Saravanan666/pypi-org huggingface dataset:
Package: anthill.customexport
IntroductionThis package adds a new tab toportal_skins/customfolder named “FS-Export”.
Using this tab you can export all scripts and other stuff you have customized
with one click to the filesystem. Now also with support for
portal_view_customizations folder.Even in times of views, content providers and viewlets there are cases where
you have stuff in custom folder and struggle with exporting it to the
filesystem.InstallationExtend your buildout with anthill.customexportMake sure your instance knows about it (eggs=, zcml=)Rerun buildoutGo to portal_skins/custom and then click on the new tabTested withPlone 2.5.x and 3.xChangelog0.3 - 2010-11-30Fixed exporter for Plone 4 environments
[spamsch]0.2 - 2009-09-01Fixed code to make export from portal_view_customiztions possible [spamsch]0.1 - 2009-08-14Initial release after repackaging
Package: anthill-PyMySQL
PyMySQLTable of ContentsRequirementsInstallationDocumentationExampleResourcesLicenseThis package contains a pure-Python MySQL client library, based onPEP 249.Most public APIs are compatible with mysqlclient and MySQLdb.NOTE: PyMySQL doesn’t support low level APIs_mysqlprovides likedata_seek,store_result, anduse_result. You should use high level APIs defined inPEP 249.
But some APIs likeautocommitandpingare supported becausePEP 249doesn’t cover
their usecase.RequirementsPython – one of the following:CPython: 2.7 and >= 3.5PyPy: Latest versionMySQL Server – one of the following:MySQL>= 5.5MariaDB>= 5.5InstallationPackage is uploaded onPyPI.You can install it with pip:$ python3 -m pip install PyMySQLTo use “sha256_password” or “caching_sha2_password” for authenticate,
you need to install additional dependency:$ python3 -m pip install PyMySQL[rsa]DocumentationDocumentation is available online:https://pymysql.readthedocs.io/For support, please refer to theStackOverflow.ExampleThe following examples make use of a simple tableCREATETABLE`users`(`id`int(11)NOTNULLAUTO_INCREMENT,`email`varchar(255)COLLATEutf8_binNOTNULL,`password`varchar(255)COLLATEutf8_binNOTNULL,PRIMARYKEY(`id`))ENGINE=InnoDBDEFAULTCHARSET=utf8COLLATE=utf8_binAUTO_INCREMENT=1;importpymysql.cursors# Connect to the databaseconnection=pymysql.connect(host='localhost',user='user',password='passwd',db='db',charset='utf8mb4',cursorclass=pymysql.cursors.DictCursor)try:withconnection.cursor()ascursor:# Create a new recordsql="INSERT INTO `users` (`email`, `password`) VALUES (%s,%s)"cursor.execute(sql,('webmaster@python.org','very-secret'))# connection is not autocommit by default. So you must commit to save# your changes.connection.commit()withconnection.cursor()ascursor:# Read a single recordsql="SELECT `id`, `password` FROM `users` WHERE `email`=%s"cursor.execute(sql,('webmaster@python.org',))result=cursor.fetchone()print(result)finally:connection.close()This example will print:{'password':'very-secret','id':1}ResourcesDB-API 2.0:https://www.python.org/dev/peps/pep-0249/MySQL Reference Manuals:https://dev.mysql.com/doc/MySQL client/server protocol:https://dev.mysql.com/doc/internals/en/client-server-protocol.html“Connector” channel in MySQL Community Slack:https://lefred.be/mysql-community-on-slack/PyMySQL mailing list:https://groups.google.com/forum/#!forum/pymysql-usersLicensePyMySQL is released under the MIT License. See LICENSE for more information.
Package: anthill.querytool
IntroductionThis package provides a complete user interface forAdvancedQueryby
Dieter Maurer. It enables you to use a powerful language to search for
content. It also provides functionality to save parametrized and conditional
queries for later use (predefined queries). Look at the examples for more information.InstallationPut anthill.querytool in eggs= and zcml=Make sure that AdvancedQuery is installed (works for Plone 3.x)Example queryA query could look like that:And(
    Eq('SearchableText', '$text'),
    ~Generic('path', {'query':'Members', 'level':-1}),

    [[if($allowed_types)]]
        In('portal_types', $allowed_types),
    [[endif]]

    [[ifnot($allowed_types)]]
        In('portal_types', ['Folder', 'Document']),
    [[endif]]

    Ge('start_date', TODAY)
)Here you see that you can parametrize queries (variable expansion enabled
using $), you also can use defined constants (only one currently active called TODAY
where TODAY=DateTime()) and you can put conditionals in your queries.Conditionals are a powerful way to enable or disable certain parts of your
query. Theifstatement checks if a given parameter exists. You can also
replaceifwithifnotthat only activates the given part if the
parameter is not set.You can save this query and call it later on like that:context.query_tool.executePredefinedQuery('contentsearch', text='Test*', allowed_types=['Folder', ])ExtensionsThis release adds some additional query operators (defined in
SearchOperators.py). The following operators are currently available:Countcounts items in search results. (e.g. Count(Eq(‘SearchableText’, ‘moses’)) )Sumcomputes sum over int result (e.g. Sum(Ge(‘commentcount’, 10)) )Avgcomputes the average over an int result (e.g. Avg(Ge(‘userviews’, 1)) )Look at SearchOperators.py for examples on how to create your own operators.Submit queryPredefined queriesThanksMarkus Reinsch for coding the predecessor of this packageDieter Maurer for his great implementation of AdvancedQueryChangelog0.2 - 2009/08/21Revamped documentation [spamsch]0.1 - 2009/08/20Initial release
Package: anthill.tal.macrorenderer
IntroductionThis package renders macros from a given page template using pure python.Sometime you may want to use page templates like code libraries where for each
functionality you have one macro. Calling macros is no problem using ZPT
use-macro but how do you call macros from pure python code and also pass
parameters? Because there do not seems to be an obvious solution for this problem
(especially the parameters part) this package was created.Render macro with namemacronamefrom a given page template:>>> from anthill.tal.macrorenderer import MacroRenderer
>>> template = ViewPageTemplateFile('template.pt')
>>> renderer = MacroRenderer(template, 'macroname')
>>> print renderer(data={'option1' : 42})Sometimes you get an exception about not enough context being provided to the
renderer (or for prior versions a TypeError).A fix is easy: Simply add acontext=self.contextto the MacroRenderer call:>>> renderer = MacroRenderer(template, 'macroname', context=self.context)Changelog0.2.1 (2009-08-24)Added fix forhttp://mail.zope.org/pipermail/zope3-dev/2007-April/022266.htmlbecause this can happen when calling macros with not enough context [spamsch]0.2 (2009-08-24)Fix for missing context (TypeError exceptions) [spamsch]0.1 (2009-08-08)Initial release
Package: anthropicautodocstrings
Source Code:https://github.com/JordanM575/anthropicautodocstringsAnthropicautodocstrings is a command-line tool with the following key features:Updates the docstrings in Python files using the Anthropic API.Can process a single file or a directory of files, including all subdirectories.Anthropicautodocstrings uses the Anthropic API to generate docstrings, so these are not guaranteed to be perfect. The claude-instant-1.2 model is used to generate the docstrings. This is the fastest. The version this was forked from is slow in comparison. To increase raw speed! This runs asyncronously.Anthropicautodocstrings will work best for code that already has good type hints. Without type hints, the Anthropic API will have to guess input and return types, which may not be accurate.RequirementsPython 3.10+A valid Anthropic API KEY. Set in the environment variables.InstallationTo install the dependencies for this tool, run the following command:$pipinstallanthropicautodocstringsUsageTo use this tool, run the following commands:$exportANTHROPIC_API_KEY=1234567890$aadsINPUT`[--replace-existing-docstrings] `[--skip-constructor-docstrings] `[--exclude-directories EXCLUDE_DIRECTORIES] `[--exclude-files EXCLUDE_FILES]Where INPUT is a Python file or directory containing Python files to update the docstrings in, API_KEY is your Anthropic API key, and the optional flags --replace-existing-docstrings and --skip-constructor-docstrings can be used to skip updating docstrings for constructors (initmethods) and replacing existing docstirngs. EXCLUDE_DIRECTORIES and EXCLUDE_FILES are comma-separated lists of directories and files to exclude from the update.ExamplesUpdate the docstrings in all Python files in the my_code directory:$aadscool_code/Update the docstrings in the my_file.py file:$aadsawesome_script.pyUpdate the docstrings in all Python files in the my_code directory and replace existing ones:$aadscool_code/--replace-existing-docstringsUpdate the docstrings in all Python files in the my_code directory, but skip updating docstrings for class constructors (initmethods):$aadscool_code/--skip-constructor-docstringsUpdate the docstrings in all Python files in the my_code directory, but exlcude the exclude_dir directory and the exclude_file_1.py and exclude_file_2.py files:$aadsmy_code/--exclude-directoriesexclude_dir--exclude-filesexclude_file_1.py,exclude_file_2.py
Package: anthros-core
[RUS]Welcome to Anthros-core!Environment, interfaces and tools for your projects, as well as an interactive console for development and testingHow to start?Install the latest version of this packageCreate a *.py file at the root of your projectImport AC: from anthros import core as acRun interactive mode: ac.interfaces.console.run()AdditionallyHow can I find out more about the components?Everything is in the help, enter: help*but for now, everything is in russian, you can help us with the translationHow can I contact the creators?Join our communities:Discord,VkYou can also write tomeor join discord (Tand#9533)
Package: anthroscore-eacl
AnthroScoreThis repository contains code to compute AnthroScore. AnthroScore is introduced in the following paper, which is accepted to EACL 2024:AnthroScore: A Computational Linguistic Measure of AnthropomorphismMyra Cheng, Kristina Gligoric, Tiziano Piccardi, Dan Jurafsky(Stanford University)Abstract:Anthropomorphism, or the attribution of human-like characteristics to non-human entities, has shaped conversations about the impacts and possibilities of technology. We present ANTHROSCORE, an automatic metric of implicit anthropomorphism in language. We use a masked language model to quantify how non-human entities are implicitly framed as human by the surrounding context. We show that ANTHROSCORE corresponds with human judgments of anthropomorphism and dimensions of anthropomorphism described in social science literature. Motivated by concerns of misleading anthropomorphism in computer science discourse, we use ANTHROSCORE to analyze 15 years of research papers and downstream news articles. In research papers, we find that anthropomorphism has steadily increased over time, and that papers related to natural language processing (NLP) and language models have the most anthropomorphism. Within NLP papers, temporal increases in anthropomorphism are correlated with key neural advancements. Building upon concerns of scientific misinformation in mass media, we identify higher levels of anthropomorphism in news headlines compared to the research papers they cite. Since ANTHROSCORE is lexicon-free, it can be directly applied to a wide range of text sources.SetupDownload the repository, eithervia pip:pip install anthroscore-eaclorvia Github:git clone https://github.com/myracheng/anthroscore.git;cd anthroscore;pip install .Install the spaCy model:python -m spacy download en_core_web_sm(The specific model used ishttps://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl#sha256=86cc141f63942d4b2c5fcee06630fd6f904788d2f0ab005cce45aadb8fb73889)Example UsageTo obtain AnthroScores for the terms "model" and "system" in
abstracts from examples/acl_50.csv (a subset of ACL Anthology papers)python get_anthroscore.py --input_file example/acl_50.csv \
    --text_column_name abstract --entities system model \
    --output_file example/results.csv --text_id_name acl_id
Package: anti
Модуль для работы с браузерами, метрикой, вебмастером и пр.Позволяет получать данные по позициямПозволяет получать суп из поиска Яндекса и ГуглаУтилиты решают проблемы получения доноров, бэклинков, ТИЦ, PR и пр.Импорт модулей:from anti import openYandex, openGoogle, SeoParser, Metrika, Webmaster0.7.1 ИзмененияДобавлен балансировщик между серверамиУменьшено количество sql запросов к базе0.7.2Балансировщик на redis0.7.3Fix: пустая выдача0.7.4Fix SeoParser: поправлена выдача страниц в индексе1.0.1Доделаны необходимые ф-ции для манипуляции с данными1.0.2Добавлена региональность1.0.3Добавлена ф-ция pages_of_site_in_index_google1.0.4Добавлены ф-ции получения возраста домена и посещений по liveinternet1.0.5Поправлена фитча с блоками. Добавлен 1ый пункт в whitelist. Добавлена ф-ция очищения текущего кэша.1.1.0Исправлена логика работы по сохранению и получению данных(Теперь все сохраняется в redis, а потом в postgres)1.1.1Добавлено получение позиций из кэша редиса1.2.0Удалены ф-ции multy. get_yandex_cache_pos получает позиции из redis. В базу данных теперь сохраняем отдельно.1.2.1Добавлена очистка кэша1.2.2Изменена работа с Webmaster и Metrika1.2.3Поправлены ошибки в seoparser1.2.4urllib и urllib2 заменены на requests1.2.5поправлена ф-ция получения PR1.2.6изменено получение данных из вебмастера + поправлена ф-ция get_normal_url_decode1.2.7Исправлена ошибка в SeoUtils1.2.8Добавлен параметр self.quote в SeoUtils1.2.9Порты вынесены в конфиг1.3.0Зависимости приведены в порядок1.3.1Исправлен фикс в выдаче normalize=True(get_yandex_cache_pos)1.3.2Добавили отдельный сервер под балансер1.3.3Поисковик поменял верстку1.3.4Изменена верстка при получении выдачи по сайту1.3.5Получение адреса страниц вместо относительного url1.3.6Исправлена ошибка: передача пустого списка как значения по умолчанию1.3.7Поправлено получение страниц в индексе1.3.8Добавлен timeout к запросам requests1.4.0Переделан функционал работы с redis. Теперь проверка идет по json.1.4.1Поправлен апдейт redis_key1.4.2Еще один hotfix для redis_key1.4.3Поменяли на контекстный менеджер1.4.3.3Обновил метод GetYaca в seoutils1.4.4Добавлен storage аргумент в функцию get_yandex_cache_pos1.5.0Добавлен декоратор на исключения1.5.3Поисковик ввел странное решение в верстку2.0.1Попробовали новую версию, решили откатить назад2.1.2Поправили ф-ции в связи с обновлением версии2.1.3Ключи в redis изменили на page:0:url и page:1:url3.0.0Переделал балансер на rabbitmq, переписал всю серверную часть3.2.5Поправлены проблемы с новым фласком, пофиксил парсинг гугла и прочие фишки3.2.6Поправлено получение страниц с гугла
Package: antibiotics
antibioticsNamedTuple / dataclasses <-> delimited text"The best treatment for acute episodes of PANDAS is to treat the strep
infection causing the symptoms, if it is still present, with antibiotics."--National Institute of Mental Healthantibioticsis a minimalist type-driven serialization/deserialization library
inspired bySerdeandcassava.It uses type annotations to automatically read and writeNamedTupleor@dataclassobjects to or from delimited text files.Out of the box, it only knows about Python scalar types andtyping.Unions
of them (includingtyping.Optional), but an extension mechanism for
arbitrary type-directed serialization and deserialization is provided
through thetype_serde_extargument to theDelimitedconstructor - seeexamples/advanced.py.ForUniontypes, serialization is driven by the runtime type,
and deserialization is attempted in the order of declaration of theUnionarguments - except thatNoneTypeis tried first if present,
to preserve the expected behavior when deserializing null/missing values
of types whose deserializers do not throw when receiving''as an argument.A typeExternalNameis also provided which may be used withtyping.Annotatedto specify the name which should be used for a member when serializing or
deserializing (e.g. to match CSV headers).Please note that as with the built-incsvmodule, file-like objects used
with this library should be opened withnewline=''.Basic examplefromantibioticsimportDelimitedfromdataclassesimportdataclassfromtypingimportNamedTuple,Optional@dataclassclassSampleDC():w:Optional[float]x:inty:boolz:strclassSampleNT(NamedTuple):w:Optional[float]x:inty:boolz:strif__name__=='__main__':dcs=list()nts=list()foriinrange(10):even=i%2==0dcs.append(SampleDC(i*3.5ifevenelseNone,i,noteven,f'_",\t_{i}'))nts.append(SampleNT(i*3.5ifevenelseNone,i,noteven,f'_",\t_{i}'))csv=Delimited()withopen('dcs.csv','w',newline='')asf:csv.write(SampleDC,dcs,f)tsv=Delimited(sep='\t',escape='\\',newline='\n')withopen('nts.tsv','w',newline='')asf:tsv.write(SampleNT,dcs,f,header=False)withopen('dcs.csv','r',newline='')asf:forrincsv.read(SampleDC,f):print(r)withopen('nts.tsv','r',newline='')asf:forrintsv.read(SampleNT,f,header=False):print(r)Example with custom external namesfromantibioticsimportDelimited,ExternalNamefromdataclassesimportdataclassfromtypingimportAnnotated,Optional@dataclassclassSampleDC():w:Annotated[Optional[float],ExternalName('BigW')]x:Annotated[int,ExternalName('Fancy X')]y:boolz:strif__name__=='__main__':dcs=list()foriinrange(10):even=i%2==0dcs.append(SampleDC(i*3.5ifevenelseNone,i,noteven,f'_",\t_{i}'))csv=Delimited()withopen('dcs.csv','w',newline='')asf:csv.write(SampleDC,dcs,f)withopen('dcs.csv','r',newline='')asf:fordcincsv.read(SampleDC,f):print(dc)DocumentationDocumentation strings and type annotations are provided for public types and
functions. We recommend viewing "nice" documentation pages usingpdoc; e.g. in the same environment as theantibioticspackage is installed, installpdocwithpip install pdoc,
then runpdoc antibiotics.Install with:pip install antibioticsOr download directlyfrom PyPI.(c) 2023 dwt | terminus data science, LLCavailable under the Apache License 2.0
Package: antibody-ngs-pipeline
# Antibody NGS PipelineBulk antibody sequence preprocessing, annotation with abstar, import to MongoDB.
This is based on the AbStar analysis pipeline: (www.github.com/briney/abstar)### installpip install antibody-ngs-pipeline### useTo run antibody_ngs_pipeline:antibody_ngs_pipelineTo run antibody_ngs_pipeline with FASTQC report on raw data:antibody_ngs_pipeline -fTo run antibody_ngs_pipeline with adapter trimming by CutAdapt:antibody_ngs_pipeline -t <path-to-adapters.fasta>To run antibody_ngs_pipeline with quality trimming by sickle:antibody_ngs_pipeline -qTo run antibody_ngs_pipeline with adapter trimming by CutAdapt, quality trimming
with sickle and get a FASTQC report on both raw data and processed data:antibody_ngs_pipeline -f -q -t <path-to-adapters.fasta>### requirements
Python 3.5+
abstar
abutils
cutadapt
pymongoDownloading from BaseSpace requires Basemount:https://basemount.basespace.illumina.com/Quality trimming requires sickle:https://github.com/najoshi/sickleFastQC report requires FastQC:https://www.bioinformatics.babraham.ac.uk/projects/fastqc/Merging paired sequences requires PANDAseq:https://github.com/neufeld/pandaseqbatch_mongoimport (from Abstar) requires MongoDB:http://www.mongodb.org/
Package: anticaptcha
anticaptcha=========Simple anticaptcha library for images with text.>>> pip install anticaptcha.. code:: pythonfrom anticaptcha import Anticaptchaac = Anticaptcha('API_TOKEN')with open('captcha.png', 'rb') as img:response = ac.createTask(img.read())task_id = response['taskId']result = ac.getTaskResult(task_id)solution = result['solution']['text']Test with: python -m pytest
Package: antichat
Python wrapper forhttps://forum.antichat.ru/Installationpip install antichatOr you can install from source with:git clone https://github.com/drygdryg/antichat_python.git
cd antichat_python
python setup.py installUsagePostingimportantichatclient=antichat.Client(username='myusername',password='mypassword')client.auth()post_id=client.make_post(thread=12345,message='Hi there!')client.delete_post(post_id=post_id,reason='No hello')Reading threadsreader=client.get_reader(thread=12345)all_posts=reader.read()# Read all posts sequentiallyfirst_posts=reader.read(limit=20)# Get only first 20 postslast_posts=reader.read(offset=400)# Skip first 400 postsposts=reader.read(start_post=6789)# Read from post ID 6789page=reader.read_page(page=5)# Read page #5
Package: antichecked
Antichecked ExceptionsAntichecked Exceptions are an alternative to checked exceptions. They're
designed to actively catch bugs, and to prevent silencing exceptions for
the sake of satisfying the compiler (as usually happens with checked
exceptions).Rather than requiring the caller to catch some exceptions, Antichecked
Exceptions intercepts some raised exceptions, and helps guarantee to the
caller, if the caller opts to catch some exception, that the exception
was actually raised by a deliberate design decision rather than a typo
or bug.
Package: anticipate
AnticipateExpect the unexpected, but get what you want.@anticipate(int)
def get_int():
  return '1'

assert get_int() == 1

@anticipate(str)
def get_str():
  return 22

assert get_str() == '22'

@anticipate([str])
def get_strs(*args):
  return args

assert list(get_strs(1, 2, 3)) == ['1', '2', '3']Works much better with your own objects or withSpringFieldChangelog0.9.0Dropped support for Python 2.6, added support for Python 3.7.Cleaned up code formattingBug FixesFixed issue that prevented use of an adaptable object (has anadaptmethod) as an anticipated list of type.0.8.0Change so that anticipating an iterable using[type]will always
return a list instead of generatorAddedanticipate_input_factoryto make it easier to implement
input handlers that need to inject values or handle input errors
differentlyMade it so you can use any object that implementsadaptas an
anticipate type so you can useSpringFieldfields as input typesImproved error messagesSplit anticipate input and output handling into separate functions to
make it easier to intercept input or output handlingCheck that the params being anticipated exist in the function
signature
Package: anticipy
AnticipyAnticipy is a tool to generate forecasts for time series. It takes a pandas Series or DataFrame as input, and
returns a DataFrame with the forecasted values for a given period of time.Features:Simple interface. Start forecasting with a single function call on a pandas DataFrame.Model selection. If you provide different multiple models (e.g. linear, sigmoidal, exponential), the tool will
compare them and choose the best fit for your data.Trend and seasonality. Support for weekly and monthly seasonality, among other types.Calendar events. Provide lists of special dates, such as holiday seasons or bank holidays, to improve model
performance.Data cleaning. The library has tools to identify and remove outliers, and to detect and handle step changes in
the data.It is straightforward to generate a simple linear model with the tool - just callforecast.run_forecast(my_dataframe): ::import pandas as pd, numpy as np
from anticipy import forecastdf = pd.DataFrame({'y': np.arange(0., 5)}, index=pd.date_range('2018-01-01', periods=5, freq='D'))
df_forecast = forecast.run_forecast(df, extrapolate_years=1)
print(df_forecast.head(12))Output: ::.        date source  is_actuals    model    y   q5  q20  q80  q95
0  2018-01-01    src        True  actuals  0.0  NaN  NaN  NaN  NaN
1  2018-01-02    src        True  actuals  1.0  NaN  NaN  NaN  NaN
2  2018-01-03    src        True  actuals  2.0  NaN  NaN  NaN  NaN
3  2018-01-04    src        True  actuals  3.0  NaN  NaN  NaN  NaN
4  2018-01-05    src        True  actuals  4.0  NaN  NaN  NaN  NaN
5  2018-01-01    src       False   linear  0.0  NaN  NaN  NaN  NaN
6  2018-01-02    src       False   linear  1.0  NaN  NaN  NaN  NaN
7  2018-01-03    src       False   linear  2.0  NaN  NaN  NaN  NaN
8  2018-01-04    src       False   linear  3.0  NaN  NaN  NaN  NaN
9  2018-01-05    src       False   linear  4.0  NaN  NaN  NaN  NaN
10 2018-01-06    src       False   linear  5.0  5.0  5.0  5.0  5.0
11 2018-01-07    src       False   linear  6.0  6.0  6.0  6.0  6.0Documentation is available inRead the Docs <https://anticipy.readthedocs.io/en/latest/>_