Saravanan666/pypi-org huggingface dataset:
Package: hbayesdm
This is the Python version ofhBayesDM(hierarchical Bayesian modeling of
Decision-Making tasks), a user-friendly package that offers hierarchical
Bayesian analysis of various computational models on an array of
decision-making tasks.hBayesDMin Python usesPyStan(Python interface forStan) for Bayesian inference.It supports Python 3.5 or higher versions and requires several packages including:NumPy,SciPy,Pandas,PyStan,Matplotlib, andArviZ.Documentation:http://hbayesdm.readthedocs.io/InstallationYou can install hBayesDM from PyPI with the following line:pipinstallhbayesdm# Install using pipIf you want to install from source (by cloning from GitHub):gitclonehttps://github.com/CCS-Lab/hBayesDM.gitcdhBayesDMcdPythonpythonsetup.pyinstall# Install from sourceCitationIf you used hBayesDM or some of its codes for your research, please citethis paper:@article{hBayesDM,title={Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the {hBayesDM} Package},author={Ahn, Woo-Young and Haines, Nathaniel and Zhang, Lei},journal={Computational Psychiatry},year={2017},volume={1},pages={24--57},publisher={MIT Press},url={doi:10.1162/CPSY_a_00002},}
Package: hb-bank-statement-machine-learning
This module add a machine learning on theaccount.bank.statementto predict
the party and the account to use for a new line.InstallDependencies for ArchLinuxsudo pacman -S cairo pkgconf gobject-introspectionDependencies for debiansudo apt-get install libcairo2-dev libgirepository1.0-devInstall the package# installs python deps
pip install hb_bank_statement_machine_learning
# install the module
trytond-admin -u hb_bank_statement_machine_learning --activate-dependenciesInstall the db by hb-tryton-devtoolspip install git+https://gitlab.com/hashbangfr/tryton-modules/hb_tryton_devtools.git#egg=hb_tryton_devtools
export TRYTON_DATABASE_URI=postgresql:///
export TRYTON_DATABASE_NAME=test
hb-tryton-admin create-db --modules hb_bank_statement_machine_learningTest packageThe package need pytest and hb-tryton-devtoolspip install pytest pytest-cov
pip install git+ssh://git@gitlab.com/hashbangfr/tryton-modules/hb_tryton_devtools.git#egg=hb_tryton_devtoolsRun the test with pytest with environ variableexport TRYTON_DATABASE_URI=postgresql:///
export TRYTON_DATABASE_NAME=test
pytest hb_bank_statement_machine_learning/testsLow levelThe machine learning is added on theacount.statement.line, the machine learning is based on the field number on the line,
this field must be filledpool = Pool()
Line = pool.get('account.statement.line')
line = Line()
line.number = 'My number'
line.set_account_and_party_from_ml()
assert line.party
assert line.accountUsageAn on_change method on the field number exist to predict the fields party and account from the interfaceCHANGELOG1.0.0 (2022-05-18)Used cache from trytond0.1.0 (2021-09-28)Implemented the machine learningImplemented the on change method on the fields number
Package: hbcapi
hbcapiDescriptionThis is the official api wrapper for hydrogenbots.clubimporting and definingimportpip install hbcapidefine the library in your codeimporthbcapiHBC=hbcapi.access("auth_token")Methodspoststatspost the servercount for your botprint(HBC.poststats(guild,shard))#guild - the guildcount of the bot shard-the shard count of the bot, print the responsevotersget an array of all of the voters that have voted for your botprint(HBC.poststats())#print the response
Package: hbc-random-number
TheProvablyFairRandomNumberGeneratoris a Python library that provides a provably fair mechanism for generating random numbers. This library uses cryptographic techniques to ensure transparency and fairness in generating random numbers, making it suitable for applications where unbiased randomness is essential.FeaturesGenerates random numbers within a specified range using provably fair methods.Uses os specific randomness source.InstallationpipinstallprovablyfairrandomnumbergeneratorUsagefromprovablyfairrandomnumbergeneratorimportgenerate_random_number# Generate a random number within a rangemin_value=1max_value=100random_number=generate_random_number(min_value,max_value)print(f"Generated Random Number:{random_number}")DependenciesPython 3.xCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-08-29)First release on PyPI.
Package: hbctool
hbctoolA command-line interface for disassembling and assembling the Hermes Bytecode.Since the React Native team created their own JavaScript engine (named Hermes) for running the React Native application, the JavaScript source code is often compiled to the Hermes bytecode. In the penetration test project, I found that some React Native applications have already been migrated to the Hermes engine. It is really head for me to analyze or patch those applications. Therefore, I created hbctool for helping any pentester to test the Hermes bytecode.Hermesis an open-source JavaScript engine optimized for running React Native apps on Android. For many apps, enabling Hermes will result in improved start-up time, decreased memory usage, and smaller app size. At this time Hermes is an opt-in React Native feature, and this guide explains how to enable it.Special thanks toErbaZZandJusmisticfor helping me research and develop this tool.For more information, please visit:https://suam.wtf/posts/react-native-application-static-analysis-en/ScreenshotThis video with MP4 format can be found at/image/hbctool_example.mp4.InstallationTo install hbctool, simply use pip:pip install hbctoolUsagePlease runhbctool --helpto show the usage.hbctool --help   
A command-line interface for disassembling and assembling
the Hermes Bytecode.

Usage:
    hbctool disasm <HBC_FILE> <HASM_PATH>
    hbctool asm <HASM_PATH> <HBC_FILE>
    hbctool --help
    hbctool --version

Operation:
    disasm              Disassemble Hermes Bytecode
    asm                 Assemble Hermes Bytecode

Args:
    HBC_FILE            Target HBC file
    HASM_PATH           Target HASM directory path

Options:
    --version           Show hbctool version
    --help              Show hbctool help manual

Examples:
    hbctool disasm index.android.bundle test_hasm
    hbctool asm test_hasm index.android.bundleFor Android, the HBC file normally locates atassetsdirectory withindex.android.bundlefilename.Supporthbctool currently supports the following Hermes Bytecode version:Hermes Bytecode version 59Hermes Bytecode version 62Hermes Bytecode version 74Hermes Bytecode version 76ContributionFeel free to create an issue or submit the merge request. Anyway you want to contribute this project. I'm very happy about it.However, please run the unit test before submiting the pull request.cd hbctool
python test.pyI use poetry to build this tool. To build it yourself, simply execute:poetry installNext StepAdd the other Hermes bytecode versionsCreate a class abstractionSupport overflow patchingDo all TODO, NOTE, FIXME in source code
Package: hbdatacache
HB DatacachePackage to manage temporary files in the cache and streamline codes with downloads and repetitive functionsimport hbdatacache

#Check if exist temp file
if (hbdatacache.check_temp_data("example_data", parameters=["teste",])):
    #Loading data from temp
    data = hbdatacache.load_temp_data("example_data", parameters=["teste",])
else:
    #Generate new data
    data = [1,2,3,4,5,6,7,8,9,10] #Your data generating...
    #Save data
    hbdatacache.save_temp_data(data, "example_data", parameters=["teste",])
Package: hbdl
hbdl the Humble Bundle downloaderA simpleHumble Bundledownloader, takes aHumble Bundleproduct URI (e.ghttps://www.humblebundle.com/downloads?key=xxxxxx) and it takes care of the download from there.Installationpip install hbdlUsageUsage: hbdl [OPTIONS] URI
Options:
  --dest-dir PATH
  --downloader [wget|curl|python]
  --help                          Show this message and exit.TodoCheck the hash of the downloaded to avoid repetitive downloadBetter logging and error reportingStandalone app / GUIAutomate the pypi publish
Package: hbf
homeBuyerFireProject OverviewA program to predict a home's sell price using data scraped from the internet.homeBuyerFireis actively developed using a Python 3.7 interpreter.Installationpip install hbfExampleStarting from the project's parent directory:python example.pyScraping Zillow |████████████████████████████████| 7/7

                                       Address  Sell Price  Beds  Baths  Home Size      Home Type  Year Built                         Heating  Cooling Parking  Lot Size
0  11705 College View Dr Silver Spring MD 20902    293000.0   2.0    2.0     1526.0  Single Family      1951.0                      Forced air  Central     NaN    6791.0
1  11714 College View Dr Silver Spring MD 20902    465000.0   4.0    3.0     2843.0  Single Family      1962.0  Baseboard, Heat pump, Electric  Central     NaN    8115.0
2         3406 Glorus Pl Silver Spring MD 20902    785000.0   5.0    5.0     5872.0  Single Family      2006.0                      Forced air  Central     NaN   10890.0
3  11703 College View Dr Silver Spring MD 20902    475000.0   4.0    2.0     1982.0  Single Family      1951.0                 Forced air, Gas  Central     NaN    6930.0
4         3400 Glorus Pl Silver Spring MD 20902    395000.0   4.0    3.0     1914.0  Single Family      1953.0                None, Forced air  Central     NaN    7178.0
5    11714 Veirs Mill Rd Silver Spring MD 20902    383000.0   6.0    3.0     1836.0  Single Family      1953.0                      Forced air  Central     NaN    6812.0
6      3402 Pendleton Dr Silver Spring MD 20902    340000.0   2.0    1.0      882.0  Single Family      1951.0                      Forced air  Central     NaN    7609.0

--- 15.11016297340393 seconds ---Contact Info@MattThomas9@AmritPatelLicense
Package: hbm
About HBMHuman Body Models (HBM)is a code base developed by Yansel González Tejeda for his PhD research at theComputer Science departmentatParis Lodron University of Salzburg. It is a rapid prototyping platform focused on modeling realistic human bodies. It contains generative models based on the successfully SCAPE (TBD) and SMPL models.HBMis written with Python in a tremendously "chatty" way to make it especially easy to understand to people with all academic backgrounds (including those without it - and me!). Note that this code is not recommended for production purposes, you can use it at your own risk though.NOTE: This is a work in progressThings will probably be wonky for a while. Until the code base becomes stable, you should expect several changes in the future. That being said, no drastic changes are planned.AcknowledgementsThe SMPL team for the trained components of the model and the SMPL code.
The SCAPE team for the trained components of the model.
Package: hbmep
hb-mepSetup InstructionsRequires Python >=3.11Create environmentpython3 -m venv .venvActivate environmentsource .venv/bin/activateInstall hbmeppip install hbmepTutorial:https://github.com/hbmep/hbmep/blob/dev/notebooks/tutorial.ipynbTo doAdd About sectionCreate tutorial docsAdd tests
Package: hboard-widget
hboard-widget中文This project provides a visualization tool for experiment information based on jupyter notebook/ jupyterlab widget.InstallationInstall with pippipinstallhboard-widgetInstall with condacondainstall-cconda-forgehboard-widgetInstall with source codeBuild from source code need following requirements:python 3.7+nodejs v14.15.0+pip 20.0.2+jupyterlab 2.0.0+The project need frontend ofhboardto be built in advance.Clone the source code:gitclonehttps://github.com/DataCanvasIO/HyperBoard.gitBuild for development:cd./hboard-widget
pipinstall-e.
jupyternbextensioninstall--py--symlink--overwrite--sys-prefixhboard_widget
jupyternbextensionenable--py--sys-prefixhboard_widget
jupyterlabextensiondevelop--overwritehboard_widgetYou need to rebuild the JS when you make a code change:cd./hboard-widget/js
yarnrunbuildExampleThe following steps shows how to implement the experiment visualization in notebookImport the required modules：importwarningswarnings.filterwarnings('ignore')fromsklearn.model_selectionimporttrain_test_splitfromhypernets.examples.plain_modelimportPlainModel,PlainSearchSpacefromhypernets.experimentimportmake_experimentfromhypernets.tabular.datasetsimportdsutilsCreate an experimentdf=dsutils.load_boston()df_train,df_eval=train_test_split(df,test_size=0.2)search_space=PlainSearchSpace(enable_lr=False,enable_nn=False,enable_dt=False,enable_dtr=True)experiment=make_experiment(PlainModel,df_train,target='target',search_space=search_space,callbacks=[],search_callbacks=[])Experiment visualization configurationsfromhboard_widgetimportExperimentSummaryexperiment_summary_widget=ExperimentSummary(experiment)display(experiment_summary_widget)Visualize the dataset informationfromhboard_widgetimportDatasetSummarydataset_summary_widget=DatasetSummary(experiment.get_data_character())display(dataset_summary_widget)Visualize the experiment processfromhboard_widgetimportExperimentProcessWidgetestimator=experiment.run(max_trials=3)widget=ExperimentProcessWidget(experiment)display(widget)Find the project in Notebookexperiment visualization notebook.ipynb.Related projectCurrently,HyperGBMhas integrated this tool. The HyperGBM experiment could call the notebook widget visualization function and display the experiment dashboard. Please refer toHyperGBM: Experiment Visualization in Notebook
Package: hbookerAPI
这是一个刺猬猫/欢乐书客API的简单实现，正在绝赞更新中本项目从另一项目HbookerAppNovelDownloader有所借鉴本项目一切开发旨在学习，请勿用于非法用途本项目仅供学习和娱乐用途使用本项目不会支持任何和钱相关的协议本项目不会支持任何涉及账户安全的操作请勿借助本项目进行刷票、伪造数据等损害社区公平的行为其他任何衍生软件行为与本项目无关目前实现的功能登录（所以猫客不登陆到底能干什么获取书架列表从书架获取书籍列表获取书籍简单信息所以这玩意现在就只能用来做做数据分析，想干别的的就先别想了TODO建立用户类，完成关注，取关等基本操作完善书架列表操作，如新增、移除书架完善书架操作，如移动书籍、收藏、移出等支持获取章节基本信息（不含正文内容）基本的数据分析支持start通过pypi安装pipinstallhbookerAPI从仓库安装将仓库clone到本地，把hbookerAPI文件夹拷贝到你的工作目录下示例使用importhbookerAPI# 初次登陆session=hbookerAPI.Session()session.login('你的账号','你的密码')# 可持久化保存session.commonUserInfo中的loginToken和account到本地# 之后登录可直接session=hbookerAPI.Session(loginToken,account)bookShelves=session.getBookShelfList()shelf=session.getBookShelf(bookShelves[0]['shelf_id'])print(shelf.books)
Package: hb-organiser
hb_organiserOrganises Humble Bundle bundles based on their platform.
Designed to work around the structure created via Talonius'hb-downloader.
Other automated HB downloaders may not work with this out of the box.InstallAs it's packaged as a Pip module,python3-pipis required. Runpip install --user hb-organiserto install only for your user and omit--userto install for all. Seehb_organiser -hfor help.Why is this needed?I mainly use Humble Bundle to purchase book bundles (that I'll totally read at some point, I swear!) and have accumulated thousands of books over the years.
I would like to have all these books hosted via aCalibreweb server for easy access but trying to weed out the books from the default structure left behind by automated downloads is a headache.To alleviate this issue, I'm making this script that will organise my libraries with a click of a key.
Duplicate items across different bundles will be ignored, all the books will be easily discovered via Calibre, and my sanity will be saved.FeaturesCalculates number of tasks so that you're not left in the dark and can tell the progress.A crude way of tracking files being corrupted via a cancelled transfer by logging what is being operated upon and clearing it once complete. If it doesn't get cleared, chances are it was corrupted.Skips copying of duplicate files by checking for its existence first (although there is a chance it may skip newer files such as updated editions of books (seeissue 6)).Immediate roadmapThe immediate goal is to put out something working.
To keep development more direct I'll outline pressing matters below:Cut down on operation timesOrganising thousands of files in one go is very time-consuming - especially when working with game bundles.
Speeding this up will save millions of hours of collective time over the years via optimisation.Future plansLonger term goals than those outlined above will be put here:Abstract all the thingsQuite a few values are hard-coded at the moment.
To make it more convenient for others, this should be changed to allow for more options.Abstract them even furtherWhy does this have to be limited to Humble Bundles?
All its doing is moving one category of files from one place to another.
This could be useful in other situations such as organising music.
Package: hbp-archive
A high-level API for interacting with the Human Brain Project archival storage at CSCS.Authors: Andrew Davison (CNRS), Shailesh Appukuttan (CNRS) and Eszter Agnes Papp (University of Oslo)Documentation:https://hbp-archive.readthedocs.ioInstallation:pip install hbp_archiveUsage:fromhbp_archiveimportContainer,PublicContainer,Project,Archive# Working with a public containercontainer=PublicContainer("https://object.cscs.ch/v1/AUTH_id/my_container")files=container.list()local_file=container.download("README.txt")print(container.read("README.txt"))number_of_files=container.count()size_in_MB=container.size("MB")# Working with a private containercontainer=Container("MyContainer",username="xyzabc")# you will be prompted for your passwordfiles=container.list()local_file=container.download("README.txt",overwrite=True)# default is not to overwrite existing filesprint(container.read("README.txt"))number_of_files=container.count()size_in_MB=container.size("MB")container.move("my_file.dat","a_subdirectory","new_name.dat")# move/rename file within a container# Reading a file directly, without downloading itwithcontainer.open("my_data.txt")asfp:data=np.loadtxt(fp)# Working with a projectmy_proj=Project('MyProject',username="xyzabc")container=my_proj.get_container("MyContainer")# Listing all your projectsarchive=Archive(username="xyzabc")projects=archive.projectscontainer=archive.find_container("MyContainer")# will search through all projectsACKNOWLEDGEMENTSThis open source software code was developed in part or in whole in the Human Brain Project, funded from the European Union's Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270, No. 785907 and No. 945539 (Human Brain Project SGA1, SGA2 and SGA3).
Package: hbp-knowledge
Curation of Neurodegeneration in BEL (CONIB)This knowledge repository, developed during theHuman Brain Pharmacome project, contains documents in Biological Expression Language (BEL) describing neurodegenerative disease phenomena such as tauopathies.Installation•Usage•Contributing•LicenseContenthbp_knowledge: This folder contains curated BEL
documents. Most have been organized by category (protein of interest, biological process of interest, pathway, etc.).
Almost BEL documents correspond to a single publication, but a select few contain content from several due to
topic-based curation.docs: This folder contains automatically generated HTML
summaries of the content that can best be accesed throughhttps://pharmacome.github.io/conib.notebooks: This folder contains various Jupyter
notebooks associated with the repository. Since many of them use PyBEL-Jupyter, they may be best viewed usingNBViewer.Curation PlanningHere are a few links for navigating the GitHub issues and pull requests:Pull requests for curationPull requests for recuration approved by a reviewerInstallationhbp_knowledgecan be installed fromPyPIwith the following command:$pipinstallhbp_knowledgeor the latest version can be installed fromGitHubwith:$pipinstallgit+https://github.com/pharmacome/conib.gitUsageThe graph can be loaded with:importhbp_knowledge# Get a dictionary of names to graphsgraphs=hbp_knowledge.get_graphs()# Get all BEL documents as a single graphgraph=hbp_knowledge.get_graph()ContributingContributions are welcome! Please submit all pull requests tohttps://github.com/pharmacome/conib.LicenseBEL scripts in this repository are licensed under the CC BY 4.0 license.Python source code in this repository is licensed under the MIT license.
Package: hbp-neuromorphic-platform
# Client software for the EBRAINS Neuromorphic Computing Platform, developed by the Human Brain ProjectFor documentation, seehttps://electronicvisions.github.io/hbp-sp9-guidebook/For more information about EBRAINS, seehttps://ebrains.eu/For more information about the Human Brain Project, seehttps://www.humanbrainproject.eu/[![Build Status](https://travis-ci.org/HumanBrainProject/hbp-neuromorphic-client.svg?branch=master)](https://travis-ci.org/HumanBrainProject/hbp-neuromorphic-client)This software was developed in the Human Brain Project,
funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation
under Specific Grant Agreements No. 720270, No. 785907 and No. 945539 (Human Brain Project SGA1, SGA2 and SGA3).
Package: hbp-sphinx-theme
HBP Collaboratory Sphinx ThemeContentsHBP Collaboratory Sphinx ThemeInstallationVia packageVia downloadContributing or modifying the themeBuild and release the themeView a workingdemoover oncollaboratory.This package provide a Sphinx theme for documentation integrated into the
Collaboratory. It can be used for standalone websites as well.InstallationVia packageDownload the package or add it to your requirements.txt file:pipinstallhbp_sphinx_themeIn your conf.py file:importhbp_sphinx_themehtml_theme="hbp_sphinx_theme"html_theme_path=[hbp_sphinx_theme.get_html_theme_path()]Via downloadDownload thehbp_sphinx_theme.zipprovided by the latestreleaseto your documentation project directory.As explained insphinx documentation, in yourconf.pyfile:html_theme='hbp_sphinx_theme'html_theme_path=['.']Contributing or modifying the themeHBP Collaboratory Sphinx Theme github project can be included in your documentation
project as a git submodule. This will enable you to modify the theme and see the
changes immediately in your browser. Please have a look at thedocumentation exampleproject on how to set up. After you are happy with the changes, you’ve made to the theme,
please make a pull request for us to review.Build and release the themeThe build and release is done using grunt.gruntreleaseChangelog
Package: hbpycounts
hbpycountsA sample python packageInstallation$pipinstallhbpycountsUsagepycountsCalculate word counts in a text file!Installation$pipinstallpycountsUsagepycountscan be used to count words in a text file and plot results
as follows:frompycounts.pycountsimportcount_wordsfrompycounts.plottingimportplot_wordsimportmatplotlib.pyplotaspltfile_path="test.txt"# path to your filecounts=count_words(file_path)fig=plot_words(counts,n=10)plt.show()ContributingInterested 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.Licensepycountswas created by Tomas Beuzen. It is licensed under the terms
of the MIT license.Creditspycountswas created withcookiecutterand
thepy-pkgs-cookiecuttertemplate.ContributingInterested 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.Licensehbpycountswas created by Hina Bandukwala. It is licensed under the terms of the MIT license.Creditshbpycountswas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.