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_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_stcorrelate(debug=False):
tr = 0.72
testlen = 800
shiftdist = 5
windowtime = 30.0
stepsize = 5.0
corrweighting = "None"
outfilename = op.join(get_test_temp_path(), "stcorrtest")
# create outputdir if it doesn't exist
create_dir(get_test_temp_path())
dodetrend = True
timeaxis = np.arange(0.0, 1.0 * testlen) * tr
testfilter = NoncausalFilter(filtertype="lfo")
sig1 = testfilter.apply(1.0 / tr, np.random.random(testlen))
sig2 = np.float64(np.roll(sig1, int(shiftdist)))
if debug:
plt.figure()
plt.plot(sig1)
plt.plot(sig2)
legend = ["Original", "Shifted"]
plt.show()
times, corrpertime, ppertime = shorttermcorr_1D(sig1,
sig2,
tr,
windowtime,
samplestep=int(stepsize //
tr),
detrendorder=0)
plength = len(times)
times, xcorrpertime, Rvals, delayvals, valid = shorttermcorr_2D(
sig1,
sig2,
tr,
windowtime,
samplestep=int(stepsize // tr),
weighting=corrweighting,
detrendorder=0,
display=False,
)
xlength = len(times)
writenpvecs(corrpertime, outfilename + "_pearson.txt")
writenpvecs(ppertime, outfilename + "_pvalue.txt")
writenpvecs(Rvals, outfilename + "_Rvalue.txt")
writenpvecs(delayvals, outfilename + "_delay.txt")
writenpvecs(valid, outfilename + "_mask.txt")
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_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_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_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_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_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_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_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_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_nullsimfunc(debug=False, display=False):
# make the lfo filter
lfofilter = tide_filt.NoncausalFilter(filtertype="lfo")
# make the starting regressor
timestep = 1.5
Fs = 1.0 / timestep
# sourcelen = 1200
# sourcedata = lfofilter.apply(Fs, np.random.rand(sourcelen))
sourcedata = tide_io.readvecs(
os.path.join(get_test_data_path(), "fmri_globalmean.txt"))[0]
sourcelen = len(sourcedata)
numpasses = 1
if display:
plt.figure()
plt.plot(sourcedata)
plt.show()
thexcorr = tide_corr.fastcorrelate(sourcedata, sourcedata)
xcorrlen = len(thexcorr)
xcorr_x = (
np.linspace(0.0, xcorrlen, xcorrlen, endpoint=False) * timestep -
(xcorrlen * timestep) / 2.0 + timestep / 2.0)
if display:
plt.figure()
plt.plot(xcorr_x, thexcorr)
plt.show()
corrzero = xcorrlen // 2
lagmin = -10
lagmax = 10
lagmininpts = int((-lagmin / timestep) - 0.5)
lagmaxinpts = int((lagmax / timestep) + 0.5)
searchstart = int(np.round(corrzero + lagmin / timestep))
searchend = int(np.round(corrzero + lagmax / timestep))
optiondict = {
"numestreps": 10000,
"showprogressbar": debug,
"detrendorder": 3,
"windowfunc": "hamming",
"corrweighting": "None",
"nprocs": 1,
"widthlimit": 1000.0,
"bipolar": False,
"fixdelay": False,
"peakfittype": "gauss",
"lagmin": lagmin,
"lagmax": lagmax,
"absminsigma": 0.25,
"absmaxsigma": 25.0,
"edgebufferfrac": 0.0,
"lthreshval": 0.0,
"uthreshval": 1.0,
"debug": False,
"enforcethresh": True,
"lagmod": 1000.0,
"searchfrac": 0.5,
"permutationmethod": "shuffle",
"hardlimit": True,
}
theprefilter = tide_filt.NoncausalFilter("lfo")
theCorrelator = tide_classes.Correlator(
Fs=Fs,
ncprefilter=theprefilter,
detrendorder=optiondict["detrendorder"],
windowfunc=optiondict["windowfunc"],
corrweighting=optiondict["corrweighting"],
)
thefitter = tide_classes.SimilarityFunctionFitter(
lagmod=optiondict["lagmod"],
lthreshval=optiondict["lthreshval"],
uthreshval=optiondict["uthreshval"],
bipolar=optiondict["bipolar"],
lagmin=optiondict["lagmin"],
lagmax=optiondict["lagmax"],
absmaxsigma=optiondict["absmaxsigma"],
absminsigma=optiondict["absminsigma"],
debug=optiondict["debug"],
peakfittype=optiondict["peakfittype"],
searchfrac=optiondict["searchfrac"],
enforcethresh=optiondict["enforcethresh"],
hardlimit=optiondict["hardlimit"],
)
if debug:
print(optiondict)
theCorrelator.setlimits(lagmininpts, lagmaxinpts)
theCorrelator.setreftc(sourcedata)
dummy, trimmedcorrscale, dummy = theCorrelator.getfunction()
thefitter.setcorrtimeaxis(trimmedcorrscale)
histograms = []
for thenprocs in [1, -1]:
for i in range(numpasses):
corrlist = tide_nullsimfunc.getNullDistributionDatax(
sourcedata,
Fs,
theCorrelator,
thefitter,
despeckle_thresh=5.0,
fixdelay=False,
fixeddelayvalue=0.0,
numestreps=optiondict["numestreps"],
nprocs=thenprocs,
showprogressbar=optiondict["showprogressbar"],
chunksize=1000,
permutationmethod=optiondict["permutationmethod"],
)
tide_io.writenpvecs(
corrlist, os.path.join(get_test_temp_path(),
"corrdistdata.txt"))
# calculate percentiles for the crosscorrelation from the distribution data
histlen = 250
thepercentiles = [0.95, 0.99, 0.995]
pcts, pcts_fit, histfit = tide_stats.sigFromDistributionData(
corrlist, histlen, thepercentiles)
if debug:
tide_stats.printthresholds(
pcts,
thepercentiles,
"Crosscorrelation significance thresholds from data:",
)
tide_stats.printthresholds(
pcts_fit,
thepercentiles,
"Crosscorrelation significance thresholds from fit:",
)
(
thehist,
peakheight,
peakloc,
peakwidth,
centerofmass,
) = tide_stats.makehistogram(np.abs(corrlist),
histlen,
therange=[0.0, 1.0])
histograms.append(thehist)
thestore = np.zeros((2, len(thehist[0])), dtype="float64")
thestore[0, :] = (thehist[1][1:] + thehist[1][0:-1]) / 2.0
thestore[1, :] = thehist[0][-histlen:]
if display:
plt.figure()
plt.plot(thestore[0, :], thestore[1, :])
plt.show()
# tide_stats.makeandsavehistogram(corrlist, histlen, 0,
# os.path.join(get_test_temp_path(), 'correlationhist'),
# displaytitle='Null correlation histogram',
# displayplots=display, refine=False)
assert True
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)