Saravanan666/pypi-org huggingface dataset:
Package: anchor-topic
Copyright (c) 2018 Michelle YuanPermission is hereby granted, free of charge, to any person obtaining a copyof this software and associated documentation files (the "Software"), to dealin the Software without restriction, including without limitation the rightsto use, copy, modify, merge, publish, distribute, sublicense, and/or sellcopies of the Software, and to permit persons to whom the Software isfurnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in allcopies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS ORIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THEAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHERLIABILITY, 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 THESOFTWARE.Description-Content-Type: UNKNOWNDescription: # anchor-topicBuild a topic model from the anchoring algorithm.  This code supports finding anchors for one corpus (Arora et al., 2013) or two corpora (Yuan et al., 2018).  It also supports monoword anchoring (Arora et al., 2013) and multiword anchoring (Lund et al., 2017).Platform: UNKNOWNClassifier: Programming Language :: Python :: 3Classifier: License :: OSI Approved :: MIT LicenseClassifier: Operating System :: OS Independent
Package: anchor-txt
anchor_txt: attributes in markdownanchor_txt adds the ability to embed attributes in markdown files so that external
tools can more easily link them to eachother and code, as well as perform
other operations.Useanchor_txt.Section.from_md_pathto load a markdown file.# Markdown Syntax
The syntax for anchor_txt attributes is simple.Headers of the form# header {#anchor}will have theanchortag extracted and available
inHeader.anchorA header creates a Section, which can have subsections.Sections have attributes, which are embedded yaml either inline or in fenced
code blocks, shown below.An inline attribute looks like one of these:``@{foo}``@{bar:2}` `Fenced code block attributes look like below. They must include the identifieryaml(orjson) and end with a@`yaml @ foo: null bar: 2 `Attributes blocks within a Section are combined through the same process asdict.update, except overlapping keys throw an error.# Developer
Runmake initto create the necessary virtualenvRunmake testfor basic tests ormake checkfor lints and formatting.# LicenseThe source code is Licensed under either ofApache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) orhttp://www.apache.org/licenses/LICENSE-2.0)MIT license ([LICENSE-MIT](LICENSE-MIT) orhttp://opensource.org/licenses/MIT)at your option.Unless you explicitly state otherwise, any contribution intentionally submitted
for inclusion in the work by you, as defined in the Apache-2.0 license, shall
be dual licensed as above, without any additional terms or conditions.
Package: anchovy-css
Anchovy CSSanchovy_css is a pure-Python CSS pre-processor. The key feature it currently
offers is arbitrary selector and media query nesting. Future releases will add
more feature parity with established competitors, adding inclusions, features
flags, and custom properties/functions.Installationanchovy_css includes wheels which can be installed using pip:pip install anchovy_css.Alternatively, anchovy_css may be installed directly from source:pip install git+https://github.com/pydsigner/anchovy_css.
Package: ancient
AncientConvert between integers and roman numerals in PythonInstallInstall from PyPi$pipinstallcnnclusteringor clone the developement version from GitHub$gitclonehttps://github.com/janjoswig/Ancient.git
$cdAncient
$pipinstall.UsageImportfromancientimportromanBasic conversionsConvert integer values to Roman numeralsforiinrange(10):print(roman.roman(i))N
I
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III
IV
V
VI
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VIII
IXBy default, the conversion follows the standard scheme using a subtractive representation for the values 4, 9, 14, etc. (e.g. IV instead of IIII). An additive representation can be selected via themappingkeyword (see alsoCustom Mappings).foriinrange(10):print(roman.roman(i,mapping="ascii-additive"))N
I
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IIII
V
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VIII
VIIIIComposition of large numbers (>4999) can be improved using an extended mapping.foriin[5000,10000,50000,100000]:print(roman.roman(i,mapping="unicode-extended"))ↁ
ↂ
ↇ
ↈInterpretation of Roman numeralsforiin["I","IV","IIII","XX","XL","C"]:print(roman.interpret_roman(i))1442040100The Roman data typeThe packag provides theRomandata type to handle Roman numeralsnumber=roman.Roman(5)print(f"{number!r}")print(f"{number!s}")Roman(5,format='ascii-std')VThe type behaves like an integer in arithmetic operationsprint(number+2)print(number-roman.Roman(1))print(number*2)print(number/2)# Integer division!VII
IV
X
IICustom MappingsA mapping of Roman symbols to integer values used for interconversions has the formmapping={"M":1000,"D":500,"C":100,"L":50,}For the conversion of integers to Roman numerals, such a mapping should have a decreasing order in the integer values. To ensure this, mappings can inherit fromroman.Symbols. Note, that only one symbol is effectively used if the same value is mapped to more than one symbols.custom_mapping=roman.Symbols()custom_mapping.update({"ↆ":50,"Ж":100,"I":1,"Ʌ":5})print(custom_mapping){'Ж':100,'ↆ':50,'Ʌ':5,'I':1}A cutsom mapping can be used in conversions instead of the default mappingsroman.roman(156,mapping=custom_mapping)'ЖↆɅI'A set of mappings is provided as instances of froman.Symbolsinroman.symbolsprint(roman.symbols.keys())dict_keys(['ascii-additive','ascii-std','ascii-variant','unicode-additive','unicode-std','unicode-extended','unicode-extended-claudian'])Mappings stored in this place can be used by their key in conversions. Instances of typeRomanhave an attributeformatthat controls the conversion and should be a valid mapping key.number=roman.Roman(100)print(number)roman.symbols["custom"]=custom_mappingnumber.format="custom"print(number)C
ЖZero and negative numbersThe package can handle negative numbersnumber=roman.Roman(-10)print(number)-XThe symbol used to represent 0 is stored on the used mappings and can be changed.print(roman.symbols["unicode-std"].nullum)N
Package: ancientMetagenomeDirCheck
AncientMetagenomeDirCheckA python package to check AncientMetagenomeDir.AncientMetagenomeDirCheck will verify a dataset, in tabular format, against a json schema (and some other checks...).InstallFrom PyPI using pippipinstallancientMetagenomeDirCheckThe latest development version, directly from GitHubpipinstall--upgrade--force-reinstallgit+https://github.com/SPAAM-workshop/AncientMetagenomeDirCheck.gitDocumentation$ancientMetagenomeDirCheck--help
Usage:ancientMetagenomeDirCheck[OPTIONS]DATASETSCHEMAancientMetagenomeDirCheck:PerformsvaliditycheckofancientMetagenomeDirdatasetsAuthor:MaximeBorryContact:<borry[at]shh.mpg.de>Homepage&Documentation:github.com/spaam-workshop/ancientMetagenomeDirCheckDATASET:pathtotsvfileofdatasettocheckSCHEMA:pathtoJSONschemafile

Options:--versionShowtheversionandexit.-v,--validityTurnonschemachecking.-d,--duplicateTurnonlineduplicatelinechecking.-i,--doiTurnonDOIduplicatechecking.-m,--markdownOutputisinmarkdownformat-dc,--duplicated_entriesTEXTCommmaseparatedlistofcolumnstocheckforduplicatedentries--helpShowthismessageandexit.
Package: ancipher
Ancipher"Alpha Numeric Cipher"🪛 InstalltionIt is a python (precisely v3) package, uploaded onPyPi.pip install ancipher📑 UsageFirstly import it:from ancipher import ancNow, useanc()(datatype: string)anc("As simple as that!")Output45 51mpl3 45 7h47!🖱️ RequirementsObviouslyPython 3A Divinemonk creation!
Package: ancli
Building argument parser from a function annotation. A simple utility inspired byFireanddocopt. Ad-hoc solution for someone who often writes scripts with a
single entry point.How?The process of building CLI withancliis very simple.Write a plain Python function with annotated parameters.Wrap it withmake_cli.Run your script.Examples1. Function with annotated parametersThe functionrunhas explicitly annotated parameters and its signature is used
to instantiateargparse.ArgumentParserinstance that accepts parameters with
specific types and default (if any) parameters. If default value is not provided,
then the parameter is considered to be required.fromancliimportmake_clidefrun(path:str,flag:bool=True,iterations:int=1):print(f'run: path={path}, flag={flag}, iterations={iterations}')if__name__=='__main__':make_cli(run)Now this snippet can be used as follows.$pythonscript.py--pathfile.txt--flag0run:path=file.txt,flag=False,iterations=12. Function without annotationsThe functions without type annotations try to infer the parameters types based
on their default values.fromancliimportmake_clidefrun(a,b=2,c=3.0):forparamin(a,b,c):print(type(param))if__name__=='__main__':make_cli(run)The parameters without default values are considered as strings.$pythonscript.py--a1--b2--c3.0<type'str'><type'int'><type'float'>3. Runninganclias a moduleRunning package as a module allows to dynamically build a CLI from some function.
You just need to specify a path to the module, and function which should be
treated as an entry point.$python-mancliexamples.functions:compute--a2--b642
Package: anclib
anclibUsing classes and methods in anclib.py it is possible to parse and analyze text files containing results related to ancestral reconstruction of DNA or protein sequences. (Rudimentary - beginning of project)AvailabilityThe anclib.py source code is available on GitHub:https://github.com/agormp/ancliband can be installed from PyPI:https://pypi.org/project/anclib/Installationpython3 -m pip install anclibUpgrading to latest version:python3 -m pip install --upgrade anclibDependenciesPythonThe anclib library depends on these other python modules, which are automatically included when using pip to install:phylotreelib librarysequencelib libraryNumPy packagepandas libraryrpy2 packageLevenshtein Python C extension moduleRThe anclib.py library requires R to be installed, along with the following R-packages:tidyverseapetidytreeHighlightsTo be written
Package: ancpbids
AboutancpBIDS is a lightweight Python library to read/query/validate/write BIDS datasets.
It can be used in workflows or analysis pipelines to handle IO specific aspects without bothering much about low level file system operations.
Its implementation is based on the BIDS schema and allows it to evolve with the BIDS specification in a generic way.
Using a plugin mechanism, contributors can extend its functionality in a controlled and clean manner.!!! ANNOUNCEMENT !!! As of version 0.22.0 the BIDSLayout has moved over toPyBIDSwhere it will be developed and maintained in future.
ancpBIDS itself does not support this interface anymore but will act as a core package to PyBIDS and downstream projects needing a lightweight IO library to handle BIDS datasets.
This documentation has not yet been updated to reflect this change.Read more onreadthedocs.io
Package: andaluh-po
Andaluh-poTransliterate español (spanish) gettext po files to andaluz spelling proposals. Generates po and mo filesTable of ContentsDescriptionUsageRoadmapSupportContributingDescriptionTheAndalusian varieties of [Spanish](Spanish:andaluz; Andalusian) are spoken in Andalusia, Ceuta, Melilla, and Gibraltar. They include perhaps the most distinct of the southern variants of peninsular Spanish, differing in many respects from northern varieties, and also from Standard Spanish. Further info:https://en.wikipedia.org/wiki/Andalusian_Spanish.This application provides with a basicgettextspanishpo filestransliteration tool, usingandaluh-pypackage. Further info:https://github.com/andalugeeks/andaluh-pyPLEASE NOTICEandalu-po performs and automatic transliteartion from spanish to andaluz, hence all english, acronym or non spanish words will be wrongly transliterated. We encourage you to manually review all po files transliterated.UsageUse from the command line with theandaluh-potool:$ andaluh-po -h
usage: andaluh-po [-h] [-d DEST] po

Transliterate español (spanish) gettext po files to andaluz spelling
proposals. Generates po and mo files.

positional arguments:
  po                    the original po file to transliterate

optional arguments:
  -h, --help            show this help message and exit
  -d DEST, --dest DEST  destination path of the transliterated po and mo
                        files, defaults to cwd

$ andaluh-po /tmp/language-pack-gnome-es-base-18.04+20180712/data/es/LC_MESSAGES/nautilus.po -d /tmp

$ head /tmp/nautilus.po -n 80 | tail -n 20
"Nautilus supports all the basic functions of a file manager and more. It can"
" search and manage your files and folders, both locally and on a network, "
"read and write data to and from removable media, run scripts, and launch "
"applications. It has three views: Icon Grid, Icon List, and Tree List. Its "
"functions can be extended with plugins and scripts."
msgstr ""
"Nautilû âmmite toâ lâ funçionê báçicâ de un hêttôh de arxibô y argunâ mâh. "
"Puede bûccâh y hêttionâh çû arxibô y carpetâ, tanto localê como remotâ, leêh"
" y êccribîh datô de y en dîppoçitibô êttraíblê, ehecutâh çecuençiâ de órdenê"
" y abrîh aplicaçionê. Tiene trêh bîttâ: rehiya de iconô, lîtta de iconô y "
"árbô. Çe puede ampliâh çu funçionalidá con çecuençiâ de órdenê y "
"complementô."

# #-#-#-#-#  nautilus-es.po (Nautilus)  #-#-#-#-#
# components/music/nautilus-music-view.c:198
#: data/org.gnome.Nautilus.desktop.in:3 src/nautilus-mime-actions.c:107
#: src/nautilus-properties-window.c:4602 src/nautilus-window.c:3084
msgid "Files"
msgstr "Arxibô"RoadmapMigrating to python3SupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow. Create a branch, add commits, and open a pull request.
Package: and-eggs
AndEggsAndEggs is a Python library that allows you to create an email bot using Gmail's API.Simple DocumentationClient ClassTheClientclass will be the main entry point of your program.Create a ClientFirst, create a new client by calling theClientconstructor:>>>fromandeggsimportClient>>>client=Client('CommandPrefix')Then, you can use theClientobject to interact with Gmail's API.Creating a CommandTo create a command, you should create an asynchronous function that will be called when the command is triggered.
It should take in aMailContextobject as its only parameter. This object contains all the information about the email that triggered the command.
To turn it into a command, use theClient.commanddecorator:>>>@client.command('CommandName')>>>asyncdefcommand(context:MailContext):>>># Do something>>>passSending an EmailTo send an email, you should use theClient.sendmethod:>>>client.send(>>>to='RecipientEmail',>>>subject='Subject',>>>body='Body',>>>is_html=False,>>>)Activating the ClientTo activate the client, you should call theClient.runmethod:>>>client.run('youremail','yourpassword')EventsEvents are triggered when certain actions happen. Below is a list of the events that can be triggered and their parameters:on_ready: Triggered when the client is started.on_message: Triggered when a new email is received. The parameter is aMailContextobject.on_send: Triggered when an email is sent. The parameter is aMailContextobject.on_command: Triggered when a command is triggered. The parameter is aMailContextobject.on_stop: Triggered when the client is stopped.TasksA task is a function that runs in the background. You can use theClient.taskdecorator to create a task:>>>@client.task(interval=60)>>>asyncdeftask():>>># Do something>>>passMailContextOn all of your commands, you will receive aMailContextobject. This object contains all the information about the email that triggered the command.Attributessender: ASenderobject.subject: The subject of the email.body: The body of the email.Sending an EmailYou can also send an email using theMailContext.sendmethod:>>>context.send(>>>'Body',>>>)If you want to format your text with html, you should use theMailContext.sendfmethod:>>>context.sendf(>>>'<p>{}</p>',>>>'Body',>>>)
Package: andeplane-ai
cognite-aiA set of AI tools for working with CDF in Python.MemoryVectorStoreStore and query vector embeddings created from CDF. This can enable a bunch of use cases where the number of vectors aren't that big.Install the package%pip install cognite-aiThen you can create vectors from text (both multiple lines or a list of strings) like thisfrom cognite.ai import MemoryVectorStore
vector_store = MemoryVectorStore(client)

vector_store.store_text("Hi, I am a software engineer working for Cognite.")
vector_store.store_text("The moon is orbiting the earth, which is orbiting the sun.")
vector_store.store_text("Coffee can be a great way to stay awake.")

vector_store.query_text("I am tired, what can I do?")Smart data framesChat with your data using LLMs. Built on top ofPandasAIversion 1.5.8. If you have loaded data into a Pandas dataframe, you can runInstall the package%pip install cognite-aiChat with your datafrom cognite.ai import load_pandasai
SmartDataframe, SmartDatalake = await load_pandasai()

workorders_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "workorders", limit=-1)
workitems_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "workitems", limit=-1)
workorder2items_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "workorder2items", limit=-1)
workorder2assets_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "workorder2assets", limit=-1)
assets_df = client.raw.rows.retrieve_dataframe("tutorial_apm", "assets", limit=-1)

from cognite.client import CogniteClient
client = CogniteClient()

smart_lake_df = SmartDatalake([workorders_df, workitems_df, assets_df, workorder2items_df, workorder2assets_df], cognite_client=client)
smart_lake_df.chat("Which workorders are the longest, and what work items do they have?")


s_workorders_df = SmartDataframe(workorders_df, cognite_client=client)
s_workorders_df.chat('Which 5 work orders are the longest?')Configure LLM parametersparams = {
    "model": "gpt-35-turbo",
    "temperature": 0.5
}

s_workorders_df = SmartDataframe(workorders_df, cognite_client=client, params=params)
Package: andeplane-pyodide-kernel
jupyterlite-pyodide-kernelA Python kernel forJupyterLitepowered byPyodide,Requirementspython >=3.8jupyterlite >=0.1.0b19InstallTo install the Pyodide kernel labextension and the CLI addons forjupyter lite, run:pipinstalljupyterlite-pyodide-kernelThen build your JupyterLite site:jupyterlitebuild⚠️ The documentation for advanced configuration is available from the main JupyterLite
documentation site:configuringcommand line interfaceUninstallTo remove the extension, run:pipuninstalljupyterlite-pyodide-kernelDevelopment InstallBelow is an short overview of getting up and running quickly. Please see thecontributing guidefor full details.Development RequirementsRecommendeda Python virtual environment provided by a tool of choice, e.g.virtualenvmambacondaEnsure the local development environment has:gitnodejs 18python >=3.8Development Quick Startgitclonehttps://github.com/jupyterlite/pyodide-kernelcdpyodide-kernel
npmrunquickstartThen, serve the built demo site, documentation, and test reports with Python's built-in
http server:jlpmserve
Package: ander
ander: A CLI tool to identify elements that are contained in both fileBasic Usage$catfile1.txt
duplicated
notduplicated

$catfile2.txt
duplicated
notduplicated

$anderfile1.txtfile2.txt
duplicatedInstallation$pipinstallanderRequirementsPython >= 3.6Some Python Libraries (seepyproject.toml)LicenseThis software is released under the MIT License, see LICENSE.
Package: andersen
Andersen一个Python的日志库的封装使用方式安装pip install andersen代码使用直接使用importandersenandersen.debug('debug')andersen.info('info')andersen.warn('warn')andersen.error('error')系统自带了一个输出到控制台的handler，日志级别为info，因此上边的代码会输出：自定义配置可以自己指定配置的文件，有两种指定方式：环境变量和函数指定指定环境变量importosos.environ.setdefault('ANDERSEN_CONFIG','log_config.toml')# 必须在导入前设置环境变量importandersenandersen.debug('debug')andersen.info('info')andersen.warn('warn')andersen.error('error')函数加载importandersenandersen.init('log_config.toml')andersen.debug('debug')andersen.info('info')andersen.warn('warn')andersen.error('error')目前只支持toml的自定义配置文件，示例文件如下[log.common]# 可选参数name="common"# 必要参数level="info"# 可选参数，这里如果配置的话则handler处没有配置的话会到这里查找。如果都找不到则会抛出异常format="[%(asctime)s] [%(levelname)s] %(message)s"# 输出到控制台的配置[[log.common.handlers]]type="std"# 可选参数format="%(asctime)s %(levelname)s %(message)s"level="info"# 输出到文件的配置[[log.common.handlers]]type="file"# 可选参数format="%(asctime)s %(levelname)s %(message)s"level="info"# 必要参数log_file="log/common.log"# 输出到按大小迭代的文件的配置[[log.common.handlers]]type="rotate"# 可选参数format="%(asctime)s %(levelname)s %(message)s"level="info"# 必要参数log_file="log/common1.log"max_bytes=1024# 输出到按时间迭代的文件的配置[[log.common.handlers]]type="time_rotate"# 可选参数format="%(asctime)s %(levelname)s %(message)s"level="info"# 必要参数log_file="log/common2.log"when='h'# 如果需要配置另一个，修改log后边的key名称即可，然后需要配置哪些类型的handler，从上边复制后修改即可[log.sample]name="sample"level="debug"format="[%(asctime)s] [%(levelname)s] %(message)s"# 输出到控制台的配置[[log.sample.handlers]]type="std"# 可选参数format="%(asctime)s %(levelname)s %(message)s"level="debug"相关函数日志配置init(conf=None,default_logger='common')初始化函数。可重复调用，只会实际执行一次。conf 如果为None则从环境变量读取配置文件，如果不存在则使用默认配置，即创建一个输出到控制台的日志对象default_logger 初始化后默认使用的日志对象generate_sample_config(filename)将示例配置写入一个toml文件。写入内容见上文filename 目标文件get_logger(key='common')获取日志对象。key必须在配置文件里已经定义。返回的是一个python的logging.Logger对象key 日志的key值，即配置中log后边所跟的参数，默认是common日志输出debug(*args,**kwargs)输出调试信息。kwargs中可选参数logger 要使用哪个日志输出信息，未指定的话使用默认日志输出sep 默认分隔符，如果指定该参数那么下边三个参数的默认值是该值list_sep 列表分隔符，即使用该参数连接参数argspara_sep 参数分隔符，即使用该参数连接args和kwargs生成的字符串dict_sep 字典分隔符，即使用该参数连接参数kwargsinfo(*args,**kwargs)输出信息。kwargs中可选参数logger 要使用哪个日志输出信息，未指定的话使用默认日志输出sep 默认分隔符，如果指定该参数那么下边三个参数的默认值是该值list_sep 列表分隔符，即使用该参数连接参数argspara_sep 参数分隔符，即使用该参数连接args和kwargs生成的字符串dict_sep 字典分隔符，即使用该参数连接参数kwargswarn(*args,**kwargs)输出警告信息。kwargs中可选参数logger 要使用哪个日志输出信息，未指定的话使用默认日志输出sep 默认分隔符，如果指定该参数那么下边三个参数的默认值是该值list_sep 列表分隔符，即使用该参数连接参数argspara_sep 参数分隔符，即使用该参数连接args和kwargs生成的字符串dict_sep 字典分隔符，即使用该参数连接参数kwargserror(*args,**kwargs)输出错误信息。kwargs中可选参数logger 要使用哪个日志输出信息，未指定的话使用默认日志输出sep 默认分隔符，如果指定该参数那么下边三个参数的默认值是该值list_sep 列表分隔符，即使用该参数连接参数argspara_sep 参数分隔符，即使用该参数连接args和kwargs生成的字符串dict_sep 字典分隔符，即使用该参数连接参数kwargs
Package: anders-sdk-jupyterlite
Cognite Python SDKThis is the Cognite Python SDK for developers and data scientists working with Cognite Data Fusion (CDF).
The package is tightly integrated with pandas, and helps you work easily and efficiently with data in Cognite Data
Fusion (CDF).DocumentationSDK DocumentationAPI DocumentationCognite Developer DocumentationPrerequisitesIn order to start using the Python SDK, you needPython3 (>= 3.5) and pipAn API key. Never include the API key directly in the code or upload the key to github. Instead, set the API key as an environment variable. See the usage example for how to authenticate with the API key.This is how you set the API key as an environment variable on Mac OS and Linux:$exportCOGNITE_API_KEY=<yourAPIkey>On Windows, you can followsthese instructionsto set the API key as an environment variable.InstallationTo install this package:$pipinstallcognite-sdkTo install this package without the pandas and NumPy support:$pipinstallcognite-sdk-coreExamplesFor a collection of scripts and Jupyter Notebooks that explain how to perform various tasks in Cognite Data Fusion (CDF)
using Python, see the GitHub repositoryhereChangelogWondering about upcoming or previous changes to the SDK? Take a look at theCHANGELOG.ContributingWant to contribute? Check outCONTRIBUTING.