def spline_fit(data,
bins = None,
range = None,
weights = None,
order = None,
filename = 'spline.fits'):
if bins is None:
bins = data.shape[1]*[10]
counts,bin_arrays = np.histogramdd(data,range=range,weights=weights)
vars,bin_arrays = np.histogramdd(data,range=range,weights=weights**2)
else:
counts,bin_arrays = np.histogramdd(data,bins=bins,range=range,weights=weights)
vars,bin_arrays = np.histogramdd(data,bins=bins,range=range,weights=weights**2)
coords = [(b[1:]+b[:-1])/2. for b in bin_arrays]
if order == None:
order = list(np.zeros_like(bins))
knots = pad_knots(bin_arrays, order)
w = 1./np.sqrt(vars)
w[~np.isfinite(w)] = np.nanmin(w)
result = glam.fit(counts,w,coords,knots,order,0)
if not filename is None:
if os.path.exists(filename):
os.system('rm '+filename)
if filename[-5:]=='.fits':
splinefitstable.write(result,filename)
else:
splinefitstable.write(result,filename+'.fits')
return result
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()
from icecube.photospline.splinetable import SplineTable from icecube.photospline.splinefitstable import write fname = "constant.fits" # make a constant spline surface spline = SplineTable() spline.ndim = 2 spline.order = [2 for i in range(spline.ndim)] spline.knots = [numpy.linspace(0, 1, 10) for i in range(spline.ndim)] nsplines = tuple(knots.size-order-1 for knots,order in zip(spline.knots, spline.order)) spline.coefficients = numpy.ones(nsplines) if os.path.exists(fname): os.unlink(fname) write(spline, fname) x = [0.5]*spline.ndim spline = I3SplineTable(fname) assert spline.eval(x) == spline.eval(x, [0,0]) == 1. # all second derivatives must be zero for i in range(len(x)): derivs = [0]*2 derivs[i] = 2 assert spline.eval(x, derivs) == 0. os.unlink(fname)
w,
bin_centers,
knots,
order,
smooth,
penalties=penalties)
spline.geometry = Geometry.SPHERICAL
spline.extents = extents[:3]
spline.ngroup = table.header['n_group']
print("Saving table to %s..." % abs_outputfile)
spline.knots = [
spline.knots[i] * axis_scale[i] for i in range(0, len(spline.knots))
]
check_exists(abs_outputfile)
splinefitstable.write(spline, abs_outputfile)
# clean up
del (w, z, bin_centers, bin_widths, order, penalties, knots, spline)
if opts.prob:
z = table.values / norm.reshape(norm.shape + (1, ))
# XXX HACK: ignore weights for normalized timing
w = 1000 * numpy.ones(table.values.shape)
# XXX HACK: don't believe anything that happens outside the
# tracking volume of the table
#scalp(table, w, low=opts.ice_bottom, high=opts.ice_top)
order, penalties, knots = spline_spec(4)
bin_centers = [b.copy() for b in table.bin_centers]
pad_knots(numpy.linspace(0, numpy.sqrt(100), 5)**2,
2), # bundle multiplicity
pad_knots(numpy.linspace(0, 250**(1. / 2),
11)**2), # distance to shower axis
pad_knots(numpy.linspace(0, 20, 11), 2), # log(energy) [log(E/GeV)]
]
order = order[0:2] + [2, 2] + [order[-1]]
penalties[2] = penalties[2][0:2] + [smooth / 1e4, smooth / 1e1
] + [penalties[2][-1]]
penalties = {
2:
[smooth / 1e5, smooth / 1e5, smooth * 1e4, smooth / 1e3, smooth / 1e1]
} # Penalize curvature
# penalties[2][0] *= 1e1
# penalties[2][1] *= 1e4
# penalties[2][-1] *= 1e1
spline = glam.fit(hes.bincontent,
weights,
hes._h_bincenters,
knots,
order,
smooth,
penalties=penalties)
spline.bias = bias
if os.path.exists(outfile):
os.unlink(outfile)
splinefitstable.write(spline, outfile)
sublist = lowmirror + sublist + highmirror
print('done')
print('\tStacking...', end=' ')
intermedtables.append((stack_tables(sublist), z))
print('done')
# We no longer need to original tables
del tables
if len(zpos) > 1:
finaltab = stack_tables(intermedtables)
else:
finaltab = intermedtables[0][0]
try:
targetfile = args[1]
except IndexError:
targetfile = os.path.normpath(
os.path.join(
os.path.abspath(sourcedir), '..',
os.path.basename(os.path.abspath(sourcedir)) + '.fits'))
finaltab.extents += extents
try:
splinefitstable.write(finaltab, targetfile)
print("Output written to", targetfile)
except Exception as inst:
print(inst)
def spline(config, inFile, outFile, plot=False):
# Load input median file
d = np.load(inFile)
d = d.item()
xbins, ybins = d['xbins'], d['ybins']
energies = d['medians']
vars = d['var']
vars[vars<1e-15] = np.inf # Variances sufficiently close to 0 aren't real
w = 1/vars
emin, emax = energies[energies!=0].min(), energies.max()
ebins = [emin] + getEbins() + [emax]
axes, knots = [],[]
binList = [xbins, ybins]
nknots = 30
step_scale = 2/5.
for bins in binList:
mids = (bins[1:]+bins[:-1])/2.
axes += [mids]
step = (bins.max() - bins.min()) * step_scale
knots += [np.linspace(bins.min()-step, bins.max()+step, nknots)]
tab = glam.fit(energies, w, axes, knots, order=(4), \
penalties={2:1e-3})
if plot:
# Look at spline fit with finer binning
fitaxes = [[],[]]
fitaxes[0] = np.linspace(xbins.min(),xbins.max(),len(xbins)*3)
fitaxes[1] = np.linspace(ybins.min(),ybins.max(),len(ybins)*3)
fit = glam.grideval(tab, fitaxes)
#err_fit = 10**glam.grideval(err_tab,axes)
fig = plt.figure(figsize=(17,6))
mpl.rc("font", family="serif")
X, Y = np.meshgrid(axes[0], axes[1])
fitX, fitY = np.meshgrid(fitaxes[0], fitaxes[1])
# Setup custom colormap
cmap = plt.cm.jet
cmap = cmap_discretize(cmap, ebins)
cmap.set_under('white')
ax = fig.add_subplot(121)
p = ax.pcolor(fitX, fitY, fit.T, cmap=cmap, vmin=emin, vmax=emax)
cb = fig.colorbar(p, ax=ax)
ax.set_title('Median Energy')
ax.set_xlabel('cos(zenith)')
ax.set_ylabel('log10(Nchannel)')
ax.set_xlim(xbins.min(), xbins.max())
ax.set_ylim(ybins.min(), ybins.max())
ax = fig.add_subplot(122)
p = ax.pcolor(X, Y, energies.T, cmap=cmap, vmin=emin, vmax=emax)
ax.set_title('Median Energy')
ax.set_xlabel('cos(zenith)')
ax.set_ylabel('log10(Nchannel)')
ax.set_xlim(xbins.min(), xbins.max())
ax.set_ylim(ybins.min(), ybins.max())
plt.show()
if outFile:
if os.path.exists(outFile):
os.remove(outFile)
splinefitstable.write(tab, outFile)