res_params.add('j', value= 7e-12,min=7e-12,max=5e-6,vary=False)
R.subtract('res',min(R.column('res')),header='res.norm') #subtract off the minimum (residual) to help Kondo fit
# do fit, here with leastsq model
result = minimize(BG, res_params, args=(R.column('T (K)'), R.column('res.norm')))
# calculate final result
final = R.column('res.norm') + result.residual + min(R.column('res'))
R.add_column(final,column_header='fit')
# write error report
report_fit(res_params)
### Print output parameters from fit ###
print 'K = ' + format_error(res_params['K'].value,res_params['K'].stderr,latex=True)
print 'Dt = ' + format_error(res_params['Dt'].value,res_params['Dt'].stderr,latex=True)
print 'rho0 = ' + str(min(R.column('res')))
### Plot resistivity data ###
R.template=SPF.JTBPlotStyle
R.figure(1)
f=plt.gcf()
f.set_size_inches((5.5,3.75),forward=True) # Set for A4 - will make wrapper for this someday
R.multiply('res',1e8,replace=False,header='res.plot') #Convert to micro Ohm cm
R.multiply('fit',1e8,replace=False,header='fit.plot')
R.plot_xy('T (K)','res.plot',linestyle='',linewidth=3,marker='o',markersize=5,label=None)
R.plot_xy('T (K)','fit.plot',linestyle='-',linewidth=2,marker='',label=None)
R.ylabel = r'$\rho\ (\mu\Omega cm)$'
#params.add('Tk', value= 10,min=5,max=50,vary=True)
# do fit, here with leastsq model
result = minimize(BG, params, args=(R.column('T (K)'), R.column('res')))
# calculate final result
final = R.column('res') + result.residual
R.add_column(final,column_header='BG')
# write error report
report_fit(params)
print 'K = '+format_error(params['K'].value,params['K'].stderr,latex=True)
print 'Dt = '+format_error(params['Dt'].value,params['Dt'].stderr,latex=True)
print 'rho0 = '+format_error(params['rho_0'].value,params['rho_0'].stderr,latex=True)
### GET SCATTERING TIME ###
rho = R.interpolate(L.column('T'))
tsf = L.column('Lam_Cu')**2*rho[:,2]*1.6e-19*1.81e28
tau = workfile()
tau.add_column(L.column('T'),'T (K)')
tau.add_column(1/tsf,r'1/$\tau_{sf}$')
tau_err = (L.column('Lam_err')/L.column('Lam_Cu'))/tsf
tau.add_column(tau_err,'1/t_err')
### FIT SCATTERING TIME ###
# And add it to t
t.add_column(m, column_header="$m^2$")
# Now we can it a straight line
t.setas = "x..y"
fit = t.lmfit(Linear, result=True, replace=False, header="Fit")
g = t["LinearModel:slope"]
gerr = t["LinearModel:slope err"] / g
g = np.sqrt(1.0 / g)
gerr /= 2.0
l = float(d["Lambda"])
th = l / (2 * g)
therr = th * (gerr)
t.inset(loc="top right", width=0.5, height=0.4)
t.plot_xy(r"Fit", r"$sin^2\theta$", "b-", label="Fit")
t.plot_xy(r"$m^2$", r"$sin^2\theta$", "ro", label="Peak Position")
t.xlabel = "Fringe $m^2$"
t.ylabel = r"$sin^2\theta$"
t.title = ""
t.legend(loc="upper left")
t.draw()
pyplot.sca(t.axes[0])
# Get the wavelength from the metadata
# Calculate thickness and report
pyplot.text(
0.05,
0.05,
"Thickness is: {} $\AA$".format(format_error(th, therr, latex=True)),
transform=main_fig.axes[0].transAxes,
)
#Now convert the angle to sin^2
t.apply(lambda x: np.sin(np.radians(x[0]/2.0))**2, 0,header=r"$sin^2\theta$")
# Now create the m^2 order
m=np.arange(len(t))+fringe_offset
m=m**2
#And add it to t
t.add_column(m, column_header='$m^2$')
#Now we can it a straight line
t.setas="x..y"
fit=t.lmfit(Linear,result=True,replace=False,header="Fit")
g=t["LinearModel:slope"]
gerr=t["LinearModel:slope err"]/g
g=np.sqrt(1.0/g)
gerr/=2.0
l=float(d['Lambda'])
th=l/(2*g)
therr=th*(gerr)
t.inset(loc="top right",width=0.5,height=0.4)
t.plot_xy(r"Fit",r"$sin^2\theta$", 'b-',label="Fit")
t.plot_xy(r"$m^2$",r"$sin^2\theta$", 'ro',label="Peak Position")
t.xlabel="Fringe $m^2$"
t.ylabel=r"$sin^2\theta$"
t.title=""
t.legend(loc="upper left")
t.draw()
pyplot.sca(t.axes[0])
# Get the wavelength from the metadata
# Calculate thickness and report
pyplot.text (0.05,0.05, "Thickness is: {} $\AA$".format(format_error(th,therr,latex=True)), transform=main_fig.axes[0].transAxes)
d.show()
#Now convert the angle to sin^2
t.apply(lambda x: np.sin(np.radians(x[0]/2.0))**2, 0,header=r"$sin^2\theta$")
# Now create the m^2 order
m=np.arange(len(t))+1
m=m**2
#And add it to t
t.add_column(m, column_header='$m^2$')
#Now we can it a straight line
t.setas="x.y"
p, pcov=t.curve_fit(linear,result=True,replace=False,header="Fit")
g=p[1]
gerr=np.sqrt(pcov[1,1])/g
g=np.sqrt(1.0/g)
gerr/=2.0
l=float(d['Lambda'])
th=l/(2*g)
therr=th*(gerr)
t.inset(loc="top right")
t.plot_xy(r"Fit",r"$sin^2\theta$", 'b-')
t.plot_xy(r"$m^2$",r"$sin^2\theta$", 'ro')
t.xlabel="Fringe $m^2$"
t.ylabel=r"$sin^2\theta$"
t.title=None
t.show()
pyplot.sca(t.axes[0])
# Get the wavelength from the metadata
# Calculate thickness and report
pyplot.text (0.05,0.05, "Thickness is: {} $\AA$".format(format_error(th,therr,latex=True)), transform=main_fig.axes[0].transAxes)
"""Scale data to stitch it together."""
from Stoner import Data
from Stoner.Util import format_error
import matplotlib.pyplot as plt
# Load and plot two sets of data
s1 = Data("Stitch-scan1.txt", setas="xy")
s2 = Data("Stitch-scan2.txt", setas="xy")
s1.plot(label="Set 1")
s2.fig = s1.fig
s2.plot(label="Set 2")
# Stitch scan 2 onto scan 1
s2.stitch(s1)
s2.plot(label="Stictched")
s2.title = "Stictching Example"
# Tidy up the plot by adding annotation fo the stirching co-efficients
labels = ["A", "B", "C"]
txt = []
lead = r"$x'\rightarrow x+A$" + "\n" + r"$y'=\rightarrow By+C$" + "\n"
for l, v, e in zip(labels, s2["Stitching Coefficients"],
s2["Stitching Coeffient Errors"]):
txt.append(format_error(v, e, latex=True, prefix=l + "="))
plt.text(0.7, 0.65, lead + "\n".join(txt), fontdict={"size": "x-small"})
plt.draw()
rho0 = d.min(r_col)[0]
A = rho0 * 40
thetaD = 300.0
d.del_rows(0, lambda x, r: any(isnan(r)))
popt, pcov = d.curve_fit(
lambda T, thetaD, rho0, A: blochGrueneisen(T, thetaD, rho0, A, 5),
xcol=t_col,
ycol=r_col,
p0=[thetaD, rho0, A])
perr = sqrt(diag(pcov))
labels = [r'\theta_D', r'\rho_0', r'A']
annotation = [
"${}$: {}\n".format(l, format_error(v, e, latex=True))
for l, v, e in zip(labels, popt, perr)
]
annotation = "\n".join(annotation)
popt = append(popt, 5)
T = d.column(t_col)
d.add_column(blochGrueneisen(T, *popt), column_header=r"Bloch")
d.plot_xy(t_col, [r_col, "Bloch"], ["ro", "b-"],
label=["Data", r"$Bloch-Gr\"ueisen Fit$"])
d.xlabel = "Temperature (K)"
d.ylabel = "Resistance ($\Omega$)"
text(0.05, 0.05, annotation, transform=d.axes[0].transAxes)
print("Initial guesses: {}".format(p0))
d.del_rows(0, lambda x, r: np.any(np.isnan(r)))
popt, pcov = d.curve_fit(bg_wrapper,
xcol=t_col,
ycol=r_col,
p0=p0,
absolute_sigma=False)
perr = np.sqrt(np.diag(pcov))
labels = [r"\theta_D", r"\rho_0", r"A"]
units = ["K", r"\Omega m", r"\Omega m"]
annotation = [
"${}$: {}\n".format(l, format_error(v, e, latex=True, mode="eng", units=u))
for l, v, e, u in zip(labels, popt, perr, units)
]
annotation = "\n".join(annotation)
popt = np.append(popt, 5)
T = d.column(t_col)
d.add_column(blochGrueneisen(T, *popt), header=r"Bloch")
d.plot_xy(
t_col,
[r_col, "Bloch"],
["ro", "b-"],
label=["Data", r"$Bloch-Gr\"ueisen Fit$"],
)
d.xlabel = "Temperature (K)"
d.ylabel = "Resistance ($\Omega$)"
d = Analysis.AnalyseFile(tau.clone)
d.del_rows('T (K)',lambda x,y:x<100 and x>230)
sc_result = minimize(phonon, sc_params, args=(d.column('T (K)'), d.column(r'1/$\tau_{sf}$')))
# calculate final result
sc_final = (d.column(r'1/$\tau_{sf}$')) + sc_result.residual
d.add_column(sc_final,column_header='fit')
# write error report
report_fit(sc_params)
e_ph = sc_params['epsilon'].value
e_ph_err = sc_params['epsilon'].stderr
print r'$\epsilon_ph$ = ' + str(e_ph) + '$\pm$' + str(e_ph_err)
print format_error(e_ph,e_ph_err,latex=True)
e_imp = sc_params['imp'].value*9.1e-31/(8.45e28*(1.6e-19**2)*params['rho_0'].value)
e_imp_err = e_imp*numpy.sqrt((sc_params['imp'].stderr/sc_params['imp'].value)**2 + (params['rho_0'].stderr/params['rho_0'].value)**2)
print r'$\epsilon_imp$ = ' + str(e_imp) + '$\pm$' + str(e_imp_err)
print format_error(e_imp,e_imp_err,latex=True)
################ PLOT SCATTERING DATA #######################
fit=SP.PlotFile(d.clone)
fit.template=SPF.JTBPlotStyle
t=SP.PlotFile(tau.clone)
t.template=SPF.JTBPlotStyle
BG=SP.PlotFile(R.clone)
BG.template=SPF.JTBPlotStyle
fit.figure()
else:
t_col=t_col[0]
r_col=d.find_col(r_pat)
if len(r_col)!=1:
raise KeyError("More than one column that might match temperaature found!")
else:
r_col=r_col[0]
rho0=d.min(r_col)[0]
A=rho0*40
thetaD=300.0
d.del_rows(0,lambda x,r:any(isnan(r)))
popt,pcov=d.curve_fit(lambda T,thetaD,rho0,A:blochGrueneisen(T,thetaD,rho0,A,5),xcol=t_col,ycol=r_col,p0=[thetaD,rho0,A])
perr=sqrt(diag(pcov))
labels=[r'\theta_D',r'\rho_0',r'A']
annotation=["${}$: {}\n".format(l,format_error(v,e,latex=True)) for l,v,e in zip(labels,popt,perr)]
annotation="\n".join(annotation)
popt=append(popt,5)
T=d.column(t_col)
d.add_column(blochGrueneisen(T,*popt),column_header=r"Bloch")
d.plot_xy(t_col,[r_col,"Bloch"],["ro","b-"],label=["Data",r"$Bloch-Gr\"ueisen Fit$"])
d.xlabel="Temperature (K)"
d.ylabel="Resistance ($\Omega$)"
text(0.05,0.05,annotation,transform=d.axes[0].transAxes)
"""Scale data to stitch it together."""
from Stoner import Data
from Stoner.Util import format_error
import matplotlib.pyplot as plt
# Load and plot two sets of data
s1 = Data("Stitch-scan1.txt", setas="xy")
s2 = Data("Stitch-scan2.txt", setas="xy")
s1.plot(label="Set 1")
s2.fig = s1.fig
s2.plot(label="Set 2")
# Stitch scan 2 onto scan 1
s2.stitch(s1)
s2.plot(label="Stictched")
s2.title = "Stictching Example"
# Tidy up the plot by adding annotation fo the stirching co-efficients
labels = ["A", "B", "C"]
txt = []
lead = r"$x'\rightarrow x+A$" + "\n" + r"$y'=\rightarrow By+C$" + "\n"
for l, v, e in zip(
labels, s2["Stitching Coefficients"], s2["Stitching Coeffient Errors"]
):
txt.append(format_error(v, e, latex=True, prefix=l + "="))
plt.text(0.7, 0.65, lead + "\n".join(txt), fontdict={"size": "x-small"})
plt.draw()