Saravanan666/pypi-org huggingface dataset:
Package: openexchangerates3
This is a simple __python3__ client implementation ofhttps://openexchangerates.orgweb services.Installopenexchangerates is avaiable on pypi repositories. Just simply install
by usingeasy_installorpip:# Remove the old one if installed
$ pip uninstall openexchangerates

# Install the Python3 version
$ pip install openexchangerates3UsageFirst of all, you have to sign up the service for an API key by visitinghttps://openexchangerates.orgThen it is a matter of cake to start using the service:>>> from openexchangerates import OpenExchangeRatesClient
>>> client = OpenExchangeRatesClient('<YOUR_API_KEY>')
>>> client.currencies()
{'AED': 'United Arab Emirates Dirham',
'AFN': 'Afghan Afghani',
'ALL': 'Albanian Lek',
'AMD': 'Armenian Dram',
'ANG': 'Netherlands Antillean Guilder',
'AOA': 'Angolan Kwanza',
'ARS': 'Argentine Peso',
'AUD': 'Australian Dollar',
...

>>> client.latest()
{'base': 'USD',
'disclaimer': '<disclaimer>',
'license': '<license>',
'rates': {'AED': 3.672701,
    'AFN': 51.621833,
    'ALL': 104.032,
    'AMD': 406.489997,
    'ANG': 1.7888,
    'AOA': 95.936967,
    'ARS': 4.995484,
    'AUD': 0.966637,
    ...You are also free to select base currency (Supported by non-free licenses):>>> client.latest(base='GBP')
{'base': 'GBP',
'disclaimer': '<disclaimer>',
'license': '<license>',
'rates': {'AED': 3.672701,
    'AFN': 51.621833,
    'ALL': 104.032,
    'AMD': 406.489997,
    'ANG': 1.7888,
    'AOA': 95.936967,
    'ARS': 4.995484,
    'AUD': 0.966637,
    ...
Package: openexr-python
OpenEXR-PythonOpenEXRis a C++-based library, and there is a python binding (pip-openexr) written by 'sanguinariojoe'.However, because it was bound directly, python intellisense does not work.For the convenience of development in the Python environment, this library wraps sanguinariojoe's Python binding into Python once again to support intellisense.OpenEXROpenEXR provides the specification and reference implementation of the EXR file format, the professional-grade image storage format of the motion picture industry.The purpose of EXR format is to accurately and efficiently represent high-dynamic-range scene-linear image data and associated metadata, with strong support for multi-part, multi-channel use cases.OpenEXR is widely used in host application software where accuracy is critical, such as photorealistic rendering, texture access, image compositing, deep compositing, and DI.InstallationStep 1 - Install OpenEXRPlease install following theofficial installation guide.Step 2 - Install OpenEXR-Pythonpipinstallopenexr-python
Package: openfaceid
OpenFaceID - Identity Verification with Facial RecognitionOpenFaceID is an open-source identity verification project that utilizes facial recognition technology to provide a
robust and secure solution for detecting and authenticating known faces. This project aims to deliver a seamless and
reliable user experience for identity verification in various applications.InstallationUse the following command to install OpenFaceID:pipinstallopenfaceidGetting StartedTo get started with OpenFaceID, you can follow the FAISS example below:importfaissfromopenfaceidimportface_detectorfromopenfaceid.vector_stores.faissimportFAISS# Threshold distance used to determine if a face is considered a match or not.# In this example, it's set to 0.25, which is specific to FAISS and the test images used during testing.# You may need to adjust this threshold based on your dataset and vector store.# A lower value means stricter matching, while a higher value allows for more leniency in matching._DISTANCE_THRESHOLD=0.25fd=face_detector.FaceDetector()# Get all image ids and vector embeddings of existing faces, i.e. loading from a databaseall_embeddings=[]# Initialize the FAISS index and add all image embeddings to itdimension=len(all_embeddings[0][1])index=faiss.IndexFlatL2(dimension)faiss_store=FAISS(index)faiss_store.add_embeddings(all_embeddings)# Load the image and perform identity verification# Replace "path-to/new_selfie.jpg" with the actual path to your new selfie imageface_embedding=fd.get_embeddings(image_path="path-to/new_selfie.jpg")scores,image_ids=faiss_store.search_with_score(face_embedding)found=scores[0]<_DISTANCE_THRESHOLDiffound:print(f"The face has been successfully matched to an existing face with the id of{image_ids[0]}.")else:print("Unknown selfie.")This code can run when initialising the server. For more examples check./examplesfolder.ContributingWe welcome contributions to OpenFaceID! If you have ideas, bug reports, or feature requests, please feel free to open an
issue or submit a pull request. For more information, see CONTRIBUTING.md.LicenseOpenFaceID is released under the Apache License 2.0. Please review the license for more details.AcknowledgementsWe would like to express our gratitude to the open-source community and the developers of the underlying facial
recognition technology that makes OpenFaceID possible.ContactFor questions or inquiries, please contact atahalimkara@gmail.com
Package: openfaker
OpenFakerOpenFakeris a Python package that utilizes OpenAI's GPT series model to generate fake data.This new project is rapidly underway, and we welcome any issues or pull requests.Basic UsageInstall with pip:pipinstallopenfakerUsefaker.Faker()to create and initialize a faker generator, which can generate data by accessing properties named
after the type of data you want.fromopenfakerimportFakerfromopenfaker.utilsimportsetup_openaisetup_openai('<api-key>')# Get your API key from https://platform.openai.com/account/api-keysfaker=Faker()faker.name()# 'John Doe'faker.address(2)# ['426 Jordy Lodge', 'Cartwrightshire, SC 88120-6700']
Package: openfalconclient
# open-falcon api python clientexample usuage```pythonfrom openfalconclient.client import FalconClientcli = FalconClient(endpoint="http://127.0.0.1:8080", user='admin', password='admin123')# ex api/v1/user/users  getr = cli.user.users.list()# /api/v1/user/name/xxxx  getr = cli.user.name['admin'].get()# /api/v1/user/u/xxxx getr = cli.user.u['1'].get()# /api/v1/user/update  putdata = {"name": "test1","cnname": "est","email": "root123@cepave.com","im": "44955834958","phone": "99999999999","qq": "904394234239"}cli.user.update.update(data=data)# /api/v1/admin/delete_user  deleteparams = {"user_id": 31}cli.admin.delete_user.delete(params=params)# /api/v1/template/7    deletecli.template['7'].delete()query = [{"endpoint": "zwhtest.net","counter": "net.if.info/region=gd1"}]cli.graph.lastpoint.post(data=query)```
Package: openfc
open Finance CenterPlease raise an issue for any suggestions, corrections, and feedback.介绍openFC用于高效获取各类金融数据，进行学习与研究。
结合 openFC-studio (https://gitee.com/danielcxh/openfc-studio) 可视化结果。安装教程通过下载源码并安装依赖的库gitclonehttps://gitee.com/danielcxh/openfc.gitcdopenfc
python3-mpipinstall-rrequirements.txt通过 PYPI 安装# pypi ：https://pypi.org/project/openfc/python3-mpipinstallopenfc使用说明API使用说明参与贡献Fork 本仓库新建 Feat_xxx 分支提交代码新建 Pull Request资助如果您对该项目感兴趣，可以通过以下方式对该项目进行资助，您的每一份资助都是为了这个项目更好的成长。当然我更期待您能参与贡献。
Package: openfdem
openfdem - An FDEM Visualiser Post-Processing PackageV4.1University of Toronto, 2022This Python package performs transformations on FDEM models in vtk/vtu format.
Functions calculated include:Extract information within the FDEM Model based on the name of the array (e.g., Stress, Strain, Temperature, etc...) Works in 2D and 3D.Extract stress-strain information for UCS and BD Simulations (Works in 2D and 3D). Optional addition of virtual strain gauges (Limited to 2D).Plotting stress vs strain curve.Calculate the Elastic Modulus of the dataset. Eavg, Esec and Etan can be evaluated. Works in 2D and 3D.Extract information of a particular cell based on a sequence of array names. This can be extended to extracting information along a line. Works in 2D and 3D.Extract information of a threshold dataset criteria based on a sequence of array names. Works in 2D and 3D.Extract mesh information and plot rosette/polar plots. Limited to 2D.Automatic detection/ User-defined assigment of loading direction when analysing mechanical simulations, namely UCS, BD, and PLT, in both 2D and 3D simulations.How to use it - TermimalAfter installation is complete, in a terminal/cmd session where you keep your Python projects, type the following command:pythonIn terminal, import fdem-visualizer module like so:importopenfdemasfdSpecify the path to folder where your models (.vtk, .vtu files) are located on your machine.model=fd.Model("abs_model_path_on_machine")You are ready to start using openfdem! See the operating module for full description of functions available.Example OpenFDEM FunctionsGetting number of points in your model:model.n_pointsOutput:11904Getting the stress-strain for platen:model.complete_stress_strain(progress_bar=True)Output:>>>ScriptIdentifyingPlaten>>>PlatenMaterialIDfoundas[1]>>>Progress:|//////////////////////////////////////////////////|100.0%Complete>>>1.51secondsPlatenStressPlatenStrain00.000000e+000.00000014.825237e+000.00925929.628823e+000.01851931.441437e+010.02777841.919164e+010.037037........572.036137e-300.240741582.036137e-300.250000592.036137e-300.259259602.036137e-300.268519612.036137e-300.277778[62rowsx2columns]>>>
Package: openfeature-provider-flagd
flagd Provider for OpenFeatureThis provider is designed to use flagd'sevaluation protocol.Installationpip install openfeature-provider-flagdConfiguration and UsageInstantiate a new FlagdProvider instance and configure the OpenFeature SDK to use it:fromopenfeatureimportapifromopenfeature.contrib.provider.flagdimportFlagdProviderapi.set_provider(FlagdProvider())Configuration optionsThe default options can be defined in the FlagdProvider constructor.Option nameType & ValuesDefaulthoststrlocalhostportint8013schemastrhttptimeoutint2LicenseApache 2.0 - SeeLICENSEfor more information.
Package: openfec-sdk
openfec_adkPython SDK forhttps://api.open.fec.gov/AboutThis SDK was autogenerated via the swagger/openapi definition provided.You can read the docs athttps://rhythmictech.github.io/openFEC-python-sdk/index.htmland check out the project on PyPi athttps://pypi.org/project/openfec-sdk/
Package: openfeed
Openfeed SDK for PythonThis SDK encapuslates the openfeed proto objects and faciliates client connections to openfeed servers.Installationpip install openfeedExample Usageimportopenfeed# new client with credentialsof_client=openfeed.OpenfeedClient("username","password")# optional state handlersof_client.on_error=lambdax:print("Error:",x)of_client.on_connected=lambdax:print("Connected")of_client.on_disconnected=lambdax:print("Disconnected")# attach a global message handlerof_client.on_message=lambdax:print("Global Message:",x)# sub to markets by symboldefon_message(msg):print("Market Data: ",msg)of_client.add_symbol_subscription("AAPL",callback=on_message)# sub to markets by exchangeof_client.add_exchange_subscription("NYSE",callback=on_message)of_client.start()Expected Outputof-client: Connected
of-client: Market Data:  subscriptionResponse
of-client: Market Data:  marketSnapshot
of-client: Market Data:  marketUpdate
of-client: Market Data:  marketUpdateOpenfeed Documentationhttps://docs.barchart.com/openfeedSubscription TypesOpenfeed supports many levels ofsubscription types.OHLCof_client.add_exchange_subscription(["NYSE"],callback=on_message,subscription_type=["OHLC"]){
  marketId: 5389879102616877808
  symbol: "AAPL"
  open {
    price: 1205600
  }
  high {
    price: 1205600
  }
  low {
    price: 1205247
  }
  close {
    price: 1205490
  }
  volume: 43635
  priceVolume: 5259897.1422
  numberTrades: 224
  tradeDate: 20201116
  transactionTime: 1605547921000000000
  openStartTime: 1605547885850000000
  closeEndTime: 1605547920964000000
}
Package: openfermioncirq
Alpha Release.OpenFermionis an open source library for
obtaining and manipulating representations of fermionic systems (including
quantum chemistry) for simulation on quantum computers.Cirqis an open source library for
writing, manipulating, and optimizing quantum circuits and running them
against quantum computers and simulators. OpenFermion-Cirq extends the functionality of
OpenFermion by providing routines and tools for using Cirq to compile and compose circuits
for quantum simulation algorithms.Getting startedInstalling OpenFermion-Cirq requires pip. Make sure that you are using an up-to-date version of it.
Once installation is complete, be sure to take a look at ouripython tutorialsandcode documentation.InstallationTo install the latest PyPI releases as libraries (in user mode):python-mpipinstall--useropenfermioncirqDeveloper installTo install the latest versions of OpenFermion, Cirq and OpenFermion-Cirq (in development mode):gitclonehttps://github.com/quantumlib/OpenFermion-CirqcdOpenFermion-Cirqpython-mpipinstall-e.How to contributeWe’d love to accept your contributions and patches to OpenFermion-Cirq.
There are a few small guidelines to follow which you can read abouthere.How to citeWhen using OpenFermion-Cirq for research projects, please cite:Jarrod R. McClean, Kevin J. Sung, Ian D. Kivlichan, Yudong Cao,
Chengyu Dai, E. Schuyler Fried, Craig Gidney, Brendan Gimby,
Pranav Gokhale, Thomas Häner, Tarini Hardikar, Vojtĕch Havlíček,
Oscar Higgott, Cupjin Huang, Josh Izaac, Zhang Jiang, Xinle Liu,
Sam McArdle, Matthew Neeley, Thomas O’Brien, Bryan O’Gorman, Isil Ozfidan,
Maxwell D. Radin, Jhonathan Romero, Nicholas Rubin, Nicolas P. D. Sawaya,
Kanav Setia, Sukin Sim, Damian S. Steiger, Mark Steudtner, Qiming Sun,
Wei Sun, Daochen Wang, Fang Zhang and Ryan Babbush.OpenFermion: The Electronic Structure Package for Quantum Computers.arXiv:1710.07629. 2017.We are happy to include future contributors as authors on later releases.Alpha DisclaimerOpenFermion-Cirq is currently in alpha.We are still making breaking changes.
Wewillbreak your code when we make new releases.
We recommend that you target a specific version of OpenFermion-Cirq, and periodically bump to the latest release.
That way you have control over when a breaking change affects you.Copyright 2018 The OpenFermion Developers.
This is not an official Google product.
Package: openfertility
Open Fertility is an open-source project dedicated to advancing fertility analysis and prediction through machine learning. The goal is to foster acommunity-driveninitiative that empowers fertility professionals, researchers, and enthusiasts.Inspired by the paperAn annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilizationthe aim is to leverage the open data they published to develop initial models.The primary objectives are to:Cultivate an inclusive and collaborative community, bringing together experts and enthusiasts.Provide comprehensive tools for training, evaluating, and applying machine learning techniques.Effective data visualization, to gain valuable insights and interpret data with clarity.Deliver intuitive user interfaces that grant easy access to the models.Get in touch if you feel interested in participating!InstallationDuring this early develoment stage, a pip install is still not provided.So, follow these steps:Download or clone the filesNavigate to the directory containing the repositoryRunpip install .Early featuresDownload the blastocyst datasetimportopenfertilityasofblasto2k=of.datasets.blasto2k.Dataset()blasto2k.download()
Package: openff-nagl-models
Open NAGL ModelsLatest releaseStatusThis repository contains NAGL models released by theOpen Force Field Initiative. They are intended to be used byOpenFF NAGL.UseInstalling this package exposes an entry point that makes it easy to access models installed in this package.A convenience function is provided to wrap this for you:>>>fromopenff.nagl_modelsimportload_nagl_model_directory_entry_points>>>load_nagl_model_directory_entry_points()[PosixPath('/home/.../openff-nagl-models/openff/nagl_models/models/am1bcc')]You can also list all available modelsfrom all entry points:>>>fromopenff.nagl_modelsimportlist_available_nagl_models>>>list_available_nagl_models()[PosixPath('/home/.../openff-nagl-models/openff/nagl_models/models/am1bcc/openff-gnn-am1bcc-0.0.1-alpha.1.pt'),PosixPath('/home/.../openff-nagl-models/openff/nagl_models/models/am1bcc/openff-gnn-am1bcc-0.1.0-rc.1.pt')]Or validate if a model name is found in the local directory, or an entry point directory:>>>fromopenff.nagl_modelsimportvalidate_nagl_model_path>>>validate_nagl_model_path("openff-gnn-am1bcc-0.0.1-alpha.1.pt")PosixPath('/home/.../openff-nagl-models/openff/nagl_models/models/am1bcc/openff-gnn-am1bcc-0.0.1-alpha.1.pt')Finally, if you want to find all models for a particular type, useget_models_by type.
These will come sorted according to semantic versioning, where the latest release is last:>>>fromopenff.nagl_modelsimportget_models_by_type>>>get_models_by_type("am1bcc")[PosixPath('/home/.../openff-nagl-models/openff/nagl_models/models/am1bcc/openff-gnn-am1bcc-0.0.1-alpha.1.pt'),PosixPath('/home/.../openff-nagl-models/openff/nagl_models/models/am1bcc/openff-gnn-am1bcc-0.1.0-rc.1.pt')]Versionsv0.0.1-alpha.1: a pre-production model to use for experimentation. We donotrecommend using this model to assign charges in scientific work.v0.1.0-rc1: a pre-production model to use to assign AM1-BCC partial charges.AcknowledgementsProject based on theComputational Molecular Science Python Cookiecutterversion 1.0.
Package: openfga-dsl-parser-python
OpenFGA DSL Parser (Python)The OpenFGA DSL Parser provides utilities for parsing DSL file string inputs into an AST representation, as well as transforming them into a JSON representation.This library is meant to be a utility for services usingOpenFGAfor their authorization solution, but need a way to translate from their DSL to the JSON format the HTTP API expects.Usagefromopenfga_dsl_parserimportdsl_to_jsoninput="""type grouprelationsdefine member as selftype resourcerelationsdefine writer as selfdefine reader as self but not writer"""json=dsl_to_json(input)print(json)DevelopmentSetup Python environment & install Maturin:python -m venv .nenvsource .venv/bin/activatepip install maturinInstall & build package using Maturin for local testing:maturin develop
Package: openfigipy
openfigipyA python wrapper around theOpen FIGI v3 APIthat leverages
pandas DataFrames to make requests and return data.Features IncludeAutomatically throttles requests to respect the API rate limitAutomatically handles the chunking and retrieval of mapping jobsAutomatically handles the pagination of search requestsQueries are given by providing the relevant method with a pandas DataFrame,
allowing easy integration with existing reference data pipelines.Getting StartedTo install this library simply runpip install openfigipy.importpandasaspdfromopenfigipyimportOpenFigiClient# api key can either be given with the api_key argument# or set as the environment variable `OPENFIGI_API_KEY`ofc=OpenFigiClient()# establish a requests sessionofc.connect()# create a dataframe of look-ups - each row represents one query that will# be batched in jobs# the column headers represent the relevant key from the open figi apidf=pd.DataFrame({'idType':['TICKER','ID_BB_GLOBAL'],'idValue':['IBM','BBG0032FLQC3'],'currency':['USD','USD'],'marketSecDes':['Equity','Equity'],'exchCode':['US',None]})print(df)#          idType       idValue currency marketSecDes# 0        TICKER           IBM      USD       Equity# 1  ID_BB_GLOBAL  BBG0032FLQC3      USD       Equityresult=ofc.map(df)print(result.head())#        q_idType     q_idValue q_currency  ... shareClassFIGI securityType2  securityDescription# 0        TICKER           IBM        USD  ...   BBG001S5S399  Common Stock                  IBM# 1  ID_BB_GLOBAL  BBG0032FLQC3        USD  ...   BBG001S5N8V8  Common Stock                 AAPLprint(result.columns.tolist())# ['q_idType',#  'q_idValue',#  'q_currency',#  'q_marketSecDes',#  'q_exchCode',#  'query_number',#  'status_code',#  'status_message',#  'figi',#  'name',#  'ticker',#  'exchCode',#  'compositeFIGI',#  'securityType',#  'marketSector',#  'shareClassFIGI',#  'securityType2',#  'securityDescription']The resulting dataframe will keep your original query columns, prefixed withq_as well as the documented response from the Open FIGI API. This is to
ensure there isn't an overlap i.e. if your query containsexchCodeand the
results do to. There are also some additional helper columns described below
too.query_number: Shows which query the result is related to, can be helpful when
a query returns multiple matches.result_number: Shows the order in which the results were returned by the Open
FIGI API. Generally the best match is shown first (i.e.result_number0)status_code: one of ('success', 'warning', 'error') as per the documentation
on the Open FIGI API.status_message: The associated message with the givenstatus_code. Helpful
for understanding why results might not have been returned.Running testsTo run all unit tests for this module:$pytestPlease be aware some tests might take some time since they call external APIs.TodoSetup testing (integration and unit testing). Learn about mockingSetup automatic documentation generation w/ Sphinxexplore if type hinting could helpSetup automatic linting and checkingSetup continuous integrationtestsdocumentationpypi publishing
Package: openfile
openfileis a trivial Python module that implements a single convenience
functionopenfile(filename,mode="rt",**kwargs)wich delegates the real
work to one of the following standard library functions:gzip.open(filename, mode, **kwargs)if the file ends with suffix.gz;bz2.open(filename, mode, **kwargs)if the file ends with suffix.bz2;lzma.open(filename, mode, **kwargs)if the file ends with suffix.xzor.lzma;open(filename, mode, **kwargs)if the file does not end with any suffix mentioned above.If thefilenameis a single dash-thensys.stdinorsys.stdoutis returned, depending onmodebeingrorw, respectively.The following keyword arguments are used by openfile:expanduser=Truewill cause openfile to callos.path.expanduserforfilename.expandvars=Truewill cause openfile to callos.path.expandvarsforfilename.makedirs=Truewill cause openfile to callos.makedirsfor the parent directoryos.path.dirname(filename)if it does not exist andmodecontains either"w"or"a".All other keyword arguments passed to openfile will be passed down to the open functions listed above.
Package: openfinance
🌴OpenFinance🌴OpenFinance is based on the idea of Financial Egalitarianism with the help of llm. Poor or Rich, Invest for all
People can easily understand the knowledge behind the complex financial market World and the secret force behind the changement in the financial market or the consequence of different events.Quick InstallIf you are a developper, you can try:pip install openfinance🤔 What is OpenFinance ?We have built a Digital Financial World called OpenFiannce Playground, it is based on the realtime worldwide event and the magnificent abilities of llm models like ChatGPTs.Key Funcitons of the world is based on the following abilities.A Temperal Financial Graph which connects the Entity Graph to the Financial Indicator GraphA agentflow framework for designing AI Agent to solve financial tasksA Web frontend available to anyone who want to contribute to the digital financial world** 💬 Web Front**Our Website will be an interface for those who are willing to contribute theirs thoughts in the Digital Finance World. People can easily set up new tools or new factors and make their own Agents using our agentflow and all kinds of toolsClick Here** 🤖 Agents**We have already developped several agents helpfully, but they are far from enough and perfert. We encourage all of you join us to build the Digital Financial World
A short list of Agents:Search Agent: An Agent for Search financial dataPercept Agent: An Agent for observing the real time event to extract usefull information and store them into the GraphAnalysis Agent: An Agent for analysis the reasons behind complex event📖 DocumentationWe will catch up as soon as possible, before that you can tryClick Here💁 Contributing💬 Contact