def get_spectrum(data, dark = None, cencol_calibration_data = None, cold_calibration_data = None,
pxwidth = 3, bg_sub = True, calib_load_path = None, calib_save_path = None,
kalpha_kbeta_calibration = True, eltname = ''):
"""
Return the XES spectrum corresponding to the given data
and element
Inputs:
eltname: string of the abbreviated element name
data: 2d array of CSPAD data
cold_calibration_data: data to use for determination of the energy
scale. If None, the first argument is used for this.
cencol_calibration_data: data to use for location of the
spectrometer's line of focus. If None, the first argument is used
for this.
pxwidth: width of the CSPAD lineout from which the spectrum is
constructed
peak_width:
TODO: deprecate or not?
bg_sub: if True, perform a constant subtraction. The subtracted
constant is the 5th percentile of the spectrum after smoothing
with a gaussian kernel of standard deviation 5
calib_load_path: path to a file with an energy calibration to load
calib_save_path: File to which to save an energy calibration if
calib_load_path is None
kalpha_kbeta_calibration: If calib_load_path is None, use k alpha
and k beta peak locations to determine an energy scale. If None
and calib_load_path is also None, do not perform an energy
calibration at all.
Output: array, array -> energy or index, normalized intensity
"""
if np.shape(data) != (391, 370):
print "WARNING: array dimensions differ from those of CSPAD140k"
if kalpha_kbeta_calibration or calib_load_path:
peak_width = 150
else:
peak_width = 15
if dark is not None:
# copy without mutating the original array
data = np.array(data, copy = True) - dark
if cencol_calibration_data is None:
cencol_calibration_data = data
cencol = center_col(cencol_calibration_data)
intensities = lineout(data, cencol, pxwidth = pxwidth)
if calib_load_path:
x = load_calib(calib_load_path)
elif kalpha_kbeta_calibration and eltname and (cold_calibration_data is not None):
x = get_energies(cold_calibration_data, cencol, save_path = calib_save_path, eltname = eltname)
else:
if kalpha_kbeta_calibration and not eltname:
print "No element identifier provided; skipping energy calibration."
elif kalpha_kbeta_calibration and not cold_calibration_data:
print "No file for calibration specified; skipping energy calibration"
x = np.array(range(len(intensities)))
if bg_sub:
smoothed = filt(intensities, 5)
floor = np.percentile(smoothed, 5)
intensities -= floor
norm = get_normalization(x, intensities, peak_width)
return x, intensities / norm
def clean_profile(profile, window, spline_smooth=0, median_smooth_perc=0.05): sl = slice(window[0], window[1]) mask = ~profile.M[sl].isnull().values y = profile.M[sl][mask].values x = profile.R[sl][mask].values tck = interpolate.splrep(x,y,s=spline_smooth) xnew = np.linspace(x[0],x[-1], len(x)) f_size = len(x) * median_smooth_perc if f_size < 2: f_size = 2 ynew = filt(interpolate.splev(xnew,tck,der=0), f_size) return ynew, xnew
def get_spectrum(data,
dark=None,
cencol_calibration_data=None,
cold_calibration_data=None,
pxwidth=3,
bg_sub=True,
calib_load_path=None,
calib_save_path=None,
energy_ref1_energy_ref2_calibration=True,
eltname=''):
"""
Return the XES spectrum corresponding to the given data
and element
Inputs:
eltname: string of the abbreviated element name
data: 2d array of CSPAD data
cold_calibration_data: data to use for determination of the energy
scale. If None, the first argument is used for this.
cencol_calibration_data: data to use for location of the
spectrometer's line of focus. If None, the first argument is used
for this.
pxwidth: width of the CSPAD lineout from which the spectrum is
constructed
peak_width:
TODO: deprecate or not?
bg_sub: if True, perform a constant subtraction. The subtracted
constant is the 5th percentile of the spectrum after smoothing
with a gaussian kernel of standard deviation 5
calib_load_path: path to a file with an energy calibration to load
calib_save_path: File to which to save an energy calibration if
calib_load_path is None
energy_ref1_energy_ref2_calibration: If calib_load_path is None, use k alpha
and k beta peak locations to determine an energy scale. If None
and calib_load_path is also None, do not perform an energy
calibration at all.
Output: array, array -> energy or index, normalized intensity
"""
if np.shape(data) != (391, 370):
print "WARNING: array dimensions differ from those of CSPAD140k"
if energy_ref1_energy_ref2_calibration or calib_load_path:
peak_width = 150
else:
peak_width = 15
if dark is not None:
# copy without mutating the original array
data = np.array(data, copy=True) - dark
if cencol_calibration_data is None:
cencol_calibration_data = data
cencol = center_col(cencol_calibration_data)
intensities = lineout(data, cencol, pxwidth=pxwidth)
# if calib_load_path:
# x = load_calib(calib_load_path)
if calib_load_path:
x = load_calib(calib_load_path)
elif energy_ref1_energy_ref2_calibration and eltname and (
cold_calibration_data is not None):
x = energies_from_data(cold_calibration_data,
cencol,
save_path=calib_save_path,
eltname=eltname)
else:
if energy_ref1_energy_ref2_calibration and not eltname:
print "No element identifier provided; skipping energy calibration."
elif energy_ref1_energy_ref2_calibration and not cold_calibration_data:
print "No file for calibration specified; skipping energy calibration"
x = np.array(range(len(intensities)))
if bg_sub:
smoothed = filt(intensities, 5)
floor = np.percentile(smoothed, 5)
intensities -= floor
norm = get_normalization(x, intensities, peak_width)
return x, intensities / norm
def get_spectrum_one_label(eltname, detid, label, cold_calibration_label, \
cencol_calibration_label, pxwidth = 10, default_bg = None,\
bg_sub = True, calib_load_path = None,\
calib_save_path = None, kalpha_kbeta_calibration = True,
run_label_filename = 'labels.txt'):
"""
Return the XES spectrum corresponding to the given label, detector ID,
and element
Inputs:
eltname: string of the abbreviated element name
detid: CSPAD detector ID (1 or 2)
label: string reference to a group of runs
cold_calibration_label: label to use for determination of the energy
scale
cencol_calibration_label: label to use for location of the
spectrometer's line of focus
pxwidth: width of the CSPAD lineout from which the spectrum is
constructed
default_bg: label to use for background subtraction if embedded blank
frames are missing
peak_width:
TODO: deprecate or not?
bg_sub: if True, perform a constant subtraction. The subtracted
constant is the 5th percentile of the spectrum after smoothing
with a gaussian kernel of standard deviation 5
calib_load_path: path to a file with an energy calibration to load
calib_save_path: File to which to save an energy calibration if
calib_load_path is None
kalpha_kbeta_calibration: If calib_load_path is None, use k alpha
and k beta peak locations to determine an energy scale. If None
and calib_load_path is also None, do not perform an energy
calibration at all.
Output: array, array -> energy or index, normalized intensity
"""
if kalpha_kbeta_calibration or calib_load_path:
peak_width = 150
else:
peak_width = 15
cencol = center_col(cencol_calibration_label,
detid,
run_label_filename=run_label_filename)
intensities = lineout(label,
detid,
cencol,
pxwidth=pxwidth,
default_bg=default_bg,
run_label_filename=run_label_filename)
if calib_load_path:
x = load_calib(calib_load_path)
elif kalpha_kbeta_calibration:
x = get_energies(cold_calibration_label,
detid,
eltname,
cencol,
save_path=calib_save_path,
run_label_filename=run_label_filename)
else:
x = np.array(range(len(intensities)))
# if lin_bg_sub:
# peak_ranges = [[e - peak_width, e + peak_width] for e in emission[eltname]]
# intensities = linear_bg_subtraction(x, intensities, peak_ranges)
if bg_sub:
smoothed = filt(intensities, 5)
floor = np.percentile(smoothed, 5)
intensities -= floor
norm = get_normalization(x, intensities, peak_width)
#plt.plot(x, smoothed/norm)
return x, intensities / norm
def get_spectrum_one_label(
eltname,
detid,
label,
cold_calibration_label,
cencol_calibration_label,
pxwidth=10,
default_bg=None,
bg_sub=True,
calib_load_path=None,
calib_save_path=None,
kalpha_kbeta_calibration=True,
run_label_filename="labels.txt",
):
"""
Return the XES spectrum corresponding to the given label, detector ID,
and element
Inputs:
eltname: string of the abbreviated element name
detid: CSPAD detector ID (1 or 2)
label: string reference to a group of runs
cold_calibration_label: label to use for determination of the energy
scale
cencol_calibration_label: label to use for location of the
spectrometer's line of focus
pxwidth: width of the CSPAD lineout from which the spectrum is
constructed
default_bg: label to use for background subtraction if embedded blank
frames are missing
peak_width:
TODO: deprecate or not?
bg_sub: if True, perform a constant subtraction. The subtracted
constant is the 5th percentile of the spectrum after smoothing
with a gaussian kernel of standard deviation 5
calib_load_path: path to a file with an energy calibration to load
calib_save_path: File to which to save an energy calibration if
calib_load_path is None
kalpha_kbeta_calibration: If calib_load_path is None, use k alpha
and k beta peak locations to determine an energy scale. If None
and calib_load_path is also None, do not perform an energy
calibration at all.
Output: array, array -> energy or index, normalized intensity
"""
if kalpha_kbeta_calibration or calib_load_path:
peak_width = 150
else:
peak_width = 15
cencol = center_col(cencol_calibration_label, detid, run_label_filename=run_label_filename)
intensities = lineout(
label, detid, cencol, pxwidth=pxwidth, default_bg=default_bg, run_label_filename=run_label_filename
)
if calib_load_path:
x = load_calib(calib_load_path)
elif kalpha_kbeta_calibration:
x = get_energies(
cold_calibration_label,
detid,
eltname,
cencol,
save_path=calib_save_path,
run_label_filename=run_label_filename,
)
else:
x = np.array(range(len(intensities)))
# if lin_bg_sub:
# peak_ranges = [[e - peak_width, e + peak_width] for e in emission[eltname]]
# intensities = linear_bg_subtraction(x, intensities, peak_ranges)
if bg_sub:
smoothed = filt(intensities, 5)
floor = np.percentile(smoothed, 5)
intensities -= floor
norm = get_normalization(x, intensities, peak_width)
# plt.plot(x, smoothed/norm)
return x, intensities / norm
