Saravanan666/pypi-org huggingface dataset:
Package: algospace
发布方本地部署指南最保密、最快速的发布方式⚡️⚡️⚡️ 一分钟极速发布 ⚡️⚡️⚡️所需环境👉 任何可以上网的计算机👉 可以跑通的算法👉Python>=3.7第一步：准备算法预测函数例如：deflandmark_detection(image_path):'''人脸关键点标注args:image_path: 本地图片路径'''# 这里是具体算法实现 #return{'output_image_path':output_image_path,# 带关键点标注的本地图片路径'dets':dets,# 目标图像检测的人脸坐标点}第二步：安装 Python 包在算法所用 Python 环境的命令行执行：pipinstallalgospace-ihttps://pypi.python.org/simple第三步：初始化配置文件进入算法根目录，命令行执行：algospaceinitalgospace命令也可以简写为asc执行后在当前目录下生成algospace-config.py配置文件。第四步：填写配置文件根据algospace-config.py中的注释信息，填写第一步准备完成的预测函数的信息。例如第一步预测函数的配置信息应当填写为：service_filepath='./main.py'service_function='landmark_detection'service_input={'image_path':{'type':'image_path','describe':'人脸图片',}}service_output={'output_image_path':{'type':'image_path','describe':'带标注点的人脸图片'},'dets':{'type':'str','describe':'目标图像检测的人脸坐标点'}}最后一步：运行！进入算法根目录，命令行执行：algospacestart也可以挂在后台运行：nohupalgospacestart>./algospace.log2>&1&🎉 算法将会自动注册、运行、发布。🎉 稍等片刻后即可在「我的算法」页面中查看新增的算法。
Package: algos-py
What is algos-py?This package contains implementations of some classic computer
science algorithms. My main goal is to understand these algorithms
and the best way to do that is to implement them myself.Along the way I practice test driven development (withpytest),
continuous integration (withtravisandappveyor), coverage
tracking (withcoverallsandcodecov), version control (withgit,githubandgitlab), documentation (withsphinxandreadthedocs)
and a lot more.How to test?To run all of the unit-tests:$pytest-n2To run unit-tests for a specific module:$pytest./tests/test_heap.pyTo run all the unit-tests and produce a coverage report:$pytest-n2--cov=srcWhere to find?Primary repository:https://github.com/algos-all/algos-pySecondary (mirror) repository:https://gitlab.com/alisianoi/algos-pyRelease procedure:$# change version in setup.py$gitaddsetup.py$gitcommit-m"Bump version to 1.0.0"$gittagv1.0.0$gitpushgithubmain&&gitpushgithub--tags$gitpushgitlabmain&&gitpushgitlab--tags$pipinstall--upgradewheel$pythonsetup.pybdist_wheel$pipinstall--upgradetwine$twinecheck./dist/algos_py-1.0.0-py3-none-any.whl$twineupload./dist/algos_py-1.0.0-py3-none-any.whl
Package: algotides
Algo TidesDescriptionThis Python application is a graphic interface for the user who owns or have access to an Algorand
node and would like to issue operations through a GUI rather than CLI. (i.e.: manage wallets and addresses,
send transactions, save a contact list)DisclaimerClone this repo only if you want to develop. If you are just a user please download the python package following
instructions below.This repo is not guaranteed to work between commits.Installation$ pip install algotidesor you can download the.tar.gzpackage and install it that way.Users shouldn't download from this repo because it is not guaranteed to work between commits.Better to install viapipSecurityPlease keep in mind that this software stores some serialized data inside<HOME_FOLDER>/.algo-tides.As a result of this any user should put over this folder permissions that are stricter than usual.It is possible to inject arbitrary code when deserializingjsonpicklefiles so please be mindful.DonationsPlease consider donating ALGO to this project at the following address:TIDESVS3UR7WQTR5J3M5ADEJVOUUS2C2YOEIU4Z6VTPU2EMQME7PSDK76AAny donation will be used to fund the development and maintenance of Algo Tides.Algo Tides will always remain a free and open source app. Donations are on a voluntary basis.RequirementsPython 3.7Packages:PySide2py-algorand-sdkjsonpickleqasyncaioifyTroubleshootqt.qpa.plugin: Could not load the Qt platform plugin "xcb"This step creates a symlink from the existing library to the needed library:on MXLinux 19.4:sudo ln -s /usr/lib/x86_64-linux-gnu/libxcb-util.so.0.0.0 /usr/lib/x86_64-linux-gnu/libxcb-util.so.1on Debian 10:sudo ln -s /usr/lib/x86_64-linux-gnu/libxcb-util.so /usr/lib/x86_64-linux-gnu/libxcb-util.so.1
Package: algotik-tse
AlgoTik-tseاین ماژول جهت مصارف آموزشی، علمی و تحقیقاتی با زبان برنامه نویسی پایتون توسعه یافته است. سعی شده امکاناتی در این ماژول در نظر گرفته شود، که بتواند نیاز به تمامی اطلاعات سازمان بورس را مرتفع نماید و مانند یک وب سرویس دریافت اطلاعات کار کند. خروجی تمامی ساب ماژول ها با فرمت دیتافریم می تواند دریافت گردد.ویژگی های ماژول:قابلیت دسترسی به داده‌های یک سهم با استفاده از نماد يا نام کامل فارسی   <---قابلیت انجام تعدیل قیمت به صورت یکجا با احتساب انواع افزایش سرمایه و پرداخت سود نقدی   <---هوشمندی در تشخیص جابجایی یک نماد بین بازارهای مختلف و یکپارچه سازی همه سوابق نمادهای دارای جابجایی   <---قابلیت دسترسی به سوابق همه شاخص‌های بازار بورس و هوشمندی در تشخیص اشتباهات املایی و نگارشی عناوین شاخص صنایع بورسی
  <---قابلیت دسترسی به سابقه داده‌های درون‌روز یک نماد شامل عمق بازار و ریز معاملات   <---قابلیت دسترسی و رصد لحظه‌ای دیده‌بان و عمق بازار در ساعت انجام معاملات در بازار   <---قابلیت تهیه لیست جامعی از مشخصات همه سهم‌های بازار   <---قابلیت دانلود دسته‌جمعی سابقه قیمت لیستی از سهم‌ها و ساخت پنل قیمت پایانی تعدیل شده برای آنها   <---قابلیت دسترسی به سابقه ۱۰ ساله قیمت دلار بازار آزاد   <---خروجی سازگار با دیتافریم پانداز و قابلیت فیلترینگ زمانی مجدد بر اساس تاریخ شمسی   <---قابلیت ارائه تاریخ شمسی، میلادی و نام ایام هفته برای داده‌های روزانه   <---همچین می‌توانید از طریقاین لینکبه آدرس تلگرامی ما دسترسی داشته باشیدالهام گرفته شده از finpy-tse و tsemodule5نصب ماژولpipinstallalgotik-tseآپدیت ماژولpipinstallalgotik-tse--upgradeفراخوانی ماژولimportalgotik_tseasattدریافت سابقه اطلاعات روزانه یک نماددریافت سابقه قیمت:att.stock(stock='شتران',start='1402-01-01',end='1402-07-01',values=0,tse_format=False,auto_adjust=True,output_type="standard",date_format="jalali",progress=True,save_to_file=False,multi_stock_drop=True,adjust_volume=False,return_type=None,)دریافت سابقه حقیقی-حقوقی:att.stock_RI(stock='شتران',start='1402-01-01',end='1402-07-01',values=0,tse_format=False,output_type="standard",date_format="jalali",progress=True,save_to_file=False,multi_stock_drop=True,)دریافت لیست کلیه دارایی های موجود در بورس تهران:att.stocklist(bourse=True,farabourse=True,payeh=True,haghe_taqadom=False,sandogh=False,payeh_color=None,output="dataframe",progress=True)دریافت اطلاعات مربوط به یک دارایی:att.stockdetail(stock='شتران')دریافت لیست سهامداران عمده سهم:att.shareholders(stock='شتران',date=None,shh_id=False)دریافت لیست افزایش سرمایه های سهم:att.stock_capital_increase(stock='شتران',)=======
History0.2.8 (2023-12-17)First release on PyPI.0.2.9 (2023-12-18)Second release on PyPI.fix bug in returns0.3.2 (2024-01-05)Third release on PyPI.Add Shareholders0.3.3 (2024-01-05)Third release on PyPI.Fix bug in change_amount of shareholders0.3.4 (2024-01-11)Fourth release on PyPI.Add Capital Increase in simple method0.3.5 (2024-01-18)Fifth release on PyPI.Add stock information in beta phase.0.3.6 (2024-02-02)sixth release on PyPI.Add stock statistics in beta phase.0.3.7 (2024-02-04)seventh release on PyPI.Add currencies and coins in beta phase.0.3.8 (2024-02-27)eighth release on PyPI.Add payeh market color in stocklist.
Package: algo-timer
Algorithm TimerOverviewAn easy-to-use algorithm timer.MechanismWe use a context-manager andwithin Python to give an convinent way to test
a specific block of code. Just see the following examples.Note that we design this plot function here to test some algorithms' runing time and you can use it to test(and plot) the time of any block of code with minor change in source code(theTimerPloterclass, specifically)EaxmplesFibnaccifromalgotimerimportTimer,TimerPloterdeffib(n):ifn<=2:return1returnfib(n-1)+fib(n-2)deffibMemo(n):cache={1:1,2:1}defrec(n):ifnnotincache:cache[n]=rec(n-1)+rec(n-2)returncache[n]returnrec(n)if__name__=='__main__':withTimer('fib, 30')ast:print('fib(30) = ',fib(30))withTimer('fib, 35')ast:print('fib(35) = ',fib(35))withTimer('fibMemo, 30')ast:print('fibMemo(30) = ',fibMemo(30))withTimer('fibMemo, 35')ast:print('fibMemo(35) = ',fibMemo(35))ploter=TimerPloter()ploter.plot()The output:fib(30) =  832040
fib, 30 Spends 0.217 s
fib(35) =  9227465
fib, 35 Spends 2.434 s
fibMemo(30) =  832040
fibMemo, 30 Spends 0.0 s
fibMemo(35) =  9227465
fibMemo, 35 Spends 0.0 sAnd we get two files:logging,csvis the time data.fib, 30, 0.217
fib, 35, 2.434
fibMemo, 30, 0.0
fibMemo, 35, 0.0AndTimer.png, a plot of the data.ClassificationfromalgotimerimportTimer,TimerPloterfromsklearnimportdatasetsfromsklearn.naive_bayesimportGaussianNBfromsklearn.neighborsimportKNeighborsClassifieriris=datasets.load_iris()withTimer('GaussianNB, Train'):gnb=GaussianNB()clf=gnb.fit(iris.data,iris.target)withTimer('GaussianNB, Test'):y_pred=clf.predict(iris.data)print("Number of mislabeled points out of a total%dpoints :%d"%(iris.data.shape[0],(iris.target!=y_pred).sum()))withTimer('KNN(K=3), Train'):neigh=KNeighborsClassifier(n_neighbors=3)clf=neigh.fit(iris.data,iris.target)withTimer('KNN(K=3), Test'):y_pred=clf.predict(iris.data)print("Number of mislabeled points out of a total%dpoints :%d"%(iris.data.shape[0],(iris.target!=y_pred).sum()))withTimer('KNN(K=5), Train'):neigh=KNeighborsClassifier(n_neighbors=5)clf=neigh.fit(iris.data,iris.target)withTimer('KNN(K=5), Test'):y_pred=clf.predict(iris.data)print("Number of mislabeled points out of a total%dpoints :%d"%(iris.data.shape[0],(iris.target!=y_pred).sum()))# plot itploter=TimerPloter()ploter.plot()The output:GaussianNB, Train Spends 0.001 s
Number of mislabeled points out of a total 150 points : 6
GaussianNB, Test Spends 0.001 s
KNN(K=3), Train Spends 0.019 s
Number of mislabeled points out of a total 150 points : 6
KNN(K=3), Test Spends 0.019 s
KNN(K=5), Train Spends 0.001 s
Number of mislabeled points out of a total 150 points : 5
KNN(K=5), Test Spends 0.01Filelogging.csv:GaussianNB, Train, 0.001
GaussianNB, Test, 0.001
KNN(K=3), Train, 0.019
KNN(K=3), Test, 0.019
KNN(K=5), Train, 0.001
KNN(K=5), Test, 0.01FileTimer.png
Package: algotradepy
Copyright (c) 2020, Petio PetrovPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Description: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.7
Provides-Extra: ibapi
Provides-Extra: polygon
Provides-Extra: dev
Package: algotrader
AlgoTradingPython Implementations of popular Algorithmic Trading Strategies, along with genetic algorithms for tuning parameters based on historical data.Algorithms -CCI Correction (Done)Stochastic Pop and Drop (Done)Percent Above 50-day SMA (Done)Moving Momentum (Done)Exponential Moving Averages (Done)Double Exponential Moving Averages (Done)Triple Exponential Moving Averages (Done)Six-Month Cycle MACD (Done)Gap Trading StrategiesHarmonic PatternsThe Last Stochastic TechniquePercent B Money FlowPre-Holiday EffectRSI2Faber’s Sector rotation Trading StrategyCVR3 VIX Market TimingSlope Performance TrendSwing ChartingIchimoku CloudTrend Quantification and Asset Allocation
Package: algotype
Algotype manualAlgorythmic font creation system.Version 0.2.39Status: AlphaAuthorsMarek Ryćko (system design and most of Python programming)Bogusław Jackowski (design consulting and Metapost programming)Piotr Strzelczyk (Python 2 Metatype1 programming, partially used in Algotype)IntroductionAlgotype is a system for creating fonts using algorythmic means, by programming
the shapes of characters (glyphs), metric information and their relationships.ThealgotypePython package is a Python part of the entire Algotype
system. The other parts, not included on the Python Package Index, PyPI, are:program files in the Metapost language, defining the shapes of font glyphs, their relationships and general font data,configuration files, defining both the installation instance and the paramteters of a particular run of the Algotype system,templates of font feature files (definitions),GOADB file or files (Glyph Order and Alias DataBase), defining the renaming of glyphs for certain types of output fonts,renaming definition files, defining the renaminig of fonts both for OTF format and for Type 1 (PFB) format, including the definitions of names of files in 256-character fonts with various encodings.Algotype is a successor of the Metatype1 system created in the years 2000–2018 by Bogusław Jackowski and Piotr Strzelczyk. The original Metatype1 system has been used to create several font families, including Latin Modern fonts and a series of TeX Gyre fonts (all of them are freele available and also included in most Linux distribiutions).The nameThe name “Agotype” reflects the fact that the purpose of the system is algorythmic type (fonts) design.The project web page is planned to be:http://algotype.org.InstallationThe installation steps are the following:install Python 3, version at least 3.6,install t1utils (t1asm,t1disasmprograms; must be available in operating system program search path),install Fontforge (a font manipulation program, available for major operating systems),install Fontforge Python support (Python >= 2.7 in 2.x line or >= 3.7 in 3.x line),usepipcommand in Python 3 to install Algotype, namely the packagealgotypepip install-ihttps://test.pypi.org/simple/algotypeThe command in your operating system may be also calledpip3orpip3.8, or similarly,install Metapost (probably as a part of TeXLive installation),install the Metapost Algotype files (for LM and TG fonts),insltall other input Algotype files (feature templates, goadb files etc),create (or edit) Algotype configuration files,create (or edit) scripts or batch files running/calling the algotype program, with config file paths and file names parameters.
Package: algovanity
algovanitygenerate vanity addresses for the Algorand blockchainThe software supports matching multiple patterns at once, and that is in fact the suggested operation mode: generate once, match many.This design also facilitates attempts in finding longer matches in a potentially shorter timespan, giving the user control in reducing the precision and diverging from the initial desired outcome.The script spawns multiple subprocesses which will run indefinitely, with a main loop printing to console some basic statistics and any new matches as they're found, waiting for a keyboard interrupt.InstallationfrompipTo install the script from the packaged release onpip, runpip3installalgovanityfrom sourceTo install the script from source, rungitclonehttps://github.com/nxet/algovanitycdalgovanity# optionally change branchgitcheckoutdev-0.1# install requirements and librarypip3install-rrequirements.txt
pip3install.Pattern formatsAddresses are formed by 58 characters in the uppercaseA-Zrange and the2-7range, user can pick any combination of these.The numbers0,1,8and9are not valid characters in Algorand addressesMatching is supported with four different pattern formats:startmatches at the beginning of the addressdefined with patterns likeADDR...orMYNAME...endmatches at the end of the addressdefined with patterns like...ADDRor...MYNAMEedgesmatches at both start and end of the addressdefined with patterns likeCOOL...ADDRorABC...XYZregexcompiles the provided pattern as is and attempts tore.fullmatchdefined with patterns like^AB[.]*CD[.]*YZ$or^NAME[2-7][A-Z2-7]+$NB: the longer the pattern, the longer the run time.Anything over 6 characters will be running for averylong time.File outputThe script creates a new file if none is available, but will always try to append new matches to the provided file instead of overwriting.Matches are dumped to file at each status update, and finally once the main loop is terminated.The file is opened/closed on-demand, allowing totail -fit for example.Examples# install packagepip3installalgovanity# match addresses starting with MYNAMEalgovanityMYNAME...# or either starting or ending with MYNAMEalgovanityMYNAME......MYNAME# but limiting to only 2 subprocessesalgovanityMYNAME......MYNAME--procs-max2# dump results to filealgovanityMYNAME...--output~/algovanity.txt# match addresses starting with COOL and ending with ADDRalgovanityCOOL...ADDR# match addresses starting with NAME plus one exact number (either 5 or 7)algovanityNAME5...NAME7...--output~/algovanity.txt# match addresses starting with NAME plus one random number from range [2-7]algovanity'^NAME[2-7][A-Z2-7]+$'--output~/algovanity.txt# match addresses starting with NAME plus two random numbers from range [2-7]algovanity'^NAME[2-7]{2}[A-Z2-7]+$'--output~/algovanity.txtLicenseThis project is licensed under GPL 3.0 - see LICENSE for details.Support this projectIf this software has been helpful please consider donating to support future development.Algorand address: NXET... -view on algoexplorer.io
Package: algovera
📚 Algovera LibraryThe Algovera Library currently comprises of a Jupyter Lab extension for using MetaMask within Jupyter Lab to interact with theOcean market, and store on decentralized storage with decentralized access control (lit protocol for decentralized access control, Estuary/Filecoin for decentralized storage)AlgoveraAlgovera is a community of individuals working to facilitate and accelerate the development of decentralised AI products and research.Website|Notion|Discord|Calendar|Twitter|YouTube🏗 SetupTo start using the extension, simply run these commands in your terminal::conda create -n algolab -c conda-forge jupyterlab

conda activate algolab

pip install algovera

jupyter lab🚧 Contributing to the LibraryThere are currently two ways to contribute to the Algovera Library: adding more functionality to the python package or improving the jupyter lab extension.First, clone the repository by runninggit clone https://github.com/AlgoveraAI/jupyterlab_extensions.git

cd jupyterlab_extensions/To start contributing, follow the commands below to set up a development environment.conda create -n algolab-dev -c conda-forge jupyterlab ipylab jupyter-packaging nodejs ipytree bqplot ipywidgets numpy

conda activate algolab-dev

python -m pip install -e ".[dev]"

jupyter labextension develop . --overwrite

jlpm

jlpm run buildAfter any changes, runjlpm run buildto see them in Jupyter Lab. Note that you might need to runjlpmorpython -m pip install -e ".[dev]"depending on whether you add new dependencies to the project.
Package: algovera-ocean-lib
ocean.pyPython library to privately & securely publish, exchange, and consume data.With ocean.py, you can:Publishdata services: downloadable files or compute-to-data. Create an ERC721data NFTfor each service, and ERC20datatokenfor access (1.0 datatokens to access).Selldatatokens via for a fixed price. Sell data NFTs.Transferdata NFTs & datatokens to another owner, andall other ERC721 & ERC20 actionsusingweb3.py,Brownie, etc.ocean.py is part of theOcean Protocoltoolset.This is in beta state. If you run into problems, please open up anew issue.🏗 Installation🏄 Quickstart: simple flow, marketplace, compute-to-data, more🦑 Development🏛 License🏗 Installation#Installtheocean.pylibrary.Allowpre-releasesforthelatestv4version.Installwheelfirsttoavoiderrors.pip install wheelpip install --pre ocean-lib⚠️ Mac users: if you encounter an "Unsupported Architecture" issue, then install including ARCHFLAGS:ARCHFLAGS="-arch x86_64" pip install ocean-lib. [Details.]🏄 QuickstartHere are flows to try out, from simple to specific detailed variants.Simple flow- the essence of Ocean - creating a data NFT & datatoken.Publish flow- a dataset is published.Consume flow- a published dataset is consumed (downloaded).Marketplace flow- a data asset is posted for sale at fixed rate, then purchased.Dispenser flow- here, a datatoken dispenser is created and datatokens are dispensed for free.Compute-to-data flow- uses C2D to build an AI model a dataset that never leaves the premises.Key-value database- use data NFTs to store arbitrary key-value pairs on-chain.Profile NFTs- enable "login with Web3" where Dapp can access private user profile data.Learn moreGet test OCEAN- from rinkebyUnderstand config parameters- envvars vs filesLearn about off-chain services- Ocean Provider for data services, Aquarius metadata storeLearn about wallets- on generating, storing, and accessing private keysGet an overview of ocean.py- key modules and functions🦑 DevelopmentDevelopers flow- to further develop ocean.pyRelease process- to do a new release of ocean.py🏛 LicenseCopyright ((C)) 2022 Ocean Protocol Foundation

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.