Saravanan666/pypi-org huggingface dataset:
Package: hawkes
Hawkesa python package for simulation and inference (maximum likelihood estimation) of Hawkes processes.Installpip install hawkesTutorialhttps://colab.research.google.com/github/omitakahiro/Hawkes/blob/master/notebooks/tutorial.ipynbFeatureThis package provides the following kernel functions:exponential functionsum of multiple exponential functionspower-law functionnon-parametric functionThis package provides the following classes of a baseline intensity:constant baselinepiecewise constant baseline modelpiecewise linear baseline modellog-linear baseline modelcustom baseline functionContactTakahiro Omihttps://sites.google.com/view/omitakahiro/home-englishtakahiro.omi.em[at]gmail.com
Package: hawkesbook
The Python package 'hawkesbook' for Hawkes Process inference, simulation, etc.To install simply runpip install hawkesbook.This package is meant to accompany the upcoming bookThe Elements of Hawkes Processeswritten by Patrick J. Laub, Young Lee, and Thomas Taimre.It implements inference, simulation, and other related method for Hawkes processes and some mutually-exciting Hawkes processes.The main design goal for this package was simplicity and readability.
Some functions are JIT-compiled to C and parallelised withnumbaso the computational performance is not completely neglected.
Everything that can benumpy-vectorised has been.Our main dependencies arenumba,numpy, andscipy(for the minimize function).As an example, in the book we have a case study which fits various Hawkes process to the arrival times of earthquakes.
The code for the fitting and analysis of that data is like:importhawkesbookashawkesimportnumpyasnpimportpandasaspdimportscipy.statsasstatsimportmatplotlib.pyplotaspltfromstatsmodels.graphics.gofplotsimportqqplot# Load data to fitquakes=pd.read_csv("japanese-earthquakes.csv")quakes.index=pd.to_datetime(quakes.Day.astype(str)+"/"+quakes.Month.astype(str)+"/"+quakes.Year.astype(str)+" "+quakes.Time,dayfirst=True)quakes.sort_index(inplace=True)# Calculate each arrival as a (fractional) number of days since the# beginning of the observation periodtimeToQuake=quakes.index-pd.Timestamp("1/1/1973")ts=np.array(timeToQuake.total_seconds()/60/60/24)# Calculate the length of the observation periodobsPeriod=pd.Timestamp("31/12/2020")-pd.Timestamp("1/1/1973")T=obsPeriod.days# Calculate the maximum likelihood estimate for the Hawkes process# with an exponentially decaying intensity𝛉_exp_mle=hawkes.exp_mle(ts,T)print("Exp Hawkes MLE fit: ",𝛉_exp_mle)# Calculate the EM estimate or the same type of Hawkes process𝛉_exp_em=hawkes.exp_em(ts,T,iters=100)print("Exp Hawkes EM fit: ",𝛉_exp_mle)# Get the likelihoods of each fit to find the better onell_mle=hawkes.exp_log_likelihood(ts,T,𝛉_exp_mle)ll_em=hawkes.exp_log_likelihood(ts,T,𝛉_exp_em)ifll_mle>ll_em:print("MLE was a better fit than EM in this case")𝛉_exp=𝛉_exp_mlell_exp=ll_mleelse:print("EM was a better fit than MLE in this case")𝛉_exp=𝛉_exp_emll_exp=ll_em# Fit instead the Hawkes with a power-law decay𝛉_pl=hawkes.power_mle(ts,T)ll_pl=hawkes.power_log_likelihood(ts,T,𝛉_pl)# Compare the BICsBIC_exp=3*np.log(len(ts))-2*ll_expBIC_pl=4*np.log(len(ts))-2*ll_plifBIC_exp<BIC_pl:print(f"The exponentially-decaying Hawkes was the better fit with BIC={BIC_exp:.2f}.")print(f"The power-law Hawkes had BIC={BIC_pl:.2f}.")else:print(f"The power-law Hawkes was the better fit with BIC={BIC_pl:.2f}.")print(f"The exponentially-decaying Hawkes had BIC={BIC_exp:.2f}.")# Create a Q-Q plot for the exponential-decay fit by# first transforming the points to a unit-rate Poisson# process as outlined by the random time change theoremtsShifted=hawkes.exp_hawkes_compensators(ts,𝛉_exp)iat=np.diff(np.insert(tsShifted,0,0))qqplot(iat,dist=stats.expon,fit=False,line="45")plt.show()
Package: hawkeslib
Welcome tohawkeslibhawkeslibstarted with the ambition of having a simple Python implementation
ofplain-vanillaHawkes (orself-excitingprocesses), i.e. those
with factorized triggering kernels with exponential decay functions.Thedocscontain tutorials, examples and a detailed API reference.
For other examples, see theexamples/folder.The following models are available:Univariate Hawkes Process (with exponential delay)Bayesian Univariate Hawkes Process (with exponential delay)Poisson Process'Bayesian' Poisson processBayesian variants implement methods for sampling from the posterior as well as calculating
marginal likelihood (e.g. for Bayesian model comparison).InstallationCython(>=0.28) andnumpy(>=1.14) andscipymust be installed prior to the installation as
they are required for the build.$ pip install -U Cython numpy scipy
$ pip install hawkeslib
Package: hawkeye
HawkEye is a v of commands that I have been building up over time to save time. Turning four&five-liners into a quick, callable function.HawkEye:#!/usr/bin/env python

from hawkeye import *

# quickly initialize a database (dictionary) and start storing things.

_data = INIT_Database("<DesiredPathname>") # can be .txt or .json or a custom file
printData(_data) # prettyprint
_navigateData = navigate(_data) # prints keys of dictionary to console so you can find what you need quicklyI’m still working on it tho.
Package: hawk-eyes
Hawk-eyesA library for Computer Vision, flexible and easy to use. The library uses a lot of models like ArcFace, RetinaFace, FaceLandmark, OCRModels .... These models will be downloaded automatically when used for the first time, please make sure your network connection is not blocked to google drive.InstallHawk-eyesis available onpypi.org, if you just want to use it for your project, install it using pip.
Requires:python>=3.8torch==1.8onnxruntime==1.7 (or onnxruntime-gpu)pip install hawk-eyesMethodsSupported methods:Retina FaceArcFaceFace similarface landmarksface anglesTrackingYolov5 inference1. Face Recognition1.1 Face detectionRetina Face: Single-stage Dense Face Localisation in the Wildimportcv2fromhawk_eyes.faceimportRetinaFaceretina=RetinaFace(model_name='retina_s')img=cv2.imread('path/to/image.png')bboxes,kpss=retina.detect(img)forboxinbboxes:box=box[:4].astype(int)cv2.rectangle(img,(box[0],box[1]),(box[2],box[3]),(0,255,0),thickness=2)cv2.imshow('image',img)cv2.waitKey(0)1.2 Face extract feature use ArcFaceArcFace: Additive Angular Margin Loss for Deep Face Recognitionimportcv2fromhawk_eyes.faceimportRetinaFacefromhawk_eyes.faceimportArcFacearc=ArcFace(model_name='arcface_s')retina=RetinaFace(model_name='retina_s')img=cv2.imread('path/to/image.png')bboxes,kpss=retina.detect(img)forbox,kpsinzip(bboxes,kpss):emb=arc.get(img,kps)print(emb)1.3 Get similarComing soon1.4 Get face landmarks, face anglesimportcv2fromhawk_eyes.faceimportRetinaFace,Landmarkretina=RetinaFace(model_name='retina_s')landmark=Landmark()img=cv2.imread('path/to/image.png')bboxes,kpss=retina.detect(img)forbox,kpsinzip(bboxes,kpss):land=landmark.get(img,box)angle=landmark.get_face_angle(img,land)print(angle)2. Trackingimportcv2fromhawk_eyes.faceimportRetinaFacefromhawk_eyes.trackingimportBYTETrackerbt=BYTETracker()retina=RetinaFace(model_name='retina_s')cap=cv2.videoCapture(0)ret,_=cap.read()whileret:ret,frame=cap.read()boxes,_=retina.detect(frame)boxes,ids=bt.predict(frame,boxes)print(ids)
Package: hawkeye-v2
HawkEyeA library for Computer Vision, flexible and easy to use. The library uses a lot of models like ArcFace, RetinaFace, FaceLandmark, OCRModels .... These models will be downloaded automatically when used for the first time, please make sure your network connection is not blocked to google drive.InstallHawkEyeis available onpypi.org, if you just want to use it for your project, install it using pip.
Requires python>=3.8, torch>=1.8pip install hawkeye-v2MethodsSupported methods:Retina FaceArcFaceFace similarface landmarksface anglesTracking1. Face Recognition1.1 Face detectionRetina Face: Single-stage Dense Face Localisation in the Wildimportcv2fromhawkeye.faceimportRetinaFaceretina=RetinaFace(model_name='retina_s')img=cv2.imread('path/to/image.png')bboxes,kpss=retina.detect(img)forboxinbboxes:box=box[:4].astype(int)cv2.rectangle(img,(box[0],box[1]),(box[2],box[3]),(0,255,0),thickness=2)cv2.imshow('image',img)cv2.waitKey(0)1.2 Face extract feature use ArcFaceArcFace: Additive Angular Margin Loss for Deep Face Recognitionimportcv2fromhawkeye.faceimportRetinaFacefromhawkeye.faceimportArcFacearc=ArcFace(model_name='arcface_s')retina=RetinaFace(model_name='retina_s')img=cv2.imread('path/to/image.png')bboxes,kpss=retina.detect(img)forbox,kpsinzip(bboxes,kpss):emb=arc.get(img,kps)print(emb)1.3 Get similarComing soon1.4 Get face landmarks, face anglesimportcv2fromhawkeye.faceimportRetinaFace,Landmarkretina=RetinaFace(model_name='retina_s')landmark=Landmark()img=cv2.imread('path/to/image.png')bboxes,kpss=retina.detect(img)forbox,kpsinzip(bboxes,kpss):land=landmark.get(img,box)angle=landmark.get_face_angle(img,land)print(angle)2. Trackingimportcv2fromhawkeye.faceimportRetinaFacefromhawkeye.trackingimportBYTETrackerbt=BYTETracker()retina=RetinaFace(model_name='retina_s')cap=cv2.videoCapture(0)ret,_=cap.read()whileret:ret,frame=cap.read()boxes,_=retina.detect(frame)boxes,ids=bt.predict(frame,boxes)print(ids)
Package: hawkflow
HawkFlow.aiMonitoring for anyone that writes codeInstall the pip packagepip install hawkflowUsage:importtimefromhawkflowclient.hawkflow_apiimport*# authenticate with your API keyhf=HawkflowAPI("YOUR_API_KEY")# start timing your code - pass through process (required) and meta (optional) parametersprint("sending timing start data to hawkflow")hf.start("your_process_name","your_meta_data")# do some workprint("sleeping for 5 seconds...")time.sleep(5)# end timing this piece of code - process (required) and meta (optional) parameters should match the startprint("sending timing end data to hawkflow")hf.end("your_process_name","your_meta_data")More examples:HawkFlow.ai Python examplesRead the docs:HawkFlow.ai documentationWhat is HawkFlow.ai?HawkFlow.ai is a new monitoring platform that makes it easier than ever to make monitoring part of your development process. Whether you are an Engineer, a Data Scientist, an Analyst, or anyone else that writes code, HawkFlow.ai helps you and your team take ownership of monitoring.
Package: hawkflowairflow
HawkFlow.ai apache airflow integrationFirst, sign up to hawkflow for free:https://hawkflow.ai/and get an API keyInstall the pip packagepip install hawkflowairflowAdd this to the top of your DAGfromhawkflowairflowimporthawkflow_callbackshawkflow_callbacks.HF_API_KEY="YOUR_HAWKFLOW_API_KEY_HERE"Add these two lines to default_args in your DAG:default_args={    
    "on_success_callback": hawkflow_callbacks.hawkflow_success_callback,
    "on_execute_callback": hawkflow_callbacks.hawkflow_start_callback
}All done. Now when your DAG runs, you will see the output in the HawkFlow UI.https://app.hawkflow.ai/loginKnown IssuesIf you are on anARM macand notice that your DAG is just hanging. You may need to put this
at the top of your DAG. Airflow is running as a different user on your mac, and the security is blocking outgoing requests.import os
os.environ['NO_PROXY'] = '*'More examplesMore examples:HawkFlow.ai Python examplesRead the docs:HawkFlow.ai documentationWhat is HawkFlow.ai?HawkFlow.ai is a new monitoring platform that makes it easier than ever to make monitoring part of your development process.
Whether you are an Engineer, a Data Scientist, an Analyst, or anyone else that writes code, HawkFlow.ai helps you and your team take ownership of monitoring.
Package: hawkmoth
Hawkmoth - Sphinx Autodoc for CHawkmoth is a minimalisticSphinxC and C++ Domainautodoc directive
extension to incorporate formatted C and C++ source code comments written inreStructuredTextinto Sphinx based documentation. It uses Clang Python Bindings
for parsing, and generates C and C++ Domain directives for C and C++ API
documentation, and more. In short, Hawkmoth is Sphinx Autodoc for C/C++.Hawkmoth aims to be a compelling alternative for documenting C and C++ projects
using Sphinx, mainly through its simplicity of design, implementation and use.NoteThe C++ support is still in early stages of development, and lacks some
fundamental features such as handling namespaces and documenting C++ specific
features other than classes.ExampleGiven C source code with rather familiar looking documentation comments:/**
 * Get foo out of bar.
 *
 * :param bar: Name of the bar.
 */
void foobar(const char *bar);and a directive in the Sphinx project:.. c:autofunction:: foobar
   :file: filename.cyou can incorporate code documentation into Sphinx. It’s as simple as that.You can document functions, their parameters and return values, structs,
classes, unions, their members, macros, function-like macros, enums, enumeration
constants, typedefs, variables, as well as have generic documentation comments
not attached to any symbols.See the documentationexamplessection for more, with sample output.DocumentationDocumentation on how to install, configure and use Hawkmoth, as well as write
documentation comments, with examples, is available for both thelatest
releaseand theversion currently in development.The same is also hosted atRead the Docs.InstallationYou can install Hawkmoth fromPyPIwith:pip install hawkmothYou’ll additionally need to install Clang and Python 3 bindings for it through
your distro’s package manager; they are not available via PyPI. For further
details, see the documentation.Alternatively, installation packages are available for:Arch LinuxIn Sphinxconf.py, addhawkmothtoextensions, and pointhawkmoth_rootat the source tree. See the extension documentation for
details.Development and ContributingHawkmoth source code is available onGitHub. The development version can be
checked out viagitusing this command:git clone https://github.com/jnikula/hawkmoth.gitPlease file bugs and feature requests as GitHubissues. Contributions are
welcome as GitHub pull requests.SeeCONTRIBUTING.rstfor more details.DependenciesDependencies and their minimum versions:Python 3.6Sphinx 3Clang library 6Python 3 Bindings for Clang library 6There are additional development and testing dependencies recorded insetup.cfg.LicenseHawkmoth is free software, released under the2-Clause BSD License.ContactIRC channel#hawkmothonOFTC IRC network. GitHubissuesanddiscussions.
Package: hawkscraper
HawkScraperHawkScraper is an interface for routing/chaining outbound API requests to various endpoints asynchronously and parsing/formatting the output.Currently Supported Endpoints:APIAtera(in progress)ClickUp(in progress)Google(Search / SiteSearch)GPTOpenAIReplicateScrapingFraseModestMoneyTrustPilotCurrently Supported Format Conversions:JSONPDF(in progress)HTMLMarkdownPlaintextDocX(in progress)References:AteraClickUpGoogle SearchOpenAIReplicateFraseModest MoneyTrustPilotReportLab PDFpython-docxmarkdown
Package: hawk-server
hawk-serverUtility function to perform the server-side of Hawk authentication, with no dependencies other than the Python standard library.Installationpipinstallhawk-serverUsagefromhawkserverimportauthenticate_hawk_headerdeflookup_credentials(id):# Return {'id': 'some-id', 'key': 'some-secret'} matching credentials,# or None if credentials can't be founddefseen_nonce(nonce,id):# Store nonce, return True if nonce previously seenerror_message,credentials=authenticate_hawk_header(lookup_credentials,seen_nonce,max_skew,header,method,host,port,path,content_type,content,)iferror_messageisnotNone:# Return error or raise exception as needed
Package: hawk-server-asyncio
hawk-server-asyncioUtility function to perform the server-side of Hawk authentication for asyncio servers, with no dependencies other than the Python standard library.Installationpipinstallhawk-server-asyncioUsagefromhawkserverimportauthenticate_hawk_headerasyncdeflookup_credentials(id):# Return {'id': 'some-id', 'key': 'some-secret'} matching credentials,# or None if credentials can't be foundasyncdefseen_nonce(nonce,id):# Store nonce, return True if nonce previously seenerror_message,credentials=awaitauthenticate_hawk_header(lookup_credentials,seen_nonce,max_skew,header,method,host,port,path,content_type,content,)iferror_messageisnotNone:# Return error or raise exception as needed
Package: hawksoft.ptaScore
hawksoft.patScore command针对pintia.cn网站教学，生成学生的多次作业成绩总表。Installation:pip install hawksoft.patScoreusage:首先“导出”学生的所有作业（或考试）成绩，并集中存放到一个目录下（这里是tests）.提供一个excel文件，为学生的点名册(这里是names.xlsx).调用如下命令，结果将会保存到指定的文件中（这里是scores.xlsx)patScore -n names.xlsx -t tests -o scores.xlsx
Package: hawksoft.relation
hawksoft.relation packagehawksoft.relation包提供了一个Relation类，是对sympy的补充。该类可以生成一个二元关系对象，实现关系的表示，包括集合，图和矩阵；实现关系的运算，包括逆关系，关系的幂，关系的自反闭包，关系的对称闭包，关系的传递闭包。同时，由于关系本来就是集合，因此也支持集合的所有运算，包括交，并和补以及子集等。安装命令：pip install hawksoft.relation或者：pip install hawksoft.relation -i https://pypi.tuna.tsinghua.edu.cn/simple该包需要sympy和networkx两个包,如果系统没有，则会自动安装。使用记住： 关系是定义在集合A（称为论域discourse）上的序偶的集合。使用过程如下：1 导入包from hawksoft.relation import Relation2 定义论域集合A：Relation.setA(1,2,3) # 该集合可以以任何对象作为元素。3 创建关系对象：r1 = Relation((1,2),(1,3))
   r2 = Relation.getUniversal() #生成一个全关系，即$A\timesA$
   r3 = Realtion.getIdendity()  #生成一个恒等关系4 关系的表示r1.showSet()   # 将关系显示为集合
    r1.showMatrix()# 将关系显示为矩阵
    r1.showGraph() # 将关系显示为图5 关系运算r5 = r2 - r1   # 关系的差
    r6 = r1 + r3   # 关系的并
    r7 = r1 ** -1  # 关系的逆关系
    r8 = r1 ** 2   # 关系的2次幂，即关系的复合。
    r9 = r1 ** 3   # 关系的3次幂 
    r10 = r1.reflectiveClosure() # 关系的自反闭包
    r10 = r1.symmetricClosure()  # 关系的对称闭包
    r10 = r1.transitiveClosure() # 关系的传递闭包举例例1:给定关系R，将R转化为一个最小的等价关系。fromhawksoft.relationimportRelationRelation.setA(1,2,3,4,5,6,7)r1=Relation((1,2),(2,3),(4,5),(5,6))r2=r1.reflectiveClosure().symmetricClosure().transitiveClosure()r2.showSet()r2.showGraph(){(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3), (4, 4
), (4, 5), (4, 6), (5, 4), (5, 5), (5, 6), (6, 4), (6, 5), (6, 6), (7, 7)}例2: 判断一个关系是满足传递性根据定理：一个关系R是传递的，当且仅当R的2次幂是R的子集。fromhawksoft.relationimportRelationRelation.setA(1,2,3,4,5,6,7)r1=Relation((1,2),(2,3),(4,5),(5,6))r2=r1**2ifr1.contains(r2):print('it is transitive')else:print('it is not transitive')it is not transitive
Package: hawksoft.serialPortComm
hawksoft.serialPortComm packagehawksoft.serialPortComm packageInstallation:usage:Provides a thread which send and receive bytes from serial port. The main thread can communicate with thread by  send and receive fucntion.Installation:pip install hawksoft.serialPortCommusage:from hawksoft import comm
comm.start('COM1',9600)
...
frames = comm.receive()
for abyte in frames:
    ...deal with abute
...
comm.send(b'\x01\x02')
...
comm.close()
Package: hawksoft.tools
hawksoft.tools packagehawksoft.tools packageInstallation:usage:md2zhihutreeselmvrenameProvides many useful command line in python.Now includes:md2zhihu: convert md file with math formula to zhihu-compatible file.tree: show directory as tree graph.selmv: move file with specified extensions from source directory to destine directory.rename: rename all file with special extension to new name.Installation:pip install hawksoft.toolsusage:md2zhihuInstallation will add a new command line: md2zhihu. you can use it like this:md2zhihu sourceFilename destineFilenameormd2zhihu sourceFilename destineDirectoryThe command will create a new file which can be imported to zhihu website.treeInstallation will add a new command line: tree. you can use it like this:treeortree [diretory]The command will show the directory as tree graph.selmvInstallation will add a new command line: selmv. you can use it as following:selmv extension fromDirectory toDirectoryThe command line will move all files with specified extension from fromDirectory to toDirectory, involving all the files in sub-fromDirectory.renameInstallation will add a new command line:rename. you can use it as following:rename extension fromDirectory newNameThe command line will rename all files with specified extension , involving all the files in sub-fromDirectory.
Package: hawqal
HawqalPython package that contains the data of countries, states
and their cities. Now it's easy for the end-user to get all
the data by just calling the relevant functionsFunctionsGet CountriesGet StatesGet CitiesGet StartedTo install the Hawqal package, use the following command:windowspipinstallhawqalmacos/linuxpip3installhawqalFeatures/ExampleFor more information, additional examples and functionality, see theHawqal Docummentation.LanguagePython 3.10.9AuthorsCapregsoftHusnain KhurshidAdditional ResourcesChangelogCode of ConductCollaborator GuideContribution Guidelines
Package: hax
HAXHAX lets you write compiled bytecode inline with pure Python. It was originally
built for exploring new improvements to CPython's compiler and peephole
optimizer.InstallationHAX supports CPython 3.7+ on all platforms.To install, just run:$pipinstallhaxExampleConsider the following function; it accepts a sequence of items, and returns a
list with each item repeated twice:defdoubled(items):out=[]foriteminitems:out+=item,itemreturnoutFor example,doubled((0, 1, 2))returns[0, 0, 1, 1, 2, 2].We can make this function faster by keepingouton the stack (instead of in a
local variable) and using theLIST_APPENDop to build it. HAX makes it
simple to inline these instructions:fromhaximport*@haxdefdoubled(items):BUILD_LIST(0)foriteminitems:LOAD_FAST("item")DUP_TOP()LIST_APPEND(3)LIST_APPEND(2)RETURN_VALUE()With the help of labeled jump targets (HAX_LABEL), the function can be further
sped up by rewriting the for-loop in bytecode, removingalltemporary
variables, and operatingentirely on the stack:fromhaximport*@haxdefdoubled(items):BUILD_LIST(0)LOAD_FAST("items")GET_ITER()HAX_LABEL("loop")FOR_ITER("return")DUP_TOP()LIST_APPEND(3)LIST_APPEND(2)JUMP_ABSOLUTE("loop")HAX_LABEL("return")RETURN_VALUE()It's important to realize that the functions HAX provides (BUILD_LIST,LOAD_FAST, ...) aren't just "emulating" their respective bytecode
instructions; the@haxdecorator detects them, and completely recompilesdoubled's code to use theactualops that we've specified here!These performance improvements are impossible to get from CPython's compiler and
optimizer alone.
Package: haxballgym
HaxBallGymHaxBallGym is a Python package that can be used to treat the gameHaxBallas though it were anOpenAI Gym-style environment for Reinforcement Learning projects.RequirementsPython >= 3.10InstallationInstall the library via pip:pipinstallhaxballgymThat's it! Runexample.pyto see if the installation was successful. The script assumes you have a recordings folder from where you run the script.RecordingsTo watch recordings, go to myHaxBall cloneand load the recording file.Discord
Package: haxball.py
Haxball.pyUsesIndeximporthaxball.pyimportasyncioloop=asyncio.get_event_loop()asyncio.ensure_future(Haxball("./bot.js").openroom())loop.run_forever()bot.jswindow.onHBLoaded=function(){constroom=window.HBInit({roomName:"My room",maxPlayers:16,public:true,noPlayer:true,token:"TOKEN"// Token: https://www.haxball.com/headlesstoken})room.setDefaultStadium("Big");room.setScoreLimit(5);room.setTimeLimit(0);functionupdateAdmins(){varplayers=room.getPlayerList();if(players.length==0)return;if(players.find((player)=>player.admin)!=null)return;room.setPlayerAdmin(players[0].id,true);};room.onPlayerJoin=function(player){updateAdmins();};room.onPlayerLeave=function(player){updateAdmins();}/**END CODE */if(typeofwindow.HBInit==='function')window.onHBLoaded()}
Package: haxcore
HaX Core packageThis repository contains the PyPi package for the core code of theHaX Cybersecurity tool.About the projectHaXis an AI-powered Cybersecurity tool designed to detect website vulnerabilities. Its cloud connection enhances capabilities for advanced analytics and modeling.
This tool is expertly designed for ethical hacking, using intelligent cyber attack simulations to identify successful outcomes. It uploads results to the cloud for advanced data analytics, refining models for future attacks.Deployment prerequisitesBefore running the application, make sure to meet the following requirementsDownload and install python latest version,check here.Install pip,check here.Install poetry,check here.Install all dependencies by runningpoetry install, the command will create a virtual environment, to understand how poetry works please refer to thisdocumentation.You can run pytest using commandpoetry run pytestWho do I talk to?You can contact us directly using one of the following:🖥️ Website:haxsec.com✉️ Support Emailsupport@haxsec.com