Saravanan666/pypi-org huggingface dataset:
Package: orbit-nrel
Offshore Renewables Balance of system and Installation ToolVersion:1.0.8Authors:Jake Nunemaker,Matt Shields,Rob HammondDocumentation:ORBIT DocsInstallationAs of version 0.5.2, ORBIT is now pip installable withpip installorbit-nrel.Development SetupThe steps below are for more advanced users that would like to modify and
and contribute to ORBIT.EnvironmentA couple of notes before you get started:It is assumed that you will be using the terminal on MacOS/Linux or the
Anaconda Prompt on Windows. The instructions refer to both as the
“terminal”, and unless otherwise noted the commands will be the same.To verify git is installed, rungit--versionin the terminal. If an error
occurs, install git using thesedirections.The listed installation process is intended to be the easiest for any OS
to get started. An alternative setup that doesn’t rely on Anaconda for
setting up an environment can be followedhere.InstructionsDownload the latest version ofMinicondafor the appropriate OS. Follow the remainingstepsfor the appropriate OS version.From the terminal, install pip by running:conda install-canaconda pipNext, create a new environment for the project with the following.conda create -n <environment_name> python=3.7 --no-default-packagesTo activate/deactivate the environment, use the following commands.conda activate <environment_name>
conda deactivate <environment_name>Clone the repository:git clonehttps://github.com/WISDEM/ORBIT.gitNavigate to the top level of the repository
(<path-to-ORBIT>/ORBIT/) and install ORBIT as an editable package
with following command.#Notethe"."attheendpip install -e .#ORifyouareyougoingtobecontributingtothecodeorbuildingdocumentationpip install -e '.[dev]'(Development only) Install the pre-commit hooks to autoformat code and
check that tests pass.pre-commit installDependenciesPython 3.7+marmot-agentsNumPySciPyMatplotlibOpenMDAO (>=3.2)Development Specificblackisortpre-commitpytestsphinxsphinx-rtd-themeRecommended packages for easy iteration and running of code:jupyterlabpandas
Package: orbit-predictor
Orbit Predictor is a Python library to propagate orbits of Earth-orbiting objects (satellites, ISS,
Santa Claus, etc) usingTLE (Two-Line Elements set)All the hard work is done by Brandon Rhodes implementation ofSGP4.We can sayOrbit predictoris kind of a “wrapper” for the python implementation of SGP4To install itYou can install orbit-predictor from pypi:pip install orbit-predictorUse exampleWhen will be the ISS over Argentina?In [1]: from orbit_predictor.sources import EtcTLESource

In [2]: from orbit_predictor.locations import ARG

In [3]: source = EtcTLESource(filename="examples/iss.tle")

In [4]: predictor = source.get_predictor("ISS")

In [5]: predictor.get_next_pass(ARG)
Out[5]: <PredictedPass ISS over ARG on 2017-11-10 22:48:10.607212>

In [6]: predicted_pass = _

In [7]: position = predictor.get_position(predicted_pass.aos)

In [8]: ARG.is_visible(position)  # Can I see the ISS from this location?
Out[8]: True

In [9]: import datetime

In [10]: position_delta = predictor.get_position(predicted_pass.los + datetime.timedelta(minutes=20))

In [11]: ARG.is_visible(position_delta)
Out[11]: False

In [12]: tomorrow = datetime.datetime.utcnow() + datetime.timedelta(days=1)

In [13]: predictor.get_next_pass(ARG, tomorrow, max_elevation_gt=20)
Out[13]: <PredictedPass ISS over ARG on 2017-11-11 23:31:36.878827>Simplified creation of predictor from TLE lines:In [1]: import datetime

In [2]: from orbit_predictor.sources import get_predictor_from_tle_lines

In [3]: TLE_LINES = (
            "1 43204U 18015K   18339.11168986  .00000941  00000-0  42148-4 0  9999",
            "2 43204  97.3719 104.7825 0016180 271.1347 174.4597 15.23621941 46156")

In [4]: predictor = get_predictor_from_tle_lines(TLE_LINES)

In [5]: predictor.get_position(datetime.datetime(2019, 1, 1))
Out[5]: Position(when_utc=datetime.datetime(2019, 1, 1, 0, 0),
    position_ecef=(-5280.795613274576, -3977.487633239489, -2061.43227648734),
    velocity_ecef=(-2.4601788971676903, -0.47182217472755117, 7.167517631852518),
    error_estimate=None)Currently you have available these sourcesMemorytlesource: in memory storage.EtcTLESource: a uniq TLE is stored in/etc/latest_tleWSTLESource: It reads a REST API currently used inside Satellogic. We are are working to make it publicly available.How to contributeWrite pep8 complaint code.Wrap the code on 100 collumns.Always use a branch for each feature and Merge Proposals.Always run the tests before to push. (test implies pep8 validation)
Package: orbits
# orbitsWARNING: This is still in early development. See the [Initial release milestone](https://github.com/vandalt/orbits/milestone/1) section for
details.Currently, only the RVModel is available. _orbits_ also provides functions to
load PyMC3 parameters and define GP kernel._orbits_ is a wrapper around
[exoplanet](https://github.com/exoplanet-dev/exoplanet) aimed at providing
pre-defined orbit models that can be quickly setup from the API or from a
configuration file. The goal is to have something similar to
[RadVel](https://github.com/California-Planet-Search/radvel), but usingexoplanetand PyMC3 as a backend.The 0.1 release supports creating an RVModel with a parameter dictionary,
defining GP kernels, and using the RVModel object for inference.## Installation_orbits_ can be installed with pip:python -m pip install orbits.To use the development version of _orbits_, clone the repository and install it:`shell git clonehttps://github.com/vandalt/orbits.gitpython-mpip install-U-e".[dev]"`## Using _orbits_
There is no detailed documentation yet, but examples will be added to theexamplesdirectory. There is currently an example with the K2-24 dataset.
This replicates tutorials from _exoplanet_ and _RadVel_.
Package: orbit-tessellation
tessellationA Python package for evaluating orbit commensurability using Delaunay tessellation. Use this in thecommensurabilitypackage to analyze galaxy potentials.InstallationInstall this package viapip:python -m pip install orbit-tessellationUsageUsetessellation.Tessellationon your point array to perform the commensurability evaluation.importnumpyasnpfromtessellationimportTessellation# generate 100 random 2D pointspoints=np.random.normal(size=(100,2))tess=Tessellation(points)This returns a tessellation object that contains info in its attributes. The.measureattribute gives the measure of the orbit (normalized to be in[0, 1)).
Package: orbitutils
This package allows for easy and very quick Monte Carlo simulation of populations of orbits, giving instantly distributions of projected separations, relative RVs between components, etc.  Includes calculations for eccentric orbits.  Handy for simulating imaging or RV observations.  This code is one of the modules that makes up theMorton (2012)validation procedure.Makes occasional use of theastropyQuantityobject to keep track of some quantities, and the astropySkyCoordobject to keep track of coordinates.  Also usespandasto save/restore populations.See below for a quick intro, and thenotebook demofor more.Installation$ pip install [--user] orbitutilsOr clone the repository and install:$ git clone https://github.com/timothydmorton/orbitutils.git
$ cd orbitutils
$ python setup.py install [--user]Basic usageSimulate a population for given primary and secondary mass(es), and orbital periods.  Eccentricity is zero by default, but can be set.fromorbitutilsimportOrbitPopulationpop=OrbitPopulation(1,1,1000,n=1e4)#Primary mass, secondary mass, orbital period (d)You can also create a distribution of secondary masses, and/or a distribution of periods:importnumpyasnpfromorbitutilsimportOrbitPopulationN=1e4M2s=np.linspace(0.1,1,N)Ps=np.logspace(1,3,N)#periods evenly log-spaced from 10 to 1000 dayspop=OrbitPopulation(1,M2s,Ps)OrbitPopulationobjects can be saved using thesave_hdfmethod:pop.save_hdf('mypop.h5')and then read back in/restored as follows:fromorbitutilsimportOrbitPopulation_FromH5pop_again=OrbitPopulation_FromH5('mypop.h5')AttributionIf you use this software for your research, please let me know.  Eventually there will be a more current publication to cite; until then, please cite my 2012 planet validation method paper which used this code:@ARTICLE{2012ApJ...761....6M,
   author = {{Morton}, T.~D.},
    title = "{An Efficient Automated Validation Procedure for Exoplanet Transit Candidates}",
  journal = {\apj},
archivePrefix = "arXiv",
   eprint = {1206.1568},
 primaryClass = "astro-ph.EP",
 keywords = {planetary systems, stars: statistics },
     year = 2012,
    month = dec,
   volume = 761,
      eid = {6},
    pages = {6},
      doi = {10.1088/0004-637X/761/1/6},
   adsurl = {http://adsabs.harvard.edu/abs/2012ApJ...761....6M},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Package: orbl-onpremise
Python API client for ORBL on-premise APIThis library is mirror of official ORBL on-premise API in terms of methods and interfaces.For your convenience, you can make API calls using sync or async (asyncio) interface.Installationpipinstallorbl_onpremiseNote that it is always recommended pinning version of your installed packages.Usage example (sync)An example of how to create an origin:fromorbl_onpremiseimportClientif__name__=='__main__':# api_token is just str with your API tokenapi_token="abcd012345"# Now create instance of Client. There should be only one per process.client=Client(base_url="http://localhost:8080",api_token=api_token)# Issue API request to create an originclient.origins.create(name="test_name")Now that we have our origin created, we can create profile inside that origin:fromorbl_onpremiseimportClientdefcreate_profiles_example(client:Client):origin_id=1# you can inspect response from `client.origins.create`withopen("image.png","rb")asf:response=client.profiles.create(image=f,origin_id=origin_id)print("Profiles Create Response:\n",response.json(),flush=True)if__name__=='__main__':# api_token is just str with your API tokenapi_token="abcd012345"# Now create instance of Client. There should be only one per process.client=Client(base_url="http://localhost:8080",api_token=api_token)# Issue API request to create a profilecreate_profiles_example(client)Now that we have our origin & profile created, we can search for profile:fromorbl_onpremiseimportClientdefsearch_profiles_example(client:Client):withopen("image.png","rb")asf:response=client.profiles.search(image=f,identify_asm=True)print("Profiles Search Response:\n",response.json(),flush=True)if__name__=='__main__':# api_token is just str with your API tokenapi_token="abcd012345"# Now create instance of Client. There should be only one per process.client=Client(base_url="http://localhost:8080",api_token=api_token)# Issue API request to search profilessearch_profiles_example(client)For more examples and usage, please refer to thedocs.Development setupTo install all the development requirements:pipinstall--upgradepip
pipinstallpoetry
poetryinstall--no-rootTo run linters & test suite:./scripts/test.shHow to releaseUpdate version in:CHANGELOG.md__init__.pypyproject.tomlLock deps to update version in poetry:poetry lockGet API Token from account settings inPyPIpoetry config pypi-token.pypi YOUR_API_TOKENpoetry buildpoetry publishRelease History0.1.1Fixed types of custom Enum classes and in corresponding methods0.1.0Initial version of packageLicenseDistributed under the MIT license. SeeLICENSEfor more information.ContributingCreate your feature branch (git checkout -b feature/fooBar)Commit your changes (git commit -am 'Add some fooBar')Push to the branch (git push origin feature/fooBar)Create a new Pull Request
Package: orbs-orderbook-sdk
Python SDK for Orb's order bookThis Python SDK allows you to query the Orb's order book and place orders.Please note that this SDK is still in development and is subject to change.Getting startedThe SDKis available on PyPIand can be installed withpip:pipinstallorbs-orderbook-sdkYou will need to request an API key from the Orbs team, and provide us with your Externally Owned Account (EOA) wallet address (NOT the private key).UsageCreating an orderSeeexamples/create_order.py.Signing an orderSeeexamples/sign_order.py.Cancelling ordersSeeexamples/cancel_order.py.Getting orders (open and filled)Seeexamples/get_orders.py.Folder structureexamples: Example scriptsorbs_orderbook: The Python SDK source codeDevelopmentInstallationEnsure you have at least Python 3.8 installedPipRunpip install -r requirements.txtto install dependenciesPoetryInstallPoetryRunpoetry installto install dependencies into a virtual environment
Package: orc2pstgrs
ReadmeThis library help you to copy your data(Table wise) from Oracle Database to Postgresql Database.Following librarys are use :importpandasaspd
importnumpyasnp
importcx_Oracle
fromsqlalchemyimportcreate_engineConnection DetailsNote:All credentials of Database connection should me correct.Have to provide all credentials for the connection like User name , password and Host etc. for both the database and also provide table name which you want to copy from Oracle DB to Postgresql DB, after providing all credentials get pandas dataframe having table name and count of rows in table as output .How to use codeImport orc2pstgrs libraryimportorc2pstgrsAfter getting the orc2pstgrs library ,then create connection with Oracle DBdataload.oracleDBconnection(host_name,port,service_name,user_name,password_orc)Create connection with PostgreSQL DBdataload.postgresDBconnection(user,password,host,port_pos,database_name)Now, ETL start here by just giving the table namedataload.TableDetails(table_name_of_orcle,table_name_use_to_save_in)OR use SQL query to Extract tabledataload.GettablewithSQL(query,table_name_use_to_save_in)Note:Libraries likecx_Oracleandsqlalchemyare very powerful so can be use  alone for ETL job.
Package: orca
OrcaOrca is a Python library for task orchestration. It’s designed for workflows like city simulation, where the data representing a model’s state is so large that it needs to be managed outside of the task graph.The building blocks of a workflow are “steps”, Python functions that can be assembled on the fly into linear or cyclical pipelines. Steps typically interact with a central data store that persists in memory while the pipeline runs. Derived tables and columns can be updated automatically as base data changes, and pipeline components are evaluated lazily to reduce unnecessary overhead.Orca is used inUrbanSimand other projects.Documentationudst.github.io/orca/Installationpip install orcaconda install orca--channelconda-forge
Package: orcanet
OrcaNet is a deep learning framework based on tensorflow in order to simplify the
training process of neural networks for astroparticle physics, in particular
for Orca, Arca and Antares. It incorporates
automated logging, plotting and validating during the training, as well as
saving and continuing the training process. Additionally, it features easy
management of multiple neural network inputs and the use of training data
which is split over multiple files.In this sense, it tackles many challenges that are usually found in
astroparticle physics, like huge datasets.Documentation is athttps://ml.pages.km3net.de/OrcaNet/.OrcaNet is a part of the Deep Learning efforts for the neutrino telescope KM3NeT.
Find more information about KM3NeT onhttp://www.km3net.org.OrcaNet is currently being developed at the official KM3NeT gitlab (https://git.km3net.de/ml/OrcaNet).However, there’s also a github mirror that can be found athttps://github.com/ViaFerrata/OrcaNet.Past studiesFor studies done using orcanet, check out the wiki page of the machine learning
working grouphttps://wiki.km3net.de/index.php/Machine_Learning_working_group.
You can find orcanet config files used in studies in the directory
orcanet_contrib/configs of the orcanet repo.InstallationOrcaNet can be installed via pip by running:pip install orcanetIn order to make use of tensorflow’s GPU acceleration, you need cuda and
cudnn installed. You can see which of these each tensorflow version needs herehttps://www.tensorflow.org/install/source#gpuContainerizationThe easiest way to run OrcaNet is with singularity.
A Singularity image of the latest stable version of OrcaNet with tensorflow
and cuda/cudnn for GPUs
is automatically uploaded to our sftp server.
Download it e.g. via:wget http://sftp.km3net.de/singularity/orcanet_v???.sifwhere v??? is the version, e.g. orcanet_v1.0.1.sif.
Run it on a CPU e.g. via:singularity shell orcanet_v???.sifIf you run it on a GPU, don’t forget the nv flag:singularity shell --nv orcanet_v???.sif
Package: orca-parser
ORCA-ParserA python module to parse data out of ORCA output filesThis module is availible at:https://pypi.org/project/orca-parser/and can be installedviapipinstallorca-parserThe module use as:importorca_parserOptimization=orca_parser.ORCAParse("Test-cases/Phenol/Opt.out")print("ORCA exited normally:",Optimization.valid)print("Job took:",Optimization.seconds(),"seconds")print("Job input line:",Optimization.parse_input())Optimization.parse_coords()print("Atoms:",Optimization.atoms)print("Final coordinates:")print(Optimization.coords[-1])Requirements:ase, numpy, pandasOrca parser contains two classes:ORCAParseReads orca output streams (.out/.log/etc)
Can read frequencies, atoms, coordinates, IR spectra, free energy (broken down into its components as well)
Can also tell you how the job finished, if it converged etcHessianToolsReads orca Hessian (.hess) outputs
Can parse the atoms and coordinates, normal modes, IR spectra.
From this .xyz trajectories of normal modes can be writtenparse_engradReads orca engrad (.engrad) outputs
returns the number of atoms, the energy (Ha), the atomic numbers, coordinates and forces
coordinates and forces are returned as (N,3) matricesUsage:Example usage can be found in Usage.py, along with example ORCA output files in the "Test-cases" folder.
Package: orcbench
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⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠉⠉⠉⠛⠛⠛⠛⠉⠉⠉⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀OrcBench: A Representative Serverless BenchmarkOrcBench generates a workload trace which can be consumed by serverless
platforms to test their service. The models used by OrcBench are modeled off of
Microsoft Azure and whos creation is outlined in our paper (referenced below).Installationpip install orcbenchQuick Startorcbench traceORpython3 -m orcbench traceThis will produce a standard trace (25% that of the original Microsoft
Workload,--scale 0.25). nd produces jobs which will sample for 10 models
(--N 10). With a runtime (--runtime 30) of 30 minutes. The seed (--seed)
can be optionally set to produce deterministic workloads. The outputted
(--out) trace is by default sent totrace.out.Referencing UsIf you use this benchmark please make sure to cite us using the following citationPDF of the paper can be found -OrcBench: A Representative Serverless Benchmark@inproceedings{hancock:benchmarking,
  author    = {Ryan Hancock and
               Sreeharsha Udayashankar and
               Ali José Mashtizadeh and
               Samer Al-Kiswany},
  title     = {OrcBench: A Representative Serverless Benchmark},
  booktitle = {Proceedings of the15th International Conference on Cloud Computing (CLOUD'22)}
  publisher = {{IEEE}},
  year      = {2022},
  doi       = {10.1109/CLOUD55607.2022.00028},
}
Package: orc-dev-cli
Welcome to orc-dev-cliOrc-dev-cli gives developers a CLI tool called orc.
Orc is used to interact with openshift osd clusters and the openshift addon RHOAM.As this is a dev tool kude-admin access to the clusters is expected.InstallationEasiest way to install orc is by using pippipinstallorc-dev-cliBasic Commandsorc rhoam statusGet the current state of an installed addon instancesorc configOpen configuration fileorc cluster deleteDelete cluster.orc cluster loginGet cluster kubeadmin login details.orc cluster statusGet basic state information on osd clusterShell CompletionBashCreate temporary bash script.Add_ORC_COMPLETE=bash_source orc > ~/.orc-complete.bash.Run script.Source the newly created~/.orc-complete.bashin~/.bashrcas follows. ~/.orc-complete.bash.Restart the shell to complete.
Package: orchard
This package provides Python bindings for [Orchard], letting you manage Docker hosts for a particular account and interact with an individual host using [docker-py].Install`bash $ pip install orchard `AuthenticatingTheorchardpackage provides two methods for instantiating an API client:`python >>> import orchard >>> orchard.with_token(my_token) <orchard.client.Client object at 0x101de0d10> >>> orchard.with_username_and_password(my_username, my_password) <orchard.client.Client object at 0x102244e10> `Managing hostsOnce you’ve instaniated aClientobject, thehostsproperty lets you list, create and delete hosts:`python >>> client.hosts [<Host: default>, <Host: host2>] >>> client.hosts[0] <Host: default> >>>client.hosts["host2"]<Host: host2> >>> host3 =client.hosts.create({"name":"host3"})>>> host3 <Host: host3> >>> host3.delete() `Interacting with DockerTo get a [docker-py] instance for a host, callhost.docker():`python >>> docker =client.hosts["default"].docker()>>> docker.containers() [] >>> c =docker.create_container("ubuntu","date") >>>docker.start(c['Id'])>>>docker.wait(c['Id'])0 >>>docker.logs(c['Id'])'Mon Oct 28 15:42:56 UTC 2013\n' >>>docker.remove_container(c['Id'])`See the [docker-py] README for a full list of methods.[Orchard]:https://www.orchardup.com[docker-py]:https://github.com/dotcloud/docker-py[CLI docs]:https://www.orchardup.com/docs/cli[Orchard API docs]:https://www.orchardup.com/docs/api
Package: orchard-client
This Python client is designed to work with the Orchard Payment Gateway API, allowing you to easily process payments.
Before using the client, you’ll need to install the orchard_client package using pip.Once installed, you can import the Orchard class and create an instance of it by providing your secret_key, client_key, and the base URL for the Orchard Payment Gateway API.
With an instance of the Orchard class created, you can then create a payload containing the information or request you want to send.
It’s important to ensure that the payload for each request is formatted correctly as JSON.You can then use any of the available methods provided by the Orchard class (send_sms, check_wallet_balance, send_payment, account_inquiry, hosted_checkout) to complete your request.
The final response from the request will be sent to the callback URL provided in the request for the send_payment endpoint. Other methods such as check_wallet_balance, send_sms, account_inquiry, and hosted_checkout will give you an instant response.Notes
Please note that this code assumes that you have the necessary credentials and permissions to use the Orchard API, and that your IP address is whitelisted by the appsNmobile Client Service team. If you don’t have these credentials, you’ll need to obtain them before you can use this client or library.For more information about the Orchard Payment Gateway API, please refer to the official documentation athttps://docs.anmgw.com/index.html.Source Codehttps://github.com/appsnmobile-solutions/orchard_client_api_pythonChange Log0.1.0 (2022-02-26)First Release