[REF] Replace duecredit with BibTeX

.codecov.yml

@@ -16,5 +16,4 @@ coverage:
 
 ignore:
   - "tedana/tests/"
-  - "tedana/due.py"
   - "tedana/_version.py"

docs/conf.py

@@ -20,6 +20,8 @@
 import os
 import sys
 
+import sphinx_rtd_theme
+
 sys.path.insert(0, os.path.abspath("sphinxext"))
 sys.path.insert(0, os.path.abspath(os.path.pardir))
 
@@ -50,6 +52,7 @@
     "sphinx.ext.napoleon",
     "sphinx.ext.todo",
     "sphinxarg.ext",
+    "sphinxcontrib.bibtex",  # for foot-citations
 ]
 
 import sphinx
@@ -127,8 +130,6 @@
 # a list of builtin themes.
 #
 # installing theme package
-import sphinx_rtd_theme
-
 html_theme = "sphinx_rtd_theme"
 
 # Theme options are theme-specific and customize the look and feel of a theme
@@ -153,6 +154,13 @@ def setup(app):
 
 html_favicon = "_static/tedana_favicon.png"
 
+# -----------------------------------------------------------------------------
+# sphinxcontrib-bibtex
+# -----------------------------------------------------------------------------
+bibtex_bibfiles = ["../tedana/resources/references.bib"]
+bibtex_style = "unsrt"
+bibtex_reference_style = "author_year"
+bibtex_footbibliography_header = ""
 
 # -- Options for HTMLHelp output ------------------------------------------
 

docs/outputs.rst

@@ -71,6 +71,8 @@ desc-ICAAccepted_components.nii.gz                  High-kappa ICA coefficient f
 desc-ICAAcceptedZ_components.nii.gz                 Z-normalized spatial component maps
 report.txt                                          A summary report for the workflow with relevant
                                                     citations.
+references.bib                                      The BibTeX entries for references cited in
+                                                    report.txt.
 tedana_report.html                                  The interactive HTML report.
 ================================================    =====================================================
 
@@ -400,28 +402,129 @@ The report is saved in a plain-text file, report.txt, in the output directory.
 
 An example report
 
-  TE-dependence analysis was performed on input data. An initial mask was generated from the first echo using nilearn's compute_epi_mask function. An adaptive mask was then generated, in which each voxel's value reflects the number of echoes with 'good' data. A monoexponential model was fit to the data at each voxel using nonlinear model fitting in order to estimate T2* and S0 maps, using T2*/S0 estimates from a log-linear fit as initial values. For each voxel, the value from the adaptive mask was used to determine which echoes would be used to estimate T2* and S0. In cases of model fit failure, T2*/S0 estimates from the log-linear fit were retained instead. Multi-echo data were then optimally combined using the T2* combination method (Posse et al., 1999). Principal component analysis in which the number of components was determined based on a variance explained threshold was applied to the optimally combined data for dimensionality reduction. A series of TE-dependence metrics were calculated for each component, including Kappa, Rho, and variance explained. Independent component analysis was then used to decompose the dimensionally reduced dataset. A series of TE-dependence metrics were calculated for each component, including Kappa, Rho, and variance explained. Next, component selection was performed to identify BOLD (TE-dependent), non-BOLD (TE-independent), and uncertain (low-variance) components using the Kundu decision tree (v2.5; Kundu et al., 2013). Rejected components' time series were then orthogonalized with respect to accepted components' time series.
+  .. note::
 
-  This workflow used numpy (Van Der Walt, Colbert, & Varoquaux, 2011), scipy (Jones et al., 2001), pandas (McKinney, 2010), scikit-learn (Pedregosa et al., 2011), nilearn, and nibabel (Brett et al., 2019).
+    The boilerplate text includes citations in LaTeX format.
+    \\citep refers to parenthetical citations, while \\cite refers to textual ones.
 
-  This workflow also used the Dice similarity index (Dice, 1945; Sørensen, 1948).
+  TE-dependence analysis was performed on input data using the tedana workflow \\citep{dupre2021te}.
+  An adaptive mask was then generated, in which each voxel's value reflects the number of echoes with 'good' data.
+  A two-stage masking procedure was applied, in which a liberal mask (including voxels with good data in at least the first echo) was used for optimal combination, T2*/S0 estimation, and denoising, while a more conservative mask (restricted to voxels with good data in at least the first three echoes) was used for the component classification procedure.
+  Multi-echo data were then optimally combined using the T2* combination method \\citep{posse1999enhancement}.
+  Next, components were manually classified as BOLD (TE-dependent), non-BOLD (TE-independent), or uncertain (low-variance).
+  This workflow used numpy \\citep{van2011numpy}, scipy \\citep{virtanen2020scipy}, pandas \\citep{mckinney2010data,reback2020pandas}, scikit-learn \\citep{pedregosa2011scikit}, nilearn, bokeh \\citep{bokehmanual}, matplotlib \\citep{Hunter:2007}, and nibabel \\citep{brett_matthew_2019_3233118}.
+  This workflow also used the Dice similarity index \\citep{dice1945measures,sorensen1948method}.
 
   References
 
-  Brett, M., Markiewicz, C. J., Hanke, M., Côté, M.-A., Cipollini, B., McCarthy, P., … freec84. (2019, May 28). nipy/nibabel. Zenodo. http://doi.org/10.5281/zenodo.3233118
-
-  Dice, L. R. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3), 297-302.
-
-  Jones E, Oliphant E, Peterson P, et al. SciPy: Open Source Scientific Tools for Python, 2001-, http://www.scipy.org/
-
-  Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., Vértes, P. E., Inati, S. J., ... & Bullmore, E. T. (2013). Integrated strategy for improving functional connectivity mapping using multiecho fMRI. Proceedings of the National Academy of Sciences, 110(40), 16187-16192.
-
-  McKinney, W. (2010, June). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference (Vol. 445, pp. 51-56).
-
-  Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830.
-
-  Posse, S., Wiese, S., Gembris, D., Mathiak, K., Kessler, C., Grosse‐Ruyken, M. L., ... & Kiselev, V. G. (1999). Enhancement of BOLD‐contrast sensitivity by single‐shot multi‐echo functional MR imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 42(1), 87-97.
-
-  Sørensen, T. J. (1948). A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. I kommission hos E. Munksgaard.
-
-  Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13(2), 22.
+  .. note::
+
+    The references are also provided in the ``references.bib`` output file.
+
+  .. code-block:: bibtex
+
+    @Manual{bokehmanual,
+        title = {Bokeh: Python library for interactive visualization},
+        author = {{Bokeh Development Team}},
+        year = {2018},
+        url = {https://bokeh.pydata.org/en/latest/},
+    }
+    @article{dice1945measures,
+        title={Measures of the amount of ecologic association between species},
+        author={Dice, Lee R},
+        journal={Ecology},
+        volume={26},
+        number={3},
+        pages={297--302},
+        year={1945},
+        publisher={JSTOR},
+        url={https://doi.org/10.2307/1932409},
+        doi={10.2307/1932409}
+    }
+    @article{dupre2021te,
+        title={TE-dependent analysis of multi-echo fMRI with* tedana},
+        author={DuPre, Elizabeth and Salo, Taylor and Ahmed, Zaki and Bandettini, Peter A and Bottenhorn, Katherine L and Caballero-Gaudes, C{\'e}sar and Dowdle, Logan T and Gonzalez-Castillo, Javier and Heunis, Stephan and Kundu, Prantik and others},
+        journal={Journal of Open Source Software},
+        volume={6},
+        number={66},
+        pages={3669},
+        year={2021},
+        url={https://doi.org/10.21105/joss.03669},
+        doi={10.21105/joss.03669}
+    }
+    @inproceedings{mckinney2010data,
+        title={Data structures for statistical computing in python},
+        author={McKinney, Wes and others},
+        booktitle={Proceedings of the 9th Python in Science Conference},
+        volume={445},
+        number={1},
+        pages={51--56},
+        year={2010},
+        organization={Austin, TX},
+        url={https://doi.org/10.25080/Majora-92bf1922-00a},
+        doi={10.25080/Majora-92bf1922-00a}
+    }
+    @article{pedregosa2011scikit,
+        title={Scikit-learn: Machine learning in Python},
+        author={Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
+        journal={the Journal of machine Learning research},
+        volume={12},
+        pages={2825--2830},
+        year={2011},
+        publisher={JMLR. org},
+        url={http://jmlr.org/papers/v12/pedregosa11a.html}
+    }
+    @article{posse1999enhancement,
+        title={Enhancement of BOLD-contrast sensitivity by single-shot multi-echo functional MR imaging},
+        author={Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R and Kiselev, Valerij G},
+        journal={Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine},
+        volume={42},
+        number={1},
+        pages={87--97},
+        year={1999},
+        publisher={Wiley Online Library},
+        url={https://doi.org/10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+        doi={10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O}
+    }
+    @software{reback2020pandas,
+        author = {The pandas development team},
+        title = {pandas-dev/pandas: Pandas},
+        month = feb,
+        year = 2020,
+        publisher = {Zenodo},
+        version = {latest},
+        doi = {10.5281/zenodo.3509134},
+        url = {https://doi.org/10.5281/zenodo.3509134}
+    }
+    @article{sorensen1948method,
+        title={A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons},
+        author={Sorensen, Th A},
+        journal={Biol. Skar.},
+        volume={5},
+        pages={1--34},
+        year={1948}
+    }
+    @article{van2011numpy,
+        title={The NumPy array: a structure for efficient numerical computation},
+        author={Van Der Walt, Stefan and Colbert, S Chris and Varoquaux, Gael},
+        journal={Computing in science \& engineering},
+        volume={13},
+        number={2},
+        pages={22--30},
+        year={2011},
+        publisher={IEEE},
+        url={https://doi.org/10.1109/MCSE.2011.37},
+        doi={10.1109/MCSE.2011.37}
+    }
+    @article{virtanen2020scipy,
+        title={SciPy 1.0: fundamental algorithms for scientific computing in Python},
+        author={Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and others},
+        journal={Nature methods},
+        volume={17},
+        number={3},
+        pages={261--272},
+        year={2020},
+        publisher={Nature Publishing Group},
+        url={https://doi.org/10.1038/s41592-019-0686-2},
+        doi={10.1038/s41592-019-0686-2}
+    }

setup.cfg

@@ -41,6 +41,7 @@ doc =
     sphinx>=1.5.3
     sphinx_rtd_theme
     sphinx-argparse
+    sphinxcontrib-bibtex
 tests =
     codecov
     coverage<5.0
@@ -50,10 +51,7 @@ tests =
     pytest
     pytest-cov
     requests
-duecredit =
-    duecredit
 all =
-    %(duecredit)s
     %(doc)s
     %(tests)s
 

tedana/__init__.py

@@ -8,42 +8,10 @@
 import warnings
 
 from ._version import get_versions
-from .due import BibTeX, Doi, due
 
 __version__ = get_versions()["version"]
 
 # cmp is not used, so ignore nipype-generated warnings
 warnings.filterwarnings("ignore", r"cmp not installed")
 
-# Citation for the package JOSS paper.
-due.cite(
-    Doi("10.21105/joss.03669"),
-    description="Publication introducing tedana.",
-    path="tedana",
-    cite_module=True,
-)
-
-# Citation for the algorithm.
-due.cite(
-    Doi("10.1016/j.neuroimage.2011.12.028"),
-    description="Introduces MEICA and tedana.",
-    path="tedana",
-    cite_module=True,
-)
-due.cite(
-    Doi("10.1073/pnas.1301725110"),
-    description="Improves MEICA and tedana.",
-    path="tedana",
-    cite_module=True,
-)
-
-# Citation for package version.
-due.cite(
-    Doi("10.5281/zenodo.1250561"),
-    description="The tedana package",
-    version=__version__,
-    path="tedana",
-    cite_module=True,
-)
-
 del get_versions