Saravanan666/pypi-org huggingface dataset:
Package: algobattle-base
Algorithmic BattleThe lab course "Algorithmic Battle" is offered by theComputer Science Theory group of RWTH Aachen Universitysince 2019. This repository contains the necessary code and
documentation to set up the lab course yourself.In an Algorithmic Battle, pairs of teams compete against one another
in order to solve a problem of your choice, e.g. a problem from the
class NP. The teams each design agenerator, that outputs
hard-to-solve instances for a given instance size, as well as asolverthat accepts an instance and outputs a solution to it as
quickly as possible.The framework is written to be completely language-agnostic regarding
the code of thegeneratorand thesolver, as each is wrapped in a
docker container that only needs to adhere to an I/O structure of your
choice (by default, in the form ofjson-files.)If you are interested in how to use the framework for a
lab course of your own, please consult the teaching concept in
thedocumentation.Installation and UsageThis project is developed and tested on all major operating systems.Please consult the officialdocumentationfor detailed instructions on installation and usage.Related projectsThis repository only includes the core framework for executing anAlgorithmic Battle. For a selection of concrete problems that you
can use to play around with the framework, have a look at thealgobattle-problemsrepository. These are problems that have been posed to students in
some form over the past years.While the framework provides all essential tools to host a tournament
yourself, e.g. in the form of a lab course, you may be interested in
thealgobattle-webproject.  Thealgobattle-webproject implements a webframework with
which you are able to comfortably manage your students teams and their
code, your problem files and their documentation as well as schedule
matches to be fought between registered student teams, using thealgobattleAPI.ContributingWe welcome any input on how to make this project accessible to as many
people as possible. If you have feedback regarding the usage of the
framework, the documentation or would even like to help us out with
corrections, new features, or translations, feel free to open an issue
or pull request. We have developed this project on the basis of
practical experience inside our lab courses, thus some design elements
may be unintuitive to you. Feel free to point out anything that
appears odd to you.FundingThe development of version4.0.0was funded byStiftung Innovation in der Hochschullehre(ProjectFRFMM-106/2022 Algobattle) and by theDepartment of Computer Science of
RWTH Aachen University.
Package: algobowl
AlgoBOWLAlgoBOWL is a group project for algorithms courses.  Students compete to create
heuristics to an NP-hard problem.  For more information, see the paper inITiCSE 2019.This is the AlgoBOWL web application, as well as associated tools (e.g., command
line interface).Getting StartedThe rest of thisREADMEassumes you're interested in hacking on the AlgoBOWL
code, and want to install the web app locally.  For other topics of interest,
check out thedocs/directory.You'll need a system running Linux and Python 3.8+.Create and activate a virtual environment to install in:$ python3 -m venv venv
$ . venv/bin/activateNext, install the app in editable mode::$ pip install -e ".[dev]"Next, copy the sample development config and setup the application::$ cp development.ini.sample development.ini
$ gearbox setup-appFinally, you can serve the app::$ gearbox serve --reload --debugHave fun!
Package: algobra
ALGOBRAThis is an algorithmic trading engine used to backtest, model, and develop trading strategies on real time/historic stock data.InstallationRun the following to install:pipinstallalgobraUsagefromalgobraimportengine# Systems check to see if your machine can utilitize native performance improvements.engine.optimize()# See engine informationprint(engine.info())Developing AlgobraTo install algobra, along with the tools you need to develop and run tests, run the following in your virtualenv.pipinstall-e.[dev]ConceptsTrading System DevelopmentTrading System DesignTrading System EnvironmentTime Series AnalysisOptimizationPerformance MeasurementsRisk ManagementTrading Strategy ImplementationExecutionConfiguring DBmysql -u root -pmysql> CREATE DATABASE securities_master;
mysql> USE securities_master;
mysql> CREATE USER ’sec_user’@’localhost’ IDENTIFIED BY ’password’;
mysql> GRANT ALL PRIVILEGES ON securities_master.* TO ’sec_user’@’localhost’;
mysql> FLUSH PRIVILEGES;Referenceschemas.sqlfor the commands to create needed tables
Package: algo-crawler
Algo CrawlerUsageInstall Packagepipinstallalgo_crawlerCrawl Solutionpython3-malgo_crawler-sBJ-ukoosaga# {'user_id': 'koosaga', 'problem_codes': ['1000', '1001' ...]Crawl Problempython3-malgo_crawler-sBJ-p1000# {'code': 'LC_1', 'level': '1', 'link': 'https://leetcode.com/problems/two-sum', 'title': 'Two Sum'}Developer SettingDeveloper settingpipinstall-rrequirements.txt
pre-commitinstallTestpython3-malgo_crawler.test
Package: algoesup
AlgoesupThis small library was written to support the writing ofalgorithmic essays,
but can be used independently of that purpose.
The library provides auxiliary functions that make it easier to:write unit tests for functionsmeasure and plot run-times for best, average and worst case inputsuse linters within Jupyter Notebook environments.Guidance on how to use the library ishere.To install in the global Python environment, open a terminal or PowerShell, and enterpipinstallalgoesupTo install in a virtual environment, activate it before entering the command above.The library supports theruffandallowedlinters, and thepytypetype checker.
You have to install them explicitly if you want to use them from within a notebook:pipinstallruffallowedpytypeNote thatpytypeis not available for Windows.Licencealgoesupis Copyright © 2023–2024 by The Open University, UK.
The code is licensed under aBSD 3-clause licence.
The documentation is licensed under aCreative Commons Attribution 4.0 International Licence
Package: algofunc
This library consists of different sorting and searching algorithms to make it
easier to use these algorithms in your code without having to type them out yourself.Library includes the following functions:binary_search( array, target )linear_search( array, target )fibonacci_search( array, target )jump_search( array, target )linear_search( array, target )ternary_search( array, target )bitonic_sort( array )bucket_sort( array )bubble_sort( array )cocktail_sort( array )comb_sort( array )counting_sort( array )cycle_sort( array )gnome_sort( array )heap_sort( array )merge_sort( array )odd_even_sort( array )pancake_sort( array )quick_sort( array )radix_sort( array )selection_sort( array )shell_sort( array )timsort( array )
Package: algoind
ALGOIND: A Technical Indicators collection in Python.🔎 What is italgoindis python3 package that contains technical indicators for backtesting and for implementing trading strategies in Python3.😃 Who I amMy name is Matteo, a 21 years old FinTech student.I created this library for my personal usage and I decided publish it because it think can be useful for someone.👋 You can find mycontacts here.The source code is currently hosted on GitHub at:https://github.com/matteoincremona/algoind/Thanks toInvestopedia.comthat provided me a vast amount of knowledge to be able to create this library.💻 How to Install it# condacondainstall-cconda-forgealgoind# PyPIpipinstallalgoind📈 FeaturesThis is the list of all the indicatorsalgoindcontains:Single Moving Average (SMA)Esponential Moving Average (EMA)Average True Range (ATR)Relative Strenght Index (RSI)Upper Bollinger Bands (BBU)Lower Bollinger Bands (BBL)Mid Bollinger Bands (BBM)Moving Average Convergence Divergence (MACD)Moving Average Convergence Divergence Signal (MACDsignal)✅ Example: How to use it# After the installation of the package:importalgoind
fromalgoindimportindicatorsasind# Let's try SMA: what should we know about it?help(ind.SMA)# Let's try SMA that takes, for example:# - The close prices of a df: "df.Close"# - The period for the calculation of the SMA: "20"SMA20=ind.SMA(df.Close,20)# To see the values of the indicator:SMA20⚙️ Discussion and DevelopmentI will be very enthusiastic if somebody would like to help me with this project.Contact meif you have anyproblemor if you want me to addnew indicators.Thank you.
Package: algokit-utils
AlgoKit Python UtilitiesA set of core Algorand utilities written in Python and released via PyPi that make it easier to build solutions on Algorand.
This project is part ofAlgoKit.The goal of this library is to provide intuitive, productive utility functions that make it easier, quicker and safer to build applications on Algorand.
Largely these functions wrap the underlying Algorand SDK, but provide a higher level interface with sensible defaults and capabilities for common tasks.NoteIf you prefer TypeScript there's an equivalentTypeScript utility library.Install|DocumentationInstallThis library can be installed using pip, e.g.:pip install algokit-utilsGuiding principlesThis library follows theGuiding Principles of AlgoKit.ContributingThis is an open source project managed by the Algorand Foundation.
See theAlgoKit contributing pageto learn about making improvements.To successfully run the tests in this repository you need to be running LocalNet viaAlgoKit:algokit localnet start
Package: algol-bayer
Capture, display and convert RAW DSLR astronomical imagesThis package was developed for astro-spectroscopic student projects
at the Dresden Gönnsdorf observatory.Installationapt-get install gphoto2
pip install algol-bayerCapture and display$ bayer_capture_sequence.sh 

    usage: bayer_capture_histograms.sh min-exp-time-s max-exp-time-s
    
           Create and display histograms by doubling the exposure times
           between min and max exposure time.
           Example: bayer_capture_histograms.sh 30 240 will create histograms for exposures of 30, 60, 120 and 240 seconds.


$ bayer_capture_sequence.sh 

    usage: bayer_capture_sequence.sh object image-count exp-time-s
    
           Capture and image sequence and store them as raw images.
    
           Example: bayer_capture_sequence.sh zetori 20 1800 will capture 20 halve hour exposures zetori_1800_00.cr2, zetori_1800_01.cr2, ...ConvertTBDLinkshttp://gphoto.org/https://pypi.org/project/rawpy/https://pypi.org/project/algol-reduction/
Package: algoliaqb
A library to help build queries for searching Algolia.This package is meant to be used with thealgoliasearchlibrary.Problem:An app I have been working on has a few points of code duplication and increased complexity. This package is a way for me to fix those issues and increase readability.Installationpip install algoliaqbFilter StringsBy defaultfilter_mapconsiders all input as strings. This is pretty straight forward. Lets say for example you have the following filter map:filter_map = {
    "group_id": "tenant_id",
}with the followingflask.args:{
    "group_id": 1
}AlgoliaQB will look forgroup_idwithinflask.args, grab the value, then remap the key and value for you.tenant_id:1Now lets try an example with a few more filters.filter_map = {
    "group_id": "tenant_id",
    "is_reported": "is_reported"
}with the followingflask.args:{
    "group_id": 1,
    "is_reported": "true
}The returned filter_string is as follows.tenant_id:1 AND is_reported:trueLibrary Usage Example:fromalgoliasearch.search_clientimportSearchClientfromalgoliaqbimportAlgoliaQueryBuilderfromflaskimportrequestaqb=AlgoliaQueryBuilder(search_param="search",filter_map={"group_id":"tenant_id","is_reported":"is_reported"})# Extract the search query from our flask apps request.args var.search_query=aqb.get_search_query(request.args)# Get the filter query from our request args.filter_query=aqb.get_filter_query(request.args)# Now that we have the filter string, we just pass it into the search function.search_client=SearchClient()index=search_client.init_index("contacts")results=index.search(search_query,{"filters":filter_query})
Package: algoliasearch
The perfect starting point to integrateAlgoliawithin your Python projectDocumentation•Django•Community Forum•Stack Overflow•Report a bug•FAQ•Support✨ FeaturesThin & minimal low-level HTTP client to interact with Algolia's APISupports Python from3.4to3.11Contains blazing-fast asynchronous methods built on top of theAsyncio💡 Getting StartedFirst, install Algolia Python API Client via thepippackage manager:pipinstall--upgrade'algoliasearch>=3.0,<4.0'Then, create objects on your index:fromalgoliasearch.search_clientimportSearchClientclient=SearchClient.create('YourApplicationID','YourAPIKey')index=client.init_index('your_index_name')index.save_objects([{'objectID':1,'name':'Foo'}])Finally, you may begin searching a object using thesearchmethod:objects=index.search('Fo')For full documentation, visit theAlgolia Python API Client.❓ TroubleshootingEncountering an issue? Before reaching out to support, we recommend heading to ourFAQwhere you will find answers for the most common issues and gotchas with the client.Use the DockerfileIf you want to contribute to this project without installing all its dependencies, you can use our Docker image. Please check ourdedicated guideto learn more.📄 LicenseAlgolia Python API Client is an open-sourced software licensed under theMIT license.
Package: algo-lib
Algo-lib (the Algorithm Library)This is a simple example package. It contains two searching algorithms. The purpose of this repo is to practice packaging a library for distribution.For package distribution information and release history, see:https://pypi.org/project/algo-lib/Quick StartInstall algo-lib from the command line using pip.python3 -m pip install --upgrade algo-libExample UsageBinary Searchfromalgo_libimportsearchlst=[1,50,99,150,40000]targetValue=99#binary search returns the index of a target value if present in a sorted listtargetIndex=search.binary(lst,0,len(lst)-1,targetValue)#targetIndex is 2Linear Searchfromalgo_libimportsearchlst=[5,1,2,100,41,-1]targetValue=-1#linear search returns the index of a target value if present in listtargetIndex=search.binary(lst,targetValue)#targetIndex is 5Merge Sortfromalgo_libimportsortlst=[5,1,2,100,41,-1]#merge sort takes a list argument, and sorts it in either increasing or decreasing ordertargetIndex=sort.merge(lst)#lst is [-1, 1, 2, 5, 41, 100]
Package: algomart
AlgoMartCollection of algorithms in pythonSorting AlgorithmBubble Sort -bubble_sortInsertion Sort -insertion_sortSelection Sort -selection_sortQuick Sort -quick_sortMerge Sort -merge_sortHeap Sort -heap_sortCounting Sort -counting_sortRadix Sort -radix_sortSample Codefromalgomart.algorithm.sortimportbubble_sortprint(bubble_sort([3,2,1]))
Package: algomatefix
This package is fork of QuickFIX for AlgoMate Inc, providing additionalfeatures, such as dynamic session creation.The following License is provided as a requirement from QuickFIX:The QuickFIX Software License, Version 1.0Copyright (c) 2001-2018 Oren MillerRedistribution and use in source and binary forms, with or withoutmodification, are permitted provided that the following conditionsare met:1. Redistributions of source code must retain the above copyrightnotice, this list of conditions and the following disclaimer.2. Redistributions in binary form must reproduce the above copyrightnotice, this list of conditions and the following disclaimer inthe documentation and/or other materials provided with thedistribution.3. The end-user documentation included with the redistribution,if any, must include the following acknowledgment:"This product includes software developed byquickfixengine.org (http://www.quickfixengine.org/)."Alternately, this acknowledgment may appear in the software itself,if and wherever such third-party acknowledgments normally appear.4. The names "QuickFIX" and "quickfixengine.org" mustnot be used to endorse or promote products derived from thissoftware without prior written permission. For writtenpermission, please contact ask@quickfixengine.org5. Products derived from this software may not be called "QuickFIX",nor may "QuickFIX" appear in their name, without prior writtenpermission of quickfixengine.orgTHIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESSED OR IMPLIEDWARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIESOF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE AREDISCLAIMED.  IN NO EVENT SHALL QUICKFIXENGINE.ORG ORITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOTLIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OFUSE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED ANDON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUTOF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OFSUCH DAMAGE.Download-URL: http://www.algomate.coDescription: UNKNOWNPlatform: UNKNOWN
Package: algomath-def
# This library is for test and portfolio
## Notes
Since it is a combination of learning and portfolio, please install at your own risk.
## How to use
will be available in “import filename”
## Available modules
Example “filename.functionname” can be used
##### mcd(Methods that return the greatest common divisor)argument(int a, int b)
x = filename.mcd(a,b)
Returns the greatest common divisor of type int## Other matters
We will add things that have been learned as functions at any time.
If you have any problems or anomalies, please let me know.
Package: algomax
Algomax-CLIA command line tools for working with EMAX API.install the package with bellow command:-> pip install algomaxuse bellow command for working with the package:-> algomax algorithm.py config.json -p params.json -m schedule.jsonExampleHere is a simple example of usingalgomaxalgorithm.pyimport os
import sys
import json
import math
from order import Order
import utils


def run_algorithm():
    config = utils.get_settings()
    trader_order = Order(config['broker_url'])

    params = utils.get_params()

    # algorithm
    data = fibonacci_algorithm(params['data'])

    # trade
    trader_order.create(data)


def isPerfectSquare(number: int): 
    squrare_number = int(math.sqrt(number)) 
    return squrare_number * squrare_number == number 


def isFibonacci(number): 
    return isPerfectSquare(5 * number * number + 4) or isPerfectSquare(5 * number * number - 4) 


def fibonacci_algorithm(data: dict):
    qunatity = data['quantity']
    if isFibonacci(qunatity):
        qunatity += 3
    else:
        qunatity -= 3

    data['quantity'] = qunatity
    return data

if __name__ == '__main__':

    run_algorithm()config.json{
    "broker_url": "http://your-broker.server",
    "access_token": "YoUr-ToKeN",
    "account_id": "your-account-id"
}params.json (your algorithm data){
    "data": {
        "agent_id": "2",
        "side_id": "1",
        "instrument_id": "instrument-id",
        "price": 1234.0,
        "quantity": 1934,
        "validity_type_id": "1",
        "validity_date": ""
    }
}mode.json (scheduling){
    "mode": "interval",
    "schedule": {
        "start_date": "2019-09-22",
        "end_date": "2019-10-23",
        "minutes_interval": 1,
        "start_time": "8:30",
        "end_time": "13:00"
    }
}Caution: utils.py, order.py -> usealgomax-enginepackage, but it's not ready yet ;)
Package: algomax-common
Algomax-CommonA common tools for working withalgomax-cliandalgomax-engine.install the package with bellow command:-> pip install algomax-commonLoggingAdding logging to your Python program is as easy as this:from algomax_common.logger import AMLoggerInitiate the loggeradd below code on top of your programs fileAMLogger.init()and, use it:AMLogger.info('order created successfully', extra=with_this_data)AMLogger.error('order creation failed', extra=order_data_and_error)Note: extra is a dictExplore log filesabove code creates a directory namedlogsin your project directory.log directory structure:logs
|____ error.json    # contains AMLogger.error() records
|____ info.json     # contains AMLogger.info() records
Package: algo-method-tools
algo-method-toolsalgo-method用の環境構築用コマンドラインツール入力例, 出力例を含むディレクトリを自動生成する.このプロジェクトの仕様は予告なしに変更される可能性があります (testコマンドに対応すると 変更されることが予想されます).インストール方法$pipinstallalgo-method-tools使い方$algo-tools--help
Usage:algo-tools[OPTIONS]TASK_NO

Arguments:TASK_NO[required]Options:--base-dirPATH[default:/home/yassu/algo-methods]--template-fileTEXT--helpShowthismessageandexit.使用例$algo-tools316writeinto/home/yassu/algo-methods/316
$cat~/algo-methods/316/
in_1.txtmain.pyout_1.txt
$cat~/algo-methods/316/main.py
$cat~/algo-methods/316/in_1.txt23123432$cat~/algo-methods/316/out_1.txt5# algo-tools/template.pyは自分用のテンプレートファイル$algo-tools316--base-dir~/repos/algo-tools--template-file~/algo-tools/template.py
writeinto/home/yassu/repos/algo-tools/316
$ls~/repos/algo-tools/316/
in_1.txtmain.pyout_1.txt
$cat~/repos/algo-tools/316/main.py
frompprintimportpprint
fromsysimportsetrecursionlimit,stdin
fromtypingimportDict,Iterable,Set
INF:int=2**62
...
$cat~/repos/algo-tools/316/in_1.txt23123432$cat~/repos/algo-tools/316/out_1.txt5オプション--template-file, -t: 使用するテンプレートファイルを指定する(指定しなければ使用されない)--base-dir, -b: ファイルを格納するディレクトリを指定する
Package: algo-ml
# This is Algo-ML
This is a machine learning module/library. Feel free to use any of the code.## How to use:
### Regression
Call the train function on the model, giving the data as the first input and there is no other input. The input data looks like this: [[x, y], [x, y], ……..].### KMeansClustering
Call the train function giving the data as input which should look like this [[x, y, ….], [x, y, ….], [x, y, ….], …….].### KNearestNeighbor
Give it data to train on (just stores the data), which takes the form [[x, y, ……, LABEL], [x, y, ……, LABEL], ………..]. Use the predict function then to predict a label
for a particular point.### That’s it! :)Change log0.0.1 (9/20/2020)
- - - - - - - - -
- First Release
Package: algomodule
Python Hashing Library for Coin AlgorithmsSupported Algorithms:Bcrypt (algomdule._bcrypt_hash)BitBlock (algomodule._bitblock_hash)Blake (algomodule._blake_hash)Dcrypt (algomodule._dcrypt_hash)Fresh (algomodule._fresh_hash)Groestl (algomodule._groestl_hash)Hefty1 (algomodule._hefty1_hash)Jackpot (algomodule._jackpot_hash)Keccak (algomodule._keccak_hash)Scrypt (algomodule._ltc_scrypt)Myriad Groestl (algomodule._mgroestl_hash)NeoScrypt (algomodule._neoscrypt_hash)Nist5 (algomodule._nist5_hash)Quark (algomodule._quark_hash)Qubit (algomodule._qubit_hash)Sha1 (algomodule._sha1_hash)Shavite3 (algomodule._shavite3_hash)Skein (algomodule._skein_hash)3S (algomodule._threes_hash)TWE (algomodule._twe_hash)x11 (algomodule._x11_hash)x13 (algomodule._x13_hash)x14 (algomodule._x14_hash)x15 (algomodule._x15_hash)x17 (algomodule._x17_hash)AuthorAhmed BodiwalaContributingAll hashing algorithms welcome either by requests or PR'sHelp fund developmentBTC (Segwit): 33NrS6heATC5cpbKf2xVBXhGjb5byjc84GBTC (Legacy): 17zjy73pGw7GvKoPVAno7HRNeXQ4B63z2EBCH: qp9jqyydj3p73ph8ehnwwygla3fztlf6yqa7pc6vuzBTC: AY5e47xJXfw9yYNM6smf9e3vG6182vzxr3DASH: XwekGtZq9sJrJJW3C3ZRA59uEHjFVnPpL1DGB: SUQSLkn4X12xMJomfVqvU4VBWUCop1GZTSDOGE: DJ1ZmrSQ8e92yZRg1XQNu2DRcQeUYaKkEjLTC: MSAkyZB96J4dRdVGoSYJ2nYXL4AvBYXEypNMC: N4gRvEF6HSx719tcJz2VLQhzSyKYeJxzZxVTC: 3PJrdRTgjoTx6SmMwQQWpVqbwJSvmPsXhFZEC: t1L6k83EcFqPuAUTHSKp4HeC94ZUAGzERPHETH: 0x03203fcb30774703d684FD3Ec5A4f9De39C8f907ETC: 0x62A12D574167d37713294fB82D9157aC511F264a