def test_fullrunhappy_v2(debug=False, display=False):
# run happy
inputargs = [
os.path.join(get_examples_path(), "sub-HAPPYTEST.nii.gz"),
os.path.join(get_examples_path(), "sub-HAPPYTEST.json"),
os.path.join(get_test_temp_path(), "happyout"),
"--estmask",
os.path.join(get_examples_path(), "sub-HAPPYTEST_smallmask.nii.gz"),
"--projmask",
os.path.join(get_examples_path(), "sub-HAPPYTEST_smallmask.nii.gz"),
"--mklthreads",
"-1",
"--model",
"model_revised",
"--fliparteries",
"--temporalglm",
"--cardiacfile",
os.path.join(
get_examples_path(),
"sub-HAPPYTEST_desc-slicerescardfromfmri_timeseries.json:cardiacfromfmri_dlfiltered",
),
"--increaseoutputlevel",
"--increaseoutputlevel",
]
happy_workflow.happy_main(happy_parser.process_args(inputargs=inputargs))
def test_fullrunrapidtide_v3(debug=False, display=False):
# run rapidtide
inputargs = [
os.path.join(get_examples_path(), "sub-RAPIDTIDETEST.nii.gz"),
os.path.join(get_test_temp_path(), "sub-RAPIDTIDETEST"),
"--corrmask",
os.path.join(get_examples_path(),
"sub-RAPIDTIDETEST_restrictedmask.nii.gz:1"),
"--legacyoutput",
"--maxpasses",
"2",
"--numnull",
"100",
"--refinetype",
"pca",
"--convergencethresh",
"0.5",
"--noglm",
"--nprocs",
"-1",
"--similaritymetric",
"mutualinfo",
"--dpoutput",
"--spcalculation",
"--globalsignalmethod",
"pca",
"--regressor",
os.path.join(
get_examples_path(),
"sub-RAPIDTIDETEST_desc-oversampledmovingregressor_timeseries.json:pass3",
),
]
rapidtide_workflow.rapidtide_main(
rapidtide_parser.process_args(inputargs=inputargs))
def test_rapidtide2x(debug=False):
recalculate = True
if recalculate:
# create outputdir if it doesn't exist
create_dir(get_test_temp_path())
# and launch the processing
theargs = ["rapidtide2x"]
theargs += [os.path.join(get_examples_path(), "fmri.nii.gz")]
theargs += [
os.path.join(get_test_temp_path(), "rapidtide2x_phase2output")
]
theargs += [
"--regressor=" +
os.path.join(get_test_data_path(),
"rapidtide2x_phase1target_reference_fmrires.txt")
]
theargs += ["--regressortstep=1.5"]
theargs += ["--limitoutput"]
theargs += ["-s", "25.0"]
theargs += ["-L"]
theargs += ["-r", "-20,20"]
theargs += ["-f", "2"]
theargs += ["--passes=3"]
theargs += ["--refineoffset"]
theargs += ["--despecklepasses=4"]
theargs += ["--accheck"]
theargs += ["--nprocs=2"]
theargs += ["--saveoptionsasjson"]
theargs += ["--detrendorder=3"]
theargs += ["--pickleft"]
theargs += [
"--corrmask=" +
os.path.join(get_examples_path(), "quartermask.nii.gz")
]
theargs += ["--noglm"]
rapidtide2x_workflow.rapidtide_main(theargs)
diffmaps = tide_util.comparerapidtideruns(
os.path.join(get_test_temp_path(), "rapidtide2x_phase2output"),
os.path.join(get_test_target_path(), "rapidtide2x_phase2target"),
)
for mapname, maps in diffmaps.items():
print("checking", mapname)
print("\trelmindiff", maps["relmindiff"])
print("\trelmaxdiff", maps["relmaxdiff"])
print("\trelmeandiff", maps["relmeandiff"])
print("\trelmse", maps["relmse"])
assert maps["relmindiff"] < 1e2
assert maps["relmaxdiff"] < 1e2
assert maps["relmeandiff"] < 1e-2
assert maps["relmse"] < 1e2
def test_fullrunhappy_v1(debug=False, display=False):
# run happy
inputargs = [
os.path.join(get_examples_path(), "sub-HAPPYTEST.nii.gz"),
os.path.join(get_examples_path(), "sub-HAPPYTEST.json"),
os.path.join(get_test_temp_path(), "happyout"),
"--mklthreads",
"-1",
"--spatialglm",
"--model",
"model_revised",
"--aliasedcorrelation",
]
happy_workflow.happy_main(happy_parser.process_args(inputargs=inputargs))
def test_rapidtide2x_phase3(debug=False):
recalculate = True
if recalculate:
# create outputdir if it doesn't exist
create_dir(get_test_temp_path())
# trigger the usage function
rapidtide2x_workflow.usage()
# and launch the processing
theargs = ["rapidtide2x"]
theargs += [os.path.join(get_examples_path(), "fmri.nii.gz")]
theargs += [
os.path.join(get_test_temp_path(), "rapidtide2x_phase3output")
]
theargs += ["--nowindow"]
theargs += ["--windowfunc=hamming"]
theargs += ["--liang", "--eckart", "--phat"]
theargs += ["--usesp"]
theargs += ["--preservefiltering"]
theargs += ["--corrmaskthresh=0.25"]
theargs += ["-I", "-B", "-a", "-M", "-m"]
theargs += ["-C", "-R", "-L", "-V", "-F", "0.01,0.08"]
theargs += ["-v", "--debug"]
theargs += ["--globalmaskmethod=mean"]
theargs += ["--mklthreads=1"]
theargs += ["--nosharedmem"]
theargs += ["-S"]
rapidtide2x_workflow.rapidtide_main(theargs)
assert True
def test_rapidtide2x_phase3(debug=False):
recalculate = True
if recalculate:
# create outputdir if it doesn't exist
create_dir(get_test_temp_path())
# trigger the usage function
rapidtide2x_workflow.usage()
# and launch the processing
theargs = ['rapidtide2x']
theargs += [os.path.join(get_examples_path(), 'fmri.nii.gz')]
theargs += [
os.path.join(get_test_temp_path(), 'rapidtide2x_phase3output')
]
theargs += ['--nowindow']
theargs += ['--windowfunc=hamming']
theargs += ['--liang', '--eckart', '--phat']
theargs += ['--usesp']
theargs += ['--preservefiltering']
theargs += ['--corrmaskthresh=0.25']
theargs += ['-I', '-B', '-a', '-M', '-m']
theargs += ['-C', '-R', '-L', '-V', '-F', '0.01,0.08']
theargs += ['-v', '--debug']
theargs += ['--globalmaskmethod=mean']
theargs += ['--mklthreads=1']
theargs += ['--nosharedmem']
theargs += ['-S']
rapidtide2x_workflow.rapidtide_main(theargs)
assert True
def test_fullrunhappy_v4(debug=False, display=False):
# run happy
inputargs = [
os.path.join(get_examples_path(), "sub-HAPPYTEST.nii.gz"),
os.path.join(get_examples_path(), "sub-HAPPYTEST.json"),
os.path.join(get_test_temp_path(), "happyout"),
"--estmask",
os.path.join(get_examples_path(), "sub-HAPPYTEST_smallmask.nii.gz"),
"--projmask",
os.path.join(get_examples_path(), "sub-HAPPYTEST_smallmask.nii.gz"),
"--legacyoutput",
"--mklthreads",
"-1",
"--model",
"model_revised",
]
happy_workflow.happy_main(happy_parser.process_args(inputargs=inputargs))
def test_rapidtide2x(debug=False):
recalculate = True
if recalculate:
# create outputdir if it doesn't exist
create_dir(get_test_temp_path())
# and launch the processing
theargs = ['rapidtide2x']
theargs += [os.path.join(get_examples_path(), 'fmri.nii.gz')]
theargs += [os.path.join(get_test_temp_path(), 'rapidtide2x_phase2output')]
theargs += ['--regressor=' + os.path.join(get_test_data_path(), 'rapidtide2x_phase1target_reference_fmrires.txt')]
theargs += ['--regressortstep=1.5']
theargs += ['--limitoutput']
theargs += ['-s', '25.0']
theargs += ['-L']
theargs += ['-r', '-20,20']
theargs += ['-f', '2']
theargs += ['--passes=3']
theargs += ['--refineoffset']
theargs += ['--despecklepasses=4']
theargs += ['--accheck']
theargs += ['--nprocs=2']
theargs += ['--saveoptionsasjson']
theargs += ['--detrendorder=3']
theargs += ['--pickleft']
theargs += ['--corrmask=' + os.path.join(get_examples_path(), 'quartermask.nii.gz')]
theargs += ['--noglm']
rapidtide2x_workflow.rapidtide_main(theargs)
diffmaps = tide_util.comparerapidtideruns(os.path.join(get_test_temp_path(), 'rapidtide2x_phase2output'), os.path.join(get_test_target_path(), 'rapidtide2x_phase2target'))
for mapname, maps in diffmaps.items():
print('checking', mapname)
print('\trelmindiff', maps['relmindiff'])
print('\trelmaxdiff', maps['relmaxdiff'])
print('\trelmeandiff', maps['relmeandiff'])
print('\trelmse', maps['relmse'])
assert maps['relmindiff'] < 1e2
assert maps['relmaxdiff'] < 1e2
assert maps['relmeandiff'] < 1e-2
assert maps['relmse'] < 1e2
def test_fullrunrapidtide_v4(debug=False, display=False):
# run rapidtide
inputargs = [
os.path.join(get_examples_path(), "sub-NIRSRAPIDTIDETEST.txt"),
os.path.join(get_test_temp_path(), "sub-NIRSRAPIDTIDETEST"),
"--globalmeaninclude",
os.path.join(get_examples_path(), "sub-NIRSRAPIDTIDETEST_mask.txt"),
"--nirs",
"--datatstep",
"0.2560",
"--globalmaskmethod",
"variance",
"--despecklepasses",
"0",
"--numnull",
"1000",
"--respdelete",
"--echocancel",
]
rapidtide_workflow.rapidtide_main(
rapidtide_parser.process_args(inputargs=inputargs))
def test_fullrunrapidtide_v5(debug=False, display=False):
# run rapidtide
inputargs = [
os.path.join(get_examples_path(), "sub-RAPIDTIDETEST_cifti.ptseries.nii"),
os.path.join(get_test_temp_path(), "sub-RAPIDTIDETEST"),
"--nprocs",
"-1",
"--passes",
"2",
"--numnull",
"0",
]
rapidtide_workflow.rapidtide_main(rapidtide_parser.process_args(inputargs=inputargs))
def test_happy_phase1(debug=False):
recalculate = True
if recalculate:
# create outputdir if it doesn't exist
create_dir(get_test_temp_path())
# and launch the processing
theargs = ["happy"]
theargs += [os.path.join(get_examples_path(), "happyfmri.nii.gz")]
theargs += [os.path.join(get_examples_path(), "happyfmri.json")]
theargs += [os.path.join(get_test_temp_path(), "happy_output")]
theargs += ["--dodlfilter"]
theargs += ["--saveinfoasjson"]
theargs += ["--glm"]
theargs += ["--numskip=0"]
theargs += ["--gridbins=2.0"]
theargs += ["--gridkernel=kaiser"]
theargs += ["--model=model_revised"]
print(" ".join(theargs))
happy_workflow.happy_main(theargs)
diffmaps = tide_util.comparehappyruns(
os.path.join(get_test_temp_path(), "happy_output"),
os.path.join(get_test_target_path(), "happy_target"),
debug=debug,
)
for mapname, maps in diffmaps.items():
print("checking", mapname)
print("\trelmindiff", maps["relmindiff"])
print("\trelmaxdiff", maps["relmaxdiff"])
print("\trelmeandiff", maps["relmeandiff"])
print("\trelmse", maps["relmse"])
assert maps["relmindiff"] < 1e2
assert maps["relmaxdiff"] < 1e2
assert maps["relmeandiff"] < 1e-2
assert maps["relmse"] < 1e2
def test_fullrunrapidtide_v1(debug=False, display=False):
# run rapidtide
inputargs = [
os.path.join(get_examples_path(), "sub-RAPIDTIDETEST.nii.gz"),
os.path.join(get_test_temp_path(), "sub-RAPIDTIDETEST"),
"--spatialfilt",
"2",
"--nprocs",
"-1",
"--passes",
"1",
"--despecklepasses",
"3",
]
rapidtide_workflow.rapidtide_main(
rapidtide_parser.process_args(inputargs=inputargs))
def test_fullrunrapidtide_v2(debug=False, display=False):
# run rapidtide
inputargs = [
os.path.join(get_examples_path(), "sub-RAPIDTIDETEST.nii.gz"),
os.path.join(get_test_temp_path(), "sub-RAPIDTIDETEST"),
"--tmask",
os.path.join(get_examples_path(), "tmask3.txt"),
"--corrmask",
os.path.join(get_examples_path(),
"sub-RAPIDTIDETEST_restrictedmask.nii.gz"),
"--globalmeaninclude",
os.path.join(get_examples_path(),
"sub-RAPIDTIDETEST_brainmask.nii.gz"),
"--globalmeanexclude",
os.path.join(get_examples_path(), "sub-RAPIDTIDETEST_nullmask.nii.gz"),
"--refineinclude",
os.path.join(get_examples_path(),
"sub-RAPIDTIDETEST_brainmask.nii.gz"),
"--refineexclude",
os.path.join(get_examples_path(), "sub-RAPIDTIDETEST_nullmask.nii.gz"),
"--spatialfilt",
"-1",
"--savelags",
"--checkpoint",
"--saveintermediatemaps",
"--nolimitoutput",
"--calccoherence",
"--nprocs",
"1",
"--passes",
"2",
"--numnull",
"0",
"--similaritymetric",
"hybrid",
"--globalsignalmethod",
"meanscale",
]
rapidtide_workflow.rapidtide_main(
rapidtide_parser.process_args(inputargs=inputargs))
def test_io(debug=True, display=False):
# create outputdir if it doesn't exist
create_dir(get_test_temp_path())
# test checkifnifti
assert tide_io.checkifnifti("test.nii") == True
assert tide_io.checkifnifti("test.nii.gz") == True
assert tide_io.checkifnifti("test.txt") == False
# test checkiftext
assert tide_io.checkiftext("test.nii") == False
assert tide_io.checkiftext("test.nii.gz") == False
assert tide_io.checkiftext("test.txt") == True
# test getniftiroot
assert tide_io.getniftiroot("test.nii") == "test"
assert tide_io.getniftiroot("test.nii.gz") == "test"
assert tide_io.getniftiroot("test.txt") == "test.txt"
# test fmritimeinfo
fmritimeinfothresh = 1e-2
tr, timepoints = tide_io.fmritimeinfo(
os.path.join(get_examples_path(), "sub-HAPPYTEST.nii.gz"))
assert np.fabs(tr - 1.16) < fmritimeinfothresh
assert timepoints == 110
tr, timepoints = tide_io.fmritimeinfo(
os.path.join(get_examples_path(), "sub-RAPIDTIDETEST.nii.gz"))
assert np.fabs(tr - 1.5) < fmritimeinfothresh
assert timepoints == 260
# test niftifile reading
sizethresh = 1e-3
happy_img, happy_data, happy_hdr, happydims, happysizes = tide_io.readfromnifti(
os.path.join(get_examples_path(), "sub-HAPPYTEST.nii.gz"))
fmri_img, fmri_data, fmri_hdr, fmridims, fmrisizes = tide_io.readfromnifti(
os.path.join(get_examples_path(), "sub-RAPIDTIDETEST.nii.gz"))
targetdims = [4, 65, 89, 64, 110, 1, 1, 1]
targetsizes = [-1.00, 2.39583, 2.395830, 2.4, 1.16, 0.00, 0.00, 0.00]
if debug:
print("happydims:", happydims)
print("targetdims:", targetdims)
print("happysizes:", happysizes)
print("targetsizes:", targetsizes)
for i in range(len(targetdims)):
assert targetdims[i] == happydims[i]
assert mse(np.array(targetsizes), np.array(happysizes)) < sizethresh
# test file writing
datathresh = 2e-3 # relaxed threshold because sub-RAPIDTIDETEST has been converted to INT16
tide_io.savetonifti(
fmri_data, fmri_hdr,
os.path.join(get_test_temp_path(), "sub-RAPIDTIDETEST_copy.nii.gz"))
(
fmricopy_img,
fmricopy_data,
fmricopy_hdr,
fmricopydims,
fmricopysizes,
) = tide_io.readfromnifti(
os.path.join(get_test_temp_path(), "sub-RAPIDTIDETEST_copy.nii.gz"))
assert tide_io.checkspacematch(fmri_hdr, fmricopy_hdr)
assert tide_io.checktimematch(fmridims, fmridims)
assert mse(fmri_data, fmricopy_data) < datathresh
# test file header comparisons
assert tide_io.checkspacematch(happy_hdr, happy_hdr)
assert not tide_io.checkspacematch(happy_hdr, fmri_hdr)
assert tide_io.checktimematch(happydims, happydims)
assert not tide_io.checktimematch(happydims, fmridims)
def test_findmaxlag(display=False, fittype="gauss", debug=False):
textfilename = op.join(get_examples_path(), "lt_rt.txt")
# set default variable values
searchfrac = 0.75
indata = tide_io.readvecs(textfilename, debug=debug)
xvecs = indata[0, :]
yvecs = indata[1, :]
# set some fit parameters
lagmin = -20.0
lagmax = 20.0
widthlimit = 1000.0
absmaxsigma = 1000.0
absminsigma = 0.1
absmaxval = 1.0
absminval = 0.0
# test over the lag range
testmaxval = 0.8
testmaxsigma = 5.0
testlags = np.linspace(-25.0, 25.0, 50, endpoint=True)
testsigmas = np.full((len(testlags)), testmaxsigma, dtype=np.float64)
testvals = np.full((len(testlags)), testmaxval, dtype=np.float64)
fml_maxlags = np.zeros(len(testlags), dtype=np.float64)
fml_maxsigmas = np.zeros(len(testlags), dtype=np.float64)
fml_maxvals = np.zeros(len(testlags), dtype=np.float64)
fml_lfailreasons = np.zeros(len(testlags), dtype=np.uint16)
fmlc_maxlags = np.zeros(len(testlags), dtype=np.float64)
fmlc_maxsigmas = np.zeros(len(testlags), dtype=np.float64)
fmlc_maxvals = np.zeros(len(testlags), dtype=np.float64)
fmlc_lfailreasons = np.zeros(len(testlags), dtype=np.uint16)
# initialize the correlation fitter
thefitter = tide_classes.SimilarityFunctionFitter(
corrtimeaxis=xvecs,
lagmin=lagmin,
lagmax=lagmax,
absmaxsigma=absmaxsigma,
absminsigma=absminsigma,
peakfittype=fittype,
debug=debug,
searchfrac=searchfrac,
zerooutbadfit=False,
)
for i in range(len(testlags)):
yvecs = tide_fit.gauss_eval(
xvecs, np.array([testvals[i], testlags[i], testsigmas[i]]))
print()
print()
print()
(
maxindex,
fml_maxlags[i],
fml_maxvals[i],
fml_maxsigmas[i],
maskval,
fml_lfailreasons[i],
peakstart,
peakend,
) = tide_fit.findmaxlag_gauss(
xvecs,
yvecs,
lagmin,
lagmax,
widthlimit,
tweaklims=False,
refine=True,
debug=debug,
searchfrac=searchfrac,
absmaxsigma=absmaxsigma,
absminsigma=absminsigma,
zerooutbadfit=False,
)
print()
print()
print()
(
maxindexc,
fmlc_maxlags[i],
fmlc_maxvals[i],
fmlc_maxsigmas[i],
maskvalc,
fmlc_lfailreasons[i],
peakstartc,
peakendc,
) = thefitter.fit(yvecs)
print(
maxindexc,
fmlc_maxlags[i],
fmlc_maxvals[i],
fmlc_maxsigmas[i],
maskvalc,
fmlc_lfailreasons[i],
peakstartc,
peakendc,
)
if debug:
print("findmaxlag_gauss results over lag range")
for i in range(len(testlags)):
print(testlags[i], fml_maxlags[i], fml_lfailreasons[i])
assert eval_fml_result(lagmin, lagmax, testlags, fml_maxlags,
fml_lfailreasons)
assert eval_fml_result(absminval, absmaxval, testvals, fml_maxvals,
fml_lfailreasons)
assert eval_fml_result(absminsigma, absmaxsigma, testsigmas, fml_maxsigmas,
fml_lfailreasons)
assert eval_fml_result(lagmin, lagmax, testlags, fmlc_maxlags,
fmlc_lfailreasons)
assert eval_fml_result(absminval, absmaxval, testvals, fmlc_maxvals,
fmlc_lfailreasons)
assert eval_fml_result(absminsigma, absmaxsigma, testsigmas,
fmlc_maxsigmas, fmlc_lfailreasons)
if display:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(testlags, fml_maxlags, "r")
ax.plot(testlags, fmlc_maxlags, "b")
ax.legend(["findmaxlag_gauss", "classes"])
plt.show()
# now test over range of sigmas
testlag = 5.0
testsigmas = np.asarray([
0.01,
0.02,
0.05,
0.1,
0.2,
0.5,
1.0,
2.0,
5.0,
10.0,
20.0,
50.0,
100.0,
200.0,
500.0,
1000.0,
2000.0,
])
testlags = np.full((len(testsigmas)), testlag, dtype=np.float64)
testvals = np.full((len(testsigmas)), testmaxval, dtype=np.float64)
fml_maxlags = np.zeros(len(testsigmas), dtype=np.float64)
fml_maxsigmas = np.zeros(len(testsigmas), dtype=np.float64)
fml_maxvals = np.zeros(len(testsigmas), dtype=np.float64)
fml_wfailreasons = np.zeros(len(testsigmas), dtype=np.uint16)
fmlc_maxlags = np.zeros(len(testsigmas), dtype=np.float64)
fmlc_maxsigmas = np.zeros(len(testsigmas), dtype=np.float64)
fmlc_maxvals = np.zeros(len(testsigmas), dtype=np.float64)
fmlc_wfailreasons = np.zeros(len(testsigmas), dtype=np.uint16)
peakstartc = np.zeros(len(testsigmas), dtype=np.int32)
peakendc = np.zeros(len(testsigmas), dtype=np.int32)
for i in range(len(testsigmas)):
yvecs = tide_fit.gauss_eval(
xvecs, np.array([testvals[i], testlags[i], testsigmas[i]]))
print()
print()
print()
(
maxindex,
fml_maxlags[i],
fml_maxvals[i],
fml_maxsigmas[i],
maskval,
fml_wfailreasons[i],
peakstart,
peakend,
) = tide_fit.findmaxlag_gauss(
xvecs,
yvecs,
lagmin,
lagmax,
widthlimit,
tweaklims=False,
refine=True,
debug=debug,
searchfrac=searchfrac,
absmaxsigma=absmaxsigma,
absminsigma=absminsigma,
zerooutbadfit=False,
)
print()
print()
print()
(
maxindexc,
fmlc_maxlags[i],
fmlc_maxvals[i],
fmlc_maxsigmas[i],
maskvalc,
fmlc_wfailreasons[i],
peakstartc[i],
peakendc[i],
) = thefitter.fit(yvecs)
print(
maxindexc,
fmlc_maxlags[i],
fmlc_maxvals[i],
fmlc_maxsigmas[i],
maskvalc,
fmlc_wfailreasons[i],
peakstartc[i],
peakendc[i],
)
if debug:
print("findmaxlag_gauss results over sigma range")
for i in range(len(testsigmas)):
print(
testsigmas[i],
fml_maxsigmas[i],
fmlc_maxlags[i],
fmlc_maxvals[i],
fml_wfailreasons[i],
)
print("\nfitter class results over lag range")
for i in range(len(testsigmas)):
print(
testsigmas[i],
fmlc_maxsigmas[i],
fmlc_maxlags[i],
fmlc_maxvals[i],
peakstartc[i],
peakendc[i],
fmlc_wfailreasons[i],
thefitter.diagnosefail(np.uint32(fmlc_wfailreasons[i])),
)
if display:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.loglog(testsigmas, fml_maxsigmas, "r")
ax.loglog(testsigmas, fmlc_maxsigmas, "b")
ax.legend(["findmaxlag_gauss", "classes"])
plt.show()
assert eval_fml_result(lagmin, lagmax, testlags, fml_maxlags,
fml_wfailreasons)
# assert eval_fml_result(absminval, absmaxval, testvals, fml_maxvals, fml_wfailreasons)
assert eval_fml_result(absminsigma, absmaxsigma, testsigmas, fml_maxsigmas,
fml_wfailreasons)
assert eval_fml_result(lagmin, lagmax, testlags, fmlc_maxlags,
fmlc_wfailreasons)
assert eval_fml_result(absminval, absmaxval, testvals, fmlc_maxvals,
fmlc_wfailreasons)
assert eval_fml_result(absminsigma, absmaxsigma, testsigmas,
fmlc_maxsigmas, fmlc_wfailreasons)
