wordtm


Namewordtm JSON
Version 0.4.6 PyPI version JSON
download
home_page
SummaryTopic Modeling Package
upload_time2024-02-05 05:22:38
maintainer
docs_urlNone
authorDr. Johnny CHENG
requires_python
license
keywords word scripture topic modeling visualization low-code pro-code network analysis bertopic lda nfm
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            wordtm 0.4.6
============

An NLP package for topic modeling on the Holy Scripture and other text
from low-code to pro-code

Installation
------------

.. code:: shell

   $ pip install wordtm

Usage
-----

``wordtm`` can be used to perform some NLP pre-processing tasks, text
exploration, including Chinese one, text visualization (word cloud), and
topic modeling (BERTopic, LDA and NMF) as follows:

.. code:: python

   from wordtm import meta, util, ta, tm, viz, pivot, quot

version Submodule
~~~~~~~~~~~~~~~~~

Provides some version information.

.. code:: python

   import wordtm
   print(wordtm.__version__)

meta Submodule
~~~~~~~~~~~~~~

Provides extracting source code of ``wordtm`` module and adding timing and code-showing features
to all functions of the module.

.. code:: python

   print(meta.get_module_info())

   print(meta.get_module_info(detailed=True))

   meta.addin_all()

quot Submodule
~~~~~~~~~~~~~~

Provides functions to extract the quotation source Scripture in OT based on the presribed NT Scripture.

.. code:: python

   cdf = util.load_word('cuv.csv')
   crom8 = util.extract2(cdf, 'Rom 8')
   
   quot.show_quot(crom8, lang='chi')

pivot Submodule
~~~~~~~~~~~~~~~

Provides a pivot table of the prescribed text.

.. code:: python

   cdf = util.load_word('cuv.csv')

   pivot.stat(cdf, chi=True)

ta Submodule
~~~~~~~~~~~~

Provides text analytics functions, including extracting the summarization of the prescribed text.

.. code:: python

   cdf = util.load_word('cuv.csv')
   crom8 = util.extract2(cdf, 'Rom 8')

   ta.summary(rom8, code=True)

tm Submodule
~~~~~~~~~~~~~

Provides text modeling functions, including LDA, NMF and BERTopics modeling.

.. code:: python

   lda = tm.lda_process("cuv.csv", chi=True, eval=True, timing=True)

   nmf = tm.nmf_process("cuv.csv", chi=True, eval=True, code=1)

   btm = tm.btm_process("cuv.csv", chi=True, cat='nt', eval=True)

util Submodule
~~~~~~~~~~~~~~

Provides loading text and text preprocessing functions.

.. code:: python

   df = util.load_word()
   cdf = util.load_word('cuv.csv')

   df.head()
   cdf.head()

   rom8 = util.extract2(df, 'Rom 8')
   crom8 = util.extract2(cdf, 'Rom 8')

viz Submodule
~~~~~~~~~~~~~

Wordcloud plotting from the prescribed text.

.. code:: python

   cdf = util.load_word('cuv.csv')

   viz.chi_wordcloud(cdf)

Contributing
------------

Interested in contributing? Check out the contributing guidelines.
Please note that this project is released with a Code of Conduct. By
contributing to this project, you agree to abide by its terms.

License
-------

``wordtm`` was created by Johnny Cheng. It is licensed under the terms
of the MIT license.

Credits
-------

``wordtm`` was created under the guidance of Jehovah, the Lord.

            

Raw data

            {
    "_id": null,
    "home_page": "",
    "name": "wordtm",
    "maintainer": "",
    "docs_url": null,
    "requires_python": "",
    "maintainer_email": "",
    "keywords": "word,scripture,topic modeling,visualization,low-code,pro-code,network analysis,BERTopic,LDA,NFM",
    "author": "Dr. Johnny CHENG",
    "author_email": "<drjohnnycheng@gmail.com>",
    "download_url": "https://files.pythonhosted.org/packages/6c/33/f330615a8873af446386d2d7ae7c6707f2272f684ef85b1cb2f4eeb4b565/wordtm-0.4.6.tar.gz",
    "platform": null,
    "description": "wordtm 0.4.6\r\n============\r\n\r\nAn NLP package for topic modeling on the Holy Scripture and other text\r\nfrom low-code to pro-code\r\n\r\nInstallation\r\n------------\r\n\r\n.. code:: shell\r\n\r\n   $ pip install wordtm\r\n\r\nUsage\r\n-----\r\n\r\n``wordtm`` can be used to perform some NLP pre-processing tasks, text\r\nexploration, including Chinese one, text visualization (word cloud), and\r\ntopic modeling (BERTopic, LDA and NMF) as follows:\r\n\r\n.. code:: python\r\n\r\n   from wordtm import meta, util, ta, tm, viz, pivot, quot\r\n\r\nversion Submodule\r\n~~~~~~~~~~~~~~~~~\r\n\r\nProvides some version information.\r\n\r\n.. code:: python\r\n\r\n   import wordtm\r\n   print(wordtm.__version__)\r\n\r\nmeta Submodule\r\n~~~~~~~~~~~~~~\r\n\r\nProvides extracting source code of ``wordtm`` module and adding timing and code-showing features\r\nto all functions of the module.\r\n\r\n.. code:: python\r\n\r\n   print(meta.get_module_info())\r\n\r\n   print(meta.get_module_info(detailed=True))\r\n\r\n   meta.addin_all()\r\n\r\nquot Submodule\r\n~~~~~~~~~~~~~~\r\n\r\nProvides functions to extract the quotation source Scripture in OT based on the presribed NT Scripture.\r\n\r\n.. code:: python\r\n\r\n   cdf = util.load_word('cuv.csv')\r\n   crom8 = util.extract2(cdf, 'Rom 8')\r\n   \r\n   quot.show_quot(crom8, lang='chi')\r\n\r\npivot Submodule\r\n~~~~~~~~~~~~~~~\r\n\r\nProvides a pivot table of the prescribed text.\r\n\r\n.. code:: python\r\n\r\n   cdf = util.load_word('cuv.csv')\r\n\r\n   pivot.stat(cdf, chi=True)\r\n\r\nta Submodule\r\n~~~~~~~~~~~~\r\n\r\nProvides text analytics functions, including extracting the summarization of the prescribed text.\r\n\r\n.. code:: python\r\n\r\n   cdf = util.load_word('cuv.csv')\r\n   crom8 = util.extract2(cdf, 'Rom 8')\r\n\r\n   ta.summary(rom8, code=True)\r\n\r\ntm Submodule\r\n~~~~~~~~~~~~~\r\n\r\nProvides text modeling functions, including LDA, NMF and BERTopics modeling.\r\n\r\n.. code:: python\r\n\r\n   lda = tm.lda_process(\"cuv.csv\", chi=True, eval=True, timing=True)\r\n\r\n   nmf = tm.nmf_process(\"cuv.csv\", chi=True, eval=True, code=1)\r\n\r\n   btm = tm.btm_process(\"cuv.csv\", chi=True, cat='nt', eval=True)\r\n\r\nutil Submodule\r\n~~~~~~~~~~~~~~\r\n\r\nProvides loading text and text preprocessing functions.\r\n\r\n.. code:: python\r\n\r\n   df = util.load_word()\r\n   cdf = util.load_word('cuv.csv')\r\n\r\n   df.head()\r\n   cdf.head()\r\n\r\n   rom8 = util.extract2(df, 'Rom 8')\r\n   crom8 = util.extract2(cdf, 'Rom 8')\r\n\r\nviz Submodule\r\n~~~~~~~~~~~~~\r\n\r\nWordcloud plotting from the prescribed text.\r\n\r\n.. code:: python\r\n\r\n   cdf = util.load_word('cuv.csv')\r\n\r\n   viz.chi_wordcloud(cdf)\r\n\r\nContributing\r\n------------\r\n\r\nInterested in contributing? Check out the contributing guidelines.\r\nPlease note that this project is released with a Code of Conduct. By\r\ncontributing to this project, you agree to abide by its terms.\r\n\r\nLicense\r\n-------\r\n\r\n``wordtm`` was created by Johnny Cheng. It is licensed under the terms\r\nof the MIT license.\r\n\r\nCredits\r\n-------\r\n\r\n``wordtm`` was created under the guidance of Jehovah, the Lord.\r\n",
    "bugtrack_url": null,
    "license": "",
    "summary": "Topic Modeling Package",
    "version": "0.4.6",
    "project_urls": {
        "Documentation": "https://drjohnnycheng.github.io/wordtm"
    },
    "split_keywords": [
        "word",
        "scripture",
        "topic modeling",
        "visualization",
        "low-code",
        "pro-code",
        "network analysis",
        "bertopic",
        "lda",
        "nfm"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "ee1c1001144e2cc29c7442485ec49fdd2068b0315d1ba893c493a6d2c3ae7c12",
                "md5": "691f04773d4e9d7e4464e6f731994b36",
                "sha256": "1a7a06bcc77389ed2bab30a3f3667c30714473908a6b8279a3ce4aef6ca57936"
            },
            "downloads": -1,
            "filename": "wordtm-0.4.6-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "691f04773d4e9d7e4464e6f731994b36",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": null,
            "size": 25552,
            "upload_time": "2024-02-05T05:22:33",
            "upload_time_iso_8601": "2024-02-05T05:22:33.254730Z",
            "url": "https://files.pythonhosted.org/packages/ee/1c/1001144e2cc29c7442485ec49fdd2068b0315d1ba893c493a6d2c3ae7c12/wordtm-0.4.6-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "6c33f330615a8873af446386d2d7ae7c6707f2272f684ef85b1cb2f4eeb4b565",
                "md5": "9237b7140e08257bdba183359c1f0e21",
                "sha256": "d716277f648b4e76bf4c85430e4ebc8f87bf0cb30b96cb8b0062737d67d4f747"
            },
            "downloads": -1,
            "filename": "wordtm-0.4.6.tar.gz",
            "has_sig": false,
            "md5_digest": "9237b7140e08257bdba183359c1f0e21",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 5753859,
            "upload_time": "2024-02-05T05:22:38",
            "upload_time_iso_8601": "2024-02-05T05:22:38.983406Z",
            "url": "https://files.pythonhosted.org/packages/6c/33/f330615a8873af446386d2d7ae7c6707f2272f684ef85b1cb2f4eeb4b565/wordtm-0.4.6.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-02-05 05:22:38",
    "github": false,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "lcname": "wordtm"
}
        
Elapsed time: 0.17179s