Saravanan666/pypi-org huggingface dataset:
Package: nelsnmp
Nelsnmp is used bynelmonand theCisco SNMP Ansible modules.Release History0.2.9Add support for HPE (Procurve)Add support for Extreme (ExtremeXOS)0.2.8Add support for very old Cisco IOS devices0.2.7Add support for Metamako to HostInfo0.2.6SnmpHandler now requires version as argument0.2.5Added Ericsson SmartEdge (Redback) support to HostInfoAdded Juniper Junos support to HostInfo0.2.4Added Arista EOS support to HostInfo0.2.3Fix to handle changed return values from pysnmp 4.3.00.2.2Changes to how Synology NAS versions are reported0.2.1Renamed HostInfo classAdded IOS-XR, IOS-XE, Cisco AireOS (WLC) support to HostInfoAdded Synology NAS support to HostInfoAdded Huawei VRP support to HostInfoAdded Alcatel Timos support to HostInfoAdded product version oid from AIRESPACE-SWITCHING-MIB0.2.0Added table oids from CISCO-ENVMON-MIB0.1.9Added oids from CISCO-RTTMON-MIB0.1.8Internal structure changes. All vendor features should be created under nelsnmp.vendors.Implemented Hostinfo check to get vendor and version (for Cisco devices)Marked nelsnmp.cisco_oids as deprecated, use nelsnmp.vendors.cisco.oidsAdded sysORTableAdded oids from CISCO-ENVMON-MIB and CISCO-ENTITY-SENSOR-MIB0.1.7Made error handling more flexible0.1.6Added oids from CISCO-CONFIG-COPY-MIB0.1.5Added ability to set snmp portAdded cdpCacheDeviceId, cdpCacheDevicePort0.1.4Created changes file
Package: nelson
==============nelson==============Projects for Georgia Tech's OMSCS program or for Udacity's advanced nanodegree programs are typically distributed with project-specific submission scripts that handle the submission to Udacity's advanced automatic feedback service.  These scripts typically rely on this underlying library, which contains most of the shared functionality.Examples---------This is simple example for GTOMSCS.  It submits the file hello_world.py to the quiz named "hello_world" within the course "cs101".::from nelson.gtomscs import submitdef main():course = 'cs101'quiz = 'hello_world'filenames = ['hello_world.py']submit(course, quiz, filenames)if __name__ == '__main__':main()For Udacity, simply import from ``nelson.udacity`` instead of ``nelson.gtomscs``.Typical output would be something like::GT Login required.Username :jbarros7Password :Save the jwt?[y,N]ySubmission includes the following files:helloworld.cUploading submission...[=========================== 100% ===========================] 376/376Waiting for results...Done!Results:--------Tests that the code compiles cleanly:                                        5/5Tests that the code prints "Hello World!":                                   5/5(Details available in helloworld-result-2017-01-04-22-41-22.json)
Package: nely-df2d
Drosophila 2D PoseLoad the model.frommodelimportDrosophila2DPosefromparserimportcreate_parsercheckpoint_path='/home/user/Desktop/DeepFly3D/weights/sh8_deepfly.tar'args=create_parser().parse_args('')model=Drosophila2DPose(checkpoint_path=checkpoint_path,**args.__dict__).cuda()Load the data.frominferenceimportpath2inpfromdatasetimportDrosophila2Datasetfromtorch.utils.dataimportDataLoaderimage_path='/home/user/Desktop/DeepFly3D/data/test/'inp=path2inp(image_path)# extract list of images under the folderdat=DataLoader(Drosophila2Dataset(inp),batch_size=8)Do the inference.frominferenceimportinferencepoints2d=inference(model,dat)
Package: nely-pyba
Python Bundle AdjustmentLoad the data.frompyba.CameraNetworkimportCameraNetworkimportpickleimportglobimportnumpyasnpimage_path='./data/test/camera_{cam_id}_img_00000{img_id}.jpg'pr_path='./data/test/df3d_2/pose_result*.pkl'd=pickle.load(open(glob.glob(pr_path)[0],'rb'))camNet=CameraNetwork(points2d=d['points2d'],calib=d,image_path=image_path)points2d is a numpy array with shape T x J x 2.
All units are in pixels. calib is a nested dictionary where keys are camera id's, indexed starting from 0 up to n_cameras-1.
values are another sets of dictionaries with keys  "R", "tvec", "intr", "distort"calib={0:{'R':array([[0.90885957,0.006461,-0.41705219],[0.01010426,0.99924554,0.03750006],[0.41697983,-0.0382963,0.90810859]]),'tvec':array([1.65191596e+00,2.22582670e-02,1.18353733e+02]),'intr':array([[1.60410e+04,0.00000e+00,2.40000e+02],[0.00000e+00,1.59717e+04,4.80000e+02],[0.00000e+00,0.00000e+00,1.00000e+00]]),'distort':array([0.,0.,0.,0.,0.])},1:{'R':array([[0.59137248,0.02689833,-0.80594979],[-0.00894927,0.9996009,0.02679478],[0.80634887,-0.00863303,0.59137718]]),'tvec':array([1.02706542e+00,-9.25820468e-02,1.18251732e+02]),'intr':array([[1.60410e+04,0.00000e+00,2.40000e+02],[0.00000e+00,1.59717e+04,4.80000e+02],[0.00000e+00,0.00000e+00,1.00000e+00]]),'distort':array([0.,0.,0.,0.,0.])},}Visualize the 2d pose.importmatplotlib.pyplotaspltimg=camNet.plot_2d(0,points='points2d')plt.figure(figsize=(20,20))plt.imshow(img,cmap='gray')plt.axis('off')Do the bundle adjustment.frompyba.pybaimportbundle_adjustbundle_adjust(camNet)Iteration     Total nfev        Cost      Cost reduction    Step norm     Optimality   
       0              1         7.1659e+05                                    7.27e+05    
       1              2         2.9376e+05      4.23e+05       1.08e+01       3.12e+05    
       2              4         2.6084e+05      3.29e+04       2.39e+00       1.85e+05    
       3              5         2.4676e+05      1.41e+04       3.04e+00       2.20e+04    
       4              7         2.4604e+05      7.20e+02       1.32e+00       1.75e+04    
       5              8         2.4579e+05      2.53e+02       2.67e+00       2.86e+04    
       6              9         2.4487e+05      9.20e+02       2.53e+00       2.18e+04    
       7             10         2.4472e+05      1.43e+02       2.48e+00       2.02e+04    
       8             11         2.4441e+05      3.18e+02       6.71e-01       1.77e+03    
       9             12         2.4440e+05      9.43e+00       6.78e-01       2.13e+03    
`ftol` termination condition is satisfied.
Function evaluations 12, initial cost 7.1659e+05, final cost 2.4440e+05, first-order optimality 2.13e+03.Visualize the resulting camera rig.fig=plt.figure(figsize=(10,10))ax3d=fig.add_subplot(111,projection='3d')camNet.draw(ax3d,size=20)camNet.plot_3d(ax3d,img_id=0,size=10)
Package: nemapi
nemapiAccess to public National Electricity Market data through nemAPI.Description & UsageCurrently users can access the following tables within the AEMO MMS Data Model provided atnemweb.NEM Dispatch Interval Data:dispatch_pricedispatch_regionsumdispatch_interconnectorresNEM Trading Interval Data:trading_pricetrading_regionsumtrading_interconnectorresThere are two main methods for returning data. Firstly a get_['table'] method which will return the most current data back a trailing number of dispatch/trading intervals the user defines. The second is a get_['table']_historical where the user can define a start_date and end_date in addition to selecting over either regionid (price and regionsum tables) or connectorid (interconnectorres tables).Note: Each data request is limited to 10,000 rows.Examplesfromnemapiimport*nemapi=nemapi()df=nemapi.get_dispatch_price(intervals=1)print('dispatch prices:\n',df)df1=nemapi.get_dispatch_regionsum_historical(start_date='2021-01-01 00:30:00',end_date='2021-01-01 12:00:00',regionid='ALL')print('dispatch prices:\n',df1)df2=nemapi.get_trading_interconnectorres_historical(start_date='2021-01-01 00:00:00',end_date='2021-01-02 00:00:00',connectorid='NSW1-QLD1')print('trading NSW1-QLD1 interconnector historical :\n',df2)InstallationUse the package managerpipto install nemapi.pipinstallnemapi
pipinstallnemapi--upgradeRequirementsPython modules required:datetimerequestspandasCompatibilityPython3LicenceTheMITLicense (MIT)Authorsnemmarketapiwas written byJonathon Emerick <jonathon.emerick@uq.net.au>_.
Package: nemcore
NetEase Cloud Music ApiClient网易云音乐核心 API 客户端。这个项目的目的是抽离一个干净的 API Client，便于二次开发和维护。主要代码来自NetEase-MusicBox，非常感谢每一位该项目的贡献者。警告，目前 API 尚未稳定，不保证兼容性。万一有新点子说不定就会改。此外欢迎 code review 和 pull request。安装使用 pip 安装pipinstallNEMCore文档生成需要额外依赖项pipinstallNEMCore[docs]单元测试需要额外依赖pipinstallNEMCore[test]使用quickstartfromnemcore.apiimportNetEaseApinetease=NetEaseApi(cookie_path='./cookies')netease.login('cloudmusic@163.com','password')# 获取我的歌单playlists=netease.get_user_playlist()# 获取日推recommend=netease.get_recommend_songs()# 签到netease.daily_task()详细的 api 文档和快速开始请参考这里。v1.0 开发计划添加测试用例规范命名和返回值结构提供可配置的缓存(是否持久化，缓存有效时间等)提供文档，挂在readthedocs.io上。重构简化 api 和实现。提供助手函数，实现一些常用操作移除 python2 支持(__future__等)，迁移到 python3.6+支持异步(考虑aiohttp)changelogcurrent0.1.8fix ImportError about filelock0.1.7BREAKING CHANGE !API 返回类型修改和标注0.1.6修复login接口登录失败错误0.1.5本版本主要修复影响运行的问题，把包构建从poetry改为setuptools。在poetry可用性足够之前不会用它了。修复from cachetools.ttl import default_timer失败的问题从poetry迁移到setuptools有一些已知的问题如下。login会抛出错误代码501，暂时没找到好办法处理。0.1.4本版本主要是对代码进行重构，将核心 Api 类清晰化，解耦无关逻辑，简化了使用。此外，提供了比较详细的入门文档，帮助使用者快速了解使用方式和 api 的响应内容。不过 api 文档不是很好，需要改进。nemcore.netease模块重命名成nemcore.apinemcore.netease.NetEase重命名成nemcore.api.NetEaseApi删除nemcore.conf模块删除nemcore.storage模块删除nemcore.parser模块删除nemcore.pdict模块添加 sphinx 文档和快速开始指引，文档已经挂到了 readthedocs.io0.1.3支持缓存。基于pickle和cachetools实现，可配置缓存时间和是否持久化
Package: nemdata
nem-dataA simple & opinionated Python command line tool to access Australian National Energy Market (NEM) data provided by the Australian Energy Market Operator (AEMO).  It features:access to the NEMDE dataset as well as the predispatch, unit-scada, trading-price, demand and interconnectors tables from MMSDM,a cache to not re-download data that already exists in~/nem-data/data,addsinterval-startandinterval-endcolumns.It is designed for use by researchers & data scientists - this tool supports my personal research work.  It is not designed for production use - seeNEMOSISfor a production grade package.SeeA Hackers Guide to AEMO & NEM Datafor more context on the data provided by AEMO.Setup$pipinstallnemdataUseCLI$nemdata--helpUsage: nemdata [OPTIONS]Downloads NEM data from AEMO.Options:-t, --table TEXT          Available tables: nemde, dispatch-price,predispatch, unit-scada, trading-price, demand,interconnectors, p5min, predispatch-sensitivities,predispatch-demand.-s, --start TEXT          Start date (YYYY-MM or YYYY-MM-DD for NEMDE).-e, --end TEXT            End date (incusive) (YYYY-MM or YYYY-MM-DD forNEMDE).--dry-run / --no-dry-run  Whether to save downloaded data to disk.--help                    Show this message and exit.Download NEMDE data for the first three days in January 2018:$nemdata-tnemde--start2018-01-01--end2018-01-03Download trading price data from MMSDM for January to March 2018:$nemdata-ttrading-price-s2018-01-e2018-03PythonDownload trading price data from MMSDM for January to Feburary 2020:importnemdatadata=nemdata.download(start="2020-01",end="2020-02",table="trading-price")Load this data back into a pandas DataFrame:data=nemdata.load()['trading-price']At this point,datawill have the trading price for all regions.DataDownloaded into into$HOME/nem-data/data/:$nemdata-ttrading-price-s2020-01-e2020-02$tree~/nem-data/Users/adam/nem-data└── data└── trading-price├── 2020-01│   ├── PUBLIC_DVD_TRADINGPRICE_202001010000.CSV│   ├── clean.csv│   ├── clean.parquet│   └── raw.zip└── 2020-02├── PUBLIC_DVD_TRADINGPRICE_202002010000.CSV├── clean.csv├── clean.parquet└── raw.zipA few things happen during data processing:rows of the raw CSV are removed to create a rectangular, single table CSV,interval-startandinterval-endtimezone aware datetime columns are added,when usingnemdata.loader.loaderfortrading-price, all data is resampled to a 5 minute frequency (both before and after the 30 to 5 minute settlement interval change).
Package: nem-ed25519
nem-ed25519===========NEM implementation ed26619 encryption modules for Python.NEM use Keccak hash function, not same SHA3.Require-------Python3 (>=3.5)how to use-----Please look [test codes folder.](test)Install------```commandlinepip install nem-ed25519orpip install git+https://github.com/namuyan/nem-ed25519.git```This version need GMP.*For Linux*```apt-get install python3-gmpy2 libgmp3-dev libmpc-devapt install libmpfr-devpip install gmpy2```*For windows*Download [pythonlibs](https://www.lfd.uci.edu/~gohlke/pythonlibs/#gmpy)`pip install gmpy2‑2.0.8‑cp36‑cp36m‑win_amd64.whl` If you use Python3.6 64bitSamples1------```pythonfrom nem_ed25519.key import secret_key, public_key, get_address# secret keysk = secret_key()# public keypk = public_key(sk)# compressed keyck = get_address(pk, main_net=True)from nem_ed25519.signature import sign, verify# sign messagesign = sign(msg=b'hello world', sk=sk, pk=pk)# verify messageverify(msg=b'hello world', sign=sign, pk=pk)from nem_ed25519.encrypt import encrypt, decrypt# encrypt/decrypt messagesk1 = secret_key()pk1 = public_key(sk1)enc = encrypt(sk=sk, pk=pk1, msg=b'Hot potato.')dec = decrypt(sk=sk1, pk=pk, enc=enc)```Samples2--------Import setting at first, and you can select encode mode.Please look at [allinone.py](test/allinone.py)```pythonfrom nem_ed25519.base import Encryptionecc = Encryption()```Author------[@namuyan_mine](http://twitter.com/namuyan_mine/)Licence-------MIT
Package: nemesis-daxfeliz
NEMESISexoplanet traNsit survEy of nearbyM-dwarfs in tESs full frameImageSDescriptionThis repository contains documentation, code, and small chunks of data
supporting the NEMESIS project.To download data products from NEMESIS Planet Candidates detected in data from Sectors 1 to 5, visit ourFiltergraph Portal.AuthorDax FelizLicenseMITInstallconda env create -f environment.yml -n nemesisUpdate envconda env export --no-builds > environment.yml
Package: nemesispy
This library contains routines for simulating and fitting
exoplanet emission spectra at arbitrary orbital phase,
thereby constraining the thermal structure and chemical
abundance of exoplanet atmospheres. It is also capable
of fitting emission spectra at multiple orbital phases
(phase curves) at the same time. This package
comes ready with some spectral data and General Circulation
Model (GCM) )data so you could start simulating spectra
straight away. There are a few demonstration routines in
thenemesispyfolder; in particulardemo_fit_eclipse.pycontains an interactive plot routine which allows you
to fit a hot Jupiter eclipse spectrum by hand by varying
its chemical abundance and temperature profile. This package
can be easily integrated with a Bayesian sampler, in particularMultiNestfor a full spectral retrieval.The radiative transfer calculations are done with the
correlated-k approximation, and are accelerated with thenumbajust-in-time compiler to match the speed of
compiled languages such as Fortran. The radiative transfer
routines are based on the well-testedNemesislibrary developed
by Patrick Irwin (University of Oxford) and collaborators.This package has the following advantageous features:Highly portable and customisable compared
to packages written in compiled languages, and
can be easily installed on computer clusters.Fast calculation speed due to just-in-time
compilation, which compiles Python code to machine
code at run time.Radiative transfer routines are benchmarked against
the extensively usedNemesislibrary.Contains interactive plotting routines that allows you
to visualise the impact of gas abundance and thermal
structure on the emission spectra.Contains routine to simulate spectra from General
Circulation Models (GCMs).Contains unit tests so that you could check if the
the code is working properly after your modifications.
Package: nemesis-scan
NemesisDescriptionA simple tool for scanning urls for vulnerabilites and sensitive information. It has lot of pre defined regexes for dom xss possibility detection, secrets leakage, hidden parameters, extra links and much more.FeaturesSupports scanning of both html and javascript urlsPre defined regexes for dom xss (sinks & sources), web services, hidden parameters, endpoints and a lot more are already present.Shannon entropy helps to find additional suspicious data that can be missed by regexes but may generate false positive so disabled by default.Usageusage: Nemesis.py [-h] [--- | -w WORDLIST | -u URL] [-o OUTPUT] [-e] [-t THREADS] [-b]

Nemesis

optional arguments:
  -h, --help            show this help message and exit
  ---, ---              Stdin
  -w WORDLIST, --wordlist WORDLIST
                        Absolute path of wordlist
  -u URL, --url URL     url to scan
  -o OUTPUT, --output OUTPUT
                        Output file
  -e, --enable-entropy  Enable entropy search
  -t THREADS, --threads THREADS
                        Number of threads
  -b, --banner          Print banner and exit

Enjoy bug huntingExampleScan a single urlNemesis -u google.comorNemesis -u https://google.com/closurelibrary.jsScan from URLsNemesis -w hakrawler.txtLimitationsOutput maybe repeated such as same links again and againOutput to file saving is in workAdditional logical errors and false positivies from faulty regexSupportIf you wanted to support me freely for the tools I create, chekoutthis out.
Package: nemf
NEMF: network-based ecosystem modeling frameworkNEMF is a ecosystem modelling framework written in python.
It is designed to offer an easy to use method for modelling ecosystems with low- to intermediate complexity.The framework offers the functionality to handle non-equilibrium, non-linear interactions.
For the typical use-cases, user do need to write any code but only provide a model configuration.
The use-cases can easily be extended with simple user written functions without needing to change any of the framework code.The framework offers an easy to use method that fits any parameter of the model to mimic the studied system.
For simplicity, the current version of the framework is limited to non-spatially resolved models (box-models).A detailed introduction and many more resources can be found at: nemf.readthedocs.ioInstallationThe package can be installed using pythons integrated package managerpip.pipinstallnemfFor a detailed description how it can be installed, seeinstall notesQuick StartSee examples.py. For a set of exemplary framework configurations seeconfiguration_filesUsageimportnemfas# A model configuration is defined in a yaml file. To read this file:model=nemf.load_model('path/to/model_configuration.yml')# To draw a graph visualizing the model configuration:nemf.interaction_graph(model)# To solve the time integration of this model call:forward_results=nemf.forward_model(model)# To also solve the inverse problem as defined in the configuration, call:inverse_results=nemf.inverse_model(model)# To plot the results callnemf.plot.output_summary(inverse_results)For details on the yaml configuration file, seeyaml documentationThe presented functions offer many (crucial) options, which are discussed in
thedocumentation.How does it work?For a conceptual description of the internals of this library, seemodel conceptExampleA simple example might be a simple NPZD model as presented below:The user needs to define the compartments and interactions between them.
If the model should be fitted, they also need to provide some constraints and some data to fit it to.After that the model automatically generates a graph to visualize the system and to find potential errors in the configuration.Once the model is configured the framework handles the time integration as well as the fitting without any further required user-interaction.The results of such a run might then look like the following:Top left shows the cost of the current model configuration. The cost is a quadratic measure of distance of the current model to the desired model.
Top right shows a output of the model after it reached its steady state for every parameter set tested.
Bottom left shows the all tested parameters sets.
Bottom right shows the full model output of the best fitted model found.
Package: nemglo
NEMGLONEMGLO, the Green-energy Load Optimisation tool for the Australian National Electricity Market (NEM) is a python tool allows users to counterfactually assess flexible load operating strategies in the NEM using historical market data. Specifically, the tool enables users to test various scenarios with differing PPA contracting structures and green-certificate schemes, a primary case study being the ability to 'validate' the integrity of "green"-hydrogen produced via grid-connected electrolysis with bundled PPA contracting. Although the tool was developed contextually for electrolyser loads, the functionalities can inherently be abstracted to commercial/industrial flexible loads seeking to consume green-energy.Customisable Tool Features:Load Operating Characteristics (Min stable load, ramp rates, etc.)Multiple Power Purchase Agreements (contract volume, strike, floor, etc.)Renewable Energy Certificate procurement + surrender (with/without temporal matching)Shadow Pricing of Grid Emissions Intensity (average & marginal emissions)Constrain to Green Energy Certification Standards for H2 (max tCO2 content per tH2)Project LinksProject Homepage:https://www.nemglo.org/Python Github Repository:https://github.com/dec-heim/NEMGLOPython Documentation:https://nemglo.readthedocs.io/en/latest/about.htmlNEMGLO App Github Repository:https://github.com/dec-heim/NEMGLO-appInstallationpipinstallnemgloFor guidance on installation and usage ofNEMGLO-app(a graphical user interface), please refer to its project page.UsageFor guidance onNEMGLOusage, see theExamplessection of the documentation.ContributingInterested in contributing? Check out thecontributing guidelines.
Please note that this project is released with aCode of Conduct. By contributing to this project, you agree to abide by its terms.LicenseNEMGLOwas created by Declan Heim as a Master's Project at UNSW. It is licensed under the terms of theBSD 3-Clause license.CreditsTheNEMGLOproject is affilitated with theUNSW Collaboration on Energy and Environmental Markets. TheNEMGLO-appwas financially supported by theUNSW Digital Grid Futures Instituteto Dec-2022.NEMGLOincorporates functionality from a suite of UNSW-CEEM tools, namely,NEMOSISto extract historical market data andNEMEDto compute emissions data from AEMO's MMS databases respectively. The structure of the optimiser codebase is further adopted from sister toolnempyundernempy licence.AcknowlgementsMany thanks to:Jay Anand, co-developer ofNEMGLO-app.Nick Gorman, Abhijith Prakash, and Shayan Naderi for pointers onNEMGLOcode development (including adopted NEM tools).Iain MacGill, Anna Bruce, Rahman Daiyan, and Jack Sheppard for advice at various stages to this project.ContactQuestions and feedback are very much welcomed. Please reach out by email todeclanheim@outlook.com