Saravanan666/pypi-org huggingface dataset:
Package: hilite-syntax
python-hilite-syntaxThis package allows you to generate syntax-highlighting files from input
files (like bnf grammar files). The goal is to enable generation of syntax
highlighting files for any language specification on the fly, for all editorsSupported Frontendslark: use lark grammar files (for the LALR parser only) to generate
the syntax file for the language the grammar is parsing.seelark-parserfor more info on writing lark grammar filesseelark-syntax.vimfor syntax-highlighting of your lark files in vimseepython-shape-plotter-examplefor several things:writing a simple LALRlarkgrammar fileusinghilite_syntaxto convert lark grammar files to vim syntax
and viewing your DSL scripts with color in vimplotting 2D shapes using matplotlib from the lark DSL fileSupported Backendsvim: generate vim syntax highlighting files for the input language.by default, it will write the syntax and ftdetect files to$HOME/.vim, so you can instantly use the generated files, but
you can change the output dirpointers to related items:seelark-syntax.vimfor highlighting lark grammar files in vimseepython-shape-plotter-examplefor the reasons mentioned inlarkaboveinstallingpip install hilite_syntaxcommand-line usagehelp: the subcommand to dump the IR graph of your langauge:hilite_syntax help <frontend> [frontend-args]
hilite_syntax help <backend> [backend-args]You must run the help commands for each frontend or backend to see what
their supported arguments are.dump: the subcommand to dump the IR graph of your langauge, after being
parsed by the frontend:hilite_syntax dump <frontend> [frontend-args]input/output: the subcommand to convert the input to an output syntax
highlighting file:hilite_syntax input <frontend> [frontend-args] output <backend> [backend-args]for example, the following will convert a lark grammar file and install
vim syntax files for it at$HOME/.vim:hilite_syntax input lark ./mydsl.lark --start root_rule \
              output vim mydsl --ext mydsl --ext dslTODO: put up a docs page for the cmdline usage (and subcmds)library usageMost of the package lives in thehilite_syntaxlibrary, and can be used
directly in your application.TODO: put up a docs page for library usageinternal architectureTODO: describe lark-specific details, vim-specific details, the
intermediate graph representation, and the supported semantic FormatTypesOTHER TODOclean up documenting these potential issues:regex node sementics need to be supported by input AND
output implementation. if only the input supports it, an error will occurfor example: lark -> vim DOES support regex and string termsstring terminals MIGHT not be supported in output. any backend
implementation should specify the supported strings
Package: hillcipher
Hill CipherThe project is aboutHill Cipher Encryption and DecryptionIt allows to encrypt the text using the Hill Cipher technique, and decryption can be done as well. Checkout the snippets below.Note: Provide the input in the capital letters.importhillcipherashckey=[[17,17,5],[21,18,21],[2,2,19]]text="PAY MORE MONEY"enc=hc.encrypt(text,key)dec=hc.decrypt(enc,key)print(enc,dec)The $2^{nd}$ parameter (i.e.,key) is optional and can be excluded, but ensure that the count of alphabets in the input string are the multiples of 3.importhillcipherashctext="PAY MORE MONEY"enc=hc.encrypt(text)dec=hc.decrypt(enc)print(enc,dec)The complete code can be found @www.github.com/Kirandeep2806/Hill-Cipher
Package: hillmaker
hillmakerhillmakeris a Python package that computes time of day and day of week specific
occupancy statistics from transaction data containing arrival and departure
timestamps. Typical use is for capacity planning problems in places like
hospital emergency departments, surgical recovery rooms or any system in which
entities arrive, occupy capacity for some amount of time, and then depart. It
gets its name from the hill-like nature of plots based on temporal occupancy
statistics.usable via a CLI, a function based API and and objected oriented APItakes a pandas DataFrame or csv file as the input data typecomputes arrival, departure and occupancy summary statistics
by time of day, day of week, and entity category based on a dataframe containing one
record per visit.computes arrival, departure and occupancy for each datetime bin in a specified date rangeselect any time bin size (minutes) that divides evenly into a day.output statistics includes sample size, mean, min, max, standard deviation,
coefficient of variation, standard error, skew, kurtosis, and percentiles.weekly and day of week plots can be created by default or on demand; numerous plot related input parameters are available,summary report for length of stay automatically createdoutputs are stored in a dictionary containing pandas dataframes and as matplotlib figures. These can be accessed by methods for further post-processing or for exporting to external files.Requires Python >= 3.10, pandas >= 1.5.0, numpy >= 1.22, pydantic >= 2.1.1, seaborn >= 0.12.2, matplotlib >= 3.7.1, and tomli >= 2.0.1 (if not using Python 3.11)MIT LicenseSeethe CHANGELOGfor details on latest and older versions.Where to get itProject repo:http://github.com/misken/hillmakerPyPI:https://pypi.python.org/pypi/hillmakerDocumentation:https://hillmaker.readthedocs.io/en/latest/intro.htmlMore Examples: See notebooks and scripts athttp://github.com/misken/hillmaker-examplesInstallationYou can install using pip:pip install hillmakerYou should also be able to install hillmaker from conda-forge shortly (?) into a virtual environment.conda config --add channels conda-forge
conda config --set channel_priority strict
conda install hillmakerIf you want to get the latest update which is not yet on PyPI or conda-forge, you can install from the GitHub repo'sdevelopbranch:pip install git+https://github.com/misken/hillmaker@developQuick StartSee theGetting Startedpage in the hillmaker docs.How to contributeUse the GitHub issue tracking system to report problems with the software, seek support, or suggest improvements.
Code contributions can be suggested using GitHub pull requests.SeeCONTRIBUTING.rstfor more details.Learn more about the history of hillmakerSee theHistorypage at the hillmaker docs.
Package: hillslopetoy
hillslopetoyA collection of tools for exploring hillslope evolution.Free software: MIT licenseDocumentation:https://hillslopetoy.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2022-11-09)First release on PyPI.
Package: hillstone-client
Hilldust(UNOFFICIAL) Yet another implementation ofHillstone™ Secure Connect VPN Clientfor Linuxand for macOS later.Usage./hilldust.py vpn.yourdomain.com:12345 username passwordAsvpn.yourdomain.com:12345is your VPN provider's address.NotesFor now, it is only a proof-of-concept and may be not available for all the users,
because it only supports auth:HMAC-SHA1-96, crypto:3DES-CBC.DependenciesPython 3scapy (Python module)cryptography (Python module)iproute2Please note different systems may have different names for Python 3.
It can be:python3withpip3, orpythonwithpip,You may use the shortcut:pip3 install -r requirements.txtto install all
Python modules the program needs.You may useip -Versionto check if your system is missing iproute2 utility.
Package: hilly
HillyTools for summarizing and viewing sporty GPX tracesInstallpipinstallhillyDev Installgit clone
cd hilly
poetry installFeaturesTraces are loaded as GeoDataFrames (convenient exploring and editing)Rasterize traces to see popular routes/trails.Label/Filter Skiing Trace to label/exclude chair-lifts.Summary for Skiing dayUsageImport a trace:importhillydf=hilly.io.load_trace("path/to/trace.gpx")If it is a ski/snowboarding trace, apply filters to identify runs/lifts:ski_df=hilly.ski.apply_filters(df)ski_df.labels_str.unique().tolist()# ['', 'lift-1', 'run-1', 'run-2', 'lift-2', 'run-3', 'lift-3', 'run-4']Thelabels_strcolumn labels points in the trace as belonging to a lift ride or run, otherwise it is empty. The number indicates a count of lift/runs.From there you can get a summary for the trace (assuming it is over a single day):summary=hilly.ski.summary(ski_df)#🏂 Ski Stats ⛷#    runs: 4#    Total Dist(km): 4.815023209198009#    Longest Run(km): 1.409849302232853#    Total time: 0 days 00:17:32#    Total lift time: 0 days 00:36:55.990000#    Time Ratio: 0.4747313841668961Or, you can view the trace:# open leaflet map showing points, dropping datetime column for proper JSONm=ski_df.drop(columns=["time"]).explore("labels",cmap="coolwarm_r")m.show_in_browser()or rasterize the trace to get a density map:arr,bounds=hilly.utils.rasterize(ski_df,runs_only=True,res=1)m=hilly.io.map_raster(arr,bounds)m.show_in_browser()Collecting TracesThere are many apps available for collecting traces. I useOpenTracksand follow the Export instructions from the settings menu.
Package: hilok
Hierarchical locksAllows the caller to lock a "directory", or an individual file.Locks are, by default, shared, recursive & timed.First optional argument to the lock manager is the "sep".Second optional argument is "flags" (default is HiLokFlags.RECURSIVE, can be also be HiLokFlags:STRICT).fromhilokimportHiLok,HiLokError,HiLokFlagsh=HiLok()# default sep is '/', can pass it in hererd=h.read("/some/path")# nonblocking, this will fail!try:wr=h.write("/some",block=False)exceptHiLokError:passrd.release()wr=h.write("/some")# timeout=0 is the the same as block=Falserd=h.read("/some/path",block=False)# with syntax is fine:withh.read("/some/path"):passLock modes:HiLokFlags.STRICT: not reentrant, the good modeHiLokFlags.RECURSIVE: fully reentrant, supports escalation (read/write/release-read) and de-escalation (write/read/release-write)HiLokFlags.RECURSIVE_WRITE: only write-locks are reentrantHiLokFlags.RECURSIVE_ONEWAY: can read-lock while holding a write, but not vice-versa
Package: hil-test-framework
Testing FrameworkFramework to test embedded systemsClone repositorycd <path-to-directory>
git clone git@gitlab.com:pass-testing-solutions/hil-test-framework.git
cd hil-test-framework/
git pull origin main
git checkout -b <your-new-branch>  # Please follow the branch naming convention as mentioned in the coding guidelinesVirtual environment on MacOS1. VirtualenvwrapperYou can install virtualenv and virtualenvwrapper with:pip3 install virtualenv
pip3 install virtualenvwrapper
mkdir ~/.virtualenvs
# Might need to query where the virtualenv and virtualenvwrapper.sh are
which virtualenv
which virtualenvwrapper.sh
which python3Add these lines in the ~/.zshrc file# Setting PATH for Python 3 installed by brew
export PATH=<path-to-your-python3>:$PATH

# Configuration for virtualenv
export WORKON_HOME=$HOME/.virtualenvs
export VIRTUALENVWRAPPER_PYTHON=<path-to-your-python3>
export VIRTUALENVWRAPPER_VIRTUALENV=<path-to-your-virtualenv>
source <path-to-your-virtualenvwrapper.sh>

source $ZSH/oh-my-zsh.shMake sure to save these changes in your file and close your terminal for them to take effect.
Now you can create a virtual environment by simply typing:mkvirtualenv <name-of-your-virtual-env>
cd <your-git-repository>
workon <name-of-your-virtual-env>
deactivate <name-of-your-virtual-env>To add the correct interpreter for your repository on PyCharm:- PyCharm IDE > Preferences > Project > Python Interpreter
- Select the 'Add' button for interpreters
- Choose the 'Existing environments' in 'Virtualenv'
- Add the python3 file from /bin/ of your venv folder
E.g. - .virtualenvs/demo_repo_venv/bin/python3After setting up the venv for the repo please install all the
requirements and allow some time for the IDE to do indexing. If your repository has a requirements.txt:pip3 install -r requirements.txt2. PipenvInstall pipenvpip3 install pipenv 
# or
brew install pipenv

pipenv shell         # will create a virtualenv for the project
pipenv install       # will install dependencies for the repo from the Pipfile and Pipfile.lock

# Install any further packages in this environment as

pipenv install rich  # rich is a python library to nicely print on terminalYour pipenv environment will be in the ~./virtualenvs folder.
Make sure to add the interpreter on your IDE if not automatically added by navigating to the virtualenv folder for your repo and selecting the correct python3 file from /bin/.MaintainersMaintainers: @julianpass and @shuparnadeb_pts
Package: hilti_profis
Hilti ProfisPython binding for generating `.pe` files, for use withHilti-Profis software.Documentation·Report Bug·Request FeatureTable of Contents1. About the Project1.1. Features2. Getting Started2.1. Prerequisites2.2. Dependencies2.3. Installation2.3.1. From github2.3.2. From Pypi2.4. Development3. Usage6. License7. Contact1. About the Project1.1. FeaturesCreate new custom.pefilesModify part/whole of existing.pefileAdd load cases to anchor design2. Getting Started2.1. PrerequisitesPython 3.11 or above2.2. DependenciesCreate the virutual environment and install dependenciespipinstallflit2.3. Installation2.3.1. From githubGet the latest version directly from githubgitclonehttps://github.com/rpakishore/Hilti-Profis.gitcdHilti-Profispipinstallflitflitinstall--depsproduction2.3.2. From Pypipipinstallhilti_profis2.4. DevelopmentDownload the git and install via flitgitclonehttps://github.com/rpakishore/Hilti-Profis.gitcdHilti-Profispipinstallflitflitinstall--pth-file3. Usagefromhilti_profis.mainimportPEanchor=PE()#Change/Add valuesanchor.Model['ProjectName']='TestProject'anchor.Model.Loads.Combos.add(Fx=1,Fy=1,Fz=2,Mx=1,My=2,Mz=3,LoadType='Seismic',Comment='LC1')#Forces in Nanchor.Model.Loads.Combos.add(Fx=1000,Fy=1000,Fz=2000,Mx=1000,My=2000,Mz=3000,LoadType='Seismic',Comment='LC2')#Forces in N; Moments in N-mmanchor.Model.apply()#Apply the changesanchor.xml_content()#Get contents of `.pe` fileanchor.save('nosync-test.pe')#Save file to destination6. LicenseSeeLICENSEfor more information.7. ContactArun Kishore -@rpakishoreProject Link:https://github.com/rpakishore/Hilti-Profis
Package: hil-webui
PyTest CommanderA UI for controlling PyTest interactively. With it, you can explore your
test hierarchy (test packages/modules/classes/methods etc.), run tests at the
click of a button and see the results presented in your web browser.FeaturesRun tests by group (module, class etc.) or individually.Clear pass/fail labelling so you can dig down and find what failed quickly,
instead of having to scroll up through many screens of terminal output.Test files and dependencies are reloaded every time so you can run, tweak your
code, then run again in a fast iteration cycle.Runs on any modern OS and browser (tested on Win10, macOS and Ubuntu with
Chrome, Firefox and Safari). IE is not supported.Automatically start and stop docker services while running if specified in adocker-compose.ymlfile in the root test directory.Tests are run in separate processes and reloaded for each invocation, allowing
tests to be tweaked and re-run without restarting the main process.PrerequisitesRequires Python3.6+ and pip.QuickstartInstall and get started running your own PyTest tests:pip install pytest-commander
pytest_commander /path/to/your/testsI recommend installing into an activatedvirtual environment. You will
want to make sure that all additional dependencies required to run your tests
are installed into the same virtual environment.When you run with default options, the web app to browse and run tests should be
opened automatically in your default browser. To view full docs for the
command-line parameters, runpytest_commander --help.Build from sourceAlternatively you may build from source. In addition to the prerequisites above,
you must ensure you have installedpipenvandnpm.git clone https://github.com/ryanc414/pytest_commander.git
cd pytest_commander
python build.py
python test.pyYou may then install the local directory into an activated virual environment,
along with other dependencies installed to run your tests:pip install .
pytest_commander /path/to/your/testsPyTest ConfigurationConfiguration for PyTest such as extra command-line arguments may be specified
by adding a pytest.ini file in the same directory. Seehttps://docs.pytest.org/en/latest/reference/customize.html#pytest-inifor more
details.Note that there is a known issue when using pytest_commander along with the
xdist plugin, used to parallelise tests among multiple processes. You will
have to remove xdist config options from your pytest.ini in order to use
pytest-commander.RenamingPreviously this project was called "pytest_web_ui". I felt that this name gave
too much emphasis to the implementation detail of the UI being a web app,
when it could easily have been implemented in some other way (e.g. as an
electron app) and still serve the same purpose. I chose the name
"pytest_commander" to reflect the true purpose of this project: giving a
friendly and intuitive interface for developers to take command of their tests.Happy testing!
Package: himalaya
Himalaya: Multiple-target linear modelsHimalaya[1]implements machine learning linear models in Python, focusing
on computational efficiency for large numbers of targets.Usehimalayaif you need a library that:estimates linear models on large numbers of targets,runs on CPU and GPU hardware,provides estimators compatible withscikit-learn’s API.Himalayais stable (with particular care for backward compatibility) and
open for public use (give it a star!).Exampleimportnumpyasnpn_samples,n_features,n_targets=10,5,4np.random.seed(0)X=np.random.randn(n_samples,n_features)Y=np.random.randn(n_samples,n_targets)fromhimalaya.ridgeimportRidgeCVmodel=RidgeCV(alphas=[1,10,100])model.fit(X,Y)print(model.best_alphas_)# [ 10. 100.  10. 100.]The modelRidgeCVuses the same API asscikit-learnestimators, with methods such asfit,predict,score, etc.The model is able to efficiently fit a large number of targets (routinely
used with 100k targets).The model selects the best hyperparameteralphafor each target
independently.More examplesCheck more examples of use ofhimalayain thegallery of examples.Tutorials usinghimalayafor fMRIHimalayawas designed primarily for functional magnetic resonance imaging
(fMRI) encoding models. In depth tutorials about usinghimalayafor fMRI
encoding models can be found atgallantlab/voxelwise_tutorials.ModelsHimalayaimplements the following models:Ridge, RidgeCVKernelRidge, KernelRidgeCVGroupRidgeCV, MultipleKernelRidgeCV, WeightedKernelRidgeSparseGroupLassoCVSee themodel descriptionsin the documentation
website.Himalaya backendsHimalayacan be used seamlessly with different backends.
The available backends arenumpy(default),cupy,torch, andtorch_cuda.
To change the backend, call:fromhimalaya.backendimportset_backendbackend=set_backend("torch")and givetorcharrays inputs to thehimalayasolvers. For convenience,
estimators implementingscikit-learn’s API can cast arrays to the correct
input type.GPU accelerationTo runhimalayaon a graphics processing unit (GPU), you can use either
thecupyor thetorch_cudabackend:fromhimalaya.backendimportset_backendbackend=set_backend("cupy")# or "torch_cuda"data=backend.asarray(data)InstallationDependenciesPython 3NumpyScikit-learnOptional (GPU backends):PyTorch (1.9+ preferred)CupyStandard installationYou may install the latest version ofhimalayausing the package managerpip, which will automatically downloadhimalayafrom the Python Package
Index (PyPI):pipinstallhimalayaInstallation from sourceTo installhimalayafrom the latest source (mainbranch), you may
call:pipinstallgit+https://github.com/gallantlab/himalaya.gitDevelopers can also installhimalayain editable mode via:gitclonehttps://github.com/gallantlab/himalayacdhimalayapipinstall--editable.Cite this packageIf you usehimalayain your work, please give it a star, and cite our
publication:[1]Dupré La Tour, T., Eickenberg, M., Nunez-Elizalde, A.O., & Gallant, J. L. (2022).
Feature-space selection with banded ridge regression.NeuroImage.
Package: himama
himama: HiMama DownloaderPython/Selenium based utility in Python, to download
pictures and videos offline.InstallationInstallPython 3.9or Higher.Run this in your Command Prompt or Terminal:python-mpipinstallhimamaDepending on which Operating System you use, the command could either bepython3or evenpython3.9.Now download and install a supported WebDriver. Currently it
tries to use:Mac - Safari DriverWindows - Edge DriverAll - Firefox Gecko DriverAll - Chrome DriverAll - Opera DriverIf you are running Linux, there may already be a package for your system
and you may want to install it through your package manager.and level of support may vary from time to time.UsageTo Download, run:himamaTo run non-interactively such as a Cron Script, set these variables
in your environment.setHIMAMA_USER=daddy@daddy.comsetHIMAMA_PASSWORD=changeMeIt needs a non-headless mode, or a virtual display to run.Questions and FeedbackI wrote this in my spare time. All feedback and patches welcome.
Package: himawari-api
Welcome to HIMAWARI API - An API to download and search HIMAWARI satellite data.The HIMAWARI-API is still in development. Feel free to try it out and to report issues or to suggest changes.QuickstartThe code in this repository provides an API to download, query and filter HIMAWARI-8 and HIMAWARI-9 satellite data.Data download and query/filtering is available:for local file systems and AWS S3.for sensors AHI.The foldertutorialsprovide the following jupyter notebooks, describing various features ofHIMAWARI_API:Downloading HIMAWARI data:download.ipynbFind and filter HIMAWARI data:find_and_filter.ipynbRead AHI and plot it with satpy:read_ahi_data.ipynbDocumentation is available at XXXXXInstallationpipHIMAWARI-API can be installed viapipon Linux, Mac, and Windows.
On Windows you can installWinPythonto get Python and pip running.Then, install the HIMAWARI-API package by typing the following command in the command terminal:pip install himawari_apiCitationIf you are using HIMAWARI-API, please cite:Ghiggi Gionata. ghiggi/himawari_api. Zenodo.https://doi.org/10.5281/zenodo.7787923If you want to cite a specific version, have a look at theZenodo site.ContributorsGionata GhiggiLicenseThe content of this repository is released under the terms of theMITlicense.
Package: hi-ml
Microsoft Health Intelligence Machine Learning ToolboxOverviewThis toolbox aims at providing low-level and high-level building blocks for Machine Learning / AI researchers and
practitioners. It helps to simplify and streamline work on deep learning models for healthcare and life sciences,
by providing tested components (data loaders, pre-processing), and deep learning models.InstallationYou can install the latest version frompypiviapip install hi-mlDocumentationThe detailed package documentation, with examples and API reference, is onreadthedocs.Getting startedExamples that illustrate the use of thehi-mltoolbox can be found onreadthedocs.ChangelogWe are relying on Github's auto-generated changelog to describe what went into a release. Please checkeach individual releaseto see a full changelog.LinksGithubhttps://github.com/microsoft/hi-mlProject InnerEyehttp://aka.ms/InnerEye
Package: hi-ml-azure
Microsoft Health Intelligence Machine Learning Azure ToolboxOverviewThis toolbox provides cloud integration tools for Machine Learning / AI researchers and
practitioners. ML components can be found in the sibling packagehi-ml.InstallationYou can install the latest version frompypiviapipinstallhi-ml-azureDocumentationThe detailed package documentation, with examples and API reference, is onreadthedocs.Getting startedExamples that illustrate the use of thehi-mltoolbox can be found onreadthedocs.ChangelogWe are relying on Github's auto-generated changelog to describe what went into a release. Please check each individual release
(available here) to see a full changelog.LinksGithubhttps://github.com/microsoft/hi-mlProject InnerEyehttp://aka.ms/InnerEye
Package: himport
HImportImport python files over http!Usagefromhimportimporthimporth=himport("https://raw.githubusercontent.com/BlackIQ/Hello-World/main/Python/examples/python.py","h")InstallRunpip install himportNoteFiles from github must be raw, and the code must be the only thing on the page. If it relies on dependiences that aren't included in python, or you have installed, you must add those too.LicenseMIT