def center_col(label, detid, run_label_filename="labels.txt"):
"""
Return the peak index of the label data summed along the zeroth axis
(perpendicular to the energy-dispersive direction
"""
summed = np.sum(data.get_label_data(label, detid, fname=run_label_filename), axis=0)
return np.argmax(summed)
def si_is_saturated(label, start=400, end=800):
from dataccess import data_access as data
imarr = data.get_label_data(label, 'si')[0]
spectrum = np.sum(imarr, axis=0)[start:end]
spectrum = spectrum - np.percentile(spectrum, 1)
saturation_metric = np.sum(np.abs(np.diff(
np.diff(spectrum)))) / np.sum(spectrum)
return saturation_metric > 0.1
def center_col(label, detid, run_label_filename='labels.txt'):
"""
Return the peak index of the label data summed along the zeroth axis
(perpendicular to the energy-dispersive direction
"""
summed = np.sum(data.get_label_data(label, detid,
fname=run_label_filename),
axis=0)
return np.argmax(summed)
def lineout(label, detid, cencol, pxwidth=10, default_bg=None, run_label_filename="labels.txt"):
"""
Return a 1d lineout
"""
raw = data.get_label_data(label, detid, default_bg=default_bg, fname=run_label_filename)
frame_dimension = len(raw)
spectrum_intensities = np.array(
[sum([raw[i][j] for j in range(cencol - pxwidth, cencol + pxwidth + 1)]) for i in range(frame_dimension)]
)
return spectrum_intensities
def lineout(label,
detid,
cencol,
pxwidth=10,
default_bg=None,
run_label_filename='labels.txt'):
"""
Return a 1d lineout
"""
raw = data.get_label_data(label,
detid,
default_bg=default_bg,
fname=run_label_filename)
frame_dimension = len(raw)
spectrum_intensities = np.array([
sum([raw[i][j] for j in range(cencol - pxwidth, cencol + pxwidth + 1)])
for i in range(frame_dimension)
])
return spectrum_intensities
def si_spectrometer_dark(run=None, **kwargs):
from dataccess import data_access
bg_label = data_access.get_label_property(str(run), 'background')
dark, _ = data_access.get_label_data(bg_label, 'si')
return dark