Saravanan666/pypi-org huggingface dataset:
Package: arklog
A python logging module with colors.Examplesimportarklogarklog.set_config_logging()arklog.debug("Debug message")arklog.info("Debug message")arklog.warning("Debug message")arklog.extra("Extra")arklog.success("Completed")importarklogarklog.set_defaults()arklog.debug("Debug message")arklog.info("Info message")arklog.warning("Warning message")arklog.extra("Extra message")arklog.error("Error message")arklog.success("Success message")
Package: arknights-farmer
arknights-farmerFarming assistant for Arknights.InstallationPrerequisitesPython >= 3.7.4pipadb from android platform-tools added to your$PATHFrom SourceClone this repository usinggit clonecommand (or just download thezipversion).Install the dependencies usingpip.$pipinstall-rrequirements.txtRun the script using the following command inside the repo directory.$python3-marknights-farmerFrom PyPi (Recommended)For Windows users (runcmdas administrator):pipinstallarknights-farmerFor Linux users:$sudopipinstallarknights-farmerUsageusage: arknights-farmer [-h] [-s STAGE [STAGE ...]] [-c] [-r REFILL] [-l] [-v]
                        [-m MANUAL]

optional arguments:
  -h, --help            show this help message and exit

required args:
  -s STAGE [STAGE ...], --stage STAGE [STAGE ...]
                        manually add stage(s) to farm task (e.g. 1-7:100
                        4-4:25 (separated by whitespace))
  -c, --cont            continue from the most recent farming session

optional args:
  -r AMOUNT, --refill AMOUNT
                        how many times you want to refill. default is 0
  -l, --list-task       list unfinished task(s) from recent farming session
  -v, --version         show version
  -m AMOUNT, --manual AMOUNT
                        manual mode (good for single stage farming like event
                        stages)Connect to ADBUse theadb devicescommand to check if your adb client is connected to your emulator.Refer to your emulator manual on how to connect your adb client.Emulator SettingMake sure to use the following display settings on your emulator:Display Resolution: 1280x720DPI: 240 DPIExamples$arknights-farmer-s1-7:100# runs 1-7 100 times (will stop whenever you run out of sanity)$arknights-farmer-s1-7:100-r10# runs 1-7 100 times with 10 times sanity refill (prioritizes using sanity potion)$arknights-farmer-s1-7:100ce-5:20# runs 1-7 100 times then CE-5 20 times$arknights-farmer-c# continues the most recent halted farming sessionBefore starting the script, make sure to set yourself in the main menu screen.You can safely stop the script usingCtrl-C. It will save the ongoing farming task(s) before quitting.ContributingPull requests are welcome.LicenseLicensed under the MIT License. SeeLICENSEfor more information.DisclaimerIt has not been clear whether it is safe to use this tool or not. I am not responsible for what happens to your account.Use at your own risk.
Package: arknights-mower
arknights-mower7*24 小时不间断长草，让你忘掉这个游戏！⚠ 注意事项本程序不支持国服以外的明日方舟区服，支持官服和 Bilibili 服。原理上，使用本程序没有任何被判定为作弊并被封号的风险，但是作者不对使用此程序造成的任何损失负责。开发组人数稀少，有时不太能及时反馈和修复 Bug，见谅一下。也欢迎更多有能力有意向的朋友参与。本软件目前仅支持 1920*1080 分辨率，使用夜神模拟器可以解决大部分问题主要功能自动打开《明日方舟》，支持官服和 Bilibili 服自动登录账户密码需要手动输入读取基建心情，根据排班表**动态换班**力求最高工休比支持**跑单操作**，可设置仅跑单模式支持调用 maa 执行除基建外的长草活动支持邮件提醒自动使用菲亚梅塔恢复指定房间心情最低干员的心情并重回岗位（工作位置不变以避免重新暖机） [参考使用场景]安装在Releases下载 Windows 可执行文件版本。
在action产物下载最新测试版本
或进入下方 qq 群文件领取，目前软件迭代速度较快，进入群内可获取一手版本使用教程下载安装后，打开软件设置 adb 地址（如 127.0.0.1:5555）配置排班表[配置方法]或前往 qq 群寻找现成的排班表导入点击开始执行详细配置内容请参考[功能文档]欢迎大家提交 Pull requests 增加更多的功能！遇到报错？想要更多功能？如果你在使用过程中遇到问题，欢迎通过提交 Issue 的方式报错或者提问。报告 Issue 时建议附上调试信息以便定位问题。也欢迎加入交流群讨论：Telegram GroupQQ Group: 239200680Star History
Package: arknights-toolkit
Arknights-Toolkit明日方舟(Arknights) 相关功能的整合库现拥有如下功能：抽卡模拟十连随机干员生成公招链接生成猜干员游戏抽卡结果查询欢迎 PR安装pipinstallarknights-toolkitpdmaddarknights-toolkit配置arknights-toolkit配套了 cli 工具用于资源下载:初始化数据与图片资源:arkkitinit更新抽卡卡池:arkkitupdategachapath/to/gacha.json示例fromarknights_toolkit.gachaimportArknightsGacha,GachaUserfromarknights_toolkit.gacha.simulateimportsimulate_imagefrompathlibimportPathimportasyncioasyncdefmain():gacha=ArknightsGacha("path/to/gacha.json")user=GachaUser()data=gacha.gacha(user,10)img=awaitsimulate_image(data[0])withPath("example_sim.png").open("wb+")asf:f.write(img)asyncio.run(main())抽卡结果:
Package: arksearch
Thiscompletely unofficialscript will perform a search ofIntel’s ARK sitefor processor information.  Using the script is fairly straightforward:InstallationFrom PyPi:pip install arksearchor via git:git clone https://github.com/major/arksearch
cd arksearch
python setup.py installUsageSearch for a processor, such as theE3-1230Choose the exact processor from a listRead the data printed belowHere’s a brief example:$ arksearch E3-1230
Found 5 processors...
[0] Intel® Xeon® Processor E3-1230 v5 (8M Cache, 3.40 GHz)
[1] Intel® Xeon® Processor E3-1230L v3 (8M Cache, 1.80 GHz)
[2] Intel® Xeon® Processor E3-1230 v3 (8M Cache, 3.30 GHz)
[3] Intel® Xeon® Processor E3-1230 v2 (8M Cache, 3.30 GHz)
[4] Intel® Xeon® Processor E3-1230 (8M Cache, 3.20 GHz)
Which processor? 0
+----------------------------------------------------------+-----------------------------------------+
| Parameter                                                | Value                                   |
+----------------------------------------------------------+-----------------------------------------+
| Status                                                   | Launched                                |
| Launch Date                                              | Q4'15                                   |
| Processor Number                                         | E3-1230V5                               |
| Intel® Smart Cache                                       | 8 MB                                    |
| DMI3                                                     | 8 GT/s                                  |
| Instruction Set                                          | 64-bit                                  |
| Instruction Set Extensions                               | SSE4.1/4.2, AVX 2.0                     |
... SNIP ...This is handy for quick, single CPU lookups. However, if you need bulk access to data, pleasesign up for an API key.Pull requests are always welcome.– Major
Package: arkserver
Ark-ServerConnectStart Listening (connect)Starts the serverReceive Connect Request (accept_wrapper)Connects to a clientDisconnectDisconnects the serverSend Disconnect (send_disconnect)Sends to a client to disconnect with the serverReceive Disconnect (parsed from read)If the server was remotely asked to shut down, it will then
initiate disconnecting to all remote clients too.RunReceive Connect RequestAccept the incoming connectionService the connectionReadRead from the incoming connectionWriteWrite to the incoming connection after reading
Package: arkvatar
Arkvatar-pyAPI Wrapper to interact with Arkvatar.Built withPythonAIOHTTPInstallationpipinstallarkvatar-pyUsageimportasynciofromarkvatarimportArkvatarasyncdefmain():arkvatar=Arkvatar()identifier="azerty@gmail.com"request=awaitarkvatar.store("Email",identifier)print(request)if__name__=='__main__':loop=asyncio.get_event_loop()loop.run_until_complete(main())AuthorsJolan Beer - HighjhackerLicenseArkvatar-py is under MIT license. See theLICENSE filefor more informations.
Package: arkwaifu-2x
Arkwaifu 2xArkwaifu 2x is a CLI tool for Arkwaifu. It supports the following features:Enlarge the Arts withreal-esrgan(realesrgan-x4plus).Enlarge the Arts withreal-esrgan(realesrgan-x4plus-anime).Re-generate thumbnails for all arts.This tool is created because the backend of Arkwaifu cannot handle hard works, like enlarging the arts with neutral
networks. So Arkwaifu 2x takes the tasks in local, reducing pressure of the backend.UsagePrerequisites:Windows 10 or abovePython: ^3.11AUSER_TOKENto grant permission, see belowThis tool is intended to run on Windows. However, it is possible to support other OS like macOS or Linux. I did not
do that because I do not have these environments to test this tool. If you wish to run this tool on other OS, feel free
to open a issue to tell me, and I will try my best to support these OS.In order to install this tool, runpy -m pip install arkwaifu-2xIn order to run this tool, set theUSER_TOKENfirst$env:USER_TOKEN="<YOUR USER_TOKEN> # PowerShell
SET USER_TOKEN=<YOUR USER_TOKEN> # CMDThen, you can run the toolarkwaifu-2xWhat isUSER_TOKEN?USER_TOKENis a token for trusted users to grant permission when they are submitting something to the website. I
designed this because I do not want everyone to be able to submit to the website, and that may cause security issues.
If you want to get aUSER_TOKEN, just open a issue for that. I will be happy to allocate aUSER_TOKENfor you. :)LicenseThe source code of this project is licensed under the MIT License.This project utilizes Real-ESRGAN and Real-ESRGAN-ncnn-vulkan, which are respectively licensed under the BSD-3-Clause
license and the MIT License.
Package: arlatentsde
Amortized reparametrization: Efficient and Scalable Variational Inference for Latent SDEsAccompanying code for theNeurIPS 2023
paperby Kevin Course and Prasanth B. Nair.Tutorials and documentation coming soon!1. InstallationInstalling the packageThe package can be installed from PyPI:pipinstallarlatentsdeReproducing the experiment environmentWe ran experiments on a Linux machine with CUDA 11.8.
We usedpoetryto manage dependencies.If you prefer a different environment manager, all dependencies are listed
in thepyproject.toml.To reproduce the experiment environment, first navigate to branch namedneurips-freeze.
Then install all optional dependencies required to run experiments,poetryinstall--withdev,expsTo download all pretrained models, datasets, and figures we userepopacker:repopackerdownloadmodels-data-figs.zip
repopackerunpackmodels-data-figs.zip2. UsageThe numerical studies can be rerun from the experiments
directory using the command-line scriptmain.py. All numerical
studies follow the same basic structure:
(i) generate / download,
(ii) train model, and
(iii) post process for plots and tables.The script has the following syntax:pythonmain.py[experiment][action]The choices of experiments and actions are provided below:Experiments:predprey: Orders of magnitude magnitude fewer NFEs experimentlorenz: Adjoint instabilities experimentmocap: Motion capture benchmarknsde-video: Neural SDE from video experimentgrad-variance: Gradient variance experimentActions:get-data: Download / generate datatrain: Train modelspost-process: Post process for plots and tables3. ReferenceCourse, K., Nair, P.B. Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs.In Proc. Advances in Neural Information Processing Systems, (2023).@inproceedings{
course2023amortized,
title={Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent {SDE}s},
author={Kevin Course and Prasanth B. Nair},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=5yZiP9fZNv}
}
Package: arlib
arlib: Common interface for archive manipulation (Tar, Zip, etc)Table of ContentsInstallationInstall from PyPIInstall from AnacondaSimple exampleOpen archiveList member namesOpen a memberLicenseDocumentationRationaleSometimes, we need to deal with different archive files. There are
several packages/modules for archive file manipulation, e.g.,zipfilefor "*.zip" files,tarfilefor "*.tar.gz" or "*.tar.bz2"
files, etc. If we want to support different archive type in our
project, probably we need to do the following:ifzipfile.is_zipfile(file):ar=zipfile.ZipFile(file)f=ar.open('member-name')# some processingelifzipfile.is_tarfile(file):ar=tarfile.open(file)f=ar.extractfile('member-name')# some processingelse:# other stuffsThe problems of the above approach are:We need repeat the above code everywhere we want to support
different archive types.Different archive manipulation modules (e.g.zipfileandtarfile) may have different API convention.arlibis designed to solve the above problems. It abstracts the
logic of archive manipulations and provides a single high level
interface for users.InstallationInstall from PyPIpipinstallarlibInstall from Anacondacondainstall-cliyugongarlibSimple exampleThe abstract classarlib.Archivedefines the common interface to
handle different archive types, such as tar file, zip file or an
directory. Three concrete classesarlib.TarArchive,arlib.ZipArchiveandarlib.DirArchiveimplement the interface
correspondingly.Open archiveThe simplest way to open an archive is usingarlib.openfunctionar=arlib.open('abc.tar.gz','r')This will determine the type of the archive automatically and return a
corresponding object of one of the three engine classes. If we don't
want the automatic engine determination mechanism, we can also
specify the class via argumentengine, e.g.ar=arlib.open('abc.tar.gz','r',engine=ZipArchive)or we can simply construct an object of the engine classar=arlib.ZipArchive('abc.tar.gz','r')List member namesThe propertymember_nameswill return a list of the names of members
contained in the archive, e.g.,print(ar.member_names)Check memberUse the methodmember_is_dirandmember_is_fileto check whether a
member is a directory or a regular filear=arlib.open('abc.tar','r')ar.member_is_dir('member_name')ar.member_is_file('member_name')Open a memberUse the methodopen_memberto open a member in the archive as a file
objectwithar.open_member('a.txt','r')asf:# do sth by using f as an opened file objectExtract members to a locationUse the methodextractto extract members to a specified locationar=arlib.open('abc.tar','r')ar.extract()# extract all the members to the current working directoryar.extract(path='d:/hello',members=['abc.txt','dir1/'])LicenseThearlibpackage is released under theMIT LicenseDocumentationhttps://arlib.readthedocs.io
Package: arline-quantum
Arline QuantumArline Quantumis an open-source library providing basic functionality for creating and manipulating quantum
circuits. It also contains a list of mock quantum hardware.Installation$pip3installarline-quantumAlternatively, Arline Quantum can be installed locally in the editable mode.
Clone Arline Quantum repository,cdto the source directory:$gitclonehttps://github.com/ArlineQ/arline_quantum.git$cdarline_quantumWe recommend to install Arline Quantum in thevirtual environment.$virtualenvvenv$sourcevenv/bin/activateIfvirtualenvis not installed on your machine, run$pip3installvirtualenvNext in order to install the Arline Quantum platform execute:$pip3install.Alternatively, Arline Quantum can be installed in the editable mode:$pip3install-e.API documentationAPI documentation is heredocumentation.
To generate HTML API documentation, run below command:$cddocs/$makehtmlRunning testsTo run unit-tests and check installed dependencies:$toxFolder structurearline_quantum
│
├── arline_quantum            # library
│   ├── gate_chain            # gate chain (circuit) class
│   ├── gate_sets             # collection of gate sets for quantum hardware
│   ├── gates                 # collection of quantum gates
│   ├── hardware              # collection of predefined mock hardware devices
│   ├── qasm_parser           # parser of .qasm circuits
│   └── qubit_connectivity    # list of hardware topologies and utils functions
│   
├── docs                      # documentation
│
└── test                      # tests
    ├── gate_chain            # tests for gate chain class
    ├── gates                 # tests for gates
    ├── hardware              # tests for quantum hardware
    ├── qasm_files            # .qasm files for gate_chain test
    └── qubit_connectivity    # tests for qubit connectivity class
Package: arlogger
ARDIC AI Logger PackageA package that sends urgent logs to specified URL to be processed and displayed. Uploads daily log files to specified URL periodically.Usagefromarloggerimport*deftest_func():logger.debug("This is a debug message")logger.info("This is an info message")logger.warning("This is a warning message")logger.error("This is an error message")logger.critical("This is a critical message")if__name__=="__main__":url=""# URL of the server that will process the incoming logsprojectName="ARDIC AI Logger Test"logger=ArLogger().setUrl(url=url,projectName=projectName).setLevel(httpLevel=logging.ERROR,localLevel=logging.DEBUG)test_func()The output will contain the following information; log time, function name that generated the log, severity of log and the message of the log.
Package: arlpy
Packages such asnumpyandscipyprovide excellent mathematical tools for
scientists and engineers using Python. However, these packages are still young
and evolving, and understandably have some gaps, especially when it comes to
domain-specific requirements. Thearlpypackage aims to fill in some of the
gaps in the areas of underwater acoustics, signal processing, and communication.
Additionally,arlpyalso includes some commonly needed utilities and plotting
routines based onbokeh.General modulesThe following modules are general and are likely to be of interest to researchers
and developers working on signal processing, communication and underwater acoustics:Signal processing (arlpy.signal)Communications (arlpy.comms)Beamforming and array processing (arlpy.bf)Stable distributions (arlpy.stable)Geographical coordinates (arlpy.geo)Underwater acoustics (arlpy.uwa)Underwater acoustic propagation modeling (arlpy.uwapm)Plotting utilities (arlpy.plot)Common utilities (arlpy.utils)Special-purpose modulesThe following modules are specific to tools available at the ARL and may not be of
general interest to others:Digital Towed Array (arlpy.dtla)ROMANIS (arlpy.romanis)HiDAQ (arlpy.hidaq)UNET (arlpy.unet)UsageInstallation:pip install arlpyTo import all general modules:import arlpyNotesPng export of bokeh plots requiresselenium,pillowandphantomjs. These are not
installed as automatic depdendencies, since they are optional and only required
for png export. These should be installed manually, if desired.Useful linksarlpy homearlpy documentation
Package: arls
ARLS: Automatically Regularized Linear System SolverArls is intended for use in solving linear systems, Ax=b, which other solversmay not be able to handle. Ill-conditioning is the usual reason for people to seekalternative solvers such as arls(). In addition, arls() has a rich set ofconstraints which can be useful in solving both difficult and easy systems.All these solvers apply to any shape of the matrix, A. That is, thesystem can have more rows than columns (over-determined), or the same numberof rows and columns (square), or fewer rows than columns (under-determined). And the matixA can be full rank, or have near zero singular values, or exactly zero singularvalues of any number.Please see the in-code comments for details of calls and returns, including example applications for each routine.Our primary solver is:x = arls1(A, b)[0]If you need to solve many systems with the same matrix, A, but different b vectors, use:x = arlsusv(A, b, U, S, Vt)[0]See details in the code comments for how to get U, S, Vt.If you need the solution to be constrained to be non-negative, use:x = arlsnn(A, b)If you need to add constraint equations which must be satisfied exactly (such as"the sum of the solution elements must be 100.0") then put those equation in aseparate system, Ex == f and call:x = arlseq(A, b, E, f)If you need to add inequality constraints, such as "x1 + x2 >= 1.0" thenput those equations in a separate system, Gx >= h and call:x = arlsgt(A, b, G, h)If you need both equality and inequality constraints, usex =arlsall(A, b, E, f, G, h)You can also constrain the shape of the solution withx = arlshape(A, b, nonneg, slope, curve)You can constrain the solution to be(1) nonnegative; and/or(2) monotonically non-decreasing (i.e., rising)or monotonically non-increasing (i.e., falling); and/or(3) concave upward (like y = x*x) or concave downward.Examples:x = arlshape(A, b, 1, 1, 0) will force x to be nonnegative and rising.x = arlshape(A, b, 0, -1, 1) will force x to be falling and concave up.See details in the code comments.
Package: arlunio
ArlunioStableSnapshotDocsCodePyPiArlunio is in early development, while it is useable we cannot make any
stability guarantees.Arlunio is a Python library that allows you to create images and animations
powered by your imagination and a little mathematics. While mathematics is very
much at the core you do not have to be a mathematician to use it!importarlunioasar# Example coming soon!InstallationArlunio is available for Python 3.6+ and can be installed using Pip:$pipinstallarlunioBe sure to check out thedocumentation(under construction) for details on how to get started with arlunio.ContributingContributions are welcome! Just bear with us while contribution guidelines are
written up
Package: armaan
TopsisA Multi-Criteria Decision Analysis MethodSubmitted By- Armaan BhardwajRoll No.- 101903292Group- 3COE11It is a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) came in the 1980s as a multi-criteria-based decision-making method. TOPSIS chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution.InstallationThis package requires pandas to be pre-installed and python to run.
Install the dependencies and devDependencies and start the server.To Install and run in cmd line:pipinstallTopsis-Armaan-101903292
python
importTopsis_Armaan_101903292
Topsis_Armaan_101903292.topsis_cal(Input_File_Name.csv,"1,1,1,1 ...","+,-,+,- ...","resultfile.csv")##Project description
This package is implementation of topsis technique of multi-criteria decision analysis. This package will accept three parameters:data.csv //file which contains the models and parameters
string of weights separated by commas(,)
string of requirements (+/-) separated by commas(,) // important install pandas,sys,operator and math libraries before installing this // You can install this package using following command pip install Topsis_Armaan_101903292##License
Â© 2022 Armaan BhardwajThis repository is licensed under the MIT license. See LICENSE for details.