Saravanan666/pypi-org huggingface dataset:
Package: hidb
hidb :wave:hidb is a file based key-value data storeInstallation$pipinstallhidbUsagehidb can be used as a python package for saving JSON object data in the local storage.InitializeInitialize the database with the location you want to store the data, here we are using the current directoryfromhidbimportfileStoreDBdb=fileStoreDB("./")Add the datacreate method takes three valueskeyIt is the unique key to recognise the datadataIt is the JSON object to storettlIt is an integer, the number of seconds the data should be available in the database, if left blank the data would persist indefinitely.data='{"name": "John Doe", "age": 20, "hobby": ["Reading", "Coding","Travelling"]}'db.create("mydata",data,20)Read from the databaseTo read from the database, simply provide the keyprint(db.read("mydata"))Delete the record from the databaseTo delete from the database, simply provide the keydb.delete("mydata")To save the database on the local storageProvide the name with which to save the filedb.saveData("mydata.pk")To load from an existing databaseTo load from an existing file, first we need to create the database instance and then load the data into it.db=fileStoreDB("./")db.loadData("mydata.pk")
Package: hiddb
HIDDB Python SDKThe official SDK for theHIDDBvector database.InstallationUse the package managerpipto install the SDK.pipinstallhiddbUsageCreate a collection within a database<your database_id>.fromhiddb.synchronousimportHIDDBhiddb=HIDDB("<key>","<secret>")# Create a collection named 'wordvectors'hiddb.create_collection(database_id="<your database_id>",collection_id="wordvectors")Create an index within this collection:# Create an index on field 'vector' within the collection and dimension 300hiddb.create_index(database_id="<your database_id>",collection_name='wordvectors',index_name="vector",dimension=300)Insert documents like that:document={"vector":[0.0]*300,"id":"my first document"}hiddb.insert_document(database_id=database_id,collection_name='wordvectors',documents=[document])Search for nearest documents:similar_words=hiddb.search_nearest_documents(database_id="<your database_id>",collection_name='wordvectors',index_name="vector",vectors=[[42.0]*300],max_neighbors=10)More examples and a more detailed documentation will follow soon 🚀ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.
Package: hiddenbot
HiddenBotDark web crawler.PrerequisitesTorAccess to onion services, we need to useTorproxy.# If tor is not installed...sudoaptinstalltor

tor&To check if you're using Tor from command line, run the following command:curl-fsSL-xsocks5://127.0.0.1:9050check.torproject.org|grepCongratulationsIf the content "Congratulations. This browser is configured to use Tor." appeared, your Tor proxy configuration is correct.Virual Machine (Optional)It's recommended to use a virtual machine for more securely connecting to dark web.Usagehiddenbotrun-uhttps://xxx...xxx.onion/# Depth (-d)hiddenbotrun-uhttps://xxx...xxx.onion/-d5# Output (-o)hiddenbotrun-uhttps://xxx...xxx.onion/-oresult.jsonhiddenbotcurrently extractstitle,descriptionandURLonly.Extracted data is saved to aJSONfile.InstallationFrom PippipinstallhiddenbotFrom Sourcegitclonehttps://github.com/hideckies/hiddenbot.gitcdhiddenbot
poetryinstall
poetryshell
hiddenbot--help
Package: hiddeneye
Installation$ pip install hiddeneye && hiddeneyeFeatureshiddeneye, ** Modern Phishing Tool **How to install ?$ pkg install python -y
$ pip install hiddeneye
$ hiddeneyeRun ?$ hiddeneyeDisclaimerWe are not responsible for your loss by this tool.
this tool is made for Education/Learning Purpose.
Dont harm anyone using this tool.Usage$ hiddeneye -h

Author : Unknown
PIP module : Parixit Sutariya
Github : github.com/Bhai4You
Blogspot : https://bhai4you.blogspot.comGithub
Package: hiddeneye-reborn
HiddenEye: Rebornis my second try on doing multi-featured tool for human mistakes exploitation.
Currently,HE: REhas mainly phishing features. But we are planning on adding more, you can follow development progress by looking at (REMIND ME TO DO ROADMAP) or Projects Tab on GitHubDisclaimerThe use of theHiddenEye: Rebornand/or its resources iscomplete responsibilityof theend-user.
Developers assumeno liabiityand arenot responsiblefor any misuse or damage caused byHiddenEye: Reborn.
Some of your actions may beillegaland youcan notuse this software to test someone withoutwritten permissionfrom person or company.InstallationHE: REis available on PyPI and can be installed using pip:pipinstallhiddeneye-rebornThat's all it takes! HE: RE is now available as a terminal command or as a package to your projects.Basic Usageto be writtenDocumentationto be writtenhttps://hiddeneye-reborn.readthedocs.ioFAQQ:Why originalHiddenEyeis no longer maintained?A:Due to low quality and bad practices used in code.Q:Is there any example usages for this tool?A:This is just a tool, and it's up to you how to use it. That's why we take no liability for your actions.
There are multiple examples of legal usage of this tool, such as people willing to improve their workflow by testing/educating their employees or collegues and educating them about human vulnerabilities social engineers may use.Q:Why are developers so slow? Why isn't anyone updating it?A:Community doesn't generate any revenue and developer can't afford to eat while working on this software, if you want opensource software to be developed - support developers.Contributingto be written
Package: hiddenfiles
Welcome to PyconfigDotfiles is a repo for managing dotfiles. The idea is to collect those time-consuming files to setup in one directory and reference them via symbolic links from there original locations. Thus, when a new machine is required to be setup, all that is required to clone the repo in that machine and create the symbolic links.Dotfiles include, but are not limited to:~/.gitconfig~/.ssh~/.aws~/.bash_profile~/.bashrc$profilein Windows PowershelletcAdditionally, files that contain data, sensitive information that should not be pushed to a repository are contained in a directory~/dotfiles. The files therein are encrypted before backedup.Additionally, scripts to perform setup of new machines and perform mundane tasks are maintained here inscripts. The repo uses Python to perform the tasks.
Package: hidden_markov
This package is an implementation of Viterbi Algorithm, Forward algorithm and the Baum Welch Algorithm. The computations are done via matrices to improve the algorithm runtime. Packagehidden_markovis tested with Python version 2.7 and Python version 3.5.DocumentationCheck this linkfor a detailed documentation of the project.If you are new to hidden markov models check out thistutorial.RequirementsnumpyLicenseCopyright (c) 2016 Rahul Ramesh.  See the LICENSE file for license rights and limitations (MIT).
Package: hideandseek
hideandseekHighly modularized deep learning training library.Why usehideandseek?Easy training & saving deep learning models along with other modules (ex: preprocessing modules) required in inferenceRun multiple deep learning experiments in parallel on multiples GPUs (powered byhydra, and python multiprocessing)Design and analyze experiments scientifically by modifying variables (powered byhydra)Modularized machine learning pipeline allows using the same script for all types of experimentsThe same training code can be run in privacy preserving setting by minimal modificationsCurrently prettifying codes. (30.10.2022.)import torch
import torch.nn as nn

# Generate data
x = torch.rand(200,1)
y = 5*x+2

network = nn.Linear(1,1)
dataset = torch.utils.data.TensorDataset(x, y)
criterion = nn.MSELoss()
cfg = {
'lr': 1e-2,
'batch_size': 32,
'epoch': 10 # optional
}

# Training configuration. All you need to train a neural network
kwargs = {
'network':network,
'dataset':dataset,
'cfg_train':cfg,
'criterion':criterion,
'name': 'Test' # optional
}
trainer = hs.N.Node(**kwargs)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
trainer.network.to(device)

# Train for predefined number of epochs
trainer.train() # Train for predefined number of epochs
trainer.train(5) # Train for specified number of epochs
trainer.train(epoch=5) # Same thing with trainer.train(5)
trainer.train(step=500) # Train for specified number of updates

trainer.network.cpu()and simply run multiple batch of experiments with a single line command such as:python train.py -m lr=1e-3,1e-2 batch_size=32,64 "random_seed=range(0,5)" \
hydra/launcher=joblib hydra.launcher.n_jobs=8
# Runs total of 2*2*5=40 batch of experiments, with 8 processes at a time. Experiment results are stored in hydra.sweep.dir which can be overridden.To doDraw figures to explain hideandseekGUI for generating experiment scripts when conducting variable sweeps
Package: hidebehind
hidebehindWhat is this?It's a simple python library to embed data (files) into image/video/audio files.Why?It was written for educational purposes only. It may contain bugs, so use it at your own risk!Installation$python3-mpipinstallhidebehindQuick start examplesFirst, readUnix philosophy:Write programs to handle text streams, because that is a universal interface.Assume you have:$echo"Dream big and dare to fail">secret1.txt
$echo"Talk is cheap. Show me the code.">secret2.txt
$wgethttps://github.com/multifrench/hidebehind/blob/main/img/Gauss.pngThen:$hideput-cGauss.png<secret1.txt>embedded.png
$hideget<embedded.png>s1-restored.txt
$cats1-restored.txtEncryptionIt's not safe to transmit unencrypted (sensitive) information over public connections.In this example I'll useGnuPG, but you can use anything you want (i.e.VeraCrypt)# Encrypt:$gpg-er<YouKeyID>-o-<secret2.txt|hideput-cGauss.png>embedded.png# Decrypt:$hideget<embedded.png|gpg-d>s2-restored.txt
$cats2-restored.txtMultiple files & compressionIn case you want to transmit more than one file at once, usetar.It's also a good idea to compress our data. I'll usebzip2.$tar-csecret?.txt|bzip2-9|hideput-cGauss.png>embedded.png# And get our data back:$hideget<embedded.png|tar-xj
$catsecret?.txtIntegrityIn addition, to be sure our files weren't corrupted, we will write down their hashes (sha256sum) into a separate file and use technique from the previous paragraph to save them.$sha256sumsecret?.txt>.shasum# Copy-paste the previous code block but add `.shasum` into the archive.$tar-csecret?.txt.shasum|bzip2-9|hideput-cGauss.png>embedded.png# Get our data back:$hideget<embedded.png|tar-xj# Now, verify the data wasn't corrupted (or it was)$sha256sum-c.shasumWhat else?Write programs to handle text streams, because that is a universal interface.Remember? I hope now you're (if you weren't) convinced it's very useful technique.
You may, for example, want to embed encrypted and compressed secret and then check its integrity. It's up to you.How does it work?Do you see the difference?Nor do I. But there is a secret embedded in it!Stay hungry. Stay foolishBasically, the idea behind it is thatthe human eye is very insensitive. It can't detect difference in less than 1%.The program slightly changes the value of blue part (of an RGB pixel).Implementation detailAssume we want to hide a sequence of bytesS(that's our secret message; it's not necessarily a string of printable characters, i.e. a binary file) into
  an imageI. For simplicity, we assume an image is anNxMx3array, whereI[i][j]is[red, green, blue]. We are only interested in0 <= blue < 256value.
  It's possible to represent it as a binary number. For example,55 = 0b110111. If we clear last least significant bit, we get0b110110 = 54. We changed the value of it only by less than0.4%!  
  Having done so, now we can store the payload there. Yes, a bit of the payload goes into one pixel. If we want to embed more, we modify two least significant bits. The maximum difference then will be1.2%, but it's still OK.
Package: hide-code
Hide_codeRelease:Dev:hide_code is a Jupyter notebook extension to selectively hide code, prompts and outputs with PDF and HTML exporting support. Check out the demo withJupyter Notebook Installationpip install hide_codejupyter nbextension install --py hide_codejupyter nbextension enable --py hide_codejupyter serverextension enable --py hide_codeJupyter Lab Installationpip instal hide_code[lab]jupyter lab buildUpgrading with nbextensionpip install hide_code --upgradejupyter nbextension install --py hide_codeNote: add--sys-prefixtojupyter nbextensionto install into virtualenv or conda environment.Changes in 0.6.0ImprovementsAdded experiemental Jupyter Lab support. SeeLab usagefor details and limitations.Added Binder demoSimplified extension installation. No longer need to use nbextension/serverextension commands after pip install.No longer supporting Python 2.7. Install hide_code==0.5.2 for Python 2.7.0.5.6 is the last version to support Python 3.6 and older.Added extras_install [all] to install hide_code and all dependencies. Usepip install hide_code[all]to install. Resolves #85.DocumentationVisit theWiki.RequirementsJupyter notebook >6.0Jupyter nbconvert >6.xpdfkit &wkhtmltopdfPython 3.7+
Package: hide-code-lab
hide_code_labis a Jupyter notebook extension to selectively hide code, prompts and outputs with PDF and HTML exporting support.This extension is composed of a Python package namedhide_code_labfor the server extension and a NPM package namedhide-code-labfor the frontend extension.RequirementsJupyterLab >= 2.0InstallNote: You will need NodeJS to install the extension.pipinstallhide_code_lab
jupyterlabbuildTroubleshootIf you are seeing the frontend extension but it is not working, check
that the server extension is enabled:jupyterserverextensionlistIf the server extension is installed and enabled but you are not seeing
the frontend, check the frontend is installed:jupyterlabextensionlistIf it is installed, try:jupyterlabclean
jupyterlabbuildContributingInstallThejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Move to hide_code_lab directory# Install server extensionpipinstall-e.# Register server extensionjupyterserverextensionenable--pyhide_code_lab--sys-prefix# Install dependenciesjlpm# Build Typescript sourcejlpmbuild# Link your development version of the extension with JupyterLabjupyterlabextensioninstall.# Rebuild Typescript source after making changesjlpmbuild# Rebuild JupyterLab after making any changesjupyterlabbuildYou can watch the source directory and run JupyterLab in watch mode to watch for changes in the extension's source and automatically rebuild the extension and application.# Watch the source directory in another terminal tabjlpmwatch# Run jupyterlab in watch mode in one terminal tabjupyterlab--watchNow every change will be built locally and bundled into JupyterLab. Be sure to refresh your browser page after saving file changes to reload the extension (note: you'll need to wait for webpack to finish, which can take 10s+ at times).Uninstallpipuninstallhide_code_lab
jupyterlabextensionuninstallhide-code-lab
Package: hideez
python-hideezPython library and commandline client for communicating with Hideez
Hardware WalletSeehttps://hideez.comfor more informationInstallpython-hideez requires Python 3.3 or higher. The easiest
way to install it is withpip.pip3installhideezCommand line client (hideezctl)The includedhideezctlpython script can perform various tasks such as
signing transactions, retrieving account info and addresses.
Package: hidefix
HIDEFIXThis Rust and Python library provides an alternative reader for theHDF5file orNetCDF4
file(which uses HDF5) which supports concurrent access to data. This is achieved by
building an index of the chunks, allowing a thread to use many file handles to
read the file. The original (native) HDF5 library is used to build the index,
but once it has been created it is no longer needed. The index can be
serialized to disk so that performing the indexing is not necessary.In Rust:usehidefix::prelude::*;letidx=Index::index("tests/data/coads_climatology.nc4").unwrap();letmutr=idx.reader("SST").unwrap();letvalues=r.values::<f32>(None,None).unwrap();println!("SST: {:?}",values);or with Python using Xarray:importxarrayasxrimporthidefixds=xr.open_dataset('file.nc',engine='hidefix')print(ds)See theexamplefor how to use hidefix forregular,parallelorconcurrentreads.MotivationThe HDF5 library requires internal locks to bethread-safesince it relies on
internal buffers which cannot be safely accessed/written to from multiple
threads. This effectively causes multi-threaded applications to use sequential
reads, while competing for the locks. And also apparently cause each other
trouble, perhaps through dropping cached chunks which other threads still need.
It can be safely used from different processes, but that requires potentially
much more overhead than multi-threaded or asynchronous code.Some basic benchmarkshidefixis intended to perform better when concurrent reads are made either
to the same dataset, same file or to different files from a single process. For
basic benchmarks the performance is on-par or slightly better compared to doing
standardsequentialreads than the native HDF5 library (through itsrust-bindings). Wherehidefixshines
is once themultiple threadsin thesame processtries to read inany wayfrom a HDF5 file simultaneously.This simple benchmark tries to read a small dataset sequentially or
concurrently using thecachedreader fromhidefixand the native reader
from HDF5. The dataset is chunked, shuffled and compressed (using gzip):$cargobench--benchconcurrency----ignoredtestshuffled_compressed::cache_concurrent_reads...bench:15,903,406ns/iter(+/-220,824)testshuffled_compressed::cache_sequential...bench:59,778,761ns/iter(+/-602,316)testshuffled_compressed::native_concurrent_reads...bench:411,605,868ns/iter(+/-35,346,233)testshuffled_compressed::native_sequential...bench:103,457,237ns/iter(+/-7,703,936)Inspiration and other projectsThis work is based in part on theDMR++
moduleof theOPeNDAPHyrax
server. Thezarrformat does something similar,
and the same approach has beentested out on
HDF5as swell.
Package: hide-frames
hide_framesEver had nested wrappers (probably from many libraries) ... and your traceback look messy? There's justfunc(*args, **kwargs)everywhere!Not anymore, usinghide_frames, you can (ironically) use a simple decorator to hide your ugly frames!Installationpip install hide_framesExampleSuppose we have a wrapper:defuseless(f):defwrapped_function(*args,**kwargs):returnf(*args,**kwargs)returnwrapped_functionIf we have a function that raises an exception and we wrap it:@uselessdefoops():raiseExceptionWe would get an ugly traceback if we call it:Traceback(mostrecentcalllast):File"myfile.py",line10,in<module>oops()File"myfile.py",line3,inwrapped_functionreturnf(*args,**kwargs)# <--- ugly^^^^^^^^^^^^^^^^^^File"myfile.py",line8,inoopsraiseExceptionExceptionWe can use the hide_frame decorator to modify ouruselessdecorator:defuseless(f):@hide_frame# <-- importantdefwrapped_function(*args,**kwargs):returnf(*args,**kwargs)returnwrapped_functionNow, our traceback looks like this:Traceback(mostrecentcalllast):File"myfile.py",line13,in<module>oops()File"myfile.py",line11,inoopsraiseExceptionExceptionHooray! No moref(*args, **kwargs)!The best part: it works with any number of decorators!If you have more than one frame that needs to be hidden, usehide_nframes()(There's another simple example intest.py)
Package: hideip
InstallationWindowspython -m pip install hideipmacOSsudo pip install hideipLinuxpip install hideipMajor FeaturesOpen file SOCKS5 proxyGet the avaiable proxy in the fileTorrent the proxyHow to use?First of all, we need the file SOCKS5 download it fromHideMefromhideipimportHideMe# The path file socks5.csvfile='socks5.csv'hide=HideMe(file)# Get the avaiable proxyproxy=hide.torrent()# Printprint(proxy)Use with requests libraryThe socks5 maybe not supportverify, therefore we need to set itFalse. We might want to turn off the InsecureRequest warning.importrequestsfromhideipimportHideMe# Turn off warning, please ignore if you don't wantfromurllib3.exceptionsimportInsecureRequestWarningrequests.packages.urllib3.disable_warnings(category=InsecureRequestWarning)# Get the proxyfile='socks5.csv'hide=HideMe(file)hideme=hide.torrent()# Requestsres=requests.get('https://httpbin.org/ip',proxies=hideme,verify=False,timeout=2)print(res.text)
Package: hide-markdown
hide-markdownHides markdown cells for easier development. This JupyterLab extension was inspired by thejupyterlab-hide-codeJupyterLab extension from (OSSCAR)
Open Software Services for Classrooms and Research – An Open Science Educational HubRequirementsJupyterLab >= 3.0Installpipinstallhide-markdownContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the hide-markdown directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseUninstallpipuninstallhide-markdown