Saravanan666/pypi-org huggingface dataset:
Package: ageml
AgeModellingDescriptionBrainAge models (Franke et al. 2010, Neuroimage) have had success in exploring the relationship between healthy and pathological ageing of the brain. Furthermore, this type of age modelling can be extended to multiple body systems and modelling of the interactions between them (Tian et al 2023, Nature Medicine). However, there is no standard for age modelling. There have been works attempting to describe proper procedures, especially for age-bias correction (de Lange and Cole 2020, Neuroimage: Clinical). In this work we developed an Open-Source software that allows anyone to do age modelling following well-established and tested methodologies for any type of clinical data. Age modelling with machine learning made easy.The objective of AgeML is to standardise procedures, lower the barrier to entry into age modelling and ensure reproducibility. The project is Open-Source to create a welcoming environment and a community to work together to improve and validate existing methodologies. We are actively seeking new developers who want to contribute to growing and expanding the package.References:
De Lange, A.-M. G., & Cole, J. H. (2020). Commentary: Correction procedures in brain-age prediction. NeuroImage: Clinical, 26, 102229.https://doi.org/10.1016/j.nicl.2020.102229Franke, K., Ziegler, G., Klöppel, S., & Gaser, C. (2010). Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters. NeuroImage, 50(3), 883–892.https://doi.org/10.1016/j.neuroimage.2010.01.005Tian, Y. E., Cropley, V., Maier, A. B., Lautenschlager, N. T., Breakspear, M., & Zalesky, A. (2023). Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nature Medicine, 29(5), 1221–1231.https://doi.org/10.1038/s41591-023-02296-6CI/CDLicenseLicensed under theApache 2.0
Package: agemo
agemoAgemo is an open-source tool with a python API that allows users to generate the Laplace Transform of the coalescence time distribution of a sample with a given demographic history.
In addition, agemo provides ways to efficiently query that distribution, by using the fact that its generating function can be represented most simply as a directed graph with all possible ancestral states of the sample as nodes. Past implementations have not made full use of this, relying on computer algebra systems instead of graph traversal to process these recursive expressions.So far, agemo has been used to compute the probabilities of the joint site frequency spectrum for blocks of a given size, under models of isolation and migration. Calculating these probabilities requires repeated differentiation of the generating function (Lohse et al, 2011) which suffers from an explosion in the number of terms when implemented naively. Using a closed-form expression for the coefficients of a series expansion of the equations associated with each edge of the graph, we can efficiently propagate these coefficients through the graph avoiding redundant operations.
Package: agenda
Simple Python module for pretty task logging.Exposes the following methods:sectionstarts a new section of application processingtaskstarts a new high-level task within the sectionsubtaskstarts a new subtask within the tasksubfailureindicates the failure of a subtask of the current taskfailureindicates the failure of a high-level tasksubpromptprints a subtask input promptpromptprints a high-level input prompt
Package: agenius
AGenius.pyAGenius.pyis aLyricsGeniusfork, making it easy to use, and async ready.Key FeaturesPythonicasync/await.Removed every possible instance of thePublic APIto make itsafer.SetupYou'll need a freeGeniusaccount to get access to
theGenius API. This provides anaccess_tokenthat is required.InstallationPython 3.9 or higherYou can use pip:# Linuxpython3-mpipinstallagenius# Windowspy-3-mpipinstallageniusExamplesImporting the package and initiating the main class:importageniusgenius=agenius.Genius(token)PUBLIC_APIhas been removed in this version. You have to pass an access token to theGenius()class.To search for a specific song, you can either search by thetitleorsong_id:# by titlesong=awaitgenius.search_song("Never Gonna Give You Up","Rick Astley")# by song_idsong=awaitgenius.search_song(song_id=84851)You can also look up artists and their songs viaartist_id's:# look up an artistartist=awaitgenius.artist(artist_id=artist_id)# look up their songssong_list=awaitgenius.artist_songs(artist_id=artist_id,per_page=10,sort="title")Configurable parameters in theGenius()class:genius.verbose=False# Turns status messages offgenius.excluded_terms=["(Remix)","(Live)"]# Exclude songs with these words in their titleMore ExamplesGet a song's lyricsimportageniusgenius=agenius.Genius(token)song=awaitgenius.search_song("Never Gonna Give You Up")lyrics=song.lyricsGet a list of an artist's songs, and get the lyrics of every one of themimportageniusgenius=agenius.Genius(token)asyncdefget_lyrics(artist_id):song_list=awaitgenius.artist_songs(artist_id,per_page=50,sort="title")lyrics={}asyncforsonginsong_list:lyrics[song["title"]]=song.lyricsreturnlyricsLicense NoticeThis program is free software: you can redistribute it and/or modify it under the terms of the
GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License,
or any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
See the GNU Lesser General Public License for more details.You should have received a copy of the GNU Lesser General Public License along with this program.
If not, seehttps://www.gnu.org/licenses/.
Package: agensgraph
# Agensgraph python driverA psycopg2 extension to use [AgensGraph][1] with Python.### DocumentationGo to [Psycopg's documentation][2] for documentation about the Python interface and to [AgensGraph][1] documentation for AgensSQL. If you want to learn more about Cypher, I recommend you to go to [Neo4j's][3] documentation.### How to```pythonimport agensgraphconn = agensgraph.connect("dbname=iomed")  # Equivalent to psycopg2.connectcur = conn.cursor()cur.execute("SET graph_path=snomed;")cur.execute("MATCH (a)-[r]->(b) RETURN a,r,b LIMIT 10;")result = cur.fetchall()```**Copyright © 2017, IOMED Medical Solutions S.L.**[1]: http://bitnine.net/wp-content/uploads/2017/06/html5/main.html[2]: http://initd.org/psycopg/docs/index.html[3]: https://neo4j.com/docs/developer-manual/current/cypher/
Package: agenshindot
AGenshinDotAGenshinDot 是GenshinDot的 Python 实现，由Graia-Ariadne驱动.声明AGenshinDot 遵循AGPLv3许可协议开放全部源代码，你可在这里找到本项目的许可证.AGenshinDot 仅供学习娱乐使用，禁止将此 Bot 用于商用和非法用途.AGenshinDot 项目及作者不对因使用本项目所造成的损失进行赔偿，也不承担任何法律责任.安装从 PyPI 安装pipinstallagenshindot# orpoetryaddagenshindot从 GitHub 安装1.直接安装poetryaddgit+https://github.com/MingxuanGame/AGenshinDot.git2.克隆后安装gitclonehttps://github.com/MingxuanGame/AGenshinDot.gitcdAGenshinDot
poetryinstall--no-dev配置所有配置均保存在运行目录config.toml.下面为配置样例：# 机器人 QQ 号account=1185285105# verifyKeyverify_key="agenshindot"# 是否启用控制台enable_console=false# 是否开启 Cookie 绑定enable_bind_cookie=false# 机器人管理员 QQ 号admins=[1060148379]# 以下为连接配置# 如果不配置则默认为 HTTP + 正向 WebSocket，连接地址为 localhost:8080# 可以同时配置多种连接# 正向 WebSocket 配置ws="ws://localhost:8080"# 等同于如下配置# ws = ["ws://localhost:8080"]# HTTP 配置http="http://localhost:8080"# 等同于如下配置# http = ["http://localhost:8080"]# 反向 WebSocket 配置[ws_reverse]# Endpointpath="/"# 验证的参数params={}# 验证的请求头headers={}# WARNING 上面的配置要保证不能缺失，也不能调换位置# 如果只需要设置 Endpoint，也可以使用下方的配置# ws_reverse = "/"# HTTP Webhook 配置[webhook]# Endpointpath="/"# 验证的请求头headers={}# WARNING 上面的配置要保证不能缺失，也不能调换位置# 如果只需要设置 Endpoint，也可以使用下方的配置# webhook = "/"# 日志配置[log]# 日志等级，详情请看 loguru 文档level="INFO"# 过期时间，过期的日志将被删除，格式请看# https://pydantic-docs.helpmanual.io/usage/types/#datetime-types# 中 `timedelta` 部分expire_time="P14DT0H0M0S"# 是否启用数据库日志db_log=false启动1.执行本项目文件夹下的bot.pypythonbot.py2.以模块形式启动python-magenshindot控制台命令WARNING
控制台属于实验性内容，不建议使用启用控制台后，会禁用标准输出中日志等级的设置在启用控制台后，可以输入以下命令执行一些操作/stop关闭 AGenshinDot./license输出许可证信息./version输出 AGenshinDot LOGO 和版本信息./execute <SQL 语句>执行 SQL 语句（危险操作）
Package: agent
# Agent: Async generators for humans**agent** provides a simple decorator to create python 3.5 [asynchronous iterators](https://docs.python.org/3/reference/compound_stmts.html#async-for) via `yield`s## ExamplesMake people wait for things for no reason!```pythonimport agentimport asyncio@agent.gen  # Shorthand decoratordef wait_for_me():yield 'Like 'yield from asyncio.sleep(1)yield 'the line 'yield from asyncio.sleep(10)yield 'at 'yield from asyncio.sleep(100)yield 'the DMV'async for part in wait_for_me():print(part)```Paginate websites in an easy asynchronous manner.```pythonimport agentimport aiohttp@agent.async_generatordef gen():page, url = 0, 'http://example.com/paginated/endpoint'while True:resp = yield from aiohttp.request('GET', url, params={'page': page})resp_json = (yield from resp.json())['data']if not resp_json:breakfor blob in resp_json['data']:yield blobpage += 1# Later on....async for blob in gen():# Do work```**The possibilities are endless!**For additional, crazier, examples take a look in the [tests directory](tests/).## Get it```bash$ pip install -U agent```## Caveats`yield from` syntax must be used as `yield` in an `async def` block is a syntax error.```pythonasync def generator():yield 1  # Syntax Error :(````asyncio.Future`s can not be yield directly, they must be wrapped by `agent.Result`.## LicenseMIT licensed. See the bundled [LICENSE](LICENSE) file for more details.
Package: agent1c-metrics
Agent1C Metrics ServiceService for collecting metrics from 1C service files
Version: 0.4.4InstallationInstall packagepip install --upgrade agent1c_metricsRun servicepython -m agent1c_metrics --reloadContributionInstall package in editable modepip install -e .Change version (major/minor/patch)bumpver update --patchBuild and publish the packagepoetry publish --build
Package: agent360
Agent360360 Monitoring (360monitoring.com) is a web service that monitors and displays statistics of
your server performance.Agent360 is OS agnostic software compatible with Python 3.7 and 3.8.
It's been optimized to have low CPU consumption and comes with an
extendable set of useful plugins.DocumentationYou can find the full documentation including the feature complete REST API atdocs.360monitoring.comanddocs.360monitoring.com/docs/api.Automatic Installation (All Linux Distributions)You can install the default configuration of Agent360 on all Linux distributions with just one click.Connect to your server via SSH.Find your USERTOKEN. To do so,go to the servers pageand then click the "Add server" button.Run the following command:wget-q-Nhttps://monitoring.platform360.io/agent360.sh&&bashagent360.shUSERTOKENAutomatic Installation (Windows)Download thesetupand install it on your Windows server.The installer will ask for your USERTOKEN which you can getfrom the servers page.Manual InstallationTo customize installation options, install Agent360 manually.Connect to your server via SSH.Run the following command, which differs depending on your server platform:Debian GNU/Linux:apt-getinstallpython3-develpython3-setuptoolspython3-pip
pip3installagent360
wget-O/etc/agent360.inihttps://monitoring.platform360.io/agent360.iniFedora/CentOS version 6 or earlier (python 2.7):yuminstallpython-develpython-setuptoolsgcc
easy_installagent360netifacespsutil
wget-O/etc/agent360.inihttps://monitoring.platform360.io/agent360.iniFedora/CentOS version 7 and later (python 3):yuminstallpython36-develpython36gcc
pip3installagent360
wget-O/etc/agent360.inihttps://monitoring.platform360.io/agent360.iniFind your USERTOKEN. To do so,go to the servers pageand then click the "Add server" button. You need this to generate a serverid.Run the following command (USERTOKEN is the one you got during the previous step):agent360helloUSERTOKEN/etc/agent360-token.iniCreate a systemd service at/etc/systemd/system/agent360.serviceby adding the following:[Unit]Description=Agent360[Service]ExecStart=/usr/local/bin/agent360User=agent360[Install]WantedBy=multi-user.targetRun the following command:chmod644/etc/systemd/system/agent360.service
systemctldaemon-reload
systemctlenableagent360
systemctlstartagent360Building Windows setupPrerequisite:InnoSetupis used as the installer, build script assumes that it is installed in the default location.Runphp windows/build.phpto create setup file.
Package: agent-automata
Agent AutomataIntroductionagent-automatais a lightweight orchestration architecture for a hierarchical group of modular, autonomous agents, with the goal of composing actions from simple autonomous agents into complex collective behavior.The core idea behind this architecture is that instead of having a complex central agent managing many commands and sub-agents, or a fixed set of agents with specific roles in a task loop, we allow agents to call each other as tools, and then establish a hierarchical, rank-based structure to control the direction of the calls:Agent A (Rank 3):
  - Agent B (Rank 2)
    - Tool 1
    - Tool 2
  - Agent C (Rank 2)
    - Tool 1
    - Tool 3
    - Agent D (Rank 1)
      - Tool 4
      - Tool 5
      - Tool 6Agent A can then potentially be included as a callable sub-agent by another agent of higher rank, and so on.InstallationRunpip install agent-automatafor the core package.
You can also runpip install agent-automata[builtins]to install some additional built-in functionality.Usage/DemoThere is very little concrete functionality included in the package--this is meant to be one component in a larger, more usable system of agents. Thedemodirectory shows a rather trivial example of specifying a simple agent and its sub-agents/tools using yaml spec files.To run the demo:Install the package with the[builtins]option.Download thedemodirectory (you can download the zip and extract just thedemodirectory).cdto thedemodirectory.Runpython run_demo.py.You should see some output from the demo agent, which creates a quiz and saves it to a file in a workspace.If you find this architecture interesting and would like more documentation on how it works, please post an issue.
Package: agent-bdi
Physiologically Inspired Framework for Complex System IntegrationIntroductionIn this study, inspired by the physiological operation of the human body, an efficient and resilient software framework was developed to aid in the integration of mature AI technologies, allowing them to coordinate with one another to accomplish more advanced goals. A navigation system for the visually impaired was then developed to validate the framework, with promising experimental results. This framework can be applied to other types of AI systems.InstructionInstall the required packages.pip install -r requirements.txtCreate config.py, rewite the MQTT settings.cp config.sample.py config.pyStart the framework.python start.pyClass DiagramThe class diagram of the integration of agents and coordination. HolonicAgent represents the core, and HeadAgents and BodyAgents represent the collection of head agents and subagents, respectively. MQTT is the fundamental communication protocol for agents in the global circulation system, and MqttClient is a private member of HolonicAgent, which allows the agent to have built-in MQTT connection and reception capabilities.All agents inherit from HolonicAgent to form a hierarchical structure, and they use the DDS to achieve neural message transmission depending on their specific behavior. Each super-agent is a DDS domain that publishes or subscribes to related topics with the required QoS, such as the DEADLINE policy to confirm the date of the data or the TRANSPORT_PRIORITY policy to define the transmission priority order, in order to achieve the purpose of a specific agent.Class diagram of integrationSequence DiagramAccording to the sequence diagram depicted in below, the DDS and MQTT serve to transmit messages for the agents. Action 1 entails generating an independent process immediately after the root agent is initialized. Action 2 entails subscribing to or publishing relevant topics within the QoS constraints. Action 3 entails recursively calling all the subagents to initiate the action. The agent main action is performed in a separate process of Action 2 until it is notified of its termination. Finally, Action 4 entails generating a global broadcast with MQTT, with a system termination notification serving as an example in this study.Sequence Diagram of Integration
Package: agentclpr
AgentCLPR简介一个基于ONNXRuntime、AgentOCR和License-Plate-Detector项目开发的中国车牌检测识别系统。车牌识别效果支持多种车牌的检测和识别（其中单层车牌识别效果较好）：单层车牌：[[[[373, 282], [69, 284], [73, 188], [377, 185]], ['苏E05EV8', 0.9923506379127502]]]
  [[[[393, 278], [318, 279], [318, 257], [393, 255]], ['VA30093', 0.7386096119880676]]]
  [[[[[487, 366], [359, 372], [361, 331], [488, 324]], ['皖K66666', 0.9409016370773315]]]]
  [[[[304, 500], [198, 498], [199, 467], [305, 468]], ['鲁QF02599', 0.995299220085144]]]
  [[[[309, 219], [162, 223], [160, 181], [306, 177]], ['使198476', 0.9938704371452332]]]
  [[[[957, 918], [772, 920], [771, 862], [956, 860]], ['陕A06725D', 0.9791222810745239]]]双层车牌：[[[[399, 298], [256, 301], [256, 232], [400, 230]], ['浙G66666', 0.8870148431461757]]]
  [[[[398, 308], [228, 305], [227, 227], [398, 230]], ['陕A00087', 0.9578166644088313]]]
  [[[[352, 234], [190, 244], [190, 171], [352, 161]], ['宁A66666', 0.9958433652812175]]]快速使用快速安装# 安装 AgentCLPR$pipinstallagentclpr# 根据设备平台安装合适版本的 ONNXRuntime# CPU 版本（推荐非 win10 系统，无 CUDA 支持的设备安装）$pipinstallonnxruntime# GPU 版本（推荐有 CUDA 支持的设备安装）$pipinstallonnxruntime-gpu# DirectML 版本（推荐 win10 系统的设备安装，可实现通用的显卡加速）$pipinstallonnxruntime-directml# 更多版本的安装详情请参考 ONNXRuntime 官网简单调用：# 导入 CLPSystem 模块fromagentclprimportCLPSystem# 初始化车牌识别模型clp=CLPSystem()# 使用模型对图像进行车牌识别results=clp('test.jpg')服务器部署：启动 AgentCLPR Server 服务$agentclprserverPython 调用importcv2importjsonimportbase64importrequests# 图片 Base64 编码defcv2_to_base64(image):data=cv2.imencode('.jpg',image)[1]image_base64=base64.b64encode(data.tobytes()).decode('UTF-8')returnimage_base64# 读取图片image=cv2.imread('test.jpg')image_base64=cv2_to_base64(image)# 构建请求数据data={'image':image_base64}# 发送请求url="http://127.0.0.1:5000/ocr"r=requests.post(url=url,data=json.dumps(data))# 打印预测结果print(r.json())
Package: agentcoreclient
Oversight AgentCore ClientThis is a library to create probes for theOversight platform.Supported environment variableVariableDescriptionOS_LOG_LEVELLog level. One ofdebug,info,warning,errororcritical. If not set, thelog_levelargument of thesetup_logger(..)method will be used.OS_AGENTCORE_IPSet the agent core Ip address. Fallback toagentCoreIpfrom the configuration and finallylocalhost.OS_AGENTCORE_PORTSet the agent core port. Fallback toagentCorePortfrom the configuration and finally7211.OS_CONFIG_FOLDERSet the configuration folder. The assets configuration files must be stored in this folder. If not set,/etcwill be used.(This environment variable is usually configured in the Dockerfile of the corresponding probe)OS_CONFIG_FILENAMEPath to the probe configuration file. If not set, theconfig_fnargument of theAgentCoreClientwill be used instead.(It is recommended to configure theconfig_fnargument when building a probe and not rely on this environment variable only)Reload local configThe local configuration files will be read only the first time this is required for an asset. If you with to reload the configuration, for example when the configuration has been changed, this can be done by adding a file namedreloadinside the configuration folder. For example:/data/config/sampleprobe/$touchreload
Package: agentcrypt
AbstractYet another implementation of the idea, to use the SSH agent for symmetric encryption.
This time for Python (2+3).Load a key and make an SSH connection with agent-forwarding:local-machine~%ssh-addlocal-machine~%ssh-Aremote-machineCreate someagentcryptenabled scripts on the remote host:remote-machine~%cat<<'_EOF_'>encryptor.pyimportsysfromagentcrypt.ioimportContainerwithContainer.create(sys.stdout)ascntr:forlineinsys.stdin:cntr.write(line)_EOF_remote-machine~%cat<<'_EOF_'>decryptor.pyimportsysfromagentcrypt.ioimportContainerwithContainer.load(sys.stdin)ascntr:print(cntr.getvalue().decode())_EOF_Use the newly created scripts with keys from the localssh-agent:remote-machine~%echo"secret data"|pythonencryptor.py>hushhush.datremote-machine~%pythondecryptor.py<hushhush.datsecretdataOr via themainguard of the package:remote-machine~%echo"secret data"|python-magentcrypt.ioenc>hushhush.datremote-machine~%python-magentcrypt.iodec<hushhush.datsecretdataMotivationProvide a convenient (passwordless) way of encrypting sensitive data for Python.Profit from the fact that agent-forwarding makes SSH keys available on remote servers.Alternativeshttps://github.com/edwardspeyer/sshovel- Pythonhttps://github.com/is/sshcrypt- Gohttps://github.com/jwhitham/safeu- C (not sure if it uses the signature as keys too)Why another implementation?The ones I found on GitHub are either not Python or they rely on calls to external programs.
I wanted a pure Python library, that runs with Versions 2.7 and 3.x.DocumentationAPI Documentation
Package: agentdesk
AgentDeskDesktops for AI agents   :computer:Explore the docs »View Demo·Report Bug·Request FeatureAgentdesk provides full featured desktop environments which can be programatically controlled by AI agents. Spin them up locally or in the cloud.▶ Built onagentda runtime daemon which exposes a REST API for interacting with the desktop.▶ Implements theToolsV1 protocolInstallationpip install agentdeskQuick StartfromagentdeskimportDesktop# Create a local VMdesktop=Desktop.local()# Launch the UI for itdesktop.view(background=True)# Open a browser to Googledesktop.open_url("https://google.com")# Take actions on the desktopdesktop.move_mouse(500,500)desktop.click()img=desktop.take_screenshot()UsageCreate a local desktopfromagentdeskimportDesktopdesktop=Desktop.local()$agentdeskcreate--providerqemu*requiresqemuCreate a remote desktop on GCEdesktop=Desktop.gce()$agentdeskcreate--providergceCreate a remote desktop on EC2desktop=Desktop.ec2()$agentdeskcreate--providerec2View the desktop in the UIdesktop.view()$agentdeskviewold_mckinny*requires dockerList desktopsDesktop.list()$agentdeskgetDelete a desktopDesktop.delete("old_mckinny")$agentdeskdeleteold_mckinnyUse the desktopdesktop.open_url("https://google.com")coords=desktop.mouse_coordinates()desktop.move_mouse(500,500)desktop.click()desktop.type_text("What kind of ducks are in Canada?")desktop.press_key('Enter')desktop.scroll()img=desktop.take_screenshot()ProcessorsProcess images to make them more accessible to LMMs.GridAdd a coordinate grid on top of the imagefromagentdesk.processorsimportGridProcessorimg=desktop.take_screenshot()processor=GridProcessor()grid_img=processor.process_b64(img)ExamplesGPT-4VSee how to use GPT-4V with AgentDesk in ournotebookoragentDevelopingPlease open an issue before creating a PR.Changes to the VM happen inagentd
Package: agent-finder-masterjefferson
agent-finderFind nearby auto insurance agents using the Google Maps Places API, and dump them to a CSV file.This script was written to help automate part of
my girlfriends job. Maybe someone else will find it useful too.Installationpip3installagent-finder-masterjeffersonUsageGoogle Maps APIYou must have a valid Google Maps API key.In your shell configuration file (.zshrc, .bash_profile, .bashrc, etc):exportGOOGLE_MAPS_API_KEY=<your-api-key>Basic search:agent-finder"Los Angeles, CA"You can also customize the CSV filename and the search radius (units = kilometers).agent-finder"Los Angeles, CA"--filenamefoo.csv--radius50