Saravanan666/pypi-org huggingface dataset:
Package: nomix
Nomix v1.0OverviewThis Python module generates random names based on parameters such as gender and nationality. It provides functions to retrieve random names and last names from various cultures.Table of ContentsInstallationFunctionsget_nameget_name_randomget_lastnameget_lastname_randomget_fullnameget_fullname_randomContributingLicenseInstallationClone the repository:gitclonehttps://github.com/tomasilluminati/NomixOr use the pip command:pipinstallnomixUsageImport the module in your projectimportnomixFunctionsget_name()Returns a random name based on the specified data type. Available data types include:"USA_MALE_NAME""USA_FEMALE_NAME""ENGLISH_MALE_NAME""ENGLISH_FEMALE_NAME""FRENCH_MALE_NAME""FRENCH_FEMALE_NAME""SPANISH_MALE_NAME""SPANISH_FEMALE_NAME""RUSSIAN_MALE_NAME""RUSSIAN_FEMALE_NAME""GERMAN_MALE_NAME""GERMAN_FEMALE_NAME""CHINESE_MALE_NAME""CHINESE_FEMALE_NAME""GREEK_MALE_NAME""GREEK_FEMALE_NAME""JAPANESE_MALE_NAME""JAPANESE_FEMALE_NAME""KOREAN_MALE_NAME""KOREAN_FEMALE_NAME""HINDI_MALE_NAME""HINDI_FEMALE_NAME""ITALIAN_MALE_NAME""ITALIAN_FEMALE_NAME""AFRICAN_MALE_NAME""AFRICAN_FEMALE_NAME""PORTUGUESE_MALE_NAME""PORTUGUESE_FEMALE_NAME"Example:importnomixname=nomix.get_name("USA_MALE_NAME")print(name)#Output: Maxwellget_name_random()Returns a random name from any available nationality and gender (No required parameters).Example:importnomixname=nomix.get_name_random()print(name)#Output: Yuliaget_lastname()Returns a random last name based on the specified data type. Available data types include:"USA_LASTNAME""ENGLISH_LASTNAME""FRENCH_LASTNAME""SPANISH_LASTNAME""RUSSIAN_LASTNAME""GERMAN_LASTNAME""CHINESE_LASTNAME""GREEK_LASTNAME""JAPANESE_LASTNAME""KOREAN_LASTNAME""HINDI_LASTNAME""ITALIAN_LASTNAME""AFRICAN_LASTNAME""PORTUGUESE_LASTNAME"Example:importnomixlastname=nomix.get_lastname("JAPANESE_LASTNAME")print(lastname)#Output: Matsuiget_lastname_random()Returns a random last name from any available nationality (No required parameters).Example:importnomixlastname=nomix.get_lastname_random()print(lastname)#Output: Sanderget_fullname()Returns a full name based on the specified genre (male/female) and nationality. If genre or nationality is not provided, it randomly selects them.Parametersgenre: (Optional) The gender of the name. It can be "MALE" for male or "FEMALE" for female.nationality: (Optional) The nationality of the name. It must be one of the following options:"USA""ENGLISH"FRENCH""SPANISH""RUSSIAN""GERMAN""CHINESE""GREEK""JAPANESE""KOREAN""HINDI""ITALIAN""AFRICAN""PORTUGUESE".Example:importnomixfull_name=nomix.get_fullname("MALE","KOREAN")print(full_name)#Output: Younghoon Seolget_fullname_random()Returns a random full name from any available nationality and gender (No required parameters).Example:importnomixfull_name=nomix.get_fullname_random()print(full_name)#Output: Anatoliy AfanasievContributingContributions are welcome! Please feel free to open a pull request.LicenseThis project is licensed under theMIT License.CopyrightCopyright (c) 2024 Tomas Illuminati
Package: nomnomdata-tools-engine
Installationpip install nomnomdata-tools-engine nomnomdata-engineCreating a new appInstructions for how to create, build and deploy a new app available here:https://support.nomnomdata.com/portal/en/kb/articles/creating-your-first-nnd-appAdditional toolkit information available here:http://developer.nomnomdata.com/
Package: nomoji
NoMoJiNoMoJi(Number-Emoji) is a simple and silly Python package to print integer numbers using Emoji digits.Plus there is also a fancyGAME_DIEto use for apps likednd-roller.This project is used as part of the PyConUS tutorialPublishing your Python project, the conda waypresented atPyConUSby:Dave ClementsBianca HendersonMahe Iram KhanValerio MaggioThis package will be published on PyPi and it will be used solely
to demonstrate how to addpipdependecies to acondapackage.Usagefromnomojiimportemojizeemojize(2)This would produce 2️⃣ (the number as keypad emoji).Similarly:emojize(10)would generate the 🔟 emoji unicode character, while:emojize(24)would result in 2️⃣4️⃣.
Package: nomoney.shop
nomoney.shopThis year, 2020, the world economy as a guarant of wealth is going to get damaged.
Currently the 'experts' have a huge spread of future predictions.That is why I created this project.It's purpose is to deliver people with the needed goods and services without the need to buy with money.Easyly list down the things you are searching for and do the same with things you can offer therefore.
If there is another user, offering the things you search and searching what your offer, you guessed it, it will show you the possible deal.Often these kind of deals aren't possible.
The following constellation is more common:
A needs Potatoes and offers Water
B needs Strawberries and offers Potatoes
C needs Water and offers StrawberriesBeing not easy to be calculated this kind of deal is easy to calculate for a computer.Getting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Have a look at thedocumentation.Introducing notesClone it, use it, extend itPrerequisitesIt should work Python3.4+ but tests are running on 3.5 and 3.7.This project makes use of the following libraries:Django- The web framework for perfectionists with deadlines.InstallingYou can install the latest release from pip:pip install ...Short exampleRunning the testsRename 'secrets.sample.py' in tests directory to 'secrets.py' and include your e-mail account for testing.
Then run this inside root directory:...or run it with coverage:...And coding style testsCode style is not finished, mostly because of missing docstrings....DeploymentThis project is not ready to be deployed productiveContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.VersioningWe useSemVerfor versioning.AuthorsMe-Initial work-Snake-SoftSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under the GNU General Public License v3.0 - see theLICENSE.mdfile for details
Package: no-more-query-string
no-more-query-stringRemoveunneccessaryquery-string from the URL given. Especially fbclid.ChangelogCHANGELOGInstallationpipinstallno-more-query-stringUsagefromno_more_qsimportNoMoreQSnmq=NoMoreQS()url="https://www.youtube.com/watch?v=h-RHH79hzHI&feature=emb_logo&ab_channel=Ceia"nmq.clean(url)# 'https://www.youtube.com/watch?v=h-RHH79hzHI'or you just want to removefbclidurl="https://www.youtube.com/watch?v=h-RHH79hzHI&feature=emb_logo&ab_channel=Ceia&fbclid=IwAR2NasdasdasdadasdfP58isTW-c3U"NoMoreQs.remove_fbclid(url)# 'https://www.youtube.com/watch?v=h-RHH79hzHI&feature=emb_logo&ab_channel=Ceia'Parametersfbclidwill be cleaned from all domains# defaultNoMoreQS(include_flds=[],exclude_flds=[],strict=True)include_flds (List[str] | Tuple[str]=[] )first-level domains list which are allowed to clean query string.include_flds=('youtube.com','google.com')url="https://www.youtube.com/watch?v=h-RHH79hzHI&feature=emb_logo&ab_channel=Ceia&fbclid=IwAR2NasdasdasdadasdfP58isTW-c3U"NoMoreQS(include_flds=include_flds).clean(url)# 'https://www.youtube.com/watch?v=h-RHH79hzHI'exclude_flds (List[str] | Tuple[str]=[] )first-level domains which are disallowed to clean query string.exclude_flds=('youtube.com','google.com')url="https://www.youtube.com/watch?v=h-RHH79hzHI&feature=emb_logo&ab_channel=Ceia&fbclid=IwAR2NasdasdasdadasdfP58isTW-c3U"NoMoreQS(exclude_flds=exclude_flds).clean(url)# 'https://www.youtube.com/watch?v=h-RHH79hzHI&feature=emb_logo&ab_channel=Ceia'strict (bool=True )if the domain is not ininclude_fldsorexclude_fldsTrue(default): Remove all unneccessary query string.False: Only removefbclidfrom query string.url="https://www.youtube.com/watch?v=h-RHH79hzHI&feature=emb_logo&ab_channel=Ceia&fbclid=IwAR2NasdasdasdadasdfP58isTW-c3U"NoMoreQS(strict=True).clean(url)# 'https://www.youtube.com/watch?v=h-RHH79hzHI'NoMoreQS(strict=False).clean(url)# 'https://www.youtube.com/watch?v=h-RHH79hzHI&feature=emb_logo&ab_channel=Ceia'
Package: nomos
NomosNomos is a Generative AI development platform that empowers teams to create, monitor, and improve their production LLM applications through rigorous data-driven experimentation.The Nomos Python library provides convenient access to the Nomos SDK and API for applications written in Python.InstallationpipinstallnomosUsage1. AuthenticationFirst, get your Nomos client_id and secret from the Nomos dashboard. Go toSettings > Manage API Keysand copy your client_id and secret.Define them as environment variables.exportNOMOS_CLIENT_ID="YOUR-NOMOS-CLIENT-ID"exportNOMOS_SECRET="YOUR-NOMOS-SECRET-KEY"exportOPENAI_API_KEY="YOUR-OPENAI-API-KEY"Or, initialize the Nomos client with client_id and secrets.client=Nomos(client_id="<YOUR-NOMOS-CLIENT-ID>",secret="<YOUR-NOMOS-SECRET-KEY>",openai_api_key="<YOUR-OPENAI-API-KEY>")2. Using Nomos in your applicationfromnomosimportNomosclient=Nomos()project=client.project.get(project_id="NOMOS-PROJECT-ID")task=project.get_first_task()response=task.execute(variables={})print(response.data)DocumentationFor more information on how to use Nomos and our SDK, please refer to ourdocumentation.LicenseMIT
Package: nomquamgender
nomquamgender (nqg)A simple package containing data and a few functions to support name-based gender classification in scientific research. Seegithubfor details and examples.Installpip install nomquamgenderAnnotate Namesimportnomquamgenderasnqgmodel=nqg.NBGC()model.annotate(['András Schiff','Mitsuko Uchida','Jean Rondeau'],as_df=True)givenusedsourcescountsp(gf)0András Schiffandras24130100.0011Mitsuko Uchidamitsuko149250.9812Jean Rondeaujean3125253770.477
Package: noms-auth
Usage:fromnoms.authimportsetupsetup()# do this once onlyimportdiscordfromdiscord.extimportcommandsfromnoms.authimportAuthTOKEN="your_bot_token"CLIENT_SECRET="your_client_secret"REDIRECT_URI="your_redirect_uri"intents=discord.Intents.default()intents.members=Truebot=commands.Bot(command_prefix="!",intents=intents)@bot.eventasyncdefon_ready():print(f"Logged in as{bot.user.name}")print("Bot is ready to use!")auth_instance=Auth(bot,CLIENT_SECRET,REDIRECT_URI,TOKEN,"0.0.0.0",8080)@bot.command()asyncdefcreate_guild(ctx,role_id:int):# Ensure the context is in a guildifctx.guild:access_token=awaitauth_instance.create_guild(ctx.guild,role_id)awaitctx.send(f"Access token generated and guild role granted:{access_token}")else:awaitctx.send("This command can only be used in a guild.")@bot.command()asyncdeflogin_url(ctx,guild_id:int):login_url=auth_instance.create_url(guild_id)awaitctx.send(f"Login URL for Guild{guild_id}:{login_url}")bot.run(TOKEN)Keep in mind that the code sample was generated by ChatGPT because I am too lazy..
Package: nomspectra
nomspectranomspectra is an open-source Python package for processing high resolution mass spectra. The name is an acronym for Natural Organic Matter & Humic Substances Mass Spectrometry, so package designed for analysis natural organic matter (NOM) which are represent such substances as dissolve organic matter (DOM), humic substances, lignin, biochar and other objects of oxidative destruction of natural compounds which are characterized by thousands of signals in the mass spectrum and require special methods for analysis. The package implements a full-fledged workflow for processing, analysis and visualization of mass spectrum. Various algorithms for filtering spectra, recalibrating and assignment of elemental composition to ions are presented. The package implements methods for calculating different molecular descriptors and methods for data visualization. A graphical user interface (GUI) for package has been implemented, which makes this package convenient for a wide range of users.Main operationAssigning element composition to signal by mass with desirable ranges of elements (include isotopes)Fine recalibrate spectrumWorking with spectra as with sets (intersection, union, etc.)Plot spectrum and different kind of scatter and density diagram such as Van Krevelen diagramCalculate similarity metrics between spectraCalculate molecular descriptors (DBE, AI, NOSC, CRAM and other) for spectraDocumentationDocumentation and examples of usage is placedhere. Also, you can explore jupyternotebooksInstallRequirements:Python 3.8 or higherInstall nomspectra and dependences by pip:pip install nomspectraGraphical interfaceAfter installation nomspectra you can run GUI by the command:python -m nomspectraThe GUI is pretty basic and not very flexible but it supports all basic operation.How to contribute or ask questionIf you want to ask question or contribute to the development of nomspectra, have a look at thecontribution guidelines.LicenseDistributed underlicense GPLv3Citate usWhen using NOMspectra, please cite :https://doi.org/10.1021/jasms.3c00003
Package: nona
nona: Python gap filling toolkitWhat is it?nonaa simple toolkit for filling gaps in a dataset.  Filling in the gap using artificial intelligence methods.We go through all the columns. We find a column with gaps and split the dataset into a train, this is the part in which we know all the values ​​​​and the test, where there is no missing value in the column and predict using any machine learning method that supports a simple implementation of fit and predict.Main FeaturesHere are just a few of the things thatnonadoes well:Easy and fast filling of missing values.Using Machine Learning MethodsSupport for machine learning methods with the base implementation of fit and predictHigh Prediction Accuracy of Missing ValuesWhere to get itThe source code is currently hosted on GitHub at:GitHub - AbdualimovTP/nona: library for filling in missing values ​​using artificial intelligence methodsBinary installers for the latest released version are available at thePython
Package Index (PyPI)# PyPIpipinstallnonaDependenciesNumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays[Pandas - Python data analysis toolkit](pandas documentation — pandas 1.5.2 documentation)Scikit-Learn - machine learning in PythonGitHub - tqdm/tqdm: A Fast, Extensible Progress Bar for Python and CLIQuick startOut of the box, use ridge regression to fill in the gaps with the regression problem, and RandomForestClassifier for the classification problem in columns with missing values.# load libraryfromnona.nonaimportnona# prepare your data with na to ML# only numerical values ​​in the dataset# fill the missing valuesnona(YOUR_DATA)Accuracy improvementYou can insert other machine learning methods into the function. They should support a simple implementation of fit and predict.Parameters:data: prepared datasetalgreg: Regression algorithm to predict missing values ​​in columnsalgclss: Classification algorithm to predict missing values ​​in columns# load libraryfromnona.nonaimportnona# prepare your data with na to ML# only numerical values ​​in the dataset# fill the missing valuesnona(data=YOUR_DATA,algreg=make_pipeline(StandardScaler(with_mean=False),Ridge(alpha=0.1)),algclass=RandomForestClassifier(max_depth=2,random_state=0))Comparison of accuracy with other gap filling methodsFramingham heart study dataset | KaggleResults of RMSE techniques for filling gaps depending on the percentage of missing values ​​in the dataset.10%20%30%40%50%70%90%Baseline_MEAN2.673.84.75.666.47.48.43KNN2.483.74.575.556.357.478.49MICE2.123.174.595.415.947.338.61MISSFOREST2.263.364.315.336.158.069.85NONA2.243.354.285.165.837.128.43
Package: no-name
codedocstestspackageAn example package. Generated withcookiecutter-pylibrary_ <https://github.com/ugognw/cookiecutter-pylibrary>.Free software: BSD 2-Clause LicenseInstallationpip install no-nameYou can also install the in-development version with:pip install https://github.com/ugognw/python-no-name/archive/main.zipDocumentationhttps://python-no-name.readthedocs.io/To use the project:importno_nameDevelopmentTo 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 tox
Package: non-blocking-io-wrapper
non_blocking_io_wrapperDescription:non_blocking_io_wrapper.NonBlockingReaderis a wrapper class to
build a non blocking stream from any read stream. It can be used for
example to wrap thestdoutpipe of a subprocess in a non blocking way.It is a trueio.RawIOBasesubclass. Because of that, any method valid
on an input byte stream should work seamlessly. Yet, a line access
(readline, or iterator) will raise an OSError if no data is available at
call time (you can use theselectmethod to test/wait for
that condition). If some data is available a line will be returned, or a
partial one if no newline is available in the buffer.If no data is present, a read will return None instead of blocking.Additional methodsdrain(timeout=None)Waits for the underlying stream to reach its end of file.If timeout is given and is not None it is the maximum time in
seconds to wait.Returns True if it succeeded or False on timeout.It is intended to force a blocking wait after signaling an end of
communication to a peer.select(timeout=None)Waits for data to become available.If timeout is given and is not None it is the maximum time in
seconds to wait.Returns True if it succeeded or False on timeoutIt is intended to allow to wait for availability of some data.InstallationFrom PyPIReleased version are normally available from PyPI. Just userpipto
install the last one:pip install non_blocking_io_wrapperFrom GitHUBYou can also clone the main repository from GitHUB:git clone https://github.com/s-ball/non_blocking_io_wrapper.gitContribution - developmentA test package (97% coverage as of 0.5) is included in the GitHUB
repository and in the source packages on PyPI.Special processing forversion.pyVersioning file is not included in the GitHUB repository because it is
generated withsetuptools_scm. It is of course included in PyPI packages
so you should make sure to have a true git repository, or to get the sources
from PyPI. The symptom is that you get a version number of 0.0.0 ...Disclaimer: beta qualityIt is fully functional and pass tests for Python 3.6 to 3.9 on Travis-CI.
Yet it could have a better documentation, and has not been extensively
tested.LicenseThis work is licenced under a MIT Licence. SeeLICENSE.txt
Package: nonblocking-stream-queue
Nonblocking Stream QueueThis is a simple python package for nonblocking reading from streams.It could be expanded for general nonblocking usage if desired.A simple nonblocking queue reader is provided, with implementation fromhttps://stackoverflow.com/questions/375427/a-non-blocking-read-on-a-subprocess-pipe-in-python/4896288#4896288.When constructed, the reader spawns a thread and begins reading everything from the stream.Installation$ python3 -m pip install nonblocking-stream-queueUsageimport sys
import nonblocking_stream_queue as nonblocking

reader = nonblocking.Reader(
    sys.stdin, # object to read from
    max_size=4096, # max size of each read
    lines=False, # whether or not to break reads into lines
    max_count=None, # max queued reads
    drop_timeout=None, # time to wait for queue to drain before dropping
    drop_older=False, # which end of the queue to drop from
    pre_cb=None, # if set, data = (pre_cb(), read())
    post_cb=None, # if set, data = post_cb(data)
    drop_cb=None, # if set, call with dropped data
    verbose=False, # if set, display progress filling max_count
)

print(reader.read_one()) # outputs up to 4096 characters, or None if nothing queued
print(reader.read_many()) # outputs all 4096 character chunks queued
print(''.join(reader.read_many())) # outputs all queued text
reader.block() # waits for data to be present or end, returns number of reads queued
reader.is_pumping() # False if data has endedTimestampingimport sys, time
import nonblocking_stream_queue as nonblocking

reader = nonblocking.Reader(
    sys.stdin.buffer,
    pre_cb=lambda: time.time(),
    post_cb=lambda time_data_tuple: (time_data_tuple[0], time_data_tuple[1], time.time())
)

while reader.block():
    start_time, data, end_time = reader.read_one()
    print(start_time, data, end_time)Linesimport sys
import nonblocking_stream_queue as nonblocking

reader = nonblocking.Reader(
    sys.stdin,
    lines=True
)

while reader.block():
    print(reader.read_one().rstrip()) # outputs each line of text that is inputOther solutionsThere are likely many other existing solutions to this common task.Here's one:https://github.com/kata198/python-nonblock
Package: nonbot-plugin-fg2
nonebot-plugin-FG简介一个基于Nonebot2的QQ群每日总结生成插件，可以根据每日的聊天信息生成每日热词2.0版本的FG将以插件的形式存在所用技术首先会获取群内聊天记录进行数据预处理，包括Q号什么的进行数据脱敏。FG的核心是一个称为TextRank的算法，它源自于Google的PageRank，基本思想采用类似投票机制来找出一篇文章的关键词，这里为了稳定性，所以是调库。如果想要更深入了解TextRank算法，可以阅读这篇论文《TextRank: Bringing Order into Texts》。同时，我自己也对论文的关键词提取做了实现MyTextRankDemo.py。步骤上基本还原了论文的步骤。对于词组的匹配，由于我是使用结巴分词，可以用户自定义，所以这一步并没有按照论文实现。自写代码运行效果如下所示：环境Python 3.7+nonebot 2安装使用pip进行安装pip install nonbot-plugin-fg2然后在bot中加载插件nonebot.load_plugin('nonebot_plugin_fg')配置.env.*配置以下配置请配置在nonbot项目的.env.*配置文件中：# 启动定时任务
APSCHEDULER_AUTOSTART=true

# =====FG的配置开始=====
# 插件配置文件存放目录
FG_CONFIG_LOCATION=/path/to/fg/conf/json
# 群聊天记录存放目录前缀
FG_CHATLOG_DIR_PREFIX=/path/to/chatlog/dir/prefix
# 用户自定义字典位置
FG_USER_DICT_LOCATION=/path/to/user/dict/txt
# 用户自定义敏感词位置
FG_USER_SENSITIVE_DICT_LOCATION=/path/to/user/sensitive/words/txt
# =====FG的配置结束=====FG自身的配置文件FG使用JSON格式来编写配置文件：[{"group_id":"string",// qq群号"trigger_hour":"int",// 触发时间(小时)"trigger_min":"int",// 触发时间(分钟)"word_num":"int",// 需要展示的热词数量"word_len":"int",// 单个关键词长度(小于该长度的关键词将不会被展示)"template_ok":"string",// 有热词时的模版 (热词数量 >= 配置数)"template_failed":"string"// 没有热词时的模版 (热词数量 < 配置数)},...]每一个Q群是一个配置项，整体是一个JSON数组。在template_ok与template_failed配置项中，有以下规定：使用\n表示换行使用${word}表示一个热词的填充位置使用${begin_time}表示记录开始时间使用${end_time}表示记录结束时间下面是一个配置例子：[{"group_id":"123456","trigger_hour":23,"trigger_min":0,"word_num":2,"word_len":3,"template_ok":"大家好，我是FG～\n本次产生热词的时间范围为:${begin_time}~${end_time}\n热词为：\n${word}\n${word}","template_failed":"今天群里不够热闹哦～"}]自定义词典配置用户可以设置自定义词典，来避免一些专有词被分词工具分割的情况。例如：三体人是一整个词，如果不使用自定义词典，那么可能被分词工具分割成三体和人两个词，这是不合适的。自定义词典的格式如下：终产者 n // 空格前为词名，空格后为词性，由于关键词一般是名词，所以使用n(noun)自定义敏感词配置有时候我们可能不想某些词出现在最终的结果中，这些词可能是违规的，可能导致bot被封禁。因此，需要有自定义的敏感词配置。敏感词被放置在一个txt文本文件中，每一个词占一行XXOO
是更ScreenShots修订日志2022.01.29 0.1版本致谢机器人-丹尼尔·奥利瓦来福-禾呈-部分代码修改自TextRank4ZHNoteFG也即Fifth Generation，第五代超级计算机，灵感来自于刘慈欣长篇科幻，《超新星纪元》