Changelog#

The current changelog contains information on NeuroCAPs versions 0.19.0 and above. For changes in earlier versions (< 0.19.0), please refer to the Changelog Archive.

Note: The versions listed in this file have been or will be deployed to PyPI.

[Versioning]#

0.minor.patch.postN

  • .minor : Introduces new features and may include potential breaking changes. Any breaking changes will be explicitly noted in the changelog (e.g., new functions or parameters, changes in parameter defaults, or function names).

  • .patch : Contains fixes for identified bugs and may include modifications or added parameters for improvements/enhancements. All fixes and modifications are backwards compatible.

  • .postN : Consists of documentation changes or metadata-related updates, such as modifications to type hints.

[0.26.10] - 2025-04-19#

  • Final round to address mutability

♻ Changed#

  • For TimeseriesExtractor, certain mutable parameters such as confound_names, fd_threshold, and dummy_scans are copied to prevent un-intentional user changes that mutate these parameters without validation.

  • Also for TimeseriesExtractor and CAP certain properties return a deep copy. However, large properties such as subject_timeseries and concatenated_timeseries return just a reference to prevent excessive memory usage.

[0.26.9] - 2025-04-19#

✨ Enhancement#

  • Improves stacking in merge_dicts to make less memory intensive for larger datasets.

  • IO operations when pickles are given in subject_timeseries_list for merge_dicts is only done once per call. Consequently, fixes issue if strings are given in subject_timeseries_list but doesn’t have the “pkl” extension since “.pkl”, “.pickle”, and “.joblib” are recognized.

[0.26.8] - 2025-04-18#

♻ Changed#

  • Exceptions no longer require message

🐛 Fixes#

  • In the merge_dicts function, fixes issue a mutability issue where if a subject only has a single run, the memory address of the numpy array in the merged dict would be the same as the original input dict. Now deepcopy used to safeguard against any unintended side effects to the original input dictionaries.

[0.26.7] - 2025-04-16#

🐛 Fixes#

  • Adds the py.typed file

[0.26.6] - 2025-04-15#

♻ Changed#

  • For str method, changed “Metadata” to “Current Object State” and made other minor tweaks.

🐛 Fixes#

  • Fixed a logged warning about condition event windows being out of bounds when condition_tr_shift is not used. Previously, the log message would never actually be logged.

📖 Documentation#

  • Improved documentation about how the default class in CAP is handles and added a logged message about this behavior.

  • Added doc string to str method.

[0.26.5] - 2025-04-13#

  • Updates related to plotting and pickling

🚀 New/Added#

  • Several functions now include a as_pickle parameter to save figures as pickle files. For CAP.cas2radar, an as_json file is added to saved plotly files in json format as opposed to pickle. This allows for further modifications of the plots outside of the functions.

♻ Changed#

  • For parameters that except files as strings such as subject_timeseries and parcel_approach, recognized extensions are now “.pkl”, “.pickle”, and “.joblib”, instead of just “.pkl”.

🐛 Fixes#

  • For file names in TimeseriesExtractor.visualize_bold, the default file name saved with an additional “run-“, this has been removed.

  • For file names in CAP.caps2plot, groups with spaces saved with whitespace, now this whitespace is replaced with underscores (“_”).

[0.26.4] - 2025-04-13#

🐛 Fixes#

  • Fix for upcoming use of clean_args in NiftiLabelsMasker.

[0.26.3] - 2025-04-13#

  • Dependency fixes

🐛 Fixes#

  • Updates minimum dependencies in pyproject toml for functions to work

  • Fixes error if using knn_dict with nilearn version < 0.11.0 due to using a parameter introduced in that version

  • Accounts for upcoming nilearn changes that add “Background” to the labels list to ensure proper plotting

  • Accounts for future deprecation in NiftiLabelsMasker that will transition from using kwargs to clean_args in order to use clean__extrapolate=False

[0.26.2] - 2025-04-11#

  • Updates for CAP

🐛 Fixes#

  • Reverse the mean and standardize properties returning None unless standardized to allow them to be cleared any time CAP.get_caps function is called since this can be an issue if standardizing is first requested, then CAP.get_caps is ran again without standardizing and then the CAP metrics are computed. This would result in incorrect scaling for predicting CAP assignment for that particular scenario. Affected version 0.26.1.

[0.26.1] - 2025-04-11#

  • Updates for CAP

🐛 Fixes#

  • Fixed performance bottleneck when stacking large timeseries by only calling NumPy’s vstack once per group instead of once per subject and run pair. Consequently, updates to the progress bar were made to reflect this.

  • If an invalid cluster selection method is called, the error now comes before concatenation instead of after.

  • The mean and stdev properties now return None unless standardized is True or truthy instead of returning empty dictionaries when standardization is False.

[0.26.0] - 2025-04-10#

  • Full source repository now archived on Zenodo, instead of just the pure source code

🚀 New/Added#

  • Added mean and standard deviation of framewise displacement of QC report, which are added at the beginning of the QC report.

♻ Changed#

  • In, TimeseriesExtractor, default for detrend changed from True to False to avoid redundancy since the default behavior for confound_names includes the cosine-basis regressors. This is also the default for NiftiLabelsMasker.

  • Now skips timeseries extraction if the number of confound regressors are equal to or greater than the length of the timeseries.

  • For CAP.caps2radar, the default for fill changed from “none” to “toself” so that the traces of the radar plot are always filled in by default.

  • For CAP, the region_means property now fully replaces the region_caps property. The only difference is that region_means includes the region names themselves and better describes what it represents.

  • Version directives under 0.25.0 have been removed to clean the docs. These directives can still be viewed on 0.25.1 documentation.

[0.25.1] - 2025-04-08#

  • Simple internal change, to explicitly change scaling to False for NiftiLabelsMasker, in case the scaling approach changes in a future version or has been changed in a past version.

[0.25.0.post1] - 2025-04-07#

📖 Documentation#

  • Added documentation note

[0.25.0] - 2025-04-07#

♻ Changed#

  • For TimeseriesExtractor, standardize is no longer passed to Nilearn. Since standardizing is the final step in signal cleaning, it is now done within this package. Only standardizing using Bessel’s correction (sample standard deviation) is used. This removes having to do external standardizing with neurocaps.analysis.standardize when censoring or extracting conditions and standardizing is True.

  • For CAP, tqdm progress bar is now also displayed for the concatenation step when progress_bar is True.

[0.24.7] - 2025-04-05#

  • Minor refactoring

🚀 New/Added#

  • dummy_scans now accepts “auto” for convenience so that {“auto”: True} does not need to be used.

[0.24.6] - 2025-04-02#

🚀 New/Added#

  • Added “Mean_High_Motion_Length” and “Std_High_Motion_Length” to qc report.

♻ Changed#

  • Qc report only produced when fd_threshold is specified, a valid and a confounds tsv file with “framewise displacement” column is found. Done since qc currently only focuses on framewise displacement.

🐛 Fixes#

Errors that could arise for some edge cases that usually won’t be used

  • Setting fd_threshold and “outlier_percentage” to 0 are now recognized.

[0.24.5] - 2025-03-30#

  • Cleanest version for JOSS consideration.

  • Some Internal refactoring done to clean code

  • Adds all to exceptions module for star import

🐛 Fixes#

  • Issue introduced in 0.24.3 specifically for condition, where if interpolation is requested and outlier percentage is used, the computation would only consider frames not being interpolated instead of all frames flagged for high motion. Added test too test suite to confirm behavior.

📖 Documentation#

  • Adds clarifications in documentation

  • Adds links to docs for the type hints

  • Adds basic docstring for many internal functions in _utils

[0.24.4.post0] - 2025-03-29#

🐛 Fixes#

  • Broken zenodo badge.

[0.24.4] - 2025-03-29#

  • Simply adds an additional conditional as a safeguard when passing sample mask to nilearn.

[0.24.3] - 2025-03-29#

  • Internal refactoring

🚀 New/Added#

  • Added the qc property and the report_qc function in TimeseriesExtractor

🐛 Fixes#

  • Type hint for output_dir in TimeseriesExtractor.timeseries_to_pickle

📖 Documentation#

  • Some docs cleaning.

[0.24.2] - 2025-03-25#

  • Some internal refactoring and name changes to internal functions for clarity

🐛 Fixes#

  • Removes wheel in requirements since it is no longer needed for bdist_wheel since setuptools v70.1.

[0.24.1] - 2025-03-25#

🐛 Fixes#

  • Update license field to comply with PEP 639 and avoid deprecation.

  • Upgraded to setuptools to 77.0.1, since this expression is supported in version 77.0.0 (which was yanked)

📖 Documentation#

  • Added some additional information in docs for user guiding.

[0.24.0] - 2025-03-24#

  • Minor internal refactoring for private functions to improve readability.

  • Some general improvements for better use of this package by others.

🚀 New/Added#

  • Added NoElbowDetectedError for instances where elbow method fails to detect elbow.

♻ Changed#

  • Uses default for mask_img for NiftiLabelsMasker, which is None, as opposed to using masks queried from data. This better aligns with standard usage of the class and the parcellation serves as a mask already and is redundant especially when atlas and data are registered to the same space.

  • In TimeseriesExtractor.visualize_bold(), run no longer needs to be specified if the given subject only has a single run.

[0.23.8.post1] - 2025-03-20#

📖 Documentation#

  • Adds additional documentation clarity and emphasis.

[0.23.8.post0] - 2025-03-20#

📖 Documentation#

  • Fixes improper documentation rendering in IDE’s

  • Streamlines documentation

[0.23.8] - 2025-03-17#

🐛 Fixes#

  • Added all to neurocaps.typing module so that the star import only restricts to public types.

[0.23.7] - 2025-03-16#

🐛 Fixes#

  • Fixes an incorrect return typehint for a CAP.caps2corr function.

  • Add optional type hint for certain parameter.

[0.23.6] - 2025-03-16#

🚀 New/Added#

  • Add type hints to all internal classes; minor code cleaning.

  • Use new types for subject timeseries and parcellations throughout docs.

♻ Changed#

  • Change some internal parameters for the private _Data class such as scrub_lim -> out_percent, fd -> fd_thresh, and shift -> tr_shift. Done for clarity.

[0.23.5] - 2025-03-13#

🐛 Fixes#

  • Updated type hints for class methods that return self from None to Self.

[0.23.4] - 2025-03-13#

  • Primarily some internal refactoring and API docs updates:

    • Some refactoring to reduce some code complexity.

    • Internal code for public classes only use private attributes to separate it from public properties. Exception for private getter classes that are inherited public classes.

♻ Changed#

  • Internal function changed from _create_regions to compute_region_means.

  • Internal CAP._raise_error function changed slightly to accept attribute names, which are preceded by the underscore instead of properties. Done so that their is a separation between the internal private attributes and public properties.

  • Property change from region_caps to region_means and now includes “Regions” key. For backward compatibility, the old region_caps behavior is still available.

📖 Documentation#

  • Name change from “neurocaps” to “NeuroCAPs” in documentation only. Package name to remain “neurocaps” for compliance with PEP 8.

  • Additional documentation fixes to enhance clarity.

[0.23.3] - 2025-03-08#

✨ Enhancement#

  • Improved error handling for custom parcel approaches. The structure of the subkeys are validated to prevent errors due to incorrect structure down the pipeline.

[0.23.2] - 2025-03-06#

♻ Changed#

  • Minor improvements to str call for clarity.

  • Added optional dependencies for benchmarking and cleaned repeating optional dependencies.

  • Created separate static internal function for computing cosine similarity between the 1D region mask and high/low amplitude of the CAP vector.

[0.23.1] - 2025-02-27#

♻ Changed#

  • Minor improvements in how run IDs are intersected to prevent errors in rare cases.

  • Update confound names in test datasets to thier modern counterparts in fMRIPrep.

🐛 Fixes#

  • Added pytest-cov and pre-commit as optional dependencies

  • Fix case in version 0.23.0 when confound_names is None but n_acompcor_separate is specified, which resulted in the no acompcor components being included for nuisance regression.

  • Also, add warning is no cosine regressors are included in confound_names but the following is detected:

    • n_acompcor_separate specified

    • “a_comp_cor” or “t_comp_cor” included in confound_names

[0.23.0] - 2025-02-25#

  • Updates pertain to TimeseriesExtractor

🚀 New/Added#

  • Added a new key to fd_threshold for optional cubic spline interpolation of censored volumes not at the beginning or end of the timeseries and is only done after nuisance regression and parcellation. By default, interpolation is not done and must explicitly be set to True.

♻ Changed#

  • Default for confounds_names changed from None to "basic". The "basic" option now performs the same functionality as confound_names=None did in previous versions.

  • Ordering of some self.signal_clean_info parameters changed.

🐛 Fixes#

  • Raises ValueError when use_confounds=False but fd_threshold, n_acompcor_separate, or ``dummy_scans = {“auto”: True} is specified. IMPORTANT:

  • Fixed issue that occured only when n_acompcor_separate is None (default), which resulted in all acompcor regressors are selected from the confounds metadata due to list slicing issue [0:None]. Not an issue when n_acompcor_separate is not None or the preproccesing pipeline directory did not have a confounds json file. FIX: The confounds metadata is only retrieved when n_acompcor_separate is not None.

  • Overall improved error handling.

[0.22.2] - 2025-02-21#

🚀 New/Added#

  • Added new “exceptions” module containing the BIDSQueryError.

📖 Documentation#

  • BIDSQueryError now documented.

  • Updated doc strings to redirect to documentation about directory structure/entities.

[0.22.1.post0] - 2025-02-19#

📖 Documentation#

  • Add clarifying information to doc strings about the entities/file naming structure.

[0.22.1] - 2025-02-18#

♻ Changed#

  • More efficient computation of transition probability

[0.22.0.post0] - 2025-02-17#

📖 Documentation#

  • Add clarifying information to docs.

[0.22.0] - 2025-02-17#

♻ Changed#

  • Change in internal logic for condition to not add plus one to the duration scan index (scans = range(start, end + 1) -> scans = range(start, end)) to reduce potential condition spillover in certain task designs such as rapid events.

📖 Documentation#

  • Remove version change directives under 0.19.0 to clean up docs.

[0.21.8] - 2025-02-13#

🚀 New/Added#

  • CAP and TimeseriesExtractor classes now have defined string dunder methods that return specific metadata.

[0.21.7] - 2025-02-11#

🐛 Fixes#

  • Fixed documentation rendering issues in VSCode.

📖 Documentation#

  • Cleaned documentation in some functions.

[0.21.6] - 2025-02-06#

🐛 Fixes#

  • CAP.outer_products property now no longer returns None when it is not None.

[0.21.5] - 2025-01-27#

🚀 New/Added#

  • Added new progress_bar parameter to CAP.calculate_metrics, CAP.caps2niftis, CAP.caps2surf, CAP.get_caps, and TimeseriesExtractor.get_bold to display tqdm progress bars.

📖 Documentation#

  • Cleans version change/version added directives and references for versions under 0.19.0 to clean up documentation.

  • Additional minor documentation cleaning.

[0.21.4] - 2025-01-24#

🐛 Fixes#

  • Fix issue in “counts” computation in CAP.calculate_metrics for case where no TRs are assigned to a specific label/CAP. Instead of “counts” being 0 in this case, it would be a 1. Issue did not affect the other metrics (“temporal fraction”, “persistence”, etc), which would correctly be 0 in such cases.

[0.21.3] - 2025-01-17#

🐛 Fixes#

  • Added ipywidgets in optional dependencies for a better experience with the “openneuro_demo” Jupyter notebook.

[0.21.2] - 2025-01-14#

🐛 Fixes#

  • Fixes warning about ignoring mandatory keys in fd_threshold and dummy_scans.

  • Also adds check to ensure that the “outlier_percentage” key is a float between 0 and 1.

  • Setuptools version pinned to 64.0 or greater.

[0.21.1] - 2025-01-10#

🐛 Fixes#

  • Better type validation for fd_threshold and dummy_scans.

📖 Documentation#

  • Slightly clearer documentation on the criteria used for fd_threshold.

[0.21.0] - 2025-01-02#

🚀 New/Added#

  • Added a new parameter, slice_time_ref in TimeseriesExtractor.get_bold to allow onset to be subtracted by slice_time_ref * tr if desired.

[0.20.0] - 2024-12-31#

🚀 New/Added#

  • Added new log message specifying the condition being extracted if condition is not None.

  • Added a new parameter, condition_tr_shift in TimeseriesExtractor.get_bold to allow a shift in the the starting and ending scan in TR units for a condition.

[0.19.4] - 2024-12-24#

📖 Documentation#

  • Links are fixed in the documentation.

🐛 Fixes#

  • Fix indexing error for visualize_bold if parcel_approach["Custom"]["nodes"] is a NumPy array instead of list.

♻ Changed#

  • Internally, the verbose parameter is set to 0 for nilearn’s fetch_atlas_aal and fetch_atlas_schaefer. and the behavior is stated in the documentation. Cosine similarity in this case is assigned np.nan

  • When creating “regions” for the “Custom” parcel approach, both a list and range can be accepted. Previously, only lists were accepted.

List method:

parcel_approach["Custom"]["regions"] = {
    "Primary Visual": {"lh": [0], "rh": [180]},
    "Early Visual": {"lh": [1, 2, 3], "rh": [181, 182, 183]},
    "Dorsal Stream Visual": {"lh": list(range(4, 10)), "rh": list(range(184, 190))},
    "Ventral Stream Visual": {"lh": list(range(10, 17)), "rh": list(range(190, 197))},
    "MT+ Complex": {"lh": list(range(17, 26)), "rh": list(range(197, 206))},
    "SomaSens Motor": {"lh": list(range(26, 31)), "rh": list(range(206, 211))},
    "ParaCentral MidCing": {"lh": list(range(31, 40)), "rh": list(range(211, 220))},
    "Premotor": {"lh": list(range(40, 47)), "rh": list(range(220, 227))},
    "Posterior Opercular": {"lh": list(range(47, 52)), "rh": list(range(227, 232))},
    "Early Auditory": {"lh": list(range(52, 59)), "rh": list(range(232, 239))},
    "Auditory Association": {"lh": list(range(59, 67)), "rh": list(range(239, 247))},
    "Insula FrontalOperc": {"lh": list(range(67, 79)), "rh": list(range(247, 259))},
    "Medial Temporal": {"lh": list(range(79, 87)), "rh": list(range(259, 267))},
    "Lateral Temporal": {"lh": list(range(87, 95)), "rh": list(range(267, 275))},
    "TPO": {"lh": list(range(95, 100)), "rh": list(range(275, 280))},
    "Superior Parietal": {"lh": list(range(100, 110)), "rh": list(range(280, 290))},
    "Inferior Parietal": {"lh": list(range(110, 120)), "rh": list(range(290, 300))},
    "Posterior Cingulate": {"lh": list(range(120, 133)), "rh": list(range(300, 313))},
    "AntCing MedPFC": {"lh": list(range(133, 149)), "rh": list(range(313, 329))},
    "OrbPolaFrontal": {"lh": list(range(149, 158)), "rh": list(range(329, 338))},
    "Inferior Frontal": {"lh": list(range(158, 167)), "rh": list(range(338, 347))},
    "Dorsolateral Prefrontal": {"lh": list(range(167, 180)), "rh": list(range(347, 360))},
    "Subcortical Regions": {"lh": list(range(360, 393)), "rh": list(range(393, 426))},
}

List and range method:

parcel_approach["Custom"]["regions"] = {
    "Primary Visual": {"lh": [0], "rh": [180]},
    "Early Visual": {"lh": [1, 2, 3], "rh": [181, 182, 183]},
    "Dorsal Stream Visual": {"lh": range(4, 10), "rh": range(184, 190)},
    "Ventral Stream Visual": {"lh": range(10, 17), "rh": range(190, 197)},
    "MT+ Complex": {"lh": range(17, 26), "rh": range(197, 206)},
    "SomaSens Motor": {"lh": range(26, 31), "rh": range(206, 211)},
    "ParaCentral MidCing": {"lh": range(31, 40), "rh": range(211, 220)},
    "Premotor": {"lh": range(40, 47), "rh": range(220, 227)},
    "Posterior Opercular": {"lh": range(47, 52), "rh": range(227, 232)},
    "Early Auditory": {"lh": range(52, 59), "rh": range(232, 239)},
    "Auditory Association": {"lh": range(59, 67), "rh": range(239, 247)},
    "Insula FrontalOperc": {"lh": range(67, 79), "rh": range(247, 259)},
    "Medial Temporal": {"lh": range(79, 87), "rh": range(259, 267)},
    "Lateral Temporal": {"lh": range(87, 95), "rh": range(267, 275)},
    "TPO": {"lh": range(95, 100), "rh": range(275, 280)},
    "Superior Parietal": {"lh": range(100, 110), "rh": range(280, 290)},
    "Inferior Parietal": {"lh": range(110, 120), "rh": range(290, 300)},
    "Posterior Cingulate": {"lh": range(120, 133), "rh": range(300, 313)},
    "AntCing MedPFC": {"lh": range(133, 149), "rh": range(313, 329)},
    "OrbPolaFrontal": {"lh": range(149, 158), "rh": range(329, 338)},
    "Inferior Frontal": {"lh": range(158, 167), "rh": range(338, 347)},
    "Dorsolateral Prefrontal": {"lh": range(167, 180), "rh": range(347, 360)},
    "Subcortical Regions": {"lh": range(360, 393), "rh": range(393, 426)},
}

[0.19.3.post0] - 2024-12-10#

📖 Documentation#

  • Additional documentation for standardize function.

[0.19.3] - 2024-12-08#

🚀 New/Added#

  • Method chaining for several methods in the CAP and TimeseriesExtractor class.

[0.19.2] - 2024-12-06#

🐛 Fixes#

  • Add type hints to properties.

  • Improve accuracy of type hints for the properties.

  • Fixes type hints for certain parameters that included numpy.ndarray.

  • Replaces any returns that implies a plot object is returned and replaces with None for clarity.

  • Raise type error when self.subject_table in CAP is set but is not a dictionary.

[0.19.1] - 2024-11-30#

  • Primarily to ensure all the latest distributions have the correct documentation links.

  • Includes some internal code changes that won’t change results.

  • TODO for future version is to support Python 3.13.

[0.19.0] - 2024-11-28#

  • Cleaning some of the API, specifically parameter names and properties, no defaults have been changed in this update.

API for 0.18.0 versions

API for 0.19.0

🚀 New/Added#

  • suffix_filename added to CAP.caps2plot, CAP.caps2surf, CAP.caps2radar, and transition_matrix. This addition was done to allow the suffix_title parameter in each of the previously listed methods to only be responsible for the title of the plots. The suffix filename will also be appended to the end of the default filename.

  • CAP class now has a cluster_scores property to consolodate the inertia, davies_bouldin, silhouette, and “variance_ratio” scores into a property instead of separate properties. Consequently, the inertia, davies_bouldin, silhouette, and “variance_ratio” have been removed.

The structure of this property is:

{
    "Cluster_Selection_Method": str,  # e.g., "elbow", "davies_bouldin", "silhouette", or "variance_ratio"
    "Scores": {
        "GroupName": {
            2: float,  # Score for 2 clusters
            3: float,  # Score for 3 clusters
            4: float,  # Score for 4 clusters
        },
    }
}

♻ Changed#

  • Any instance of file_name in a parameter name has been changed to the more conventional parameter name filename. For instance, suffix_file_name now becomes suffix_filename and file_names becomes filenames. This change effects the following functions: merge_dicts, standardize, change_dtypes, CAP.calculate_metrics, CAP.caps2niftis, TimeseriesExtractor.timeseries_to_pickle, and TimeseriesExtractor.visualize_bold.

  • Warning logged whenever file name parameter is used but output_dir is not specified.

📖 Documentation#

  • Fix doc parameter error for CAP.caps2niftis that used suffix_title instead of suffix_file_name, which is now suffix_filename.

  • In documentation, version labels restricted to changes or additions make from 0.18.0 and above for less clutter.

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