Saravanan666/pypi-org huggingface dataset:
Package: raven-aiohttp-redux
A transport for theSentry Python SDKwhich supports Python 3’s asyncio interface.
For more information about Sentry and the python SDK, see ourPython Documentationfor framework integrations
and other goodies.Requirementsraven-python>=5.4.0python>=3.4.2aiohttp>=2.0Usageraven-aiohttpships two asyncio based transports forraven.Client:AioHttpTransportandQueuedAioHttpTransport.AioHttpTransportAll messages to the sentry server will be produced by “Fire And Forget”Each new message spawns it ownsasyncio.Task, amount of them is not limitedimportasynciofromravenimportClientfromraven_aiohttpimportAioHttpTransportclient=Client(transport=AioHttpTransport)try:1/0exceptZeroDivisionError:client.captureException()# graceful shutdown waits until all pending messages are sendloop=asyncio.get_event_loop()loop.run_until_complete(client.remote.get_transport().close())QueuedAioHttpTransportAll messages to the sentry server will be produced by queue systemWhen transport is created it spawns limited amount ofasyncio.Taskwhich sends messages one by one from internalasyncio.QueueimportasynciofromfunctoolsimportpartialfromravenimportClientfromraven_aiohttpimportQueuedAioHttpTransportclient=Client(transport=partial(QueuedAioHttpTransport,workers=5,qsize=1000))try:1/0exceptZeroDivisionError:client.captureException()# graceful shutdown waits until internal queue is emptyloop=asyncio.get_event_loop()loop.run_until_complete(client.remote.get_transport().close())ResourcesSentryPython DocumentationIssue TrackerIRC Channel(irc.freenode.net, #sentry)
Package: ravenc
Ravenc is a tool for copying a single video title from a DVD whilst
transcoding it. The purpose of this is to make the video available
to devices you may have which do not have a DVD player.Altenatively you can transcode multimedia files on your filesystem.Ravenc is designed to work with a clean DVD source like the ones you get
on bought DVD disks.
Ravenc was created because Acidrip is no longer being maintained
and I found myself needing a DVD ripper.The design of Ravenc was heavily influenced by Acidrip but has now
significantly changed.
Ravenc is, in fact, a simple front-end to avlib/avconv which is
itself a front-end to some video encoding and decoding libraries. Everything
that Ravenc does can be achieved from the command line, Ravenc just
simplifies the process.
Package: ravendb
Official Python client for RavenDB NoSQL DatabaseInstallationInstall fromPyPi, asravendb.pipinstallravendbIntroductionPython client API (v5.2) forRavenDB, a NoSQL document database.Although new API isn't compatible with the previous one, it comes withmany improvements and new features.Package has been reworked to match Java and other RavenDB clientsType-hinted entire project and API results- using the API is now much more comfortable with IntelliSenseReleasesAll client versions 5.2.x are fully compatible with and support RavenDB server releases 5.4 and 6.0.Click hereto view all Releases and Changelog.What's new?5.2.5+Changes available in thereleasessection.5.2.4Bulk insert dependenciesbugfix5.2.3CountersCounters indexes5.2.2New feature -Bulk insertBugfixes - Cluster-wide operations (here)5.2.1Bugfixes - Serialization while loading/querying (here)5.2.0SubscriptionsDocument streamsSecured subscriptionsQueryingMajor bugfixesSpatial querying and indexingHighlighting fixesCustom document parsers & loaders5.2.0b3New featuresConditional LoadSelectFields & FacetsProjectionsMoreLikeThisSuggestionsImprovementsCompare exchangeQueryingDocumentConventionsPatchingSpatial queriesAggregations5.2.0b2Lazy OperationsLazy loadingLazy queryingLazy compare exchange operationsStructureImportant classes are now available to import from the top levelravendbmodule...and many bugfixes5.2.0b1QueryingSimpler, well type hinted queryingGroup by, aggregationsSpatial queryingBoost, fuzzy, proximitySubclauses supportStatic IndexesStore fields, index fields, pick analyzers & more usingAbstractIndexCreationTaskFull indexes CRUDIndex related commands (priority, erros, start/stop, pause, lock)Additional assemblies, map-reduce, index query with results "of_type"CRUDType hints for results and includesSupport for dataclassesAttachmentsNew attachments APIBetter type hintsHTTPSSupport for https connectionCertificates CRUD operationsLazy loadNew featureCluster Transactions, Compare ExchangeNew featureComing soon, work in progressCounters & Time SeriesReplication & ETL CommandsStreaming (ready, will be merged on v5.4 -https://github.com/ravendb/ravendb-python-client/pull/168)DemoWorking with secured serverfromravendbimportDocumentStoreURLS=["https://raven.server.url"]DB_NAME="SecuredDemo"CERT_PATH="path\\to\\cert.pem"classUser:def__init__(self,name:str,tag:str):self.name=nameself.tag=tagstore=DocumentStore(URLS,DB_NAME)store.certificate_pem_path=CERT_PATHstore.initialize()user=User("Gracjan","Admin")withstore.open_session()assession:session.store(user,"users/1")session.save_changes()withstore.open_session()assession:user=session.load("users/1",User)assertuser.name=="Gracjan"assertuser.tag=="Admin"RavenDB Documentationhttps://ravendb.net/docs/article-page/5.3/pythonGitHubhttps://github.com/ravendb/ravendb-python-clientBug Trackerhttp://issues.hibernatingrhinos.com/issues/RDBC
Package: ravendb-embedded
Overviewravendb-embeddedis a RavenDB package for running RavenDB in embedded mode.EmbeddedServer().start_server()withEmbeddedServer().get_document_store("Embedded")asstore:withstore.open_session()assession:session.store(User(name="Ilay",age=4))session.save_changes()InstallationInstallravendb-embeddedfromPyPiusing:pipinstallravendb-embeddedInstallingravendb-embeddedfrom pip will also provide you with a copy of the RavenDB server binary files.UsageStart a serverTo start the RavenDB server, call thestart_server()method from anEmbeddedServerinstance.EmbeddedServer.start_server()For more control over your server, you can passserver_optionsto thestart_server()method.ServerOptionsframework_version: The framework version to run the server with.data_directory: Where to save the database data (if None, the files will be saved in the RavenDB folder in the base folder).server_url: The URL the server will be opened on (if None, the server will open on localhost).dotnet_path: The location ofdotnet.exe(if .NET Core is not installed on your machine, you can downloaddotnet binariesand provide the path).command_line_args: A list of allserver command arguments.server_options=ServerOptions(data_directory="MYPATH/RavenDBDataDir")EmbeddedServer().start_server(server_options)SecurityYou can secureravendb-embeddedusing thesecured()method:secured(server_pfx_certificate_path,client_pem_certificate_path,server_pfx_certificate_password=None,ca_certificate_path=None)Provide the path to.pfxand.pemfiles, and optionally a password and CA certificate file.Minimal setup requires both a.pfxserver and a.pemclient certificate.Get Document StoreAfter initializing and starting the server, you can use theget_document_storemethod to obtain aDocumentStoreand start working with RavenDB as usual.get_document_storemethod can take either just thedatabase_nameorDatabaseOptions.DatabaseOptionsdatabase_name: The name of the database.skip_creating_database:get_document_storewill create a new database if it does not exist. If this option is set to True, the database won’t be created (Default False).Open RavenDB Studio in the BrowserTo open RavenDB Studio fromravendb-embedded, use theopen_studio_in_browsermethod, and the studio will open automatically in your default browser.
Package: ravendb-test-driver
RavenDB Test Driverravendb-test-driveris a package for writing integration tests against RavenDB server.SetupInstall from PyPi:pip install ravendb-test-driverUsageInheritRavenTestDriverto your test class or create an instance within your class.Unittest example:fromravendb_test_driverimportRavenTestDriverfromunittestimportTestCaseclassTestBasic(TestCase):defsetUp(self):super().setUp()self.test_driver=RavenTestDriver()deftest_1(self):withself.test_driver.get_document_store()asstore:withstore.open_session()assession:person={"Name":"John"}session.store(person,"people1")session.save_changes()PyPihttps://pypi.org/project/ravendb-test-driver/Githubhttps://github.com/ravendb/ravendb-python-testdriver
Package: raven-formats
Raven FormatsTools to work with formats used byRaven SoftwareinMUA/XML2games.UsageXMLB Compile/Decompileusage: xmlb.py [-h] [-d] [--no_indent] input output

positional arguments:
  input            input file (supports glob)
  output           output file (wildcards will be replaced by input file name)

optional arguments:
  -h, --help       show this help message and exit
  -d, --decompile  decompile input XMLB file to XML/JSON file
  --no_indent      disable indent in decompiled XML/JSON fileZSND Compile/Decompileusage: zsnd.py [-h] [-d] input output

positional arguments:
  input            input file (supports glob)
  output           output file (wildcards will be replaced by input file name)

optional arguments:
  -h, --help       show this help message and exit
  -d, --decompile  decompile input ZSND file to JSON file
Package: raven-harakiri
UWSGI allows loggingpython tracebackswhen worker commits harakiri.
This packages sends them to yoursentryinstance.Installationpip install raven-harakiriUsageraven-harakiri <uwsgi_log_string_with_harakiri> –dsn=<your_sentry_dsn>First argument (or stdin from other process, if piped) should be text with python tracebacker log.
Package: raven-hydro
Raven-hydro is a Python-based installer for the hydrologic modelling frameworkRaven.Free software: Apache Software License v2.0AboutRavenis a hydrological modeling framework that lets hydrologists build hydrological models by combining different hydrological processes together. It can also be used to emulate a variety of existing lumped and distributed models. Model structure, parameters, initial conditions and forcing files are configured in text files, which Raven parses to build and run hydrological simulations. A detailed description about modeling capability of Raven can be found in thedocs.PurposeThe goal of this project is to provide a multiplatform installer for the Raven hydrological model usingscikit-build-coreandpybind11. The compiled binary is built with support forNetCDF4, and uses a customCMakeLists.txt, rather than the one provided with theRaven source code, in order to leveragepipfor handling the installation of binaries and libraries.This project differs fromRavenPyby solely providing a means for downloading, compiling, and installing the Raven binary (with NetCDF4 support) on multiple platforms and as well as providing version control and updating viapip/PyPI.FeaturesDownload and/or compile the Raven hydrological model withpipMultiplatform support (macOS, Linux, Windows, BSD, etc.)InstallationWarningBy default, this build of Raven requires that NetCDF4 libraries are installed on the system, exposed on the$PATH, and discoverable using theFindNetCDF.cmakehelper script.On Linux, this can be provided by thelibnetcdf-devsystem library; On macOS by thenetcdfhomebrew package; And on Windows by using UNIDATA'spre-built binaries.This can also be provided by thelibnetcdfpackage available atconda-forge, though this is not guaranteed to work on macOS at time of writing (further research required).raven-hydrocan be installed from PyPI using the following command:$pipinstallraven-hydroTo installraven-hydrofrom PyPIwithout NetCDF support:$pipinstallraven-hydro-Ccmake.define.USE_NETCDF=falseFor development purposes, we recommend cloning the repository and performing an--editableinstallation:$gitclonegit@github.com:Ouranosinc/raven-hydro
$cdraven-hydro
$pipinstall--editable.
Package: ravenml
ravenMLCLI tool for machine learning model training.InstallationPyPIInstall from pip via:pipinstallravenmlConda Installation (development)Create the conda environment from theenvironment.ymlfile using:condaenvcreate-fenvironment.ymlActivate the conda environment with:condaactivateravenml# may require source activate ravenml depending on system setupInstall ravenML from the root of this repository using:pipinstall--editable.ConfigurationravenML must be configured with the name of the S3 buckets you wish to pullJigsaw-created
datasets from and upload trained models to.After installation, set this configuration by running:ravenmlconfigupdateYou can check your configuration anytime by runningravenml config show, and update it anytime withravenml config update.Training PluginsravenML provides core functionality while unique model training pipelines are implemented
via plugins dynamically loaded at runtime. A default set of plugins is located atravenML-plugins. See the README there
for more information about how plugins work and how to make your own.To install all default plugins for use with ravenML you just need to clone the repository
and run a script.Clone the respository with:gitclonehttps://github.com/autognc/ravenML-pluginsInstall default plugins by navigating to the downloadedravenMLdirectory and using:./install_all.shTo test your installation runravenml train listand verify that the training plugin names appear on your console.ContributingCommitizenWe will use commitizen for all commit messages. The repository is set up to use
commitizen via the.czrcfile. If you have commitizen already installed globally,
you can use it to commit for this repository.If you do not have commitizen installed, follow the instructions on theirGitHub.If you do not have npm installed, you will need to do that before installing commitizen.
npm is distributed with Node.js. Install Node.jshere.
Package: ravenpy
A Python wrapper to setup and run the hydrologic modelling frameworkRaven.Free software: MIT licenseDocumentation:https://ravenpy.readthedocs.ioRavenPyis a Python wrapper forRaven, accompanied by utility functions that facilitate model configuration, calibration, and evaluation.Ravenis an hydrological modeling framework that lets hydrologists build hydrological models by combining different hydrological processes together. It can also be used to emulate a variety of existing lumped and distributed models. Model structure, parameters, initial conditions and forcing files are configured in text files, which Raven parses to build and run hydrological simulations. A detailed description about modeling capability ofRavencan be found in thedocs.RavenPyprovides a Python interface toRaven, automating the creation of configuration files and allowing the model to be launched from Python. Results, or errors, are automatically parsed and exposed within the programming environment. This facilitates the launch of parallel simulations, multi-model prediction ensembles, sensitivity analyses and other experiments involving a large number of model runs.Note that version 0.12 includes major changes compared to the previous 0.11 release, and breaks backward compatibility. The benefits of these changes are a much more intuitive interface for configuring and running the model.FeaturesConfigure, run and parse Raven outputs from PythonUtility command to create grid weight filesExtract physiographic information about watershedsAlgorithms to estimate model parameters from ungauged watershedsExposes outputs (flow, storage) asxarray.DataArrayobjectsInstallPlease see the detailedinstallation docs.AcknowledgementsRavenPy’s development has been funded byCANARIEandOuranosand would be not be possible without the help of Juliane Mai and James Craig.This package was created withCookiecutterand theOuranosinc/cookiecutter-pypackageproject template.
Package: raven-pydf
This is the official implementation of the DataFrame specification provided by Raven Computing.Getting StartedInstall via:pip install raven-pydfAfter installation you can use the entire DataFrame API by importing one class:fromraven.struct.dataframeimportDataFrame# read a DataFrame file into memorydf=DataFrame.read("/path/to/myFile.df")# show the first 10 rows on stdoutprint(df.head(10))Alternatively, you can import all concrete Column types directly, for example:fromraven.struct.dataframeimport(DefaultDataFrame,IntColumn,DoubleColumn,StringColumn)# create a DataFrame with 3 columns and 3 rowsdf=DefaultDataFrame(IntColumn("A",[1,2,3]),DoubleColumn("B",[4.4,5.5,6.6]),StringColumn("C",["cat","dog","horse"]))print(df)# _| A B   C# 0| 1 4.4 cat# 1| 2 5.5 dog# 2| 3 6.6 horseCompatibilityThis library requiresPython3.7or higher.Internally, this library usesNumpyfor array operations. The minimum required version is v1.19.0DocumentationThe unified documentation is availablehere.Additional features implemented in Python are documented in theWiki.LicenseThis library is licensed under the Apache License Version 2 - see theLICENSEfor details.
Package: raven-sh
This is a wrapper executing a command and sending its stdout/stderr to the
Sentry server.Useful to work with cron jobs. Unless misconfigured, the wrapper itself is
quiet. It launches the program, captures its output, and if the program has
been ended with non-zero exit code, builds a message and puts it to the remote
server.WarningDon’t try to launch scripts producing a lot of data to
stdout / stderr with this wrapper, as it stores everything in
memory and thus can easily make your system swap.Example of cron task:SENTRY_DSN='http://...../'
*/30 * * * *  raven-sh -- bash -c 'echo hello world; exit 1'
Package: raver
Ratio versioning checker.QuickstartInstallationRaver can be installed usingpip install raverfor any Python version >=3.9. Or, for
Poetry managed projects, usepoetry add-Draverto add it as a development
dependency.Using raverTo just check if Raver can fetch and check your current version, useraverin your
favorite terminal while in your project directory (wherepyproject.tomlis).Raver detects any Python module that is set up using Poetry by default. It will then
check if the version in the module/package’s main file contains a__version__ ="..."line that matches the version in yourpyproject.tomlfile.Check w.r.t. git referenceTo check the current version versus a git branch as reference (e.g.origin/master),
use:raver--reforigin/masterwhereorigin/masteris the reference branch. It then performs a comparison usinggit diffto check for committed changes.-rworks as well as a shorthand.Check changelogTo check whether a changelog entry exists for the current version. There are two
supported changelog methods, file and directory.raver--changelog./path/to/changelog.rstwhere the changelog file can be of any extension. Raver checks whether the earlier
detected Python file version (only{major}.{minor}.{patch}, no metadata) is included
in the document.raver--changelog./path/to/changelog/is also allowed, where the changelog directory has to contain an entry in the format ofv{major}.{minor}.{patch}*of the current version (the star is a glob wildcard).TOML configurationRaver supports TOML configuration! Ittakes precedence overany command-line
parameters. The following would be a sensible default for raver:[tool.raver]module="raver"# Change to your package name or remove to use Poetry's entry.reference="origin/main"changelog="./doc/source/changelog.rst"debug=falseDeveloper guidePython packaging informationThis project is packaged usingpoetry. Packaging
information as well as dependencies are stored inpyproject.toml.Installing the project and its development dependencies can be done usingpoetry install.VersioningThis project usessemantic versioning. Version increments are
checked usingRaver.ChangelogChangelog format as described byhttps://keepachangelog.com/has been adopted.SeeChangelog.
Package: ravf
Raw Astro Video Format (RAVF) PackageIntroductionPython implementation of a reader/writer for the Raw Astro Video Format.Specification:RawAstroVideoFormat.mdRAVF is loosely based on theAstro Digital Videospecification with an emphasis on writing in a variety of raw formats natively on CPU-bound hardware, where the goal is to get the data written quickly at higher frame rates sometimes needed for occultations.Install:pip install ravfUpgrade:pip install --upgrade ravf
Package: ravin
Ravin - Power optimizer for IoT DeploymentsWith Ravin, you can get the best configuration for all the IoT devices in a IoT deployment for minimim power consumption with just one line of code. Ravin is super easy to use and handles everything for you. Just specify the data of various configuration of the devices in your deployment and voila! you get the best configuration for each device.OverviewRavin was written with fast use in mind. It provides the following key featuresSingle Objective OptimizationUsageIn the following paragraphs, I am going to describe how you can get and use Scrapeasy for your own projects.Getting itTo download ravin, either fork this github repo or simply use Pypi via pip.$pipinstallravinUsing itravin was programmed with ease-of-use in mind. First, import iot_dp from Scrapeasyfromravinimportiot_dpLicenseMIT LicenseCopyright (c) 2020 Vineet DhaimodkerPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: ravi-utils
ravi_utilsSimple utilities for quick scriptsInstallpip install ravi_utilsSample usage# -*- coding: utf-8 -*-

import logging
import sys

from ravi_utils.common import CommonOps
from ravi_utils.common_logging import LogOps
from ravi_utils.common_conf import ConfOps

def main():
    """Main function"""

    LogOps.setup_logging("./log", "main", logging.DEBUG)
    CommonOps.param_check(1)
    conf = ConfOps.init_conf(sys.argv[1], [
        "some_option"
    ])

    logging.info("start one")
    logging.debug("conf value: " + conf["some_option"])

if __name__ == '__main__':
    main()
Package: raViz
RaRa helps easily generate geographic visualization using the folium package.Github LinkInstallationpipinstallraVizUsageimportraimportpandasaspddf=pd.read_csv('mycsv.csv')ra=ra.Map(df)ra.createMap()ra.saveMap('MyMap.html')ContributingAnyone is welcome to contribute, if you'd like send a pull request for major changes with the changes you'd like to make.LicenseMIT