def getHist(data,var_name,mask=None,edges=None):
h = Histogram(edges)
if not mask is None:
h.fill(data[var_name][mask])
else:
h.fill(data[var_name])
return h
def getHist(data, var_name, mask=None, edges=None):
h = Histogram(edges)
if not mask is None:
h.fill(data[var_name][mask])
else:
h.fill(data[var_name])
return h
def getOnOffHist(data,var_name,phases,mask=None,edges=None):
(on_phase,off_phase,alpha) = phases
on_mask = PhotonData.get_mask(data,phases=on_phase)
off_mask = PhotonData.get_mask(data,phases=off_phase)
if not mask is None:
on_mask &= mask
off_mask &= mask
hon = Histogram(edges)
hon.fill(data[var_name][on_mask])
hoff = Histogram(edges)
hoff.fill(data[var_name][off_mask])
hoffs = copy.deepcopy(hoff)
hoffs *= alpha
return (hon,hoff,hoffs)
def getOnOffHist(data, var_name, phases, mask=None, edges=None):
(on_phase, off_phase, alpha) = phases
on_mask = PhotonData.get_mask(data, phases=on_phase)
off_mask = PhotonData.get_mask(data, phases=off_phase)
if not mask is None:
on_mask &= mask
off_mask &= mask
hon = Histogram(edges)
hon.fill(data[var_name][on_mask])
hoff = Histogram(edges)
hoff.fill(data[var_name][off_mask])
hoffs = copy.deepcopy(hoff)
hoffs *= alpha
return (hon, hoff, hoffs)
print 'SIGMA ', sigma_mu_fn(1.0)
print 'SIGMA ', sigma_mu_fn(10.0)
print 'Asimov q0 UL ', find_fn_root(lambda t: fn_qmu(mub+t,mub+t,mub),0,100,dlnl)
print 'Asimov qmu UL ', ul_asimov_qmu, sigma
print 'Asimov UL ', ul_asimov, ul_asimov_upper-ul_asimov, ul_asimov_lower-ul_asimov
print -2*poisson_delta_lnl(mub,mub,mub+ul_asimov)
print -2*poisson_delta_lnl(mub,mub,mub+ul_asimov_qmu)
print fn_qmu(mub,mub,mub+ul_asimov_qmu)
print dlnl
qmu = -2*poisson_delta_lnl(mub,mub,mub+ul_asimov_qmu)
h = Histogram(Axis.create(0,100,100))
h.fill(ul_mc)
h.normalize().cumulative().plot()
plt.axhline(0.5-0.34,color='g')
plt.axhline(0.5+0.34,color='g')
plt.axvline(ul_asimov_qmu,color='k')
plt.axvline(ul_asimov_qmu+sigma,color='k',linestyle='--')
plt.axvline(ul_asimov_qmu-sigma,color='k',linestyle='--')
# plt.axvline(ul_asimov_qmu-sigma/(ul_asimov_qmu+sigma)*ul_asimov_qmu,
# color='k',linestyle='--')
plt.gca().grid(True)
def make_image_plot(self,subplot,h,fig,fig2,title,rpsf68,rpsf95,
smooth=False,
resid_type=None,mc_resid=None,**kwargs):
plt.figure(fig.get_label())
cb_label='Counts'
if resid_type == 'significance':
kwargs['vmin'] = -5
kwargs['vmax'] = 5
kwargs['levels'] = [-5.0,-3.0,3.0,5.0]
cb_label = 'Significance [$\sigma$]'
elif resid_type == 'fractional':
kwargs['vmin'] = -1.0
kwargs['vmax'] = 1.0
kwargs['levels'] = [-1.0,-0.5,0.5,1.0]
cb_label = 'Fractional Residual'
if smooth:
kwargs['beam_size'] = [self._rsmooth,self._rsmooth,0.0,4]
axim = h.plot(subplot=subplot,cmap='ds9_b',**kwargs)
h.plot_circle(rpsf68,color='w',lw=1.5)
h.plot_circle(rpsf95,color='w',linestyle='--',lw=1.5)
h.plot_marker(marker='+',color='w',linestyle='--')
ax = h.ax()
ax.set_title(title)
cb = plt.colorbar(axim,orientation='horizontal',
shrink=0.9,pad=0.15,
fraction=0.05)
cb.set_label(cb_label)
cat = Catalog.get('3fgl')
cat.plot(h,ax=ax,src_color='w',label_threshold=5.0)
if resid_type is None: return
plt.figure(fig2.get_label())
ax2 = fig2.add_subplot(subplot)
z = h.counts[10:-10,10:-10]
if resid_type == 'significance':
zproj_axis = Axis.create(-6,6,120)
elif resid_type == 'fractional':
zproj_axis = Axis.create(-1.0,1.0,120)
else:
zproj_axis = Axis.create(-10,10,120)
hz = Histogram(zproj_axis)
hz.fill(np.ravel(z))
nbin = np.prod(z.shape)
hz_mc = Histogram(zproj_axis)
if mc_resid:
for mch in mc_resid:
z = mch.counts[10:-10,10:-10]
hz_mc.fill(np.ravel(z))
hz_mc /= float(len(mc_resid))
fn = lambda t : 1./np.sqrt(2*np.pi)*np.exp(-t**2/2.)
hz.plot(label='Data',linestyle='None')
if resid_type == 'significance':
plt.plot(hz.axis().center,
fn(hz.axis().center)*hz.axis().width*nbin,
color='k',label='Gaussian ($\sigma = 1$)')
hz_mc.plot(label='MC',hist_style='line')
plt.gca().grid(True)
plt.gca().set_yscale('log')
plt.gca().set_ylim(0.5)
ax2.legend(loc='upper right',prop= {'size' : 10 })
data_stats = 'Mean = %.2f\nRMS = %.2f'%(hz.mean(),hz.stddev())
mc_stats = 'MC Mean = %.2f\nMC RMS = %.2f'%(hz_mc.mean(),
hz_mc.stddev())
ax2.set_xlabel(cb_label)
ax2.set_title(title)
ax2.text(0.05,0.95,
'%s\n%s'%(data_stats,mc_stats),
verticalalignment='top',
transform=ax2.transAxes,fontsize=10)
def make_image_plot(self,
subplot,
h,
fig,
fig2,
title,
rpsf68,
rpsf95,
smooth=False,
resid_type=None,
mc_resid=None,
**kwargs):
plt.figure(fig.get_label())
cb_label = 'Counts'
if resid_type == 'significance':
kwargs['vmin'] = -5
kwargs['vmax'] = 5
kwargs['levels'] = [-5.0, -3.0, 3.0, 5.0]
cb_label = 'Significance [$\sigma$]'
elif resid_type == 'fractional':
kwargs['vmin'] = -1.0
kwargs['vmax'] = 1.0
kwargs['levels'] = [-1.0, -0.5, 0.5, 1.0]
cb_label = 'Fractional Residual'
if smooth:
kwargs['beam_size'] = [self._rsmooth, self._rsmooth, 0.0, 4]
axim = h.plot(subplot=subplot, cmap='ds9_b', **kwargs)
h.plot_circle(rpsf68, color='w', lw=1.5)
h.plot_circle(rpsf95, color='w', linestyle='--', lw=1.5)
h.plot_marker(marker='x', color='w', linestyle='--')
ax = h.ax()
ax.set_title(title)
cb = plt.colorbar(axim,
orientation='horizontal',
shrink=0.9,
pad=0.15,
fraction=0.05)
if kwargs.get('zscale', None) is not None:
import matplotlib.ticker
cb.locator = matplotlib.ticker.MaxNLocator(nbins=5)
cb.update_ticks()
cb.set_label(cb_label)
cat = Catalog.get('3fgl')
cat.plot(h,
ax=ax,
src_color='w',
label_threshold=self._srclabels_thresh)
if resid_type is None: return
plt.figure(fig2.get_label())
ax2 = fig2.add_subplot(subplot)
z = h.counts[10:-10, 10:-10]
if resid_type == 'significance':
zproj_axis = Axis.create(-6, 6, 120)
elif resid_type == 'fractional':
zproj_axis = Axis.create(-1.0, 1.0, 120)
else:
zproj_axis = Axis.create(-10, 10, 120)
hz = Histogram(zproj_axis)
hz.fill(np.ravel(z))
nbin = np.prod(z.shape)
hz_mc = Histogram(zproj_axis)
if mc_resid:
for mch in mc_resid:
z = mch.counts[10:-10, 10:-10]
hz_mc.fill(np.ravel(z))
hz_mc /= float(len(mc_resid))
fn = lambda t: 1. / np.sqrt(2 * np.pi) * np.exp(-t**2 / 2.)
hz.plot(label='Data', linestyle='None')
if resid_type == 'significance':
plt.plot(hz.axis().center,
fn(hz.axis().center) * hz.axis().width * nbin,
color='k',
label='Gaussian ($\sigma = 1$)')
hz_mc.plot(label='MC', hist_style='line')
plt.gca().grid(True)
plt.gca().set_yscale('log')
plt.gca().set_ylim(0.5)
ax2.legend(loc='upper right', prop={'size': 10})
data_stats = 'Mean = %.2f\nRMS = %.2f' % (hz.mean(), hz.stddev())
mc_stats = 'MC Mean = %.2f\nMC RMS = %.2f' % (hz_mc.mean(),
hz_mc.stddev())
ax2.set_xlabel(cb_label)
ax2.set_title(title)
ax2.text(0.05,
0.95,
'%s\n%s' % (data_stats, mc_stats),
verticalalignment='top',
transform=ax2.transAxes,
fontsize=10)
cm = CompositeModel()
# cm.addModel(KingFn.create(1.0,3.0,0.5*nevent))
cm.addModel(PolyFn.create(3,[0,1,2]))
# cm.addModel(KingFn.create(0.2,3.0,0.5*nevent,3))
print cm.param()
nbin = 80
h0 = Histogram([0,5.0],nbin)
h1 = Histogram([0,5.0],nbin)
h0.fill(cm.rnd(nevent,10.0))
lnlfn = OnOffBinnedLnL(h0._counts,h1._counts,h0._xedges,1.0,cm)
plt.figure()
sig = np.linspace(0.9,1.1,100)
p = cm.param().makeParameterArray(1,sig)
#print lnlfn.eval(p)
#print lnlfn.eval(p[0])
nevent = 10000
cm = CompositeModel()
# cm.addModel(KingFn.create(1.0,3.0,0.5*nevent))
cm.addModel(PolyFn.create(3, [0, 1, 2]))
# cm.addModel(KingFn.create(0.2,3.0,0.5*nevent,3))
print cm.param()
nbin = 80
h0 = Histogram([0, 5.0], nbin)
h1 = Histogram([0, 5.0], nbin)
h0.fill(cm.rnd(nevent, 10.0))
lnlfn = OnOffBinnedLnL(h0._counts, h1._counts, h0._xedges, 1.0, cm)
plt.figure()
sig = np.linspace(0.9, 1.1, 100)
p = cm.param().makeParameterArray(1, sig)
#print lnlfn.eval(p)
#print lnlfn.eval(p[0])
plt.plot(sig, lnlfn.eval(p))
lnlfn.fit()