groups in :attr:`gromacs.analysis.plugins.com.Worker.results.group_names`.
:Keywords:
*nframesavg*
number of initial and final frames that are averaged in
order to compute the drift of the COM of each group
[5000]
*refgroup*
group name whose com is taken as the reference and subtracted from
all other coms for the distance calculations. If supplied,
additional result 'com_relative_*refgroup*' is created.
:Returns: a dictionary of the results and also sets
:attr:`gromacs.analysis.plugins.com.Worker.results`.
"""
from gromacs.formats import XVG
logger.info("Preparing COM graphs as XVG objects.")
self.results = AttributeDict( (k, XVG(fn)) for k,fn in self.parameters.filenames.items() )
# compute RMSD of COM and shift of COM (drift) between avg pos
# over first/last 5,000 frames
nframesavg = kwargs.pop('nframesavg', 5000)
ngroups = len(self.parameters.group_names)
xcom = self.results['com'].array
refgroup = kwargs.pop('refgroup', None)
if not refgroup is None:
if not refgroup in self.parameters.group_names:
errmsg = "refgroup=%s must be one of %r" % (refgroup, self.parameters.group_names)
logger.error(errmsg)
raise ValueError(errmsg)
nreference = 1 + 3 * self.parameters.group_names.index(refgroup) # 1-based !!
reference_com = xcom[nreference:nreference+3]
xcom[1:] -= numpy.vstack(ngroups * [reference_com]) # can't use broadcast
logger.debug("distances computed with refgroup %r", refgroup)
self.store_xvg('com_relative_%s' % refgroup, xcom,
names=['time']+self.parameters.group_names)
def vlength(v):
return numpy.sqrt(numpy.sum(v**2, axis=0)) # distances over time step
logger.debug("drift calculated between %d-frame averages at beginning and end",nframesavg)
records = []
for i in xrange(1, 3*ngroups+1, 3):
x = xcom[i:i+3]
r = vlength(x - x.mean(axis=1)[:,numpy.newaxis]) # distances over time step
#r0 = vlength(r - r[:,0][:,numpy.newaxis]) # distances over time step from r(t=0)
#h,edges = numpy.histogram(r, bins=kwargs.get('bins', 100), normed=True)
#m = 0.5*(edges[1:]+edges[:-1])
#c = h.cumsum(dtype=float) # integral
#c /= c[-1] # normalized (0 to 1)
#median = m[c < 0.5][-1]
#g = h/(4*numpy.pi*m**2)
#import scipy.integrate
#radint = lambda y: 4*numpy.pi*scipy.integrate.simps(m**2*y, x=m)
#g /= radint(g) # properly normalized radial distribution function
rmsd = numpy.sqrt(numpy.mean(r**2)) # radial spread sqrt(radint(m**2 * g))
median = numpy.median(r) # radius that contains 50% of the observations
dx = x[:,:nframesavg].mean(axis=1) - x[:,-nframesavg:].mean(axis=1)
drift = vlength(dx)
records.append((rmsd, median, drift))
self.store_xvg('distance', numpy.transpose(records), names="rmsd,median,drift")
return self.results
def plot(self, **kwargs):
"""Plot all results in one graph, labelled by the result keys.
:Keywords:
observables
select one or more of the stored results. Can be a list
or a string (a key into the results dict). ``None``
plots everything [``None``]
figure
- ``True``: save figures in the given formats
- "name.ext": save figure under this filename (``ext`` -> format)
- ``False``: only show on screen [``False``]
formats : sequence
sequence of all formats that should be saved [('png', 'pdf')]
plotargs
keyword arguments for pylab.plot()
"""
import pylab
figure = kwargs.pop('figure', False)
observables = asiterable(kwargs.pop('observables', self.results.keys()))
extensions = kwargs.pop('formats', ('pdf','png'))
for name in observables:
result = self.results[name]
try:
result.plot(**kwargs) # This requires result classes with a plot() method!!
except AttributeError:
warnings.warn("Sorry, plotting of result %(name)r is not implemented" % vars(),
category=UserWarning)
# quick labels -- relies on the proper ordering
labels = [str(n)+" "+dim for n in self.parameters.group_names
for dim in 'xyz']
if not kwargs.get('columns', None) is None:
# select labels according to columns; only makes sense
# if plotting against the time (col 0)
if kwargs['columns'][0] == 0:
labels = numpy.array([None]+labels)[kwargs['columns'][1:]]
else:
labels = ()
pylab.legend(labels, loc='best')
if figure is True:
for ext in extensions:
self.savefig(ext=ext)
elif figure:
self.savefig(filename=figure)
