Saravanan666/pypi-org huggingface dataset:
Package: d2-nip-eval
D2 nip-evalD2 nip-eval is a package aimed to help in providing an API for converting nip expressions into python eval statementstranspile_nip_expressions(expressions: list[str]) -> list[str]transpile_nip_expression(expression: str) -> strkeep_item(expressions: list[str], item_data: HoveredItem)HoveredItemclassHoveredItem:Name:str# e.g., DAGGER, SPIRIT, CHILLING GRAND CHARM OF VITAQuality:str# e.g., gray, normal, magic, rare, set, unique, runeword, runeText:strBaseItem:dictItem:Union[dict,None]NTIPAliasType:intNTIPAliasClassID:intNTIPAliasClass:Union[int,None]NTIPAliasQuality:intNTIPAliasStat:Union[dict,None]NTIPAliasFlag:dictUsagePython Example:importrequestsfromd2_nip_to_evalimportlexerdata=requests.get(url='https://raw.githubusercontent.com/blizzhackers/kolbot/master/d2bs/kolbot/pickit/kolton.nip').textexpressions=lexer.transpile_nip_expressions(data.split('\n'))forexpressioninexpressions:print(expression)hovered_item={"Name":"War Traveler","Quality":"unique","Text":"WAR TRAVELER|BATTLE BOOTS|DEFENSE: 139|DURABILITY: 13 OF 48|REQUIRED STRENGTH: 95|REQUIRED LEVEL: 42|+25% FASTER RUN/WALK|ADDS 15-25 DAMAGE|+190% ENHANCED DEFENSE|+10 TO STRENGTH|+10 TO VITALITY|40% SLOWER STAMINA DRAIN|ATTACKER TAKES DAMAGE OF 10|50% BETTER CHANCE OF GETTING MAGIC ITEMS","BaseItem":{"DisplayName":"Battle Boots","NTIPAliasClassID":388,"NTIPAliasType":15,"NTIPAliasStatProps":{"72":18,"73":18,"31":{"min":39,"max":47},"0x400000":{"min":0,"max":1}},"dimensions":[2,2],"sets":["ALDURSADVANCE"],"uniques":["WARTRAVELER"],"NTIPAliasClass":1},"Item":{"DisplayName":"War Traveler","NTIPAliasClassID":388,"NTIPAliasType":15,"NTIPAliasStatProps":{"3":{"min":10,"max":10},"0":{"min":10,"max":10},"80":{"min":30,"max":50},"72":30,"73":30,"96":{"min":25,"max":25},"16,0":{"min":150,"max":190},"21":15,"22":25,"78":{"min":5,"max":10},"154":{"min":40,"max":40}}},"NTIPAliasType":15,"NTIPAliasClassID":388,"NTIPAliasClass":None,"NTIPAliasQuality":7,"NTIPAliasStat":{"21":15,"22":25,"78":10,"31":139,"72":13,"73":48,"80":50,"0":10,"3":10,"16":190,"96":25,"154":40},"NTIPAliasFlag":{"0x10":True,"0x400000":False,"0x4000000":False}}should_keep_item=lexer.keep_item(expressions,hovered_item)print(should_keep_item)
Package: d2p-utils
D2P_utilsD2P Utils,resusable componentsFree software: MIT licenseDocumentation:https://d2p-utils.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History1.0.0 (2020-04-26)First release on PyPI.
Package: d2ssect
d2ssectd2ssectcalculates an alignment-free distance between samples based on frequencies of shared kmers. Specifically, it provides a fast implementation of theD2S statisticwhich can be used as a standalone distance measure, or as input to a range of methods (eg seethese tools) for phylogenetic and network analysis.Installationd2ssectis available viapypi.  Installation requires python 3.7 or greater as well as thejellyfishprogram and libraries.  We recommend installation into a conda environment as followscondacreate-nd2ssectpython=3.7kmer-jellyfish
condaactivated2ssect
pipinstalld2ssect
d2ssect-hAlternatively, you may use an existing Jellyfish installation, or install Jellyfish without using conda. If using this method please note that;Jellyfish version 2 is required (Jellyfish 1 will not work)Installation of Jellyfish via linux package managers will not work as this installs the jellyfish binary but not libraries and headers needed byd2ssectOnce Jellyfish is installed you should then be able to installd2ssectusing pip or pip3 as followspipinstalld2ssectUsageLets say we have a collection of fastq files corresponding to sequencing reads from different samples. We want to compare these withd2ssect.  First count kmers in these files usingjellyfishforfin*.fastq;dojellyfishcount-m21-s10000000$f-o${f%.fastq}.jf;doneNote that the command above will create a corresponding.jffile for every.fastqfile in the current directory. By keeping the base names of thejfandfastqfiles identical we can then rund2ssectas follows;d2ssect-l*.jf-f*.fastqOutputsd2ssectprovides information on progress (sent to stderr) and will eventually produce a matrix of pairwise D2S values (one for each pair of samples) sent to stdout.
Package: d2vs
what is thisdiablo 2 vision system is a library to assist with the automation of d2, using only pixels, keyboard and mouseinstallation$pipinstalld2vsNOTE: this can run via CPU, but via GPU is far superior. You must install CUDA and the appropriate python jazz
to get that working, for me with CUDA10.1:$condainstalltorchtorchvisioncudatoolkit=10.1-cpytorchusage(586_gold.png)importnumpyasnpfromd2vs.ocrimportOCRfromPILimportImage# Initiate OCRocr=OCR()# Load an Imageimg=Image.open("586_gold.png")# Scan the imagebounds,text,item_type=ocr.read(img)# Print out the data for demo purposestop_left,top_right,bottom_right,bottom_left=boundsprint(top_left,top_right,bottom_right,bottom_left)# ([2, 2], [158, 2], [158, 32], [2, 32])print(text)# '586 Gold'print(item_type)# 'Normal'project goalsHave fun automating single player! Not for profitOCR with near 100% accuracyVisually determine where you are in game, area level and world coordinate systemClick from world coords to screen coordsPath through unexplored areas to a goalFacilitate complete d2 bot from lvl 1 to 99Pick itan example map reading from d2vs of black marshnodes for the static map Harrogathdevelopmentsetup$gitclone...
$pipinstall-rrequirements.dev.txtrunning tests$pytestdistributing$pythonsetup.pysdistbdist_wheel
$twineuploaddist/*
Package: d2y
D2Y Python SDKThe D2Y Python SDK is a convenient and user-friendly library for interacting with the D2Y Exchange API. It simplifies the process of accessing and managing data from the D2Y platform and offers an easy-to-use interface for developers.InstallationTo install the D2Y Python SDK, use pip:pip install d2yDocumentationFor comprehensive documentation, including getting started guides and detailed examples, please visit ourofficial SDK documentation.Running testsCreate a .env file inside the tests folder and populate with the following variables:API_KEY,API_SECRET,TEST_INSTRUMENT_NAME,LINKED_SIGNER_KEYFor tests to pass the api key and secret should be from a fresh user, linked signer key should be associated with the same user and the instrument name should have an empty bookOptionally specifyAPI_BASE_URLenv var if you want to test locally, by default it points to testnetCreate venv and install packages from requirements.txt and enter venvsource venv/bin/activateBy default the last published version of d2y packge is installed. But for testing of the current d2y changes you must runpip install -e .Run testspython tests/test_sdk.pyUpdating packageIncrement version number in setup.pyDelete existingdist/directoryAdd any new required packages to setup.pyBuild package:python setup.py sdist bdist_wheelUpload to PyPI:twine upload dist/*TODOAdd constants for different environents like CHAIN_ID
Package: d33ps3curity-sql-tools
d33ps3curity-sql-toolsA simple middleware tool for analyzing Django ORM SQL execution. You can learn how to build this middleware package by following the steps below.UsageAdd the follow line of code to your middleware in your project settings.py file.d33ps3curity-sql-tools.middleware.new_middlewareHow it worksThe tool uses Django built in features to inspect the SQL generated from queries executed by the application. 3rd party tools are used to format and highlight the SQL presented in the terminal window.
Package: d365
D365-PYA nice tool to make accessing your Dynamics 365 API easier. Use in conjunction with a webserver such as FlaskInstallpip install d365Examplesfrom d365 import *#initializes connectiond = d365(
'cooldomain.crm.dynamics.com',
'clientidfromazure',
'clientsecretfromazure',
'redirectthatyouapprovedinazure'
)#returns authorization urld.authorize()#returns token data using authorization coded.authenticate('codeprovidedbytheredirect')#returns token data using refresh tokend.refresh('refreshtokenprovidedbyauthenticatefunction')#lists records with a possible query stringd.list('accounts','?$top=10&$select=name')#gets specific recordd.get('accounts','56959192-354e-eb11-a813-000d3a1026fe')#creates a recordd.create('contacts',{'lastname':'thisisatest'})#updates a recordd.update('contacts','6e1900e0-ac03-ed11-82e4-000d3a124166',{'firstname':'cool'})#deletes a recordd.delete('contacts','6e1900e0-ac03-ed11-82e4-000d3a124166')More Info on How to Use the Dynamics 365 APIhttps://docs.microsoft.com/en-us/previous-versions/dynamicscrm-2016/developers-guide/mt607689(v=crm.8)
Package: d365fw
Python Microsoft Dynamic 365 FrameWorkPython Microsoft Dynamic 365 FrameWorkThe Microsoft Dynamics 365 FrameWork is a basic REpresentational State
Transfer (REST) Application Programming Interface (API). It provides
integration toMicrosoft Dataverse Web APIresources.Table of ContentsQuick StartAuthenticationUsageQuick StartInstallationD365FW can be installed withpython-mpipinstalld365fworpipinstalld365fwImport the modulefromD365FW.D365FWimportD365FWAuthenticationTheD365FWframework allows user to authenticate the system usingOAuth. It accepts a fix list of
valid credentials to login to the system.hostname:the unique organization name for the environmentclient_id:the client (application) ID of the Azure registered applicationclient_secret:the client secret (key) of the Azure registered applicationtenant_id:the tenant (directory) ID of the environment# Create an instance of D365FW object and logind365fw=D365fw(hostname=hostname,client_id=client_id,client_secret=client_secret,tenant_id=tenant_id)UsageCreate# Create payloadpayload={# Account Name'name':f'Microsoft Dynamics'}# Make a request to create the Account# Get the return unique identifier (ID)# The payload need to be serialized to JSON formatted str (json.dumps)account_id=d365fw.accounts.create(json.dumps(payload))Read# Make a request to read the Accountread_account=d365fw.accounts.read(account_id)Update# Create payloadpayload={# Account Name'name':f'Power Platform'}# Make a request to update the Account with unique identifier (ID)# Update the Account Name with the newly generated Account Nameupdate_account=d365fw.accounts.update(account_id,json.dumps(payload))Delete# Make a request to delete the Account with unique identifier (ID)delete_account=d365fw.accounts.delete(account_id)
Package: d3blocks
D3Blocks builds on the graphics of d3 javascript (d3js) to create the most visually attractive and useful charts with only a few lines of Python code!
Thedocumentation pagescontains detailed information about the working of the blocks with many examples.Seeherean overview for all the blogsInstallation (Pypi)pipinstalld3blocks# Normal installationpipinstall-Ud3blocks# Force updateInstallation (clone)gitclonehttps://github.com/d3blocks/d3blocks.gitcdd3blocks
pipinstall-U.Import d3blocks packagefromd3blocksimportd3blocks# Initialized3=D3Blocks()Supported chartsnrBlockFunctionBlog1D3graphd3.d3graph()D3graph2Elasticgraphd3.elasticgraph()Elasticgraph3Sankeyd3.sankey()Sankey4Movingbubblesd3.movingbubbles()Movingbubbles5Scatterd3.scatter()Scatter6Heatmapd3.heatmap()Heatmap7Chord diagramd3.chord()D3Blocks8Timeseriesd3.timeseries()D3Blocks9Image sliderd3.imageslider()D3Blocks10Violin plotd3.violin()D3Blocks11Particlesd3.particles()D3Blocks12Treemapd3.treemap()D3Blocks13Mapsd3.maps()D3Blocks14Circlepackingd3.circlepacking()D3Blocks-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Referencesbl.ocksobservablehq
Package: d3cryp7.py
d3cryp7.py is a Python module for image analysis and recognition designed to decode, decrypt and understand the contents of an image.Installdocker pull bkvaluemeal/d3cryp7.py
pip install d3cryp7.pyUsagedocker run -d bkvaluemeal/d3cryp7.py
d3cryp7 --host localhost --port 80LicenseCopyright (c) 2016, Justin Willis

Permission to use, copy, modify, and/or distribute this software for any
purpose with or without fee is hereby granted, provided that the above
copyright notice and this permission notice appear in all copies.

THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
Package: d3g
3D-Geometry-in-PythonExplore 3D geometry in python!Installation-pipinstalld3gThis project repo will help you find out thei. Distances between the two points,two lines and a point and a plane.ii. Magnitude of a vector, dot product and cross product of two vectors.iii. Angle between two lines, two planes and a line and a plane.Example 1: Find the distance between the points (2,1,0) and (5,1,4)fromd3g.distanceimportp2pp2p(p1=(2,1,0),p2=(5,1,4))Output-25.0Example 2: Find the distance between the point (1,2,3) and the plane 2x + 3y + 4z = 1fromd3g.distanceimportpointToPlanepointToPlane(p1=(1,2,3),plane=[2,3,4,1])Output-3.5282114253639856Example 3: Find the distance between the point (1,2,3) and the linefromd3g.distanceimportpointToLinepointToLine(p1=(1,2,3),line=[[-3,5,-6],[2,4,2]])Output-14.899105176791087Example 4: Find the distance between the lines:line 1 :line 2 :fromd3g.distanceimportlineToLinelineToLine(line1=[[-3,5,-6],[2,4,2]],line2=[[5,-4,-7],[3,5,1]])Output-10.9577109184094Example 5: Find the magnitude of the vector v = 5i + 3j - 4kfromd3g.vectorimportmagnitudemagnitude(vector=[5,3,-4])Output-7.0710678118654755Example 6: Find the dot product of the vectors i + 2j + 3k and 2i + 3j + kfromd3g.vectorimportdotProductdotProduct(vector1=[1,2,3],vector2=[2,3,1])Output-11Example 7: Find the cross product of the vectors i + 2j + 3k and 2i + 3j + kfromd3g.vectorimportcrossProductcrossProduct(vector1=[1,2,3],vector2=[2,3,1])Output-[-7,5,-1]Example 8: Find the angle between the lines:line 1 :line 2 :fromd3g.angleimportlineslines(line1=[[-3,5,-6],[2,4,2]],line2=[[5,-4,-7],[3,5,1]])Output-14.963217433307127Example 9: Find the angle between the planes, plane1: x + y + z = 1 plane2: x + 2y + 3z = 4fromd3g.angleimportplanesplanes(plane1=[1,1,1,1],plane2=[1,2,3,4])Output-22.207654298596495Example 10: Find the angle between the line and a plane, line:plane: x + 2y + 3z = 4fromd3g.angleimportlineAndPlanelineAndPlane(line=[[-3,5,-6],[2,4,2]],plane=[1,2,3,4])Output-60.79406775260059
Package: d3gem
For harnessing those Flawless Square Emeraldsd3gemis a very simple utility tool for helping with Diablo 3 gem crafting.
What it does is calculating whether you have enough lesser gems to make few greater ones,
and how many you are missing (or will have left).InstallationUsepipto get thed3gemcommand in your shell:$ pip install d3gemOr just download the package and rund3gem.pydirectly (e.g. withpython ./d3gem.py).UsageSpecify which gem(s) you are trying to make and you will see how many Flawless Square ones
you will need for that:$ d3gem 1fst
You DON'T have enough lesser gems to make 1 Flawless Star gem(s).
The equivalent of 81 Flawless Square gem(s) is missing for that.You can also say gems what you already have instock, so that you’ll see how many
you are still missing to perform your craft:$ d3gem 1fst -s 20fsq,1rsq,1st
You DON'T have enough lesser gems to make 1 Flawless Star gem(s).
The equivalent of 25 Flawless Square gem(s) is missing for that.or, alternatively, how many you’ll have left:$ d3gem 1st -s 20fsq,1rsq
You DO have enough lesser gems to make 1 Star gem(s).
Afterwards you will still have the equivalent of 2 Flawless Square gem(s).Typed3gem--helpto get the list of gem class abbrevations.TODOsMake target gems optional and show what’s the highest gem you can craft with given stock
and how many more you need for even higher classMaybe store stock in config file?…Suggestions and pull requests welcome :)
Package: d3graph
Interactive force-directed network creator (d3graph)d3graphis a python package that simplifies the task of creatinginteractiveandstand-alonenetworks ind3 javascriptusingpython.
For this package I was inspired by d3 javascript examples but there was no python package that could create such interactive networks. Here it is; a library that automatically creates D3 javascript and HTML code based on an input adjacency matrix in python! This library does not require you any additional installation, downloads or setting paths to your systems environments. You just need python and this library. All other is taken care off. Huray!This library will create an interactive and stand-alone network that is build on d3 javascript.d3graphonly requirs an adjacency matrix in the form of an pandas dataframe. Each column and index name represents a node whereas values >0 in the matrix represents an edge. Node links are build from rows to columns. Building the edges from row to columns only matters in directed cases. The network nodes and edges can be adjusted in weight, color etc, based on user defined paramters.⭐️ Star this repo if you like it ⭐️BlogsRead the blogCreating beautiful stand-alone interactive D3 charts with Pythonto get a structured overview and usage ofd3graph.Documentation pagesOn thedocumentation pagesyou can find detailed information about the working of thed3graphwith many examples.InstallationInstall from PyPIpipinstalld3graphImport packagefromd3graphimportd3graphExamplesClick on the following image to load the interactiveTitanicnetwork that is created withd3graph. Note that the relations are determined usingHNet. Click here to go to the page with code to make the network.Example: Changing node propertiesExample: Convert source-target list to adjacency matrixExample: Breaking of networks using sliderContributeAll kinds of contributions are welcome!CitationPlease cite d3graph in your publications if this is useful for your research. See column right for citation information.MaintainerErdogan Taskesen, github:erdogantContributions are welcome.If you wish to buy me aCoffeefor this work, it is very appreciated :)
Package: d3heatmap
d3heatmapd3heatmapis a Python package to create interactive heatmaps based on d3js.Theaimof d3heatmap is to create interactive heatmaps that can be used stand-alone and being visual attractive.This library does not require any additional installation of javascript, or downloads or setting paths to your systems environments. You just need python and pip install this library. There are two main functions to create a heatmap and there are some differences between the two. Read below for more details. Have fun!Functionalitiesd3heatmap.matrixAllows none symetric adjacency matrices.Colormap can be changed.No clustering.round-ish elements.d3heatmap.heatmapAllows Clustering.Colormap is fixed.Advanced cluster coloring. Clusters are colored and within each cluster the color is incremental based on the value.Adjacency matrix must be symetric.Star this repo if you like it! ⭐️Installationpip install d3heatmapAlternatively, install d3heatmap from the GitHub source:gitclonehttps://github.com/erdogant/d3heatmap.gitcdd3heatmap
pipinstall-U.Import d3heatmapfromd3heatmapimportd3heatmapasd3Example 1: plot using the heatmap functiondf=d3.import_example()# Create heatmappaths=results=d3.heatmap(df)Klik on the figure for the interactive example.Example 2: plot using the matrix functiondf=d3.import_example(size=(6,20))# Create heatmappaths=d3.matrix(df)Example 3: plot using the matrix function# The dataframe contains more columns then rows. Adjust the size and color differently.df=d3.import_example(size=(6,20))# Create heatmappaths=d3.matrix(df,fontsize=10,title='Hooray!',description='d3 matrix is created using https://github.com/erdogant/d3heatmap.',path='c:/temp/example/d3_matrix.html',width=600,height=300,cmap='interpolateGreens',vmin=1)Example 4: Matrix with parameters changed:# The dataframe contains more columns then rows. Adjust the size and color differently.df=d3.import_example(size=(6,20))# Create heatmappaths=d3.matrix(df,fontsize=10,title='Hooray!',description='d3 matrix is created using https://github.com/erdogant/d3heatmap.',path='c:/temp/example/d3_matrix.html',width=600,height=300,cmap='interpolateGreens',vmin=1)Example 4: Comparison heatmap vs matrix:There are quit some differences between theheatmapvsmatrixfunctionality.df=d3.import_example()results=d3.heatmap(df,title='d3heatmap with the heatmap function.',path='heatmap.html')results=d3.matrix(df,title='d3heatmap with the matrix function.',cmap='interpolatePRGn',path='matrix.html',width=700,height=700)CitationPlease cite d3heatmap in your publications if this is useful for your research. See right column for citation information.Referenceshttps://github.com/erdogant/d3heatmaphttps://d3-graph-gallery.comhttps://https://d3js.org/MaintainerErdogan Taskesen, github:erdogantThis work is created and maintained in my free time. If you wish to buy me aCoffeefor this work, it is very appreciated.Contributions are welcome.Star it if you like it!
Package: d3m
Common code for D3M projectThis package provides a core package for D3M project with common code available.
It contains standard interfaces, reference implementations, and utility implementations.InstallationThis package works with Python 3.6-3.8 and pip 19+.You can install latest stable version fromPyPI:$ pip3 install d3mTo install the latest development version:$ pip3 install -e git+https://gitlab.com/datadrivendiscovery/d3m.git@devel#egg=d3mWhen cloning the repository, clone it recursively to get also git submodules:$ git clone --recursive https://gitlab.com/datadrivendiscovery/d3m.gitChangelogSeeHISTORY.mdfor a summary of changes between versions of this package.DocumentationThe documentation for the package is available athttps://datadrivendiscovery.org/.ContributingSeeCODE_STYLE.mdfor our coding style and contribution guide. Please ensure any merge requests you open follow this guide.Repository structuremasterbranch contains latest stable release of the package.develbranch is a staging branch for the next release.Releases aretagged.About the Data Driven Discovery of Models ProgramDARPA's Data Driven Discovery of Models (D3M) Program is researching ways to get machines to build
machine learning pipelines automatically. It is split into three layers:
TA1 (primitives), TA2 (systems which combine primitives automatically into pipelines
and executes them), and TA3 (end-users interfaces).