Update README.md

README.md

@@ -59,3 +59,19 @@ Unit tests are written in [pytest](https://docs.pytest.org/en/7.3.x/) and execut
 > Kemp, C., Xu, Y., & Regier, T. (2018). Semantic Typology and Efficient Communication. Annual Review of Linguistics, 4(1), 109–128. https://doi.org/10.1146/annurev-linguistics-011817-045406
 
 </details>
+
+## Citation
+
+```
+@article{imel2025ultk,
+  author    = {Imel, Nathaniel and Haberland, Claire and Steinert-Threlkeld, Shane},
+  title     = {The Unnatural Language ToolKit (ULTK)},
+  journal   = {Proceedings of the Society for Computation in Linguistics},
+  volume    = {8},
+  number    = {1},
+  pages     = {46},
+  year      = {2025},
+  doi       = {10.7275/scil.3144},
+  url       = {https://doi.org/10.7275/scil.3144}
+}
+```

src/examples/__init__.py

@@ -1,3 +1 @@
-"""Minimal examples demonstrating how to use ULTK.
-
-"""
+"""Minimal examples demonstrating how to use ULTK."""

src/examples/modals/scripts/analyze.py

@@ -30,11 +30,10 @@
             pn.aes(
                 fill="degree_iff",
                 # shape="type",
-            ),        
+            ),
             color="black",
             size=6,
         )
-
         + pn.geom_point(  # The natural languages
             natural_data,
             color="red",
@@ -48,10 +47,10 @@
             size=6,  # orig 9
             nudge_x=1,
             # color="white",
-        )         
+        )
         + pn.scale_fill_continuous(
             "cividis",
-            name="naturalness", 
+            name="naturalness",
         )
         + pn.theme_classic()
         + pn.xlab("Complexity")
@@ -66,6 +65,7 @@
 
     import numpy as np
     from scipy.stats import ttest_1samp, linregress
+
     print(
         ttest_1samp(
             explored_data["distance"].values,
@@ -79,4 +79,4 @@
             explored_data["degree_iff"].values,
         )
     )
-    # breakpoint()
+    # breakpoint()

src/examples/modals/scripts/combine_data.py

@@ -31,8 +31,11 @@ def yaml_to_dataframe(filename: str, keys: list[str]) -> pd.DataFrame:
     )
 
     from ultk.effcomm.tradeoff import pareto_min_distances
+
     all_points = all_data[["complexity", "comm_cost"]].values
-    pareto_points = all_data[all_data["type"] == "dominant"][["complexity", "comm_cost"]].values
+    pareto_points = all_data[all_data["type"] == "dominant"][
+        ["complexity", "comm_cost"]
+    ].values
     min_distances = pareto_min_distances(points=all_points, pareto_points=pareto_points)
     all_data["distance"] = min_distances
 

src/ultk/effcomm/__init__.py

@@ -1,4 +1,4 @@
-"""Tools for measuring languages for communicative efficiency. 
+"""Tools for measuring languages for communicative efficiency.
 
 Submodules divide the labor of a computational experiment performing an efficiency analysis of a language into several parts: generating and sampling the space of possible languages, measuring their properties, and determining which languages optimize efficient trade-offs w.r.t these properties.
 

src/ultk/effcomm/sampling.py

@@ -1,5 +1,4 @@
-"""Functions for sampling expressions into languages.
-"""
+"""Functions for sampling expressions into languages."""
 
 import copy
 from typing import Any

src/ultk/language/__init__.py

@@ -2,7 +2,7 @@
 
 At the current stage of development, ULTK focuses on supporting abstractions to model the mapping between expressions and meanings of a language. So far, we leave almost everything besides this basic mapping (morphosyntax, phonology, phonetic inventories, among other features of human languages) to future work.
 
-The `ultk.language.language` submodule contains classes for constructing a language, which can contain one or more expressions. 
+The `ultk.language.language` submodule contains classes for constructing a language, which can contain one or more expressions.
 
 The `ultk.language.semantics` submodule contains classes for defining a universe (meaning space) of referents (denotations) and meanings (categories).
 """

src/ultk/language/semantics.py

@@ -1,20 +1,20 @@
 """Classes for modeling the meanings of a language.
 
-    Meanings are modeled as things which map linguistic forms to objects of reference. The linguistic forms and objects of reference can in principle be very detailed, and future work may elaborate the meaning classes and implement a Form class.
+Meanings are modeled as things which map linguistic forms to objects of reference. The linguistic forms and objects of reference can in principle be very detailed, and future work may elaborate the meaning classes and implement a Form class.
 
-    In efficient communication analyses, simplicity and informativeness can be measured as properties of semantic aspects of a language. E.g., a meaning is simple if it is easy to represent, or to compress into some code; a meaning is informative if it is easy for a listener to recover a speaker's intended literal meaning.
+In efficient communication analyses, simplicity and informativeness can be measured as properties of semantic aspects of a language. E.g., a meaning is simple if it is easy to represent, or to compress into some code; a meaning is informative if it is easy for a listener to recover a speaker's intended literal meaning.
 
-    Examples:
+Examples:
 
-        >>> from ultk.language.semantics import Referent, Meaning, Universe
-        >>> from ultk.language.language import Expression
-        >>> # construct the meaning space for numerals
-        >>> numerals_universe = NumeralUniverse(referents=[NumeralReferent(str(i)) for i in range(1, 100)])
-        >>> # construct a list of referents for the expression 'a few'
-        >>> a_few_refs = [NumeralReferent(name=str(i)) for i in range(2, 6)]
-        >>> a_few_meaning = NumeralMeaning(referents=a_few_refs, universe=numerals_universe)
-        >>> # define the expression
-        >>> a_few = NumeralExpression(form="a few", meaning=a_few_meaning)
+    >>> from ultk.language.semantics import Referent, Meaning, Universe
+    >>> from ultk.language.language import Expression
+    >>> # construct the meaning space for numerals
+    >>> numerals_universe = NumeralUniverse(referents=[NumeralReferent(str(i)) for i in range(1, 100)])
+    >>> # construct a list of referents for the expression 'a few'
+    >>> a_few_refs = [NumeralReferent(name=str(i)) for i in range(2, 6)]
+    >>> a_few_meaning = NumeralMeaning(referents=a_few_refs, universe=numerals_universe)
+    >>> # define the expression
+    >>> a_few = NumeralExpression(form="a few", meaning=a_few_meaning)
 """
 
 from dataclasses import dataclass