def store_xvg(self, name, a, **kwargs):
"""Store array *a* as :class:`~gromacs.formats.XVG` in result *name*.
kwargs are passed to :class:`gromacs.formats.XVG`.
This is a helper method that simplifies the task of storing
results in the form of a numpy array as a data file on disk in
the xmgrace format and also as a :class:`~gromacs.formats.XVG`
instance in the :attr:`gromacs.analysis.core.Worker.results`
dictionary.
"""
from gromacs.formats import XVG
kwargs.pop('filename', None) # ignore filename
filename = self.plugindir(name + '.xvg')
xvg = XVG(**kwargs)
xvg.set(a)
xvg.write(filename)
self.results[name] = xvg
self.parameters.filenames[name] = filename
return filename
def store_xvg(self, name, a, **kwargs):
"""Store array *a* as :class:`~gromacs.formats.XVG` in result *name*.
kwargs are passed to :class:`gromacs.formats.XVG`.
This is a helper method that simplifies the task of storing
results in the form of a numpy array as a data file on disk in
the xmgrace format and also as a :class:`~gromacs.formats.XVG`
instance in the :attr:`gromacs.analysis.core.Worker.results`
dictionary.
"""
from gromacs.formats import XVG
kwargs.pop('filename',None) # ignore filename
filename = self.plugindir(name+'.xvg')
xvg = XVG(**kwargs)
xvg.set(a)
xvg.write(filename)
self.results[name] = xvg
self.parameters.filenames[name] = filename
return filename
def analyze(self, **kwargs):
"""Load results from disk into :attr:`_Dihedrals.results` and compute PMF.
The PMF W(phi) in kT is computed from each dihedral
probability distribution P(phi) as
W(phi) = -kT ln P(phi)
It is stored in :attr:`_Dihedrals.results` with the key *PMF*.
:Keywords:
*bins*
bins for histograms (passed to numpy.histogram(new=True))
:Returns: a dictionary of the results and also sets
:attr:`_Dihedrals.results`.
"""
bins = kwargs.pop("bins", 361)
results = AttributeDict()
# get graphs that were produced by g_angle
for name, f in self.parameters.filenames.items():
try:
results[name] = XVG(f)
except IOError:
pass # either not computed (yet) or some failure
# compute individual distributions
ts = results["timeseries"].array # ts[0] = time, ts[1] = avg
dih = ts[2:]
phi_range = (-180.0, 180.0)
Ndih = len(dih)
p = Ndih * [None] # histograms (prob. distributions), one for each dihedral i
for i in xrange(Ndih):
phis = dih[i]
p[i], e = numpy.histogram(phis, bins=bins, range=phi_range, normed=True, new=True)
P = numpy.array(p)
phi = 0.5 * (e[:-1] + e[1:]) # midpoints of bin edges
distributions = numpy.concatenate((phi[numpy.newaxis, :], P)) # phi, P[0], P[1], ...
xvg = XVG()
xvg.set(distributions)
xvg.write(self.parameters.filenames["distributions"])
results["distributions"] = xvg
del xvg
# compute PMF (from individual distributions)
W = -numpy.log(P) # W(phi)/kT = -ln P
W -= W.min(axis=1)[:, numpy.newaxis] # minimum at 0 kT
pmf = numpy.concatenate((phi[numpy.newaxis, :], W), axis=0)
xvg = XVG()
xvg.set(pmf)
xvg.write(self.parameters.filenames["PMF"])
results["PMF"] = xvg
self.results = results
return results
def analyze(self, **kwargs):
"""Load results from disk into :attr:`_Dihedrals.results` and compute PMF.
The PMF W(phi) in kT is computed from each dihedral
probability distribution P(phi) as
W(phi) = -kT ln P(phi)
It is stored in :attr:`_Dihedrals.results` with the key *PMF*.
:Keywords:
*bins*
bins for histograms (passed to numpy.histogram(new=True))
:Returns: a dictionary of the results and also sets
:attr:`_Dihedrals.results`.
"""
bins = kwargs.pop('bins', 361)
results = AttributeDict()
# get graphs that were produced by g_angle
for name, f in self.parameters.filenames.items():
try:
results[name] = XVG(f)
except IOError:
pass # either not computed (yet) or some failure
# compute individual distributions
ts = results['timeseries'].array # ts[0] = time, ts[1] = avg
dih = ts[2:]
phi_range = (-180., 180.)
Ndih = len(dih)
p = Ndih * [None] # histograms (prob. distributions), one for each dihedral i
for i in xrange(Ndih):
phis = dih[i]
p[i],e = numpy.histogram(phis, bins=bins, range=phi_range, normed=True, new=True)
P = numpy.array(p)
phi = 0.5*(e[:-1]+e[1:]) # midpoints of bin edges
distributions = numpy.concatenate((phi[numpy.newaxis, :], P)) # phi, P[0], P[1], ...
xvg = XVG()
xvg.set(distributions)
xvg.write(self.parameters.filenames['distributions'])
results['distributions'] = xvg
del xvg
# compute PMF (from individual distributions)
W = -numpy.log(P) # W(phi)/kT = -ln P
W -= W.min(axis=1)[:, numpy.newaxis] # minimum at 0 kT
pmf = numpy.concatenate((phi[numpy.newaxis, :], W), axis=0)
xvg = XVG()
xvg.set(pmf)
xvg.write(self.parameters.filenames['PMF'])
results['PMF'] = xvg
self.results = results
return results
