Saravanan666/pypi-org huggingface dataset:
Package: rawsocket
Only in linux, sorry.RationaleIf you need raw sockets in your python script, you have to run it as root
or make it SUID root. Alternatively, you can use file capabilities to grant
extended priviliges to a binary. Unfortunately, this only works on binaries
and not on scripts, which means you have to set the capability on the
python binary.How does it work?As mentioned above, you can only set capabilties on a binary. Therefore,
this module has a helper binary, which will get the RAW_NET capability.
This binary is spawned by the rawsocket python module every time you create
a new raw socket. The helper uses its capability to create the socket and
then passes the file descriptor back to the calling program.In linux you can pass file descriptors through unix domain sockets by using
thesendmsg()system call andSCM_RIGHTS. See alsofd passing.InstallationUse PIP:pip install rawsocketOr use the standard method:python setup.py installThis will install the extension module and the helper binary. But it willnotset the file capability on the helper binary. You have two choices:Grant every user access to the helper (simple,not recommended):setcap cap_net_raw+ep rawsocket-helperGrant access only to users which are member of a specific group
(recommended):chmod 750 rawsocket-helper
chown root:yourgroup rawsocket-helper
setcap cap_net_raw+ep rawsocket-helperExampleimportsocket,rawsocketfd=rawsocket.rawsocket_fd()s=socket.fromfd(fd,socket.AF_PACKET,socket.SOCK_RAW)You see easy as pie ;)LicenseRedistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:1. Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS”
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
Package: raw-sql-migrate
StatusLatest stableDevelopGoalRaw-sql-migrate is tool for managing your raw SQL migrations.DocsSee <http://rsm.readthedocs.org/en/latest/> page for full docs.Short guideCreate rsm.yaml or rsm.py in your project dir with next structure:database:engine:engine backend modulehost:database hostport:database portname:database nameuser:user namepassword:user passwordhistory_table_name:migration history table nameRSM_CONFIG={'database':{'engine':enginebackendmodule,'host':databasehost,'port':databaseport,'name':databasename,'user':username,'password':userpassword,},'history_table_name':migrationhistorytablename,'packages':['package_a','package_b','package_c.package_d',],}Create first migrationrsmcreatepackage_a.package_binitialEdit migration file found package_a/package_b/migrations/0001_initial.py. Example:defforward(database_api):database_api.execute('''
        CREATE TABLE test (
           id INT PRIMARY KEY NOT NULL,
           test_value BIGINT NOT NULL,
        );
        CREATE INDEX test_value_index ON test(test_value);
        ''')defbackward(database_api):database_api.execute('DROP TABLE test;')Run migrations:rsmmigratepackage_a.package_bMigrating backwards:rsmmigratepackage_a.package_b0
Package: raw-tcp-connection
Your short description here.romnnn.github.io/raw_tcp_connection$pipinstallraw_tcp_connectionSee theofficial documentationfor more information.importraw_tcp_connectionDevelopmentFor detailed instructions seeCONTRIBUTING.TestsYou can run tests with$invoketest$invoketest--min-coverage=90# Fail when code coverage is below 90%$invoketype-check# Run mypy type checksLinting and formattingLint and format the code with$invokeformat$invokelintAll of this happens when you runinvokepre-commit.NoteThis project is still in the alpha stage and should not be considered production ready.
Package: rawtools
Utility library for consuming and manipulating x-ray volume data in.rawformat.FeaturesConvert float32, uint16, and uint8 versions of.rawXRT volume dataExtract image slices from.rawfilesRead, parse, and generate metadata files (.dat) for XRT dataConvert proprietary.nsihdrfiles into.rawformat (Windows only). Batch conversion supported as CLI and minimal GUI.Generate previews of XRT volumes for quality control (e.g., maximum density projections, midslice extraction, etc.)Planned FeaturesGenerate dummy.rawfiles for testing downstream pipelinesBatch conversion of.nsihdron Linux systemsDensity adjustment (similar to automated adjustment MATLAB scripts forrootseg)Related Projectsxrcap: root crown image analysis pipelineDynamicRoots: tool for reconstructing and analyzing the growth of a plant root systemTopoRoot: high-throughput computational method that computes fine-grained architectural traits from 3D CT images of excavated maize root crownsrootseg: ML-based segmentation for soil rich samplesxrt-dmt: data management tool for tracking and archiving XRT (meta)dataCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.3.0 (2021-07-18)AddedLicenseBasic tests on core functionality for scaling dataChangedUpdated URL for any references to GitHub repoUpdated docstring header forraw2imgmoduleUpdated README to be more descriptive0.2.0 (2021-01-04)Added efX-SDK libraryAdded module to export/convertnsihdrfiles to uintrawfiles using the efX-SDK (Windows only)Tentatively implemented a GUI for the nsihdr2raw export toolTweaked logging path for log files generated relative to input data fornsihdr2raw0.1.4 (2020-10-27)Code clean up, remove unnecessary transposing of data, improved debug statements and logging0.1.3 (2020-10-26)Fixed font file missing when installing via pip0.1.2 (2020-09-25)Fix typo in log messages when checking expected file size to actual file size (.raw)0.1.0 (2020-06-19)Initial version
Package: rawtypes
rawtypesA code generator using libclang for a python extension or zig.parsestruct / union / anonymousfunction pointer typedeffunctionmanglingpointer handlingconst referencedefault argumentpython generatorctypes structadd method to ctypes structpyi type annotationzig generatormethod callvariadic argumentclang.cindexThis library is use 'clang.cindex` fromhttps://raw.githubusercontent.com/llvm/llvm-project/llvmorg-13.0.0/clang/bindings/python/clang/.
And placement in rawtypes.clang.fromrawtypes.clangimportcindexgeneratedhttps://github.com/ousttrue/pydearis generated extension. Includedimguiandpicovg
Package: rax
🦖Rax: Learning-to-Rank using JAXRaxis a Learning-to-Rank library written in JAX. Rax provides off-the-shelf
implementations of ranking losses and metrics to be used with JAX. It provides
the following functionality:Ranking losses (rax.*_loss):rax.softmax_loss,rax.pairwise_logistic_loss, ...Ranking metrics (rax.*_metric):rax.mrr_metric,rax.ndcg_metric, ...Transformations (rax.*_t12n):rax.approx_t12n,rax.gumbel_t12n, ...RankingA ranking problem is different from traditional classification/regression
problems in that its objective is to optimize for the correctness of therelative orderof alist of examples(e.g., documents) for a given
context (e.g., a query).Raxprovides support for ranking problems within
the JAX ecosystem. It can be used in, but is not limited to, the following
applications:Search: ranking a list of documents with respect to a query.Recommendation: ranking a list of items given a user as context.Question Answering: finding the best answer from a list of candidates.Dialogue System: finding the best response from a list of responses.SynopsisIn a nutshell, given the scores and labels for a list of items, Rax can compute
various ranking losses and metrics:importjax.numpyasjnpimportraxscores=jnp.array([2.2,-1.3,5.4])# output of a model.labels=jnp.array([1.0,0.0,0.0])# indicates doc 1 is relevant.rax.ndcg_metric(scores,labels)# computes a ranking metric.# 0.63092977rax.pairwise_hinge_loss(scores,labels)# computes a ranking loss.# 2.1All of the Rax losses and metrics are purely functional and compose well with
standard JAX transformations. Additionally, Rax provides ranking-specific
transformations so you can build new ranking losses. An example israx.approx_t12n, which can be used to transform any (non-differentiable)
ranking metric into a differentiable loss. For example:loss_fn=rax.approx_t12n(rax.ndcg_metric)loss_fn(scores,labels)# differentiable approx ndcg loss.# -0.63282484jax.grad(loss_fn)(scores,labels)# computes gradients w.r.t. scores.# [-0.01276882  0.00549765  0.00727116]InstallationSeehttps://github.com/google/jax#installationfor instructions on installing JAX.We suggest installing the latest stable version of Rax by running:$ pip install raxExamplesSee theexamples/directory for complete examples on how to use Rax.Citing RaxIf you use Rax, please consider citing ourpaper:@inproceedings{jagerman2022rax,
  title = {Rax: Composable Learning-to-Rank using JAX},
  author  = {Rolf Jagerman and Xuanhui Wang and Honglei Zhuang and Zhen Qin and
  Michael Bendersky and Marc Najork},
  year  = {2022},
  booktitle = {Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining}
}
Package: rax-autoscaler
RAX-AUTOSCALERUses the rackspace APIs to allow for scaling based on aggregate metricsacross a cluster.Can be used and installed on the auto-scale group members or on adedicated management instance.-   GitHub repository-   PyPI package[Stories in Ready] [Documentation Status] [Travis CI status][Coverage Status]Installationpip install rax-autoscalerConfigurationRead the DocsWhat's NewV0.4 is here with a brand new plugin raxmon-autoscale, check it out inthe documentation above. Along for the ride are a host of improvements,and fixes. Read the commit log if you have any questions and pleasecreate an issue if you see any regressions.Upgrading from 0.2V0.3 is a major change from 0.2 and as such there are many breakingchanges in the configuration file.Here is what you need to fix to upgrade successfully.The monitoring information is now a plugin and needs to be moved to theplugin section. The only monitoring plugin available in 0.2 was raxmonso you should specify the raxmon plugin and just move the check_type,check_config, metric_name, thresholds down.Old Config"autoscale_groups": {"group0": {"group_id": "group id","scale_up_policy": "scale up policy id","scale_down_policy": "scale down policy id","check_type": "agent.load_average","check_config": {},"metric_name": "1m","scale_up_threshold": 0.6,"scale_down_threshold": 0.4,"webhooks": {"scale_up": {"pre": ["url","url"],"post": ["url"]},"scale_down": {"pre": ["url","url"],"post": ["url"]}}}}New Config"autoscale_groups": {"group0": {"group_id": "704a2260-f4b9-46a9-be83-dbfb19919ee0","scale_up_policy": "74588ebe-7ca7-4950-9b86-e622a11295b6","scale_down_policy": "b22f4fce-132f-40af-b8c6-0e22704b1241","webhooks": {"scale_up": {"pre": ["url","url"],"post": ["url"]},"scale_down": {"pre": ["url","url"],"post": ["url"]}},"plugins":{"raxmon":{"scale_up_threshold": 0.6,"scale_down_threshold": 0.4,"check_type": "agent.load_average","load_balancers": [17443]}}}}Contributing-   Fork rax-autoscaler repository, and clone it on your laptop-   Create _your feature_ branch from _devel_ branch:git checkout -b my-new-feature origin/devel-   Commit, comment, push to your fork on GitHub, and create new _PullRequest_RequirementsYou might want to install pip packages in your Python virtualenv:pip install -r requirements-dev.txtLicenseCopyright 2014 Rackspace US, Inc.Licensed under the Apache License, Version 2.0 (the "License");you may not use this file except in compliance with the License.You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, softwaredistributed under the License is distributed on an "AS IS" BASIS,WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions andlimitations under the License.
Package: rax-cloudfeeds
cloudfeeds_clientThis python client is a Cloud Feeds library, that is built to be used with Rackspace's CloudCreating Auth ClientImplement AuthClient with following two methodsclass AuthClient:

    def fetch_auth_token(self):
        #Logic to generate auth token
        return ""


    def get_auth_header(self):
        return {
            "${HEADER_NAME}": self.fetch_auth_token()
        }Creating Feed Clientfeed_url = "https://feed.com/type/events"
auth_client = AuthClient()
feed_client = FeedClient(feed_url, auth_client)** Reading Events **To read events,feed_client.fetch(url, headers, params)All fields url, headers and params are optional.** Get an Event **To read one particular event,feed_client.fetch_event(event_id, url, headers)url and headers are optional** Publish an Event **To publish an event,feed_client.create_entry(entry, url, headers)url and headers are optionalTo create an entry json to publish, there are entry, content, event classes defined and
also method to transform the object to json as feed requires** Work In Progress **We are currently working on setting up Redis, so that we can queue the events and publish the events by reading the queue
Package: raxopy
Instalaciónpy -m pip install raxopyFuncionesPor el momento, la librería tiene las funciones:
- isprime(x),
- isamstrong(x),
- ispair(x),
- factorial(x),
- tobinary(x),
- tohexadecimal(x),
- tooctal(x)
- createpassword(x)Mas informaciónSi teneis alguna duda, podeis contactar al creador a través del gmail‘raxopy@gmail.com’
Package: ray
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute:Learn more aboutRay AI Libraries:Data: Scalable Datasets for MLTrain: Distributed TrainingTune: Scalable Hyperparameter TuningRLlib: Scalable Reinforcement LearningServe: Scalable and Programmable ServingOr more aboutRay Coreand its key abstractions:Tasks: Stateless functions executed in the cluster.Actors: Stateful worker processes created in the cluster.Objects: Immutable values accessible across the cluster.Monitor and debug Ray applications and clusters using theRay dashboard.Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growingecosystem of community integrations.Install Ray with:pip install ray. For nightly wheels, see theInstallation page.Why Ray?Today’s ML workloads are increasingly compute-intensive. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands.Ray is a unified way to scale Python and AI applications from a laptop to a cluster.With Ray, you can seamlessly scale the same code from a laptop to a cluster. Ray is designed to be general-purpose, meaning that it can performantly run any kind of workload. If your application is written in Python, you can scale it with Ray, no other infrastructure required.More InformationDocumentationRay Architecture whitepaperExoshuffle: large-scale data shuffle in RayOwnership: a distributed futures system for fine-grained tasksRLlib paperTune paperOlder documents:Ray paperRay HotOS paperRay Architecture v1 whitepaperGetting InvolvedPlatformPurposeEstimated Response TimeSupport LevelDiscourse ForumFor discussions about development and questions about usage.< 1 dayCommunityGitHub IssuesFor reporting bugs and filing feature requests.< 2 daysRay OSS TeamSlackFor collaborating with other Ray users.< 2 daysCommunityStackOverflowFor asking questions about how to use Ray.3-5 daysCommunityMeetup GroupFor learning about Ray projects and best practices.MonthlyRay DevRelTwitterFor staying up-to-date on new features.DailyRay DevRel
Package: ray-chunkit-chung-gum-gum-pistol
gum gum pistolMinimal example to create a pypi package using poetry(Screen cap fromhttps://onepiece.fandom.com/wiki/Gomu_Gomu_no_Mi/Techniques)Pypi:https://pypi.org/project/ray-chunkit-chung-gum-gum-pistol/Github:https://github.com/ray-chunkit-chung/essential-poetryHow to useInstall gum-gum-pistolpipinstallray-chunkit-chung-gum-gum-pistolThis gum-gum-pistol has two functionsgumload
gumshootThat's it!Use typer to create gum-gum-pistolhttps://typer.tiangolo.com/tutorial/package/Step 1 Install pythonpython-mvenv.venv# .venv\Scripts\activatesource.venv/bin/activate
python-mpipinstall--upgradepip
pipinstall--upgrade-rrequirements.txtStep 2 Init project and dependenciespoetrynewray-chunkit-chung-gum-gum-pistolcdray-chunkit-chung-gum-gum-pistol
poetryaddtyper[all]poetryaddpytest[all]Step 3 Define appDefine app path[tool.poetry.scripts]gum="ray_chunkit_chung_gum_gum_pistol.main:app"Install local for testpoetryinstallTest app localgum--help
gumload
gumshootStep 4 Distribute appBuild wheel filepoetrybuildTest by changing to a new venv and install from local distpipinstallray-chunkit-chung-gum-gum-pistol\dist\ray_chunkit_chung_gum_gum_pistol-0.1.0-py3-none-any.whlStep 5 Upload to pypiCreate PYPI_TOKEN and export to env. Then config pypi-token in poetry. Then publish to pypipoetryconfigpypi-token.pypi$PYPI_TOKENpoetrypublish--buildTest by changing to a new venv and install from pypipipinstallray-chunkit-chung-gum-gum-pistol
gum--help
Package: ray-cpp
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute:Learn more aboutRay AI Libraries:Data: Scalable Datasets for MLTrain: Distributed TrainingTune: Scalable Hyperparameter TuningRLlib: Scalable Reinforcement LearningServe: Scalable and Programmable ServingOr more aboutRay Coreand its key abstractions:Tasks: Stateless functions executed in the cluster.Actors: Stateful worker processes created in the cluster.Objects: Immutable values accessible across the cluster.Monitor and debug Ray applications and clusters using theRay dashboard.Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growingecosystem of community integrations.Install Ray with:pip install ray. For nightly wheels, see theInstallation page.Why Ray?Today’s ML workloads are increasingly compute-intensive. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands.Ray is a unified way to scale Python and AI applications from a laptop to a cluster.With Ray, you can seamlessly scale the same code from a laptop to a cluster. Ray is designed to be general-purpose, meaning that it can performantly run any kind of workload. If your application is written in Python, you can scale it with Ray, no other infrastructure required.More InformationDocumentationRay Architecture whitepaperExoshuffle: large-scale data shuffle in RayOwnership: a distributed futures system for fine-grained tasksRLlib paperTune paperOlder documents:Ray paperRay HotOS paperRay Architecture v1 whitepaperGetting InvolvedPlatformPurposeEstimated Response TimeSupport LevelDiscourse ForumFor discussions about development and questions about usage.< 1 dayCommunityGitHub IssuesFor reporting bugs and filing feature requests.< 2 daysRay OSS TeamSlackFor collaborating with other Ray users.< 2 daysCommunityStackOverflowFor asking questions about how to use Ray.3-5 daysCommunityMeetup GroupFor learning about Ray projects and best practices.MonthlyRay DevRelTwitterFor staying up-to-date on new features.DailyRay DevRel
Package: raydbg
RayDBGA tool for static and dynamic analysis of Ray programs, using LLM.Installationpip3installraydbg# Setup CodeQL (optional)raydbginitUsageMake sure you have setOPENAI_KEYenvironment variable to your OpenAI API key if you want to use LLM for more accurate analysis.
You can also use--no-llmto disable LLM feature.Static Analysis:raydbgstatic--target<path_to_ray_file>Dynamic Analysis:raydbgdynamic--target<path_to_ray_file>Examplegitclonehttps://github.com/shouc/raydbg.gitcdraydbgRun static analysis:raydbgstatic--targetexamples/pi.pyRun dynamic analysis:raydbgdynamic--targetexamples/pi.py
Package: rayflare
[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
[![codecov](https://codecov.io/gh/qpv-research-group/rayflare/branch/devel/graph/badge.svg)](https://codecov.io/gh/qpv-research-group/rayflare)
[![Codacy Badge](https://app.codacy.com/project/badge/Grade/7ff9180e5f7a460192440895d823ff15)](https://www.codacy.com/gh/qpv-research-group/rayflare?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=qpv-research-group/rayflare&amp;utm_campaign=Badge_Grade)
[![Documentation Status](https://readthedocs.org/projects/rayflare/badge/?version=latest)](https://rayflare.readthedocs.io/en/latest/?badge=latest)
[![status](https://joss.theoj.org/papers/15647ef7b3dd688b47c1b802a4f50a67/status.svg)](https://joss.theoj.org/papers/15647ef7b3dd688b47c1b802a4f50a67)Important: Please check out the [news & updates](https://rayflare.readthedocs.io/en/latest/news.html) page for the most recent updates
and changes, including any possible backwards compatibility issues.  If you have questions, issues, etc., please check the
documentation and (open and closed) [issues](https://github.com/qpv-research-group/rayflare/issues) first,
or open a new issue using the relevant template.# rayflare
Open-source, integrated optical modelling of complex stacks. RayFlare incorporates the transfer-matrix method (TMM),
ray-tracing and rigorous coupled-wave analysis (RCWA/FMM), in addition to an angular redistribution matrix method which allows multiple
methods to be coupled across a single structure to calculate total absorption/reflection/transmission, absorption per
layer, and absorption profiles.You can view RayFlare’s documentation, including installation instructions [here](https://rayflare.readthedocs.io/en/latest/).
The contributing guidelines are [here](CONTRIBUTING.md) and the Code of Conduct is [here](CODE_OF_CONDUCT.md). This package
is distributed under a [GNU GPL (version 3) license](GNU_GPL_v3.txt). If you have questions, issues, etc., please check the
documentation first or open an [issue](https://github.com/qpv-research-group/rayflare/issues) using the relevant template.If you use RayFlare in your work, please cite the [JOSS paper](https://doi.org/10.21105/joss.03460):Pearce, P. M., (2021). RayFlare: flexible optical modelling of solar cells. Journal of Open Source Software, 6(65), 3460.
https://doi.org/10.21105/joss.03460![poster](poster.png “RayFlare poster”)