def showSqFlucts(modes, *args, **kwargs):
"""Show square fluctuations using :func:`~matplotlib.pyplot.plot`.
.. plot::
:context:
:include-source:
plt.figure(figsize=(6,4))
showSqFlucts( p38_anm[0] )
showSqFlucts( p38_anm[1] )
plt.legend(prop={'size': 10})
.. plot::
:context:
:nofigs:
plt.close('all')"""
import matplotlib.pyplot as plt
sqf = calcSqFlucts(modes)
if not 'label' in kwargs:
kwargs['label'] = str(modes)
show = plt.plot(sqf, *args, **kwargs)
plt.xlabel('Indices')
plt.ylabel('Square fluctuations')
plt.title(str(modes))
return show
def showScaledSqFlucts(modes, *args, **kwargs):
"""Show scaled square fluctuations using :func:`~matplotlib.pyplot.plot`.
Modes or mode sets given as additional arguments will be scaled to have
the same mean squared fluctuations as *modes*.
.. plot::
:context:
:include-source:
plt.figure(figsize=(5,4))
showScaledSqFlucts(p38_pca[0], p38_anm[2])
plt.legend(prop={'size': 10})
.. plot::
:context:
:nofigs:
plt.close('all')"""
import matplotlib.pyplot as plt
sqf = calcSqFlucts(modes)
mean = sqf.mean()
args = list(args)
modesarg = []
i = 0
while i < len(args):
if isinstance(args[i], (VectorBase, ModeSet, NMA)):
modesarg.append(args.pop(i))
else:
i += 1
show = [plt.plot(sqf, *args, label=str(modes), **kwargs)]
plt.xlabel('Indices')
plt.ylabel('Square fluctuations')
for modes in modesarg:
sqf = calcSqFlucts(modes)
scalar = mean / sqf.mean()
show.append(plt.plot(sqf * scalar, *args,
label='{0:s} (x{1:.2f})'.format(str(modes), scalar),
**kwargs))
return show
def showNormedSqFlucts(modes, *args, **kwargs):
"""Show normalized square fluctuations using :func:`~matplotlib.pyplot.
plot`.
.. plot::
:context:
:include-source:
plt.figure(figsize=(5,4))
showNormedSqFlucts(p38_pca[0], p38_anm[2])
plt.legend(prop={'size': 10})
.. plot::
:context:
:nofigs:
plt.close('all')"""
import matplotlib.pyplot as plt
sqf = calcSqFlucts(modes)
args = list(args)
modesarg = []
i = 0
while i < len(args):
if isinstance(args[i], (VectorBase, ModeSet, NMA)):
modesarg.append(args.pop(i))
else:
i += 1
show = [plt.plot(sqf/(sqf**2).sum()**0.5, *args,
label='{0:s}'.format(str(modes)), **kwargs)]
plt.xlabel('Indices')
plt.ylabel('Square fluctuations')
for modes in modesarg:
sqf = calcSqFlucts(modes)
show.append(plt.plot(sqf/(sqf**2).sum()**0.5, *args,
label='{0:s}'.format(str(modes)), **kwargs))
return show
