.mailmap

+Raphaël Bleuse <cs@research.bleuse.net>      <raphael.bleuse@uni.lu>
+Martin Rosalie <martin.rosalie@univ-perp.fr> <martin.rosalie@uni.lu>

CONTRIBUTORS.rst

@@ -2,10 +2,10 @@
 Credits (in alphabetical order)
 ===============================
 
-* Raphaël BLEUSE <raphael.bleuse@uni.lu>
+* Raphaël BLEUSE <cs@research.bleuse.net>
 * Pascal BOUVRY <pascal.bouvry@uni.lu>
 * Grégoire DANOY <gregoire.danoy@uni.lu>
 * Emmanuel KIEFFER <emmanuel.kieffer@uni.lu>
 * Jeff MEDER <jeffmeder2303@gmail.com>
 * Maya Alexandra OLSZEWSKI <maya.olszewski@gmail.com>
-* Martin ROSALIE <martin.rosalie@uni.lu>
+* Martin ROSALIE <martin.rosalie@univ-perp.fr>

README.rst

@@ -26,11 +26,32 @@ as a Scalable Vector Graphics (SVG) file.
 Citation
 --------
 
-If you use ``cate`` for your research, please cite the following paper:
+``cate`` is developed as a research project.
+
+The motivation for a tool such as ``cate``, an introduction to linking
+matrices, and a description of the optimization logic are described, *inter
+alia*, in a publication presented at the conference Graph Drawing 2018.
+
+This publication is available in the conference proceedings
+(`doi:10.1007/978-3-030-04414-5\_8 <https://doi.org/10.1007/978-3-030-04414-5_8>`__, paywalled),
+or on arXiv (`arxiv:1807.11853 <https://arxiv.org/abs/1807.11853>`__).
+
+
+If you use ``cate`` for a publication, please cite as follows:
+
+  Maya Olszewski, Jeff Meder, Emmanuel Kieffer, Raphaël Bleuse, Martin Rosalie,
+  Grégoire Danoy, and Pascal Bouvry.
+  **Visualizing the Template of a Chaotic Attractor.**
+  In *Graph Drawing*, volume 11282 of Lecture Notes in Computer Science, 106–119.
+  Springer, 2018.
+  `doi:10.1007/978-3-030-04414-5\_8 <https://doi.org/10.1007/978-3-030-04414-5_8>`__.
+
+Or you may use, at your convenience, the following
+`BibTeX entry <https://gitlab.uni.lu/PCOG/cate/raw/master/doc/OlszewskiM2018Visualizing.bib>`__:
 
 .. code-block:: bibtex
 
-   @article{OlszewskiM2018Visualizing,
+   @inproceedings{OlszewskiM2018Visualizing,
      author    = {Olszewski, Maya and
                   Meder, Jeff and
                   Kieffer, Emmanuel and
@@ -39,20 +60,24 @@ If you use ``cate`` for your research, please cite the following paper:
                   Danoy, Gr{\'{e}}goire and
                   Bouvry, Pascal},
      title     = {{V}isualizing the {T}emplate of a {C}haotic {A}ttractor},
-     journal   = {arXiv e-prints},
+     booktitle = {Graph Drawing},
+     series    = {Lecture Notes in Computer Science},
+     volume    = 11282,
+     pages     = {106--119},
+     publisher = {Springer},
      year      = 2018,
-     month     = jul,
-     url       = {https://arxiv.org/abs/1807.11853},
-     archivePrefix = {arXiv},
-     eprint    = {1807.11853},
+     month     = sep,
+     doi       = {10.1007/978-3-030-04414-5_8},
+     isbn      = {978-3-030-04413-8},
    }
 
 
 Installation
 ------------
 
-``cate`` is available as a regular Python package.  It hence can easily be
-installed with ``pip``.
+``cate`` is packaged as a regular Python package, and is published on
+`PyPI <https://pypi.org/project/cate/>`__.  It hence can easily be installed
+with ``pip``.
 
 For more details on how to install a Python package, one can refer to
 https://packaging.python.org/tutorials/installing-packages/

doc/OlszewskiM2018Visualizing.bib

+@inproceedings{OlszewskiM2018Visualizing,
+  author    = {Olszewski, Maya and
+               Meder, Jeff and
+               Kieffer, Emmanuel and
+               Bleuse, Rapha{\"{e}}l and
+               Rosalie, Martin and
+               Danoy, Gr{\'{e}}goire and
+               Bouvry, Pascal},
+  title     = {{V}isualizing the {T}emplate of a {C}haotic {A}ttractor},
+  booktitle = {Graph Drawing},
+  series    = {Lecture Notes in Computer Science},
+  volume    = 11282,
+  pages     = {106--119},
+  publisher = {Springer},
+  year      = 2018,
+  month     = sep,
+  doi       = {10.1007/978-3-030-04414-5_8},
+  isbn      = {978-3-030-04413-8},
+  language  = english,
+}

setup.cfg

@@ -4,7 +4,7 @@
 name = cate
 url = https://gitlab.uni.lu/pcog/cate
 maintainer = Raphaël Bleuse
-maintainer_email = raphael.bleuse@uni.lu
+maintainer_email = cs@research.bleuse.net
 version = attr: src.cate.__version__
 description = Tool to draw the templates of chaotic attractors.
 long_description = file: README.rst

src/cate/_svg.py

@@ -462,7 +462,7 @@ class SVGExporter(_export.Exporter, alias='svg'):
                 self.drawer.draw_crossing(
                     left=(lstrand, lpos),
                     right=(rstrand, rpos),
-                    orientation=template.matrix[lstrand][rstrand] > 0
+                    orientation=template.crossings[lstrand, rstrand] > 0
                 )
                 uncrossed -= {lstrand, rstrand}
                 self.positions[lstrand], self.positions[rstrand] = \

src/cate/core.py

@@ -184,19 +184,28 @@ class Template:
 
     @property
     def crossings(self):
-        """Mapping of (crossing, arity) for each crossing of the template."""
+        """
+        Mapping of {crossing: arity} for each (oriented) crossing of the template.
+        """
         return {
-            (j, i): abs(self.matrix[i][j])
+            (j, i): self.matrix[i][j]
             for i in range(len(self.matrix))
             for j in range(i)
-            if self.matrix[i][j]  # keep only non-zero crossings
+            if self.matrix[i][j]  # keep only crossings with non-zero arity
         }
 
     def _optimize_crosslevels(self):
         """Compute a sequence of crossing levels of minimum depth."""
         initial_state = _OptimizerState(
             tuple(range(self.size)),
-            _multiset.FrozenMultiset(self.crossings)
+            _multiset.FrozenMultiset(
+                {
+                    # the optimization loop needs the arity of each crossing
+                    # without their orientation: drop orientation
+                    crossing: abs(arity)
+                    for crossing, arity in self.crossings.items()
+                }
+            )
         )
         final_state = _OptimizerState(
             tuple(final_position(self.matrix)),