Saravanan666/pypi-org huggingface dataset:
Package: pcapy
This file is an extract from the the online documentation athttps://www.coresecurity.com/corelabs-research/open-source-tools/pcapyWhat is Pcapy?Pcapy is a Python extension module that enables software written in
Python to access the routines from the pcap packet capture library.From libpcap’s documentation: “Libpcap is a system-independent
interface for user-level packet capture. Libpcap provides a portable
framework for low-level network monitoring. Applications include
network statistics collection, security monitoring, network debugging,
etc.”What makes pcapy different from the others?works with Python threads.works both in UNIX with libpcap and Windows with WinPcap.provides a simpler Object Oriented API.Getting PcapyCurrent and past releases are available fromhttps://www.coresecurity.com/corelabs-research/open-source-tools/pcapySetupQuick startGrab the latest stable release, unpack it and run ‘python setup.py
install’ from the directory where you placed it. Isn’t that easy?RequirementsA Python interpreter. Versions 2.1.3 and newer are known to work.A C++ compiler. GCC G++ 2.95, as well as Microsoft Visual Studio
6.0, are known to work.Libpcap 0.7.2 or newer. Windows user are best to check WinPcap 3.0
or newer.A recent release of Pcapy.Compiling the source and installingAs this extension is written in C++ it needs to be compiled for the
host system before it can be accessed from Python. Fortunately this
process has been made easy by the setup.py script. In order to compile
and install the source execute the following command from the
directory where the pcapy’s distribution has been unpacked: ‘python
setup.py install’. This will install the extension into the default
Python’s modules path; note that you might need special permissions to
write there. For more information on what commands and options are
available from setup.py, run ‘python setup.py –help-commands’.This extension has been tested under Linux and Windows systems
and is known to work there, but it ought to work out-of-the-box on any
system where Python and libpcap are available.LicensingThis software is provided under under a slightly modified version of
the Apache Software License. See the accompanying LICENSE file for
more information.Contact UsWhether you want to report a bug, send a patch or give some
suggestions on this package, drop us a few lines atoss@coresecurity.com.
Package: pcapy-binary
This file is an extract from the the online documentation athttps://www.coresecurity.com/corelabs-research/open-source-tools/pcapyWhat is Pcapy?Pcapy is a Python extension module that enables software written in
Python to access the routines from the pcap packet capture library.From libpcap’s documentation: “Libpcap is a system-independent
interface for user-level packet capture. Libpcap provides a portable
framework for low-level network monitoring. Applications include
network statistics collection, security monitoring, network debugging,
etc.”What makes pcapy different from the others?works with Python threads.works both in UNIX with libpcap and Windows with WinPcap.provides a simpler Object Oriented API.Getting PcapyCurrent and past releases are available fromhttps://www.coresecurity.com/corelabs-research/open-source-tools/pcapySetupQuick startGrab the latest stable release, unpack it and run ‘python setup.py
install’ from the directory where you placed it. Isn’t that easy?RequirementsA Python interpreter. Versions 2.1.3 and newer are known to work.A C++ compiler. GCC G++ 2.95, as well as Microsoft Visual Studio
6.0, are known to work.Libpcap 0.7.2 or newer. Windows user are best to check WinPcap 3.0
or newer.A recent release of Pcapy.Compiling the source and installingAs this extension is written in C++ it needs to be compiled for the
host system before it can be accessed from Python. Fortunately this
process has been made easy by the setup.py script. In order to compile
and install the source execute the following command from the
directory where the pcapy’s distribution has been unpacked: ‘python
setup.py install’. This will install the extension into the default
Python’s modules path; note that you might need special permissions to
write there. For more information on what commands and options are
available from setup.py, run ‘python setup.py –help-commands’.This extension has been tested under Linux and Windows systems
and is known to work there, but it ought to work out-of-the-box on any
system where Python and libpcap are available.LicensingThis software is provided under under a slightly modified version of
the Apache Software License. See the accompanying LICENSE file for
more information.Contact UsWhether you want to report a bug, send a patch or give some
suggestions on this package, drop us a few lines atoss@coresecurity.com.
Package: pcapy-ng
This file is an extract from the the online documentation athttps://github.com/stamparm/pcapy-ng/blob/master/README.mdWhat is Pcapy-NG?Pcapy-NG is a Python extension module that enables software written in
Python to access the routines from the pcap packet capture library. It is
a replacement of Pcapy, which is not maintained any more and stopped working
altogether on Python3.10.From libpcap’s documentation: “Libpcap is a system-independent
interface for user-level packet capture. Libpcap provides a portable
framework for low-level network monitoring. Applications include
network statistics collection, security monitoring, network debugging,
etc.”What makes pcapy different from the others?works with Python threads.works both in UNIX with libpcap and Windows with WinPcap.provides a simpler Object Oriented API.SetupQuick startGrab the latest stable release, unpack it and run ‘python setup.py
install’ from the directory where you placed it. Isn’t that easy?RequirementsA Python interpreter. Versions 2.1.3 and newer are known to work.A C++ compiler. GCC G++ 2.95, as well as Microsoft Visual Studio
6.0, are known to work.Libpcap 0.7.2 or newer. Windows user are best to check WinPcap 3.0
or newer.A recent release of Pcapy.Compiling the source and installingAs this extension is written in C++ it needs to be compiled for the
host system before it can be accessed from Python. Fortunately this
process has been made easy by the setup.py script. In order to compile
and install the source execute the following command from the
directory where the pcapy’s distribution has been unpacked: ‘python
setup.py install’. This will install the extension into the default
Python’s modules path; note that you might need special permissions to
write there. For more information on what commands and options are
available from setup.py, run ‘python setup.py –help-commands’.This extension has been tested under Linux and Windows systems
and is known to work there, but it ought to work out-of-the-box on any
system where Python and libpcap are available.LicensingThis software is provided under under the Apache Software License.
See the accompanying LICENSE file for more information.Contact UsWhether you want to report a bug, send a patch or give some
suggestions on this package, drop a few lines atmiroslav@sqlmap.org.
Package: pcapyplus
Python extension module to access libpcap.Pcapyplus is a maintained fork of the no longer maintained pcapy by CORE
Security Technologies available at:https://github.com/helpsystems/pcapyIn order to support this project the following features were dropped:Support for Windows systems.Support for Python 2.Documentationpip3installtoxtox-edocInstallTo install thepcapypluspackage a C++ build system is required, along with
thelibpcapdevelopment headers. In Ubuntu systems, run:sudoaptinstallbuild-essentiallibpcap-devFinally, installpcapypluswith:pip3installpcapyplusChangelog1.0.0 (2022-05-19)NewFirst stable release.0.1.0 (2021-02-01)NewDevelopment preview.LicenseCopyright (C) 2021-2022 Hewlett Packard Enterprise Development LP.
Copyright (C) 2014-2021 CORE Security Technologies

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 at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed 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 and limitations
under the License.
Package: pcarpet
pcarpetpcarpetis a small python package that creates acarpet plotfrom fMRI data and decomposes it withPCA.Citation:Sirmpilatze et al. eLife 2022;11:e74813. DOI:https://doi.org/10.7554/eLife.74813For an overview of the project, please refer to theREADME filein the Github repository
and to thedocumentation.Licensepcarpetis licensed under the terms of the MIT license. See the file
“LICENSE” for information on the history of this software, terms & conditions
for usage, and a DISCLAIMER OF ALL WARRANTIES.All trademarks referenced herein are property of their respective holders.Copyright (c) 2021–, Nikoloz Sirmpilatze, German Primate Center.
Package: pcase
pcase测试用例生成工具功能简介各因素之间完全独立，且每个因素取值改变不会影响其他因素的测试结果，采用最小组合数方法，总体而言包含了各因素的各组合即可各因素之间完全独立，各个因素取值改变可能会影响其他因素的测试结果，采用正交试验设计，使用最小行数选取正交表，可能存在某个因素的某个选项缺失的情况各因素之间完全独立，各个因素取值改变可能会影响其他因素的测试结果，采用正交试验设计，使用最适合行数选取正交表，用例组合相对全面各因素之间完全独立，各个因素取值改变可能会影响其他因素的测试结果，采用全量组合，用例组合完全覆盖samplefrom pcase.caseCreate import CaseCreate

# 定义不同因素的多个选项
input1_list = ['标准-全局与计算', '标准-仅计算', "融合-全局与计算"]
input1_list = [('标准-全局与计算', 0), ('标准-仅计算', 2), ("融合-全局与计算", 1)]
input2_list = ['SDN-3.0-POC', 'SDN-2.0-POC', 'VLAN-POC', 'SDN-3.0-常规', 'VLAN-常规']
input2_list = [('SDN-3.0-POC', 1), ('SDN-2.0-POC', 3), ('VLAN-POC', 4), ('SDN-3.0-常规', 0), ('VLAN-常规', 2)]
input3_list = ['repl', 'sanc', 'pair', 'local', 'tpsc']
input4_list = ['2个管理节点', '3个管理节点', '4个管理节点']
input_factors = [input1_list, input2_list, input3_list, input4_list]

# 初始化实例
ca = CaseCreate()

# 产生不同组合
#计算方式 1、2、3、4
cases_info = ca.generate_group(*input_factors, ctype=3, csvt=False, print_status=True)

# 定义用例模板
case_temple = {
    '用例名称': '验证典型配置全链路安装【{title}】',
    '前置条件': '1.安装环境已准备充分 \r\n2. 物理硬件、网络配置服务部署安装需求 \r\n3. 已拉起fb，并成功登录webinstaller',
    '用例步骤': '1.webinstaller配置为{group} \r\n2.点击安装，观察安装过程 \r\n3.检查安装结果，检查配置、服务',
    '预期结果': '3.配置一致；部署成功；服务正常',
    '优先级': '{priority}'
}
ca.build_case_template(define_case=case_temple)

# 替换模板中参数，生成实际用例
cases_params = []
for case in cases_info:
    case_params = {
        'group': case['group'],
        'title': '+'.join(case['group'][:3]),
        'priority': f'P{case["priority"]}',
    }
    cases_params.append(case_params)
cases = ca.assemble_cases(params=cases_params, print_status=True)

# 用例写入excel
ca.WriteCase2Excel(epath='hacases.xlsx', case_list=cases)
Package: pcaspy
PCASpy /’pi:kas,pai/ provides not only the low level python binding to EPICS Portable Channel Access Server
but also the necessary high level abstraction to ease the server tool programming.Check outPCASpy documentsto get started.Home:https://github.com/paulscherrerinstitute/pcaspy/wikiDocuments:https://pcaspy.readthedocs.ioDownloads:https://pypi.python.org/pypi/pcaspySource Repo:https://github.com/paulscherrerinstitute/pcaspyIssue Tracker:https://github.com/paulscherrerinstitute/pcaspy/issues
Package: pcatk
PCA analysis toolkitAnalysis toolkit for the Pre-Cancer Atlas project. This toolkit is meant for exploratory analyses that changes rapidly.Images, mostly acquired through theCyCIF technology, are assumed to be processed first bymcmicro-nf, where most of the heavy computation for illumination correction, stitching, registration, nuclei & cell segmentation, and feature quantification are done. The images are then passed to this toolkit for rapid iterations of exploratory analyses.ModulesconvertUnpack and repack ome.tif files to TIFF images.exemplarSample, render, and assemble single cell images.featurePixel binarization as feature generation.measureMini-tile and region-wise Pearson correlation coefficient.utilUtility functions.externalUseful external code.
Package: pcats-api-client
PCATS: Bayesian Causal Inference for General Type of TreatmentThe PCATS application programming interface (API) implements two Bayesian’s non parametric causal inference modeling, Bayesian’s Gaussian process regression and Bayesian additive regression tree, and provides estimates of averaged causal treatment (ATE) and conditional averaged causal treatment (CATE) for adaptive or non-adaptive treatment. The API is able to handle general types of treatment - binary, multilevel, continuous and their combinations, as well as general type of outcomes including bounded summary scores such as health related quality of life and survival outcomes. In addition, the API is able to deal with missing data using user supplied multiply imputed missing data. Summary tables and interactive figures of the results are generated and downloadable.
Package: pcax
pcaxMinimal Principal Component Analsys (PCA) implementation using jax.The aim of this project is to provide a JAX-based PCA implementation, eliminating the need for unnecessary data transfer to CPU or conversions to Numpy. This can provide performance benefits when working with large datasets or in GPU-intensive workflowUsageimportpcax# Fit the PCA model with 3 components on your data Xstate=pcax.fit(X,n_components=3)# Transform X to its principal componentsX_pca=pcax.transform(state,X)# Recover the original X from its principal componentsX_recover=pcax.recover(state,X_pca)Installationpcaxcan be installed from PyPI viapippip install pcaxAlternatively, it can be installed directly from the GitHub repository:pip install git+git://github.com/alonfnt/pcax.git
Package: pcbasic
PC-BASICA free, cross-platform emulator for the GW-BASIC family of interpreters.PC-BASIC is a free, cross-platform interpreter for GW-BASIC, Advanced BASIC (BASICA), PCjr Cartridge Basic and Tandy 1000 GWBASIC.
It interprets these BASIC dialects with a high degree of accuracy, aiming for bug-for-bug compatibility.
PC-BASIC emulates the most common video and audio hardware on which these BASICs used to run.
PC-BASIC runs plain-text, tokenised and protected .BAS files.
It implements floating-point arithmetic in the Microsoft Binary Format (MBF) and can therefore
read and write binary data files created by GW-BASIC.PC-BASIC is free and open source software released under the GPL version 3.See also thePC-BASIC home page.Quick Start GuideThis quick start guide covers installation and elementary use of PC-BASIC. For more information, please refer to thePC-BASIC documentation.If you find bugs, pleaseopen an issue on GitHub. It would be most helpful if you could include a short bit of BASIC code that triggers the bug.InstallationPC-BASIC desktop installers for Windows, Mac, and Linux can be downloaded fromGitHub.Python users can obtain the PC-BASIC package fromPyPIthroughpip3 install pcbasic.BASIC survival kitPC-BASIC has a 1980s-style interface operated by executing
typed commands. There is no menu, nor are there any of the visual clues
that we've come to expect of modern software.A few essential commands to help you get around:CommandEffectFILESshow current working directory and its contentsLOAD "PROGRAM"loads the program file namedPROGRAM.BASinto memoryLISTdisplays the BASIC code of the current programRUNstarts the current programSAVE "PROGRAM",Asaves the current program to a text file namedPROGRAM.BASNEWimmediately deletes the current program from memorySYSTEMexits PC-BASIC immediately, discarding any unsaved programUse one of the key combinationsCtrl+Break,Ctrl+Scroll Lock,Ctrl+CorF12+Bto interrupt a running program.Program locationIf started through the start-menu shortcut, PC-BASIC looks for programs in the shortcut's start-in folder.OnWindows, this is yourDocumentsfolder by default.OnMacandLinuxthis is your home directory~/by default.If started from the command prompt, PC-BASIC looks for programs in the current working directory.Seethe documentation on accessing your drivesfor more information.External resourcesSee thecollection of GW-BASIC programs and tutorials.
Package: pcbnewTransition
Pcbnew TransitionThis library allows you to easily support all, KiCAD 7, 6 and 5 in your KiCAD
plugins. It basically monkeypatches KiCAD Python module, so it matches KiCAD 7
API.UsageInstead of:importpcbnewfrompcbnewimportEDA_ANGLEUse:frompcbnewTransitionimportpcbnewfrompcbnewTransition.pcbnewimportEDA_ANGLEAnd then, use the API for KiCAD 8. In case you need to distinguish the versions,
you can:frompcbnewTransitionimportKICAD_VERSION,isV6ifisV6():# something...
Package: pcb-tools
Tools to handle Gerber and Excellon files in Python.Usage Example:importgerberfromgerber.renderimportGerberCairoContext# Read gerber and Excellon filestop_copper=gerber.read('example.GTL')nc_drill=gerber.read('example.txt')# Rendering contextctx=GerberCairoContext()# Create SVG imagetop_copper.render(ctx)nc_drill.render(ctx,'composite.svg')Rendering ExamplesTop Composite renderingBottom Composite renderingSource code for this example can be foundon github.Install from source:$gitclonehttps://github.com/curtacircuitos/pcb-tools.git$cdpcb-tools$pipinstall-rrequirements.txt$pythonsetup.pyinstallDocumentationYou can find the documentation for PCB-Tools onreadthedocs.Development and TestingDependencies for developing and testing pcb-tools are listed in test-requirements.txt. Use of a virtual environment is strongly recommended.$virtualenvvenv$sourcevenv/bin/activate(venv)$pipinstall-rtest-requirements.txt(venv)$pipinstall-e.We use nose to run pcb-tools’s suite of unittests and doctests.(venv)$nosetests
Package: pccora
PC-CORA parser for Python. Supports the format described athttps://badc.nerc.ac.uk/data/ukmo-rad-hires/pc-coradata.html(accessed at 2015-12-05).This format is used forradiosonde data.A radiosonde (Sonde is French and German for probe) is a battery-powered telemetry instrument package carried into the atmosphere usually by a weather balloon that measures various atmospheric parameters and transmits them by radio to a ground receiver. (Wikipedia)This format is produced by oldVaisalaequipments. Newer data is probably available in the NetCDF.HistoryI was asked by a co-worker to look at some Python code with a PC-CORA parser.
This co-worker also needed further analysis and processing, involving some
data being created as CSV, netCDF, or plotted.I decided to write a module for PC-CORA inspired by theoriginal script,
but using Python3, OO, and packaging as a Python package to be distributed
to thePYPI.This way we could use it in scripts, or other internal applications. And it
would also be easier for others to find it and re-use.The code in this repository was used on aDoctoral Thesispublished in 2018,
about radiosonde, GCOS, radio occultation, and weather prediction.Example>>> from pccora import PCCORAParser
>>> pccora_parser = PCCORAParser()
>>> pccora_parser.parse_file('./123456789.EDT')
>>> print(pccora_parser.get_header())
>>> print(pccora_parser.get_identification())
>>> print(pccora_parser.get_data())Obtaining DataThere are datasets available at theCEDA website(Centre for Environmental Data Archival),
however, access is restricted.NOAA’s ESRL(Earth System Research Laboratory)
has an FTP server with some data in the the old PC-CORA sounding data format.
Just search for FTP for instructions on how to access the Physical Sciences
Division FTP server. Some valid files can be found at/psd3/cruises/AERO_1999/RHB/balloon/Raw(accessed 2016-01-17).RequirementsPython 3.6 or superior, and theconstruct libraryare the minimum requirements.Installationpip install pccoraOr, to use the bleeding edge version, git clone this repository, and have a
look at the scripts folders for an example how to use the module from
within a local folder. You may have to uninstall the pip module first.python setup.py installThe PYPI URL ishttps://pypi.python.org/pypi/pccora.LicenseLicensed under the MIT License.
Package: pccs
pccs-pythonPCCS library for PythonPCCS (Practical Color Coordinate System / 日本色研配色体系) is a color
notation system which is developed by Japan Color Research Institute
(日本色彩研究所) in 1966.This library provides conversions between PCCS and Munsell color system
notation based on a scientific algorythm.RequirementsPython 3.9 or newerExamplesReferences
Package: pcdebuginformation
hybridtoolkitGreetings.Hello Reader,I am Ravi Nain. I have created this python package to retrieve system details quick and easy.
This will be useful for developers who need to understand the system or IT guy who want to ensure the Operating system security.This ToolKit is designed to get quick information of Linux Distribution Systems.If you want to contribute please reach me in github issues or via my email (ravinain100@gmail.com)Github Code
Package: pcdiff
pcdiff: Differential operators on point cloudsSimple and small library to compute differential operators (gradient, divergence, Laplacian) on point clouds.InstallationThe recommended installation method is by using pip:pipinstallpcdiffExample usageSeedemo.pyinexamplesfor a full visual demo. For a quick start:importnumpyasnpfrompcdiffimportknn_graph,estimate_basis,build_grad_div# Random point cloudpos=np.random.rand(1000,3)# Generate kNN graphedge_index=knn_graph(pos,20)# Estimate normals and local framesbasis=estimate_basis(pos,edge_index)# Build gradient and divergence operators (Scipy sparse matrices)grad,div=build_grad_div(pos,*basis,edge_index)# ... use gradient and divergence in any task you likeFor sake of simplicity, every operation is written in Numpy and can be accelerated with Numba or Jax. If you would like to use these operators in PyTorch, please refer the github repository forDeltaConv:pip install deltaconvand use the operators fromdeltaconv.geometry.CitationIf you find this library useful in your own work, please cite our paper on DeltaConv, a convolution for point clouds that uses these operators:@Article{Wiersma2022DeltaConv,author={Ruben Wiersma, Ahmad Nasikun, Elmar Eisemann, Klaus Hildebrandt},journal={Transactions on Graphics},title={DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds},year={2022},month=jul,number={4},volume={41},doi={10.1145/3528223.3530166},publisher={ACM},}
Package: pcdm
PCDMProperty Casualty Data ModelSQLAlchemy implementation ofOMG Property Casualty Data ModelDistributionAccording to theObject Management Group:Anyone can download specifications from the OMG website for free, write software implementations that conform to the specifications, and use them, give them away, or sell them. Neither OMG membership nor license is required for this.Installation and DeploymentThis repository requires sqlalchemy, so install it if you don't have it:pip3 install sqlalchemyorpip3 install -r requirements.txtThe file deploy_sqlite contains a script that can be used to deploy a SQLite database:git clone https://github.com/genedan/PCDM
cd PCDM
python3 deploy_sqlite.pyPyPIThis packages is also avaiable on PyPI:pip3 install pcdm
Package: pcdsdaq
Thepcdsdaqmodule provides a higher-level interface for user control of the
LCLS1 DAQ through the LCLS1 DAQ’s Python C libraries,pydaq,pycdb,
andpyami. It provides utilities for controlling DAQ runs and incorporating
the DAQ intoblueskyplans.Requirementspython>= 3.6bluesky>= 1.2.0ophyd>= 1.1.0pydaqpycdbpyami