pystc: A simple but extensible Python module for sentences
==========================================================
Introduction
============
A *sentence* is an atomic sentence or a (compound) sentence.
An *atomic sentence* consists of a *predicate* and *constants* the predicate has
as its arguments.
There is no variable, no function.
The number of arguments of a predicate is called the *arity* of the predicate.
A compound sentence can be obtained by joining sentences with *connectives*.
``pystc`` is a simple but extensible Python module for sentences.
It provides functionality to define sentences as you like
by adding constants, predicates, and connectives, whether logical or non-logical.
Constants, predicates, and connectives can be introduced by specifying
their names and how they are associated with other well-defined objects or
functions.
Installation
============
.. code:: shell-session
$ pip install pystc
Usage
=====
Let us first create atomic sentences that can be built from a predicate ``=``
and constants ``T``, ``F``,
.. code:: python
from pystc import AtomicSentence
# Let us add predicate "=" so that the arity for it is 2.
AtomicSentence.add_predicate("=",2)
AtomicSentence.add_constant("T")
AtomicSentence.add_constant("F")
# Now, sentences are ready to create.
s1 = AtomicSentence("=","T","T")
s2 = AtomicSentence("=","T","F")
s3 = AtomicSentence("=","F","T")
assert str(s2) == "=(T,F)"
assert AtomicSentence.read("=(T,F)") == s2
Let us next construct compound sentences.
A ``Sentence`` in ``pystc`` module is simply a recursive type defined to be:
.. code:: python
Sentence = Union[str, AtomicSentence, Tuple["Sentence"]]
Although it is loosely defined for simplicity,
sentences are implicitly expected to fall into one of the following cases:
1. An AtomicSentence object, say ``s2``.
1. The string representation of an AtomicSentence object, say ``"=(T,F)"``.
1. A tuple such that the initial entry is a connective name and the other entries are Sentence objects, say ``("&", s2, ("!", s2))``.
As the connective names ``"&"`` and ``"!"`` appears just above,
let us introduce these connectives in the following codeblock
and inteprete sentences.
.. code:: python
from pystc import SentenceConverter
# Let us set an atomic sentence type and how each symbol is interpreted.
SentenceConverter.set_atom_type(AtomicSentence)
SentenceConverter.set_constant_destination("T", True)
SentenceConverter.set_constant_destination("F", False)
SentenceConverter.set_predicate_destination("=", lambda li,w: li[0]==li[1])
SentenceConverter.set_connective_destination("&",lambda li,w: not False in li)
SentenceConverter.set_connective_destination("|",lambda li,w: True in li)
SentenceConverter.set_connective_destination("!",lambda li,w: not li[0])
assert SentenceConverter.convert(s2) == False
assert SentenceConverter.convert("=(T,F)") == False
assert SentenceConverter.convert(("&", s2, ("!", s2))) == False
assert SentenceConverter.convert(("&", "=(T,F)", ("!", s2))) == False
For another example of usage, let us convert sentences into strings in infix
notation.
.. code:: python
# Clear all class variables
SentenceConverter.clear()
SentenceConverter.set_atom_type(AtomicSentence)
SentenceConverter.set_constant_destination("T", "T")
SentenceConverter.set_constant_destination("F", "F")
SentenceConverter.set_predicate_destination("=", lambda li,w: f"{li[0]}={li[1]}")
SentenceConverter.set_connective_destination("&",lambda li,w: "("+" & ".join(li)+")")
SentenceConverter.set_connective_destination("|",lambda li,w: "("+" | ".join(li)+")")
SentenceConverter.set_connective_destination("!",lambda li,w: "!"+li[0])
assert SentenceConverter.convert("=(T,F)") == "T=F"
assert SentenceConverter.convert(("&", s2, ("!", s2))) == "(T=F & !T=F)"
Let us not forget to clear class variables after everything is finished.
.. code:: python
SentenceConverter.clear()
AtomicSentence.clear()
Bugs/Requests/Discussions
=========================
Please report bugs and requests from `GitHub Issues <https://github.com/toda-lab/pystc/issues>`__ , and
ask questions from `GitHub Discussions <https://github.com/toda-lab/pystc/discussions>`__ .
License
=======
Please see `LICENSE <https://github.com/toda-lab/pystc/blob/main/LICENSE>`__ .
Raw data
{
"_id": null,
"home_page": "https://github.com/toda-lab/pystc",
"name": "pystc",
"maintainer": null,
"docs_url": null,
"requires_python": "<4.0,>=3.8",
"maintainer_email": null,
"keywords": "logic, artificial intelligence",
"author": "Takahisa Toda",
"author_email": "toda@disc.lab.uec.ac.jp",
"download_url": "https://files.pythonhosted.org/packages/21/db/940b6e86e9d3c6f7de1355a19a834e4d1705711ddbbcca95f3e45a914e12/pystc-2.0.0.tar.gz",
"platform": null,
"description": "pystc: A simple but extensible Python module for sentences\n==========================================================\n\nIntroduction\n============\nA *sentence* is an atomic sentence or a (compound) sentence.\nAn *atomic sentence* consists of a *predicate* and *constants* the predicate has\nas its arguments.\nThere is no variable, no function.\nThe number of arguments of a predicate is called the *arity* of the predicate.\nA compound sentence can be obtained by joining sentences with *connectives*.\n\n``pystc`` is a simple but extensible Python module for sentences.\nIt provides functionality to define sentences as you like \nby adding constants, predicates, and connectives, whether logical or non-logical.\nConstants, predicates, and connectives can be introduced by specifying\ntheir names and how they are associated with other well-defined objects or\nfunctions.\n\nInstallation\n============\n\n.. code:: shell-session\n\n $ pip install pystc\n\nUsage\n=====\n\nLet us first create atomic sentences that can be built from a predicate ``=``\nand constants ``T``, ``F``, \n\n.. code:: python\n\n from pystc import AtomicSentence\n\n # Let us add predicate \"=\" so that the arity for it is 2.\n AtomicSentence.add_predicate(\"=\",2) \n AtomicSentence.add_constant(\"T\")\n AtomicSentence.add_constant(\"F\")\n\n # Now, sentences are ready to create.\n s1 = AtomicSentence(\"=\",\"T\",\"T\")\n s2 = AtomicSentence(\"=\",\"T\",\"F\")\n s3 = AtomicSentence(\"=\",\"F\",\"T\")\n\n assert str(s2) == \"=(T,F)\"\n assert AtomicSentence.read(\"=(T,F)\") == s2\n\nLet us next construct compound sentences.\nA ``Sentence`` in ``pystc`` module is simply a recursive type defined to be:\n\n.. code:: python\n\n Sentence = Union[str, AtomicSentence, Tuple[\"Sentence\"]]\n\nAlthough it is loosely defined for simplicity, \nsentences are implicitly expected to fall into one of the following cases:\n\n1. An AtomicSentence object, say ``s2``.\n1. The string representation of an AtomicSentence object, say ``\"=(T,F)\"``.\n1. A tuple such that the initial entry is a connective name and the other entries are Sentence objects, say ``(\"&\", s2, (\"!\", s2))``.\n\nAs the connective names ``\"&\"`` and ``\"!\"`` appears just above, \nlet us introduce these connectives in the following codeblock\nand inteprete sentences.\n\n.. code:: python\n\n from pystc import SentenceConverter\n\n # Let us set an atomic sentence type and how each symbol is interpreted.\n SentenceConverter.set_atom_type(AtomicSentence)\n SentenceConverter.set_constant_destination(\"T\", True)\n SentenceConverter.set_constant_destination(\"F\", False)\n SentenceConverter.set_predicate_destination(\"=\", lambda li,w: li[0]==li[1])\n SentenceConverter.set_connective_destination(\"&\",lambda li,w: not False in li)\n SentenceConverter.set_connective_destination(\"|\",lambda li,w: True in li)\n SentenceConverter.set_connective_destination(\"!\",lambda li,w: not li[0])\n\n assert SentenceConverter.convert(s2) == False\n assert SentenceConverter.convert(\"=(T,F)\") == False\n assert SentenceConverter.convert((\"&\", s2, (\"!\", s2))) == False\n assert SentenceConverter.convert((\"&\", \"=(T,F)\", (\"!\", s2))) == False\n\nFor another example of usage, let us convert sentences into strings in infix\nnotation.\n\n.. code:: python\n\n # Clear all class variables\n SentenceConverter.clear()\n\n SentenceConverter.set_atom_type(AtomicSentence)\n SentenceConverter.set_constant_destination(\"T\", \"T\")\n SentenceConverter.set_constant_destination(\"F\", \"F\")\n SentenceConverter.set_predicate_destination(\"=\", lambda li,w: f\"{li[0]}={li[1]}\")\n SentenceConverter.set_connective_destination(\"&\",lambda li,w: \"(\"+\" & \".join(li)+\")\")\n SentenceConverter.set_connective_destination(\"|\",lambda li,w: \"(\"+\" | \".join(li)+\")\")\n SentenceConverter.set_connective_destination(\"!\",lambda li,w: \"!\"+li[0])\n\n assert SentenceConverter.convert(\"=(T,F)\") == \"T=F\"\n assert SentenceConverter.convert((\"&\", s2, (\"!\", s2))) == \"(T=F & !T=F)\"\n\nLet us not forget to clear class variables after everything is finished.\n\n.. code:: python\n\n SentenceConverter.clear()\n AtomicSentence.clear()\n\n\nBugs/Requests/Discussions\n=========================\n\nPlease report bugs and requests from `GitHub Issues <https://github.com/toda-lab/pystc/issues>`__ , and \nask questions from `GitHub Discussions <https://github.com/toda-lab/pystc/discussions>`__ .\n\nLicense\n=======\n\nPlease see `LICENSE <https://github.com/toda-lab/pystc/blob/main/LICENSE>`__ .\n\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "A simple but extensible Python module for sentences",
"version": "2.0.0",
"project_urls": {
"Homepage": "https://github.com/toda-lab/pystc",
"Repository": "https://github.com/toda-lab/pystc"
},
"split_keywords": [
"logic",
" artificial intelligence"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "7f5cc98b04feb4c7dd749e7fa3804cf95d06fc9141d889b2f25b8b502a48ebb0",
"md5": "d6374091a426e8e5dd5d5fe9a950bcb0",
"sha256": "dec1011da7299b17b162ead59fa096e80f9c361fddcf99b150c943f9f135a783"
},
"downloads": -1,
"filename": "pystc-2.0.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "d6374091a426e8e5dd5d5fe9a950bcb0",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<4.0,>=3.8",
"size": 6489,
"upload_time": "2024-03-28T15:24:14",
"upload_time_iso_8601": "2024-03-28T15:24:14.484332Z",
"url": "https://files.pythonhosted.org/packages/7f/5c/c98b04feb4c7dd749e7fa3804cf95d06fc9141d889b2f25b8b502a48ebb0/pystc-2.0.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "21db940b6e86e9d3c6f7de1355a19a834e4d1705711ddbbcca95f3e45a914e12",
"md5": "3f32f1ad3e7bf1b5e3e5c7a3189dcfbc",
"sha256": "f8303eba5b2058ee666c1c65cacf124c0c441c209d1ebcd3db66361f4cc0eb3e"
},
"downloads": -1,
"filename": "pystc-2.0.0.tar.gz",
"has_sig": false,
"md5_digest": "3f32f1ad3e7bf1b5e3e5c7a3189dcfbc",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<4.0,>=3.8",
"size": 5932,
"upload_time": "2024-03-28T15:24:16",
"upload_time_iso_8601": "2024-03-28T15:24:16.429642Z",
"url": "https://files.pythonhosted.org/packages/21/db/940b6e86e9d3c6f7de1355a19a834e4d1705711ddbbcca95f3e45a914e12/pystc-2.0.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-03-28 15:24:16",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "toda-lab",
"github_project": "pystc",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"tox": true,
"lcname": "pystc"
}
JSON
