Saravanan666/pypi-org huggingface dataset:
Package: omni-pro
Saas MS LibrarySaas MS Library is a Python library designed to be a utility for OMS microservices. This library provides an easy-to-use and highly customizable solution for reusing application utilities and clients, allowing developers to focus on creating the most efficient and scalable microservice.With Saas MS Library, you will be able to handle AWS databases and clients. Our library is compatible with Python 3.10 and later versions, and is under constant development to improve its performance and extend its functionality.FeaturesDatabase Handler (MongoDB, Redis, PostgreSQL, etc.)AWS Client Handler (Cognito, S3, etc.)DecoratorsInstallationTo install saas-ms-library, simply run the following command:pip install saas-ms-libraryExamples of useHere are some examples of how to use saas-ms-library in your projects:Create environment variablesGRPC_PORT=50051
GRPC_MAX_WORKERS=10
SERVICE_ID=saas-ms-user
REDIS_HOST=redis
REDIS_PORT=6379
REDIS_DB=0Usefrom protos.v1.users import user_pb2, user_pb2_grpc
from omni.pro.decorators import resources_decorator
from omni.pro.util import HTTPStatus, Resource, deep_search, nested


class  UserService(user_pb2_grpc.UsersServiceServicer):
    @resources_decorator([Resource.AWS_COGNITO, Resource.MONGODB])
    def  UserCreate(
        self, request: user_pb2.UserCreateRequest, context
        ) -> user_pb2.UserCreateResponse:
            response = context.cognito_client.create_user(
                username=data["username"],
                password=data["password"],
                name=data["name"],
                email=data["email"],
                tenant=data["tenant"],
                language_code=data["language"]["code"],
                timezone_code=data["timezone"]["code"],
            )
            att_list = nested(response, "User.Attributes") or  nested(response, "UserAttributes", default=[])
            sub_dict = deep_search(att_list, key, value)
            user = context.db_manager.get_document(
                context.db_name, UserDocument, sub=sub_dict.get("Value")
            )
            return  user_pb2.UserCreateResponse(
                response_standard=base_pb2.ResponseStandard(
                    success=True,
                    message="User created",
                    status_code=HTTPStatus.CREATED
                ),
                user=user.to_proto(),
            )LicenseSaas MS Library is licensed under the modified MIT License.
Package: omnipy-examples
omnipy-examplesExample projects that that makes use of theomnipypackage for
type-driven, scalable and interoperable data wrangling!Main installation instructionsInstall:pip install omnipy-examplesRun example scripts:Example:omnipy-examples isajsonFor help on the command line interface:omnipy-examples --helpFor help on a particular example:omnipy-examples isajson --helpOutput of flow runsThe output will by default appear in thedatadirectory, with a timestamp.It is recommended to install a file viewer that are capable of browsing tar.gz files.
For instance, the "File Expander" plugin in PyCharm is excellent for this.To unpack the compressed files of a run on the command line
(just make sure to replace the datetime string from this example):for f in $(ls data/2023_02_03-12_51_51/*.tar.gz); do mkdir ${f%.tar.gz}; tar xfzv $f -C ${f%.tar.gz}; doneRun with the Prefect engineOmnipy is integrated with the powerfulPrefectdata flow orchestration library.To run an local example using theprefectengine, e.g.:omnipy-examples --engine prefect isajsonAfter completion of some runs, you can check the flow logs and orchestration options in the Prefect UI:prefect server startTo set up a kubernetes-based deployment on our NIRD test setup, run e.g.:prefect config set PREFECT_API_URL=https://prefect.fairtracks.sigma2.no/apiprefect deploy -n isajsonThe configuration of this job is found in the fileprefect.yaml.More info on Prefect configuration will come soon...Development setupInstall Poetry:curl -sSL https://install.python-poetry.org | python3 -Install dependencies:poetry install --with devUpdate all dependencies:poetry updateUpdate single dependency, e.g.:poetry update omnipyIf a dependency is not updated to the latest version available on Pypi, you might need to clear
the pip cache of poetry:poetry cache clear pypi --allFor mypy support in PyCharmIn PyCharm, install "Mypy" plugin (not "Mypy (Official)")which mypyto get path to mypy binaryIn the PyCharm settings for the mypy plugin:Select the mypy binarySelectpyproject.tomlas the mypy config fileFor automatic formatting and lintingI have added my typical setup for automatic formatting and linting. The main alternative is to use black, which is easier to set up, but it does
not have as many options. I am not fully happy with my config, but I at least like it better than black.In PyCharm -> File Watchers:Click arrow down iconSelectwatchers.xml
Package: omnipyrig
omnipyrigA package that allows the deveplopment of amateur radio applications using the amazing Omni-Rig libraryprerequisiteOmniRig (http://dxatlas.com/omnirig/)python (https://www.python.org/downloads/)InstallationPyPi:https://pypi.org/project/omnipyrig/pip install omnipyrigusageimport omnipyrig

#create a new instance
OmniClient = omnipyrig.OmniRigWrapper()
#set the frequency
OmniClient.setFrequency("A",14222000)
#set the mode
OmniClient.setMode(OmniClient.MODE_SSB_U)how it works?the package uses win32com to dispatch omnirig objectit then wrap it's properties and methodsconstants & methodsconstants:mode enumerationMODE_SSB_LMODE_SSB_UMODE_CW_UMODE_FMMODE_AMMODE_RTTY_LMODE_CW_LMODE_DATA_LMODE_RTTY_UMODE_DATA_FMMODE_FM_NMODE_DATA_UMODE_AM_NMODE_PSKMODE_DATA_FM_Nrit/xitRIT_ONRIT_OFFXIT_ONXIT_OFFsplitSPLIT_ONSPLIT_OFFvfoVFO_AAVFO_ABVFO_BBVFO_BAmethods:client.showParams()client.setFrequency(vfo_selector, frequency)client.setMode(mode)client.setRit(state)client.setXit(state)client.setRitOffset(offset)client.setSplit(state)client.setPitch(pitch)client.setVfoA()client.setVfoB()client.setVfoAB()client.setVfoBA()73,Gil 4Z1KD
Package: omnipyseed
omnipyseedOmnipyseed is a minimal Python package that seeds all the basic Random Number
Generators (RNGs) in a Python script. The purpose of omnipyseed is to simplify
the process of seeding numerical simulations which use packages such as
Numpy and Pytorch.Dependenciesomnipyseeddoes not depend on any non-essential Python package.InstallationYou can installomnipyseedusing pip:pip3 (or pip) install omnipyseedOr you can clone this repository to any of your local directories and install
it from there.$ git clone https://github.com/gdetor/omnipyseed.git
$ cd omnipyseed/
$ pip install .How to useimport numpy as np
from omnipyseed import set_manual_seeds    # import the seeding module from omnipyseed


if __name__ == '__main__':
    set_manual_seeds.universal_seed(13)     # call the universal_seed function to seed
                                            # Python and Numpy
    x = np.linspace(-1, 1, 10)
    y = np.sin(2*np.pi*5*x)
    print(y)CaveatPlease do not useomnipyseedif you'd like to use different random states
for different random parts of your project. Instead you should instantiate
generator objects with a seed and pass it around (e.g.,
numpy.random.default_rng(seed)).
Package: omnisolver
Omnisolver is a collection of Binary Quadratic Model solvers and a framework for implementing them.Installation and getting startedInstalling this package alone won't give you much benefit, unless of course you want to develop new omnisolver plugins. In that case, you can installomnisolverfrom pip:pipinstallomnisolverAlgorithms for solving QUBO and Ising models are implemented in Omnisolverplugins. For instance, to use parallel tempering algorithm you need to install theomnisolver-ptpackage:pipinstallomnisolver-ptYou can run theomnisolver pt -hcommand to see the command line usage.CitingIf you used the package or one of its plugins, please cite:@misc{https://doi.org/10.48550/arxiv.2112.11131,
  doi = {10.48550/ARXIV.2112.11131},
  
  url = {https://arxiv.org/abs/2112.11131},
  
  author = {Jałowiecki, Konrad and Pawela, Łukasz},
  
  keywords = {Software Engineering (cs.SE), Quantum Physics (quant-ph), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences},
  
  title = {Omnisolver: an extensible interface to Ising spin glass solvers},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}
Package: omnisolver-bruteforce
Bruteforce (a.k.a. exhaustive search) Plugin forOmnisolverInstallationCurrently, theomnisolver-bruteforcepackage requires working CUDA installation.To install the plugin first set theCUDAHOMEenvironmental library to your CUDA instalaltion location, e.g.:# Rmember, your actual location  may vary!exportCUDAHOME=/usr/local/cudaand then run:pipinstallomnisolver-bruteforceWarningIf you don't set theCUDAHOMEdirectory, an attempt will be made to deduce it based on the location of yournvcccompiler.
However, this process might not work in all the cases and should not be relied on.Command line usageusage: omnisolver bruteforce-gpu [-h] [--output OUTPUT] [--vartype {SPIN,BINARY}] [--num_states NUM_STATES] [--suffix_size SUFFIX_SIZE] [--grid_size GRID_SIZE]
                                 [--block_size BLOCK_SIZE] [--dtype {float,double}]
                                 input

Bruteforce (a.k.a exhaustive search) sampler using CUA-enabled GPU

positional arguments:
  input                 Path of the input BQM file in COO format. If not specified, stdin is used.

optional arguments:
  -h, --help            show this help message and exit
  --output OUTPUT       Path of the output file. If not specified, stdout is used.
  --vartype {SPIN,BINARY}
                        Variable type
  --num_states NUM_STATESCitingIf you used the Omnisolver package or one of its plugins, please cite:@misc{https://doi.org/10.48550/arxiv.2112.11131,
  doi = {10.48550/ARXIV.2112.11131},
  
  url = {https://arxiv.org/abs/2112.11131},
  
  author = {Jałowiecki, Konrad and Pawela, Łukasz},
  
  keywords = {Software Engineering (cs.SE), Quantum Physics (quant-ph), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences},
  
  title = {Omnisolver: an extensible interface to Ising spin glass solvers},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}
Package: omnisolver-pt
Parallel Tempering Plugin forOmnisolverInstallationTo install the plugin run:pipinstallomnisolver-ptCommand line usageusage: omnisolver pt [-h] [--output OUTPUT] [--vartype {SPIN,BINARY}] [--num_replicas NUM_REPLICAS] [--num_pt_steps NUM_PT_STEPS]
                     [--num_sweeps NUM_SWEEPS] [--beta_min BETA_MIN] [--beta_max BETA_MAX]
                     input

Parallel tempering sampler

positional arguments:
  input                 Path of the input BQM file in COO format. If not specified, stdin is used.

optional arguments:
  -h, --help            show this help message and exit
  --output OUTPUT       Path of the output file. If not specified, stdout is used.
  --vartype {SPIN,BINARY}
                        Variable type
  --num_replicas NUM_REPLICAS
                        number of replicas to simulate (default 10)
  --num_pt_steps NUM_PT_STEPS
                        number of parallel tempering steps
  --num_sweeps NUM_SWEEPS
                        number of Monte Carlo sweeps per parallel tempering step
  --beta_min BETA_MIN   inverse temperature of the hottest replica
  --beta_max BETA_MAX   inverse temperature of the coldest replicaCitingIf you used the Omnisolver package or one of its plugins, please cite:@misc{https://doi.org/10.48550/arxiv.2112.11131,
  doi = {10.48550/ARXIV.2112.11131},
  
  url = {https://arxiv.org/abs/2112.11131},
  
  author = {Jałowiecki, Konrad and Pawela, Łukasz},
  
  keywords = {Software Engineering (cs.SE), Quantum Physics (quant-ph), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences},
  
  title = {Omnisolver: an extensible interface to Ising spin glass solvers},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}
Package: omnisolver-test-tools
omnisolver-test-toolsPurposeThis meta-package tracks versions of test tools (e.g. pytest) used in Omnisolver and it's official
plugins.
Therefore, it reduces the burden of maintaining such dependencies in each repository separately.InstallationYou typically don't installomnisolver-test-toolsmanually. Instead, the package gets installed
whenever you install Omnisolver or one of its official plugins withtestrequirements, e.g. by
runningpipinstallomnisolver[test]which is only useful if you plan to run tests or contribute to Omnisolver.
Package: omnisoot
OMNISOOTomnisoot (previously eptlsoot) is an object-oriented computational tool for the simulation of the gas phase synthesis of nanoparticles including soot (Carbon Black), graphene, nickel, and carbon nanotubes in flames and reactors. omnisoot can be used for scientific research or process design and optimization of commercial reactors. It accommodates a variety of particle dynamics, inception, growth, and oxidation models that can be combined and used for target simulations in Premixed and Counterflow diffusion flames as well as Plug Flow, Constant Volume, Partially Stirred, and Shock Tube reactors. Reaction mechanisms are processed byCanterathat is used to compute thermodynamic and kinetics properties of the gas mixture. The code is designed to handle high particle volume fractions. omnisoot can be used from Python and a web interface which provides post-processing and visualization tools. A series of examples are also available in the form of Jupyter notebooks and Python scripts.
omnisoot has been registered for copyright on 31 May 2023 byCanadian Intellectual Property Office (CIPO)under regirstration number 1203139.
Package: omniSparql
omniSPARQLomniSparqlis a python module to query triples from the OMNIBENCHMARK triplestore. Its main usage is to retrieve lineage information in an OMNIBENCHMARK (see thelineage query fileof summary metric projects).UsageMinimal usageThe typical use ofomniSparqlconsists in forming a SPARQL based on your need and running it on your triplestore URL:importomniSparqlasomni## get a queryquery=omni.getSparqlQuery.CLASS_METHOD## run the queryout=omni.query_from_sparql(query,URL=TRIPLESTORE_URL)Detailled usageLet's start by getting a query. Several SPARQL queries are available in thegetSparqlQueryclass, which you can explore with thehelppage of the page;importomniSparqlasomnihelp(omni.getSparqlQuery)Help on class getSparqlQuery in module omniSparql.sparql:

class getSparqlQuery(builtins.object)
 |  Available SPARQL queries. One or multiple input to define when called. 
 |  
 |  Returns: 
 |      A string which contains a SPARQL command to use with `query_from_sparql`.
 |  
 |  Methods defined here:

 [...]
 |  imported_datasets_from_project(project_name)
 |      Retrieves all datasets imported by a project.
 |      
 |      Args: 
 |          project_name (str): abbreviated name of the project, e.g. 'omni_batch_processed'
 |      Returns: 
 |          `query`: the query, `project_name`
 |          `full_name`: project full name
 |          `short_name`: project short name
 |          `keyword`: keyword associated to the dataset
 |          `creator`: the mail of the author of the dataset
 |          `dateCreated`
 |          `descr`: decription of the dataset
 |          `originID`: ID of dataset
 [...]As an example, we can select the query showed above;imported_datasets_from_projectand store it for the next step. As explained in the class method documentation, this function helps to identify which renku datasets were imported by a project, only by providing its name. Let's use it to query all datasets imported by one of theirisomnibenchmark;iris_accuracymetric project.Prepare the query:q=omni.getSparqlQuery.imported_datasets_from_project(project_name='iris_accuracy')q"\n        PREFIX ns1:<http://www.w3.org/ns/prov#>\n        PREFIX ns2:<https://swissdatasciencecenter.github.io/renku-ontology#>\n        PREFIX ns3 [...]"The output of any SPARQL query fromgetSparqlQuerycan be used to query a specified triplestore with thequery_from_sparqlfunction (you can ask the URL of your benchmark by contacting the dev team)-out=omni.query_from_sparql(q,URL="http://imlspenticton.uzh.ch/omni_iris_sparql")All queries typically return a dictionary or a list of dictionaries with fields describing your output. In this case, the function retrieves the following information about the datasets imported by the specified project:out[0].keys()
> dict_keys(['query', 'full_name', 'short_name', 'keyword', 'creator', 'dateCreated', 'descr', 'originID'])
Package: omnispectrum
全方位地震动反应谱生成安装pip install omnispectrum例程spectrumimportomnispectrum# acc加速度时程，dt，是否进行基线修正spect=omnispectrum.spectrum(ew,t,False)# 计算反应谱，start，end，step(开始周期，结束周期，步长)spect.get_sa(0,2,0.1)print(spect.sa)omnispectrumimportomnispectrum# csv为三向地震动，格式参照实例文件omni=omnispectrum.OmniSpectrum('022DLB.csv')omni.any_angle_spectrum()批量转换生成word报告importomnispectrumcsv2docx(path,output,baseline_correction=False)作为模块使用python-momnispectrum.omnispectrum--ipath
python-momnispectrum.omnispectrum--helpExample例程
Package: omnitagger
A CLI that beautifies your (old) music files, automatically adds metatags
based on fingerprinting, filename and metadata.Home-page:https://github.com/kkoomen/omnitaggerAuthor: Kim Koomen
Author-email:koomen@protonmail.comLicense: MIT
Description: # omnitagger[![Package Version](https://img.shields.io/pypi/v/omnitagger.svg)](https://pypi.python.org/pypi/omnitagger)
[![Code Climate](https://codeclimate.com/github/kkoomen/omnitagger/badges/gpa.svg)](https://codeclimate.com/github/kkoomen/omnitagger)Omnitagger is a CLI tool for beautifying your music files and adding metadata
automatically to your files. It provides fingerprint lookup via
[acoustid](https://github.com/beetbox/pyacoustid). Wether this is specified or
not: after that it will check for theartist - title.{mp3,flac,ogg}format.
If that pattern isn’t found either, it will check the directory the file is in,
since it is a common case that when you download an album, the structure is:`artist-name/├── title.mp3 ├── title.mp3 ├── title.mp3 └── title.mp3 `If that isn’t an options either, it will skip the file and continue to the next
one. For more information on how omnitagger works, visit the
[How it works](https://github.com/kkoomen/omnitagger/wiki/How-it-works) page on
the wiki.# InstallationNOTE: If you have python 3.7+ installed, you won’t be able to use the
fingerprint module. This due to it needs the Chromaprint module and this one is
not compatible yet with Python 3.7+.$ pip install omnitaggerDownload the [Chromaprint](https://beets.readthedocs.io/en/v1.4.7/plugins/chroma.html#installing-the-library) library.# Documentation
Visit the [documentation](https://github.com/kkoomen/omnitagger/wiki/Documentation)
on the wiki for further details on how to use omnitagger.# The purpose of omnitagger
The purpose of omnitagger was a personal issue we all can relate to in the early
days: You download an album or song using a YouTube converter (or oldschool
Limewire/Frostwire back in those days) and the names do look horrible. Names
with numbers in front of it while it is already in the metatags, underscores
instead of spaces… you get it. This was an issue me and a friend of mine
always had and I suddenly had the idea to create a python script to make from an
ugly and horrible-looking filename a good-looking filename with metatags set
correctly automatically. Once I had the idea I kept building and building and
sometimes even rebuilding the whole script to make it better and more efficient.# LicenseMIT.Keywords: music,beautifier,mp3,ogg,flac,local,tagger
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python :: 3.0
Classifier: Programming Language :: Python :: 3.1
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Package: omnium
Designed to fit into rose/cylc workflowAlso easy to use as a standalone utility.Allows easy adding of new analysers to workflow.Conversion from fields files to NetCDF4.Sync’ing from work computer to local mirror.Data visualization with 2D and 3D displays.Use MPI with master/slave config to farm out work.Dependenciesirispyqtgraph (data visualization)rsyncmpi4py
Package: omniunibot
omniunibot🤖 An omnipotent universal message bot library for pythonSupported platformsFeishuSlackDingtalkFeaturesNon-blocking mode for sending messagesSend to multiple platforms with one-line code💻 Installation(via pip)pip install -U omniunibot(via source)clone this repo &&pip install .📜 UsagePrepare a config fileDefault config path:$HOME/configs/omniunibot.jsonConfig example{"server":{"bind":"tcp://*:58655","interval":0.5},"client":{"bind":"tcp://localhost:58655"},"log":{"level":"DEBUG","dir":"/home/ubuntu/logs/omniunibot"},"channel_groups":{"test_channels":[{"platform":"Slack","webhook":"https://hooks.slack.com/services/xxxx/xxxx/xxxx"},{"platform":"Lark","webhook":"https://open.feishu.cn/open-apis/bot/v2/hook/1a166e72-xxxx-xxxx-xxxx-3ae4f0fb51b7","secret":"xxx"},{"platform":"DingTalk","webhook":"https://oapi.dingtalk.com/robot/send?access_token=xxx","secret":"xxx"}]}}Start the bot serverpython-momniunibot.serverUse the client-side code in your codeExample:./example/client_example.py
Package: omnivector
OmniVector provides a simple interface to vector databases. We integrate the main functionalities of different vector dbs,
generally indexing and searching, into a single interface. This allows us to easily switch between different vector dbs.db=WeaviateDB()# or PineconeDB() or LanceDB()encoder=SentenceTransformerEmbedder("paraphrase-MiniLM-L6-v2",device="cpu")docs=["the cat is on the table","the table is on the cat","the dog is mining bitcoins"]ids=list(range(4,len(docs)+4))embeddings=encoder.embed(docs)db.create_index(ids,docs,embeddings)search_vector=encoder.embed(["the dog is mining bitcoins"])[0]print(db.vector_search(search_vector,k=1))Free software: MIT licenseDocumentation:https://omnivector.readthedocs.io.FeaturesThe AbstractDB requires setting OMNIVECTOR_CONFIG env variable to a config file (an example is in config.yaml)CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: omnivoreql
OmnivoreQL: Omnivore API client for PythonThis is a Python client for theOmnivore API.How to useTo use omnivoreql in your Python project, you can follow these steps:Install the omnivoreql package using pip:pipinstallomnivoreqlImport the package into your project and Create a new instance of the client:fromomnivoreqlimportOmnivoreQLomnivoreql_client=OmnivoreQL("your_api_token_here")Use the methods of the OmnivoreQL class to interact with the Omnivore API.profile=omnivoreql_client.get_profile()result=omnivoreql_client.save_url("https://www.google.com")articles=omnivoreql_client.get_articles()username=profile['me']['profile']['username']slug=articles['search']['edges'][0]['node']['slug']articles=omnivoreql_client.get_article(username,slug)labels=omnivoreql_client.get_labels()subscriptions=omnivoreql_client.get_subscriptions()