Saravanan666/pypi-org huggingface dataset:
Package: texpro
TeXpro is a collection of wrapper classes to display and save objects used inTex projects, such as tables, plots, images, equations, and tex snippets.  The wrapper classes display these assets as rich content in Jupyter Notebooks, while saving them as separate.texfiles that can be readily included in any (La)TeX document.  Thus, with minimal code and without copy-pasting, results can be shown in a Jupyter notebook, main TeX document, TeX presentation, and other documents.  TeXpro maintains a consistent folder and file structure, based on the TeX labels.ExampleFor an interactive example, see example/texpro_demo.ipynb and the example TeX file example/texpro_demo.tex.DevelopmentTeXpro is build usingflit.  To build the package, (i) install flit, (ii) download the source code and (iii) run the following command inside the main folder:python3-mflitinstall-sThis will install the required dependencies as well as TeXpro.
Package: texrocket
TexRocketA basic template engine for LaTeX. Create dynamic documents by parsing JSON data and populating a custom template.The script parses a template and fills it with data read from json files. Provide different json files to have multiple files with the same structure but varying content.Especially useful for translations.RequirementsMake sure you have the following programs installed on your system before moving forward.pythonLaTeXInstallationEasily install TexRocket by usingpippipinstalltexrocketUsageAt any time you can run thetexrocketscript with the-hor--helpflag to display usage informationusage: texrocket [-h] [-i INPUT_JSON] [-o OUTPUT_DIR] [-v] input_tex

Dynamic LaTeX generation from JSON, developed by Alberto Mosconi

positional arguments:
  input_tex             the LaTeX template file

options:
  -h, --help            show this help message and exit
  -i INPUT_JSON, --input-json INPUT_JSON
                        the JSON file or directory of files
  -o OUTPUT_DIR, --output-dir OUTPUT_DIR
                        the directory for the output files
  -v, --verbose         print verbose output and save LaTeX logsAn example using all possible arguments istexrocketTEMPLATE.tex-iinput.json-oout/-vThis will create anout/folder, with the documents generated using theTEMPLATE.textemplate and the values insideinput.jsonout/
├── source/
│   └── TEMPLATE_input.tex
├── logs/
│   └── TEMPLATE_input.log
└── TEMPLATE_input.pdf! All the JSON files MUST have the same structure, the engine will create multiple documents (one for each.jsonfile) starting fromTEMPLATE.tex.The generated files have the following naming scheme<TEMPLATE FILENAME>_<JSON FILENAME>.pdfIf the input JSON file is namedmain.jsonthen this will not show up<TEMPLATE FILENAME>.pdfSyntaxValuesTo display a certain JSON value reference it with the following syntax<section.title>{"section":{"title":"The section title","text":"some text"}}The example above will be rendered as"The section title".LoopsYou can avoid repetition of LaTeX code by using loops, which can be nested\begin{itemize}%startloop: first\item<name>\begin{itemize}%startloop: second\item<name>%endloop\end{itemize}%endloop\end{itemize}{"first":[{"name":"first main list item","second":[{"name":"secondary list item"}]},{"name":"second main list item","second":[{"name":"secondary list item"}]}]}
Package: texsnip
TeXSnipA small single-module Python package to generate LaTeX text and equations for your favorite vector graphics tool, with no dependencies required! Except LaTeX, of course.To get started, simply run:pip install texsnipWith a few lines of Python code, you can create .pdf files that you can then drag'n'drop into Inkscape, Illustrator, Corel Draw, or most other vector graphics programms.fromtexsnipimportSnip# Here, you can modify the LaTeX preamble, for example to configure fonts.# We use the 'libertine' package, the fonts for the current ACM SIGGRAPH template.preamble=r"\usepackage{libertine}"# Write the rendering equation to a file called 'rendering-equation.pdf'Snip("rendering-equation",8,r"$L_o = L_e + \int_\Omega L_i f \cos\theta_i \,\mathrm{d}\omega_i$").generate(preamble)If .pdfs are not supported, don't worry: the script allows you to easily create .png files as well.Snip("rendering-equation",8,r"$L_o = L_e + \int_\Omega L_i f \cos\theta_i \,\mathrm{d}\omega_i$").generate_png(preamble)If you are using these in presentation slides, you can assemble a list of Snips in a .pptx file (uses .png conversion)fromtexsnipimportSnip,pptx_snips# Here, you can modify the LaTeX preamble, for example to configure fonts.# We use the 'libertine' package, the fonts for the current ACM SIGGRAPH template.preamble=r"\usepackage{libertine}"snips=[# Write the rendering equation to a file called 'rendering-equation.pdf'Snip("rendering-equation",14,r"$L_o = L_e + \int_\Omega L_i f \cos\theta_i \,\mathrm{d}\omega_i$"),# Sometimes, you need individual termsSnip("omega_i",14,r"$\omega_i$"),# Or you might want captions for your illustrations with LaTeX typesettingSnip("a-caption",14,r"\textsf{a) Some \textcolor[RGB]{200,110,5}{cool}illustration}")]# Lets create a snips.pptx with all these snips in it# This will also create the corresponding .pdf and .png files for use in other applicationspptx_snips(snips,preamble=preamble)DependenciesPython >= 3.6LaTeX with pdfcrop (requires Perl) and xcolor, graphicx, inputenc, fontencTo generate .png images, you will additionally need:pip install pdf2imagewhich requires Poppler to be installed and in the path.To generate .pptx files, you will need the .png dependencies and also:pip install python-pptx PyPDF2
Package: texsoup-alt
TexSoupTexSoup is a fault-tolerant, Python3 package for searching, navigating, and modifying LaTeX documents.Getting StartedInstallationAPI ReferenceCreated byAlvin Wan+contributors.Getting StartedTo parse a $LaTeX$ document, pass an open filehandle or a string into theTexSoupconstructor.fromTexSoupimportTexSoupsoup=TexSoup("""\begin{document}\section{Hello\textit{world}.}\subsection{Watermelon}(n.) A sacred fruit. Also known as:\begin{itemize}\item red lemon\item life\end{itemize}Here is the prevalence of each synonym.\begin{tabular}{c c}red lemon & uncommon\\life & common\end{tabular}\end{document}""")With the soupified $\LaTeX$, you can now search and traverse the document tree.
The code below demonstrates the basic functions that TexSoup provides.>>>soup.section# grabs the first `section`\section{Hello\textit{world}.}>>>soup.section.name'section'>>>soup.section.string'Hello\\textit{world}.'>>>soup.section.parent.name'document'>>>soup.tabular\begin{tabular}{cc}redlemon&uncommon\\life&common\end{tabular}>>>soup.tabular.args[0]'c c'>>>soup.item\itemredlemon>>>list(soup.find_all('item'))[\itemredlemon,\itemlife]For more use cases, seethe Quickstart Guide. Or, try TexSouponline, via repl.it →Links:Quickstart Guide: how and when to use TexSoupExample Use Cases: counting references, resolving imports, and moreInstallationPipTexSoup is published via PyPi, so you can install it viapip. The package
name isTexSoup:$pipinstalltexsoupFrom sourceAlternatively, you can install the package from source:$gitclonehttps://github.com/alvinwan/TexSoup.git
$cdTexSoup
$pipinstall.
Package: texstrip
texstripis a small tool that strips (removes) comments from TeX sources. I made this tool to relieve myself from manually deleting comments when submitting TeX sources to [arXiv](https://arxiv.org/help/faq/whytex).It removes both inline comments (i.e., things between%and the next\n) and comment environments (i.e., things within\begin{comment}and\end{comment}),
copies the supplementary files to a new folder for a clean submission.LaTeX is a complex beast and this tool, despite its humble goal, might fail on corner cases. I fixed many of them during my own use but YMMV. If you do encounter glitches, please submit a PR and let’s make it better together!Getting texstripYou’ll needlatexpand.texstripcan be installed frompipbypip install texstripUsage(More text to be added here. For now, refer to the help message.)texstrip --helpTODO: add an example
Package: text11
the text11 system for the cmd lineText11 is a collection of programs/utilities that add functionality to a text
console to provide similar features/experience to that of a full blown X11
session such as auto plugging of devices, application notification areas,
auto launchers and session managerswe try not to enforce any specific component and make each utility capable of
operation on its own or where this is not possible, import the required feature
locally without requiring a separate daemon (eg config server)ComponentsSession-AgentManages logins and user processes, takes steps to remove redundant redundancies
by running one instance of a daemon/service where posible and sharing the
instance between multiple sessionsFeaturescgroup supportasylum supportShared sessions (sessions shared between all instances)consolekit support?Display-AgentHandles raw displays such as adding/destroying screensFeaturesSupport remote displaysWindow-AgentManages windows on a displayFeaturesSupport ssh connections on remote machinesPower-AgentHandles power events and applies appropriate policiesFeaturesacpi lid close support for locking sessionsPassword-AgentHandles user password requestsNetwork-AgentHandles network related tasks such as running scripts on connect, actual network connection code
may be handed out to a 3rd party program (eg network-manager)Input-Agentresponsible for opening devices and converting key presses using keymaps as well as arbitrating input
to logged in consoleSettings-Agentagent responsible for managing settings and updating apps when a setting has been changed
look into gconf?Download-AgentDisk-Agentmanages auto maounting of discs or prompting the userNotifcation-Agentcollates events and notifcations and passes them to intrested programsCut-and-Paste-Agentcuts and pastes texts and other data to diffrent locationsHost/Service-DiscoveryAvahi based
Package: text2aks
Text2AksThis project is under active development and anything can change in the futureWhat is text2aks ?Text2Aksis a simple library that can generate simple pictures with text input.InstallingInstall and update using pip:$ pip install text2aksHow dose it work ?You have to havePillowinstalled on your system.In the below example I will show you how can you genrate image with text2aksfromtext2aksimportText2Aks,Fontsfromtext2aks.elementsimportmake_darkerfromPILimportImagemy_image='path to my image'text:str="Nothing is more difficult, and therefore more precious, than to be able to decide"wrtier_of_text:str='Napoleon Bonaparte'withImage.open(my_image)asraw_image:darker_image=make_darker(raw_image)image_maker=Text2Aks(darker_image,Fonts.VAZIR_BOLD,font_size=50)image_maker.genrate(text,wrtier_of_text,'ltr')# left ro right (ltr) | right to left (rtl)result:Image.Image=image_maker.resault()# If you want to save it in your file system you can use save method of Image class# or if you want just takes a look at it you can use show method.# result.save('name of new image , 'JPEG')# result.show()You can find all available fonts for this project in thefonts_datamoduelThe only available font in this version of the project, isVazirfont. it's an open-source and free font that you can use it in your projects.You can use different weights of it in this project :fromtext2aks.fonts_dataimportFontsFonts.VAZIR_BLACK# "Vazirmatn-Black.ttf"Fonts.VAZIR_BOLD# "Vazirmatn-Bold.ttf"Fonts.VAZIR_EXTRABOLD# "Vazirmatn-ExtraBold.ttf"Fonts.VAZIR_EXTRALIGHT# "Vazirmatn-ExtraLight.ttf"Fonts.VAZIR_LIGHT# "Vazirmatn-Light.ttf"Fonts.VAZIR_MEDIUM# "Vazirmatn-Medium.ttf"Fonts.VAZIR_REGULAR# "Vazirmatn-Regular.ttf"Fonts.VAZIR_SEMIBOLD# "Vazirmatn-SemiBold.ttf"Fonts.VAZIR_THIN# "Vazirmatn-Thin.ttf"LICENSEMIT License

Copyright (c) 2022 Iman Hosseini Pour

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: text2array
text2arrayConvert your NLP text data to arrays!text2arrayhelps you process your NLP text dataset into Numpy ndarray objects that are
ready to use for e.g. neural network inputs.text2arrayhandles data shuffling,
batching, padding, and converting into arrays. Say goodbye to these tedious works!Documentationhttps://text2array.readthedocs.ioContributingPull requests are welcome! To start contributing, first installflit.pip install flitNext, install this library and its dependencies in development mode.flit install --symlinkLastly, setup the pre-commit hook.ln -s ../../pre-commit.sh .git/hooks/pre-commitTests, the linter, and the type checker can be run withpytest,flake8, andmypyrespectively.LicenseApache License, Version 2.0
Package: text2class
Text2ClassBuild multi-class text classifiers using state-of-the-art pre-trained contextualized language models, e.g. BERT. Only a few hundred samples per class are necessary to get started.BackgroundThis project is based on our study:Transfer Learning Robustness in Multi-Class Categorization by Fine-Tuning Pre-Trained Contextualized Language Models.CitationTo cite this work, use the following BibTeX citation.@article{transfer2019multiclass,
  title={Transfer Learning Robustness in Multi-Class Categorization by Fine-Tuning Pre-Trained Contextualized Language Models},
  author={Liu, Xinyi and Wangperawong, Artit},
  journal={arXiv preprint arXiv:1909.03564},
  year={2019}
}Installationpip install text2classExample usageCreate a dataframe with two columns, such as 'text' and 'label'. No text pre-processing is necessary.import pandas as pd
from text2class.text_classifier import TextClassifier

df = pd.read_csv("data.csv")

train = df.sample(frac=0.9,random_state=200)
test = df.drop(train.index)

cls = TextClassifier(
	num_labels=3,
	data_column="text",
	label_column="label",
	max_seq_length=128
)

cls.fit(train)

predictions = cls.predict(test["text"])Advanced usageModel typeThe default model is an uncased Bidirectional Encoder Representations from Transformers (BERT) consisting of 12 transformer layers, 12 self-attention heads per layer, and a hidden size of 768. Below are all models currently supported that you can specify withhub_module_handle. We expect that more will be added in the future. For more information, seeBERT's GitHub.https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1
https://tfhub.dev/google/bert_uncased_L-24_H-1024_A-16/1
https://tfhub.dev/google/bert_cased_L-12_H-768_A-12/1
https://tfhub.dev/google/bert_cased_L-24_H-1024_A-16/1
https://tfhub.dev/google/bert_chinese_L-12_H-768_A-12/1
https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1

cls = TextClassifier(
	num_labels=3,
	data_column="text",
	label_column="label",
	max_seq_length=128,
	hub_module_handle="https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1"
)ContributingText2Class is an open-source project founded and maintained to better serve the machine learning and data science community. Please feel free to submit pull requests to contribute to the project. By participating, you are expected to adhere to Text2Class'scode of conduct.Questions?For questions or help using Text2Class, please submit a GitHub issue.
Package: text2code
text2codeParses arbitrary strings and returns a dictionary.Usagefrom text2code import text2code as t2c

d = t2c("""
def f(x, y):
    return x + y

def a(b, c):
    return b + c
""")The result of this code would be a dictionary with 2 keys:f, anda, both of which would be the functions defined above.
Package: text2digits
Installationpip3 install text2digitsUsagePython 3 only!from text2digits import text2digits
t2d = text2digits.Text2Digits()
t2d.convert("twenty ten and twenty one")
> 2010 and 21It can handle a variety of phrases. Spoken/Informal and formal language:"A random string" -> 'A random string'
"I am thirty six years old with a child that is four. I would like to get him four cars!" -> 'I am 36 years old with a child that is 4. I would like to get him 4 cars!'
"I was born in twenty ten" -> 'I was born in 2010'
"I was born in nineteen sixty four" -> 'I was born in 1964'
"I am the fourth cousin" -> 'I am the 4 cousin'
"I am twenty nine" -> 'I am 29'
"it was twenty ten and was negative thirty seven degrees" -> 'it was 2010 and was negative 37 degrees'
"thirty twenty one" -> '3021'
"one thousand six hundred sixty six" -> '1666'
"one thousand and six hundred and sixty six" -> '1666'
"sixteen sixty six" -> '1666'
"eleven hundred twelve" -> '1112'
"Sixteen and seven" -> '16 and 7'
"twenty ten and twenty one" -> '2010 and 21'
"I was born in nineteen ninety two and am twenty six years old!" -> 'I was born in 1992 and am 26 years old!'
"three forty five" -> '345'I find this useful if using Alexa/Lex to convert audio to text and have to convert the text to digits.Improvements/IssuesStill need to add support for decimal numbersNeed to add support for negative numbersAcknowledgementsI have heavily used code from the SO answers from here:https://stackoverflow.com/questions/493174/is-there-a-way-to-convert-number-words-to-integersand improved upon them
Package: text2digits-careless25
UsagePython 3 only!from t2d import Text2Digits
Text2Digits().convert("I was born in nineteen ninety two and am twenty six years old!")Output:"i was born in 1992 and am 26 years old!"I find this useful if using Alexa/Lex to convert audio to text and have to convert the text to digits.Improvements/IssuesStill need to add support for decimal numbersNeed to add support for negative numbersAcknowledgementsI have heavily used code from the SO answers from here:https://stackoverflow.com/questions/493174/is-there-a-way-to-convert-number-words-to-integersand improved upon them
Package: text2digits-custom
Disclaimer and acknowledgementThis code is forked fromhttps://github.com/ShailChoksi/text2digitsHe is the original author and maintainer of the source code. Kudos and thanks for the contribution.
This is a custom fork meant for a specific use case with small changes on top of the original repo.Installationpip3 install text2digits_customUsagePython 3 only!from text2digits import text2digits
t2d = text2digits.Text2Digits()
t2d.convert("twenty ten and twenty one")
> 2010 and 21It can handle a variety of phrases. Spoken/Informal and formal language:"A random string" -> 'A random string'
"I am thirty six years old with a child that is four. I would like to get him four cars!" -> 'I am 36 years old with a child that is 4. I would like to get him 4 cars!'
"I was born in twenty ten" -> 'I was born in 2010'
"I was born in nineteen sixty four" -> 'I was born in 1964'
"I am the fourth cousin" -> 'I am the 4 cousin'
"I am twenty nine" -> 'I am 29'
"it was twenty ten and was negative thirty seven degrees" -> 'it was 2010 and was negative 37 degrees'
"thirty twenty one" -> '3021'
"one thousand six hundred sixty six" -> '1666'
"one thousand and six hundred and sixty six" -> '1666'
"sixteen sixty six" -> '1666'
"eleven hundred twelve" -> '1112'
"Sixteen and seven" -> '16 and 7'
"twenty ten and twenty one" -> '2010 and 21'
"I was born in nineteen ninety two and am twenty six years old!" -> 'I was born in 1992 and am 26 years old!'
"three forty five" -> '345'I find this useful if using Alexa/Lex to convert audio to text and have to convert the text to digits.Improvements/IssuesStill need to add support for decimal numbersNeed to add support for negative numbersAcknowledgementsI have heavily used code from the SO answers from here:https://stackoverflow.com/questions/493174/is-there-a-way-to-convert-number-words-to-integersand improved upon them
Package: text2emotion
What is emotion?Emotion is a biological state associated with the nervous system brought on by neurophysiological changes variously associated with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure.
(Source: Wikipedia)Human being can easily identify the emotions from text and experience it. But what about the machines, are they able to identify the emotions from text?text2emotion is the python package which will help you to extract the emotions from the content.Processes any textual message and recognize the emotions embedded in it.Compatible with 5 different emotion categories as Happy, Angry, Sad, Surprise and Fear.Features1. Text Pre-processingAt first we have the major goal to perform data cleaning and make the content suitable for emotion analysis.Remove the unwanted textual part from the message.Perform the natural language processing techniques.Bring out the well pre-processed text from the text pre-processing.2. Emotion InvestigationDetect emotion from every word that we got from pre-processed text and take a count of it for further analytical process.Find the appropriate words that express emotions or feelings.Check the emotion category of each word.Store the count of emotions relevant to the words found.3. Emotion AnalysisAfter emotion investigation, there is the time of getting the significant output for the textual message we input earlier.The output will be in the form of dictionary.There will be keys as emotion categories and values as emotion score.Higher the score of a particular emotion category, we can conclude that the message belongs to that category.How to use?Check Demo on ColabApp DeploymentHere's the code implementation withStreamlit Appfor the users.Enter the text.Hit the submit button.Tada!! Get the output in visual form.Check Demo of AppLet's experience the library, test your multiple use cases on web app and check whether the library performs as per your expectations.
Package: text2excel
text2excelThis program converts CSV Or TSV text files to Microsoft Excel format. It
usesopenpyxlto create Excel files.As input it takes tab-separated*.txtfiles (TSV), or any CSV files
(Comma-Separated Values) that can be auto-detected by the Python standard
librarycsvmodule.You'll find thetext2excel source on GitHubExample$printf"one\ttwo\tthree\n1\t2\t3\n"|teemy_data_file.txt
onetwothree123$text2excel--numbersmy_data_file.txt
Savedtofile:my_data_file.xlsxInstallationTo install or upgradetext2excelfromPyPI, I recommend usingpipx:pipxinstalltext2excel
pipxupgradetext2excelIf you don't havepipx, you could also usepipwith your preferred Python version:python3-mpipinstall--user--upgrade-strategyeager--upgradetext2excelNewsPlease see thechangelogfor more details.ContributingDo you want to help out with this project?Please check theCONTRIBUTINGguide.CreditsThis project was originally based ona Gist by Konrad Förstner.