def process_binned(self, E1, E2, Estep, killzone_mask=None):
"""
Process spectrum binning
Parameters:
E1 - low edge of lowest bin
E2 - high edge of highest bin
Estep = bin width
killzone_mask - optional mask of regions to ignore entirely
See Calibration.process for prerequisites and results
"""
calib = calibrate.load(self.calibration_file)
# zero out killzones of calibration matrix
# (this causes those pixels to be ignored)
if killzone_mask is not None:
calib.calibration_matrix[killzone_mask] = 0
exposure = Exposure()
exposure.load_multi(self.exposure_files)
exposure.apply_filters(self.incident_energy, self.filters)
spectrum = binned_emission_spectrum(calib.calibration_matrix, exposure,
E1, E2, Estep, self.I0)
self._set_spectrum(spectrum)
def process_binned(self, E1, E2, Estep, killzone_mask=None):
"""
Process spectrum binning
Parameters:
E1 - low edge of lowest bin
E2 - high edge of highest bin
Estep = bin width
killzone_mask - optional mask of regions to ignore entirely
See Calibration.process for prerequisites and results
"""
calib = calibrate.load(self.calibration_file)
# zero out killzones of calibration matrix
# (this causes those pixels to be ignored)
if killzone_mask is not None:
calib.calibration_matrix[killzone_mask] = 0
exposure = Exposure()
exposure.load_multi(self.exposure_files)
exposure.apply_filters(self.incident_energy, self.filters)
spectrum = binned_emission_spectrum(calib.calibration_matrix,
exposure,
E1,
E2,
Estep,
self.I0)
self._set_spectrum(spectrum)
def diagnose(self, return_spectra=False, filters=None, progress=None):
"""
Process all calibration exposures and fit to gaussians, returning parameters of fit
Example:
>>> import minixs as mx
>>> from matplotlib import pyplot as pt
>>> c = mx.calibrate.Calibration('example.calib')
>>> d, spectra = c.diagnose(return_spectra = True, filters=[mx.filter.HighFilter(1000)])
>>> d[:,3] # sigma for gaussian fits
array([ 0.4258905 , 0.54773887, 0.58000567, 0.57056559, 0.56539868,
0.58693027, 0.60704443, 0.61898894, 0.62726828, 0.63519546,
0.65309853, 0.66317984, 0.67826396, 0.69466781, 0.75039033,
0.78887514, 0.84248593, 0.8974527 ])
>>> s = spectra[5]
>>> pt.plot(s.emission, s.intensity, 'o')
>>> pt.plot(s.emission, mx.gauss.gauss_model(d[5,1:], s.emission))
>>> pt.show()
Parameters
----------
return_spectra: whether to return processed calibration spectra
Returns
-------
(diagnostics, [processed_spectra])
diagnostics: an array with one row for each calibration exposure
the columns are:
incident beam energy
amplitude
E0
sigma
the best Gaussian fit to the data is given by:
exp(-(E-E0)**2/(2*sigma**2))
if `return_spectra` is True, then a list of XES spectra will be returned (one for each calibration exposure)
"""
emin, emax = self.energy_range()
emission_energies = np.arange(emin, emax, .2)
diagnostics = np.zeros((len(self.energies), 4))
if return_spectra:
spectra = []
for i in range(len(self.energies)):
if progress:
msg = "Processing calibration exposure %d / %d" % (i, len(self.energies))
prog = i / float(len(self.energies))
progress.update(msg, prog)
energy = self.energies[i]
exposure = Exposure(self.exposure_files[i])
if filters is not None:
exposure.apply_filters(energy, filters)
s = process_spectrum(self.calibration_matrix, exposure, emission_energies, 1, self.dispersive_direction, self.xtals)
x = s[:,0]
y = s[:,1]
fit, ier = gauss_leastsq((x,y), (y.max(), energy, 1.0))
if not (0 < ier < 5):
continue
diagnostics[i,0] = energy
diagnostics[i,1:] = fit
if return_spectra:
xes = EmissionSpectrum()
xes.incident_energy = energy
xes.exposure_files = [exposure.filename]
xes._set_spectrum(s)
spectra.append(xes)
diagnostics = diagnostics[np.where(diagnostics[:,0] != 0)]
if return_spectra:
return (diagnostics, spectra)
else:
return diagnostics
