Python compute_distancesの例

コード例 #1

ファイル: ljsim.py プロジェクト: simphys/exercises

p.plot(ts, Es, label='Eges')
p.plot(ts, Epots, label='Epot')

# Temperature
p.new(xlabel='time', ylabel='temperature')
p.plot(ts, Ts, label='T')

# Pressure
p.new(xlabel='time', ylabel='pressure')
p.plot(ts, Ps, label='P')

# RDF
datafile = open(datafilename, 'r')
ts, Es, Epots, Ekins, Ts, Ps, traj2 = pickle.load(datafile)
datafile.close()
p.new(xlabel='distance', ylabel='probability')

hist, bins = np.histogram(compute_distances(traj2[-1]),
                          bins=np.linspace(0.8, 5, 100))
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
hist /= (2 * np.pi * density * (bins[1] - bins[0]) *
         (bins[:-1] + (bins[1] - bins[0]) / 2)**2)

p.bar(center, hist / N, align='center',
      width=width)  # kA, warum erst hier durch N geteilt werden darf

p.make(ncols=3)

print "Finished."

コード例 #2

ファイル: ljanalyze.py プロジェクト: simphys/exercises

Ts100 = compute_running_average(Ts, 100)

p.new(title='Temperature (from t=100)', xlabel='time', ylabel='temperature')
p.plot(ts, Ts, label='T')
p.plot(ts10, Ts10, label='running av 10')
p.plot(ts100, Ts100, label='running av 100')

# Pressure
#Average
Ps10 = compute_running_average(Ps, 10)
Ps100 = compute_running_average(Ps, 100)
p.new(title='Pressure (from t=100)', xlabel='time', ylabel='pressure')
p.plot(ts, Ps, label='P')
p.plot(ts10, Ps10, label='running av 10')
p.plot(ts100, Ps100, label='running av 100')

# RDF
#Average
l = min(len(traj), 100)
m = N * (N - 1) / 2
dist = np.empty(l * m)
for i in range(1, l + 1):
    dist[(i - 1) * m:i * m] = compute_distances(traj[-i])
p.new(title='RDF (last 100 trajectories)',
      xlabel='distance',
      ylabel='probability')
p.hist(dist, bins=100, range=(0.8, 5), normed=True, log=False, label='RDF')

p.make(ncols=2)

print "Finished."

コード例 #3

ファイル: ljanalyze.py プロジェクト: simphys/exercises

#Average
Ts10=compute_running_average(Ts, 10)
Ts100=compute_running_average(Ts, 100)

p.new(title='Temperature (from t=100)',xlabel='time',ylabel='temperature')
p.plot(ts,Ts, label='T')
p.plot(ts10,Ts10, label='running av 10')
p.plot(ts100,Ts100, label='running av 100')

# Pressure
#Average
Ps10=compute_running_average(Ps, 10)
Ps100=compute_running_average(Ps, 100)
p.new(title='Pressure (from t=100)', xlabel='time',ylabel='pressure')
p.plot(ts,Ps, label='P')
p.plot(ts10,Ps10, label='running av 10')
p.plot(ts100,Ps100, label='running av 100')

# RDF
#Average
l = min(len(traj),100)
m = N*(N-1)/2
dist = np.empty(l*m)
for i in range(1,l+1):
    dist[(i-1)*m:i*m] = compute_distances(traj[-i])  
p.new(title='RDF (last 100 trajectories)', xlabel='distance',ylabel='probability')
p.hist(dist, bins=100, range=(0.8,5), normed=True,  log=False, label='RDF')

p.make(ncols= 2)

print "Finished."

コード例 #4

ファイル: ljsim.py プロジェクト: simphys/exercises

    p.plot(tpart[-1, 0, n], tpart[-1, 1, n], "o", c=colors[n], alpha=0.8, ms=7, mew=2)

# Total energy
p.new(xlabel="time", ylabel="energy")
p.plot(ts, Es, label="Eges")

# Energies
p.new(xlabel="time", ylabel="energy")
p.plot(ts, Ekins, label="Ekin")
p.plot(ts, Es, label="Eges")
p.plot(ts, Epots, label="Epot")

# Temperature
p.new(xlabel="time", ylabel="temperature")
p.plot(ts, Ts, label="T")

# Pressure
p.new(xlabel="time", ylabel="pressure")
p.plot(ts, Ps, label="P")

# RDF
datafile = open(datafilename, "r")
ts, Es, Epots, Ekins, Ts, Ps, traj2 = pickle.load(datafile)
datafile.close()
p.new(xlabel="distance", ylabel="probability")
p.hist(compute_distances(traj2[-1]), bins=100, range=(0.8, 5), normed=True, log=False, label="RDF")

p.make(ncols=3)

print "Finished."

コード例 #5

ファイル: ljsim.py プロジェクト: simphys/exercises

# Energies
p.new(xlabel='time',ylabel='energy')
p.plot(ts,Ekins, label='Ekin')
p.plot(ts,Es, label='Eges')
p.plot(ts,Epots, label='Epot')

# Temperature
p.new(xlabel='time',ylabel='temperature')
p.plot(ts,Ts, label='T')

# Pressure
p.new(xlabel='time',ylabel='pressure')
p.plot(ts,Ps, label='P')

# RDF
datafile = open(datafilename, 'r')
ts, Es, Epots, Ekins, Ts, Ps, traj2 = pickle.load(datafile)
datafile.close()
p.new(xlabel='distance',ylabel='probability')

hist, bins = np.histogram(compute_distances(traj2[-1]),bins = np.linspace(0.8,5,100))
width = 0.7*(bins[1]-bins[0])
center = (bins[:-1]+bins[1:])/2
hist /=(2*np.pi*density*(bins[1]-bins[0])*(bins[:-1]+(bins[1]-bins[0])/2)**2)

p.bar(center, hist/N, align = 'center', width = width) # kA, warum erst hier durch N geteilt werden darf

p.make(ncols= 3)

print "Finished."

コード例 #6

ファイル: ljsim.py プロジェクト: simphys/exercises

# Energies
p.new(xlabel='time', ylabel='energy')
p.plot(ts, Ekins, label='Ekin')
p.plot(ts, Es, label='Eges')
p.plot(ts, Epots, label='Epot')

# Temperature
p.new(xlabel='time', ylabel='temperature')
p.plot(ts, Ts, label='T')

# Pressure
p.new(xlabel='time', ylabel='pressure')
p.plot(ts, Ps, label='P')

# RDF
datafile = open(datafilename, 'r')
ts, Es, Epots, Ekins, Ts, Ps, traj2 = pickle.load(datafile)
datafile.close()
p.new(xlabel='distance', ylabel='probability')
p.hist(compute_distances(traj2[-1]),
       bins=100,
       range=(0.8, 5),
       normed=True,
       log=False,
       label='RDF')

p.make(ncols=3)

print "Finished."