def _xrf_plot(x=None, y=None, mca=None, win=1, new=True, as_mca2=False, _larch=None,
wxparent=None, size=None, side='left', force_draw=True, wintitle=None,
**kws):
"""xrf_plot(energy, data[, win=1], options])
Show XRF trace of energy, data
Parameters:
--------------
energy : array of energies
counts : array of counts
mca: Group counting MCA data (rois, etc)
as_mca2: use mca as background MCA
win: index of Plot Frame (0, 1, etc). May create a new Plot Frame.
new: flag (True/False, default False) for whether to start a new plot.
color: color for trace (name such as 'red', or '#RRGGBB' hex string)
style: trace linestyle (one of 'solid', 'dashed', 'dotted', 'dot-dash')
linewidth: integer width of line
marker: symbol to draw at each point ('+', 'o', 'x', 'square', etc)
markersize: integer size of marker
See Also: xrf_oplot, plot
"""
plotter = _getDisplay(wxparent=wxparent, win=win, size=size,
_larch=_larch, wintitle=wintitle, xrf=True)
if plotter is None:
return
plotter.Raise()
if x is None:
return
if isLarchMCAGroup(x):
mca = x
y = x.counts
x = x.energy
if as_mca2:
new = False
if isLarchMCAGroup(mca):
plotter.plotmca(mca, as_mca2=True, new=False, **kws)
elif y is not None:
plotter.oplot(x, y, mca=mca, as_mca2=True, **kws)
elif new:
if isLarchMCAGroup(mca):
plotter.plotmca(mca, **kws)
elif y is not None:
plotter.plot(x, y, mca=mca, **kws)
else:
plotter.oplot(x, y, mca=mca, **kws)
def _xrf_plot(x=None, y=None, mca=None, win=1, new=True, as_mca2=False, _larch=None,
wxparent=None, size=None, side='left', force_draw=True, wintitle=None,
**kws):
"""xrf_plot(energy, data[, win=1], options])
Show XRF trace of energy, data
Parameters:
--------------
energy : array of energies
counts : array of counts
mca: Group counting MCA data (rois, etc)
as_mca2: use mca as background MCA
win: index of Plot Frame (0, 1, etc). May create a new Plot Frame.
new: flag (True/False, default False) for whether to start a new plot.
color: color for trace (name such as 'red', or '#RRGGBB' hex string)
style: trace linestyle (one of 'solid', 'dashed', 'dotted', 'dot-dash')
linewidth: integer width of line
marker: symbol to draw at each point ('+', 'o', 'x', 'square', etc)
markersize: integer size of marker
See Also: xrf_oplot, plot
"""
plotter = _getDisplay(wxparent=wxparent, win=win, size=size,
_larch=_larch, wintitle=wintitle, xrf=True)
if plotter is None:
_larch.raise_exception(None, msg='No Plotter defined')
plotter.Raise()
if x is None:
return
if isLarchMCAGroup(x):
mca = x
y = x.counts
x = x.energy
if as_mca2:
new = False
if isLarchMCAGroup(mca):
plotter.plotmca(mca, as_mca2=True, new=False, **kws)
elif y is not None:
plotter.oplot(x, y, mca=mca, as_mca2=True, **kws)
elif new:
if isLarchMCAGroup(mca):
plotter.plotmca(mca, **kws)
elif y is not None:
plotter.plot(x, y, mca=mca, **kws)
else:
plotter.oplot(x, y, mca=mca, **kws)
def xrf_calib_compute(mca, apply=False, _larch=None):
"""compute linear energy calibration
from init_calib dictionary found from xrf_calib_fitrois()
To exclude lines from the calibration, first run
>>> xrf_calib_fitrois(mca)
then remove items (by ROI name) from the mca.init_calib dictionay
"""
if not isLarchMCAGroup(mca):
print('Not a valid MCA')
return
if not hasattr(mca, 'init_calib'):
xrf_calib_fitrois(mca, _larch=_larch)
# current calib
offset, slope = mca.offset, mca.slope
x = np.array([c[3] for c in mca.init_calib.values()])
y = np.array([c[0] for c in mca.init_calib.values()])
_s, _o, r, p, std = linregress(x, y)
mca.new_calib = (_o, _s)
mca.cal_x = x
mca.cal_y = y
if apply:
xrf_calib_apply(mca, offset=_o, slope=_s)
def xrf_calib_compute(mca, apply=False, _larch=None):
"""compute linear energy calibration
from init_calib dictionary found from xrf_calib_fitrois()
To exclude lines from the calibration, first run
>>> xrf_calib_fitrois(mca)
then remove items (by ROI name) from the mca.init_calib dictionay
"""
if not isLarchMCAGroup(mca):
print( 'Not a valid MCA')
return
if not hasattr(mca, 'init_calib'):
xrf_calib_fitrois(mca, _larch=_larch)
# current calib
offset, slope = mca.offset, mca.slope
x = np.array([c[3] for c in mca.init_calib.values()])
y = np.array([c[0] for c in mca.init_calib.values()])
_s, _o, r, p, std = linregress(x, y)
mca.new_calib = (_o, _s)
mca.cal_x = x
mca.cal_y = y
if apply:
xrf_calib_apply(mca, offset=_o, slope=_s)
def xrf_calib_init_roi(mca, roiname):
"""initial calibration step for MCA:
find energy locations for one ROI
"""
if not isLarchMCAGroup(mca):
print( 'Not a valid MCA')
return
energy = 1.0*mca.energy
chans = 1.0*np.arange(len(energy))
counts = mca.counts
bgr = getattr(mca, 'bgr', None)
if bgr is not None:
counts = counts - bgr
if not hasattr(mca, 'init_calib'):
mca.init_calib = OrderedDict()
roi = None
for xroi in mca.rois:
if xroi.name == roiname:
roi = xroi
break
if roi is None:
return
words = roiname.split()
elem = words[0].title()
family = 'Ka'
if len(words) > 1:
family = words[1].title()
if family == 'Lb':
family = 'Lb1'
eknown = xray_line(elem, family)[0]/1000.0
llim = max(0, roi.left - roi.bgr_width)
hlim = min(len(chans)-1, roi.right + roi.bgr_width)
segcounts = counts[llim:hlim]
maxcounts = max(segcounts)
ccen = llim + np.where(segcounts==maxcounts)[0]
ecen = ccen * mca.slope + mca.offset
bkgcounts = counts[llim] + counts[hlim]
if maxcounts < 2*bkgcounts:
mca.init_calib[roiname] = (eknown, ecen, 0.0, ccen, None)
else:
model = GaussianModel() + ConstantModel()
params = model.make_params(amplitude=maxcounts,
sigma=(chans[hlim]-chans[llim])/2.0,
center=ccen-llim, c=0.00)
params['center'].min = -10
params['center'].max = hlim - llim + 10
params['c'].min = -10
out = model.fit(counts[llim:hlim], params, x=chans[llim:hlim])
ccen = llim + out.params['center'].value
ecen = ccen * mca.slope + mca.offset
fwhm = out.params['fwhm'].value * mca.slope
mca.init_calib[roiname] = (eknown, ecen, fwhm, ccen, out)
def xrf_background(energy,
counts=None,
group=None,
width=4,
compress=2,
exponent=2,
slope=None,
_larch=None):
"""fit background for XRF spectra. Arguments:
xrf_background(energy, counts=None, group=None, width=4,
compress=2, exponent=2, slope=None)
Arguments
---------
energy array of energies OR an MCA group. If an MCA group,
it will be used to give ``counts`` and ``mca`` arguments
counts array of XRF counts (or MCA.counts)
group group for outputs
width full width (in keV) of the concave down polynomials
for when its full width is 100 counts. default = 4
compress compression factor to apply to spectra. Default is 2.
exponent power of polynomial used. Default is 2, should be even.
slope channel to energy conversion, from energy calibration
(default == None --> found from input energy array)
outputs (written to group)
-------
bgr background array
bgr_info dictionary of parameters used to calculate background
"""
if isLarchMCAGroup(energy):
group = energy
counts = group.counts
energy = group.energy
if slope is None:
slope = (energy[-1] - energy[0]) / len(energy)
xbgr = XRFBackground(counts,
width=width,
compress=compress,
exponent=exponent,
slope=slope)
if group is not None:
group.bgr = xbgr.bgr
group.bgr_info = xbgr.parinfo
def xrf_calib_apply(mca, offset=None, slope=None, _larch=None):
"""apply calibration to MCA
either supply offset and slope arguments (in keV and keV/chan)
or run xrf_calib_compute(mca) to estimate these from ROI peaks
"""
if not isLarchMCAGroup(mca):
print('Not a valid MCA')
return
if (offset is None or slope is None) and not hasattr(mca, 'new_calib'):
print('must supply offset and slope or run xrf_calib_compute()!')
return
if (offset is None or slope is None):
offset, slope = mca.new_calib
mca.offset = offset
mca.slope = slope
npts = len(mca.energy)
mca.energy = offset + slope * np.arange(npts)
def xrf_calib_apply(mca, offset=None, slope=None, _larch=None):
"""apply calibration to MCA
either supply offset and slope arguments (in keV and keV/chan)
or run xrf_calib_compute(mca) to estimate these from ROI peaks
"""
if not isLarchMCAGroup(mca):
print( 'Not a valid MCA')
return
if (offset is None or slope is None) and not hasattr(mca, 'new_calib'):
print( 'must supply offset and slope or run xrf_calib_compute()!')
return
if (offset is None or slope is None):
offset, slope = mca.new_calib
mca.offset = offset
mca.slope = slope
npts = len(mca.energy)
mca.energy = offset + slope*np.arange(npts)
def xrf_background(energy, counts=None, group=None, width=4,
compress=2, exponent=2, slope=None,
_larch=None):
"""fit background for XRF spectra. Arguments:
xrf_background(energy, counts=None, group=None, width=4,
compress=2, exponent=2, slope=None)
Arguments
---------
energy array of energies OR an MCA group. If an MCA group,
it will be used to give ``counts`` and ``mca`` arguments
counts array of XRF counts (or MCA.counts)
group group for outputs
width full width (in keV) of the concave down polynomials
for when its full width is 100 counts. default = 4
compress compression factor to apply to spectra. Default is 2.
exponent power of polynomial used. Default is 2, should be even.
slope channel to energy conversion, from energy calibration
(default == None --> found from input energy array)
outputs (written to group)
-------
bgr background array
bgr_info dictionary of parameters used to calculate background
"""
if isLarchMCAGroup(energy):
group = energy
counts = group.counts
energy = group.energy
if slope is None:
slope = (energy[-1] - energy[0])/len(energy)
xbgr = XRFBackground(counts, width=width, compress=compress,
exponent=exponent, slope=slope)
if group is not None:
group.bgr = xbgr.bgr
group.bgr_info = xbgr.parinfo
def xrf_calib_fitrois(mca, _larch=None):
"""initial calibration step for MCA:
find energy locations for all ROIs
"""
if not isLarchMCAGroup(mca):
print('Not a valid MCA')
return
energy = 1.0 * mca.energy
chans = 1.0 * np.arange(len(energy))
counts = mca.counts
bgr = getattr(mca, 'bgr', None)
if bgr is not None:
counts = counts - bgr
calib = OrderedDict()
for roi in mca.rois:
words = roi.name.split()
elem = words[0].title()
family = 'ka'
if len(words) > 1:
family = words[1]
try:
eknown = xray_line(elem, family, _larch=_larch)[0] / 1000.0
except:
continue
llim = max(0, roi.left - roi.bgr_width)
hlim = min(len(chans) - 1, roi.right + roi.bgr_width)
fit = fit_peak(chans[llim:hlim],
counts[llim:hlim],
'Gaussian',
background='constant',
_larch=_larch)
ccen = fit.params.center.value
ecen = ccen * mca.slope + mca.offset
fwhm = 2.354820 * fit.params.sigma.value * mca.slope
calib[roi.name] = (eknown, ecen, fwhm, ccen, fit)
mca.init_calib = calib
def xrf_calib_fitrois(mca, _larch=None):
"""initial calibration step for MCA:
find energy locations for all ROIs
"""
if not isLarchMCAGroup(mca):
print( 'Not a valid MCA')
return
energy = 1.0*mca.energy
chans = 1.0*np.arange(len(energy))
counts = mca.counts
bgr = getattr(mca, 'bgr', None)
if bgr is not None:
counts = counts - bgr
calib = OrderedDict()
for roi in mca.rois:
words = roi.name.split()
elem = words[0].title()
family = 'ka'
if len(words) > 1:
family = words[1]
try:
eknown = xray_line(elem, family, _larch=_larch)[0]/1000.0
except:
continue
llim = max(0, roi.left - roi.bgr_width)
hlim = min(len(chans)-1, roi.right + roi.bgr_width)
fit = fit_peak(chans[llim:hlim], counts[llim:hlim],
'Gaussian', background='constant',
_larch=_larch)
ccen = fit.params['center'].value
ecen = ccen * mca.slope + mca.offset
fwhm = 2.354820 * fit.params['sigma'].value * mca.slope
calib[roi.name] = (eknown, ecen, fwhm, ccen, fit)
mca.init_calib = calib
def xrf_background(energy, counts=None, group=None, width=None, exponent=2, **kws):
"""fit background for XRF spectra.
xrf_background(energy, counts=None, group=None, exponent=2)
Arguments
---------
energy array of energies OR an MCA group. If an MCA group,
it will be used to give ``counts`` and ``mca`` arguments
counts array of XRF counts (or MCA.counts)
group group for outputs
width full width (in keV) of the concave down polynomials when its
value is ~1% of max counts. Default width is (energy range)/4.0
exponent power of polynomial used. Default is 2, should be even.
Outputs (written to group)
-------
bgr background array
bgr_info dictionary of parameters used to calculate background
"""
if isLarchMCAGroup(energy):
group = energy
energy = group.energy
if counts is None:
counts = group.counts
nchans = len(counts)
slope = energy[1] - energy[0]
if width is None:
width = max(energy)/4.0
tcounts = 1.0 * counts
tcounts[np.where(tcounts<0.01)] = 0.01
bgr = -1.0*np.ones(nchans)
# use 1% of 99% percentile of counts as height at which
# the polynomial should have full width = width
max_count = np.percentile(tcounts, [99])[0]
indices = np.linspace(-nchans, nchans, 2*nchans+1) * (2.0 * slope / width)
polynom = 0.01 * max_count * indices**exponent
polynom = np.compress((polynom <= max_count), polynom)
max_index = int(len(polynom)/2 - 1)
for chan in range(nchans-1):
chan0 = max((chan - max_index), 0)
chan1 = min((chan + max_index), (nchans-1))
chan1 = max(chan1, chan0) + 1
idx0 = chan0 - chan + max_index
idx1 = chan1 - chan + max_index
offset = tcounts[chan] - polynom[idx0:idx1]
test = tcounts[chan0:chan1] - offset
bgr[chan0:chan1] = np.maximum(bgr[chan0:chan1],
min(test)+offset)
bgr[np.where(bgr <= 0)] = 0.0
if group is not None:
group.bgr = bgr
group.bgr_info = dict(width=width, exponent=exponent)
