Saravanan666/pypi-org huggingface dataset:
Package: achat
achatachat is an asynchronous wrapper over thechatsonic apifor the python programming language.Featuresasynchronous supportmessage history supportconvenient customization of all query parametersInstallationTo install the latest stable version, usepip install achatExamplesUsing chat instanceimportasynciofromachatimportSonicChatasyncdefmain():chat=SonicChat(token="your api token here")try:awaitchat.start()response=awaitchat.ask("Who is Elon Musk?")print(response.message)response=awaitchat.ask("When was he born?")print(response.message)finally:awaitchat.close()asyncio.run(main())Using async context managerimportasynciofromachatimportSonicChatasyncdefmain():asyncwithSonicChat(token="your api token here")aschat:response=awaitchat.ask("Who is Elon Musk?")print(response.message)response=awaitchat.ask("When was he born?")print(response.message)asyncio.run(main())DocumentationComing soonDependenciesaiohttp
Package: acheeve
acheeveThe repository coordinates multiple chembees.
A reminder: A chembee object manifests itself through
the below shown SOLID design pattern.Thus, theacheeveobject handles multiple chembees, for example to evaluate an algorithm:The above pattern, lets us implement a swarm of chembees for a wide array of endpoints fast. But we only need one API to stick them together.The concept is at the moment evaluated by comparing three different datasets (chembees). If the method proves valuable, the above pattern will be used for ourSaaSproducts.Data setsIt turns out data is easily abstracted. However, thechembee_datasetsmodule implements the classes in a way that violate the original software pattern.
Testing the new pattern it turns out, that the imagined SOLID pattern is more useful than the intuition and data operations should indeed not be part of any data class.This aligns with knowledge about data modelling.Commercial usageCurrently, we license the software under AGPL 3.0 or later. According to the software pattern, you have to open-source your data when using the package.You can easily do so usingveritas-archiveCiteWhen using the package for your scientific work please cite the repo and the connected papers and thesis workReferencesPublication quality RDKit by proteinsandwavefunctions
Package: acheron
AcheronAboutAcheron is a plotting application and data recorder for USB and TCP devices communicating via the Asphodel protocol.The Asphodel communication protocol was developed by Suprock Technologies (http://www.suprocktech.com)LicenseAcheron is licensed under the ISC license.The ISC license is a streamlined version of the BSD license, and permits usage in both open source and propretary projects.
Package: achn
achnDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nbnx--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallbnxnbdev# activate conda environment$condaactivatebnx# make sure the bnx package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the bnx package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/bnx.gitor fromconda$condainstall-cdsm-72bnxor frompypi$pipinstallbnxDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.
Package: aciclean
ACIClean is a tool that helps you clean up your Cisco ACI infrastructure
by detecting and reporting on unused objects. It uses theACI COBRA
Python SDKprovided by Cisco.ACIClean currently detects and reports on the following objects:VLAN PoolsAAEPsPhysical DomainsLeafswitch ProfilesAccessportsPCs and VPCsInstallationTo install ACIClean, follow these steps:Install the ACI COBRA Python SDK by following the instructionshere.If you dont’t have the SDK modules handy, run the following commands to install an ACI v5.2.7 SDK module:pip installhttps://github.com/cubinet-code/aci_cobra_sdk/raw/main/acicobra-5.2.7.0.7-py2.py3-none-any.whlpip installhttps://github.com/cubinet-code/aci_cobra_sdk/raw/main/acimodel-5.2.7.0.7-py2.py3-none-any.whlRunpip install acicleanto install this module and command line script.UsageTo use ACIClean, run the aciclean.py script. The script will detect and
report on any unused objects in your Cisco ACI infrastructure.The following APIC credentials will be read from the environment, if they exist:ACI_APIC_URLACI_APIC_USERACI_APIC_PASSWORDaciclean --help

Usage: aciclean.py [OPTIONS]

Options:
  --url TEXT       APIC URL including protocol.
  --user TEXT      APIC user.  [default: (admin)]
  --password TEXT  APIC password.
  -w, --write      Write report to aciclean_report.txt
  -r, --remove     WARNING: !!! This will remove all policies without
                   relationships from the APIC !!!
  --help           Show this message and exit.ContributingContributions to ACIClean are welcome! If you find a bug or have a
feature request, please open an issue on the GitHub repository. If you
would like to contribute code, please fork the repository and submit a
pull request.LicenseACIClean is licensed under the MIT License.
Package: aciClient
aciClientA python wrapper to the Cisco ACI REST-API.Python VersionWe support Python 3.6 and up. Python 2 is not supported and there is no plan to add support for it.Installationpip install aciClientInstallation for Developinggit clone https://github.com/netcloud/aciclient.git
pip install -r requirements.txt
python setup.py developUsageInitialisationUsername/passwordimportaciClientimportlogginglogging.basicConfig(level=logging.INFO)logger=logging.getLogger(__name__)aciclient=aciClient.ACI(apic_hostname,apic_username,apic_password,refresh=False)try:aciclient.login()aciclient.getJson(uri)aciclient.postJson(config)aciclient.deleteMo(dn)aciclient.logout()exceptExceptionase:logger.exception("Stack Trace")For automatic authentication token refresh you can set variablerefreshto Trueaciclient=aciClient.ACI(apic_hostname,apic_username,apic_password,refresh=True)Certificate/signatureimportaciClientimportlogginglogging.basicConfig(level=logging.INFO)logger=logging.getLogger(__name__)aciclient=aciClient.ACICert(apic_hostname,path_to_privatekey_file,certificate_dn)try:aciclient.getJson(uri)aciclient.postJson(config)aciclient.deleteMo(dn)exceptExceptionase:logger.exception("Stack Trace")Examplesget configtenants=aciclient.getJson('class/fvTenant.json?order-by=fvTenant.dn|asc')formointenants:print(f'tenant DN:{mo["fvTenant"]["attributes"]["dn"]}')post configconfig={"fvTenant":{"attributes":{"dn":"uni/tn-XYZ"}}}aciclient.postJson(config)delete MOsaciclient.deleteMo('uni/tn-XYZ')create snapshotYou can specify a tenant in variabletarget_dnor not provide any to do a fabric-wide snapshot.aci.snapshot(description='test',target_dn='/uni/tn-test')Testingpip install -r requirements.txt
python -m pytestContributingPlease readCONTRIBUTING.mdfor details on our code
of conduct, and the process for submitting pull requests to this project.AuthorsMarcel Zehnder-Initial workAndreas Graber-Migration to open sourceRichard Strnad-Paginagtion for large requests, various small stuffDario Kaelin-Added snapshot creationLicenseThis project is licensed under MIT - see theLICENSE.mdfile for details.
Package: acid-chess
The Chess Computer for nerds, by nerds.Picture by PictureACID Chess is a chess computer written in Python, which can be used with any? board. By filming the board, the
contour of the board is recognized, and the positions of the individual pieces can be determined. TwoNeural Networkswere trained for the board and squares recognition.FeaturesYou can play against an engine, Stockfish or Maia are available, or play a game against another human. In both variants,
a PGN is generated, which you can load later in the analysis board at Lichess, or so, for analysis.Engine play against Stockfish or MaiaUse polyglot opening booksPGN exportsPlanned FeaturesClockPlay on Lichess... seeissuesfor detailsTechnologyPython as a programming languageQt (PySide6) as toolkit for the GUI (with own extension for reactive bindings)PyTorch (Lightning ) for the development of AI modelsI want to play against ACID!We have tested ACID Chess with four different boards and were able to complete games without significant flaws. There
will be problems on unknown boards, but every tester makes ACID Chess better!Regardless of the chosen installation method: ACID Chess saves images of data that cannot be classified sufficiently.
Please provide us with this data. Create anissueand upload a ZIP
file as an attachment.<3There are two ways to install ACID Chess.as binary: for users who want to try ACID Chess and don't want to deal with installing Python etc.check out the project via git and install the dependencies manually for people who want to develop on ACID Chess themselves.Modern hardware, preferably NVIDIA GPU or Mac M[0-9]+is recommended!Known bugs and limitationsafter switching cameras you will see an "Image capture failed: timed out waiting for a preview frame" error in the logs. Workaroud: Select camara you want to use and restart the appResourcesDocumentationhttps://acid-chess.readthedocs.ioSourcecodehttps://github.com/ierror/acid-chessContributingContributions are always welcome. Please discuss major changes via issue first before submitting a pull request.Data AttributionGoogle Programmable Search EngineRest API was used to search for
Creative Commons licensed images of chess boards used for training the neural network models.Notebookfor collecting the dataCSVto document the AttributionContactMastodon@boerni@chaos.socialDiscord
Package: acidfile-optelgroup
acidfileThis project was forked fromhttps://github.com/nilp0inter/acidfileto fix issues with more recent versions of Python.acidfilemodule provides the ACIDFile object. This object can be used as a
regular file object but instead of write one copy of the data, it will write
several copies to disk in an ACID manner.This algorithm was explained byElvis Pfützenreuterin his blog postAchieving ACID transactions with common files.Latest stable version can be found onPyPI.acidfileis compatible with python 2.7, 3.6 and up, and pypyInstallationLatest version can be installed viapip$pipinstall--upgradeacidfile-optelgroupRunning the testsClone this repository and install the develop requirements.$gitclonehttps://gitlab.com/optelgroup-public/acidfile.git$cdacidfile$pipinstall-rrequirements/develop.txt$pythonsetup.pydevelop$toxUsage examplesBasic usage>>>fromacidfileimportACIDFile>>>myfile=ACIDFile('/tmp/myfile.txt','w')>>>myfile.write(b'Some important data.')>>>myfile.close()At the close invocation two copies will be written to disk:myfile.txt.0and
belowmyfile.txt.1. Each one will have an creation timestamp and a HMAC
signature.>>>myfile=ACIDFile('/tmp/myfile.txt','r')>>>printmyfile.read()'Some important data.'>>>myfile.close()If any of the files is damaged due to turning off without proper shutdown or
disk failure, manipulation, etc. It will be detected by the internal HMAC and
the other’s file data would be used instead.NoteIf you want to read anacidfile, never pass the full path of the
real file, instead use the file name that you use in the creation step.✗ ACIDFile(‘/tmp/myfile.txt.0’, ‘r’)✗ ACIDFile(‘/tmp/myfile.txt.1’, ‘r’)✓ ACIDFile(‘/tmp/myfile.txt’, ‘r’)Context managerACIDFile can (and should) be used as a regular context manager:>>>withACIDFile('/tmp/myfile.txt','w')asmyfile:...myfile.write(b'Some important data.')Number of copiesThe number of inner copies of the data can be configured through thecopiesparameter.Checksum KeyThe key used for compute and check the internal HMAC signature can be setted
by thekeyparameter.It’s recommended to change that key in order to protect against fraud, making
more difficult for a tamperer to put a fake file in place of the legitimate
one.News1.2.2 (fork)Fix missing parameter in hmacAdd support to Python 3.6 - 3.10Rewrote the ci/cd pipeline using gitlab-ci1.2.1Using io.open in setup.py to read README and NEWS. This fix some
problems installing the package.Python 3.4 support.1.2.0Python 2.6 support.Added Python 3.2 and pypy to tox tests.Added flattr button :DFixed flake8 and pylint warnings.1.1.0Python 3 support.Changed testing framework tobehavebecause python 3 support.Usingtoxfor multiple python version testing.1.0.0First stable release.Documentation.0.0.1Initial development.
Package: acid.senza.templates
Senza template for automatic deployment of PostgreSQL instancesThis package provides an external template for the stups-senza tool (https://github.com/zalando-stups/senza), allowing rapid deployment of PostgreSQL nodes on AWS. It’s designed to work together with an external tool that runs
senza with all necessary parameters and deploy DB instances automatically, therefore, the template is a non-interactive one. Compared to the PostgresApp template (included with senza) it adds the following actions:NAT gateways are detected based on the customer DNS zone.Correct Etcd endpoints in the current account are detected for a specific region.Non-interactive mode is the default one, all parameters can be supplied with environment variables (-voption during senza init).pg_hba.conf is configured by default to reject non-SSL connections.Standby and superuser passwords are automatically generated.All passwords and scalyr keys are encrypted.zmon2 group is automatically picked from the current account.EBS is always used.Installation$sudopip3install--upgradesenza.templates.acidUsage$senzainit-tbase[-vparam=name]deployment.yamlBelow is the list of parameters supported by the template:team_name: the name of the team to deploy the template (used as part of the DNS name for the resulting instance).team_region: AWS region of the team to deploy the template (by default, eu-west-1 and eu-central-1 are supported).team_gateway_zone: the DNS zone the application runs at, to look for the NAT gateways.add_replica_loadbalancer: whether to add a separate load-balancer to serve requests for the replica (default: false).instance_type: AWS EC2 instance type to deploy the DB on (default: t2.medium).volume_size: initial size of the DB EBS volume in GBs (default: 10).volume_type: AWS type of the EBS volume (default: gp2).volume_iops: number of the IO operations per second for the provision IO EBS volumes.snapshot_id: ID of the existing EBS snapshot to initialize the new database from.scalyr_account_key: Key to the scalyr account to log the database activity.pgpassword_admin: password to the admin account.postgresql_conf: a JSON dictionary of the key-value parameters for the PostgreSQL.ExamplesInitialization:$senzainit-tbase-vteam_name=foo-v'team_region=eu-west-1'-v'team_gateway_zone=foo.example.com'-v'hosted_zone=db.example.com'-vinstance_type=m3.medium' -v 'postgresql_conf='{shared_buffers: 1GB}'deploy.yamlDeployment:$senzacreatedeploy.yamlbarThe steps above result in the deployment of the new PostgreSQL cluster consisting of 3 t2.medium instances, available under
the name ofbar.db.example.comand accessible to the application running in the account associated with the DNS zonefoo.example.com. They only work in the AWS environment configured for STUPS and senza.Senza it a powerful tool developed by Zalando to deploy applications on AWS. If you are not familiar with senza-based
deployments, please, refer to the STUPS documentation:http://stups.readthedocs.io/en/latest/.LicenseApache 2.0Releasing$./release.sh<NEW_VERSION>
Package: acids-msprior
MSPriorA multi(scale/stream) prior model for realtime temporal learningDisclaimerThis is an experimental project thatwillbe subject to lots of changes.Installationpipinstallacids-mspriorUsageMSPrior assumes you haveA pretrained RAVE model exportedwithout streamingas a torchscript.tsfileThe dataset on which RAVE has been trained (a folder of audio files).1. PreprocessingMSPrior operates on the latent representation yielded by RAVE. Therefore, we start by encoding the entirety of the audio dataset into a latent dataset.mspriorpreprocess--audio/path/to/audio/folder--out_path/path/to/output/folder--rave/path/to/pretrained/rave.ts2. TrainingMSPrior has several possible configurations. The default is a ALiBi-Transformer with a skip prediction backend, which can run in realtime on powerful computers (e.g. Apple M1-2 chips, GPU enabled Linux stations). A less demanding configuration is a large GRU. Both configurations can launched be usingmspriortrain--configconfiguration--db_path/path/to/preprocessed/dataset--nametraining_name--pretrained_embedding/path/to/pretrained/rave.tsHere are the different configurations availableNameDescriptiondecoder_onlyUnconditional autoregressive models, relying solely on previous samples to produce a prediction. The recurrent mode uses a Gated Recurrent Unit instead of a Transformer, suitable for small datasets and lower computational requirements.recurrentencoder_decoderEncoder / decoder autoregressive mode, where the generation process is conditioned by an external input (aka seq2seq). The continuous version is based oncontinuous featuresinstead of a discrete token sequence.encoder_decoder_continuousThe configurationsdecoder_onlyandrecurrentare readily usable, the seq2seq variants depends on another project calledrave2vecthat will be open sourced in the near future.3. ExportExport your model to a.tsfile that you can load inside thenn~ external for Max/MSP and PureData.mspriorexport--run/path/to/your/runWARNINGIf you are training on top of acontinuousrave (i.e. anything but thediscreteconfiguration), you shoud pass the--continuousflag during exportmspriorexport--run/path/to/your/run--continuous4. Realtime usageOnce exported, you can load the model inside MaxMSP following the image below.Note that additional inputs (e.g. semantic) are only available when using seq2seq models. The last output yields the perplexity of the model.FundingThis work is funded by the DAFNE+ N° 101061548 project, and is led at IRCAM in the STMS lab.
Package: acid-vault
Python password vault.Python password vault to keep track of password either locally or centralized in your own cloud.
As this is a hobby project I cannot guarantee any functionality or that no data loss will occur, but as I use it personally I will do my best to avoid it.
Currently development is done on Python 3.9 and the client runs on Win10 while the cloud is run on Raspbian on a Raspberry Pi 2.PrerequisitescryptographyparamikopillowSetupInstall prerequisitesClone repo or pip install acid_vaultRun VaultGui.pyw (For GUI)Setup your vault in file menu (Only necessary for Cloud and/or Steganography)Setup SSH for cloud (For remote storage of vault)Host - URL to hostPort - Port to use on hostUsername - Username to login with at hostPassword - Password to login with at host, will not be saved and has to be entered each time program is started. Recomended usage is through key exchange, see belowSetup Steganography (For hiding the vault in an image)File location - path to vault storage E.g. images/picture.pngOriginal file - path to local file with the original png picture to compare against (Important that its a png and not jpeg as jpeg compression is not stable)CheckSteganography(If Steganography is to be used)Chose Local/Remote (Where to store vault)Basic usageAdd passwords by pressing "Add Password" button.Chose a password in password box.Press Save passwords to save passwords in vault.Press Load passwords to load passwords into vault (Will clear any unsaved data).Lock/Unlock - Will lock/unlock the data kept by program while its running to avoid overhead of getting data from the cloud.If vault detect that the user has not used the UI for 5 minutes it will lock itself.The file menu has options to save/load backups both as encrypted and unencrypted locally where the user chose.
Package: acie
Aadhaar Card Information ExtractorThis project is an implementation of OCR (Optical Character Recognition) to extract relevant information from an Aadhaar card.Deployed App availablehereIMPORTANTPlease ensure you have pytesseract installed in the following path:C:\Program Files\Tesseract-OCR\tesseract.exeUSAGEJust typeaciefollowed by the filename argument, for example:acie filename.jpg
Package: acinf
AboutThis software provides basic CLI and API functionality for controlling a single
fan and reading sensor values from the AC Infinity Controller 69 Pro.Installpip install acinfThis script was authored for python 3.10 and Linux, but other python versions and operating
systems may work.UsageIf everything is installed correctly, commandacinfshould have the following output:$ acinf
usage: acinf [-h] [--log-level {debug,info,warning,error}] mac_address {get,set} [value]
acinf: error: the following arguments are required: mac_address, actionTo set fan level to 5 use command for exampleMAC addressDE:AD:BE:EF:CA:FE:acinf DE:AD:BE:EF:CA:FE set 5To show all values to stdout in json format:$ acinf DE:AD:BE:EF:CA:FE get
{
    "temperature_c": 18.56,
    "temperature_f": 65.408,
    "humidity": 70.03,
    "vpd_kpa": 0.61
}To retrieve just a single value as ASCII float to stdout, combinegetwith any
of the following valuestemperature_c,temperature_f,humidity,vpd_kpa:$ acinf DE:AD:BE:EF:CA:FE get temperature_f
65.408MAC addressTo determine the MAC address of your AC Infinity controller on Linux, ensure the
bluetooth package is installed and executebluetoothctl, then, commandscan on,
examine devices marked ‘NEW’, or, enter commanddevices.AC infinity controllers are marked ‘ACI-E’, as in the example below:linux# bluetoothctl
Agent registered
[CHG] Controller 00:11:22:33:44:66 Pairable: yes
[bluetooth]# scan on
Discovery started
[CHG] Controller 00:11:22:33:44:66 Discovering: yes
[NEW] Device AB:CD:EF:AB:CD:EF AB-CD-EF-AB-CD-EF
[NEW] Device DE:AD:BE:EF:CA:FE ACI-E
[NEW] Device AB:CD:EF:AB:CD:FF AB-CD-EF-AB-CD-FFPairingIf you haven’t previously paired with your device, do that now. Press and Hold
the bluetooth button on the controller until it begins flashing, then,
enter bluetoothctl command,pair DE:AD:BE:EF:CA:FE.AboutThis software is not affiliated with AC Infinity. AC Infinity is a trademark of
AC Infinity Inc. This software is not guaranteed to work with any particular
AC Infinity product. It is not guaranteed to work at all. Use at your own risk.CaveatsAlthough 4 fans may be connected to the controller, this software only supports
controlling a single fan.Do not execute more than 1 of these processes at a time. All ‘get’ and ‘set’ commands
are failed if they exceed 1 minute, and so any scheduled execution of this command
should be limited to approximately once per 2 minutes.This API and software has a very slow startup time, because it reconnects
to the bluetooth device each time. Although it is possible to re-use a connection,
I found persistent connections to be unreliable over long durations.Errors may often report to stderr, about timeout or failure to discover or connect,
especially for long receiver distances, but all get or set operations are
automatically retried for a full minute before failure.ContributingThis project is not very serious, if you wish to expand it for more features and devices,
consider making a pull requests and becoming a co-maintainer and feel free to fork.
Thanks, enjoy, and best wishes!
Package: ac-infinity-ble
AC Infinity BLEAC Infinity BLE ControllerInstallationInstall this via pip (or your favourite package manager):pip install ac-infinity-bleContributors ✨Thanks goes to these wonderful people (emoji key):This project follows theall-contributorsspecification. Contributions of any kind welcome!CreditsThis package was created withCopierand thebrowniebroke/pypackage-templateproject template.