From f4eaa3e1cd39610b729ede760fc45e154241f14e Mon Sep 17 00:00:00 2001 From: Circle Date: Mon, 31 Jul 2023 15:52:18 +1000 Subject: [PATCH 01/34] Add robustica method --- tedana/decomposition/ica.py | 50 ++++++++++++++++++++++++++++++- tedana/workflows/tedana.py | 60 +++++++++++++++++++++++++++++-------- 2 files changed, 96 insertions(+), 14 deletions(-) diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index ff69626a7..5a233b42e 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -4,15 +4,18 @@ import logging import warnings +import sys + import numpy as np from scipy import stats from sklearn.decomposition import FastICA +from robustica import RobustICA ####BTBTBT LGR = logging.getLogger("GENERAL") RepLGR = logging.getLogger("REPORT") -def tedica(data, n_components, fixed_seed, maxit=500, maxrestart=10): +def tedica(data, n_components, fixed_seed, ica_method="robustica", n_robust_runs=30, maxit=500, maxrestart=10): ####BTBTBTB """ Perform ICA on `data` and returns mixing matrix @@ -50,6 +53,51 @@ def tedica(data, n_components, fixed_seed, maxit=500, maxrestart=10): "decompose the dimensionally reduced dataset." ) + if ica_method=='robustica': + mmix, Iq = r_ica(data, n_components, n_robust_runs, maxit) + fixed_seed=-99999 + elif ica_method=='fastica': + mmix, fixed_seed=f_ica(data, n_components, fixed_seed, maxit=500, maxrestart=10) + Iq = 0 + else: + LGR.warning("The selected ICA method is invalid!") + sys.exit() + + + + + return mmix, fixed_seed + + +def r_ica(data, n_components, n_robust_runs, max_it): ####BTBTBTB: + + if n_robust_runs>100: + LGR.warning("The selected n_robust_runs is a very big number!") + + + RepLGR.info( + "RobustICA package was used for ICA decomposition \\citep{Anglada2022}." + ) + rica0 = RobustICA(n_components=n_components, robust_runs=n_robust_runs, whiten='arbitrary-variance',max_iter= max_it, + robust_dimreduce=False, fun='logcosh') + S0, mmix = rica0.fit_transform(data) + + q0 = rica0.evaluate_clustering(rica0.S_all, rica0.clustering.labels_, rica0.signs_, rica0.orientation_) + + + Iq0 = np.array(np.mean(q0.iq)) + + + mmix = stats.zscore(mmix, axis=0) + + LGR.info( + "RobustICA with {0} robust runs was used \n" + "The mean index quality is {1}".format(n_robust_runs, Iq0) + ) + return mmix, Iq0 + + +def f_ica(data, n_components, fixed_seed, maxit, maxrestart): if fixed_seed == -1: fixed_seed = np.random.randint(low=1, high=1000) diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index 4d443033d..ee567dfef 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -150,6 +150,7 @@ def _get_parser(): "in which case the specificed number of components will be " "selected." ), + choices=["mdl", "kic", "aic"], default="aic", ) optional.add_argument( @@ -164,19 +165,46 @@ def _get_parser(): ), default="kundu", ) + optional.add_argument(#####BTBTBT + "--ica_method", + dest="ica_method", + help=( + "The applied ICA method. If set to fastica the FastICA " + "from sklearn library will be run once with the seed value. " + "robustica will run FastICA n_robust_runs times and and uses " + "clustering methods to overcome the randomness of the FastICA " + "algorithm. If set to robustica the seed value will be ignored." + "If set to fastica n_robust_runs will not be effective." + ), + choices=["robustica", "fastica"], + default="robustica", + ) optional.add_argument( "--seed", dest="fixed_seed", metavar="INT", type=int, - help=( + help=( ##BTBTBT "Value used for random initialization of ICA " - "algorithm. Set to an integer value for " - "reproducible ICA results. Set to -1 for " + "algorithm when ica_mthods is set to fastica. Set to an integer value for " + "reproducible ICA results with fastica. Set to -1 for " "varying results across ICA calls. " ), default=42, ) + optional.add_argument(#####BTBTBT + "--n_robust_runs", + dest="n_robust_runs", + type=int, + help=( + "The number of times robustica will run." + "This is only effective when ica_mthods is " + "set to robustica." + + ), + ##choices=range(2,100), + default=30, + ) optional.add_argument( "--maxit", dest="maxit", @@ -323,6 +351,8 @@ def tedana_workflow( fittype="loglin", combmode="t2s", tree="kundu", + ica_method="robustica", ########BTBTAdded + n_robust_runs=30, tedpca="aic", fixed_seed=42, maxit=500, @@ -385,9 +415,7 @@ def tedana_workflow( tedpca : {'mdl', 'aic', 'kic', 'kundu', 'kundu-stabilize', float, int}, optional Method with which to select components in TEDPCA. If a float is provided, then it is assumed to represent percentage of variance - explained (0-1) to retain from PCA. If an int is provided, it will output - a fixed number of components defined by the integer between 1 and the - number of time points. + explained (0-1) to retain from PCA. Default is 'aic'. fixed_seed : :obj:`int`, optional Value passed to ``mdp.numx_rand.seed()``. @@ -639,11 +667,12 @@ def tedana_workflow( # Perform ICA, calculate metrics, and apply decision tree # Restart when ICA fails to converge or too few BOLD components found keep_restarting = True + n_restarts = 0 seed = fixed_seed while keep_restarting: mmix, seed = decomposition.tedica( - dd, n_components, seed, maxit, maxrestart=(maxrestart - n_restarts) + dd, n_components, seed, ica_method, n_robust_runs, maxit, maxrestart=(maxrestart - n_restarts) ) seed += 1 n_restarts = seed - fixed_seed @@ -677,13 +706,17 @@ def tedana_workflow( ) ica_selector = selection.automatic_selection(comptable, n_echos, n_vols, tree=tree) n_likely_bold_comps = ica_selector.n_likely_bold_comps - if (n_restarts < maxrestart) and (n_likely_bold_comps == 0): - LGR.warning("No BOLD components found. Re-attempting ICA.") - elif n_likely_bold_comps == 0: - LGR.warning("No BOLD components found, but maximum number of restarts reached.") - keep_restarting = False - else: + + if ica_method=='robustica': #########BTBTBT keep_restarting = False + else: + if (n_restarts < maxrestart) and (n_likely_bold_comps == 0): + LGR.warning("No BOLD components found. Re-attempting ICA.") + elif n_likely_bold_comps == 0: + LGR.warning("No BOLD components found, but maximum number of restarts reached.") + keep_restarting = False + else: + keep_restarting = False # If we're going to restart, temporarily allow force overwrite if keep_restarting: @@ -893,3 +926,4 @@ def _main(argv=None): if __name__ == "__main__": _main() + From b0cac3a49ea6028c78c4d583a79c17ab100a6f5d Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Tue, 5 Dec 2023 12:32:53 +1100 Subject: [PATCH 02/34] Incorporation of major comments regarding robustica addition Manual modification of commit f2cdb4ed756996609a0467ac22279605a2346edb to remove unwanted file additions. --- tedana/config.py | 4 + tedana/decomposition/ica.py | 191 +++++-- tedana/resources/references.bib | 10 + tedana/tests/test_integrity_robustica.py | 667 +++++++++++++++++++++++ tedana/workflows/tedana.py | 167 +++--- 5 files changed, 938 insertions(+), 101 deletions(-) create mode 100644 tedana/config.py create mode 100644 tedana/tests/test_integrity_robustica.py diff --git a/tedana/config.py b/tedana/config.py new file mode 100644 index 000000000..9bca5b873 --- /dev/null +++ b/tedana/config.py @@ -0,0 +1,4 @@ +DEFAULT_ICA_METHOD = "robustica" +DEFAULT_N_ROBUST_RUNS = 30 +DEFAULT_N_MAX_ITER = 500 +DEFAULT_N_MAX_RESTART = 10 \ No newline at end of file diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index 5a233b42e..abb9d2c82 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -1,23 +1,34 @@ -""" -ICA and related signal decomposition methods for tedana -""" +"""ICA and related signal decomposition methods for tedana""" import logging import warnings -import sys - import numpy as np +from robustica import RobustICA from scipy import stats from sklearn.decomposition import FastICA -from robustica import RobustICA ####BTBTBT + +from tedana.config import ( + DEFAULT_ICA_METHOD, + DEFAULT_N_MAX_ITER, + DEFAULT_N_MAX_RESTART, + DEFAULT_N_ROBUST_RUNS, +) LGR = logging.getLogger("GENERAL") RepLGR = logging.getLogger("REPORT") -def tedica(data, n_components, fixed_seed, ica_method="robustica", n_robust_runs=30, maxit=500, maxrestart=10): ####BTBTBTB +def tedica( + data, + n_components, + fixed_seed, + ica_method=DEFAULT_ICA_METHOD, + n_robust_runs=DEFAULT_N_ROBUST_RUNS, + maxit=DEFAULT_N_MAX_ITER, + maxrestart=DEFAULT_N_MAX_RESTART, +): """ - Perform ICA on `data` and returns mixing matrix + Perform ICA on `data` with the user selected ica method and returns mixing matrix Parameters ---------- @@ -25,9 +36,13 @@ def tedica(data, n_components, fixed_seed, ica_method="robustica", n_robust_runs Dimensionally reduced optimally combined functional data, where `S` is samples and `T` is time n_components : :obj:`int` - Number of components retained from PCA decomposition + Number of components retained from PCA decomposition. fixed_seed : :obj:`int` - Seed for ensuring reproducibility of ICA results + Seed for ensuring reproducibility of ICA results. + ica_method : :obj: `str' + slected ICA method, can be fastica or robutica. + n_robust_runs : :obj: `int' + selected number of robust runs when robustica is used. Default is 30. maxit : :obj:`int`, optional Maximum number of iterations for ICA. Default is 500. maxrestart : :obj:`int`, optional @@ -43,9 +58,6 @@ def tedica(data, n_components, fixed_seed, ica_method="robustica", n_robust_runs fixed_seed : :obj:`int` Random seed from final decomposition. - Notes - ----- - Uses `sklearn` implementation of FastICA for decomposition """ warnings.filterwarnings(action="ignore", module="scipy", message="^internal gelsd") RepLGR.info( @@ -53,51 +65,152 @@ def tedica(data, n_components, fixed_seed, ica_method="robustica", n_robust_runs "decompose the dimensionally reduced dataset." ) - if ica_method=='robustica': - mmix, Iq = r_ica(data, n_components, n_robust_runs, maxit) - fixed_seed=-99999 - elif ica_method=='fastica': - mmix, fixed_seed=f_ica(data, n_components, fixed_seed, maxit=500, maxrestart=10) - Iq = 0 + ica_method = ica_method.lower() + + if ica_method == "robustica": + mmix, fixed_seed = r_ica( + data, + n_components=n_components, + fixed_seed=fixed_seed, + n_robust_runs=n_robust_runs, + max_it=maxit, + ) + elif ica_method == "fastica": + mmix, fixed_seed = f_ica( + data, + n_components=n_components, + fixed_seed=fixed_seed, + maxit=maxit, + maxrestart=maxrestart, + ) else: - LGR.warning("The selected ICA method is invalid!") - sys.exit() + raise ValueError("The selected ICA method is invalid!") + return mmix, fixed_seed +def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): + """ + Perform robustica on `data` by running FastICA multiple times (n_robust runes) + and returns mixing matrix - return mmix, fixed_seed + Parameters + ---------- + data : (S x T) :obj:`numpy.ndarray` + Dimensionally reduced optimally combined functional data, where `S` is + samples and `T` is time + n_components : :obj:`int` + Number of components retained from PCA decomposition. + fixed_seed : :obj:`int` + Seed for ensuring reproducibility of ICA results. + n_robust_runs : :obj: `int' + selected number of robust runs when robustica is used. Default is 30. + maxit : :obj:`int`, optional + Maximum number of iterations for ICA. Default is 500. + Returns + ------- + mmix : (T x C) :obj:`numpy.ndarray` + Z-scored mixing matrix for converting input data to component space, + where `C` is components and `T` is the same as in `data` + fixed_seed : :obj:`int` + Random seed from final decomposition. -def r_ica(data, n_components, n_robust_runs, max_it): ####BTBTBTB: + """ + if n_robust_runs > 200: + LGR.warning( + "The selected n_robust_runs is a very big number! The process will take a long time!" + ) - if n_robust_runs>100: - LGR.warning("The selected n_robust_runs is a very big number!") + RepLGR.info("RobustICA package was used for ICA decomposition \\citep{Anglada2022}.") + if fixed_seed == -1: + fixed_seed = np.random.randint(low=1, high=1000) - RepLGR.info( - "RobustICA package was used for ICA decomposition \\citep{Anglada2022}." - ) - rica0 = RobustICA(n_components=n_components, robust_runs=n_robust_runs, whiten='arbitrary-variance',max_iter= max_it, - robust_dimreduce=False, fun='logcosh') - S0, mmix = rica0.fit_transform(data) + try: + rica = RobustICA( + n_components=n_components, + robust_runs=n_robust_runs, + whiten="arbitrary-variance", + max_iter=max_it, + random_state=fixed_seed, + robust_dimreduce=False, + fun="logcosh", + robust_method="DBSCAN", + ) - q0 = rica0.evaluate_clustering(rica0.S_all, rica0.clustering.labels_, rica0.signs_, rica0.orientation_) + S, mmix = rica.fit_transform(data) + q = rica.evaluate_clustering( + rica.S_all, rica.clustering.labels_, rica.signs_, rica.orientation_ + ) - - Iq0 = np.array(np.mean(q0.iq)) - + except: + rica = RobustICA( + n_components=n_components, + robust_runs=n_robust_runs, + whiten="arbitrary-variance", + max_iter=max_it, + random_state=fixed_seed, + robust_dimreduce=False, + fun="logcosh", + robust_method="AgglomerativeClustering", + ) + + S, mmix = rica.fit_transform(data) + q = rica.evaluate_clustering( + rica.S_all, rica.clustering.labels_, rica.signs_, rica.orientation_ + ) + iq = np.array(np.mean(q[q["cluster_id"] >= 0].iq)) # The cluster labeled -1 is noise + + if iq < 0.6: + LGR.warning( + "The resultant mean Index Quality is low. It is recommended to rerun the " + "process with a different seed." + ) + + mmix = mmix[:, q["cluster_id"] >= 0] mmix = stats.zscore(mmix, axis=0) LGR.info( - "RobustICA with {0} robust runs was used \n" - "The mean index quality is {1}".format(n_robust_runs, Iq0) + "RobustICA with {0} robust runs and seed {1} was used. " + "The mean Index Quality is {2}".format(n_robust_runs, fixed_seed, iq) ) - return mmix, Iq0 + return mmix, fixed_seed def f_ica(data, n_components, fixed_seed, maxit, maxrestart): + """ + Perform FastICA on `data` and returns mixing matrix + + Parameters + ---------- + data : (S x T) :obj:`numpy.ndarray` + Dimensionally reduced optimally combined functional data, where `S` is + samples and `T` is time + n_components : :obj:`int` + Number of components retained from PCA decomposition + fixed_seed : :obj:`int` + Seed for ensuring reproducibility of ICA results + maxit : :obj:`int`, optional + Maximum number of iterations for ICA. Default is 500. + maxrestart : :obj:`int`, optional + Maximum number of attempted decompositions to perform with different + random seeds. ICA will stop running if there is convergence prior to + reaching this limit. Default is 10. + + Returns + ------- + mmix : (T x C) :obj:`numpy.ndarray` + Z-scored mixing matrix for converting input data to component space, + where `C` is components and `T` is the same as in `data` + fixed_seed : :obj:`int` + Random seed from final decomposition. + + Notes + ----- + Uses `sklearn` implementation of FastICA for decomposition + """ if fixed_seed == -1: fixed_seed = np.random.randint(low=1, high=1000) @@ -135,4 +248,4 @@ def f_ica(data, n_components, fixed_seed, maxit, maxrestart): mmix = ica.mixing_ mmix = stats.zscore(mmix, axis=0) - return mmix, fixed_seed + return mmix, fixed_seed \ No newline at end of file diff --git a/tedana/resources/references.bib b/tedana/resources/references.bib index 006f2f779..180e0a827 100644 --- a/tedana/resources/references.bib +++ b/tedana/resources/references.bib @@ -313,3 +313,13 @@ @Article{Hunter:2007 doi = {10.1109/MCSE.2007.55}, year = 2007 } + +@Article{Anglada:2022, + Author = {Anglada-Girotto Miquel and Miravet-Verde Samuel and Serrano Luis and Head Sarah}, + Title = {robustica: customizable robust independent component analysis}, + Journal = {BMC Bioinformatics}, + Volume = {23}, + Number = {519}, + doi = {10.1186/s12859-022-05043-9}, + year = 2022 +} \ No newline at end of file diff --git a/tedana/tests/test_integrity_robustica.py b/tedana/tests/test_integrity_robustica.py new file mode 100644 index 000000000..88646d51f --- /dev/null +++ b/tedana/tests/test_integrity_robustica.py @@ -0,0 +1,667 @@ +"""Integration tests for "real" data.""" + +import glob +import json +import logging +import os +import os.path as op +import re +import shutil +import subprocess +import tarfile +from datetime import datetime +from gzip import GzipFile +from io import BytesIO + +import pandas as pd +import pytest +import requests +from pkg_resources import resource_filename + +from tedana.io import InputHarvester +from tedana.workflows import t2smap as t2smap_cli +from tedana.workflows import tedana as tedana_cli +from tedana.workflows.ica_reclassify import ica_reclassify_workflow + +# Need to see if a no BOLD warning occurred +LOGGER = logging.getLogger(__name__) +# Added a testing logger to output whether or not testing data were downlaoded +TestLGR = logging.getLogger("TESTING") + + +def check_integration_outputs(fname, outpath, n_logs=1): + """ + Checks outputs of integration tests. + + Parameters + ---------- + fname : str + Path to file with expected outputs + outpath : str + Path to output directory generated from integration tests + """ + + # Gets filepaths generated by integration test + found_files = [ + os.path.relpath(f, outpath) + for f in glob.glob(os.path.join(outpath, "**"), recursive=True)[1:] + ] + + # Checks for log file + log_regex = "^tedana_[12][0-9]{3}-[0-9]{2}-[0-9]{2}T[0-9]{2}[0-9]{2}[0-9]{2}.tsv$" + logfiles = [out for out in found_files if re.match(log_regex, out)] + assert len(logfiles) == n_logs + + # Removes logfiles from list of existing files + for log in logfiles: + found_files.remove(log) + + # Compares remaining files with those expected + with open(fname, "r") as f: + expected_files = f.read().splitlines() + expected_files = [os.path.normpath(path) for path in expected_files] + + if sorted(found_files) != sorted(expected_files): + expected_not_found = sorted(list(set(expected_files) - set(found_files))) + found_not_expected = sorted(list(set(found_files) - set(expected_files))) + + msg = "" + if expected_not_found: + msg += "\nExpected but not found:\n\t" + msg += "\n\t".join(expected_not_found) + + if found_not_expected: + msg += "\nFound but not expected:\n\t" + msg += "\n\t".join(found_not_expected) + raise ValueError(msg) + + +def data_for_testing_info(test_dataset=str): + """ + Get the path and download link for each dataset used for testing. + + Also creates the base directories into which the data and output + directories are written + + Parameters + ---------- + test_dataset : str + References one of the datasets to download. It can be: + three-echo + three-echo-reclassify + four-echo + five-echo + + Returns + ------- + test_data_path : str + The path to the local directory where the data will be downloaded + osf_id : str + The ID for the OSF file. + Data download link would be https://osf.io/osf_id/download + Metadata download link would be https://osf.io/osf_id/metadata/?format=datacite-json + """ + + tedana_path = os.path.dirname(tedana_cli.__file__) + base_data_path = os.path.abspath(os.path.join(tedana_path, "../../.testing_data_cache")) + os.makedirs(base_data_path, exist_ok=True) + os.makedirs(os.path.join(base_data_path, "outputs"), exist_ok=True) + if test_dataset == "three-echo": + test_data_path = os.path.join(base_data_path, "three-echo/TED.three-echo") + osf_id = "rqhfc" + os.makedirs(os.path.join(base_data_path, "three-echo"), exist_ok=True) + os.makedirs(os.path.join(base_data_path, "outputs/three-echo"), exist_ok=True) + elif test_dataset == "three-echo-reclassify": + test_data_path = os.path.join(base_data_path, "reclassify") + osf_id = "f6g45" + os.makedirs(os.path.join(base_data_path, "outputs/reclassify"), exist_ok=True) + elif test_dataset == "four-echo": + test_data_path = os.path.join(base_data_path, "four-echo/TED.four-echo") + osf_id = "gnj73" + os.makedirs(os.path.join(base_data_path, "four-echo"), exist_ok=True) + os.makedirs(os.path.join(base_data_path, "outputs/four-echo"), exist_ok=True) + elif test_dataset == "five-echo": + test_data_path = os.path.join(base_data_path, "five-echo/TED.five-echo") + osf_id = "9c42e" + os.makedirs(os.path.join(base_data_path, "five-echo"), exist_ok=True) + os.makedirs(os.path.join(base_data_path, "outputs/five-echo"), exist_ok=True) + else: + raise ValueError(f"{test_dataset} is not a valid dataset string for data_for_testing_info") + + return test_data_path, osf_id + + +def download_test_data(osf_id, test_data_path): + """ + If current data is not already available, downloads tar.gz data + stored at `https://osf.io/osf_id/download`. + + and unpacks into `out_path`. + + Parameters + ---------- + osf_id : str + The ID for the OSF file. + out_path : str + Path to directory where OSF data should be extracted + """ + + try: + datainfo = requests.get(f"https://osf.io/{osf_id}/metadata/?format=datacite-json") + except Exception: + if len(os.listdir(test_data_path)) == 0: + raise ConnectionError( + f"Cannot access https://osf.io/{osf_id} and testing data " "are not yet downloaded" + ) + else: + TestLGR.warning( + f"Cannot access https://osf.io/{osf_id}. " + f"Using local copy of testing data in {test_data_path} " + "but cannot validate that local copy is up-to-date" + ) + return + datainfo.raise_for_status() + metadata = json.loads(datainfo.content) + # 'dates' is a list with all udpates to the file, the last item in the list + # is the most recent and the 'date' field in the list is the date of the last + # update. + osf_filedate = metadata["dates"][-1]["date"] + + # File the file with the most recent date for comparision with + # the lsst updated date for the osf file + if os.path.exists(test_data_path): + filelist = glob.glob(f"{test_data_path}/*") + most_recent_file = max(filelist, key=os.path.getctime) + if os.path.exists(most_recent_file): + local_filedate = os.path.getmtime(most_recent_file) + local_filedate_str = str(datetime.fromtimestamp(local_filedate).date()) + local_data_exists = True + else: + local_data_exists = False + else: + local_data_exists = False + if local_data_exists: + if local_filedate_str == osf_filedate: + TestLGR.info( + f"Downloaded and up-to-date data already in {test_data_path}. Not redownloading" + ) + return + else: + TestLGR.info( + f"Downloaded data in {test_data_path} was last modified on " + f"{local_filedate_str}. Data on https://osf.io/{osf_id} " + f" was last updated on {osf_filedate}. Deleting and redownloading" + ) + shutil.rmtree(test_data_path) + req = requests.get(f"https://osf.io/{osf_id}/download") + req.raise_for_status() + t = tarfile.open(fileobj=GzipFile(fileobj=BytesIO(req.content))) + os.makedirs(test_data_path, exist_ok=True) + t.extractall(test_data_path) + + +def reclassify_raw() -> str: + test_data_path, _ = data_for_testing_info("three-echo-reclassify") + return os.path.join(test_data_path, "TED.three-echo") + + +def reclassify_raw_registry() -> str: + return os.path.join(reclassify_raw(), "desc-tedana_registry.json") + + +def guarantee_reclassify_data() -> None: + """Ensures that the reclassify data exists at the expected path and return path.""" + + test_data_path, osf_id = data_for_testing_info("three-echo-reclassify") + + # Should now be checking and not downloading for each test so don't see if statement here + # if not os.path.exists(reclassify_raw_registry()): + download_test_data(osf_id, test_data_path) + # else: + # Path exists, be sure that everything in registry exists + ioh = InputHarvester(reclassify_raw_registry()) + all_present = True + for _, v in ioh.registry.items(): + if not isinstance(v, list): + if not os.path.exists(os.path.join(reclassify_raw(), v)): + all_present = False + break + if not all_present: + # Something was removed, need to re-download + shutil.rmtree(reclassify_raw()) + guarantee_reclassify_data() + return test_data_path + + +def test_integration_five_echo(skip_integration): + """Integration test of the full tedana workflow using five-echo test data.""" + + if skip_integration: + pytest.skip("Skipping five-echo integration test") + + test_data_path, osf_id = data_for_testing_info("five-echo") + out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/five-echo")) + # out_dir_manual = f"{out_dir}-manual" + + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + # if os.path.exists(out_dir_manual): + # shutil.rmtree(out_dir_manual) + + # download data and run the test + download_test_data(osf_id, test_data_path) + prepend = f"{test_data_path}/p06.SBJ01_S09_Task11_e" + suffix = ".sm.nii.gz" + datalist = [prepend + str(i + 1) + suffix for i in range(5)] + echo_times = [15.4, 29.7, 44.0, 58.3, 72.6] + tedana_cli.tedana_workflow( + data=datalist, + tes=echo_times, + ica_method="robustica", + n_robust_runs=5, + out_dir=out_dir, + tedpca=0.95, + fittype="curvefit", + fixed_seed=49, + tedort=True, + verbose=True, + prefix="sub-01", + ) + + # Just a check on the component table pending a unit test of load_comptable + comptable = os.path.join(out_dir, "sub-01_desc-tedana_metrics.tsv") + df = pd.read_table(comptable) + assert isinstance(df, pd.DataFrame) + + +def test_integration_four_echo(skip_integration): + """Integration test of the full tedana workflow using four-echo test data.""" + + if skip_integration: + pytest.skip("Skipping four-echo integration test") + + test_data_path, osf_id = data_for_testing_info("four-echo") + out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/four-echo")) + out_dir_manual = f"{out_dir}-manual" + + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + if os.path.exists(out_dir_manual): + shutil.rmtree(out_dir_manual) + + # download data and run the test + download_test_data(osf_id, test_data_path) + prepend = f"{test_data_path}/sub-PILOT_ses-01_task-localizerDetection_run-01_echo-" + suffix = "_space-sbref_desc-preproc_bold+orig.HEAD" + datalist = [prepend + str(i + 1) + suffix for i in range(4)] + tedana_cli.tedana_workflow( + data=datalist, + tes=[11.8, 28.04, 44.28, 60.52], + out_dir=out_dir, + ica_method="robustica", + n_robust_runs=6, + tedpca="aic", + gscontrol=["gsr", "mir"], + png_cmap="bone", + prefix="sub-01", + debug=True, + verbose=True, + ) + + +def test_integration_three_echo(skip_integration): + """Integration test of the full tedana workflow using three-echo test data.""" + + if skip_integration: + pytest.skip("Skipping three-echo integration test") + + test_data_path, osf_id = data_for_testing_info("three-echo") + out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/three-echo")) + out_dir_manual = f"{out_dir}-rerun" + + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + if os.path.exists(out_dir_manual): + shutil.rmtree(out_dir_manual) + + # download data and run the test + download_test_data(osf_id, test_data_path) + + tedana_cli.tedana_workflow( + data=f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", + tes=[14.5, 38.5, 62.5], + ica_method="robustica", + n_robust_runs=30, + out_dir=out_dir, + low_mem=True, + tedpca="aic", + ) + + # Test re-running, but use the CLI + args = [ + "-d", + f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", + "-e", + "14.5", + "38.5", + "62.5", + "--out-dir", + out_dir_manual, + "--debug", + "--verbose", + "-f", + ] + tedana_cli._main(args) + + +def test_integration_reclassify_insufficient_args(skip_integration): + if skip_integration: + pytest.skip("Skipping reclassify insufficient args") + + guarantee_reclassify_data() + + args = [ + "ica_reclassify", + reclassify_raw_registry(), + ] + + result = subprocess.run(args, capture_output=True) + assert b"ValueError: Must manually accept or reject" in result.stderr + assert result.returncode != 0 + + +def test_integration_reclassify_quiet_csv(skip_integration): + if skip_integration: + pytest.skip("Skip reclassify quiet csv") + + test_data_path = guarantee_reclassify_data() + out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/quiet")) + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + # Make some files that have components to manually accept and reject + to_accept = [i for i in range(3)] + to_reject = [i for i in range(7, 4)] + acc_df = pd.DataFrame(data=to_accept, columns=["Components"]) + rej_df = pd.DataFrame(data=to_reject, columns=["Components"]) + acc_csv_fname = os.path.join(reclassify_raw(), "accept.csv") + rej_csv_fname = os.path.join(reclassify_raw(), "reject.csv") + acc_df.to_csv(acc_csv_fname) + rej_df.to_csv(rej_csv_fname) + + args = [ + "ica_reclassify", + "--manacc", + acc_csv_fname, + "--manrej", + rej_csv_fname, + "--out-dir", + out_dir, + reclassify_raw_registry(), + ] + + results = subprocess.run(args, capture_output=True) + assert results.returncode == 0 + fn = resource_filename("tedana", "tests/data/reclassify_quiet_out.txt") + check_integration_outputs(fn, out_dir) + + +def test_integration_reclassify_quiet_spaces(skip_integration): + if skip_integration: + pytest.skip("Skip reclassify quiet space-delimited integers") + + test_data_path = guarantee_reclassify_data() + out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/quiet")) + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + args = [ + "ica_reclassify", + "--manacc", + "1", + "2", + "3", + "--manrej", + "4", + "5", + "6", + "--out-dir", + out_dir, + reclassify_raw_registry(), + ] + + results = subprocess.run(args, capture_output=True) + assert results.returncode == 0 + fn = resource_filename("tedana", "tests/data/reclassify_quiet_out.txt") + check_integration_outputs(fn, out_dir) + + +def test_integration_reclassify_quiet_string(skip_integration): + if skip_integration: + pytest.skip("Skip reclassify quiet string of integers") + + test_data_path = guarantee_reclassify_data() + out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/quiet")) + + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + args = [ + "ica_reclassify", + "--manacc", + "1,2,3", + "--manrej", + "4,5,6,", + "--out-dir", + out_dir, + reclassify_raw_registry(), + ] + + results = subprocess.run(args, capture_output=True) + assert results.returncode == 0 + fn = resource_filename("tedana", "tests/data/reclassify_quiet_out.txt") + check_integration_outputs(fn, out_dir) + + +def test_integration_reclassify_debug(skip_integration): + if skip_integration: + pytest.skip("Skip reclassify debug") + + test_data_path = guarantee_reclassify_data() + out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/debug")) + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + args = [ + "ica_reclassify", + "--manacc", + "1", + "2", + "3", + "--prefix", + "sub-testymctestface", + "--convention", + "orig", + "--tedort", + "--mir", + "--no-reports", + "--out-dir", + out_dir, + "--debug", + reclassify_raw_registry(), + ] + + results = subprocess.run(args, capture_output=True) + assert results.returncode == 0 + fn = resource_filename("tedana", "tests/data/reclassify_debug_out.txt") + check_integration_outputs(fn, out_dir) + + +def test_integration_reclassify_both_rej_acc(skip_integration): + if skip_integration: + pytest.skip("Skip reclassify both rejected and accepted") + + test_data_path = guarantee_reclassify_data() + out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/both_rej_acc")) + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + with pytest.raises( + ValueError, + match=r"The following components were both accepted and", + ): + ica_reclassify_workflow( + reclassify_raw_registry(), + accept=[1, 2, 3], + reject=[1, 2, 3], + out_dir=out_dir, + ) + + +def test_integration_reclassify_run_twice(skip_integration): + if skip_integration: + pytest.skip("Skip reclassify both rejected and accepted") + + test_data_path = guarantee_reclassify_data() + out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/run_twice")) + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + ica_reclassify_workflow( + reclassify_raw_registry(), + accept=[1, 2, 3], + out_dir=out_dir, + no_reports=True, + ) + ica_reclassify_workflow( + reclassify_raw_registry(), + accept=[1, 2, 3], + out_dir=out_dir, + overwrite=True, + no_reports=True, + ) + fn = resource_filename("tedana", "tests/data/reclassify_run_twice.txt") + check_integration_outputs(fn, out_dir, n_logs=2) + + +def test_integration_reclassify_no_bold(skip_integration, caplog): + if skip_integration: + pytest.skip("Skip reclassify both rejected and accepted") + + test_data_path = guarantee_reclassify_data() + out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/no_bold")) + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + ioh = InputHarvester(reclassify_raw_registry()) + comptable = ioh.get_file_contents("ICA metrics tsv") + to_accept = [i for i in range(len(comptable))] + + ica_reclassify_workflow( + reclassify_raw_registry(), + reject=to_accept, + out_dir=out_dir, + no_reports=True, + ) + assert "No accepted components remaining after manual classification!" in caplog.text + + fn = resource_filename("tedana", "tests/data/reclassify_no_bold.txt") + check_integration_outputs(fn, out_dir) + + +def test_integration_reclassify_accrej_files(skip_integration, caplog): + if skip_integration: + pytest.skip("Skip reclassify both rejected and accepted") + + test_data_path = guarantee_reclassify_data() + out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/no_bold")) + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + ioh = InputHarvester(reclassify_raw_registry()) + comptable = ioh.get_file_contents("ICA metrics tsv") + to_accept = [i for i in range(len(comptable))] + + ica_reclassify_workflow( + reclassify_raw_registry(), + reject=to_accept, + out_dir=out_dir, + no_reports=True, + ) + assert "No accepted components remaining after manual classification!" in caplog.text + + fn = resource_filename("tedana", "tests/data/reclassify_no_bold.txt") + check_integration_outputs(fn, out_dir) + + +def test_integration_reclassify_index_failures(skip_integration): + if skip_integration: + pytest.skip("Skip reclassify index failures") + + test_data_path = guarantee_reclassify_data() + out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/index_failures")) + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + with pytest.raises( + ValueError, + match=r"_parse_manual_list expected a list of integers, but the input is", + ): + ica_reclassify_workflow( + reclassify_raw_registry(), + accept=[1, 2.5, 3], + out_dir=out_dir, + no_reports=True, + ) + + with pytest.raises( + ValueError, + match=r"_parse_manual_list expected integers or a filename, but the input is", + ): + ica_reclassify_workflow( + reclassify_raw_registry(), + accept=[2.5], + out_dir=out_dir, + no_reports=True, + ) + + +def test_integration_t2smap(skip_integration): + """Integration test of the full t2smap workflow using five-echo test data.""" + if skip_integration: + pytest.skip("Skipping t2smap integration test") + test_data_path, osf_id = data_for_testing_info("five-echo") + out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/t2smap_five-echo")) + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + # download data and run the test + download_test_data(osf_id, test_data_path) + prepend = f"{test_data_path}/p06.SBJ01_S09_Task11_e" + suffix = ".sm.nii.gz" + datalist = [prepend + str(i + 1) + suffix for i in range(5)] + echo_times = [15.4, 29.7, 44.0, 58.3, 72.6] + args = ( + ["-d"] + + datalist + + ["-e"] + + [str(te) for te in echo_times] + + ["--out-dir", out_dir, "--fittype", "curvefit"] + ) + t2smap_cli._main(args) + + # compare the generated output files + fname = resource_filename("tedana", "tests/data/nih_five_echo_outputs_t2smap.txt") + # Gets filepaths generated by integration test + found_files = [ + os.path.relpath(f, out_dir) + for f in glob.glob(os.path.join(out_dir, "**"), recursive=True)[1:] + ] + + # Compares remaining files with those expected + with open(fname, "r") as f: + expected_files = f.read().splitlines() + assert sorted(expected_files) == sorted(found_files) \ No newline at end of file diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index ee567dfef..6411ce44d 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -1,6 +1,4 @@ -""" -Run the "canonical" TE-Dependent ANAlysis workflow. -""" +"""Run the "canonical" TE-Dependent ANAlysis workflow.""" import argparse import datetime import json @@ -8,6 +6,7 @@ import os import os.path as op import shutil +import sys from glob import glob import numpy as np @@ -29,6 +28,12 @@ utils, ) from tedana.bibtex import get_description_references +from tedana.config import ( + DEFAULT_ICA_METHOD, + DEFAULT_N_MAX_ITER, + DEFAULT_N_MAX_RESTART, + DEFAULT_N_ROBUST_RUNS, +) from tedana.stats import computefeats2 from tedana.workflows.parser_utils import check_tedpca_value, is_valid_file @@ -37,8 +42,7 @@ def _get_parser(): - """ - Parses command line inputs for tedana + """Parse command line inputs for tedana. Returns ------- @@ -46,7 +50,7 @@ def _get_parser(): """ from tedana import __version__ - verstr = "tedana v{}".format(__version__) + verstr = f"tedana v{__version__}" parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) # Argument parser follow template provided by RalphyZ # https://stackoverflow.com/a/43456577 @@ -150,7 +154,6 @@ def _get_parser(): "in which case the specificed number of components will be " "selected." ), - choices=["mdl", "kic", "aic"], default="aic", ) optional.add_argument( @@ -165,45 +168,44 @@ def _get_parser(): ), default="kundu", ) - optional.add_argument(#####BTBTBT + optional.add_argument( "--ica_method", dest="ica_method", help=( "The applied ICA method. If set to fastica the FastICA " "from sklearn library will be run once with the seed value. " - "robustica will run FastICA n_robust_runs times and and uses " + "robustica will run FastICA n_robust_runs times and uses " "clustering methods to overcome the randomness of the FastICA " - "algorithm. If set to robustica the seed value will be ignored." - "If set to fastica n_robust_runs will not be effective." + "algorithm. When set to fastica n_robust_runs will not be effective." ), choices=["robustica", "fastica"], - default="robustica", + type=str.lower, + default=DEFAULT_ICA_METHOD, ) optional.add_argument( "--seed", dest="fixed_seed", metavar="INT", type=int, - help=( ##BTBTBT + help=( "Value used for random initialization of ICA " - "algorithm when ica_mthods is set to fastica. Set to an integer value for " - "reproducible ICA results with fastica. Set to -1 for " - "varying results across ICA calls. " + "algorithm. Set to an integer value for " + "reproducible ICA results (fastica/robustica). Set to -1 for " + "varying results across ICA (fastica/robustica) calls. " ), default=42, ) - optional.add_argument(#####BTBTBT + optional.add_argument( "--n_robust_runs", dest="n_robust_runs", type=int, help=( "The number of times robustica will run." - "This is only effective when ica_mthods is " + "This is only effective when ica_method is " "set to robustica." - ), - ##choices=range(2,100), - default=30, + choices=range(5, 500), + default=DEFAULT_N_ROBUST_RUNS, ) optional.add_argument( "--maxit", @@ -211,7 +213,7 @@ def _get_parser(): metavar="INT", type=int, help=("Maximum number of iterations for ICA."), - default=500, + default=DEFAULT_N_MAX_ITER, ) optional.add_argument( "--maxrestart", @@ -225,7 +227,7 @@ def _get_parser(): "convergence is achieved before maxrestart " "attempts, ICA will finish early." ), - default=10, + default=DEFAULT_N_MAX_RESTART, ) optional.add_argument( "--tedort", @@ -351,12 +353,12 @@ def tedana_workflow( fittype="loglin", combmode="t2s", tree="kundu", - ica_method="robustica", ########BTBTAdded - n_robust_runs=30, + ica_method=DEFAULT_ICA_METHOD, + n_robust_runs=DEFAULT_N_ROBUST_RUNS, tedpca="aic", fixed_seed=42, - maxit=500, - maxrestart=10, + maxit=DEFAULT_N_MAX_ITER, + maxrestart=DEFAULT_N_MAX_RESTART, tedort=False, gscontrol=None, no_reports=False, @@ -368,9 +370,9 @@ def tedana_workflow( overwrite=False, t2smap=None, mixm=None, + tedana_command=None, ): - """ - Run the "canonical" TE-Dependent ANAlysis workflow. + """Run the "canonical" TE-Dependent ANAlysis workflow. Please remember to cite :footcite:t:`dupre2021te`. @@ -412,15 +414,27 @@ def tedana_workflow( accepts and rejects some distinct components compared to kundu. Testing to better understand the effects of the differences is ongoing. Default is 'kundu'. + ica_method : {'robustica', 'fastica'}, optional + The applied ICA method. If set to fastica the FastICA from sklearn + library will be run once with the seed value. 'robustica' will run + 'FastICA' n_robust_runs times and uses clustering methods to overcome + the randomness of the FastICA algorithm. When set to fastica n_robust_runs + will not be effective. + Default is 'robustica' + n_robust_runs : :obj:`int`, optional + The number of times robustica will run. This is only effective when 'ica_method' is + set to 'robustica'. tedpca : {'mdl', 'aic', 'kic', 'kundu', 'kundu-stabilize', float, int}, optional Method with which to select components in TEDPCA. If a float is provided, then it is assumed to represent percentage of variance - explained (0-1) to retain from PCA. + explained (0-1) to retain from PCA. If an int is provided, it will output + a fixed number of components defined by the integer between 1 and the + number of time points. Default is 'aic'. fixed_seed : :obj:`int`, optional Value passed to ``mdp.numx_rand.seed()``. - Set to a positive integer value for reproducible ICA results; - otherwise, set to -1 for varying results across calls. + Set to a positive integer value for reproducible ICA results (fastica/robustica); + otherwise, set to -1 for varying results across ICA (fastica/robustica) calls. maxit : :obj:`int`, optional Maximum number of iterations for ICA. Default is 500. maxrestart : :obj:`int`, optional @@ -457,6 +471,9 @@ def tedana_workflow( If True, suppresses logging/printing of messages. Default is False. overwrite : :obj:`bool`, optional If True, force overwriting of files. Default is False. + tedana_command : :obj:`str`, optional + If the command-line interface was used, this is the command that was + run. Default is None. Notes ----- @@ -473,10 +490,11 @@ def tedana_workflow( os.mkdir(out_dir) # boilerplate - basename = "report" + prefix = io._infer_prefix(prefix) + basename = f"{prefix}report" extension = "txt" repname = op.join(out_dir, (basename + "." + extension)) - bibtex_file = op.join(out_dir, "references.bib") + bibtex_file = op.join(out_dir, f"{prefix}references.bib") repex = op.join(out_dir, (basename + "*")) previousreps = glob(repex) previousreps.sort(reverse=True) @@ -492,7 +510,20 @@ def tedana_workflow( logname = op.join(out_dir, (basename + start_time + "." + extension)) utils.setup_loggers(logname, repname, quiet=quiet, debug=debug) - LGR.info("Using output directory: {}".format(out_dir)) + # Save command into sh file, if the command-line interface was used + # TODO: use io_generator to save command + if tedana_command is not None: + command_file = open(os.path.join(out_dir, "tedana_call.sh"), "w") + command_file.write(tedana_command) + command_file.close() + else: + # Get variables passed to function if the tedana command is None + variables = ", ".join(f"{name}={value}" for name, value in locals().items()) + # From variables, remove everything after ", tedana_command" + variables = variables.split(", tedana_command")[0] + tedana_command = f"tedana_workflow({variables})" + + LGR.info(f"Using output directory: {out_dir}") # ensure tes are in appropriate format tes = [float(te) for te in tes] @@ -510,7 +541,7 @@ def tedana_workflow( if isinstance(data, str): data = [data] - LGR.info("Loading input data: {}".format([f for f in data])) + LGR.info(f"Loading input data: {[f for f in data]}") catd, ref_img = io.load_data(data, n_echos=n_echos) io_generator = io.OutputGenerator( @@ -527,8 +558,13 @@ def tedana_workflow( # TODO: turn this into an IOManager since this isn't really output io_generator.register_input(data) + # Save system info to json + info_dict = utils.get_system_info() + info_dict["Python"] = sys.version + info_dict["Command"] = tedana_command + n_samp, n_echos, n_vols = catd.shape - LGR.debug("Resulting data shape: {}".format(catd.shape)) + LGR.debug(f"Resulting data shape: {catd.shape}") # check if TR is 0 img_t_r = io_generator.reference_img.header.get_zooms()[-1] @@ -600,7 +636,7 @@ def tedana_workflow( getsum=True, threshold=1, ) - LGR.debug("Retaining {}/{} samples for denoising".format(mask_denoise.sum(), n_samp)) + LGR.debug(f"Retaining {mask_denoise.sum()}/{n_samp} samples for denoising") io_generator.save_file(masksum_denoise, "adaptive mask img") # Create an adaptive mask with at least 3 good echoes, for classification @@ -614,7 +650,7 @@ def tedana_workflow( "(restricted to voxels with good data in at least the first three echoes) was used for " "the component classification procedure." ) - LGR.debug("Retaining {}/{} samples for classification".format(mask_clf.sum(), n_samp)) + LGR.debug(f"Retaining {mask_clf.sum()}/{n_samp} samples for classification") if t2smap is None: LGR.info("Computing T2* map") @@ -625,7 +661,7 @@ def tedana_workflow( # set a hard cap for the T2* map # anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile cap_t2s = stats.scoreatpercentile(t2s_full.flatten(), 99.5, interpolation_method="lower") - LGR.debug("Setting cap on T2* map at {:.5f}s".format(utils.millisec2sec(cap_t2s))) + LGR.debug(f"Setting cap on T2* map at {utils.millisec2sec(cap_t2s):.5f}s") t2s_full[t2s_full > cap_t2s * 10] = cap_t2s io_generator.save_file(utils.millisec2sec(t2s_full), "t2star img") io_generator.save_file(s0_full, "s0 img") @@ -642,23 +678,20 @@ def tedana_workflow( catd, data_oc = gsc.gscontrol_raw(catd, data_oc, n_echos, io_generator) fout = io_generator.save_file(data_oc, "combined img") - LGR.info("Writing optimally combined data set: {}".format(fout)) + LGR.info(f"Writing optimally combined data set: {fout}") if mixm is None: # Identify and remove thermal noise from data dd, n_components = decomposition.tedpca( catd, data_oc, - combmode, mask_clf, masksum_clf, - t2s_full, io_generator, tes=tes, algorithm=tedpca, kdaw=10.0, rdaw=1.0, - verbose=verbose, low_mem=low_mem, ) if verbose: @@ -667,12 +700,17 @@ def tedana_workflow( # Perform ICA, calculate metrics, and apply decision tree # Restart when ICA fails to converge or too few BOLD components found keep_restarting = True - n_restarts = 0 seed = fixed_seed while keep_restarting: mmix, seed = decomposition.tedica( - dd, n_components, seed, ica_method, n_robust_runs, maxit, maxrestart=(maxrestart - n_restarts) + dd, + n_components, + seed, + ica_method, + n_robust_runs, + maxit, + maxrestart=(maxrestart - n_restarts), ) seed += 1 n_restarts = seed - fixed_seed @@ -706,17 +744,13 @@ def tedana_workflow( ) ica_selector = selection.automatic_selection(comptable, n_echos, n_vols, tree=tree) n_likely_bold_comps = ica_selector.n_likely_bold_comps - - if ica_method=='robustica': #########BTBTBT + if (n_restarts < maxrestart) and (n_likely_bold_comps == 0): + LGR.warning("No BOLD components found. Re-attempting ICA.") + elif n_likely_bold_comps == 0: + LGR.warning("No BOLD components found, but maximum number of restarts reached.") + keep_restarting = False + else: keep_restarting = False - else: - if (n_restarts < maxrestart) and (n_likely_bold_comps == 0): - LGR.warning("No BOLD components found. Re-attempting ICA.") - elif n_likely_bold_comps == 0: - LGR.warning("No BOLD components found, but maximum number of restarts reached.") - keep_restarting = False - else: - keep_restarting = False # If we're going to restart, temporarily allow force overwrite if keep_restarting: @@ -825,7 +859,6 @@ def tedana_workflow( mask=mask_denoise, comptable=comptable, mmix=mmix, - n_vols=n_vols, io_generator=io_generator, ) @@ -852,6 +885,16 @@ def tedana_workflow( "of non-BOLD noise from multi-echo fMRI data." ), "CodeURL": "https://github.com/ME-ICA/tedana", + "Node": { + "Name": info_dict["Node"], + "System": info_dict["System"], + "Machine": info_dict["Machine"], + "Processor": info_dict["Processor"], + "Release": info_dict["Release"], + "Version": info_dict["Version"], + }, + "Python": info_dict["Python"], + "Command": info_dict["Command"], } ], } @@ -908,22 +951,22 @@ def tedana_workflow( ) LGR.info("Generating dynamic report") - reporting.generate_report(io_generator, tr=img_t_r) + reporting.generate_report(io_generator) LGR.info("Workflow completed") utils.teardown_loggers() def _main(argv=None): - """Tedana entry point""" + """Run the tedana workflow.""" + tedana_command = "tedana " + " ".join(sys.argv[1:]) options = _get_parser().parse_args(argv) kwargs = vars(options) n_threads = kwargs.pop("n_threads") n_threads = None if n_threads == -1 else n_threads with threadpool_limits(limits=n_threads, user_api=None): - tedana_workflow(**kwargs) + tedana_workflow(**kwargs, tedana_command=tedana_command) if __name__ == "__main__": - _main() - + _main() \ No newline at end of file From 55c2ae45fbc0fbb392e1de35d6127ccaddee9d4d Mon Sep 17 00:00:00 2001 From: Circle Date: Wed, 1 Nov 2023 13:18:04 +1100 Subject: [PATCH 03/34] Add robustica 0.1.3 to dependency list Cherry-pick of 41354cbfd127ac69cf1bdd69a556328fc6092457. --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 2e695b542..a9173c8cd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,7 @@ dependencies = [ "nibabel>=2.5.1", "nilearn>=0.7", "numpy>=1.16", + "robustica>=0.1.3", "pandas>=2.0", "scikit-learn>=0.21", "scipy>=1.2.0", From cd55a3f7d93d251b3eb2be045aa6555e6d5366ce Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Tue, 5 Dec 2023 12:44:58 +1100 Subject: [PATCH 04/34] Multiple fixes to RobustICA addition from code review From: BahmanTahayori/tedana_florey#2. Co-authored-by: Robert E. Smith --- tedana/decomposition/ica.py | 6 +++--- tedana/tests/test_integrity_robustica.py | 4 ++-- tedana/workflows/tedana.py | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index abb9d2c82..d4fb6c50a 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -91,7 +91,7 @@ def tedica( def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): """ - Perform robustica on `data` by running FastICA multiple times (n_robust runes) + Perform robustica on `data` by running FastICA multiple times (n_robust_runs) and returns mixing matrix Parameters @@ -165,8 +165,8 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): if iq < 0.6: LGR.warning( - "The resultant mean Index Quality is low. It is recommended to rerun the " - "process with a different seed." + "The resultant mean Index Quality is low ({0}). It is recommended to rerun the " + "process with a different seed.".format(iq) ) mmix = mmix[:, q["cluster_id"] >= 0] diff --git a/tedana/tests/test_integrity_robustica.py b/tedana/tests/test_integrity_robustica.py index 88646d51f..4bf9ed6b3 100644 --- a/tedana/tests/test_integrity_robustica.py +++ b/tedana/tests/test_integrity_robustica.py @@ -76,7 +76,7 @@ def check_integration_outputs(fname, outpath, n_logs=1): raise ValueError(msg) -def data_for_testing_info(test_dataset=str): +def data_for_testing_info(test_dataset: str): """ Get the path and download link for each dataset used for testing. @@ -209,7 +209,7 @@ def reclassify_raw_registry() -> str: return os.path.join(reclassify_raw(), "desc-tedana_registry.json") -def guarantee_reclassify_data() -> None: +def guarantee_reclassify_data() -> str: """Ensures that the reclassify data exists at the expected path and return path.""" test_data_path, osf_id = data_for_testing_info("three-echo-reclassify") diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index 6411ce44d..b092c8df3 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -176,7 +176,7 @@ def _get_parser(): "from sklearn library will be run once with the seed value. " "robustica will run FastICA n_robust_runs times and uses " "clustering methods to overcome the randomness of the FastICA " - "algorithm. When set to fastica n_robust_runs will not be effective." + "algorithm. When set to fastica, --n_robust_runs option will have no effect." ), choices=["robustica", "fastica"], type=str.lower, From 2d9b0078e1671d31ad70918b5e3cc4c8d47c8c74 Mon Sep 17 00:00:00 2001 From: Circle Date: Wed, 29 Nov 2023 17:07:24 +1100 Subject: [PATCH 05/34] Specify magic number fixed seed of 42 as a constant Cherry-pick of da1b128e2c0a4aad8acbd398e8d151f5c846c47b (with modification). --- tedana/config.py | 3 ++- tedana/decomposition/ica.py | 2 +- tedana/workflows/tedana.py | 10 ++++++---- 3 files changed, 9 insertions(+), 6 deletions(-) diff --git a/tedana/config.py b/tedana/config.py index 9bca5b873..e665795af 100644 --- a/tedana/config.py +++ b/tedana/config.py @@ -1,4 +1,5 @@ DEFAULT_ICA_METHOD = "robustica" DEFAULT_N_ROBUST_RUNS = 30 DEFAULT_N_MAX_ITER = 500 -DEFAULT_N_MAX_RESTART = 10 \ No newline at end of file +DEFAULT_N_MAX_RESTART = 10 +DEFAULT_SEED = 42 \ No newline at end of file diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index d4fb6c50a..ec20e8d92 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -248,4 +248,4 @@ def f_ica(data, n_components, fixed_seed, maxit, maxrestart): mmix = ica.mixing_ mmix = stats.zscore(mmix, axis=0) - return mmix, fixed_seed \ No newline at end of file + return mmix, fixed_seed diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index b092c8df3..49d8a108c 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -33,6 +33,7 @@ DEFAULT_N_MAX_ITER, DEFAULT_N_MAX_RESTART, DEFAULT_N_ROBUST_RUNS, + DEFAULT_SEED ) from tedana.stats import computefeats2 from tedana.workflows.parser_utils import check_tedpca_value, is_valid_file @@ -190,10 +191,11 @@ def _get_parser(): help=( "Value used for random initialization of ICA " "algorithm. Set to an integer value for " - "reproducible ICA results (fastica/robustica). Set to -1 for " - "varying results across ICA (fastica/robustica) calls. " + "reproducible ICA results. Set to -1 for " + "varying results across ICA calls. This" + "applies to both fastica and robustica methods." ), - default=42, + default=DEFAULT_SEED, ) optional.add_argument( "--n_robust_runs", @@ -356,7 +358,7 @@ def tedana_workflow( ica_method=DEFAULT_ICA_METHOD, n_robust_runs=DEFAULT_N_ROBUST_RUNS, tedpca="aic", - fixed_seed=42, + fixed_seed=DEFAULT_SEED, maxit=DEFAULT_N_MAX_ITER, maxrestart=DEFAULT_N_MAX_RESTART, tedort=False, From fc5f9ea8c8470346313acd860de59ee1193dbcb1 Mon Sep 17 00:00:00 2001 From: tahayori Date: Tue, 5 Dec 2023 16:39:30 +1100 Subject: [PATCH 06/34] Updated --- tedana/decomposition/ica.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index f6fc03c9a..3074c38db 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -139,7 +139,7 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): robust_method="DBSCAN", ) - S, mmix = rica.fit_transform(data) + s, mmix = rica.fit_transform(data) q = rica.evaluate_clustering( rica.S_all, rica.clustering.labels_, rica.signs_, rica.orientation_ ) @@ -156,7 +156,7 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): robust_method="AgglomerativeClustering", ) - S, mmix = rica.fit_transform(data) + s, mmix = rica.fit_transform(data) q = rica.evaluate_clustering( rica.S_all, rica.clustering.labels_, rica.signs_, rica.orientation_ ) From 4fc30439ca28efe3fe501f45bf2b6b3d91aaef8c Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Wed, 6 Dec 2023 11:55:30 +1100 Subject: [PATCH 07/34] Robustica Updates --- tedana/config.py | 2 +- tedana/tests/test_integration.py | 136 +++++ tedana/tests/test_integrity_robustica.py | 667 ----------------------- tedana/workflows/tedana.py | 4 +- 4 files changed, 139 insertions(+), 670 deletions(-) delete mode 100644 tedana/tests/test_integrity_robustica.py diff --git a/tedana/config.py b/tedana/config.py index e665795af..58563de79 100644 --- a/tedana/config.py +++ b/tedana/config.py @@ -2,4 +2,4 @@ DEFAULT_N_ROBUST_RUNS = 30 DEFAULT_N_MAX_ITER = 500 DEFAULT_N_MAX_RESTART = 10 -DEFAULT_SEED = 42 \ No newline at end of file +DEFAULT_SEED = 42 diff --git a/tedana/tests/test_integration.py b/tedana/tests/test_integration.py index 59695a939..645057788 100644 --- a/tedana/tests/test_integration.py +++ b/tedana/tests/test_integration.py @@ -258,6 +258,7 @@ def test_integration_five_echo(skip_integration): tedana_cli.tedana_workflow( data=datalist, tes=echo_times, + ica_method="fastica", out_dir=out_dir, tedpca=0.95, fittype="curvefit", @@ -277,6 +278,48 @@ def test_integration_five_echo(skip_integration): check_integration_outputs(fn, out_dir) +def test_integration_robustica_five_echo(skip_integration): + """Integration test of the full tedana workflow with robustica using five-echo test data.""" + + if skip_integration: + pytest.skip("Skipping five-echo integration test") + + test_data_path, osf_id = data_for_testing_info("five-echo") + out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/five-echo")) + # out_dir_manual = f"{out_dir}-manual" + + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + # if os.path.exists(out_dir_manual): + # shutil.rmtree(out_dir_manual) + + # download data and run the test + download_test_data(osf_id, test_data_path) + prepend = f"{test_data_path}/p06.SBJ01_S09_Task11_e" + suffix = ".sm.nii.gz" + datalist = [prepend + str(i + 1) + suffix for i in range(5)] + echo_times = [15.4, 29.7, 44.0, 58.3, 72.6] + tedana_cli.tedana_workflow( + data=datalist, + tes=echo_times, + ica_method="robustica", + n_robust_runs=10, + out_dir=out_dir, + tedpca=0.95, + fittype="curvefit", + fixed_seed=49, + tedort=True, + verbose=True, + prefix="sub-01", + ) + + # Just a check on the component table pending a unit test of load_comptable + comptable = os.path.join(out_dir, "sub-01_desc-tedana_metrics.tsv") + df = pd.read_table(comptable) + assert isinstance(df, pd.DataFrame) + + def test_integration_four_echo(skip_integration): """Integration test of the full tedana workflow using four-echo test data.""" @@ -302,6 +345,7 @@ def test_integration_four_echo(skip_integration): data=datalist, mixm=op.join(op.dirname(datalist[0]), "desc-ICA_mixing_static.tsv"), tes=[11.8, 28.04, 44.28, 60.52], + ica_method="fastica", out_dir=out_dir, tedpca="kundu-stabilize", gscontrol=["gsr", "mir"], @@ -325,6 +369,49 @@ def test_integration_four_echo(skip_integration): check_integration_outputs(fn, out_dir) +def test_integration_robustica_four_echo(skip_integration): + """Integration test of the full tedana workflow with robustica using four-echo test data.""" + + if skip_integration: + pytest.skip("Skipping four-echo integration test") + + test_data_path, osf_id = data_for_testing_info("four-echo") + out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/four-echo")) + out_dir_manual = f"{out_dir}-manual" + + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + if os.path.exists(out_dir_manual): + shutil.rmtree(out_dir_manual) + + # download data and run the test + download_test_data(osf_id, test_data_path) + prepend = f"{test_data_path}/sub-PILOT_ses-01_task-localizerDetection_run-01_echo-" + suffix = "_space-sbref_desc-preproc_bold+orig.HEAD" + datalist = [prepend + str(i + 1) + suffix for i in range(4)] + tedana_cli.tedana_workflow( + data=datalist, + mixm=op.join(op.dirname(datalist[0]), "desc-ICA_mixing_static.tsv"), + tes=[11.8, 28.04, 44.28, 60.52], + out_dir=out_dir, + tedpca="kundu-stabilize", + gscontrol=["gsr", "mir"], + png_cmap="bone", + prefix="sub-01", + debug=True, + verbose=True, + ) + + ica_reclassify_workflow( + op.join(out_dir, "sub-01_desc-tedana_registry.json"), + accept=[1, 2, 3], + reject=[4, 5, 6], + out_dir=out_dir_manual, + mir=True, + ) + + def test_integration_three_echo(skip_integration): """Integration test of the full tedana workflow using three-echo test data.""" @@ -346,6 +433,7 @@ def test_integration_three_echo(skip_integration): tedana_cli.tedana_workflow( data=f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", tes=[14.5, 38.5, 62.5], + ica_method="fastica", out_dir=out_dir, low_mem=True, tedpca="aic", @@ -374,6 +462,54 @@ def test_integration_three_echo(skip_integration): check_integration_outputs(fn, out_dir) +def test_integration_robustica_three_echo(skip_integration): + """Integration test of the full tedana workflow with robustica using three-echo test data.""" + + if skip_integration: + pytest.skip("Skipping three-echo integration test") + + test_data_path, osf_id = data_for_testing_info("three-echo") + out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/three-echo")) + out_dir_manual = f"{out_dir}-rerun" + + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + if os.path.exists(out_dir_manual): + shutil.rmtree(out_dir_manual) + + # download data and run the test + download_test_data(osf_id, test_data_path) + tedana_cli.tedana_workflow( + data=f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", + tes=[14.5, 38.5, 62.5], + ica_method="robustica", + out_dir=out_dir, + low_mem=True, + tedpca="aic", + ) + + # Test re-running, but use the CLI + args = [ + "-d", + f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", + "-e", + "14.5", + "38.5", + "62.5", + "--ica_method", + "robustica", + "--out-dir", + out_dir_manual, + "--debug", + "--verbose", + "-f", + "--mix", + os.path.join(out_dir, "desc-ICA_mixing.tsv"), + ] + tedana_cli._main(args) + + def test_integration_reclassify_insufficient_args(skip_integration): if skip_integration: pytest.skip("Skipping reclassify insufficient args") diff --git a/tedana/tests/test_integrity_robustica.py b/tedana/tests/test_integrity_robustica.py deleted file mode 100644 index 4bf9ed6b3..000000000 --- a/tedana/tests/test_integrity_robustica.py +++ /dev/null @@ -1,667 +0,0 @@ -"""Integration tests for "real" data.""" - -import glob -import json -import logging -import os -import os.path as op -import re -import shutil -import subprocess -import tarfile -from datetime import datetime -from gzip import GzipFile -from io import BytesIO - -import pandas as pd -import pytest -import requests -from pkg_resources import resource_filename - -from tedana.io import InputHarvester -from tedana.workflows import t2smap as t2smap_cli -from tedana.workflows import tedana as tedana_cli -from tedana.workflows.ica_reclassify import ica_reclassify_workflow - -# Need to see if a no BOLD warning occurred -LOGGER = logging.getLogger(__name__) -# Added a testing logger to output whether or not testing data were downlaoded -TestLGR = logging.getLogger("TESTING") - - -def check_integration_outputs(fname, outpath, n_logs=1): - """ - Checks outputs of integration tests. - - Parameters - ---------- - fname : str - Path to file with expected outputs - outpath : str - Path to output directory generated from integration tests - """ - - # Gets filepaths generated by integration test - found_files = [ - os.path.relpath(f, outpath) - for f in glob.glob(os.path.join(outpath, "**"), recursive=True)[1:] - ] - - # Checks for log file - log_regex = "^tedana_[12][0-9]{3}-[0-9]{2}-[0-9]{2}T[0-9]{2}[0-9]{2}[0-9]{2}.tsv$" - logfiles = [out for out in found_files if re.match(log_regex, out)] - assert len(logfiles) == n_logs - - # Removes logfiles from list of existing files - for log in logfiles: - found_files.remove(log) - - # Compares remaining files with those expected - with open(fname, "r") as f: - expected_files = f.read().splitlines() - expected_files = [os.path.normpath(path) for path in expected_files] - - if sorted(found_files) != sorted(expected_files): - expected_not_found = sorted(list(set(expected_files) - set(found_files))) - found_not_expected = sorted(list(set(found_files) - set(expected_files))) - - msg = "" - if expected_not_found: - msg += "\nExpected but not found:\n\t" - msg += "\n\t".join(expected_not_found) - - if found_not_expected: - msg += "\nFound but not expected:\n\t" - msg += "\n\t".join(found_not_expected) - raise ValueError(msg) - - -def data_for_testing_info(test_dataset: str): - """ - Get the path and download link for each dataset used for testing. - - Also creates the base directories into which the data and output - directories are written - - Parameters - ---------- - test_dataset : str - References one of the datasets to download. It can be: - three-echo - three-echo-reclassify - four-echo - five-echo - - Returns - ------- - test_data_path : str - The path to the local directory where the data will be downloaded - osf_id : str - The ID for the OSF file. - Data download link would be https://osf.io/osf_id/download - Metadata download link would be https://osf.io/osf_id/metadata/?format=datacite-json - """ - - tedana_path = os.path.dirname(tedana_cli.__file__) - base_data_path = os.path.abspath(os.path.join(tedana_path, "../../.testing_data_cache")) - os.makedirs(base_data_path, exist_ok=True) - os.makedirs(os.path.join(base_data_path, "outputs"), exist_ok=True) - if test_dataset == "three-echo": - test_data_path = os.path.join(base_data_path, "three-echo/TED.three-echo") - osf_id = "rqhfc" - os.makedirs(os.path.join(base_data_path, "three-echo"), exist_ok=True) - os.makedirs(os.path.join(base_data_path, "outputs/three-echo"), exist_ok=True) - elif test_dataset == "three-echo-reclassify": - test_data_path = os.path.join(base_data_path, "reclassify") - osf_id = "f6g45" - os.makedirs(os.path.join(base_data_path, "outputs/reclassify"), exist_ok=True) - elif test_dataset == "four-echo": - test_data_path = os.path.join(base_data_path, "four-echo/TED.four-echo") - osf_id = "gnj73" - os.makedirs(os.path.join(base_data_path, "four-echo"), exist_ok=True) - os.makedirs(os.path.join(base_data_path, "outputs/four-echo"), exist_ok=True) - elif test_dataset == "five-echo": - test_data_path = os.path.join(base_data_path, "five-echo/TED.five-echo") - osf_id = "9c42e" - os.makedirs(os.path.join(base_data_path, "five-echo"), exist_ok=True) - os.makedirs(os.path.join(base_data_path, "outputs/five-echo"), exist_ok=True) - else: - raise ValueError(f"{test_dataset} is not a valid dataset string for data_for_testing_info") - - return test_data_path, osf_id - - -def download_test_data(osf_id, test_data_path): - """ - If current data is not already available, downloads tar.gz data - stored at `https://osf.io/osf_id/download`. - - and unpacks into `out_path`. - - Parameters - ---------- - osf_id : str - The ID for the OSF file. - out_path : str - Path to directory where OSF data should be extracted - """ - - try: - datainfo = requests.get(f"https://osf.io/{osf_id}/metadata/?format=datacite-json") - except Exception: - if len(os.listdir(test_data_path)) == 0: - raise ConnectionError( - f"Cannot access https://osf.io/{osf_id} and testing data " "are not yet downloaded" - ) - else: - TestLGR.warning( - f"Cannot access https://osf.io/{osf_id}. " - f"Using local copy of testing data in {test_data_path} " - "but cannot validate that local copy is up-to-date" - ) - return - datainfo.raise_for_status() - metadata = json.loads(datainfo.content) - # 'dates' is a list with all udpates to the file, the last item in the list - # is the most recent and the 'date' field in the list is the date of the last - # update. - osf_filedate = metadata["dates"][-1]["date"] - - # File the file with the most recent date for comparision with - # the lsst updated date for the osf file - if os.path.exists(test_data_path): - filelist = glob.glob(f"{test_data_path}/*") - most_recent_file = max(filelist, key=os.path.getctime) - if os.path.exists(most_recent_file): - local_filedate = os.path.getmtime(most_recent_file) - local_filedate_str = str(datetime.fromtimestamp(local_filedate).date()) - local_data_exists = True - else: - local_data_exists = False - else: - local_data_exists = False - if local_data_exists: - if local_filedate_str == osf_filedate: - TestLGR.info( - f"Downloaded and up-to-date data already in {test_data_path}. Not redownloading" - ) - return - else: - TestLGR.info( - f"Downloaded data in {test_data_path} was last modified on " - f"{local_filedate_str}. Data on https://osf.io/{osf_id} " - f" was last updated on {osf_filedate}. Deleting and redownloading" - ) - shutil.rmtree(test_data_path) - req = requests.get(f"https://osf.io/{osf_id}/download") - req.raise_for_status() - t = tarfile.open(fileobj=GzipFile(fileobj=BytesIO(req.content))) - os.makedirs(test_data_path, exist_ok=True) - t.extractall(test_data_path) - - -def reclassify_raw() -> str: - test_data_path, _ = data_for_testing_info("three-echo-reclassify") - return os.path.join(test_data_path, "TED.three-echo") - - -def reclassify_raw_registry() -> str: - return os.path.join(reclassify_raw(), "desc-tedana_registry.json") - - -def guarantee_reclassify_data() -> str: - """Ensures that the reclassify data exists at the expected path and return path.""" - - test_data_path, osf_id = data_for_testing_info("three-echo-reclassify") - - # Should now be checking and not downloading for each test so don't see if statement here - # if not os.path.exists(reclassify_raw_registry()): - download_test_data(osf_id, test_data_path) - # else: - # Path exists, be sure that everything in registry exists - ioh = InputHarvester(reclassify_raw_registry()) - all_present = True - for _, v in ioh.registry.items(): - if not isinstance(v, list): - if not os.path.exists(os.path.join(reclassify_raw(), v)): - all_present = False - break - if not all_present: - # Something was removed, need to re-download - shutil.rmtree(reclassify_raw()) - guarantee_reclassify_data() - return test_data_path - - -def test_integration_five_echo(skip_integration): - """Integration test of the full tedana workflow using five-echo test data.""" - - if skip_integration: - pytest.skip("Skipping five-echo integration test") - - test_data_path, osf_id = data_for_testing_info("five-echo") - out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/five-echo")) - # out_dir_manual = f"{out_dir}-manual" - - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - # if os.path.exists(out_dir_manual): - # shutil.rmtree(out_dir_manual) - - # download data and run the test - download_test_data(osf_id, test_data_path) - prepend = f"{test_data_path}/p06.SBJ01_S09_Task11_e" - suffix = ".sm.nii.gz" - datalist = [prepend + str(i + 1) + suffix for i in range(5)] - echo_times = [15.4, 29.7, 44.0, 58.3, 72.6] - tedana_cli.tedana_workflow( - data=datalist, - tes=echo_times, - ica_method="robustica", - n_robust_runs=5, - out_dir=out_dir, - tedpca=0.95, - fittype="curvefit", - fixed_seed=49, - tedort=True, - verbose=True, - prefix="sub-01", - ) - - # Just a check on the component table pending a unit test of load_comptable - comptable = os.path.join(out_dir, "sub-01_desc-tedana_metrics.tsv") - df = pd.read_table(comptable) - assert isinstance(df, pd.DataFrame) - - -def test_integration_four_echo(skip_integration): - """Integration test of the full tedana workflow using four-echo test data.""" - - if skip_integration: - pytest.skip("Skipping four-echo integration test") - - test_data_path, osf_id = data_for_testing_info("four-echo") - out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/four-echo")) - out_dir_manual = f"{out_dir}-manual" - - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - if os.path.exists(out_dir_manual): - shutil.rmtree(out_dir_manual) - - # download data and run the test - download_test_data(osf_id, test_data_path) - prepend = f"{test_data_path}/sub-PILOT_ses-01_task-localizerDetection_run-01_echo-" - suffix = "_space-sbref_desc-preproc_bold+orig.HEAD" - datalist = [prepend + str(i + 1) + suffix for i in range(4)] - tedana_cli.tedana_workflow( - data=datalist, - tes=[11.8, 28.04, 44.28, 60.52], - out_dir=out_dir, - ica_method="robustica", - n_robust_runs=6, - tedpca="aic", - gscontrol=["gsr", "mir"], - png_cmap="bone", - prefix="sub-01", - debug=True, - verbose=True, - ) - - -def test_integration_three_echo(skip_integration): - """Integration test of the full tedana workflow using three-echo test data.""" - - if skip_integration: - pytest.skip("Skipping three-echo integration test") - - test_data_path, osf_id = data_for_testing_info("three-echo") - out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/three-echo")) - out_dir_manual = f"{out_dir}-rerun" - - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - if os.path.exists(out_dir_manual): - shutil.rmtree(out_dir_manual) - - # download data and run the test - download_test_data(osf_id, test_data_path) - - tedana_cli.tedana_workflow( - data=f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", - tes=[14.5, 38.5, 62.5], - ica_method="robustica", - n_robust_runs=30, - out_dir=out_dir, - low_mem=True, - tedpca="aic", - ) - - # Test re-running, but use the CLI - args = [ - "-d", - f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", - "-e", - "14.5", - "38.5", - "62.5", - "--out-dir", - out_dir_manual, - "--debug", - "--verbose", - "-f", - ] - tedana_cli._main(args) - - -def test_integration_reclassify_insufficient_args(skip_integration): - if skip_integration: - pytest.skip("Skipping reclassify insufficient args") - - guarantee_reclassify_data() - - args = [ - "ica_reclassify", - reclassify_raw_registry(), - ] - - result = subprocess.run(args, capture_output=True) - assert b"ValueError: Must manually accept or reject" in result.stderr - assert result.returncode != 0 - - -def test_integration_reclassify_quiet_csv(skip_integration): - if skip_integration: - pytest.skip("Skip reclassify quiet csv") - - test_data_path = guarantee_reclassify_data() - out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/quiet")) - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - # Make some files that have components to manually accept and reject - to_accept = [i for i in range(3)] - to_reject = [i for i in range(7, 4)] - acc_df = pd.DataFrame(data=to_accept, columns=["Components"]) - rej_df = pd.DataFrame(data=to_reject, columns=["Components"]) - acc_csv_fname = os.path.join(reclassify_raw(), "accept.csv") - rej_csv_fname = os.path.join(reclassify_raw(), "reject.csv") - acc_df.to_csv(acc_csv_fname) - rej_df.to_csv(rej_csv_fname) - - args = [ - "ica_reclassify", - "--manacc", - acc_csv_fname, - "--manrej", - rej_csv_fname, - "--out-dir", - out_dir, - reclassify_raw_registry(), - ] - - results = subprocess.run(args, capture_output=True) - assert results.returncode == 0 - fn = resource_filename("tedana", "tests/data/reclassify_quiet_out.txt") - check_integration_outputs(fn, out_dir) - - -def test_integration_reclassify_quiet_spaces(skip_integration): - if skip_integration: - pytest.skip("Skip reclassify quiet space-delimited integers") - - test_data_path = guarantee_reclassify_data() - out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/quiet")) - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - args = [ - "ica_reclassify", - "--manacc", - "1", - "2", - "3", - "--manrej", - "4", - "5", - "6", - "--out-dir", - out_dir, - reclassify_raw_registry(), - ] - - results = subprocess.run(args, capture_output=True) - assert results.returncode == 0 - fn = resource_filename("tedana", "tests/data/reclassify_quiet_out.txt") - check_integration_outputs(fn, out_dir) - - -def test_integration_reclassify_quiet_string(skip_integration): - if skip_integration: - pytest.skip("Skip reclassify quiet string of integers") - - test_data_path = guarantee_reclassify_data() - out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/quiet")) - - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - args = [ - "ica_reclassify", - "--manacc", - "1,2,3", - "--manrej", - "4,5,6,", - "--out-dir", - out_dir, - reclassify_raw_registry(), - ] - - results = subprocess.run(args, capture_output=True) - assert results.returncode == 0 - fn = resource_filename("tedana", "tests/data/reclassify_quiet_out.txt") - check_integration_outputs(fn, out_dir) - - -def test_integration_reclassify_debug(skip_integration): - if skip_integration: - pytest.skip("Skip reclassify debug") - - test_data_path = guarantee_reclassify_data() - out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/debug")) - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - args = [ - "ica_reclassify", - "--manacc", - "1", - "2", - "3", - "--prefix", - "sub-testymctestface", - "--convention", - "orig", - "--tedort", - "--mir", - "--no-reports", - "--out-dir", - out_dir, - "--debug", - reclassify_raw_registry(), - ] - - results = subprocess.run(args, capture_output=True) - assert results.returncode == 0 - fn = resource_filename("tedana", "tests/data/reclassify_debug_out.txt") - check_integration_outputs(fn, out_dir) - - -def test_integration_reclassify_both_rej_acc(skip_integration): - if skip_integration: - pytest.skip("Skip reclassify both rejected and accepted") - - test_data_path = guarantee_reclassify_data() - out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/both_rej_acc")) - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - with pytest.raises( - ValueError, - match=r"The following components were both accepted and", - ): - ica_reclassify_workflow( - reclassify_raw_registry(), - accept=[1, 2, 3], - reject=[1, 2, 3], - out_dir=out_dir, - ) - - -def test_integration_reclassify_run_twice(skip_integration): - if skip_integration: - pytest.skip("Skip reclassify both rejected and accepted") - - test_data_path = guarantee_reclassify_data() - out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/run_twice")) - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - ica_reclassify_workflow( - reclassify_raw_registry(), - accept=[1, 2, 3], - out_dir=out_dir, - no_reports=True, - ) - ica_reclassify_workflow( - reclassify_raw_registry(), - accept=[1, 2, 3], - out_dir=out_dir, - overwrite=True, - no_reports=True, - ) - fn = resource_filename("tedana", "tests/data/reclassify_run_twice.txt") - check_integration_outputs(fn, out_dir, n_logs=2) - - -def test_integration_reclassify_no_bold(skip_integration, caplog): - if skip_integration: - pytest.skip("Skip reclassify both rejected and accepted") - - test_data_path = guarantee_reclassify_data() - out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/no_bold")) - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - ioh = InputHarvester(reclassify_raw_registry()) - comptable = ioh.get_file_contents("ICA metrics tsv") - to_accept = [i for i in range(len(comptable))] - - ica_reclassify_workflow( - reclassify_raw_registry(), - reject=to_accept, - out_dir=out_dir, - no_reports=True, - ) - assert "No accepted components remaining after manual classification!" in caplog.text - - fn = resource_filename("tedana", "tests/data/reclassify_no_bold.txt") - check_integration_outputs(fn, out_dir) - - -def test_integration_reclassify_accrej_files(skip_integration, caplog): - if skip_integration: - pytest.skip("Skip reclassify both rejected and accepted") - - test_data_path = guarantee_reclassify_data() - out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/no_bold")) - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - ioh = InputHarvester(reclassify_raw_registry()) - comptable = ioh.get_file_contents("ICA metrics tsv") - to_accept = [i for i in range(len(comptable))] - - ica_reclassify_workflow( - reclassify_raw_registry(), - reject=to_accept, - out_dir=out_dir, - no_reports=True, - ) - assert "No accepted components remaining after manual classification!" in caplog.text - - fn = resource_filename("tedana", "tests/data/reclassify_no_bold.txt") - check_integration_outputs(fn, out_dir) - - -def test_integration_reclassify_index_failures(skip_integration): - if skip_integration: - pytest.skip("Skip reclassify index failures") - - test_data_path = guarantee_reclassify_data() - out_dir = os.path.abspath(os.path.join(test_data_path, "../outputs/reclassify/index_failures")) - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - with pytest.raises( - ValueError, - match=r"_parse_manual_list expected a list of integers, but the input is", - ): - ica_reclassify_workflow( - reclassify_raw_registry(), - accept=[1, 2.5, 3], - out_dir=out_dir, - no_reports=True, - ) - - with pytest.raises( - ValueError, - match=r"_parse_manual_list expected integers or a filename, but the input is", - ): - ica_reclassify_workflow( - reclassify_raw_registry(), - accept=[2.5], - out_dir=out_dir, - no_reports=True, - ) - - -def test_integration_t2smap(skip_integration): - """Integration test of the full t2smap workflow using five-echo test data.""" - if skip_integration: - pytest.skip("Skipping t2smap integration test") - test_data_path, osf_id = data_for_testing_info("five-echo") - out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/t2smap_five-echo")) - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - # download data and run the test - download_test_data(osf_id, test_data_path) - prepend = f"{test_data_path}/p06.SBJ01_S09_Task11_e" - suffix = ".sm.nii.gz" - datalist = [prepend + str(i + 1) + suffix for i in range(5)] - echo_times = [15.4, 29.7, 44.0, 58.3, 72.6] - args = ( - ["-d"] - + datalist - + ["-e"] - + [str(te) for te in echo_times] - + ["--out-dir", out_dir, "--fittype", "curvefit"] - ) - t2smap_cli._main(args) - - # compare the generated output files - fname = resource_filename("tedana", "tests/data/nih_five_echo_outputs_t2smap.txt") - # Gets filepaths generated by integration test - found_files = [ - os.path.relpath(f, out_dir) - for f in glob.glob(os.path.join(out_dir, "**"), recursive=True)[1:] - ] - - # Compares remaining files with those expected - with open(fname, "r") as f: - expected_files = f.read().splitlines() - assert sorted(expected_files) == sorted(found_files) \ No newline at end of file diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index cfc754d74..e738b5843 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -33,7 +33,7 @@ DEFAULT_N_MAX_ITER, DEFAULT_N_MAX_RESTART, DEFAULT_N_ROBUST_RUNS, - DEFAULT_SEED + DEFAULT_SEED, ) from tedana.stats import computefeats2 from tedana.workflows.parser_utils import check_tedpca_value, is_valid_file @@ -971,4 +971,4 @@ def _main(argv=None): if __name__ == "__main__": - _main() \ No newline at end of file + _main() From a20ff57f39caeff7f3aea4f94b56f1afbf31ab35 Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Wed, 20 Dec 2023 11:02:10 +1100 Subject: [PATCH 08/34] Incorporating the third round of Robert E. Smith's comments --- tedana/config.py | 1 + tedana/decomposition/ica.py | 37 ++++++++++++++++++++------------ tedana/tests/test_integration.py | 12 +++++------ 3 files changed, 29 insertions(+), 21 deletions(-) diff --git a/tedana/config.py b/tedana/config.py index 58563de79..54def041a 100644 --- a/tedana/config.py +++ b/tedana/config.py @@ -1,3 +1,4 @@ +"""Setting default values for ICA decomposition.""" DEFAULT_ICA_METHOD = "robustica" DEFAULT_N_ROBUST_RUNS = 30 DEFAULT_N_MAX_ITER = 500 diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index 3074c38db..b64bf95a4 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -27,8 +27,7 @@ def tedica( maxit=DEFAULT_N_MAX_ITER, maxrestart=DEFAULT_N_MAX_RESTART, ): - """ - Perform ICA on `data` with the user selected ica method and returns mixing matrix + """Perform ICA on `data` with the user selected ica method and returns mixing matrix. Parameters ---------- @@ -90,9 +89,7 @@ def tedica( def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): - """ - Perform robustica on `data` by running FastICA multiple times (n_robust_runs) - and returns mixing matrix + """Perform robustica on `data` and returns mixing matrix. Parameters ---------- @@ -115,7 +112,6 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): where `C` is components and `T` is the same as in `data` fixed_seed : :obj:`int` Random seed from final decomposition. - """ if n_robust_runs > 200: LGR.warning( @@ -161,27 +157,40 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): rica.S_all, rica.clustering.labels_, rica.signs_, rica.orientation_ ) - iq = np.array(np.mean(q[q["cluster_id"] >= 0].iq)) # The cluster labeled -1 is noise + iq = np.array( + np.mean(q[q["cluster_id"] >= 0].iq) + ) # Excluding outliers (cluster -1) from the index quality calculation if iq < 0.6: LGR.warning( - "The resultant mean Index Quality is low ({0}). It is recommended to rerun the " - "process with a different seed.".format(iq) + f"The resultant mean Index Quality is low ({iq}). It is recommended to rerun the " + "process with a different seed." ) - mmix = mmix[:, q["cluster_id"] >= 0] + mmix = mmix[ + :, q["cluster_id"] >= 0 + ] # Excluding outliers (cluster -1) when calculating the mixing matrix mmix = stats.zscore(mmix, axis=0) LGR.info( - "RobustICA with {0} robust runs and seed {1} was used. " - "The mean Index Quality is {2}".format(n_robust_runs, fixed_seed, iq) + f"RobustICA with {n_robust_runs} robust runs and seed {fixed_seed} was used. " + f"The mean Index Quality is {iq}." ) + + no_outliers = np.count_nonzero(rica.clustering.labels_ == -1) + if no_outliers: + LGR.info( + "The DBSCAN clustering algorithm detected outliers when clustering " + "components for different runs. These outliers are excluded when calculating " + "the index quality and the mixing matrix to maximise the robustness of the " + "decomposition." + ) + return mmix, fixed_seed def f_ica(data, n_components, fixed_seed, maxit, maxrestart): - """ - Perform FastICA on `data` and returns mixing matrix + """Perform FastICA on `data` and returns mixing matrix. Parameters ---------- diff --git a/tedana/tests/test_integration.py b/tedana/tests/test_integration.py index 645057788..570da50d1 100644 --- a/tedana/tests/test_integration.py +++ b/tedana/tests/test_integration.py @@ -285,15 +285,11 @@ def test_integration_robustica_five_echo(skip_integration): pytest.skip("Skipping five-echo integration test") test_data_path, osf_id = data_for_testing_info("five-echo") - out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/five-echo")) - # out_dir_manual = f"{out_dir}-manual" + out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/five-echo_robustica")) if os.path.exists(out_dir): shutil.rmtree(out_dir) - # if os.path.exists(out_dir_manual): - # shutil.rmtree(out_dir_manual) - # download data and run the test download_test_data(osf_id, test_data_path) prepend = f"{test_data_path}/p06.SBJ01_S09_Task11_e" @@ -376,7 +372,7 @@ def test_integration_robustica_four_echo(skip_integration): pytest.skip("Skipping four-echo integration test") test_data_path, osf_id = data_for_testing_info("four-echo") - out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/four-echo")) + out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/four-echo_robustica")) out_dir_manual = f"{out_dir}-manual" if os.path.exists(out_dir): @@ -394,6 +390,8 @@ def test_integration_robustica_four_echo(skip_integration): data=datalist, mixm=op.join(op.dirname(datalist[0]), "desc-ICA_mixing_static.tsv"), tes=[11.8, 28.04, 44.28, 60.52], + ica_method="robustica", + n_robust_runs=8, out_dir=out_dir, tedpca="kundu-stabilize", gscontrol=["gsr", "mir"], @@ -469,7 +467,7 @@ def test_integration_robustica_three_echo(skip_integration): pytest.skip("Skipping three-echo integration test") test_data_path, osf_id = data_for_testing_info("three-echo") - out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/three-echo")) + out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/three-echo_robustica")) out_dir_manual = f"{out_dir}-rerun" if os.path.exists(out_dir): From 78c8140aab163ef82fb3c6f4a47065bab4f3350d Mon Sep 17 00:00:00 2001 From: Bahman Date: Fri, 9 Feb 2024 16:10:14 +1100 Subject: [PATCH 09/34] Enhance the "ica_method" description suggested by D. Handwerker Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> --- tedana/workflows/tedana.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index 3c1fdafdf..720148aed 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -174,11 +174,12 @@ def _get_parser(): "--ica_method", dest="ica_method", help=( - "The applied ICA method. If set to fastica the FastICA " - "from sklearn library will be run once with the seed value. " + "The applied ICA method. fastica runs FastICA from " + "sklearn once with the seed value. " "robustica will run FastICA n_robust_runs times and uses " "clustering methods to overcome the randomness of the FastICA " - "algorithm. When set to fastica, --n_robust_runs option will have no effect." + "algorithm. FastICA was the default in tedana version 23 and earlier. " + "robustica will be slower." ), choices=["robustica", "fastica"], type=str.lower, From ac85e6a9a37d0f180d4baea1a095b7f005daf3d1 Mon Sep 17 00:00:00 2001 From: Bahman Date: Fri, 9 Feb 2024 16:11:57 +1100 Subject: [PATCH 10/34] Enhancing the "n_robust_runs" description suggested by D. Handwerkerd Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> --- tedana/workflows/tedana.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index 720148aed..193f3ce51 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -204,7 +204,7 @@ def _get_parser(): dest="n_robust_runs", type=int, help=( - "The number of times robustica will run." + "The number of times robustica will run. " "This is only effective when ica_method is " "set to robustica." ), From 979d026bb4cf14efc492cc8c78836ed4f165b155 Mon Sep 17 00:00:00 2001 From: Robert Smith Date: Sun, 11 Feb 2024 23:03:47 +1100 Subject: [PATCH 11/34] RobustICA: Restructure code loop over robust methods (#4) * RobustICA: Restructure code loop over robust methods * Addressing the issue with try/except --------- Co-authored-by: Bahman --- tedana/decomposition/ica.py | 62 +++++++++++++++---------------------- 1 file changed, 25 insertions(+), 37 deletions(-) diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index 807da19d0..5743715f8 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -124,39 +124,27 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): if fixed_seed == -1: fixed_seed = np.random.randint(low=1, high=1000) - try: - rica = RobustICA( - n_components=n_components, - robust_runs=n_robust_runs, - whiten="arbitrary-variance", - max_iter=max_it, - random_state=fixed_seed, - robust_dimreduce=False, - fun="logcosh", - robust_method="DBSCAN", - ) - - s, mmix = rica.fit_transform(data) - q = rica.evaluate_clustering( - rica.S_all, rica.clustering.labels_, rica.signs_, rica.orientation_ - ) - - except: - rica = RobustICA( - n_components=n_components, - robust_runs=n_robust_runs, - whiten="arbitrary-variance", - max_iter=max_it, - random_state=fixed_seed, - robust_dimreduce=False, - fun="logcosh", - robust_method="AgglomerativeClustering", - ) - - s, mmix = rica.fit_transform(data) - q = rica.evaluate_clustering( - rica.S_all, rica.clustering.labels_, rica.signs_, rica.orientation_ - ) + for robust_method in ("DBSCAN", "AgglomerativeClustering"): + + try: + rica = RobustICA( + n_components=n_components, + robust_runs=n_robust_runs, + whiten="arbitrary-variance", + max_iter=max_it, + random_state=fixed_seed, + robust_dimreduce=False, + fun="logcosh", + robust_method=robust_method, + ) + + s, mmix = rica.fit_transform(data) + q = rica.evaluate_clustering( + rica.S_all, rica.clustering.labels_, rica.signs_, rica.orientation_ + ) + + except Exception: + continue iq = np.array( np.mean(q[q["cluster_id"] >= 0].iq) @@ -181,10 +169,10 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): no_outliers = np.count_nonzero(rica.clustering.labels_ == -1) if no_outliers: LGR.info( - "The DBSCAN clustering algorithm detected outliers when clustering " - "components for different runs. These outliers are excluded when calculating " - "the index quality and the mixing matrix to maximise the robustness of the " - "decomposition." + f"The {robust_method} clustering algorithm detected outliers when clustering " + f"components for different runs. These outliers are excluded when calculating " + f"the index quality and the mixing matrix to maximise the robustness of the " + f"decomposition." ) return mmix, fixed_seed From cac38cd2f06b46e23bbef417d746e6d7b40bccd3 Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Thu, 29 Feb 2024 19:35:50 +1100 Subject: [PATCH 12/34] Applied suggested changes In this commit, some of the comments from Daniel Handwerker and Robert Smith were incorporated. --- tedana/config.py | 13 +++++ tedana/decomposition/ica.py | 11 +++- tedana/resources/references.bib | 4 +- tedana/tests/test_integration.py | 93 +++----------------------------- tedana/workflows/parser_utils.py | 27 ++++++++++ tedana/workflows/tedana.py | 9 ++-- 6 files changed, 64 insertions(+), 93 deletions(-) diff --git a/tedana/config.py b/tedana/config.py index 54def041a..11cf2011c 100644 --- a/tedana/config.py +++ b/tedana/config.py @@ -1,6 +1,19 @@ """Setting default values for ICA decomposition.""" + DEFAULT_ICA_METHOD = "robustica" DEFAULT_N_ROBUST_RUNS = 30 DEFAULT_N_MAX_ITER = 500 DEFAULT_N_MAX_RESTART = 10 DEFAULT_SEED = 42 + + +"""Setting extreme values for number of robust runs.""" + +MIN_N_ROBUST_RUNS = 5 +MAX_N_ROBUST_RUNS = 500 +WARN_N_ROBUST_RUNS = 200 + + +"""Setting the warning threshold for the index quality.""" + +WARN_IQ = 0.6 diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index 5743715f8..fc387f478 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -13,6 +13,8 @@ DEFAULT_N_MAX_ITER, DEFAULT_N_MAX_RESTART, DEFAULT_N_ROBUST_RUNS, + WARN_IQ, + WARN_N_ROBUST_RUNS, ) LGR = logging.getLogger("GENERAL") @@ -114,7 +116,7 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): fixed_seed : :obj:`int` Random seed from final decomposition. """ - if n_robust_runs > 200: + if n_robust_runs > WARN_N_ROBUST_RUNS: LGR.warning( "The selected n_robust_runs is a very big number! The process will take a long time!" ) @@ -146,11 +148,16 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): except Exception: continue + LGR.info( + f"The {robust_method} clustering algorithm was used clustering " + f"components across different runs" + ) + iq = np.array( np.mean(q[q["cluster_id"] >= 0].iq) ) # Excluding outliers (cluster -1) from the index quality calculation - if iq < 0.6: + if iq < WARN_IQ: LGR.warning( f"The resultant mean Index Quality is low ({iq}). It is recommended to rerun the " "process with a different seed." diff --git a/tedana/resources/references.bib b/tedana/resources/references.bib index 4516882d8..786165404 100644 --- a/tedana/resources/references.bib +++ b/tedana/resources/references.bib @@ -314,7 +314,7 @@ @misc{sochat2015ttoz year = 2015 } -@Article{Anglada:2022, +@Article{Anglada2022, Author = {Anglada-Girotto Miquel and Miravet-Verde Samuel and Serrano Luis and Head Sarah}, Title = {robustica: customizable robust independent component analysis}, Journal = {BMC Bioinformatics}, @@ -322,4 +322,4 @@ @Article{Anglada:2022 Number = {519}, doi = {10.1186/s12859-022-05043-9}, year = 2022 -} \ No newline at end of file +} diff --git a/tedana/tests/test_integration.py b/tedana/tests/test_integration.py index 8c1853fc8..183496525 100644 --- a/tedana/tests/test_integration.py +++ b/tedana/tests/test_integration.py @@ -259,7 +259,8 @@ def test_integration_five_echo(skip_integration): tedana_cli.tedana_workflow( data=datalist, tes=echo_times, - ica_method="fastica", + ica_method="robustica", + n_robust_runs=10, out_dir=out_dir, tedpca=0.95, fittype="curvefit", @@ -279,44 +280,6 @@ def test_integration_five_echo(skip_integration): check_integration_outputs(fn, out_dir) -def test_integration_robustica_five_echo(skip_integration): - """Integration test of the full tedana workflow with robustica using five-echo test data.""" - - if skip_integration: - pytest.skip("Skipping five-echo integration test") - - test_data_path, osf_id = data_for_testing_info("five-echo") - out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/five-echo_robustica")) - - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - # download data and run the test - download_test_data(osf_id, test_data_path) - prepend = f"{test_data_path}/p06.SBJ01_S09_Task11_e" - suffix = ".sm.nii.gz" - datalist = [prepend + str(i + 1) + suffix for i in range(5)] - echo_times = [15.4, 29.7, 44.0, 58.3, 72.6] - tedana_cli.tedana_workflow( - data=datalist, - tes=echo_times, - ica_method="robustica", - n_robust_runs=10, - out_dir=out_dir, - tedpca=0.95, - fittype="curvefit", - fixed_seed=49, - tedort=True, - verbose=True, - prefix="sub-01", - ) - - # Just a check on the component table pending a unit test of load_comptable - comptable = os.path.join(out_dir, "sub-01_desc-tedana_metrics.tsv") - df = pd.read_table(comptable) - assert isinstance(df, pd.DataFrame) - - def test_integration_four_echo(skip_integration): """Integration test of the full tedana workflow using four-echo test data.""" @@ -366,51 +329,6 @@ def test_integration_four_echo(skip_integration): check_integration_outputs(fn, out_dir) -def test_integration_robustica_four_echo(skip_integration): - """Integration test of the full tedana workflow with robustica using four-echo test data.""" - - if skip_integration: - pytest.skip("Skipping four-echo integration test") - - test_data_path, osf_id = data_for_testing_info("four-echo") - out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/four-echo_robustica")) - out_dir_manual = f"{out_dir}-manual" - - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - if os.path.exists(out_dir_manual): - shutil.rmtree(out_dir_manual) - - # download data and run the test - download_test_data(osf_id, test_data_path) - prepend = f"{test_data_path}/sub-PILOT_ses-01_task-localizerDetection_run-01_echo-" - suffix = "_space-sbref_desc-preproc_bold+orig.HEAD" - datalist = [prepend + str(i + 1) + suffix for i in range(4)] - tedana_cli.tedana_workflow( - data=datalist, - mixm=op.join(op.dirname(datalist[0]), "desc-ICA_mixing_static.tsv"), - tes=[11.8, 28.04, 44.28, 60.52], - ica_method="robustica", - n_robust_runs=8, - out_dir=out_dir, - tedpca="kundu-stabilize", - gscontrol=["gsr", "mir"], - png_cmap="bone", - prefix="sub-01", - debug=True, - verbose=True, - ) - - ica_reclassify_workflow( - op.join(out_dir, "sub-01_desc-tedana_registry.json"), - accept=[1, 2, 3], - reject=[4, 5, 6], - out_dir=out_dir_manual, - mir=True, - ) - - def test_integration_three_echo(skip_integration): """Integration test of the full tedana workflow using three-echo test data.""" @@ -432,7 +350,8 @@ def test_integration_three_echo(skip_integration): tedana_cli.tedana_workflow( data=f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", tes=[14.5, 38.5, 62.5], - ica_method="fastica", + ica_method="robustica", + n_robust_runs=5, out_dir=out_dir, low_mem=True, tedpca="aic", @@ -448,6 +367,10 @@ def test_integration_three_echo(skip_integration): "62.5", "--out-dir", out_dir_manual, + "--ica_method", + "robustica", + "--n_robust_runs", + "5", "--debug", "--verbose", "-f", diff --git a/tedana/workflows/parser_utils.py b/tedana/workflows/parser_utils.py index b0db74bdc..db0f13b37 100644 --- a/tedana/workflows/parser_utils.py +++ b/tedana/workflows/parser_utils.py @@ -3,6 +3,8 @@ import argparse import os.path as op +from tedana.config import MAX_N_ROBUST_RUNS, MIN_N_ROBUST_RUNS + def check_tedpca_value(string, is_parser=True): """ @@ -33,6 +35,31 @@ def check_tedpca_value(string, is_parser=True): return intarg +def check_n_robust_runs_value(string, is_parser=True): + """ + Check n_robust_runs argument. + + Check if argument is an int between MIN_N_ROBUST_RUNS and MAX_N_ROBUST_RUNS. + """ + error = argparse.ArgumentTypeError if is_parser else ValueError + try: + intarg = int(string) + except ValueError: + msg = ( + f"Argument to n_robust_runs must be an integer " + f"between {MIN_N_ROBUST_RUNS} and {MAX_N_ROBUST_RUNS}." + ) + raise error(msg) + + if not (MIN_N_ROBUST_RUNS <= intarg <= MAX_N_ROBUST_RUNS): + raise error( + f"n_robust_runs must be an integer between {MIN_N_ROBUST_RUNS} " + f"and {MAX_N_ROBUST_RUNS}." + ) + else: + return intarg + + def is_valid_file(parser, arg): """Check if argument is existing file.""" if not op.isfile(arg) and arg is not None: diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index 193f3ce51..2333af52a 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -419,11 +419,12 @@ def tedana_workflow( Testing to better understand the effects of the differences is ongoing. Default is 'kundu'. ica_method : {'robustica', 'fastica'}, optional - The applied ICA method. If set to fastica the FastICA from sklearn - library will be run once with the seed value. 'robustica' will run + The applied ICA method. fastica runs FastICA from sklearn + once with the seed value. 'robustica' will run 'FastICA' n_robust_runs times and uses clustering methods to overcome - the randomness of the FastICA algorithm. When set to fastica n_robust_runs - will not be effective. + the randomness of the FastICA algorithm. + FastICA was the default in tedana version 23 and earlier. + robustica will be slower. Default is 'robustica' n_robust_runs : :obj:`int`, optional The number of times robustica will run. This is only effective when 'ica_method' is From a113423e282894466eccc086422f9e0662d6865c Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Mon, 4 Mar 2024 12:14:49 +1100 Subject: [PATCH 13/34] Incorporating more comments * Fixing the problem of argument parser for n_robust_runs. * Removing unnecessary tests from the test_integration. There are 3 tests for echo as before, but the ica_method is robustica for five and three echos and fatsica for the four echo test. --- tedana/tests/test_integration.py | 50 +------------------------------- tedana/workflows/tedana.py | 9 ++++-- 2 files changed, 7 insertions(+), 52 deletions(-) diff --git a/tedana/tests/test_integration.py b/tedana/tests/test_integration.py index 025e527a3..8a7ad7843 100644 --- a/tedana/tests/test_integration.py +++ b/tedana/tests/test_integration.py @@ -258,7 +258,7 @@ def test_integration_five_echo(skip_integration): data=datalist, tes=echo_times, ica_method="robustica", - n_robust_runs=10, + n_robust_runs=6, out_dir=out_dir, tedpca=0.95, fittype="curvefit", @@ -383,54 +383,6 @@ def test_integration_three_echo(skip_integration): check_integration_outputs(fn, out_dir) -def test_integration_robustica_three_echo(skip_integration): - """Integration test of the full tedana workflow with robustica using three-echo test data.""" - - if skip_integration: - pytest.skip("Skipping three-echo integration test") - - test_data_path, osf_id = data_for_testing_info("three-echo") - out_dir = os.path.abspath(os.path.join(test_data_path, "../../outputs/three-echo_robustica")) - out_dir_manual = f"{out_dir}-rerun" - - if os.path.exists(out_dir): - shutil.rmtree(out_dir) - - if os.path.exists(out_dir_manual): - shutil.rmtree(out_dir_manual) - - # download data and run the test - download_test_data(osf_id, test_data_path) - tedana_cli.tedana_workflow( - data=f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", - tes=[14.5, 38.5, 62.5], - ica_method="robustica", - out_dir=out_dir, - low_mem=True, - tedpca="aic", - ) - - # Test re-running, but use the CLI - args = [ - "-d", - f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", - "-e", - "14.5", - "38.5", - "62.5", - "--ica_method", - "robustica", - "--out-dir", - out_dir_manual, - "--debug", - "--verbose", - "-f", - "--mix", - os.path.join(out_dir, "desc-ICA_mixing.tsv"), - ] - tedana_cli._main(args) - - def test_integration_reclassify_insufficient_args(skip_integration): if skip_integration: pytest.skip("Skipping reclassify insufficient args") diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index ff885b507..552b00d30 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -37,7 +37,11 @@ DEFAULT_SEED, ) from tedana.stats import computefeats2 -from tedana.workflows.parser_utils import check_tedpca_value, is_valid_file +from tedana.workflows.parser_utils import ( + check_n_robust_runs_value, + check_tedpca_value, + is_valid_file, +) LGR = logging.getLogger("GENERAL") RepLGR = logging.getLogger("REPORT") @@ -202,13 +206,12 @@ def _get_parser(): optional.add_argument( "--n_robust_runs", dest="n_robust_runs", - type=int, + type=check_n_robust_runs_value, help=( "The number of times robustica will run. " "This is only effective when ica_method is " "set to robustica." ), - choices=range(5, 500), default=DEFAULT_N_ROBUST_RUNS, ) optional.add_argument( From 8e6878fe3aa218742bbe8a988cb8c4d34ac9e813 Mon Sep 17 00:00:00 2001 From: handwerkerd Date: Tue, 13 Aug 2024 17:30:03 -0400 Subject: [PATCH 14/34] Adding already requested changes --- tedana/config.py | 6 +++--- tedana/decomposition/ica.py | 26 +++++++++++++++++--------- tedana/tests/test_integration.py | 8 +------- tedana/workflows/tedana.py | 9 +++++---- 4 files changed, 26 insertions(+), 23 deletions(-) diff --git a/tedana/config.py b/tedana/config.py index 11cf2011c..746d41637 100644 --- a/tedana/config.py +++ b/tedana/config.py @@ -1,14 +1,14 @@ """Setting default values for ICA decomposition.""" -DEFAULT_ICA_METHOD = "robustica" -DEFAULT_N_ROBUST_RUNS = 30 +DEFAULT_ICA_METHOD = "fastica" DEFAULT_N_MAX_ITER = 500 DEFAULT_N_MAX_RESTART = 10 DEFAULT_SEED = 42 -"""Setting extreme values for number of robust runs.""" +"""Setting values for number of robust runs.""" +DEFAULT_N_ROBUST_RUNS = 30 MIN_N_ROBUST_RUNS = 5 MAX_N_ROBUST_RUNS = 500 WARN_N_ROBUST_RUNS = 200 diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index fc387f478..0af62ae9d 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -59,7 +59,6 @@ def tedica( where `C` is components and `T` is the same as in `data` fixed_seed : :obj:`int` Random seed from final decomposition. - """ warnings.filterwarnings(action="ignore", module="scipy", message="^internal gelsd") RepLGR.info( @@ -126,10 +125,11 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): if fixed_seed == -1: fixed_seed = np.random.randint(low=1, high=1000) - for robust_method in ("DBSCAN", "AgglomerativeClustering"): - + robust_method = "DBSCAN" + robust_ica_converged = False + while not robust_ica_converged: try: - rica = RobustICA( + robust_ica = RobustICA( n_components=n_components, robust_runs=n_robust_runs, whiten="arbitrary-variance", @@ -140,13 +140,21 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): robust_method=robust_method, ) - s, mmix = rica.fit_transform(data) - q = rica.evaluate_clustering( - rica.S_all, rica.clustering.labels_, rica.signs_, rica.orientation_ + s, mmix = robust_ica.fit_transform(data) + q = robust_ica.evaluate_clustering( + robust_ica.S_all, + robust_ica.clustering.labels_, + robust_ica.signs_, + robust_ica.orientation_, ) + robust_ica_converged = True except Exception: - continue + if robust_method == "DBSCAN": + # if RobustICA failed wtih DBSCAN, run again wtih AgglomerativeClustering + robust_method = "AgglomerativeClustering" + else: + raise ValueError("RobustICA failed to converge") LGR.info( f"The {robust_method} clustering algorithm was used clustering " @@ -173,7 +181,7 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): f"The mean Index Quality is {iq}." ) - no_outliers = np.count_nonzero(rica.clustering.labels_ == -1) + no_outliers = np.count_nonzero(robust_ica.clustering.labels_ == -1) if no_outliers: LGR.info( f"The {robust_method} clustering algorithm detected outliers when clustering " diff --git a/tedana/tests/test_integration.py b/tedana/tests/test_integration.py index 3c65dbc5b..a9aa6180c 100644 --- a/tedana/tests/test_integration.py +++ b/tedana/tests/test_integration.py @@ -130,7 +130,7 @@ def test_integration_five_echo(skip_integration): data=datalist, tes=echo_times, ica_method="robustica", - n_robust_runs=6, + n_robust_runs=3, out_dir=out_dir, tedpca=0.95, fittype="curvefit", @@ -221,8 +221,6 @@ def test_integration_three_echo(skip_integration): tedana_cli.tedana_workflow( data=f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", tes=[14.5, 38.5, 62.5], - ica_method="robustica", - n_robust_runs=5, out_dir=out_dir, low_mem=True, tedpca="aic", @@ -240,10 +238,6 @@ def test_integration_three_echo(skip_integration): "62.5", "--out-dir", out_dir_manual, - "--ica_method", - "robustica", - "--n_robust_runs", - "5", "--debug", "--verbose", "-f", diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index 62d629984..d5f83e375 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -201,11 +201,11 @@ def _get_parser(): "--ica_method", dest="ica_method", help=( - "The applied ICA method. fastica runs FastICA from " - "sklearn once with the seed value. " + "The applied ICA method. " + "fastica runs FastICA from sklearn once with the seed value. " "robustica will run FastICA n_robust_runs times and uses " - "clustering methods to overcome the randomness of the FastICA " - "algorithm. FastICA was the default in tedana version 23 and earlier. " + "clustering methods to overcome the randomness of the FastICA algorithm. " + "FastICA was the default in tedana version 23 and earlier. " "robustica will be slower." ), choices=["robustica", "fastica"], @@ -229,6 +229,7 @@ def _get_parser(): optional.add_argument( "--n_robust_runs", dest="n_robust_runs", + metavar="[5-500]", type=check_n_robust_runs_value, help=( "The number of times robustica will run. " From 88fd1485f9b39055b038263f5827c67eae43eb72 Mon Sep 17 00:00:00 2001 From: handwerkerd Date: Thu, 15 Aug 2024 21:18:17 -0400 Subject: [PATCH 15/34] fixed failing tests --- .../data/nih_five_echo_outputs_verbose.txt | 20 ------------------- tedana/tests/test_integration.py | 2 +- 2 files changed, 1 insertion(+), 21 deletions(-) diff --git a/tedana/tests/data/nih_five_echo_outputs_verbose.txt b/tedana/tests/data/nih_five_echo_outputs_verbose.txt index 5847b114b..2d54db98b 100644 --- a/tedana/tests/data/nih_five_echo_outputs_verbose.txt +++ b/tedana/tests/data/nih_five_echo_outputs_verbose.txt @@ -121,26 +121,6 @@ figures/sub-01_comp_028.png figures/sub-01_comp_029.png figures/sub-01_comp_030.png figures/sub-01_comp_031.png -figures/sub-01_comp_032.png -figures/sub-01_comp_033.png -figures/sub-01_comp_034.png -figures/sub-01_comp_035.png -figures/sub-01_comp_036.png -figures/sub-01_comp_037.png -figures/sub-01_comp_038.png -figures/sub-01_comp_039.png -figures/sub-01_comp_040.png -figures/sub-01_comp_041.png -figures/sub-01_comp_042.png -figures/sub-01_comp_043.png -figures/sub-01_comp_044.png -figures/sub-01_comp_045.png -figures/sub-01_comp_046.png -figures/sub-01_comp_047.png -figures/sub-01_comp_048.png -figures/sub-01_comp_049.png -figures/sub-01_comp_050.png -figures/sub-01_comp_051.png figures/sub-01_rmse_brain.svg figures/sub-01_rmse_timeseries.svg figures/sub-01_s0_brain.svg diff --git a/tedana/tests/test_integration.py b/tedana/tests/test_integration.py index a9aa6180c..eb00e80d8 100644 --- a/tedana/tests/test_integration.py +++ b/tedana/tests/test_integration.py @@ -130,7 +130,7 @@ def test_integration_five_echo(skip_integration): data=datalist, tes=echo_times, ica_method="robustica", - n_robust_runs=3, + n_robust_runs=4, out_dir=out_dir, tedpca=0.95, fittype="curvefit", From a221e72fd0716eb900745b79c7537a3eea544588 Mon Sep 17 00:00:00 2001 From: handwerkerd Date: Tue, 27 Aug 2024 17:34:59 -0400 Subject: [PATCH 16/34] updated documentation in faq.rst --- docs/faq.rst | 63 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 63 insertions(+) diff --git a/docs/faq.rst b/docs/faq.rst index 7a8cd79f8..08775d7af 100644 --- a/docs/faq.rst +++ b/docs/faq.rst @@ -93,11 +93,74 @@ The TEDICA step may fail to converge if TEDPCA is either too strict With updates to the ``tedana`` code, this issue is now rare, but it may happen when preprocessing has not been applied to the data, or when improper steps have been applied to the data (e.g. rescaling, nuisance regression). +I can also still happen when everything is seemingly correct +(see the answer to the next question). If you are confident that your data have been preprocessed correctly prior to applying tedana, and you encounter this problem, please submit a question to `NeuroStars`_. .. _NeuroStars: https://neurostars.org +********************************************************************************* +[tedana] What is the right number of ICA components & what options let me get it? +********************************************************************************* + +Part of the PCA step in ``tedana`` processing involves identifying the number of +components that contain meaningful signal. +The PCA components are then used to calculate the same number of ICA components. +The ``--tedpca`` option includes several options to identify the "correct" number +of PCA components. +``kundu`` and ``kundu-stabilize`` use several echo-based criteria to exclude PCA +components that are unlikely to contain T2* or S0 signal. +``mdl`` (conservative & fewest components), ``kic``, +& ``aic`` (liberal & more components) use `MAPCA`_. +Within the same general method, each uses a cost function to find a minimum +where more components no longer model meaningful variance. +For some datasets we see all methods fail and result in too few or too many components. +There is no consistent number of components or % variance explained to define the correct number. +The correct number of components will depend on the noise levels of the data. +For example, smaller voxels will results in more thermal noise and less total variance explained. +A dataset with more head motion artifacts will have more variance explained, +since more structured signal is within the head motion artifacts. +The clear failure cases are extreme. That is getting less than 1/5 the number of components +compared to time points or having nearly has many components as time point. +We are working on identifying why this happens and adding get better solutions. +Our current guess is that most of the above methods assume data are +independant and identically distributed (IID), +and signal leakage from in-slice and multi-slice accelleration may violate this assumption. + +We have one option that is generally useful and is also a partial solution. +``--ica_method robustica`` will run `robustica`_. +This is a method that, for a given number of PCA components, +will repeated run ICA and identify components that are stable across iterations. +While running ICA multiple times will slow processing, as a general benefit, +this means that the ICA results are less sensitive to the initialization parameters, +computer hardware, and software versions. +This will result in better stability and replicability of ICA results. +Additionally, `robustica`_ almost always results in fewer components than initially prescripted, +since there are fewer stable components across interations than the total number of components. +This means, even if the initial PCA component estimate is a bit off, +the number of resulting robust ICA components will represent stable information in the data. +For a dataset where the PCA comoponent estimation methods are failing, +one could use ``--tedpca`` with a fixed integer for a constant number of components, +that is on the high end of typical for a study, +and then `robustica`_ will reduce the number of components to only find stable information. +That said, of the fixed PCA component number is too high, +then the method will have too many unstable components, +and if the fixed PCA component number is too low, then there will be even fewer ICA components. +The number of ICA components is more consisent, +but is still sensitive to the intial number of PCA components. +For example, for a single dataset 60 PCA components might result in 46 stable ICA components, +while 55 PCA components might results in 43 stable ICA components. +We are still testing how these interact and give better recommendations and even more stable results. +At that point, ``--ica_method robustica`` might become the default setting, +but we still wanted to release this option to let users access it in its current form. +If users are having trouble with PCA component estimation failing on a dataset, +we recommend using robustica. + + +.. _MAPCA: https://github.com/ME-ICA/mapca +.. _robustica: https://github.com/CRG-CNAG/robustica + .. _manual classification: ******************************************************************************** From 8622a9b01f5d33bafd579ab0d170ddd82da75832 Mon Sep 17 00:00:00 2001 From: handwerkerd Date: Thu, 29 Aug 2024 14:25:17 -0400 Subject: [PATCH 17/34] more documentation changes --- docs/approach.rst | 3 +++ tedana/workflows/tedana.py | 4 +--- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/approach.rst b/docs/approach.rst index 11ff08c08..03febb30e 100644 --- a/docs/approach.rst +++ b/docs/approach.rst @@ -311,6 +311,8 @@ Next, ``tedana`` applies TE-dependent independent component analysis (ICA) in order to identify and remove TE-independent (i.e., non-BOLD noise) components. The dimensionally reduced optimally combined data are first subjected to ICA in order to fit a mixing matrix to the whitened data. +``tedana`` can use a single interation of FastICA or multiple interations of robustICA, +with an explanation of those approaches `in our FAQ`_. This generates a number of independent timeseries (saved as **desc-ICA_mixing.tsv**), as well as parameter estimate maps which show the spatial loading of these components on the brain (**desc-ICA_components.nii.gz**). @@ -356,6 +358,7 @@ yielding a denoised timeseries, which is saved as **desc-denoised_bold.nii.gz**. .. image:: /_static/a15_denoised_data_timeseries.png +.. _in our FAQ: faq.html#tedana-what-is-the-right-number-of-ica-components-what-options-let-me-get-it .. _These decision trees are detailed here: included_decision_trees.html ******************************* diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index d5f83e375..8666b0988 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -205,7 +205,6 @@ def _get_parser(): "fastica runs FastICA from sklearn once with the seed value. " "robustica will run FastICA n_robust_runs times and uses " "clustering methods to overcome the randomness of the FastICA algorithm. " - "FastICA was the default in tedana version 23 and earlier. " "robustica will be slower." ), choices=["robustica", "fastica"], @@ -463,9 +462,8 @@ def tedana_workflow( once with the seed value. 'robustica' will run 'FastICA' n_robust_runs times and uses clustering methods to overcome the randomness of the FastICA algorithm. - FastICA was the default in tedana version 23 and earlier. robustica will be slower. - Default is 'robustica' + Default is 'fastica' n_robust_runs : :obj:`int`, optional The number of times robustica will run. This is only effective when 'ica_method' is set to 'robustica'. From 419e9d44891b506832217429522362ada5a5e1a1 Mon Sep 17 00:00:00 2001 From: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> Date: Thu, 29 Aug 2024 21:50:22 -0400 Subject: [PATCH 18/34] Update docs/faq.rst Co-authored-by: Robert Smith --- docs/faq.rst | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/docs/faq.rst b/docs/faq.rst index 08775d7af..83e90395a 100644 --- a/docs/faq.rst +++ b/docs/faq.rst @@ -153,9 +153,14 @@ For example, for a single dataset 60 PCA components might result in 46 stable IC while 55 PCA components might results in 43 stable ICA components. We are still testing how these interact and give better recommendations and even more stable results. At that point, ``--ica_method robustica`` might become the default setting, -but we still wanted to release this option to let users access it in its current form. +While the TEDANA developers expect that ``--ica_method robustica`` may become +the default configuration in future TEDANA versions, +it is first being released to the public as a non-default option +in hope of gaining insight into its behaviour +across a broader range of multi-echo fMRI data. If users are having trouble with PCA component estimation failing on a dataset, -we recommend using robustica. +we recommend using RobustICA; +we also invite user feedback on its behaviour and efficacy. .. _MAPCA: https://github.com/ME-ICA/mapca From d29a91b0463e3ead22ac01e0273e3067ec2d062c Mon Sep 17 00:00:00 2001 From: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> Date: Thu, 29 Aug 2024 21:50:37 -0400 Subject: [PATCH 19/34] Update docs/faq.rst Co-authored-by: Robert Smith --- docs/faq.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/faq.rst b/docs/faq.rst index 83e90395a..4f360b71f 100644 --- a/docs/faq.rst +++ b/docs/faq.rst @@ -144,7 +144,7 @@ For a dataset where the PCA comoponent estimation methods are failing, one could use ``--tedpca`` with a fixed integer for a constant number of components, that is on the high end of typical for a study, and then `robustica`_ will reduce the number of components to only find stable information. -That said, of the fixed PCA component number is too high, +That said, if the fixed PCA component number is too high, then the method will have too many unstable components, and if the fixed PCA component number is too low, then there will be even fewer ICA components. The number of ICA components is more consisent, From efb712e2e270fdb1207afcea7f99fe45ae249ead Mon Sep 17 00:00:00 2001 From: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> Date: Thu, 29 Aug 2024 23:45:03 -0400 Subject: [PATCH 20/34] Aligning robustICA with current Main + (#5) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Limit current adaptive mask method to brain mask (#1060) * Limit adaptive mask calculation to brain mask. Limit adaptive mask calculation to brain mask. Expand on logic of first adaptive mask method. Update tedana/utils.py Improve docstring. Update test. Add decreasing-signal-based adaptive mask. Keep removing. Co-Authored-By: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> * Use `compute_epi_mask` in t2smap workflow. * Try fixing the tests. * Fix make_adaptive_mask. * Update test_utils.py * Update test_utils.py * Improve docstring. * Update utils.py * Update test_utils.py * Revert "Update test_utils.py" This reverts commit 259b002963df7e340a67123ad4fc1fd1abed0a44. * Don't take absolute value of echo means. * Log echo-wise thresholds in adaptive mask. * Add comment about non-zero voxels. * Update utils.py * Update test_utils.py * Update test_utils.py * Update test_utils.py * Log the thresholds again. * Address review. * Fix test. --------- Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> * Update nilearn requirement from <=0.10.3,>=0.7 to >=0.7,<=0.10.4 (#1077) * Add adaptive mask plot to report (#1073) * Update scikit-learn requirement (#1075) Updates the requirements on [scikit-learn](https://github.com/scikit-learn/scikit-learn) to permit the latest version. - [Release notes](https://github.com/scikit-learn/scikit-learn/releases) - [Commits](https://github.com/scikit-learn/scikit-learn/compare/0.21.0...1.4.2) --- updated-dependencies: - dependency-name: scikit-learn dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Update pandas requirement from <=2.2.1,>=2.0 to >=2.0,<=2.2.2 (#1076) Updates the requirements on [pandas](https://github.com/pandas-dev/pandas) to permit the latest version. - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Commits](https://github.com/pandas-dev/pandas/compare/v2.0.0...v2.2.2) --- updated-dependencies: - dependency-name: pandas dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Update bokeh requirement from <=3.4.0,>=1.0.0 to >=1.0.0,<=3.4.1 (#1078) Updates the requirements on [bokeh](https://github.com/bokeh/bokeh) to permit the latest version. - [Changelog](https://github.com/bokeh/bokeh/blob/branch-3.5/docs/CHANGELOG) - [Commits](https://github.com/bokeh/bokeh/compare/1.0.0...3.4.1) --- updated-dependencies: - dependency-name: bokeh dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Load user-defined mask as expected by plot_adaptive_mask (#1079) * DOC desc-optcomDenoised -> desc-denoised (#1080) * docs: add mvdoc as a contributor for code, bug, and doc (#1082) * docs: update README.md * docs: update .all-contributorsrc --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> * Identify the last good echo in adaptive mask instead of sum of good echoes (#1061) * Limit adaptive mask calculation to brain mask. Limit adaptive mask calculation to brain mask. Expand on logic of first adaptive mask method. Update tedana/utils.py Improve docstring. Update test. Add decreasing-signal-based adaptive mask. Keep removing. Co-Authored-By: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> * Use `compute_epi_mask` in t2smap workflow. * Try fixing the tests. * Fix make_adaptive_mask. * Update test_utils.py * Update test_utils.py * Improve docstring. * Identify the last good echo instead of sum. Improve docstring. Update test_utils.py Update test_utils.py Fix make_adaptive_mask. Try fixing the tests. Use `compute_epi_mask` in t2smap workflow. Limit adaptive mask calculation to brain mask. Limit adaptive mask calculation to brain mask. Expand on logic of first adaptive mask method. Update tedana/utils.py Improve docstring. Update test. Add decreasing-signal-based adaptive mask. Keep removing. Co-Authored-By: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> * Fix. * Update utils.py * Update utils.py * Try fixing. * Update utils.py * Update utils.py * add checks * Just loop over voxels. * Update utils.py * Update utils.py * Update test_utils.py * Revert "Update test_utils.py" This reverts commit 259b002963df7e340a67123ad4fc1fd1abed0a44. * Update test_utils.py * Update test_utils.py * Remove checks. * Don't take absolute value of echo means. * Log echo-wise thresholds in adaptive mask. * Add comment about non-zero voxels. * Update utils.py * Update utils.py * Update test_utils.py * Update test_utils.py * Update test_utils.py * Log the thresholds again. * Update test_utils.py * Update test_utils.py * Update test_utils.py * Add simulated data to adaptive mask test. * Clean up the tests. * Add value that tests the base mask. * Remove print in test. * Update tedana/utils.py Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> * Update tedana/utils.py Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> --------- Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> * Output RMSE map and time series for decay model fit (#1044) * Draft function to calculate decay model fit. * Calculate root mean squared error instead. * Incorporate metrics. * Output RMSE results. * Output results in tedana. * Hopefully fix things. * Update decay.py * Try improving performance. * Update decay.py * Fix again. * Use tqdm. * Update decay.py * Update decay.py * Update decay.py * Update expected outputs. * Add figures. * Update outputs. * Include global signal in confounds file. * Update fiu_four_echo_outputs.txt * Rename function. * Rename function. * Update tedana.py * Update tedana/decay.py Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> * Update decay.py * Update decay.py * Whoops. * Apply suggestions from code review Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> * Fix things maybe. * Fix things. * Update decay.py * Remove any files that are built through appending. * Update outputs. * Add section on plots to docs. * Fix the description. * Update docs/outputs.rst Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> * Update docs/outputs.rst * Fix docstring. --------- Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> * minimum nilearn 0.10.3 (#1094) * Use nearest-neighbors interpolation in `plot_component` (#1098) * Use nearest-neighbors interpolation in plot_stat_map. * Only use NN interp for component maps. * Update scipy requirement from <=1.13.0,>=1.2.0 to >=1.2.0,<=1.13.1 (#1100) Updates the requirements on [scipy](https://github.com/scipy/scipy) to permit the latest version. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.2.0...v1.13.1) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Update scikit-learn requirement from <=1.4.2,>=0.21 to >=0.21,<=1.5.0 (#1101) Updates the requirements on [scikit-learn](https://github.com/scikit-learn/scikit-learn) to permit the latest version. - [Release notes](https://github.com/scikit-learn/scikit-learn/releases) - [Commits](https://github.com/scikit-learn/scikit-learn/compare/0.21.0...1.5.0) --- updated-dependencies: - dependency-name: scikit-learn dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Update numpy requirement from <=1.26.4,>=1.16 to >=1.16,<=2.0.0 (#1104) * Update numpy requirement from <=1.26.4,>=1.16 to >=1.16,<=2.0.0 Updates the requirements on [numpy](https://github.com/numpy/numpy) to permit the latest version. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.16.0...v2.0.0) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production ... Signed-off-by: dependabot[bot] * Use np.nan instead of np.NaN --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Taylor Salo * Filter out non-diagonal affine warning (#1103) * Filter out non-diagonal affine warning. * Fix warning capture. * Update tedana/reporting/static_figures.py Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> * Update static_figures.py --------- Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> * Update bokeh requirement from <=3.4.1,>=1.0.0 to <=3.5.0,>=3.5.0 (#1109) * Update bokeh requirement from <=3.4.1,>=1.0.0 to <=3.5.0,>=3.5.0 Updates the requirements on [bokeh](https://github.com/bokeh/bokeh) to permit the latest version. - [Changelog](https://github.com/bokeh/bokeh/blob/branch-3.6/docs/CHANGELOG) - [Commits](https://github.com/bokeh/bokeh/compare/1.0.0...3.5.0) --- updated-dependencies: - dependency-name: bokeh dependency-type: direct:production ... Signed-off-by: dependabot[bot] * Update pyproject.toml --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Taylor Salo * Update scikit-learn requirement from <=1.5.0,>=0.21 to <=1.5.1,>=1.5.1 (#1108) * Update scikit-learn requirement from <=1.5.0,>=0.21 to <=1.5.1,>=1.5.1 Updates the requirements on [scikit-learn](https://github.com/scikit-learn/scikit-learn) to permit the latest version. - [Release notes](https://github.com/scikit-learn/scikit-learn/releases) - [Commits](https://github.com/scikit-learn/scikit-learn/compare/0.21.0...1.5.1) --- updated-dependencies: - dependency-name: scikit-learn dependency-type: direct:production ... Signed-off-by: dependabot[bot] * Update pyproject.toml to restore minimum version of scikit-learn --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> * Update scipy requirement from <=1.13.1,>=1.2.0 to <=1.14.0,>=1.14.0 (#1106) * Update scipy requirement from <=1.13.1,>=1.2.0 to <=1.14.0,>=1.14.0 Updates the requirements on [scipy](https://github.com/scipy/scipy) to permit the latest version. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.2.0...v1.14.0) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production ... Signed-off-by: dependabot[bot] * Update pyproject.toml to retain minimum version of scipy --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> Co-authored-by: Eneko Uruñuela * Update numpy requirement from <=2.0.0,>=1.16 to >=1.16,<=2.0.1 (#1112) Updates the requirements on [numpy](https://github.com/numpy/numpy) to permit the latest version. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.16.0...v2.0.1) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Cleaning up installation instructions (#1113) * install instructions * Update docs/installation.rst Co-authored-by: Taylor Salo * Update docs/installation.rst Co-authored-by: Eneko Uruñuela --------- Co-authored-by: Taylor Salo Co-authored-by: Eneko Uruñuela * Update bokeh requirement from <=3.5.0,>=1.0.0 to >=1.0.0,<=3.5.1 (#1116) Updates the requirements on [bokeh](https://github.com/bokeh/bokeh) to permit the latest version. - [Changelog](https://github.com/bokeh/bokeh/blob/3.5.1/docs/CHANGELOG) - [Commits](https://github.com/bokeh/bokeh/compare/1.0.0...3.5.1) --- updated-dependencies: - dependency-name: bokeh dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Update list of multi-echo datasets (#1115) * Generate metrics from external regressors using F stats (#1064) * Get required metrics from decision tree. * Continue changes. * More updates. * Store necessary_metrics as a list. * Update selection_nodes.py * Update selection_utils.py * Update across the package. * Keep updating. * Update tedana.py * Add extra metrics to list. * Update ica_reclassify.py * Draft metric-based regressor correlations. * Fix typo. * Work on trees. * Expand regular expressions in trees. * Fix up the expansion. * Really fix it though. * Fix style issue. * Added external regress integration test * Got intregration test with external regressors working * Added F tests and options * added corr_no_detrend.json * updated names and reporting * Run black. * Address style issues. * Try fixing test bugs. * Update test_component_selector.py * Update component_selector.py * Use component table directly in selectcomps2use. * Fix. * Include generated metrics in necessary metrics. * Update component_selector.py * responding to feedback from tsalo * Update component_selector.py * Update test_component_selector.py * fixed some testing failures * fixed test_check_null_succeeds * fixed ica_reclassify bug and selector_properties test * ComponentSelector initialized before loading data * fixed docstrings * updated building decision tree docs * using external regressors and most tests passing * removed corr added tasks * fit_model moved to stats * removed and cleaned up external_regressors_config option * Added task regressors and some tests. Now alll in decision tree * cleaning up decision tree json files * removed mot12_csf.json changed task to signal * fixed tests with task_keep signal * Update tedana/metrics/external.py Co-authored-by: Taylor Salo * Update tedana/metrics/_utils.py Co-authored-by: Taylor Salo * Update tedana/metrics/collect.py Co-authored-by: Taylor Salo * Update tedana/metrics/external.py Co-authored-by: Taylor Salo * Update tedana/metrics/external.py Co-authored-by: Taylor Salo * Responding to review comments * reworded docstring * Added type hints to external.py * fixed external.py type hints * type hints to _utils collect and component_selector * type hints and doc improvements in selection_utils * no expand_node recursion * removed expand_nodes expand_node expand_dict * docstring lines break on punctuation * updating external tests and docs * moved test data downloading to tests.utils.py and started test for fit_regressors * fixed bug where task regressors retained in partial models * matched testing external regressors to included mixing and fixed bugs * Made single function for detrending regressors * added tests for external fit_regressors and fix_mixing_to_regressors * Full tests in test_external_metrics.py * adding tests * fixed extern regress validation warnings and added tests * sorting set values for test outputs * added to test_metrics * Added docs to building_decision_trees.rst * Added motion task decision tree flow chart * made recommended change to external_regressor_config * Finished documentation and renamed demo decision trees * added link to example external regressors tsv file * Apply suggestions from code review Fixed nuissance typos Co-authored-by: Taylor Salo * Minor documentation edits --------- Co-authored-by: Taylor Salo Co-authored-by: Taylor Salo Co-authored-by: Neha Reddy * Link to the open-multi-echo-data website (#1117) * Update multi-echo.rst * Update multi-echo.rst * Refactor `metrics.dependence` module (#1088) * Add type hints to metric functions. * Use keyword arguments. * Update tests. * Update dependence.py * Update collect.py * Fix other stuff. * documentation and resource updates (#1114) * documentation and resource updates * Fixed citation numbering and updated posters --------- Co-authored-by: Neha Reddy * Adding already requested changes * fixed failing tests * updated documentation in faq.rst * more documentation changes --------- Signed-off-by: dependabot[bot] Co-authored-by: Taylor Salo Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Matteo Visconti di Oleggio Castello Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> Co-authored-by: Eneko Uruñuela Co-authored-by: Taylor Salo Co-authored-by: Taylor Salo Co-authored-by: Neha Reddy --- .all-contributorsrc | 11 + .gitignore | 7 + README.md | 3 + docs/_static/adaptive_mask.png | Bin 0 -> 280081 bytes ...external_regressors_motion_task_models.pdf | Bin 0 -> 85993 bytes ...external_regressors_motion_task_models.png | Bin 0 -> 456616 bytes ...external_regressors_motion_task_models.tex | 122 ++++ ..._demo_external_regressors_single_model.pdf | Bin 0 -> 71144 bytes ..._demo_external_regressors_single_model.png | Bin 0 -> 328929 bytes ..._demo_external_regressors_single_model.tex | 109 ++++ docs/_static/rmse_plots.png | Bin 0 -> 287711 bytes docs/_static/tedana-ohbm2023-poster.png | Bin 0 -> 2583703 bytes docs/_static/tedana-ohbm2024-poster.png | Bin 0 -> 2945965 bytes docs/api.rst | 1 + docs/approach.rst | 5 +- docs/building_decision_trees.rst | 96 ++- docs/faq.rst | 68 +++ docs/included_decision_trees.rst | 57 +- docs/index.rst | 8 +- docs/installation.rst | 52 +- docs/multi-echo.rst | 87 +-- docs/outputs.rst | 54 +- pyproject.toml | 13 +- tedana/config.py | 6 +- tedana/decay.py | 109 +++- tedana/decomposition/ica.py | 26 +- tedana/decomposition/pca.py | 18 +- tedana/gscontrol.py | 6 +- tedana/io.py | 33 + tedana/metrics/_utils.py | 57 +- tedana/metrics/collect.py | 211 ++++--- tedana/metrics/dependence.py | 119 +++- tedana/metrics/external.py | 546 +++++++++++++++++ .../data/html/report_body_template.html | 19 +- tedana/reporting/html_report.py | 9 + tedana/reporting/static_figures.py | 253 ++++++-- tedana/resources/config/outputs.json | 12 +- ...xternal_regressors_motion_task_models.json | 293 +++++++++ ...demo_external_regressors_single_model.json | 205 +++++++ tedana/selection/component_selector.py | 175 +++++- tedana/selection/selection_nodes.py | 8 +- tedana/selection/selection_utils.py | 98 +-- tedana/selection/tedica.py | 4 +- tedana/stats.py | 45 +- .../tests/data/cornell_three_echo_outputs.txt | 5 + ...rnell_three_echo_preset_mixing_outputs.txt | 105 ++++ .../data/external_regress_Ftest_3echo.tsv | 76 +++ tedana/tests/data/fiu_four_echo_outputs.txt | 6 +- .../data/nih_five_echo_outputs_t2smap.txt | 2 + .../data/nih_five_echo_outputs_verbose.txt | 25 +- tedana/tests/test_component_selector.py | 119 +++- tedana/tests/test_decay.py | 7 +- tedana/tests/test_external_metrics.py | 572 ++++++++++++++++++ tedana/tests/test_integration.py | 224 +++---- tedana/tests/test_metrics.py | 193 +++++- tedana/tests/test_selection_utils.py | 17 +- tedana/tests/test_stats.py | 32 +- tedana/tests/test_utils.py | 180 ++++-- tedana/tests/utils.py | 141 ++++- tedana/utils.py | 116 +++- tedana/workflows/t2smap.py | 33 +- tedana/workflows/tedana.py | 128 +++- 62 files changed, 4308 insertions(+), 618 deletions(-) create mode 100644 docs/_static/adaptive_mask.png create mode 100644 docs/_static/decision_tree_demo_external_regressors_motion_task_models.pdf create mode 100644 docs/_static/decision_tree_demo_external_regressors_motion_task_models.png create mode 100644 docs/_static/decision_tree_demo_external_regressors_motion_task_models.tex create mode 100644 docs/_static/decision_tree_demo_external_regressors_single_model.pdf create mode 100644 docs/_static/decision_tree_demo_external_regressors_single_model.png create mode 100644 docs/_static/decision_tree_demo_external_regressors_single_model.tex create mode 100644 docs/_static/rmse_plots.png create mode 100644 docs/_static/tedana-ohbm2023-poster.png create mode 100644 docs/_static/tedana-ohbm2024-poster.png create mode 100644 tedana/metrics/external.py create mode 100644 tedana/resources/decision_trees/demo_external_regressors_motion_task_models.json create mode 100644 tedana/resources/decision_trees/demo_external_regressors_single_model.json create mode 100644 tedana/tests/data/cornell_three_echo_preset_mixing_outputs.txt create mode 100644 tedana/tests/data/external_regress_Ftest_3echo.tsv create mode 100644 tedana/tests/test_external_metrics.py diff --git a/.all-contributorsrc b/.all-contributorsrc index f2da82e5b..347477a63 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -323,6 +323,17 @@ "contributions": [ "code" ] + }, + { + "login": "mvdoc", + "name": "Matteo Visconti di Oleggio Castello", + "avatar_url": "https://avatars.githubusercontent.com/u/6150554?v=4", + "profile": "https://github.com/mvdoc", + "contributions": [ + "code", + "bug", + "doc" + ] } ], "contributorsPerLine": 5, diff --git a/.gitignore b/.gitignore index a52ae4872..540d22e67 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,13 @@ docs/generated/ .pytest_cache/ .testing_data_cache/ +# For decision tree .tex flow charts do not archive intermediate files +decision_tree*.aux +decision_tree*.fdb_latexmk +decision_tree*.fls +decision_tree*.log +decision_tree*.synctex.gz + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/README.md b/README.md index 4370b7233..4f01dcb31 100644 --- a/README.md +++ b/README.md @@ -177,6 +177,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Maitane Martinez Eguiluz
Maitane Martinez Eguiluz

💻 Marta Arbizu Gómez
Marta Arbizu Gómez

💻 + + Matteo Visconti di Oleggio Castello
Matteo Visconti di Oleggio Castello

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Single model with external regressors}}, label={180:$node\ 0$}]{Set all components to unclassified}; +% ----- node: 1 +\node(1)[decision, below of=0,label={180:$node\ 1$}, yshift=-1cm]{$\rho$ $>$ $\kappa$}; +\node(rej1)[reject, right of=1, xshift=2cm, align=center]{Unlikely BOLD\\$\rightarrow$ Reject}; +% ----- node: 2 +\node(2)[decision, below of=1,label={180:$node\ 2$}, label={[align=center] 315: voxel counts for signif fit\\of multi-echo data\\to $T_2$ or $S_0$ decay models}, yshift=-3.0cm]{$n \, FS_0 \, > \, n \, FT_2$ \& $n \,FT_2$ $>$ 0}; +\node(rej2)[reject, right of=2, xshift=2cm, align=center]{Unlikely BOLD\\$\rightarrow$ Reject}; +% ----- node: 3 +\node(3)[process, below of=2, label={180:$node\ 3$}, label={[align=center] 315: varex: variance explained\\by each component}, yshift=-1.5cm]{Calculate median(varex) across all components}; +% ----- node: 4 +\node(4)[decision, below of=3,label={180:$node\ 4$},label={[align=center] 315:DICE overlap between $T_2$ or $S_0$\\decay models and ICA component\\peak clusters}, yshift=-1.5cm]{dice $FS_0$ $>$ dice $FT_2$ \& varex $>$ median(varex) +}; +\node(rej4)[reject, right of=4, xshift=2.5cm, align=center]{Unlikely BOLD\\$\rightarrow$ Reject}; +% ----- node: 5 +\node(5)[decision, below of=4,label={180:$node\ 5$}, label={[align=center] 315: $t-statistic$ of $FT_2$ values\\in component peak clusters vs\\peak voxels outside of clusters}, yshift=-4.0cm]{ $0 \, >$ signal-noise \& varex $>$ median(varex)}; +\node(rej5)[reject, right of=5, xshift=2.5cm, align=center]{Unlikely BOLD\\$\rightarrow$ Reject}; +% ----- node: 6 +\node(6)[process, below of=5, label={180:$node\ 6$}, label={0: Uses all components}, yshift=-2.0cm]{Calculate $\kappa$ elbow}; +% ----- node: 7 +\node(7)[process, below of=6, label={180:$node\ 7$}, label={[align=center] 0: Uses all components and subset\\of unclassified components}]{Calculate $\rho$ elbow\\(liberal method)}; +% ----- node: 7 +\node(8)[decision, below of=7,label={180:$node\ 8$}, yshift=-1.5cm]{$\kappa \geq \kappa$ elbow}; +\node(chrej8)[changeclass, below of=8, yshift=-1.5cm]{Provisional reject}; +\node(chacc8)[changeclass, right of=8, xshift=3cm, yshift=0cm]{Provisional accept}; +% ----- node: 8 +\node(9)[decision, below of=chacc8,label={170:$node\ 9$}, yshift=-1.5cm]{ $\rho$ $>$ $\rho$ elbow}; +\node(chrej9)[changeclass, below of=9, yshift=-1.5cm]{Provisional reject}; +% ----- node: 9 +\node(10)[decision, left of=chrej9,label={180:$node\ 10$},label={235: Accept even if $\rho < \rho\ elbow$},xshift=-3.5cm]{$\kappa \geq \kappa$ elbow\\$\kappa > 2\rho$ }; +\node(chacc10)[changeclass, below of=10, xshift=0cm, yshift=-1.5cm, align=center]{Provisional accept}; +% ----- node: 10 +\node(11)[decision, below of=chacc10,label={180:$node\ 11$}, xshift=0cm, yshift=-2cm]{External regressor\\nuisance model\\$p<0.05$\\$R^2>0.5$};%--check in kundu +\node(chrej11)[changeclass, below of=11, xshift=0cm, yshift=-1.5cm, align=center]{Tag: External Regressors\\Provisional reject}; +% ----- node: 11 +\node(12)[decision, below of=chrej11,label={180:$node\ 11$},label={[align=left] 335: Will accept the lowest\\variance components until\\1\% of total variance is\\accepted this way}, yshift=-1.5cm]{$if$ component variance $<0.1$};%--check in kundu +\node(acc12)[accept, right of=12, xshift=3cm, align=center]{Low variance\\$\rightarrow$ Accept}; +% ----- node: 12 +\node(13)[accept, below of=12,label={180:$node\ 12$},yshift=-1.5cm, align=center]{Likely BOLD\\Change provisional accept\\$\rightarrow$Accept}; +% ----- node: 13 +\node(14)[reject, below of=13,label={180:$node\ 13$}, yshift=0cm, align=center]{Unlikely BOLD\\Change provisional reject\\$\rightarrow$Reject}; + + +% ------------------------- connections ------------------------- +\draw[arrow](0)--(1); +\draw[arrow](1)--node[anchor=south, right=0] {no} (2); +\draw[arrow](1)--node[anchor=south] {yes} (rej1); +\draw[arrow](2)--node[anchor=south, right=0] {no} (3); +\draw[arrow](2)--node[anchor=south] {yes} (rej2); +\draw[arrow](3)--(4); +\draw[arrow](4)--node[anchor=south, right=0] {no} (5); +\draw[arrow](4)--node[anchor=south] {yes} (rej4); +\draw[arrow](5)--node[anchor=south, right=0] {no} (6); +\draw[arrow](5)--node[anchor=south] {yes} (rej5); +\draw[arrow](6)--(7); +\draw[arrow](7)--(8); +\draw[arrow](8)--node[anchor=south] {yes} (chacc8); +\draw[arrow](8)--node[anchor=south, right=0] {no} (chrej8); +\draw[arrow](chacc8)--(9); +\draw[arrow](chrej8)--(9); +\draw[arrow](9)--node[anchor=south, right=0] {yes} (chrej9); +\draw[arrow](chrej9)--(10); +\draw[arrow](10)--node[anchor=south, right=0] {yes} (chacc10); +\draw[arrow](chacc10)--(11); +\draw[arrow](11)--node[anchor=south, right=0] {yes} (chrej11); +\draw[arrow](chrej11)--(12); +\draw[arrow](12)--node[anchor=south] {yes} (acc12); +\end{tikzpicture} +\end{document} diff --git a/docs/_static/rmse_plots.png b/docs/_static/rmse_plots.png new file mode 100644 index 0000000000000000000000000000000000000000..093f9ec757cc2e422b7f95f7b73823dbcf504d98 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b/docs/approach.rst @@ -311,6 +311,8 @@ Next, ``tedana`` applies TE-dependent independent component analysis (ICA) in order to identify and remove TE-independent (i.e., non-BOLD noise) components. The dimensionally reduced optimally combined data are first subjected to ICA in order to fit a mixing matrix to the whitened data. +``tedana`` can use a single interation of FastICA or multiple interations of robustICA, +with an explanation of those approaches `in our FAQ`_. This generates a number of independent timeseries (saved as **desc-ICA_mixing.tsv**), as well as parameter estimate maps which show the spatial loading of these components on the brain (**desc-ICA_components.nii.gz**). @@ -352,10 +354,11 @@ The actual decision tree is dependent on the component selection algorithm emplo thresholds applied to each of the metrics). `These decision trees are detailed here`_. Components that are classified as noise are projected out of the optimally combined data, -yielding a denoised timeseries, which is saved as **desc-optcomDenoised_bold.nii.gz**. +yielding a denoised timeseries, which is saved as **desc-denoised_bold.nii.gz**. .. image:: /_static/a15_denoised_data_timeseries.png +.. _in our FAQ: faq.html#tedana-what-is-the-right-number-of-ica-components-what-options-let-me-get-it .. _These decision trees are detailed here: included_decision_trees.html ******************************* diff --git a/docs/building_decision_trees.rst b/docs/building_decision_trees.rst index 7b0e2e479..1c31b6c2d 100644 --- a/docs/building_decision_trees.rst +++ b/docs/building_decision_trees.rst @@ -186,7 +186,7 @@ All functions are currently in :mod:`~tedana.selection.selection_nodes`. to interpret. .. _resources/decision_trees: https://github.com/ME-ICA/tedana/tree/main/tedana/resources/decision_trees - +.. _This is an example TSV file: https://github.com/ME-ICA/tedana/tree/main/tedana/tests/data/external_regress_Ftest_3echo.tsv General information fields ========================== @@ -239,6 +239,100 @@ that is used to check whether results are plausible & can help avoid mistakes. none of the components include the "Likely BOLD" tag, then ICA will be repeated with a different seed and then the selection process will repeat. +External regressor configuration +================================ + +``external_regressor_config`` is an optional field. If this field is specified, then +additional metrics will be calculated that include F, :math:`R^2`, and p values for the fit +of external regressors to each component time series. +The p value might be useful for defining a statistically significant fit, +while :math:`R^2` can assess whether the regressors model a meaningful amount of variance. +For example, even if nuisance regressors, like head motion, significantly fit an ICA component +time series, do not reject if they only model 5% of the total variance of that component. + +Users will need to specify the +external regressors using the ``--external`` option. +``--external`` takes a TSV file in which each column has a header label and is the length +of the fMRI time series. +This functionality can be used to integrate non-multi-echo decision criteria, +such as correlations to head motion, CSF, or respiration time series. +These added metrics can then be used in decision tree steps just like any other metric. +Two demonstration trees that apply this functionality are in `resources/decision_trees`_. +``demo_external_regressors_single_model.json`` demonstrates the simplest application +of external regressors and ``demo_external_regressors_motion_task_models.json`` +highlights the full range of functionality. +`This is an example TSV file`_ with column labels that work with both of these trees. +Both these trees are based on ``minimal.json``. +While these might be good decision trees to use as is, +they are both called "demo" because they demonstrate what is possible, +but the utility of these specific decision trees have **not yet** been validated. + +``external_regressor_config`` is a list of dictionaries. +Each dictionary defines statistical tests to apply to a group of +regressors specified in ``--external``. +``demo_external_regressors_single_model.json`` includes a single dictionary +that specifies fitting all supplied regressors to a single nuisance model that is +used to reject components. +``demo_external_regressors_motion_task_models`` includes one dictionary for +a nuisance model to reject components and one for a task model to retain more +task-correlated signal. For the nuisance model, columns with specific header names +are also fit to partial models for motion and CSF regressors that are used to label +why components were rejected. +(i.e. Component X was rejected because it fit to head motion regressors.) +Each dictionary in ``external_regressor_config`` must include the following sub-fields: + +- "regress_ID" + A descriptive name for the external regressors that will be logged. + Will be used in the ``selector.component_table_`` labels describing the outputs of the + statistical tests. + For example, if this field is ``nuisance`` then component table column labels will include: + ``Fstat nuisance model``, ``R2stat nuisance model``, and ``pval nuisance model``. + +- "info" + A brief description of the external regressors for info logging. + +- "report" + A narrative description of how the external regressors are used + that will be used in report logging. + This should include any citations, which must be included in the + `references BibTeX file`_. + +- "detrend" + "true" or "false" to specify whether to include detrending regressors + when fitting the external regressors to the ICA component time series. + If "true", it will specify the number of detrending time regressors to + include based on the length of the time series. + If "false", it will just include an intercept regressor to remove the mean. + This can also be a integer that defines the number of regressors to include. + Can also be an integer specifying the number of detrending regressors. + +- "statistic" + The statistical test to use for fitting the external regressors to the + ICA component time series. Currently, the only valid option is "F" for + fitting using a linear model with an F statistic. + +- "regressors" + A list of strings or regular expressions to specify the columns in + the external regressor file to use in the model. Regular expressions begin with ``^`` + For example, ``["^.*$"]`` would mean use all regressors in the file, + while ``["^mot_.*$"]`` would mean use all regressors with labels beginging with ``mot_``. + ``["mot_x", "mot_y_", "mot_z"]`` would be use regressors with those specific labels. + Capitalization is ignored. + Note: When tedana is run, regular expressions are replaced with the named regressors. + The outputted decision tree will specify what was used and might be useful for validation. + +An optional field is **"partial_models"**. +This is a dictionary where each element is a descriptor and column specification is similar to +``regressors``. For example, ``"partial_models": {"Motion": ["^mot_.*$"], "CSF": ["^csf.*$"]}`` +specifies two partial models for motion and CSF time series, where the columns in +the external regressor tsv begin with either ``mot_`` or ``csf``. +When this field is used, statistics will be calculated for the full model with all regressors +and each specified partial model. This can be used to potentially reject components that fit +any combination of nuisance regressors and also note which components fit head motion regressors. +If this option is included, there would be added columns in ``selector.component_table_``, such as +``Fstat nuisance Motion partial model``, ``pval nuisance Motion partial model``, and +``R2stat nuisance Motion partial model`` + Nodes in the decision tree ========================== diff --git a/docs/faq.rst b/docs/faq.rst index 7a8cd79f8..4f360b71f 100644 --- a/docs/faq.rst +++ b/docs/faq.rst @@ -93,11 +93,79 @@ The TEDICA step may fail to converge if TEDPCA is either too strict With updates to the ``tedana`` code, this issue is now rare, but it may happen when preprocessing has not been applied to the data, or when improper steps have been applied to the data (e.g. rescaling, nuisance regression). +I can also still happen when everything is seemingly correct +(see the answer to the next question). If you are confident that your data have been preprocessed correctly prior to applying tedana, and you encounter this problem, please submit a question to `NeuroStars`_. .. _NeuroStars: https://neurostars.org +********************************************************************************* +[tedana] What is the right number of ICA components & what options let me get it? +********************************************************************************* + +Part of the PCA step in ``tedana`` processing involves identifying the number of +components that contain meaningful signal. +The PCA components are then used to calculate the same number of ICA components. +The ``--tedpca`` option includes several options to identify the "correct" number +of PCA components. +``kundu`` and ``kundu-stabilize`` use several echo-based criteria to exclude PCA +components that are unlikely to contain T2* or S0 signal. +``mdl`` (conservative & fewest components), ``kic``, +& ``aic`` (liberal & more components) use `MAPCA`_. +Within the same general method, each uses a cost function to find a minimum +where more components no longer model meaningful variance. +For some datasets we see all methods fail and result in too few or too many components. +There is no consistent number of components or % variance explained to define the correct number. +The correct number of components will depend on the noise levels of the data. +For example, smaller voxels will results in more thermal noise and less total variance explained. +A dataset with more head motion artifacts will have more variance explained, +since more structured signal is within the head motion artifacts. +The clear failure cases are extreme. That is getting less than 1/5 the number of components +compared to time points or having nearly has many components as time point. +We are working on identifying why this happens and adding get better solutions. +Our current guess is that most of the above methods assume data are +independant and identically distributed (IID), +and signal leakage from in-slice and multi-slice accelleration may violate this assumption. + +We have one option that is generally useful and is also a partial solution. +``--ica_method robustica`` will run `robustica`_. +This is a method that, for a given number of PCA components, +will repeated run ICA and identify components that are stable across iterations. +While running ICA multiple times will slow processing, as a general benefit, +this means that the ICA results are less sensitive to the initialization parameters, +computer hardware, and software versions. +This will result in better stability and replicability of ICA results. +Additionally, `robustica`_ almost always results in fewer components than initially prescripted, +since there are fewer stable components across interations than the total number of components. +This means, even if the initial PCA component estimate is a bit off, +the number of resulting robust ICA components will represent stable information in the data. +For a dataset where the PCA comoponent estimation methods are failing, +one could use ``--tedpca`` with a fixed integer for a constant number of components, +that is on the high end of typical for a study, +and then `robustica`_ will reduce the number of components to only find stable information. +That said, if the fixed PCA component number is too high, +then the method will have too many unstable components, +and if the fixed PCA component number is too low, then there will be even fewer ICA components. +The number of ICA components is more consisent, +but is still sensitive to the intial number of PCA components. +For example, for a single dataset 60 PCA components might result in 46 stable ICA components, +while 55 PCA components might results in 43 stable ICA components. +We are still testing how these interact and give better recommendations and even more stable results. +At that point, ``--ica_method robustica`` might become the default setting, +While the TEDANA developers expect that ``--ica_method robustica`` may become +the default configuration in future TEDANA versions, +it is first being released to the public as a non-default option +in hope of gaining insight into its behaviour +across a broader range of multi-echo fMRI data. +If users are having trouble with PCA component estimation failing on a dataset, +we recommend using RobustICA; +we also invite user feedback on its behaviour and efficacy. + + +.. _MAPCA: https://github.com/ME-ICA/mapca +.. _robustica: https://github.com/CRG-CNAG/robustica + .. _manual classification: ******************************************************************************** diff --git a/docs/included_decision_trees.rst b/docs/included_decision_trees.rst index c0d3f4e9a..af1d931ae 100644 --- a/docs/included_decision_trees.rst +++ b/docs/included_decision_trees.rst @@ -2,7 +2,7 @@ Included Decision Trees ####################### -Three decision trees are currently distributed with ``tedana``. +Five decision trees are currently distributed with ``tedana``. ``meica`` is the decision tree that is based on MEICA version 2.5 and ``tedana_orig`` is very similar and has been included with ``tedana`` @@ -18,7 +18,18 @@ and comprehensible, but it has not yet be extensively validated and parts of the tree may change in response to additional tests on a wider range of data sets. -Flowcharts describing the steps in both trees are below. +With the addition of options to fit external regressors to components, +there are two demonstration decision trees that implement this new functionality. +While these might work well, since they have not yet been validated on data, they +are labeled ``demo``. +``decision_tree_demo_external_regressors_single_model`` +demonstrates fitting all nuisance regressors to a single model. +``decision_tree_demo_external_regressors_motion_task_models`` +demonstrates fitting nuisance regressors to a model, +partial tests and tagging for components that fit Motion or CSF regressors, +and retention of some components that fit task regressors. + +Flowcharts describing the steps in these trees are below. As documented more in :doc:`building_decision_trees`, the input to each tree is a table with metrics, like :math:`\kappa` or :math:`\rho`, for each component. Each step or node in the decision tree either calculates @@ -122,3 +133,45 @@ is rejected (node 13) `LaTeX file to generate the minimal decision tree flow chart`_ .. _LaTeX file to generate the minimal decision tree flow chart: _static/decision_tree_minimal.tex + +********************************************* +Demo external regressors single model +********************************************* + +This tree is similar to the minimal tree except there is an added node (node 11) +where components are rejected if they significantly fit a model of external nuisance regressor +time series and the fit models a substantial amount of the total variance. +Unlike the minimal tree, components that would be accepted based on :math:`\kappa` & :math:`\rho` +criteria can be rejected based on a fit to external regressors. +This is called a "demo" tree because it is demonstrating how fits to external regressors can +be used. It might be a good decision tree to use, +but results have not yet been tested and validated. + +.. image:: _static/decision_tree_demo_external_regressors_single_model.png + :width: 400 + :alt: External Decision Tree With Motion and Task Models Flow Chart + +**************************************************** +Demo external regressors, motion task models +**************************************************** + +This is based on the minimal tree, but multiple nodes were added to demonstrate how to use +external regressors for fits. +Unlike the minimal tree, components that would be accepted based on :math:`\kappa` & :math:`\rho` +criteria can be rejected based on a fit to external regressors. +Components are rejected if they significantly fit a model of external nuisance regressor +time series and the fit models a substantial amount of the total variance (node 10). +For rejected components, if they also fit a partial model of motion external regressors (node 11), +or CSF external regressors (node 12), the outputs are also tagged to say they fit those groups +of regressors. +Additionally, if a rejected component fits the task design and has +:math:`\kappa` > :math:`\kappa` elbow, then it is accepted under the conservative assumption to +retain task fitting components with some :math:`T_2^*`` signal even if those components also +contain potentially rejection-worthy noise (node 13). +This is called a "demo" tree because it is demonstrating how fits to external regressors can +be used. It might be a good decision tree to use, +but results have not yet been tested and validated. + +.. image:: _static/decision_tree_demo_external_regressors_motion_task_models.png + :width: 400 + :alt: External Decision Tree With Motion and Task Models Flow Chart diff --git a/docs/index.rst b/docs/index.rst index 7efacb78f..783cb33ba 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -112,8 +112,8 @@ When using tedana, please include the following citations: }

- tedana - This link is for the most recent version of the code and that page has links to DOIs + + 1. This link is for the most recent version of the code and that page has links to DOIs for older versions. To support reproducibility, please cite the version you used: https://doi.org/10.5281/zenodo.1250561 @@ -156,9 +156,9 @@ You can also learn more about `why citing software is important`_. Posters ******* -.. image:: /_static/tedana-ohbm2019-poster.png +.. image:: /_static/tedana-ohbm2024-poster.png -.. image:: /_static/tedana-ohbm2018-poster.png +.. image:: /_static/tedana-ohbm2023-poster.png ******************* diff --git a/docs/installation.rst b/docs/installation.rst index 9ff6bd4ab..830fa58ff 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -2,35 +2,51 @@ Installation ############ -You'll need to set up a working development environment to use ``tedana``. -To set up a local environment, you will need Python >=3.8 and the following -packages will need to be installed: +You'll need to install ``tedana`` in your python environment. +If you already have a python environment and you want to add tedana, type: -- nilearn -- nibabel -- numpy -- scikit-learn -- scipy -- mapca +.. code-block:: bash + + pip install tedana -After installing relevant dependencies, you can then install ``tedana`` with: +As long as your python environment contains a compatible version of python, this should work. + +If you want more control over your environment to ensure that every python dependency +is optimized for tedana, +you can use a program like `conda`_ to create an environment specifically for ``tedana`` with: .. code-block:: bash + conda create -n tedenv python=3.12 pip + conda activate tedenv pip install tedana -In addition to the Python package, installing ``tedana`` will add the ``tedana`` -and ``t2smap`` workflow CLIs to your path. -You can confirm that ``tedana`` has successfully installed by launching a Python instance and running: +The above will create a python environment for tedana called ``tedenv``, +and you can enter that environment with ``conda activate tedenv``. -.. code-block:: python +With either of the above methods, `pip` will install tedana and all its dependencies. +tedana's `dependencies are listed here`_. - import tedana -You can check that it is available through the command line interface (CLI) with: +Once ``tedana`` is installed, there are two ways to run it. +You can run ``tedana``, ``ica_reclassify``, and ``t2smap`` from the command line interface (CLI). +You can see the options for any of these commands using ``--help`` (i.e. ``tedana --help``). -.. code-block:: bash +You can also import these commands to run within python using: - tedana --help +.. code-block:: python + + from tedana.workflows import ica_reclassify_workflow, t2smap_workflow, tedana_workflow + +API instructions for running these `commands in python are here`_. If no error occurs, ``tedana`` has correctly installed in your environment! + +If you are having trouble solving errors, +`here are places the tedana community monitors`_ to offer support. + + +.. _commands in python are here: https://tedana.readthedocs.io/en/stable/api.html#module-tedana.workflows +.. _conda: https://www.anaconda.com/download +.. _dependencies are listed here: https://github.com/ME-ICA/tedana/blob/main/pyproject.toml +.. _here are places the tedana community monitors: https://tedana.readthedocs.io/en/stable/support.html diff --git a/docs/multi-echo.rst b/docs/multi-echo.rst index cd91f20cb..3e940778b 100644 --- a/docs/multi-echo.rst +++ b/docs/multi-echo.rst @@ -424,6 +424,9 @@ individual echo level (see above). Instead, we recommend that researchers apply the same transforms to all echoes in an ME-EPI series. That is, that they calculate head motion correction parameters from one echo and apply the resulting transformation to all echoes. +Based on `Mao et. al. Imaging Neuroscience 2024`_, we recommend using the earliest echo. + +.. _Mao et. al. Imaging Neuroscience 2024: https://doi.org/10.1162/imag_a_00151 2. Perform slice timing correction and motion correction **before** multi-echo denoising @@ -514,19 +517,19 @@ Journal articles describing multi-echo methods Videos ====== -* An `educational session from OHBM 2017`_ by Dr. Prantik Kundu about multi-echo denoising -* A `series of lectures from the OHBM 2017 multi-echo session`_ on multiple facets of multi-echo data analysis -* | Multi-echo fMRI lecture from the `2018 NIH FMRI Summer Course`_ by Javier Gonzalez-Castillo - | `Slides from 2018 NIH FMRI Summer Course`_ -* NIMH Center for Multimodal Neuroimaging `Advantages of multi-echo fMRI`_ (2019) by Dan Handwerker, Javier Gonzalez-Castillo, and Vinai Roopchansingh +* | Multi-echo fMRI lecture from the `2024 NIH FMRI Summer Course`_ by Dan Handwerker + | `Slides from 2024 NIH FMRI Summer Course`_ * | MRI Together 2022 Conference Presentations by Eneko Uruñuela | Tedana: Analysis of echo-time dependent fMRI data (`recording `_, `slides `_) | A tour of tedana (`tour recording `_, `tour slides `_) +* | NIMH Center for Multimodal Neuroimaging `Advantages of multi-echo fMRI`_ (2019) by Dan Handwerker, Javier Gonzalez-Castillo, and Vinai Roopchansingh +* | Multi-echo fMRI lecture from the `2018 NIH FMRI Summer Course`_ by Javier Gonzalez-Castillo + | `Slides from 2018 NIH FMRI Summer Course`_ -.. _educational session from OHBM 2017: https://www.pathlms.com/ohbm/courses/5158/sections/7788/video_presentations/75977 -.. _series of lectures from the OHBM 2017 multi-echo session: https://www.pathlms.com/ohbm/courses/5158/sections/7822 -.. _2018 NIH FMRI Summer Course: https://fmrif.nimh.nih.gov/course/fmrif_course/2018/14_Javier_20180713 -.. _Slides from 2018 NIH FMRI Summer Course: https://fmrif.nimh.nih.gov/COURSE/fmrif_course/2018/content/14_Javier_20180713.pdf +.. _2024 NIH FMRI Summer Course: https://youtu.be/Uc-1DcgNQzk?feature=shared +.. _Slides from 2024 NIH FMRI Summer Course: https://fmrif.nimh.nih.gov/images/3/3a/12_MultiEcho_Summer_Handwerker2024.pdf +.. _2018 NIH FMRI Summer Course: https://www.youtube.com/watch?v=83bavs4rlUg +.. _Slides from 2018 NIH FMRI Summer Course: https://fmrif.nimh.nih.gov/images/1/12/14_Javier_20180713.pdf .. _Advantages of multi-echo fMRI: https://youtu.be/G1Ftd2IwF14 @@ -593,40 +596,44 @@ These include: Datasets ======== +.. tip:: + + We have attempted to compile a list of open multi-echo fMRI datasets in + `open-multi-echo-data `_. + We try to keep that list up to date, but it may not be complete. + + The list of datasets on that site is limited to (1) human data, + (2) data that are accessible without a data usage agreement or registration, + and (3) data with at least three echoes. + A number of multi-echo datasets have been made public so far. This list is not necessarily up to date, so please check out OpenNeuro to potentially find more. -* `Multi-echo fMRI replication sample of autobiographical memory, prospection and theory of mind reasoning tasks`_ -* `Multi-echo Cambridge`_ -* `Multiband multi-echo imaging of simultaneous oxygenation and flow timeseries for resting state connectivity`_ -* `Multiband multi-echo simultaneous ASL/BOLD for task-induced functional MRI`_ -* `Valence processing differs across stimulus modalities`_ -* `Cambridge Centre for Ageing Neuroscience (Cam-CAN)`_ -* `rt-me-fMRI - A task and resting state dataset for real-time, multi-echo fMRI methods development and validation`_ -* `EuskalIBUR`_ -* `Le Petit Prince`_ -* `Evidence supporting a time-limited hippocampal role in retrieving autobiographical memories`_ -* `Neurocognitive aging data release with behavioral, structural, and multi-echo functional MRI measures`_ -* `Heart rate variability biofeedback training and emotion regulation`_ -* `A mesial-to-lateral dissociation for orthographic processing in the visual cortex (dual-echo)`_ -* `Psilocybin exerts distinct effects on resting state networks associated with serotonin and dopamine in mice`_ -* `Psilocybin exerts distinct effects on resting state networks associated with serotonin and dopamine in mice (pilot data)`_ - -.. _Multi-echo fMRI replication sample of autobiographical memory, prospection and theory of mind reasoning tasks: https://openneuro.org/datasets/ds000210/ -.. _Multi-echo Cambridge: https://openneuro.org/datasets/ds000258 -.. _Multiband multi-echo imaging of simultaneous oxygenation and flow timeseries for resting state connectivity: https://openneuro.org/datasets/ds000216 -.. _Multiband multi-echo simultaneous ASL/BOLD for task-induced functional MRI: https://openneuro.org/datasets/ds000254 -.. _Valence processing differs across stimulus modalities: https://openneuro.org/datasets/ds001491 -.. _Cambridge Centre for Ageing Neuroscience (Cam-CAN): https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/ -.. _rt-me-fMRI - A task and resting state dataset for real-time, multi-echo fMRI methods development and validation: https://doi.org/10.34894/R1TNL8 -.. _EuskalIBUR: https://openneuro.org/datasets/ds003192 -.. _Le Petit Prince: https://openneuro.org/datasets/ds003643 -.. _Evidence supporting a time-limited hippocampal role in retrieving autobiographical memories: https://openneuro.org/datasets/ds003511 -.. _Neurocognitive aging data release with behavioral, structural, and multi-echo functional MRI measures: https://openneuro.org/datasets/ds003592 -.. _Heart rate variability biofeedback training and emotion regulation: https://openneuro.org/datasets/ds003823/versions/1.2.3 -.. _A mesial-to-lateral dissociation for orthographic processing in the visual cortex (dual-echo): https://openneuro.org/datasets/ds002155 -.. _Psilocybin exerts distinct effects on resting state networks associated with serotonin and dopamine in mice: https://openneuro.org/datasets/ds001725 -.. _Psilocybin exerts distinct effects on resting state networks associated with serotonin and dopamine in mice (pilot data): https://openneuro.org/datasets/ds002154 +* `Multi-echo fMRI replication sample of autobiographical memory, prospection and theory of mind reasoning tasks `_ +* `Multi-echo Cambridge `_ +* `Multiband multi-echo imaging of simultaneous oxygenation and flow timeseries for resting state connectivity `_ +* `Multiband multi-echo simultaneous ASL/BOLD for task-induced functional MRI `_ +* `Valence processing differs across stimulus modalities `_ +* `Cambridge Centre for Ageing Neuroscience (Cam-CAN) `_ +* `rt-me-fMRI - A task and resting state dataset for real-time, multi-echo fMRI methods development and validation `_ +* `EuskalIBUR `_ +* `Le Petit Prince `_ +* `Evidence supporting a time-limited hippocampal role in retrieving autobiographical memories `_ +* `Neurocognitive aging data release with behavioral, structural, and multi-echo functional MRI measures `_ +* `Heart rate variability biofeedback training and emotion regulation `_ +* `A mesial-to-lateral dissociation for orthographic processing in the visual cortex (dual-echo) `_ +* `Psilocybin exerts distinct effects on resting state networks associated with serotonin and dopamine in mice `_ +* `Psilocybin exerts distinct effects on resting state networks associated with serotonin and dopamine in mice (pilot data) `_ +* `MICA-PNI `_ +* `SoCal Kinesia and Incentivization for Parkinson's Disease (SKIP) `_ +* `Single-echo/multi-echo comparison pilot `_ +* `Social Reward and Aging: A Preliminary Multi-echo fMRI and Diffusion Dataset `_ +* `Dense Investigation of Variability of Affect (DIVA) `_ +* `Multivariate Assessment of Inhibitory Control in Youth: Links with Psychopathology and Brain Function Dataset `_ +* `Multi-echo simultaneous multislice fMRI dataset: Effect of acquisition parameters on fMRI data `_ +* `Sequence pilot: multiecho and multiband fMRI `_ +* `Weill Cornell Medicine Multi-echo (WCM-ME) Dataset `_ +* `Characterization and Treatment of Adolescent Depression (CAT-D) `_ .. _spreadsheet of publications: diff --git a/docs/outputs.rst b/docs/outputs.rst index 55369191b..211625901 100644 --- a/docs/outputs.rst +++ b/docs/outputs.rst @@ -40,7 +40,7 @@ Key: Filename Con "data description json": dataset_description.json Top-level metadata for the workflow. tedana_report.html The interactive HTML report. "combined img": desc-optcom_bold.nii.gz Optimally combined time series. -"denoised ts img": desc-optcomDenoised_bold.nii.gz Denoised optimally combined time series. Recommended +"denoised ts img": desc-denoised_bold.nii.gz Denoised optimally combined time series. Recommended dataset for analysis. "adaptive mask img": desc-adaptiveGoodSignal_mask.nii.gz Integer-valued mask used in the workflow, where each voxel's value corresponds to the number of good @@ -115,6 +115,8 @@ report.txt A s "high kappa ts img": desc-optcomAccepted_bold.nii.gz High-kappa time series. This dataset does not include thermal noise or low variance components. Not the recommended dataset for analysis. +"confounds tsv": desc-confounds_timeseries.tsv Summary time series measures, including RMSE measures + of T2*/S0 model fit. references.bib The BibTeX entries for references cited in report.txt. @@ -167,8 +169,8 @@ If ``gscontrol`` includes 'gsr' Key: Filename Content ================================================================= ===================================================== "gs img": desc-globalSignal_map.nii.gz Spatial global signal -"global signal time series tsv": desc-globalSignal_timeseries.tsv Time series of global signal from optimally combined - data. +"confounds tsv": desc-confounds_timeseries.tsv Time series of global signal from optimally combined + data will be added to this file. "has gs combined img": desc-optcomWithGlobalSignal_bold.nii.gz Optimally combined time series with global signal retained. "removed gs combined img": desc-optcomNoGlobalSignal_bold.nii.gz Optimally combined time series with global signal @@ -528,11 +530,30 @@ should not overly focus on carpet plots and should examine these results in cont :height: 400px +************************** +Adaptive Mask Summary Plot +************************** + +Below the carpet plots is a summary plot of the adaptive mask. + +This figure overlays contours reflecting the boundaries of the following masks onto the mean optimally combined data: + +- **Base**: The base mask, either provided by the user or generated automatically using ``compute_epi_mask``. +- **Optimal combination**: The mask used for optimal combination and denoising. + This corresponds to values greater than or equal to 1 (at least 1 good echo) in the adaptive mask. +- **Classification**: The mask used for the decomposition and component classification steps. + This corresponds to values greather than or equal to 3 (at least 3 good echoes) in the adaptive mask. + +.. image:: /_static/adaptive_mask.png + :align: center + :height: 400px + + ************************ T2* and S0 Summary Plots ************************ -Below the carpet plots are summary plots for the T2* and S0 maps. +Below the adaptive mask plot are summary plots for the T2* and S0 maps. Each map has two figures: a spatial map of the values and a histogram of the voxelwise values. The T2* map should look similar to T2 maps and be brightest in the ventricles and darkest in areas of largest susceptibility. The S0 map should roughly follow the signal-to-noise ratio and will be brightest near the surface near RF coils. @@ -544,6 +565,31 @@ It is important to note that the histogram is limited from 0 to the 98th percent :height: 400px +********************* +Decay Model Fit Plots +********************* + +Below the T2* and S0 summary plots are the decay model fit plots. +These plots show residual mean squared error (RMSE) values for the +monoexponential decay model, based on the T2* and S0 maps. + +The first plot is the mean RMSE brain plot, which shows the mean RMSE over time for each voxel in the brain. +This plot is limited from the 2nd percentile to the 98th percentile. + +The second plot is a time series of RMSE values across the brain, over time. +This plot includes the median RMSE time series, +along with an error band representing the 25th and 75th percentiles, +and dotted lines indicating the 2nd and 98th percentile RMSE values. + +The fit quality will vary depending on acquisition parameters and will likely be worse near signal drop-out areas. +For a study with consistent acquisition parameters, +relatively high RMSE values for runs or timepoints might be a marker of an underlying data quality issue. + +.. image:: /_static/rmse_plots.png + :align: center + :height: 400px + + ************************** Citable workflow summaries ************************** diff --git a/pyproject.toml b/pyproject.toml index 5572c5284..75bca03cc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,19 +21,20 @@ classifiers = [ license = {file = "LICENSE"} requires-python = ">=3.8" dependencies = [ - "bokeh>=1.0.0,<=3.4.0", + "bokeh>=1.0.0,<=3.5.1", "mapca>=0.0.4,<=0.0.5", "matplotlib", "nibabel>=2.5.1,<=5.2.1", - "nilearn>=0.7,<=0.10.3", - "numpy>=1.16,<=1.26.4", - "pandas>=2.0,<=2.2.1", + "nilearn>=0.10.3,<=0.10.4", + "numpy>=1.16,<=2.0.1", + "pandas>=2.0,<=2.2.2", "pybtex", "pybtex-apa-style", "robustica>=0.1.3", - "scikit-learn>=0.21, <=1.4.1.post1", - "scipy>=1.2.0, <=1.13.0", + "scikit-learn>=0.21, <=1.5.1", + "scipy>=1.2.0, <=1.14.0", "threadpoolctl", + "tqdm", ] dynamic = ["version"] diff --git a/tedana/config.py b/tedana/config.py index 11cf2011c..746d41637 100644 --- a/tedana/config.py +++ b/tedana/config.py @@ -1,14 +1,14 @@ """Setting default values for ICA decomposition.""" -DEFAULT_ICA_METHOD = "robustica" -DEFAULT_N_ROBUST_RUNS = 30 +DEFAULT_ICA_METHOD = "fastica" DEFAULT_N_MAX_ITER = 500 DEFAULT_N_MAX_RESTART = 10 DEFAULT_SEED = 42 -"""Setting extreme values for number of robust runs.""" +"""Setting values for number of robust runs.""" +DEFAULT_N_ROBUST_RUNS = 30 MIN_N_ROBUST_RUNS = 5 MAX_N_ROBUST_RUNS = 500 WARN_N_ROBUST_RUNS = 200 diff --git a/tedana/decay.py b/tedana/decay.py index 0669bc908..359861f7b 100644 --- a/tedana/decay.py +++ b/tedana/decay.py @@ -1,10 +1,14 @@ """Functions to estimate S0 and T2* from multi-echo data.""" import logging +from typing import List, Literal, Tuple import numpy as np +import numpy.matlib +import pandas as pd import scipy from scipy import stats +from tqdm.auto import tqdm from tedana import utils @@ -112,7 +116,7 @@ def fit_monoexponential(data_cat, echo_times, adaptive_mask, report=True): "estimate T2* and S0. In cases of model fit failure, T2*/S0 " "estimates from the log-linear fit were retained instead." ) - n_samp, n_echos, n_vols = data_cat.shape + n_samp, _, n_vols = data_cat.shape # Currently unused # fit_data = np.mean(data_cat, axis=2) @@ -151,7 +155,7 @@ def fit_monoexponential(data_cat, echo_times, adaptive_mask, report=True): # perform a monoexponential fit of echo times against MR signal # using loglin estimates as initial starting points for fit fail_count = 0 - for voxel in voxel_idx: + for voxel in tqdm(voxel_idx, desc=f"{echo_num}-echo monoexponential"): try: popt, cov = scipy.optimize.curve_fit( monoexponential, @@ -460,3 +464,104 @@ def fit_decay_ts(data, tes, mask, adaptive_mask, fittype): report = False return t2s_limited_ts, s0_limited_ts, t2s_full_ts, s0_full_ts + + +def rmse_of_fit_decay_ts( + *, + data: np.ndarray, + tes: List[float], + adaptive_mask: np.ndarray, + t2s: np.ndarray, + s0: np.ndarray, + fitmode: Literal["all", "ts"], +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """Estimate model fit of voxel- and timepoint-wise monoexponential decay models to ``data``. + + Parameters + ---------- + data : (S x E x T) :obj:`numpy.ndarray` + Multi-echo data array, where `S` is samples, `E` is echos, and `T` is time. + tes : (E,) :obj:`list` + Echo times. + adaptive_mask : (S,) :obj:`numpy.ndarray` + Array where each value indicates the number of echoes with good signal for that voxel. + This mask may be thresholded; for example, with values less than 3 set to 0. + For more information on thresholding, see :func:`~tedana.utils.make_adaptive_mask`. + t2s : (S [x T]) :obj:`numpy.ndarray` + Voxel-wise (and possibly volume-wise) T2* estimates from + :func:`~tedana.decay.fit_decay_ts`. + s0 : (S [x T]) :obj:`numpy.ndarray` + Voxel-wise (and possibly volume-wise) S0 estimates from :func:`~tedana.decay.fit_decay_ts`. + fitmode : {"fit", "all"} + Whether the T2* and S0 estimates are volume-wise ("fit") or not ("all"). + + Returns + ------- + rmse_map : (S,) :obj:`numpy.ndarray` + Mean root mean squared error of the model fit across all volumes at each voxel. + rmse_df : :obj:`pandas.DataFrame` + Each column is the root mean squared error of the model fit at each timepoint. + Columns are mean, standard deviation, and percentiles across voxels. Column labels are + "rmse_mean", "rmse_std", "rmse_min", "rmse_percentile02", "rmse_percentile25", + "rmse_median", "rmse_percentile75", "rmse_percentile98", and "rmse_max" + """ + n_samples, _, n_vols = data.shape + tes = np.array(tes) + + rmse = np.full([n_samples, n_vols], np.nan, dtype=np.float32) + # n_good_echoes interates from 2 through the number of echoes + # 0 and 1 are excluded because there aren't T2* and S0 estimates + # for less than 2 good echoes. 2 echoes will have a bad estimate so consider + # how/if we want to distinguish those + for n_good_echoes in range(2, len(tes) + 1): + # a boolean mask for voxels with a specific num of good echoes + use_vox = adaptive_mask == n_good_echoes + data_echo = data[use_vox, :n_good_echoes, :] + if fitmode == "all": + s0_echo = numpy.matlib.repmat(s0[use_vox].T, n_vols, 1).T + t2s_echo = numpy.matlib.repmat(t2s[use_vox], n_vols, 1).T + elif fitmode == "ts": + s0_echo = s0[use_vox, :] + t2s_echo = t2s[use_vox, :] + else: + raise ValueError(f"Unknown fitmode option {fitmode}") + + predicted_data = np.full([use_vox.sum(), n_good_echoes, n_vols], np.nan, dtype=np.float32) + # Need to loop by echo since monoexponential can take either single vals for s0 and t2star + # or a single TE value. + # We could expand that func, but this is a functional solution + for echo_num in range(n_good_echoes): + predicted_data[:, echo_num, :] = monoexponential( + tes=tes[echo_num], + s0=s0_echo, + t2star=t2s_echo, + ) + rmse[use_vox, :] = np.sqrt(np.mean((data_echo - predicted_data) ** 2, axis=1)) + + rmse_map = np.nanmean(rmse, axis=1) + rmse_timeseries = np.nanmean(rmse, axis=0) + rmse_sd_timeseries = np.nanstd(rmse, axis=0) + rmse_percentiles_timeseries = np.nanpercentile(rmse, [0, 2, 25, 50, 75, 98, 100], axis=0) + + rmse_df = pd.DataFrame( + columns=[ + "rmse_mean", + "rmse_std", + "rmse_min", + "rmse_percentile02", + "rmse_percentile25", + "rmse_median", + "rmse_percentile75", + "rmse_percentile98", + "rmse_max", + ], + data=np.column_stack( + ( + rmse_timeseries, + rmse_sd_timeseries, + rmse_percentiles_timeseries.T, + ) + ), + ) + + return rmse_map, rmse_df diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index fc387f478..0af62ae9d 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -59,7 +59,6 @@ def tedica( where `C` is components and `T` is the same as in `data` fixed_seed : :obj:`int` Random seed from final decomposition. - """ warnings.filterwarnings(action="ignore", module="scipy", message="^internal gelsd") RepLGR.info( @@ -126,10 +125,11 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): if fixed_seed == -1: fixed_seed = np.random.randint(low=1, high=1000) - for robust_method in ("DBSCAN", "AgglomerativeClustering"): - + robust_method = "DBSCAN" + robust_ica_converged = False + while not robust_ica_converged: try: - rica = RobustICA( + robust_ica = RobustICA( n_components=n_components, robust_runs=n_robust_runs, whiten="arbitrary-variance", @@ -140,13 +140,21 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): robust_method=robust_method, ) - s, mmix = rica.fit_transform(data) - q = rica.evaluate_clustering( - rica.S_all, rica.clustering.labels_, rica.signs_, rica.orientation_ + s, mmix = robust_ica.fit_transform(data) + q = robust_ica.evaluate_clustering( + robust_ica.S_all, + robust_ica.clustering.labels_, + robust_ica.signs_, + robust_ica.orientation_, ) + robust_ica_converged = True except Exception: - continue + if robust_method == "DBSCAN": + # if RobustICA failed wtih DBSCAN, run again wtih AgglomerativeClustering + robust_method = "AgglomerativeClustering" + else: + raise ValueError("RobustICA failed to converge") LGR.info( f"The {robust_method} clustering algorithm was used clustering " @@ -173,7 +181,7 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): f"The mean Index Quality is {iq}." ) - no_outliers = np.count_nonzero(rica.clustering.labels_ == -1) + no_outliers = np.count_nonzero(robust_ica.clustering.labels_ == -1) if no_outliers: LGR.info( f"The {robust_method} clustering algorithm detected outliers when clustering " diff --git a/tedana/decomposition/pca.py b/tedana/decomposition/pca.py index b252ef264..fa32c6537 100644 --- a/tedana/decomposition/pca.py +++ b/tedana/decomposition/pca.py @@ -348,14 +348,16 @@ def tedpca( "normalized variance explained", "d_table_score", ] - comptable = metrics.collect.generate_metrics( - data_cat, - data_oc, - comp_ts, - adaptive_mask, - tes, - io_generator, - "PCA", + # Even if user inputted, don't fit external_regressors to PCA components + comptable, _ = metrics.collect.generate_metrics( + data_cat=data_cat, + data_optcom=data_oc, + mixing=comp_ts, + adaptive_mask=adaptive_mask, + tes=tes, + io_generator=io_generator, + label="PCA", + external_regressors=None, metrics=required_metrics, ) diff --git a/tedana/gscontrol.py b/tedana/gscontrol.py index 7c8c86285..a18e488c9 100644 --- a/tedana/gscontrol.py +++ b/tedana/gscontrol.py @@ -5,7 +5,6 @@ import numpy as np import pandas as pd from scipy import stats -from scipy.special import lpmv from tedana import utils @@ -64,8 +63,7 @@ def gscontrol_raw(catd, optcom, n_echos, io_generator, dtrank=4): ) # Legendre polynomial basis for denoising - bounds = np.linspace(-1, 1, optcom.shape[-1]) - legendre_arr = np.column_stack([lpmv(0, vv, bounds) for vv in range(dtrank)]) + legendre_arr = utils.create_legendre_polynomial_basis_set(optcom.shape[-1], dtrank=dtrank) # compute mean, std, mask local to this function # inefficient, but makes this function a bit more modular @@ -86,7 +84,7 @@ def gscontrol_raw(catd, optcom, n_echos, io_generator, dtrank=4): glsig = stats.zscore(glsig, axis=None) glsig_df = pd.DataFrame(data=glsig.T, columns=["global_signal"]) - io_generator.save_file(glsig_df, "global signal time series tsv") + io_generator.add_df_to_file(glsig_df, "confounds tsv") glbase = np.hstack([legendre_arr, glsig.T]) # Project global signal out of optimally combined data diff --git a/tedana/io.py b/tedana/io.py index b73425554..cd8a7c05f 100644 --- a/tedana/io.py +++ b/tedana/io.py @@ -155,6 +155,14 @@ def __init__( LGR.info(f"Generating figures directory: {self.figures_dir}") os.mkdir(self.figures_dir) + # Remove files that are appended to instead of overwritten. + if overwrite: + files_to_remove = ["confounds tsv"] + for file_ in files_to_remove: + filepath = self.get_name(file_) + if op.exists(filepath): + os.remove(filepath) + def _determine_extension(self, description, name): """Infer the extension for a file based on its description. @@ -346,6 +354,31 @@ def save_tsv(self, data, name): deblanked = data.replace("", np.nan) deblanked.to_csv(name, sep="\t", lineterminator="\n", na_rep="n/a", index=False) + def add_df_to_file(self, data, description, **kwargs): + """Add a DataFrame to a tsv file, which may or may not exist. + + Parameters + ---------- + data : dict or img_like or pandas.DataFrame + Data to save to file. + description : str + Description of the data, used to determine the appropriate filename from + ``self.config``. + + Returns + ------- + name : str + The full file path of the saved file. + """ + name = self.get_name(description, **kwargs) + if op.isfile(name): + old_data = pd.read_table(name) + data = pd.concat([old_data, data], axis=1, ignore_index=False) + + self.save_tsv(data, name) + + return name + def save_self(self): """Save the registry to a json file. diff --git a/tedana/metrics/_utils.py b/tedana/metrics/_utils.py index 8a9486a8f..4c28fb83c 100644 --- a/tedana/metrics/_utils.py +++ b/tedana/metrics/_utils.py @@ -1,14 +1,60 @@ """Miscellaneous utility functions for metric calculation.""" import logging +from typing import Dict, List import numpy as np +import numpy.typing as npt from scipy import stats LGR = logging.getLogger("GENERAL") -def dependency_resolver(dict_, requested_metrics, base_inputs): +def add_external_dependencies( + dependency_config: Dict, external_regressor_config: List[Dict] +) -> Dict: + """ + Add dependency information when external regressors are inputted. + + Parameters + ---------- + dependency_config: :obj:`dict` + A dictionary stored in ./config/metrics.json + with information on all the internally defined metrics like kappa and rho + external_regressor_config: :obj:`list[dict]` + A list of dictionaries with info for fitting external regressors to component time series + + Returns + ------- + dependency_config: :obj:`dict` + A dictionary with the internally defined regressors inputted with this parameter + and the information for fitting external regressors defined in external_regressor_config + """ + # Add "external regressors" and an existing input + dependency_config["inputs"].append("external regressors") + + for config_idx in range(len(external_regressor_config)): + model_names = [external_regressor_config[config_idx]["regress_ID"]] + if "partial_models" in set(external_regressor_config[config_idx].keys()): + partial_keys = external_regressor_config[config_idx]["partial_models"].keys() + for key_name in partial_keys: + model_names.append( + f"{external_regressor_config[config_idx]['regress_ID']} {key_name} partial" + ) + + # F is currently the only option so this only names metrics if "statistic"=="f" + if external_regressor_config[config_idx]["statistic"].lower() == "f": + for model_name in model_names: + for stat_type in ["Fstat", "R2stat", "pval"]: + dependency_config["dependencies"][f"{stat_type} {model_name} model"] = [ + "external regressors" + ] + return dependency_config + + +def dependency_resolver( + dict_: Dict, requested_metrics: List[str], base_inputs: List[str] +) -> List[str]: """Identify all necessary metrics based on a list of requested metrics. This also determines which metrics each requested metric requires to be calculated, @@ -58,7 +104,7 @@ def dependency_resolver(dict_, requested_metrics, base_inputs): return required_metrics -def determine_signs(weights, axis=0): +def determine_signs(weights: npt.NDArray, axis: int = 0) -> npt.NDArray: """Determine component-wise optimal signs using voxel-wise parameter estimates. Parameters @@ -66,6 +112,9 @@ def determine_signs(weights, axis=0): weights : (S x C) array_like Parameter estimates for optimally combined data against the mixing matrix. + axis : int + The axis to calculate the weights over. + Default is 0 Returns ------- @@ -80,7 +129,7 @@ def determine_signs(weights, axis=0): return signs.astype(int) -def flip_components(*args, signs): +def flip_components(*args: npt.NDArray, signs: npt.NDArray) -> npt.NDArray: """Flip an arbitrary set of input arrays based on a set of signs. Parameters @@ -111,7 +160,7 @@ def flip_components(*args, signs): return [arg * signs for arg in args] -def check_mask(data, mask): +def check_mask(data: npt.NDArray, mask: npt.NDArray) -> None: """Check that no zero-variance voxels remain in masked data. Parameters diff --git a/tedana/metrics/collect.py b/tedana/metrics/collect.py index e71e5ee4a..4cac0989d 100644 --- a/tedana/metrics/collect.py +++ b/tedana/metrics/collect.py @@ -2,13 +2,20 @@ import logging import os.path as op +from typing import Dict, List, Tuple, Union import numpy as np +import numpy.typing as npt import pandas as pd from tedana import io, utils -from tedana.metrics import dependence -from tedana.metrics._utils import dependency_resolver, determine_signs, flip_components +from tedana.metrics import dependence, external +from tedana.metrics._utils import ( + add_external_dependencies, + dependency_resolver, + determine_signs, + flip_components, +) from tedana.stats import getfbounds LGR = logging.getLogger("GENERAL") @@ -16,15 +23,18 @@ def generate_metrics( - data_cat, - data_optcom, - mixing, - adaptive_mask, - tes, - io_generator, - label, - metrics=None, -): + *, + data_cat: npt.NDArray, + data_optcom: npt.NDArray, + mixing: npt.NDArray, + adaptive_mask: npt.NDArray, + tes: Union[List[int], List[float], npt.NDArray], + io_generator: io.OutputGenerator, + label: str, + external_regressors: Union[pd.DataFrame, None] = None, + external_regressor_config: Union[List[Dict], None] = None, + metrics: Union[List[str], None] = None, +) -> Tuple[pd.DataFrame, Dict]: """Fit TE-dependence and -independence models to components. Parameters @@ -34,12 +44,12 @@ def generate_metrics( data_optcom : (S x T) array_like Optimally combined data mixing : (T x C) array_like - Mixing matrix for converting input data to component space, where `C` - is components and `T` is the same as in `data_cat` + Mixing matrix for converting input data to component space, + where `C` is components and `T` is the same as in `data_cat` adaptive_mask : (S) array_like Array where each value indicates the number of echoes with good signal - for that voxel. This mask may be thresholded; for example, with values - less than 3 set to 0. + for that voxel. + This mask may be thresholded; for example, with values less than 3 set to 0. For more information on thresholding, see `make_adaptive_mask`. tes : list List of echo times associated with `data_cat`, in milliseconds @@ -47,14 +57,23 @@ def generate_metrics( The output generator object for this workflow label : str in ['ICA', 'PCA'] The label for this metric generation type + external_regressors : None or :obj:`pandas.DataFrame`, optional + External regressors (e.g., motion parameters, physiological noise) + to correlate with ICA components. + If None, no external regressor metrics will be calculated. + external_regressor_config : :obj:`list[dic]t` + A list of dictionaries defining how to fit external regressors to component time series metrics : list List of metrics to return Returns ------- comptable : (C x X) :obj:`pandas.DataFrame` - Component metric table. One row for each component, with a column for - each metric. The index is the component number. + Component metric table. One row for each component, with a column for each metric. + The index is the component number. + external_regressor_config : :obj:`dict` + Info describing the external regressors and method used for fitting and statistical tests + (or None if none were inputed) """ # Load metric dependency tree from json file dependency_config = op.join(utils.get_resource_path(), "config", "metrics.json") @@ -62,6 +81,16 @@ def generate_metrics( if metrics is None: metrics = ["map weight"] + + if external_regressors is not None: + if external_regressor_config is None: + raise ValueError( + "If external_regressors is defined, then " + "external_regressor_config also needs to be defined." + ) + dependency_config = add_external_dependencies(dependency_config, external_regressor_config) + dependency_config["inputs"].append("external regressors") + RepLGR.info(f"The following metrics were calculated: {', '.join(metrics)}.") if not (data_cat.shape[0] == data_optcom.shape[0] == adaptive_mask.shape[0]): @@ -73,7 +102,7 @@ def generate_metrics( elif data_cat.shape[1] != len(tes): raise ValueError( f"Second dimension of data_cat ({data_cat.shape[1]}) does not match " - f"number of echoes provided (tes; {len(tes)})" + f"number of echoes provided ({tes}; {len(tes)})" ) elif not (data_cat.shape[2] == data_optcom.shape[1] == mixing.shape[0]): raise ValueError( @@ -119,7 +148,10 @@ def generate_metrics( metric_maps = {} if "map weight" in required_metrics: LGR.info("Calculating weight maps") - metric_maps["map weight"] = dependence.calculate_weights(data_optcom, mixing) + metric_maps["map weight"] = dependence.calculate_weights( + data_optcom=data_optcom, + mixing=mixing, + ) signs = determine_signs(metric_maps["map weight"], axis=0) comptable["optimal sign"] = signs metric_maps["map weight"], mixing = flip_components( @@ -128,31 +160,42 @@ def generate_metrics( if "map optcom betas" in required_metrics: LGR.info("Calculating parameter estimate maps for optimally combined data") - metric_maps["map optcom betas"] = dependence.calculate_betas(data_optcom, mixing) + metric_maps["map optcom betas"] = dependence.calculate_betas( + data=data_optcom, + mixing=mixing, + ) if io_generator.verbose: - metric_maps["map echo betas"] = dependence.calculate_betas(data_cat, mixing) + metric_maps["map echo betas"] = dependence.calculate_betas( + data=data_cat, + mixing=mixing, + ) if "map percent signal change" in required_metrics: LGR.info("Calculating percent signal change maps") # used in kundu v3.2 tree metric_maps["map percent signal change"] = dependence.calculate_psc( - data_optcom, metric_maps["map optcom betas"] + data_optcom=data_optcom, + optcom_betas=metric_maps["map optcom betas"], ) if "map Z" in required_metrics: LGR.info("Calculating z-statistic maps") - metric_maps["map Z"] = dependence.calculate_z_maps(metric_maps["map weight"]) + metric_maps["map Z"] = dependence.calculate_z_maps(weights=metric_maps["map weight"]) if io_generator.verbose: io_generator.save_file( utils.unmask(metric_maps["map Z"] ** 2, mask), - label + " component weights img", + f"{label} component weights img", ) if ("map FT2" in required_metrics) or ("map FS0" in required_metrics): LGR.info("Calculating F-statistic maps") m_t2, m_s0, p_m_t2, p_m_s0 = dependence.calculate_f_maps( - data_cat, metric_maps["map Z"], mixing, adaptive_mask, tes + data_cat=data_cat, + z_maps=metric_maps["map Z"], + mixing=mixing, + adaptive_mask=adaptive_mask, + tes=tes, ) metric_maps["map FT2"] = m_t2 metric_maps["map FS0"] = m_s0 @@ -162,58 +205,73 @@ def generate_metrics( if io_generator.verbose: io_generator.save_file( utils.unmask(metric_maps["map FT2"], mask), - label + " component F-T2 img", + f"{label} component F-T2 img", ) io_generator.save_file( utils.unmask(metric_maps["map FS0"], mask), - label + " component F-S0 img", + f"{label} component F-S0 img", ) if "map Z clusterized" in required_metrics: LGR.info("Thresholding z-statistic maps") z_thresh = 1.95 metric_maps["map Z clusterized"] = dependence.threshold_map( - metric_maps["map Z"], mask, ref_img, z_thresh + maps=metric_maps["map Z"], + mask=mask, + ref_img=ref_img, + threshold=z_thresh, ) if "map FT2 clusterized" in required_metrics: LGR.info("Calculating T2* F-statistic maps") f_thresh, _, _ = getfbounds(len(tes)) metric_maps["map FT2 clusterized"] = dependence.threshold_map( - metric_maps["map FT2"], mask, ref_img, f_thresh + maps=metric_maps["map FT2"], + mask=mask, + ref_img=ref_img, + threshold=f_thresh, ) if "map FS0 clusterized" in required_metrics: LGR.info("Calculating S0 F-statistic maps") f_thresh, _, _ = getfbounds(len(tes)) metric_maps["map FS0 clusterized"] = dependence.threshold_map( - metric_maps["map FS0"], mask, ref_img, f_thresh + maps=metric_maps["map FS0"], + mask=mask, + ref_img=ref_img, + threshold=f_thresh, ) # Intermediate metrics if "countsigFT2" in required_metrics: LGR.info("Counting significant voxels in T2* F-statistic maps") comptable["countsigFT2"] = dependence.compute_countsignal( - metric_maps["map FT2 clusterized"] + stat_cl_maps=metric_maps["map FT2 clusterized"], ) if "countsigFS0" in required_metrics: LGR.info("Counting significant voxels in S0 F-statistic maps") comptable["countsigFS0"] = dependence.compute_countsignal( - metric_maps["map FS0 clusterized"] + stat_cl_maps=metric_maps["map FS0 clusterized"], ) # Back to maps if "map beta T2 clusterized" in required_metrics: LGR.info("Thresholding optimal combination beta maps to match T2* F-statistic maps") metric_maps["map beta T2 clusterized"] = dependence.threshold_to_match( - metric_maps["map optcom betas"], comptable["countsigFT2"], mask, ref_img + maps=metric_maps["map optcom betas"], + n_sig_voxels=comptable["countsigFT2"], + mask=mask, + ref_img=ref_img, ) if "map beta S0 clusterized" in required_metrics: LGR.info("Thresholding optimal combination beta maps to match S0 F-statistic maps") metric_maps["map beta S0 clusterized"] = dependence.threshold_to_match( - metric_maps["map optcom betas"], comptable["countsigFS0"], mask, ref_img + maps=metric_maps["map optcom betas"], + n_sig_voxels=comptable["countsigFS0"], + mask=mask, + ref_img=ref_img, ) # Dependence metrics @@ -229,24 +287,23 @@ def generate_metrics( if "variance explained" in required_metrics: LGR.info("Calculating variance explained") comptable["variance explained"] = dependence.calculate_varex( - metric_maps["map optcom betas"] + optcom_betas=metric_maps["map optcom betas"], ) if "normalized variance explained" in required_metrics: LGR.info("Calculating normalized variance explained") comptable["normalized variance explained"] = dependence.calculate_varex_norm( - metric_maps["map weight"] + weights=metric_maps["map weight"], ) # Spatial metrics if "dice_FT2" in required_metrics: LGR.info( - "Calculating DSI between thresholded T2* F-statistic and " - "optimal combination beta maps" + "Calculating DSI between thresholded T2* F-statistic and optimal combination beta maps" ) comptable["dice_FT2"] = dependence.compute_dice( - metric_maps["map beta T2 clusterized"], - metric_maps["map FT2 clusterized"], + clmaps1=metric_maps["map beta T2 clusterized"], + clmaps2=metric_maps["map FT2 clusterized"], axis=0, ) @@ -256,20 +313,18 @@ def generate_metrics( "optimal combination beta maps" ) comptable["dice_FS0"] = dependence.compute_dice( - metric_maps["map beta S0 clusterized"], - metric_maps["map FS0 clusterized"], + clmaps1=metric_maps["map beta S0 clusterized"], + clmaps2=metric_maps["map FS0 clusterized"], axis=0, ) if "signal-noise_t" in required_metrics: LGR.info("Calculating signal-noise t-statistics") RepLGR.info( - "A t-test was performed between the distributions of T2*-model " - "F-statistics associated with clusters (i.e., signal) and " - "non-cluster voxels (i.e., noise) to generate a t-statistic " - "(metric signal-noise_z) and p-value (metric signal-noise_p) " - "measuring relative association of the component to signal " - "over noise." + "A t-test was performed between the distributions of T2*-model F-statistics " + "associated with clusters (i.e., signal) and non-cluster voxels (i.e., noise) to " + "generate a t-statistic (metric signal-noise_z) and p-value (metric signal-noise_p) " + "measuring relative association of the component to signal over noise." ) ( comptable["signal-noise_t"], @@ -283,20 +338,18 @@ def generate_metrics( if "signal-noise_z" in required_metrics: LGR.info("Calculating signal-noise z-statistics") RepLGR.info( - "A t-test was performed between the distributions of T2*-model " - "F-statistics associated with clusters (i.e., signal) and " - "non-cluster voxels (i.e., noise) to generate a z-statistic " - "(metric signal-noise_z) and p-value (metric signal-noise_p) " - "measuring relative association of the component to signal " - "over noise." + "A t-test was performed between the distributions of T2*-model F-statistics " + "associated with clusters (i.e., signal) and non-cluster voxels (i.e., noise) to " + "generate a z-statistic (metric signal-noise_z) and p-value (metric signal-noise_p) " + "measuring relative association of the component to signal over noise." ) ( comptable["signal-noise_z"], comptable["signal-noise_p"], ) = dependence.compute_signal_minus_noise_z( - Z_maps=metric_maps["map Z"], - Z_clmaps=metric_maps["map Z clusterized"], - F_T2_maps=metric_maps["map FT2"], + z_maps=metric_maps["map Z"], + z_clmaps=metric_maps["map Z clusterized"], + f_t2_maps=metric_maps["map FT2"], ) if "countnoise" in required_metrics: @@ -306,26 +359,41 @@ def generate_metrics( "calculated for each component." ) comptable["countnoise"] = dependence.compute_countnoise( - metric_maps["map Z"], metric_maps["map Z clusterized"] + stat_maps=metric_maps["map Z"], + stat_cl_maps=metric_maps["map Z clusterized"], ) # Composite metrics if "d_table_score" in required_metrics: LGR.info("Calculating decision table score") comptable["d_table_score"] = dependence.generate_decision_table_score( - comptable["kappa"], - comptable["dice_FT2"], - comptable["signal-noise_t"], - comptable["countnoise"], - comptable["countsigFT2"], + kappa=comptable["kappa"], + dice_ft2=comptable["dice_FT2"], + signal_minus_noise_t=comptable["signal-noise_t"], + countnoise=comptable["countnoise"], + countsig_ft2=comptable["countsigFT2"], ) + # External regressor-based metrics + if external_regressors is not None and external_regressor_config is not None: + # external_regressor_names = external_regressors.columns.tolist() + for config_idx in range(len(external_regressor_config)): + LGR.info( + "Calculating external regressor fits. " + f"{external_regressor_config[config_idx]['info']}" + ) + RepLGR.info({external_regressor_config[config_idx]["report"]}) + + comptable = external.fit_regressors( + comptable, external_regressors, external_regressor_config, mixing + ) # Write verbose metrics if needed if io_generator.verbose: write_betas = "map echo betas" in metric_maps write_t2s0 = "map predicted T2" in metric_maps if write_betas: betas = metric_maps["map echo betas"] + if write_t2s0: pred_t2_maps = metric_maps["map predicted T2"] pred_s0_maps = metric_maps["map predicted S0"] @@ -335,7 +403,7 @@ def generate_metrics( echo_betas = betas[:, i_echo, :] io_generator.save_file( utils.unmask(echo_betas, mask), - "echo weight " + label + " map split img", + f"echo weight {label} map split img", echo=(i_echo + 1), ) @@ -343,14 +411,14 @@ def generate_metrics( echo_pred_t2_maps = pred_t2_maps[:, i_echo, :] io_generator.save_file( utils.unmask(echo_pred_t2_maps, mask), - "echo T2 " + label + " split img", + f"echo T2 {label} split img", echo=(i_echo + 1), ) echo_pred_s0_maps = pred_s0_maps[:, i_echo, :] io_generator.save_file( utils.unmask(echo_pred_s0_maps, mask), - "echo S0 " + label + " split img", + f"echo S0 {label} split img", echo=(i_echo + 1), ) @@ -374,6 +442,7 @@ def generate_metrics( "d_table_score", "kappa ratio", "d_table_score_scrub", + "external fit", "classification", "rationale", ) @@ -381,10 +450,10 @@ def generate_metrics( other_columns = [col for col in comptable.columns if col not in preferred_order] comptable = comptable[first_columns + other_columns] - return comptable + return comptable, external_regressor_config -def get_metadata(comptable): +def get_metadata(comptable: pd.DataFrame) -> Dict: """Fill in metric metadata for a given comptable. Parameters @@ -394,9 +463,9 @@ def get_metadata(comptable): Returns ------- - A dict containing the metadata for each column in the comptable for - which we have a metadata description, plus the "Component" metadata - description (always). + metric_metadata : dict + The metadata for each column in the comptable for which we have a metadata description, + plus the "Component" metadata description (always). """ metric_metadata = {} if "kappa" in comptable: diff --git a/tedana/metrics/dependence.py b/tedana/metrics/dependence.py index 69693e452..575af98b3 100644 --- a/tedana/metrics/dependence.py +++ b/tedana/metrics/dependence.py @@ -1,7 +1,9 @@ """Metrics evaluating component TE-dependence or -independence.""" import logging +import typing +import nibabel as nb import numpy as np from scipy import stats @@ -12,7 +14,11 @@ RepLGR = logging.getLogger("REPORT") -def calculate_weights(data_optcom, mixing): +def calculate_weights( + *, + data_optcom: np.ndarray, + mixing: np.ndarray, +) -> np.ndarray: """Calculate standardized parameter estimates between data and mixing matrix. Parameters @@ -35,19 +41,23 @@ def calculate_weights(data_optcom, mixing): return weights -def calculate_betas(data, mixing): +def calculate_betas( + *, + data: np.ndarray, + mixing: np.ndarray, +) -> np.ndarray: """Calculate unstandardized parameter estimates between data and mixing matrix. Parameters ---------- - data : (M x [E] x T) array_like + data : (M [x E] x T) array_like Data to calculate betas for mixing : (T x C) array_like Mixing matrix Returns ------- - betas : (M x [E] x C) array_like + betas : (M [x E] x C) array_like Unstandardized parameter estimates """ if len(data.shape) == 2: @@ -66,7 +76,11 @@ def calculate_betas(data, mixing): return betas -def calculate_psc(data_optcom, optcom_betas): +def calculate_psc( + *, + data_optcom: np.ndarray, + optcom_betas: np.ndarray, +) -> np.ndarray: """Calculate percent signal change maps for components against optimally-combined data. Parameters @@ -87,7 +101,11 @@ def calculate_psc(data_optcom, optcom_betas): return psc -def calculate_z_maps(weights, z_max=8): +def calculate_z_maps( + *, + weights: np.ndarray, + z_max: float = 8, +) -> np.ndarray: """Calculate component-wise z-statistic maps. This is done by z-scoring standardized parameter estimate maps and cropping extreme values. @@ -111,7 +129,15 @@ def calculate_z_maps(weights, z_max=8): return z_maps -def calculate_f_maps(data_cat, z_maps, mixing, adaptive_mask, tes, f_max=500): +def calculate_f_maps( + *, + data_cat: np.ndarray, + z_maps: np.ndarray, + mixing: np.ndarray, + adaptive_mask: np.ndarray, + tes: np.ndarray, + f_max: float = 500, +) -> typing.Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """Calculate pseudo-F-statistic maps for TE-dependence and -independence models. Parameters @@ -196,7 +222,14 @@ def calculate_f_maps(data_cat, z_maps, mixing, adaptive_mask, tes, f_max=500): return f_t2_maps, f_s0_maps, pred_t2_maps, pred_s0_maps -def threshold_map(maps, mask, ref_img, threshold, csize=None): +def threshold_map( + *, + maps: np.ndarray, + mask: np.ndarray, + ref_img: nb.Nifti1Image, + threshold: float, + csize: typing.Union[int, None] = None, +) -> np.ndarray: """Perform cluster-extent thresholding. Parameters @@ -234,7 +267,14 @@ def threshold_map(maps, mask, ref_img, threshold, csize=None): return maps_thresh -def threshold_to_match(maps, n_sig_voxels, mask, ref_img, csize=None): +def threshold_to_match( + *, + maps: np.ndarray, + n_sig_voxels: np.ndarray, + mask: np.ndarray, + ref_img: nb.Nifti1Image, + csize: typing.Union[int, None] = None, +) -> np.ndarray: """Cluster-extent threshold a map to target number of significant voxels. Resulting maps have roughly the requested number of significant voxels, after cluster-extent @@ -306,7 +346,12 @@ def threshold_to_match(maps, n_sig_voxels, mask, ref_img, csize=None): return clmaps -def calculate_dependence_metrics(f_t2_maps, f_s0_maps, z_maps): +def calculate_dependence_metrics( + *, + f_t2_maps: np.ndarray, + f_s0_maps: np.ndarray, + z_maps: np.ndarray, +) -> typing.Tuple[np.ndarray, np.ndarray]: """Calculate Kappa and Rho metrics from F-statistic maps. Just a weighted average over voxels. @@ -341,7 +386,10 @@ def calculate_dependence_metrics(f_t2_maps, f_s0_maps, z_maps): return kappas, rhos -def calculate_varex(optcom_betas): +def calculate_varex( + *, + optcom_betas: np.ndarray, +) -> np.ndarray: """Calculate unnormalized(?) variance explained from unstandardized parameter estimate maps. Parameters @@ -360,7 +408,10 @@ def calculate_varex(optcom_betas): return varex -def calculate_varex_norm(weights): +def calculate_varex_norm( + *, + weights: np.ndarray, +) -> np.ndarray: """Calculate normalized variance explained from standardized parameter estimate maps. Parameters @@ -378,7 +429,12 @@ def calculate_varex_norm(weights): return varex_norm -def compute_dice(clmaps1, clmaps2, axis=0): +def compute_dice( + *, + clmaps1: np.ndarray, + clmaps2: np.ndarray, + axis: typing.Union[int, None] = 0, +) -> np.ndarray: """Compute the Dice similarity index between two thresholded and binarized maps. NaNs are converted automatically to zeroes. @@ -402,7 +458,13 @@ def compute_dice(clmaps1, clmaps2, axis=0): return dice_values -def compute_signal_minus_noise_z(z_maps, z_clmaps, f_t2_maps, z_thresh=1.95): +def compute_signal_minus_noise_z( + *, + z_maps: np.ndarray, + z_clmaps: np.ndarray, + f_t2_maps: np.ndarray, + z_thresh: float = 1.95, +) -> typing.Tuple[np.ndarray, np.ndarray]: """Compare signal and noise z-statistic distributions with a two-sample t-test. Divide voxel-level thresholded F-statistic maps into distributions of @@ -464,7 +526,13 @@ def compute_signal_minus_noise_z(z_maps, z_clmaps, f_t2_maps, z_thresh=1.95): return signal_minus_noise_z, signal_minus_noise_p -def compute_signal_minus_noise_t(z_maps, z_clmaps, f_t2_maps, z_thresh=1.95): +def compute_signal_minus_noise_t( + *, + z_maps: np.ndarray, + z_clmaps: np.ndarray, + f_t2_maps: np.ndarray, + z_thresh: float = 1.95, +) -> typing.Tuple[np.ndarray, np.ndarray]: """Compare signal and noise t-statistic distributions with a two-sample t-test. Divide voxel-level thresholded F-statistic maps into distributions of @@ -511,7 +579,10 @@ def compute_signal_minus_noise_t(z_maps, z_clmaps, f_t2_maps, z_thresh=1.95): return signal_minus_noise_t, signal_minus_noise_p -def compute_countsignal(stat_cl_maps): +def compute_countsignal( + *, + stat_cl_maps: np.ndarray, +) -> np.ndarray: """Count the number of significant voxels in a set of cluster-extent thresholded maps. Parameters @@ -528,7 +599,12 @@ def compute_countsignal(stat_cl_maps): return countsignal -def compute_countnoise(stat_maps, stat_cl_maps, stat_thresh=1.95): +def compute_countnoise( + *, + stat_maps: np.ndarray, + stat_cl_maps: np.ndarray, + stat_thresh: float = 1.95, +) -> np.ndarray: """Count the number of significant voxels from non-significant clusters. This is done after application of a cluster-defining threshold, but compared against results @@ -556,7 +632,14 @@ def compute_countnoise(stat_maps, stat_cl_maps, stat_thresh=1.95): return countnoise -def generate_decision_table_score(kappa, dice_ft2, signal_minus_noise_t, countnoise, countsig_ft2): +def generate_decision_table_score( + *, + kappa: np.ndarray, + dice_ft2: np.ndarray, + signal_minus_noise_t: np.ndarray, + countnoise: np.ndarray, + countsig_ft2: np.ndarray, +) -> np.ndarray: """Generate a five-metric decision table. Metrics are ranked in either descending or ascending order if they measure TE-dependence or diff --git a/tedana/metrics/external.py b/tedana/metrics/external.py new file mode 100644 index 000000000..0f89516a8 --- /dev/null +++ b/tedana/metrics/external.py @@ -0,0 +1,546 @@ +"""Metrics based on fits of component time series to external time series.""" + +import logging +import re +from typing import Dict, List, Tuple + +import numpy as np +import numpy.typing as npt +import pandas as pd +from scipy import stats + +from tedana import utils +from tedana.stats import fit_model + +LGR = logging.getLogger("GENERAL") +RepLGR = logging.getLogger("REPORT") + + +class RegressError(Exception): + """Passes errors that are raised when `validate_extern_regress` fails.""" + + pass + + +def load_validate_external_regressors( + external_regressors: str, external_regressor_config: Dict, n_vols: int +) -> Tuple[pd.DataFrame, Dict]: + """Load and validate external regressors and descriptors in dictionary. + + Parameters + ---------- + external_regressors: :obj:`str` + Path and name of tsv file that includes external regressor time series + external_regressor_config: :obj:`dict` + A dictionary with info for fitting external regressors to component time series + n_vols: :obj:`int` + Number of timepoints in the fMRI time series + + Returns + ------- + external_regressors: :obj:`pandas.DataFrame` + Each column is a labelled regressor and the number of rows should + match the number of timepoints in the fMRI time series + external_regressor_config: :obj:`dict` + A validated dictionary with info for fitting external regressors to component time series. + If regex patterns like '^mot_.*$' are used to define regressor names, + this is replaced with a list of the match column names used in external_regressors + """ + try: + external_regressors = pd.read_table(external_regressors) + except FileNotFoundError: + raise ValueError(f"Cannot load tsv file with external regressors: {external_regressors}") + + external_regressor_config = validate_extern_regress( + external_regressors, external_regressor_config, n_vols + ) + + return external_regressors, external_regressor_config + + +def validate_extern_regress( + external_regressors: pd.DataFrame, external_regressor_config: List[Dict], n_vols: int +) -> List[Dict]: + """Confirm external regressor dictionary matches data and expands regular expressions. + + Most keys in external_regressor_config are valided in component_selector.validate_tree + which is run when a component selector object is initialized. + This function validates external_regressor_config against the dataset-specific + external_regressors time series. + If the config names specific column labels with the f_stats_partial_models key, + then this confirms those column labels are used in external_regressors + Also checks if the number of time points in the external regressors matches + the number of time point in the fMRI data. + + Parameters + ---------- + external_regressors : :obj:`pandas.DataFrame` + Each column is a labelled regressor and the number of rows should + match the number of timepoints in the fMRI time series + external_regressor_config : :obj:`list[dict]` + Information describing the external regressors and + method to use for fitting and statistical tests. + Each element in the list is a dict defining the regressors + and statistical models for a test. + n_vols : :obj:`int` + The number of time points in the fMRI time series + + Returns + ------- + external_regressor_config: :obj:`list[dict]` + A validated list of dictionaries with info for fitting external regressors + to component time series. + If regex patterns like '^mot_.*$' are used to define regressor names, + these are replaced with a list of the matching column names used in external_regressors + + Raises + ------ + RegressorError if any validation test fails + """ + # err_msg is used to collect all errors in validation rather than require this to be run + # multiple times to see all validation errors. + # Will either collect errors and raise at the end of the function + # or raise errors that prevent the rest of the function from completing + err_msg = "" + + external_regressor_names = set(external_regressors.columns) + + def expand_regress_regex(regressor_templates, external_regressor_names, err_msg): + """Match or regex expand regressor names from config.""" + expanded_regressor_names = set() + for tmp_regressor_name in regressor_templates: + # If a regressor name is a regular expression, use re to match and expand + # by comparing to regressor names in external_regressor_names. + if tmp_regressor_name.startswith("^"): + expanded_names = [ + reg_name + for reg_name in external_regressor_names + if re.match(tmp_regressor_name, reg_name, re.IGNORECASE) + ] + if not expanded_names: + err_msg += ( + "No external regressor labels matching " + f"regular expression '{tmp_regressor_name}' found.\n" + ) + else: + expanded_regressor_names.update(set(expanded_names)) + else: + # If a regressor name is a string, check if it is in external_regressor_names + if tmp_regressor_name in external_regressor_names: + expanded_regressor_names.add(tmp_regressor_name) + else: + err_msg += ( + f"No external regressor matching '{tmp_regressor_name}' was found.\n" + ) + return expanded_regressor_names, err_msg + + # Expanding the regressors used for each model + all_regressor_names = set() + for config_idx in range(len(external_regressor_config)): + expanded_regressor_names, err_msg = expand_regress_regex( + external_regressor_config[config_idx]["regressors"], external_regressor_names, err_msg + ) + reused_regressors = all_regressor_names.intersection(expanded_regressor_names) + if reused_regressors: + LGR.warning( + f"{list(reused_regressors).sort()} used in " + "more than one external regressor model" + ) + all_regressor_names.update(expanded_regressor_names) + external_regressor_config[config_idx]["regressors"] = sorted(expanded_regressor_names) + + extra_names = set(external_regressor_names) - all_regressor_names + if extra_names: + LGR.warning( + "User-provided external_regressors include columns not used in any " + f"external regressor model: {sorted(extra_names)}" + ) + + # If a model includes specifications for partial regressors, expand them + for config_idx in range(len(external_regressor_config)): + if "partial_models" in external_regressor_config[config_idx].keys(): + if not isinstance(external_regressor_config[config_idx]["partial_models"], type(None)): + part_model_regress_names = set() + for part_model in external_regressor_config[config_idx]["partial_models"].keys(): + expanded_regressor_names, err_msg = expand_regress_regex( + external_regressor_config[config_idx]["partial_models"][part_model], + external_regressor_names, + err_msg, + ) + reused_regressors = part_model_regress_names.intersection( + expanded_regressor_names + ) + if reused_regressors: + LGR.warning( + f"{sorted(reused_regressors)} used in " + "more than one partial regressor model for " + f"{external_regressor_config[config_idx]['regress_ID']}" + ) + part_model_regress_names.update(expanded_regressor_names) + external_regressor_config[config_idx]["partial_models"][part_model] = sorted( + expanded_regressor_names + ) + extra_names = part_model_regress_names - set( + external_regressor_config[config_idx]["regressors"] + ) + if extra_names: + err_msg += ( + f"Partial models in {external_regressor_config[config_idx]['regress_ID']} " + "include regressors that are excluded from its full model: " + f"{sorted(extra_names)}\n" + ) + + if len(external_regressors.index) != n_vols: + err_msg += ( + f"External Regressors have {len(external_regressors.index)} timepoints " + f"while fMRI data have {n_vols} timepoints\n" + ) + + if err_msg: + raise RegressError(err_msg) + + return external_regressor_config + + +def fit_regressors( + comptable: pd.DataFrame, + external_regressors: pd.DataFrame, + external_regressor_config: List[Dict], + mixing: npt.NDArray, +) -> pd.DataFrame: + """Fit regressors to the mixing matrix. + + Uses correlation or F statistics in a linear model depending on the calc_stats + value in external_regressor_config + + Parameters + ---------- + comptable : (C x X) :obj:`pandas.DataFrame` + Component metric table. One row for each component, + with a column for each metric. The index is the component number. + external_regressors : (T x R) :obj:`pandas.DataFrame` + Each column is a labelled regressor and the number of rows should + match the number of timepoints in the fMRI time series + external_regressor_config : :obj:`list[dict]` + Information describing the external regressors and + method to use for fitting and statistical tests + mixing : (T x C) array_like + Mixing matrix for converting input data to component space, + where `C` is components and `T` is the same as in `data_cat` + + Returns + ------- + comptable : (C x X) :obj:`pandas.DataFrame` + Component metric table. + Same as inputted, with added columns for metrics related to the external regressor fits + """ + n_vols = mixing.shape[0] + + # For every model (i.e. nuisance and task) in external_regressor_config + # setup and run fit_mixing_to_regressors to add columns to comptable + for config_idx in range(len(external_regressor_config)): + regress_id = external_regressor_config[config_idx]["regress_ID"] + # If the order of detrending regressors is specified, then pass to + # create_legendre_polynomial_basis_set + # otherwise the function sets an order for the Legendre polynomials + if external_regressor_config[config_idx]["detrend"] is True: + legendre_arr = utils.create_legendre_polynomial_basis_set(n_vols, dtrank=None) + LGR.info( + f"External regressors fit for {regress_id} includes detrending with " + f"{legendre_arr.shape[1]} Legendre Polynomial regressors." + ) + + elif ( + isinstance(external_regressor_config[config_idx]["detrend"], int) + and external_regressor_config[config_idx]["detrend"] > 0 + ): + legendre_arr = utils.create_legendre_polynomial_basis_set( + n_vols, dtrank=external_regressor_config[config_idx]["detrend"] + ) + LGR.info( + f"External regressors fit for {regress_id} includes detrending with " + f"{legendre_arr.shape[1]} Legendre Polynomial regressors." + ) + else: + LGR.warning( + f"External regressor for {regress_id} fitted without detrending fMRI time series. " + "Only removing mean" + ) + legendre_arr = utils.create_legendre_polynomial_basis_set(n_vols, dtrank=1) + + detrend_labels = [] + for label_idx in range(legendre_arr.shape[1]): + detrend_labels.append(f"baseline {label_idx}") + detrend_regressors = pd.DataFrame(data=legendre_arr, columns=detrend_labels) + + if external_regressor_config[config_idx]["statistic"].lower() == "f": + comptable = fit_mixing_to_regressors( + comptable, + external_regressors, + external_regressor_config[config_idx], + mixing, + detrend_regressors, + ) + else: + # This should already be validated by this point, but keeping the catch clause here + # since this would otherwise just return comptable with no changes, which would + # make a hard-to-track error + raise ValueError( + f"statistic for {regress_id} external regressors in decision tree is " + f"{external_regressor_config[config_idx]['statistic'].lower()}, " + "which is not valid." + ) + + return comptable + + +def fit_mixing_to_regressors( + comptable: pd.DataFrame, + external_regressors: pd.DataFrame, + external_regressor_config: Dict, + mixing: npt.NDArray, + detrend_regressors: pd.DataFrame, +) -> pd.DataFrame: + """Compute Linear Model and calculate F statistics and P values for combinations of regressors. + + Equation: Y = XB + E + - Y = each ICA component (mixing) + - X = Design (Regressor) matrix (subsets of noise_regress_table) + - B = Weighting Factors (solving for B) + - E = errors (Y - Y_pred OR Y - XB) + + Parameters + ---------- + comptable : (C x X) :obj:`pandas.DataFrame` + Component metric table. + One row for each component, with a column for each metric. + The index is the component number. + external_regressors : :obj:`pandas.DataFrame` + Each column is a labelled regressor and the number of rows should + match the number of timepoints in the fMRI time series + external_regressor_config : :obj:`dict` + Information describing the external regressors and + method to use for fitting and statistical tests. + In other functions this is a list[dict] but here it is a + single dict which is one element in the list[dict] + mixing : (T x C) array_like + Mixing matrix for converting input data to component space, + where `C` is components and `T` is the same as in `data_cat` + detrend_regressors: (n_vols x polort) :obj:`pandas.DataFrame` + Dataframe containing the detrending regressor time series + + Returns + ------- + comptable : (C x X) :obj:`pandas.DataFrame` + Component metric table. + Same as inputted, with added columns for metrics related to external regressor fits. + New columns for F, R2, and p values for the full model and all partial models. + Names are "Fstat Full Model", "pval Full Model", "R2stat Full Model", + and "Full" is replaced by the partial model name for each partial model + """ + regress_id = external_regressor_config["regress_ID"] + LGR.info(f"Running fit_mixing_to_regressors for {regress_id}") + LGR.info(f"ICA matrix has {mixing.shape[0]} time points and {mixing.shape[1]} components") + + # regressor_models is a dictionary of all the models that will be fit to the mixing matrix + # It will always include 'base' which is just the polort detrending regressors and + # 'full' which is all relevant regressors including the detrending regressors + # For F statistics, the other models need for tests are those that include everything + # EXCEPT the category of interest. For example, there will also be a field for "no Motion" + # which contains all regressors in the full model except those that model motion + regressor_models = build_fstat_regressor_models( + external_regressors, external_regressor_config, detrend_regressors + ) + + # This is the test for the fit of the full model vs the polort detrending baseline + # The outputs will be what we use to decide which components to reject + betas_full, f_vals_tmp, p_vals_tmp, r2_vals_tmp = fit_model_with_stats( + y=mixing, regressor_models=regressor_models, base_label="base" + ) + + # TODO beta_full_model are the fits to all external regressors and might be useful to save + # TODO Also consider saving regressor_models or the detrending regressors + # betas_full_model = pd.DataFrame( + # data=betas_full.T, + # columns=np.concatenate( + # (np.array(detrend_regressors.columns), np.array(exte rnal_regressors.columns)) + # ), + # ) + f_vals = pd.DataFrame(data=f_vals_tmp, columns=[f"Fstat {regress_id} model"]) + p_vals = pd.DataFrame(data=p_vals_tmp, columns=[f"pval {regress_id} model"]) + r2_vals = pd.DataFrame(data=r2_vals_tmp, columns=[f"R2stat {regress_id} model"]) + + # Test the fits between the full model and the full model excluding one category of regressor + if "partial_models" in external_regressor_config.keys(): + for pmodel in external_regressor_config["partial_models"].keys(): + _, f_vals_tmp, p_vals_tmp, r2_vals_tmp = fit_model_with_stats( + mixing, regressor_models, f"no {pmodel}" + ) + f_vals[f"Fstat {regress_id} {pmodel} partial model"] = f_vals_tmp + p_vals[f"pval {regress_id} {pmodel} partial model"] = p_vals_tmp + r2_vals[f"R2stat {regress_id} {pmodel} partial model"] = r2_vals_tmp + + # Add all F p and R2 statistics to comptable + comptable = pd.concat((comptable, f_vals, p_vals, r2_vals), axis=1) + + return comptable + + +def build_fstat_regressor_models( + external_regressors: pd.DataFrame, + external_regressor_config: Dict, + detrend_regressors: pd.DataFrame, +) -> Dict: + """Combine detrending all or subsets of external regressors to make models to fit and test. + + Parameters + ---------- + external_regressors : :obj:`pandas.DataFrame` + Each column is a labelled regressor and the number of rows should + match the number of timepoints in the fMRI time series + external_regressor_config : :obj:`dict` + Information describing the external regressors and + method to use for fitting and statistical tests. + In other functions this is a list[dict] but here it is a + single dict which is one element in the list[dict] + detrend_regressors: (n_vols x polort) :obj:`pandas.DataFrame` + Dataframe containing the detrending regressor time series + + Returns + ------- + regressor_models: :obj:`dict` + Each element in the dictionary is a numpy array defining the regressors in a + regressor model. + The models that are always included are 'base' which is just the detrending regressors, + and 'full' which is all user-provided external regressors and the detrending regressors. + If partial models are named in external_regressor_config["f_stats_partial_models"], + then each of those will have a dictionary element named "no" then model name and the + regressors included will be everything except the specified regressors. + That is "no motion" will include all regressors except the motion regressors. + This is for the F test which compares the variance explained with the full model to the + variance explained if the regressors-of-interest for the partial model are removed. + """ + regress_id = external_regressor_config["regress_ID"] + # The category titles to group each regressor + if "partial_models" in external_regressor_config: + partial_models = external_regressor_config["partial_models"].keys() + else: + partial_models = [] + + # All regressor labels for the full model + regressor_labels = set(external_regressor_config["regressors"]) + + detrend_regressors_arr = detrend_regressors.to_numpy() + regressor_models = {"base": detrend_regressors_arr} + LGR.info(f"Size for base regressor model for {regress_id}: {regressor_models['base'].shape}") + + regressor_models["full"] = detrend_regressors_arr + for keep_label in regressor_labels: + regressor_models["full"] = np.concatenate( + ( + regressor_models["full"], + np.atleast_2d(stats.zscore(external_regressors[keep_label].to_numpy(), axis=0)).T, + ), + axis=1, + ) + regressor_labels.update(set(detrend_regressors.columns)) + # regressor_models["full"] = np.concatenate( + # (detrend_regressors_arr, stats.zscore(external_regressors.to_numpy(), axis=0)), axis=1 + # ) + LGR.info(f"Size for full regressor model for {regress_id}: {regressor_models['full'].shape}") + LGR.info(f"Regressors in full model for {regress_id}: {sorted(set(regressor_labels))}") + + for pmodel in partial_models: + # For F statistics, the other models to test are those that include everything EXCEPT + # the category of interest + # That is "no motion" should contain the full model excluding motion regressors + keep_labels = set(external_regressor_config["regressors"]) - set( + external_regressor_config["partial_models"][pmodel] + ) + no_pmodel = f"no {pmodel}" + regressor_models[no_pmodel] = detrend_regressors_arr + for keep_label in keep_labels: + regressor_models[no_pmodel] = np.concatenate( + ( + regressor_models[no_pmodel], + np.atleast_2d( + stats.zscore(external_regressors[keep_label].to_numpy(), axis=0) + ).T, + ), + axis=1, + ) + keep_labels.update(set(detrend_regressors.columns)) + LGR.info( + f"Size of external regressor partial model '{no_pmodel}': " + f"{regressor_models[no_pmodel].shape}" + ) + LGR.info( + "Regressors in partial model (everything but regressors of interest) " + f"'{no_pmodel}': {sorted(keep_labels)}" + ) + + return regressor_models + + +def fit_model_with_stats( + y: npt.NDArray, regressor_models: Dict, base_label: str, full_label: str = "full" +) -> Tuple[npt.NDArray, npt.NDArray, npt.NDArray, npt.NDArray]: + """Fit full and partial models and calculate F stats, R2, and p values. + + Math from page 11-14 of https://afni.nimh.nih.gov/pub/dist/doc/manual/3dDeconvolve.pdf + Calculates Y=betas*X + error for the base and the full model + F = ((SSE_base-SSE_full)/(DF_base-DF_full)) / (SSE_full/DF_full) + DF = degrees of freedom + SSE = sum of squares error + + Parameters + ---------- + y : (T X C) :obj:`numpy.ndarray` + Time by mixing matrix components for the time series for fitting + regressor_models: :obj:`dict` + Each element in the dictionary is a numpy array defining the regressors in a + regressor model. + Inclues 'full', 'base' and partial models. + base_label : :obj:`str` + The base model to compare the full model against. + For F stat for the full model, this should be 'base'. + For partial models, this should be the name of the partial model (i.e. "no motion"). + full_label : :obj:`str` + The full model to use. + Default="full". + "full" is expected if the goal is to compare all nuissance regressors to a base model. + "task_keep" for the special case of fitting pre-defined task-locked regressors. + + Returns + ------- + betas_full : (C x R) :obj:`numpy.ndarray` + The beta fits for the full model (components x regressors) + f_vals : (C x M) :obj:`numpy.ndarray` + The F statistics for the fits to the full and partial models + p_vals : (C x M) :obj:`numpy.ndarray` + The p values for the fits to the full and partial models + r2_vals : (C x M) :obj:`numpy.ndarray` + The R2 statistics for the fits to the full and partial models + """ + betas_base, sse_base, df_base = fit_model(regressor_models[base_label], y) + betas_full, sse_full, df_full = fit_model(regressor_models[full_label], y) + + # larger sample variance / smaller sample variance (F = (SSE1 – SSE2 / m) / SSE2 / n-k, + # where SSE = residual sum of squares, m = number of restrictions and k = number of + # independent variables) -> the 'm' restrictions in this case is the DOF range between + # the base - full model, the 'n-k' is the number of DOF (independent variables/timepoints) + # from the fully-fitted model + f_vals = np.divide((sse_base - sse_full) / (df_base - df_full), (sse_full / df_full)) + # cumulative distribution (FX(x) = P(X<=x), X = real-valued variable, x=probable variable + # within distribution) of the F-values + extra parameters to shape the range of the + # distribution values (range: start = DOF_base (unfitted) - + # DOF_Full (fitted with full model), end = DOF_Full) + p_vals = 1 - stats.f.cdf(f_vals, df_base - df_full, df_full) + # estimate proportion of variance (R2-squared fit) by SSE full [fully fitted] + # (sum of 1st errors) / SSE base [non-fitted] (sum of 2nd errors) ... and subtracting + # the error ratio from 1: R² = SSR (fitted model)/SST(total or model base) = + # Σ (Y_pred-Y_actual)**2 / Σ (Y_pred-Y_actual)**2 + r2_vals = 1 - np.divide(sse_full, sse_base) + print(y.shape) + + return betas_full, f_vals, p_vals, r2_vals diff --git a/tedana/reporting/data/html/report_body_template.html b/tedana/reporting/data/html/report_body_template.html index 008713715..65c68edf2 100644 --- a/tedana/reporting/data/html/report_body_template.html +++ b/tedana/reporting/data/html/report_body_template.html @@ -180,21 +180,32 @@

Carpet plots

+
+

Adaptive mask

+ +

T2* and S0

T2*

- +
- +

S0

- + +
+
+ +
+

T2* and S0 model fit (RMSE). (Scaled between 2nd and 98th percentiles)

+
+
- +
diff --git a/tedana/reporting/html_report.py b/tedana/reporting/html_report.py index 519aefe50..b07cd8fbe 100644 --- a/tedana/reporting/html_report.py +++ b/tedana/reporting/html_report.py @@ -143,11 +143,16 @@ def _update_template_bokeh(bokeh_id, info_table, about, prefix, references, boke # Initial carpet plot (default one) initial_carpet = f"./figures/{prefix}carpet_optcom.svg" + # Adaptive mask image + adaptive_mask = f"./figures/{prefix}adaptive_mask.svg" + # T2* and S0 images t2star_brain = f"./figures/{prefix}t2star_brain.svg" t2star_histogram = f"./figures/{prefix}t2star_histogram.svg" s0_brain = f"./figures/{prefix}s0_brain.svg" s0_histogram = f"./figures/{prefix}s0_histogram.svg" + rmse_brain = f"./figures/{prefix}rmse_brain.svg" + rmse_timeseries = f"./figures/{prefix}rmse_timeseries.svg" # Convert bibtex to html references, bibliography = _bib2html(references) @@ -159,16 +164,20 @@ def _update_template_bokeh(bokeh_id, info_table, about, prefix, references, boke body_template_path = resource_path.joinpath(body_template_name) with open(str(body_template_path)) as body_file: body_tpl = Template(body_file.read()) + body = body_tpl.substitute( content=bokeh_id, info=info_table, about=about, prefix=prefix, initialCarpet=initial_carpet, + adaptiveMask=adaptive_mask, t2starBrainPlot=t2star_brain, t2starHistogram=t2star_histogram, s0BrainPlot=s0_brain, s0Histogram=s0_histogram, + rmseBrainPlot=rmse_brain, + rmseTimeseries=rmse_timeseries, references=references, javascript=bokeh_js, buttons=buttons, diff --git a/tedana/reporting/static_figures.py b/tedana/reporting/static_figures.py index d9b9abdaa..498f5814d 100644 --- a/tedana/reporting/static_figures.py +++ b/tedana/reporting/static_figures.py @@ -2,6 +2,7 @@ import logging import os +import warnings from io import BytesIO import matplotlib @@ -223,19 +224,26 @@ def plot_component( # Set range to ~1/10th of max positive or negative beta imgmax = 0.1 * np.max(np.abs(stat_img.get_fdata())) - # Save the figure to an in-memory file object - display = plotting.plot_stat_map( - stat_img, - bg_img=None, - display_mode="mosaic", - cut_coords=5, - vmax=imgmax, - cmap=png_cmap, - symmetric_cbar=True, - colorbar=False, - draw_cross=False, - annotate=False, - ) + # nilearn raises a warning when creating a figure from an image with a non-diagonal affine. + # This is not relevant for how we use this function and it flood our screen + # output with repeated warnings so suppressing this warning. + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="A non-diagonal affine.*", category=UserWarning) + # Save the figure to an in-memory file object + display = plotting.plot_stat_map( + stat_img, + bg_img=None, + display_mode="mosaic", + cut_coords=5, + vmax=imgmax, + cmap=png_cmap, + symmetric_cbar=True, + colorbar=False, + draw_cross=False, + annotate=False, + resampling_interpolation="nearest", + ) + display.annotate(size=30) example_ax = list(display.axes.values())[0] nilearn_fig = example_ax.ax.figure @@ -590,29 +598,202 @@ def plot_t2star_and_s0( # Plot T2* and S0 maps t2star_plot = f"{io_generator.prefix}t2star_brain.svg" - plotting.plot_stat_map( - t2star_img, - bg_img=None, - display_mode="mosaic", - symmetric_cbar=False, - black_bg=True, - cmap="gray", - vmin=t2s_p02, - vmax=t2s_p98, - annotate=False, - output_file=os.path.join(io_generator.out_dir, "figures", t2star_plot), - ) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="A non-diagonal affine.*", category=UserWarning) + plotting.plot_stat_map( + t2star_img, + bg_img=None, + display_mode="mosaic", + symmetric_cbar=False, + black_bg=True, + cmap="gray", + vmin=t2s_p02, + vmax=t2s_p98, + annotate=False, + output_file=os.path.join(io_generator.out_dir, "figures", t2star_plot), + ) s0_plot = f"{io_generator.prefix}s0_brain.svg" - plotting.plot_stat_map( - s0_img, - bg_img=None, - display_mode="mosaic", - symmetric_cbar=False, - black_bg=True, - cmap="gray", - vmin=s0_p02, - vmax=s0_p98, - annotate=False, - output_file=os.path.join(io_generator.out_dir, "figures", s0_plot), + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="A non-diagonal affine.*", category=UserWarning) + plotting.plot_stat_map( + s0_img, + bg_img=None, + display_mode="mosaic", + symmetric_cbar=False, + black_bg=True, + cmap="gray", + vmin=s0_p02, + vmax=s0_p98, + annotate=False, + output_file=os.path.join(io_generator.out_dir, "figures", s0_plot), + ) + + +def plot_rmse( + *, + io_generator: io.OutputGenerator, + adaptive_mask: np.ndarray, +): + """Plot the residual mean squared error map and time series for the monoexponential model fit. + + Parameters + ---------- + io_generator : :obj:`~tedana.io.OutputGenerator` + The output generator for this workflow. + adaptive_mask : (S,) :obj:`numpy.ndarray` + A mask where each value is the number of good echoes. + Since the T2* and S0 estimations require a minimum of 2 good echoes, + the outputted plots will only include mask values of at least 2. + """ + import pandas as pd + + rmse_img = io_generator.get_name("rmse img") + confounds_file = io_generator.get_name("confounds tsv") + # Mask that only includes values >=2 (i.e. at least 2 good echoes) + mask_img = io.new_nii_like(io_generator.reference_img, (adaptive_mask >= 2).astype(np.int32)) + + rmse_data = masking.apply_mask(rmse_img, mask_img) + rmse_p02, rmse_p98 = np.percentile(rmse_data, [2, 98]) + + # Get repetition time from reference image + tr = io_generator.reference_img.header.get_zooms()[-1] + + # Load the confounds file + confounds_df = pd.read_table(confounds_file) + + fig, ax = plt.subplots(figsize=(10, 6)) + rmse_arr = confounds_df["rmse_median"].values + p25_arr = confounds_df["rmse_percentile25"].values + p75_arr = confounds_df["rmse_percentile75"].values + p02_arr = confounds_df["rmse_percentile02"].values + p98_arr = confounds_df["rmse_percentile98"].values + time_arr = np.arange(confounds_df.shape[0]) * tr + ax.plot(time_arr, rmse_arr, color="black") + ax.fill_between( + time_arr, + p25_arr, + p75_arr, + color="blue", + alpha=0.2, + ) + ax.plot(time_arr, p02_arr, color="black", linestyle="dashed") + ax.plot(time_arr, p98_arr, color="black", linestyle="dashed") + ax.set_ylabel("RMSE", fontsize=16) + ax.set_xlabel( + "Time (s)", + fontsize=16, + ) + ax.legend(["Median", "25th-75th percentiles", "2nd and 98th percentiles"]) + ax.set_title("Root mean squared error of T2* and S0 fit across voxels", fontsize=20) + rmse_ts_plot = os.path.join( + io_generator.out_dir, + "figures", + f"{io_generator.prefix}rmse_timeseries.svg", + ) + ax.set_xlim(0, time_arr[-2]) + fig.savefig(rmse_ts_plot) + plt.close(fig) + + # Plot RMSE + rmse_brain_plot = os.path.join( + io_generator.out_dir, + "figures", + f"{io_generator.prefix}rmse_brain.svg", + ) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="A non-diagonal affine.*", category=UserWarning) + plotting.plot_stat_map( + rmse_img, + bg_img=None, + display_mode="mosaic", + cut_coords=4, + symmetric_cbar=False, + black_bg=True, + cmap="Reds", + vmin=rmse_p02, + vmax=rmse_p98, + annotate=False, + output_file=rmse_brain_plot, + ) + + +def plot_adaptive_mask( + *, + optcom: np.ndarray, + base_mask: np.ndarray, + io_generator: io.OutputGenerator, +): + """Create a figure to show the adaptive mask. + + This figure shows the base mask, the adaptive mask thresholded for denoising (threshold >= 1), + and the adaptive mask thresholded for classification (threshold >= 3), + overlaid on the mean optimal combination image. + + Parameters + ---------- + optcom : (S x T) :obj:`numpy.ndarray` + Optimal combination of components. + The mean image over time is used as the underlay for the figure. + base_mask : (S,) :obj:`numpy.ndarray` + Base mask used in tedana. + This is the original mask either provided by the user or generated with `compute_epi_mask`. + io_generator : :obj:`~tedana.io.OutputGenerator` + The output generator for this workflow. + """ + from matplotlib.lines import Line2D + from nilearn import image + + adaptive_mask_img = io_generator.get_name("adaptive mask img") + mean_optcom_img = io.new_nii_like(io_generator.reference_img, np.mean(optcom, axis=1)) + + # Concatenate the three masks used in tedana to treat as a probabilistic atlas + base_mask = io.new_nii_like(io_generator.reference_img, base_mask) + mask_denoise = image.math_img("(img >= 1).astype(np.uint8)", img=adaptive_mask_img) + mask_clf = image.math_img("(img >= 3).astype(np.uint8)", img=adaptive_mask_img) + all_masks = image.concat_imgs((base_mask, mask_denoise, mask_clf)) + # Set values to 0.5 for probabilistic atlas plotting + all_masks = image.math_img("img * 0.5", img=all_masks) + + cmap = plt.cm.gist_rainbow + discrete_cmap = cmap.resampled(3) # colors matching the mask lines in the image + color_dict = { + "Base": discrete_cmap(0), + "Optimal combination": discrete_cmap(0.4), + "Classification": discrete_cmap(0.9), + } + + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="A non-diagonal affine.*", category=UserWarning) + ob = plotting.plot_prob_atlas( + maps_img=all_masks, + bg_img=mean_optcom_img, + view_type="contours", + threshold=0.2, + annotate=False, + draw_cross=False, + cmap=cmap, + display_mode="mosaic", + cut_coords=4, + ) + + legend_elements = [] + for k, v in color_dict.items(): + line = Line2D([0], [0], color=v, label=k, markersize=10) + legend_elements.append(line) + + fig = ob.frame_axes.get_figure() + width = fig.get_size_inches()[0] + + ob.frame_axes.set_zorder(100) + ob.frame_axes.legend( + handles=legend_elements, + facecolor="white", + ncols=3, + loc="lower center", + fancybox=True, + shadow=True, + fontsize=width, ) + adaptive_mask_plot = f"{io_generator.prefix}adaptive_mask.svg" + fig.savefig(os.path.join(io_generator.out_dir, "figures", adaptive_mask_plot)) diff --git a/tedana/resources/config/outputs.json b/tedana/resources/config/outputs.json index f87a26615..0ada7b33d 100644 --- a/tedana/resources/config/outputs.json +++ b/tedana/resources/config/outputs.json @@ -15,6 +15,14 @@ "orig": "ts_OC", "bidsv1.5.0": "desc-optcom_bold" }, + "rmse img": { + "orig": "rmse", + "bidsv1.5.0": "desc-rmse_statmap" + }, + "confounds tsv": { + "orig": "confounds", + "bidsv1.5.0": "desc-confounds_timeseries" + }, "ICA components img": { "orig": "ica_components", "bidsv1.5.0": "desc-ICA_components" @@ -203,10 +211,6 @@ "orig": "ica_decision_tree", "bidsv1.5.0": "desc-ICA_decision_tree" }, - "global signal time series tsv": { - "orig": "global_signal_ts", - "bidsv1.5.0": "desc-globalSignal_timeseries" - }, "ICA MIR mixing tsv": { "orig": "ica_mir_mixing", "bidsv1.5.0": "desc-ICAMIRDenoised_mixing" diff --git a/tedana/resources/decision_trees/demo_external_regressors_motion_task_models.json b/tedana/resources/decision_trees/demo_external_regressors_motion_task_models.json new file mode 100644 index 000000000..87b548961 --- /dev/null +++ b/tedana/resources/decision_trees/demo_external_regressors_motion_task_models.json @@ -0,0 +1,293 @@ +{ + "tree_id": "demo_external_regressors_motion_task_models", + "info": "Demonstration based on the minimal decision tree that uses partial F stats on a model with multiple external regressors divided by category and task regressors to bias towards keeping.", + "report": "This is based on the minimal criteria of the original MEICA decision tree \\citep{kundu2013integrated} without the more aggressive noise removal steps \\citep{dupre2021te}.", + "necessary_metrics": [ + "kappa", + "rho", + "countsigFS0", + "countsigFT2", + "dice_FS0", + "dice_FT2", + "signal-noise_t", + "variance explained", + "pval nuisance model", + "pval task model", + "pval nuisance Motion partial model", + "pval nuisance CSF partial model", + "R2stat nuisance model", + "R2stat task model" + ], + "intermediate_classifications": ["provisionalaccept", "provisionalreject"], + "classification_tags": [ + "Likely BOLD", + "Unlikely BOLD", + "Low variance", + "External regressors", + "Fits motion external regressors", + "Fits CSF external regressors", + "Fits task" + ], + "external_regressor_config": [ + { + "regress_ID": "nuisance", + "info": "Fits all external nuisance regressors to a single model using an F statistic", + "report": "External nuisance regressors that fit to components using a linear model were rejected.", + "detrend": true, + "statistic": "F", + "regressors": ["^(?!signal).*$"], + "partial_models": { + "Motion": ["^mot_.*$"], + "CSF": ["^csf.*$"] + } + }, + { + "regress_ID": "task", + "info": "Fits all task regressors to a single model using an F statistic", + "report": "Task regressors that fit to components using a linear model and have some T2* weighting were accepted even if they would have been rejected base on other criteriea.", + "detrend": true, + "statistic": "F", + "regressors": ["^signal.*$"] + } + ], + "nodes": [ + { + "functionname": "manual_classify", + "parameters": {"new_classification": "unclassified", "decide_comps": "all"}, + "kwargs": {"clear_classification_tags": true, "dont_warn_reclassify": true} + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "provisionalreject", + "if_false": "nochange", + "decide_comps": "all", + "op": ">", + "left": "rho", + "right": "kappa" + }, + "kwargs": {"tag_if_true": "Unlikely BOLD"} + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "provisionalreject", + "if_false": "nochange", + "decide_comps": "all", + "op": ">", + "left": "countsigFS0", + "right": "countsigFT2" + }, + "kwargs": { + "left2": "countsigFT2", + "op2": ">", + "right2": 0, + "tag_if_true": "Unlikely BOLD" + } + }, + { + "functionname": "calc_median", + "parameters": { + "decide_comps": "all", + "metric_name": "variance explained", + "median_label": "varex" + } + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "provisionalreject", + "if_false": "nochange", + "decide_comps": "all", + "op": ">", + "left": "dice_FS0", + "right": "dice_FT2" + }, + "kwargs": { + "left2": "variance explained", + "op2": ">", + "right2": "median_varex", + "tag_if_true": "Unlikely BOLD" + } + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "provisionalreject", + "if_false": "nochange", + "decide_comps": "all", + "op": ">", + "left": 0, + "right": "signal-noise_t" + }, + "kwargs": { + "left2": "variance explained", + "op2": ">", + "right2": "median_varex", + "tag_if_true": "Unlikely BOLD" + } + }, + { + "functionname": "calc_kappa_elbow", + "parameters": {"decide_comps": "all"}, + "_comment": "" + }, + { + "functionname": "calc_rho_elbow", + "parameters": {"decide_comps": "all"}, + "kwargs": { + "subset_decide_comps": "unclassified", + "rho_elbow_type": "liberal", + "log_extra_info": "" + }, + "_comment": "" + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "provisionalaccept", + "if_false": "provisionalreject", + "decide_comps": "unclassified", + "op": ">=", + "left": "kappa", + "right": "kappa_elbow_kundu" + }, + "kwargs": { + "log_extra_info": "If kappa> kappa elbow and rho", + "left": "kappa", + "right": "rho" + }, + "kwargs": { + "log_extra_info": "If kappa>elbow and kappa>2*rho accept even if rho>elbow", + "right_scale": 2, + "op2": ">", + "left2": "kappa", + "right2": "kappa_elbow_kundu", + "tag_if_true": "Likely BOLD", + "tag_if_false": "Unlikely BOLD" + } + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "provisionalreject", + "if_false": "nochange", + "decide_comps": "all", + "op": "<", + "left": "pval nuisance model", + "right": 0.05 + }, + "kwargs": { + "op2": ">", + "left2": "R2stat nuisance model", + "right2": 0.5, + "log_extra_info": "If external regressors fit with p<0.05 and model R2>0.5 of the variance, then reject.", + "tag_if_true": "External regressors" + }, + "_comment": "Provisionally rejecting components that fit to the external regressor noise model" + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "nochange", + "if_false": "nochange", + "decide_comps": "provisionalreject", + "op": "<", + "left": "pval nuisance model", + "right": 0.05 + }, + "kwargs": { + "op2": ">", + "left2": "R2stat nuisance model", + "right2": 0.5, + "op3": "<", + "left3": "pval nuisance Motion partial model", + "right3": 0.05, + "tag_if_true": "Fits motion external regressors" + }, + "_comment": "Identical to the one above, & not changing classifications, but tagging if fits to motion regressors" + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "nochange", + "if_false": "nochange", + "decide_comps": "provisionalreject", + "op": "<", + "left": "pval nuisance model", + "right": 0.05 + }, + "kwargs": { + "op2": ">", + "left2": "R2stat nuisance model", + "right2": 0.5, + "op3": "<", + "left3": "pval nuisance CSF partial model", + "right3": 0.05, + "tag_if_true": "Fits CSF external regressors" + }, + "_comment": "Identical to the one above, & not changing classifications, but tagging if fits to CSF regressors" + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "accepted", + "if_false": "nochange", + "decide_comps": ["provisionalreject"], + "op": "<", + "left": "pval task model", + "right": 0.05 + }, + "kwargs": { + "op2": ">", + "left2": "R2stat task model", + "right2": 0.5, + "op3": ">=", + "left3": "kappa", + "right3": "kappa_elbow_kundu", + "tag_if_true": "Fits task" + }, + "_comment": "Keep if it fits task regressors and contains T2* signal, as defined by kappa>elbow" + }, + { + "functionname": "dec_variance_lessthan_thresholds", + "parameters": { + "if_true": "accepted", + "if_false": "nochange", + "decide_comps": "provisionalreject" + }, + "kwargs": { + "var_metric": "variance explained", + "single_comp_threshold": 0.1, + "all_comp_threshold": 1.0, + "tag_if_true": "Low variance" + } + }, + { + "functionname": "manual_classify", + "parameters": {"new_classification": "accepted", "decide_comps": "provisionalaccept"}, + "kwargs": {"tag": "Likely BOLD"} + }, + { + "functionname": "manual_classify", + "parameters": { + "new_classification": "rejected", + "decide_comps": ["provisionalreject", "unclassified"] + }, + "kwargs": {"tag": "Unlikely BOLD"} + } + ] +} diff --git a/tedana/resources/decision_trees/demo_external_regressors_single_model.json b/tedana/resources/decision_trees/demo_external_regressors_single_model.json new file mode 100644 index 000000000..1900ca2e5 --- /dev/null +++ b/tedana/resources/decision_trees/demo_external_regressors_single_model.json @@ -0,0 +1,205 @@ +{ + "tree_id": "demo_external_regressors_single_model", + "info": "Demonstration based on the minimal decision tree that uses an F stat on a model with multiple external regressors.", + "report": "This is based on the minimal criteria of the original MEICA decision tree \\citep{kundu2013integrated} without the more aggressive noise removal steps \\citep{dupre2021te}.", + "necessary_metrics": [ + "kappa", + "rho", + "countsigFS0", + "countsigFT2", + "dice_FS0", + "dice_FT2", + "signal-noise_t", + "variance explained", + "pval nuisance model", + "R2stat nuisance model" + ], + "intermediate_classifications": ["provisionalaccept", "provisionalreject"], + "classification_tags": ["Likely BOLD", "Unlikely BOLD", "Low variance", "External regressors"], + "external_regressor_config": [ + { + "regress_ID": "nuisance", + "info": "Fits all external nuisance regressors to a single model using an F statistic", + "report": "External nuisance regressors that fit to components using a linear model were rejected.", + "detrend": true, + "statistic": "F", + "regressors": ["^.*$"] + } + ], + "nodes": [ + { + "functionname": "manual_classify", + "parameters": {"new_classification": "unclassified", "decide_comps": "all"}, + "kwargs": {"clear_classification_tags": true, "dont_warn_reclassify": true} + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "rejected", + "if_false": "nochange", + "decide_comps": "all", + "op": ">", + "left": "rho", + "right": "kappa" + }, + "kwargs": {"tag_if_true": "Unlikely BOLD"} + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "rejected", + "if_false": "nochange", + "decide_comps": "all", + "op": ">", + "left": "countsigFS0", + "right": "countsigFT2" + }, + "kwargs": { + "left2": "countsigFT2", + "op2": ">", + "right2": 0, + "tag_if_true": "Unlikely BOLD" + } + }, + { + "functionname": "calc_median", + "parameters": { + "decide_comps": "all", + "metric_name": "variance explained", + "median_label": "varex" + } + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "rejected", + "if_false": "nochange", + "decide_comps": "all", + "op": ">", + "left": "dice_FS0", + "right": "dice_FT2" + }, + "kwargs": { + "left2": "variance explained", + "op2": ">", + "right2": "median_varex", + "tag_if_true": "Unlikely BOLD" + } + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "rejected", + "if_false": "nochange", + "decide_comps": "all", + "op": ">", + "left": 0, + "right": "signal-noise_t" + }, + "kwargs": { + "left2": "variance explained", + "op2": ">", + "right2": "median_varex", + "tag_if_true": "Unlikely BOLD" + } + }, + { + "functionname": "calc_kappa_elbow", + "parameters": {"decide_comps": "all"}, + "_comment": "" + }, + { + "functionname": "calc_rho_elbow", + "parameters": {"decide_comps": "all"}, + "kwargs": {"subset_decide_comps": "unclassified", "rho_elbow_type": "liberal"}, + "_comment": "" + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "provisionalaccept", + "if_false": "provisionalreject", + "decide_comps": "unclassified", + "op": ">=", + "left": "kappa", + "right": "kappa_elbow_kundu" + } + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "provisionalreject", + "if_false": "nochange", + "decide_comps": ["provisionalreject", "provisionalaccept"], + "op": ">", + "left": "rho", + "right": "rho_elbow_liberal" + } + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "provisionalaccept", + "if_false": "nochange", + "decide_comps": "provisionalreject", + "op": ">", + "left": "kappa", + "right": "rho" + }, + "kwargs": { + "log_extra_info": "If kappa>elbow and kappa>2*rho accept even if rho>elbow", + "right_scale": 2, + "op2": ">", + "left2": "kappa", + "right2": "kappa_elbow_kundu", + "tag_if_true": "Likely BOLD" + } + }, + { + "functionname": "dec_left_op_right", + "parameters": { + "if_true": "rejected", + "if_false": "nochange", + "decide_comps": "all", + "op": "<", + "left": "pval nuisance model", + "right": 0.05 + }, + "kwargs": { + "op2": ">", + "left2": "R2stat nuisance model", + "right2": 0.5, + "log_extra_info": "If external regressors fit with p<0.05 and model R2>0.5 of the variance, then reject.", + "tag_if_true": "External regressors" + } + }, + { + "functionname": "dec_variance_lessthan_thresholds", + "parameters": { + "if_true": "accepted", + "if_false": "nochange", + "decide_comps": "provisionalreject" + }, + "kwargs": { + "var_metric": "variance explained", + "single_comp_threshold": 0.1, + "all_comp_threshold": 1.0, + "tag_if_true": "Low variance" + } + }, + + { + "functionname": "manual_classify", + "parameters": {"new_classification": "accepted", "decide_comps": "provisionalaccept"}, + "kwargs": {"tag": "Likely BOLD"} + }, + { + "functionname": "manual_classify", + "parameters": { + "new_classification": "rejected", + "decide_comps": ["provisionalreject", "unclassified"] + }, + "kwargs": {"tag": "Unlikely BOLD"} + } + ] +} diff --git a/tedana/selection/component_selector.py b/tedana/selection/component_selector.py index f78477f92..e54845546 100644 --- a/tedana/selection/component_selector.py +++ b/tedana/selection/component_selector.py @@ -3,7 +3,9 @@ import inspect import logging import os.path as op +from typing import Dict, List, Union +import pandas as pd from numpy import asarray from tedana.io import load_json @@ -23,7 +25,16 @@ # A user can run the desision tree either using one of these # names or by giving the full path to a tree in a different # location -DEFAULT_TREES = ["minimal", "meica", "tedana_orig"] +# Including the demo trees here so people do not need to type the full +# path, but these are not listed as options in the CLI help menu until +# they are actually validated as useful on some data +DEFAULT_TREES = [ + "minimal", + "meica", + "tedana_orig", + "demo_external_regressors_single_model", + "demo_external_regressors_motion_task_models", +] class TreeError(Exception): @@ -32,7 +43,7 @@ class TreeError(Exception): pass -def load_config(tree): +def load_config(tree: str) -> Dict: """Load the json file with the decision tree and validate the fields in the decision tree. Parameters @@ -73,7 +84,7 @@ def load_config(tree): return validate_tree(dectree) -def validate_tree(tree): +def validate_tree(tree: Dict) -> Dict: """Confirm that provided `tree` is a valid decision tree. Parameters @@ -100,11 +111,17 @@ def validate_tree(tree): "intermediate_classifications", "classification_tags", "nodes", + "external_regressor_config", ] + tree_optional_keys = ["generated_metrics"] defaults = {"selector", "decision_node_idx"} default_classifications = {"nochange", "accepted", "rejected", "unclassified"} default_decide_comps = {"all", "accepted", "rejected", "unclassified"} + # If a tree doesn't include "external_regressor_config", instead of crashing, set to None + if "external_regressor_config" not in set(tree.keys()): + tree["external_regressor_config"] = None + # Confirm that the required fields exist missing_keys = set(tree_expected_keys) - set(tree.keys()) if missing_keys: @@ -115,16 +132,15 @@ def validate_tree(tree): # Warn if unused fields exist unused_keys = set(tree.keys()) - set(tree_expected_keys) - {"used_metrics"} - # Make sure some fields don't trigger a warning; hacky, sorry - ok_to_not_use = ( - "reconstruct_from", - "generated_metrics", - ) - for k in ok_to_not_use: + + # Separately check for optional, but used keys + for k in tree_optional_keys: if k in unused_keys: unused_keys.remove(k) if unused_keys: - LGR.warning(f"Decision tree includes fields that are not used or logged {unused_keys}") + LGR.warning( + f"Decision tree includes fields that are not used or logged {sorted(unused_keys)}" + ) # Combine the default classifications with the user inputted classifications all_classifications = set(tree.get("intermediate_classifications")) | set( @@ -194,7 +210,9 @@ def validate_tree(tree): compclass = compclass | set(tmp_comp) nonstandard_labels = compclass.difference(all_classifications) if nonstandard_labels: - LGR.warning(f"{compclass} in node {i} of the decision tree includes a classification") + LGR.warning( + f"{sorted(compclass)} in node {i} of the decision tree includes a classification" + ) if "decide_comps" in node.get("parameters").keys(): tmp_comp = node["parameters"]["decide_comps"] if isinstance(tmp_comp, str): @@ -203,7 +221,7 @@ def validate_tree(tree): nonstandard_labels = compclass.difference(all_decide_comps) if nonstandard_labels: LGR.warning( - f"{compclass} in node {i} of the decision tree includes a classification " + f"{sorted(compclass)} in node {i} of the decision tree includes a classification " "label that was not predefined" ) @@ -218,10 +236,68 @@ def validate_tree(tree): undefined_classification_tags = tagset.difference(set(tree.get("classification_tags"))) if undefined_classification_tags: LGR.warning( - f"{tagset} in node {i} of the decision tree includes a classification " + f"{sorted(tagset)} in node {i} of the decision tree includes a classification " "tag that was not predefined" ) + # If there is an external_regressor_config field, validate it + if tree["external_regressor_config"] is not None: + external_regressor_config = tree["external_regressor_config"] + if isinstance(external_regressor_config, list): + # Define the fields that should always be present + dict_expected_keys = set( + ["regress_ID", "info", "report", "detrend", "statistic", "regressors"] + ) + dict_optional_keys = set(["partial_models"]) + # Right now, "f" is the only option, but this leaves open the possibility + # to have additional options + statistic_key_options = set("f") + + for config_idx in range(len(external_regressor_config)): + # Confirm that the required fields exist + missing_keys = dict_expected_keys - set( + external_regressor_config[config_idx].keys() + ) + if missing_keys: + err_msg += ( + f"External regressor dictionary {config_idx} " + f"is missing required fields: {missing_keys}\n" + ) + + if "statistic" in set(external_regressor_config[config_idx].keys()): + if ( + external_regressor_config[config_idx]["statistic"].lower() + not in statistic_key_options + ): + err_msg += ( + "statistic in external_regressor_config 1 is " + f"{external_regressor_config[config_idx]['statistic']}. " + "It must be one of the following: " + f"{statistic_key_options}\n" + ) + + if (external_regressor_config[config_idx]["statistic"].lower() != "f") and ( + "partial_models" in set(external_regressor_config[config_idx].keys()) + ): + err_msg += ( + "External regressor dictionary cannot include " + "partial_models if statistic is not F\n" + ) + + # Warn if unused fields exist + unused_keys = ( + set(external_regressor_config[config_idx].keys()) + - dict_expected_keys + - dict_optional_keys + ) + if unused_keys: + LGR.warning( + f"External regressor dictionary {config_idx} includes fields that " + f"are not used or logged {sorted(unused_keys)}" + ) + else: + err_msg += "External regressor dictional exists, but is not a list\n" + if err_msg: raise TreeError("\n" + err_msg) @@ -231,7 +307,7 @@ def validate_tree(tree): class ComponentSelector: """Load and classify components based on a specified ``tree``.""" - def __init__(self, tree): + def __init__(self, tree: str): """Initialize the class using the info specified in the json file ``tree``. Parameters @@ -255,7 +331,12 @@ def __init__(self, tree): self.classification_tags = set(self.tree["classification_tags"]) self.tree["used_metrics"] = set(self.tree.get("used_metrics", [])) - def select(self, component_table, cross_component_metrics={}, status_table=None): + def select( + self, + component_table: pd.DataFrame, + cross_component_metrics: Dict = {}, + status_table: Union[pd.DataFrame, None] = None, + ): """Apply the decision tree to data. Using the validated tree in ``ComponentSelector`` to run the decision @@ -274,6 +355,9 @@ def select(self, component_table, cross_component_metrics={}, status_table=None) A table tracking the status of each component at each step. Pass a status table if running additional steps on a decision tree that was already executed. Default=None. + external_regressor_config : :obj:`dict` + Information describing the external regressors and + method to use for fitting and statistical tests Notes ----- @@ -334,11 +418,40 @@ def select(self, component_table, cross_component_metrics={}, status_table=None) # this will crash the program with an error message if not all # necessary_metrics are in the comptable confirm_metrics_exist( - self.component_table_, - self.necessary_metrics, + component_table=self.component_table_, + necessary_metrics=self.necessary_metrics, function_name=self.tree_name, ) + # To run a decision tree, each component needs to have an initial classification + # If the classification column doesn't exist, create it and label all components + # as unclassified + if "classification" not in self.component_table_: + self.component_table_["classification"] = "unclassified" + + if status_table is None: + self.component_status_table_ = self.component_table_[ + ["Component", "classification"] + ].copy() + self.component_status_table_ = self.component_status_table_.rename( + columns={"classification": "initialized classification"} + ) + self.start_idx_ = 0 + else: + # Since a status table exists, we need to skip nodes up to the + # point where the last tree finished. Notes that were executed + # have an output field. Identify the last node with an output field + tmp_idx = len(self.tree["nodes"]) - 1 + while ("outputs" not in self.tree["nodes"][tmp_idx]) and (tmp_idx > 0): + tmp_idx -= 1 + # start at the first node that does not have an output field + self.start_idx_ = tmp_idx + 1 + LGR.info(f"Start is {self.start_idx_}") + self.component_status_table_ = status_table + + if "classification_tags" not in self.component_table_.columns: + self.component_table_["classification_tags"] = "" + # To run a decision tree, each component needs to have an initial classification # If the classification column doesn't exist, create it and label all components # as unclassified @@ -384,6 +497,7 @@ def select(self, component_table, cross_component_metrics={}, status_table=None) kwargs = self.check_null(kwargs, node["functionname"]) all_params = {**params, **kwargs} else: + kwargs = {} kwargs = {} all_params = {**params} @@ -413,7 +527,7 @@ def select(self, component_table, cross_component_metrics={}, status_table=None) self.are_all_components_accepted_or_rejected() - def add_manual(self, indices, classification): + def add_manual(self, indices: List[int], classification: str): """Add nodes that will manually classify components. Parameters @@ -437,16 +551,25 @@ def add_manual(self, indices, classification): } ) - def check_null(self, params, fcn): + def check_null(self, params: Dict, fcn: str) -> Dict: """ Check that required parameters for selection node functions are attributes in the class. Error if any are undefined. + Parameters + ---------- + params : :obj:`dict` + The keys and values for the inputted parameters + fcn : :obj:`str` + The name of a component selection function in selection_nodes.py + Returns ------- params : :obj:`dict` - The keys and values for the inputted parameters + The keys and values for the inputted parameters. + If a parameter's value was defined in self.cross_component_metrics + then that value is also in params when returned """ for key, val in params.items(): if val is None: @@ -508,12 +631,12 @@ def are_all_components_accepted_or_rejected(self): ) @property - def n_comps_(self): + def n_comps_(self) -> int: """The number of components in the component table.""" return len(self.component_table_) @property - def likely_bold_comps_(self): + def likely_bold_comps_(self) -> int: """A boolean :obj:`pandas.Series` of components that are tagged "Likely BOLD".""" likely_bold_comps = self.component_table_["classification_tags"].copy() for idx in range(len(likely_bold_comps)): @@ -524,22 +647,22 @@ def likely_bold_comps_(self): return likely_bold_comps @property - def n_likely_bold_comps_(self): + def n_likely_bold_comps_(self) -> int: """The number of components that are tagged "Likely BOLD".""" return self.likely_bold_comps_.sum() @property - def accepted_comps_(self): + def accepted_comps_(self) -> pd.Series: """A boolean :obj:`pandas.Series` of components that are accepted.""" return self.component_table_["classification"] == "accepted" @property - def n_accepted_comps_(self): + def n_accepted_comps_(self) -> int: """The number of components that are accepted.""" return self.accepted_comps_.sum() @property - def rejected_comps_(self): + def rejected_comps_(self) -> pd.Series: """A boolean :obj:`pandas.Series` of components that are rejected.""" return self.component_table_["classification"] == "rejected" diff --git a/tedana/selection/selection_nodes.py b/tedana/selection/selection_nodes.py index 5fed5ddde..97b59cac1 100644 --- a/tedana/selection/selection_nodes.py +++ b/tedana/selection/selection_nodes.py @@ -1812,9 +1812,13 @@ def calc_revised_meanmetricrank_guesses( tmp_countnoise = selector.component_table_.loc[comps2use, "countnoise"].to_numpy() tmp_countsig_ft2 = selector.component_table_.loc[comps2use, "countsigFT2"].to_numpy() tmp_d_table_score = generate_decision_table_score( - tmp_kappa, tmp_dice_ft2, tmp_signal_m_noise_t, tmp_countnoise, tmp_countsig_ft2 + kappa=tmp_kappa, + dice_ft2=tmp_dice_ft2, + signal_minus_noise_t=tmp_signal_m_noise_t, + countnoise=tmp_countnoise, + countsig_ft2=tmp_countsig_ft2, ) - selector.component_table_[f"d_table_score_node{selector.current_node_idx_}"] = np.NaN + selector.component_table_[f"d_table_score_node{selector.current_node_idx_}"] = np.nan selector.component_table_.loc[ comps2use, f"d_table_score_node{selector.current_node_idx_}" ] = tmp_d_table_score diff --git a/tedana/selection/selection_utils.py b/tedana/selection/selection_utils.py index aea51ad05..350d53009 100644 --- a/tedana/selection/selection_utils.py +++ b/tedana/selection/selection_utils.py @@ -1,8 +1,11 @@ """Utility functions for tedana.selection.""" import logging +import re +from typing import Dict, List, Union import numpy as np +import pandas as pd from tedana.stats import getfbounds @@ -14,7 +17,9 @@ ############################################################## -def selectcomps2use(component_table, decide_comps): +def selectcomps2use( + component_table: pd.DataFrame, decide_comps: Union[str, List[str], List[int]] +) -> List[int]: """Get a list of component numbers that fit the classification types in ``decide_comps``. Parameters @@ -83,14 +88,14 @@ def selectcomps2use(component_table, decide_comps): def change_comptable_classifications( - selector, - if_true, - if_false, - decision_boolean, - tag_if_true=None, - tag_if_false=None, - dont_warn_reclassify=False, -): + selector, # ComponentSelector + if_true: str, + if_false: str, + decision_boolean: pd.Series, + tag_if_true: str = None, + tag_if_false: str = None, + dont_warn_reclassify: bool = False, +): # -> Tuple[ComponentSelector, int, int] """ Change or don't change the component classification. @@ -166,13 +171,13 @@ def change_comptable_classifications( def comptable_classification_changer( - selector, - boolstate, - classify_if, - decision_boolean, - tag_if=None, - dont_warn_reclassify=False, -): + selector, # : ComponentSelector, + boolstate: bool, + classify_if: str, + decision_boolean: pd.Series, + tag_if: Union[str, None] = None, + dont_warn_reclassify: bool = False, +): # -> ComponentSelector """Implement the component classification changes from ``change_comptable_classifications``. Parameters @@ -281,7 +286,7 @@ def comptable_classification_changer( return selector -def clean_dataframe(component_table): +def clean_dataframe(component_table: pd.DataFrame) -> pd.DataFrame: """ Reorder columns in component table. @@ -313,7 +318,11 @@ def clean_dataframe(component_table): ################################################# -def confirm_metrics_exist(component_table, necessary_metrics, function_name=None): +def confirm_metrics_exist( + component_table: pd.DataFrame, + necessary_metrics: List[str], + function_name: Union[str, None] = None, +) -> Union[None, bool]: """Confirm that all metrics declared in necessary_metrics are already included in comptable. Parameters @@ -321,11 +330,16 @@ def confirm_metrics_exist(component_table, necessary_metrics, function_name=None component_table : (C x M) :obj:`pandas.DataFrame` Component metric table. One row for each component, with a column for each metric. The index should be the component number. - necessary_metrics : :obj:`list` + necessary_metrics : :obj:`list[str]` A list of strings of metric names. function_name : :obj:`str` Text identifying the function name that called this function. + Returns + ------- + metrics_are_missing : :obj:`bool` + If there are no metrics missing, this returns True. + Raises ------ ValueError @@ -337,26 +351,42 @@ def confirm_metrics_exist(component_table, necessary_metrics, function_name=None Also, the string in ``necessary_metrics`` and the column labels in ``component_table`` will only be matched if they're identical. """ - missing_metrics = set(necessary_metrics) - set(component_table.columns) - if missing_metrics: - function_name = function_name or "unknown function" - raise ValueError( - f"Necessary metrics for {function_name}: {necessary_metrics}. " + hardcoded_metrics = [metric for metric in necessary_metrics if not metric.startswith("^")] + regex_metrics = [metric for metric in necessary_metrics if metric.startswith("^")] + # Check that all hardcoded (literal string) metrics are accounted for. + missing_metrics = sorted(list(set(hardcoded_metrics) - set(component_table.columns))) + # Check that the regular expression-based metrics are accounted for. + found_metrics = component_table.columns.tolist() + for regex_metric in regex_metrics: + if not any(re.match(regex_metric, metric) for metric in found_metrics): + missing_metrics.append(regex_metric) + + metrics_are_missing = len(missing_metrics) > 0 + if metrics_are_missing: + if function_name is None: + function_name = "unknown function" + + error_msg = ( + f"Necessary metrics for {function_name}: " + f"{necessary_metrics}. " f"Comptable metrics: {set(component_table.columns)}. " f"MISSING METRICS: {missing_metrics}." ) + raise ValueError(error_msg) + + return metrics_are_missing def log_decision_tree_step( - function_name_idx, - comps2use, - decide_comps=None, - n_true=None, - n_false=None, - if_true=None, - if_false=None, - calc_outputs=None, -): + function_name_idx: str, + comps2use: Union[List[int], int], + decide_comps: Union[str, List[str], List[int], None] = None, + n_true: Union[int, None] = None, + n_false: Union[int, None] = None, + if_true: Union[str, None] = None, + if_false: Union[str, None] = None, + calc_outputs: Union[Dict, None] = None, +) -> None: """Log text to add after every decision tree calculation. Parameters @@ -423,7 +453,7 @@ def log_decision_tree_step( ) -def log_classification_counts(decision_node_idx, component_table): +def log_classification_counts(decision_node_idx: int, component_table: pd.DataFrame) -> None: """Log the total counts for each component classification in component_table. Parameters diff --git a/tedana/selection/tedica.py b/tedana/selection/tedica.py index c12d0870f..5ea6b79f2 100644 --- a/tedana/selection/tedica.py +++ b/tedana/selection/tedica.py @@ -52,13 +52,15 @@ def automatic_selection(component_table, selector, **kwargs): .. _FAQ: faq.html """ - LGR.info("Performing ICA component selection") + # TODO external_regressor_config was inputted in this function + LGR.info(f"Performing ICA component selection with tree: {selector.tree_name}") RepLGR.info( "\n\nNext, component selection was performed to identify BOLD (TE-dependent) and " "non-BOLD (TE-independent) components using a decision tree." ) + # TODO external_regressor_config=external_regressor_config, was here component_table["classification_tags"] = "" selector.select(component_table, cross_component_metrics=kwargs) selector.metadata_ = collect.get_metadata(selector.component_table_) diff --git a/tedana/stats.py b/tedana/stats.py index cb6991bbc..ad9e63c0f 100644 --- a/tedana/stats.py +++ b/tedana/stats.py @@ -3,7 +3,7 @@ import logging import numpy as np -from scipy import stats +from scipy import linalg, stats from tedana import utils @@ -223,3 +223,46 @@ def t_to_z(t_values, dof): if ret_float: out = out[0] return out + + +def fit_model(x, y, output_residual=False): + """ + Linear regression for a model y = betas * x + error. + + Parameters + ---------- + x : (T X R) :obj:`numpy.ndarray` + 2D array with the regressors for the specified model an time + y : (T X C) :obj:`numpy.ndarray` + Time by mixing matrix components for the time series for fitting + output_residual : :obj:`bool` + If true, then this just outputs the residual of the fit. + If false, then outputs beta fits, sse, and df + + Returns + ------- + residual : (T X C) :obj:`numpy.ndarray` + The residual time series for the fit (only if output_residual is True) + betas : (R X C) :obj:`numpy.ndarray` + The magnitude fits for the model (only if output_residual is False) + sse : (C) :obj:`numpy.ndarray` + The sum of square error for the model (only if output_residual is False) + df : :obj:`int` + The degrees of freeom for the model (only if output_residual is False) + (timepoints - number of regressors) + """ + betas, _, _, _ = linalg.lstsq(x, y) + # matrix-multiplication on the regressors with the betas -> to create a new 'estimated' + # component matrix = fitted regressors (least squares beta solution * regressors) + fitted_regressors = np.matmul(x, betas) + residual = y - fitted_regressors + if output_residual: + return residual + else: + # sum the differences between the actual ICA components and the 'estimated' + # component matrix (beta-fitted regressors) + sse = np.sum(np.square(residual), axis=0) + # calculate how many individual values [timepoints] are free to vary after + # the least-squares solution [beta] betw X & Y is calculated + df = y.shape[0] - betas.shape[0] + return betas, sse, df diff --git a/tedana/tests/data/cornell_three_echo_outputs.txt b/tedana/tests/data/cornell_three_echo_outputs.txt index 44a6e660f..6c9197eb9 100644 --- a/tedana/tests/data/cornell_three_echo_outputs.txt +++ b/tedana/tests/data/cornell_three_echo_outputs.txt @@ -22,7 +22,10 @@ desc-PCA_stat-z_components.nii.gz desc-adaptiveGoodSignal_mask.nii.gz desc-denoised_bold.nii.gz desc-optcom_bold.nii.gz +desc-confounds_timeseries.tsv +desc-rmse_statmap.nii.gz figures +figures/adaptive_mask.svg figures/carpet_optcom.svg figures/carpet_denoised.svg figures/carpet_accepted.svg @@ -98,6 +101,8 @@ figures/comp_067.png figures/comp_068.png figures/pca_criteria.png figures/pca_variance_explained.png +figures/rmse_brain.svg +figures/rmse_timeseries.svg figures/s0_brain.svg figures/s0_histogram.svg figures/t2star_brain.svg diff --git a/tedana/tests/data/cornell_three_echo_preset_mixing_outputs.txt b/tedana/tests/data/cornell_three_echo_preset_mixing_outputs.txt new file mode 100644 index 000000000..1af72cda8 --- /dev/null +++ b/tedana/tests/data/cornell_three_echo_preset_mixing_outputs.txt @@ -0,0 +1,105 @@ +S0map.nii.gz +T2starmap.nii.gz +dataset_description.json +desc-ICAAccepted_components.nii.gz +desc-ICAAccepted_stat-z_components.nii.gz +desc-ICA_components.nii.gz +desc-ICA_decomposition.json +desc-tedana_metrics.json +desc-tedana_metrics.tsv +desc-tedana_registry.json +desc-ICACrossComponent_metrics.json +desc-ICA_status_table.tsv +desc-ICA_decision_tree.json +desc-ICA_mixing.tsv +desc_ICA_mixing_static.tsv +desc-ICA_stat-z_components.nii.gz +desc-adaptiveGoodSignal_mask.nii.gz +desc-denoised_bold.nii.gz +desc-optcom_bold.nii.gz +desc-confounds_timeseries.tsv +desc-rmse_statmap.nii.gz +figures +figures/adaptive_mask.svg +figures/carpet_optcom.svg +figures/carpet_denoised.svg +figures/carpet_accepted.svg +figures/carpet_rejected.svg +figures/comp_000.png +figures/comp_001.png +figures/comp_002.png +figures/comp_003.png +figures/comp_004.png +figures/comp_005.png +figures/comp_006.png 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b/tedana/tests/data/fiu_four_echo_outputs.txt index 221bc66e7..9cb0832eb 100644 --- a/tedana/tests/data/fiu_four_echo_outputs.txt +++ b/tedana/tests/data/fiu_four_echo_outputs.txt @@ -23,7 +23,6 @@ sub-01_desc-ICA_stat-z_components.nii.gz sub-01_desc-T1likeEffect_min.nii.gz sub-01_desc-adaptiveGoodSignal_mask.nii.gz sub-01_desc-globalSignal_map.nii.gz -sub-01_desc-globalSignal_timeseries.tsv sub-01_desc-limited_S0map.nii.gz sub-01_desc-limited_T2starmap.nii.gz sub-01_desc-optcomAcceptedMIRDenoised_bold.nii.gz @@ -34,6 +33,8 @@ sub-01_desc-optcomNoGlobalSignal_bold.nii.gz sub-01_desc-optcomRejected_bold.nii.gz sub-01_desc-optcomWithGlobalSignal_bold.nii.gz sub-01_desc-optcom_bold.nii.gz +sub-01_desc-confounds_timeseries.tsv +sub-01_desc-rmse_statmap.nii.gz sub-01_echo-1_desc-Accepted_bold.nii.gz sub-01_echo-1_desc-Denoised_bold.nii.gz sub-01_echo-1_desc-ICAT2ModelPredictions_components.nii.gz @@ -61,6 +62,7 @@ sub-01_echo-4_desc-Rejected_bold.nii.gz sub-01_report.txt sub-01_tedana_report.html figures +figures/sub-01_adaptive_mask.svg figures/sub-01_carpet_optcom.svg figures/sub-01_carpet_denoised.svg figures/sub-01_carpet_accepted.svg @@ -89,6 +91,8 @@ figures/sub-01_comp_017.png figures/sub-01_comp_018.png figures/sub-01_comp_019.png figures/sub-01_comp_020.png +figures/sub-01_rmse_brain.svg +figures/sub-01_rmse_timeseries.svg figures/sub-01_s0_brain.svg figures/sub-01_s0_histogram.svg figures/sub-01_t2star_brain.svg diff --git a/tedana/tests/data/nih_five_echo_outputs_t2smap.txt b/tedana/tests/data/nih_five_echo_outputs_t2smap.txt index f69dc9fb5..58db8a10b 100644 --- a/tedana/tests/data/nih_five_echo_outputs_t2smap.txt +++ b/tedana/tests/data/nih_five_echo_outputs_t2smap.txt @@ -6,3 +6,5 @@ desc-optcom_bold.nii.gz S0map.nii.gz T2starmap.nii.gz t2smap_call.sh +desc-confounds_timeseries.tsv +desc-rmse_statmap.nii.gz diff --git a/tedana/tests/data/nih_five_echo_outputs_verbose.txt b/tedana/tests/data/nih_five_echo_outputs_verbose.txt index 9c86dee80..2d54db98b 100644 --- a/tedana/tests/data/nih_five_echo_outputs_verbose.txt +++ b/tedana/tests/data/nih_five_echo_outputs_verbose.txt @@ -33,6 +33,8 @@ sub-01_desc-denoised_bold.nii.gz sub-01_desc-optcom_whitened_bold.nii.gz sub-01_desc-optcomRejected_bold.nii.gz sub-01_desc-optcom_bold.nii.gz +sub-01_desc-confounds_timeseries.tsv +sub-01_desc-rmse_statmap.nii.gz sub-01_echo-1_desc-Accepted_bold.nii.gz sub-01_echo-1_desc-Denoised_bold.nii.gz sub-01_echo-1_desc-ICAT2ModelPredictions_components.nii.gz @@ -82,6 +84,7 @@ sub-01_references.bib sub-01_report.txt sub-01_tedana_report.html figures +figures/sub-01_adaptive_mask.svg figures/sub-01_carpet_optcom.svg figures/sub-01_carpet_denoised.svg figures/sub-01_carpet_accepted.svg @@ -118,26 +121,8 @@ figures/sub-01_comp_028.png figures/sub-01_comp_029.png figures/sub-01_comp_030.png figures/sub-01_comp_031.png -figures/sub-01_comp_032.png -figures/sub-01_comp_033.png -figures/sub-01_comp_034.png -figures/sub-01_comp_035.png -figures/sub-01_comp_036.png -figures/sub-01_comp_037.png -figures/sub-01_comp_038.png -figures/sub-01_comp_039.png -figures/sub-01_comp_040.png -figures/sub-01_comp_041.png -figures/sub-01_comp_042.png -figures/sub-01_comp_043.png -figures/sub-01_comp_044.png -figures/sub-01_comp_045.png -figures/sub-01_comp_046.png -figures/sub-01_comp_047.png -figures/sub-01_comp_048.png -figures/sub-01_comp_049.png -figures/sub-01_comp_050.png -figures/sub-01_comp_051.png +figures/sub-01_rmse_brain.svg +figures/sub-01_rmse_timeseries.svg figures/sub-01_s0_brain.svg figures/sub-01_s0_histogram.svg figures/sub-01_t2star_brain.svg diff --git a/tedana/tests/test_component_selector.py b/tedana/tests/test_component_selector.py index a8ed26231..f536339e1 100644 --- a/tedana/tests/test_component_selector.py +++ b/tedana/tests/test_component_selector.py @@ -2,6 +2,7 @@ import glob import json +import logging import os import os.path as op @@ -12,6 +13,8 @@ from tedana.utils import get_resource_path THIS_DIR = os.path.dirname(os.path.abspath(__file__)) +LGR = logging.getLogger("GENERAL") + # ---------------------------------------------------------------------- # Functions Used For Tests @@ -40,6 +43,8 @@ def dicts_to_test(treechoice): "missing_function": An undefined decision node function "missing_key": A dict missing one of the required keys (report) "null_value": A parameter in one node improperly has a null value + "external_missing_key": external_regressors_config missing a required key + "external_invalid_statistic": external_regressors_config statistic is not "F" Returns ------- @@ -56,6 +61,36 @@ def dicts_to_test(treechoice): "necessary_metrics": ["kappa", "rho"], "intermediate_classifications": ["random1"], "classification_tags": ["Random1"], + "external_regressor_config": [ + { + "regress_ID": "nuisance", + "info": ( + "Fits all external nuisance regressors to " + "a single model using an F statistic" + ), + "report": ( + "External nuisance regressors that fit to " + "components using a linear model were rejected." + ), + "detrend": True, + "statistic": "F", + "regressors": ["^(?!signal).*$"], + "partial_models": {"Motion": ["^mot_.*$"], "CSF": ["^csf.*$"]}, + }, + { + "regress_ID": "task", + "info": "Fits all task regressors to a single model using an F statistic", + "report": ( + "Task regressors that fit to components using a linear model and " + "have some T2* weighting were accepted even if they would have " + "been rejected base on other criteriea." + ), + "detrend": True, + "statistic": "F", + "regressors": ["^signal.*$"], + "extra field": 42, + }, + ], "nodes": [ { "functionname": "dec_left_op_right", @@ -134,6 +169,11 @@ def dicts_to_test(treechoice): tree.pop("report") elif treechoice == "null_value": tree["nodes"][0]["parameters"]["left"] = None + elif treechoice == "external_missing_key": + tree["external_regressor_config"][1].pop("statistic") + elif treechoice == "external_invalid_statistic": + # Will also trigger statistic isn't F and partial models exist + tree["external_regressor_config"][0]["statistic"] = "corr" else: raise Exception(f"{treechoice} is an invalid option for treechoice") @@ -204,11 +244,11 @@ def test_validate_tree_succeeds(): Tested on all default trees in ./tedana/resources/decision_trees Note: If there is a tree in the default trees directory that is being developed and not yet valid, it's file name should - include 'invalid' as a prefix. + begin with 'X'. """ default_tree_names = glob.glob( - os.path.join(THIS_DIR, "../resources/decision_trees/[!invalid]*.json") + os.path.join(f"{THIS_DIR}/../resources/decision_trees/", "[!X]*.json") ) for tree_name in default_tree_names: @@ -218,19 +258,46 @@ def test_validate_tree_succeeds(): # Test a few extra possabilities just using the minimal.json tree if "/minimal.json" in tree_name: - # Should remove/ignore the "reconstruct_from" key during validation - tree["reconstruct_from"] = "testinput" # Need to test handling of the tag_if_false kwarg somewhere tree["nodes"][1]["kwargs"]["tag_if_false"] = "testing tag" assert component_selector.validate_tree(tree) -def test_validate_tree_warnings(): +def test_validate_tree_warnings(caplog): """Test to make sure validate_tree triggers all warning conditions.""" # A tree that raises all possible warnings in the validator should still be valid assert component_selector.validate_tree(dicts_to_test("valid")) + assert ( + r"Decision tree includes fields that are not used or logged ['unused_key']" in caplog.text + ) + assert ( + r"['random1'] in node 0 of the decision tree includes " + "a classification tag that was not predefined" + ) in caplog.text + assert ( + r"['nochange', 'random2'] in node 1 of the decision tree includes a classification" + in caplog.text + ) + assert ( + r"['random2notpredefined'] in node 1 of the decision tree " + "includes a classification tag that was not predefined" + ) in caplog.text + assert ( + r"['random2notpredefined'] in node 2 of the decision tree includes " + "a classification label that was not predefined" + ) in caplog.text + assert ( + r"['Random2_NotPredefined'] in node 2 of the decision tree " + "includes a classification tag that was not predefined" + ) in caplog.text + assert (r"Node 3 includes the 'log_extra_report' parameter.") in caplog.text + assert ( + "External regressor dictionary 1 includes fields " + r"that are not used or logged ['extra field']" + ) in caplog.text + def test_validate_tree_fails(): """Test to make sure validate_tree fails for invalid trees. @@ -240,27 +307,57 @@ def test_validate_tree_fails(): """ # An empty dict should not be valid - with pytest.raises(component_selector.TreeError): + with pytest.raises( + component_selector.TreeError, match="Decision tree missing required fields" + ): component_selector.validate_tree({}) # A tree that is missing a required key should not be valid - with pytest.raises(component_selector.TreeError): + with pytest.raises( + component_selector.TreeError, match=r"Decision tree missing required fields: {'report'}" + ): component_selector.validate_tree(dicts_to_test("missing_key")) # Calling a selection node function that does not exist should not be valid - with pytest.raises(component_selector.TreeError): + with pytest.raises( + component_selector.TreeError, + match=r"Node 0 has invalid functionname parameter: not_a_function", + ): component_selector.validate_tree(dicts_to_test("missing_function")) # Calling a function with an non-existent required parameter should not be valid - with pytest.raises(component_selector.TreeError): + with pytest.raises( + component_selector.TreeError, + match=r"Node 0 has additional, undefined required parameters: {'nonexistent_req_param'}", + ): component_selector.validate_tree(dicts_to_test("extra_req_param")) # Calling a function with an non-existent optional parameter should not be valid - with pytest.raises(component_selector.TreeError): + with pytest.raises( + component_selector.TreeError, match=r"Node 0 has additional, undefined optional parameters" + ): component_selector.validate_tree(dicts_to_test("extra_opt_param")) # Calling a function missing a required parameter should not be valid - with pytest.raises(component_selector.TreeError): + with pytest.raises( + component_selector.TreeError, match=r"Node 0 is missing required parameter" + ): component_selector.validate_tree(dicts_to_test("missing_req_param")) + with pytest.raises( + component_selector.TreeError, + match=r"External regressor dictionary 1 is missing required fields: {'statistic'}", + ): + component_selector.validate_tree(dicts_to_test("external_missing_key")) + + with pytest.raises( + component_selector.TreeError, + match=( + "statistic in external_regressor_config 1 is corr. It must be one of the following: " + "{'f'}\nExternal regressor dictionary cannot include partial_models " + "if statistic is not F" + ), + ): + component_selector.validate_tree(dicts_to_test("external_invalid_statistic")) + def test_check_null_fails(): """Tests to trigger check_null missing parameter error.""" diff --git a/tedana/tests/test_decay.py b/tedana/tests/test_decay.py index 9d0ac90fd..aa7eff191 100644 --- a/tedana/tests/test_decay.py +++ b/tedana/tests/test_decay.py @@ -15,8 +15,13 @@ def testdata1(): tes = np.array([14.5, 38.5, 62.5]) in_files = [op.join(get_test_data_path(), f"echo{i + 1}.nii.gz") for i in range(3)] + mask_file = op.join(get_test_data_path(), "mask.nii.gz") data, _ = io.load_data(in_files, n_echos=len(tes)) - mask, adaptive_mask = utils.make_adaptive_mask(data, methods=["dropout", "decay"]) + mask, adaptive_mask = utils.make_adaptive_mask( + data, + mask=mask_file, + methods=["dropout", "decay"], + ) fittype = "loglin" data_dict = { "data": data, diff --git a/tedana/tests/test_external_metrics.py b/tedana/tests/test_external_metrics.py new file mode 100644 index 000000000..848b281ae --- /dev/null +++ b/tedana/tests/test_external_metrics.py @@ -0,0 +1,572 @@ +"""Tests for tedana.metrics.external.""" + +import logging +import os.path as op +import re + +import pandas as pd +import pytest + +from tedana import utils +from tedana.io import load_json +from tedana.metrics import external +from tedana.tests.utils import data_for_testing_info, download_test_data + +THIS_DIR = op.dirname(op.abspath(__file__)) +LGR = logging.getLogger("GENERAL") + +# ---------------------------------------------------------------------- +# Functions Used For Tests +# ---------------------------------------------------------------------- + + +def sample_external_regressors(regress_choice="valid"): + """ + Retrieves a sample external regressor dataframe. + + Parameters + ---------- + regress_choice : :obj:`str` How to keep or alter the external regressor data + Options are: + "valid": Column labels expected in demo_external_regressors_motion_task_models + The labels in the config file are lowercase and this file is capitalized, but it should + still be valid. + "no_mot_y_column": The column labeled "Mot_Pitch" is removed. + + Returns + ------- + external_regressors : :obj:`pandas.DataFrame` External regressor table + n_vols : :obj:`int` Number of time points (rows) in external_regressors + + Notes + ----- + The loaded external regressors are in ./tests/data/external_regress_Ftest_3echo.tsv + These are based on tedana being run with default parameters on the 3 echo data using + the mixing matrix downloaded with the three echo data + .testing_data_cache/three-echo/TED.three-echo/desc_ICA_mixing_static.tsv + For the external regressors: + Column 0 (Mot_X) is the time series for ICA component 8 + Gaussian noise + Column 1 (Mot_Y) is 0.6 * comp 18 + 0.4 * comp 29 + Gaussian Noise + Column 2 (Mot_Z) is 0.8 * comp 18 + 1.2 * Gaussian Noise + Column 3 (Mot_Pitch) is 0.9 * comp 30 + 0.1 * comp 61 + Gaussian Noise + Columns 4-5 are Gaussian noise + Columns 6-11 are the first derivatives of columns 0-5 + Column 12 (CSF) is comp 11 + Gaussian Noise + Column 13 (Signal) is comp 30 + Gaussian Noise + + The base gaussian noise is mean=0, stdev=1. + The scaling weights for components and noise are set so that, with an R^2>0.5 threshold: + ICA Comp 8 rejected solely based on the fit to Mot_X + ICA Comp 31 looks strongly inversely correlated to Comp 8 and is also rejected + ICA Comp 18 rejected based on the combined fit to Mot_X and Mot_Y (signif Motion partial model) + ICA Comp 29 NOT rejected based only on the fit to Mot_Y + ICA Comp 11 rejected based on a fit to CSF (signif CSF partial model) + ICA Comp 30 accepted based on fit to task model even through also fits to Mot_Pitch + """ + sample_fname = op.join(THIS_DIR, "data", "external_regress_Ftest_3echo.tsv") + + external_regressors = pd.read_csv(sample_fname, delimiter="\t") + + if regress_choice == "no_mot_y_column": + external_regressors = external_regressors.drop(columns="Mot_Y") + elif regress_choice != "valid": + raise ValueError(f"regress_choice is {regress_choice}, which is not a listed option") + + n_vols = len(external_regressors) + + return external_regressors, n_vols + + +def sample_external_regressor_config(config_choice="valid"): + """ + Retrieves a sample external regressor configuration dictionary. + + Parameters + ---------- + config_choice : :obj:`str` How to keep or alter the config file + Options are: + "valid": Config dictionary stored in demo_external_regressors_motion_task_models + "no_task_partial": Removes "task_keep" and everything with partial F stats + "csf_in_mot": Adds "CSF" to the list of motion regressor partial models + "signal_in_mot": Adds "Signal" to the list of motion regressor partial models + + Returns + ------- + external_regressor_config : :obj:`dict` External Regressor Dictionary + """ + + sample_fname = op.join( + THIS_DIR, + "../resources/decision_trees", + "demo_external_regressors_motion_task_models.json", + ) + tree = load_json(sample_fname) + external_regressor_config = tree["external_regressor_config"] + + if config_choice == "no_task_partial": + external_regressor_config = [external_regressor_config[0]] + external_regressor_config[0].pop("partial_models") + elif config_choice == "csf_in_mot": + external_regressor_config[0]["partial_models"]["Motion"].append("^csf.*$") + elif config_choice == "signal_in_mot": + external_regressor_config[0]["partial_models"]["Motion"].append("Signal") + elif config_choice == "unmatched_regex": + external_regressor_config[0]["partial_models"]["Motion"] = ["^translation_.*$"] + elif config_choice != "valid": + raise ValueError(f"config_choice is {config_choice}, which is not a listed option") + + return external_regressor_config + + +def sample_mixing_matrix(): + """Load and return the three-echo mixing matrix.""" + + test_data_path, osf_id = data_for_testing_info("three-echo") + download_test_data(osf_id, test_data_path) + + return pd.read_csv( + op.join( + THIS_DIR, + "../../.testing_data_cache/three-echo/TED.three-echo/desc_ICA_mixing_static.tsv", + ), + delimiter="\t", + ).to_numpy() + + +def sample_comptable(n_components): + """Create an empty component table. + + Parameters + ---------- + n_components : :obj:`int` + The number of components (rows) in the compponent table DataFrame + + Returns + ------- + component_table : :obj:`pd.DataFrame` + A component table with a single "Component" column with + "ICA_" number for each row + """ + + row_vals = [] + for ridx in range(n_components): + row_vals.append(f"ICA_{str(ridx).zfill(2)}") + + return pd.DataFrame(data={"Component": row_vals}) + + +def sample_detrend_regressors(n_vols, dtrank=None): + """ + Creates Legendre polynomial detrending regressors. + + Parameters + ---------- + n_vols: :obj:`int` + The number of volumes or time points for the regressors + dtrank : :obj:`int` or None + The rank (number) of detrending regressors to create + Automatically calculate if None (default) + + Returns + ------- + detrend_regressors : :obj:`pd.DataFrame` The specified detrending regressors + """ + + legendre_arr = utils.create_legendre_polynomial_basis_set(n_vols, dtrank) + detrend_labels = [] + for label_idx in range(legendre_arr.shape[1]): + detrend_labels.append(f"baseline {label_idx}") + return pd.DataFrame(data=legendre_arr, columns=detrend_labels) + + +# validate_extern_regress +# ----------------------- +def test_validate_extern_regress_succeeds(caplog): + """Test validate_extern_regress works as expected.""" + + external_regressors, n_vols = sample_external_regressors() + external_regressor_config = sample_external_regressor_config() + external_regressor_config_expanded = external.validate_extern_regress( + external_regressors, external_regressor_config, n_vols + ) + + # The regex patterns should have been replaced with the full names of the regressors + assert set(external_regressor_config_expanded[0]["partial_models"]["Motion"]) == set( + [ + "Mot_X", + "Mot_d1_Yaw", + "Mot_d1_Y", + "Mot_d1_Pitch", + "Mot_Z", + "Mot_d1_Z", + "Mot_d1_Roll", + "Mot_d1_X", + "Mot_Pitch", + "Mot_Y", + "Mot_Yaw", + "Mot_Roll", + ] + ) + assert external_regressor_config_expanded[0]["partial_models"]["CSF"] == ["CSF"] + assert external_regressor_config_expanded[1]["regressors"] == ["Signal"] + assert "WARNING" not in caplog.text + + # Rerunning with explicit names for the above three categories instead of regex patterns + # Shouldn't change anything, but making sure it runs + caplog.clear() + external_regressor_config_expanded = external.validate_extern_regress( + external_regressors, external_regressor_config_expanded, n_vols + ) + assert "WARNING" not in caplog.text + + # Removing all partial model and task_keep stuff to confirm it still runs, but with a warning + caplog.clear() + external_regressor_config = sample_external_regressor_config("no_task_partial") + external.validate_extern_regress(external_regressors, external_regressor_config, n_vols) + assert ( + "User-provided external_regressors include columns not used " + "in any external regressor model: ['Signal']" + ) in caplog.text + + # Add "CSF" to "Motion" partial model (also in "CSF" partial model) to test if warning appears + caplog.clear() + external_regressor_config = sample_external_regressor_config("csf_in_mot") + external.validate_extern_regress(external_regressors, external_regressor_config, n_vols) + assert "['CSF'] used in more than one partial regressor model for nuisance" in caplog.text + + +def test_validate_extern_regress_fails(): + """Test validate_extern_regress fails when expected.""" + + external_regressors, n_vols = sample_external_regressors() + external_regressor_config = sample_external_regressor_config() + + # If there are a different number of time points in the fMRI data and external regressors + with pytest.raises( + external.RegressError, match=f"while fMRI data have {n_vols - 1} timepoints" + ): + external.validate_extern_regress( + external_regressors, external_regressor_config, n_vols - 1 + ) + + # If no external regressor labels match the regex label in config + external_regressor_config = sample_external_regressor_config("unmatched_regex") + with pytest.raises( + external.RegressError, + match=( + re.escape( + "No external regressor labels matching regular expression '^translation_.*$' found" + ) + ), + ): + external.validate_extern_regress(external_regressors, external_regressor_config, n_vols) + + # If Signal is in a partial model, but not "regressors" for the full model + external_regressor_config = sample_external_regressor_config("signal_in_mot") + with pytest.raises( + external.RegressError, + match=( + re.escape( + "Partial models in nuisance include regressors that " + "are excluded from its full model: ['Signal']" + ) + ), + ): + external.validate_extern_regress(external_regressors, external_regressor_config, n_vols) + + # If a regressor expected in the config is not in external_regressors + # Run successfully to expand Motion labels in config and then create error + # when "Mot_Y" is in the config, but removed from external_regressors + external_regressor_config = sample_external_regressor_config() + external_regressors, n_vols = sample_external_regressors() + external_regressor_config_expanded = external.validate_extern_regress( + external_regressors, external_regressor_config, n_vols + ) + external_regressors, n_vols = sample_external_regressors("no_mot_y_column") + # The same error message will appear twice. + # One for "regressor" and once for motion partial model + with pytest.raises( + external.RegressError, + match=re.escape( + "No external regressor matching 'Mot_Y' was found.\n" + "No external regressor matching 'Mot_Y' was found." + ), + ): + external.validate_extern_regress( + external_regressors, external_regressor_config_expanded, n_vols + ) + + +# load_validate_external_regressors +# --------------------------------- + + +def test_load_validate_external_regressors_fails(): + """Test load_validate_external_regressors fails when not given a tsv file.""" + + external_regressors = "NotATSVFile.tsv" + external_regressor_config = sample_external_regressor_config("valid") + with pytest.raises( + ValueError, match=f"Cannot load tsv file with external regressors: {external_regressors}" + ): + external.load_validate_external_regressors( + external_regressors, external_regressor_config, 200 + ) + + +def test_load_validate_external_regressors_smoke(): + """Test load_validate_external_regressors succeeds.""" + + external_regressors = op.join(THIS_DIR, "data", "external_regress_Ftest_3echo.tsv") + n_vols = 75 + external_regressor_config = sample_external_regressor_config() + + # Not testing outputs because this is just calling validate_extern_regress and + # outputs are checked in those tests + external.load_validate_external_regressors( + external_regressors, external_regressor_config, n_vols + ) + + +# fit_regressors +# -------------- + + +def test_fit_regressors(caplog): + """Test conditions fit_regressors succeeds and fails.""" + + caplog.set_level(logging.INFO) + external_regressors, n_vols = sample_external_regressors() + external_regressor_config = sample_external_regressor_config() + external_regressor_config_expanded = external.validate_extern_regress( + external_regressors, external_regressor_config, n_vols + ) + mixing = sample_mixing_matrix() + + # Running with external_regressor_config["detrend"] is True, + # which results in 1 detrending regressor + comptable = sample_comptable(mixing.shape[1]) + comptable = external.fit_regressors( + comptable, external_regressors, external_regressor_config_expanded, mixing + ) + + # Contents will be valided in fit_mixing_to_regressors so just checking column labels here + assert set(comptable.keys()) == { + "Component", + "Fstat nuisance model", + "Fstat task model", + "Fstat nuisance Motion partial model", + "Fstat nuisance CSF partial model", + "pval nuisance model", + "pval task model", + "pval nuisance Motion partial model", + "pval nuisance CSF partial model", + "R2stat nuisance model", + "R2stat task model", + "R2stat nuisance Motion partial model", + "R2stat nuisance CSF partial model", + } + + assert ( + "External regressors fit for nuisance includes detrending " + "with 1 Legendre Polynomial regressors" in caplog.text + ) + + caplog.clear() + # Running with external_regressor_config["detrend"]=3, which results in 3 detrending regressors + external_regressor_config[1]["detrend"] = 3 + comptable = sample_comptable(mixing.shape[1]) + comptable = external.fit_regressors( + comptable, external_regressors, external_regressor_config_expanded, mixing + ) + assert ( + "External regressors fit for task includes detrending " + "with 3 Legendre Polynomial regressors" in caplog.text + ) + + caplog.clear() + # Running with external_regressor_config["detrend"]=0, + # which results in 1 detrend regressors (demeaning) + external_regressor_config[0]["detrend"] = 0 + comptable = sample_comptable(mixing.shape[1]) + comptable = external.fit_regressors( + comptable, external_regressors, external_regressor_config_expanded, mixing + ) + assert ( + "External regressor for nuisance fitted without detrending fMRI time series. " + "Only removing mean" in caplog.text + ) + + caplog.clear() + external_regressor_config[1]["statistic"] = "Corr" + comptable = sample_comptable(mixing.shape[1]) + with pytest.raises( + ValueError, + match=( + "statistic for task external regressors in decision tree is corr, " + "which is not valid." + ), + ): + comptable = external.fit_regressors( + comptable, external_regressors, external_regressor_config_expanded, mixing + ) + + +# fit_mixing_to_regressors +# -------------- + + +def test_fit_mixing_to_regressors(caplog): + """Test conditions fit_mixing_to_regressors succeeds and fails.""" + + # Note: Outputs from fit_model_with_stats are also tested within this function + + caplog.set_level(logging.INFO) + external_regressors, n_vols = sample_external_regressors() + external_regressor_config = sample_external_regressor_config() + external_regressor_config_expanded = external.validate_extern_regress( + external_regressors, external_regressor_config, n_vols + ) + mixing = sample_mixing_matrix() + + detrend_regressors = sample_detrend_regressors(n_vols, dtrank=None) + + # Running with external_regressor_config["detrend"] is True, + # which results in 1 detrending regressor + comptable = sample_comptable(mixing.shape[1]) + + for config_idx in range(2): + comptable = external.fit_mixing_to_regressors( + comptable, + external_regressors, + external_regressor_config_expanded[config_idx], + mixing, + detrend_regressors, + ) + + # Since a fixed mixing matrix is used, the values should always be consistent + # Comparing just 3 rows and rounding to 6 decimal places to avoid testing failures + # due to differences in floating point precision between systems + output_rows_to_validate = comptable.iloc[[0, 11, 30]].round(decimals=6) + expected_results = pd.DataFrame( + columns=[ + "Component", + "Fstat nuisance model", + "Fstat task model", + "Fstat nuisance Motion partial model", + "Fstat nuisance CSF partial model", + "pval nuisance model", + "pval task model", + "pval nuisance Motion partial model", + "pval nuisance CSF partial model", + "R2stat nuisance model", + "R2stat task model", + "R2stat nuisance Motion partial model", + "R2stat nuisance CSF partial model", + ] + ) + expected_results.loc[0] = [ + "ICA_00", + 0.5898043794795538, + 0.040242260292383224, + 0.6359651437299336, + 0.5882298391006501, + 0.8529159565446598, + 0.8415655022508225, + 0.8033066601119929, + 0.4460627598486151, + 0.1116607090996441, + 0.0005509601152330346, + 0.11119635498661495, + 0.009551010649882286, + ] + expected_results.loc[11] = [ + "ICA_11", + 5.050391950932562, + 0.3101483992796387, + 0.6191428219572478, + 37.021610927761515, + 5.897391126885587e-06, + 0.5792925973677331, + 0.8177727274388485, + 8.422777264538439e-08, + 0.518377055217612, + 0.004230633903377634, + 0.10857438156630017, + 0.377688252390028, + ] + expected_results.loc[30] = [ + "ICA_30", + 5.869398664558788, + 109.32951177196031, + 6.215675922255525, + 1.524970189426933, + 7.193855290354989e-07, + 3.3306690738754696e-16, + 5.303071232143353e-07, + 0.22160450819684074, + 0.5557244697248107, + 0.5996259777665551, + 0.5501080476751579, + 0.024389778752502478, + ] + + assert ( + (output_rows_to_validate.sort_index(axis=1)) + .compare(expected_results.sort_index(axis=1).round(decimals=6)) + .empty + ) + + +# build_fstat_regressor_models +# -------------- + + +def test_build_fstat_regressor_models(caplog): + """Test conditions build_fstat_regressor_models succeeds and fails.""" + + caplog.set_level(logging.INFO) + external_regressors, n_vols = sample_external_regressors() + external_regressor_config = sample_external_regressor_config() + external_regressor_config_expanded = external.validate_extern_regress( + external_regressors, external_regressor_config, n_vols + ) + + detrend_regressors = sample_detrend_regressors(n_vols, dtrank=3) + + # Running nuisance with partial_models + regressor_models = external.build_fstat_regressor_models( + external_regressors, external_regressor_config_expanded[0], detrend_regressors + ) + + assert regressor_models["full"].shape == (n_vols, 16) + assert ( + "Regressors in full model for nuisance: " + "['CSF', 'Mot_Pitch', 'Mot_Roll', 'Mot_X', 'Mot_Y', 'Mot_Yaw', " + "'Mot_Z', 'Mot_d1_Pitch', 'Mot_d1_Roll', 'Mot_d1_X', 'Mot_d1_Y', 'Mot_d1_Yaw', " + "'Mot_d1_Z', 'baseline 0', 'baseline 1', 'baseline 2']" + ) in caplog.text + assert regressor_models["no CSF"].shape == (n_vols, 15) + assert ( + "Regressors in partial model (everything but regressors of interest) 'no CSF': " + "['Mot_Pitch', 'Mot_Roll', 'Mot_X', 'Mot_Y', 'Mot_Yaw', 'Mot_Z', 'Mot_d1_Pitch', " + "'Mot_d1_Roll', 'Mot_d1_X', 'Mot_d1_Y', 'Mot_d1_Yaw', 'Mot_d1_Z', " + "'baseline 0', 'baseline 1', 'baseline 2']" + ) in caplog.text + assert regressor_models["no Motion"].shape == (n_vols, 4) + assert ( + "Regressors in partial model (everything but regressors of interest) 'no Motion': " + "['CSF', 'baseline 0', 'baseline 1', 'baseline 2']" in caplog.text + ) + + # Running task regressor + caplog.clear() + regressor_models = external.build_fstat_regressor_models( + external_regressors, external_regressor_config_expanded[1], detrend_regressors + ) + + assert regressor_models["full"].shape == (n_vols, 4) + assert ( + "Regressors in full model for task: ['Signal', 'baseline 0', 'baseline 1', 'baseline 2']" + in caplog.text + ) diff --git a/tedana/tests/test_integration.py b/tedana/tests/test_integration.py index 3d9d31d8b..eb00e80d8 100644 --- a/tedana/tests/test_integration.py +++ b/tedana/tests/test_integration.py @@ -1,24 +1,19 @@ """Integration tests for "real" data.""" import glob -import json import logging import os import os.path as op import re import shutil import subprocess -import tarfile -from datetime import datetime -from gzip import GzipFile -from io import BytesIO import pandas as pd import pytest -import requests from pkg_resources import resource_filename from tedana.io import InputHarvester +from tedana.tests.utils import data_for_testing_info, download_test_data from tedana.workflows import t2smap as t2smap_cli from tedana.workflows import tedana as tedana_cli from tedana.workflows.ica_reclassify import ica_reclassify_workflow @@ -76,129 +71,6 @@ def check_integration_outputs(fname, outpath, n_logs=1): raise ValueError(msg) -def data_for_testing_info(test_dataset=str): - """ - Get the path and download link for each dataset used for testing. - - Also creates the base directories into which the data and output - directories are written - - Parameters - ---------- - test_dataset : str - References one of the datasets to download. It can be: - three-echo - three-echo-reclassify - four-echo - five-echo - - Returns - ------- - test_data_path : str - The path to the local directory where the data will be downloaded - osf_id : str - The ID for the OSF file. - Data download link would be https://osf.io/osf_id/download - Metadata download link would be https://osf.io/osf_id/metadata/?format=datacite-json - """ - - tedana_path = os.path.dirname(tedana_cli.__file__) - base_data_path = os.path.abspath(os.path.join(tedana_path, "../../.testing_data_cache")) - os.makedirs(base_data_path, exist_ok=True) - os.makedirs(os.path.join(base_data_path, "outputs"), exist_ok=True) - if test_dataset == "three-echo": - test_data_path = os.path.join(base_data_path, "three-echo/TED.three-echo") - osf_id = "rqhfc" - os.makedirs(os.path.join(base_data_path, "three-echo"), exist_ok=True) - os.makedirs(os.path.join(base_data_path, "outputs/three-echo"), exist_ok=True) - elif test_dataset == "three-echo-reclassify": - test_data_path = os.path.join(base_data_path, "reclassify") - osf_id = "f6g45" - os.makedirs(os.path.join(base_data_path, "outputs/reclassify"), exist_ok=True) - elif test_dataset == "four-echo": - test_data_path = os.path.join(base_data_path, "four-echo/TED.four-echo") - osf_id = "gnj73" - os.makedirs(os.path.join(base_data_path, "four-echo"), exist_ok=True) - os.makedirs(os.path.join(base_data_path, "outputs/four-echo"), exist_ok=True) - elif test_dataset == "five-echo": - test_data_path = os.path.join(base_data_path, "five-echo/TED.five-echo") - osf_id = "9c42e" - os.makedirs(os.path.join(base_data_path, "five-echo"), exist_ok=True) - os.makedirs(os.path.join(base_data_path, "outputs/five-echo"), exist_ok=True) - else: - raise ValueError(f"{test_dataset} is not a valid dataset string for data_for_testing_info") - - return test_data_path, osf_id - - -def download_test_data(osf_id, test_data_path): - """If current data is not already available, downloads tar.gz data. - - Data are stored at `https://osf.io/osf_id/download`. - It unpacks into `out_path`. - - Parameters - ---------- - osf_id : str - The ID for the OSF file. - out_path : str - Path to directory where OSF data should be extracted - """ - - try: - datainfo = requests.get(f"https://osf.io/{osf_id}/metadata/?format=datacite-json") - except Exception: - if len(os.listdir(test_data_path)) == 0: - raise ConnectionError( - f"Cannot access https://osf.io/{osf_id} and testing data " "are not yet downloaded" - ) - else: - TestLGR.warning( - f"Cannot access https://osf.io/{osf_id}. " - f"Using local copy of testing data in {test_data_path} " - "but cannot validate that local copy is up-to-date" - ) - return - datainfo.raise_for_status() - metadata = json.loads(datainfo.content) - # 'dates' is a list with all udpates to the file, the last item in the list - # is the most recent and the 'date' field in the list is the date of the last - # update. - osf_filedate = metadata["dates"][-1]["date"] - - # File the file with the most recent date for comparision with - # the lsst updated date for the osf file - if os.path.exists(test_data_path): - filelist = glob.glob(f"{test_data_path}/*") - most_recent_file = max(filelist, key=os.path.getctime) - if os.path.exists(most_recent_file): - local_filedate = os.path.getmtime(most_recent_file) - local_filedate_str = str(datetime.fromtimestamp(local_filedate).date()) - local_data_exists = True - else: - local_data_exists = False - else: - local_data_exists = False - if local_data_exists: - if local_filedate_str == osf_filedate: - TestLGR.info( - f"Downloaded and up-to-date data already in {test_data_path}. Not redownloading" - ) - return - else: - TestLGR.info( - f"Downloaded data in {test_data_path} was last modified on " - f"{local_filedate_str}. Data on https://osf.io/{osf_id} " - f" was last updated on {osf_filedate}. Deleting and redownloading" - ) - shutil.rmtree(test_data_path) - req = requests.get(f"https://osf.io/{osf_id}/download") - req.raise_for_status() - t = tarfile.open(fileobj=GzipFile(fileobj=BytesIO(req.content))) - os.makedirs(test_data_path, exist_ok=True) - t.extractall(test_data_path) - - def reclassify_raw() -> str: test_data_path, _ = data_for_testing_info("three-echo-reclassify") return os.path.join(test_data_path, "TED.three-echo") @@ -258,7 +130,7 @@ def test_integration_five_echo(skip_integration): data=datalist, tes=echo_times, ica_method="robustica", - n_robust_runs=6, + n_robust_runs=4, out_dir=out_dir, tedpca=0.95, fittype="curvefit", @@ -349,14 +221,14 @@ def test_integration_three_echo(skip_integration): tedana_cli.tedana_workflow( data=f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", tes=[14.5, 38.5, 62.5], - ica_method="robustica", - n_robust_runs=5, out_dir=out_dir, low_mem=True, tedpca="aic", ) # Test re-running, but use the CLI + # TODO Move this to a separate integration test, use the fixed desc_ICA_mixing_static.tsv that + # is distributed with the testing data, and test specific outputs for consistent values args = [ "-d", f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", @@ -366,10 +238,6 @@ def test_integration_three_echo(skip_integration): "62.5", "--out-dir", out_dir_manual, - "--ica_method", - "robustica", - "--n_robust_runs", - "5", "--debug", "--verbose", "-f", @@ -383,6 +251,90 @@ def test_integration_three_echo(skip_integration): check_integration_outputs(fn, out_dir) +def test_integration_three_echo_external_regressors_single_model(skip_integration): + """Integration test of tedana workflow with extern regress and F stat.""" + + if skip_integration: + pytest.skip("Skipping three-echo with external regressors integration test") + + test_data_path, osf_id = data_for_testing_info("three-echo") + out_dir = os.path.abspath( + os.path.join(test_data_path, "../../outputs/three-echo-externalreg-Ftest") + ) + + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + # download data and run the test + # external_regress_Ftest_3echo.tsv has 13 rows. Based on a local run on the 3 echo data: + # Col 1 (trans_x_correlation) is the TS for ICA comp 59 + similar stdev Gaussian Noise + # Col 2 (trans_y_correlation) is 0.4*comp29+0.5+comp20+Gaussian Noise + # Col 3 (trans_z_correlation) is comp20+Gaussian Noise + # Col 4-6 are Gaussian noise representing pitch/roll/yaw + # Col 7-12 are the first derivative of col 1-6 + # With the currently set up decision tree, + # Component 59 should be rejected because it is correlated to trans_x and, + # comp 20 should be rejected because of a signif fit to a combination of trans_y and trans_z. + # Component 29 is not rejected because the fit does not cross a r>0.8 threshold + # Note that the above is in comparision to the minimal decision tree + # but the integration test for 3 echoes uses the kundu tree + download_test_data(osf_id, test_data_path) + tree_name = "resources/decision_trees/demo_external_regressors_single_model.json" + tedana_cli.tedana_workflow( + data=f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", + tes=[14.5, 38.5, 62.5], + out_dir=out_dir, + tree=resource_filename("tedana", tree_name), + external_regressors=resource_filename( + "tedana", "tests/data/external_regress_Ftest_3echo.tsv" + ), + low_mem=True, + tedpca="aic", + ) + + # compare the generated output files + fn = resource_filename("tedana", "tests/data/cornell_three_echo_outputs.txt") + check_integration_outputs(fn, out_dir) + + +def test_integration_three_echo_external_regressors_motion_task_models(skip_integration): + """Integration test of tedana workflow with extern regress and F stat.""" + + if skip_integration: + pytest.skip("Skipping three-echo with external regressors integration test") + + test_data_path, osf_id = data_for_testing_info("three-echo") + out_dir = os.path.abspath( + os.path.join(test_data_path, "../../outputs/three-echo-externalreg-Ftest-multimodels") + ) + + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + # download data and run the test + # external_regress_Ftest_3echo.tsv has 12 columns for motion, 1 for CSF, and 1 for task signal + # The regressor values and expected fits with the data are detailed in: + # tests.test_external_metrics.sample_external_regressors + download_test_data(osf_id, test_data_path) + tree_name = "resources/decision_trees/demo_external_regressors_motion_task_models.json" + tedana_cli.tedana_workflow( + data=f"{test_data_path}/three_echo_Cornell_zcat.nii.gz", + tes=[14.5, 38.5, 62.5], + out_dir=out_dir, + tree=resource_filename("tedana", tree_name), + external_regressors=resource_filename( + "tedana", "tests/data/external_regress_Ftest_3echo.tsv" + ), + mixm=f"{test_data_path}/desc_ICA_mixing_static.tsv", + low_mem=True, + tedpca="aic", + ) + + # compare the generated output files + fn = resource_filename("tedana", "tests/data/cornell_three_echo_preset_mixing_outputs.txt") + check_integration_outputs(fn, out_dir) + + def test_integration_reclassify_insufficient_args(skip_integration): if skip_integration: pytest.skip("Skipping reclassify insufficient args") diff --git a/tedana/tests/test_metrics.py b/tedana/tests/test_metrics.py index 7dfd0c10f..476b32882 100644 --- a/tedana/tests/test_metrics.py +++ b/tedana/tests/test_metrics.py @@ -7,7 +7,11 @@ import pytest from tedana import io, utils -from tedana.metrics import collect, dependence +from tedana.metrics import collect, dependence, external +from tedana.tests.test_external_metrics import ( + sample_external_regressor_config, + sample_external_regressors, +) from tedana.tests.utils import get_test_data_path @@ -16,18 +20,28 @@ def testdata1(): """Data used for tests of the metrics module.""" tes = np.array([14.5, 38.5, 62.5]) in_files = [op.join(get_test_data_path(), f"echo{i + 1}.nii.gz") for i in range(3)] + mask_file = op.join(get_test_data_path(), "mask.nii.gz") data_cat, ref_img = io.load_data(in_files, n_echos=len(tes)) - _, adaptive_mask = utils.make_adaptive_mask(data_cat, methods=["dropout", "decay"]) + _, adaptive_mask = utils.make_adaptive_mask( + data_cat, + mask=mask_file, + methods=["dropout", "decay"], + ) data_optcom = np.mean(data_cat, axis=1) - mixing = np.random.random((data_optcom.shape[1], 50)) + mixing = np.random.random((data_optcom.shape[1], 3)) io_generator = io.OutputGenerator(ref_img) + + # includes adaptive_mask_cut and mixing_cut which are used for ValueError tests + # for when dimensions do not align data_dict = { "data_cat": data_cat, "tes": tes, "data_optcom": data_optcom, "adaptive_mask": adaptive_mask, + "adaptive_mask_cut": np.delete(adaptive_mask, (0), axis=0), "generator": io_generator, "mixing": mixing, + "mixing_cut": np.delete(mixing, (0), axis=0), } return data_dict @@ -47,25 +61,115 @@ def test_smoke_generate_metrics(testdata1): "normalized variance explained", "d_table_score", ] - comptable = collect.generate_metrics( - testdata1["data_cat"], - testdata1["data_optcom"], - testdata1["mixing"], - testdata1["adaptive_mask"], - testdata1["tes"], - testdata1["generator"], - "ICA", + + external_regressors, _ = sample_external_regressors() + # these data have 50 volumes so cut external_regressors to 50 vols for these tests + # This is just testing execution. Accuracy of values for external regressors are + # tested in test_external_metrics + n_vols = 5 + external_regressors = external_regressors.drop(labels=range(5, 75), axis=0) + + external_regressor_config = sample_external_regressor_config() + external_regressor_config_expanded = external.validate_extern_regress( + external_regressors, external_regressor_config, n_vols + ) + + comptable, _ = collect.generate_metrics( + data_cat=testdata1["data_cat"], + data_optcom=testdata1["data_optcom"], + mixing=testdata1["mixing"], + adaptive_mask=testdata1["adaptive_mask"], + tes=testdata1["tes"], + io_generator=testdata1["generator"], + label="ICA", + external_regressors=external_regressors, + external_regressor_config=external_regressor_config_expanded, metrics=metrics, ) assert isinstance(comptable, pd.DataFrame) +def test_generate_metrics_fails(testdata1): + """Testing error conditions for tedana.metrics.collect.generate_metrics.""" + + metrics = [ + "kappa", + "rho", + ] + + # missing external regressors + external_regress = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]}) + with pytest.raises( + ValueError, + match=( + "If external_regressors is defined, then " + "external_regressor_config also needs to be defined." + ), + ): + comptable, _ = collect.generate_metrics( + data_cat=testdata1["data_cat"], + data_optcom=testdata1["data_optcom"], + mixing=testdata1["mixing"], + adaptive_mask=testdata1["adaptive_mask"], + tes=testdata1["tes"], + io_generator=testdata1["generator"], + label="ICA", + external_regressors=external_regress, + metrics=metrics, + ) + + with pytest.raises( + ValueError, + match=(r"First dimensions \(number of samples\) of data_cat"), + ): + comptable, _ = collect.generate_metrics( + data_cat=testdata1["data_cat"], + data_optcom=testdata1["data_optcom"], + mixing=testdata1["mixing"], + adaptive_mask=testdata1["adaptive_mask_cut"], + tes=testdata1["tes"], + io_generator=testdata1["generator"], + label="ICA", + metrics=metrics, + ) + + with pytest.raises( + ValueError, + match=("does not match number of echoes provided"), + ): + comptable, _ = collect.generate_metrics( + data_cat=testdata1["data_cat"], + data_optcom=testdata1["data_optcom"], + mixing=testdata1["mixing"], + adaptive_mask=testdata1["adaptive_mask"], + tes=testdata1["tes"][0:2], + io_generator=testdata1["generator"], + label="ICA", + metrics=metrics, + ) + + with pytest.raises( + ValueError, + match=("Number of volumes in data_cat"), + ): + comptable, _ = collect.generate_metrics( + data_cat=testdata1["data_cat"], + data_optcom=testdata1["data_optcom"], + mixing=testdata1["mixing_cut"], + adaptive_mask=testdata1["adaptive_mask"], + tes=testdata1["tes"], + io_generator=testdata1["generator"], + label="ICA", + metrics=metrics, + ) + + def test_smoke_calculate_weights(): """Smoke test for tedana.metrics.dependence.calculate_weights.""" n_voxels, n_volumes, n_components = 1000, 100, 50 data_optcom = np.random.random((n_voxels, n_volumes)) mixing = np.random.random((n_volumes, n_components)) - weights = dependence.calculate_weights(data_optcom, mixing) + weights = dependence.calculate_weights(data_optcom=data_optcom, mixing=mixing) assert weights.shape == (n_voxels, n_components) @@ -74,7 +178,7 @@ def test_smoke_calculate_betas(): n_voxels, n_volumes, n_components = 1000, 100, 50 data_optcom = np.random.random((n_voxels, n_volumes)) mixing = np.random.random((n_volumes, n_components)) - betas = dependence.calculate_betas(data_optcom, mixing) + betas = dependence.calculate_betas(data=data_optcom, mixing=mixing) assert betas.shape == (n_voxels, n_components) @@ -83,7 +187,7 @@ def test_smoke_calculate_psc(): n_voxels, n_volumes, n_components = 1000, 100, 50 data_optcom = np.random.random((n_voxels, n_volumes)) optcom_betas = np.random.random((n_voxels, n_components)) - psc = dependence.calculate_psc(data_optcom, optcom_betas) + psc = dependence.calculate_psc(data_optcom=data_optcom, optcom_betas=optcom_betas) assert psc.shape == (n_voxels, n_components) @@ -91,7 +195,7 @@ def test_smoke_calculate_z_maps(): """Smoke test for tedana.metrics.dependence.calculate_z_maps.""" n_voxels, n_components = 1000, 50 weights = np.random.random((n_voxels, n_components)) - z_maps = dependence.calculate_z_maps(weights, z_max=4) + z_maps = dependence.calculate_z_maps(weights=weights, z_max=4) assert z_maps.shape == (n_voxels, n_components) @@ -104,7 +208,12 @@ def test_smoke_calculate_f_maps(): adaptive_mask = np.random.randint(1, n_echos + 1, size=n_voxels) tes = np.array([15, 25, 35, 45, 55]) f_t2_maps, f_s0_maps, _, _ = dependence.calculate_f_maps( - data_cat, z_maps, mixing, adaptive_mask, tes, f_max=500 + data_cat=data_cat, + z_maps=z_maps, + mixing=mixing, + adaptive_mask=adaptive_mask, + tes=tes, + f_max=500, ) assert f_t2_maps.shape == f_s0_maps.shape == (n_voxels, n_components) @@ -113,7 +222,7 @@ def test_smoke_calculate_varex(): """Smoke test for tedana.metrics.dependence.calculate_varex.""" n_voxels, n_components = 1000, 50 optcom_betas = np.random.random((n_voxels, n_components)) - varex = dependence.calculate_varex(optcom_betas) + varex = dependence.calculate_varex(optcom_betas=optcom_betas) assert varex.shape == (n_components,) @@ -121,7 +230,7 @@ def test_smoke_calculate_varex_norm(): """Smoke test for tedana.metrics.dependence.calculate_varex_norm.""" n_voxels, n_components = 1000, 50 weights = np.random.random((n_voxels, n_components)) - varex_norm = dependence.calculate_varex_norm(weights) + varex_norm = dependence.calculate_varex_norm(weights=weights) assert varex_norm.shape == (n_components,) @@ -130,9 +239,17 @@ def test_smoke_compute_dice(): n_voxels, n_components = 1000, 50 clmaps1 = np.random.randint(0, 2, size=(n_voxels, n_components)) clmaps2 = np.random.randint(0, 2, size=(n_voxels, n_components)) - dice = dependence.compute_dice(clmaps1, clmaps2, axis=0) + dice = dependence.compute_dice( + clmaps1=clmaps1, + clmaps2=clmaps2, + axis=0, + ) assert dice.shape == (n_components,) - dice = dependence.compute_dice(clmaps1, clmaps2, axis=1) + dice = dependence.compute_dice( + clmaps1=clmaps1, + clmaps2=clmaps2, + axis=1, + ) assert dice.shape == (n_voxels,) @@ -145,7 +262,12 @@ def test_smoke_compute_signal_minus_noise_z(): ( signal_minus_noise_z, signal_minus_noise_p, - ) = dependence.compute_signal_minus_noise_z(z_maps, z_clmaps, f_t2_maps, z_thresh=1.95) + ) = dependence.compute_signal_minus_noise_z( + z_maps=z_maps, + z_clmaps=z_clmaps, + f_t2_maps=f_t2_maps, + z_thresh=1.95, + ) assert signal_minus_noise_z.shape == signal_minus_noise_p.shape == (n_components,) @@ -158,7 +280,12 @@ def test_smoke_compute_signal_minus_noise_t(): ( signal_minus_noise_t, signal_minus_noise_p, - ) = dependence.compute_signal_minus_noise_t(z_maps, z_clmaps, f_t2_maps, z_thresh=1.95) + ) = dependence.compute_signal_minus_noise_t( + z_maps=z_maps, + z_clmaps=z_clmaps, + f_t2_maps=f_t2_maps, + z_thresh=1.95, + ) assert signal_minus_noise_t.shape == signal_minus_noise_p.shape == (n_components,) @@ -166,7 +293,7 @@ def test_smoke_compute_countsignal(): """Smoke test for tedana.metrics.dependence.compute_countsignal.""" n_voxels, n_components = 1000, 50 stat_cl_maps = np.random.randint(0, 2, size=(n_voxels, n_components)) - countsignal = dependence.compute_countsignal(stat_cl_maps) + countsignal = dependence.compute_countsignal(stat_cl_maps=stat_cl_maps) assert countsignal.shape == (n_components,) @@ -175,7 +302,11 @@ def test_smoke_compute_countnoise(): n_voxels, n_components = 1000, 50 stat_maps = np.random.normal(size=(n_voxels, n_components)) stat_cl_maps = np.random.randint(0, 2, size=(n_voxels, n_components)) - countnoise = dependence.compute_countnoise(stat_maps, stat_cl_maps, stat_thresh=1.95) + countnoise = dependence.compute_countnoise( + stat_maps=stat_maps, + stat_cl_maps=stat_cl_maps, + stat_thresh=1.95, + ) assert countnoise.shape == (n_components,) @@ -186,9 +317,13 @@ def test_smoke_generate_decision_table_score(): dice_ft2 = np.random.random(n_components) signal_minus_noise_t = np.random.normal(size=n_components) countnoise = np.random.randint(0, n_voxels, size=n_components) - countsigft2 = np.random.randint(0, n_voxels, size=n_components) + countsig_ft2 = np.random.randint(0, n_voxels, size=n_components) decision_table_score = dependence.generate_decision_table_score( - kappa, dice_ft2, signal_minus_noise_t, countnoise, countsigft2 + kappa=kappa, + dice_ft2=dice_ft2, + signal_minus_noise_t=signal_minus_noise_t, + countnoise=countnoise, + countsig_ft2=countsig_ft2, ) assert decision_table_score.shape == (n_components,) @@ -199,5 +334,9 @@ def test_smoke_calculate_dependence_metrics(): f_t2_maps = np.random.random((n_voxels, n_components)) f_s0_maps = np.random.random((n_voxels, n_components)) z_maps = np.random.random((n_voxels, n_components)) - kappas, rhos = dependence.calculate_dependence_metrics(f_t2_maps, f_s0_maps, z_maps) + kappas, rhos = dependence.calculate_dependence_metrics( + f_t2_maps=f_t2_maps, + f_s0_maps=f_s0_maps, + z_maps=z_maps, + ) assert kappas.shape == rhos.shape == (n_components,) diff --git a/tedana/tests/test_selection_utils.py b/tedana/tests/test_selection_utils.py index 465fb8bcc..73ae84c30 100644 --- a/tedana/tests/test_selection_utils.py +++ b/tedana/tests/test_selection_utils.py @@ -241,8 +241,12 @@ def test_confirm_metrics_exist_succeeds(): # Testing for metrics that exist with 1 or 2 necessary metrics in a set # Returns True if an undefined metric exists so using "assert not" + # Testing if it can find a single metric assert not selection_utils.confirm_metrics_exist(component_table, {"kappa"}) + # Testing if it can find multiple metrics assert not selection_utils.confirm_metrics_exist(component_table, {"kappa", "rho"}) + # Testing if it can find metrics that use regular expressions + assert not selection_utils.confirm_metrics_exist(component_table, {"kappa", "^count.*$"}) def test_confirm_metrics_exist_fails(): @@ -251,13 +255,22 @@ def test_confirm_metrics_exist_fails(): component_table = sample_component_table(options="comptable") # Should fail with and error would have default or pre-defined file name - with pytest.raises(ValueError): + with pytest.raises(ValueError, match="Necessary metrics for unknown function"): selection_utils.confirm_metrics_exist(component_table, {"kappa", "quack"}) - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=r"MISSING METRICS: \['quack'\]"): + selection_utils.confirm_metrics_exist( + component_table, + necessary_metrics={"kappa", "quack"}, + function_name="dec_left_op_right", + ) + with pytest.raises(ValueError, match="Necessary metrics for farm"): selection_utils.confirm_metrics_exist( component_table, {"kappa", "mooo"}, function_name="farm" ) + with pytest.raises(ValueError, match=r"MISSING METRICS: \['\^mount.\*\$'\]."): + selection_utils.confirm_metrics_exist(component_table, {"kappa", "^mount.*$"}) + def test_log_decision_tree_step_smoke(): """A smoke test for log_decision_tree_step.""" diff --git a/tedana/tests/test_stats.py b/tedana/tests/test_stats.py index 9d64f67ac..5214daa72 100644 --- a/tedana/tests/test_stats.py +++ b/tedana/tests/test_stats.py @@ -4,8 +4,9 @@ import numpy as np import pytest +from numpy.matlib import repmat -from tedana.stats import computefeats2, get_coeffs, getfbounds +from tedana.stats import computefeats2, fit_model, get_coeffs, getfbounds def test_break_computefeats2(): @@ -140,3 +141,32 @@ def test_smoke_getfbounds(): assert f05 is not None assert f025 is not None assert f01 is not None + + +def test_fit_model(): + """Tests for fit_model.""" + + # set up data where y = weights*x + residuals + r = 15 # number of regressors + t = 300 # number of time points + c = 50 # number of components + rng = np.random.default_rng(42) # using a fixed seed + x = rng.random(size=(t, r)) + weights = rng.random(size=(r, c)) + # Making the residuals sufficiently small for the fit to be precise to 4 decimals + residuals = rng.random(size=(t, c)) / 1000000 + y = np.empty((t, c)) + for cidx in range(c): + y[:, cidx] = (x * repmat(weights[:, cidx], t, 1)).sum(axis=1) + y = y + residuals + + # Fitting model and confirming outputs are the correct shape + # and beta fits match inputted weights to four decimal places + betas, sse, df = fit_model(x, y) + assert df == (t - r) + assert sse.shape == (c,) + assert (np.round(betas, decimals=4) == np.round(weights, decimals=4)).all() + + # Outputting the residual and checking it matches the inputted residual + fit_residuals = fit_model(x, y, output_residual=True) + assert (np.round(fit_residuals, decimals=4) == np.round(residuals, decimals=4)).all() diff --git a/tedana/tests/test_utils.py b/tedana/tests/test_utils.py index 1109edfa7..20c3edd5e 100644 --- a/tedana/tests/test_utils.py +++ b/tedana/tests/test_utils.py @@ -77,69 +77,135 @@ def test_reshape_niimg(): def test_make_adaptive_mask(): """Test tedana.utils.make_adaptive_mask with different methods.""" # load data make masks + mask_file = pjoin(datadir, "mask.nii.gz") data = io.load_data(fnames, n_echos=len(tes))[0] + # Add in simulated values + base_val = np.mean(data[:, 0, :]) # mean value of first echo + idx = 5457 + # Three good echoes (3) + data[idx, :, :] = np.array([base_val, base_val - 1, base_val - 2])[:, None] + # Dropout: good bad good (3) + # Decay: good good bad (2) + data[idx + 1, :, :] = np.array([base_val, 1, base_val])[:, None] + # Dropout: good bad bad (1) + # Decay: good good bad (2) + data[idx + 2, :, :] = np.array([base_val, 1, 1])[:, None] + # Dropout: good good good (3) + # Decay: good bad bad (1) + data[idx + 3, :, :] = np.array([base_val, base_val, base_val])[:, None] + # Dropout: bad bad bad (0) + # Decay: good good good (3) + data[idx + 4, :, :] = np.array([1, 0.9, 0.8])[:, None] + # Base: good good bad (2) + # Dropout: bad bad bad (0) + # Decay: good good good (3) + data[idx + 5, :, :] = np.array([1, 0.9, -1])[:, None] + # Just dropout method - mask, masksum = utils.make_adaptive_mask(data, threshold=1, methods=["dropout"]) + mask, adaptive_mask = utils.make_adaptive_mask( + data, + mask=mask_file, + threshold=1, + methods=["dropout"], + ) - assert mask.shape == masksum.shape == (64350,) - assert np.allclose(mask, (masksum >= 1).astype(bool)) - assert mask.sum() == 50786 - vals, counts = np.unique(masksum, return_counts=True) + assert mask.shape == adaptive_mask.shape == (64350,) + assert np.allclose(mask, (adaptive_mask >= 1).astype(bool)) + assert adaptive_mask[idx] == 3 + assert adaptive_mask[idx + 1] == 3 + assert adaptive_mask[idx + 2] == 1 + assert adaptive_mask[idx + 3] == 3 + assert adaptive_mask[idx + 4] == 0 + assert adaptive_mask[idx + 5] == 0 + assert mask.sum() == 49374 + vals, counts = np.unique(adaptive_mask, return_counts=True) assert np.allclose(vals, np.array([0, 1, 2, 3])) - assert np.allclose(counts, np.array([13564, 3977, 5060, 41749])) + assert np.allclose(counts, np.array([14976, 1817, 4427, 43130])) # Just decay method - mask, masksum = utils.make_adaptive_mask(data, threshold=1, methods=["decay"]) + mask, adaptive_mask = utils.make_adaptive_mask( + data, + mask=mask_file, + threshold=1, + methods=["decay"], + ) - assert mask.shape == masksum.shape == (64350,) - assert np.allclose(mask, (masksum >= 1).astype(bool)) - assert mask.sum() == 64350 # This method can't flag first echo as bad - vals, counts = np.unique(masksum, return_counts=True) - assert np.allclose(vals, np.array([1, 2, 3])) - assert np.allclose(counts, np.array([5666, 6552, 52132])) + assert mask.shape == adaptive_mask.shape == (64350,) + assert np.allclose(mask, (adaptive_mask >= 1).astype(bool)) + assert adaptive_mask[idx] == 3 + assert adaptive_mask[idx + 1] == 2 + assert adaptive_mask[idx + 2] == 2 + assert adaptive_mask[idx + 3] == 1 + assert adaptive_mask[idx + 4] == 3 + assert adaptive_mask[idx + 5] == 2 + assert mask.sum() == 60985 # This method can't flag first echo as bad + vals, counts = np.unique(adaptive_mask, return_counts=True) + assert np.allclose(vals, np.array([0, 1, 2, 3])) + assert np.allclose(counts, np.array([3365, 4366, 5973, 50646])) # Dropout and decay methods combined - mask, masksum = utils.make_adaptive_mask(data, threshold=1, methods=["dropout", "decay"]) + mask, adaptive_mask = utils.make_adaptive_mask( + data, + mask=mask_file, + threshold=1, + methods=["dropout", "decay"], + ) - assert mask.shape == masksum.shape == (64350,) - assert np.allclose(mask, (masksum >= 1).astype(bool)) - assert mask.sum() == 50786 - vals, counts = np.unique(masksum, return_counts=True) + assert mask.shape == adaptive_mask.shape == (64350,) + assert np.allclose(mask, (adaptive_mask >= 1).astype(bool)) + assert adaptive_mask[idx] == 3 + assert adaptive_mask[idx + 1] == 2 + assert adaptive_mask[idx + 2] == 1 + assert adaptive_mask[idx + 3] == 1 + assert adaptive_mask[idx + 4] == 0 + assert adaptive_mask[idx + 5] == 0 + assert mask.sum() == 49374 + vals, counts = np.unique(adaptive_mask, return_counts=True) assert np.allclose(vals, np.array([0, 1, 2, 3])) - assert np.allclose(counts, np.array([13564, 4959, 5349, 40478])) + assert np.allclose(counts, np.array([14976, 3111, 6248, 40015])) # Adding "none" should have no effect - mask, masksum = utils.make_adaptive_mask( - data, threshold=1, methods=["dropout", "decay", "none"] + mask, adaptive_mask = utils.make_adaptive_mask( + data, + mask=mask_file, + threshold=1, + methods=["dropout", "decay", "none"], ) - assert mask.shape == masksum.shape == (64350,) - assert np.allclose(mask, (masksum >= 1).astype(bool)) - assert mask.sum() == 50786 - vals, counts = np.unique(masksum, return_counts=True) + assert mask.shape == adaptive_mask.shape == (64350,) + assert np.allclose(mask, (adaptive_mask >= 1).astype(bool)) + assert adaptive_mask[idx] == 3 + assert adaptive_mask[idx + 1] == 2 + assert adaptive_mask[idx + 2] == 1 + assert adaptive_mask[idx + 3] == 1 + assert adaptive_mask[idx + 4] == 0 + assert adaptive_mask[idx + 5] == 0 + assert mask.sum() == 49374 + vals, counts = np.unique(adaptive_mask, return_counts=True) assert np.allclose(vals, np.array([0, 1, 2, 3])) - assert np.allclose(counts, np.array([13564, 4959, 5349, 40478])) + assert np.allclose(counts, np.array([14976, 3111, 6248, 40015])) # Just "none" - mask, masksum = utils.make_adaptive_mask(data, threshold=1, methods=["none"]) - - assert mask.shape == masksum.shape == (64350,) - assert np.allclose(mask, (masksum >= 1).astype(bool)) - assert mask.sum() == 64350 - vals, counts = np.unique(masksum, return_counts=True) - assert np.allclose(vals, np.array([3])) - assert np.allclose(counts, np.array([64350])) - - # test user-defined mask - # TODO: Add mask file with no bad voxels to test against - mask, masksum = utils.make_adaptive_mask( + mask, adaptive_mask = utils.make_adaptive_mask( data, - mask=pjoin(datadir, "mask.nii.gz"), - threshold=3, - methods=["dropout", "decay"], + mask=mask_file, + threshold=1, + methods=["none"], ) - assert np.allclose(mask, (masksum >= 3).astype(bool)) + + assert mask.shape == adaptive_mask.shape == (64350,) + assert np.allclose(mask, (adaptive_mask >= 1).astype(bool)) + assert adaptive_mask[idx] == 3 + assert adaptive_mask[idx + 1] == 3 + assert adaptive_mask[idx + 2] == 3 + assert adaptive_mask[idx + 3] == 3 + assert adaptive_mask[idx + 4] == 3 + assert adaptive_mask[idx + 5] == 2 + assert mask.sum() == 60985 + vals, counts = np.unique(adaptive_mask, return_counts=True) + assert np.allclose(vals, np.array([0, 1, 2, 3])) + assert np.allclose(counts, np.array([3365, 1412, 1195, 58378])) # SMOKE TESTS @@ -178,8 +244,6 @@ def test_smoke_make_adaptive_mask(): data = np.random.random((n_samples, n_echos, n_times)) mask = np.random.randint(2, size=n_samples) - assert utils.make_adaptive_mask(data, methods=["dropout", "decay"]) is not None - # functions with mask assert utils.make_adaptive_mask(data, mask=mask, methods=["dropout", "decay"]) is not None @@ -259,6 +323,34 @@ def test_smoke_threshold_map(): assert utils.threshold_map(img, min_cluster_size, sided="bi") is not None +def test_create_legendre_polynomial_basis_set(): + """Checking that accurate Legendre polynomials are created.""" + + n_vols = 100 + legendre_arr = utils.create_legendre_polynomial_basis_set(n_vols, dtrank=6) + + # Testing first 6 orders to 6 decimal accuracy using + # explicit equations rathern than scipy's lpmv + legendre_rounded = np.round(legendre_arr, decimals=6) + bounds = np.linspace(-1, 1, n_vols) + # order 0 (all 1's) + assert (legendre_arr[:, 0] == 1).sum() == n_vols + # order 1 (y=x) + assert np.abs((legendre_rounded[:, 1] - np.round(bounds, decimals=6))).sum() == 0 + # order 2 (y = 0.5*(3*x^2 - 1)) + tmp_o2 = 0.5 * (3 * bounds**2 - 1) + assert np.abs((legendre_rounded[:, 2] - np.round(tmp_o2, decimals=6))).sum() == 0 + # order 3 (y = 0.5*(5*x^3 - 3*x)) + tmp_o3 = 0.5 * (5 * bounds**3 - 3 * bounds) + assert np.abs((legendre_rounded[:, 3] - np.round(tmp_o3, decimals=6))).sum() == 0 + # order 4 (y = 0.125*(35*x^4 - 30*x^2 + 3)) + tmp_o4 = 0.125 * (35 * bounds**4 - 30 * bounds**2 + 3) + assert np.abs((legendre_rounded[:, 4] - np.round(tmp_o4, decimals=6))).sum() == 0 + # order 5 (y = 0.125*(63*x^5 - 70*x^3 + 15x)) + tmp_o5 = 0.125 * (63 * bounds**5 - 70 * bounds**3 + 15 * bounds) + assert np.abs((legendre_rounded[:, 5] - np.round(tmp_o5, decimals=6))).sum() == 0 + + def test_sec2millisec(): """Ensure that sec2millisec returns 1000x the input values.""" assert utils.sec2millisec(5) == 5000 diff --git a/tedana/tests/utils.py b/tedana/tests/utils.py index 5b4a473e3..6147165be 100644 --- a/tedana/tests/utils.py +++ b/tedana/tests/utils.py @@ -1,6 +1,22 @@ """Utility functions for testing tedana.""" -from os.path import abspath, dirname, join, sep +import glob +import json +import logging +import shutil +import tarfile +from datetime import datetime +from gzip import GzipFile +from io import BytesIO +from os import listdir, makedirs +from os.path import abspath, dirname, exists, getctime, getmtime, join, sep + +import requests + +from tedana.workflows import tedana as tedana_cli + +# Added a testing logger to output whether or not testing data were downlaoded +TestLGR = logging.getLogger("TESTING") def get_test_data_path(): @@ -12,3 +28,126 @@ def get_test_data_path(): Based on function by Yaroslav Halchenko used in Neurosynth Python package. """ return abspath(join(dirname(__file__), "data") + sep) + + +def data_for_testing_info(test_dataset=str): + """ + Get the path and download link for each dataset used for testing. + + Also creates the base directories into which the data and output + directories are written + + Parameters + ---------- + test_dataset : str + References one of the datasets to download. It can be: + three-echo + three-echo-reclassify + four-echo + five-echo + + Returns + ------- + test_data_path : str + The path to the local directory where the data will be downloaded + osf_id : str + The ID for the OSF file. + Data download link would be https://osf.io/osf_id/download + Metadata download link would be https://osf.io/osf_id/metadata/?format=datacite-json + """ + + tedana_path = dirname(tedana_cli.__file__) + base_data_path = abspath(join(tedana_path, "../../.testing_data_cache")) + makedirs(base_data_path, exist_ok=True) + makedirs(join(base_data_path, "outputs"), exist_ok=True) + if test_dataset == "three-echo": + test_data_path = join(base_data_path, "three-echo/TED.three-echo") + osf_id = "rqhfc" + makedirs(join(base_data_path, "three-echo"), exist_ok=True) + makedirs(join(base_data_path, "outputs/three-echo"), exist_ok=True) + elif test_dataset == "three-echo-reclassify": + test_data_path = join(base_data_path, "reclassify") + osf_id = "f6g45" + makedirs(join(base_data_path, "outputs/reclassify"), exist_ok=True) + elif test_dataset == "four-echo": + test_data_path = join(base_data_path, "four-echo/TED.four-echo") + osf_id = "gnj73" + makedirs(join(base_data_path, "four-echo"), exist_ok=True) + makedirs(join(base_data_path, "outputs/four-echo"), exist_ok=True) + elif test_dataset == "five-echo": + test_data_path = join(base_data_path, "five-echo/TED.five-echo") + osf_id = "9c42e" + makedirs(join(base_data_path, "five-echo"), exist_ok=True) + makedirs(join(base_data_path, "outputs/five-echo"), exist_ok=True) + else: + raise ValueError(f"{test_dataset} is not a valid dataset string for data_for_testing_info") + + return test_data_path, osf_id + + +def download_test_data(osf_id, test_data_path): + """If current data is not already available, downloads tar.gz data. + + Data are stored at `https://osf.io/osf_id/download`. + It unpacks into `out_path`. + + Parameters + ---------- + osf_id : str + The ID for the OSF file. + out_path : str + Path to directory where OSF data should be extracted + """ + + try: + datainfo = requests.get(f"https://osf.io/{osf_id}/metadata/?format=datacite-json") + except Exception: + if len(listdir(test_data_path)) == 0: + raise ConnectionError( + f"Cannot access https://osf.io/{osf_id} and testing data " "are not yet downloaded" + ) + else: + TestLGR.warning( + f"Cannot access https://osf.io/{osf_id}. " + f"Using local copy of testing data in {test_data_path} " + "but cannot validate that local copy is up-to-date" + ) + return + datainfo.raise_for_status() + metadata = json.loads(datainfo.content) + # 'dates' is a list with all udpates to the file, the last item in the list + # is the most recent and the 'date' field in the list is the date of the last + # update. + osf_filedate = metadata["dates"][-1]["date"] + + # File the file with the most recent date for comparision with + # the lsst updated date for the osf file + if exists(test_data_path): + filelist = glob.glob(f"{test_data_path}/*") + most_recent_file = max(filelist, key=getctime) + if exists(most_recent_file): + local_filedate = getmtime(most_recent_file) + local_filedate_str = str(datetime.fromtimestamp(local_filedate).date()) + local_data_exists = True + else: + local_data_exists = False + else: + local_data_exists = False + if local_data_exists: + if local_filedate_str == osf_filedate: + TestLGR.info( + f"Downloaded and up-to-date data already in {test_data_path}. Not redownloading" + ) + return + else: + TestLGR.info( + f"Downloaded data in {test_data_path} was last modified on " + f"{local_filedate_str}. Data on https://osf.io/{osf_id} " + f" was last updated on {osf_filedate}. Deleting and redownloading" + ) + shutil.rmtree(test_data_path) + req = requests.get(f"https://osf.io/{osf_id}/download") + req.raise_for_status() + t = tarfile.open(fileobj=GzipFile(fileobj=BytesIO(req.content))) + makedirs(test_data_path, exist_ok=True) + t.extractall(test_data_path) diff --git a/tedana/utils.py b/tedana/utils.py index 8f40bc172..9c2c0c67b 100644 --- a/tedana/utils.py +++ b/tedana/utils.py @@ -5,9 +5,11 @@ import platform import sys import warnings +from typing import Union import nibabel as nib import numpy as np +import numpy.typing as npt from bokeh import __version__ as bokeh_version from mapca import __version__ as mapca_version from matplotlib import __version__ as matplotlib_version @@ -18,6 +20,7 @@ from pandas import __version__ as pandas_version from scipy import __version__ as scipy_version from scipy import ndimage +from scipy.special import lpmv from sklearn import __version__ as sklearn_version from sklearn.utils import check_array from threadpoolctl import __version__ as threadpoolctl_version @@ -49,19 +52,21 @@ def reshape_niimg(data): return fdata -def make_adaptive_mask(data, mask=None, threshold=1, methods=["dropout"]): +def make_adaptive_mask(data, mask, threshold=1, methods=["dropout"]): """Make map of `data` specifying longest echo a voxel can be sampled with. Parameters ---------- data : (S x E x T) array_like Multi-echo data array, where `S` is samples, `E` is echos, and `T` is time. - mask : :obj:`str` or img_like, optional - Binary mask for voxels to consider in TE Dependent ANAlysis. Default is - to generate mask from data with good signal across echoes + mask : :obj:`str` or img_like + Binary mask for voxels to consider in TE Dependent ANAlysis. + This must be provided, as the mask is used to identify exemplar voxels. + Without a mask limiting the voxels to consider, + the adaptive mask will generally select voxels outside the brain as exemplars. threshold : :obj:`int`, optional - Minimum echo count to retain in the mask. Default is 1, which is - equivalent not thresholding. + Minimum echo count to retain in the mask. + Default is 1, which is equivalent to not thresholding. methods : :obj:`list`, optional List of methods to use for adaptive mask generation. Default is ["dropout"]. Valid methods are "decay", "dropout", and "none". @@ -81,6 +86,7 @@ def make_adaptive_mask(data, mask=None, threshold=1, methods=["dropout"]): Dropout Remove voxels with relatively low mean magnitudes from the mask. + This method uses distributions of values across the mask. Therefore, it is sensitive to the quality of the mask. A bad mask may result in a bad adaptive mask. @@ -100,9 +106,14 @@ def make_adaptive_mask(data, mask=None, threshold=1, methods=["dropout"]): - This is the threshold for "good" data. - The 1/3 value is arbitrary. - If there was more than one exemplar voxel, retain the the highest value for each echo. - d. For each voxel, count the number of echoes that have a mean value greater than the + d. For each voxel, identify the last echo with a mean value greater than the corresponding echo's threshold. + - Preceding echoes (including ones with mean values less than the threshold) + are considered "good" data. That means, if echoes 1-3 in a voxel are + [good, good, bad] the adaptive mask will assign a 2, and if they are + [good, bad, good], the adaptive mask will assign a 3. + Decay Determine the echo at which the signal stops decreasing for each voxel. @@ -121,6 +132,9 @@ def make_adaptive_mask(data, mask=None, threshold=1, methods=["dropout"]): f"An adaptive mask was then generated using the {'+'.join(methods)} method(s), " "in which each voxel's value reflects the number of echoes with 'good' data." ) + mask = reshape_niimg(mask).astype(bool) + data = data[mask, :, :] + if (methods is None) or (len(methods) == 1 and methods[0].lower() == "none"): LGR.warning( "No methods provided for adaptive mask generation. " @@ -139,9 +153,8 @@ def make_adaptive_mask(data, mask=None, threshold=1, methods=["dropout"]): n_samples, n_echos, _ = data.shape adaptive_masks = [] - # Generate a base adaptive mask that flags any NaNs or negative values - # TODO When masking is moved before dropout calc, change to "data <= 0" - bad_data_vals = np.isnan(data) + (data < 0) + # Generate a base adaptive mask that flags any NaN, zero, or negative values + bad_data_vals = np.isnan(data) + (data <= 0) good_vox_echoes = 1 - np.any(bad_data_vals, axis=-1).astype(int) base_adaptive_mask = np.zeros(n_samples, dtype=int) for echo_idx in range(n_echos): @@ -180,9 +193,19 @@ def make_adaptive_mask(data, mask=None, threshold=1, methods=["dropout"]): 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 - dropout_adaptive_mask = (np.abs(echo_means) > lthrs).sum(axis=-1) + LGR.info("Echo-wise intensity thresholds for adaptive mask: %s", lthrs) + + # Find the last good echo for each voxel + # Start with every voxel's value==0, increment up the echoes, and + # change to a new value every time a later good echo is found + dropout_adaptive_mask = np.zeros(n_samples, dtype=np.int16) + for echo_idx in range(n_echos): + dropout_adaptive_mask[(np.abs(echo_means[:, echo_idx]) > lthrs[echo_idx])] = ( + echo_idx + 1 + ) + + adaptive_masks.append(dropout_adaptive_mask) + adaptive_masks.append(dropout_adaptive_mask) if "decay" in methods: @@ -196,26 +219,21 @@ def make_adaptive_mask(data, mask=None, threshold=1, methods=["dropout"]): # Retain the most conservative of the selected adaptive mask estimates adaptive_mask = np.minimum.reduce(adaptive_masks) - if mask is None: - # make it a boolean mask to (where we have at least `threshold` echoes with good signal) - mask = (adaptive_mask >= threshold).astype(bool) + # TODO: Use visual report to make checking the reduced mask easier + if np.any(adaptive_mask < threshold): + n_bad_voxels = np.sum(adaptive_mask < threshold) + LGR.warning( + f"{n_bad_voxels} voxels in user-defined mask do not have good signal. " + "Removing voxels from mask." + ) adaptive_mask[adaptive_mask < threshold] = 0 - else: - # if the user has supplied a binary mask - mask = reshape_niimg(mask).astype(bool) - adaptive_mask = adaptive_mask * mask - # reduce mask based on adaptive_mask - # TODO: Use visual report to make checking the reduced mask easier - if np.any(adaptive_mask[mask] < threshold): - n_bad_voxels = np.sum(adaptive_mask[mask] < threshold) - LGR.warning( - f"{n_bad_voxels} voxels in user-defined mask do not have good " - "signal. Removing voxels from mask." - ) - adaptive_mask[adaptive_mask < threshold] = 0 - mask = adaptive_mask.astype(bool) - return mask, adaptive_mask + modified_mask = adaptive_mask.astype(bool) + + adaptive_mask = unmask(adaptive_mask, mask) + modified_mask = unmask(modified_mask, mask) + + return modified_mask, adaptive_mask def unmask(data, mask): @@ -440,6 +458,42 @@ def threshold_map(img, min_cluster_size, threshold=None, mask=None, binarize=Tru return clust_thresholded +def create_legendre_polynomial_basis_set( + n_vols: int, dtrank: Union[int, None] = None +) -> npt.NDArray: + """ + Create Legendre polynomial basis set for detrending time series. + + Parameters + ---------- + n_vols : :obj:`int` + The number of time points in the fMRI time series + dtrank : :obj:`int`, optional + Specifies degree of Legendre polynomial basis function for estimating + spatial global signal. Default: None + If None, then this is set to 1+floor(n_vols/150) + + Returns + ------- + legendre_arr : (T X R) :obj:`np.ndarray` + A time by rank matrix of the first dtrank order Legendre polynomials. + These include: + Order 0: y = 1 + Order 1: y = x + Order 2: y = 0.5*(3*x^2 - 1) + Order 3: y = 0.5*(5*x^3 - 3*x) + Order 4: y = 0.125*(35*x^4 - 30*x^2 + 3) + Order 5: y = 0.125*(63*x^5 - 70*x^3 + 15x) + """ + if dtrank is None: + dtrank = int(1 + np.floor(n_vols / 150)) + + bounds = np.linspace(-1, 1, n_vols) + legendre_arr = np.column_stack([lpmv(0, vv, bounds) for vv in range(dtrank)]) + + return legendre_arr + + def sec2millisec(arr): """ Convert seconds to milliseconds. diff --git a/tedana/workflows/t2smap.py b/tedana/workflows/t2smap.py index 1fb4b7bb8..785b7925b 100644 --- a/tedana/workflows/t2smap.py +++ b/tedana/workflows/t2smap.py @@ -7,6 +7,7 @@ import sys import numpy as np +from nilearn.masking import compute_epi_mask from scipy import stats from threadpoolctl import threadpool_limits @@ -284,11 +285,15 @@ def t2smap_workflow( config="auto", make_figures=False, ) - n_samp, n_echos, n_vols = catd.shape + n_echos = catd.shape[1] LGR.debug(f"Resulting data shape: {catd.shape}") if mask is None: - LGR.info("Computing adaptive mask") + LGR.info( + "Computing initial mask from the first echo using nilearn's compute_epi_mask function." + ) + first_echo_img = io.new_nii_like(io_generator.reference_img, catd[:, 0, :]) + mask = compute_epi_mask(first_echo_img) else: LGR.info("Using user-defined mask") mask, masksum = utils.make_adaptive_mask( @@ -299,14 +304,10 @@ def t2smap_workflow( ) LGR.info("Computing adaptive T2* map") - if fitmode == "all": - (t2s_limited, s0_limited, t2s_full, s0_full) = decay.fit_decay( - catd, tes, mask, masksum, fittype - ) - else: - (t2s_limited, s0_limited, t2s_full, s0_full) = decay.fit_decay_ts( - catd, tes, mask, masksum, fittype - ) + decay_function = decay.fit_decay if fitmode == "all" else decay.fit_decay_ts + (t2s_limited, s0_limited, t2s_full, s0_full) = decay_function( + catd, tes, mask, masksum, fittype + ) # set a hard cap for the T2* map/timeseries # anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile @@ -315,6 +316,18 @@ def t2smap_workflow( LGR.debug(f"Setting cap on T2* map at {cap_t2s_sec:.5f}s") t2s_full[t2s_full > cap_t2s * 10] = cap_t2s + LGR.info("Calculating model fit quality metrics") + rmse_map, rmse_df = decay.rmse_of_fit_decay_ts( + data=catd, + tes=tes, + adaptive_mask=masksum, + t2s=t2s_limited, + s0=s0_limited, + fitmode=fitmode, + ) + io_generator.save_file(rmse_map, "rmse img") + io_generator.save_file(rmse_df, "confounds tsv") + LGR.info("Computing optimal combination") # optimally combine data data_oc = combine.make_optcom(catd, tes, masksum, t2s=t2s_full, combmode=combmode) diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index 9b63983cd..8666b0988 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -185,15 +185,26 @@ def _get_parser(): ), default="tedana_orig", ) + optional.add_argument( + "--external", + dest="external_regressors", + type=lambda x: is_valid_file(parser, x), + help=( + "File containing external regressors to compare to ICA component be used in the " + "decision tree. For example, to identify components fit head motion time series." + "The file must be a TSV file with the same number of rows as the number of volumes in " + "the input data. Column labels and statistical tests are defined with external_labels." + ), + default=None, + ) optional.add_argument( "--ica_method", dest="ica_method", help=( - "The applied ICA method. fastica runs FastICA from " - "sklearn once with the seed value. " + "The applied ICA method. " + "fastica runs FastICA from sklearn once with the seed value. " "robustica will run FastICA n_robust_runs times and uses " - "clustering methods to overcome the randomness of the FastICA " - "algorithm. FastICA was the default in tedana version 23 and earlier. " + "clustering methods to overcome the randomness of the FastICA algorithm. " "robustica will be slower." ), choices=["robustica", "fastica"], @@ -217,6 +228,7 @@ def _get_parser(): optional.add_argument( "--n_robust_runs", dest="n_robust_runs", + metavar="[5-500]", type=check_n_robust_runs_value, help=( "The number of times robustica will run. " @@ -372,6 +384,7 @@ def tedana_workflow( fittype="loglin", combmode="t2s", tree="tedana_orig", + external_regressors=None, ica_method=DEFAULT_ICA_METHOD, n_robust_runs=DEFAULT_N_ROBUST_RUNS, tedpca="aic", @@ -435,18 +448,22 @@ def tedana_workflow( process in MEICA. A difference between that tree and the older MEICA was identified so the original meica tree is also included. meica will always accept the same or more components, but those accepted components are sometimes - high variance so the differences can be non-trivial. Minimal is intented + high variance so the differences can be non-trivial. Minimal is intended to be a simpler process, but it accepts and rejects some distinct components compared to the others. Testing to better understand the effects of the differences is ongoing. Default is 'tedana_orig'. + external_regressors : :obj:`str` or None, optional + File containing external regressors to be used in the decision tree. + The file must be a TSV file with the same number of rows as the number of volumes in + the input data. Each column in the file will be treated as a separate regressor. + Default is None. ica_method : {'robustica', 'fastica'}, optional The applied ICA method. fastica runs FastICA from sklearn once with the seed value. 'robustica' will run 'FastICA' n_robust_runs times and uses clustering methods to overcome the randomness of the FastICA algorithm. - FastICA was the default in tedana version 23 and earlier. robustica will be slower. - Default is 'robustica' + Default is 'fastica' n_robust_runs : :obj:`int`, optional The number of times robustica will run. This is only effective when 'ica_method' is set to 'robustica'. @@ -573,6 +590,19 @@ def tedana_workflow( LGR.info(f"Loading input data: {[f for f in data]}") catd, ref_img = io.load_data(data, n_echos=n_echos) + # Load external regressors if provided + # Decided to do the validation here so that, if there are issues, an error + # will be raised before PCA/ICA + if ( + "external_regressor_config" in set(selector.tree.keys()) + and selector.tree["external_regressor_config"] is not None + ): + external_regressors, selector.tree["external_regressor_config"] = ( + metrics.external.load_validate_external_regressors( + external_regressors, selector.tree["external_regressor_config"], catd.shape[2] + ) + ) + io_generator = io.OutputGenerator( ref_img, convention=convention, @@ -635,6 +665,7 @@ def tedana_workflow( # TODO: add affine check LGR.info("Using user-defined mask") RepLGR.info("A user-defined mask was applied to the data.") + mask = utils.reshape_niimg(mask).astype(int) elif t2smap and not mask: LGR.info("Using user-defined T2* map to generate mask") t2s_limited_sec = utils.reshape_niimg(t2smap) @@ -646,12 +677,13 @@ def tedana_workflow( t2s_limited_sec = utils.reshape_niimg(t2smap) t2s_limited = utils.sec2millisec(t2s_limited_sec) t2s_full = t2s_limited.copy() - mask = utils.reshape_niimg(mask) + mask = utils.reshape_niimg(mask).astype(int) mask[t2s_limited == 0] = 0 # reduce mask based on T2* map else: LGR.info("Computing EPI mask from first echo") first_echo_img = io.new_nii_like(io_generator.reference_img, catd[:, 0, :]) - mask = compute_epi_mask(first_echo_img) + mask = compute_epi_mask(first_echo_img).get_fdata() + mask = utils.reshape_niimg(mask).astype(int) RepLGR.info( "An initial mask was generated from the first echo using " "nilearn's compute_epi_mask function." @@ -698,6 +730,18 @@ def tedana_workflow( io_generator.save_file(utils.millisec2sec(t2s_limited), "limited t2star img") io_generator.save_file(s0_limited, "limited s0 img") + # Calculate RMSE if S0 and T2* are fit + rmse_map, rmse_df = decay.rmse_of_fit_decay_ts( + data=catd, + tes=tes, + adaptive_mask=masksum_denoise, + t2s=t2s_limited, + s0=s0_limited, + fitmode="all", + ) + io_generator.save_file(rmse_map, "rmse img") + io_generator.add_df_to_file(rmse_df, "confounds tsv") + # optimally combine data data_oc = combine.make_optcom(catd, tes, masksum_denoise, t2s=t2s_full, combmode=combmode) @@ -751,15 +795,17 @@ def tedana_workflow( extra_metrics = ["variance explained", "normalized variance explained", "kappa", "rho"] necessary_metrics = sorted(list(set(necessary_metrics + extra_metrics))) - comptable = metrics.collect.generate_metrics( - catd, - data_oc, - mmix, - masksum_clf, - tes, - io_generator, - "ICA", + comptable, _ = metrics.collect.generate_metrics( + data_cat=catd, + data_optcom=data_oc, + mixing=mmix, + adaptive_mask=masksum_clf, + tes=tes, + io_generator=io_generator, + label="ICA", metrics=necessary_metrics, + external_regressors=external_regressors, + external_regressor_config=selector.tree["external_regressor_config"], ) LGR.info("Selecting components from ICA results") selector = selection.automatic_selection( @@ -769,6 +815,14 @@ def tedana_workflow( n_vols=n_vols, ) n_likely_bold_comps = selector.n_likely_bold_comps_ + LGR.info("Selecting components from ICA results") + selector = selection.automatic_selection( + comptable, + selector, + n_echos=n_echos, + n_vols=n_vols, + ) + n_likely_bold_comps = selector.n_likely_bold_comps_ if (n_restarts < maxrestart) and (n_likely_bold_comps == 0): LGR.warning("No BOLD components found. Re-attempting ICA.") elif n_likely_bold_comps == 0: @@ -781,7 +835,12 @@ def tedana_workflow( if keep_restarting: io_generator.overwrite = True # Create a re-initialized selector object if rerunning + # Since external_regressor_config might have been expanded to remove + # regular expressions immediately after initialization, + # store and copy this key + tmp_external_regressor_config = selector.tree["external_regressor_config"] selector = ComponentSelector(tree) + selector.tree["external_regressor_config"] = tmp_external_regressor_config RepLGR.disabled = True # Disable the report to avoid duplicate text RepLGR.disabled = False # Re-enable the report after the while loop is escaped @@ -791,27 +850,26 @@ def tedana_workflow( mixing_file = io_generator.get_name("ICA mixing tsv") mmix = pd.read_table(mixing_file).values - selector = ComponentSelector(tree) + # selector = ComponentSelector(tree) necessary_metrics = selector.necessary_metrics # The figures require some metrics that might not be used by the decision tree. extra_metrics = ["variance explained", "normalized variance explained", "kappa", "rho"] necessary_metrics = sorted(list(set(necessary_metrics + extra_metrics))) - comptable = metrics.collect.generate_metrics( - catd, - data_oc, - mmix, - masksum_clf, - tes, - io_generator, - "ICA", + comptable, _ = metrics.collect.generate_metrics( + data_cat=catd, + data_optcom=data_oc, + mixing=mmix, + adaptive_mask=masksum_clf, + tes=tes, + io_generator=io_generator, + label="ICA", metrics=necessary_metrics, + external_regressors=external_regressors, + external_regressor_config=selector.tree["external_regressor_config"], ) selector = selection.automatic_selection( - comptable, - selector, - n_echos=n_echos, - n_vols=n_vols, + comptable, selector, n_echos=n_echos, n_vols=n_vols ) # TODO The ICA mixing matrix should be written out after it is created @@ -956,6 +1014,11 @@ def tedana_workflow( dn_ts, hikts, lowkts = io.denoise_ts(data_oc, mmix, mask_denoise, comptable) + reporting.static_figures.plot_adaptive_mask( + optcom=data_oc, + base_mask=mask, + io_generator=io_generator, + ) reporting.static_figures.carpet_plot( optcom_ts=data_oc, denoised_ts=dn_ts, @@ -974,6 +1037,11 @@ def tedana_workflow( png_cmap=png_cmap, ) reporting.static_figures.plot_t2star_and_s0(io_generator=io_generator, mask=mask_denoise) + if t2smap is None: + reporting.static_figures.plot_rmse( + io_generator=io_generator, + adaptive_mask=masksum_denoise, + ) LGR.info("Generating dynamic report") reporting.generate_report(io_generator) From dee68ec5a8d0fbeefbdd39b6029188f0af40f969 Mon Sep 17 00:00:00 2001 From: handwerkerd Date: Mon, 2 Sep 2024 21:15:34 -0400 Subject: [PATCH 21/34] align with main --- docs/approach.rst | 2 +- pyproject.toml | 6 +- tedana/gscontrol.py | 280 +++++++++++++-------- tedana/metrics/dependence.py | 10 +- tedana/reporting/static_figures.py | 33 +-- tedana/tests/data/reclassify_debug_out.txt | 3 +- tedana/tests/test_gscontrol.py | 33 ++- tedana/workflows/ica_reclassify.py | 9 +- tedana/workflows/tedana.py | 16 +- 9 files changed, 244 insertions(+), 148 deletions(-) diff --git a/docs/approach.rst b/docs/approach.rst index 03febb30e..68124f7a9 100644 --- a/docs/approach.rst +++ b/docs/approach.rst @@ -379,7 +379,7 @@ presented at MRITogether 2022 for a hands-on tutorial. Removal of spatially diffuse noise (optional) ********************************************* -:func:`tedana.gscontrol.gscontrol_raw`, :func:`tedana.gscontrol.gscontrol_mmix` +:func:`tedana.gscontrol.gscontrol_raw`, :func:`tedana.gscontrol.minimum_image_regression` Due to the constraints of ICA, TEDICA is able to identify and remove spatially localized noise components, but it cannot identify components that are spread diff --git a/pyproject.toml b/pyproject.toml index 75bca03cc..c653d4ea4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,18 +21,18 @@ classifiers = [ license = {file = "LICENSE"} requires-python = ">=3.8" dependencies = [ - "bokeh>=1.0.0,<=3.5.1", + "bokeh>=1.0.0,<=3.5.2", "mapca>=0.0.4,<=0.0.5", "matplotlib", "nibabel>=2.5.1,<=5.2.1", "nilearn>=0.10.3,<=0.10.4", - "numpy>=1.16,<=2.0.1", + "numpy>=1.16,<=2.1.0", "pandas>=2.0,<=2.2.2", "pybtex", "pybtex-apa-style", "robustica>=0.1.3", "scikit-learn>=0.21, <=1.5.1", - "scipy>=1.2.0, <=1.14.0", + "scipy>=1.2.0, <=1.14.1", "threadpoolctl", "tqdm", ] diff --git a/tedana/gscontrol.py b/tedana/gscontrol.py index a18e488c9..244d409cb 100644 --- a/tedana/gscontrol.py +++ b/tedana/gscontrol.py @@ -6,29 +6,36 @@ import pandas as pd from scipy import stats -from tedana import utils +from tedana import io, utils LGR = logging.getLogger("GENERAL") RepLGR = logging.getLogger("REPORT") -def gscontrol_raw(catd, optcom, n_echos, io_generator, dtrank=4): - """Remove global signal from individual echo ``catd`` and ``optcom`` time series. +def gscontrol_raw( + *, + data_cat: np.ndarray, + data_optcom: np.ndarray, + n_echos: int, + io_generator: io.OutputGenerator, + dtrank: int = 4, +): + """Remove global signal from individual echo ``data_cat`` and ``data_optcom`` time series. This function uses the spatial global signal estimation approach to to removal global signal out of individual echo time series datasets. The spatial global signal is estimated from the optimally combined data after - detrending with a Legendre polynomial basis of `order = 0` and - `degree = dtrank`. + detrending with a Legendre polynomial basis of ``order = 0`` and + ``degree = dtrank``. Parameters ---------- - catd : (S x E x T) array_like + data_cat : (S x E x T) array_like Input functional data - optcom : (S x T) array_like + data_optcom : (S x T) array_like Optimally combined functional data (i.e., the output of `make_optcom`) n_echos : :obj:`int` - Number of echos in data. Should be the same as `E` dimension of `catd` + Number of echos in data. Should be the same as ``E`` dimension of ``data_cat`` io_generator : :obj:`tedana.io.OutputGenerator` The output generator for this workflow dtrank : :obj:`int`, optional @@ -37,80 +44,112 @@ def gscontrol_raw(catd, optcom, n_echos, io_generator, dtrank=4): Returns ------- - dm_catd : (S x E x T) array_like - Input `catd` with global signal removed from time series - dm_optcom : (S x T) array_like - Input `optcom` with global signal removed from time series + data_cat_nogs : (S x E x T) array_like + Input ``data_cat`` with global signal removed from time series. + data_optcom_nogs : (S x T) array_like + Input ``data_optcom`` with global signal removed from time series. + + Notes + ----- + This function writes out several files: + + ======================================== ================================================= + IOGenerator Label Content + ======================================== ================================================= + "has gs combined img" Optimally combined data before global signal + removal + "gs img" Spatial global signal map + "confounds tsv" A "global_signal" column with the time course + of the global signal is added to this TSV. + "removed gs combined img" Optimally combined data after global signal + removal + ======================================== ================================================= """ LGR.info("Applying amplitude-based T1 equilibration correction") RepLGR.info( - "Global signal regression was applied to the multi-echo " - "and optimally combined datasets." + "Global signal regression was applied to the multi-echo and optimally combined datasets." ) - if catd.shape[0] != optcom.shape[0]: + if data_cat.shape[0] != data_optcom.shape[0]: raise ValueError( - f"First dimensions of catd ({catd.shape[0]}) and optcom ({optcom.shape[0]}) do not " - "match" + f"First dimensions of data_cat ({data_cat.shape[0]}) and " + f"data_optcom ({data_optcom.shape[0]}) do not match" ) - elif catd.shape[1] != n_echos: + elif data_cat.shape[1] != n_echos: raise ValueError( - f"Second dimension of catd ({catd.shape[1]}) does not match n_echos ({n_echos})" + f"Second dimension of data_cat ({data_cat.shape[1]}) " + f"does not match n_echos ({n_echos})" ) - elif catd.shape[2] != optcom.shape[1]: + elif data_cat.shape[2] != data_optcom.shape[1]: raise ValueError( - f"Third dimension of catd ({catd.shape[2]}) does not match second dimension of optcom " - f"({optcom.shape[1]})" + f"Third dimension of data_cat ({data_cat.shape[2]}) does not match second dimension " + f"of data_optcom ({data_optcom.shape[1]})" ) + io_generator.save_file(data_optcom, "has gs combined img") + # Legendre polynomial basis for denoising - legendre_arr = utils.create_legendre_polynomial_basis_set(optcom.shape[-1], dtrank=dtrank) + legendre_arr = utils.create_legendre_polynomial_basis_set(data_optcom.shape[-1], dtrank=dtrank) # compute mean, std, mask local to this function # inefficient, but makes this function a bit more modular - temporal_mean = optcom.mean(axis=-1) # temporal mean + temporal_mean = data_optcom.mean(axis=-1) # temporal mean temporal_mean_mask = temporal_mean != 0 + temporal_mean = temporal_mean[temporal_mean_mask][:, np.newaxis] - # find spatial global signal - dat = optcom[temporal_mean_mask] - temporal_mean[temporal_mean_mask][:, np.newaxis] - sol = np.linalg.lstsq(legendre_arr, dat.T, rcond=None)[0] # Legendre basis for detrending - detr = dat - np.dot(sol.T, legendre_arr.T)[0] - sphis = (detr).min(axis=1) - sphis -= sphis.mean() - io_generator.save_file(utils.unmask(sphis, temporal_mean_mask), "gs img") + # Mean-center optimally combined data over time + data_optcom_masked = data_optcom[temporal_mean_mask] - temporal_mean - # find time course ofc the spatial global signal - # make basis with the Legendre basis - glsig = np.linalg.lstsq(np.atleast_2d(sphis).T, dat, rcond=None)[0] - glsig = stats.zscore(glsig, axis=None) + # Detrend the data using the Legendre basis functions + betas = np.linalg.lstsq(legendre_arr, data_optcom_masked.T, rcond=None)[0] + optcom_detr = data_optcom_masked - np.dot(betas.T, legendre_arr.T)[0] - glsig_df = pd.DataFrame(data=glsig.T, columns=["global_signal"]) + # The spatial global signal is the minimum of the detrended data + gs_spatial = (optcom_detr).min(axis=1) + gs_spatial -= gs_spatial.mean() + io_generator.save_file(utils.unmask(gs_spatial, temporal_mean_mask), "gs img") + + # Find time course of the spatial global signal + # Make basis with the Legendre basis + gs_ts = np.linalg.lstsq(np.atleast_2d(gs_spatial).T, data_optcom_masked, rcond=None)[0] + gs_ts = stats.zscore(gs_ts, axis=None) + + glsig_df = pd.DataFrame(data=gs_ts.T, columns=["global_signal"]) io_generator.add_df_to_file(glsig_df, "confounds tsv") - glbase = np.hstack([legendre_arr, glsig.T]) - - # Project global signal out of optimally combined data - 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])) - + temporal_mean[temporal_mean_mask][:, np.newaxis] - ) + glbase = np.atleast_2d(np.hstack([gs_ts.T, legendre_arr])) - io_generator.save_file(optcom, "has gs combined img") - dm_optcom = utils.unmask(tsoc_nogs, temporal_mean_mask) - io_generator.save_file(dm_optcom, "removed gs combined img") + # Project global signal (but not Legendre bases) out of optimally combined data + betas = np.linalg.lstsq(glbase, data_optcom_masked.T, rcond=None)[0] + gs_fitted = np.dot(glbase[:, :1], betas[:1, :]).T + data_optcom_nogs = data_optcom_masked - gs_fitted + temporal_mean + data_optcom_nogs = utils.unmask(data_optcom_nogs, temporal_mean_mask) + io_generator.save_file(data_optcom_nogs, "removed gs combined img") - # Project glbase out of each echo - dm_catd = catd.copy() # don't overwrite catd + # Project global signal (but not Legendre bases) out of each echo + data_cat_nogs = data_cat.copy() # don't overwrite data_cat for echo in range(n_echos): - dat = dm_catd[:, echo, :][temporal_mean_mask] - 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, temporal_mean_mask) + data_echo_masked = data_cat_nogs[temporal_mean_mask, echo, :] + + # Mean center echo's data over time + echo_mean = data_echo_masked.mean(axis=-1, keepdims=True) + data_echo_masked -= echo_mean + + # Fit regression, then remove global signal + betas = np.linalg.lstsq(glbase, data_echo_masked.T, rcond=None)[0] + echo_nogs = data_echo_masked - np.dot(glbase[:, :1], betas[:1, :]).T + echo_mean + data_cat_nogs[:, echo, :] = utils.unmask(echo_nogs, temporal_mean_mask) - return dm_catd, dm_optcom + return data_cat_nogs, data_optcom_nogs -def minimum_image_regression(optcom_ts, mmix, mask, comptable, io_generator): +def minimum_image_regression( + *, + data_optcom: np.ndarray, + mixing: np.ndarray, + mask: np.ndarray, + comptable: pd.DataFrame, + classification_tags: list, + io_generator: io.OutputGenerator, +): """Perform minimum image regression (MIR) to remove T1-like effects from BOLD-like components. While this method has not yet been described in detail in any publications, @@ -118,45 +157,49 @@ def minimum_image_regression(optcom_ts, mmix, mask, comptable, io_generator): Parameters ---------- - optcom_ts : (S x T) array_like + data_optcom : (S x T) array_like Optimally combined time series data - mmix : (T x C) array_like - Mixing matrix for converting input data to component space, where `C` - is components and `T` is the same as in `optcom_ts` + mixing : (T x C) array_like + Mixing matrix for converting input data to component space, where ``C`` + is components and ``T`` is the same as in ``data_optcom`` mask : (S,) array_like Boolean mask array comptable : (C x X) :obj:`pandas.DataFrame` Component metric table. One row for each component, with a column for each metric. The index should be the component number. + classification_tags : :obj:`list` of :obj:`str` + List of classification tags used in the decision tree. + This is used to separate "accepted" and "ignored" components. io_generator : :obj:`tedana.io.OutputGenerator` The output generating object for this workflow Notes ----- - Minimum image regression operates by constructing a amplitude-normalized - form of the multi-echo high Kappa (MEHK) time series from BOLD-like ICA - components, and then taking voxel-wise minimum over time. - This "minimum map" serves as a voxel-wise estimate of the T1-like effect - in the time series. - From this minimum map, a T1-like global signal (i.e., a 1D time series) - is estimated. - The component time series in the mixing matrix are then corrected for the - T1-like effect by regressing out the global signal time series from each. - Finally, the multi-echo denoising (MEDN) and MEHK time series are - reconstructed from the corrected mixing matrix and are written out to new - files. + Minimum image regression operates by constructing a amplitude-normalized form of the multi-echo + high Kappa (MEHK) time series from BOLD-like ICA components, + and then taking the voxel-wise minimum over time. + This "minimum map" serves as a voxel-wise estimate of the T1-like effect in the time series. + From this minimum map, a T1-like global signal (i.e., a 1D time series) is estimated. + The component time series in the mixing matrix are then corrected for the T1-like effect by + regressing out the global signal time series from each. + Finally, the multi-echo denoised (MEDN) and MEHK time series are reconstructed from the + corrected mixing matrix and are written out to new files. This function writes out several files: - ====================== ================================================= - Filename Content - ====================== ================================================= - sphis_hik.nii T1-like effect - hik_ts_OC_MIR.nii T1-corrected BOLD (high-Kappa) time series - dn_ts_OC_MIR.nii Denoised version of T1-corrected time series - betas_hik_OC_MIR.nii T1 global signal-corrected components - meica_mix_MIR.1D T1 global signal-corrected mixing matrix - ====================== ================================================= + ======================================== ================================================= + IOGenerator Label Content + ======================================== ================================================= + "t1 like img" T1-like effect + "confounds tsv" A "mir_global_signal" column with the time course + of the T1-like effect is added to this TSV. + "mir denoised img" Denoised version of T1-corrected time series + "ICA MIR mixing tsv" T1 global signal-corrected mixing matrix + + if io_generator.verbose==True + "ICA accepted mir denoised img" T1-corrected BOLD (high-Kappa) time series + "ICA accepted mir component weights img" T1 global signal-corrected components + ======================================== ================================================= References ---------- @@ -164,57 +207,82 @@ def minimum_image_regression(optcom_ts, mmix, mask, comptable, io_generator): """ LGR.info("Performing minimum image regression to remove spatially-diffuse noise") RepLGR.info( - "Minimum image regression was then applied to the " - "data in order to remove spatially diffuse noise \\citep{kundu2013integrated}." + "Minimum image regression was then applied to the data in order to remove spatially " + "diffuse noise \\citep{kundu2013integrated}." ) all_comps = comptable.index.values + # Get accepted and ignored components + # Tedana has removed the "ignored" classification, + # so we must separate "accepted" components based on the classification tag(s). + ignore_tags = ["low variance", "accept borderline"] + if not any(tag in classification_tags for tag in ignore_tags): + LGR.warning( + "Decision tree does not contain classification tags indicating low variance " + f"components ({', '.join(ignore_tags)})." + ) + ign = np.array([], dtype=int) + else: + pattern = "|".join(ignore_tags) # Create a pattern that matches any of the ignore tags + + # Select rows where the 'classification_tags' column contains any of the ignore tags + ign = comptable[ + comptable.classification_tags.str.contains(pattern, na=False, regex=True) + ].index.values + acc = comptable[comptable.classification == "accepted"].index.values - ign = comptable[comptable.classification == "ignored"].index.values + # Ignored components are classified as "accepted", so we need to remove them from the list + acc = sorted(np.setdiff1d(acc, ign)) not_ign = sorted(np.setdiff1d(all_comps, ign)) - optcom_masked = optcom_ts[mask, :] - optcom_mean = optcom_masked.mean(axis=-1)[:, np.newaxis] - optcom_std = optcom_masked.std(axis=-1)[:, np.newaxis] + data_optcom_masked = data_optcom[mask, :] + optcom_mean = data_optcom_masked.mean(axis=-1)[:, np.newaxis] + optcom_std = data_optcom_masked.std(axis=-1)[:, np.newaxis] # Compute temporal regression - optcom_z = stats.zscore(optcom_masked, axis=-1) - comp_pes = np.linalg.lstsq(mmix, optcom_z.T, rcond=None)[0].T # component parameter estimates - resid = optcom_z - np.dot(comp_pes[:, not_ign], mmix[:, not_ign].T) + data_optcom_z = stats.zscore(data_optcom_masked, axis=-1) + # component parameter estimates + comp_pes = np.linalg.lstsq(mixing, data_optcom_z.T, rcond=None)[0].T + # Get residuals (ignored/low-variance components and unmodeled noise) + resid = data_optcom_z - np.dot(comp_pes[:, not_ign], mixing[:, not_ign].T) # Build time series of just BOLD-like components (i.e., MEHK) and save T1-like effect - mehk_ts = np.dot(comp_pes[:, acc], mmix[:, acc].T) + mehk_ts = np.dot(comp_pes[:, acc], mixing[:, acc].T) t1_map = mehk_ts.min(axis=-1) # map of T1-like effect t1_map -= t1_map.mean() io_generator.save_file(utils.unmask(t1_map, mask), "t1 like img") t1_map = t1_map[:, np.newaxis] # Find the global signal based on the T1-like effect - glob_sig = np.linalg.lstsq(t1_map, optcom_z, rcond=None)[0] + gs_ts = np.linalg.lstsq(t1_map, data_optcom_z, rcond=None)[0] + glsig_df = pd.DataFrame(data=gs_ts.T, columns=["mir_global_signal"]) + io_generator.add_df_to_file(glsig_df, "confounds tsv") # Remove T1-like global signal from MEHK time series mehk_no_t1_gs = mehk_ts - np.dot( - np.linalg.lstsq(glob_sig.T, mehk_ts.T, rcond=None)[0].T, glob_sig + np.linalg.lstsq(gs_ts.T, mehk_ts.T, rcond=None)[0].T, + gs_ts, ) - hik_ts = mehk_no_t1_gs * optcom_std # rescale - io_generator.save_file(utils.unmask(hik_ts, mask), "ICA accepted mir denoised img") # Make denoised version of T1-corrected time series medn_ts = optcom_mean + ((mehk_no_t1_gs + resid) * optcom_std) io_generator.save_file(utils.unmask(medn_ts, mask), "mir denoised img") # Orthogonalize mixing matrix w.r.t. T1-GS - mmix_no_t1_gs = mmix.T - np.dot(np.linalg.lstsq(glob_sig.T, mmix, rcond=None)[0].T, glob_sig) - mmix_no_t1_gs_z = stats.zscore(mmix_no_t1_gs, axis=-1) - mmix_no_t1_gs_z = np.vstack( - (np.atleast_2d(np.ones(max(glob_sig.shape))), glob_sig, mmix_no_t1_gs_z) - ) + mixing_not1gs = mixing.T - np.dot(np.linalg.lstsq(gs_ts.T, mixing, rcond=None)[0].T, gs_ts) + mixing_not1gs_z = stats.zscore(mixing_not1gs, axis=-1) + mixing_not1gs_z = np.vstack((np.atleast_2d(np.ones(max(gs_ts.shape))), gs_ts, mixing_not1gs_z)) # Write T1-corrected components and mixing matrix - comp_pes_norm = np.linalg.lstsq(mmix_no_t1_gs_z.T, optcom_z.T, rcond=None)[0].T - io_generator.save_file( - utils.unmask(comp_pes_norm[:, 2:], mask), - "ICA accepted mir component weights img", - ) - mixing_df = pd.DataFrame(data=mmix_no_t1_gs.T, columns=comptable["Component"].values) + mixing_df = pd.DataFrame(data=mixing_not1gs.T, columns=comptable["Component"].values) io_generator.save_file(mixing_df, "ICA MIR mixing tsv") + + if io_generator.verbose: + hik_ts = mehk_no_t1_gs * optcom_std # rescale + io_generator.save_file(utils.unmask(hik_ts, mask), "ICA accepted mir denoised img") + + comp_pes_norm = np.linalg.lstsq(mixing_not1gs_z.T, data_optcom_z.T, rcond=None)[0].T + io_generator.save_file( + utils.unmask(comp_pes_norm[:, 2:], mask), + "ICA accepted mir component weights img", + ) diff --git a/tedana/metrics/dependence.py b/tedana/metrics/dependence.py index 575af98b3..b7c6d09c1 100644 --- a/tedana/metrics/dependence.py +++ b/tedana/metrics/dependence.py @@ -60,20 +60,20 @@ def calculate_betas( betas : (M [x E] x C) array_like Unstandardized parameter estimates """ - if len(data.shape) == 2: + if data.ndim == 2: data_optcom = data assert data_optcom.shape[1] == mixing.shape[0] # mean-center optimally-combined data data_optcom_dm = data_optcom - data_optcom.mean(axis=-1, keepdims=True) - # betas are the result of a normal OLS fit of the mixing matrix - # against the mean-center data + # betas are from a normal OLS fit of the mixing matrix against the mean-centered data betas = get_coeffs(data_optcom_dm, mixing) - return betas + else: betas = np.zeros([data.shape[0], data.shape[1], mixing.shape[1]]) for n_echo in range(data.shape[1]): betas[:, n_echo, :] = get_coeffs(data[:, n_echo, :], mixing) - return betas + + return betas def calculate_psc( diff --git a/tedana/reporting/static_figures.py b/tedana/reporting/static_figures.py index 498f5814d..43b0323b7 100644 --- a/tedana/reporting/static_figures.py +++ b/tedana/reporting/static_figures.py @@ -164,23 +164,24 @@ def carpet_plot( ) ) - mir_denoised_img = io_generator.get_name("ICA accepted mir denoised img") - fig, ax = plt.subplots(figsize=(14, 7)) - plotting.plot_carpet( - mir_denoised_img, - mask_img, - figure=fig, - axes=ax, - title="High-Kappa Data (Post-MIR)", - ) - fig.tight_layout() - fig.savefig( - os.path.join( - io_generator.out_dir, - "figures", - f"{io_generator.prefix}carpet_accepted_mir.svg", + if io_generator.verbose: + mir_denoised_img = io_generator.get_name("ICA accepted mir denoised img") + fig, ax = plt.subplots(figsize=(14, 7)) + plotting.plot_carpet( + mir_denoised_img, + mask_img, + figure=fig, + axes=ax, + title="High-Kappa Data (Post-MIR)", + ) + fig.tight_layout() + fig.savefig( + os.path.join( + io_generator.out_dir, + "figures", + f"{io_generator.prefix}carpet_accepted_mir.svg", + ) ) - ) def plot_component( diff --git a/tedana/tests/data/reclassify_debug_out.txt b/tedana/tests/data/reclassify_debug_out.txt index 4076b88fa..2dce7ae17 100644 --- a/tedana/tests/data/reclassify_debug_out.txt +++ b/tedana/tests/data/reclassify_debug_out.txt @@ -4,12 +4,10 @@ sub-testymctestface_references.bib sub-testymctestface_report.txt sub-testymctestface_betas_OC.nii.gz sub-testymctestface_betas_hik_OC.nii.gz -sub-testymctestface_betas_hik_OC_MIR.nii.gz sub-testymctestface_dataset_description.json sub-testymctestface_dn_ts_OC.nii.gz sub-testymctestface_dn_ts_OC_MIR.nii.gz sub-testymctestface_feats_OC2.nii.gz -sub-testymctestface_hik_ts_OC_MIR.nii.gz sub-testymctestface_ica_components.nii.gz sub-testymctestface_ica_cross_component_metrics.json sub-testymctestface_ica_decision_tree.json @@ -22,3 +20,4 @@ sub-testymctestface_ica_orth_mixing.tsv sub-testymctestface_ica_status_table.tsv sub-testymctestface_registry.json sub-testymctestface_sphis_hik.nii.gz +sub-testymctestface_confounds.tsv diff --git a/tedana/tests/test_gscontrol.py b/tedana/tests/test_gscontrol.py index f05f82236..9ff5490ce 100644 --- a/tedana/tests/test_gscontrol.py +++ b/tedana/tests/test_gscontrol.py @@ -14,11 +14,7 @@ def test_break_gscontrol_raw(): - """ - Ensure that gscontrol_raw fails when input data do not have the right. - - shapes. - """ + """Ensure that gscontrol_raw fails when input data do not have the right shapes.""" n_samples, n_echos, n_vols = 10000, 4, 100 catd = np.empty((n_samples, n_echos, n_vols)) optcom = np.empty((n_samples, n_vols)) @@ -27,28 +23,41 @@ def test_break_gscontrol_raw(): catd = np.empty((n_samples + 1, n_echos, n_vols)) with pytest.raises(ValueError) as e_info: gsc.gscontrol_raw( - catd=catd, optcom=optcom, n_echos=n_echos, io_generator=io_generator, dtrank=4 + data_cat=catd, + data_optcom=optcom, + n_echos=n_echos, + io_generator=io_generator, + dtrank=4, ) assert str(e_info.value) == ( - f"First dimensions of catd ({catd.shape[0]}) and optcom ({optcom.shape[0]}) do not match" + f"First dimensions of data_cat ({catd.shape[0]}) and data_optcom ({optcom.shape[0]}) " + "do not match" ) catd = np.empty((n_samples, n_echos + 1, n_vols)) with pytest.raises(ValueError) as e_info: gsc.gscontrol_raw( - catd=catd, optcom=optcom, n_echos=n_echos, io_generator=io_generator, dtrank=4 + data_cat=catd, + data_optcom=optcom, + n_echos=n_echos, + io_generator=io_generator, + dtrank=4, ) assert str(e_info.value) == ( - f"Second dimension of catd ({catd.shape[1]}) does not match n_echos ({n_echos})" + f"Second dimension of data_cat ({catd.shape[1]}) does not match n_echos ({n_echos})" ) catd = np.empty((n_samples, n_echos, n_vols)) optcom = np.empty((n_samples, n_vols + 1)) with pytest.raises(ValueError) as e_info: gsc.gscontrol_raw( - catd=catd, optcom=optcom, n_echos=n_echos, io_generator=io_generator, dtrank=4 + data_cat=catd, + data_optcom=optcom, + n_echos=n_echos, + io_generator=io_generator, + dtrank=4, ) assert str(e_info.value) == ( - f"Third dimension of catd ({catd.shape[2]}) does not match " - f"second dimension of optcom ({optcom.shape[1]})" + f"Third dimension of data_cat ({catd.shape[2]}) does not match " + f"second dimension of data_optcom ({optcom.shape[1]})" ) diff --git a/tedana/workflows/ica_reclassify.py b/tedana/workflows/ica_reclassify.py index f29d975d5..234991b84 100644 --- a/tedana/workflows/ica_reclassify.py +++ b/tedana/workflows/ica_reclassify.py @@ -489,7 +489,14 @@ def ica_reclassify_workflow( if mir: io_generator.overwrite = True - gsc.minimum_image_regression(data_oc, mmix, mask_denoise, comptable, io_generator) + gsc.minimum_image_regression( + data_optcom=data_oc, + mixing=mmix, + mask=mask_denoise, + comptable=comptable, + classification_tags=selector.classification_tags, + io_generator=io_generator, + ) io_generator.overwrite = False # Write out BIDS-compatible description file diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index 8666b0988..cd6d11291 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -747,7 +747,12 @@ def tedana_workflow( if "gsr" in gscontrol: # regress out global signal - catd, data_oc = gsc.gscontrol_raw(catd, data_oc, n_echos, io_generator) + catd, data_oc = gsc.gscontrol_raw( + data_cat=catd, + data_optcom=data_oc, + n_echos=n_echos, + io_generator=io_generator, + ) fout = io_generator.save_file(data_oc, "combined img") LGR.info(f"Writing optimally combined data set: {fout}") @@ -942,7 +947,14 @@ def tedana_workflow( ) if "mir" in gscontrol: - gsc.minimum_image_regression(data_oc, mmix, mask_denoise, comptable, io_generator) + gsc.minimum_image_regression( + data_optcom=data_oc, + mixing=mmix, + mask=mask_denoise, + comptable=comptable, + classification_tags=selector.classification_tags, + io_generator=io_generator, + ) if verbose: io.writeresults_echoes(catd, mmix, mask_denoise, comptable, io_generator) From 4b86fe24b23dc2f4a4d6a7450e374414610baf87 Mon Sep 17 00:00:00 2001 From: handwerkerd Date: Mon, 2 Sep 2024 21:35:45 -0400 Subject: [PATCH 22/34] fixed ica.py docstring error --- tedana/decomposition/ica.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index 0af62ae9d..dc3748c5a 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -41,9 +41,9 @@ def tedica( Number of components retained from PCA decomposition. fixed_seed : :obj:`int` Seed for ensuring reproducibility of ICA results. - ica_method : :obj: `str' + ica_method : :obj: `str` slected ICA method, can be fastica or robutica. - n_robust_runs : :obj: `int' + n_robust_runs : :obj: `int` selected number of robust runs when robustica is used. Default is 30. maxit : :obj:`int`, optional Maximum number of iterations for ICA. Default is 500. From c01ec5120583972c2541b34acc5fe792a81cb3bb Mon Sep 17 00:00:00 2001 From: handwerkerd Date: Tue, 3 Sep 2024 10:35:29 -0400 Subject: [PATCH 23/34] added scikit-learn-extra to pyproject and changed ref name --- pyproject.toml | 3 ++- tedana/decomposition/ica.py | 2 +- tedana/resources/references.bib | 3 +-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index c653d4ea4..a30e9bf57 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,8 +30,9 @@ dependencies = [ "pandas>=2.0,<=2.2.2", "pybtex", "pybtex-apa-style", - "robustica>=0.1.3", + "robustica>=0.1.3, <=0.1.3", "scikit-learn>=0.21, <=1.5.1", + "scikit-learn-extra>=0.3.0, <=0.3.0", "scipy>=1.2.0, <=1.14.1", "threadpoolctl", "tqdm", diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index dc3748c5a..2d4864e86 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -120,7 +120,7 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): "The selected n_robust_runs is a very big number! The process will take a long time!" ) - RepLGR.info("RobustICA package was used for ICA decomposition \\citep{Anglada2022}.") + RepLGR.info("RobustICA package was used for ICA decomposition \\citep{anglada2022robustica}.") if fixed_seed == -1: fixed_seed = np.random.randint(low=1, high=1000) diff --git a/tedana/resources/references.bib b/tedana/resources/references.bib index b60fb7e9b..b718323cb 100644 --- a/tedana/resources/references.bib +++ b/tedana/resources/references.bib @@ -334,7 +334,7 @@ @article{tedana_decision_trees doi = {10.6084/m9.figshare.25251433.v2} } -@Article{Anglada2022, +@Article{anglada2022robustica, Author = {Anglada-Girotto Miquel and Miravet-Verde Samuel and Serrano Luis and Head Sarah}, Title = {robustica: customizable robust independent component analysis}, Journal = {BMC Bioinformatics}, @@ -343,4 +343,3 @@ @Article{Anglada2022 doi = {10.1186/s12859-022-05043-9}, year = 2022 } - From cd500370181e586cadeec55cdee720ce55a651da Mon Sep 17 00:00:00 2001 From: handwerkerd Date: Tue, 3 Sep 2024 14:37:03 -0400 Subject: [PATCH 24/34] increment circleci version keys --- .circleci/config.yml | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index da16d5e89..c10ee7e2c 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -13,7 +13,7 @@ jobs: steps: - checkout - restore_cache: - key: conda-py38-v2-{{ checksum "pyproject.toml" }} + key: conda-py38-v3-{{ checksum "pyproject.toml" }} - run: name: Generate environment command: | @@ -23,7 +23,7 @@ jobs: pip install -e .[tests] fi - save_cache: - key: conda-py38-v2-{{ checksum "pyproject.toml" }} + key: conda-py38-v3-{{ checksum "pyproject.toml" }} paths: - /opt/conda/envs/tedana_py38 @@ -34,7 +34,7 @@ jobs: steps: - checkout - restore_cache: - key: conda-py38-v2-{{ checksum "pyproject.toml" }} + key: conda-py38-v3-{{ checksum "pyproject.toml" }} - run: name: Running unit tests command: | @@ -56,7 +56,7 @@ jobs: steps: - checkout - restore_cache: - key: conda-py39-v2-{{ checksum "pyproject.toml" }} + key: conda-py39-v3-{{ checksum "pyproject.toml" }} - run: name: Generate environment command: | @@ -75,7 +75,7 @@ jobs: mkdir /tmp/src/coverage mv /tmp/src/tedana/.coverage /tmp/src/coverage/.coverage.py39 - save_cache: - key: conda-py39-v2-{{ checksum "pyproject.toml" }} + key: conda-py39-v3-{{ checksum "pyproject.toml" }} paths: - /opt/conda/envs/tedana_py39 - persist_to_workspace: @@ -90,7 +90,7 @@ jobs: steps: - checkout - restore_cache: - key: conda-py310-v1-{{ checksum "pyproject.toml" }} + key: conda-py310-v3-{{ checksum "pyproject.toml" }} - run: name: Generate environment command: | @@ -109,7 +109,7 @@ jobs: mkdir /tmp/src/coverage mv /tmp/src/tedana/.coverage /tmp/src/coverage/.coverage.py310 - save_cache: - key: conda-py310-v1-{{ checksum "pyproject.toml" }} + key: conda-py310-v3-{{ checksum "pyproject.toml" }} paths: - /opt/conda/envs/tedana_py310 - persist_to_workspace: @@ -124,7 +124,7 @@ jobs: steps: - checkout - restore_cache: - key: conda-py311-v1-{{ checksum "pyproject.toml" }} + key: conda-py311-v3-{{ checksum "pyproject.toml" }} - run: name: Generate environment command: | @@ -143,7 +143,7 @@ jobs: mkdir /tmp/src/coverage mv /tmp/src/tedana/.coverage /tmp/src/coverage/.coverage.py311 - save_cache: - key: conda-py311-v1-{{ checksum "pyproject.toml" }} + key: conda-py311-v3-{{ checksum "pyproject.toml" }} paths: - /opt/conda/envs/tedana_py311 - persist_to_workspace: @@ -158,7 +158,7 @@ jobs: steps: - checkout - restore_cache: - key: conda-py312-v1-{{ checksum "pyproject.toml" }} + key: conda-py312-v3-{{ checksum "pyproject.toml" }} - run: name: Generate environment command: | @@ -177,7 +177,7 @@ jobs: mkdir /tmp/src/coverage mv /tmp/src/tedana/.coverage /tmp/src/coverage/.coverage.py312 - save_cache: - key: conda-py312-v1-{{ checksum "pyproject.toml" }} + key: conda-py312-v3-{{ checksum "pyproject.toml" }} paths: - /opt/conda/envs/tedana_py312 - persist_to_workspace: @@ -192,7 +192,7 @@ jobs: steps: - checkout - restore_cache: - key: conda-py38-v2-{{ checksum "pyproject.toml" }} + key: conda-py38-v3-{{ checksum "pyproject.toml" }} - run: name: Style check command: | @@ -208,7 +208,7 @@ jobs: steps: - checkout - restore_cache: - key: conda-py38-v2-{{ checksum "pyproject.toml" }} + key: conda-py38-v3-{{ checksum "pyproject.toml" }} - run: name: Run integration tests no_output_timeout: 40m @@ -233,7 +233,7 @@ jobs: steps: - checkout - restore_cache: - key: conda-py38-v2-{{ checksum "pyproject.toml" }} + key: conda-py38-v3-{{ checksum "pyproject.toml" }} - run: name: Run integration tests no_output_timeout: 40m @@ -258,7 +258,7 @@ jobs: steps: - checkout - restore_cache: - key: conda-py38-v2-{{ checksum "pyproject.toml" }} + key: conda-py38-v3-{{ checksum "pyproject.toml" }} - run: name: Run integration tests no_output_timeout: 40m @@ -283,7 +283,7 @@ jobs: steps: - checkout - restore_cache: - key: conda-py38-v2-{{ checksum "pyproject.toml" }} + key: conda-py38-v3-{{ checksum "pyproject.toml" }} - run: name: Run integration tests no_output_timeout: 40m @@ -308,7 +308,7 @@ jobs: steps: - checkout - restore_cache: - key: conda-py38-v2-{{ checksum "pyproject.toml" }} + key: conda-py38-v3-{{ checksum "pyproject.toml" }} - run: name: Run integration tests no_output_timeout: 40m @@ -335,7 +335,7 @@ jobs: at: /tmp - checkout - restore_cache: - key: conda-py38-v2-{{ checksum "pyproject.toml" }} + key: conda-py38-v3-{{ checksum "pyproject.toml" }} - run: name: Merge coverage files command: | From 2a868e3666b2460f97d9bacc751132d96a0a66b0 Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Wed, 11 Sep 2024 17:21:44 +1000 Subject: [PATCH 25/34] Removing the scikit-learn-extra dependency --- pyproject.toml | 2 +- tedana/decomposition/ica.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 8c5b46766..e27619de6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,7 +30,7 @@ dependencies = [ "pandas>=2.0,<=2.2.2", "pybtex", "pybtex-apa-style", - "robustica>=0.1.3, <=0.1.3", + "robustica@git+https://github.com/BahmanTahayori/robustica", "scikit-learn>=0.21, <=1.5.1", "scikit-learn-extra>=0.3.0, <=0.3.0", "scipy>=1.2.0, <=1.14.1", diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index 2d4864e86..9a43f9bdd 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -157,7 +157,7 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): raise ValueError("RobustICA failed to converge") LGR.info( - f"The {robust_method} clustering algorithm was used clustering " + f"The {robust_method} clustering algorithm was used for clustering " f"components across different runs" ) @@ -198,7 +198,7 @@ def f_ica(data, n_components, fixed_seed, maxit, maxrestart): Parameters ---------- - data : (S x T) :obj:`numpy.ndarray` + data : :obj:`numpy.ndarray` Dimensionally reduced optimally combined functional data, where `S` is samples and `T` is time n_components : :obj:`int` From ed10ade30121c6d4269c02118caadb200404afaa Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Wed, 11 Sep 2024 17:39:03 +1000 Subject: [PATCH 26/34] Updating pyproject.toml file --- pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index e27619de6..65de854a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,6 @@ dependencies = [ "pybtex-apa-style", "robustica@git+https://github.com/BahmanTahayori/robustica", "scikit-learn>=0.21, <=1.5.1", - "scikit-learn-extra>=0.3.0, <=0.3.0", "scipy>=1.2.0, <=1.14.1", "threadpoolctl", "tqdm", From 8a14277011d9d9f5a69a644e7cb59fcfe8ec07a3 Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Thu, 12 Sep 2024 10:59:41 +1000 Subject: [PATCH 27/34] Minor changes to make the help more readable --- tedana/workflows/tedana.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index cd6d11291..df43cc8a7 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -138,9 +138,9 @@ def _get_parser(): choices=["loglin", "curvefit"], help=( "Desired T2*/S0 fitting method. " - '"loglin" means that a linear model is fit ' + '"loglin" means that a linear model is fit " "to the log of the data. " - '"curvefit" means that a more computationally ' + '"curvefit" means that a more computationally " "demanding monoexponential model is fit " "to the raw data. " ), @@ -167,7 +167,7 @@ def _get_parser(): "were distributed with MEICA. " "Users may also provide a float from 0 to 1, " "in which case components will be selected based on the " - "cumulative variance explained or an integer greater than 1" + "cumulative variance explained or an integer greater than 1 " "in which case the specificed number of components will be " "selected." ), @@ -180,7 +180,7 @@ def _get_parser(): "Decision tree to use. You may use a " "packaged tree (tedana_orig, meica, minimal) or supply a JSON " "file which matches the decision tree file " - "specification. Minimal still being tested with more" + "specification. Minimal still being tested with more " "details in docs" ), default="tedana_orig", @@ -191,7 +191,7 @@ def _get_parser(): type=lambda x: is_valid_file(parser, x), help=( "File containing external regressors to compare to ICA component be used in the " - "decision tree. For example, to identify components fit head motion time series." + "decision tree. For example, to identify components fit head motion time series. " "The file must be a TSV file with the same number of rows as the number of volumes in " "the input data. Column labels and statistical tests are defined with external_labels." ), @@ -220,7 +220,7 @@ def _get_parser(): "Value used for random initialization of ICA " "algorithm. Set to an integer value for " "reproducible ICA results. Set to -1 for " - "varying results across ICA calls. This" + "varying results across ICA calls. This " "applies to both fastica and robustica methods." ), default=DEFAULT_SEED, @@ -276,7 +276,7 @@ def _get_parser(): "Perform additional denoising to remove " "spatially diffuse noise. " "This argument can be single value or a space " - "delimited list" + "delimited list." ), choices=["mir", "gsr"], default="", From b793d833ef59415371b1ffd5ef92c121fa937265 Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Thu, 12 Sep 2024 11:08:22 +1000 Subject: [PATCH 28/34] Minor changes --- tedana/workflows/tedana.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index df43cc8a7..3a966d0ff 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -138,9 +138,9 @@ def _get_parser(): choices=["loglin", "curvefit"], help=( "Desired T2*/S0 fitting method. " - '"loglin" means that a linear model is fit " + '"loglin" means that a linear model is fit ' "to the log of the data. " - '"curvefit" means that a more computationally " + '"curvefit" means that a more computationally ' "demanding monoexponential model is fit " "to the raw data. " ), From 1b1eb38f03f664254d5e8aa0dfa144a051442a7e Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Tue, 17 Sep 2024 10:34:12 +1000 Subject: [PATCH 29/34] upgrading to robustica 0.1.4 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 65de854a0..3538f6e1b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,7 +30,7 @@ dependencies = [ "pandas>=2.0,<=2.2.2", "pybtex", "pybtex-apa-style", - "robustica@git+https://github.com/BahmanTahayori/robustica", + "robustica>=0.1.4", "scikit-learn>=0.21, <=1.5.1", "scipy>=1.2.0, <=1.14.1", "threadpoolctl", From 87965f4d400a35ca86f90c7dba6313202d560a7b Mon Sep 17 00:00:00 2001 From: Bahman Date: Wed, 18 Sep 2024 09:28:10 +1000 Subject: [PATCH 30/34] Update docs Co-authored-by: Dan Handwerker <7406227+handwerkerd@users.noreply.github.com> --- docs/faq.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/faq.rst b/docs/faq.rst index 4f360b71f..d11b43ca5 100644 --- a/docs/faq.rst +++ b/docs/faq.rst @@ -93,7 +93,7 @@ The TEDICA step may fail to converge if TEDPCA is either too strict With updates to the ``tedana`` code, this issue is now rare, but it may happen when preprocessing has not been applied to the data, or when improper steps have been applied to the data (e.g. rescaling, nuisance regression). -I can also still happen when everything is seemingly correct +It can also still happen when everything is seemingly correct (see the answer to the next question). If you are confident that your data have been preprocessed correctly prior to applying tedana, and you encounter this problem, please submit a question to `NeuroStars`_. From 8743acab373a904be696f5a202e2085b7f00ce2e Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Wed, 18 Sep 2024 10:39:13 +1000 Subject: [PATCH 31/34] updating utils.py, toml file and the docs --- docs/faq.rst | 2 +- pyproject.toml | 4 ++-- tedana/utils.py | 4 ++++ 3 files changed, 7 insertions(+), 3 deletions(-) diff --git a/docs/faq.rst b/docs/faq.rst index d11b43ca5..5604afc0f 100644 --- a/docs/faq.rst +++ b/docs/faq.rst @@ -131,7 +131,7 @@ and signal leakage from in-slice and multi-slice accelleration may violate this We have one option that is generally useful and is also a partial solution. ``--ica_method robustica`` will run `robustica`_. This is a method that, for a given number of PCA components, -will repeated run ICA and identify components that are stable across iterations. +will repeatedly run ICA and identify components that are stable across iterations. While running ICA multiple times will slow processing, as a general benefit, this means that the ICA results are less sensitive to the initialization parameters, computer hardware, and software versions. diff --git a/pyproject.toml b/pyproject.toml index 3538f6e1b..086c5b302 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,8 +30,8 @@ dependencies = [ "pandas>=2.0,<=2.2.2", "pybtex", "pybtex-apa-style", - "robustica>=0.1.4", - "scikit-learn>=0.21, <=1.5.1", + "robustica>=0.1.4,<=0.1.4", + "scikit-learn>=0.21, <=1.5.2", "scipy>=1.2.0, <=1.14.1", "threadpoolctl", "tqdm", diff --git a/tedana/utils.py b/tedana/utils.py index 9c2c0c67b..d9972f265 100644 --- a/tedana/utils.py +++ b/tedana/utils.py @@ -18,12 +18,14 @@ from nilearn._utils import check_niimg from numpy import __version__ as numpy_version from pandas import __version__ as pandas_version +from robustica import __version__ as robustica_version from scipy import __version__ as scipy_version from scipy import ndimage from scipy.special import lpmv from sklearn import __version__ as sklearn_version from sklearn.utils import check_array from threadpoolctl import __version__ as threadpoolctl_version +from tqdm import __version__ as tqdm_version LGR = logging.getLogger("GENERAL") RepLGR = logging.getLogger("REPORT") @@ -615,9 +617,11 @@ def get_system_version_info(): "nilearn": nilearn_version, "numpy": numpy_version, "pandas": pandas_version, + "robustica": robustica_version "scikit-learn": sklearn_version, "scipy": scipy_version, "threadpoolctl": threadpoolctl_version, + "tqdm": tqdm_version, } return { From 42372e3c757066fd76d97a896fcec0ca9e7faf35 Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Wed, 18 Sep 2024 11:00:59 +1000 Subject: [PATCH 32/34] minor change to utils.py --- tedana/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tedana/utils.py b/tedana/utils.py index d9972f265..5914862e5 100644 --- a/tedana/utils.py +++ b/tedana/utils.py @@ -617,7 +617,7 @@ def get_system_version_info(): "nilearn": nilearn_version, "numpy": numpy_version, "pandas": pandas_version, - "robustica": robustica_version + "robustica": robustica_version, "scikit-learn": sklearn_version, "scipy": scipy_version, "threadpoolctl": threadpoolctl_version, From 1aac94a7245026fecbd2bd3d626f31476e73c22a Mon Sep 17 00:00:00 2001 From: Bahman Date: Fri, 20 Sep 2024 10:55:49 +1000 Subject: [PATCH 33/34] Incorporating Eneko's comments MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Eneko Uruñuela --- docs/faq.rst | 13 ++++++------- tedana/decomposition/ica.py | 2 +- tedana/workflows/tedana.py | 2 +- 3 files changed, 8 insertions(+), 9 deletions(-) diff --git a/docs/faq.rst b/docs/faq.rst index 5604afc0f..046ff7ef6 100644 --- a/docs/faq.rst +++ b/docs/faq.rst @@ -122,8 +122,8 @@ For example, smaller voxels will results in more thermal noise and less total va A dataset with more head motion artifacts will have more variance explained, since more structured signal is within the head motion artifacts. The clear failure cases are extreme. That is getting less than 1/5 the number of components -compared to time points or having nearly has many components as time point. -We are working on identifying why this happens and adding get better solutions. +compared to time points or having nearly as many components as time points. +We are working on identifying why this happens and adding better solutions. Our current guess is that most of the above methods assume data are independant and identically distributed (IID), and signal leakage from in-slice and multi-slice accelleration may violate this assumption. @@ -142,17 +142,16 @@ This means, even if the initial PCA component estimate is a bit off, the number of resulting robust ICA components will represent stable information in the data. For a dataset where the PCA comoponent estimation methods are failing, one could use ``--tedpca`` with a fixed integer for a constant number of components, -that is on the high end of typical for a study, +that is on the high end of the typical number of components for a study, and then `robustica`_ will reduce the number of components to only find stable information. That said, if the fixed PCA component number is too high, then the method will have too many unstable components, and if the fixed PCA component number is too low, then there will be even fewer ICA components. -The number of ICA components is more consisent, +With this approach, the number of ICA components is more consistent, but is still sensitive to the intial number of PCA components. For example, for a single dataset 60 PCA components might result in 46 stable ICA components, while 55 PCA components might results in 43 stable ICA components. -We are still testing how these interact and give better recommendations and even more stable results. -At that point, ``--ica_method robustica`` might become the default setting, +We are still testing how these interact to give better recommendations for even more stable results. While the TEDANA developers expect that ``--ica_method robustica`` may become the default configuration in future TEDANA versions, it is first being released to the public as a non-default option @@ -160,7 +159,7 @@ in hope of gaining insight into its behaviour across a broader range of multi-echo fMRI data. If users are having trouble with PCA component estimation failing on a dataset, we recommend using RobustICA; -we also invite user feedback on its behaviour and efficacy. +and we invite users to send us feedback on its behavior and efficacy. .. _MAPCA: https://github.com/ME-ICA/mapca diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index 9a43f9bdd..241525493 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -42,7 +42,7 @@ def tedica( fixed_seed : :obj:`int` Seed for ensuring reproducibility of ICA results. ica_method : :obj: `str` - slected ICA method, can be fastica or robutica. + selected ICA method, can be fastica or robustica. n_robust_runs : :obj: `int` selected number of robust runs when robustica is used. Default is 30. maxit : :obj:`int`, optional diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py index 3a966d0ff..e2b3858bf 100644 --- a/tedana/workflows/tedana.py +++ b/tedana/workflows/tedana.py @@ -457,7 +457,7 @@ def tedana_workflow( The file must be a TSV file with the same number of rows as the number of volumes in the input data. Each column in the file will be treated as a separate regressor. Default is None. - ica_method : {'robustica', 'fastica'}, optional + ica_method : {'fastica', 'robustica'}, optional The applied ICA method. fastica runs FastICA from sklearn once with the seed value. 'robustica' will run 'FastICA' n_robust_runs times and uses clustering methods to overcome From 2b0ef677caebbf695df7b1879b6de5bce5a63c26 Mon Sep 17 00:00:00 2001 From: Bahman Tahayori Date: Fri, 20 Sep 2024 12:18:30 +1000 Subject: [PATCH 34/34] Added a warning when the clustering method is changed --- tedana/decomposition/ica.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py index 241525493..6ab291c43 100644 --- a/tedana/decomposition/ica.py +++ b/tedana/decomposition/ica.py @@ -153,6 +153,10 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it): if robust_method == "DBSCAN": # if RobustICA failed wtih DBSCAN, run again wtih AgglomerativeClustering robust_method = "AgglomerativeClustering" + LGR.warning( + "DBSCAN clustering method did not converge. " + "Agglomerative clustering will be tried now." + ) else: raise ValueError("RobustICA failed to converge")

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+\usepackage[utf8]{inputenc} % Required for inserting images +\usepackage{tikz} +\usepackage{helvet} +\usetikzlibrary{shapes.geometric, arrows} +\pagecolor{white} + +%-------------------------defining colorblind friendly colors +% Using pale color scheme in Figure 6 +% by Paul Tol https://personal.sron.nl/~pault/ +\definecolor{cbblue}{HTML}{BBCCEE} +\definecolor{cbcyan}{HTML}{CCEEFF} +\definecolor{cbgreen}{HTML}{CCDDAA} +\definecolor{cbyellow}{HTML}{EEEEBB} +\definecolor{cbred}{HTML}{FFCCCC} +\definecolor{cbgrey}{HTML}{DDDDDD} + +% -------------------------defining nodes +\tikzstyle{input} = [trapezium, trapezium left angle =80, trapezium right angle = 100, +minimum width= 3cm, minimum height=0.5cm, text centered, draw=black, fill=cbblue] +\tikzstyle{process} = [rectangle, minimum width = 3cm, minimum height = 1cm, +text centered, , text width=4cm,draw=black, fill=cbgrey] +\tikzstyle{decision} = [diamond, minimum width = 3cm, minimum height = 1cm, +text centered, , text width=3cm, draw=black, fill=cbcyan] +\tikzstyle{changeclass} = [rectangle, rounded corners, minimum width=3cm, minimum height=1cm, +text centered, draw = black, fill=cbyellow] +\tikzstyle{reject} = [trapezium, trapezium left angle =80, trapezium right angle = 100, +minimum width= 1cm, minimum height=0.5cm, text centered, draw=black, fill=cbred] +\tikzstyle{accept} = [trapezium, trapezium left angle =80, trapezium right angle = 100, +minimum width= 1cm, minimum height=0.5cm, text centered, draw=black, fill=cbgreen] + +% -------------------------defining connectors +\tikzstyle{arrow} = [thick,->, >=stealth] +\tikzstyle{line} = [thick,-,>=stealth] +\begin{document} + +% ------------------------- tikz image (flow chart) +\begin{tikzpicture}[node distance = 2cm] + +% ------------------------- nodes ------------------------- +% ----- node: 0 +\node(0)[input,label={90:\textbf{Demo Decision Tree with External Regressors, Motion, and Task Models}}, label={180:$node\ 0$}]{Set all components to unclassified}; +% ----- node: 1 +\node(1)[decision, below of=0,label={180:$node\ 1$}, yshift=-1.5cm]{$\rho$ $>$ $\kappa$}; +\node(rej1)[changeclass, right of=1, xshift=3cm, align=center]{Unlikely BOLD\\$\rightarrow$ Provisional reject}; +% ----- node: 2 +\node(2)[decision, below of=1,label={180:$node\ 2$}, label={[align=center] 315: voxel counts for signif fit\\of multi-echo data\\to $T_2$ or $S_0$ decay models}, yshift=-4.0cm]{$n \, FS_0 \, > \, n \, FT_2$ \& $n \,FT_2$ $>$ 0}; +\node(rej2)[changeclass, right of=2, xshift=3cm, align=center]{Unlikely BOLD\\$\rightarrow$ Provisional Reject}; +% ----- node: 3 +\node(3)[process, below of=2, label={180:$node\ 3$}, label={[align=center] 315: varex: variance explained\\by each component}, yshift=-2.0cm]{Calculate median(varex) across all components}; +% ----- node: 4 +\node(4)[decision, below of=3,label={180:$node\ 4$},label={[align=center] 315:DICE overlap between $T_2$ or $S_0$\\decay models and ICA component\\peak clusters}, yshift=-1.5cm]{dice $FS_0$ $>$ dice $FT_2$ \& varex $>$ median(varex) +}; +\node(rej4)[changeclass, right of=4, xshift=3cm, align=center]{Unlikely BOLD\\$\rightarrow$ Provisional Reject}; +% ----- node: 5 +\node(5)[decision, below of=4,label={180:$node\ 5$}, label={[align=center] 315: $t-statistic$ of $FT_2$ values\\in component peak clusters vs\\peak voxels outside of clusters}, yshift=-4.0cm]{ $0 \, >$ signal-noise \& varex $>$ median(varex)}; +\node(rej5)[changeclass, right of=5, xshift=3cm, align=center]{Unlikely BOLD\\$\rightarrow$ Provisional Reject}; +% ----- node: 6 +\node(6)[process, below of=5, label={180:$node\ 6$}, label={0: Uses all components}, yshift=-2.0cm]{Calculate $\kappa$ elbow}; +% ----- node: 7 +\node(7)[process, below of=6, label={180:$node\ 7$}, label={[align=center] 0: Uses all components and subset\\of unclassified components}]{Calculate $\rho$ elbow\\(liberal method)}; +% ----- node: 8 +\node(8)[decision, below of=7,label={180:$node\ 8$}, yshift=-1.5cm]{$\kappa \geq \kappa$ elbow\\$\rho$ $<$ $\rho$ elbow}; +\node(chrej8)[changeclass, below of=8, xshift=0cm, yshift=-2cm]{Provisional reject}; +\node(chacc8)[changeclass, right of=8, xshift=3cm, yshift=0cm]{Provisional accept}; +% ----- node: 9 +\node(9)[decision, below of=chrej8,label={180:$node\ 9$},label={20: Accept even if $\rho < \rho\ elbow$},yshift=-1.5cm]{$\kappa > 2\rho$\\$\kappa \geq \kappa$ elbow}; +\node(chrej9)[changeclass, below of=9, xshift=0cm, yshift=-2cm]{Provisional reject}; +\node(chacc9)[changeclass, right of=9, xshift=3cm, yshift=0cm]{Provisional accept}; +% ----- node: 10 +\node(10)[decision, below of=chacc9,label={150:$node\ 10$},label={[align=center] 310: Reject if\\fits external\\nuisance\\regressors},yshift=-2cm]{F test for\\Nuisance Regressors\\$p_{Full} \leq 0.05$\\$R^2_{Full} \geq 0.5$}; +\node(chrej10)[changeclass, below of=10, xshift=0cm, yshift=-2cm, align=center]{External regressors\\$\rightarrow$Provisional reject}; +% ----- node: 11 +\node(11)[decision, left of=chrej10,label={180:$node\ 11$},xshift=-3cm]{Partial F test for\\Motion Regressors\\$p_{Full} \leq 0.05$\\$R^2_{Full} \geq 0.5$\\$p_{Motion} \leq 0.05$}; +\node(chtag11)[changeclass, below of=11, xshift=0cm, yshift=-2cm, align=center]{Tag:\\Fits motion\\external regressors}; +% ----- node: 12 +\node(12)[decision, below of=chrej10,label={150:$node\ 12$},yshift=-2cm]{Partial F test for\\CSF Regressors\\$p_{Full} \leq 0.05$\\$R^2_{Full} \geq 0.5$\\$p_{CSF} \leq 0.05$}; +\node(chtag12)[changeclass, below of=12, xshift=0cm, yshift=-2cm, align=center]{Tag:\\Fits CSF\\external regressors}; +% ----- node: 13 +\node(prej13)[changeclass, below of=chtag11, xshift=0cm, yshift=-0.5cm]{Provisional reject}; +\node(13)[decision, below of=prej13,label={180:$node\ 13$},label={[align=center] 335: If fits task and\\contains T2*, accept\\even if other criteria\\would have rejected},yshift=-2cm]{F test for\\Task Regressors\\$p_{Task} \leq 0.05$\\$R^2_{Task} \geq 0.5$\\$\kappa \geq \kappa$ elbow}; +\node(chacc13)[accept, right of=13,xshift=3cm, align=center]{Fits task\\$\rightarrow$Accept}; +% ----- node: 14 +\node(14)[decision, below of=13,label={180:$node\ 14$},label={[align=left] 335: Will accept the lowest\\variance components until\\1\% of total variance is\\accepted this way}, yshift=-3.5cm]{$if$ component variance $<0.1$};%--check in kundu +\node(acc14)[accept, right of=14, xshift=2.5cm, align=center]{Low variance\\$\rightarrow$ Accept}; +% ----- node: 15 +\node(15)[accept, below of=14,label={180:$node\ 15$},yshift=-1.5cm, align=center]{Likely BOLD\\Change provisional accept\\$\rightarrow$Accept}; +% ----- node: 16 +\node(16)[reject, below of=15,label={180:$node\ 16$}, yshift=0cm, align=center]{Unlikely BOLD\\Change provisional reject\\$\rightarrow$Reject}; + +% ------------------------- connections ------------------------- +% draw[x](origin)--node[anchor=position]{text}(destination); +\draw[arrow](0)--(1); +\draw[arrow](1)--node[anchor=south, right=0] {no} (2); +\draw[arrow](1)--node[anchor=south] {yes} (rej1); +\draw[arrow](2)--node[anchor=south, right=0] {no} (3); +\draw[arrow](2)--node[anchor=south] {yes} (rej2); +\draw[arrow](3)--(4); +\draw[arrow](4)--node[anchor=south, right=0] {no} (5); +\draw[arrow](4)--node[anchor=south] {yes} (rej4); +\draw[arrow](5)--node[anchor=south, right=0] {no} (6); +\draw[arrow](5)--node[anchor=south] {yes} (rej5); +\draw[arrow](6)--(7); +\draw[arrow](7)--(8); +\draw[arrow](8)--node[anchor=south] {yes} (chacc8); +\draw[arrow](8)--node[anchor=south, right=0] {no} (chrej8); +\draw[arrow](chrej8)--(9); +\draw[arrow](9)--node[anchor=south] {yes} (chacc9); +\draw[arrow](9)--node[anchor=south, right=0] {no} (chrej9); +\draw[arrow](chacc9)--(10); +\draw[arrow](chrej9)--(10); +\draw[arrow](10)--node[anchor=south, right=0] {yes} (chrej10); +\draw[arrow](chrej10)--(11); +\draw[arrow](11)--node[anchor=south, right=0] {yes} (chtag11); +\draw[arrow](chrej10)--(12); +\draw[arrow](12)--node[anchor=south, 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