def niftidecomp_workflow(
decompaxis,
datafile,
outputroot,
datamaskname=None,
decomptype="pca",
pcacomponents=0.5,
icacomponents=None,
varnorm=True,
demean=True,
sigma=0.0,
):
print(f"Will perform {decomptype} analysis along the {decompaxis} axis")
if decompaxis == "temporal":
decompaxisnum = 1
transposeifspatial = lambda *a, **k: None
else:
decompaxisnum = 0
transposeifspatial = np.transpose
# save the command line
tide_io.writevec([" ".join(sys.argv)], outputroot + "_commandline.txt")
# read in data
print("reading in data arrays")
(
datafile_img,
datafile_data,
datafile_hdr,
datafiledims,
datafilesizes,
) = tide_io.readfromnifti(datafile)
if datamaskname is not None:
(
datamask_img,
datamask_data,
datamask_hdr,
datamaskdims,
datamasksizes,
) = tide_io.readfromnifti(datamaskname)
xsize, ysize, numslices, timepoints = tide_io.parseniftidims(datafiledims)
xdim, ydim, slicethickness, tr = tide_io.parseniftisizes(datafilesizes)
# check dimensions
if datamaskname is not None:
print("checking mask dimensions")
if not tide_io.checkspacedimmatch(datafiledims, datamaskdims):
print("input mask spatial dimensions do not match image")
exit()
if not (tide_io.checktimematch(datafiledims, datamaskdims)
or datamaskdims[4] == 1):
print("input mask time dimension does not match image")
exit()
# save the command line
tide_io.writevec([" ".join(sys.argv)], outputroot + "_commandline.txt")
# smooth the data
if sigma > 0.0:
print("smoothing data")
for i in range(timepoints):
datafile_data[:, :, :,
i] = tide_filt.ssmooth(xdim, ydim, slicethickness,
sigma, datafile_data[:, :, :,
i])
# allocating arrays
print("reshaping arrays")
numspatiallocs = int(xsize) * int(ysize) * int(numslices)
rs_datafile = datafile_data.reshape((numspatiallocs, timepoints))
print("masking arrays")
if datamaskname is not None:
if datamaskdims[4] == 1:
proclocs = np.where(datamask_data.reshape(numspatiallocs) > 0.5)
else:
proclocs = np.where(
np.mean(datamask_data.reshape((numspatiallocs, timepoints)),
axis=1) > 0.5)
rs_mask = datamask_data.reshape(
(numspatiallocs, timepoints))[proclocs, :]
rs_mask = np.where(rs_mask > 0.5, 1.0, 0.0)[0]
else:
datamaskdims = [1, xsize, ysize, numslices, 1]
themaxes = np.max(rs_datafile, axis=1)
themins = np.min(rs_datafile, axis=1)
thediffs = (themaxes - themins).reshape(numspatiallocs)
proclocs = np.where(thediffs > 0.0)
procdata = rs_datafile[proclocs, :][0]
print(rs_datafile.shape, procdata.shape)
# normalize the individual images
if demean:
print("demeaning array")
themean = np.mean(procdata, axis=decompaxisnum)
print("shape of mean", themean.shape)
for i in range(procdata.shape[1 - decompaxisnum]):
if decompaxisnum == 1:
procdata[i, :] -= themean[i]
else:
procdata[:, i] -= themean[i]
else:
themean = np.ones(procdata.shape[1 - decompaxisnum])
if varnorm:
print("variance normalizing array")
thevar = np.var(procdata, axis=decompaxisnum)
print("shape of var", thevar.shape)
for i in range(procdata.shape[1 - decompaxisnum]):
if decompaxisnum == 1:
procdata[i, :] /= thevar[i]
else:
procdata[:, i] /= thevar[i]
procdata = np.nan_to_num(procdata)
else:
thevar = np.ones(procdata.shape[1 - decompaxisnum])
# applying mask
if datamaskdims[4] > 1:
procdata *= rs_mask
# now perform the decomposition
if decomptype == "ica":
print("performing ica decomposition")
if icacomponents is None:
print("will return all significant components")
else:
print("will return", icacomponents, "components")
thefit = FastICA(n_components=icacomponents).fit(
transposeifspatial(procdata)) # Reconstruct signals
if icacomponents is None:
thecomponents = transposeifspatial(thefit.components_[:])
print(thecomponents.shape[1], "components found")
else:
thecomponents = transposeifspatial(
thefit.components_[0:icacomponents])
print("returning first", thecomponents.shape[1],
"components found")
else:
print("performing pca decomposition")
if pcacomponents < 1.0:
print(
"will return the components accounting for",
pcacomponents * 100.0,
"% of the variance",
)
else:
print("will return", pcacomponents, "components")
if decomptype == "pca":
thepca = PCA(n_components=pcacomponents)
else:
thepca = SparsePCA(n_components=pcacomponents)
thefit = thepca.fit(transposeifspatial(procdata))
thetransform = thepca.transform(transposeifspatial(procdata))
theinvtrans = transposeifspatial(
thepca.inverse_transform(thetransform))
if pcacomponents < 1.0:
thecomponents = transposeifspatial(thefit.components_[:])
print("returning", thecomponents.shape[1], "components")
else:
thecomponents = transposeifspatial(
thefit.components_[0:pcacomponents])
# save the eigenvalues
print("variance explained by component:",
100.0 * thefit.explained_variance_ratio_)
tide_io.writenpvecs(
100.0 * thefit.explained_variance_ratio_,
outputroot + "_explained_variance_pct.txt",
)
if decompaxis == "temporal":
# save the components
print("writing component timecourses")
tide_io.writenpvecs(thecomponents, outputroot + "_components.txt")
# save the singular values
print("writing singular values")
tide_io.writenpvecs(np.transpose(thesingvals),
outputroot + "_singvals.txt")
# save the coefficients
print("writing out the coefficients")
coefficients = thetransform
print("coefficients shape:", coefficients.shape)
theheader = datafile_hdr
theheader["dim"][4] = coefficients.shape[1]
tempout = np.zeros((numspatiallocs, coefficients.shape[1]),
dtype="float")
tempout[proclocs, :] = coefficients[:, :]
tide_io.savetonifti(
tempout.reshape(
(xsize, ysize, numslices, coefficients.shape[1])),
datafile_hdr,
outputroot + "_coefficients",
)
# unnormalize the dimensionality reduced data
for i in range(numspatiallocs):
theinvtrans[i, :] = thevar[i] * theinvtrans[i, :] + themean[i]
else:
# save the component images
print("writing component images")
theheader = datafile_hdr
theheader["dim"][4] = thecomponents.shape[1]
tempout = np.zeros((numspatiallocs, thecomponents.shape[1]),
dtype="float")
tempout[proclocs, :] = thecomponents[:, :]
tide_io.savetonifti(
tempout.reshape(
(xsize, ysize, numslices, thecomponents.shape[1])),
datafile_hdr,
outputroot + "_components",
)
# save the coefficients
print("writing out the coefficients")
coefficients = np.transpose(thetransform)
tide_io.writenpvecs(coefficients, outputroot + "_coefficients.txt")
# unnormalize the dimensionality reduced data
for i in range(timepoints):
theinvtrans[:, i] = thevar[i] * theinvtrans[:, i] + themean[i]
print("writing fit data")
theheader = datafile_hdr
theheader["dim"][4] = theinvtrans.shape[1]
tempout = np.zeros((numspatiallocs, theinvtrans.shape[1]),
dtype="float")
tempout[proclocs, :] = theinvtrans[:, :]
tide_io.savetonifti(
tempout.reshape((xsize, ysize, numslices, theinvtrans.shape[1])),
datafile_hdr,
outputroot + "_fit",
)
def glmfilt(inputfile, numskip, outputroot, evfilename):
# initialize some variables
evdata = []
evisnifti = []
thedims_in = []
thedims_ev = []
thesizes_ev = []
# read the datafile and the evfiles
nim_input, nim_data, nim_header, thedims_in, thesizes_in = tide_io.readfromnifti(inputfile)
xdim, ydim, slicedim, tr = tide_io.parseniftisizes(thesizes_in)
print(xdim, ydim, slicedim, tr)
xsize, ysize, numslices, timepoints = tide_io.parseniftidims(thedims_in)
print(xsize, ysize, numslices, timepoints)
numregressors = 0
for i in range(0, len(evfilename)):
print("file ", i, " has name ", evfilename[i])
# check to see if file is nifti or text
fileisnifti = tide_io.checkifnifti(evfilename[i])
fileisparfile = tide_io.checkifparfile(evfilename[i])
if fileisnifti:
# if file is nifti
print("reading voxel specific regressor from ", evfilename[i])
(
nim_evinput,
ev_data,
ev_header,
thedims_evinput,
thesizes_evinput,
) = tide_io.readfromnifti(evfilename[i])
evisnifti.append(True)
evdata.append(1.0 * ev_data)
thedims_ev.append(thedims_evinput)
thesizes_ev.append(thesizes_evinput)
numregressors += 1
elif fileisparfile:
# check to see if file a par file
print("reading 6 global regressors from an FSL parfile")
evtimeseries = tide_io.readvecs(evfilename[i])
print("timeseries length = ", len(evtimeseries[0, :]))
for j in range(0, 6):
evisnifti.append(False)
evdata.append(1.0 * evtimeseries[j, :])
thedims_evinput = 1.0 * thedims_in
thesizes_evinput = 1.0 * thesizes_in
thedims_ev.append(thedims_evinput)
thesizes_ev.append(thesizes_evinput)
numregressors += 1
else:
# if file is text
print("reading global regressor from ", evfilename[i])
evtimeseries = tide_io.readvec(evfilename[i])
print("timeseries length = ", len(evtimeseries))
evisnifti.append(False)
evdata.append(1.0 * evtimeseries)
thedims_evinput = 1.0 * thedims_in
thesizes_evinput = 1.0 * thesizes_in
thedims_ev.append(thedims_evinput)
thesizes_ev.append(thesizes_evinput)
numregressors += 1
for j in range(0, numregressors):
for i in range(0, 4):
if thedims_in[i] != thedims_ev[j][i]:
print("Input file and ev file ", j, " dimensions do not match")
print("dimension ", i, ":", thedims_in[i], " != ", thedims_ev[j][i])
exit()
if timepoints - numskip != thedims_ev[j][4]:
print("Input file and ev file ", j, " dimensions do not match")
print("dimension ", 4, ":", timepoints, "!= ", thedims_ev[j][4], "+", numskip)
exit()
print("will perform GLM with ", numregressors, " regressors")
meandata = np.zeros((xsize, ysize, numslices), dtype="float")
fitdata = np.zeros((xsize, ysize, numslices, numregressors), dtype="float")
Rdata = np.zeros((xsize, ysize, numslices), dtype="float")
trimmeddata = 1.0 * nim_data[:, :, :, numskip:]
for z in range(0, numslices):
print("processing slice ", z)
for y in range(0, ysize):
for x in range(0, xsize):
regressorvec = []
for j in range(0, numregressors):
if evisnifti[j]:
regressorvec.append(evdata[j][x, y, z, :])
else:
regressorvec.append(evdata[j])
if np.max(trimmeddata[x, y, z, :]) - np.min(trimmeddata[x, y, z, :]) > 0.0:
thefit, R = tide_fit.mlregress(regressorvec, trimmeddata[x, y, z, :])
meandata[x, y, z] = thefit[0, 0]
Rdata[x, y, z] = R
for j in range(0, numregressors):
fitdata[x, y, z, j] = thefit[0, j + 1]
# datatoremove[x, y, z, :, j] = thefit[0, j + 1] * regressorvec[j]
else:
meandata[x, y, z] = 0.0
Rdata[x, y, z] = 0.0
for j in range(0, numregressors):
fitdata[x, y, z, j] = 0.0
# datatoremove[x, y, z, :, j] = 0.0 * regressorvec[j]
# totaltoremove[x, y, z, :] = np.sum(datatoremove[x, y, z, :, :], axis=1)
# filtereddata[x, y, z, :] = trimmeddata[x, y, z, :] - totaltoremove[x, y, z, :]
# first save the things with a small numbers of timepoints
print("fitting complete: about to save the fit data")
theheader = nim_header
theheader["dim"][4] = 1
tide_io.savetonifti(meandata, theheader, outputroot + "_mean")
for j in range(0, numregressors):
tide_io.savetonifti(fitdata[:, :, :, j], theheader, outputroot + "_fit" + str(j))
tide_io.savetonifti(Rdata, theheader, outputroot + "_R")
Rdata = None
print()
print("Now constructing the array of data to remove")
# datatoremove = np.zeros((xsize, ysize, numslices, timepoints - numskip, numregressors), dtype='float')
totaltoremove = np.zeros((xsize, ysize, numslices, timepoints - numskip), dtype="float")
# filtereddata = 1.0 * totaltoremove
for z in range(0, numslices):
print("processing slice ", z)
for y in range(0, ysize):
for x in range(0, xsize):
if np.max(trimmeddata[x, y, z, :]) - np.min(trimmeddata[x, y, z, :]) > 0.0:
for j in range(0, numregressors):
totaltoremove[x, y, z, :] += fitdata[x, y, z, j] * regressorvec[j]
else:
totaltoremove[x, y, z, :] = 0.0
print("Array construction done. Saving files")
# now save the things with full timecourses
theheader = nim_header
theheader["dim"][4] = timepoints - numskip
tide_io.savetonifti(totaltoremove, theheader, outputroot + "_totaltoremove")
filtereddata = trimmeddata - totaltoremove
totaltoremove = None
tide_io.savetonifti(trimmeddata, theheader, outputroot + "_trimmed")
trimmeddata = None
tide_io.savetonifti(filtereddata, theheader, outputroot + "_filtered")
def roisummarize(args):
# grab the command line arguments then pass them off.
try:
args = _get_parser().parse_args()
except SystemExit:
_get_parser().print_help()
raise
# set the sample rate
if args.samplerate == "auto":
args.samplerate = 1.0
else:
samplerate = args.samplerate
args, thefilter = pf.postprocessfilteropts(args, debug=args.debug)
print("loading fmri data")
input_img, input_data, input_hdr, thedims, thesizes = tide_io.readfromnifti(
args.inputfilename)
print("loading template data")
template_img, template_data, template_hdr, templatedims, templatesizes = tide_io.readfromnifti(
args.templatefile)
print("checking dimensions")
if not tide_io.checkspacematch(input_hdr, template_hdr):
print(
"template file does not match spatial coverage of input fmri file")
sys.exit()
print("reshaping")
xsize = thedims[1]
ysize = thedims[2]
numslices = thedims[3]
numtimepoints = thedims[4]
numvoxels = int(xsize) * int(ysize) * int(numslices)
templatevoxels = np.reshape(template_data, numvoxels).astype(int)
if numtimepoints > 1:
inputvoxels = np.reshape(input_data,
(numvoxels, numtimepoints))[:, args.numskip:]
print("filtering")
for thevoxel in range(numvoxels):
if templatevoxels[thevoxel] > 0:
inputvoxels[thevoxel, :] = thefilter.apply(
args.samplerate, inputvoxels[thevoxel, :])
print("summarizing")
timecourses = summarize4Dbylabel(inputvoxels,
templatevoxels,
normmethod=args.normmethod,
debug=args.debug)
print("writing data")
tide_io.writenpvecs(timecourses, args.outputfile + "_timecourses")
else:
inputvoxels = np.reshape(input_data, (numvoxels))
numregions = np.max(templatevoxels)
template_hdr["dim"][4] = numregions
outputvoxels, regionstats = summarize3Dbylabel(inputvoxels,
templatevoxels,
debug=args.debug)
tide_io.savetonifti(
outputvoxels.reshape((xsize, ysize, numslices)),
template_hdr,
args.outputfile + "_meanvals",
)
tide_io.writenpvecs(np.array(regionstats),
args.outputfile + "_regionstats.txt")
def polyfitim(
datafile,
datamask,
templatefile,
templatemask,
outputroot,
regionatlas=None,
order=1,
):
# read in data
print("reading in data arrays")
(
datafile_img,
datafile_data,
datafile_hdr,
datafiledims,
datafilesizes,
) = tide_io.readfromnifti(datafile)
(
datamask_img,
datamask_data,
datamask_hdr,
datamaskdims,
datamasksizes,
) = tide_io.readfromnifti(datamask)
(
templatefile_img,
templatefile_data,
templatefile_hdr,
templatefiledims,
templatefilesizes,
) = tide_io.readfromnifti(templatefile)
(
templatemask_img,
templatemask_data,
templatemask_hdr,
templatemaskdims,
templatemasksizes,
) = tide_io.readfromnifti(templatemask)
if regionatlas is not None:
(
regionatlas_img,
regionatlas_data,
regionatlas_hdr,
regionatlasdims,
regionatlassizes,
) = tide_io.readfromnifti(regionatlas)
xsize = datafiledims[1]
ysize = datafiledims[2]
numslices = datafiledims[3]
timepoints = datafiledims[4]
# check dimensions
print("checking dimensions")
if not tide_io.checkspacedimmatch(datafiledims, datamaskdims):
print("input mask spatial dimensions do not match image")
exit()
if datamaskdims[4] == 1:
print("using 3d data mask")
datamask3d = True
else:
datamask3d = False
if not tide_io.checktimematch(datafiledims, datamaskdims):
print("input mask time dimension does not match image")
exit()
if not tide_io.checkspacedimmatch(datafiledims, templatefiledims):
print(templatefiledims,
"template file spatial dimensions do not match image")
exit()
if not templatefiledims[4] == 1:
print("template file time dimension is not equal to 1")
exit()
if not tide_io.checkspacedimmatch(datafiledims, templatemaskdims):
print("template mask spatial dimensions do not match image")
exit()
if not templatemaskdims[4] == 1:
print("template mask time dimension is not equal to 1")
exit()
if regionatlas is not None:
if not tide_io.checkspacedimmatch(datafiledims, regionatlasdims):
print("template mask spatial dimensions do not match image")
exit()
if not regionatlasdims[4] == 1:
print("regionatlas time dimension is not equal to 1")
exit()
# allocating arrays
print("allocating arrays")
numspatiallocs = int(xsize) * int(ysize) * int(numslices)
rs_datafile = datafile_data.reshape((numspatiallocs, timepoints))
if datamask3d:
rs_datamask = datamask_data.reshape(numspatiallocs)
else:
rs_datamask = datamask_data.reshape((numspatiallocs, timepoints))
rs_datamask_bin = np.where(rs_datamask > 0.9, 1.0, 0.0)
rs_templatefile = templatefile_data.reshape(numspatiallocs)
rs_templatemask = templatemask_data.reshape(numspatiallocs)
rs_templatemask_bin = np.where(rs_templatemask > 0.1, 1.0, 0.0)
if regionatlas is not None:
rs_regionatlas = regionatlas_data.reshape(numspatiallocs)
numregions = int(np.max(rs_regionatlas))
fitdata = np.zeros((numspatiallocs, timepoints), dtype="float")
# residuals = np.zeros((numspatiallocs, timepoints), dtype='float')
# newtemplate = np.zeros((numspatiallocs), dtype='float')
# newmask = np.zeros((numspatiallocs), dtype='float')
if regionatlas is not None:
lincoffs = np.zeros((numregions, timepoints), dtype="float")
sqrcoffs = np.zeros((numregions, timepoints), dtype="float")
offsets = np.zeros((numregions, timepoints), dtype="float")
rvals = np.zeros((numregions, timepoints), dtype="float")
else:
lincoffs = np.zeros(timepoints, dtype="float")
sqrcoffs = np.zeros(timepoints, dtype="float")
offsets = np.zeros(timepoints, dtype="float")
rvals = np.zeros(timepoints, dtype="float")
if regionatlas is not None:
print("making region masks")
regionvoxels = np.zeros((numspatiallocs, numregions), dtype="float")
for region in range(0, numregions):
thisregion = np.where((rs_regionatlas *
rs_templatemask_bin) == (region + 1))
regionvoxels[thisregion, region] = 1.0
# mask everything
print("masking template")
maskedtemplate = rs_templatefile * rs_templatemask_bin
# cycle over all images
print("now cycling over all images")
for thetime in range(0, timepoints):
print("fitting timepoint", thetime)
# get the appropriate mask
if datamask3d:
for i in range(timepoints):
thisdatamask = rs_datamask_bin
else:
thisdatamask = rs_datamask_bin[:, thetime]
if regionatlas is not None:
for region in range(0, numregions):
voxelstofit = np.where(
regionvoxels[:, region] * thisdatamask > 0.5)
voxelstoreconstruct = np.where(regionvoxels[:, region] > 0.5)
if order == 2:
thefit, R = tide_fit.mlregress(
[
rs_templatefile[voxelstofit],
np.square(rs_templatefile[voxelstofit]),
],
rs_datafile[voxelstofit, thetime][0],
)
else:
thefit, R = tide_fit.mlregress(
rs_templatefile[voxelstofit],
rs_datafile[voxelstofit, thetime][0],
)
lincoffs[region, thetime] = thefit[0, 1]
offsets[region, thetime] = thefit[0, 0]
rvals[region, thetime] = R
if order == 2:
sqrcoffs[region, thetime] = thefit[0, 2]
fitdata[voxelstoreconstruct, thetime] += (
sqrcoffs[region, thetime] *
np.square(rs_templatefile[voxelstoreconstruct]) +
lincoffs[region, thetime] *
rs_templatefile[voxelstoreconstruct] +
offsets[region, thetime])
else:
fitdata[voxelstoreconstruct,
thetime] += (lincoffs[region, thetime] *
rs_templatefile[voxelstoreconstruct] +
offsets[region, thetime])
# newtemplate += nan_to_num(maskeddata[:, thetime] / lincoffs[region, thetime]) * rs_datamask
# newmask += rs_datamask * rs_templatemask_bin
else:
voxelstofit = np.where(thisdatamask > 0.5)
voxelstoreconstruct = np.where(rs_templatemask > 0.5)
thefit, R = tide_fit.mlregress(
rs_templatefile[voxelstofit], rs_datafile[voxelstofit,
thetime][0])
lincoffs[thetime] = thefit[0, 1]
offsets[thetime] = thefit[0, 0]
rvals[thetime] = R
fitdata[voxelstoreconstruct, thetime] = (
lincoffs[thetime] * rs_templatefile[voxelstoreconstruct] +
offsets[thetime])
# if datamask3d:
# newtemplate += nan_to_num(maskeddata[:, thetime] / lincoffs[thetime]) * rs_datamask
# else:
# newtemplate += nan_to_num(maskeddata[:, thetime] / lincoffs[thetime]) * rs_datamask[:, thetime]
# newmask += rs_datamask[:, thetime] * rs_templatemask_bin
residuals = rs_datafile - fitdata
# write out the data files
print("writing time series")
if order == 2:
tide_io.writenpvecs(sqrcoffs, outputroot + "_sqrcoffs.txt")
tide_io.writenpvecs(lincoffs, outputroot + "_lincoffs.txt")
tide_io.writenpvecs(offsets, outputroot + "_offsets.txt")
tide_io.writenpvecs(rvals, outputroot + "_rvals.txt")
if regionatlas is not None:
for region in range(0, numregions):
print(
"region",
region + 1,
"slope mean, std:",
np.mean(lincoffs[:, region]),
np.std(lincoffs[:, region]),
)
print(
"region",
region + 1,
"offset mean, std:",
np.mean(offsets[:, region]),
np.std(offsets[:, region]),
)
else:
print("slope mean, std:", np.mean(lincoffs), np.std(lincoffs))
print("offset mean, std:", np.mean(offsets), np.std(offsets))
print("writing nifti series")
tide_io.savetonifti(
fitdata.reshape((xsize, ysize, numslices, timepoints)),
datafile_hdr,
outputroot + "_fit",
)
tide_io.savetonifti(
residuals.reshape((xsize, ysize, numslices, timepoints)),
datafile_hdr,
outputroot + "_residuals",
)
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)