Saravanan666/pypi-org huggingface dataset:
Package: xcs-rc
XCS-RCAccuracy-based Learning Classifier SystemswithRule Combiningmechanism, shortlyXCS-RCfor Python3, loosely based on Martin Butz's XCS Java code (2001). Read my PhD thesisherefor the complete algorithmic description.Rule Combiningis novel function that employs inductive reasoning, replacingall Darwinian genetic operation like mutation and crossover. It can handlebinariesandreal, reaching bettercorrectness rateandpopulation sizequicker than several XCS instances. My earlier papers comparing them can be obtained athereandhere.Relevant linksPyPIGithub repoExamples:Classic problems: multiplexer, Markov envChurn datasetFlappy BirdOpenAI GymInstallationpip install xcs-rcInitializationimport xcs_rc
agent = xcs_rc.Agent()Classic Reinforcement Learning cycle# input: binary string, e.g., "100110" or decimal array
state = str(randint(0, 1))

# pick methods: 0 = explore, 1 = exploit, 2 = explore_it
action = agent.next_action(state, pick_method=1)

# determine reward and apply it, e.g.,
reward = agent.maxreward if action == int(state[0]) else 0.0
agent.apply_reward(reward)Partially Observable Markov Decision Process (POMDP) environment# create env and agent
env = xcs_rc.MarkovEnv('maze4')  # maze4 is built-in
env.add_agents(num=1, tcomb=100, xmax=50)
agent = env.agents[0]

for episode in range(8000):
    steps = env.one_episode(pick_method=2)  # returns the number of taken stepsData classificationagent.train(X_train, y_train)
cm = agent.test(X_test, y_test)  # returns the confusion matrix
preds, probs = agent.predict(X)  # returns lists of predictions and probabilitiesPrint population, save it to CSV file, or use append modeagent.pop.print(title="Population")
agent.save('xcs_population.csv', title="Final XCS Population")
agent.save('xcs_pop_every_100_cycles.csv', title="Cycle: ###", save_mode='a')Finally, inserting rules to population# automatically load the last set (important for append mode)
agent.load("xcs_population.csv", empty_first=True)
agent.pop.add(my_list_of_rules)  # from a list of classifiersMain ParametersXCS-RC Parameterstcomb:combining period, number of learning cycles before the next rule combiningpredtol:prediction tolerance, maximum difference between two classifiers to be combinedprederrtol:prediction error tolerance, threshold for deletion of inappropriately combined rulesHow to Setagent.tcomb = 50 # perform rule combining every 50 cycles
agent.predtol = 20.0 # combines rules whose prediction value differences <= 20.0
agent.prederrtol = 10.0 # remove if error > 10.0, after previously below itParameters from original XCSall related to mutation and crossover is removedthe others are kept and accessible (e.g.,agent.alpha = 0.15)ResultsClassical Problems:multiplexerandMarkov environment:Flappy Bird from PyGame Learning Environment:Youtube: CartPole-v0 Benchmark from OpenAI Gym:
Package: xctemplateutils
===========xctemplateutils===========xctemplateutils provides a list of useful command line programs thathelps ease the pain of manually dealing with messy .xctemplate files.How to use it=========* Installpip install xctemplateutils* Generate <key> and <dict> for <Definitions> elementgenerate_xctemplate.py -d <folder_name>For example, suppose you have MagicalRecord copied intoa subfolder MagicalRecord/ of your .xctemplate folder @MyXCTemplate.xctemplate/MagicalRecord. You can:cd MyXCTemplate.xctemplate; generate_xctemplate.py -d MagicalRecord* Generate <string> for <Nodes> elementgenerate_xctemplate.py -n <folder_name>
Package: xcube-4d-viewer
xcube_4d_viewerThis repository is a plugin for the xcube server.xcube (https://xcube.readthedocs.io/en/latest/overview.html) is a Python package for generating and exploiting data
cubes powered by xarray, dask, and zarr. It also provides a web API and server which can be used to access and
visualise these data cubes.This repository serves as an API extension to the xcube server, allowing xcube data cubes to be analysed and
visualised within Earthwave's 4D viewer. It computes configuration details and
heatmap/3D heatmap/terrain tiles from the server's data cubes and provides them in a format expected by the 4D viewer.In order to connect to Earthwave's 4D viewer, the xcube server needs to be registered within a Middle Tier service.
Please contactsupport@earthwave.co.ukfor information on how to proceed.This work is done as part of the DeepESDL project (https://deepesdl.readthedocs.io/en/latest/).
Package: xcut
# Xcut
Xcut is an Enhanced cut command, which is used to help grep column.[![](https://img.shields.io/pypi/pyversions/xcut.svg?longCache=True)](https://pypi.org/pypi/xcut/)
[![](https://img.shields.io/pypi/v/xcut.svg?maxAge=36000)](https://pypi.org/pypi/xcut/)
[![Build Status](https://travis-ci.org/ahuigo/xcut.svg?branch=master)](https://travis-ci.org/ahuigo/xcut)## Installpip install xcut
pip3 install xcutxcut –help## Usage
Let’s test a file namedtest.csv> ~ cat test/test.csv
name,gender,job
Jack,male,coder
Lucy,female,artistCut fields> ~ xcut -f job,name test/test.csv
job,name
coder,Jack
artist,Lucy### Set title type
The default title type is head:-t head> ~ xcut -f name,gender test/test.csvSet title type to index:-t index> ~ xcut -f 1,3 -t index  test/test.csv
1,3
name,job
Jack,coder
Lucy,artistSet title type to custom(–titles TITLES)> ~ xcut -f ‘职业,姓名’ –titles ‘姓名,性别,职业’ test/test.csv -od $’t’
职业 姓名
job name
coder       Jack
artist      LucySet title type to kv(-t kv)> ~ echo ‘key1=v1,key2=v2,key3=v3’ | xcut -f key3,key2 -t kv
key3,key2
v3,v2### Set input delimiter(d)> ~ xcut -f job,name test/test.csv -d ‘,’ -od ‘`’
job`name
coder`Jack
artist`Lucy### Set output delimiter(od)> ~ xcut -f job,name test/test.csv -od ‘`’
job`name
coder`Jack
artist`Lucy### pretty output
You could set the output delimiter(od), also you can print it viapretty> ~ xcut -f ‘职业,姓名’ –titles ‘姓名,性别,职业’ test/test.csv -od $’tt’ –pretty
职业                  姓名
—————————————-
job                 name
coder               Jack
artist              Lucy### Use csv format
Not only you could  parse csv format file with–from-csv:$ echo ‘Lucy,”98,99”,23’ | python xcut -f scores,name –titles name,scores,age
scores,name
“98,Lucy$ echo ‘Lucy,”98,99”,23’ | python xcut -f scores,name –titles name,scores,age –from-csv
scores,name
98,99,LucyYou could also save the output to csv format  with(–to-csv)$ echo ‘Lucy,”98,99”,23’ | python xcut -f scores,name –titles name,scores,age –from-csv –to-csv
scores,name
“98,99”,Lucy## Required
1. python>=3.5
2. click
Package: xd
A list of handy commands which makes life easier.Installpip install xd -U
# pip install git+https://github.com/damnever/xd.git@master -UUsageUsage: xd [OPTIONS] COMMAND [ARGS]...

  A list of handy commands which makes life easier.

Options:
  --help  Show this message and exit.

Commands:
  batch    Execute a program multiple times in parallel.
  config   Manipulate config files: json, yaml, ini and toml.
  gitinit  Init a git project, or creates a LICENSE/.gitignore file.
  version  Show version.
  watch    Execute a program periodically.xd --help
xd <subcommand> --help
Package: xdantic
xdanticLightweight configuration library on top of PydanticGetting startedThis notebook shows some example usages of xdantic.It explains how to provide values to the configuration object, how to
access values and how nested configurations work.The configuration management is simple, including the config object
itself with all the fields that you implementfromtypingimportLiteralfrompydanticimportHttpUrl,FieldfromxdanticimportXConfigclassDemoConfig(XConfig):"""The object `DemoConfig` is the central config object thatwill hold your values and can be extended to your needs with new values.Here, you can leverage the power of Pydantic and Python typehints to create a configurationthat is safe, documented and easy to use.Check https://pydantic-docs.helpmanual.io/usage/types/ to learn more about field types in Pydantic."""DB_HOST:HttpUrl# will be validate to be a httpUrlDB_PORT:int=Field(default=3333,le=64000,ge=1024)# will be validated to be in the given rangeDB_TYPE:Literal["postgres","mysql"]="mysql"# can only take on values present in the `Literal` definitionCreate a configTo create a config object with parsed values you can simply call the
initializer method and XConfig will automatically create the object
using values from e.g. environment variables.Now that we have theDemoConfigwe are ready to use our config in the
code. The next cell provides an example of parsing values to the config
from environment variables and accessing them:importosos.environ['DB_PORT']='8888'# note, how this value will take presedence over the defaultos.environ['DB_HOST']='https://postgres-develop.cluster.svc.local'os.environ['DB_TYPE']='postgres'print(DemoConfig())Nested configurationSometime, it is desired to nest configurations to have a structure likeconfig.db.usernamne. This can be easily achieved by creating a
separate class that inherits frompydantic.BaseModeland add it as an
attribute to the config object (DemoConfig). In the cell below, we
overwrite the definitions from above to create the nested config object:frompydanticimportBaseModel,PositiveFloat,HttpUrl# for objects that will be nested into the root config object, inherit from `BaseModel`classModelConfig(BaseModel):lr:PositiveFloat=1e-3# overwrite the `XConfig` definition from above to include the sub-config object.classDemoConfig(XConfig):MODEL_CFG:ModelConfig=ModelConfig()DB_HOST:HttpUrl# will be validate to be a httpUrlDB_PORT:int=Field(default=3333,le=64000,ge=1024)# will be validated to be in the given rangeDB_TYPE:Literal["postgres","mysql"]="mysql"# can only take on values present in the `Literal` definitionos.environ['MODEL_CFG__LR']='0.1'print(DemoConfig())Reading from .env file {#reading-from-env-file}In the notebooks folder there is a file called '.env' containing some
config values. XConfig objects will also parse .env files automatically
if they exist.os.environ.pop('MODEL_CFG__LR',None)print(ModelConfig())
Package: xdart
xdartX-ray Data Analysis in Real TimeInstallationRecommended method:Install Anaconda if you don't already have it (Miniconda recommended)https://docs.conda.io/en/latest/miniconda.htmlFrom the terminal, create a new environment with python < 3.12:conda create -n xdart python=3.11Activate the xdart environment:conda activate xdartInstall xdart using pip:pip install xdart --upgradeAlternative:If you already have conda installed and a working environment you want to use, you can simply install xdart in that environment:pip install xdart --upgradeRunningOnce installed you can run the program by simply typingxdartin the terminal.Important: Make sure you have activated the conda environment xdart was installed in.conda activate xdart
xdart
Package: xdat
What isxdat?xdat (eXtended Data Analysis Toolkit), is a set of utilities that help streamline data science projects.Monkey-patches common packages to add useful functionalityHelps with standardization of plots in projectsIncludes useful tools (like caching)Warning: this isn't even in "alpha" yet, and will likely have changes that are not backwards-compatible.InstallationUbuntu 20.04> pip install xdatOptionally, can also install::> sudo apt -q -y install libeigen3-dev
> pip install git+https://github.com/madrury/py-glm.git
Package: xdatbus
xdatbusXdatbus is a Python package designed specifically for Vienna Ab-initio Simulation Package (VASP) users conducting
ab-initio molecular dynamics (AIMD) simulations, as well as biased MD simulations. The name of the package is derived
from XDATCAR, which represents the combined AIMD trajectories generated by VASP. Documentation for the package can be
accessedhereand the Jupyter
Notebooktutorialis also available.InstallationMake sure you have a Python interpreter, preferably version 3.10 or higher. Then, you can simply install xdatbus from
PyPI usingpip:pipinstallxdatbusIf you'd like to use the latest unreleased version on the main branch, you can install it directly from GitHub:pipinstall-Ugit+https://github.com/jcwang587/xdatbusThe package is also availabe from conda-based installation. It is generally recommended you first create a separate
environment, then you can install via the xdatbus channel on Anaconda cloud:condainstall--channelxdatbusxdatbusIf you plan to use PLUMED to analyze biased MD sampling results, you can also install the conda version of PLUMED
together:condainstall-cxdatbus-cconda-forgexdatbusplumedGet StartedThis is a brief example demonstrating how to use the basic function of xdatbus to aggregate multiple xdatcar files into
a single file and unwrap the coordinates into an .xyz file:importosfromxdatbusimportxdc_aggregate,xdc_unwrapxdc_dir="./xdatcar_dir"xdb_dir=os.path.dirname(xdc_dir)xdb_path=os.path.join(xdb_dir,"XDATBUS")xyz_path=os.path.join(xdb_dir,"XDATBUS_unwrap.xyz")xdc_aggregate(xdc_dir=xdc_dir,output_dir=xdb_dir)xdc_unwrap(xdc_path=xdb_path,output_path=xyz_path)There are also entry points included with the installation for the Command Line Interface (CLI) to perform similar
tasks (do not include the$when copying):$xdc_aggregate--xdc_dir./xdatcar--output_dir./$xdc_unwrap--xdc_path./XDATBUS--output_path./XDATBUS_unwrap.xyzMajor Changelog0.2.2Enabled CLI with enhanced interaction through therichpackage.0.2.0Implemented the preparation of training data forMACEmachine learning
interatomic potentials.
Package: xdbx
[toc]项目背景目前每次我们存数据库的时候都会有这样的问题，所有的数据在同步。或者说在入库时我们需要写入库的相关代码【day by day】，本着：DRY - Don't Repeat Yourself(不要重复你自己)原则于是我想到了我们可以异步及批量数据操作器。项目构想我们只需要关注数据的问题，不用再太多费心操作相关的创建表，修改表相关字段问题项目支持的数据库类型MysqlSqlServerPostgresKafKaElasticSearchMongo文件说明DBOP(Database Operation)数据库操作相关代码x_sqlserver.py文件用来存储处理x_sqlserver数据的管道x_mysql.py文件用来存储处理x_mysql数据的管道x_kafka.py文件用来存储处理kafka数据的管道x_mongo.py文件用来存储处理Mongo数据的管道SqlServer将SqlServer进行了封装，会自动智能的去创建一些表和字段相关的东西，会省爬虫开发者一些时间MySQL将MySQL进行了封装，会自动智能的去创建一些表和字段相关的东西，会省爬虫开发者一些时间。因为mysql<=5.5版本可能有些创建更新时间不稳定的问题，我已经把相关的代码先暂时不开放，如果有更好的方案我们再优化一下。PostgreSQL将PostgreSQL进行了封装，会自动智能的去创建一些表和字段相关的东西，会省爬虫开发者一些时间。Kafka将Kafka进行了封装,对平时我们爬虫的一些常规数据存储做操作，利用单例模式开发支持多线程操作【加锁】基本实例# 导入mysqlfromxdbximportx_mysql# 导入sqlserver# from xdbx import x_mssql# 数据库ipx_mysql.host='127.0.0.1'# 数据库端口 【mysql默认为3306】x_mysql.port=3306# 数据库用户名x_mysql.username='root'# 数据库密码x_mysql.password='123456'# 数据库名【需要先创建好的数据库】x_mysql.db='test'# 插入一条x_mysql.insert_one(item={'a':1,'b':2},table='ceshi_20211229')# 插入多条x_mysql.insert_many(items=[{'a':1,'b':2},{'a':1,'b':2},{'a':1,'b':2},{'a':1,'b':2}],table='ceshi_20211229')TODO-LIST支持数据库的连接参数重写操作智能创建表和字段操作数据同一个表字段不同时，会到表中智能增加字段批量数据插入操作支持单例多线程加锁操作创建表时会自动报警钉钉通知消息添加查询功能添加更新功能添加主键功能支持字符串,字典和列表类型Q&AQ0:解决触发器的问题注：相同数据库中不能有相同的触发器，虽然作用于这个表，但是他的范围是相对于数据库，相当于函数名Q1:解决字段名大小写不同判断有误的问题使用字段做对比时全进行转换成小写后再对比
Package: xdcc-dl
XDCC DownloadermasterdevelopAn XDCC File downloader based on theirclibframework.InstallationEither install the program usingpip install xdcc-dlorpython setup.py installPlease not that python 2 is no longer supported, the project requires python 3 to run.UsageMessage-based CLIXDCC Packlists usually list xdcc commands in the following form:/msg <BOTNAME> xdcc send #<PACKNUMBER>By supplying this message as a positional parameter, the pack can be downloaded.Examples:# This is the xdcc message:  '/msg the_bot xdcc send #1'

# This command downloads pack 1 from the_bot
$ xdcc-dl "/msg the_bot xdcc send #1"

# It's possible to download a range of packs (1-10 in this case):
$ xdcc-dl "/msg the_bot xdcc send #1-10"

# Range stepping is also possible:
$ xdcc-dl "/msg the_bot xdcc send #1-10;2"
# (This will download packs 1,3,5,7,9)

# Explicitly specifying the packs to download as a comma-separated list:
$ xdcc-dl "/msg the_bot xdcc send #1,2,5,8"
# (This will download packs 1,2,5,8)

# you can also specify the destination file or directory:
$ xdcc-dl "/msg the_bot xdcc send #1" -o /home/user/Downloads/test.txt

# if the bot is on a different server than irc.rizon.net, a server
# has to be specified:
$ xdcc-dl "/msg the_bot xdcc send #1" --server irc.freenode.org

# To specify different levels of verbosity, pass the `--verbose` or
# `--quiet` flag
$ xdcc-dl -v ...
$ xdcc-dl -q ...As a library:xdcc-dl is built to be used as a library for use in other projects.
To make use of the XDCC downloader in your application, you will first need to
create a list ofXDCCPackobjects.This can be done manually using the constructor, the XDCCPack.from_xdcc_message
class method or by using anXDCC Search EngineOnce this list of XDCCPacks is created, use thedownload_packsfunction in
thexdcc module.An example on how to use the library is listed below:fromxdcc_dl.xdccimportdownload_packsfromxdcc_dl.pack_searchimportSearchEnginesfromxdcc_dl.entitiesimportXDCCPack,IrcServer# Generate packsmanual=XDCCPack(IrcServer("irc.rizon.net"),"bot",1)from_message=XDCCPack.from_xdcc_message("/msg bot xdcc send #2-10")search_results=SearchEngines.SUBSPLEASE.value.search("Test")combined=[manual]+from_message+search_results# Start downloaddownload_packs(combined)Projects using xdcc-dltoktokkieFurther InformationChangelogLicense (GPLv3)GitlabGithubProgstatsPyPi
Package: xdcetl
XDC ETLXDC ETL lets you convert blockchain data into convenient formats like CSVs and relational databases.Do you just want to query XDC data right away? Use thepublic dataset in BigQuery.Full documentation available here.QuickstartInstall XDC ETL:pip3installxdc-etlExport blocks and transactions (Schema,Reference):>xdcetlexport_blocks_and_transactions--start-block0--end-block100000\--blocks-outputblocks.csv--transactions-outputtransactions.csv\--provider-uriHttps://rpc.XDC.orgExport ERC20 and ERC721 transfers (Schema,Reference):>xdcetlexport_token_transfers--start-block0--end-block500000\--provider-urifile://$HOME/Library/Ethereum/geth.ipc--outputtoken_transfers.csvExport traces (Schema,Reference):>xdcetlexport_traces--start-block0--end-block500000\--provider-urifile://$HOME/Library/Ethereum/parity.ipc--outputtraces.csvStream blocks, transactions, logs, token_transfers continually to console (Reference):>pip3installxdc-etl[streaming]>xdcetlstream--start-block500000-eblock,transaction,log,token_transfer--log-filelog.txt\--provider-uriHttps://rpc.XDC.orgFind other commandshere.For the latest version, check out the repo and call>pip3install-e.>python3xdcetl.pyRunning Tests>pip3install-e.[dev,streaming]>exportETHEREUM_ETL_RUN_SLOW_TESTS=True
>exportPROVIDER_URL=<your_porvider_uri>
>pytest-vvRunning Tox Tests>pip3installtox
>toxRunning in DockerInstall Docker:https://docs.docker.com/get-docker/Build a docker image> docker build -t xdc-etl:latest .
 > docker image lsRun a container out of the image> docker run -v $HOME/output:/xdc-etl/output xdc-etl:latest export_all -s 0 -e 5499999 -b 100000 -p  Https://rpc.XDC.org
 > docker run -v $HOME/output:/xdc-etl/output xdc-etl:latest export_all -s 2018-01-01 -e 2018-01-01 -p  Https://rpc.XDC.orgRun streaming to console or Pub/Sub> docker build -t xdc-etl:latest .
 > echo "Stream to console"
 > docker run xdc-etl:latest stream --start-block 500000 --log-file log.txt
 > echo "Stream to Pub/Sub"
 > docker run -v /path_to_credentials_file/:/xdc-etl/ --env GOOGLE_APPLICATION_CREDENTIALS=/xdc-etl/credentials_file.json xdc-etl:latest stream --start-block 500000 --output projects/<your-project>/topics/crypto_ethereumIf running on Apple M1 chip add the--platform linux/x86_64option to thebuildandruncommands e.g.:docker build --platform linux/x86_64 -t xdc-etl:latest .
docker run --platform linux/x86_64 xdc-etl:latest stream --start-block 500000Projects using Ethereum ETLGoogle- Public BigQuery Ethereum datasetsNansen- Analytics platform for EthereumUseful Links on Orginal ETH ETLSchemaCommand ReferenceDocumentationPublic Datasets in BigQueryExporting the BlockchainQuerying in Amazon AthenaQuerying in Google BigQueryQuerying in KaggleAirflow DAGsPostgres ETLEthereum 2.0 ETL
Package: xdcget
xdcget: a command line tool to collect webxdc apps from git repositoriesThe main purpose for this tool is to maintain a cache of released webxdc apps
and to export release files so they can be imported from thexdcstore botwhich in turn can be contacted by Delta Chat users
in order to be able to search and share webxdc apps in chats.Getting startedInstallxdcgetcommand line tool from a local checkout:pip install -e .Initialize config files:xdcget initEditxdcget.iniand make sure that you set environment variables
containing your credentials for Codeberg/Github API usage.
See below for how to get API credentials.Run theupdatecommand to retrieve newest webxdc app releases
for repositories listed inxdcget.iniand export them to theexportdirectory:xdcget updateGetting a Codeberg API access tokenLogin with Codeberg and openhttps://codeberg.org/user/settings/applicationsto generate a new token.  This token does not need any special "scopes"
it's only used for querying releases of public repositories.
You can copy the resulting API token into your clipboard
and then set it into the environment variables you declared in the config file:# bash example
export XDCGET_CODEBERG_USER=<your-codeberg-user-name>
export XDCGET_CODEBERG_TOKEN=<paste-your-codeberg-access-token-here>Getting a Github API access tokenLogin with github and openhttps://github.com/settings/tokensto generate a new token.  This token does not need any access
to your private repos -- it's only used for querying releases
of public repositories.  You may give it 90 days or other expiration
times as you feel fine with.
You can copy the resulting API token into your clipboard
and then set it into the environment variables you declared in the config file:# bash example
export XDCGET_GITHUB_USER=<your-github-user-name>
export XDCGET_GITHUB_TOKEN=<paste-your-github-access-token-here>ContributingInstall tox:pip install toxWe useblackto format the code andruffas linter. After modifying the code, run:tox -e lintRun automated tests with:toxIMPORTANT:Pull Requests with new features / bug fixes should come with automated tests.Building and publishing xdcget releasesQuick notes on requirements for testing and releasing:pip install tox build twineto install development dependenciestoxto run testspython -m buildto build the distribution filescreate API-tokens on PyPIto be able to upload to PyPI repositories.Use git to tag a release before uploading (e.g. "git tag" and "git push --tags")
The version of xdcget (also obtained viaxdcget --version) is dynamically
computed usingsetuptools-scmtwine upload dist/*to upload all built distribution files
Package: xdclient
XDClient is a command line git log collector client for agilean/lean team effectiveness analysis toolX-Developer.Currently it supports both python 2.7.x and 3.5+.Why Should I Use This?The reason to use xdclient is that it provides ability to intergrate team effectiveness analysis with CI(Continuous Intergration Server) via command line.It allows you to pre-install the package and add anexec commandin your pipeline file.FeaturesConfig APPID/KEY and TeamID via parametersGenerates git logSpecify master/dev(default) branchesCommunicates with X-Developer analysis server and response statusSend git log to X-Developer and start analysisInstallationpip install xdclientUsageSpecify theappidappkeyteamidfrom your X-Developer account.python -m xdclient -i {appid} -k {appkey} -t {teamid}Options--version             show program's version number and exit
  -h, --help            show this help message and exit
  -i APPID, --appid=APPID
                        The information of appid in your home page
  -k APPKEY, --appkey=APPKEY
                        The information of appkey in your home page
  -t TEAM, --team=TEAM  The team id you created
  -p PATTERN, --pattern=PATTERN
                        Sensitive words
  -f FORCE, --force=FORCE
                        Force Analysis
  -m MASTER, --master=MASTER
                        MasterResourcesX-Developer
Package: xdebugtoolkit
Installationsudoeasy_installxdebugtoolkit[xdot]orsudopipinstallxdebugtoolkit[xdot]Toolscg2dot- converter fromXdebug cachegrindfiles to thedotformat.cgsplit- splitter for appended cachegrind files. May be useful in
case yourxdebug.profiler_appendoption is set to 1.xdot-pygoocanvas- completely rewrittenxdotin order to make
it utilize PyGooCanvas which gives extra performance on large graphs.
You’ll need to install PyGooCanvas in addition toxdot
requirements.