Saravanan666/pypi-org huggingface dataset:
Package: sdss-mangadap
The MaNGA Data Analysis PipelineThe MaNGA data-analysis pipeline (MaNGA DAP) is the survey-led software package
that has analyzed all galaxy data produced by the MaNGA data-reduction pipeline
(MaNGA DRP).  Its goal is to produce high-level, science-ready data products
derived from MaNGA spectra.  The products currently provided are:Spatially stacked spectraStellar kinematicsNebular emission-line properties: fluxes, equivalent widths, and
kinematicsSpectral indices: absorption-line (e.g., H-delta) and bandhead/color
(e.g., TiO, D4000) measurementsThat is, the DAP currently focuses on "model-independent" properties.
Higher-level, model-dependent properties, such as stellar-population parameters,
are outside of the scope of the current pipeline.See our full documentation atsdss-mangadap.readthedocs.io.SDSS Data Release VersionsSDSS-IV/MaNGA DR15is based on version 2.2.1SDSS-IV/MaNGA DR17is based on version
3.1.2.  Note that the version of the code that produced the DR17 was version
3.1.0; version 3.1.1 and 3.1.2 only include documentation updates.CitationIf you use the DAP software and/or its output products, please cite the
following two papers:Overview:Westfall et al. (2019, AJ, 158, 231)Emission-line Modeling:Belfiore et al. (2019, AJ, 158, 160)Additionally, if you use SDSS-IV/MaNGA data, please see:How to Cite SDSSSDSS Technical PublicationsInstallationpip install sdss-mangadapSeehttps://sdss-mangadap.readthedocs.io/en/latest/installation.html
Package: sdss-obstools
ObserverToolsThis is a home for Python 3 tools which came from the sdss-hub:~/bin directory.
It is designed primarily for SDSS-V usage. Individual file documentation will
be on confluence athttps://wiki.sdss.org/display/APO/Observing+Scripts.ModeratorsDylan Gatlin, Dmitry BizyaevAuthorsIn addition to the moderators who maintain this repo, scripts were written by
the following authors: Elena Malanushenko, Jon Brinkmann, Viktor Malanushenko,
Kaike Pan, Stephen Bailey, BernieInstallationFor observers at on their personal laptops, they can install these scripts viapip install sdss-obstools. The pip page can be foundhere. This will install the scripts
in your current pip environment. To run, you must have proper tunnels to
Influx/sdss5 servers.StructureFiles that were once under sdss-hub:~/bin are now under old_bin, and Python 3
scripts are now under bin. Any non-user tools are stored under python. All
user tools have tests.Code GuidelinesAll scripts designed for users should follow the SDSS Coding Standards, include
a main function,
and use argparse if arguments are needed. Prefer pathlib and fitsio. Scripts
should try to be runnable on sdss-hub if possible. Anything in old_bin will
be left there, but they are intended primarily for reference and you should
avoid using them.If a script is moved to bin, it should have a test file in tests that will run
it in a few ways that we will likely use it during normal observing. Tests
are critical for us maintaining code dependability.TODOPythonThe best way of installing all dependencies is to create an pyenv
environment. This project is usually tested on Python 3.9. All the requirements
can be found in requirements.txtUbuntuThese libraries were needed on Ubuntu 20.04sudoaptinstalllibxt-devlibbz2-devsaods9xpa-toolsLicenseObserverTools is licensed under a 3-clause BSD style license - see the
LICENSE.md file.
Package: sdss-opscore
opscoreThe opscore productProvides core services used by most actors.actorcoreandopscoreprovide direct dependencies for the actors, whileactorkeysprovides the definitions of standard messages that will be broadcast by each actor.InstallationTo install usingpippip install sdss-opscoreTo install from sourcegit clone git@github.com:sdss/opscore
cd opscore
pip install .
Package: sdss-skymakercam
skymakercamVirtual camera based on sdss-basecam using remote catalogfromlvmtanrun:poetry run container_start --name lvm.allfromlvmpwirun:poetry run container_start --name=lvm.sci.pwi --simulatorfrom skymakercam run:poetry run python utils/plot_skymakercam.py -v -c python/skymakercam/etc/cameras.yaml lvm.sci.agw.camMove lvm.sci.foc for focusing - relative 10000 steps.Move lvm.sci.km absolute in degree "DEG".Move lvm.sci.pwi for target.Press 'o' for find stars and recenter.Press 'q' to quit.use it in your own python code:With thisconfig example python/skymakercam/etc/cameras.yamland the actors running from before, it can be used like this:import asyncio
from logging import DEBUG, INFO
from skymakercam.camera import SkymakerCameraSystem, SkymakerCamera

async def example_skymakercam(camname, exptime, verb, config):

   cs = SkymakerCameraSystem(SkymakerCamera, camera_config=config, verbose=verb)
   cam = await cs.add_camera(name=camname, uid=cs._config[camname]["uid"])

   # eg: expose or do whatever u do with a sdss-basecam type camera.
   exp = await cam.expose(exptime, camname)
   

verb = DEBUG
camname = "lvm.sci.agw.cam"
config = "python/skymakercam/etc/cameras.yaml"

asyncio.run(example_skymakercam(camname, 5.0, verb, config))
Package: sdss-target-selection
target_selectionCode to perform target selection for BHM/MWM using catalogdb.InstallationTo installtarget_selectiondo$pipinstallnumpy$pipinstallsdss-target-selectionDue to a misconfiguration inpymange,numpyneeds to be installed before you can installtarget_selection.DevelopmentNew code must follow theSDSS Coding Standards. Linting checks run as a GitHub workflow after each commit. The workflow also check that the package and dependencies can be installed. Pull Requests that don't pass the checks cannot be merged.To test the code offline, you must installflake8andisortand run the following commands from the root of the package$flake8.--count--show-source--statistics$isort-cpython/bin/If the command exit without error, everything should be fine.
Package: sdss-tpm-multicast-client
tpm_multicast_clientListens to the TPM multicast stream and unpack the status dictionary using Jeff Hagen'stpmdgramcode.InstallationTo install withpipdopip install sdss-tpm-multicast-clientTo install from source dopoetry installMore information on developing with Poetry can be foundhere.UsageThe library provides a single function,listen_to_multicast, that creates a connection to the TPM broadcast, unpacks the datagram, and passes it to a callback function. A basic examplefrompprintimportpprintfromtpm_multicast_clientimport__version__,listen_to_multicastdefpprint_datagram(data:dict):pprint(data)asyncdefmain():_,protocol=awaitlisten_to_multicast(pprint_datagram)awaitprotocol.run_forever()if__name__=="__main__":main()The callback,pprint_datagramin this case, can be a function or a coroutine. In the former case it will be called withloop.call_soon(). In the latter the coroutine is scheduled as a task.CLIThe library provides a simple CLI scripttpm-multicast-clientthat prints the output of the datagram to stdout. It's basically equivalent to the example above.
Package: sdss-tree
treeThis product contains the definition(s) of the SDSS Science Archiver Server (SAS) filesystem environment tree(s).  It
sets up the SDSS environment configuration files for loading either via theModules Environment Manageror dynamically with atree.Treepython package.  Available environments are a SDSS "working" environment, i.e.sdssworkor any number of environments for public Data
Releases, i.e.dr16.cfg.  See full documentation athttp://sdss-tree.readthedocs.io/en/latest/.Developer InstallLocal InstallTo install tree for development locally:git clone https://github.com/sdss/tree
cd tree
pip install -e ".[dev,docs]"Install at UtahTo install at Utah for use with modules,git clone https://github.com/sdss/tree main
cd main
python bin/setup_tree.py -vReplacemainwith the desired branch or tag name as needed. Also, see the instructions on theWikiInstall as a DependencyTo install as a Python package dedendency for software use, install the latest tag straight from PyPi:pip install sdss-treeBuild Sphinx DocsWithin the top level repo directory, run thesdsstoolscommands:# build the Sphinx documentation
sdss docs.build

# open the docs locally in a browser
sdss docs.showDocumentation is automatically built and pushed to Read The Docs.TestingTests are created usingpytest.  Navigate to thetestsdirectory from the top level and run withpytest.cd tests
pytestCreating ReleasesNew releases ofsdss-treeare created automatically, and pushed toPyPi, when new tags are pushed to Github.  See theCreate ReleaseGithub Action andReleasesfor the list.New tag names follow the Python semantic versioning syntax, i.e.X.Y.Z.Useful linksGitHub:https://github.com/sdss/treeDocumentation:https://sdss-tree.readthedocs.orgIssues:https://github.com/sdss/tree/issues
Package: sdtbu-acm-tool
山东工商学院acm辅助工具OJ网站账号移植[ 介绍 ]将一个账号所做过的所有题移植到另一个账号，部分题目无法转移[ 纯新手教程 ]如果您不是第一次接触python，可以跳过这一步下载python3.9并安装（Mac用户自行下载任意大于3.6的python版本），安装时请勾选Add Python 3.9 to PATHWindows下载下载开发工具，这里推荐使用Pycharm（Mac用户自行下载），下载后安装打开Pycharm并创建新项目，并在下方找到Terminal，点击Create New Terminal（中文终端），在弹出的窗口中输入pip install sdtbu-acm-tool，等待安装完成[ 使用说明 ]1. 安装依赖如果您的python版本大于3.9或者您的操作系统非Windows系统，则无法开启验证码自动识别模式，需要手动输入验证码如果确定您的python版本小于3.9且您的操作系统为Windows系统，则使用以下命令安装依赖pipinstallddddocr-ihttps://pypi.mirrors.ustc.edu.cn/simple2. 用户登录「 校内使用 」必须保证您的电脑已经连接到校园网，然后使用以下代码登录fromacm.ojimportOJuser=OJ(user_id='您的账号',password='您的密码',mode='校内',# 访问模式 [校内/校外]code_mode='自动',# 验证码识别模式 [自动/手动]).login()或者使用以下代码登录fromacm.ojimportOJuser=OJ('你的账号','你的密码').login()「 校外使用 」在使用之前，您需要先获取wvpn的token获取方法：打开链接：https://wvpn.sdtbu.edu.cn并扫码登录, 如果您已经登录过wvpn，则跳过此步骤打开开发者工具(通常为F12)，切换到Network选项卡(中文为"网络")，然后点击刷新按钮(或者按下F5)此时会出现大量网络请求，点击第一个网络请求,此时「 wengine_vpn_ticketwvpn_sdtbu_edu_cn 」的值便是我们所需的token，如果该值显示不完整，请调整窗口直至调整至完全显示为止(以下操作将采用图中的token值演示)然后您可以使用以下代码进行登录fromacm.ojimportOJuser=OJ(user_id='您的账号',password='您的密码',mode='校外',# 访问模式 [校内/校外]code_mode='自动',# 验证码识别模式 [自动/手动]wvpn_token='39a7de9aad39f158').login()或者使用以下代码登录fromacm.ojimportOJuser=OJ('你的账号','你的密码',wvpn_token='39a7de9aad39f158').login()3. 功能演示[ 移植账号 ]注意：校外访问模式同时只能登录一个账号，如果您已经登录了一个账号，那么您将无法再登录另一个账号，如果您需要登录另一个账号，那么您需要先退出当前账号，然后再登录另一个账号一个转移账号的例子:fromacm.ojimportOJuser_1=OJ('你的账号','你的密码',wvpn_token='39a7de9aad39f158').login()user_1.save('user_1.json')# 保存账号信息user_2=OJ('你的账号','你的密码',wvpn_token='39a7de9aad39f158').login()user_2.load_and_submit('user_1.json',10# 题目提交间隔时间（单位: 秒），速度过快可能会导致提交失败)# 加载账号信息并提交题目[ 提交题目 ]一个提交题目的例子:fromacm.ojimportOJuser=OJ('你的账号','你的密码',wvpn_token='39a7de9aad39f158').login()user.submit(10,# 题目ID'你的代码类型',# 代码类型'你的代码',# 代码)其中代码类型有以下几种:CC++PascalJavaRubyPython[ 用户注册 ]一个用户注册的例子:fromacm.ojimportOJuser=OJ('需要注册的账号','你的密码',wvpn_token='39a7de9aad39f158').register()[ 修改用户信息 ]一个修改用户信息的例子:fromacm.ojimportOJuser=OJ('你的账号','你的密码',wvpn_token='39a7de9aad39f158').login()user.modify(nickname='你的新昵称',# 昵称email='你的新邮箱',# 邮箱password='你的新密码',# 密码)只需要修改其中几项即可，无需全部填写[ 获取已完成题目信息 ]一个获取已完成题目信息的例子:fromacm.ojimportOJuser=OJ('你的账号','你的密码',wvpn_token='39a7de9aad39f158').login()datas=user.info()print(datas)[ 测试提交代码 ]一个测试提交代码的例子:fromacm.ojimportOJuser=OJ('你的账号','你的密码',wvpn_token='39a7de9aad39f158').login()user.submit_test_code()效果如下[ 贡献代码 ]如果您想帮助我们完善测试数据库，您可以将您账户的代码贡献给我们，您可以使用以下代码保存您的数据fromacm.ojimportOJuser=OJ('你的账号','你的密码',wvpn_token='39a7de9aad39f158').login()user.save('data.json')# 保存账号信息然后将生成的data.json文件发送至我们的邮箱1838696034@qq.com，我们会将您的数据添加到测试数据库中，感谢您对我们的支持注意事项请勿使用此项目进行非法操作，否则后果自负此仓库仅供学习交流使用，不得用于非法商业用途反馈邮箱:1838696034@qq.com
Package: sdtf
Streaming Decision Trees & ForestsSDTF(StreamingDecisionTrees &Forests) is for exploring streaming options of decision trees and random forests.The package includes two ensemble implementations (Stream Decision ForestandCascade Stream Forest).Based onscikit-learnfork.Documentation:https://sdtf.neurodata.io/Installation Guide:https://sdtf.neurodata.io/#installBenchmark Visual:https://sdtf.neurodata.io/visual.htmlAPI Reference:https://sdtf.neurodata.io/api.html
Package: sdtfile
Sdtfile is a Python library to read SDT files produced by Becker & Hickl
SPCM software. SDT files contain time correlated single photon counting
instrumentation parameters and measurement data. Currently only the
“Setup & Data”, “DLL Data”, and “FCS Data” formats are supported.Becker & Hickl GmbHis a manufacturer of
equipment for photon counting.Author:Christoph GohlkeLicense:BSD 3-ClauseVersion:2023.9.28QuickstartInstall the sdtfile package and all dependencies from thePython Package Index:python -m pip install -U sdtfileSeeExamplesfor using the programming interface.Source code and support are available onGitHub.RequirementsThis revision was tested with the following requirements and dependencies
(other versions may work):CPython3.9.13, 3.10.11, 3.11.5, 3.12rcNumpy1.25.2Revisions2023.9.28Update structs to SPCM v.9.66 (breaking).Shorten MEASURE_INFO struct to meas_desc_block_length.2023.8.30Fix linting issues.Add py.typed marker.Drop support for Python 3.8 and numpy < 1.22 (NEP29).2022.9.28Convert docstrings to Google style with Sphinx directives.2022.2.2Add type hints.Drop support for Python 3.7 and numpy < 1.19 (NEP29).2021.11.18Fix reading FLIM files created by Prairie View software (#5).2021.3.21…Refer to the CHANGES file for older revisions.ReferencesW Becker. The bh TCSPC Handbook. 9th Edition. Becker & Hickl GmbH 2021.
pp 879.SPC_data_file_structure.h header file. Part of the Becker & Hickl
SPCM software installation.ExamplesRead image and metadata from a “SPC Setup & Data File”:>>> sdt = SdtFile('image.sdt')
>>> sdt.header.revision
588
>>> sdt.info.id[1:-1]
'SPC Setup & Data File'
>>> int(sdt.measure_info[0].scan_x[0])
128
>>> len(sdt.data)
1
>>> sdt.data[0].shape
(128, 128, 256)
>>> sdt.times[0].shape
(256,)Read data and metadata from a “SPC Setup & Data File” with multiple data sets:>>> sdt = SdtFile('fluorescein.sdt')
>>> len(sdt.data)
4
>>> sdt.data[3].shape
(1, 1024)
>>> sdt.times[3].shape
(1024,)Read image data from a “SPC FCS Data File” as numpy array:>>> sdt = SdtFile('fcs.sdt')
>>> sdt.info.id[1:-1]
'SPC FCS Data File'
>>> len(sdt.data)
1
>>> sdt.data[0].shape
(512, 512, 256)
>>> sdt.times[0].shape
(256,)
Package: sdtk-visllc
Copyright (C) 2020-2022 Vis LLC - All Rights Reserved

This 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
(at your option) 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 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, see <https ://www.gnu.org/licenses/>.Main Project Page:https://sourceforge.net/projects/simple-data-toolkit/
Package: sdt_metrics
sdt_metrics - Signal Detection Theory metrics for Python==================================================================:Authors: Roger Lew:email:   rogerlew@gmail.comDependencies:* Numpy* SciPy* MatplotlibChange Log--------------v 0.1.2.1:- metric validation plots have isopleths and pcolor plots- bph is symmetric about HI==FAv 0.1.2.0:- Python 3k compatible- ROC plots can show bias isoplethsv 0.1.1.1:- first pypi release
Package: sdto
sdto - subdomain takeover finderSubdomain takeover scannerCurrent count of fingerprints:80What is subdomain takeover?Supported Servicesacquia
activecampaign
aftership
agilecrm
aha
airee
anima
announcekit
aws/s3
bigcartel
bitbucket
brightcove
campaignmonitor
canny
cargo
cargocollective
cloudfront
desk
fastly
feedpress
flexbe
flywheel
frontify
gemfury
getresponse
ghost
gitbook
github
hatenablog
helpjuice
helprace
helpscout
heroku
hubspot
intercom
jazzhr
jetbrains
kajabi
kinsta
launchrock
mashery
netlify
ngrok
pagewiz
pantheon
pingdom
proposify
readme
readthedocs
s3bucket
shopify
shortio
simplebooklet
smartjob
smartling
smugmug
sprintful
statuspage
strikingly
surge
surveygizmo
surveysparrow
tave
teamwork
thinkific
tictail
tilda
tumbler
uberflip
unbounce
uptimerobot
uservoice
vend
webflow
wishpond
wix
wordpress
worksites.net
wufoo
zendeskInstallation:to use as python librarypipinstallsdtoto use as a CLI toolpipinstallsdto[cli]or:gitclonehttps://github.com/scanfactory/sdto.gitcdsdto
poetryinstallUsage as a CLI toolExamples:python3-msdto-twww.domain.com
python3-msdto-twww.domain.com-fpath/to/custom-fingerprints-file.json
python3-msdto-thttps://www.domain.com/
python3-msdto-thttp://www.domain.com/
python3-msdto-twww.domain.com--no-ssl
python3-msdto-twww.domain.com-v--timeout30python3-msdto-twww.domain.com-H"user-agent""your-custom-user-agent"-H"another-header""header-value"python3-msdto-twww.domain.com-Fjson
python3-msdto-twww.domain.com-ooutput.txt
python3-msdto-twww.domain.com-Fjson-ooutput.json
python3-msdto-twww.domain.com-Ftxt-ooutput.txt
python3-msdto-twww.domain.com-phttp://127.0.0.1:8080python3-msdto-lsubdomains-list.txtDocker supportBuild the image:docker build -t sdto .Run the container:docker run -it --rm sdto -t www.domain.com -vUsing custom fingerprintsYou can specify custom fingerprints file via-f path/to/file.jsonparameter.
The expected json file format:{"AWS/S3":{"pattern":"The specified bucket does not exist"},"BitBucket":{"pattern":"Repository not found"},"Fastly":{"pattern":"Fastly pattern\\: unknown domain\\:","process_200":true}}Note thatpatternvalue is expected to be a python regexp.Usage as a python libraryExample:importrefromaiohttpimportClientSessionfromsdtoimportcheck_target,RegexFingerprintasyncdefmain():asyncwithClientSession()ascs:fingerprint=awaitcheck_target(cs=cs,target="sub.domain.com",ssl=True,proxy=None,fingerprints=[RegexFingerprint("Github",re.compile(r"There isn\'t a Github Pages site here\."),process_200=False,)])ifnotfingerprint:print("No match")else:print(fingerprint.name)
Package: sdu
Script Development UtilitiesA set of utilities to make developing scripts simpler and easierTable of contentsGoalsInstallationFrom PyPiFrom sourceUsage & DocumentationModulesautocompleteclipathstype_conversionsvalidationContributingGoalsThere are several goals with this project:Provide a useful API for well developed functions with error catching and testing for commonly annoying situationsProvide cross-platform implementations of functions (where possible)Give people access to source code to learn how the functions work and just pull what's neededServe as a repository of general knowledge to share solutions to issues with peopleInstallationFrom PyPiRunpip install sduorsudo pip3 install sduFrom sourceClone this repo: (put github/source code link here)Runpip install .orsudo pip3 install .in the root directoryUsage & DocumentationFor usage please see the  API documentation (https://kieranwood.ca/sdu).For details on how contributing to the project, please seeCONTRIBUTING.md, for details on upcoming changes seeour roadmap.For most recent changes seeCHANGELOG.md.ModulesautocompleteThis module is used to generate autocomplete files for bashcliA module for helpful utilities with generating CLI's such as:Clearing the terminalpathsThis module contains many useful utilities for dealing with system paths such as:A pre and post processing pipeline for system pathsAbility to add paths to the PATH variabletype_conversionsQuick and common conversions between types with sensible options such as:Converting a dictionary (and second dimension dictionary keys) to defaultdict(s)validationContains a set of common validation schemes such as:Validating number input is between a set value at the command lineValidate provided string is an accepted valueChangelogV0.1.1; September 24thFixing logo on pypi :)Bug fixesFixed logo 404 on PyPiV0.1.0; September 24thFocus was on adding more functionality, and modernizing the package setup.FeaturesAdded tests forpaths,type_conversionsandvalidationRemovedtype_conversion.stringify_listsince functionality can be one-lined usingstr.joinAddedtype_conversion.dict_to_defaultdict; Takes in a dictionary, and a default Callable then converts the dict to a defaultdictChanged From Apache V2 to MIT liscenceAddedpaths.add_to_pathfor adding specified paths to system pathsAdded testing forsdu.paths,sdu.validation,sdu.autocomplete, andsdu.type_conversionsChangedsdu.terminaltosdu.cliAddedsdu.autocompletefor generating bash autocomplete filesDocumentation ImprovementsChanged to second point releases (from 0.0.x to 0.x.0)Improved the index documentation to be more clear and concise of an overviewAdded bullet points to each package for a quick overview of what each module can be used forOverhauled readme to be more usefulBug FixesFixed some preprocessing bugs with windows inpath.preprocess_paths()V0.0.1; February 8th 2020Released existing code for the project, looking to properly implement in v0.1.0FeaturesTraversing directories in terminal and GUIValidating number selectionsNormalized serialization and de-serialization of pathsDocumentation improvementsCreated documentation site
Package: sdu-bkjws
master: [![Build Status](https://travis-ci.org/Trim21/sdu_bkjws.svg?branch=master)](https://travis-ci.org/Trim21/sdu_bkjws)dev: [![Build Status](https://travis-ci.org/Trim21/sdu_bkjws.svg?branch=dev)](https://travis-ci.org/Trim21/sdu_bkjws)pypi:[https://pypi.python.org/pypi/sdu-bkjws](https://pypi.python.org/pypi/sdu-bkjws)一个用来解析山东大学(shandong university)教务系统的库 包括查成绩 获取课表 查询成绩排名(似乎清华用的也是这个系统)python2未经测试## [Docs](https://github.com/Trim21/sdu_bkjws/blob/dev/docs/index.md)## todo:- 获取教学计划- 添加课程(bkjwxk)- 查询课程信息(bkjwxk)## [changelog](https://github.com/Trim21/sdu_bkjws/blob/master/CHANGELOG.md#100-2017-07-03)## for contributor:### method of test1. create a `keys.json` in `test` dir```json{"student_id": "student_id","password": "password"}```then```python -m unittest test```or2. export `student_id` and `password` to environment