def getCostPerNeighbor(self, lowerTime, upperTime):
sentPerRecipient = []
for node in self.itervalues():
sentPerRecipient.extend(node.getCostPerNeighbor(lowerTime,upperTime))
# Find max and min
maxNgh = -1
minNgh = sys.maxint
for i in sentPerRecipient:
if(i > maxNgh):
maxNgh = i
if(i < minNgh):
minNgh = i
if(len(sentPerRecipient) == 0):
return -1,-1,-1,-1
elif(len(sentPerRecipient) == 1):
return sentPerRecipient[0],0,sentPerRecipient[0],sentPerRecipient[0]
else:
return average(sentPerRecipient), standardDeviation(sentPerRecipient),minNgh,maxNgh
def MC2(l,f,err,edge,lowl,highl,reps):
"""Monte-carlo method, creates datasets by applying the variance in the
data to the best-fit model. reps is the number of runs to make"""
global output
auto_edge(l,f,err,edge,lowl,highl) #get best-fit
lne,fne,ene = get_edge(l,f,err,edge,lowl,highl)
spectrum = results[0][2] * (lne/l_edge)**results[0][0] * N.where(lne>l_edge,1,\
N.exp(-(lne/l_edge)**3*sig*results[0][1]))
goodpoints = where(ene<9)
sd = standardDeviation(fne[goodpoints] - spectrum[goodpoints])
print "Standard deviation of residuals:",sd
output = zeros(reps)*1.0
indeces = zeros(reps)*1.0 #if you want to store both parameters
for count in range(reps):
if count%100 == 0 : print count
fnew = spectrum + randn(len(spectrum))*sd
output[count] = auto_edge(lne,fnew,ene,edge,lowl,highl)
indeces[count] = results[0][0] #you want indeces?
return output,indeces #you want indeces?
np = universe.numberOfPoints() P = np/natoms #if (rotskipval < 100.0): # rotskipratio=1.0 #else: # rotskipratio=100.0/rotskipval # Print averages of the quantu energy estimator print "Number of Atoms:", natoms print "Number of Beads:", P print "Number of Steps:", npoints #data = trajectory.temperature #print "Temperature:", mean(data), "+/-", standardDeviation(data)/sqrt(npoints), " kj/mol" data = trajectory.quantum_energy_primitive print "Primitive estimator:", mean(data/Units.k_B), "+/-", standardDeviation(data/Units.k_B)/sqrt(npoints), " K" data2 = trajectory.potential_energy pot_eng = mean(data2/Units.k_B) pot_eng_error = standardDeviation(data2/Units.k_B)/sqrt(npoints) print "Potential estimator:", pot_eng, "+/-", pot_eng_error, " K" print "Kinetic estimator:", mean(data-data2)/Units.k_B #data = trajectory.kinetic_energy #print "Kinetic estimator:", mean(data/Units.k_B), "+/-", standardDeviation(data/Units.k_B)/sqrt(npoints), " K" #data = trajectory.spring_energy #print "Spring estimator:", mean(data/Units.k_B), "+/-", standardDeviation(data/Units.k_B)/sqrt(npoints), " K" #data = trajectory.quantum_energy_virial #print "Virial estimator:", mean(data), "+/-", standardDeviation(data)/sqrt(npoints), " kj/mol" #data = trajectory.quantum_energy_centroid_virial
traj = argv[1] trajectory = Trajectory(None, traj) npoints = len(trajectory) universe = trajectory.universe natoms = universe.numberOfAtoms() time=trajectory.time np = universe.numberOfPoints() P = np/natoms # Print averages of the quantu energy estimator print "Number of Atoms:", natoms print "Number of Beads:", P print "Number of Steps:", npoints Temp = trajectory.temperature Eprim = trajectory.quantum_energy_primitive Ecenv = trajectory.quantum_energy_centroid_virial Epot = trajectory.potential_energy Erot = trajectory.quantum_energy_rotation print "Temperature:", mean(Temp), "+/-", standardDeviation(Temp)/sqrt(npoints), " K" print "Primitive estimator:", mean(Eprim/Units.k_B), "+/-", standardDeviation(Eprim/Units.k_B)/sqrt(npoints), " K" print "Centroid Virial estimator:", mean(Ecenv/Units.k_B), "+/-", standardDeviation(Ecenv/Units.k_B)/sqrt(npoints), " K" print "Potential estimator:", mean(Epot/Units.k_B), "+/-", standardDeviation(Epot/Units.k_B)/sqrt(npoints), " K" print "Kinetic estimator:",mean(Eprim-Epot)/Units.k_B, "+/-", standardDeviation(Eprim/Units.k_B)/sqrt(npoints)-standardDeviation(Epot/Units.k_B)/sqrt(npoints), " K" print "Rotational Energy estimator:", mean(Erot/Units.k_B), "+/-", standardDeviation(Erot/Units.k_B)/sqrt(npoints), " K" print "Total Energy:", mean(Eprim+Erot)/Units.k_B, "+/-", sqrt((standardDeviation(Eprim)/Units.k_B/sqrt(npoints))**2+(standardDeviation(Erot)/Units.k_B/sqrt(npoints))**2), " K" trajectory.close()
Eprim = trajectory.quantum_energy_primitive
Ecenv = trajectory.quantum_energy_centroid_virial
Epot = trajectory.potential_energy
Erot = trajectory.quantum_energy_rotation
#print "Temperature:", mean(Temp), "+/-", standardDeviation(Temp)/sqrt(npoints), " K"
#print "Primitive estimator:", mean(Eprim/Units.k_B), "+/-", standardDeviation(Eprim/Units.k_B)/sqrt(npoints), " K"
#print "Centroid Virial estimator:", mean(Ecenv/Units.k_B), "+/-", standardDeviation(Ecenv/Units.k_B)/sqrt(npoints), " K"
#print "Potential estimator:", mean(Eprim)/Units.k_B, "+/-", standardDeviation(Eprim)/Units.k_B, " K"
##print "Potential estimator:", mean(Epot/Units.k_B), "+/-", standardDeviation(Epot/Units.k_B)/sqrt(npoints), " K"
#print "Kinetic estimator:",mean(Ecenv)/Units.k_B, "+/-", standardDeviation(Ecenv)/Units.k_B, " K"
##print "Kinetic estimator:",mean(Eprim-Epot)/Units.k_B, "+/-", standardDeviation(Eprim/Units.k_B)/sqrt(npoints)-standardDeviation(Epot/Units.k_B)/sqrt(npoints), " K"
#print "Rotational Energy estimator:", mean(Erot)/Units.k_B, "+/-", standardDeviation(Erot)/Units.k_B, " K"
#
#print "Total Energy:", mean(Eprim)/Units.k_B+mean(Erot)/Units.k_B, "+/-", sqrt((standardDeviation(Eprim)/Units.k_B)**2+(standardDeviation(Erot)/Units.k_B)**2), " K"
print "Potential estimator:", mean(Eprim), "+/-", standardDeviation(
Eprim), " kj/mol"
#print "Potential estimator:", mean(Epot/Units.k_B), "+/-", standardDeviation(Epot/Units.k_B)/sqrt(npoints), " K"
print "Kinetic estimator:", mean(Ecenv), "+/-", standardDeviation(
Ecenv), " kj/mol"
#print "Kinetic estimator:",mean(Eprim-Epot)/Units.k_B, "+/-", standardDeviation(Eprim/Units.k_B)/sqrt(npoints)-standardDeviation(Epot/Units.k_B)/sqrt(npoints), " K"
print "Rotational Energy estimator:", mean(Erot), "+/-", standardDeviation(
Erot), " kj/mol"
print "Total Energy:", mean(Eprim) + mean(Erot), "+/-", sqrt(
(standardDeviation(Eprim))**2 + (standardDeviation(Erot))**2), " kj/mol"
trajectory.close()
def __init__(self, master, mode_projector, indices):
from Scientific.Statistics import mean, standardDeviation
title = "Normal mode projections (Temperature: %5.1f K)" \
% mode_projector.temperature
PlotWindow.__init__(self, master, title)
self.mode_projector = mode_projector
plot_range = 0.
for i in indices:
series = mode_projector[i][:, 1]
average = mean(series)
deviation = standardDeviation(series)
lower = Numeric.minimum.reduce(series)
lower = min(lower, average-deviation)
upper = Numeric.maximum.reduce(series)
upper = max(upper, average+deviation)
lower, upper = plotRange(lower, upper)
plot_range = max(plot_range, upper-lower)
nmodes = mode_projector.numberOfModes()
for i in range(len(indices)):
if indices[i] < 0:
indices[i] = nmodes+indices[i]
next_indices = []
step = indices[1] - indices[0]
i = indices[-1]
while len(next_indices) < 4:
i = i + step
if i >= nmodes:
break
next_indices.append(i)
previous_indices = []
i = indices[0]
while len(previous_indices) < 4:
i = i - step
if i < 6:
break
previous_indices.insert(0, i)
if next_indices or previous_indices:
frame = Frame(self)
frame.pack(side=BOTTOM, fill=X, expand=YES)
if previous_indices:
Button(frame, text="Previous",
command=lambda s=self, pi=previous_indices:
s.modeProjection(pi)).pack(side=LEFT)
if next_indices:
Button(frame, text="Next",
command=lambda s=self, ni=next_indices:
s.modeProjection(ni)).pack(side=RIGHT)
for i in range(len(indices)):
number = indices[i]
data = mode_projector[number]
fluctuation = self.mode_projector.amplitude(number)
average = mean(data[:, 1])
deviation = standardDeviation(data[:, 1])
box = Frame(self, border=2, relief=SUNKEN)
box.pack(side=TOP, fill=BOTH, expand=YES)
frame = Frame(box, background='grey')
frame.pack(side=TOP, fill=X, expand=NO)
Label(frame, text="Mode %d" % number,
background='grey').pack(side=LEFT)
Button(frame, text="Animation", background='grey',
command=lambda s=self, n=number:
s.animateMode(n, 1.)).pack(side=RIGHT)
Button(frame, text="View", background='grey',
command=lambda s=self, n=number:
s.viewMode(n)).pack(side=RIGHT)
minv = Numeric.minimum.reduce(data[:,1])
maxv = Numeric.maximum.reduce(data[:,1])
minv, maxv = plotRange(minv, maxv)
middle = 0.5*(maxv+minv)
if maxv-minv < plot_range:
minv = middle-0.5*plot_range
maxv = middle+0.5*plot_range
plot_objects = [PolyLine([(data[1, 0], average-fluctuation),
(data[1, 0], average+fluctuation)],
color='blue', width=3),
PolyLine([(data[-1, 0], average-deviation),
(data[-1, 0], average+deviation)],
color='green', width=3)]
plot_objects.insert(0, PolyLine(data, color = 'red'))
plot = PlotCanvas(box, 400, 100, zoom=1,
select=self.master._selectRange)
plot.pack(side=LEFT, fill=BOTH, expand=YES)
plot.draw(PlotGraphics(plot_objects), 'automatic', (minv, maxv))
plot.bind('<Double-Button-1>', lambda event, d=data:
externalPlot(d))
self.registerPlot(plot)
self.setSelection(plot)
def __init__(self, master, mode_projector, indices):
from Scientific.Statistics import mean, standardDeviation
title = "Normal mode projections (Temperature: %5.1f K)" \
% mode_projector.temperature
PlotWindow.__init__(self, master, title)
self.mode_projector = mode_projector
plot_range = 0.
for i in indices:
series = mode_projector[i][:, 1]
average = mean(series)
deviation = standardDeviation(series)
lower = Numeric.minimum.reduce(series)
lower = min(lower, average - deviation)
upper = Numeric.maximum.reduce(series)
upper = max(upper, average + deviation)
lower, upper = plotRange(lower, upper)
plot_range = max(plot_range, upper - lower)
nmodes = mode_projector.numberOfModes()
for i in range(len(indices)):
if indices[i] < 0:
indices[i] = nmodes + indices[i]
next_indices = []
step = indices[1] - indices[0]
i = indices[-1]
while len(next_indices) < 4:
i = i + step
if i >= nmodes:
break
next_indices.append(i)
previous_indices = []
i = indices[0]
while len(previous_indices) < 4:
i = i - step
if i < 6:
break
previous_indices.insert(0, i)
if next_indices or previous_indices:
frame = Frame(self)
frame.pack(side=BOTTOM, fill=X, expand=YES)
if previous_indices:
Button(frame,
text="Previous",
command=lambda s=self, pi=previous_indices: s.
modeProjection(pi)).pack(side=LEFT)
if next_indices:
Button(frame,
text="Next",
command=lambda s=self, ni=next_indices: s.
modeProjection(ni)).pack(side=RIGHT)
for i in range(len(indices)):
number = indices[i]
data = mode_projector[number]
fluctuation = self.mode_projector.amplitude(number)
average = mean(data[:, 1])
deviation = standardDeviation(data[:, 1])
box = Frame(self, border=2, relief=SUNKEN)
box.pack(side=TOP, fill=BOTH, expand=YES)
frame = Frame(box, background='grey')
frame.pack(side=TOP, fill=X, expand=NO)
Label(frame, text="Mode %d" % number,
background='grey').pack(side=LEFT)
Button(frame,
text="Animation",
background='grey',
command=lambda s=self, n=number: s.animateMode(n, 1.)).pack(
side=RIGHT)
Button(frame,
text="View",
background='grey',
command=lambda s=self, n=number: s.viewMode(n)).pack(
side=RIGHT)
minv = Numeric.minimum.reduce(data[:, 1])
maxv = Numeric.maximum.reduce(data[:, 1])
minv, maxv = plotRange(minv, maxv)
middle = 0.5 * (maxv + minv)
if maxv - minv < plot_range:
minv = middle - 0.5 * plot_range
maxv = middle + 0.5 * plot_range
plot_objects = [
PolyLine([(data[1, 0], average - fluctuation),
(data[1, 0], average + fluctuation)],
color='blue',
width=3),
PolyLine([(data[-1, 0], average - deviation),
(data[-1, 0], average + deviation)],
color='green',
width=3)
]
plot_objects.insert(0, PolyLine(data, color='red'))
plot = PlotCanvas(box,
400,
100,
zoom=1,
select=self.master._selectRange)
plot.pack(side=LEFT, fill=BOTH, expand=YES)
plot.draw(PlotGraphics(plot_objects), 'automatic', (minv, maxv))
plot.bind('<Double-Button-1>',
lambda event, d=data: externalPlot(d))
self.registerPlot(plot)
self.setSelection(plot)