def prepare():
fit_interface.ResetFitterParameters()
hdtv.options.Set("table", "classic")
hdtv.options.Set("uncertainties", "short")
spec_interface.LoadSpectra(testspectrum)
for i in range(5):
spec_interface.LoadSpectra(testspectrum)
spectra.Get(str(i)).cal = [0, (i + 1) * 0.5]
yield
spectra.Clear()
def prepare():
fit_interface.ResetFitterParameters()
hdtv.options.Set("table", "classic")
hdtv.options.Set("uncertainties", "short")
spec_interface.LoadSpectra(testspectrum)
yield
spectra.Clear()
def test_cmd_fit_various():
hdtvcmd("fit function peak activate theuerkauf")
spec_interface.LoadSpectra(testspectrum)
assert len(spec_interface.spectra.dict) == 1
f, ferr = setup_fit()
assert f == ""
assert ferr == ""
f, ferr = hdtvcmd("fit execute")
assert "WorkFit on spectrum: 0" in f
assert ".0 |" in f
assert ".1 |" in f
assert "2 peaks in WorkFit" in f
f, ferr = hdtvcmd("fit store")
assert f == "Storing workFit with ID 0"
assert ferr == ""
f, ferr = hdtvcmd("fit clear", "fit list")
assert ferr == ""
assert "Fits in Spectrum 0" in f
f, ferr = hdtvcmd(
"fit hide 0",
"fit show 0",
"fit show decomposition 0",
"fit hide decomposition 0",
"fit show residuals 0",
"fit hide residuals 0")
assert ferr == ""
assert f == ""
f, ferr = hdtvcmd("fit activate 0")
assert "Activating fit 0" in f
assert ferr == ""
f, ferr = hdtvcmd("fit delete 0",
"fit function peak activate ee")
assert ferr == ""
assert f == ""
def test_cmd_fit_parameter(peak, bg, integrate, likelihood):
spec_interface.LoadSpectra(testspectrum)
f, ferr = setup_fit()
hdtvcmd("fit marker background set 415", "fit marker background set 450")
f, ferr = hdtvcmd(
f"fit function peak activate {peak}",
f"fit function background activate {bg}",
f"fit parameter integrate {integrate}",
f"fit parameter likelihood {likelihood}",
)
assert f == ""
assert ferr == ""
f, ferr = hdtvcmd("fit execute")
assert "WorkFit on spectrum: 0" in f
assert ".0 |" in f
assert ".1 |" in f
assert "2 peaks in WorkFit" in f
f, ferr = hdtvcmd("fit parameter status")
assert re.search(f"Peak model.*{peak}", f, re.M)
assert re.search(f"Background model.*{bg}", f, re.M)
assert f"likelihood: {likelihood}" in f
assert f"integrate: {integrate}" in f
assert ferr == ""
f, ferr = hdtvcmd("fit store")
assert f == "Storing workFit with ID 0"
assert ferr == ""
f, ferr = hdtvcmd("fit clear", "fit list")
assert ferr == ""
assert "Fits in Spectrum 0" in f
f, ferr = hdtvcmd("fit parameter reset")
assert ferr == ""
def prepare():
fit_interface.ResetFitterParameters()
hdtv.options.Set("table", "classic")
hdtv.options.Set("uncertainties", "short")
for _ in range(3):
spec_interface.LoadSpectra(testspectrum)
spectra.ActivateObject("0")
yield
spectra.Clear()
def test_cmd_fit_peakfind():
spec_interface.LoadSpectra(testspectrum)
assert len(spec_interface.spectra.dict) == 1
f, ferr = hdtvcmd("fit peakfind -a -t 0.002")
assert "Search Peaks in region" in f
assert "Found 68 peaks" in f
# This was not needed before commits around ~b41833c9c66f9ba5dbdcfc6fc4468b242360641f
# However, some small change has happened that prevents a correct fit of a single
# double peak at ~1540 keV. When I fit it manually, using a similar fit region
# and internal background only, it works.
assert "WARNING: Adding invalid fit" in ferr
def test_cmd_fit_copy():
spec_interface.LoadSpectra(testspectrum)
f, ferr = setup_fit()
hdtvcmd("fit marker background set 415", "fit marker background set 450")
f, ferr = hdtvcmd(
"fit function peak activate theuerkauf",
"fit function background activate polynomial",
"fit parameter integrate True",
"fit parameter likelihood poisson",
"fit execute",
)
workFit = spec_interface.spectra.workFit
newFit = copy.copy(workFit)
assert workFit == newFit
assert workFit.fitter == newFit.fitter
def prepare():
fit_interface.ResetFitterParameters()
hdtv.options.Set("table", "classic")
hdtv.options.Set("uncertainties", "short")
spec_interface.LoadSpectra(testspectrum)
yield
spectra.Clear()
bins_h1 = linspace(0.5, NBINS + 0.5, NBINS)
spectrum_h1 = BG_PER_BIN * ones(NBINS)
spectrum_h1 = spectrum_h1 + PEAK_VOLUME * norm.pdf(
bins_h1, loc=0.5 * NBINS, scale=PEAK_WIDTH)
savetxt(testspectrum_h1, spectrum_h1)
bins_h2 = linspace(0.5, NBINS + 0.5, int(0.5 * NBINS))
spectrum_h2 = 0.5 * BG_PER_BIN * ones(int(0.5 * NBINS))
spectrum_h2 = spectrum_h2 + 2. * PEAK_VOLUME * norm.pdf(
bins_h2, loc=0.5 * NBINS, scale=PEAK_WIDTH)
savetxt(testspectrum_h2, spectrum_h2)
def test_mfile_fit_volume_with_binning(region1, region2, peak, expected_volume,
calfactor, temp_file):
spectra.Clear()
bins = linspace(0.5, NBINS + 0.5, int(NBINS / calfactor))
spectrum = 1. / calfactor * BG_PER_BIN * ones(int(NBINS / calfactor))
spectrum = spectrum + calfactor * PEAK_VOLUME * norm.pdf(
bins, loc=0.5 * NBINS, scale=PEAK_WIDTH)
savetxt(temp_file, spectrum)
spec_interface.LoadSpectra(temp_file)
spectra.ApplyCalibration("0", [0, calfactor])
spectra.SetMarker("region", region1)
spectra.SetMarker("region", region2)
spectra.SetMarker("peak", peak)
spectra.ExecuteFit()
fit = spectra.workFit
fit_volume = fit.ExtractParams()[0][0]["vol"].nominal_value
integral_volume = fit.ExtractIntegralParams()[0][0]["vol"].nominal_value
assert isclose(integral_volume, expected_volume, abs_tol=sqrt(PEAK_VOLUME))
assert isclose(fit_volume, expected_volume, abs_tol=sqrt(PEAK_VOLUME))
def test_backgroundRegions(
model,
nparams,
step,
nregions,
settings,
errormessage,
bgparams,
bg_volume,
temp_file,
test_spectrum,
):
spec_interface.LoadSpectra(test_spectrum.filename)
command = ["fit function background activate " + model]
if settings != "":
command.append(settings)
for i in range(nregions):
command.append(
"fit marker background set %f"
% ((step + test_spectrum.bg_regions[i][0]) * test_spectrum.nbins_per_step)
)
command.append(
"fit marker background set %f"
% ((step + test_spectrum.bg_regions[i][1]) * test_spectrum.nbins_per_step)
)
command.append(
"fit marker region set %f"
% (
(step + 0.5) * test_spectrum.nbins_per_step
- 3.0 * test_spectrum.peak_width * test_spectrum.nbins_per_step
)
)
command.append(
"fit marker region set %f"
% (
(step + 0.5) * test_spectrum.nbins_per_step
+ 3.0 * test_spectrum.peak_width * test_spectrum.nbins_per_step
)
)
command.append(
"fit marker peak set %f" % ((step + 0.5) * test_spectrum.nbins_per_step)
)
command.append("fit execute")
command.append("fit store")
for i in range(nregions):
command.append("fit marker background delete %i" % (i))
command.append("fit marker region delete 0")
command.append("fit marker peak delete 0")
f, ferr = hdtvcmd(*command)
if WRITE_BATCHFILE:
batchfile = os.path.join(
os.path.curdir,
"test",
"share",
model + "_" + str(nparams) + "_" + str(step) + "_background.hdtv",
)
bfile = open(batchfile, "w")
bfile.write("spectrum get " + test_spectrum.filename + "\n")
for c in command:
bfile.write(c + "\n")
bfile.close()
if errormessage != "":
hdtvcmd("fit delete 0", "spectrum delete 0")
assert errormessage in ferr
else:
assert ferr == ""
fitxml.WriteXML(spectra.Get("0").ID, temp_file)
fitxml.ReadXML(spectra.Get("0").ID, temp_file, refit=True, interactive=False)
hdtvcmd("fit delete 0", "spectrum delete 0")
# Parse xml file manually and check correct output
tree = ET.parse(temp_file)
root = tree.getroot()
# Check the number of fits
fits = root.findall("fit")
assert len(fits) == 1
# Check the number and positions of background markers
bgMarkers = fits[0].findall("bgMarker")
assert len(bgMarkers) == nregions
for i in range(len(bgMarkers)):
assert isclose(
float(bgMarkers[i].find("begin").find("cal").text),
(step + test_spectrum.bg_regions[i][0]) * test_spectrum.nbins_per_step,
abs_tol=BG_MARKER_TOLERANCE,
)
assert isclose(
float(bgMarkers[i].find("end").find("cal").text),
(step + test_spectrum.bg_regions[i][1]) * test_spectrum.nbins_per_step,
abs_tol=BG_MARKER_TOLERANCE,
)
# Check the number (and values) of background parameters
background = fits[0].find("background")
assert background.get("backgroundModel") == model
assert int(background.get("nparams")) == nparams
# Check the background parameters
if len(bgparams) > 0:
params = background.findall("param")
for i in range(len(params)):
if i < len(bgparams):
assert isclose(
float(params[i].find("value").text),
bgparams[i],
abs_tol=N_SIGMA * float(params[i].find("error").text),
)
# Check the fit result
# All results will be compared
assert isclose(
float(fits[0].find("peak").find("cal").find("vol").find("value").text),
test_spectrum.peak_volume,
abs_tol=N_SIGMA
* float(fits[0].find("peak").find("cal").find("vol").find("error").text),
)
assert isclose(
float(fits[0].find("peak").find("cal").find("width").find("value").text),
SIGMA_TO_FWHM * test_spectrum.peak_width * test_spectrum.nbins_per_step,
abs_tol=N_SIGMA
* float(fits[0].find("peak").find("cal").find("width").find("error").text),
)
# Check the integration result
integrals = fits[0].findall("integral")
assert len(integrals) == 3
assert integrals[0].get("integraltype") == "tot"
assert integrals[1].get("integraltype") == "bg"
assert integrals[2].get("integraltype") == "sub"
# Peak volume
assert isclose(
float(integrals[2].find("uncal").find("vol").find("value").text),
test_spectrum.peak_volume,
abs_tol=N_SIGMA
* float(integrals[2].find("uncal").find("vol").find("error").text),
)
# Background volume
if bg_volume > 0.0:
assert isclose(
float(integrals[1].find("uncal").find("vol").find("value").text),
bg_volume,
abs_tol=sqrt(bg_volume),
)
else:
assert isclose(
float(integrals[1].find("uncal").find("vol").find("value").text),
test_spectrum.bg_per_bin
* 6.0
* test_spectrum.peak_width
* test_spectrum.nbins_per_step,
abs_tol=N_SIGMA
* sqrt(
test_spectrum.bg_per_bin
* 6.0
* test_spectrum.peak_width
* test_spectrum.nbins_per_step
),
)
def test_spectra_reload_one():
spectra.Pop("3")
spec_interface.LoadSpectra(testspectrum)
res = get_list()
assert "3 | AV" in res
def test_backgroundRegions(model, nparams, step, nregions, settings, errormessage, bgparams, bg_volume, temp_file, test_spectrum):
spec_interface.LoadSpectra(test_spectrum.filename)
command = ['fit function background activate ' + model]
if settings is not "":
command.append(settings)
for i in range(nregions):
command.append('fit marker background set %f' % ((step+test_spectrum.bg_regions[i][0])*test_spectrum.nbins_per_step))
command.append('fit marker background set %f' % ((step+test_spectrum.bg_regions[i][1])*test_spectrum.nbins_per_step))
command.append('fit marker region set %f' % ((step+0.5)*test_spectrum.nbins_per_step - 3.*test_spectrum.peak_width*test_spectrum.nbins_per_step))
command.append('fit marker region set %f' % ((step+0.5)*test_spectrum.nbins_per_step + 3.*test_spectrum.peak_width*test_spectrum.nbins_per_step))
command.append('fit marker peak set %f' % ((step+0.5)*test_spectrum.nbins_per_step))
command.append('fit execute')
command.append('fit store')
for i in range(nregions):
command.append('fit marker background delete %i' % (i))
command.append('fit marker region delete 0')
command.append('fit marker peak delete 0')
f, ferr = hdtvcmd(*command)
if WRITE_BATCHFILE:
batchfile = os.path.join(os.path.curdir, 'test', 'share', model + '_' + str(nparams) + '_' + str(step) + '_background.hdtv')
bfile = open(batchfile, 'w')
bfile.write('spectrum get '+ test_spectrum.filename + '\n')
for c in command:
bfile.write(c + '\n')
bfile.close()
if errormessage is not '':
hdtvcmd('fit delete 0', 'spectrum delete 0')
assert errormessage in ferr
else:
assert ferr == ''
fitxml.WriteXML(spectra.Get("0").ID, temp_file)
fitxml.ReadXML(spectra.Get("0").ID, temp_file, refit=True, interactive=False)
hdtvcmd('fit delete 0', 'spectrum delete 0')
# Parse xml file manually and check correct output
tree = ET.parse(temp_file)
root = tree.getroot()
# Check the number of fits
fits = root.findall('fit')
assert len(fits) == 1
# Check the number and positions of background markers
bgMarkers = fits[0].findall('bgMarker')
assert len(bgMarkers) == nregions
for i in range(len(bgMarkers)):
assert isclose(float(bgMarkers[i].find('begin').find('cal').text), (step+test_spectrum.bg_regions[i][0])*test_spectrum.nbins_per_step, abs_tol=BG_MARKER_TOLERANCE)
assert isclose(float(bgMarkers[i].find('end').find('cal').text), (step+test_spectrum.bg_regions[i][1])*test_spectrum.nbins_per_step, abs_tol=BG_MARKER_TOLERANCE)
# Check the number (and values) of background parameters
background = fits[0].find('background')
assert background.get('backgroundModel') == model
assert int(background.get('nparams')) == nparams
# Check the background parameters
if len(bgparams) > 0:
params = background.findall('param')
for i in range(len(params)):
if i < len(bgparams):
assert isclose(float(params[i].find('value').text), bgparams[i], abs_tol=N_SIGMA*float(params[i].find('error').text))
# Check the fit result
# All results will be compared
assert isclose(float(fits[0].find('peak').find('cal').find('vol').find('value').text), test_spectrum.peak_volume, abs_tol=N_SIGMA*float(fits[0].find('peak').find('cal').find('vol').find('error').text))
assert isclose(float(fits[0].find('peak').find('cal').find('width').find('value').text), SIGMA_TO_FWHM*test_spectrum.peak_width*test_spectrum.nbins_per_step, abs_tol=N_SIGMA*float(fits[0].find('peak').find('cal').find('width').find('error').text))
# Check the integration result
integrals = fits[0].findall('integral')
assert len(integrals) == 3
assert integrals[0].get('integraltype') == 'tot'
assert integrals[1].get('integraltype') == 'bg'
assert integrals[2].get('integraltype') == 'sub'
# Peak volume
assert isclose(float(integrals[2].find('uncal').find('vol').find('value').text), test_spectrum.peak_volume, abs_tol=N_SIGMA*float(integrals[2].find('uncal').find('vol').find('error').text))
# Background volume
if bg_volume > 0.:
assert isclose(float(integrals[1].find('uncal').find('vol').find('value').text), bg_volume, abs_tol=sqrt(bg_volume))
else:
assert isclose(float(integrals[1].find('uncal').find('vol').find('value').text), test_spectrum.bg_per_bin*6.*test_spectrum.peak_width*test_spectrum.nbins_per_step, abs_tol=N_SIGMA*sqrt(test_spectrum.bg_per_bin*6.*test_spectrum.peak_width*test_spectrum.nbins_per_step))
def test_interpolation_incomplete():
spec_interface.LoadSpectra(testspectrum)
assert len(spec_interface.spectra.dict) == 1
f, ferr = setup_interpolation_incomplete()
assert f == ""
assert "Background fit failed" in ferr
