def main():
with open('../Data/calibration.txt') as f: #calibration readings file
x0 = [
0, 0, 0, 1, 1, 1, 0, 0, 0
] #% Structure of vector: x0 = [ALFAxy, ALFAzx, ALFAzy, Sx, Sy, Sz, bx, by, bz]
accelData = []
for row in f:
vector = row.split(',')
vector[0] = float(vector[0])
vector[1] = float(vector[1])
vector[2] = float(vector[2])
accelData.append(vector)
x = opt((lambda x: minimize(x, accelData)), x0, method='BFGS').x
N = [[1, 0, 0], [x[0], 1, 0], [x[1], x[2], 1]]
S = [[x[3], 0, 0], [0, x[4], 0], [0, 0, x[5]]]
b = [x[6], x[7], x[8]]
print("\nN =\n" + str(np.array(N)))
print("\nS =\n" + str(np.array(S)))
print("\nb = " + str(np.array(b)))
print("\nRMSE before calibration: " +
'{:5f}'.format(rmse(accelData) * gval) + " m/s^2")
print("RMSE after calibration: " +
'{:5f}'.format(rmse(correction(accelData, N, S, b)) * gval) +
" m/s^2")
def _optimization(self,
max_scf=100,
log=True,
scf=True,
readguess=None,
argnum=[0],
taskname='Output',
method='BFGS',
penalize=None,
**otherargs):
print readguess
print max_scf
if self.verbose:
t0 = time.clock()
self._print_head_optimization(taskname, max_scf, readguess,
**otherargs)
name = taskname
rguess = None
### If initial guess
if readguess:
pguess = name + '.npy'
out = False
ene = self._singlepoint(max_scf,
max_d,
printcoef=pguess,
name=name,
output=False)
if ene == 99999:
raise NameError('SCF did not converved')
rguess = pguess
pguess = None
record = False
args = self._energy_args(max_scf=max_scf,
max_d=max_scf,
log=log,
printguess=pguess,
readguess=rguess,
name=name,
write=record)
ene_function = rhfenergy
grad_fun = function_grads_algopy(
ene_function,
argnum) ## This defines de gradient function of autograd
opt = self.select_method.get(
method, lambda: self._BFGS) ## This selects the opt method
res = opt(ene_function, grad_fun, args, argnum, log, name, **otherargs)
if self.verbose:
timer = time.clock() - t0
self._print_tail_optimization(res, timer)
self.status = bool(res.status == 0 or res.status == 1)
self._optupdateparam(argnum, res.x)
return res, timer
def data_fit(spectre):
mu1 = np.argmax(spectre[0:int(np.argmax(spectre) * 0.9)])
mu2 = int(np.argmax(spectre) * 1.2) + np.argmax(
spectre[int(np.argmax(spectre) * 1.1):len(spectre) - 1])
if spectre[mu1] > spectre[mu2]:
pars = [
np.max(spectre) * 5., 1., 1.,
np.argmax(spectre) * 1., 0.1, spectre[mu1] * 5., 1., 1., mu1 * 1.
]
else:
pars = [
np.max(spectre) * 5., 1., 1.,
np.argmax(spectre) * 1., 0.1, spectre[mu2] * 5., 1., 1., mu2 * 1.
]
print('')
print('Parameters acquisition...')
print('')
out = opt(errfunc_voigt, pars, args=(np.arange(len(spectre)), spectre))[0]
out1 = out[0:4].tolist()
out2 = out[5:].tolist()
dist = int((abs(out2[3] - out1[3]) / 2.) * 0.8)
print('')
print('Plotting spectre...')
print('')
plt.figure(100)
plt.plot(spectre)
plt.plot(V(np.arange(len(spectre)), *out1) + out[4])
plt.plot(V(np.arange(len(spectre)), *out2) + out[4])
plt.plot(Vbis(np.arange(len(spectre)), *out))
integral1 = sum(spectre[int(out1[3] - dist) + 1:int(out1[3] + dist)]) + (
spectre[int(out1[3]) - dist] + spectre[int(out1[3]) + dist]) * 0.5
integral2 = sum(spectre[int(out2[3] - dist) + 1:int(out2[3] + dist)]) + (
spectre[int(out2[3]) - dist] + spectre[int(out2[3]) + dist]) * 0.5
return (integral1, out[3], integral2, out2[3])
def get_m_exponent(m, Tc, start_temp, end_temp, num_temp):
"""
Takes in Cv array and underlying temps params and returns critical exponent and error
"""
# temps
temps = linspace(start_temp, end_temp, num=num_temp)
# trim the function to only T < Tc
crit_temp_idx = argmin(abs(temps - Tc))
m_trimmed = m[:crit_temp_idx]
temps_trimmed = temps[:crit_temp_idx]
# fit the function
m_best_params, m_cov = opt(lambda t, b, c, d: tc_fit(t, Tc, b, c, d),
xdata=temps_trimmed,
ydata=m_trimmed,
p0=[1 / 8, 0, 0])
# get best params and errors
(best_beta, best_c, best_d) = m_best_params
err_params = sqrt(diag(m_cov))
best_beta_err = err_params[0]
# print out best params estimated with this method
print("Estimated critical exponent = {:.3} +/- {:.1}".format(
best_beta, best_beta_err))
# make best fit array with
best_fit = tc_fit(temps_trimmed, Tc, best_beta, best_c, best_d)
# plot results
fig = plt.figure(figsize=(16, 8))
plt.title("Magnetization fit with " +
"$(T_c - T)^{beta}$" + " and $beta =$ {:.3} $+/-$ {:.1}".format(
abs(best_beta), best_beta_err))
plt.scatter(temps_trimmed, m_trimmed, c='k')
plt.plot(temps_trimmed, best_fit, c='darkcyan', label="Best fit")
plt.ylabel("Magnetization")
plt.xlabel("Temperature in units of $J/k_b$")
plt.legend()
plt.show()
return best_beta, best_beta_err, best_fit
def refine(self, idx, fun=None, yvec=None):
"""use quadratic interpolation to locate a fine maximum of
the peak position and value
Arguments:
idx: index of the peak to interpolate
"""
pos = self._idx[idx]
if yvec is not None:
y = yvec
else:
y = self.y
if fun:
from scipy.optimize import broyden1 as opt
# val = fun(self.val[idx])
sur = fun(y[pos-1:pos+2])
else:
# val = self.val[idx]
sur = y[pos-1:pos+2]
if sur[1] > sur[0] and sur[1] >= sur[2]:
c = sur[1]
b = (sur[2] - sur[0])/2
a = (sur[2] + sur[0])/2 - c
lpos = - b/2/a
fpos = float(pos) + lpos
if fun:
ival = a*lpos*lpos + b*lpos + c
# print "rpos = %d; rf(val) = %f; f(val) = %f; dpos = %f;"%(pos, sur[1], ival, lpos)
fval = opt(lambda x: fun(x)-ival, self.val[idx]/2)
else:
fval = a*lpos*lpos + b*lpos + c
# print "rpos = %d; rval = %f; val = %f; dpos = %f; pos = %f"%(pos, sur[1], fval, lpos, fpos)
else:
fpos = pos
fval = sur[1]
return np.interp(fpos, np.arange(len(self.x)), self.x), fval.tolist()
def refine(self, idx, fun=None, xvec=None):
"""use quadratic interpolation to locate a fine maximum of
the peak position and value
Arguments:
idx: index of the peak to interpolate
"""
pos = self.pos[idx]
if xvec is not None:
x = xvec
else:
x = self.x
if fun:
from scipy.optimize import broyden1 as opt
#val = fun(self.val[idx])
sur = fun(x[pos - 1:pos + 2])
else:
#val = self.val[idx]
sur = x[pos - 1:pos + 2]
if sur[1] > sur[0] and sur[1] > sur[2]:
c = sur[1]
b = (sur[2] - sur[0]) / 2
a = (sur[2] + sur[0]) / 2 - c
lpos = -b / 2 / a
fpos = float(pos) + lpos
if fun:
ival = a * lpos * lpos + b * lpos + c
#print "rpos = %d; rf(val) = %f; f(val) = %f; dpos = %f;"%(pos,sur[1],ival, lpos)
fval = opt(lambda x: fun(x) - ival, self.val[idx] / 2)
else:
fval = a * lpos * lpos + b * lpos + c
#print "rpos = %d; rval = %f; val = %f; dpos = %f; pos = %f"%(pos,sur[1],fval, lpos, fpos)
else:
fpos = pos
fval = sur[1]
return fpos, fval.tolist()
def fitLeastSq(fn, p0, xs, ys, yerrs=None, log_fit=0):
""" Example fitting a parabola:
fn = lambda p,xs: p[0]+p[1]*xs**2
xs,ys=np.arange(-10,10),fn((1,2),xs)
plt.plot(xs,ys,lw=3)
pars = fitLeastSq(fn,[15,2],xs,ys)
plt.plot(xs,fn(pars,xs),'r--',lw=3)"""
if yerrs is not None:
if log_fit:
fit_func = lambda p: np.log10(ys) - np.log10(fn(p, xs))
else:
fit_func = lambda p: (ys - fn(p, xs)) / yerrs
else:
if log_fit:
fit_func = lambda p: np.log10(ys) - np.log10(fn(p, xs))
else:
fit_func = lambda p: (ys - fn(p, xs))
pars, res = opt(fit_func, p0)
return pars
def get_cv_exponent(Cv, Tc, start_temp, end_temp, num_temp):
"""
Takes in Cv array and underlying temps params and returns critical exponent and error
"""
# temps
temps = linspace(start_temp, end_temp, num=num_temp)
# fit the function
# split it in two
cv_best_params, cv_cov = opt(lambda t, a, c: tc_fit_abs(t, Tc, a, c),
xdata=temps,
ydata=Cv,
p0=[0.1, 0])
# get best params and errors
(best_alpha, best_c) = cv_best_params
err_params = sqrt(diag(cv_cov))
best_alpha_err = err_params[0]
# print out best params estimated with this method
print("Estimated critical exponent = {:.3} +/- {:.1}".format(
best_alpha, best_alpha_err))
# make best fit array with
best_fit = tc_fit_abs(temps, Tc, best_alpha, best_c)
# plot results
fig = plt.figure(figsize=(16, 8))
plt.title(
"Heat capacity fit with " + "$|T_c - T|^{-alpha}$" +
" and $alpha =$ {:.3} $+/-$ {:.1}".format(best_alpha, best_alpha_err))
plt.scatter(temps, Cv, c='k')
plt.plot(temps, best_fit, c='darkcyan', label="Best fit")
plt.ylabel("Heat capacity")
plt.xlabel("Temperature in units of $J/k_b$")
plt.legend()
plt.show()
return best_alpha, best_alpha_err, best_fit
def _optimization(self,max_scf=100,log=True,scf=True,readguess=None,argnum=[0],taskname='Output', method='BFGS',penalize=None,**otherargs):
name=taskname
record = False
maxsteps = 100
rhf_old = 1000
grad_fun = []
lbda = 1.0
rguess = None
if readguess:
pguess = name +'.npy'
out = False
ene = self._singlepoint(maxsteps,maxsteps,pguess,name,False)
if ene == 99999:
raise NameError('SCF did not converved')
rguess= pguess
if self.verbose:
self.tape.write(' \n Optimization ...\n')
self.tape.write(' ---Start--- \n')
self._optprintparam(max_scf,rguess)
ene_function = rhfenergy
pguess = None
record = False
args=[np.log(self.sys.alpha),self.sys.coef,self.sys.xyz,self.sys.l,self.sys.charges,self.sys.atom,self.sys.natoms,self.sys.nbasis,
self.sys.list_contr,self.sys.ne,
max_scf,max_scf,log,True,pguess,rguess,
name,record,self.sys.alpha] # Last term is only used for Algopy
grad_fun = self._algo_gradfun(ene_function,args,argnum) ## This defines de gradient function of autograd
opt = self.select_method.get(method,lambda: self._BFGS) ## This selects the opt method
t0 = time.clock()
res = opt(ene_function,grad_fun,args,argnum,log,name,**otherargs)
timer = time.clock()-t0
self._optupdateparam(argnum,res.x)
self.status = bool(res.status == 0 or res.status==1)
return res,timer
for i in range(len(rho)):
rho[i] = l/Ts[i] + (betas/(1 + lambdas*Ts[i])).sum()
rho_norm = rho/beta
rho_norm_error = np.ones_like(rho_norm)
for i in range(len(rho_norm_error)):
x = ((betas*lambdas)/((1+lambdas*Ts[i])**2)).sum()
x = x*Ts_error[i]/beta
x = x + (l*Ts_error[i])/(beta*Ts[i]**2)
rho_norm_error[i] = x
Text1 = "Fitansatz: "
Text1 = Text1 + r"$y = a\cdot\sin^2\left(\frac{\pi\cdot x}{b}\right)$"
p_opt,kov = opt(differentieller_Fit,z1/np.amax(z1),rho_norm/np.amax(rho_norm))#,sigma=rho_norm_error)
z1_fit = np.linspace(np.amin(z1/np.amax(z1)),np.amax(z1/np.amax(z1)),1000)
rho1_fit = differentieller_Fit(z1_fit,*p_opt)
x = np.linspace(0,4000)
diff1 = differentieller_Fit(x,0.120,5440)
int1 = integrate.cumtrapz(diff1, x, initial=0) # kumulierte Intergralwerte
#int1 = integrale_Kennlinie(x,0.120,5440)
Text2 = r"$a = ($"
Text2 = Text2 + "{0:.2f}".format(p_opt[0])
Text2 = Text2 + r"$\pm$"
Text2 = Text2 + "{0:.2f}".format(np.sqrt(kov[0][0]))
Text2 = Text2 + r"$)$"
Text3 = r"$b = ($"
random.shuffle(mm)
m = mm[0] # m is the index of largest projection axis
####### U construction #######
def unitary(x0):
for i in range(m):
M[m][i] = x0[i]
M[i][m] = -x0[i]
for i in range(m + 1, n):
M[m][i] = x0[i - 1]
M[i][m] = -x0[i - 1]
U = LA.expm(M)
return U
def Energy(x0):
U = unitary(x0)
dQ = (U - I).dot(P)
E = E0 + g0.T.dot(dQ) + 0.5 * dQ.T.dot(H0).dot(dQ)
return E
####### optimization #######
res = opt(Energy, x0, approx_grad=1)
x = res[0] # optimized parameters
Q = unitary(x).dot(P) + C
####### output result #######
print np.reshape(Q, (n / 3, 3))
row_alpha = []
row_beta = []
row_alpha_err = []
row_beta_err = []
for y in range(64):
points = (tools.get_points(paths=paths,
coords=(x, y),
freqs=freqs,
directory=pathcore,
extension=extension))
frequencies, svalues = zip(*points)
svalues = np.array(svalues)
try:
popt, pcov = opt(tools.lit_fit,
frequencies,
svalues,
p0=[2800, 30e-6],
bounds=([1e-8, 1e-8], [np.inf, np.inf]),
method='trf')
perr = np.sqrt(np.diag(pcov))
row_alpha.append(popt[1])
row_beta.append(popt[0])
row_alpha_err.append(perr[1])
row_beta_err.append(perr[0])
except:
row_alpha.append(0)
row_beta.append(0)
row_alpha_err.append(0)
row_beta_err.append(0)
z_alpha.append(row_alpha)
z_beta.append(row_beta)
z_alpha_err.append(row_alpha_err)
Q=np.empty((n,3))
sample=open('GeomO','r')
for i in range(n):
line=sample.readline()
word=line.split()
Q[i,0]=float(word[2])
Q[i,1]=float(word[3])
Q[i,2]=float(word[4])
sample.close() # reading geometry
def Energy(Q):
f=open('Geom_tmp','w')
f.write(str(np.reshape(Q,(n,3))))
f.close()
result=os.popen('Cal_Energy.sh','r')
E=result.read()
if (len(E)!=0):
E=float(E)
f=open('Geom_app','a')
f.write('\n\n'+str(np.reshape(Q,(n,3))))
f.write('\n\n'+str(E))
f.close()
return E
else:
return 0
res=opt(Energy,Q,approx_grad=1)
x=res[0] #optimized parameters
print np.reshape(x,(n,3))
print Energy(x)
def data_analisys(file):
f = h5py.File(file, 'r')
max_y = 0
max_x = 0
events = f['Events']
for label in np.arange(len(events)):
mix = np.max(events[str(label + 1)]['spots_x'][...])
if max_x < mix: max_x = mix
miy = np.max(events[str(label + 1)]['spots_y'][...])
if max_y < miy: max_y = miy
[max_y, max_x] = [max_y + 1, max_x + 1]
tot_img = np.zeros([max_y, max_x])
print('')
print('Creating total image...')
print('')
xcms = []
ycms = []
for label in np.arange(len(events)):
xs = events[str(label + 1)]['spots_x'][...]
ys = events[str(label + 1)]['spots_y'][...]
zs = events[str(label + 1)]['spots_z'][...]
max_val = np.max(zs)
k = np.log(max_val) / max_val
pounds = np.exp(zs * k)
xcms.append(sum(pounds * xs) / sum(pounds))
ycms.append(sum(pounds * ys) / sum(pounds))
for ind in np.arange(len(xs)):
tot_img[ys[ind], xs[ind]] += zs[ind]
print('')
print('Shifting data...')
print('')
[py, px] = np.unravel_index(tot_img.argmax(), [max_y, max_x])
pars = [py, 0., 0.]
maxs = []
for col in np.arange(max_x):
maxs.append(np.argmax(tot_img[:, col]))
out = opt(errfunc, pars, args=(np.arange(len(maxs)), maxs))[0]
truemaxs = []
trueargs = []
for col in np.arange(len(maxs)):
if (maxs[col] - parabola(col, *out))**2 / parabola(col, *out) < 1.:
truemaxs.append(maxs[col])
trueargs.append(col)
out = opt(errfunc,
pars,
args=(np.asarray(trueargs) * 1.0, np.asarray(truemaxs) * 1.0))[0]
x_0_pol = parabola(np.arange(max_x), *out)
shift_0 = max(x_0_pol)
shape_row = max_y + int(max(x_0_pol)) - int(min(x_0_pol))
print('')
print('Creating spectrum...')
print('')
spectre = np.zeros(shape_row)
for label in np.arange(len(xcms)):
delta = shift_0 - parabola(xcms[label], *out)
spectre[int(round(ycms[label] + delta))] += 1
return (spectre)
