Saravanan666/pypi-org huggingface dataset:
Package: commonmeta-py
commonmeta-pycommonmeta-py is a Python library to implement Commonmeta, the common Metadata Model for Scholarly Metadata. Use commonmeta-py to convert scholarly metadata, in a variety of formats, listed below. Commonmeta-py is work in progress, the first release on PyPi (version 0.5.0) was on February 16, 2023. Up until version 0.5.1, the library was called talbot. Commonmeta-py is modelled after thecommonmeta-ruby ruby gem.commonmeta-py uses semantic versioning. Currently, its major version number is still at 0, meaning the API is not yet stable, and breaking changes are expected in the internal API and commonmeta JSON format.InstallationStable versionpip (or pip3) install commonmeta-pyDev versionpip install git+https://github.com/front-matter/commonmeta-py.git#egg=commonmeta-pySupported Metadata FormatsCommometa-py reads and/or writes these metadata formats:FormatNameContent TypeReadWriteCommonmetacommonmetaapplication/vnd.commonmeta+jsonyesyesCrossRef XMLcrossref_xmlapplication/vnd.crossref.unixref+xmlyesyesCrossrefcrossrefapplication/vnd.crossref+jsonyesn/aDataCitedataciteapplication/vnd.datacite.datacite+jsonyesyesSchema.org (in JSON-LD)schema_orgapplication/vnd.schemaorg.ld+jsonyesyesRDF XMLrdf_xmlapplication/rdf+xmlnolaterRDF Turtleturtletext/turtlenolaterCSL-JSONcslpplication/vnd.citationstyles.csl+jsonyesyesFormatted text citationcitationtext/x-bibliographyn/ayesCodemetacodemetaapplication/vnd.codemeta.ld+jsonyeslaterCitation File Format (CFF)cffapplication/vnd.cff+yamlyeslaterJATSjatsapplication/vnd.jats+xmllaterlaterCSVcsvtext/csvnolaterBibTexbibtexapplication/x-bibtexlateryesRISrisapplication/x-research-info-systemsyesyesInvenioRDMinveniordmapplication/vnd.inveniordm.v1+jsonlateryesJSON Feedjson_feed_itemapplication/feed+jsonyeslatercommonmeta: the Commonmeta format is the native format for the library and used internally.Planned: we plan to implement this format for the v1.0 public release.Later: we plan to implement this format in a later release.DocumentationDocumentation (work in progress) for using the library is available at thecommonmeta-py Documentationwebsite and includes several interactive Jupyter Notebooks .MetaPlease note that this project is released with aContributor Code of Conduct. By participating in this project you agree to abide by its terms.License:MIT
Package: common-mimetypes
Common MIME types not available in standard library mimetypes modulePackage:http://pypi.python.org/pypi/common-mimetypesProject:https://github.com/iki/common-mimetypesIssues:https://github.com/iki/common-mimetypes/issuesUpdates:https://github.com/iki/common-mimetypes/commits/master.atomInstall viapip:pip installcommon-mimetypesInstall viaeasy_install:easy_installcommon-mimetypesSources viagit:git clonehttps://github.com/iki/common-mimetypesSources viahg-git:hg clonegit://github.com/iki/common-mimetypes
Package: common-mlops
common-mlopsThis is a set of utilities for end-to-end lifecycle of Machine Learning applications in Python for Inria-Chile.
Could be used like a framework and tooling for rapid development of data science and MLOps functions.Here’s the folder structure for the repository:common-mlops/
|
├── data/
│   ├── prepared/
│   └── raw/
│   └── metrics/
├── model/
├── mlops/
|   └── *.py
├── test/
|   └── test_*.py
├── venv/
├── check_credentials.sh
├── install.sh
├── LICENSE
├── README.md
├── requirements.txt
├── s3*.py
└── setup.pyThere are the folloging folders in our repository:data/ is all versions of the dataset.data/raw/ is the data obtained from an external source.data/prepared/ is for data modified internally.data/metrics/ is for tracking the performance metrics of our models.model/ is for machine learning models.mlops/ is the source code. Python classes used by the Python scripts.test/ is the pyunit of source code.venv/ is the virtual environment.check_credentials.sh bash for check the AWS credentials.install.sh bash for check the installation.LICENSErequirements.txt the dependencies.s3*.py Python scripts for bash executions.setup.py the configuration of the package.Installation LinuxPerform the following steps in order to install this program. Replace 'access_key_id' and 'secret_access_key' by the account AWS Credentials associated to poc-inriacl.gitclonehttps://gitlab.com/Inria-Chile/common-mlops.git
./common-mlops/install.shaccess_key_idsecret_access_key
pip3uninstallcommon-mlops# Version of testpip3install-ihttps://test.pypi.org/simple/common-mlops# Version of productionpip3installcommon-mlopsUpdates in test.pypi.orgPerform the following steps in order to publish new versions to test.pypi.org:sudorm-rfbuildcommon_mlops.egg-infodist
bumpversion--current-version1.0.0minorsetup.pymlops/__init__.py
python3setup.pysdistbdist_wheel# Version of testtwinecheckdist/*
twineupload--repository-urlhttps://test.pypi.org/legacy/dist/*# Version of productiontwineuploaddist/*TestsRun the unit tests:python3-munittestUsageSee the example of usage in:https://gitlab.com/Inria-Chile/oceania/data-readhttp://twiki.inriadev.cl/twiki/bin/view/Sandbox/OceaniaClassDiagramPOCS3DVCDockerBuild the image:dockerbuild-tcommon-mlops.dockerpullcommon-mlopsRun the image:dockerrun-v$HOME/.aws:/root/.aws:ro-eS3_ACCESS_KEY_ID=access_key_id-eS3_SECRET_ACCESS_KEY=secret_access_key-eAWS_ACCESS_KEY_ID=access_key_id-eAWS_SECRET_ACCESS_KEY=secret_access_keycommon-mlops.Referenceshttps://test.pypi.org/project/common-mlopshttps://pypi.org/project/common-mlopshttps://anno-ai.medium.com/mlops-and-data-managing-large-ml-datasets-with-dvc-and-s3-part-1-d5b8f2fb8280https://martinfowler.com/articles/cd4ml.htmlhttps://christophergs.com/machine%20learning/2020/03/14/how-to-monitor-machine-learning-models/
Package: commonnexus
commonnexusThis package provides functionality to read and write the NEXUS file format as specified inMaddison, Swofford, and Maddison (1997). "NEXUS: An extensible file format for systematic information". Systematic Biology. 46 (4): 590–621.doi:10.1093/sysbio/46.4.590Rather than trying to rip out relevant portions of a NEXUS file as quickly as possible, the implementation
incommonnexustries to do "the right thing" according to the specification, i.e. parse a file token by
token. Thus, we sacrifice speed for correctness and the ability to support weird edge cases likeComments do not break tokens. Thus,AssuMP[comment]TiONSis processed asASSUMPTIONS.InstallInstallcommonnexusfromPyPI:pipinstallcommonnexusOverviewcommonnexusprovides aPython APIas well as ashell commandto manipulate (the data in) NEXUS files.In particular, it allows reading NEXUS>>>fromcommonnexusimportNexus>>>nex=Nexus.from_file('docs/characters.nex')>>>nex.CHARACTERS.get_matrix()['t1'].values()odict_values(['1','0','0','1','0','1','0','0','0','0'])and writing NEXUS>>>fromcommonnexusimportNexus>>>fromcommonnexus.blocksimportData>>>nex=Nexus.from_file('docs/characters.nex')>>>print(Nexus.from_blocks(Data.from_data(nex.CHARACTERS.get_matrix())))#NEXUSBEGINDATA;DIMENSIONSNCHAR=10;FORMATDATATYPE=STANDARDMISSING=?GAP=-SYMBOLS="01";MATRIXt11001010000t20101000100t30011101010t40001100001t50001100001;END;Command line usageInstalling thecommonnexuspackage will also install a command line interfacecommonnexus, which provides several
sub-commands to manipulate NEXUS files.Runcommonnexus -hto get an overview of available sub-commands or find detailed documentation
with exampleson ReadTheDocs.Python APIThe Python API tries to convert NEXUS constructs to appropriate Python objects, e.g.NEXUS content is alistofCommandobjects,missing states in a CHARACTERS MATRIX are conveyed asNonevalues, etc.This allows for dealing with NEXUS data in a way that is abstracted from the NEXUS formatting
conventionsFor a detailed documentation of the Python API, refer to thedocs on ReadTheDocs.See alsohttps://doi.org/10.1093/sysbio/46.4.590https://github.com/dlce-eva/python-nexushttp://wiki.christophchamp.com/index.php?title=NEXUS_file_format
Package: commonnn-clustering
Common-nearest-neighbour clusteringThecommonnnPython package provides a flexible interface to use thecommon-nearest-neighbour (CommonNN) clustering procedure. While the method can be applied to arbitrary data, this implementation was made before the background of processing trajectories from Molecular Dynamics (MD) simulations. In this context the cluster result can serve as a suitable basis for the construction of a core-set Markov-state (cs-MSM) model to capture the essential dynamics of the underlying molecular processes.The commonnn packageThe package provides a main module:cluster: User interface to (hierarchical) CommoNN clusteringFurther, it contains among others the modules:plot: Convenience functions to evaluate cluster results_types: Direct access to generic types representing needed cluster components_fit: Direct access to generic clustering proceduresFeatures:Flexible: The clustering can be done for data sets in different input formats. Internal parts of the procedure can be exchanged. Interfacing with external methods is made easy.Convenient: Integration of functionality, which may be handy in the context of MD data analysis.Fast: Core functionalities have been implemented in Cython.Please refer to the following papers for the scientific background (and consider citing if you find the method useful):B. Keller, X. Daura, W. F. van GunsterenJ. Chem. Phys.,2010,132, 074110.O. Lemke, B.G. KellerJ. Chem. Phys.,2016,145, 164104.O. Lemke, B.G. KellerAlgorithms,2018,11, 19.DocumentationThe package documentation is availablehereonline or underdocs/index.html.
The sources for the documentation can be found underdocsrc/and can be build using Sphinx.InstallRefer to thedocumentationfor more details. Install from PyPi$pipinstallcommonnn-clusteringor clone the development version and install from a local branch$gitclonehttps://github.com/bkellerlab/CommonNNClustering.git
$cdCommonNNClustering
$pipinstall.Quickstart>>>fromcommonnnimportcluster>>># 2D data points (list of lists, 12 points in 2 dimensions)>>>data_points=[# point index...[0,0],# 0...[1,1],# 1...[1,0],# 2...[0,-1],# 3...[0.5,-0.5],# 4...[2,1.5],# 5...[2.5,-0.5],# 6...[4,2],# 7...[4.5,2.5],# 8...[5,-1],# 9...[5.5,-0.5],# 10...[5.5,-1.5],# 11...]>>>clustering=cluster.Clustering(data_points)>>>clustering.fit(radius_cutoff=1.5,similarity_cutoff=1,v=False)>>>clustering.labelsarray([1,1,1,1,1,0,0,0,0,2,2,2])Alternative scikit-learn implementationWe provide an alternative approach to CommonNN clustering in the spirit of the scikit-learn project withinscikit-learn-extra.Development historyThe present development repository has diverged with changes from the original one undergithub.com/janjoswig/CommonNNClustering.A previous implementation of the clustering can be found undergithub.com/bettinakeller/CNNClustering.
Package: common-notifier
Not On PyPiThis is a reserved package name to preventdependency confusion / substitution attack. If installed, will immediately raise an error indicating that the wrong PyPi repository was used.If someone would like to use this package name, I would be happy to discuss handoff. Please contact me through a Github issue onKyleKing/not-on-pypior if I don't respond, you can try mygmailaccountdev.act.kyle
Package: commonocean-io
This repository includes the commonocean_io python package for representing benchmarks for marine motion planning. In addition, we provide two tutorials to exemplify the usage. For an extensive scenario documentation, consider reading thedocumentation for the XML format.
​
The structure of the repository is:
​.
├── documentation                   # Documentation of scenario specification
└── commonocean                     # Source files
    ├── common                      # Folders which represent the package structure
    ├── ...                         # ...
    └── doc                         # ´Read the Docs´ documentation for the commonocean-io package​Installation instructions​
Create a new Anaconda environment for Python 3.8 (here called co38).
​
Run in your Terminal window:condacreate−nco38python=3.8Activate your environmentcondaactivateco38Install the package by simply using pip and, if you want to use the jupyter notebook, also install jupyterpipinstallcommonocean-io
pipinstalljupyterNow everything is installed and you can start jupyter notebook to run thetutorials$ jupyter notebookChangelogCompared to version 2022.2, the following features have been added or changed:AddedNew obstacle type "Waters Boundary" due to requirement inCommonOcean DCFixedCreation of static obstacles for traffic signs such that no dublicate obstacles are createdChangedRestructured State class so that it is now based on the commonroad-io state class and definition of State classes for different vessel dynamics.Removed duplicate classes and functions from commonroad-io to reduce maintance effortThe packages is no longer compatible with Python 3.7Updated documentationContibutors and Reference​
We thank all the contibutors for helping develop this project (see contributors.txt).
​If you use our converter for research, please consider citing our paper:@inproceedings{Krasowski2022a,
	author = {Krasowski, Hanna and Althoff, Matthias},
	title = {CommonOcean: Composable Benchmarks for Motion Planning on Oceans},
	booktitle = {Proc. of the IEEE International Conference on Intelligent Transportation Systems},
	year = {2022},
}
Package: commonocean-vessel-models
Python Vessel Models of CommonOcean​
This package contains all vessel models of theCommonOcean benchmarks.
The code is based on theCommonRoad vehicle models repository.
​
We provide implementations of the vessel dynamics and vessel parameters.
​Documentation​
For a detailed explanation of the vehicle models, please have a look at thedocumentation.
​Installation​
To use vehicle models and parameters, runpip install commonocean-vessel-models​Code examples​
For an extended simulation example, we refer to ourgitlab repository. A simple simulation example for using the point-mass model in combination with an odeint solver would be
​fromscipy.integrateimportodeintimportnumpy​fromvesselmodels.parameters_vessel_1importparameters_vessel_1fromvesselmodels.vessel_dynamics_pmimportvessel_dynamics_pm​deffunc_PM(x,t,u,p):f=vessel_dynamics_pm(x,u,p)returnf​tStart=0# start timetFinal=1# start time​# load vehicle parametersp=parameters_vessel_1()​# initial state for simulationx0_PM=[0,0,10.0,0.0]​t=numpy.arange(0,tFinal,0.01)u=[1.4,0]x=odeint(func_PM,x0_PM,t,args=(u,p))​​Contribute​
If you want to contribute new vessel models, you can create a merge request in ourrepository, or contact us viamail.
​Contibutors and Reference​
We thank all the contributors for helping develop this project (see contributors.txt).
​If you use our converter for research, please consider citing our paper:@inproceedings{Krasowski2022a,
	author = {Krasowski, Hanna and Althoff, Matthias},
	title = {CommonOcean: Composable Benchmarks for Motion Planning on Oceans},
	booktitle = {Proc. of the IEEE International Conference on Intelligent Transportation Systems},
	year = {2022},
}
Package: common-prstub
Not On PyPiThis is a reserved package name to preventdependency confusion / substitution attack. If installed, will immediately raise an error indicating that the wrong PyPi repository was used.If someone would like to use this package name, I would be happy to discuss handoff. Please contact me through a Github issue onKyleKing/not-on-pypior if I don't respond, you can try mygmailaccountdev.act.kyle
Package: common-py-lib
Python librariesCommand lineBuild a package:python setup.py sdist bdist_wheelInstall to AWS CodeArtifact:./install_AWS_CodeArtifacts.sh [Domain Name] [Repo Name] [AWS profile name if has multiple]Install to PyPi:./install_pypi.sh [PyPi username] [PyPi password]Use the package (You need to login to the AWS CodeArtifacts using CLI):pip install common-py-libChange Notes2022-11-02 (v1.4): Initial release with logging and aws services support2022-11-04 (v1.5.0): Easy maintenance of the Version2022-11-15 (v1.6.0): Refactor logging and AWS helper2022-12-28 (v1.7.0): Add AppConfig support
Package: common-pyutil
common-pyutilBunch of common utility functions I've used in various projects. This package
provides a uniform interface to them.FeaturesPure python stdlib with no external dependencies (exceptrequests)Bunch of useful modules like:A simple hierarchical argument parser.Functional programming library.ATimercontext for easy monitoringATagdecorator for function taggingA logging module to get generate a logger with sensible defaults.AGetclass with progress tracking.
Package: commonregex
Find all times, dates, links, phone numbers, emails, ip addresses, and prices in a string.Usage>>> from commonregex import CommonRegex
>>> parsed_text = CommonRegex("John, please get that article on www.linkedin.com to me by 5:00PM
                               on Jan 9th 2012. 4:00 would be ideal, actually. If you have any
                               questions, You can reach me at (519)-236-2723 or get in touch with
                               my associate at harold.smith@gmail.com")
>>> parsed_text.times
['5:00PM', '4:00']
>>> parsed_text.dates
['Jan 9th 2012']
>>> parsed_text.links
['www.linkedin.com']
>>> parsed_text.phones
['(519)-236-2727']
>>> parsed_text.emails
['harold.smith@gmail.com']Alternatively, you can generate a single CommonRegex instance and use it to parse multiple segments of text.>>> parser = CommonRegex()
>>> parser.times("When are you free?  Do you want to meet up for coffee at 4:00?")
['4:00']Please note that this module is currently English/US specific.CommonRegex Ports:[CommonRegexJS] (https://github.com/talyssonoc/CommonRegexJS)[CommonRegexScala] (https://github.com/everpeace/CommonRegexScala)[CommonRegexJava] (https://github.com/talyssonoc/CommonRegexJava)
Package: commonregex-improved
CommonRegex Improved (crim)An improved version of commonly used regular expressions in PythonInspired by and improved uponCommonRegexThis is a collection of commonly used regular expressions. This library provides a simple API interface to match the strings corresponding to specified patterns.Installationpipinstall--upgradecommonregex-improvedUsageimportcrimasCommonRegextext="John, please get that article on www.linkedin.com to me by 5:00PM on Jan 9th 2012. 4:00 would be ideal, actually or 5:30 P.M. If you have any questions, You can reach me at (519)-236-2723x341 or get in touch with my associate at harold_smith@gmail.com. You can find my ip address at 127.0.0.1 or at 64.248.67.225. I also have a secret protected with md5 8a2292371ee60f8212096c06fe3335fd. The internal webpage to get the article from is https://internal.sharepoint.edu.au"date_list=CommonRegex.dates(text)# ['Jan 9th 2012']time_list=CommonRegex.times(text)# ['5:00PM', '4:00 ', '5:30 P.M.']url_list=CommonRegex.links(text)# ['www.linkedin.com', 'gmail.com', 'https://internal.sharepoint.edu.au']phone_list=CommonRegex.phones_with_exts(text)# ['(519)-236-2723x341']ip_list=CommonRegex.ips(text)# ['127.0.0.1', '64.248.67.225']email_list=CommonRegex.emails(text)# ['harold_smith@gmail.com']md5_list=CommonRegex.md5_hashes(text)# ['8a2292371ee60f8212096c06fe3335fd']⚔️ Performance benchmarkCommonRegexis awesome!So why re-implement the popular original commonregex project? The API calls to each of the regular expressions are really slow.It takes 12 seconds for a total of 2999 calls to Dates function in the original version of CommonRegex. While the improved version of CommonRegex with the same number of calls merely takes 2 seconds.You can find more detailed results aboutoriginalandimprovedversions.Features / Supported Methodsdates(text: str)times(text: str)phones(text: str)phones_with_exts(text: str)links(text: str)emails(text: str)ipv4s(text: str)ipv6s(text: str)ips(text: str)not_known_ports(text: str)prices(text: str)hex_colors(text: str)credit_cards(text: str)visa_cards(text: str)master_cards(text: str)btc_address(text: str)street_addresses(text: str)zip_codes(text: str)po_boxes(text: str)ssn_numbers(text: str)md5_hashes(text: str)sha1_hashes(text: str)sha256_hashes(text: str)isbn13s(text: str)isbn10s(text: str)mac_addresses(text: str)iban_numbers(text: str)git_repos(text: str)
Package: commonroad-agent
CommonRoad Agent SimulationThis repository comprises module to perform multi agent simulations withCommonRoadscenarios.
Dynamic obstacles in a scenario can be replaced by agents with certain behavior models implemented in this repository.
This allows to perfom simulations with interacting traffic participants that do not follow predefined trajectories but react on other traffic participants.Supported Behavior ModelsIf nothing specified the dynamic obstacles in a scenario move according to the predefined trajectories.
If assigned like shown in theexamples, the scenario can be simulated forward with thestep()while the considered dynamic obstacles behave according to the specified behavior model.
Currently, the following behavior models are implemented:IDMThe IDM is a well known car following model. It does not include lane changes and is suited best for highway scenarios. An example can be foundhereMOBILMOBIL is an extension of the IDM with lane change decisions. An example can be foundhereLEVEL_0_POLICYThe level 0 policy defines a basic behavior of an interaction unaware traffic participant. This policy will be used to train higher level agents with reasoning hierarchy.InstallationThe following need to be installed on your machine:RequirementsPython >= 3.6Installation via pip:pip install commonroad-agent
Package: commonroad-all
CommonRoadThis is a meta-package which automatically installs all CommonRoad tools currently released on PyPi.For more details to the individual tool, please see ourwebsite.ToolsThe current version of the meta-package installs the following CommonRoad tools with the specified versions:commonroad-io = "2023.3"commonroad-vehicle-models = "3.0.2"commonroad-drivability-checker = "2023.1"commonroad-dataset-converter = "2023.2"sumocr = "2023.2"commonroad-scenario-designer = "0.8.0"commonroad-route-planner = "2022.3"commonroad-crime = "0.3.0"commonroad-openscenario-converter = "0.1.0"commonroad-reach = "2023.1.1"InstallationTo automatically install the above mentioned tools, please run:pip install commonroad-all