Saravanan666/pypi-org huggingface dataset:
Package: qgym
QGYM – A Gym for Training and Benchmarking RL-Based Quantum Compilationqgymis a software framework that provides environments for training and benchmarking RL-based quantum compilers.
It is built on top of OpenAI Gym and abstracts parts of the compilation process that are irrelevant to AI researchers.qgymincludes three environments:InitialMapping,Routing, andScheduling, each of which is customizable and extensible.DocumentationWe have created anextensive documentationwith code snippets.
Please feel free to contact us vias.feld@tudelft.nlif you have any questions, or by creating aGitHub issue.Getting StartedWhat follows are some simple steps to get you running.
You could also have a look at someJupyter Notebooksthat we have created for a tutorial at theIEEE International Conference on Quantum Computing and Engineering (QCE’22).Installing with pipTo install theqgymusepip install qgymIf you would also like to use the notebooks, additional packages are required, which can simply be installed by using
In this case, usepip install qgym[tutorial]Currentlyqgymhas support for Python 3.7, 3.8, 3.9, 3.10 and 3.11.PublicationThe paper onqgymhas been presented in the1st International Workshop on Quantum Machine Learning: From Foundations to Applications (QML@QCE'23).
The publication can be found oncomputer.orgYou can find the preprint of the paper onarxiv.@inproceedings{van2023qgym,
  title={qgym: A Gym for training and benchmarking RL-based quantum compilation},
  author={Van Der Linde, Stan and De Kok, Willem and Bontekoe, Tariq and Feld, Sebastian},
  booktitle={2023 IEEE International Conference on Quantum Computing and Engineering (QCE)},
  volume={2},
  pages={26--30},
  year={2023},
  organization={IEEE}
}TeamBuilding qgym is a joint effort.Core developersStan van der LindeWillem de KokTariq BontekoeSebastian FeldContributors and Power UsersJoris HenstraRares Oancea
Package: qh
qhQuick Http web-service construction.Getting from python to an http-service exposing them to the world,
in the easiest way machinely possible.Harnesses the great power ofpy2httpwithout all the responsibilities.This is meant for the desireable lightening fast development cycles during
proof-of-conceptualization.
As you move towards production, consider using one of those boring grown-up tools out there...To install:pip install qhExamplesWhen dealing only with simple (json) types...importqhfromqhimportmk_http_service_appdefpoke():return'here is a peek'deffoo(x:int):returnx+2defbar(name='world'):returnf"Hello{name}!"app=mk_http_service_app([foo,bar,poke])app.run()Bottle v0.12.19 server starting up (using WSGIRefServer())...
Listening on http://127.0.0.1:8080/
Hit Ctrl-C to quit.Now grab a browser and go tohttp://127.0.0.1:8080/ping(it's a GET route that the app gives you for free, to test if alive){"ping": "pong"}Now try some post requests:Run this script somewhere. For example, with curl try things like:curl http://127.0.0.1:8080/ping
# should get {"ping": "pong"}

curl -X POST http://127.0.0.1:8080/poke
# should get "here is a peek"

curl -H "Content-Type: application/json" -X POST -d '{"x": 3}' http://127.0.0.1:8080/foo
# (should get 5)

curl -H "Content-Type: application/json" -X POST -d '{"name": "qh"}' http://127.0.0.1:8080/bar
# should get "hello qh!"Now be happy (or go try the other function by doing some post requests).When your types get complicatedTo deploy the above, we would just need to doapp=mk_http_service_app([poke,foo,bar])But what if we also wanted to handle this:defadd_numpy_arrays(a,b):return(a+b).tolist()Here the a and b are assumed to be numpy arrays (or.tolist()would fail).
Out of the box, qh can only handle json types(str, list, int, float), so we need to preprocess the input.qhmakes that easy too.Here we provide a name->conversion_func mapping (but could express it otherwise)fromqh.transimportmk_json_handler_from_name_mappingimportnumpyasnpinput_trans=mk_json_handler_from_name_mapping({"a":np.array,"b":np.array})app=mk_http_service_app([poke,foo,bar,add_numpy_arrays],input_trans=input_trans)app.run()Now try it out:curl -H "Content-Type: application/json" -X POST -d '{"a": [1,2,3], "b": [4,5,6]}' http://127.0.0.1:8080/add_numpy_arrays
# should get [5, 7, 9]
Package: qh3
What isqh3?qh3is a maintained fork of theaioquiclibrary.
It is lighter, faster, and more adapted to mass usage. Regularly improved and expect a better time to initial response in issues and PRs.qh3is a library for the QUIC network protocol in Python. It features
a minimal TLS 1.3 implementation, a QUIC stack, and an HTTP/3 stack.QUIC was standardized inRFC 9000and HTTP/3 inRFC 9114.qh3is regularly tested for interoperability against otherQUIC implementations.To learn more aboutqh3pleaseread the documentation.Why should I useqh3?qh3has been designed to be embedded into Python client and server
libraries wishing to support QUIC and/or HTTP/3. The goal is to provide a
common codebase for Python libraries in the hope of avoiding duplicated effort.Both the QUIC and the HTTP/3 APIs follow the “bring your own I/O” pattern,
leaving actual I/O operations to the API user. This approach has a number of
advantages including making the code testable and allowing integration with
different concurrency models.This library is the lowest level you can find for handling QUIC and HTTP/3. Here are higher libraries:mid-way:urllib3.futurehighest and easiest:niquests(Recommended!)FeaturesQUIC stack conforming withRFC 9000HTTP/3 stack conforming withRFC 9114minimal TLS 1.3 implementation conforming withRFC 8446IPv4 and IPv6 supportconnection migration and NAT rebindinglogging TLS traffic secretslogging QUIC events in QLOG formatHTTP/3 server push supportRequirementsqh3requires Python 3.7 or greater.Running the examplesqh3comes with a number of examples illustrating various QUIC use cases.You can browse these examples here:https://github.com/Ousret/qh3/tree/main/examplesLicenseqh3is released under theBSD license.
Package: qham
Qham - A QUICK INSIGHT INTO QUANTUM HAMILTONIAN SIMULATIONSQham is a Python SDK designed to bridge the gap between theoretical physics and practical quantum computing applications. It provides a comprehensive suite of tools for exploring, simulating, and analyzing Hamiltonian processes across statistical mechanics and quantum mechanics.At the heart of Qham lies a mission to democratize the understanding of Hamiltonian dynamics, unravel the complexities of physical systems, and streamline the development of quantum algorithms. By amalgamating theoretical concepts with practical computational tools, Qham endeavors to make the intricate study of Hamiltonian systems both accessible and intuitive.OverviewQham is a Python library that consists of the following components:ComponentDescriptionqhamA lightweight Quantum Hamiltonian Simulations for high-performance Quantum researchqham.fhmFermiHubbard Modalqham.hbmHeisenberg Modalqham.qhoQuantum Harmonic Oscillatorqham.TFIMTransverse Field Ising ModelQham, can be used for,a Hamiltonian processing in quantum mechanics and statistical mechanics.a quantum python package whichl will give a good introduction quantum hamilotnian simulations.InstallationSee the Qham[Installation][]guide for detailed installation instructions (including building from source).Currently,qhamsupports releases of Python 3.6 onwards;
To install the current release:$pipinstall--upgradeqhamGetting StartedMinimal Exampleimportqham# Initialize the TFIM simulationsim=TFIMSimulation(size=10,beta=0.4,h=0.05,steps=100)# Run the simulationsim.run_simulation()# Plot the final lattice configurationsim.plot_lattice()ResourcesPyPiDocumentationIssue trackingContributingWe appreciate all contributions, feedback and issues. If you plan to contribute new features, utility functions, or extensions to the core, please go through our [Contribution Guidelines][].To contribute, start working through theqhamcodebase, read the [Documentation][], navigate to the [Issues][] tab and start looking through interesting issues.Asking for helpIf you have any questions, please:Read the docs.Look it up in our Github Discussions (or add a new question).Search through the issues.Licenseqham is open-source and released under theMIT License.
Package: qhbayes
QHBayesBayesian methods for inferring mass eruption rate from column height (or vice versa) for volcanic eruptionsWhat is it?QHBayesuses Bayesian methods to explore the relationship between the mass eruption rate (Q) of a volcanic eruption and the height reached by the volcanic eruption column (H) that is produced.The mass eruption rate is a quantity that is very important in volcanology and in the dispersion of volcanic ash in the atmosphere, but it is very difficult to measure directly.Often the mass eruption rate is inferred from observations of the height of the volcanic eruption column, since the eruption column is often much easier to measure.  The eruption column height is linked to the mass eruption rate through the fluid dynamics of turbulent buoyant plumes, but there are often external volcanological and atmospheric effects that contribute and complicate the relationship.Datasets of the mass eruption rate and eruption column height have been compiled and used to determine an empirical relationship these quantities, using linear regression.  This has then been used to infer the mass eruption rate from the plume height.QHBayesgoes further, by using Bayesian methods to perform the regression.  Bayesian methods:allow us to incorporate a range ofuncertaintiesquantitatively into our model;provide a meaningful quantitative comparison of different models;Main FeaturesHow do I get set up?Summary of set upConfigurationDependenciesDatabase configurationHow to run testsDeployment instructionsContribution guidelinesWriting testsCode reviewOther guidelinesWho do I talk to?Repo owner or adminOther community or team contact
Package: qhdl
QHDLCircuit Components as and QHDL in PythonDevelopment of QHDL happens onGithub.
You can read the full documentation atReadTheDocs.InstallationTo install the latest released version of QHDL, run this command in your terminal:$pipinstallqhdlThis is the preferred method to install QHDL, as it will always install the most recent stable release.If you don’t havepipinstalled, thePython installation guidecan guide
you through the process.To install the latest development version of QHDL fromGithub.$pipinstallgit+https://github.com/mabuchilab/qhdl.git@develop#egg=qhdlUsageTo use QHDL in a project:import qhdlHistory0.1.0 (2018-12-06)Initial release
Package: qhist
QHist– A Quick Histogram drawer forROOT::TTreefor smoother HEP analysis!Examples:Simple draw from tree and parameter (branch name):h1 = QHist()
h1.trees = t1
h1.params = 'M'
h1.draw()Overlaid comparison between branches, compact syntax:QHist(trees=t1, params=['mu_PT/1e3','pi_PT/1e3'], xlabel='PT [GeV]', xmax=60).draw()Compare between trees, with filtering, and reusable via templating:H = QHist(trees=[t1, t2, t3], filters=['mu_PT>20e3'])
H(params='APT'  , xmin=0  , xlabel='PT-asymmetry').draw()
H(params='M/1e3', xmax=120, xlabel='Mass [GeV]').draw()Read more onhttps://qhist.readthedocs.ioInstallation & dependenciesIt’s available frompip install qhist.
The package requires an existing installation ofPyROOT.DisclaimerThis packacge was written and used during my PhD in 2013-2017 at EPFL (Lausanne) and LHCb collaboration (CERN),
for the work inZ->tau taucross-section measurement andH->mu tausearches at LHCb (8TeV).
I hope it can be of a good use for future analysis…
Package: qh-python-manilaclient
Python bindings to the OpenStack Manila APIThis is a client for the OpenStack Manila API. There’s a Python API (themanilaclientmodule), and a command-line script (manila). Each
implements 100% of the OpenStack Manila API.See theOpenStack CLI guidefor information on how to use themanilacommand-line tool. You may also want to look at theOpenStack API documentation.The project is hosted onLaunchpad, where bugs can be filed. The code is
hosted onGithub. Patches must be submitted usingGerrit,notGithub
pull requests.This code is a fork ofCinderclientof Grizzly release and then it was
developed separately. Cinderclient code is a fork ofJacobian’s python-cloudserversIf you need API support for the Rackspace
API solely or the BSD license, you should use that repository.
python-manilaclient is licensed under the Apache License like the rest of
OpenStack.Contents:Command-line APIPython APICommand-line APIInstalling this package gets you a shell command,manila, that you
can use to interact with any Rackspace compatible API (including OpenStack).You’ll need to provide your OpenStack username and password. You can do this
with the--os-username,--os-passwordand--os-tenant-nameparams, but it’s easier to just set them as environment variables:export OS_USERNAME=foouser
export OS_PASSWORD=barpass
export OS_TENANT_NAME=fooprojectYou will also need to define the authentication url either with param--os-auth-urlor as an environment variable:export OS_AUTH_URL=http://example.com:5000/v2.0/Since Keystone can return multiple regions in the Service Catalog, you
can specify the one you want with--os-region-name(orexport OS_REGION_NAME). It defaults to the first in the list returned.You’ll find complete documentation on the shell by runningmanila help, seemanila help COMMANDfor help on a specific command.Python APIThere’s also a complete Python API, but it has not yet been documented.Quick-start using keystone:# use v2.0 auth with http://example.com:5000/v2.0/
>>> from manilaclient.v1 import client
>>> nt = client.Client(USER, PASS, TENANT, AUTH_URL, service_type="share")
>>> nt.shares.list()
[...]License: Apache License, Version 2.0PyPi- package installationOnline DocumentationLaunchpad project- release managementBlueprints- feature specificationsBugs- issue trackingSourceHow to ContributeRelease Notes
Package: qhsdk
qhsdkqhsdk的介绍qhsdk主要是为https://qhkch.com/提供 SDK 的 Python 库, 您可以通过奇货可查机构VIP接口文档了解和查询详细数据接口！qhsdk服务于www.qhkch.comqhsdk的特色qhsdk主要改进如下:qhsdk支持Python 3.7及以上版本;目前提供已提供奇货可查全部接口;提供完善的接口文档, 提高qhsdk的易用性;安装方法pip install qhsdk升级方法pip install qhsdk --upgrade快速入门目标数据: 奇货可查-商品-持仓数据接口示例代码:importqhsdkasqhpro=qh.pro_api(token="此处输入您的token, 请联系奇货可查获取！")variety_positions_df=pro.variety_positions(fields="shorts",code="rb1810",date="2018-08-08")print(variety_positions_df)示例结果:broker  short  short_chge
0    银河期货  60987       -4228
1    永安期货  57520       -1071
2    中信期货  38120        -620
3    国泰君安  36498         528
4    方正中期  32105        4444
5    海通期货  29638       -2783
6    东海期货  29250         450
7    光大期货  28458         -84
8    南华期货  27853        -144
9    中辉期货  26101        -553
10   中大期货  23761        1572
11   鲁证期货  22501        -598
12   兴证期货  22262        -842
13   东证期货  21675        -686
14   徽商期货  18966        -607
15   中信建投  18583        -625
16   华泰期货  17076       -5797
17   国投安信  16808         349
18   申银万国  14876         376
19   广发期货  14588       -2196
20   大地期货      0      -14603特别说明致谢特别感谢AkShare项目提供借鉴学习的机会;声明qhsdk提供的数据仅供参考, 不构成任何投资建议;任何基于qhsdk进行研究的投资者请注意数据风险;qhsdk的使用请遵循奇货可查网站的用户协议;qhsdk使用产生的所有问题的最终解释权归奇货可查网站所有;版本更新说明0.0.1
发布测试版本

0.0.2
调试接口

0.0.3
增加lxml=4.4.1

0.0.4
更新说明文档

0.0.5
新增通过用户名和密码登录并访问VIP资源功能

0.0.6
修正导入问题

0.0.7
更新 README 文档

0.0.8
第二版接口测试

0.0.9
更新说明文档

0.1.0
更新测试文件

0.1.1
预览版

0.1.2
增加 token 使用说明

0.1.3
增加 inventory 参数类型二

0.1.4
新增: 合约持仓数据接口, 商品持仓数据接口, 修复部分接口与文档一致

0.1.5
新增: 增加 broker_positions_process 中 start_date 和 end_date 可选参数

0.1.6
新增: 增加 variety_calc_positions 接口

0.1.7
修改: 修改 variety_all 接口, 新增 3 个字段

0.1.8
新增: 新增 variety_net_position_list 接口

0.1.9
新增: 同步文档 2021年11月25日 版本

0.1.10
新增: inventory_new 接口
修复：返回空值

0.1.11
修复: 修改 url 地址文档构建cddocspipinstall-rrequirements.txtsphinx-build-bhtml._build
Package: qhub-jupyterhub-theme
Please submit issues tohttps://github.com/quansight/qhub/issuesCustom JupyterHub Template for QHubThis repo contains html jinja2 templates for customising the
appearance of JupyterHub. Each HTML file here will override the files
inhttps://github.com/jupyterhub/jupyterhub/tree/master/share/jupyter/hub/templates.UsageInstallqhub_jupyterhub_themein your environmentpipinstallqhub_jupyterhub_themeAdd the following to the jupyterhub configuration to pickup the new
jinja2 templates directory and static files.fromqhub_jupyterhub_themeimporttheme_extra_handlers,theme_template_pathsc.JupyterHub.extra_handlers=theme_extra_handlersc.JupyterHub.template_paths=theme_template_pathsFinally customize the templates via thetemplate_vars. Current
options are:hub_titlehub_subtitlewelcomelogoprimary_colorsecondary_color`accent_color'text_colorh1_colorh2_colornavbar_text_colornavbar_hover_colorInternal options:cdsdashboards_enabledcdsdashboards_restrictedqhub_theme_extra_js_urlsInspiration is in the test jupyterhub configurationtest_jupyterhub_config.py.c.JupyterHub.template_vars={'hub_title':'This is QHub','hub_subtitle':'your scalable open source data science laboratory.','welcome':'have fun.',}TestingInstall the development environmentcondaenvinstall-fenvironment.yamlYou do not need to restart jupyterhub to see changes incustomandtemplates. Run jupyterhub via the test scriptjupyterhub--configtest_jupyterhub_config.pyTo run in VSCode, here is a launch.json config:{
    "name": "JupyterHub test",
    "type": "python",
    "request": "launch",
    "module": "jupyterhub",
    "args": ["-f", "./test_jupyterhub_config.py"],
    "cwd": "${workspaceFolder}"
}You would need to make sure the Python virtualenv you've set up for this is active in the project.ChangelogVersion 0.3.6Extend navbar links #16Version 0.3.5Updates to the version display style #15Version 0.3.4Updades to ReadmeExpose navbar color options #12Add option to display Qhub version #13Version 0.3.3Simplify the JupyterHub config (backwards-compatible)Added testing docs for VScodeVersion 0.3.2Added Dashboards menu page and headerAdded custom js tag functionality #11Version 0.3.1Add text color and defaults to template options #9Version 0.3.0Adds colors! :tada:
Package: qhub-ops
QHub-OpsIs a tool for initializing and maintaining the state of QHub
deployments on Digital Ocean, Amazon Web Services, and Google Cloud
Platform.Installation:pipinstallqhub-opsConfigurationQHub is entirely controlled from a configuration file. Example
configuration files can be found intests/assets. Documentation
on creating a configuration file is detailedhere.Templates for each file are at the following paths:AWS :: tests/assets/config_aws.yamlDO  :: tests/assets/config_do.yamlGCP :: tests/assets/config_gcp.yamlInitializing the Provider TemplateThe exact naming of the configuration file is needed to trigger the CI
actions when the configuration is changed.mkdir<repository-name># mv <config-filename> <repository-name>/qhub-ops-config.yamlqhub-opsrender-c<repository-name>/qhub-ops-config.yaml-o<repository-name>/--forceAfter initialising the provider templates, follow the instructions in
docs on deploying the infrastructure at<repository-name>/docs/installation.md. At this current moment some
bootstrapping is required before github-actions can manage the
infrastructure as code. All of these instructions are automated inscripts/00-guided-install.sh. Note that you will need to set the
environment variables inintallation.mdfor this script to
succeed. You will be prompted several times for use actions such as
setting oauth provider and dns.scripts/00-guided-install.shTerraform Module DependenciesThis project depends on the terraform modules repository:https://github.com/Quansight/qhub-terraform-modulesArchitectureThe architecture diagrams for each cloud provider is inarchitecturefolder.
To generate them, just run the following command:python<diagram_file>.pyLicenseqhub-ops is BSD3 licensed
Package: qian
Quantitative Investment Algorithm Nexus (QIAN)"Nexus" is a term that refers to a connection, link, or central point of intersection. In the context of the acronym, it signifies the integration and interconnectedness of the various quantitative investment algorithms that Qian Capital runs. It represents the idea of algorithms coming together as a cohesive unit, forming a central hub or network, where they collaborate and interact to generate investment insights and drive decision-making. The term "nexus" conveys the notion of a powerful and dynamic network of algorithms working in synergy to analyze data, identify patterns, and optimize investment strategies. For Qian Capital, QIAN represents, and is the name of, our flagship super-intelligent AI trader that accounts for all of Qian Capital's quantitative investing algorithms, including all of the sub-models, inputs, etc. QIAN is the super-intelligent AGI that makes the final alpha generation, portfolio optimization, execution decisions.
Package: qianfan
百度千帆大模型平台 SDKDocumentation|GitHub|Cookbook针对百度智能云千帆大模型平台，我们推出了一套 Python SDK（下称千帆 SDK），方便用户通过代码接入并调用千帆大模型平台的能力。如何安装目前千帆 SDK 已发布到 PyPI ，用户可使用 pip 命令进行安装。安装千帆 SDK 需要 3.7.0 或更高的 Python 版本pip install qianfan在安装完成后，用户即可在代码内引入千帆 SDK 并使用importqianfan快速使用在使用千帆 SDK 之前，用户需要百度智能云控制台 - 安全认证页面获取 Access Key 与 Secret Key，并在千帆控制台中创建应用，选择需要启用的服务，具体流程参见平台说明文档。在获得了 Access Key 与 Secret Key 后，用户即可开始使用 SDK：importosimportqianfanos.environ["QIANFAN_ACCESS_KEY"]="..."os.environ["QIANFAN_SECRET_KEY"]="..."chat_comp=qianfan.ChatCompletion(model="ERNIE-Bot")resp=chat_comp.do(messages=[{"role":"user","content":"你好，千帆"}],top_p=0.8,temperature=0.9,penalty_score=1.0)print(resp["result"])除了通过环境变量设置外，千帆 SDK 还提供了.env文件和通过代码配置的方式，详细参见SDK 配置部分。除了模型调用外，千帆 SDK 还提供模型训练、数据管理等诸多功能，如何使用请参考SDK 使用文档。其他认证方式这里是一些其他认证方式，请仅在无法获取 Access Key 与 Secret Key 时使用。这些认证方式已经过时，将在未来从 SDK 中移除。API Key (AK) 和 Secret Key (SK）是用户在调用千帆模型相关功能时所需要的凭证。具体获取流程参见平台的应用接入使用说明文档，但该认证方式无法使用训练、发布模型等功能，若需使用请使用 Access Key 和 Secret Key 的方式进行认证。在获得并配置了 AK 以及 SK 后，用户即可开始使用 SDK：importosimportqianfanos.environ["QIANFAN_AK"]="..."os.environ["QIANFAN_SK"]="..."chat_comp=qianfan.ChatCompletion(model="ERNIE-Bot")resp=chat_comp.do(messages=[{"role":"user","content":"你好，千帆"}],top_p=0.8,temperature=0.9,penalty_score=1.0)print(resp["result"])适用范围：功能API KeyAccess KeyChat 对话✅✅Completion 续写✅✅Embedding 向量化✅✅Plugin 插件调用✅✅文生图✅✅大模型调优❌✅大模型管理❌✅大模型服务❌✅数据集管理❌✅功能导览千帆平台提供了大模型相关的诸多能力，SDK 提供了对各能力的调用，具体介绍可以查看SDK 文档或者GitHub 仓库。大模型能力[Doc][GitHub]Chat 对话Completion 续写Embedding 向量化Plugin 插件调用Text2Image 文生图模型调优[Doc][GitHub]模型管理[Doc][GitHub]模型服务[Doc][GitHub]数据集管理[Doc][GitHub]Prompt 管理[Doc][GitHub]其他Tokenizer [Doc][GitHub]接口流控 [Doc][GitHub]还可以通过API References查看每个接口的详细说明。联系我们如使用过程中遇到什么问题，或对SDK功能有建议，可通过如下方式联系我们GitHub issues百度智能云工单（百度专家即时服务）LicenseApache-2.0
Package: qianglie
墙裂地址发布页 ·墙裂永久域名：https://www.qianglie.com关注墙裂推特：@QiangLie永久域名被大陆地区封锁，请通过VPN或以下方式访问墙裂获取最新域名可以发送邮件至qianglie@mail.com请把这页内容复制到你的记事本上或妥善保存在其他地方比如有道云笔记，以免找不到我们！防屏蔽地址：https://cdn.qianglie.biz备用地址（每日更新）：https://vk.com/qianglie如果因各种原因，以上方法都不行，可以发送邮件到 qianglie#vk.com  # 换成 @ ，索取最新地址。
Package: qianxun-wechat-sdk
千寻微信框架的 Python SDK介绍千寻微信框架的 Python SDK，基于千寻微信框架的 HTTP API 接口，封装了一些常用的接口，方便开发者使用。安装pipinstallqianxun-wechat-sdk使用示例importqianxun.EmojiasEmoji# 导入 qianxun Emoji 表情模块fromqianxun.SDKimportRobot# 导入 qianxun SDK 框架# 创建一个回调函数来接收 qianxun 框架的回调事件defcallback(request):print('='*50+'回调事件'+'='*50,end='\n\n')print(request)ifrequest['event']==10014:print('账号变动事件(10014)')ifrequest['event']==10008:print('收到群聊消息(10008)')ifrequest['event']==10009:print('收到私聊消息(10009)')ifrequest['event']==10010:print('自己发出消息(10010)')ifrequest['event']==10006:print('收到转账事件(10006)')ifrequest['event']==10013:print('撤回事件(10013)')ifrequest['event']==10011:print('好友请求(10011)')ifrequest['event']==10007:print('支付事件(10007)')if__name__=='__main__':# 创建一个机器人实例，传入机器人的 ip 和端口，以及机器人的 wxid (可选)robot=Robot(host='127.0.0.1',port=7777,bot_wxid='')# 调用机器人的登录接口, 获取机器人的 wxidrobot.bot_wxid=robot.getWeChatList()['result'][0]['wxid']# 创建一个机器人的回调事件，传入回调函数和端口robot.callbackEvents(callback_fun=callback,port=5000)# 调用机器人的发送文本消息接口，传入机器人的 wxid 和消息内容robot.sendTextMessage(wxid='filehelper',msg=f'你好{Emoji.小丑脸}测试{Emoji.表情_捂脸}')