name='rdf')
n = 0
for medium, values in sorted(datadict['rdf'].iteritems()):
i1 = signal.find_peaks_cwt(values[2], np.arange(1, 20), noise_perc=1)[0]
i2 = signal.find_peaks_cwt(values[2], np.arange(20, 40), noise_perc=1)[1:3]
peakindex = np.append(i1, i2)
mx = values[1][peakindex]
my = values[2][peakindex]
print medium + ":"
for i in range(0, 3):
print "(%f | %f)" % (mx[i], my[i])
p.plot(mx, my, c[n] + '*', ms=11, mew=1)
p.plot(values[1], values[2], c[n] + '-', label=medium)
n += 1
p.make()
'''f.loadFunction(lambda x, *p: p[1]*x**p[0], [1,1])
p.new(xlabel=r'time [ps]', ylabel=r'mean aquare displacement',name='regimes', xscale='log',yscale='log')
for medium, values in sorted(datadict['msd'].iteritems()):
i = values[1] < 600
j = values[1][i] < 0.3
f.loadData(values[1][i][j], values[2][i][j], scale='log')
x = np.linspace(0.1, 10,2)
p.plot(x, f(x), 'k-')
j = values[1][i] > 0.5
f.loadData(values[1][i][j], values[2][i][j], scale='log')
x = np.linspace(0.1, 1,2)
p.plot(x, f(x), 'k-')
# --- Auslenkung ---
p.new(title='Auslenkung',xlabel='t',ylabel='x')
for v,w,name in x_s: p.plot(v,w,label=name)
# --- Auslenkung ---
p.new(title='Plots für ausgeschaltete Anregung',xlabel='t')
v,w,name = x_s[0]
p.plot(v,w,label='Auslenkung x')
v,w,name = v_s[0]
p.plot(v,w,label='Geschwindigkeit v')
v,w,name = a_s[0]
p.plot(v,w,label='Beschleunigung a')
# --- Energie ---
p.new(title='Gesamte Energie', yscale='log',xlabel='t',ylabel='E')
for v,w,name in E_s: p.plot(v,w,label=name)
# --- Amplitude ---
p.new(title='Amplitude',xlabel=r'$\omega$',ylabel=r'$\hat{x}$')
f_amp = lambda x, *p: p[0]/np.sqrt((p[1]*p[1]-x*x)**2+(2*p[2]*x)**2)
alpha = Fmax/m
omeganull = np.sqrt(D/m)
gamma = b/m/2
f = Fitter(f_amp, [alpha, omeganull, gamma])
f.loadData(w_s, amp_s, scale='linear')
p.plot(w_s,amp_s,'+',label=r'Simulation mit $\alpha = %.4f$, $\gamma = %.4f$, $\omega_0 = %.4f$'%(alpha, gamma, omeganull))
p.plot(f.x,f.y,'-',label=r'Fitt mit $\alpha = %.4f \pm %.4f$, $\gamma = %.4f \pm %.4f$, $\omega_0 = %.4f \pm %.4f$'%(f.params[0],f.std[0],f.params[2],f.std[2],f.params[1],f.std[1]))
p.make(ncols = 2, savewindow = False)
datadict[type][0] = np.append(datadict[type][0], volume)
datadict[type][1] = np.append(datadict[type][1], energy)
datadict[type][2] = np.append(datadict[type][2], lattice)
print "%s, run %2s) lattice constant = %f, cell volume = %f, total energy = %f \n" % (
type, runnumber, lattice, volume, energy)
with open('data.pkl', 'wb') as f:
pickle.dump(datadict, f)
else:
with open('data.pkl', 'rb') as f:
datadict = pickle.load(f)
# ==== PLOT ====
for type, [volume, energy, lattice] in datadict.items():
sort = np.argsort(volume)
lattice = np.array(lattice)[sort]
volume = np.array(volume)[sort]
energy = np.array(energy)[sort]
p.new(xlabel=r'cell volume [Ang$^3$]',
ylabel=r'total energy [eV]',
name=type + "_energy")
p.plot(volume, energy, '-', label=type)
p.new(xlabel=r'cell volume [Ang$^3$]',
ylabel=r'lattice constant [Ang]',
name=type + "_lattice")
p.plot(volume, lattice, 'x', label=type)
p.make(ncols=2)
lattice = float(l)
except:
continue
datadict[type][0] = np.append(datadict[type][0], volume)
datadict[type][1] = np.append(datadict[type][1], energy)
datadict[type][2] = np.append(datadict[type][2], lattice)
print "%s, run %2s) lattice constant = %f, cell volume = %f, total energy = %f \n"%(type,
runnumber,lattice,volume,energy)
with open('data.pkl', 'wb') as f:
pickle.dump(datadict, f)
else:
with open('data.pkl', 'rb') as f:
datadict = pickle.load(f)
# ==== PLOT ====
for type, [volume,energy,lattice] in datadict.items():
sort = np.argsort(volume)
lattice = np.array(lattice)[sort]
volume = np.array(volume)[sort]
energy = np.array(energy)[sort]
p.new(xlabel=r'cell volume [Ang$^3$]', ylabel=r'total energy [eV]',name=type+"_energy")
p.plot(volume, energy, '-', label=type)
p.new(xlabel=r'cell volume [Ang$^3$]',ylabel=r'lattice constant [Ang]',name=type+"_lattice")
p.plot(volume, lattice, 'x', label=type)
p.make(ncols = 2)
# --- Energie ---
p.new(title='Gesamte Energie', yscale='log', xlabel='t', ylabel='E')
for v, w, name in E_s:
p.plot(v, w, label=name)
# --- Amplitude ---
p.new(title='Amplitude', xlabel=r'$\omega$', ylabel=r'$\hat{x}$')
f_amp = lambda x, *p: p[0] / np.sqrt((p[1] * p[1] - x * x)**2 +
(2 * p[2] * x)**2)
alpha = Fmax / m
omeganull = np.sqrt(D / m)
gamma = b / m / 2
f = Fitter(f_amp, [alpha, omeganull, gamma])
f.loadData(w_s, amp_s, scale='linear')
p.plot(
w_s,
amp_s,
'+',
label=r'Simulation mit $\alpha = %.4f$, $\gamma = %.4f$, $\omega_0 = %.4f$'
% (alpha, gamma, omeganull))
p.plot(
f.x,
f.y,
'-',
label=
r'Fitt mit $\alpha = %.4f \pm %.4f$, $\gamma = %.4f \pm %.4f$, $\omega_0 = %.4f \pm %.4f$'
% (f.params[0], f.std[0], f.params[2], f.std[2], f.params[1], f.std[1]))
p.make(ncols=2, savewindow=False)
p.new(xlabel=r'distance [nm]', ylabel=r'radial distribution function',name='rdf')
n = 0
for medium, values in sorted(datadict['rdf'].iteritems()):
i1 = signal.find_peaks_cwt(values[2], np.arange(1,20),noise_perc=1)[0]
i2 = signal.find_peaks_cwt(values[2], np.arange(20,40),noise_perc=1)[1:3]
peakindex = np.append(i1,i2)
mx = values[1][peakindex]
my = values[2][peakindex]
print medium+":"
for i in range(0,3):
print "(%f | %f)"%(mx[i],my[i])
p.plot(mx, my, c[n]+'*',ms=11, mew=1)
p.plot(values[1], values[2], c[n]+'-', label=medium)
n += 1
p.make()
'''f.loadFunction(lambda x, *p: p[1]*x**p[0], [1,1])
p.new(xlabel=r'time [ps]', ylabel=r'mean aquare displacement',name='regimes', xscale='log',yscale='log')
for medium, values in sorted(datadict['msd'].iteritems()):
i = values[1] < 600
j = values[1][i] < 0.3
f.loadData(values[1][i][j], values[2][i][j], scale='log')
x = np.linspace(0.1, 10,2)
p.plot(x, f(x), 'k-')
j = values[1][i] > 0.5
