def test_reimport_calibrate_write_read(temp_file, region1, region2, peak,
calfactor1, calfactor2, calfactor3):
"""
Write/Read fit and change calibration in between
Fit should appear at the new calibrated position
"""
test_store_fit(region1, region2, peak, calfactor1, calfactor2)
spectra.ActivateFit(None)
fit = list(spectra.Get("0").dict.items())[0][1]
markers_initial = get_markers(fit)
integral_initial = fit.ExtractIntegralParams()[0][0]
fitxml.WriteXML(spectra.Get("0").ID, temp_file)
spectra.Get("0").Clear()
spectra.ApplyCalibration(0, [0, calfactor3])
fitxml.ReadXML(spectra.Get("0").ID, temp_file)
fit = list(spectra.Get("0").dict.items())[0][1]
markers_final = get_markers(fit)
integral_final = fit.ExtractIntegralParams()[0][0]
assert isclose(markers_initial[1],
markers_final[1] / calfactor3 * calfactor2)
assert isclose(markers_initial[3],
markers_final[3] / calfactor3 * calfactor2)
assert isclose(markers_initial[5],
markers_final[5] / calfactor3 * calfactor2)
for key in ["pos", "width_cal"]:
assert isclose(
integral_initial[key].nominal_value,
integral_final[key].nominal_value / calfactor3 * calfactor2)
def fit_write_and_save(filename):
spectra.ExecuteFit()
spectra.StoreFit()
spectra.ClearFit()
out_original = list_fit()
print('Saving fits to file %s' % filename)
fitxml.WriteXML(spectra.Get("0").ID, filename)
print('Deleting all fits')
spectra.Get("0").Clear()
print('Reading fits from file %s' % filename)
fitxml.ReadXML(spectra.Get("0").ID, filename)
assert out_original == list_fit()
def test_write_read_xml(temp_file, region1, region2, peak, calfactor1,
calfactor2):
"""
Write/read fit to/from xml
Fit should appear at the same position as before
"""
test_store_fit(region1, region2, peak, calfactor1, calfactor2)
spectra.ActivateFit(None)
out_original = list_fit()
spectra.ClearFit()
fitxml.WriteXML(spectra.Get("0").ID, temp_file)
spectra.Get("0").Clear()
fitxml.ReadXML(spectra.Get("0").ID, temp_file)
assert out_original == list_fit()
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_old_xml(xmlfile):
fitxml.ReadXML(spectra.Get("0").ID, xmlfile, refit=True, interactive=False)
fit_interface.ListFits()
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_old_xml(xmlfile):
# Check whether hdtv recognizes the format of the XML file at all
# by invoking the general ReadXML function.
# Note that this does not ensure the correct reading of the XML file.
fitxml.ReadXML(spectra.Get("0").ID, xmlfile, refit=True, interactive=False)
def test_calbin(temp_file, test_spectrum):
command = ['spectrum get ' + test_spectrum.filename]
step = 2 # Use the third spectrum which has a constant background and Poissonian fluctuations
# Fit the peak in step 2
command.append('fit marker background set %f' %
((step + test_spectrum.bg_regions[0][0]) *
test_spectrum.nbins_per_step))
command.append('fit marker background set %f' %
((step + test_spectrum.bg_regions[0][1]) *
test_spectrum.nbins_per_step))
command.append('fit marker background set %f' %
((step + test_spectrum.bg_regions[1][0]) *
test_spectrum.nbins_per_step))
command.append('fit marker background set %f' %
((step + test_spectrum.bg_regions[1][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')
f, ferr = hdtvcmd(*command)
if WRITE_BATCHFILE:
batchfile = os.path.join(os.path.curdir, 'test', 'share',
'calbin.hdtv')
with open(batchfile, 'w') as bfile:
for c in command:
bfile.write(c + '\n')
assert ferr == ''
# Write the fit result to a temporary XML file
fitxml.WriteXML(spectra.Get("0").ID, temp_file)
fitxml.ReadXML(spectra.Get("0").ID,
temp_file,
refit=True,
interactive=False)
# Parse XML file manually
tree = ET.parse(temp_file)
root = tree.getroot()
# Check the number of fits
fits = root.findall('fit')
assert len(fits) == 1
# Read out the main fitted peak properties (position, volume, width) and their uncertainties
pos_value_init = float(
fits[0].find('peak').find('cal').find('pos').find('value').text)
pos_error_init = abs(
float(fits[0].find('peak').find('cal').find('pos').find('error').text))
vol_value_init = float(
fits[0].find('peak').find('cal').find('vol').find('value').text)
vol_error_init = abs(
float(fits[0].find('peak').find('cal').find('vol').find('error').text))
width_value_init = float(
fits[0].find('peak').find('cal').find('width').find('value').text)
width_error_init = abs(
float(
fits[0].find('peak').find('cal').find('width').find('error').text))
# Calbin the spectrum with standard settings, read in the fitted value again and check whether they have changed
command = ['spectrum calbin 0 -s 1']
command.append('fit execute')
command.append('fit store')
f, ferr = hdtvcmd(*command)
assert ferr == ''
if WRITE_BATCHFILE:
batchfile = os.path.join(os.path.curdir, 'test', 'share',
'calbin.hdtv')
with open(batchfile, 'a') as bfile:
for c in command:
bfile.write(c + '\n')
assert ferr == ''
fitxml.WriteXML(spectra.Get("0").ID, temp_file)
fitxml.ReadXML(spectra.Get("0").ID,
temp_file,
refit=False,
interactive=False)
tree = ET.parse(temp_file)
root = tree.getroot()
fits = root.findall('fit')
# It is a little bit unsatisfactory to accumulate the fits multiple times in temp_file, but I found no way to erase the content of temp_file
# open(temp_file, 'w').close() wouldn't work
assert len(fits) == 3
pos_value_1 = float(
fits[2].find('peak').find('cal').find('pos').find('value').text)
pos_error_1 = abs(
float(fits[2].find('peak').find('cal').find('pos').find('error').text))
vol_value_1 = float(
fits[2].find('peak').find('cal').find('vol').find('value').text)
vol_error_1 = abs(
float(fits[2].find('peak').find('cal').find('vol').find('error').text))
width_value_1 = float(
fits[2].find('peak').find('cal').find('width').find('value').text)
width_error_1 = abs(
float(
fits[2].find('peak').find('cal').find('width').find('error').text))
# For the fit of the position, allow it to deviate by N_SIGMA times the combined standard deviations of the fitted positions PLUS the width of a single bin.
assert isclose(
pos_value_init - pos_value_1,
0.,
abs_tol=N_SIGMA *
sqrt(pos_error_init * pos_error_init + pos_error_1 * pos_error_1) + 1.)
assert isclose(
vol_value_init - vol_value_1,
0.,
abs_tol=N_SIGMA *
sqrt(vol_error_init * vol_error_init + vol_error_1 * vol_error_1))
assert isclose(width_value_init - width_value_1,
0.,
abs_tol=N_SIGMA * sqrt(width_error_init * width_error_init +
width_error_1 * width_error_1))
# Calbin the spectrum with a factor of 2, read in the fitted value again and check whether they have changed
command = ['spectrum calbin 0 -b 2 -s 0']
command.append('fit execute')
command.append('fit store')
f, ferr = hdtvcmd(*command)
assert ferr == ''
if WRITE_BATCHFILE:
batchfile = os.path.join(os.path.curdir, 'test', 'share',
'calbin.hdtv')
with open(batchfile, 'a') as bfile:
for c in command:
bfile.write(c + '\n')
assert ferr == ''
fitxml.WriteXML(spectra.Get("0").ID, temp_file)
fitxml.ReadXML(spectra.Get("0").ID,
temp_file,
refit=False,
interactive=False)
tree = ET.parse(temp_file)
root = tree.getroot()
fits = root.findall('fit')
assert len(fits) == 7
pos_value_2 = float(
fits[6].find('peak').find('cal').find('pos').find('value').text)
pos_error_2 = abs(
float(fits[6].find('peak').find('cal').find('pos').find('error').text))
vol_value_2 = float(
fits[6].find('peak').find('cal').find('vol').find('value').text)
vol_error_2 = abs(
float(fits[6].find('peak').find('cal').find('vol').find('error').text))
width_value_2 = float(
fits[6].find('peak').find('cal').find('width').find('value').text)
width_error_2 = abs(
float(
fits[6].find('peak').find('cal').find('width').find('error').text))
assert isclose(
pos_value_init - pos_value_2,
0.,
abs_tol=N_SIGMA *
sqrt(pos_error_init * pos_error_init + pos_error_2 * pos_error_2) + 2.)
assert isclose(
vol_value_init - vol_value_2,
0.,
abs_tol=N_SIGMA *
sqrt(vol_error_init * vol_error_init + vol_error_2 * vol_error_2))
assert isclose(width_value_init - width_value_2,
0.,
abs_tol=N_SIGMA * sqrt(width_error_init * width_error_init +
width_error_2 * width_error_2))
assert isclose(pos_error_init, pos_error_2, rel_tol=0.2)
assert isclose(vol_error_init, vol_error_2, rel_tol=0.2)
assert isclose(pos_error_init, pos_error_2, rel_tol=0.2)
