Saravanan666/pypi-org huggingface dataset:
Package: ultralite
# UltraliteFor when portability is critical, but you still need some HTTP convenience## Author
by Cathal Garvey, copyright 2014, proudly licensed under the
[GNU Affero General Public License](https://www.gnu.org/licenses/agpl-3.0.txt).Twitter: @onetruecathalEmail:cathalgarvey@cathalgarvey.meGithub:https://github.com/cathalgarveyminiLock.io: JjmYYngs7akLZUjkvFkuYdsZ3PyPHSZRBKNm6qTYKZfAM## About
Anyone who’s coded in Python for long will have discovered [Kenneth Reitz’s
“Requests” library](http://docs.python-requests.org/en/latest/), which makes
HTTP operations far more clean and straightforward than provided for by the
Python standard library (though things are improving on that score).Requests is as simple to install aspip install requests, and as pip comes
installed with newer distributions of Python, this is really the best way you
can get good HTTP in Python.However, there are times when you want your code to be portable and not to
depend on even the easiest third-party libraries, and for that there’s
Ultralite.The goal is to support 90% of the convenience of requests, while remaining
short and in-lineable, so you can literally copy/paste all of the code in this
module and drop it into your own code to get a taste of requests, anywhere.
And all of this while passing flake8 and pyflakes.## How do I use this?
The intended use-case is to copy/paste this into your own modules or libraries.
You can also usepip install ultralightto install for use in the interactive
interpreter or to list it as a dependency in setup.py.When copy/pasting, just copy the entire Ultralite class from this module, and
import the standard library modules it requires. Default headers can be added
to the class attribute “_default_headers”; these will be added toallrequests’ header dictionaries, with user-provided headers overwriting them.At present this only supports HEAD and GET requests, but will handle
POST/PUT/DELETE in the near future also in a manner similar to requests
(enabled by improvements in the standard library).Features provided that mimic requests:Ultralite methods “get”/”head”/”put”/”post”/”delete” (not all implemented)Methods return Ultralite.UltraliteResponse objects which are requests-like:UltraliteResponse.headers -> dictUltraliteResponse.status_code -> intUltraliteResponse.reason -> strUltraliteResponse.content -> bytesUltraliteResponse.text -> unicode decoding of UltraliteResponse.contentUltraliteResponse.raw -> http.client.HTTPResponse / urllib.error.XXXUltraliteResponse.request -> urllib.request.RequestUltraliteResponse.raise_for_status() -> Raise Ultralite.UltraliteError if
http response code is not within 2XX range, otherwise do nothing.UltraliteResponse.json() -> attempt json-decoding UltraliteResponse.text## Does this depend on anything?
Nope. This is valid pure-stdlib python as of Python 3.3+.
Package: ultralytics-hub
Ultralytics pip Package TemplateThis is a minimal template repository for reserving Python package names on PyPI and deploying v0.0.0 placeholders. It provides a streamlined way to ensure that your package name is available for future development and releases.What is this repository for?Name Reservation: Ensures the package name you desire is reserved on PyPI.Placeholder Deployment: Allows for an initial v0.0.0 release to indicate the package name is taken but the package is not yet functional.Quick StartStep 1: Clone the RepositoryClone this repository to your local machine.gitclonehttps://github.com/ultralytics/pip.git<your-package-name>Step 2: Update Package InformationEdit thesetup.pyfile to update the package name, author, and other meta-information.Step 3: Create a Virtual Environment (Optional)It's a good practice to create a virtual environment.python3-mvenvvenvsourcevenv/bin/activate# On Windows, use `venv\Scripts\activate`Step 4: Install DependenciesIf your package has dependencies, install them.pipinstall-rrequirements.txtStep 5: Package and UploadPackage your code and upload it to PyPI.pythonsetup.pysdistbdist_wheel
twineuploaddist/*Now your package name is reserved and a v0.0.0 placeholder is deployed on PyPI.RecommendationsOnce you start the actual development, replace this README with one that describes your package's functionality.Add aLICENSEfile appropriate for your project.Update therequirements.txtandsetup.pyas your project evolves.SupportFor issues and feature requests related to this template, please open an issue on GitHub.LicenseThis project is licensed under the AGPL-3.0 License. For more details, see theLICENSEfile.
Package: ultralytics-hub-sdk
Ultralytics pip Package TemplateThis is a minimal template repository for reserving Python package names on PyPI and deploying v0.0.0 placeholders. It provides a streamlined way to ensure that your package name is available for future development and releases.What is this repository for?Name Reservation: Ensures the package name you desire is reserved on PyPI.Placeholder Deployment: Allows for an initial v0.0.0 release to indicate the package name is taken but the package is not yet functional.Quick StartStep 1: Clone the RepositoryClone this repository to your local machine.gitclonehttps://github.com/ultralytics/pip.git<your-package-name>Step 2: Update Package InformationEdit thesetup.pyfile to update the package name, author, and other meta-information.Step 3: Create a Virtual Environment (Optional)It's a good practice to create a virtual environment.python3-mvenvvenvsourcevenv/bin/activate# On Windows, use `venv\Scripts\activate`Step 4: Install DependenciesIf your package has dependencies, install them.pipinstall-rrequirements.txtStep 5: Package and UploadPackage your code and upload it to PyPI.pythonsetup.pysdistbdist_wheel
twineuploaddist/*Now your package name is reserved and a v0.0.0 placeholder is deployed on PyPI.RecommendationsOnce you start the actual development, replace this README with one that describes your package's functionality.Add aLICENSEfile appropriate for your project.Update therequirements.txtandsetup.pyas your project evolves.SupportFor issues and feature requests related to this template, please open an issue on GitHub.LicenseThis project is licensed under the AGPL-3.0 License. For more details, see theLICENSEfile.
Package: ultralyticsplus
ultralytics+Extra features forultralytics/ultralytics.installationpipinstallultralyticspluspush to 🤗 hubultralyticsplus--exp_dirruns/detect/train--hf_model_idHF_USERNAME/MODELNAMEload from 🤗 hubfromultralyticsplusimportYOLO,render_result# load modelmodel=YOLO('HF_USERNAME/MODELNAME')# set model parametersmodel.overrides['conf']=0.25# NMS confidence thresholdmodel.overrides['iou']=0.45# NMS IoU thresholdmodel.overrides['agnostic_nms']=False# NMS class-agnosticmodel.overrides['max_det']=1000# maximum number of detections per image# set imageimage='https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'# perform inferenceresults=model.predict(image,imgsz=640)# parse resultsresult=results[0]boxes=result.boxes.xyxy# x1, y1, x2, y2scores=result.boxes.confcategories=result.boxes.clsscores=result.probs# for classification modelsmasks=result.masks# for segmentation models# show results on imagerender=render_result(model=model,image=image,result=result)render.show()
Package: ultra-random-uuid
Ultra Random UUIDPython Ultra Random UUIDFree software: MIT licenseDocumentation:https://ultra-random-uuid.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-03-10)First release on PyPI.
Package: ultra-rest-client
python_rest_api_clientA sample Python client for communicating with the UltraDNS REST APIDependencies and InstallationThis sample code depends on the requests library, which can be found at:http://docs.python-requests.org/en/latest/If you have pip installed, you can add the client and requests to your environment with:pip install ultra_rest_clientFunctionalityThe sample code does not attempt to implement a client for all available UltraDNS REST API functionality.  It provides access to basic functionality.  Adding additional functionality should be relatively straightforward, and any contributions from the UltraDNS community would be greatly appreciated.  See sample.py for an example of how to use this library in your own code.QuestionsPlease contact UltraDNS support if you have any questions or encounter any issues with this code.
Package: ultra-rl
ULTRAUnprotected Left Turn for Robust AgentsUltra provides a gym-based environment using SMARTS for tackling intersection navigation and more specifically unprotected left turn.Here is the summary of key features:Customizable scenarios with different levels of difficulty.Analysis tools to evaluate traffic designs including low-mid-high densities.Tools to support analyzing social-vehicle behaviors.Configurable train and test parameters.Benchmark results to train and test custom RL algorithms against.IndexGet started by choosing an option below.Flowchart of ULTRA's interface with SMARTSSetting up ULTRATrain and Evaluate a Baseline AgentCreate a Custom AgentMore about ULTRA AgentsULTRA supports RLlibMulti-agent Experiments in ULTRACiting ULTRAFor a longer introduction to ULTRA, including its purpose, concepts, and benchmarks, please seeULTRA: A reinforcement learning generatlization benchmark for autonomous driving.If you use ULTRA in your research, you can use to the following citation.@misc{elsayed20_ultra,
  author          = {Elsayed, Mohamed and Hassanzadeh, Kimia and Nguyen, Nhat M,
                  and Alban, Montgomery and Zhu, Xiru and Graves, Daniel and
                  Luo, Jun},
  journal         = {Machine Learning for Autonomous Driving Workshoo, Neural
                  Information Processing Systems},
  title           = {ULTRA: A reinforcement learning generalization benchmark
                  for autonomous driving},
  url             = {https://ml4ad.github.io/files/papers2020/ULTRA: A
                  reinforcement learning generalization benchmark for autonomous
                  driving.pdf},
  year            = 2020,
}
Package: ultra-simple-config
Ultra Simple Config for PythonInstallationpipinstallultra-simple-configUsageCreate a yml file with your configuration, for example:db:url:localhostport:5432format:utf8user:name:blubbyThen load it in your python code:fromultra_simple_configimportload_configThen use it on your method like that:@load_config(location="tests/example_config/db.yaml")deftest_loading(cfg:DictConfig):# then you can use it like this:print(cfg.db.url)
Package: ultraspy
What is ultraspy?Ultraspy is a package designed to efficiently manipulate ultrasound data using
GPU. The most common beamforming or Doppler methods are implemented (such as
DAS, RF to I/Qs, Color/Power Doppler, ...), along with some state-of-the-art
methods (Capon beamforming, Vector Doppler, alias-free Doppler velocity, ...).
A set of metrics (PSL, FWHM, SNR) is also provided so anyone can validate the
quality of their ultrasound data and beamforming operations.The package is designed to work with both RF and I/Q signals, in 2D or 3D, and
with any type of probe (linear, convex, or matrix). The core code can run both
on CPU and GPU, making it ideal for any real-time application. All beamforming
parameters (f-number, compounding, apodizationâ€¦) can be freely customized at
any time for research purposes.The package has been thought to be as flexible as possible, so that anyone
could eventually clone it and add its own research methods and test it in real
time. A set of tutorials is provided to facilitate user learning and adoption,
along with some instruction on how to contribute to the lib if you feel like
your research method should be added to help the community.FeaturesGeneral beamforming methods, flexible to Radio-Frequency or In-phase
Quadrature data, working on CPU and GPU. Mainly DAS and FDMAS for the
plane-wave imaging, but also TFM for Beam Focusing imagingAdvanced beamforming methods (p-DAS or Capon), with a dedicated tutorial to
understand how these are implemented and how to implement your own methodsBasic Doppler methods (Color and Power maps), and their dedicated utilities
functions (matched filtering, RF to I/Qs conversion)Advanced Doppler methods, such as a proposition for alias-free alias-free
Doppler velocities (using dual-wavelength method). This still lacks of
methods, and should include Vector Doppler or so in future releasesBasic metrics for evaluation of the data quality (SNR), or of our beamforming
algorithms (FWHM, PSL, CNR)DocumentationFull documentation can be found in the 'docs' folder, and is also available athttps://ultraspy.readthedocs.io/en/latest. You will find there all the detailed
information about how to install ultraspy and how to use it.InstallationInstallation can be easily done using pypi:.. code-block:: console$ pip install ultraspyAlso, if you want to run it on GPU, you need to install the proper version ofcupybased on your CUDA version:.. code-block:: console$ pip install cupy-cudaXXxContributeIssue Tracker:https://gitlab.com/pecarlat/ultraspy/-/issues/Source Code:https://gitlab.com/pecarlat/ultraspyDocumentation:https://ultraspy.readthedocs.io/en/latestLicenseThe project is under the MIT license.
Package: ultrayolo
UltrayoloTensorflow 2 implementation of yolo version 3. You only look once (YOLO) is a state-of-the-art, real-time object detection systemFeaturesUltrayolo implements YOLO object detection paper with the following backbones:Darknet: the original architectureResNet:ResNet50V2, ResNet101V2, ResNet152V2DenseNet:DenseNet121, DenseNet169, DenseNet201MobileNetV2:MobileNetV2Free software: Apache Software License 2.0Documentation:https://ultrayolo.readthedocs.io.TODO[ ] add support to tf.data to load datasets[ ] add mean average precision evaluation scriptCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History
Package: ultr-bias-toolkit
Personal toolkit for bias estimation in unbiased learning to rankInstallationpip install ultr-bias-toolkitOffline bias estimation methodsWe implement multiple offline position bias estimation methods, includingthree intervention harvestingapproaches:from ultr_bias_toolkit.bias.naive import NaiveCtrEstimator
from ultr_bias_toolkit.bias.intervention_harvesting import PivotEstimator, AdjacentChainEstimator, AllPairsEstimator

estimators = {
    "CTR Rate": NaiveCtrEstimator(),
    "Pivot One": PivotEstimator(pivot_rank=1),
    "Adjacent Chain": AdjacentChainEstimator(),
    "Global All Pairs": AllPairsEstimator(),
}
examination_dfs = []

for name, estimator in estimators.items():
    examination_df = estimator(df)
    examination_df["estimator"] = name
    examination_dfs.append(examination_df)

examination_df = pd.concat(examination_dfs)
examination_df.head()
Package: ultyas
Ultyas - Ultisnips to YASnippet Conversion ToolUltyas is a command-line tool, written byJames Cherti, designed to simplify the process of converting code snippets from Ultisnips to YASnippet format.With Ultyas, you can effortlessly migrate your code snippets to the YASnippet format, saving you valuable time and effort.Installationsudo pip install ultyasUsageultyas~/.vim/UltiSnips/python.snippets-o~/.emacs.d/snippets/python-mode/LicenseDistributed under terms of the GNU General Public License version 3.LinksUltyas @PyPIUltyas @GitHub
Package: ulula
Ulula is an ultra-lightweight 2D hydro code for teaching and experimentation. The code is designed
to be as short as possible and easy to understand, while not compromising on performance.You can find the code in thePublic BitBucket Repository.
Ulula is in the testing phase and comes with absolutely zero warranty! Please see theDocumentationfor more information. Ulula can be referenced
via itsASCL entry.Main Developer: Benedikt Diemer (diemer@umd.edu)License:        MIT. Copyright (c) 2020-2021
Package: ulutil
# ulutilThis is a collection of utilities I found useful during the course of my
research in the Church lab.  It is an upstream dependency for thevdjpackage, and was used to make lots of figures, etc.### Installation`bash pip install ulutil `or`bash git clonehttps://github.com/churchlab/ulutil.gitcd ulutil python setup.py install `There may be varied dependencies for the different submodules.
Package: uma
umaumais a Python package to collect pyomo results as pandas dataframe for easier access.Installationumacan be installed from PyPI using:pip install umaumarequires numpy and pandas to work.Usageumaworks if the model solution is optimal. If the solution is infeasible, an error will be returned.fromumaimportget_df,get_dict,get_valuefrompyomo.environimport*frompyomo.optimportSolverFactorymodel=ConcreteModel()model.x=Var(initialize=-1.2,bounds=(-2,2))model.y=Var(initialize=1.0,bounds=(-2,2))model.obj=Objective(expr=(1-model.x)**2+100*(model.y-model.x**2)**2,sense=minimize)solver=SolverFactory('ipopt')solver.solve(model)df=get_df(model)print(df.head())dict1=get_dict(model)print(dict1)x=get_value(model.x)print(x)
Package: umaat
umaat-Ultimate Machine-learning Algorithm Accuracy TestThis is a package that houses the functions which can produce accuracy results for each algorithm listed under the categories of Clustering, Regression & Classification based on passing the arguments - independent and dependent variables/features from a given dataset, based on  which the user can choose the best category and algorithm suitable for their  dataset and then implement the machine learning model with the sameDevelopment Status:Under Development (Early release)Developed by:Vishal Balaji SivaramanInstallation:Use the package managerpipto install umaatFor installation of latest packagepipinstallumaatFor installation of a particular version of packagepipinstallumaat==versionnumberofpackageTo import umaat:importumaatTo use a particular function in umaat:fromumaatimportmodel_accuracyma=model_accuracy()ma.accuracy_test(X,y)License:MIT
Package: umacajadada
reads a file asynchronously during its execution while somewhere some other process writes to the same fileTested against Windows 10 / Python 3.10 / Anaconda - should work everywhere - pure Pythonpip install umacajadadaread_async(file:str,asthread:bool=True,mode:str="r",action=lambdaline:sys.stderr.write((str(line)+"\n")),stoptrigger:list|tuple=(False,),):Thisfunction`read_async`readsafileasynchronouslyduringitsexecutionwhilesomewheresomeotherprocesswritestothesamefile.Args:file(str):Thepathtothefiletoberead.asthread(bool,optional):IfTrue,thefunctionwillstartasathread.DefaultisTrue.mode(str,optional):Themodeinwhichthefileisopened.Defaultis'r'(readmode).action(function,optional):Afunctiontobeappliedtoeachlineofthefile.Defaultistowritethelinetostderr.stoptrigger(list|tuple,optional):Alistortuple(preferablyalist)thattriggersthefunctiontostopreadingwhenitslastelementisTrue.Defaultis[False].encoding(str,optional):Thecharacterencodingofthefile.Defaultis'utf-8'.errors(str,optional):Howtohandleencodingerrors.Defaultis'backslashreplace'.Returns:kthread.KThreadorNone:Ifas_threadisTrue,itreturnsaKThreadobject.Otherwise,itreturnsNone.Theadvantageofthisfunctionisthatitallowsforreal-timeprocessingofthefilewhileitisbeingwrittenbyanotherprocess.Thisisparticularlyusefulinscenarioswherethefileiscontinuouslyupdated,andtheupdatesneedtobeprocessedimmediately,suchasinlogmonitoringorreal-timedataanalysis.Exampleusage:fromthreadingimportTimerfromtimeimporttimenewfile=f'c:\\testfilex{str(time()).replace(".","_")}.txt'stoptrigger=[False,]t=read_async(file=newfile,as_thread=True,mode="r",action=lambdaline:sys.stderr.write((str(line)+"\n")),stoptrigger=stoptrigger,)Timer(5,lambda:stoptrigger.append(True)).start()# Stops after 5 seconds and doesn't print anymore, but os.system goes on.os.system("ping 8.8.8.8 -n 10 > "+newfile+"")ifnotasthread:return_read_file_async(file,mode=mode,action=action)else:return_start_as_tread(file,mode,action,stoptrigger)
Package: uMAIA
uMAIAToolbox for the processing and analysis of MALDI-MSI dataInstallationTo begin, create a new environment withconda create --name uMAIA_env python==3.9activate the environment withconda activate uMAIA_envconda install jupyter withconda install jupyterinstall the required packagespip install -r requirements.txtIf you are experiencing issues during the installation with caches files, you may need to remove them.
You can do so with the following commands:conda clean -apython -m pip cache purgerm ~/.cache/pip -rfconda update --allGurobiThe molecule matching portion of the pipeline requires Gurobi. You will need to (1) install the Gurobi installer and (2) activate a license that is free for those are affiliated with an academic institution. Concerning (1), download the archive withLinux:wget https://packages.gurobi.com/9.1/gurobi9.1.2_linux64.tar.gzto the directory you want to install it in.
or MacOS:https://packages.gurobi.com/9.1/gurobi9.1.2_macos_universal2.pkgUnpack the archive withLinux:tar -xzvf gurobi9.1.2_linux64.tar.gzor on MacOS, simply double click the .pkg file and walk through the installation instructionsActivate the license withLinux:gurobi912/linux64/bin/grbgetkey <licensekey>MacOS:grbgetkey <licensekey>GPU usage restrictionsIf you intend on applying uMAIA's normalisation algorithm on a GPU, please note that JAX will automatically use between 75 and 90 % of available GPU memory. To indicate that you would only like to allocate memory that is needed, you may export an environment variable after activating the environment withexport XLA_PYTHON_CLIENT_PREALLOCATE=false
Package: umakaparser
UmakaParserThis tool is to make a JSON file forUmaka Viewer.​First, you need to prepare a metadata file in theSPARQL Builder Meatadata (SBM)format.If you want to get such a metadata file,TripleDataProfilercan generate it for a SPARQL endpoint.​Then, if you have ontology files for the target endpoint or RDF dataset, you need to make asset files by this tool as follows.​umakaparser --build-index [--dist <Path to put an asset file>] <ontology files in Turtle>​If you have ontology files only in RDF/XML, this tool converts them into those in Turtle as follows.​umakaparser --convert <files in RDF/XML>​Finally, this tool generates a JSON file that can be accepted byUmaka Vieweras follows.​umakaparser --build [--a <Path to asset files>|--d <Path to put a generated JSON file>] <an SBM file>​The JSON file structure ishere.