Saravanan666/pypi-org huggingface dataset:
Package: nixstrings
nixstringsLinux/UNIX/*NIX strings command, in CythonOver the years I've found many programs in digital forensics need this kind of
functionality, some do it by piping strings with a subprocess, others use
regexes, others implement a listcomp of their own.Of those, usually the regex is the fastest, but it turns out you can actually go
a bit faster with the help of C(ython).This module has specialized functions that compile the comparisson of characters
values to get the results as fast as possible. On synthethic benchmarks, theascii_strings()function is almost 5x faster than the equivalent regex (that
would berx = re.compile(f'[{ascii_printables}]{{4,}}')and thenrx.findall(data)), but on real case scenarios it's usually just 2x faster
before I/O becomes the main bottleneck.
Package: nixtlats
NixtlaNixtlaTSForecast using TimeGPTNixtlaTSoffers a collection of classes and methods to interact with the API of TimeGPT.🕰️ TimeGPT: Revolutionizing Time-Series AnalysisDeveloped by Nixtla, TimeGPT is a cutting-edge generative pre-trained transformer model dedicated to prediction tasks. 🚀 By leveraging the most extensive dataset ever – financial, weather, energy, and sales data – TimeGPT brings unparalleled time-series analysis right to your terminal! 👩‍💻👨‍💻In seconds, TimeGPT can discern complex patterns and predict future data points, transforming the landscape of data science and predictive analytics.⚙️ Fine-Tuning: For Precision PredictionIn addition to its core capabilities, TimeGPT supports fine-tuning, enhancing its specialization for specific prediction tasks. 🎯 This feature is like training a machine learning model on a targeted data subset to improve its task-specific performance, making TimeGPT an even more versatile tool for your predictive needs.🔄NixtlaTS: Your Gateway to TimeGPTWithNixtlaTS, you can easily interact with TimeGPT through simple API calls, making the power of TimeGPT readily accessible in your projects.💻 InstallationGetNixtlaTSup and running with a simple pip command:pipinstallnixtlats>=0.1.0🎈 Quick StartGet started with TimeGPT now:df=pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short.csv')fromnixtlatsimportTimeGPTtimegpt=TimeGPT(# defaults to os.environ.get("TIMEGPT_TOKEN")token='my_token_provided_by_nixtla')fcst_df=timegpt.forecast(df,h=24,level=[80,90])
Package: niyofetch
Niyofetch - Your System Info, But BetterFor Chads who use Arch (or any other distro) and aren't afraid to show it.🖥️ Flex your system, regardless of the OS 🌐You say: "I use Arch BTW!", but secretly love Ubuntu? With Niyofetch, you can have your cake and eat it too.🚀 Quick Start1️⃣ Install Niyofetch with pip:pipinstallniyofetch2️⃣ Set an environment variable to your preferred distro:exportshow_distro="Arch Linux"3️⃣ Run Niyofetch:niyofetchVoila, flex time💻 Customize to your likingFeeling like Gentoo today? Or maybe Fedora? Niyofetch got you covered:exportshow_distro="Gentoo"niyofetch🕶️ Ultimate FlexYou've already flexed your system withshow_distro. Now, let's go a step further. Useniyofetchfor the ultimate flex on r/unixporn:aliasneofetch=niyofetchNow, everyneofetchis aniyofetch. Prepare to post your elite setup onr/unixporn!🔗 LinksDependenciesInstallationWiki📝 LicenseThis project is MIT licensed. See theLICENSE.mdfile for details.🙏 AcknowledgementsNiyofetch is based on the originalneofetchproject.
Package: njab
(not) Just Another Biomarker (nJAB)njabis a collection of some python function building on top ofpandas,scikit-learn,statsmodels,pingoin,numpyand more...It aims to formalize a procedure for biomarker discovery which was first developed for
a paper on alcohol-related liver disease, based on mass spectrometry-based proteomics
measurements of blood plasma samples:Niu, L., Thiele, M., Geyer, P. E., Rasmussen, D. N., Webel, H. E.,Santos, A., Gupta, R., Meier, F., Strauss, M., Kjaergaard, M., Lindvig,K., Jacobsen, S., Rasmussen, S., Hansen, T., Krag, A., & Mann, M. (2022).“Noninvasive Proteomic Biomarkers for Alcohol-Related Liver Disease.”Nature Medicine 28 (6): 1277–87.nature.com/articles/s41591-022-01850-yThe approach was formalized for an analysis of inflammation markers of a cohort of patients with alcohol related cirrhosis,
based on OLink-based proteomics measurments of blood plasma samples:Mynster Kronborg, T., Webel, H., O’Connell, M. B., Danielsen, K. V., Hobolth, L., Møller, S., Jensen, R. T., Bendtsen, F., Hansen, T., Rasmussen, S., Juel, H. B., & Kimer, N. (2023).Markers of inflammation predict survival in newly diagnosed cirrhosis: a prospective registry study.Scientific Reports, 13(1), 1–11.nature.com/articles/s41598-023-47384-2InstallationInstall using pip fromPyPiversion.pip install njabor directly from githubpip install git+https://github.com/RasmussenLab/njab.gitTutorialsThe tutorial can be found on the documentation of the project with output
or can be run directly in colab.Explorative Analysis of survival datasetThe tutorial builds on a dataset example of survival of prostatic cancer.The main steps in the tutorial are:Data loading and inspectionUncontrolled binary and t-tests for binary and continous variables respectivelyANCOVA analysis controlling for age and weight, corrected for multiple testingKaplan-Meier plots of for significant featuresBiomarker discovery tutrialAll steps are describe in the tutorial, where you could load your own data with minor adaptions.
The tutorial build on an curatedAlzheimer dataset from omiclearn. See theAlzheimer Datasection for more information.The main steps in the tutorial are:Load and prepare data for machine learningFind a good set of features using cross validationEvaluate and inspect your model retrained on the entire training dataDocumentationPlease find the documentation undernjab.readthedocs.io
Package: njet
njet: Lightweight automatic differentiationA lightweight AD package, using forward-mode automatic differentiation, in order to determine the
higher-order derivatives of a given function in several variables.FeaturesHigher-order (forward-mode) automatic differentiation in several variables.Support for NumPy, SymPy and mpmath.Differentiation of expressions containing nested higher-order derivatives.Complex differentiation (Wirtinger calculus) possible.Faa di Bruno's formula for vector-valued functions implemented.Lightweight and easy to use.InstallationInstall this module with pippipinstallnjetQuickstartAn example function we want to differentiatefromnjet.functionsimportexpf=lambdax,y,z:exp(-0.23*x**2-0.33*x*y-0.11*z**2)Generate a class to handle the derivatives of the given function (in this example up to order 3)fromnjetimportderivedf=derive(f,order=3)Evaluate the derivatives at a specific pointdf(0.4,2.1,1.73){(0,0,0):0.5255977986928584,(0,0,1):-0.2000425221825019,(1,0,0):-0.46094926945363685,(0,1,0):-0.06937890942745731,(0,0,2):-0.03949533176976862,(0,2,0):0.009158016044424365,(1,0,1):0.1754372919540542,(0,1,1):0.026405612928090252,(2,0,0):0.1624775219121247,(1,1,0):-0.11260197000076322,(2,1,0):0.2827794849469999,(1,1,1):0.04285630978229049,(0,1,2):0.005213383793609458,(0,2,1):-0.0034855409065079135,(0,3,0):-0.0012088581178640162,(3,0,0):0.2815805411804125,(2,0,1):-0.061838944839754675,(0,0,3):0.10305063303187477,(1,2,0):0.03775850015116166,(1,0,2):0.034637405962087094}The indices here correspond to the powers of the variables x, y, z
in the multivariate Taylor expansion. They can be translated to
the tensor indices of the corresponding multilinear map using a
built-in routine. Example:Obtain the gradient and the Hessiandf.grad(){(2,):-0.2000425221825019,(0,):-0.46094926945363685,(1,):-0.06937890942745731}df.hess(){(2,2):-0.03949533176976862,(1,1):0.009158016044424365,(0,2):0.1754372919540542,(1,2):0.026405612928090252,(0,0):0.1624775219121247,(0,1):-0.11260197000076322}Further readinghttps://njet.readthedocs.io/en/latest/index.htmlLicensenjet: Automatic Differentiation LibraryCopyright (C) 2021, 2022, 2023 by Malte Titzenjet is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.njet is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License
along with njet. If not, seehttps://www.gnu.org/licenses/.
Package: njinn
Njinn CLI & ClientInstallationYou can install the Njinn CLI fromPyPI:pip install njinnThe client is supported on Python 3.7 and above.Njinn CLI UsagePack installnjinn pack install <repository_url>Use --help to see available options and defaults.Njinn Client UsageThe client allows interacting with Njinn via its REST API.To see available query string parameters, please refer to official Njinn Documentation.Summaryapi=NjinnAPI(host="https://njinn.io",token="*****")# GET /workflowsapi.workflows(limit=2)# GET /workflows/1api.workflows(1)# POST /workflowsapi.create(Workflow(title="Workflow 1"))# PUT /workflows/1api.workflows(1).save()# DELETE /workflows/1api.workflows(1).delete()Get running executions### GET /executions?workflow=1&state=RUNNINGexecutions=api.executions(workflow=1,state="RUNNING")Add a label to executionexecution=api.executions(1)execution.labels["my_label"]="new"# PATCH /executions/1 {...}execution.save(fields="labels")Cancel execution# POST /executions/1/cancel {...}api.executions(1).cancel()Run Workflow# POST /workflows/1/run {...}execution=api.workflows(1).run(input={"variable_1":"value_1"})print(execution.state)Create webhook# POST /hooks {...}webhook=api.create(Webhook(name="W1",title="W1",workflow=1))Disable webhookwebhook=api.hooks(1)webhook.is_active=False# PUT /hooks/1 {...}webhook.save()Delete webhook# DELETE /hooks/1api.webhooks(1).delete()Create config# POST /configs {...}api.create(Config(name="W1",title="W1",values={"key_1":{"value":"value_1","is_secret":False}}))Update configconfig=api.configs(1)config.values["key_1"]={"value":"value_1","is_secret":False}# PUT /configs/1 {...}config.save()Duplicate configconfig=api.configs(1)# POST /configs/1/duplicate {...}duplicate=config.duplicate(name="new",title="New")print(duplicate.id)Get execution log# GET /executions/1/loglog=api.executions(1).log()print(log)Get task execution log# GET /executions/1/tasks/task_1/loglog=api.executions(1).tasks("task_1").log()print(log)
Package: njinn-worker
Njinn WorkerInstallationYou can install the Njinn Worker fromPyPI:pip install njinn-workerThe worker is supported on Python 3.7 and above.UsageWorker registrationnjinn-worker <registration_token>If it's already registered you can run the worker by callingnjinn-workerDockerThe Njinn Worker is also available from dockerhub as njinn/worker. You can also set the registration key via the environment variableNJINN_WORKER_TOKEN
Package: nj-lab01
Lab 1: Repos, Git, and PackagesWelcome to Lab 1! This Python package is designed to provide a basic code that, when run, displays the date and time of your local time zone.InstallationTo install the package, use the following command:pip install nj_lab01-1.3UsageAfter installation, you can use the command line to display the date and time. Open your terminal and run:nj-lab01This will print the current date and time in your local time zone.Project Structuresetup.py:This file contains package information and dependencies.src directory:Contains the Python modules needed for the project.script directory:Contains the command-line script for displaying date and time.README.md:The file you are currently reading, providing information about the project.Examplenj-lab01Current date and time: 2024-02-13 12:34:56 (Your Time Zone)Feel free to explore the source code in the src directory for more details on the implementation.ContributionIf you'd like to contribute to this project or report issues, please visit the GitHub repositoryavailable here.Happy coding!
Package: njord
Njord Python PackageOverviewThe njord python package provides a convenient wrapper to programmatically working with NordVPN servers on Mac/Linux via OpenVPN.PrerequisitessudoopenvpnBecause njord relies onsudoandopenvpn, it only works on OSX (including Apple silicone) and Linux. Linux is currently untested. Nord has anofficial clientthat may better serve your needs on Linux. Windows isn't supported.Installing OpenVPNOSX$ brew install openvpnUbuntu$ sudo apt-get install openvpnInstallation$ pip install njordUsageAuthenticationAuthentication is handled viaservice credentials. Generate these via your Nord account atServices > NordVPN > Manual SetupCopy the service credentials username and password. It's reccomended that you use a .env file for storing them. Njord will check for a .env file on initialization as well as os env variables. Authentication can also be done explicitly during init.# .env
NORD_USER=xxxxxxxxxxxxx
NORD_PASSSWORD=xxxxxxxxxxxfromnjordimportVPN# Using .env file or os envvpn=VPN()# Explicitvpn=VPN(user="xxxxxxxx",password="xxxxxxxxx")Connecting & DisconnectingfromnjordimportVPNvpn=VPN()vpn.connect()vpn.protected()>>Truevpn.disconnect()vpn.protected()>>FalseMIT LicenseCopyright (c) 2023 Adam SmithPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: njswpose
IntroductionThis package is used to estimate human body keypoints, developed by Waterway Research Insitutue of NanjingE-mail:1126557295@qq.comInstallationpipinstallnjswposeExamplefromPILimportImageimportPILfromtorchvision.transformsimporttransformsfromnjswposeimportpose_detectionimage=Image.open('./test.jpg').convert('RGB')loader=transforms.Compose([transforms.ToTensor()])image=loader(image)pose_detection(image,detect_threshold=0.55,vis=True)
Package: njupt
Python API Wrappers for NJUPT.   南京邮电大学网站接口 Python 封装【阅读文档】功能特性正方教务获取课程成绩和绩点获取等级考试信息获取全部课程获取可选课程获取课表校园卡系统获取校园卡余额充值校园卡获取账单获取网费余额充值网费充值寝室电费图书馆系统获取基本信息早锻炼系统查询早锻炼次数和记录安装pipenvinstallnjupt# or pip install njupt# 仅支持python3使用示例这里是一些简单的使用案例fromnjuptimportZhengfangzf=Zhengfang(account='B1xxxxxxx',password='password')>>>zf.get_courses()[{'class_end':9,'class_start':8,'day':1,'name':'市场营销','room':'教4－101','teacher':'王波(男)','week':'第1-15周|单周','interval':2,'week_end':15,'week_start':1},...]fromnjuptimportCardcard=Card(account='11020xxxxxxxxxx',password='passwd')>>>card.recharge_xianlin_elec(amount=2.33,building_name='兰苑11栋',big_room_id='403',small_room_id='1'){'success':True,'code':0,'Msg':'缴费成功！'}更多示例参见examples谁在使用？NJUPT-iCal: 生成课表ics文件并导入系统日历
Package: njuseg
Contents1 Installation and Usage1.1 Installation1.2 Usage2 Performance3 Speed1 Installation and Usage1.1 InstallationInstallation through pip:pipinstallnjuseg1.2 UsagefromnjusegimportSegmentersegmenter=Segmenter.load_model(model_pth,use_gpu=True)sentences=['美国联邦储备委员会 16 日发布的全国经济形势调查报告显示，去年 12 月初至今年 1 月上旬，美国经济继续温和扩张，但美国企业对经济前景的乐观程度有所下降。','美联储注意到了市场对全球经济放缓等风险因素的担心，但当前美国经济发生衰退的风险并未上升。']segmented_sentences=segmenter.seg(sentences)2 Performance2.1 In domain:with pretrained unigram + bigram embeddingPKUMSRCTB5CTB6NLPCC96.6396.5298.1496.1395.823 SpeedOn CPU: 20 k characters per second
On single NVIDIA GTX 1080 GPU: 160 k characters per second
Package: nk225op
#nk225opnk225opis module for fetching latest NK225(Nikkei225) Options Data from JPX Server.nk225opはJPX(日本証券取引所)のサーバーから、最新の日経２２５オプション清算値を取得するモジュールです。ブラウザでお気軽に試す場合　⇒Jupyter Notebook example on Google ColabDemo:　 (２月限と３月限　権利価格　20500～20750 の清算値とIVデータ表示)fromnk225opimportnk225opasnknk([201902,201903],[20500,20750])Output:lbPRICEIV02/C2050034018.328902/C2062526017.582602/C2075019517.2045.............(以下省略）Install:　(Google Colaboratory 上での利用方法 )!pip install nk225opブラウザでお気軽に試す場合　⇒Jupyter Notebook example on Google Colab※ローカル環境にInstallする場合pip install nk225opRequirementPandasbs4利用できる関数:defnk225op(maturities=None,strike_range=None):"""Download latest Options DATA(NK255) from JPX:param maturities: List#eg1(monthly type): [201902]#eg2(weekly type): [20190125]#eg3: [201902 , 201903 , 20190125]:param strike_range: List#eg: [19000,20000]:return: DataFrameOptionsPriceList :Examples-------->>>  nk225op([201902],[20500, 21000])"""deflatest_csv_path():"""JPXサーバ内にある最新の清算値CSVファイルへのPATHを返す"""defcsv_to_df(csv_path,max_maturity_day=90):"""清算値CSVファイルをDataFrameに変換Parameters----------csv_path : strmax_maturity_day :  int , default 90最大、SQ期日が何日後までのデータを取得するか（規定値９０日後）Returns-------DataFrame"""
Package: nka
Input Sequence Length
Transformer models like BERT / RoBERTa / DistilBERT etc. the runtime and the memory requirement grows quadratic with the input length. This limits transformers to inputs of certain lengths. A common value for BERT & Co. are 512 word pieces, which corresponde to about 300-400 words (for English). Longer texts than this are truncated to the first x word pieces.By default, the provided methods use a limit fo 128 word pieces, longer inputs will be truncated. You can get and set the maximal sequence length like this:An example package. Generated with cookiecutter-pylibrary.Free software: MIT licenseInstallationpip install nkaYou can also install the in-development version with:pip install https://github.com/tobibias/neural-keyword-assignment/archive/main.zipDocumentationhttps://neural-keyword-assignment.readthedocs.io/DevelopmentTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.0 (2022-06-22)First release on PyPI.
Package: nkdfu
Nitrokey DFU tool - nkdfunkdfuis a Python DFU tool for updating Nitrokeys' firmware. Currently supports Nitrokey Pro only.Based onpython-dfuproject, which brings implementation of USB DFU 1.1 spec.Call$ nkdfu FIRMWARE_PATH <flags>Seenkdfu --helpfor details.InstallationFrom Repository$pip3installnkdfuDirectly from releasesIt is possible to install it directly from the releases page:$pip3installhttps://github.com/Nitrokey/nkdfu/releases/download/v0.1/nkdfu-0.1-py3-none-any.whlLicenseLicense follows upstream - GPLv2. SeeLICENSEfor details.
Package: nkeys
NKEYS for Python3A public-key signature system based onEd25519for the NATS ecosystem.AboutThe NATS ecosystem will be moving toEd25519keys for identity, authentication and authorization for entities such as Accounts, Users, Servers and Clusters.Ed25519 is fast and resistant to side channel attacks. Generation of a seed key is all that is needed to be stored and kept safe, as the seed can generate both the public and private keys.The NATS system will utilize Ed25519 keys, meaning that NATS systems will never store or even have access to any private keys. Authentication will utilize a random challenge response mechanism.Dealing with 32 byte and 64 byte raw keys can be challenging. NKEYS is designed to formulate keys in a much friendlier fashion and references work done in cryptocurrencies, specificallyStellar.	Bitcoin and others used a form of Base58 (or Base58Check) to endode raw keys. Stellar utilized a more traditonal Base32 with a CRC16 and a version or prefix byte. NKEYS utilizes a similar format where the prefix will be 1 byte for public and private keys and will be 2 bytes for seeds. The base32 encoding of these prefixes will yield friendly human readbable prefixes, e.g. 'N' = server, 'C' = cluster, 'O' = operator, 'A' = account, and 'U' = user. 'P' is used for private keys. For seeds, the first encoded prefix is 'S', and the second character will be the type for the public key, e.g. "SU" is a seed for a user key pair, "SA" is a seed for an account key pair.InstallationpipinstallnkeysBasic API Usageimportnkeysimportos# Create an NKEYS KeyPair from a seed file.user=Nonewithopen("user.nkey",'rb',buffering=0)asf:# We compute the size of the file to allocate the required# bytearray size in order to have control over the memory# and be able to wipe it once the keys are not needed anymore.seed=bytearray(os.fstat(f.fileno()).st_size)f.readinto(seed)user=nkeys.from_seed(seed)# Sign some data with the KeyPair user.data=b'arGTKH8q7XDmgy0'sig=user.sign(data)# Verify the signaturetry:user.verify(data,sig)exceptnkeys.ErrInvalidSignaturease:print("Error:",e)# Access the seed, the only thing that needs to be stored and kept safe.print(user.seed)# Remove any secrets that were in use by the KeyPair.user.wipe()LicenseUnless otherwise noted, the NATS source files are distributed under the Apache Version 2.0 license found in the LICENSE file.
Package: nkf
Python Interface to NKFCopyright (c) 2012-2024 SATOH Fumiyasu @ OSSTech Corp., JapanCopyright (c) 2005 Matsumoto, Tadashi (original author)License: BSDDevelopment home:https://github.com/fumiyas/python-nkfAuthor's home:https://fumiyas.github.io/How to InstallInstall from PyPI:$sudopipinstallnkfInstall from source tree:$make$sudomakeinstallUsage## flag is same as the flags of nkf itselfimportnkfoutput=nkf.nkf(flag,input)## For example, to convert from euc-jp to utf-8output=nkf.nkf('-Ew','some euc-jp string')## Guess character encodinginput_encoding=nkf.guess('some string')guess()function guesses an input string encoding and returns
one of next strings:BINARYASCIIShift_JISCP932EUC-JPEUCJP-MSCP51932ISO-2022-JPCP50221CP50220UTF-8UTF-16UTF-32
Package: nkia
NK Artificial IntelligenceWelcome to NKIA or NKAI in English. This project is used to categorize products based on its caracteristics, there is 29 different classes of food products. Another usage is to distinguish between food and non-food products.RequirementsInstall requirements:pip install -r requirements.txt --userInstall NLTK stopwords:python -m nltk.downloader stopwordsUsageImport classify class:from nkia.ml.classify_products import ClassifyProducts as cpPredict product class:cp().inference_from_category_model(product_name=['frozen fish'])Returns: String containing the predicted class nameParameters:inference_from_category_model(ingredients=[''], allergics=[''], description=[''], product_name=[''])All the parameters must be in a single string format.Predict if product is a food or not:cp().inference_from_food_model(product_name=['chocolate cake'])Returns: String 'food' or 'not food'Note: Food classifier just has the product name as parameter.Examplescp().inference_from_category_model(ingredients=['sugar salt vanilla orange'], allergics=['peanuts'], description=['this is an awesome product, hummmm'], product_name=['very delicious candy'])