def localfit_goodchs(data, linename, goodchs, category=None):
elo, ehi = mass.STANDARD_FEATURES[linename] - 50, mass.STANDARD_FEATURES[
linename] + 50
edges = np.arange(elo, ehi, 1)
p_energy = []
for ds in data:
if not ds.channum in goodchs: continue
if category is not None:
good = ds.cuts.good(**category)
else:
good = ds.good()
p_energy.extend(ds.p_energy[good])
p_energy = np.asarray(p_energy, dtype=np.float32)
counts, _ = np.histogram(p_energy, edges)
fitter = mass.getfitter(linename)
fitter.fit(counts, edges, plot=False)
params = fitter.last_fit_params[:]
params[fitter.param_meaning["tail_frac"]] = 0.25
params[fitter.param_meaning["dP_dE"]] = 1
params[fitter.param_meaning["resolution"]] = 6
fitter.fit(counts,
edges,
params=params,
hold=[2],
vary_tail=True,
plot=False)
# hold[2]: fixing dP_dE
#print "---------------------------"
#print fitter.result_string()
#print "---------------------------"
return fitter
def linefit(hist, bin_edges, linename):
fitter = mass.getfitter(linename)
fitter.fit(hist, bin_edges, plot=False)
params = fitter.last_fit_params[:]
params[fitter.param_meaning["tail_frac"]] = 0.25
params[fitter.param_meaning["dP_dE"]] = 1
params[fitter.param_meaning["resolution"]] = 6
fitter.fit(hist, bin_edges, params, hold=[2], vary_tail=True, plot=False)
return fitter
def localfit(ds, linename, category=None):
'''
NOTE parameters of MultiLorentzianComplexFitter
param_meaning = {
"resolution": 0,
"peak_ph": 1,
"dP_dE": 2,
"amplitude": 3,
"background": 4,
"bg_slope": 5,
"tail_frac": 6,
"tail_length": 7
}
'''
if category is not None:
good = ds.cuts.good(**category)
else:
good = ds.good()
elo, ehi = mass.STANDARD_FEATURES[linename] - 50, mass.STANDARD_FEATURES[
linename] + 50
edges = np.arange(elo, ehi, 1)
counts, _ = np.histogram(ds.p_energy[good], edges)
fitter = mass.getfitter(linename)
fitter.fit(counts, edges, plot=False)
params = fitter.last_fit_params[:]
params[fitter.param_meaning["tail_frac"]] = 0.25
params[fitter.param_meaning["dP_dE"]] = 1
params[fitter.param_meaning["resolution"]] = 6
fitter.fit(counts,
edges,
params=params,
hold=[2],
vary_tail=True,
plot=False)
# hold[2]: fixing dP_dE
#print "---------------------------"
#print fitter.result_string()
#print "---------------------------"
return fitter
plt.figure()
for fv in fvs:
hist, bin_edges = np.histogram(fv[g], nbins, hist_range)
plt.hist(fv[g], bin_edges, hist_range, histtype='step')
plt.title(title)
nbins = 200
hist_range = (6800., 7000.)
plt.figure()
for ene in enes:
hist, bin_edges = np.histogram(ene[g], nbins, hist_range)
plt.hist(ene[g], bin_edges, hist_range, histtype='step')
plt.title(title)
linename = "CoKAlpha"
fitter = mass.getfitter(linename)
nbins = 100
hist_range = (mass.STANDARD_FEATURES[linename] - 50,
mass.STANDARD_FEATURES[linename] + 50)
vtail = False
plt.figure()
ax1 = plt.subplot(311)
hist, bin_edges = np.histogram(enes[0][g], bins=nbins, range=hist_range)
fitter.fit(hist,
bin_edges[0:-1],
axis=ax1,
color='red',
vary_bg=True,
vary_bg_slope=False,
vary_tail=vtail)