Saravanan666/pypi-org huggingface dataset:
Package: one
Notallnorany: just theone.oneis a simple function to check if there is a unique value
that evaluates to True in an iterable, and return it. Optionally,
it receives a callable as the test function.Examples:>>>fromoneimportone>>>one((True,False,False))True>>>one((True,False,True))False>>>one((0,0,'a'))'a'>>>one((0,False,None))False>>>one((True,True))False>>>one(('',1))1>>>one((10,20,30,42),lambdai:i>40)42Installpip install oneUsagefrom one import onePatterns and use casesonecould replace the following patterns:true_values=[iforiiniterableifcmp(i)]iflen(true_values)==1:returntrue_values[0]returnFalse# using onereturnone(iterable,cmp)Another pattern, is in a complex conditionif((aandnotbandnotc)or(bandnotaandnotc)or(candnotaandnotb)):do_something()# using oneifone((a,b,c)):do_something()A very frequent case is when you have values that must exclude each others.classShopStore(models.Model):address=models.CharField(max_length=200,null=True,blank=True)is_online=models.BooleanField(default=False)defclean(self):ifnotone((self.address,self.is_online)):raisemodels.ValidationError(u'A shop must be online or physical, but not both')Send me your examples!
Package: oneapi2langchain
LangChain 实现闭源大模型的统一本项目的目的是将闭源大模型，如科大星火 文心一言 通义千问等闭源大模型进行Langchain的实现，即继承了langchain的BaseChatModel的实现。（仓库初步构建，欢迎贡献源码。）背景随着AIGC的快速发展，市面上出现了越来越多的大模型，其中包括开源大模型如qwen，baichuan,chatglm等，开源的模型已经有很好社区来支持openai格式的API接口，之所以使用openai格式的API接口，很重要的一个因素是可以轻松的使用langchain(目前最流行)。但是闭源大模型由于接口不统一，导致很难使用langchain提供的生态，因为我才有想法构建本项目。支持的闭源模型讯飞星火文心一言通义千问百川安装(要用官方源，国内源可能同步更新不及时)pipinstalloneapi2langchain-ihttps://pypi.org/simple/示例代码fromoneapi2langchainimportChatSpark# 完全支持langchainfromdotenvimportload_dotenv# 加载 APP_ID 等配置load_dotenv()if__name__=="__main__":llm=ChatSpark()# # 测试 string  okret=llm.predict("你是谁",stop=['科大讯飞'])print(ret)# 测试 message  ok# ret = llm.predict_messages([ChatMessage(role="user",content="你是谁")])# print(ret)# 测试 invoke  ok# ret = llm.invoke("你是谁")# print(ret)# 测试流式# for i in llm.stream("你是谁"):#     print(i)致谢spark-ai-python参考并引用langchain参考并引用
Package: oneapi-sdk
Lord of the Rings SDKThis is a Python SDK for the Lord of the Rings API.Installationpipinstalloneapi-sdkUsageTo use the SDK, you will need to get an API key fromThe One API.Basic usage:importoneapioneapi.access_token="YOUR_API_KEY"# Get all moviesmovies=oneapi.Movie.list()# Get all quotes from the first moviequotes=movies[0].quotesFilters:importoneapi# Filter syntax is {"field": ("operator", "value")}# Operators are "=", "!=", "<", ">", ">=", "exists", "not_exists"# Get all quotes from the first moviequotes=oneapi.Quote.list(filter={"movie":('=','5cd95395de30eff6ebccde5c')})Pagination:importoneapi# You can manually paginate through the results using .list(limit, offset)quotes_1=oneapi.Quote.list(limit=10,offset=0)quotes_2=oneapi.Quote.list(limit=10,offset=10)# Or you can use the built-in paginationquotes=oneapi.Quote.list(limit=10)forquoteinquotes.auto_paginate():print(quote.dialog)
Package: oneat-slim
OneatThis project provides static and action classification networks for LSTM based networks to recoganize cell events such as division, apoptosis, cell rearrangement for various imaging modalities.Installation & UsageInstallationThis package can be installed bypip install --user oneatadditionally ensure that your installed tensorflow version is not over 2.3.4If you are building this from the source, clone the repository and install viagitclonehttps://github.com/Kapoorlabs-CAPED/CAPED-AI-oneat/cdCAPED-AI-oneat

pipinstall--user-e.# or, to install in editable mode AND grab all of the developer tools# (this is required if you want to contribute code back to NapaTrackMater)pipinstall--user-rrequirements.txtPipenv installPipenv allows you to install dependencies in a virtual environment.# install pipenv if you don't already have it installedpipinstall--userpipenv# clone the repository and sync the dependenciesgitclonehttps://github.com/Kapoorlabs-CAPED/CAPED-AI-oneat/cdCAPED-AI-oneat
pipenvsync# make the current package availablepipenvrunpythonsetup.pydevelop# you can run the example notebooks by starting the jupyter notebook inside the virtual envpipenvrunjupyternotebookExamplesoneat comes with different options to combine segmentation with classification or to just use classification independently of any segmentation during the model prediction step. We summarize this in the table below:Example DatasetDataSetTrained ModelNotebook CodeExample timelapseOneat modelNapari notebookExample timelapseOneat modelNapari notebookExample timelapseOneat modelNapari notebookTroubleshooting & SupportTheimage.sc forumis the best place to start getting help and support. Make sure to use the tagoneat, since we are monitoring all questions with this tag.If you have technical questions or found a bug, feel free toopen an issue.
Package: one-block
one-blockone-blockhas started as I needed to start usingSlack's Block Kitfor the
design of my application. While there area few Python packages availablealready, they were either using a license I was uncomfortable with, or have simply not been kept up-to-date.Note, this is still a pre-release version, and as such, features might change or go away entirely.Available featuresWhile this is an on-going effort, this was initially driven by the fact I needed something to be able to move quickly.
As such, it's implementing all the existing blocks, but some things might not be available, e.g. specifyingblock_ids.
Documentation is also non-existent, but something I plan on adding as soon as possible.It is meant to be fairly simple to use, with re-usability at the forefront of its design.RoadmapSupport for all blocksDocumentationPython package publishingTestsType annotationsAppendixAlternative Python ImplementationsASU-CodeDevils/slack-blockkit- Did not seem to be well maintainedNickLambourne/slackblocks- BSD licensed and maintenance queries
Package: oneblue
oneblueA Simple Colormap forMatplotlib.RequirementsEnsureNumPy(NumPyon GitHub), andMatplotlib(Matplotlibon GitHub) is installed already before installingoneblue.One of most simple ways to install them is installing it withconda:$condainstallnumpymatplotlibInstallationCurrently the latest version ofmikutoolkitcan be installed withpipas following:$pipinstalloneblue--upgradeorfrom sourcelike other packages.ImportationTo accessoneblueand its functions import it in your Python code like this:>>>frommatplotlibimportpyplotasplt>>>plt.style.use("whatever_u_like_ggplot_4_instance")>>>fromoneblueimportoneb1ue>>>withplt.rc_context({"image.cmap":oneb1ue.cmap}):...............plt.show()Comparison with other Colormaps inMatplotlibColormap  NameNebula[^1]  Likelihood-mapIM2C  2019[^2]  D-1SST of Yellow Sea Cold Water Mass Area at 15:00, Dec 24th, 2006"Greys""Greys_r""Seismic"oneblue.oneb1ue.cmaponeblue.oneb1ue.dmapReferences[^1]:@sleepwalking/Nebula: Zero-data (yet trainable) probabilistic fundamental frequency estimator.
[^2]:IMMCExtraThe Colormaps are inspired by the theme colors of Hoshino-Ichika and Luo-Tianyi.NameHoshino-Ichika (Game Character)Luo-Tianyi (Virtual Singer of VOCALOID)Theme Color#33AAEE#66CCFFVision
Package: onebone
Summaryoneboneis an open-source software for signal analysis about predictive maintenance, being used for research activities atⓒ ONEPREDICT Corp.. It includes modules for preprocessing, health feature, and more. If you need to analyze signals for industrial equipments like turbines, a rotary machinery or componets like gears, bearings, give onebone a try!The directory is as follows:.
├── docs
├── onebone
│ ├── feature
│ ├── math
│ ├── preprocessing
│ ├── signal
│ └── utils
├── tests
├── tools
├── README.md
├── Makefile
└── pyproject.tomlDocumentationThis reference manual details functions included in onebone, describing what they are and what they do. For learning how to use onebone, see the completedocumentation.Contributors ✨Thanks goes to these wonderful people (emoji key):Kyunghwan Kim💻📖👀Kangwhi Kim💻📖👀DaeyeopN💻Kibum Fred Kim💻👀Kibum Park💻👀sewon.kim💻jeonjuna💻SunjinKim💻Hyunjae Kim💻This project follows theall-contributorsspecification. Contributions of any kind welcome!Getting startedPrerequisiteonebone requires Python 3.8+.Installationonebone can be installed via pip fromPyPI$pipinstalloneboneIt can be checked as follows whether the onebone has been installed.>>>importonebone>>>onebone.__version__UsageIt assumes that the user has already installed the onebone package.You can import directly the function, for example:>>>fromonebone.featureimporttacho_to_rpmCall for contributeWe appreciate and welcome contributions. Small improvements or fixes are always appreciated; issues labeled as "good first issue" may be a good starting point.Writing code isn't the only way to contribute to onebone. You can also:triage issuesreview pull requestshelp with outreach and onboard new contributorsIf you're unsure where to start or how your skills fit in, reach out! You can ask here, on GitHub, by leaving a comment on a relevant issue that is already open.If you want to use an code for signal analysis, but it's not in onebone, make a issue.Please followthis guideto contribute to onebone.If you are new to contributing to open source,this guidehelps explain why, what, and how to get involved.ReferencesNumpyScipyMatlab
Package: onebot
OneBotOneBot is an ircbot based on irc3Free software: BSD licenseDocumentation:http://onebot.rtfd.org.FeaturesDatabase pluginLast.FM pluginTrakt.TV pluginAdvanced user systemAdvanced ACLURL informationUtility commandsThese features are available through plugins.History1.3.3 (2019-08-26)irc3’sasyncplugin got renamed toasynchronious.This means we now requireirc3>=1.1.3In the URL plugin: read the response in 100KiB chunks and timeout1.3.2 (2018-08-23)Actually release the below fix1.3.1 (2018-08-23)AddCompatible: curltoUser-Agentforurlinfo.py(contributed by@mrngmPR #39)1.3.0 (2018-08-13)Support Python 3.7 (Requiresirc3>=1.1)1.2.1 (2018-03-05)Change urlinfo character limit to 320, helps twitter urls (contributed by@mrngmPR #38)1.2.0 (2017-10-31)Truncate too long<title>tags in urlinfo output.Drop Python 3.3, 3.4 support1.1.0 (2017-04-01)PSA Plugin (contributed by@joostrijneveld:PR #32)Various contributions by@Mattbox:PR #36* Use musicbrainz tags instead of last.fm
* Tests for PSA plugin
* reload commandRemovecomparecommand as it’s dead on Last.fmRemovembidlookups on Last.fm as they’re broken1.0.0 (2016-07-17)I get around to finally posting this shit.0.1.0 (2015-??)First production usage0.0.0 (2014-05-21)Start of development
Package: onebot-qqguild-extension
OneBot QQ频道 扩展标准QQ频道 扩展标准旨在统一不同实现的扩展方法，避免混乱局面的出现。注意，本仓库是 OneBot QQ频道 扩展标准的最新草案，发布在https://he0119.github.io/onebot-qqguild-extension/。本地预览要在本地预览 OneBot QQ频道 扩展标准文档，请参考下面命令：poetryinstall
mkdocsserve贡献如果你有兴趣帮助完善 OneBot QQ频道 扩展标准，可积极参与discussions中的讨论、提出 RFC 或帮助更新标准文档，具体贡献方式请参考贡献指南。鸣谢OneBot: 文档的复制粘贴对象
Package: onebrain
Onebrain-SDK安装首先镜像里需安装onebrain-sdk。可以采用whl包本地安装的形式。
一般来说，平台提供的镜像里应该已经集成onebrain-sdk了。打最新的whl包gitclonehttps://gitee.com/onebrain/onebrain-sdk.git
pythonsetup.pybdist_wheel-donebrain将whl包上传至镜像安装whl包pipinstallonebrain-0.0.1-py3-none-any.whlSDK说明
使用示例fromonebrain.apiimportOnebrainClientrun_id="my_train_job"# 创建与平台通信的client对象client=OnebrainClient()然后就可以通过client对象来上传指标到平台了
以pytorch训练任务为例# ...deftrain(dataloader:DataLoader,model:nn.Module,loss_fn,optimizer:torch.optim.Optimizer,step):image_size=len(dataloader.dataset)print(f"数据集的图片总数: {image_size}")forbatch,(x_train,y_train)inenumerate(dataloader):x_train,y_train=x_train.to(device),y_train.to(device)y_hat=model(x_train)loss=loss_fn(y_hat,y_train)loss.backward()optimizer.step()optimizer.zero_grad()ifbatch%100==0:loss,current=loss.item(),batch*len(x)print(f'batch:{batch}, loss: {loss}, , image_num:{current}/{image_size}')# 上传指标client.log_metric(key="loss",value=loss,step=step)forepochinrange(epochs):print(f"Epoch {epoch}\n --------------------------------")train(train_dataloader,model,loss_fn,optimizer,epoch)log_metric方法参数说明：run_id:  训练任务的唯一标识，默认从环境变量获取，填充为平台生成的resourceIdkey：指标名称value: 指标值timestamp:  时间戳，默认为当前时间step: 训练的step备注：在OnebrainClient对象实例化之初，会根据容器的环境变量判断该以何种方式上报平台。
默认为RestfulApi，也支持使用MQ上报指标。当环境变量不存在时，实例化会失败并报错。
Package: onecache
OneCachePython cache for sync and async code.Cache uses LRU algoritm. Cache can optionally have TTL.Tested in python 3.7, 3.9, and pypy3.9 for windows, mac and linux (see github status badge), it should work in versions between them. It may work for python3.6UsagefromonecacheimportCacheDecoratorfromonecacheimportAsyncCacheDecoratorclassCounter:def__init__(self,count=0):self.count=count@pytest.mark.asyncioasyncdeftest_async_cache_counter():"""Test async cache, counter case."""counter=Counter()@AsyncCacheDecorator()asyncdefmycoro(counter:Counter):counter.count+=1returncounter.countassert1==(awaitmycoro(counter))assert1==(awaitmycoro(counter))deftest_cache_counter():"""Test async cache, counter case."""counter=Counter()@CacheDecorator()defsample(counter:Counter):counter.count+=1returncounter.countassert1==(sample(counter))assert1==(sample(counter))Decorator classes supports the following argumentsmaxsize (int): Maximun number of items to be cached. default: 512ttl (int): time to expire in milliseconds, if None, it does not expire. default: Noneskip_args (bool): apply cache as the function doesn't have any arguments, default: Falsecache_class (class): Class to use for cache instance. default: LRUCacherefresh_ttl (bool): if cache with ttl, This flag makes key expiration timestamp to be refresh per access. default: Falsethread_safe (bool): tell decorator to use thread safe lock. default=Falsemax_mem_size (int): max mem size in bytes. Ceil for sum of cache values sizes. default=None which means no limit. For pypy this value is ignored as the objects can change by the JIT compilation.If num of records exceds maxsize, it drops the oldest.DevelopmentInstall packages with pip-tools:pipinstallpip-tools
pip-compile
pip-compiletest-requirements.in
pip-syncrequirements.txttest-requirements.txtContributeForkcreate a branchfeature/your_featurecommit - push - pull requestThanks :)
Package: onec-checkgrammar
onec-checkgrammarПроверка орфографии элементов формМожно проверять исходные файлы внешних обработок/отчетов и файлов конфигурацииУстановкаИз пакетовpipinstallonec-checkgrammarИз репозиторияpipinstall.ИспользованиеПростая проверкаonec-checkgrammar./srcБудут выбраны все файлы форм в формате xml. Из них извлечены элементы для которых заданы:ЗаголовкиПодсказкиРасширенные подсказкиПроверка нескольких папокonec-checkgrammar./src1./src2Исключение форм из проверкиonec-checkgrammar--skipТест_*./src1Исключит все формы начинающиеся с префикса "Тест_". Подробнее: документация globРезультат проверки в формате JUnitonec-checkgrammar--junitjunit.xml./srcРезультат проверки в отдельный файлonec-checkgrammar--outputtemp.txt./srcВ файл будут добавлены все слова с ошибками. Удобно подготовить файл словаря исключенийСловари исключенийДля исключения терминов или каких-то других слов можно использовать словариЯвное указаниеonec-checkgrammar--dictdict.txt./srcСлова должны быть разделены переносом строкМожно указать несколько словарейonec-checkgrammar--dictdict1.txt--dictdict2.txt./srcНастройки BSL language serverЕсли у Вас в настройках указаны исключения для опечаток, можно использовать их повторноonec-checkgrammar--bsl-settings--dictdict1.txt./srcБудут взяты настройки из файла.bsl-language-server.jsononec-checkgrammar-bsl/my-bsl.json./srcЯвное указание файла настроек
Package: onec_dtools
onec_dtools- библиотека для работы с бинарными файлами 1С:Предприятие 8 (1CD, cf, epf, efd и т.д.) без
использования технологической платформы.Установкаpip install onec_dtoolsИспользованиеПолное описание всех возможностей библиотеки доступно вдокументации.Простой пример, демонстрирующий чтение всех данных (включая BLOB) из таблицы V8USERS:import onec_dtools

with open('1Cv8.1CD', 'rb') as f:
    db = onec_dtools.DatabaseReader(f)
    if row.is_empty:
            continue
    for row in db.tables['V8USERS']:
        print(row.as_list(True))Распаковка и запаковки CF файла:import onec_dtools

onec_dtools.extract('D:/sample.cf', 'D:/unpack')
onec_dtools.build('D:/unpack', 'D:/repacked.cf')
Package: oneclick-client
oneclick-clientThis is a simple library for working with the Spectrum OneClick API.
The basic working methods are implemented, the response can be returned in json, like a python dict, or in xml, like a bs4 object. You can also use xml or json in requests, passing them as text.Installationpip install oneclick-clientLaunch examplesSee example.py for more working examples.# importfromoneclick_clientimportspectrumassp# initializationapi=sp.SpectrumClient(server='https://192.168.0.1:8888',user='spectrum',password='spectrum',verify=False,timeout=5)# get devices, output in json (python dict)devices=api.get(out_format='json',app='devices',params={'throttlesize':10000,'landscape':'0x1000000','attr':['0x129e7','0x129fa','0x1006e']})# create servicemodel=api.post(out_format='json',app='model',params={'landscapeid':'0x1000000','mtypeid':'0x1046f','attr':['0x12a51','0x1006e'],'val':['7','test_srv'],})mh=model['create-model-response']['model']['@mh']# update service name, output in xml (bs4 obj)update=api.put(app=f"model/{mh}",params={'attr':'0x1006e','val':'test_srv_update',})# request with data in xml and output in json (python dict)xml_request="""<!-- xml body request GET ALL ASA MODELS --><rs:model-request throttlesize="10000"xmlns:rs="http://www.ca.com/spectrum/restful/schema/request"xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://www.ca.com/spectrum/restful/schema/request ../../../xsd/Request.xsd "><rs:target-models><rs:models-search><rs:search-criteriaxmlns="http://www.ca.com/spectrum/restful/schema/filter"><filtered-models><is-derived-from><attribute id="AttributeID.MTYPE_HANDLE"><value>0x2101ea</value> <!-- CiscoASA --></attribute></is-derived-from></filtered-models></rs:search-criteria></rs:models-search></rs:target-models><rs:requested-attribute id="0x1006e" /> <!-- Device Name --><rs:requested-attribute id="0x10000" /> <!-- Model Type Name --><rs:requested-attribute id="0x10032" /> <!-- Manufacturer --><rs:requested-attribute id="0x129fa" /> <!-- Model Handle --><rs:requested-attribute id="0x1027f" /> <!-- IP Address --><rs:requested-attribute id="0x10004" /> <!-- Contact status --><rs:requested-attribute id="0x110df" /> <!-- MAC Address --><rs:requested-attribute id="0x82002d" /> <!-- Last verified unix timestamp  --></rs:model-request>"""asa_models=api.post(out_format='json',app='models',data=xml_request)
Package: one-cli-plugin-commands
plugin-commandsThis is a one-cli plugin that allow to create new commands during runtime to the CLI.Configuration# one.yamlrequired_version:">=0.7.1"plugins:commands:package:one-cli-plugin-commands==0.1.4module:'plugin_commands'commands:-name:installcommand:'npminstall'volumes:['.:/work']help:'npminstall'-name:buildcommand:'npmrunbuild'volumes:['.:/work']help:'npmrunbuild'-name:startvolumes:['.:/work']command:'npmstart'ports:['4100:4100']help:'npmstart'environment:['TEST':'test']Usageoneinstall
onebuild
onestartParameters-name:<command_name>image:<string(docker_image)># default toentrypoint:<string(entrypoint)># default to Nonevolumes:<list(volumes)># ['.:/work', '.:/app']command:<string(command)>ports:<list(ports)># ['3000:3000', '4100:4100']environment:<list(environments)># ['ENV': 'env']help:<string(help)>DevelopmentDependenciesPython 3Python Virtual Environment# Create environmentpython3-mvenvenv# To activate the environmentsourceenv/bin/activate# When you finish you can exit typingdeactivateInstall dependenciespip3install--editable.AuthorManaged byDNX Solutions.LicenseApache 2 Licensed. SeeLICENSEfor full details.
Package: one-cli-plugin-dnx-assume
plugin-dnx-assumeThis is a one-cli plugin that adds extra assume role entry command to the CLI.Configuration# one.yamlrequired_version:">=0.7.1"plugins:dnx-assume:package:one-cli-plugin-dnx-assume==0.1.3module:'plugin_dnx_assume'parameters:aws_role:<redact>aws_account_id:<redact>Usageonednx-assumeDevelopmentDependenciesPython 3Python Virtual Environment# Create environmentpython3-mvenvenv# To activate the environmentsourceenv/bin/activate# When you finish you can exit typingdeactivateInstall dependenciespip3install--editable.AuthorManaged byDNX Solutions.LicenseApache 2 Licensed. SeeLICENSEfor full details.
Package: one-cli-plugin-foundation
plugin-foundationThis is a one-cli plugin to help create new DNX foundations.Configuration# one.yamlrequired_version:">=0.7.1"plugins:foundation:package:one-cli-plugin-foundation==0.0.1module:'plugin_foundation'UsageonefoundationDevelopmentDependenciesPython 3Python Virtual Environment# Create environmentpython3-mvenvenv# To activate the environmentsourceenv/bin/activate# When you finish you can exit typingdeactivateInstall dependenciespip3install--editable.AuthorManaged byDNX Solutions.LicenseApache 2 Licensed. SeeLICENSEfor full details.
Package: one-cli-plugin-kube
plugin-kubeThis is a one-cli plugin that adds Kubernetes tools alias to the CLI.Configuration# one.yamlrequired_version:">=0.7.1"plugins:kube:package:one-cli-plugin-kube==0.5.2module:'plugin_kube'parameters:cluster_name:<redact>aws_default_region:<redact>aws_assume_role:# Optional: default to falsekubeconfig:# Optional: Override default kubeconfig pathUsageonekubectl...
onehelm...
onekube-bashDevelopmentDependenciesPython 3Python Virtual Environment# Create environmentpython3-mvenvenv# To activate the environmentsourceenv/bin/activate# When you finish you can exit typingdeactivateInstall dependenciespip3install--editable.AuthorManaged byDNX Solutions.LicenseApache 2 Licensed. SeeLICENSEfor full details.
Package: onecloud
# OneCloud APIAPI клиент для управления серверами на платформе 1Cloud.ru# УстановкаВоспользуйтесь `pip` или `easy_install`:```bash$ pip install --upgrade onecloud```or```bash$ easy_install --upgrade onecloud```# Совместимость с версиями Python:Python 2.6 or 2.7, 3.3+ полностью поддерживаются.# Дополнительные компоненты:Использует всего лишь один дополнительный компонент* [requests](https://github.com/kennethreitz/requests)# Использование```pythonfrom onecloud import apia = api.OneCloud(api_key='API ключ из личного кабинета', timeout=5)```#Возможности:- Актуальный баланс:```pythona.get_balance()```- Список Ваших серверов:```pythona.get_servers_list()```- Включить сервер:```pythona.power_on_server(self, server_id)```- Перезагрузить сервер:```pythona.reboot_server(self, server_id)```- Выключить сервер:```pythona.power_off_server(self, server_id)```- Создать новый сервер:```pythona.create_server(self, name, cpu, ram, hdd, image_id, dc_location, hdd_type, is_high_performance) ...```- ...в выбранном ДатаЦентре```pythona.get_dc_locations(self)```- ...и установить ОС из списка образов:```pythona.get_images_list()```- Создать свой образ операционной системы:```pythona.create_image(self, name, tech_name, server_id)```- Создать частную сеть:```pythona.create_private_network(self, name)```- Список созданных сетей:```pythona.get_private_networks_list(self)```- Подключить сервер к частной сети:```pythona.connect_server_to_network(self, server_id, network_id)```и т.д.более подробно:# Документация по APIhttps://1cloud.ru/api