def fit(self, xrange=None, bins=100, hist_nsig=3, dADU=150):
if xrange is None:
self._set_hist_range(dADU, bins, hist_nsig)
else:
self.xrange = xrange
hist = np.histogram(self.signals, bins=bins, range=self.xrange)
x = (hist[1][1:] + hist[1][:-1]) / 2.
y = hist[0]
ntot = sum(y)
#
# Starting values for two Gaussian fit. The relative
# normalizations are initially set at the expected line ratio
# of K-alpha/K-beta = 0.88/0.12. The relative peak locations
# and relative widths are fixed in fe55_lines(...) above.
#
p0 = (ntot * 0.88, self.median, self.stdev / 2., ntot * 0.12)
self.pars, pcov = scipy.optimize.curve_fit(fe55_lines, x, y, p0=p0)
kalpha_peak, kalpha_sigma = self.pars[1], self.pars[2]
kalpha_peak_error = np.sqrt(pcov[1][1])
# when there is only one peak, check if it is kalpha.
if kalpha_peak < self.xrange[0] or (
self.pars[3] > self.pars[0]
and 6.49 / 5.889 * self.pars[1] < self.xrange[1]):
kalpha_peak = 6.49 / 5.889 * self.pars[1]
kalpha_peak_error = 6.49 / 5.889 * np.sqrt(pcov[1][1])
fe55_yield = Fe55Yield(self.ccdtemp)
Ne, Ne_error = fe55_yield.alpha()
self.gain = Ne / kalpha_peak
# self.gain_error = float(self.gain*np.sqrt((Ne_error/Ne)**2 +
# (kalpha_peak_error/kalpha_peak)**2))
self.gain_error = float(self.gain * kalpha_peak_error / kalpha_peak)
return kalpha_peak, kalpha_sigma
def __init__(self, imfile, mask_files=(), ccdtemp_par=-100):
self.ccd = MaskedCCD(imfile, mask_files=mask_files)
self.md = afwImage.readMetadata(imfile, 0)
try:
ccdtemp = self.md.get('CCDTEMP')
except:
ccdtemp = ccdtemp_par
self.fe55_yield = Fe55Yield(ccdtemp).alpha()
self._footprint_signal = self._footprint_signal_spans
def __init__(self, image, ccdtemp=None):
if (image.getWidth() == imUtils.full_segment.getWidth()
and image.getHeight() == imUtils.full_segment.getHeight()):
# Trim to imaging area.
self.image = image.Factory(image, imUtils.imaging)
else:
message = "XrayCte constructor: must pass an untrimmed image"
raise RuntimeError(message)
self.imarr = image.getArray()
stat_control = afwMath.MEANCLIP | afwMath.STDEVCLIP | afwMath.STDEV
stats = afwMath.makeStatistics(image, stat_control)
self.mean = stats.getValue(afwMath.MEANCLIP)
self.stdev = stats.getValue(afwMath.STDEVCLIP)
# self.stdev = stats.getValue(afwMath.STDEV)
self.fe55_yield = Fe55Yield(ccdtemp).alpha()
def __init__(self, ccd, amp, bg_reg=(10, 10)):
self.ccd = ccd
self.amp = amp
self.ccdtemp = ccd.md.get('CCDTEMP')
self.fe55_yield = Fe55Yield(self.ccdtemp)
raw_image = ccd[amp]
try:
self.imarr = raw_image.getArray()
except AttributeError:
self.imarr = raw_image.getImage().getArray()
self.image = imutils.trim(raw_image, imaging=ccd.amp_geom.imaging)
self.image -= self._bg_image(*bg_reg)
flags = afwMath.MEANCLIP | afwMath.STDEVCLIP
stats = afwMath.makeStatistics(self.image, flags, self.ccd.stat_ctrl)
self.mean = stats.getValue(afwMath.MEANCLIP)
self.stdev = stats.getValue(afwMath.STDEVCLIP)
self.footprint_signal = self._footprint_signal_spans
def __init__(self,
exptime=1,
gain=5,
ccdtemp=-95,
full_well=None,
geometry=AmplifierGeometry()):
self.exptime = exptime
self.gain = gain
self.ccdtemp = ccdtemp
self.full_well = full_well
self.geometry = geometry
self.fe55_yield = Fe55Yield(ccdtemp)
self.image = afwImage.ImageF(geometry.full_segment)
self.imarr = self.image.Factory(self.image,
geometry.imaging).getArray()
self.ny, self.nx = self.imarr.shape
self.npix = self.nx * self.ny
self._sigma = -1
def fe55_gain_fitter(signals,
ccdtemp=-95,
make_plot=False,
xrange=None,
bins=100,
hist_nsig=10,
title='',
plot_filename=None,
interactive=True,
ylog=True):
"""
Function to fit the distribution of charge cluster DN values from
a Fe55 dataset. A two Gaussian model of Mn K-alpha and K-beta
lines is assumed with the ratio between the K-alpha and K-beta
energies fixed at 5.889/6.49 and the the Gaussian width of the
lines set equal.
The gain (Ne/DN), location and sigma of the K-alpha peak (in units
of DN) are returned as a tuple.
If make_plot=True, then a matplotlib plot of the distribution and
fit is displayed.
If xrange is not None, then that 2-element tuple is used as the
histogram x-range.
If xrange is None, then the histogram x-range is set to
+/- hist_nsig*clipped_stdev about the median of the signal
distribution.
"""
flags = afwMath.MEDIAN | afwMath.STDEVCLIP
try:
stats = afwMath.makeStatistics(signals.tolist(), flags)
except:
print signals
raise
median = stats.getValue(afwMath.MEDIAN)
stdev = stats.getValue(afwMath.STDEVCLIP)
if xrange is None:
# Set range of histogram to include both Kalpha and Kbeta peaks.
xmin = max(median - hist_nsig * stdev, 200)
xmax = min(median * 1785. / 1620. + hist_nsig * stdev, 1000)
xrange = xmin, xmax
# Save pylab interactive state.
pylab_interactive_state = pylab.isinteractive()
# Determine distribution mode and take that as the location of the
# Kalpha peak
hist = np.histogram(signals, bins=bins, range=xrange)
xpeak = hist[1][np.where(hist[0] == max(hist[0]))][0]
xrange = max(0, xpeak - 200), xpeak * 1785. / 1620. + 200
hist = np.histogram(signals, bins=bins, range=xrange)
yrange = 1, max(hist[0]) * 1.5
if make_plot:
if interactive:
pylab.ion()
else:
pylab.ioff()
# fig = pylab.figure()
# axes = fig.add_subplot(111)
win = plot.Window()
hist = pylab.hist(signals,
bins=bins,
range=xrange,
histtype='bar',
color='b',
log=ylog)
if ylog:
plot.setAxis(xrange, yrange)
else:
pylab.ioff()
# hist = np.histogram(signals, bins=bins, range=xrange)
x = (hist[1][1:] + hist[1][:-1]) / 2.
y = hist[0]
ntot = sum(y)
#
# Starting values for two Gaussian fit. The relative
# normalizations are initially set at the expected line ratio
# of K-alpha/K-beta = 0.88/0.12. The relative peak locations
# and relative widths are fixed in fe55_lines(...) above.
#
p0 = (ntot * 0.88, median, stdev / 2., ntot * 0.12)
pars, _ = scipy.optimize.curve_fit(fe55_lines, x, y, p0=p0)
kalpha_peak, kalpha_sigma = pars[1], pars[2]
fe55_yield = Fe55Yield(ccdtemp)
gain = fe55_yield.alpha()[0] / kalpha_peak
if make_plot:
pylab.xlabel('Bias Corrected Event Signal (DN)')
pylab.ylabel('Entries / bin')
xx = np.linspace(x[0], x[-1], 1000)
pylab.plot(xx, fe55_lines(xx, *pars), 'r--', markersize=3, linewidth=1)
pylab.annotate(("K-alpha peak = %i DN\n\n" + "Gain = %.2f e-/DN\n\n") %
(kalpha_peak, gain), (0.5, 0.7),
xycoords='axes fraction')
win.set_title(title)
if plot_filename is not None:
pylab.savefig(plot_filename)
# Restore pylab interactive state.
pylab.interactive(pylab_interactive_state)
return gain, kalpha_peak, kalpha_sigma