Rename package from pensive to sofic

.github/workflows/publish.yml

@@ -26,7 +26,7 @@ jobs:
         run: |
           set -euo pipefail
           tag="${GITHUB_REF_NAME#v}"
-          built=$(ls dist/pensive-*.tar.gz | sed -E 's|.*/pensive-(.+)\.tar\.gz|\1|')
+          built=$(ls dist/sofic-*.tar.gz | sed -E 's|.*/sofic-(.+)\.tar\.gz|\1|')
           echo "tag=$tag built=$built"
           [ "$tag" = "$built" ] || { echo "::error::Built version $built != tag $tag"; exit 1; }
 
@@ -42,7 +42,7 @@ jobs:
     runs-on: ubuntu-latest
     environment:
       name: pypi
-      url: https://pypi.org/p/pensive
+      url: https://pypi.org/p/sofic
     permissions:
       id-token: write
       contents: write

.gitignore

@@ -13,7 +13,7 @@ MANIFEST
 .venv/
 
 # Packaging
-pensive.egg-info/
+sofic.egg-info/
 
 # Test and coverage
 .coverage*

CONTRIBUTING.md

@@ -1,11 +1,11 @@
 Contributing
 ------------
 
-If you'd like a feature added to ``pensive`` or notice any problems, please file an issue, or, even better, open a pull request. We'll work with you to ensure that the code is tested and documented.
+If you'd like a feature added to ``sofic`` or notice any problems, please file an issue, or, even better, open a pull request. We'll work with you to ensure that the code is tested and documented.
 
-It's appropriate to file an issue when you encounter a problem with `pensive`. Be sure to include
+It's appropriate to file an issue when you encounter a problem with `sofic`. Be sure to include
 
-* the `pensive` version you're using
+* the `sofic` version you're using
 * the operating system and Python version
 * full tracebacks
 

LICENSE.txt

@@ -1,6 +1,6 @@
 BSD 3-Clause License
 
-Copyright (c) 2026, pensive contributors.
+Copyright (c) 2026, sofic contributors.
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

README.rst

@@ -1,8 +1,8 @@
 =======
-pensive
+sofic
 =======
 
-``pensive`` is a Python package for hidden Markov models, symbolic dynamics,
+``sofic`` is a Python package for hidden Markov models, symbolic dynamics,
 finite state machines, and other stochastic symbol generators.
 
 Basic Information
@@ -11,12 +11,12 @@ Basic Information
 Documentation
 ~~~~~~~~~~~~~~
 
-https://pensive.readthedocs.io
+https://sofic.readthedocs.io
 
 Repository
 ~~~~~~~~~~
 
-https://github.com/dit/pensive
+https://github.com/dit/sofic
 
 Dependencies
 ~~~~~~~~~~~~
@@ -35,8 +35,8 @@ Clone the repository and install development dependencies with ``uv``:
 
 .. code-block:: bash
 
-   git clone https://github.com/dit/pensive.git
-   cd pensive
+   git clone https://github.com/dit/sofic.git
+   cd sofic
    uv sync --extra dev
 
 Run tests with ``uv run pytest``. See the ``generalinfo`` page in the Sphinx
@@ -47,7 +47,7 @@ Introduction
 
 Many natural and engineered processes produce sequences of symbols whose
 statistics are governed by latent structure: hidden states, transition rules,
-or algebraic constraints on allowed paths. ``pensive`` collects algorithms and
+or algebraic constraints on allowed paths. ``sofic`` collects algorithms and
 data structures for representing, simulating, and analyzing such generators
 behind a single, composable Python API.
 
@@ -56,15 +56,15 @@ construction, validation, simulation, visualization, (de)serialization, and a
 large library of structural and information-theoretic measures. The three
 main families are:
 
-* **Stochastic generators** (``pensive.generators``) — Markov chains, hidden
+* **Stochastic generators** (``sofic.generators``) — Markov chains, hidden
   Markov models (Moore and Mealy presentations), ε-machines, probabilistic
   finite automata, mixed-state presentations, and quasiprobabilistic
   generators. These assign probabilities to sequences.
-* **Finite automata** (``pensive.automata``) — DFAs, NFAs, transducers
+* **Finite automata** (``sofic.automata``) — DFAs, NFAs, transducers
   (Mealy/Moore machines), regular languages, Büchi automata, visibly pushdown
   and nested-word automata, and residual finite-state automata. These recognize
   or transform languages.
-* **Symbolic shifts** (``pensive.shifts``) — shifts of finite type, sofic
+* **Symbolic shifts** (``sofic.shifts``) — shifts of finite type, sofic
   shifts, topological Markov chains, and Dyck/sofic-Dyck shifts. These describe
   the *support* (set of allowed sequences) of a process.
 
@@ -77,15 +77,15 @@ Installation
 
 .. code-block:: bash
 
-   pip install pensive
+   pip install sofic
 
 Optional extras:
 
-* ``pensive[viz]`` — Graphviz diagrams in terminals and Jupyter
-* ``pensive[bayes]`` — PyMC/ArviZ backends for Bayesian inference
-* ``pensive[test]`` — pytest, hypothesis, and graphviz for the test suite
-* ``pensive[docs]`` — Sphinx, IPython, and matplotlib for the docs
-* ``pensive[dev]`` — linting, type checking, docs, and all of the above
+* ``sofic[viz]`` — Graphviz diagrams in terminals and Jupyter
+* ``sofic[bayes]`` — PyMC/ArviZ backends for Bayesian inference
+* ``sofic[test]`` — pytest, hypothesis, and graphviz for the test suite
+* ``sofic[docs]`` — Sphinx, IPython, and matplotlib for the docs
+* ``sofic[dev]`` — linting, type checking, docs, and all of the above
 
 Quickstart
 ----------
@@ -102,7 +102,7 @@ Stochastic generators
 
 .. code-block:: python
 
-   from pensive import MarkovChain
+   from sofic import MarkovChain
 
    mc = MarkovChain(initial_distribution={"sunny": 0.5, "rainy": 0.5})
    mc.add_transition("sunny", "sunny", 0.9)
@@ -120,7 +120,7 @@ states and ``P(target | source)`` on edges:
 
 .. code-block:: python
 
-   from pensive import MooreHMM
+   from sofic import MooreHMM
 
    moore = MooreHMM(
        observation_alphabet=frozenset({"H", "T"}),
@@ -144,7 +144,7 @@ states and ``P(target | source)`` on edges:
 
 .. code-block:: python
 
-   from pensive import MealyHMM
+   from sofic import MealyHMM
 
    gm = MealyHMM(
        observation_alphabet=frozenset({0, 1}),
@@ -159,16 +159,16 @@ states and ``P(target | source)`` on edges:
    gm.is_unifilar()              # True
    gm.word_probability([1, 0, 1])  # 0.1667
 
-Many canonical models ship in ``pensive.examples``, so the golden mean is
-also just ``from pensive.examples import golden_mean; gm = golden_mean(0.5)``.
+Many canonical models ship in ``sofic.examples``, so the golden mean is
+also just ``from sofic.examples import golden_mean; gm = golden_mean(0.5)``.
 
 **ε-machine** — the minimal unifilar (causal-state) presentation of a stationary
 process. Build one from any HMM, from an observed sequence, or directly, and
 read off computational-mechanics quantities:
 
 .. code-block:: python
 
-   from pensive import EpsilonMachine
+   from sofic import EpsilonMachine
 
    eps = EpsilonMachine.from_hmm(gm)          # minimize an HMM presentation
    # eps = EpsilonMachine.from_sequence(data, method="cssr", Lmax=4)  # infer
@@ -187,7 +187,7 @@ be checked as you go):
 
 .. code-block:: python
 
-   from pensive import DFA
+   from sofic import DFA
 
    dfa = DFA(
        input_alphabet=frozenset({"a", "b"}),
@@ -212,7 +212,7 @@ when you need one:
 
 .. code-block:: python
 
-   from pensive import NFA
+   from sofic import NFA
 
    nfa = NFA(
        input_alphabet=frozenset({"a", "b"}),
@@ -233,7 +233,7 @@ inverts bits:
 
 .. code-block:: python
 
-   from pensive import MealyMachine
+   from sofic import MealyMachine
 
    inv = MealyMachine(
        input_alphabet=frozenset({"0", "1"}),
@@ -258,7 +258,7 @@ forbidden words. The golden-mean shift forbids ``11``:
 
 .. code-block:: python
 
-   from pensive import ShiftOfFiniteType
+   from sofic import ShiftOfFiniteType
 
    sft = ShiftOfFiniteType.from_forbidden_words(
        {("1", "1")},
@@ -276,7 +276,7 @@ topological entropy or extract the measure of maximal entropy:
 .. code-block:: python
 
    import numpy as np
-   from pensive import TopologicalMarkovChain
+   from sofic import TopologicalMarkovChain
 
    tmc = TopologicalMarkovChain.from_adjacency(
        np.array([[1, 1], [1, 0]], dtype=float),   # golden-mean adjacency
@@ -292,7 +292,7 @@ NFA). This is the even shift (even-length runs of ``0`` between ``1``\ s):
 
 .. code-block:: python
 
-   from pensive import SoficShift
+   from sofic import SoficShift
 
    even = SoficShift(symbol_alphabet=frozenset({0, 1}))
    even.add_transition("even", "even", 0)
@@ -342,7 +342,7 @@ Every model can also be round-tripped through YAML:
 
    text = eps.to_yaml()
    restored = EpsilonMachine.from_yaml(text)
-   # or: pensive.model_to_yaml(eps) / pensive.model_from_yaml(text)
+   # or: sofic.model_to_yaml(eps) / sofic.model_from_yaml(text)
 
 Information anatomy (advanced)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -352,7 +352,7 @@ transient-information anatomy of a process (James, Burke & Crutchfield, 2013):
 
 .. code-block:: python
 
-   from pensive.examples import golden_mean_bidirectional, tent_map_misiurewicz_bidirectional
+   from sofic.examples import golden_mean_bidirectional, tent_map_misiurewicz_bidirectional
 
    bidir = golden_mean_bidirectional(0.5)
    bidir.statistical_complexity()   # 1.5850 bits  (C±)
@@ -366,4 +366,4 @@ transient-information anatomy of a process (James, Burke & Crutchfield, 2013):
 License
 -------
 
-``pensive`` is distributed under the BSD 3-Clause License; see ``LICENSE.txt``.
+``sofic`` is distributed under the BSD 3-Clause License; see ``LICENSE.txt``.

docs/automata/algorithms.rst

@@ -1,19 +1,19 @@
 .. algorithms.rst
-.. py:module:: pensive.automata.algorithms
+.. py:module:: sofic.automata.algorithms
 
 **********
 Algorithms
 **********
 
-Standard automata operations on :class:`~pensive.automata.base.LabeledAutomaton`
+Standard automata operations on :class:`~sofic.automata.base.LabeledAutomaton`
 instances. The implementations cover subset construction, DFA equivalence and
 minimization, Brzozowski double reversal, Moore refinement, Hopcroft refinement,
 and state-elimination conversion to regular expressions
 :cite:`RabinScott1959,Brzozowski1962,Moore1956,Hopcroft1971,Kleene1956`.
 
 .. ipython::
 
-   In [1]: from pensive.automata import DFA, trim, minimize, equivalent
+   In [1]: from sofic.automata import DFA, trim, minimize, equivalent
 
    In [2]: dfa = DFA(
       ...:     input_alphabet=frozenset({0}),
@@ -43,6 +43,6 @@ API
 .. autofunction:: minimize_moore
 .. autofunction:: minimize_brzozowski
 .. autofunction:: equivalent
-.. autofunction:: pensive.automata.regex.automaton_to_regex
+.. autofunction:: sofic.automata.regex.automaton_to_regex
 
 .. autodata:: MinimizationAlgorithm

docs/automata/atomaton.rst

@@ -1,5 +1,5 @@
 .. atomaton.rst
-.. py:module:: pensive.automata.atomaton
+.. py:module:: sofic.automata.atomaton
 
 ********
 Átomaton

docs/automata/automata.rst

@@ -4,7 +4,7 @@
 Automata
 ********
 
-The :mod:`pensive.automata` package provides finite automata, transducers, and
+The :mod:`sofic.automata` package provides finite automata, transducers, and
 regular-language algebra. The finite-automata foundations follow the classical
 DFA/NFA and regular-language literature :cite:`RabinScott1959,HopcroftUllman1979`.
 

docs/automata/buchi.rst

@@ -1,5 +1,5 @@
 .. buchi.rst
-.. py:module:: pensive.automata.buchi
+.. py:module:: sofic.automata.buchi
 
 *****************
 Büchi Automata
@@ -14,5 +14,5 @@ API
 .. autoclass:: BuchiAutomaton
    :members: accepts_lasso, accepts_omega
 
-.. autofunction:: pensive.automata.buchi_simulation.accepts_lasso_buchi
-.. autofunction:: pensive.automata.buchi_simulation.accepts_omega_buchi
+.. autofunction:: sofic.automata.buchi_simulation.accepts_lasso_buchi
+.. autofunction:: sofic.automata.buchi_simulation.accepts_omega_buchi

docs/automata/dfa.rst

@@ -1,5 +1,5 @@
 .. dfa.rst
-.. py:module:: pensive.automata.dfa
+.. py:module:: sofic.automata.dfa
 
 ***
 DFA
@@ -11,7 +11,7 @@ and no ε-transitions, following the standard finite-automata model
 
 .. ipython::
 
-   In [1]: from pensive.automata import DFA
+   In [1]: from sofic.automata import DFA
 
    In [2]: dfa = DFA(
       ...:     input_alphabet=frozenset({0, 1}),

docs/automata/icdfa.rst

@@ -1,5 +1,5 @@
 .. icdfa.rst
-.. py:module:: pensive.automata.icdfa
+.. py:module:: sofic.automata.icdfa
 
 *****
 ICDFA
@@ -23,7 +23,7 @@ API
 .. autofunction:: count_icdfa
 .. autofunction:: count_icdfa_empty
 
-.. autofunction:: pensive.automata.idfa.iter_idfa_strings
-.. autofunction:: pensive.automata.idfa.rank_idfa_string
-.. autofunction:: pensive.automata.idfa.unrank_idfa_string
-.. autofunction:: pensive.automata.idfa.count_accessible_idfa
+.. autofunction:: sofic.automata.idfa.iter_idfa_strings
+.. autofunction:: sofic.automata.idfa.rank_idfa_string
+.. autofunction:: sofic.automata.idfa.unrank_idfa_string
+.. autofunction:: sofic.automata.idfa.count_accessible_idfa