Saravanan666/pypi-org huggingface dataset:
Package: igym
internet-gymGym for your AI to connect with the internet and play around. This framework is written with language models in mind as the actions are described in natural language and agent has to understand that in order to get to target. Readhowbelow.InstallationInstall using pipy as:pip install igymUsageThe syntax is same as OpenAI gym while the internals are completely different.fromigymimportInternetEnv,DefaultActions# initialise the environmentenv=InternetEnv("path/to/chromedriver")target="Google Elon Musk"# type instruction and send to your modelwhileTrue:action=model(target,DefaultActions,env.state)env.step(action)iftarget_achieved:breakThere are ofcourse challenges here such as open-ended generations and no rewards. Still confused, check out thisnotebook.How?At the core of this framework isigym.core.Actionwhich has two attributes and one method:Action.text: Template string that explains the purpose of this Action in natural languageAction.args: This dictionary is automatically extracted from thetextand before calling each action user has to fill this usingAction.fill_values()method. Any discrepancy is automatically caughtAction.step(): This method recieves theselenium WebDriverobject and it performs the action on the driver.ThetextofActionandstep()are thus a pair and agent has to navigate this to complete the objective.SampleA sample using Language models can be found in thisnotebook.Why?There are a tonne of good ideas and philosophy of what intelligence exactly is, some of the things:Machine Learning Street Talk - Francois Chollet - On the Measure Of IntelligenceARC ChallengeIntelligence WikipediaAGI Wikiand on ... and on ... and on. However only a few people write about the use the AI as atooland when they do mention it, it is usually as the assumption and focus is on generalisation or solving a specific problem / benchmark.I am arguing that form follows function and thus the building blocks of AGI should be based on function.This project aims to test the ability of language models to travel the internet and answer questions. If it can travel the internet, then it can answer questions far better than any human individual can based on the amount of cached information on Google.
Package: ihan
IHAN Client===========This is the installation and usage guide for the `IHAN <https://www.ihan.ee/>`_ Client. This utility is used for feeding and back filling log files to the IHAN service.Requirements------------Python 2.7 and Python 3.4+ are supported.On most Ubuntu systems you should be able to install Python and virtualenv via the following:.. code-block:: bash$ sudo apt update$ sudo apt install \python-pip \python-virtualenvInstallation------------.. code-block:: bash$ virtualenv ~/.ihan$ source ~/.ihan/bin/activate$ pip install --upgrade ihanLogging In----------Make sure you have an account setup at `IHAN <http://www.ihan.ee/>`_ and you have your email address and password for your IHAN account to hand... code-block:: bash$ source ~/.ihan/bin/activate$ ihan loginEnter your email address and password when prompted. If authentication is successful API credentials will be written to ``~/.ihan/config``. From this point on the ``live`` and ``backfill`` commands should work without issue.Shipping Logs-------------Make sure the user account that is running has read access to the main nginx log file. If it doesn't please run the following. Replace ``your_user_name`` with your unix username (found via ``whoami``)... code-block:: bash$ sudo chown your_user_name:www-data /var/log/nginx/access.log$ sudo chmod u+r /var/log/nginx/access.log.. code-block:: bash$ sudo apt install screen$ screen$ ihan live /var/log/nginx/access.logOnce that's running type ``CTRL-A`` and then ``CTRL-D`` to return to your regular shell.Backfill Log Files------------------If the log file is not compressed, run the following:.. code-block:: bash$ screen$ ihan backfill /var/log/nginx/access.logIf it is compressed, run the following:.. code-block:: bash$ screen$ gunzip -c /var/log/nginx/access.log.gz | ihan backfill -Once that's running type ``CTRL-A`` and then ``CTRL-D`` to return to your regular shell.
Package: ihatefacebook
ihatefacebookihatefacebok is a Python script that allows you to get notified by email whenever a Facebook page adds a new event,
without having to interact with Facebook yourself. The script runs by default every time your machine starts up
(after login), but you can also set it up with theruncommand as acronjob. Currently supports macOS only.Installation$ pip install ihatefacebookUsage$ ihatefacebook COMMAND [OPTION] [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  config  Add or list email and SMTP server configurations
  page    Add, remove or list pages to be scraped for events
  run     Run scraping utility with currently set configurations and pagesSubcommands$ ihatefacebook config OPTION [ARGS]

  Add or list email and SMTP server configurations

Options:
  -a, --add key=value   Add user.email, smtp.host, smtp.port configurations, with
                            the format key=val
  -p, --password        Set password for the user.email added using the -a option
  -l, --list            List currently set configurations
  --help                Show this message and exit.$ ihatefacebook page OPTION [ARGS]

  Add, remove or list pages to be scraped for events

Options:
  -a, --add PAGE_ID     Add a page to the list of pages to be scraped, using its
                            Facebook page ID
  -r, --remove PAGE_ID  Remove a page using its Facebook page ID
  -l, --list            List all pages from the list of pages to be scraped
  --help                Show this message and exit.Requirementspipinstalls needed dependencies but you'll need to have these on your machine as well:Google ChromeChromeDriver, make sure it's the version corresponding to that of the browser,
and have it located in/usr/local/bin/.NotesThe scraping code could break anytime Facebook change their DOM, if you notice this before I update the code,
please let me know.If you're using Gmail as your SMTP server, you'll need to turn onless secure app access.The script useslaunchdon macOS for launching every time your machine starts up,ihatefacebookcallslaunchdand adds needed configuration
at installation. However, you wanna check the status of the script inlaunchdto make sure it'll work properly. This
can be done usinglaunchctl list | grep ihatefacebook(or useLaunchControl),
0 means OK. Check~/Library/Logs/com.hot3eed.ihatefacebook/log.error(or useConsole) for debugging. All this hard manual labor
could be automated in a future update.TODOAdd Linux support.Add Windows support.LicenseApache License 2.0
Package: i-hate-papers
I "Hate" PapersCreate easily readable versions of papers via OpenAII often need to read a paper to provide background on a related topic.
In these cases the technical depth of a paper can be a major obstacle.
So I created I Hate Papers to create easily digestible versions of
academic research.Currently works with:An arXiv paper IDA local.texfileA local.mdfileA local.htmlfile (experimental)Installationpip install i-hate-papersExample use# First set your OpenAI API key
❱ export OPENAI_API_KEY=...

# Summarise a arXiv paper ID
❱ i_hate_papers 2106.09685

# Summarise a latex file
❱ i_hate_papers path/to/some-paper.tex

# Summarise a html file
❱ i_hate_papers path/to/some-paper.htmlExample outputExample HTML(includes rendered math using MathJax)Example MarkdownReference❱ i_hate_papers --help
usage: i_hate_papers [-h] [--verbosity {0,1,2}] [--no-input] [--no-html] [--no-open] [--no-footer] 
                     [--no-glossary] [--detail-level {0,1,2}] [--model MODEL] INPUT

Summarise an academic paper

You must set the OPENAI_API_KEY environment variable using your OpenAi.com API key

positional arguments:
  INPUT                 arXiv paper ID (example: 1234.56789) or path to a .tex/.html/.md file

options:
  -h, --help            show this help message and exit
  --verbosity {0,1,2}   Set the logging verbosity (0 = quiet, 1 = info logging, 2 = debug logging). Default is 1
  --no-input            Don't prompt for file selection, just use the largest tex file
  --no-html             Skip HTML file generation
  --no-open             Don't open the HTML file when complete (macOS only)
  --no-footer           Don't include a footer containing metadata
  --no-glossary         Don't include a glossary
  --detail-level {0,1,2}
                        How detailed should the summary be? (0 = minimal detail, 1 = normal, 2 = more detail)
  --model MODEL         What model to use to generate the summariesRelease processFor internal use:export VERSION=0.1.1
poetry version $VERSION
git ci -a -m "Releasing version $VERSION"
git tag "v$VERSION"
git push origin main refs/tags/v$VERSION
poetry publish --build
Package: ihatepyc
ihatepycAutomaticpycachefolder deleterUsage$ihatepycwatchThe above command will watch the current directory (you change change this by providing a --path /path/to/directory if you want a specific place) every 3 seconds and delete all__pycache__folders found within the directory, and sub directories. It will also print out the deleted folders because of the verbose option, you can delete it if needed.Installation$pipinstallihatepycor$pip3installihatepycHelp$ihatepyc--help$ihatepycwatch--helpLicenseMITAuthorcombogangreal(contact@combogang.com)(combogang on discord)NoteThe actual project on relies on theclicklibrary for the command line interface, but the build process is done with the basepip install buildmodule andtwine.
Package: ihc-mdapy
mdapyIntroductionWelcome to the "mdapy" repository! This repository contains the implementation of the Maximum Dissimilarity Algorithm in Python. The algorithm is commonly used in clustering and sampling to select the most dissimilar data points from a set. This README provides an overview of the project, a theoretical explanation of the algorithm, and examples of how to execute the code.The Maximum Dissimilarity AlgorithmThe Maximum Dissimilarity Algorithm is a technique used to select a specified number of data points from a larger dataset in such a way that the selected points are maximally dissimilar to each other. The algorithm is particularly useful in scenarios where you need to create diverse subsets of data for analysis or for initializing cluster centroids in clustering algorithms.In this case, directional variables can also be considered, in which case, the distance between two angles is calculated as the shortest distance between the two angles in a circle. For example, the distance between 350° and 10° is 20°, not 340°. This is the approach used in this implementation of the algorithm (seeGuanche et al. (2013)for more details).Algorithm StepsInitialization: Start with an empty subset, S. Choose the first data point arbitrarily or based on some criteria and add it to S.Iteration: Repeat the following steps until you have selected the desired number of data points (n_clusters).Finding the Most Dissimilar Point: For each data point not in S, calculate the minimum distance from that point to the points in S.Selecting the Maximum Dissimilarity Point: Choose the data point with the greatest dissimilarity (maximum distance calculated in the previous step) and include it in subset S.Output: Return the subset S, which contains n_clusters data points that are maximally dissimilar to each other.How to Execute the CodeYou can use the Maximum Dissimilarity Algorithm by following these steps:Clone this repository to your local machine:gitclonehttps://github.com/yourusername/mdapy.gitIn the same directory, execute the following command to install the package:pipinstall.Import the max_diss_alg function from the mdapy package:frommdapyimportmax_diss_algCreate a tuple with the data arrays to use (must have same lengths) and choose the number of data points to select:tp=[14.93,10.42,7.46,7.63,12.20,12.05,14.49,8.62]hs=[1.82,1.45,0.26,0.90,1.30,5.71,1.40,2.18]dr=[268,272,289,293,276,273,276,292]data=(tp,hs,dr)n_clusters=3Aditionally, choose the seed point to start the algorithm (optional) and, in case any of the variables is direcciotal, provide the position on the tuple of that array.seed_index=np.argmax(tp)dir_indices=[2]Call the max_diss_alg function and store the output data:clustered_data=max_diss_alg(data,n_clusters,seed_index,dir_indices)References[1]Guanche, Y., Guanche, R., Camus, P., Mendez, F. J., & Medina, R. (2013). A multivariate approach to estimate design loads for offshore wind turbines. Wind Energy, 16(7), 1091-1106. (Apen)
Package: ihcmqtt-gateway
Ihc Mqtt GatewayA simple IHC-MQTT Gateway; given connection details to an IHC Controller it downloads the project
file and subscribes on events from dataline inputs and outputs. Furthermore it subscribes to topics
in MQTT corresponding to each dataline output in the IHC project file.The gateway can easily be run as a Python module:python3 -m ihcmqtt.gatewayIt accepts the command line parameters:-h: a short help message-v: increase verbosity, possible levels are: 1, 2 and 3-c: specify a configuration file to useThe configuration file may look like this:controller_url=http://192.168.178.1.10
controller_username=ihcuser
controller_password=ihcpassword
broker_host=localhost
broker_port=1883These are the required settings. Specifying amapfileconfiguration item will make the gateway print out
IHC resource identifiers and corresponding MQTT topic names to said file.By default topic names are derived from name and position tags in the IHC project file and prefixed with "house".
The prefix can be changed by specifyingtopic_prefixin the configuration file.An example of those extra, non-mandatory, configuration items could be:mapfile=/var/ihcmqtt/ihcmqtt.map
topic_prefix=cottageThe gateway connects to the MQTT broker with a client name of "ihcmqtt-gateway-PID" - with PID being the
process id of the gateway.When run as a Python module, the gateway will exit cleanly on SIGINT.
Package: iheart-cli
Command line client foriHeartRadioThis project is meant to be a learning exercise. It is heavily inspired bypianobarwhich I love!The project is a cross platform iHeartRadio console client implemented in Python. It is simply an alternative to playing iHeartRadio on the browser; only plays Live and Artist radio stations which are available without needing to create an account.FeaturesPlay Live and Artist radio stationsPlay Song radio stations (Which are essentially Artist stations, but created using song names)Save song urls into local playlists maintained as json filesEach playlist is saved as a separate json file and can be freely copied between machinesPlay songs in a playlist sequentially or on shufflePlayaNONradioPlayinternet-radioDependenciesThis project does not implement an audio player. It uses VLC for cross platform audio playback. It can be installed from theVLC websiteInstallation/UsageStable release using pip -Great read$ pip install -U iheart-cli
$ iheart --helpLatest code from repository (might contains bugs and incomplete features)$ git clone https://github.com/shashfrankenstien/iheart-cli.git
$ cd iHeart-cli
$ python3 -m iheart --helpTODOAdd more / better testsaNONradio and internet-radio are very slow to start (almost 1 minute) - look into vlc documentation
Package: iheartradio
iheartradio rhythmboxPrerequisitesPython 3RhythmboxoptionalInstallsudoaptinstallpython3python3-pip
pipinstalliheartradioRuniheartradioA radio.json file will be generated with every stream available for each station in Canada. You can select US radio stations as well, see below.Run Rhythmbox modeiheartradio--rhythmboxAdd --rhythmbox flag to write to default rhythmbox cache at ~/.local/share/rhythmbox/rhythmdb.xmlNote:Make sure you have exited rhythmboxbefore running the script. Otherwise rhythmbox will overwrite the cache. Reopen it once the script is complete.Script will update existing stations and/or add new stations if you run it again in the future.Country CodeYou can select US radio stations by adding --country USiheartradio--rhythmbox--countryUSThere are over 1000 US radio stations, this will take a while and I suggest you don't do it very often as it could cause a temp ban.AuthorGrant Hutchinson(hutchgrant)LicenseReleased under theMIT License
Package: ihela-oauth2-provider
iHela Provider is a simple Django app to enable oAuth2 authentication with django-allauth.Quick startInstalldjango-allauthpip installdjango-allauthand following theinstall guideAdd “ihelaprovider” to your INSTALLED_APPS setting like this:INSTALLED_APPS=[..."allauth","allauth.account","allauth.socialaccount","ihelaprovider",]Include the polls URLconf in your project urls.py like this:path("oAuth2/",include("ihelaprovider.urls")),Runpython manage.py migrateto create the polls models.Start the development server and visithttp://127.0.0.1:8000/admin/to the iHela provider with the application credentials given (you’ll need the Admin app enabled).VisitiHelaanddjango-allauth.
Package: ihela-python-client
This repository provides a Python client for iHela platform.Get startedInstall the package withpip installihela-python-clientimport the package for using the provided functions to communicate with the iHela API. Theredirect_urimust be registered with the client created by iHela both for test and production.Init a bill and redirect the client to the bill url.Verify after if a bill has been paid.Visit the github (https://github.com/ubuviz/ihela-pyhton-client) page for more details.
Package: ihih
ihih is an attempt to provide simple configuration parsers (for Python) with a
dictionary-like interface.It try to be flexible and let you alter the syntax by sub-classing it.More informations can be found in the docs directory or online:https://romain-dartigues.gitlab.io/python-ihih/http://python-ihih.readthedocs.org/Features“flat” configuration file formatFlat by opposition to, “nested” (like JSON, nginx, …)
and “section” (INI files, …).Simple key / value systemSimple, obvious, supporting the features you are used to (ignore blank lines
and spaces, support comments, automatic unquoting, …).Lazy value interpolationExample:my_banner   = $myhostname MY-PROGRAM
my_hostname = server.example.netDictionary-like interfaceExample:>>>printconf['my_banner']server.example.netMY-PROGRAMConfiguration precedenceIn the following example, user configuration file will take precedence over
system-wide configuration file:conf=IHIH(('/etc/example.conf',os.path.join(os.path.expanduser('~'),'.example.conf')))Default on the initializationExample:conf=IHIH('/etc/example.conf',{'lang':'en','TZ':'UTC'})
Package: ihk-ausbildungsnachweis-utilities
IHK Ausbildungsnachweis UtilitiesSome tools to write your IHK Ausbildungsnachweise in plain XML and generate a PDF from it.
This comes especially handy when you organise your Ausbildungsnachweise in a git repository
like GitHub, GitLab or Bitbucket and use common techniques like PRs for reviewing.
The current functionality also includes the ability to sign an Ausbildungsnachweis PDF which
comes especially handy when integrating into a CI/CD process.Installationpip install ihk_ausbildungsnachweis_utilitiesUsageToDoExamplesExamples that demonstrate the usage of the tool can be found in theexamples/directory.DocumentationToDO
Package: ihm
This is a Python package to assist in handlingmmCIFandBinaryCIFfiles compliant with theintegrative/hybrid modeling (IHM)extension. It works with Python 2.7 or Python 3.To handle non-integrative theoretical models (for example, homology models),
see thepython-modelcifpackage
which supports files compliant with theModelCIFextension.Pleasesee the documentationor someworked examplesfor more details.Installation with conda, Homebrew or pipIf you are usingAnaconda Python, install withconda install -c conda-forge ihmOn a Mac withHomebrew, install withbrew tap salilab/salilab; brew install ihmOn a Fedora or RedHat Enterprise Linux box, install withdnf copr enable salilab/salilab; dnf install python3-ihmAlternatively, install with pip:pip install ihm(Note that pip builds a C extension module for faster parsing of mmCIF files.
This requires that your system has a C compiler. If you don't want to build
the C extension module, install withpip install ihm --install-option="--without-ext".)Installation from source codeTo build and install from a clone of the GitHub repository, runpython setup.py build
python setup.py installNote that a C extension module is built for faster parsing of mmCIF files.
This requires that your system has a C compiler
andSWIG. If either of these components are missing, you
can choose to build without the extension by adding--without-extto bothsetup.pycommand lines above.If you want to read or writeBinaryCIFfiles, you will also need the
Pythonmsgpackpackage.TestingThere are a number of testcases in thetestdirectory. Each one can be run
like a normal Python script to test the library. They can also be all run at
once usingnoseorpytest. They will also test
the C extension module if it is first built withpython setup.py build_ext --inplace.
Package: ihpc
🔵⬜⬜⬜🟦🟦🟦⬜🟦🟦🟦⬜🟦⬜🟦🟦🟦⬜🟦🟦🟦⬜⬜⬜🟦iHPCPython Interface for Scheduling HPC JobsExplore the docs »View Demo·Report Bug·Request Feature
Package: ihs29x
ihs29xA simple spec-driven streaming ("SAX-style") parser for IHS 297 and 298 files, based on the specifications provided bytwoninetyex.
It handles 297 and 298 files in either fixed or comma delimited format.
In accordance with my reading of the spec, it even handles intermixed formats within a single file, based on (sub-)file headers.The only interesting function isihs29x.stream_records, which may be called on an open file-like object in text mode.Live documentation is available athttps://derrickturk.github.io/ihs29x-python/docs/ihs29x.html.Available under the MIT license.UsageFor example, try:importsysfromihs29ximportstream_recordsdefmain(argv:list[str])->int:iflen(argv)!=2:print(f'Usage{argv[0]}<298-or-297-file>',file=sys.stderr)return2withopen(argv[1])asf:forrinstream_records(f):print(r)return0if__name__=='__main__':sys.exit(main(sys.argv))(c) 2023 terminus, LLC
Package: ihs-dl
The IHS Teardown Analysis provides detailed Photos, PDFs and BOMs of various electronics. Very useful ... the problem is the price-tag. Well with this program you can legally obtain the full photo collection of all tear-downs.Installationpip3 install ihs-dlUsageihs-dlScreenshotsBrief ExplainationAfter some digging, I found a way to download the photos and PDFs.
The preview photos on the IHS Teardown Website only artificially limited and have a unique DeviceID and a PhotoID.
PDFs have a different retrieving system that I don't understand as of yet.
But I managed to use the query's ProductID to retrieve and download the associated photos. Each Photo has an associated JSON file (Title, Description etc). And after some HTML parsing, URL retrieving, photo downloading and renaming with the synced JSON we have full access to the IHS Teardown Archive.Disclaimer and CopyrightThe photos are publicly available and neither encrypted nor protected. There's no copyright infringement involved in any process.
Be aware that every photo obtained is in the intellectual property of IHS Markit Ltd and may be illegal to redistribute.
Package: ihsm-analytics
# Project NameTODO: Write a project description## InstallationTODO: Describe the installation process## UsageTODO: Write usage instructions## ContributingFork it!Create your feature branch:git checkout -b my-new-featureCommit your changes:git commit -am ‘Add some feature’Push to the branch:git push origin my-new-featureSubmit a pull request :D## HistoryTODO: Write history## CreditsTODO: Write credits## LicenseTODO: Write license