def reco_energy_plot(config, out=False, batch=False):
# Load median file information
simBase = getSimBase(config)
inFile = '%s_median.npy' % simBase
d = np.load(inFile)
d = d.item()
xbins, ybins, ebins = d['xbins'], d['ybins'], d['ebins']
# Calculate median energy value from bin
energies = d['medians']
energies = energies.T
# Trim down the top of the grid
ycts = energies.sum(axis=1)
yidx = np.where(ycts==0)[0][0] + 2
ybins = ybins[:yidx]
energies = energies[:yidx]
emin, emax = energies[energies!=0].min(), energies[energies!=0].max()
emin, emax = 3.75, 8
ebins = [emin] + getEbins() + [emax]
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
X, Y = np.meshgrid(xbins, ybins)
cmap = plt.cm.jet
cmap = cmap_discretize(cmap, ebins)
cmap.set_under('white')
tPars = {'fontsize':16}
p = ax.pcolor(X, Y, energies, cmap=cmap, vmin=emin, vmax=emax)
cb = fig.colorbar(p, ax=ax, ticks=ebins)
cb.ax.set_yticklabels(['%.2f' % ebin for ebin in ebins])
cb.set_label(r'$\mathrm{log}_{10}(E/\mathrm{GeV})$',
rotation=270, labelpad=20, **tPars)
#ax.set_title('Median Energy vs Zenith and Nchannel')
ax.set_xlabel(r'$\mathrm{cos}(\theta_\mathrm{reco})$', **tPars)
ax.set_ylabel(r'$\mathrm{log}_{10}(N_\mathrm{channel})$', **tPars)
ax.set_xlim(xbins.min(), xbins.max())
ax.set_ylim(ybins.min(), ybins.max())
if out != False:
#outFile = '%s/%s_Median_Energy' % (outPrefix, config)
plt.savefig(out, dpi=300, bbox_inches='tight')
if not batch:
plt.show()
def eres(config, out=False, batch=False, old=False):
# Basic setup
fig, ax = plt.subplots()
lw = 2
ms = 7*lw
pltParams = {'fmt':'.', 'lw':lw, 'ms':ms}
d = {}
configs = ['IC59','IC79','IC86','IC86-II','IC86-III','IC86-IV']
if config in configs:
configs = [config]
# Energy binning information
eList = getEbins() + [100]
eStrings = ['%s' % float(i) for i in eList]
ePairs = [[eStrings[i], eStrings[i+1]] for i in range(len(eStrings)-1)]
minE, maxE = np.asarray(eList[:-1]), np.asarray(eList[1:])
if maxE[-1] == 100:
maxE[-1] = maxE[-2] + 1
emids = (minE + maxE) / 2.
xL = emids - minE
xR = maxE - emids
xR[-1] = 0
# Read in distribution information
for cfg in configs:
d[cfg] = {}
for key in ['median','sigL','sigR']:
d[cfg][key] = []
for emin, emax in ePairs:
median, sigL, sigR = readDist(cfg, emin, emax, old=old)
d[cfg]['median'] += [median]
d[cfg]['sigL'] += [sigL]
d[cfg]['sigR'] += [sigR]
if len(configs) > 1:
pltParams['label'] = cfg
ax.errorbar(emids, d[cfg]['median'],
yerr=[d[cfg]['sigL'], d[cfg]['sigR']], **pltParams)
emids += .02
else:
#ax.errorbar(emids, d[cfg]['median'], xerr=[xL, xR],
# yerr=[d[cfg]['sigL'], d[cfg]['sigR']], **pltParams)
#ax.arrow(emids[-1], d[cfg]['median'][-1], 0.5, 0.0,
# fc='b', ec='b', head_width=.1, head_length=.2)
ax.errorbar(emids, d[cfg]['median'],
yerr=[d[cfg]['sigL'], d[cfg]['sigR']], **pltParams)
# Create combined distribution (start unweighted)
ave_median = np.average([d[key]['median'] for key in d.keys()], axis=0)
ave_sigL = np.average([d[key]['sigL'] for key in d.keys()], axis=0)
ave_sigR = np.average([d[key]['sigR'] for key in d.keys()], axis=0)
if len(configs) > 1:
ax.errorbar(emids, ave_median, yerr=[ave_sigL, ave_sigR],
fmt='kx', label='Total')
ax.set_xlim(3.5, 8)
tPars = {'fontsize':16}
#ax.set_xlabel(r'Reconstructed Energy ($log_{10}(E/\mathrm{GeV})$)',
# **tPars)
ax.set_xlabel(r'Energy of Bin Center ($log_{10}(E/\mathrm{GeV})$)',
**tPars)
ax.set_ylabel(r'True Energy ($log_{10}(E/\mathrm{GeV})$)', **tPars)
#ax.set_title('Energy Distributions for Cuts', fontsize=16)
if len(configs) > 1:
plt.legend(loc='lower right')
if out != False:
plt.savefig(out, dpi=300, bbox_inches='tight')
if not batch:
plt.show()
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)
