Saravanan666/pypi-org huggingface dataset:
Package: rsfsup
rsfsupPython interface to the Rohde & Schwarz FSUP Signal Source AnalyzerInstallation$ pip install rsfsupUsageIt is possible to configure the instrument and read the trace data with this library.
The first example shows reading the spectrum analyzer trace in a context manager.>>>fromrsfsupimportCommChannel>>>withCommChannel("<ip address>")asfsup:...data=fsup.spectrum.read()>>>importmatplotlib.pyplotasplt>>>plt.plot(*data)[<matplotlib.lines.Line2Dat...>]>>>plt.show()The next example shows switching to SSA mode to measure phase noise. This one also shows
using the CommChannel directly, which is useful in interactive sessions where features
of the instrument can be accessed using tab completion.>>>fromrsfsupimportCommChannel>>>cc=CommChannel("<ip address>")>>>fsup=cc.get_instrument()>>>fsup.mode="SSA">>>data=fsup.ssa.read()>>>importmatplotlib.pyplotasplt>>>plt.semilogx(*data)>>>plt.show()>>>cc.close()It is possible to change the resolution bandwith and video bandwidth.In[1]:fromrsfsupimportCommChannelIn[2]:cc=CommChannel("<ip address>")In[3]:fsup=cc.get_instrument()In[4]:fsup.spectrum.bandwidth.resolution_bandwidthOut[4]:'50.0 kHz'In[5]:fsup.spectrum.bandwidth.video_bandwidthOut[5]:'50.0 kHz'In[6]:fsup.spectrum.bandwidth.rbw_vbw_ratioOut[6]:1.0In[7]:fsup.spectrum.bandwidth.rbw_vbw_ratio=0.1In[8]:fsup.spectrum.bandwidth.rbw_vbw_ratioOut[8]:0.1In[9]:fsup.spectrum.bandwidth.resolution_bandwidthOut[9]:'50.0 kHz'In[10]:fsup.spectrum.bandwidth.video_bandwidthOut[10]:'500.0 kHz'In[11]:fsup.spectrum.bandwidth.video_bandwidth="3 MHz"In[12]:fsup.spectrum.bandwidth.video_bandwidthOut[12]:'3.0 MHz'Supported features:Spectrum analyzerConfigurationMarkersTriggerRead trace, frequency and time domainPhase noise (PLL Cross correlation)File system managementDocumentation
Package: rsgis
ContentCurrent releaseFuture releaseInstallationQuick tutorialCommand line UseRunning the testsAuthorsLicensersgisA python package for basic to Advanced GIS operations.Current_ReleaseMetadata support module for landsatLandsat module for landsat bands extraction and manipulationFuture_ReleaseMetadata support for other file formats(sentinel,NetCDF etc)A one liner for basic to advanced GIS operationsNDVI,NDWI,NDBIImage subsetting/clipping supportInstallationThe package is available on the python package index(Pypi),to install use the command below;Python version 3.0 +pipinstallrsgisQuick_TutorialUsing the landsat module#import the modulefromrsgisimportlandsat#Extract all filesdata=landsat.extract(path/to/landsat/dataset)#To check path to extracted datadata.path#Extract specific bands#Here we will extract the metadata = 0, band1 = 1 and band2 = 2data=landsat.extract(path/to/landsat/dataset,bands=[0,1,2])#Get metadata pathdata.get_metadata()#Get path(s) to specific band(s)#In this example, we will be getting band4 and band5data.get_bands(4,5)Using the Metadata module with the Landsat Module#import the classfromrsgisimportMetadata,landsatdata=landsat.extract(path/to/landsat/dataset)meta_path=data.get_metadata()mtd=Metadata(meta_path)#usagemtd.get('SUN')#to get a single parameter. Might return a list of dict if found multiple match. Be specific to avoid this.mtd.get_all()#returns a dict of all available parameters in the metadata filemtd.get_some(['sun','path','row'])#To get multiple parameters. Returns a list of values.Command_Line_UseChange directory into the rsgis folder to runpythonmain.py--dir_path[path/to/metadata/file]--get[parametrtoget]#This command returns the result of the --get queryRunning_The_TestsPaste a metadata file in the data dir(delete old one) in tests and cd into the test dir then run in terminalpython metadata_test.pyIf no error,then you can go ahead to do some magic.ContributingPRs and issues are welcomed and not limited to the following:Code documentationRefactoringAdding support for other metadata file types etcAuthorsJolaiya Emmanuel-Twitter,EmailOke Matthew-EmailLicenseThis project is licensed under the Apache 2.0 License - see theLICENSE.mdfile for details
Package: rshare
项目介绍这是一个 Python 和 Rust 混合编程的项目，主要目的是比较 Rust 和 Python 的性能差异。目前这个项目是rshare，我们
在项目中通过 PYO3 和 Maturin 来将 Rust 代码包装到 Python 中，以使得用户可以通过 Python 代码来调用 Rust 写的函数。当然其中是有性能损失的。说明目前已经支持 64 位操作系统，包括 Windows，Linux，macOS 和 ARM64 架构的 Linux 系统。安装通过pip install rshare --upgrade -i https://pypi.org/simple来安装rshare体验 Rust 的极致性能！使用安装依赖库请先确认是否在本地安装以下依赖库pipinstallnumpyrsharetalib注意其中 talib 的安装需要参考：ta-lib-python运行测试代码importtimeimportnumpyasnpimportrshareasrkimporttalibdata_num=100000000# 调整此数值data_np=np.random.rand(data_num)timeperiod=10# Rust 和 C 对比start_py=time.time()result_talib=talib.SMA(data_np,timeperiod)end_py=time.time()print(f"基于 C 语言的 TA-Lib 耗时:{end_py-start_py}seconds")start_py=time.time()result_np_rs=rk.calculate_moving_average_rs(data=data_np,window_size=timeperiod)end_py=time.time()print(f"基于 Rust 的耗时:{end_py-start_py}seconds")可以通过调整 data_num 数值来设置不同的数据量，从而比较性能差异！构建maturinbuild# 会创建一个可以分发的轮子包，但不会安装它。maturinbuild--release# 创建的轮子包是为发布准备的，具有更高的性能，但编译时间更长。maturindevelop# 非优化插入 Python 环境maturindevelop--release# 优化插入 Python 环境Python 测试importrshareasrk
rk.fetch_title("https://www.baidu.com")版本说明目前主要是说明开发的软件版本：Python 3.12.1Rust 1.75注意事项需要重新编译部分内容更新推送到 main 分支修改Cargo.toml中的版本号打标签：git tag v0.1.x通过推送标签进行升级：git push origin tag v0.1.x镜像推送基于原始镜像：ghcr.io/rust-cross/manylinux2014-cross:aarch64docker build -t jindaxiang/newopen .docker tag local-image:tagname jindaxiang/new-repo:tagnamedocker push jindaxiang/new-repo:tagname查看PyPI 版本 RShare 版本贡献代码maturin 项目maturin 文档PyO3 项目PyO3 文档关注 .vscode用于 debug 代码，新增 .vscode 文件夹Python 环境设置利用 conda 创建虚拟环境，此处注意 Python 的版本condacreate-nrsharepython进入环境condaactivatersharepipinstallmaturin
python-mpipinstallTA-Lib# 仅用于测试 talib
Package: rshiny-server-cmd
rshiny-server-cmdCommand line wrapper to run a named R Shiny Server script/folder.This project is used inContainDS Dashboards, which is a user-friendly
way to launch Jupyter notebooks as shareable dashboards inside JupyterHub. Also works with Streamlit and other
visualization frameworks.Install and RunInstall using pip.pip install rshiny-server-cmdThe file to start is specified on the command line, for example:rshiny-server-cmd ~/Dev/myrfile.RBy default the server will listen on port 8888To specify a different port, use the --port flag.rshiny-server-cmd --port=8888 ~/Dev/myrfile.RTo run directly in python:python -m rshiny_server_cmd.main <rest of command line>Other command line args--debug--ip
Package: rsHRF
Resting state HRF estimation and deconvolution.Please refer tohttps://github.com/compneuro-da/rsHRFfor MATLAB versionThe basic ideaThis toolbox is aimed to retrieve the onsets of pseudo-events triggering an hemodynamic response from resting state fMRI BOLD voxel-wise signal.
It is based on point process theory, and fits a model to retrieve the optimal lag between the events and the HRF onset, as well as the HRF shape, using either the canonical shape with two derivatives, or a (smoothed) Finite Impulse Response.Once that the HRF has been retrieved for each voxel, it can be deconvolved from the time series (for example to improve lag-based connectivity estimates), or one can map the shape parameters everywhere in the brain (including white matter), and use the shape as a pathophysiological indicator.How to use the toolboxThe input is voxelwise BOLD signal, already preprocessed according to your favorite recipe. Important thing are:bandpass filter in the 0.01-0.08 Hz interval (or something like that)z-score the voxel BOLD time seriesTo be on the safe side, these steps are performed again in the code.The input can be images (3D or 4D), or directly matrices of [observation x voxels].It is possible to use a temporal mask to exclude some time points (for example after scrubbing).The demos allow you to run the analyses on several formats of input data.Python Package and BIDS-appA BIDS-App has been made for easy and reproducible analysis. Its documentation can be accessed at:http://bids-apps.neuroimaging.io/rsHRF/CollaboratorsGuorong WuNigel ColenbierSofie Van Den BosscheDaniele MarinazzoMadhur Tandon (Python - BIDS)Asier Erramuzpe (Python - BIDS)Amogh Johri   (Python - BIDS)ReferencesGuo-Rong Wu, Wei Liao, Sebastiano Stramaglia, Ju-Rong Ding, Huafu Chen, Daniele Marinazzo*. "A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data." Medical Image Analysis, 2013, 17:365-374.PDFGuo-Rong Wu, Daniele Marinazzo. "Sensitivity of the resting state hemodynamic response function estimation to autonomic nervous system fluctuations." Philosophical Transactions of the Royal Society A, 2016, 374: 20150190.PDFGuo-Rong Wu, Daniele Marinazzo. "Retrieving the Hemodynamic Response Function in resting state fMRI: methodology and applications." PeerJ PrePrints, 2015.PDF
Package: rsi-calculator
RSI CalculatorA library to calculating RSI.Installpip3 install rsi-calculatorUsingIn another scriptfromrsi_calculatorimportrsi# rsi(prices = None, periods = 14)print(rsi([15,20,25,30,20,15,20,25,30,20,15,20,25,30,100]))In command line-h, --help            show this help message and exit-pr PRICES [PRICES ...], --prices PRICES [PRICES ...]Prices-pe PERIODS, --periods PERIODSPeriodsrsi -pr 15 20 25 30 20 15 20 25 30 20 15 20 25 30 100
Package: rsilk
✨ rsilk ✨The python binding forsilk-rsUsageencodeimportrsilkwithopen("tests/input.pcm","rb")asf:data=f.read()output=rsilk.encode(data,24000,24000)withopen("output.silk","wb")asf:f.write(output)decodeimportrsilkwithopen("input.silk","rb")asf:data=f.read()output=rsilk.decode(data,24000)withopen("output.pcm","wb")asf:f.write(output)ExceptionsSilkErrorPublic functionsdef decode(src: bytes, sample_rate: int, frame_size: int = 0, frames_per_packet: int = 1, more_internal_decoder_frames: bool = False, in_band_fec_offset: int = 0, loss: bool = False) -> bytesdef encode(input: bytes, sample_rate: int, bit_rate: int, max_internal_sample_rate: int = 24000, packet_loss_percentage: int = 0, complexity: int = 2, use_inband_fec: bool = False, use_dtx: bool = False, tencent: bool = True) -> bytes
Package: rsi-scraper
RSI-ScraperOverviewWeb Scraper for RSI (used instarcitizen-api.com)This module allows you to easy retrive online information related to the RSI website.RequirementsPython>=3.7requests>=2.25.1lxml>=4.6.3Installationpip install rsi-scraperModulesModuleDescriptionOrganizationFind an organization by SIDOrganizationMembersList members of an organization by page of 32ProgressTrackerGet all progress tracker teamsProgressTrackerInfoGet progress tracker information using the slugRoadmapList cards of the Roadmap by specified versionShipSearch ship using multiple parametersStarmapSystemsGet systems from the starmap by nameStarmapTunnelsGet tunnels from the starmap by idStarmapSpeciesGet species from the starmap by nameStarmapAffiliationsGet affiliations from the starmap by nameStarmapStarSystemsGet star-system from the starmap by codeStarmapCelestialObjectsGet celestial objects from the starmap by nameStarmapSearchSearch object from the starmap by nameStarmapRouteSearchFind routes from position to destionationStatsGet general information like the numbers of citizensTelemetryGet some telemetry infoUserFind information about a user by handleVersionGet a list of all versionsUsagefromrsi_scraperimportUser,Organization,Shipuser=User('dymerz').execute()organization=Organization('PROTECTORA').execute()ship=Ship(name='Cutlass Black').execute()Optional Environment Variables# Used in the HTTP header of requestsVERSION = "DEVELOPMENT"# Define the HTTP proxy to use (e.g:http://127.0.0.1:8000).HTTP_PROXY = ""ContributingFork the repo on GitHubClone the project to your own machineCommit changes to your own branchPush your work back up to your forkSubmit a Pull request so that we can review your changesNOTE: Be sure to merge the latest from "upstream" before making a pull request!SeeCONTRIBUTING.md.RightsThis is an unofficial Star Citizen tool, not affiliated with the Cloud Imperium group of companies. All content on this site not authored by its host or users are property of their respective owners.robertsspaceindustries.comThis project is not endorsed by or affiliated with the Cloud Imperium or Roberts Space Industries group of companies.All game content and materials are copyright Cloud Imperium Rights LLC, Cloud Imperium Rights Ltd, Star Citizen®, Squadron 42®, Roberts Space Industries®, and Cloud Imperium® are registered trademarks of Cloud Imperium Rights LLC.All rights reserved.
Package: rskeyring
rskeyring (Rust Keyring)A C-level, Rust keyring library bind to Python (https://github.com/DK26/pyrust-keyring).The Rust library (crate) itself was created by hwchen (https://github.com/hwchen/keyring-rs).MotivationSince usingpypi.orgkeyring library to store sensitive data with the PyInstaller libraryisn't possible at the moment due to the errorkeyring.errors.NoKeyringErrorand sinceit has been like this for a long time now, I've decided to try to bind the Rust programming languagekeyring library (as I'm still learning it) to Python and been able to do so successfully.Tested successfully on Windows 10 to work with PyInstaller.Installationpip install rskeyringUsageStore or Update Passwordimportrskeyringfromgetpassimportgetpassusername=input("Username: ")password=getpass()try:rskeyring.set_password("service",username,password)exceptOSError:print(f"Unable to create or update service for{username}."f"\nPlease make sure you have the proper permissions")Retrieve Passwordimportrskeyringusername=input("Username: ")try:password=rskeyring.get_password("service",username)print(password)exceptOSError:print(f"Unable to get{username}'s password from 'service'")Delete Passwordimportrskeyringusername=input("Username: ")try:rskeyring.delete_password("service",username)exceptOSError:print(f"Unable to remove{username}from 'service'")ExceptionsCurrently the external Rustkerying-rslibrary doesn't provide any concrete error details.At this stage, we just throw a generalOSErrorwith an error message originated by the underlying Rust library itself.e.g.OSError: Windows Vault ErrorUnit Testspython -m unittest tests.lib_unittestManual Use of Compiled LibraryThesetuptools_rustshould be able to automatically compile & copy the rust libraries upon callingpip install rskeyring.If you wish to compile and import the Rust libraries to your python setup manually, please refer to the following notes:CompileIn order to compile the Rust code, you'll need to have therustuptoolchain.To install therustuptoolchain, go tohttps://rustup.rsFrom within this directory, execute the next command to compile:cargo build --releaseWindowsCopy the filepyrust-keyring\target\release\rskeyring.dllto your Python project. Make sure you rename its extension from.dllto.pyd.MacOSCopy the filepyrust-keyring/target/release/rskeyring.dylibto your Python project. Make sure you rename its extension from.dylibto.so.Docshelp(rskeyring)
Package: rskfd
rskfd PackageDescriptionThis repository contains the rskfd package, which is a python package containing tools for instrument and data handling.
Data handling mainly focuses on reading writing I/Q files in publich R&S formats.
Instrument Control is only based on raw socket connections.Note:Software is provided as isPackage APIs are subject to change, so consider saving the version you're usingThis is not an official Rohde & Schwarz package / software / productUsageTBD: A detailed documentation will follow ...InstallingUse pip install rskfd to install packageFolder structureHere's a folder structure for a Pandoc document:rskfd/     # Root directory.
|- iq_data_handling/        # Folder contains classes/scripts to handle iq data (files).
|- remote_control/          # Folder contains classes/scripts to handle remote connections to measurement instruments.
|- signal_generation/       # Folder contains classes/scripts to generate signals.
|- helper/                  # Folder contains classes/scripts for additional tools not directly related to the above.Setup generic data---
title: rskfd Python Package
author: Florian Ramian
rights:  tbd
language: en-US
tags: [document]
abstract: |
  rskfd package for instrument control and I/Q data handling.
---
Package: rskit
rskitToolkit for building recommender systemsProvide CLI interface for running recommendation algorithmsContains abstractions you can leverage to build custom recommendersInstallationpipinstallrskitDevelopmentFor development, you may use below to create a Python interpreter that resides invenvin the current working directory, and to install all of rskit's dependencies:$ virtualenv venv 
$ source venv/bin/activate
$ pip install -e .
$ pip install -r requirements-dev.txt
$ rskit --help # should workBecause the script installs the package as editable, you can make changes in the source tree and use therskitcommand to immediately validate them. If this does not appear to work, check that you are using a the proper virtual environment, and that the package is indeed installed in editable mode:$ which rskit # should point into your virtualenv
/path/to/my/venv/bin/rskit
$ pip list --local | grep rskit # should point to the source tree
rskit                0.0.1                /project/rskitPlease refer to theMakefilefor supplementary development tasks.
In particular, the following targets may be relevant when validating changes before committing:$ make lint # check rskit's source for code style errors
$ make test # run all testsMotivationOur motivation is to simplify and standardise the process of building, benchmarking and deploying recommender systems.ContributeTo discuss ideas and to report problems, open an issue or open an issue or send a pull request. All contributions are welcomed.
Package: rsklpr
Robust Local Polynomial Regression with Similarity KernelsTL;DRThis library is useful to perform robust locally weighted regression in Python when:There are no particular assumptions on the underlying function except that it is "reasonably smooth". In particular,
you don't know which parametric model to specify or if an appropriate model exists.There are no particular assumptions on the type and intensity of noise present.There are no particular assumptions on the presence of outliers and their extent.You may want to predict in locations not explicitly present in the dataset but also not too far from existing
observations or far outside the areas where observations exist.The independent inputs are univariate or multivariate.The dependent variable is univariate.You want a straightforward hassle-free way to tune the model and the smoothness of fit.You may want to calculate confidence intervals.You may want to filter noise to recover the original underlying process.You may want to impute or resample the data.If the above use cases hold then this library could be useful for you. Have a look at this notebookhttps://nbviewer.org/github/yaniv-shulman/rsklpr/tree/main/docs/usage.ipynbfor an example of how to use
this library to perform regression easily.InstallationInstall fromPyPIusing pip (preferred method):pipinstallrsklprDetailsLocal polynomial regression (LPR) is a powerful and flexible statistical technique that has gained increasing popularity
in recent years due to its ability to model complex relationships between variables. Local polynomial regression
generalizes the polynomial regression and moving average methods by fitting a low-degree polynomial to a nearest
neighbors subset of the data at the location. The polynomial is fitted using weighted ordinary least squares, giving
more weight to nearby points and less weight to points further away. Local polynomial regression is however susceptible
to outliers and high leverage points which may cause an adverse impact on the estimation accuracy. This library
implements a variant of LPR presented in theRobust Local Polynomial Regression with Similarity Kernels draft paperwhich uses a generalized similarity kernel
that assign robust weights to mitigate the adverse effect of outliers in the local neighborhood by estimating and
utilizing the density at the local locations.Experimental resultsThe experimental results and demonstration of the library for various experimental settings are available as interactive
Jupyter notebooks athttps://nbviewer.org/github/yaniv-shulman/rsklpr/tree/main/src/experiments/KDE ImplementationKDE implementation is a copy of the code from statsmodelshttps://www.statsmodels.org/stable/index.html. The copy is done to
remove statsmodels as a dependency of this package since statsmodels is quite heavy and pulls a lot of additional
packages.Contribution and feedbackThe paper is work in progress and the library in early stages of development but both are in a useful state.
Contributions and feedback are most welcome both to the paper and the code. Please seeCONTRIBUTE.mdfor further details.
Package: rslib
Documentation谛听组推荐系统离线训练框架RSLib主要功能Faster Deployment (sql-as-backbone)State-of-the-art Recurrent Model (transformer-xl etc.)Distributed DL (horovod etc.)Deep Learning Accelerator (tvm etc.)Utility Classes (file2hdfs etc.)设计思路十问十答InstallTo install the current release:$ pip install rslibDemodataframe2hive功能demo功能描述:
通过洛阁组通过的hdfs上传接口实现本地dataframe上传至hive表('\t'分割)的功能。由于hive数据导入时不进行类型检查(不支持schema on write)，我们不提供直接插入现有表分区的操作，而是建一张新表。用户需要管理好dataframe的列名。
由于洛阁接口的问题，上传文件会有报错信息，本接口有报错重连机制，一般是能上传成功的。大文件不建议上传，不过测试下来也比较稳定，1.3G文件能在10分钟内上传完成。环境要求(在user_profile/basic镜像基础上)$ apt-get update && apt-get install -y krb5-user krb5-config libkrb5-dev
$ pip install requests-kerberos==0.12.0 hdfs==2.5.8 kerberos==1.3.0
$ pip install rslib
$ pip install requirements.txt  #custom path
$ kinit -kt code/data/up_recommend.keytab up_recommend  #custom path示例python代码import pandas as pd
from rslib.utils import dataupload
df = pd.DataFrame({'bb': [1, 2, 3], 'c': [2, 2, 3], 'aa': ['4', '5', '6']})
table = 'up_nsh_tmp.diting_rslib_test_20191021'
dataupload.pandas2hive(df, table)  #no partition
dataupload.pandas2hive(df, table, partition='2019-10-21')  #add partition
Package: rs-lib
RS_LibLibrary for general Remote Sensing codePrerequisitesPython 3 version 3.4 and abovePostgreSQL server version 7.4 to 12PostgreSQL client library version 9.1Build prerequisitesSetuptools (pip install --upgrade setuptools)The pg_config program: it is usually installed by the libpq-dev package or by postgresql server but sometimes it is not in a PATH directory. Having it in the PATH greatly streamlines the installation, so try running pg_config --version.InstallJust runpip install rs-libDevelopment installClone this repository.Cd into the cloned folderRunpip install .[dev]All the development dependencies will be installed, currently: pytest and numpyUsageImportimportrs_libasrs# Create an instance of the Config classconfig=rs.Config()# Load a config fileconfig.use(path_to_config_file)# Pass the config class to a functionresult=gru_orto(schema,table,priority,config)Config fileAs the library relies on several database functions and file paths, a config file must be used. An example of such a config file can be seenhere
Package: rsl.mime
RSL - WSDL 1 MIME ExtensionThis module provides an implementation for the WSDL 1 MIME Extension.Seerslfor more details.CHANGES0.2.3 (2009-01-12)fixed packaging0.2.2 (2008-12-14)fixed missing CHANGES.txt file0.2.1 (2008-12-14)raise exception on illegal mime:content definitions in WSDL0.2.0 (2008-12-03)Initial release
Package: rsLogger
Logger ClassDescriptionThe Logger class is a utility for logging messages in your application. It provides a simple and flexible way to log messages to different output sources.FeaturesLog messages to consoleLog messages to fileLog messages to external services (e.g., database, cloud storage)Support for different log levels (e.g., info, warning, error)Customizable log formatInstallationTo use the Logger class in your project, you can install it via npm:
Package: rsLoggerTwo
Logger ClassDescriptionThe Logger class is a utility for logging messages in your application. It provides a simple and flexible way to log messages to different output sources.FeaturesLog messages to consoleLog messages to fileLog messages to external services (e.g., database, cloud storage)Support for different log levels (e.g., info, warning, error)Customizable log formatInstallationTo use the Logger class in your project, you can install it via npm: