def plotRock(ax, xdat, ydat, yerrdat, norm, labelstr, lc, fc, **kwargs):
xdat = np.array(xdat)
ydat = np.array(ydat) /norm
yerrdat = np.array(yerrdat)/norm
# We will plot how much above the norm is A2/A1
# in units of the norm
ydat = ydat - 1.
maxy = ydat.max()
ax.errorbar( xdat, ydat/maxy, yerr=yerrdat/maxy, \
capsize=0., elinewidth = 1. ,\
fmt='.', ecolor=lc, mec=fc, \
mew=1., ms=5.,\
marker='o', mfc='lightblue', \
label=labelstr+', $S_{\mathbf{\pi}}$=%.2g'%(maxy+1))
Gaus = True
if Gaus:
# Fit data with a Gaussian
fitdat = np.transpose( np.vstack( (xdat,ydat/maxy)))
sig0 = kwargs.pop('sig0', 100.)
p0 = [1.0, 0.1, sig0]
fun = fitlibrary.fitdict['GaussianNoOffset'].function
pG, errorG = fitlibrary.fit_function( p0, fitdat, fun)
#print "Fitting with Gaussian:"
print "...Fitting with sig0=%.2f --> %.2f"%(sig0, pG[2])
print
#print pG
#print errorG
fitX, fitY = fitlibrary.plot_function(pG, \
np.linspace( xdat.min(), xdat.max(),120),fun)
ax.plot( fitX, fitY, '-', c=lc, lw=1.0)
erad = True
if erad:
# Returns 1/e^2 radius
return np.abs(pG[2]), errorG[2]
else:
return 2.3548*np.abs(pG[2]), 2.3548*errorG[2]
else:
# Fit data with a Lorentzian
fitdat = np.transpose( np.vstack( (xdat,ydat/maxy)))
sig0 = kwargs.pop('sig0', 100.)
print "...Fitting with sig0=%.2f"%sig0
p0 = [1.0, 0.1, sig0]
fun = fitlibrary.fitdict['LorentzianNoOffset'].function
pL, errorL = fitlibrary.fit_function( p0, fitdat, fun)
print "Fitting with Lorentzian:"
print p0
print pL
#print errorL
fitX, fitY = fitlibrary.plot_function(pL, \
np.linspace( xdat.min(), xdat.max(),120),fun)
ax.plot( fitX, fitY, '-', c=lc, lw=1.0)
return np.abs(pL[2]), errorL[2]
def plotRock(ax, xdat, ydat, yerrdat, norm, labelstr, lc, fc):
xdat = np.array(xdat)
ydat = np.array(ydat) /norm
yerrdat = np.array(yerrdat)/norm
# We will plot how much above the norm is A2/A1
# in units of the norm
ydat = ydat - 1.
maxy = ydat.max()
ax.errorbar( xdat, ydat/maxy, yerr=yerrdat/maxy, \
capsize=0., elinewidth = 1. ,\
fmt='.', ecolor=lc, mec=fc, \
mew=1., ms=5.,\
marker='o', mfc='lightblue', \
label=labelstr+', $B_{\mathrm{tof}}$=%.2g'%(maxy+1))
# Fit data with a Gaussian
fitdat = np.transpose( np.vstack( (xdat,ydat/maxy)))
p0 = [1.0, 0., 10., 0.1]
fun = fitlibrary.fitdict['Gaussian'].function
pG, errorG = fitlibrary.fit_function( p0, fitdat, fun)
#print "Fitting with Gaussian:"
#print pG
#print errorG
fitX, fitY = fitlibrary.plot_function(pG, \
np.linspace( xdat.min(), xdat.max(),120),fun)
ax.plot( fitX, fitY, '-', c=lc, lw=1.0)
return np.sqrt(2.)*pG[2], np.sqrt(2.)*errorG[2]
def tof_temperature( points, tfermi, number, i, ax_tof, ax_T, ax_TF, axTFN, legend_dict):
shots = points[:,k['SEQ:shot']]
p0 = [ 50., 1/5.]
fitfun = fitlibrary.fitdict['Temperature'].function
tofdat = points[:,k['ODT:odttof']]
sizdat = points[:,k['CPP:ax0w']]
try:
Tfit, Terror = fitlibrary.fit_function( p0, points[:, [k['ODT:odttof'],k['CPP:ax0w']]], fitfun)
getTcmd = 'getTrange -T %.2f %04d:%04d' % (depth/5., sorted(shots)[0], sorted(shots)[-1] )
getT = subprocess.Popen(getTcmd.split(), stdout=subprocess.PIPE).communicate()[0]
except:
Tfit = [0., 0.]
Terror = [0., 0.]
#print points[:, [k['ODT:odttof'],k['CPP:ax0w']]]
#print "Python Fit: T = %.3f +/- %.3f uK " % (T, Terr)
#print "Gnuplot Fit: T = %.3f +/- %.3f uK " % (float(getT.split()[3]), float(getT.split()[4]))
T = unc.ufloat(( Tfit[1], Terror[1]))
TfitX, TfitY = fitlibrary.plot_function( Tfit, tofdat, fitfun, xlim=(0.,6.0))
ax_tof.plot( tofdat, sizdat, 'o', color=colors[i], markeredgewidth=0.8, markersize=4)
ax_tof.plot( TfitX, TfitY, '-', color=colors[i], markeredgewidth=0.3, markersize=12, alpha=0.5)
#******* T TOF
xT = numpy.array( [points[0,k['FESHBACH:bias']]*6.8 ] )
yT = numpy.array( [T.nominal_value] )
yTerr = numpy.array( [T.std_dev()] )
if yTerr[0] < maxerror* yT[0]:
for ax in ax_T:
ax.errorbar(xT, yT, yerr=yTerr, fmt='o', ecolor=colors[i], markeredgecolor=colors[i], markerfacecolor="None", markeredgewidth=2., markersize=8, alpha=1.0)
#******* T/TF TOF
TF = T/tfermi
yT = numpy.array( [TF.nominal_value] )
yTerr = numpy.array( [TF.std_dev()] )
if yTerr[0] < maxerror* yT[0]:
for ax in ax_TF:
plot1 = ax.errorbar(xT, yT, yerr=yTerr, fmt='o', ecolor=colors[i], markeredgecolor=colors[i], markerfacecolor="None", markeredgewidth=2., markersize=8, alpha=1.0)
if 'Ballistic Expansion' not in legend_dict.keys():
legend_dict['Ballistic Expansion'] = plot1
#******* T/TF vs. N TOF
xT = numpy.array( [number.nominal_value] )
yT = numpy.array( [TF.nominal_value] )
xTerr = numpy.array( [number.std_dev()] )
yTerr = numpy.array( [TF.std_dev()] )
if yTerr[0] < maxerror* yT[0]:
axTFN.errorbar(xT,yT, xerr=xTerr, yerr=yTerr, fmt='o', ecolor=colors[i], markeredgecolor=colors[i], markerfacecolor="None", markeredgewidth=2., markersize=8, alpha=1.0)
return T, TF, TfitX, TfitY
def fit(self,data):
fitdata, n = self.limits(data)
if n == 0:
display("No points in the specified range [x0:xf], [y0:yf]")
return None,None
if self.func == 'Gaussian':
if not self.dofit:
return fitlibrary.plot_function(self.a[:,0] , fitdata[:,0],fitlibrary.gaus1d_function)
else:
display("Fitting to a Gaussian")
self.a, self.ae=fitlibrary.fit_function(self.a0[:,0],fitdata,fitlibrary.gaus1d_function)
return fitlibrary.plot_function(self.a[:,0] , fitdata[:,0],fitlibrary.gaus1d_function)
if self.func == 'Sine':
if not self.dofit:
return fitlibrary.plot_function(self.a[:,0] , fitdata[:,0],fitlibrary.sine_function)
else:
display("Fitting to a Sine")
self.a, self.ae=fitlibrary.fit_function(self.a0[:,0],fitdata,fitlibrary.sine_function)
return fitlibrary.plot_function(self.a[:,0] , fitdata[:,0],fitlibrary.sine_function)
if self.func == 'ExpSine':
if not self.dofit:
return fitlibrary.plot_function(self.a[:,0] , fitdata[:,0],fitlibrary.expsine_function)
else:
display("Fitting to a ExpSine")
self.a, self.ae=fitlibrary.fit_function(self.a0[:,0],fitdata,fitlibrary.expsine_function)
return fitlibrary.plot_function(self.a[:,0] , fitdata[:,0],fitlibrary.expsine_function)
if self.func == 'Temperature':
if not self.dofit:
return fitlibrary.plot_function(self.a[:,0] , fitdata[:,0],fitlibrary.temperature_function)
else:
display("Fitting to a ExpSine")
self.a, self.ae=fitlibrary.fit_function(self.a0[:,0],fitdata,fitlibrary.temperature_function)
return fitlibrary.plot_function(self.a[:,0] , fitdata[:,0],fitlibrary.temperature_function)
if self.func == 'Exp':
if not self.dofit:
return fitlibrary.plot_function(self.a[:,0] , fitdata[:,0],fitlibrary.exp_function)
else:
display("Fitting to a Exp")
self.a, self.ae=fitlibrary.fit_function(self.a0[:,0],fitdata,fitlibrary.exp_function)
return fitlibrary.plot_function(self.a[:,0] , fitdata[:,0],fitlibrary.exp_function)
def tof_temperature( points, tfermi, number, i, ax_tof, ax_T, ax_TF, axTFN, legend_dict, xT):
shots = points[:,k['SEQ:shot']]
fcpow = points[0,k['EVAP:finalcpow']]
U0 = points[0,k['ODTCALIB:maxdepth']]
tofdat = points[:,k['ODT:odttof']]
sizdat = points[:,k['CPP:ax0w']]
mintofindex = tofdat.argmin()
s_guess = sizdat[mintofindex]
T_guess =fcpow/10. *U0 / 5.
print "Fitting TOF Temperature, starting with guess r0 = %.3f, T = %.3f" % (s_guess, T_guess)
p0 = [ s_guess, T_guess]
fitfun = fitlibrary.fitdict['Temperature'].function
todelete=[]
tofdat_clean =[]
sizdat_clean =[]
for j,tof in enumerate(tofdat):
if sizdat[j] > 400. or sizdat[j] < 0.:
todelete.append(j)
else:
tofdat_clean.append( tofdat[j] )
sizdat_clean.append( sizdat[j] )
tofdat = numpy.array(tofdat_clean)
sizdat = numpy.array(sizdat_clean)
fitdat= numpy.transpose(numpy.array( [tofdat.tolist(), sizdat.tolist()] ))
try:
Tfit, Terror = fitlibrary.fit_function( p0, fitdat, fitfun)
#getTcmd = 'getTrange -T %.2f %04d:%04d' % (depth/5., sorted(shots)[0], sorted(shots)[-1] )
#getT = subprocess.Popen(getTcmd.split(), stdout=subprocess.PIPE).communicate()[0]
except:
Tfit = [0., 0.]
Terror = [0., 0.]
#print points[:, [k['ODT:odttof'],k['CPP:ax0w']]]
#print "Python Fit: T = %.3f +/- %.3f uK " % (T, Terr)
#print "Gnuplot Fit: T = %.3f +/- %.3f uK " % (float(getT.split()[3]), float(getT.split()[4]))
T = unc.ufloat(( Tfit[1], Terror[1]))
TfitX, TfitY = fitlibrary.plot_function( Tfit, tofdat, fitfun, xlim=(0.,6.0))
ax_tof.plot( tofdat, sizdat, 'o', color=colors[i], markeredgewidth=0.8, markersize=4)
ax_tof.plot( TfitX, TfitY, '-', color=colors[i], markeredgewidth=0.3, markersize=12, alpha=0.5)
#******* T TOF
#xT = numpy.array( [points[0,k['EVAP:image']]/1000. ] )
yT = numpy.array( [T.nominal_value] )
yTerr = numpy.array( [T.std_dev()] )
if yTerr[0] < maxerror* yT[0]:
for ax in ax_T:
ax.errorbar(xT, yT, yerr=yTerr, fmt='o', ecolor=colors[i], markeredgecolor=colors[i], markerfacecolor="None", markeredgewidth=2., markersize=8, alpha=1.0)
#******* T/TF TOF
TF = T/tfermi
yT = numpy.array( [TF.nominal_value] )
yTerr = numpy.array( [TF.std_dev()] )
if yTerr[0] < maxerror* yT[0]:
for ax in ax_TF:
plot1 = ax.errorbar(xT, yT, yerr=yTerr, fmt='o', ecolor=colors[i], markeredgecolor=colors[i], markerfacecolor="None", markeredgewidth=2., markersize=8, alpha=1.0)
if 'Ballistic Expansion' not in legend_dict.keys():
legend_dict['Ballistic Expansion'] = plot1
#******* T/TF vs. N TOF
xT = numpy.array( [number.nominal_value] )
yT = numpy.array( [TF.nominal_value] )
xTerr = numpy.array( [number.std_dev()] )
yTerr = numpy.array( [TF.std_dev()] )
if yTerr[0] < maxerror* yT[0]:
axTFN.errorbar(xT,yT, xerr=xTerr, yerr=yTerr, fmt='o', ecolor=colors[i], markeredgecolor=colors[i], markerfacecolor="None", markeredgewidth=2., markersize=8, alpha=1.0)
return T, TF, TfitX, TfitY
def plotkey_ratio(
ax,
gk,
key,
cond1,
cond2,
dat,
base,
marker="o",
ms=5.0,
mec="def",
mfc="def",
labelstr="def",
fit=False,
save=False,
):
if mec == "def":
mec = gdat[gk]["ec"]
if mfc == "def":
mfc = gdat[gk]["fc"]
if labelstr == "def":
labelstr = gdat[gk]["label"]
dat1 = np.copy(dat)
for c in cond1:
dat1 = dat1[dat1[:, K(c[0])] == c[1]]
dat2 = np.copy(dat)
for c in cond2:
dat2 = dat2[dat2[:, K(c[0])] == c[1]]
plotkey = "DIMPLELATTICE:knob05"
x1 = np.unique(dat1[:, K(plotkey)])
x2 = np.unique(dat2[:, K(plotkey)])
set1 = set(x1.tolist())
set2 = set(x2.tolist())
common = list(set1 & set2)
num = dat1
den = dat2
rval = []
rerr = []
for c in common:
numi = num[num[:, K(plotkey)] == c][:, K(key)]
deni = den[den[:, K(plotkey)] == c][:, K(key)]
val = ufloat((np.mean(numi), stats.sem(numi))) / ufloat((np.mean(deni), stats.sem(deni)))
rval.append(val.nominal_value)
rerr.append(val.std_dev())
rval = np.array(rval) / base
rerr = np.array(rerr) / base
xc = np.array(common)
print "\t%s: x points in common = " % key, len(xc)
if len(xc) > 0:
ax.errorbar(
fx(xc),
rval / base,
yerr=rerr / base,
capsize=0.0,
elinewidth=1.0,
fmt=".",
ecolor=mec,
mec=mec,
mew=1.0,
marker=marker,
mfc=mfc,
ms=ms,
label=labelstr,
)
if save:
fname = key.replace(":", "_") + "_" + labelstr[-3:] + ".rawdatNOTCorrected"
X = np.transpose(np.vstack((fx(xc), rval / base, rerr / base)))
np.savetxt(fname, X, fmt="%10.2f", delimiter="\t", newline="\n")
if fit:
fitdat = np.transpose(np.vstack((fx(xc), rval / base)))
fun = fitlibrary.fitdict["Gaussian"].function
p0 = [0.6, 0.01, 20.0, 1.0]
pFit, error = fitlibrary.fit_function(p0, fitdat, fun)
fitX, fitY = fitlibrary.plot_function(pFit, fitdat[:, 0], fun)
# ax.plot( fitX, fitY, '-', color='gray', lw=4.5,zorder=1)
print "1/e width Gaussian = ", pFit[2]
fitdat = np.transpose(np.vstack((fx(xc), rval / base - 1.0)))
fun = fitlibrary.fitdict["GaussianNoOffset"].function
p0 = [0.5, 0.0, 12.0]
pFit, error = fitlibrary.fit_function(p0, fitdat, fun)
pFit = [0.6134, -3.027, 20.7434] # 190a0 data
# pFit = [0.5, 0.0, 12.] #400a0
fitX, fitY = fitlibrary.plot_function(pFit, fitdat[:, 0], fun)
ax.plot(fitX, fitY + 1.0, "-", color="gray", lw=6.5, zorder=1)
print "1/e width GaussianNoOffset = ", pFit[2]
fwhm = 2 * pFit[2] * np.sqrt(-1 * np.log(1.0 / 2.0))
print "FWHM = ", fwhm
print "pFit = ", pFit
ax.text(0.05, 0.05, "FWHM=%.0f mrad" % (fwhm * np.pi / 180.0 * 1000), transform=ax.transAxes, fontsize=10)
Vpfit, Verror = fitlibrary.fit_function( p0, d[:, [0,2]], fit.function)
Hleg = "%s\nwH = %.2f um at %.0f MIL, M2=%.2f" % ( dat, Hpfit[0], Hpfit[1], Hpfit[2] )
Vleg = "wV = %.2f um at %.0f MIL, M2=%.2f" % ( Vpfit[0], Vpfit[1], Vpfit[2] )
astigmatism = Hpfit[1] - Vpfit[1]
postdat = numpy.array( [[ i, Hpfit[0], Vpfit[0], astigmatism]] )
ax2.plot( postdat[:,0], postdat[:,1], 'o', color=colors[i], markeredgewidth=0.3, markersize=12)
ax2.plot( postdat[:,0], postdat[:,2], 'x', color=colors[i], markeredgewidth=1.0, markersize=12)
ax3.plot( postdat[:,0], postdat[:,3], '^', markerfacecolor="None", markeredgecolor=colors[i], markeredgewidth=1.0, markersize=12)
print "\t" + Hleg
print "\t" + Vleg
HfitX, HfitY = fitlibrary.plot_function( Hpfit, d[:,0], fit.function)
VfitX, VfitY = fitlibrary.plot_function( Vpfit, d[:,0], fit.function)
msize=8
ax1.plot( d[:,0], d[:,1], 'o', color = colors[i], markersize=msize, markeredgewidth=0.3, label=Hleg)
ax1.plot( d[:,0], d[:,2], 'x', color = colors[i], markersize=msize, markeredgewidth=1.0, label=Vleg )
ax1.plot( HfitX, HfitY, linestyle = '-', color = colors[i])
ax1.plot( VfitX, VfitY, linestyle = '-', color = colors[i])
Hlegb = "%s\nX Position of Waist" %dat
Vlegb = "Y Position of Waist"
camPixSize = 4.65
