Saravanan666/pypi-org huggingface dataset:
Package: alphanum-code
This module generates unique consecutive alphanumeric codes of specified size.
A comment can be associated with a code on request.Requirementspython >= 2.7SQLAchemyPackage depencies are included in alphanum_code package and will be automatically installed.
For more details,  seerequirements.txt.InstallInstall from PyPI:pip install alphanum_codeInstall from source:`bash git clonehttps://github.com/ylaizet/alphanum_codecd alphanum_code pip install-e. `Usage>>> from alphanum_code import AlphaNumCodeManager
>>> dbname = "sqlite:///test_alphanum.sqlite"
>>> manager = AlphaNumCodeManager(dbname)
>>> first_code = manager.next_code("with comment")
>>> print("my first code:", first_code)NotesAlphanumeric order is digits then letters :0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ.Letters in the alphanumeric code are UPPERCASE.TipsAtmanagerinstanciation, you can set:code_sizeto specify the lenght of the code you want to generate each timeinit_codeto specify the starting point for code generationTestInstall Pytestpip install pytestRun test from base directorypython -m pytest tests/
Package: alpha-orm
alpha-ormAn extraordinary python database ormFeaturesAutomatically creates tables and columns.No configuration required, simply create database.Currently supported databases include mysql.ExamplesSetup (MySQL)importalphaorm.AlphaORMasDBDB.setup('mysql',{'host':'localhost','user':'root','password':'','database':'alphaorm'})CREATE#--------------------------------------#	CREATE 1#--------------------------------------product=DB.create('product')product.name='Running shoes'product.price=5000DB.store(product)#--------------------------------------#	CREATE 2#--------------------------------------author=DB.create('author')author.name='Chimamanda Adichie'book=DB.create('book')book.title='Purple Hibiscus'book.author=authorDB.store(book)READ#--------------------------------------#	READ 1 [get all records]#--------------------------------------books=DB.getAll('book')forbookinbooks:print(f'{book.title}by{book.author.name}')#--------------------------------------#	READ 2 [filter one]#--------------------------------------book=DB.find('book','id = :bid',{'bid':1})print(f'{book.title}by{book.author.name}')#--------------------------------------#	READ 3 [filter all]#--------------------------------------author=DB.find('author','name = :authorName',{'authorName':'William Shakespare'})booksByShakespare=DB.findAll('book','author_id = :authorId',{'authorId':author.getID()})print('Books by William Shakespare are :')forbookinbooksByShakespare:print(book.title)UPDATE#--------------------------------------#	UPDATE#--------------------------------------product=DB.find('product','id = :pid',{'pid':1})product.price=500book=DB.find('book','id = :bid',{'bid':1})book.author.name='New author'book.isbn='3847302-SD'book.title='New Title'DB.store(book)print(book)DELETE#--------------------------------------#	DELETE 1 [delete single record]#--------------------------------------book=DB.find('book','id = :bid',{'bid':1})DB.drop(book)#--------------------------------------#	DELETE 2 [delete all records]#--------------------------------------DB.dropAll('book')
Package: alpha-pca
Alpha-PCAAlpha-PCA is more robust to outliers than standard PCA.Standard PCA is a special case of alpha PCA (when alpha=1).UsageUsageThe model is inherited from a sklearn module and works the same way as thestandard PCA.It also supportsPyTorchtensors (on cpu and GPU).fromalpha_pcaimportAlphaPCAimporttorchX=torch.randn(16,10)# also works with numpypca=AlphaPCA(n_components=5,alpha=0.7,random_state=123)# alpha=1 -> standard PCApca.fit(X)# to project X in the latent spaceX_transformed=pca.transform(X)# (16, 10) -> (16, 5)# fit inverseX_=pca.inverse_transform(X_transformed)# (16, 5) -> (16, 10)# directly approximate X_ == inverse_transform(transform(X))X_=pca.approximate(X)# (16, 10) -> (16, 10)# Find the optimal alpha via a reconstruction lossbest_alpha=pca.compute_optimal_alpha(X,n_components=5)
Package: alphaquantum
============
AlphaQuantum.. image::https://img.shields.io/pypi/v/alphaquantum.svg:target:https://pypi.python.org/pypi/alphaquantum.. image::https://img.shields.io/travis/antobzzll/alphaquantum.svg:target:https://travis-ci.com/antobzzll/alphaquantum.. image::https://readthedocs.org/projects/alphaquantum/badge/?version=latest:target:https://alphaquantum.readthedocs.io/en/latest/?version=latest:alt: Documentation StatusOpen source algorithmic trading platformFree software: Apache Software License 2.0Documentation:https://alphaquantum.readthedocs.io.FeaturesTODOCreditsThis package was created with Cookiecutter_ and theaudreyr/cookiecutter-pypackage_ project template... _Cookiecutter:https://github.com/audreyr/cookiecutter.. _audreyr/cookiecutter-pypackage:https://github.com/audreyr/cookiecutter-pypackage
Package: alphaserve
Project AlphaServecurrently running on python3 and FlaskIdeacreate a webserver, that receives control commands from clients using:Flaskis used for running the webserverHammer.jsserves implementations for mouse and click eventssocket.iolets us establish an TCP connection, so that the commands are sent fasterTODOSfix CSS for remote to properly render and disable scrolling when using the touchpadfix the scrolling baradd argument option for en-/disabling the debug mode
Package: alphastats
DocumentationStreamlit WebAppAn open-source Python package for downstream mass spectrometry downstream data analysis from theMann Group at the University of Copenhagen.CitationInstallationTroubleshootingLicenseHow to contributeChangelogCitationPublication:AlphaPeptStats: an open-source Python package for automated and scalable statistical analysis of mass spectrometry-based proteomicsCitation:Krismer, E., Bludau, I.,  Strauss M. & Mann M. (2023). AlphaPeptStats: an open-source Python package for automated and scalable statistical analysis of mass spectrometry-based proteomics. Bioinformaticshttps://doi.org/10.1093/bioinformatics/btad461InstallationAlphaPeptStats can be used aspython library (pip-installation), orGraphical User Interface (either pip-installation or one-click installer).Further we provide a Dockerimage for the GUI.Pip InstallationAlphaStats can be installed in an existing Python 3.8/3.9/3.10 environment with a singlebashcommand.pipinstallalphastatsIn case you want to use the Graphical User Interface, use following command in the command line:alphastatsguiAlphaStats can be imported as a Python package into any Python script or notebook with the commandimport alphastats.
A briefJupyter notebook tutorialon how to use the API is also present in thenbs folder.One Click InstallerOne click Installer for MacOS, Windows and Linux can be foundhere.Docker ImageWe provide two Dockerfiles, one for the library and one for the Graphical User Interface.
The Image can be pulled from Dockerhubdockerpullelenakrismer/alphapeptstats_streamlitGUITroubleshootingIn case of issues, check out the following:Issues: Try a few different search terms to find out if a similar problem has been encountered beforeLicenseAlphaStats was developed by theMann Group at the University of Copenhagenand is freely available with anApache License. External Python packages (available in therequirementsfolder) have their own licenses, which can be consulted on their respective websites.How to contributeIf you like this software, you can give us astarto boost our visibility! All direct contributions are also welcome. Feel free to post a newissueor clone the repository and create apull requestwith a new branch. For an even more interactive participation, check out thediscussionsand thethe Contributors License Agreement.ChangelogSee theHISTORY.mdfor a full overview of the changes made in each version.FAQHow can I resolve the Microsoft visual error message when installing: error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools"?Please, find a description on how to update required toolshere.How to resolve ERROR:: Could not find a local HDF5 installation. on Mac M1?Before installing AlphaPeptStats you might need to install pytables first:conda install -c anaconda pytables
Package: alphatools-jv
About projectA small wrapper over smartapi to backtest basic trading strategies.
Easy installation and usageInstallationpip install alphatools_jvUsageCreating and running a strategyfromalphatools.backtesting_appimportBackTestingAppfromdatetimeimportdatetimeclassTestSmartApiApp(BackTestingApp):defon_md(self,data_row):# your strat code goes hereprint("New row found:{}".format(data_row))app=TestSmartApiApp('/Users/jaskiratsingh/projects/smart-api-creds.ini')app.set_start_date(datetime.strptime('2022-12-20 11:39:00+05:30','%Y-%m-%d%H:%M:%S%z'))app.set_end_date(datetime.strptime('2022-12-29 11:39:00+05:30','%Y-%m-%d%H:%M:%S%z'))app.set_interval('ONE_MINUTE')app.add_instrument(53825,"NFO")app.add_instrument(48756,"NFO")app.load_data()# Loads the data into a dataframeapp.get_candle_info_df()# Returns the entire simulation dataframeapp.simulate()# Starts simulation from the beginning# To place a trade, use trade api to send the orders to the pnl calculator.# Pnl calculator uses last tick prices to calculate the observed Pnlapp.trade(53825,1)# Buys 1 unit for token 53825app.trade(53825,-3)# Sells 3 units for token 53825app.get_total_pnl()# Returns pnl after all trades are madeHelper for instrumentsfromalphatools.utils.token_managerimportTokenManagertok=TokenManager()# Refer documentation for more overridesinfo=tok.get_instrument(53825)# Returns instrument infoinfo=tok.get_instrument('NIFTY23FEB23FUT')# Returns instrument info
Package: alpha-trainer
Alpha Trainer - Machine Learning Game Training FrameworkThe Alpha Trainer package is a versatile framework designed for training machine learning models for custom games. It simplifies the process of training and evaluating models on custom game environments. This README provides an overview of the package and focuses on the main function,simulate_game.InstallationYou can install thealpha_trainerpackage using pip:pipinstallalpha_trainerThe "Example" section now includes a code snippet showing how to use thesimulate_gamefunction within the README.md file. You can customize the example to match your specific use case and provide more detailed information as needed.ExampleHere's an example of how to use thesimulate_gamefunction:fromalpha_trainerimportsimulate_game,AlphaTrainableGame,AlphaMove# Define your custom game classclassMyGame(AlphaTrainableGame):# Implement your custom game logic here# Simulate a game and collect datagame_results=simulate_game(MyGame,num_simulations=1000,model=my_model)# Use the collected data to train and evaluate your machine learning model# ...
Package: alphaturtle
> To Draw Alphabet And Number Using Turtle ModuleAbout Turtle:This project is made using in-built python library called “Turtle”.Turtle graphics is a popular way for introducing programming to kids.It was part of the original Logo programming language developed by Wally Feurzig and Seymour Papert in 1966.InstallationGui Installation(How To Use):gitclonehttps://github.com/C0DE-SLAYER/alphaturtlecdalphaturtlepipinstall-rrequirement.txtpythonalphaturtle_gui.pyTo Use Function On Your Own / CLI(How To Use):pipinstallalphaturtleUsage/ExamplesGui Usage :1.Run usingpython alphaturtle_gui.py2.Fill All The Requried Field3.Hit The Draw Button :CLI Usage :If you have install alphaturtle using pip just run this in terminal/powershell/cmd :alphaturtle -i pythonFor help runalphaturtle -hUse Function Own :Include alphaturtle in your project :from alphaturtle import * # import aplhaturtle 
import turtle # importing turtle module
SetVar(2, 5, 20, 'black', 'yellow', 0) # setting values

drawLetterP() # calling function to draw P 
whiteSpace()  # drawing whitespace
drawLetterY() # calling function to draw Y 
whiteSpace()  # drawing whitespace
drawLetterT() # calling function to draw T 
whiteSpace()  # drawing whitespace
drawLetterH() # calling function to draw H 
whiteSpace()  # drawing whitespace
drawLetterO() # calling function to draw O 
whiteSpace()  # drawing whitespace
drawLetterN() # calling function to draw N 

turtle.hideturtle() # turtle method to hide the cursor
turtle.done() # To see the outputLimitationsThe code works fine for all capatial alphabets and numericals ,but not for special characters except dot and hipanLicenseMIT
Package: alphatwirl
A Python library for summarizing event data into multivariate categorical dataDescriptionAlphaTwirlis a Python library that summarizes event data into multivariate categorical data as data frames. Event data, input to AlphaTwirl, are data with one entry (or row) for one event: for example, data inROOTTTreeswith one entry per collision event of anLHCexperiment atCERN. Event data are often large—too large to be loaded in memory—because they have as many entries as events. Multivariate categorical data, the output of AlphaTwirl, have one row for one category. They are usually small—small enough to be loaded in memory—because they only have as many rows as categories. Users can, for example, import them as data frames intoRandpandas, which usually load all data in memory, and can perform categorical data analyses with a rich set of data operations available in R and pandas.Quick startJupyter Notebook:Quick start of AlphaTwirlPublicationTai Sakuma,"AlphaTwirl: A Python library for summarizing event data into multivariate categorical data",
EPJ Web of Conferences214, 02001 (2019),doi:10.1051/epjconf/201921402001,1905.06609LicenseAlphaTwirl is licensed under the BSD license.ContactTai Sakuma -tai.sakuma@gmail.com
Package: alphav
alphavalpha vantage api wrapperDescriptionusing a symbol object that pulls the data once the property is accessed.The data is then saved for the next property calls.ExamplefromalphavimportSymbolimportos# generate symbolapikey=os.environ.get('API_KEY')s=Symbol('IBM',apikey)# print the data it providesprint(s.balance_sheet)print(s.earnings)print(s.income_statement)print(s.cash_flow)print(s.overview)print(s.global_quote)print(s.time_series_daily)print(s.time_series_monthly)print(s.time_series_monthly_adjusted)print(s.time_series_weekly)print(s.time_series_weekly_adjusted)The properties are of data object, supporting# for balance sheet dataprop.main# the main data slice, annual by defaultprop.annual# annual data when proviededprop.quarterly# quarterly data when providedprop.set_main('quarterly')prop.main# will be prop.annual# for the rest of the dataprop.main# only
Package: alphavantage
alphavantagealphavantage is a Python wrapper for the Alpha Vantage API.The API wrapper can be used to retrieve historical prices such as intraday or daily prices for global equities and ETFs.StatusThe API aims to support equity time-series data as a first step.The package is currently in alpha status. It has not been used extensively yet and therefore mainly of the potential quirks of Alpha Vantage's actual API may not be accounted for. We plan on using this wrapper for price history charting in ourcompany lookup and ratings tool.Design ConsiderationThis library is intended to provide a simple wrapper with minimal dependencies, and does not intend to introduce pydata stack dependencies (numpy, pandas, etc.) in the future. Differences with existing wrappers for the Alpha Vantage API include:Library DifferencesNo Pandas dependencies or optional dependencyFocuses on simplifying data for ingestingAvoids logical branching making the code simpler (only two if statements at moment)Provides symbology mapping referencesThe library carries out some conveniences versus using the API without a wrapper.ConveniencesConverts timestamps to UTC time when applicable.Simplifies record field names i.e. "4. close" -> "close".Appends the timestamp field to record vs. having the timestamp act as dictionary key.Uses time ascending list versus a dictionary for price record data structure.Returns multiple tickers over a given parameter set using threads.Maps ticker symbology from other vendors.Excludes intraday data in daily price history requests.Examplesfromalphavantage.price_historyimport(AdjustedPriceHistory,get_results,PriceHistory,IntradayPriceHistory,filter_dividends)# weekly priceshistory=PriceHistory(period='W',output_size='compact')results=history.get('AAPL')# intraday prices, 5 minute intervalhistory=IntradayPriceHistory(utc=True,interval=5)results=history.get('AAPL')# adjusted daily priceshistory=AdjustedPriceHistory(period='D')results=history.get('AAPL')dividends=list(filter_dividends(results.records))# Return multiple tickersparameters={'output_size':'compact','period':'D'}tickers=['AAPL','MSFT']results=dict(get_results(PriceHistory,tickers,parameters))ContributingContributions are welcome. Someone can immediately contribute by building out wrappers for the rest of the API such as FX rates or crypto prices.Getting StartedInstallingpipinstallalphavantageDeveloper InstallationThese instructions assume Python 3.6. It is recommended that you use conda or a virtualenv.For conda install follow:Download theconda installer.
And follow setupinstructions.Conda Environmentcondacreate--name<environment_name>python=3.6
activate<environment_name>
condainstall--filerequirements.txt

pythonsetup.pyinstallbdist_wheeldebian installationInstructionFollow the instructions in the link provided.DO NOT SUDO PIP INSTALL. Alias the preferred Python installation by adding, for example:aliaspython='/usr/bin/python3.6'When using Pippipinstall--upgradepip
pipinstallwheel
pipinstall-rrequirements.txt

pythonsetup.pyinstallbdist_wheelRunning the Testspy.testRunning Coverage Reportpy.test--cov
Package: alpha-vantage-cli
Command line interface for Alpha Vantage APIs (WIP)Command line interface to get stock data from the Alpha Vantage APIAlpha Vantage offers an API for financial data and other popular finance indicators.
This library provides a series of commands that you can use to query the API from your terminal in an easy way.Getting startedGet an alpha vantage free api key. Visithttp://www.alphavantage.co/support/#api-keyInstallalpha-vantage-cli:pipinstallalpha-vantage-cliSet your api key:av set-keyTry it out:avstockquoteibmUsage examplesav--helpOutput:Usage: av [OPTIONS] COMMAND [ARGS]...

  Unofficial Alpha Vantage command line interaface.

  Get stocks data from the command line.

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  crypto   Manages the Cryptocurrences APIs (Not yet implemented)
  data     Manages the Fundamental Data APIs (Not yet implemented)
  econ     Manages the Economic Indicators APIs (Not yet implemented)
  forex    Manages the Forex APIs (Not yet implemented)
  intel    Manages the Alpha Intelligence APIs (Not yet implemented)
  set-key  Set your API key so that you can send requests to Alpha...
  stock    Manages the Core Stocks APIs
  tech     Manages the Technical Indicators APIs (Not yet implemented)Get quote for stockavstockquoteaaplSample output:{'Global Quote': {'01. symbol': 'AAPL', '02. open': '151.2100', '03. high': '151.3500', '04. low': '148.3700', '05. price': '150.7000', '06. volume': '162278841', '07. latest trading day': '2022-09-16', '08. previous close': '152.3700', '09. change': '-1.6700', '10. change percent': '-1.0960%'}}Download monthly data as CSVavstockmonthlyibm--datatype=csv>ibm.csv
Package: alpha-vantage-py
Alpha Vantage Pythonalpha_vantageis a simple Python wrapper around the Alpha Vantage API:fromalpha_vantageimportClientfromalpha_vantage.functionsimportTimeSeries# setup API client and TimeSeries interface for retrieving IBM stock dataclient=Client("API_TOKEN")ts=TimeSeries(client=client,symbol="IBM")# retrieve historic stock data on a daily leveldaily=ts.daily()# loop over resultsfordayindaily.timeseries:print(f"-{day.timestamp}:{day.high}")# close clientclient.close()Installationalpha_vantage is available on PyPi:$python-mpipinstallalpha-vantage-pyFeaturesEasy integration with your Python projectAlpha Vantage endpoints wrapped in functions
Package: alpha-viewer
alpha_viewerClass to view Alphafold modelsalpha_viewer is based on the alphafold2 colab notebook visualization. It automatically chooses the best available prediction. alpha_viewer includes functions to plot PAE (plot_pae) and pLDDT (plot_pLDDT). It also allows for coloring of the py3Dmol view based on pLDDT withshow_confidence. alpha_viewer contains.obs, a pandas data-frame, that can be used for custom annotations based on aa postion within the chain(s). You can use a key of.obsto color the py3Dmol view of your protein(s) based that annotation withshow_annotation. This can also be used to inspect substructures withshow_substructures.alpha-viewer has been tested to work withmonomer,monomer_ptmandmultimermodels.
So far it's sadly not possible to save the py3Dmol view for exporting.If you use alpha_viewer in your publication please cite:Installyou can install this repository from pypi with:pip install alpha-viewerIt's recommended to use alpha_viewer within jupyterlab. Please enable jupyterlab extensions and installjupyter-widgets/jupyterlab-managerandjupyterlab_3dmolA tutorial notebook can be found inhttps://github.com/Intron7/alpha_viewer/tree/main/tutorials
Package: alphawaves
Alpha Waves DatasetRepository with basic scripts for using the Alpha Waves Dataset developed at GIPSA-lab [1]. The dataset files and their documentation are all available athttps://zenodo.org/record/2348892The code of this repository was developed inPython 3.9 and 3.10using MNE-Python [2, 3] as a tool for the EEG processing.The package can be downloaded with pip:pip install alphawavesAlternatively, you might want to clone the GitHub repository on your local computer, and install the package by running:python setup.py developAll dependencies are listed inrequirements.txtfor your interest.Then, to ensure that your code finds the right scripts, open a Python shell and type:import alphawavesNote that you might want to create avirtual environmentbefore doing all these installations, e.g.:conda create -n eegalpha python=3.9References[1] Cattan et al. "EEG Alpha Waves dataset"DOI[2] Gramfort et al. "MNE software for processing MEG and EEG data"DOI[3] Gramfort et al. "MEG and EEG data analysis with MNE-Python"DOI
Package: alphaz-next
The alphaz-next library is a Python library that is designed to simplify the setup of RESTAPi using FastAPI & pydantic. It provides some usefull toolkit to setup Logger, Config, ...Installing Alphaz-NextTo install alphaz-next, if you already have Python, you can install with:pip install alphaz-nextHow to import alphaz-nextTo access alphaz-next and its functions import it in your Python code like this:from alphaz-next import DataBase, Logger
from alphaz-next.models.config.config_settings import create_config_settings
Package: alphinity
alphinityalphinity encodes integers to alphabetize in ascending order.fromalphinityimportencodeencode(100)# -> "ccv"# passesassertsorted(map(encode,range(10**6)))==[*map(encode,range(10**6))]For stable encoding,fromalphinity.v1importencodeNotesAlphabetization doesn't work strictly infinitely, just through very, very high integers i.e.,1e35.Non-negative integers only.