help='Print data used for fitting, do not fit nor plot')
args = parser.parse_args()
binx = args.binx[0]
biny = args.biny[0]
if (not (binx != 0 and ((binx & (binx - 1)) == 0))
or not (biny != 0 and ((biny & (biny - 1)) == 0))):
print('Binning factor must be a power of 2')
exit()
shift = args.shift[0]
energy_low_cut = args.cut[0]
energy_high_cut = args.cut[1]
try:
his = hisfile.HisFile(args.infile)
except GeneralError as err:
print(err.msg)
exit()
dim, x_axis, y_axis, data = his.load_histogram(args.his[0])
if dim != 2:
print('Data must be a 2D histogram!')
exit()
# Rebinng histogram using reshape trick
x_axis = x_axis.reshape((x_axis.shape[0] // binx, binx)).mean(1)
y_axis = y_axis.reshape((y_axis.shape[0] // biny, biny)).mean(1)
data_rebinned = data.reshape(
(data.shape[0] // binx, binx, data.shape[1] // biny, -1)).sum(3).sum(1)
def load(self, file_name):
"""Load his file (also tar gzipped files)"""
self.hisfile = hisfile.HisFile(file_name)
def process(self, pause, show, verbose, rounding):
"""For each file in file_list, for each spectrum in a given files
performes fit and adds result to entry in the dict
"""
for file_name, spectra in self.file_list.items():
his = hisfile.HisFile(file_name, rounding)
for hisId, lines in spectra.items():
data = his.load_histogram(hisId)
for line in lines:
xmin = line['min']
xmax = line['max']
if data[0] != 1:
raise GeneralError('Only 1D histograms are suitable' +
'for this calibration')
data_x, data_y = data[1][xmin:xmax], data[3][xmin:xmax]
data_dy = numpy.sqrt(numpy.abs(data[3][xmin:xmax]))
# 0 counts have error 1 (poisson!)
for i, dy in enumerate(data_dy):
if dy == 0:
data_dy[i] = 1
try:
fitter = peak_fitter.PeakFitter(line['model'])
result = fitter.fit(data_x, data_y, data_dy)
except ValueError:
msg = ('Fit problems with spectrum {} line {}: {}')
raise GeneralError(
msg.format(hisId, line['line'],
'numerical issue encountered'))
except peak_fitter.GeneralError as err:
msg = ('Fit problems with spectrum {} line {}: {}')
raise GeneralError(
msg.format(hisId, line['line'], err.msg))
if line['model'].startswith("gauss_doublet"):
line['dx'] = result.params['mu'].stderr
line['x0'] = result.params['mu'].value
line['x1'] = result.params['mu1'].value
line['Area'] = result.params['A'].value
line['Area1'] = result.params['A1'].value
line['dA'] = result.params['A'].stderr
line['dA1'] = result.params['A1'].stderr
else:
x0 = result.params['mu'].value
dx = result.params['mu'].stderr
line['x0'] = x0
line['dx'] = dx
line['Area'], line['dA'] = self._find_area(
fitter, x0, dx, xmin, data_x, data_y)
line['redchi'] = result.redchi
if result.params['mu'].stderr == 0:
msg = ('Warning, line {} in spectrum {}:' +
' could not determine uncertainity\n')
sys.stderr.write(msg.format(line['line'], hisId))
if show == 'plot' or show == 'png':
x = numpy.linspace(data_x[0], data_x[-1], 1000)
y0 = fitter.fit_func(result.params, x)
plt.clf()
plt.xlabel('Channel')
plt.ylabel('Counts')
plt.title('Spectrum {} line {}'.format(
hisId, line['line']))
plt.plot(x, y0)
plt.errorbar(data_x, data_y, data_dy, fmt='o')
xpos = (plt.xlim()[0] +
(plt.xlim()[1] - plt.xlim()[0]) * 0.1)
ypos = (plt.ylim()[1] -
(plt.ylim()[1] - plt.ylim()[0]) * 0.1)
text = ('$\mu$ = {0:.2f}\n' +
'$\chi^2/\mathcal{{N}}$' +
' = {1:.2f}').format(result.params['mu'].value,
result.redchi)
plt.text(xpos, ypos, r'{}'.format(text))
if show == 'png':
png_name = '{}_{}_{:.0f}.png'.format(
file_name, hisId, float(line['line']))
plt.savefig(png_name)
print('File', png_name, 'saved')
elif show == 'plot':
plt.draw()
time.sleep(pause)
elif show == 'text':
msg = ('Line {} in spectrum {},' + ' x0 = {:.2f},' +
' redchi = {:.2f}\n').format(
line['line'], hisId,
result.params['mu'].value, result.redchi)
sys.stderr.write(msg)
elif show == 'quiet':
pass
else:
raise GeneralException(
'Unknown show method {}'.format(show))
if verbose:
sys.stderr.write('{}\n'.format(20 * '-'))
sys.stderr.write('Line {} spectrum {}\n'.format(
line['line'], hisId))
sys.stderr.write('Reduced Chi2: {:.3f}\n'.format(
result.redchi))
for key, par in result.params.items():
sys.stderr.write('{} {:.3f} +/- {:.3f}\n'.format(
key, par.value, par.stderr))
fwhm_factor = 2 * math.sqrt(math.log(2) * 2)
sys.stderr.write('{} {:.3f} +/- {:.3f}\n'.format(
'FWHM', fwhm_factor * result.params['s'].value,
fwhm_factor * result.params['s'].stderr))