def get_energy(self):
"""
Returns array of energy for each channel in the MCA spectra.
"""
energy = channel_to_energy(self.channels, offset=self.offset,
slope=self.slope, quad=self.quad)
return energy
def med_copy_rois(med, source_mca=0):
"""
This procedure copies the ROIs defined for one Mca in the Med to all of
the other Mcas.
Parameters:
-----------
* source_mca: The index number of the Mca from which the ROIs are to
be copied. This number ranges from 0 to self.n_detectors-1.
The default is the first Mca (index=0).
Notes:
------
The ROIs are copied by their position
in energy rather than in channels. This is very useful when
copying ROIs when the calibration parameters for each Mca in
the Med are not identical.
"""
units = "channel"
if energy:
units = "keV"
kws = dict(offset = med.mca[source_mca].offset,
slope = med.mca[source_mca].slope,
quad = med.mca[source_mca].quad,
clip = med.mca[source_mca].clip)
left, right, label, bgr_width = [], [], [], []
for roi in med.mca[source_mca].rois:
left.append(channel_to_energy(roi.left, **kws))
right.append(channel_to_energy(roi.right, **kws))
label.append(roi.label)
bgr_width.append(roi.bgr)
for j in range(med.n_detectors):
off = med.mca[j].offset
slope = med.mca[j].slope
quad = med.mca[j].quad
clip = len(med.mca[j].data)
med.mca[j].rois = []
for k in range(len(left)):
l = energy_to_channel(left[k],offset=off,slope=slope,quad=quad,clip=clip)
r = energy_to_channel(right[k],offset=off,slope=slope,quad=quad,clip=clip)
roi = ROI(left=int(l),right=int(r), label=label[k], bgr_width=bgr_width[k])
def fit_gaussian(): for dataset in datasets: guess_channel = int(dataset.center) guess = (guess_channel, dataset.count_diff[guess_channel], fitradius) fitrange = slice(guess_channel - fitradius, guess_channel + fitradius) dataset.channel_fit = channel[fitrange] dataset.count_fit = dataset.count_diff[fitrange] data = scipy.odr.RealData( dataset.channel_fit, dataset.count_fit, sy=np.sqrt(dataset.count_fit)) odr = scipy.odr.ODR(data, scipy.odr.Model( lambda p, x: gaussian(x, *p)), beta0=guess) result = odr.run() dataset.popt = result.beta dataset.center = result.beta[0] dataset.center_error = result.sd_beta[0] dataset.center_energy, dataset.center_energy_error = channel_to_energy( dataset.center, dataset.center_error)
import constants from calibration import channel, channel_to_energy from datasets import datasets from detector import detector_inv_efficiency bb_angles, bb_diff_cross = np.loadtxt( 'src/bb-diff-cross.dat', usecols=[0, 2], unpack=True) angles = [] counts = [] for dataset in datasets: angles.append(dataset.angle) channel = np.argmax(dataset.count_diff) energy, _ = channel_to_energy(channel) count = np.sum(dataset.count_diff) * detector_inv_efficiency(energy) counts.append(count) angles = np.array(angles) counts = np.array(counts) counts_err = np.sqrt(counts) time = 300 # s rate = counts / time rate_err = rate / counts * counts_err # day not given, assume middle of july date_inital = datetime.date(1971, 7, 15) date_experiment = datetime.date(2018, 12, 17) ratio_remaining = np.power( 0.5, (date_experiment - date_inital).days / (30.17 * 365.25))