def test():
from matplotlib import pyplot
# make some dat
import _test_dat as test_dat
chans = num.arange(2048)
offset = 1.0
slope = .01
en = offset + slope*chans
data = test_dat.data1(en)
pyplot.plot(en,data,'ko')
#
bgr = Background(bottom_width=4.,compress=4)
bgr.calc(data,slope=slope)
print bgr
pyplot.plot(en,bgr.bgr,'r')
#
pyplot.show()
def test_fit():
from matplotlib import pyplot
import _test_dat as test_dat
from xrf_bgr import Background
#############################
# Data
#############################
chans = num.arange(2048)
offset = 1.0
slope = .01
en = offset + slope * chans
data = test_dat.data1(en)
pyplot.subplot(211)
pyplot.plot(en, data, 'ko')
#############################
# fit
#############################
p1 = XrfPeak(label='p1', energy=4.)
p2 = XrfPeak(label='p2', energy=7.)
bgr = Background(bottom_width=4, compress=4)
xspec = XrfSpectrum(data=data,
chans=chans,
peaks=[p1, p2],
bgr=bgr,
energy_offset=1.,
energy_slope=0.01,
chi_exp=0.5,
guess=True)
# lets see how good the initial guess is
pyplot.plot(en, xspec.peaks[0].calc(en), 'r')
pyplot.plot(en, xspec.peaks[1].calc(en), 'r')
# Now do the fit
xspec.fit(opt_bgr=False, quiet=0)
print xspec
pyplot.subplot(212)
en = xspec.energy_offset + xspec.energy_slope * chans
pyplot.plot(en, data, 'ko')
pyplot.plot(en, xspec.predicted, 'g')
pyplot.plot(en, xspec.bgr.bgr, 'k-')
#############################
pyplot.show()
def test_fit():
from matplotlib import pyplot
import _test_dat as test_dat
from xrf_bgr import Background
#############################
# Data
#############################
chans = num.arange(2048)
offset = 1.0
slope = .01
en = offset + slope*chans
data = test_dat.data1(en)
pyplot.subplot(211)
pyplot.plot(en,data,'ko')
#############################
# fit
#############################
p1 = XrfPeak(label='p1',energy=4.)
p2 = XrfPeak(label='p2',energy=7.)
bgr = Background(bottom_width=4,compress=4)
xspec = XrfSpectrum(data=data,chans=chans,peaks=[p1,p2],bgr=bgr,
energy_offset=1.,energy_slope=0.01,chi_exp=0.5,
guess=True)
# lets see how good the initial guess is
pyplot.plot(en,xspec.peaks[0].calc(en),'r')
pyplot.plot(en,xspec.peaks[1].calc(en),'r')
# Now do the fit
xspec.fit(opt_bgr=False,quiet=0)
print xspec
pyplot.subplot(212)
en = xspec.energy_offset + xspec.energy_slope*chans
pyplot.plot(en,data,'ko')
pyplot.plot(en,xspec.predicted,'g')
pyplot.plot(en,xspec.bgr.bgr,'k-')
#############################
pyplot.show()
def test_peak():
from matplotlib import pyplot
# make some dat
import _test_dat as test_dat
chans = num.arange(2048)
offset = 1.0
slope = .01
en = offset + slope*chans
data = test_dat.data1(en)
pyplot.plot(en,data,'ko')
#
p1 = XrfPeak(label='1',energy=4.,ampl=550,fwhm=.5)
print p1.get_params()
p2 = XrfPeak(label='1',energy=7.,ampl=850,fwhm=.6)
print p2.get_params()
pyplot.plot(en,p1.calc(en),'r')
pyplot.plot(en,p2.calc(en),'r')
#
(y,(mi,ma)) = p1._calc_range(en)
print mi,ma
pyplot.plot(en[mi:ma],y,'g')
#
pyplot.show()
def test_peak():
from matplotlib import pyplot
# make some dat
import _test_dat as test_dat
chans = num.arange(2048)
offset = 1.0
slope = .01
en = offset + slope * chans
data = test_dat.data1(en)
pyplot.plot(en, data, 'ko')
#
p1 = XrfPeak(label='1', energy=4., ampl=550, fwhm=.5)
print p1.get_params()
p2 = XrfPeak(label='1', energy=7., ampl=850, fwhm=.6)
print p2.get_params()
pyplot.plot(en, p1.calc(en), 'r')
pyplot.plot(en, p2.calc(en), 'r')
#
(y, (mi, ma)) = p1._calc_range(en)
print mi, ma
pyplot.plot(en[mi:ma], y, 'g')
#
pyplot.show()