def zfix(s, cut='llh', nbins=100, thetaMax=40., minE=5.0,
plot=False, out=False):
from icecube.photospline import spglam as glam
thetaMax *= degree
zbins = np.linspace(1, np.cos(thetaMax), nbins+1)[::-1]
ebins = getEbins()
t = np.log10(s['MC_energy'])
r = np.log10(s['ML_energy'])
z = np.pi - s['zenith']
# Calculate cut values
c0 = np.logical_not(np.isnan(r))
ecut = r >= minE
c0 *= ecut
if cut != None:
c0 *= s['cuts'][cut]
# Store median and standard deviation info
x1 = np.cos(z)[c0]
if x1.min() > zbins.min():
zbins = zbins[zbins >= x1.min()]
y = (r - t)[c0]
medians, sigL, sigR, vars = getMedian(x1, y, zbins)
w = 1/vars
nknots = 30
step_scale = 2/5.
step = (zbins.max() - zbins.min()) * step_scale
mids = (zbins[1:] + zbins[:-1]) / 2.
axes = [mids]
knots = [np.linspace(zbins.min()-step, zbins.max()+step, nknots)]
tab = glam.fit(medians, w, axes, knots, order=(4), penalties={2:1e-4})
if plot:
fig, ax = plt.subplots()
#ax.set_title('Energy Resolution vs Reconstructed Zenith', fontsize=18)
ax.set_xlabel(r'$\cos(\mathrm{\theta_{reco}})$', fontsize=16)
ax.set_ylabel(r'$\log_{10}(E_{\mathrm{LLH}}/E_{\mathrm{true}})$', fontsize=16)
lw = 2
ms = 7*lw
pltParams = dict(fmt='.', lw=lw, ms=ms)
# Energy resolution vs zenith
zbins = np.linspace(1, np.cos(thetaMax), 21)[::-1]
x = getMids(zbins)
medians, sigL, sigR, vars = getMedian(x1, y, zbins)
ax.errorbar(x, medians, yerr=(sigL,sigR), **pltParams)
# Spline fit
fitx = np.linspace(x.min(), x.max(), len(x)*3)
fit = glam.grideval(tab, [fitx])
ax.plot(fitx, fit)
ax.set_xlim(0.8,1)
if out:
plt.savefig(out)
plt.show()
fit = glam.grideval(tab, [np.cos(z)])
return r - fit
def makeTable(bintype='logdist', plot=False):
# Starting parameters
my.setupShowerLLH(verbose=False)
s = load_sim(bintype=bintype)
outFile = '%s/IT73_sim/Zfix_%s.fits' % (my.llh_data, bintype)
nbins = 100
thetaMax = 40.
minE = 4.0
thetaMax *= np.pi / 180.
zbins = np.linspace(1, np.cos(thetaMax), nbins+1)[::-1]
ebins = getEbins()
t = np.log10(s['MC_energy'])
r = np.log10(s['ML_energy'])
z = np.pi - s['zenith']
# Calculate cut values
c0 = s['cuts']['llh']
ecut = r >= minE
c0 *= ecut
# Store median and standard deviation info
x1 = np.cos(z)[c0]
if x1.min() > zbins.min():
zbins = zbins[zbins >= x1.min()]
y = (r - t)[c0]
medians, sigL, sigR, vars = getMedian(x1, y, zbins)
w = 1/vars
nknots = 30
step_scale = 2/5.
step = (zbins.max() - zbins.min()) * step_scale
mids = (zbins[1:] + zbins[:-1]) / 2.
axes = [mids]
knots = [np.linspace(zbins.min()-step, zbins.max()+step, nknots)]
tab = glam.fit(medians, w, axes, knots, order=(4), penalties={2:1e-4})
if os.path.exists(outFile):
os.remove(outFile)
splinefitstable.write(tab, outFile)
if plot:
fig, ax = plt.subplots()
ax.set_title('Energy Resolution vs Reconstructed Zenith', fontsize=18)
ax.set_xlabel('Cos(zenith)', fontsize=16)
ax.set_ylabel('Ereco - Etrue (median)', fontsize=16)
lw = 2
ms = 7*lw
pltParams = dict(fmt='.', lw=lw, ms=ms)
# Energy resolution vs zenith
x = getMids(zbins)
ax.errorbar(x, medians, yerr=(sigL,sigR), **pltParams)
# Spline fit
fitx = np.linspace(x.min(), x.max(), len(x)*3)
fit = glam.grideval(tab, [fitx])
ax.plot(fitx, fit)
plt.show()
def core_res(s, cut=None, nbins=40, minE=4, title=True, zcorrect=False,
out=False):
# Setup plot
fig, ax = plt.subplots()
if title:
ax.set_title('Core Resolution vs True Energy', fontsize=18)
ax.set_xlabel(r'$\log_{10}(E/\mathrm{GeV})$', fontsize=16)
ax.set_ylabel(r'$\vec{x}_{\mathrm{LLH}} - \vec{x}_{\mathrm{true}} [m]$',
fontsize=16)
lw = 2
ms = 7*lw
pltParams = dict(fmt='.', lw=lw, ms=ms)
# Group into larger bins in energy
Ebins = getEbins()
ebins = np.linspace(Ebins.min(), Ebins.max(), nbins+1)
if ebins[-1] != Ebins[-1]:
ebins.append(Ebins[-1])
# Plot Ereco histograms for each energy bin in Etrue
r = np.log10(s['ML_energy'])
if zcorrect:
r = zfix(s)
c0 = np.logical_not(np.isnan(s['ML_energy']))
ecut = (r >= minE)
c0 *= ecut
if cut != None:
c0 *= s['cuts'][cut]
t = np.log10(s['MC_energy'])[c0]
tx, ty = s['MC_x'][c0], s['MC_y'][c0]
rx, ry = s['ML_x'][c0], s['ML_y'][c0]
# Store median and standard deviation info
x = getMids(ebins)
y = np.sqrt((rx-tx)**2 + (ry-ty)**2)
medians, sigL, sigR, vars = getMedian(t, y, ebins)
ax.errorbar(x, medians, yerr=(sigL,sigR), **pltParams)
if out:
plt.savefig(out)
plt.show()
def eres(s, cut=None, xaxis='energy', nbins=20, minE=4.0, rt='t',
thetaMax=55., zcorrect=False, title=True, out=False, ax=None):
titleDict = {'zenith':'Zenith Angle', 'energy':'Energy',
'core':'Core Position'}
xDict = {'zenith':r'$\cos(\mathrm{\theta_{true}})$',
'energy':r'$\log_{10}(E/\mathrm{GeV})$',
'core':'Core Position (m)'}
rtDict = {'r':'Reconstructed', 't':'True'}
# Setup plot
if ax == None:
fig, ax = plt.subplots()
if title:
ax.set_title('Energy Resolution vs %s %s' % \
(rtDict[rt], titleDict[xaxis]), fontsize=18)
ax.set_xlabel(xDict[xaxis], fontsize=16)
ax.set_ylabel(r'$\log_{10}(E_{\mathrm{LLH}}/E_{\mathrm{true}})$',
fontsize=16)
lw = 2
ms = 7*lw
pltParams = dict(fmt='.', lw=lw, ms=ms)
if xaxis == 'energy':
Ebins = getEbins()
bins = np.linspace(Ebins.min(), Ebins.max(), nbins+1)
if xaxis == 'zenith':
bins = np.linspace(1, np.cos(thetaMax * degree), nbins+1)[::-1]
if xaxis == 'core':
bins = np.linspace(0, 1000, nbins+1)
t = np.log10(s['MC_energy'])
r = np.log10(s['ML_energy'])
if zcorrect:
r = zfix(s)
if rt == 't':
e = t
z = s['MC_zenith']
cx, cy = s['MC_x'], s['MC_y']
if rt == 'r':
e = r
z = np.pi - s['zenith']
cx, cy = s['ML_x'], s['ML_y']
# Calculate cut values
c0 = np.logical_not(np.isnan(r))
#ecut = (t >= minE) if rt=='t' else (r >= minE)
ecut = (r >= minE)
c0 *= ecut
if cut != None:
c0 *= s['cuts'][cut]
# Store median and standard deviation info
x = getMids(bins)
x1 = {'energy':e, 'zenith':np.cos(z), 'core':np.sqrt(cx**2+cy**2)}
x1 = x1[xaxis]
y = r - t
medians, sigL, sigR, vars = getMedian(x1[c0], y[c0], bins)
#if xaxis == 'zenith':
# x = (np.arccos(x) / degree)[::-1]
# medians, sigL, sigR = medians[::-1], sigL[::-1], sigR[::-1]
ax.errorbar(x, medians, yerr=(sigL,sigR), **pltParams)
if out:
plt.savefig(out)
#if ax == None:
plt.show()
