From 28b26228fd64cf421971a31909826c6625cd3bd0 Mon Sep 17 00:00:00 2001 From: Ross Markello Date: Fri, 11 May 2018 14:26:59 -0400 Subject: [PATCH 01/10] [TST] Added some tests for utils.py Updated circle.yml to trigger new tests. More tests needed for all the functions that operate on data files? --- circle.yml | 2 +- tedana/tests/test_utils.py | 116 +++++++++++++++++++++++++++++++++---- tedana/utils/utils.py | 73 +++++++++++------------ 3 files changed, 142 insertions(+), 49 deletions(-) diff --git a/circle.yml b/circle.yml index 0ad2fe8e9..462960e88 100644 --- a/circle.yml +++ b/circle.yml @@ -38,7 +38,7 @@ jobs: name: run tests command: | source activate venv - py.test ./tedana/tests/test_tedana.py + py.test ./tedana/tests no_output_timeout: "40m" post: bash <(curl -s https://codecov.io/bash) diff --git a/tedana/tests/test_utils.py b/tedana/tests/test_utils.py index 102f36a89..655e348e4 100644 --- a/tedana/tests/test_utils.py +++ b/tedana/tests/test_utils.py @@ -2,42 +2,134 @@ Tests for tedana.utils """ +import nibabel as nib +import numpy as np +import pytest +from tedana import utils -def test_load_image(): - pass +rs = np.random.RandomState(1234) -def test_cat2echos(): - pass +def test_get_dtype(): + # various combinations of input types + good_inputs = [ + (['echo1.func.gii', 'echo2.func.gii', 'echo3.func.gii'], 'GIFTI'), + ('echo1.func.gii', 'GIFTI'), + (['echo1.nii.gz', 'echo2.nii.gz', 'echo3.nii.gz'], 'NIFTI'), + ('echo1.nii.gz', 'NIFTI'), + (['echo1.unknown', 'echo2.unknown', 'echo3.unknown'], 'OTHER'), + ('echo1.unknown', 'OTHER'), + (nib.Nifti1Image(np.zeros((10,)*3), + affine=np.diag(np.ones(4))), 'NIFTI') + ] + for (input, expected) in good_inputs: + assert utils.get_dtype(input) == expected -def test_make_adaptive_mask(): + with pytest.raises(ValueError): # mixed arrays don't work + utils.get_dtype(['echo1.func.gii', 'echo1.nii.gz']) + + with pytest.raises(TypeError): # non-img_like inputs don't work + utils.get_dtype(rs.rand(100, 100)) + + +def test_getfbounds(): + good_inputs = range(1, 12) + bad_inputs = [ + (0, ValueError), + (12, ValueError), + (10.5, TypeError) + ] + + for n_echos in good_inputs: + utils.getfbounds(n_echos) + + for (n_echos, err) in bad_inputs: + with pytest.raises(err): + utils.getfbounds(n_echos) + + +def test_unmask(): + # generate boolean mask + get number of True values + mask = rs.choice([0, 1], size=(100,)).astype(bool) + n_data = mask.sum() + + inputs = [ + (rs.rand(n_data, 3), float), # 2D float + (rs.rand(n_data, 3, 3), float), # 3D float + (rs.randint(10, size=(n_data, 3)), int), # 2D int + (rs.randint(10, size=(n_data, 3, 3)), int) # 3D int + ] + + for (input, dtype) in inputs: + out = utils.unmask(input, mask) + assert out.shape == (100,) + input.shape[1:] + assert out.dtype == dtype + + +def test_fitgaussian(): + # not sure a good way to test this + # it's straight out of the scipy cookbook, so hopefully its robust? + assert utils.fitgaussian(rs.rand(100, 100)).size == 5 + + +def test_dice(): + arr = rs.choice([0, 1], size=(100, 100)) + # identical arrays should have a Dice index of 1 + assert utils.dice(arr, arr) == 1.0 + # inverted arrays should have a Dice index of 0 + assert utils.dice(arr, np.logical_not(arr)) == 0.0 + # float arrays should return same as integer or boolean array + assert utils.dice(arr * rs.rand(100, 100), arr * rs.rand(100, 100)) == 1.0 + # empty arrays still work + assert utils.dice(np.array([]), np.array([])) == 0.0 + # different size arrays raise a ValueError + with pytest.raises(ValueError): + utils.dice(arr, rs.choice([0, 1], size=(20, 20))) + + +def test_andb(): + # test with a range of dimensions and ensure output dtype is int + for ndim in range(1, 5): + shape = (10,) * ndim + out = utils.andb([rs.randint(10, size=shape) for f in range(5)]) + assert out.shape == shape + assert out.dtype == int + + # confirm error raised when dimensions are not the same + with pytest.raises(ValueError): + utils.andb([rs.randint(10, size=(10, 10)), + rs.randint(10, size=(20, 20))]) + + +# should tedana ship with test data for these? +def test_load_image(): pass -def test_make_min_mask(): +def test_load_data(): pass -def test_unmask(): +def test_make_adaptive_mask(): pass -def test_moments(): +def test_make_min_mask(): pass -def test_gaussian(): +def test_filewrite(): pass -def test_fitgaussian(): +def test_new_nii_like(): pass -def test_dice(): +def test_new_gii_like(): pass -def test_andb(): +def test_new_gii_darray(): pass diff --git a/tedana/utils/utils.py b/tedana/utils/utils.py index 7a8b46d66..74a30a3dd 100644 --- a/tedana/utils/utils.py +++ b/tedana/utils/utils.py @@ -42,10 +42,7 @@ def get_dtype(data): raise TypeError('Input data format cannot be detected.') dtype = data.valid_exts[0] - if dtype in FORMATS.keys(): - return FORMATS[dtype] - - return 'OTHER' + return FORMATS.get(dtype, 'OTHER') def getfbounds(n_echos): @@ -62,9 +59,11 @@ def getfbounds(n_echos): """ if not isinstance(n_echos, int): - raise IOError('Input n_echos must be int') - elif n_echos <= 0: - raise ValueError('Input n_echos must be greater than 0') + raise TypeError('Input n_echos must be type int. Type {} ' + 'invalid'.format(type(n_echos))) + elif n_echos <= 0 or n_echos > 11: + raise ValueError('Input `n_echos` must be >0 and <12. Provided ' + 'value: {}'.format(n_echos)) idx = n_echos - 1 F05s = [None, None, 18.5, 10.1, 7.7, 6.6, 6.0, 5.6, 5.3, 5.1, 5.0] @@ -94,10 +93,12 @@ def load_image(data): return fdata elif get_dtype(data) == 'NIFTI': data = check_niimg(data).get_data() + elif isinstance(data, nib.spatialimages.SpatialImage): + data = check_niimg(data).get_data() - fdata = data.reshape((-1,) + data.shape[3:]) + fdata = data.reshape((-1,) + data.shape[3:]).squeeze() - return fdata.squeeze() + return fdata def load_data(data, n_echos=None): @@ -127,18 +128,18 @@ def load_data(data, n_echos=None): if get_dtype(data) == 'GIFTI': # TODO: deal with L/R split GIFTI files pass if len(data) == 1: # a z-concatenated file was provided - if n_echos is None: - raise ValueError('Number of echos `n_echos` must be specified ' - 'if z-concatenated data file provided.') data = data[0] elif len(data) == 2: # inviable -- need more than 2 echos raise ValueError('Cannot run `tedana` with only two echos: ' '{}'.format(data)) - else: # individual echo files were provided + else: # individual echo files were provided (surface or volumetric) fdata = np.stack([load_image(f) for f in data], axis=1) return np.atleast_3d(fdata), data[0] - # we have a z-cat file + # we have a z-cat file -- we need to know how many echos are in it! + if n_echos is None: + raise ValueError('Number of echos `n_echos` must be specified if only ' + 'one data file is provided.') img = check_niimg(data) (nx, ny), nz = img.shape[:2], img.shape[2] // n_echos fdata = load_image(img.get_data().reshape(nx, ny, nz, n_echos, -1, order='F')) @@ -180,24 +181,26 @@ def make_adaptive_mask(data, minimum=True, getsum=False): if minimum: return make_min_mask(data) - n_samp, n_echos, n_vols = data.shape + # take temporal mean of echos and extract non-zero values in first echo echo_means = data.mean(axis=-1) # temporal mean of echos - first_echo = echo_means[..., 0] - # make a map of longest echo with which a voxel can be sampled, with min - # value of map as X value of voxel that has median value in the 1st echo - # N.B. larger factor (%ile??) leads to bias to lower TEs - perc_33 = np.percentile(first_echo[first_echo.nonzero()], 33, - interpolation='higher') # why take 33rd %ile? - med_val = (first_echo == perc_33) - lthrs = np.vstack([echo_means[..., echo][med_val] / 3 for echo in - range(n_echos)]) # why divide by three? - lthrs = lthrs[:, lthrs.sum(0).argmax()] - mthr = np.ones(data.shape[:-1]) - for echo in range(n_echos): - mthr[..., echo] *= lthrs[echo] - - masksum = (np.abs(echo_means) > mthr).astype('int').sum(axis=-1) - mask = (masksum != 0) + first_echo = echo_means[echo_means[:, 0] != 0, 0] + + # get 33rd %ile of `first_echo` and find corresponding index + perc_33 = np.percentile(first_echo, 33, interpolation='higher') # QUESTION: why 33%ile? + med_val = (echo_means[:, 0] == perc_33) + + # extract values from all echos at relevant index + lthrs = np.squeeze(echo_means[med_val].T) / 3 # QUESTION: why divide by 3? + + # if multiple samples were extracted per echo, keep the one w/the highest signal + if lthrs.ndim > 1: + lthrs = lthrs[:, lthrs.sum(axis=0).argmax()] + + # determine samples where absolute value is greater than echo-specific thresholds + # and count # of echos that pass criterion + masksum = (np.abs(echo_means) > lthrs).sum(axis=-1) + # make it a boolean mask to (where we have at least 1 echo with good signal) + mask = masksum.astype(bool) if getsum: return mask, masksum @@ -271,8 +274,7 @@ def filewrite(data, filename, ref_img, gzip=False, copy_header=True, 'formats are compatible'.format(dtype, FORMATS[ext])) if dtype == 'NIFTI': - out = new_nii_like(ref_img, data, - copy_header=copy_header) + out = new_nii_like(ref_img, data, copy_header=copy_header) name = '{}.{}'.format(root, 'nii.gz' if gzip else 'nii') out.to_filename(name) elif dtype == 'GIFTI': @@ -584,10 +586,9 @@ def andb(arrs): Integer array of summed `arrs` """ - # coerce to integer and ensure same shape + # coerce to integer and ensure all arrays are the same shape arrs = [check_array(arr, dtype=int, ensure_2d=False, allow_nd=True) for arr in arrs] - same_shape = [arr1.shape == arr2.shape for arr1 in arrs for arr2 in arrs] - if not np.all(same_shape): + if not np.all([arr1.shape == arr2.shape for arr1 in arrs for arr2 in arrs]): raise ValueError('All input arrays must have same shape.') # sum across arrays From 1c9b5209990f1347085128026b708aed3f03ebaa Mon Sep 17 00:00:00 2001 From: Ross Markello Date: Fri, 11 May 2018 16:56:43 -0400 Subject: [PATCH 02/10] [DOC] Supercharged logging Added a bunch of logging statements through the codebase, options to augment (--debug) or stifle (--quiet) them, and some comments throughout. Also updated `setup.py` so that once installed, `tedana` is callable from the command line (it had erroneous pointers to meica). Hoping I didn't change any of the results in the process of doing this... --- Dockerfile | 4 +-- setup.py | 10 +++---- tedana/cli/run.py | 31 ++++++++++++++++++-- tedana/decomp/eigendecomp.py | 31 +++++++++++--------- tedana/io/output_data.py | 53 ++++++++++++++++++++--------------- tedana/model/fit.py | 21 +++++++------- tedana/model/t2smap.py | 16 +++++++---- tedana/select/select_comps.py | 45 +++++++++++++++++++---------- tedana/workflows/t2smap.py | 7 ++--- tedana/workflows/tedana.py | 22 +++++++++------ 10 files changed, 150 insertions(+), 90 deletions(-) diff --git a/Dockerfile b/Dockerfile index 57f3e15c3..68d730693 100644 --- a/Dockerfile +++ b/Dockerfile @@ -98,7 +98,7 @@ RUN conda create -y -q --name default --channel vida-nyu python=3.6.1 \ && sync && conda clean -tipsy && sync \ && /bin/bash -c "source activate default \ && pip install -q --no-cache-dir \ - nipype ipython scikit-learn scipy ipdb mdp nilearn nibabel>=2.1.0" \ + nipype ipython scikit-learn scipy ipdb mdp nilearn 'nibabel>=2.1.0'" \ && sync ENV PATH=/opt/conda/envs/default/bin:$PATH @@ -110,7 +110,7 @@ RUN conda create -y -q --name py27 python=2.7 \ && sync && conda clean -tipsy && sync \ && /bin/bash -c "source activate default \ && pip install -q --no-cache-dir \ - nipype ipython scikit-learn scipy ipdb mdp nilearn nibabel>=2.1.0" \ + nipype ipython scikit-learn scipy ipdb mdp nilearn 'nibabel>=2.1.0'" \ && sync USER root diff --git a/setup.py b/setup.py index 735306881..715133fb4 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- # @Author: oesteban -""" meica setup script """ +""" tedana setup script """ def main(): @@ -16,7 +16,7 @@ def main(): # For Python 3: use a locals dictionary # http://stackoverflow.com/a/1463370/6820620 ldict = locals() - # Get version and release info, which is all stored in meica/info.py + # Get version and release info, which is all stored in tedana/info.py module_file = op.join(this_path, 'tedana', 'info.py') with open(module_file) as infofile: pythoncode = [line for line in infofile.readlines() if not line.strip().startswith('#')] @@ -40,10 +40,8 @@ def main(): tests_require=ldict['TESTS_REQUIRES'], extras_require=ldict['EXTRA_REQUIRES'], entry_points={'console_scripts': [ - 'ama=meica.cli.run_alignp_mepi_anat:main', - 'meica=meica.cli.run_meica:main', - 't2smap=meica.cli.run_t2smap:main', - 'tedana=meica.cli.run_tedana:main' + 't2smap=tedana.cli.run:run_t2smap', + 'tedana=tedana.cli.run:main' ]}, packages=find_packages(exclude=("tests",)), zip_safe=False, diff --git a/tedana/cli/run.py b/tedana/cli/run.py index 4b2b1d7d3..88295b6fe 100644 --- a/tedana/cli/run.py +++ b/tedana/cli/run.py @@ -1,6 +1,9 @@ import argparse from tedana import workflows +import logging +logging.basicConfig(format='[%(levelname)s]: ++ %(message)s', level=logging.INFO) + def get_parser(): """ @@ -100,7 +103,7 @@ def get_parser(): dest='filecsdata', help='Save component selection data', action='store_true', - default=True) + default=False) parser.add_argument('--label', dest='label', help='Label for output directory.', @@ -109,14 +112,38 @@ def get_parser(): dest='fixed_seed', help='Seeded value for ICA, for reproducibility.', default=42) + parser.add_argument('--debug', + dest='debug', + help=argparse.SUPPRESS, + action='store_true', + default=False) + parser.add_argument('--quiet', + dest='quiet', + help=argparse.SUPPRESS, + action='store_true', + default=False) return parser def main(argv=None): - """Entry point""" + """Tedana entry point""" options = get_parser().parse_args(argv) + if options.debug and not options.quiet: + logging.getLogger().setLevel(logging.DEBUG) + elif options.quiet: + logging.getLogger().setLevel(logging.WARNING) workflows.tedana.main(**vars(options)) +def run_t2smap(argv=None): + """T2smap entry point""" + options = get_parser().parse_args(argv) + if options.debug and not options.quiet: + logging.basicConfig(level=logging.DEBUG) + elif options.quiet: + logging.getLogger().setLevel(logging.WARNING) + workflows.t2smap.main(**vars(options)) + + if __name__ == '__main__': main() diff --git a/tedana/decomp/eigendecomp.py b/tedana/decomp/eigendecomp.py index 9c31d8faa..883302158 100644 --- a/tedana/decomp/eigendecomp.py +++ b/tedana/decomp/eigendecomp.py @@ -5,7 +5,6 @@ from tedana import model, utils import logging -logging.basicConfig(format='[%(levelname)s]: %(message)s', level=logging.INFO) lgr = logging.getLogger(__name__) F_MAX = 500 @@ -33,12 +32,12 @@ def eimask(dd, ees=None): ees = range(dd.shape[1]) imask = np.zeros([dd.shape[0], len(ees)], dtype=bool) for ee in ees: - lgr.info('++ Creating eimask for echo {}'.format(ee)) + lgr.debug('Creating eimask for echo {}'.format(ee)) perc98 = stats.scoreatpercentile(dd[:, ee, :].flatten(), 98, interpolation_method='lower') lthr, hthr = 0.001 * perc98, 5 * perc98 - lgr.info('++ Eimask threshold boundaries: ' - '{:.03f} {:.03f}'.format(lthr, hthr)) + lgr.debug('Eimask threshold boundaries: ' + '{:.03f} {:.03f}'.format(lthr, hthr)) m = dd[:, ee, :].mean(axis=1) imask[np.logical_and(m > lthr, m < hthr), ee] = True @@ -94,13 +93,13 @@ def tedpca(catd, OCcatd, combmode, mask, t2s, t2sG, stabilize, ste = np.array([int(ee) for ee in str(ste).split(',')]) if len(ste) == 1 and ste[0] == -1: - lgr.info('++ Computing PCA of optimally combined multi-echo data') + lgr.info('Computing PCA of optimally combined multi-echo data') d = OCcatd[utils.make_min_mask(OCcatd[:, np.newaxis, :])][:, np.newaxis, :] elif len(ste) == 1 and ste[0] == 0: - lgr.info('++ Computing PCA of spatially concatenated multi-echo data') + lgr.info('Computing PCA of spatially concatenated multi-echo data') d = catd[mask].astype('float64') else: - lgr.info('++ Computing PCA of echo #%s' % ','.join([str(ee) for ee in ste])) + lgr.info('Computing PCA of echo #%s' % ','.join([str(ee) for ee in ste])) d = np.stack([catd[mask, ee] for ee in ste - 1], axis=1).astype('float64') eim = np.squeeze(eimask(d)) @@ -141,6 +140,7 @@ def tedpca(catd, OCcatd, combmode, mask, t2s, t2sG, stabilize, vTmix = v.T vTmixN = ((vTmix.T - vTmix.T.mean(0)) / vTmix.T.std(0)).T + lgr.info('Making initial component selection guess from PCA results') _, ctb, betasv, v_T = model.fitmodels_direct(catd, v.T, eimum, t2s, t2sG, tes, combmode, ref_img, mmixN=vTmixN, full_sel=False) @@ -148,17 +148,18 @@ def tedpca(catd, OCcatd, combmode, mask, t2s, t2sG, stabilize, ctb = np.vstack([ctb.T[:3], sp]).T # Save state - lgr.info('++ Saving PCA') + fname = op.abspath('pcastate.pkl') + lgr.info('Saving PCA results to: {}'.format(fname)) pcastate = {'u': u, 's': s, 'v': v, 'ctb': ctb, 'eigelb': eigelb, 'spmin': spmin, 'spcum': spcum} try: - with open('pcastate.pkl', 'wb') as handle: + with open(fname, 'wb') as handle: pickle.dump(pcastate, handle) except TypeError: - lgr.warning('++ Could not save PCA solution.') + lgr.warning('Could not save PCA solution.') else: # if loading existing state - lgr.info('++ Loading PCA') + lgr.info('Loading PCA from: {}'.format('pcastate.pkl')) with open('pcastate.pkl', 'rb') as handle: pcastate = pickle.load(handle) u, s, v = pcastate['u'], pcastate['s'], pcastate['v'] @@ -202,7 +203,7 @@ def tedpca(catd, OCcatd, combmode, mask, t2s, t2sG, stabilize, dd = u.dot(np.diag(s*np.array(pcsel, dtype=np.int))).dot(v) n_components = s[pcsel].shape[0] - lgr.info('++ Selected {0} components. Kappa threshold: {1:.02f}, ' + lgr.info('Selected {0} components with Kappa threshold: {1:.02f}, ' 'Rho threshold: {2:.02f}'.format(n_components, kappa_thr, rho_thr)) dd = stats.zscore(dd.T, axis=0).T # variance normalize timeseries @@ -211,7 +212,7 @@ def tedpca(catd, OCcatd, combmode, mask, t2s, t2sG, stabilize, return n_components, dd -def tedica(n_components, dd, conv, fixed_seed, cost, final_cost): +def tedica(n_components, dd, conv, fixed_seed, cost, final_cost, verbose=False): """ Performs ICA on `dd` and returns mixing matrix @@ -230,6 +231,8 @@ def tedica(n_components, dd, conv, fixed_seed, cost, final_cost): Initial cost function for ICA finalcost : {'tanh', 'pow3', 'gaus', 'skew'} str, optional Final cost function for ICA + verbose : bool, optional + Whether to print messages regarding convergence process. Default: False Returns ------- @@ -247,7 +250,7 @@ def tedica(n_components, dd, conv, fixed_seed, cost, final_cost): mdp.numx_rand.seed(fixed_seed) icanode = mdp.nodes.FastICANode(white_comp=n_components, approach='symm', g=cost, fine_g=final_cost, coarse_limit=climit*100, - limit=climit, verbose=True) + limit=climit, verbose=verbose) icanode.train(dd) smaps = icanode.execute(dd) # noqa mmix = icanode.get_recmatrix().T diff --git a/tedana/io/output_data.py b/tedana/io/output_data.py index 3d68f9783..2dce827b8 100644 --- a/tedana/io/output_data.py +++ b/tedana/io/output_data.py @@ -4,7 +4,6 @@ from tedana import model, utils import logging -logging.basicConfig(format='[%(levelname)s]: %(message)s', level=logging.INFO) lgr = logging.getLogger(__name__) @@ -37,7 +36,7 @@ def gscontrol_mmix(OCcatd, mmix, mask, acc, rej, midk, ref_img): Compute temporal regression """ dat = (OCcatd[Gmask] - Gmu[Gmask][:, np.newaxis]) / Gstd[mask][:, np.newaxis] - solG = np.linalg.lstsq(mmix, dat.T)[0] + solG = np.linalg.lstsq(mmix, dat.T, rcond=None)[0] resid = dat - np.dot(solG.T, mmix.T) """ @@ -46,19 +45,18 @@ def gscontrol_mmix(OCcatd, mmix, mask, acc, rej, midk, ref_img): bold_ts = np.dot(solG.T[:, acc], mmix[:, acc].T) sphis = bold_ts.min(axis=-1) sphis -= sphis.mean() - lgr.info(sphis.shape) utils.utils.filewrite(utils.utils.unmask(sphis, mask), 'sphis_hik', ref_img) """ Find the global signal based on the T1-like effect """ - sol = np.linalg.lstsq(np.atleast_2d(sphis).T, dat) + sol = np.linalg.lstsq(np.atleast_2d(sphis).T, dat, rcond=None) glsig = sol[0] """ T1 correct time series by regression """ - bold_noT1gs = bold_ts - np.dot(np.linalg.lstsq(glsig.T, bold_ts.T)[0].T, glsig) + bold_noT1gs = bold_ts - np.dot(np.linalg.lstsq(glsig.T, bold_ts.T, rcond=None)[0].T, glsig) utils.utils.filewrite(utils.unmask(bold_noT1gs * Gstd[mask][:, np.newaxis], mask), 'hik_ts_OC_T1c.nii', ref_img) @@ -72,7 +70,7 @@ def gscontrol_mmix(OCcatd, mmix, mask, acc, rej, midk, ref_img): """ Orthogonalize mixing matrix w.r.t. T1-GS """ - mmixnogs = mmix.T - np.dot(np.linalg.lstsq(glsig.T, mmix)[0].T, glsig) + mmixnogs = mmix.T - np.dot(np.linalg.lstsq(glsig.T, mmix, rcond=None)[0].T, glsig) mmixnogs_mu = mmixnogs.mean(-1) mmixnogs_std = mmixnogs.std(-1) mmixnogs_norm = (mmixnogs - mmixnogs_mu[:, np.newaxis]) / mmixnogs_std[:, np.newaxis] @@ -81,7 +79,7 @@ def gscontrol_mmix(OCcatd, mmix, mask, acc, rej, midk, ref_img): """ Write T1-GS corrected components and mixing matrix """ - sol = np.linalg.lstsq(mmixnogs_norm.T, dat.T) + sol = np.linalg.lstsq(mmixnogs_norm.T, dat.T, rcond=None) utils.filewrite(utils.unmask(sol[0].T[:, 2:], mask), 'betas_hik_OC_T1c', ref_img) np.savetxt('meica_mix_T1c.1D', mmixnogs) @@ -157,20 +155,26 @@ def write_split_ts(data, mmix, mask, acc, rej, midk, ref_img, suffix=''): # get variance explained by retained components betas = model.get_coeffs(utils.unmask(dmdata.T, mask), mask, mmix)[mask] varexpl = (1 - ((dmdata.T - betas.dot(mmix.T))**2.).sum() / (dmdata**2.).sum()) * 100 - lgr.info('++ Variance explained: {:.02f}%'.format(varexpl)) + lgr.info('Variance explained by ICA decomposition: {:.02f}%'.format(varexpl)) # create component and de-noised time series and save to files hikts = betas[:, acc].dot(mmix.T[acc, :]) midkts = betas[:, midk].dot(mmix.T[midk, :]) lowkts = betas[:, rej].dot(mmix.T[rej, :]) dnts = data[mask] - lowkts - midkts + if len(acc) != 0: - utils.filewrite(utils.unmask(hikts, mask), 'hik_ts_{0}'.format(suffix), ref_img) + fout = utils.filewrite(utils.unmask(hikts, mask), 'hik_ts_{0}'.format(suffix), ref_img) + lgr.info('Writing high-Kappa time series: {}'.format(op.abspath(fout))) if len(midk) != 0: - utils.filewrite(utils.unmask(midkts, mask), 'midk_ts_{0}'.format(suffix), ref_img) + fout = utils.filewrite(utils.unmask(midkts, mask), 'midk_ts_{0}'.format(suffix), ref_img) + lgr.info('Writing mid-Kappa time series: {}'.format(op.abspath(fout))) if len(rej) != 0: - utils.filewrite(utils.unmask(lowkts, mask), 'lowk_ts_{0}'.format(suffix), ref_img) - utils.filewrite(utils.unmask(dnts, mask), 'dn_ts_{0}'.format(suffix), ref_img) + fout = utils.filewrite(utils.unmask(lowkts, mask), 'lowk_ts_{0}'.format(suffix), ref_img) + lgr.info('Writing low-Kappa time series: {}'.format(op.abspath(fout))) + + fout = utils.filewrite(utils.unmask(dnts, mask), 'dn_ts_{0}'.format(suffix), ref_img) + lgr.info('Writing denoised time series: {}'.format(op.abspath(fout))) return varexpl @@ -304,22 +308,25 @@ def writeresults(ts, mask, comptable, mmix, n_vols, acc, rej, midk, empty, ref_i Reference image to dictate how outputs are saved to disk """ - lgr.info('++ Writing optimally combined time series') - utils.filewrite(ts, 'ts_OC', ref_img) - lgr.info('++ Writing Kappa-filtered optimally combined timeseries') + fout = utils.filewrite(ts, 'ts_OC', ref_img) + lgr.info('Writing optimally-combined time series: {}'.format(op.abspath(fout))) + varexpl = write_split_ts(ts, mmix, mask, acc, rej, midk, ref_img, suffix='OC') - lgr.info('++ Writing signal versions of components') + ts_B = model.get_coeffs(ts, mask, mmix) - utils.filewrite(ts_B, 'betas_OC', ref_img) + fout = utils.filewrite(ts_B, 'betas_OC', ref_img) + lgr.info('Writing full ICA coefficient feature set: {}'.format(op.abspath(fout))) if len(acc) != 0: - utils.filewrite(ts_B[:, acc], 'betas_hik_OC', ref_img) - lgr.info('++ Writing optimally combined high-Kappa features') - writefeats(split_ts(ts, mmix, mask, acc)[0], - mmix[:, acc], mask, ref_img, suffix='OC2') - lgr.info('++ Writing component table') + fout = utils.filewrite(ts_B[:, acc], 'betas_hik_OC', ref_img) + lgr.info('Writing denoised ICA coefficient feature set: {}'.format(op.abspath(fout))) + fout = writefeats(split_ts(ts, mmix, mask, acc)[0], + mmix[:, acc], mask, ref_img, suffix='OC2') + lgr.info('Writing Z-normalized spatial component maps: {}'.format(op.abspath(fout))) + writect(comptable, n_vols, acc, rej, midk, empty, ctname='comp_table.txt', varexpl=varexpl) + lgr.info('Writing component table: {}'.format(op.abspath('comp_table.txt'))) def writeresults_echoes(catd, mmix, mask, acc, rej, midk, ref_img): @@ -346,6 +353,6 @@ def writeresults_echoes(catd, mmix, mask, acc, rej, midk, ref_img): """ for i_echo in range(catd.shape[1]): - lgr.info('++ Writing Kappa-filtered echo #{:01d} timeseries'.format(i_echo+1)) + lgr.info('Writing Kappa-filtered echo #{:01d} timeseries'.format(i_echo+1)) write_split_ts(catd[:, i_echo, :], mmix, mask, acc, rej, midk, ref_img, suffix='e%i' % (i_echo+1)) diff --git a/tedana/model/fit.py b/tedana/model/fit.py index d19f8b152..f71fbf4ad 100644 --- a/tedana/model/fit.py +++ b/tedana/model/fit.py @@ -7,7 +7,6 @@ from scipy.special import lpmv import logging -logging.basicConfig(format='[%(levelname)s]: %(message)s', level=logging.INFO) lgr = logging.getLogger(__name__) F_MAX = 500 @@ -101,7 +100,7 @@ def fitmodels_direct(catd, mmix, mask, t2s, t2sG, tes, combmode, ref_img, """ # compute optimal combination of raw data - tsoc = model.make_optcom(catd, t2sG, tes, mask, combmode).astype(float)[mask] + tsoc = model.make_optcom(catd, t2sG, tes, mask, combmode, verbose=False).astype(float)[mask] # demean optimal combination tsoc_dm = tsoc - tsoc.mean(axis=-1, keepdims=True) @@ -158,6 +157,7 @@ def fitmodels_direct(catd, mmix, mask, t2s, t2sG, tes, combmode, ref_img, Br_clmaps_R2 = np.zeros([n_voxels, n_components]) Br_clmaps_S0 = np.zeros([n_voxels, n_components]) + lgr.info('Fitting TE- and S0-dependent models to components') for i in range(n_components): # size of B is (n_components, nx*ny*nz) B = np.atleast_3d(betamask)[:, :, i].T @@ -217,7 +217,9 @@ def fitmodels_direct(catd, mmix, mask, t2s, t2sG, tes, combmode, ref_img, # full selection including clustering criteria seldict = None if full_sel: - lgr.info('++ Performing spatial clustering of components') + lgr.info('Performing spatial clustering of components') + csize = np.max([int(n_voxels * 0.0005) + 5, 20]) + lgr.debug('Using minimum cluster size: {}'.format(csize)) for i in range(n_components): # save out files out = np.zeros((n_samp, 4)) @@ -230,7 +232,6 @@ def fitmodels_direct(catd, mmix, mask, t2s, t2sG, tes, combmode, ref_img, continue # TODO: pass through GIFTI file data as below ccimg = utils.new_nii_like(ref_img, out) - csize = np.max([int(n_voxels * 0.0005) + 5, 20]) # Do simple clustering on F sel = spatclust(ccimg, min_cluster_size=csize, @@ -299,7 +300,7 @@ def get_coeffs(data, mask, X, add_const=False): if add_const: # add intercept, if specified X = np.column_stack([X, np.ones((len(X), 1))]) - betas = np.linalg.lstsq(X, mdata)[0].T + betas = np.linalg.lstsq(X, mdata, rcond=None)[0].T if add_const: # drop beta for intercept, if specified betas = betas[:, :-1] betas = utils.unmask(betas, mask) @@ -438,7 +439,7 @@ def gscontrol_raw(catd, optcom, n_echos, ref_img, dtrank=4): Input `optcom` with global signal removed from time series """ - lgr.info('++ Applying amplitude-based T1 equilibration correction') + lgr.info('Applying amplitude-based T1 equilibration correction') # Legendre polynomial basis for denoising bounds = np.linspace(-1, 1, optcom.shape[-1]) @@ -451,7 +452,7 @@ def gscontrol_raw(catd, optcom, n_echos, ref_img, dtrank=4): # find spatial global signal dat = optcom[Gmask] - Gmu[Gmask][:, np.newaxis] - sol = np.linalg.lstsq(Lmix, dat.T)[0] # Legendre basis for detrending + sol = np.linalg.lstsq(Lmix, dat.T, rcond=None)[0] # Legendre basis for detrending detr = dat - np.dot(sol.T, Lmix.T)[0] sphis = (detr).min(axis=1) sphis -= sphis.mean() @@ -459,13 +460,13 @@ def gscontrol_raw(catd, optcom, n_echos, ref_img, dtrank=4): # find time course ofc the spatial global signal # make basis with the Legendre basis - glsig = np.linalg.lstsq(np.atleast_2d(sphis).T, dat)[0] + glsig = np.linalg.lstsq(np.atleast_2d(sphis).T, dat, rcond=None)[0] glsig = stats.zscore(glsig, axis=None) np.savetxt('glsig.1D', glsig) glbase = np.hstack([Lmix, glsig.T]) # Project global signal out of optimally combined data - sol = np.linalg.lstsq(np.atleast_2d(glbase), dat.T)[0] + sol = np.linalg.lstsq(np.atleast_2d(glbase), dat.T, rcond=None)[0] tsoc_nogs = dat - np.dot(np.atleast_2d(sol[dtrank]).T, np.atleast_2d(glbase.T[dtrank])) + Gmu[Gmask][:, np.newaxis] @@ -477,7 +478,7 @@ def gscontrol_raw(catd, optcom, n_echos, ref_img, dtrank=4): dm_catd = catd.copy() # don't overwrite catd for echo in range(n_echos): dat = dm_catd[:, echo, :][Gmask] - sol = np.linalg.lstsq(np.atleast_2d(glbase), dat.T)[0] + sol = np.linalg.lstsq(np.atleast_2d(glbase), dat.T, rcond=None)[0] e_nogs = dat - np.dot(np.atleast_2d(sol[dtrank]).T, np.atleast_2d(glbase.T[dtrank])) dm_catd[:, echo, :] = utils.unmask(e_nogs, Gmask) diff --git a/tedana/model/t2smap.py b/tedana/model/t2smap.py index ced02fe2d..8813fdaef 100644 --- a/tedana/model/t2smap.py +++ b/tedana/model/t2smap.py @@ -2,7 +2,6 @@ from tedana import utils import logging -logging.basicConfig(format='[%(levelname)s]: %(message)s', level=logging.INFO) lgr = logging.getLogger(__name__) @@ -35,7 +34,7 @@ def fit(data, mask, tes, masksum, start_echo): x = np.array([np.ones(i_echo), neg_tes]) X = np.sort(x)[:, ::-1].transpose() - beta, _, _, _ = np.linalg.lstsq(X, B) + beta, _, _, _ = np.linalg.lstsq(X, B, rcond=None) t2s = 1. / beta[1, :].transpose() s0 = np.exp(beta[0, :]).transpose() @@ -68,7 +67,7 @@ def fit(data, mask, tes, masksum, start_echo): return t2sa_ts, s0va_ts, t2saf_ts, s0vaf_ts -def make_optcom(data, t2s, tes, mask, combmode): +def make_optcom(data, t2s, tes, mask, combmode, verbose=True): """ Optimally combine BOLD data across TEs. @@ -86,6 +85,8 @@ def make_optcom(data, t2s, tes, mask, combmode): How to combine data. Either 'ste' or 't2s'. useG : :obj:`bool`, optional Use G. Default is False. + verbose : :obj:`bool`, optional + Print status messages. Default: True Returns ------- @@ -98,12 +99,15 @@ def make_optcom(data, t2s, tes, mask, combmode): tes = np.array(tes)[np.newaxis] # (1 x E) array_like if t2s.ndim == 1: - lgr.info('++ Optimally combining data with voxel-wise T2 estimates') + msg = 'Optimally combining data with voxel-wise T2 estimates' ft2s = t2s[mask, np.newaxis] else: - lgr.info('++ Optimally combining data with voxel- and volume-wise T2 estimates') + msg = 'Optimally combining data with voxel- and volume-wise T2 estimates' ft2s = t2s[mask, :, np.newaxis] + if verbose: + lgr.info(msg) + if combmode == 'ste': alpha = mdata.mean(axis=-1) * tes else: @@ -168,7 +172,7 @@ def t2sadmap(data, tes, mask, masksum, start_echo): x = np.column_stack([np.ones(echo), [-te for te in tes[:echo]]]) X = np.repeat(x, n_vols, axis=0) - beta = np.linalg.lstsq(X, B)[0] + beta = np.linalg.lstsq(X, B, rcond=None)[0] t2s = 1. / beta[1, :].T s0 = np.exp(beta[0, :]).T diff --git a/tedana/select/select_comps.py b/tedana/select/select_comps.py index 9c8a0bd86..c342ceedc 100644 --- a/tedana/select/select_comps.py +++ b/tedana/select/select_comps.py @@ -7,7 +7,6 @@ from tedana import model, utils import logging -logging.basicConfig(format='[%(levelname)s]: %(message)s', level=logging.INFO) lgr = logging.getLogger(__name__) @@ -97,9 +96,8 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o if filecsdata: import bz2 - if seldict is not None: - lgr.info('++ Saving component selection data') + lgr.info('Saving component selection data') with bz2.BZ2File('compseldata.pklbz', 'wb') as csstate_f: pickle.dump(seldict, csstate_f) else: @@ -107,7 +105,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o with bz2.BZ2File('compseldata.pklbz', 'rb') as csstate_f: seldict = pickle.load(csstate_f) except FileNotFoundError: - lgr.warning('++ Failed to load component selection data') + lgr.warning('Failed to load component selection data') return None # List of components @@ -158,7 +156,11 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o counts_FR2_Z[ii, :] = [len(signal_FR2_Z), len(noise_FR2_Z)] try: ttest = stats.ttest_ind(signal_FR2_Z, noise_FR2_Z, equal_var=True) - mwu = stats.norm.ppf(stats.mannwhitneyu(signal_FR2_Z, noise_FR2_Z)[1]) + # avoid DivideByZero RuntimeWarning + if signal_FR2_Z.size > 0 and noise_FR2_Z.size > 0: + mwu = stats.norm.ppf(stats.mannwhitneyu(signal_FR2_Z, noise_FR2_Z)[1]) + else: + mwu = -np.inf tt_table[ii, 0] = np.abs(mwu) * ttest[0] / np.abs(ttest[0]) tt_table[ii, 1] = ttest[1] except Exception: # TODO: what is the error that might be caught here? @@ -176,7 +178,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o Step 1: Reject anything that's obviously an artifact a. Estimate a null variance """ - + lgr.debug('Rejecting gross artifacts based on Rho/Kappa values and S0/R2 counts') rej = ncl[utils.andb([seldict['Rhos'] > seldict['Kappas'], countsigFS0 > countsigFR2]) > 0] ncl = np.setdiff1d(ncl, rej) @@ -184,6 +186,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o Step 2: Compute 3-D spatial FFT of Beta maps to detect high-spatial frequency artifacts """ + lgr.debug('Computing 3D spatial FFT of beta maps to detect high-spatial frequency artifacts') # spatial information is important so for NIFTI we convert back to 3D space if utils.get_dtype(ref_img) == 'NIFTI': dim1 = np.prod(ref_img.shape[:2]) @@ -215,6 +218,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o """ Step 3: Create feature space of component properties """ + lgr.debug('Creating feature space of component properties') fdist_pre = fdist.copy() fdist_pre[fdist > np.median(fdist) * 3] = np.median(fdist) * 3 fdist_z = (fdist_pre - np.median(fdist_pre)) / fdist_pre.std() @@ -236,6 +240,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o Step 3: Make initial guess of where BOLD components are and use DBSCAN to exclude noise components and find a sample set of 'good' components """ + lgr.debug('Making initial guess of BOLD components') # epsmap is [index,level of overlap with dicemask, # number of high Rho components] F05, F025, F01 = utils.getfbounds(n_echos) @@ -275,7 +280,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o # Make guess of Kundu et al 2011 plus remove high frequencies, # generally high variance, and high variance given low Kappa tt_lim = stats.scoreatpercentile(tt_table[tt_table[:, 0] > 0, 0], - 75, interpolation_method='lower')/3 + 75, interpolation_method='lower') / 3 KRguess = np.setdiff1d(np.setdiff1d(nc[KRelbow == 2], rej), np.union1d(nc[tt_table[:, 0] < tt_lim], np.union1d(np.union1d(nc[spz > 1], @@ -292,13 +297,22 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o rej = np.union1d(rej, rejB) ncl = np.setdiff1d(ncl, rej) + lgr.debug('Using DBSCAN to find optimal set of "good" BOLD components') for ii in range(20000): - db = DBSCAN(eps=.005 + ii * .005, min_samples=3).fit(fz.T) + eps = .005 + ii * .005 + db = DBSCAN(eps=eps, min_samples=3).fit(fz.T) # it would be great to have descriptive names, here + # DBSCAN found more than three non-noisy (i.e., labels_ == -1) clusters cond1 = db.labels_.max() > 1 + # DBSCAN didn't detect more classes than the total # of components / 6 cond2 = db.labels_.max() < len(nc) / 6 + # not totally sure if 0 is a special label for DBSCAN + # my intuition here is that we're confirming DBSCAN labelled previously + # rejected components as noise (i.e., no overlap between `rej` and + # labelled DBSCAN components) cond3 = np.intersect1d(rej, nc[db.labels_ == 0]).shape[0] == 0 + # DBSCAN labelled less than half of the total components as noisy cond4 = np.array(db.labels_ == -1, dtype=int).sum() / float(len(nc)) < .5 if cond1 and cond2 and cond3 and cond4: @@ -306,17 +320,17 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o np.intersect1d(nc[db.labels_ == 0], nc[seldict['Rhos'] > model.getelbow_mod(Rhos_sorted, val=True)]).shape[0]]) - lgr.debug('++ Found solution', ii, db.labels_) db = None epsmap = np.array(epsmap) + lgr.debug('Found DBSCAN solutions for {}/20000 eps resolutions'.format(len(epsmap))) group0 = [] dbscanfailed = False if len(epsmap) != 0: # Select index that maximizes Dice with guessmask but first # minimizes number of higher Rho components ii = int(epsmap[np.argmax(epsmap[epsmap[:, 2] == np.min(epsmap[:, 2]), 1], 0), 0]) - lgr.info('++ Component selection tuning: {:.05f}'.format(epsmap[:, 1].max())) + lgr.debug('Component selection tuning: {:.05f}'.format(epsmap[:, 1].max())) db = DBSCAN(eps=.005+ii*.005, min_samples=3).fit(fz.T) ncl = nc[db.labels_ == 0] ncl = np.setdiff1d(ncl, rej) @@ -326,7 +340,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o to_clf = np.setdiff1d(nc, np.union1d(ncl, rej)) if len(group0) == 0 or len(group0) < len(KRguess) * .5: dbscanfailed = True - lgr.info('++ DBSCAN based guess failed. Using elbow guess method.') + lgr.debug('DBSCAN guess failed; using elbow guess method instead') ncl = np.setdiff1d(np.setdiff1d(nc[KRelbow == 2], rej), np.union1d(nc[tt_table[:, 0] < tt_lim], np.union1d(np.union1d(nc[spz > 1], @@ -338,8 +352,8 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o group_n1 = [] to_clf = np.setdiff1d(nc, np.union1d(group0, rej)) if len(group0) < 2 or (len(group0) < 4 and float(len(rej))/len(group0) > 3): - lgr.warning('++ Extremely limited reliable BOLD signal space. ' - 'Not filtering further into midk etc.') + lgr.warning('Extremely limited reliable BOLD signal space! ' + 'Not filtering components beyond BOLD/non-BOLD guesses.') midkfailed = True min_acc = np.array([]) if len(group0) != 0: @@ -391,6 +405,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o yy = aa * xx - (clf_.intercept_[0]) / ww[1] plt.plot(xx, yy, '-') """ + lgr.debug('Attempting to classify midk components') # Tried getting rid of accepting based on SVM altogether, # now using only rejecting toacc_hi = np.setdiff1d(nc[utils.andb([fdist <= np.max(fdist[group0]), @@ -426,7 +441,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o veinBout = utils.unmask(veinmaskB, mask) utils.filewrite(veinBout, 'veins50', ref_img) """ - + lgr.debug('Computing variance associated with low T2* areas (e.g., draining veins)') tsoc_B_Zcl = np.zeros(seldict['tsoc_B'].shape) tsoc_B_Zcl[seldict['Z_clmaps'] != 0] = np.abs(seldict['tsoc_B'])[seldict['Z_clmaps'] != 0] sig_B = [stats.scoreatpercentile(tsoc_B_Zcl[tsoc_B_Zcl[:, ii] != 0, ii], 25) @@ -479,6 +494,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o Step 4: Learn joint TE-dependence spatial and temporal models to move remaining artifacts to ignore class """ + lgr.debug('Learning joint TE-dependence spatial/temporal models to ignore remaining artifacts') to_ign = [] @@ -531,7 +547,6 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, o orphan = np.setdiff1d(nc, list(ncl) + list(to_ign) + list(midk) + list(rej)) if savecsdiag: - diagstep_keys = ['Rejected components', 'Kappa-Rho cut point', 'Kappa cut', 'Rho cut', 'DBSCAN failed to converge', 'Kappa-Rho guess', 'Dice rejected', 'rej_supp', 'to_clf', diff --git a/tedana/workflows/t2smap.py b/tedana/workflows/t2smap.py index 9ab4859d6..f4dd89076 100644 --- a/tedana/workflows/t2smap.py +++ b/tedana/workflows/t2smap.py @@ -2,7 +2,6 @@ from tedana import model, utils import logging -logging.basicConfig(format='[%(levelname)s]: %(message)s', level=logging.INFO) lgr = logging.getLogger(__name__) @@ -31,16 +30,16 @@ def main(options): ref_img = data[0] if isinstance(data, list) else data - lgr.info("++ Computing Mask") + lgr.info('Computing adaptive mask') mask, masksum = utils.make_adaptive_mask(catd, minimum=False, getsum=True) utils.filewrite(masksum, 'masksum%s' % suf, ref_img, copy_header=False) - lgr.info("++ Computing Adaptive T2* map") + lgr.info('Computing adaptive T2* map') t2s, s0, t2ss, s0vs, t2saf, s0vaf = model.t2sadmap(catd, tes, mask, masksum, 2) utils.filewrite(t2ss, 't2ss%s' % suf, ref_img, copy_header=False) utils.filewrite(s0vs, 's0vs%s' % suf, ref_img, copy_header=False) - lgr.info("++ Computing optimal combination") + lgr.info('Computing optimal combination') tsoc = np.array(model.make_optcom(catd, t2s, tes, mask, combmode), dtype=float) diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index 513bfd854..64f5558fc 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -6,7 +6,6 @@ from tedana import (decomp, io, model, select, utils) import logging -logging.basicConfig(format='[%(levelname)s]: %(message)s', level=logging.INFO) lgr = logging.getLogger(__name__) """ @@ -26,7 +25,7 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, no_gscontrol=False, kdaw=10., rdaw=1., conv=2.5e-5, ste=-1, combmode='t2s', dne=False, initcost='tanh', finalcost='tanh', stabilize=False, fout=False, filecsdata=False, label=None, - fixed_seed=42): + fixed_seed=42, debug=False, quiet=False): """ Parameters ---------- @@ -85,9 +84,10 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, n_echos = len(tes) # coerce data to samples x echos x time array - lgr.info('++ Loading input data: {}'.format(data)) + lgr.info('Loading input data: {}'.format([op.abspath(f) for f in data])) catd, ref_img = utils.load_data(data, n_echos=n_echos) n_samp, n_echos, n_vols = catd.shape + lgr.debug('Resulting data shape: {}'.format(catd.shape)) if fout: fout = ref_img @@ -102,7 +102,10 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, out_dir = 'TED' out_dir = op.abspath(out_dir) if not op.isdir(out_dir): + lgr.info('Creating output directory: {}'.format(out_dir)) os.mkdir(out_dir) + else: + lgr.info('Using output directory: {}'.format(out_dir)) if mixm is not None and op.isfile(mixm): shutil.copyfile(mixm, op.join(out_dir, 'meica_mix.1D')) @@ -118,10 +121,11 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, os.chdir(out_dir) - lgr.info('++ Computing Mask') + lgr.info('Computing adapative mask') mask, masksum = utils.make_adaptive_mask(catd, minimum=False, getsum=True) + lgr.debug('Retaining {}/{} samples'.format(mask.sum(), n_samp)) - lgr.info('++ Computing T2* map') + lgr.info('Computing T2* map') t2s, s0, t2ss, s0s, t2sG, s0G = model.t2sadmap(catd, tes, mask, masksum, start_echo=1) @@ -129,6 +133,7 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, # anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile cap_t2s = stats.scoreatpercentile(t2s.flatten(), 99.5, interpolation_method='lower') + lgr.debug('Setting cap on T2* map at {:.5f}'.format(cap_t2s * 10)) t2s[t2s > cap_t2s * 10] = cap_t2s utils.filewrite(t2s, op.join(out_dir, 't2sv'), ref_img) utils.filewrite(s0, op.join(out_dir, 's0v'), ref_img) @@ -145,13 +150,13 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, catd, OCcatd = model.gscontrol_raw(catd, OCcatd, n_echos, ref_img) if mixm is None: - lgr.info("++ Doing ME-PCA and ME-ICA") n_components, dd = decomp.tedpca(catd, OCcatd, combmode, mask, t2s, t2sG, stabilize, ref_img, tes=tes, kdaw=kdaw, rdaw=rdaw, ste=ste) mmix_orig = decomp.tedica(n_components, dd, conv, fixed_seed, cost=initcost, - final_cost=finalcost) + final_cost=finalcost, verbose=debug) np.savetxt(op.join(out_dir, '__meica_mix.1D'), mmix_orig) + lgr.info('Making second component selection guess from ICA results') seldict, comptable, betas, mmix = model.fitmodels_direct(catd, mmix_orig, mask, t2s, t2sG, tes, combmode, @@ -164,6 +169,7 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, n_echos, t2s, s0, strict_mode=strict, filecsdata=filecsdata) else: + lgr.info('Using supplied mixing matrix from ICA') mmix_orig = np.loadtxt(op.join(out_dir, 'meica_mix.1D')) seldict, comptable, betas, mmix = model.fitmodels_direct(catd, mmix_orig, mask, t2s, t2sG, @@ -179,7 +185,7 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, acc, rej, midk, empty = io.ctabsel(ctab) if len(acc) == 0: - lgr.warning('++ No BOLD components detected!!! Please check data and results!') + lgr.warning('No BOLD components detected! Please check data and results!') io.writeresults(OCcatd, mask, comptable, mmix, n_vols, acc, rej, midk, empty, ref_img) io.gscontrol_mmix(OCcatd, mmix, mask, acc, rej, midk, ref_img) From 0e2172089eafa249a03546d226f2e98f833ba420 Mon Sep 17 00:00:00 2001 From: Ross Markello Date: Fri, 11 May 2018 17:47:54 -0400 Subject: [PATCH 03/10] [RF] Removed unnecessary dependencies, updated Dockerfile --- Dockerfile | 61 +++++++++++------------------------------------- requirements.txt | 2 -- tedana/info.py | 2 -- 3 files changed, 13 insertions(+), 52 deletions(-) diff --git a/Dockerfile b/Dockerfile index 68d730693..21635bd19 100644 --- a/Dockerfile +++ b/Dockerfile @@ -31,44 +31,9 @@ RUN apt-get update -qq && apt-get install -yq --no-install-recommends \ && chmod -R 777 /neurodocker && chmod a+s /neurodocker ENTRYPOINT ["/neurodocker/startup.sh"] -RUN apt-get update -qq && apt-get install -yq --no-install-recommends git vim libxml2-dev libnlopt-dev libxslt-dev\ +RUN apt-get update -qq && apt-get install -yq --no-install-recommends git vim \ && apt-get clean \ - && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* - -#-------------------- -# Install AFNI latest -#-------------------- -ENV PATH=/opt/afni:$PATH -RUN apt-get update -qq && apt-get install -yq --no-install-recommends ed gsl-bin libglu1-mesa-dev libglib2.0-0 libglw1-mesa \ - libgomp1 libjpeg62 libxm4 netpbm tcsh xfonts-base xvfb \ - && libs_path=/usr/lib/x86_64-linux-gnu \ - && if [ -f $libs_path/libgsl.so.19 ]; then \ - ln $libs_path/libgsl.so.19 $libs_path/libgsl.so.0; \ - fi \ - # Install libxp (not in all ubuntu/debian repositories) \ - && apt-get install -yq --no-install-recommends libxp6 \ - || /bin/bash -c " \ - curl --retry 5 -o /tmp/libxp6.deb -sSL http://mirrors.kernel.org/debian/pool/main/libx/libxp/libxp6_1.0.2-2_amd64.deb \ - && dpkg -i /tmp/libxp6.deb && rm -f /tmp/libxp6.deb" \ - # Install libpng12 (not in all ubuntu/debian repositories) \ - && apt-get install -yq --no-install-recommends libpng12-0 \ - || /bin/bash -c " \ - curl -o /tmp/libpng12.deb -sSL http://mirrors.kernel.org/debian/pool/main/libp/libpng/libpng12-0_1.2.49-1%2Bdeb7u2_amd64.deb \ - && dpkg -i /tmp/libpng12.deb && rm -f /tmp/libpng12.deb" \ - # Install R \ - && apt-get install -yq --no-install-recommends \ - r-base-dev r-cran-rmpi \ - || /bin/bash -c " \ - curl -o /tmp/install_R.sh -sSL https://gist.githubusercontent.com/kaczmarj/8e3792ae1af70b03788163c44f453b43/raw/0577c62e4771236adf0191c826a25249eb69a130/R_installer_debian_ubuntu.sh \ - && /bin/bash /tmp/install_R.sh" \ - && apt-get clean \ - && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* \ - && echo "Downloading AFNI ..." \ - && mkdir -p /opt/afni \ - && curl -sSL --retry 5 https://afni.nimh.nih.gov/pub/dist/tgz/linux_openmp_64.tgz \ - | tar zx -C /opt/afni --strip-components=1 \ - && /opt/afni/rPkgsInstall -pkgs ALL \ - && rm -rf /tmp/* +&& rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* # Create new user: neuro RUN useradd --no-user-group --create-home --shell /bin/bash neuro @@ -93,24 +58,24 @@ RUN echo "Downloading Miniconda installer ..." \ #----------------------------- # Create py3 conda environment #----------------------------- -RUN conda create -y -q --name default --channel vida-nyu python=3.6.1 \ - numpy pandas reprozip traits \ +RUN conda create -y -q --name py36 python=3.6 \ + numpy scikit-learn mdp nilearn 'nibabel>=2.1.0' \ && sync && conda clean -tipsy && sync \ - && /bin/bash -c "source activate default \ - && pip install -q --no-cache-dir \ - nipype ipython scikit-learn scipy ipdb mdp nilearn 'nibabel>=2.1.0'" \ - && sync -ENV PATH=/opt/conda/envs/default/bin:$PATH + && bash -c "source activate py36 \ + && pip install -q --no-cache-dir \ + https://github.com/ME-ICA/tedana/archive/master.tar.gz" \ + && sync \ + && sed -i '$isource activate py36' $ND_ENTRYPOINT #------------------------------ # Create py27 conda environment #------------------------------ RUN conda create -y -q --name py27 python=2.7 \ - numpy pandas reprozip traits \ + numpy scikit-learn mdp nilearn 'nibabel>=2.1.0' \ && sync && conda clean -tipsy && sync \ - && /bin/bash -c "source activate default \ - && pip install -q --no-cache-dir \ - nipype ipython scikit-learn scipy ipdb mdp nilearn 'nibabel>=2.1.0'" \ + && bash -c "source activate py27 \ + && pip install -q --no-cache-dir \ + https://github.com/ME-ICA/tedana/archive/master.tar.gz" \ && sync USER root diff --git a/requirements.txt b/requirements.txt index 53ef7df8f..525c193fe 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,7 +3,5 @@ scikit-learn mdp nilearn nibabel>=2.1.0 -pybids>=0.4.0 -nipype duecredit sphinx-argparse diff --git a/tedana/info.py b/tedana/info.py index 7fabfaf0f..5e4448748 100644 --- a/tedana/info.py +++ b/tedana/info.py @@ -30,8 +30,6 @@ 'mdp', 'nilearn', 'nibabel>=2.1.0', - 'pybids>=0.4.0', - 'nipype', ] TESTS_REQUIRES = [ From 6fcf2d30f8404bcee7ff06bbd0f3bd7de9b1728f Mon Sep 17 00:00:00 2001 From: Ross Markello Date: Sat, 12 May 2018 21:22:18 -0400 Subject: [PATCH 04/10] [FIX] Added scipy as dependency --- Dockerfile | 4 ++-- requirements.txt | 1 + tedana/info.py | 1 + 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/Dockerfile b/Dockerfile index 21635bd19..37b7d900f 100644 --- a/Dockerfile +++ b/Dockerfile @@ -59,7 +59,7 @@ RUN echo "Downloading Miniconda installer ..." \ # Create py3 conda environment #----------------------------- RUN conda create -y -q --name py36 python=3.6 \ - numpy scikit-learn mdp nilearn 'nibabel>=2.1.0' \ + numpy scikit-learn mdp nilearn scipy 'nibabel>=2.1.0' \ && sync && conda clean -tipsy && sync \ && bash -c "source activate py36 \ && pip install -q --no-cache-dir \ @@ -71,7 +71,7 @@ RUN conda create -y -q --name py36 python=3.6 \ # Create py27 conda environment #------------------------------ RUN conda create -y -q --name py27 python=2.7 \ - numpy scikit-learn mdp nilearn 'nibabel>=2.1.0' \ + numpy scikit-learn mdp nilearn scipy 'nibabel>=2.1.0' \ && sync && conda clean -tipsy && sync \ && bash -c "source activate py27 \ && pip install -q --no-cache-dir \ diff --git a/requirements.txt b/requirements.txt index 525c193fe..94cebd44b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,6 @@ numpy scikit-learn +scipy mdp nilearn nibabel>=2.1.0 diff --git a/tedana/info.py b/tedana/info.py index 5e4448748..a88a72925 100644 --- a/tedana/info.py +++ b/tedana/info.py @@ -30,6 +30,7 @@ 'mdp', 'nilearn', 'nibabel>=2.1.0', + 'scipy' ] TESTS_REQUIRES = [ From 73cca7087d7e55869e5212238dfa79ff8c03306f Mon Sep 17 00:00:00 2001 From: Ross Markello Date: Sun, 13 May 2018 18:17:15 -0400 Subject: [PATCH 05/10] [TST] Update circle and travis --- .travis.yml | 36 ++++++++++++++++++++++++++++++++---- Dockerfile | 11 ----------- circle.yml | 11 +++++------ tedana/info.py | 17 +++++++++++------ 4 files changed, 48 insertions(+), 27 deletions(-) diff --git a/.travis.yml b/.travis.yml index 132faa621..ec63a3b0b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -2,11 +2,39 @@ language: python sudo: false python: - - 3.6 + - 3.6 + +matrix: + include: + - python: 3.6 + env: + - STYLE=1 + - python: 3.6 + env: + - COVERAGE=1 before_install: - - python -m pip install --upgrade pip - - pip install "flake8<3.0" flake8-putty + - python -m pip install --upgrade pip + - pip install --upgrade virtualenv + - if [ "${STYLE}" == "1" ]; then + pip install "flake8<3.0" flake8-putty; + fi + - if [ "${COVERAGE}" == "1" ]; then + pip install coverage coveralls codecov pytest pytest-cov; + fi script: -- flake8 tedana + - | + if [ "${STYLE}" == "1" ]; then + flake8 tedana + else + if [ "${COVERAGE}" == "1" ]; then + TEST_ARGS="--cov-report term-missing --cov=tedana"; + fi + py.test --ignore=tedana/tests/test_tedana.py ${TEST_ARGS} tedana; + fi + +after_success: + - if [ "${COVERAGE}" == "1" ]; then + coveralls; codecov; + fi diff --git a/Dockerfile b/Dockerfile index 37b7d900f..247e73027 100644 --- a/Dockerfile +++ b/Dockerfile @@ -67,17 +67,6 @@ RUN conda create -y -q --name py36 python=3.6 \ && sync \ && sed -i '$isource activate py36' $ND_ENTRYPOINT -#------------------------------ -# Create py27 conda environment -#------------------------------ -RUN conda create -y -q --name py27 python=2.7 \ - numpy scikit-learn mdp nilearn scipy 'nibabel>=2.1.0' \ - && sync && conda clean -tipsy && sync \ - && bash -c "source activate py27 \ - && pip install -q --no-cache-dir \ - https://github.com/ME-ICA/tedana/archive/master.tar.gz" \ - && sync - USER root # User-defined instruction diff --git a/circle.yml b/circle.yml index 462960e88..bdb9ba852 100644 --- a/circle.yml +++ b/circle.yml @@ -14,18 +14,18 @@ jobs: - checkout - run: - name: download data + name: Download input test data command: curl -L -o /home/neuro/data/zcat_ffd.nii.gz https://www.dropbox.com/s/ljeqskdmnc6si9d/zcat_ffd.nii.gz?dl=0 - run: - name: download and unzip processed data + name: Download expected output data command: | curl -L -o /home/neuro/data/test_TED.tar.gz https://www.dropbox.com/s/x5xhzs3x6p3ukjl/test_TED.tar.gz?dl=0 tar -xvzf /home/neuro/data/test_TED.tar.gz --no-same-owner -C /home/neuro/data/ - run: - name: install dependencies + name: Create test environment command: | conda create --name venv python=3.6 source activate venv @@ -33,12 +33,11 @@ jobs: pip install -r requirements.txt python setup.py install - # run tests! - run: - name: run tests + name: Run tests command: | source activate venv - py.test ./tedana/tests + py.test ./tedana/tests/test_tedana.py no_output_timeout: "40m" post: bash <(curl -s https://codecov.io/bash) diff --git a/tedana/info.py b/tedana/info.py index a88a72925..e970ff6b0 100644 --- a/tedana/info.py +++ b/tedana/info.py @@ -16,9 +16,9 @@ __status__ = 'Prototype' __url__ = 'https://github.com/me-ica/tedana' __packagename__ = 'tedana' -__description__ = ("TE-Dependent Analysis (tedana) of multi-echo functional " - "magnetic resonance imaging (fMRI) data.") -__longdesc__ = ("To do.") +__description__ = ('TE-Dependent Analysis (tedana) of multi-echo functional ' + 'magnetic resonance imaging (fMRI) data.') +__longdesc__ = ('To do.') DOWNLOAD_URL = ( 'https://github.com/ME-ICA/{name}/archive/{ver}.tar.gz'.format( @@ -34,12 +34,17 @@ ] TESTS_REQUIRES = [ - "codecov", - "pytest", + 'codecov', + 'pytest', + 'pytest-cov' ] EXTRA_REQUIRES = { - 'doc': ['sphinx>=1.5.3', 'sphinx_rtd_theme', 'sphinx-argparse'], + 'doc': [ + 'sphinx>=1.5.3', + 'sphinx_rtd_theme', + 'sphinx-argparse' + ], 'tests': TESTS_REQUIRES, 'duecredit': ['duecredit'], } From 1a1ff2a7494fc7bbfa73667fc6a6571394cfd960 Mon Sep 17 00:00:00 2001 From: Ross Markello Date: Sun, 13 May 2018 19:06:04 -0400 Subject: [PATCH 06/10] [RF] Logging and doc-strings --- tedana/cli/run.py | 2 +- tedana/decomposition/_utils.py | 6 ++-- tedana/decomposition/eigendecomp.py | 18 +++++----- tedana/model/combine.py | 4 +-- tedana/model/fit.py | 10 +++--- tedana/model/monoexponential.py | 21 ++++++++++-- tedana/selection/_utils.py | 2 +- tedana/selection/select_comps.py | 34 ++++++++++--------- tedana/tests/test_tedana.py | 51 ++++++++++++++--------------- tedana/utils/io.py | 24 +++++++------- tedana/utils/utils.py | 8 +++-- tedana/workflows/t2smap.py | 8 ++--- tedana/workflows/tedana.py | 24 +++++++------- 13 files changed, 115 insertions(+), 97 deletions(-) diff --git a/tedana/cli/run.py b/tedana/cli/run.py index 88295b6fe..f9ad88862 100644 --- a/tedana/cli/run.py +++ b/tedana/cli/run.py @@ -139,7 +139,7 @@ def run_t2smap(argv=None): """T2smap entry point""" options = get_parser().parse_args(argv) if options.debug and not options.quiet: - logging.basicConfig(level=logging.DEBUG) + logging.getLogger().setLevel(logging.DEBUG) elif options.quiet: logging.getLogger().setLevel(logging.WARNING) workflows.t2smap.main(**vars(options)) diff --git a/tedana/decomposition/_utils.py b/tedana/decomposition/_utils.py index e9e6dedef..a2d4d9f97 100644 --- a/tedana/decomposition/_utils.py +++ b/tedana/decomposition/_utils.py @@ -6,7 +6,7 @@ import numpy as np from scipy import stats -lgr = logging.getLogger(__name__) +LGR = logging.getLogger(__name__) def eimask(dd, ees=None): @@ -30,11 +30,11 @@ def eimask(dd, ees=None): ees = range(dd.shape[1]) imask = np.zeros([dd.shape[0], len(ees)], dtype=bool) for ee in ees: - lgr.debug('Creating eimask for echo {}'.format(ee)) + LGR.debug('Creating eimask for echo {}'.format(ee)) perc98 = stats.scoreatpercentile(dd[:, ee, :].flatten(), 98, interpolation_method='lower') lthr, hthr = 0.001 * perc98, 5 * perc98 - lgr.debug('Eimask threshold boundaries: ' + LGR.debug('Eimask threshold boundaries: ' '{:.03f} {:.03f}'.format(lthr, hthr)) m = dd[:, ee, :].mean(axis=1) imask[np.logical_and(m > lthr, m < hthr), ee] = True diff --git a/tedana/decomposition/eigendecomp.py b/tedana/decomposition/eigendecomp.py index 4bf628a0d..59bc0e8c2 100644 --- a/tedana/decomposition/eigendecomp.py +++ b/tedana/decomposition/eigendecomp.py @@ -12,7 +12,7 @@ from tedana.decomposition._utils import eimask from tedana.selection._utils import (getelbow_cons, getelbow_mod) -lgr = logging.getLogger(__name__) +LGR = logging.getLogger(__name__) F_MAX = 500 Z_MAX = 8 @@ -67,13 +67,13 @@ def tedpca(catd, OCcatd, combmode, mask, t2s, t2sG, stabilize, ste = np.array([int(ee) for ee in str(ste).split(',')]) if len(ste) == 1 and ste[0] == -1: - lgr.info('Computing PCA of optimally combined multi-echo data') + LGR.info('Computing PCA of optimally combined multi-echo data') d = OCcatd[utils.make_min_mask(OCcatd[:, np.newaxis, :])][:, np.newaxis, :] elif len(ste) == 1 and ste[0] == 0: - lgr.info('Computing PCA of spatially concatenated multi-echo data') + LGR.info('Computing PCA of spatially concatenated multi-echo data') d = catd[mask].astype('float64') else: - lgr.info('Computing PCA of echo #%s' % ','.join([str(ee) for ee in ste])) + LGR.info('Computing PCA of echo #%s' % ','.join([str(ee) for ee in ste])) d = np.stack([catd[mask, ee] for ee in ste - 1], axis=1).astype('float64') eim = np.squeeze(eimask(d)) @@ -114,7 +114,7 @@ def tedpca(catd, OCcatd, combmode, mask, t2s, t2sG, stabilize, vTmix = v.T vTmixN = ((vTmix.T - vTmix.T.mean(0)) / vTmix.T.std(0)).T - lgr.info('Making initial component selection guess from PCA results') + LGR.info('Making initial component selection guess from PCA results') _, ctb, betasv, v_T = model.fitmodels_direct(catd, v.T, eimum, t2s, t2sG, tes, combmode, ref_img, mmixN=vTmixN, full_sel=False) @@ -123,17 +123,17 @@ def tedpca(catd, OCcatd, combmode, mask, t2s, t2sG, stabilize, # Save state fname = op.abspath('pcastate.pkl') - lgr.info('Saving PCA results to: {}'.format(fname)) + LGR.info('Saving PCA results to: {}'.format(fname)) pcastate = {'u': u, 's': s, 'v': v, 'ctb': ctb, 'eigelb': eigelb, 'spmin': spmin, 'spcum': spcum} try: with open(fname, 'wb') as handle: pickle.dump(pcastate, handle) except TypeError: - lgr.warning('Could not save PCA solution') + LGR.warning('Could not save PCA solution') else: # if loading existing state - lgr.info('Loading PCA from: {}'.format('pcastate.pkl')) + LGR.info('Loading PCA from: {}'.format('pcastate.pkl')) with open('pcastate.pkl', 'rb') as handle: pcastate = pickle.load(handle) u, s, v = pcastate['u'], pcastate['s'], pcastate['v'] @@ -177,7 +177,7 @@ def tedpca(catd, OCcatd, combmode, mask, t2s, t2sG, stabilize, dd = u.dot(np.diag(s*np.array(pcsel, dtype=np.int))).dot(v) n_components = s[pcsel].shape[0] - lgr.info('Selected {0} components with Kappa threshold: {1:.02f}, ' + LGR.info('Selected {0} components with Kappa threshold: {1:.02f}, ' 'Rho threshold: {2:.02f}'.format(n_components, kappa_thr, rho_thr)) dd = stats.zscore(dd.T, axis=0).T # variance normalize timeseries diff --git a/tedana/model/combine.py b/tedana/model/combine.py index fced339d6..a2f4b8e21 100644 --- a/tedana/model/combine.py +++ b/tedana/model/combine.py @@ -5,7 +5,7 @@ import numpy as np from tedana import utils -lgr = logging.getLogger(__name__) +LGR = logging.getLogger(__name__) def make_optcom(data, t2s, tes, mask, combmode, verbose=True): @@ -46,7 +46,7 @@ def make_optcom(data, t2s, tes, mask, combmode, verbose=True): ft2s = t2s[mask, :, np.newaxis] if verbose: - lgr.info(msg) + LGR.info(msg) if combmode == 'ste': alpha = mdata.mean(axis=-1) * tes diff --git a/tedana/model/fit.py b/tedana/model/fit.py index 3b4aa14b9..0876840dd 100644 --- a/tedana/model/fit.py +++ b/tedana/model/fit.py @@ -12,7 +12,7 @@ from tedana import model, utils -lgr = logging.getLogger(__name__) +LGR = logging.getLogger(__name__) F_MAX = 500 Z_MAX = 8 @@ -119,7 +119,7 @@ def fitmodels_direct(catd, mmix, mask, t2s, t2sG, tes, combmode, ref_img, Br_clmaps_R2 = np.zeros([n_voxels, n_components]) Br_clmaps_S0 = np.zeros([n_voxels, n_components]) - lgr.info('Fitting TE- and S0-dependent models to components') + LGR.info('Fitting TE- and S0-dependent models to components') for i in range(n_components): # size of B is (n_components, nx*ny*nz) B = np.atleast_3d(betamask)[:, :, i].T @@ -179,9 +179,9 @@ def fitmodels_direct(catd, mmix, mask, t2s, t2sG, tes, combmode, ref_img, # full selection including clustering criteria seldict = None if full_sel: - lgr.info('Performing spatial clustering of components') + LGR.info('Performing spatial clustering of components') csize = np.max([int(n_voxels * 0.0005) + 5, 20]) - lgr.debug('Using minimum cluster size: {}'.format(csize)) + LGR.debug('Using minimum cluster size: {}'.format(csize)) for i in range(n_components): # save out files out = np.zeros((n_samp, 4)) @@ -345,7 +345,7 @@ def gscontrol_raw(catd, optcom, n_echos, ref_img, dtrank=4): Input `optcom` with global signal removed from time series """ - lgr.info('Applying amplitude-based T1 equilibration correction') + LGR.info('Applying amplitude-based T1 equilibration correction') # Legendre polynomial basis for denoising bounds = np.linspace(-1, 1, optcom.shape[-1]) diff --git a/tedana/model/monoexponential.py b/tedana/model/monoexponential.py index 24b608eb3..6b11203a6 100644 --- a/tedana/model/monoexponential.py +++ b/tedana/model/monoexponential.py @@ -7,7 +7,7 @@ def fit_decay(data, tes, mask, masksum, start_echo): """ - Fit voxel-wise monoexponential decay models to estimate T2* and S0 maps. + Fit voxel-wise monoexponential decay models to `data` Parameters ---------- @@ -82,8 +82,23 @@ def fit_decay(data, tes, mask, masksum, start_echo): def fit_decay_ts(data, mask, tes, masksum, start_echo): """ - Fit voxel- and timepoint-wise monoexponential decay models to estimate - T2* and S0 timeseries. + Fit voxel- and timepoint-wise monoexponential decay models to `data` + + Parameters + ---------- + data : (S x E x T) array_like + Multi-echo data array, where `S` is samples, `E` is echos, and `T` is + time + tes : (E, ) list + Echo times + mask : (S, ) array_like + Boolean array indicating samples that are consistently (i.e., across + time AND echoes) non-zero + masksum : (S, ) array_like + Valued array indicating number of echos that have sufficient signal in + given sample + start_echo : int + First echo to consider """ nx, ny, nz, n_echos, n_trs = data.shape echodata = data[mask] diff --git a/tedana/selection/_utils.py b/tedana/selection/_utils.py index 9d59e34ad..4c1411329 100644 --- a/tedana/selection/_utils.py +++ b/tedana/selection/_utils.py @@ -6,7 +6,7 @@ import numpy as np from sklearn import svm -lgr = logging.getLogger(__name__) +LGR = logging.getLogger(__name__) def do_svm(X_train, y_train, X_test, svmtype=0): diff --git a/tedana/selection/select_comps.py b/tedana/selection/select_comps.py index 846a13bfc..75346e55f 100644 --- a/tedana/selection/select_comps.py +++ b/tedana/selection/select_comps.py @@ -13,7 +13,7 @@ from tedana.selection._utils import (getelbow_cons, getelbow_mod, getelbow_aggr, do_svm) -lgr = logging.getLogger(__name__) +LGR = logging.getLogger(__name__) def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, @@ -36,6 +36,8 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, Comma-separated list of indices of manually accepted components n_echos : int Number of echos in original data + t2s : (S,) array_like + s0 : (S,) array_like olevel : int, optional Default: 2 oversion : int, optional @@ -62,7 +64,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, if filecsdata: import bz2 if seldict is not None: - lgr.info('Saving component selection data') + LGR.info('Saving component selection data') with bz2.BZ2File('compseldata.pklbz', 'wb') as csstate_f: pickle.dump(seldict, csstate_f) else: @@ -70,7 +72,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, with bz2.BZ2File('compseldata.pklbz', 'rb') as csstate_f: seldict = pickle.load(csstate_f) except FileNotFoundError: - lgr.warning('Failed to load component selection data') + LGR.warning('Failed to load component selection data') return None # List of components @@ -143,7 +145,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, Step 1: Reject anything that's obviously an artifact a. Estimate a null variance """ - lgr.debug('Rejecting gross artifacts based on Rho/Kappa values and S0/R2 counts') + LGR.debug('Rejecting gross artifacts based on Rho/Kappa values and S0/R2 counts') rej = ncl[utils.andb([seldict['Rhos'] > seldict['Kappas'], countsigFS0 > countsigFR2]) > 0] ncl = np.setdiff1d(ncl, rej) @@ -151,7 +153,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, Step 2: Compute 3-D spatial FFT of Beta maps to detect high-spatial frequency artifacts """ - lgr.debug('Computing 3D spatial FFT of beta maps to detect high-spatial frequency artifacts') + LGR.debug('Computing 3D spatial FFT of beta maps to detect high-spatial frequency artifacts') # spatial information is important so for NIFTI we convert back to 3D space if utils.get_dtype(ref_img) == 'NIFTI': dim1 = np.prod(ref_img.shape[:2]) @@ -183,7 +185,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, """ Step 3: Create feature space of component properties """ - lgr.debug('Creating feature space of component properties') + LGR.debug('Creating feature space of component properties') fdist_pre = fdist.copy() fdist_pre[fdist > np.median(fdist) * 3] = np.median(fdist) * 3 fdist_z = (fdist_pre - np.median(fdist_pre)) / fdist_pre.std() @@ -205,7 +207,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, Step 3: Make initial guess of where BOLD components are and use DBSCAN to exclude noise components and find a sample set of 'good' components """ - lgr.debug('Making initial guess of BOLD components') + LGR.debug('Making initial guess of BOLD components') # epsmap is [index,level of overlap with dicemask, # number of high Rho components] F05, F025, F01 = utils.getfbounds(n_echos) @@ -262,13 +264,13 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, rej = np.union1d(rej, rejB) ncl = np.setdiff1d(ncl, rej) - lgr.debug('Using DBSCAN to find optimal set of "good" BOLD components') + LGR.debug('Using DBSCAN to find optimal set of "good" BOLD components') for ii in range(20000): eps = .005 + ii * .005 db = DBSCAN(eps=eps, min_samples=3).fit(fz.T) # it would be great to have descriptive names, here - # DBSCAN found more than three non-noisy (i.e., labels_ == -1) clusters + # DBSCAN found at least three non-noisy clusters cond1 = db.labels_.max() > 1 # DBSCAN didn't detect more classes than the total # of components / 6 cond2 = db.labels_.max() < len(nc) / 6 @@ -288,14 +290,14 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, db = None epsmap = np.array(epsmap) - lgr.debug('Found DBSCAN solutions for {}/20000 eps resolutions'.format(len(epsmap))) + LGR.debug('Found DBSCAN solutions for {}/20000 eps resolutions'.format(len(epsmap))) group0 = [] dbscanfailed = False if len(epsmap) != 0: # Select index that maximizes Dice with guessmask but first # minimizes number of higher Rho components ii = int(epsmap[np.argmax(epsmap[epsmap[:, 2] == np.min(epsmap[:, 2]), 1], 0), 0]) - lgr.debug('Component selection tuning: {:.05f}'.format(epsmap[:, 1].max())) + LGR.debug('Component selection tuning: {:.05f}'.format(epsmap[:, 1].max())) db = DBSCAN(eps=.005+ii*.005, min_samples=3).fit(fz.T) ncl = nc[db.labels_ == 0] ncl = np.setdiff1d(ncl, rej) @@ -305,7 +307,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, to_clf = np.setdiff1d(nc, np.union1d(ncl, rej)) if len(group0) == 0 or len(group0) < len(KRguess) * .5: dbscanfailed = True - lgr.debug('DBSCAN guess failed; using elbow guess method instead') + LGR.debug('DBSCAN guess failed; using elbow guess method instead') ncl = np.setdiff1d(np.setdiff1d(nc[KRelbow == 2], rej), np.union1d(nc[tt_table[:, 0] < tt_lim], np.union1d(np.union1d(nc[spz > 1], @@ -317,7 +319,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, group_n1 = [] to_clf = np.setdiff1d(nc, np.union1d(group0, rej)) if len(group0) < 2 or (len(group0) < 4 and float(len(rej))/len(group0) > 3): - lgr.warning('Extremely limited reliable BOLD signal space! ' + LGR.warning('Extremely limited reliable BOLD signal space! ' 'Not filtering components beyond BOLD/non-BOLD guesses.') midkfailed = True min_acc = np.array([]) @@ -370,7 +372,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, yy = aa * xx - (clf_.intercept_[0]) / ww[1] plt.plot(xx, yy, '-') """ - lgr.debug('Attempting to classify midk components') + LGR.debug('Attempting to classify midk components') # Tried getting rid of accepting based on SVM altogether, # now using only rejecting toacc_hi = np.setdiff1d(nc[utils.andb([fdist <= np.max(fdist[group0]), @@ -406,7 +408,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, veinBout = utils.unmask(veinmaskB, mask) utils.filewrite(veinBout, 'veins50', ref_img) """ - lgr.debug('Computing variance associated with low T2* areas (e.g., draining veins)') + LGR.debug('Computing variance associated with low T2* areas (e.g., draining veins)') tsoc_B_Zcl = np.zeros(seldict['tsoc_B'].shape) tsoc_B_Zcl[seldict['Z_clmaps'] != 0] = np.abs(seldict['tsoc_B'])[seldict['Z_clmaps'] != 0] sig_B = [stats.scoreatpercentile(tsoc_B_Zcl[tsoc_B_Zcl[:, ii] != 0, ii], 25) @@ -459,7 +461,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, Step 4: Learn joint TE-dependence spatial and temporal models to move remaining artifacts to ignore class """ - lgr.debug('Learning joint TE-dependence spatial/temporal models to ignore remaining artifacts') + LGR.debug('Learning joint TE-dependence spatial/temporal models to ignore remaining artifacts') to_ign = [] diff --git a/tedana/tests/test_tedana.py b/tedana/tests/test_tedana.py index 0555837e2..abbeacbaa 100644 --- a/tedana/tests/test_tedana.py +++ b/tedana/tests/test_tedana.py @@ -38,30 +38,29 @@ def test_outputs(): """ nifti_test_list = [ - 't2sv.nii', - 's0v.nii', - 't2ss.nii', - 's0vs.nii', - 't2svG.nii', - 's0vG.nii', - 'T1gs.nii', - 'tsoc_orig.nii', - 'tsoc_nogs.nii', - 'veins_l0.nii', - 'veins_l1.nii', - 'ts_OC.nii', - 'hik_ts_OC.nii', - 'midk_ts_OC.nii', - 'lowk_ts_OC.nii', - 'dn_ts_OC.nii', - 'betas_OC.nii', - 'betas_hik_OC.nii', - 'feats_OC2.nii', - 'betas_hik_OC_T1c.nii', - 'dn_ts_OC_T1c.nii', - 'hik_ts_OC_T1c.nii', - 'sphis_hik.nii'] - test_dir = Path('/home/neuro/data/TED/') - res_dir = Path('/home/neuro/code/TED/') + 't2sv.nii', + 's0v.nii', + 't2ss.nii', + 's0vs.nii', + 't2svG.nii', + 's0vG.nii', + 'T1gs.nii', + 'tsoc_orig.nii', + 'tsoc_nogs.nii', + 'veins_l0.nii', + 'veins_l1.nii', + 'ts_OC.nii', + 'hik_ts_OC.nii', + 'midk_ts_OC.nii', + 'lowk_ts_OC.nii', + 'dn_ts_OC.nii', + 'betas_OC.nii', + 'betas_hik_OC.nii', + 'feats_OC2.nii', + 'betas_hik_OC_T1c.nii', + 'dn_ts_OC_T1c.nii', + 'hik_ts_OC_T1c.nii', + 'sphis_hik.nii' + ] for fn in nifti_test_list: - compare_nifti(fn, test_dir, res_dir) + compare_nifti(fn, Path('/home/neuro/data/TED/'), Path('/home/neuro/code/TED/')) diff --git a/tedana/utils/io.py b/tedana/utils/io.py index 378fd0825..5f81a54d5 100644 --- a/tedana/utils/io.py +++ b/tedana/utils/io.py @@ -9,7 +9,7 @@ from tedana import model, utils -lgr = logging.getLogger(__name__) +LGR = logging.getLogger(__name__) def gscontrol_mmix(OCcatd, mmix, mask, acc, rej, midk, ref_img): @@ -160,7 +160,7 @@ def write_split_ts(data, mmix, mask, acc, rej, midk, ref_img, suffix=''): # get variance explained by retained components betas = model.get_coeffs(utils.unmask(dmdata.T, mask), mask, mmix)[mask] varexpl = (1 - ((dmdata.T - betas.dot(mmix.T))**2.).sum() / (dmdata**2.).sum()) * 100 - lgr.info('Variance explained by ICA decomposition: {:.02f}%'.format(varexpl)) + LGR.info('Variance explained by ICA decomposition: {:.02f}%'.format(varexpl)) # create component and de-noised time series and save to files hikts = betas[:, acc].dot(mmix.T[acc, :]) @@ -170,16 +170,16 @@ def write_split_ts(data, mmix, mask, acc, rej, midk, ref_img, suffix=''): if len(acc) != 0: fout = utils.filewrite(utils.unmask(hikts, mask), 'hik_ts_{0}'.format(suffix), ref_img) - lgr.info('Writing high-Kappa time series: {}'.format(op.abspath(fout))) + LGR.info('Writing high-Kappa time series: {}'.format(op.abspath(fout))) if len(midk) != 0: fout = utils.filewrite(utils.unmask(midkts, mask), 'midk_ts_{0}'.format(suffix), ref_img) - lgr.info('Writing mid-Kappa time series: {}'.format(op.abspath(fout))) + LGR.info('Writing mid-Kappa time series: {}'.format(op.abspath(fout))) if len(rej) != 0: fout = utils.filewrite(utils.unmask(lowkts, mask), 'lowk_ts_{0}'.format(suffix), ref_img) - lgr.info('Writing low-Kappa time series: {}'.format(op.abspath(fout))) + LGR.info('Writing low-Kappa time series: {}'.format(op.abspath(fout))) fout = utils.filewrite(utils.unmask(dnts, mask), 'dn_ts_{0}'.format(suffix), ref_img) - lgr.info('Writing denoised time series: {}'.format(op.abspath(fout))) + LGR.info('Writing denoised time series: {}'.format(op.abspath(fout))) return varexpl @@ -314,24 +314,24 @@ def writeresults(ts, mask, comptable, mmix, n_vols, acc, rej, midk, empty, ref_i """ fout = utils.filewrite(ts, 'ts_OC', ref_img) - lgr.info('Writing optimally-combined time series: {}'.format(op.abspath(fout))) + LGR.info('Writing optimally-combined time series: {}'.format(op.abspath(fout))) varexpl = write_split_ts(ts, mmix, mask, acc, rej, midk, ref_img, suffix='OC') ts_B = model.get_coeffs(ts, mask, mmix) fout = utils.filewrite(ts_B, 'betas_OC', ref_img) - lgr.info('Writing full ICA coefficient feature set: {}'.format(op.abspath(fout))) + LGR.info('Writing full ICA coefficient feature set: {}'.format(op.abspath(fout))) if len(acc) != 0: fout = utils.filewrite(ts_B[:, acc], 'betas_hik_OC', ref_img) - lgr.info('Writing denoised ICA coefficient feature set: {}'.format(op.abspath(fout))) + LGR.info('Writing denoised ICA coefficient feature set: {}'.format(op.abspath(fout))) fout = writefeats(split_ts(ts, mmix, mask, acc)[0], mmix[:, acc], mask, ref_img, suffix='OC2') - lgr.info('Writing Z-normalized spatial component maps: {}'.format(op.abspath(fout))) + LGR.info('Writing Z-normalized spatial component maps: {}'.format(op.abspath(fout))) writect(comptable, n_vols, acc, rej, midk, empty, ctname='comp_table.txt', varexpl=varexpl) - lgr.info('Writing component table: {}'.format(op.abspath('comp_table.txt'))) + LGR.info('Writing component table: {}'.format(op.abspath('comp_table.txt'))) def writeresults_echoes(catd, mmix, mask, acc, rej, midk, ref_img): @@ -358,7 +358,7 @@ def writeresults_echoes(catd, mmix, mask, acc, rej, midk, ref_img): """ for i_echo in range(catd.shape[1]): - lgr.info('Writing Kappa-filtered echo #{:01d} timeseries'.format(i_echo+1)) + LGR.info('Writing Kappa-filtered echo #{:01d} timeseries'.format(i_echo+1)) write_split_ts(catd[:, i_echo, :], mmix, mask, acc, rej, midk, ref_img, suffix='e%i' % (i_echo+1)) diff --git a/tedana/utils/utils.py b/tedana/utils/utils.py index ff2c31bbb..1cb202ad6 100644 --- a/tedana/utils/utils.py +++ b/tedana/utils/utils.py @@ -50,6 +50,8 @@ def get_dtype(data): def getfbounds(n_echos): """ + Gets estimated F-statistic boundaries based on number of echos + Parameters ---------- n_echos : int @@ -82,11 +84,11 @@ def load_image(data): Parameters ---------- data : (X x Y x Z [x T]) array_like or img_like object - Data array or data file to be loaded / reshaped + Data array or data file to be loaded and reshaped Returns ------- - fdata : (S x T) np.ndarray + fdata : (S [x T]) np.ndarray Reshaped `data`, where `S` is samples and `T` is time """ @@ -145,7 +147,7 @@ def load_data(data, n_echos=None): 'one data file is provided.') img = check_niimg(data) (nx, ny), nz = img.shape[:2], img.shape[2] // n_echos - fdata = load_image(img.get_data().reshape(nx, ny, nz, n_echos, -1, order='F')) + fdata = load_image(img.get_data().reshape(nx, ny, nz, n_echos, -1)) # create reference image ref_img = img.__class__(np.zeros((nx, ny, nz)), affine=img.affine, diff --git a/tedana/workflows/t2smap.py b/tedana/workflows/t2smap.py index 718e2d7fc..bdcfcc7f7 100644 --- a/tedana/workflows/t2smap.py +++ b/tedana/workflows/t2smap.py @@ -2,7 +2,7 @@ import numpy as np from tedana import model, utils -lgr = logging.getLogger(__name__) +LGR = logging.getLogger(__name__) def main(options): @@ -30,16 +30,16 @@ def main(options): ref_img = data[0] if isinstance(data, list) else data - lgr.info('Computing adaptive mask') + LGR.info('Computing adaptive mask') mask, masksum = utils.make_adaptive_mask(catd, minimum=False, getsum=True) utils.filewrite(masksum, 'masksum%s' % suf, ref_img, copy_header=False) - lgr.info('Computing adaptive T2* map') + LGR.info('Computing adaptive T2* map') t2s, s0, t2ss, s0vs, t2saf, s0vaf = model.t2sadmap(catd, tes, mask, masksum, 2) utils.filewrite(t2ss, 't2ss%s' % suf, ref_img, copy_header=False) utils.filewrite(s0vs, 's0vs%s' % suf, ref_img, copy_header=False) - lgr.info('Computing optimal combination') + LGR.info('Computing optimal combination') tsoc = np.array(model.make_optcom(catd, t2s, tes, mask, combmode), dtype=float) diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index d3b853752..01ef81937 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -6,7 +6,7 @@ from tedana import (decomposition, model, selection, utils) import logging -lgr = logging.getLogger(__name__) +LGR = logging.getLogger(__name__) """ PROCEDURE 2 : Computes ME-PCA and ME-ICA @@ -84,10 +84,10 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, n_echos = len(tes) # coerce data to samples x echos x time array - lgr.info('Loading input data: {}'.format([op.abspath(f) for f in data])) + LGR.info('Loading input data: {}'.format([op.abspath(f) for f in data])) catd, ref_img = utils.load_data(data, n_echos=n_echos) n_samp, n_echos, n_vols = catd.shape - lgr.debug('Resulting data shape: {}'.format(catd.shape)) + LGR.debug('Resulting data shape: {}'.format(catd.shape)) if fout: fout = ref_img @@ -102,10 +102,10 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, out_dir = 'TED' out_dir = op.abspath(out_dir) if not op.isdir(out_dir): - lgr.info('Creating output directory: {}'.format(out_dir)) + LGR.info('Creating output directory: {}'.format(out_dir)) os.mkdir(out_dir) else: - lgr.info('Using output directory: {}'.format(out_dir)) + LGR.info('Using output directory: {}'.format(out_dir)) if mixm is not None and op.isfile(mixm): shutil.copyfile(mixm, op.join(out_dir, 'meica_mix.1D')) @@ -121,11 +121,11 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, os.chdir(out_dir) - lgr.info('Computing adapative mask') + LGR.info('Computing adapative mask') mask, masksum = utils.make_adaptive_mask(catd, minimum=False, getsum=True) - lgr.debug('Retaining {}/{} samples'.format(mask.sum(), n_samp)) + LGR.debug('Retaining {}/{} samples'.format(mask.sum(), n_samp)) - lgr.info('Computing T2* map') + LGR.info('Computing T2* map') t2s, s0, t2ss, s0s, t2sG, s0G = model.fit_decay(catd, tes, mask, masksum, start_echo=1) @@ -133,7 +133,7 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, # anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile cap_t2s = stats.scoreatpercentile(t2s.flatten(), 99.5, interpolation_method='lower') - lgr.debug('Setting cap on T2* map at {:.5f}'.format(cap_t2s * 10)) + LGR.debug('Setting cap on T2* map at {:.5f}'.format(cap_t2s * 10)) t2s[t2s > cap_t2s * 10] = cap_t2s utils.filewrite(t2s, op.join(out_dir, 't2sv'), ref_img) utils.filewrite(s0, op.join(out_dir, 's0v'), ref_img) @@ -156,7 +156,7 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, mmix_orig = decomposition.tedica(n_components, dd, conv, fixed_seed, cost=initcost, final_cost=finalcost, verbose=debug) np.savetxt(op.join(out_dir, '__meica_mix.1D'), mmix_orig) - lgr.info('Making second component selection guess from ICA results') + LGR.info('Making second component selection guess from ICA results') seldict, comptable, betas, mmix = model.fitmodels_direct(catd, mmix_orig, mask, t2s, t2sG, tes, combmode, @@ -169,7 +169,7 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, n_echos, t2s, s0, strict_mode=strict, filecsdata=filecsdata) else: - lgr.info('Using supplied mixing matrix from ICA') + LGR.info('Using supplied mixing matrix from ICA') mmix_orig = np.loadtxt(op.join(out_dir, 'meica_mix.1D')) seldict, comptable, betas, mmix = model.fitmodels_direct(catd, mmix_orig, mask, t2s, t2sG, @@ -185,7 +185,7 @@ def main(data, tes, mixm=None, ctab=None, manacc=None, strict=False, acc, rej, midk, empty = utils.ctabsel(ctab) if len(acc) == 0: - lgr.warning('No BOLD components detected! Please check data and results!') + LGR.warning('No BOLD components detected! Please check data and results!') utils.writeresults(OCcatd, mask, comptable, mmix, n_vols, acc, rej, midk, empty, ref_img) utils.gscontrol_mmix(OCcatd, mmix, mask, acc, rej, midk, ref_img) From 35d55686845ac810518f23fca86c75e02b7b135d Mon Sep 17 00:00:00 2001 From: Ross Markello Date: Sun, 13 May 2018 22:04:13 -0400 Subject: [PATCH 07/10] [TST] Testing data and more utils tests --- .gitignore | 2 - tedana/tests/data/echo1.nii.gz | Bin 0 -> 534936 bytes tedana/tests/data/echo2.nii.gz | Bin 0 -> 504830 bytes tedana/tests/data/echo3.nii.gz | Bin 0 -> 475338 bytes tedana/tests/test_utils.py | 74 +++++++++++++++++++++++++++++---- tedana/utils/utils.py | 6 +-- 6 files changed, 69 insertions(+), 13 deletions(-) create mode 100755 tedana/tests/data/echo1.nii.gz create mode 100755 tedana/tests/data/echo2.nii.gz create mode 100755 tedana/tests/data/echo3.nii.gz diff --git a/.gitignore b/.gitignore index 02a7b22b3..7bbc71c09 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,3 @@ -data/ - # 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+datadir = pjoin(dirname(__file__), 'data') +fnames = [pjoin(datadir, 'echo{}.nii.gz'.format(n)) for n in range(1, 4)] def test_get_dtype(): @@ -102,28 +105,83 @@ def test_andb(): rs.randint(10, size=(20, 20))]) -# should tedana ship with test data for these? def test_load_image(): - pass + fimg = nib.load(fnames[0]) + exp_shape = (64350, 5) + + # load filepath to image + assert utils.load_image(fnames[0]).shape == exp_shape + # load img_like object + assert utils.load_image(fimg).shape == exp_shape + # load array + assert utils.load_image(fimg.get_data()).shape == exp_shape def test_load_data(): - pass + fimg = [nib.load(f) for f in fnames] + exp_shape = (64350, 3, 5) + + # list of filepath to images + d, ref = utils.load_data(fnames) + assert d.shape == exp_shape + assert isinstance(ref, str) + assert ref == fnames[0] + + # list of img_like + d, ref = utils.load_data(fimg) + assert d.shape == exp_shape + assert isinstance(ref, nib.Nifti1Image) + assert ref == fimg[0] + + # imagine z-cat img + d, ref = utils.load_data(fnames[0], n_echos=3) + assert d.shape == (21450, 3, 5) + assert isinstance(ref, nib.Nifti1Image) + assert ref.shape == (39, 50, 11) + + with pytest.raises(ValueError): + utils.load_data(fnames[0]) def test_make_adaptive_mask(): - pass + # load data make masks + data = utils.load_data(fnames)[0] + minmask = utils.make_adaptive_mask(data) + mask, masksum = utils.make_adaptive_mask(data, minimum=False, getsum=True) + + # minimum mask different than adaptive mask + assert not np.allclose(minmask, mask) + # getsum doesn't change mask values + assert np.allclose(mask, utils.make_adaptive_mask(data, minimum=False)) + # shapes are all the same + assert mask.shape == masksum.shape == (64350,) + assert np.allclose(mask, masksum.astype(bool)) + # mask has correct # of entries + assert mask.sum() == 50786 + # masksum has correct values + vals, counts = np.unique(masksum, return_counts=True) + assert np.allclose(vals, np.array([0, 1, 2, 3])) + assert np.allclose(counts, np.array([13564, 3977, 5060, 41749])) def test_make_min_mask(): - pass + # load data make mask + data = utils.load_data(fnames)[0] + minmask = utils.make_min_mask(data) - -def test_filewrite(): - pass + assert minmask.shape == (64350,) + assert minmask.sum() == 58378 def test_new_nii_like(): + data, ref = utils.load_data(fnames) + nimg = utils.new_nii_like(ref, data) + + assert isinstance(nimg, nib.Nifti1Image) + assert nimg.shape == (39, 50, 33, 3, 5) + + +def test_filewrite(): pass diff --git a/tedana/utils/utils.py b/tedana/utils/utils.py index 1cb202ad6..6b068619f 100644 --- a/tedana/utils/utils.py +++ b/tedana/utils/utils.py @@ -33,11 +33,11 @@ def get_dtype(data): """ if isinstance(data, list): - dtypes = np.unique([splitext_addext(d)[1] for d in data]) + dtypes = np.unique([get_dtype(d) for d in data]) if dtypes.size > 1: raise ValueError('Provided data detected to have varying formats: ' '{}'.format(dtypes)) - dtype = dtypes[0] + return dtypes[0] elif isinstance(data, str): dtype = splitext_addext(data)[1] else: # img_like? @@ -147,7 +147,7 @@ def load_data(data, n_echos=None): 'one data file is provided.') img = check_niimg(data) (nx, ny), nz = img.shape[:2], img.shape[2] // n_echos - fdata = load_image(img.get_data().reshape(nx, ny, nz, n_echos, -1)) + fdata = load_image(img.get_data().reshape(nx, ny, nz, n_echos, -1, order='F')) # create reference image ref_img = img.__class__(np.zeros((nx, ny, nz)), affine=img.affine, From 533a07095c2653c58415111432d0b6c580d4d39b Mon Sep 17 00:00:00 2001 From: Elizabeth DuPre Date: Sun, 13 May 2018 23:14:18 -0400 Subject: [PATCH 08/10] [DOC] Add numpydoc requirement to setup.py --- tedana/info.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tedana/info.py b/tedana/info.py index e970ff6b0..7db3275ad 100644 --- a/tedana/info.py +++ b/tedana/info.py @@ -43,7 +43,8 @@ 'doc': [ 'sphinx>=1.5.3', 'sphinx_rtd_theme', - 'sphinx-argparse' + 'sphinx-argparse', + 'numpydoc' ], 'tests': TESTS_REQUIRES, 'duecredit': ['duecredit'], From 603389df5c964ce856459280e53a37dbc6f7dcec Mon Sep 17 00:00:00 2001 From: Ross Markello Date: Mon, 14 May 2018 10:33:40 -0400 Subject: [PATCH 09/10] [DOC] Add codecov badge to README.md --- README.md | 40 ++++++++++++++++++++++------------------ 1 file changed, 22 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index 764b32b4d..15652f907 100644 --- a/README.md +++ b/README.md @@ -1,25 +1,28 @@ # tedana -TE-Dependent Analysis (_tedana_) is a Python module for denoising multi-echo fMRI data. +`TE`-`de`pendent `ana`lysis (_tedana_) is a Python module for denoising multi-echo functional magnetic resonance imaging (fMRI) data. [![CircleCI](https://circleci.com/gh/ME-ICA/tedana.svg?style=shield)](https://circleci.com/gh/ME-ICA/tedana) [![Documentation Status](https://readthedocs.org/projects/tedana/badge/?version=latest)](http://tedana.readthedocs.io/en/latest/?badge=latest) +[![Codecov](https://codecov.io/gh/me-ica/tedana/branch/master/graph/badge.svg)](https://codecov.io/gh/me-ica/tedana) [![License](https://img.shields.io/badge/License-LGPL%202.0-blue.svg)](https://opensource.org/licenses/LGPL-2.1) [![Join the chat at https://gitter.im/ME-ICA/tedana](https://badges.gitter.im/ME-ICA/tedana.svg)](https://gitter.im/ME-ICA/tedana?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) ## About -tedana is part of the ME-ICA pipeline, and therefore assumes that you're working with already preprocessed data. If you're in need of a preprocessing pipeline, we recommend [FMRIPREP](https://github.com/poldracklab/fmriprep/), which has been tested for compatibility with multi-echo fMRI data. +`tedana` originally came about as a part of the [`ME-ICA`](https://github.com/me-ica/me-ica) pipeline. +The ME-ICA pipeline orignially performed both pre-processing and TE-dependent analysis of multi-echo fMRI data; however, `tedana` now assumes that you're working with data which has been previously preprocessed. +If you're in need of a pre-processing pipeline, we recommend [`fmriprep`](https://github.com/poldracklab/fmriprep/) which has been tested for compatibility with multi-echo fMRI data and `tedana`. ### Why Multi-Echo? -Multi-echo fMRI data collection entails acquires multiple TEs (commonly called [echo times](http://mriquestions.com/tr-and-te.html)) for each collected fMRI volume. -Our signal of interest, Blood Oxygen-Level Dependent or [BOLD signal](http://www.fil.ion.ucl.ac.uk/spm/course/slides10-zurich/Kerstin_BOLD.pdf), is known to decay at a set rate within each fMRI volume. -Collecting multiple echos therefore allows us to infer if components of fMRI signal are BOLD-related or driven by acquisition artifacts, like participant motion. -For a review, see [Kundu et al. (2017), _NeuroImage_](https://paperpile.com/shared/eH3PPu). +Multi-echo fMRI data is obtained by acquiring multiple TEs (commonly called [echo times](http://mriquestions.com/tr-and-te.html)) for each MRI volume during data collection. +While fMRI signal contains important neural information (termed the blood oxygen-level dependent, or [BOLD signal](http://www.fil.ion.ucl.ac.uk/spm/course/slides10-zurich/Kerstin_BOLD.pdf)), it also contains "noise" (termed non-BOLD signal) caused by things like participant motion and changes in breathing. +Because the BOLD signal is known to decay at a set rate, collecting multiple echos allows us to assess whether components of the fMRI signal are BOLD- or non-BOLD. +For a comprehensive review, see [Kundu et al. (2017), _NeuroImage_](https://paperpile.com/shared/eH3PPu). -In tedana, we combine all collected echos, then decompose the resulting time series into components that can be classified as BOLD or non-BOLD based. This is performed in a series of steps including: +In `tedana`, we take the time series from all the collected TEs, combine them, and decompose the resulting data into components that can be classified as BOLD or non-BOLD. This is performed in a series of steps including: * Principal components analysis * Independent components analysis @@ -29,7 +32,9 @@ More information and documentation can be found at https://tedana.readthedocs.io ## Installation -You'll need to set up a working development environment to use tedana. We provide a Dockerfile for this purpose (check out [tips on using Docker](https://neurohackweek.github.io/docker-for-scientists/)), or you can set up your environment locally. If you choose the latter, make sure the following packages are installed: +You'll need to set up a working development environment to use `tedana`. +We provide a Dockerfile for this purpose (check out [tips on using Docker](https://neurohackweek.github.io/docker-for-scientists/)), but you can also set up your environment locally. +If you choose the latter, the following packages will need to be installed as dependencies: mdp nilearn @@ -38,21 +43,20 @@ numpy pybids>=0.4.0 scikit-learn -tedana will eventually be hosted on PyPi. In the mean time, you can still install it with `pip` using: +`tedana` will eventually be hosted on PyPi. In the interim, you can still install it with `pip` using: ``` pip install https://github.com/ME-ICA/tedana/archive/master.tar.gz ``` -## Development +## Getting involved -We :yellow_heart: new contributors ! To get started, check out [our contributing guidelines](https://github.com/ME-ICA/tedana/blob/master/CONTRIBUTING.md). +We :yellow_heart: new contributors ! +To get started, check out [our contributing guidelines](https://github.com/ME-ICA/tedana/blob/master/CONTRIBUTING.md). -Want to learn more about our plans for developing tedana ? Check out [our roadmap](https://github.com/ME-ICA/tedana/projects). Have a question, comment, or suggestion ? Open or comment on one of [our issues](https://github.com/ME-ICA/tedana/issues) ! +Want to learn more about our plans for developing `tedana` ? +Check out [our roadmap](https://github.com/ME-ICA/tedana/projects). +Have a question, comment, or suggestion ? +Open or comment on one of [our issues](https://github.com/ME-ICA/tedana/issues) ! -We ask that all contributions to tedana respect our [code of conduct](https://github.com/ME-ICA/tedana/blob/master/Code_of_Conduct.md). - -### :earth_americas: Mozilla Global Sprint (10-11 May, 2018) :earth_africa: - -This year, tedana will be participating in the [Mozilla Global Sprint](https://foundation.mozilla.org/opportunity/global-sprint/) ! -Check out issues tagged [![mozsprint](https://img.shields.io/badge/-mozsprint-0052cc.svg)](https://github.com/ME-ICA/tedana/labels/mozsprint) for good places to get started during the sprint. +We ask that all contributions to `tedana` respect our [code of conduct](https://github.com/ME-ICA/tedana/blob/master/Code_of_Conduct.md). From eea263b61179f08291ff37c0f42c6ce5fba3b861 Mon Sep 17 00:00:00 2001 From: Elizabeth DuPre Date: Mon, 14 May 2018 15:55:13 -0400 Subject: [PATCH 10/10] Convert code comment to TODO --- tedana/selection/select_comps.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tedana/selection/select_comps.py b/tedana/selection/select_comps.py index ef39b6d96..3b193342e 100644 --- a/tedana/selection/select_comps.py +++ b/tedana/selection/select_comps.py @@ -274,7 +274,7 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2, cond1 = db.labels_.max() > 1 # DBSCAN didn't detect more classes than the total # of components / 6 cond2 = db.labels_.max() < len(nc) / 6 - # not totally sure if 0 is a special label for DBSCAN + # TODO: confirm if 0 is a special label for DBSCAN # my intuition here is that we're confirming DBSCAN labelled previously # rejected components as noise (i.e., no overlap between `rej` and # labelled DBSCAN components)

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