Saravanan666/pypi-org huggingface dataset:
Package: gator-eda
🐊 GatorGator is a combination of a task runner and a logging system. Every job is
managed by a wrapper layer that monitors its progress, resource usage, and
captures the messages that it emits via STDOUT or STDERR. Execution is managed
in a hierarchical structure, with jobs at the leaves reporting back to layers of
parent processes.NOTEThis project is under development is not feature complete, nor has it
been battle tested.Setting UpGator has been developed usingpoetryfor package management:$>python3-mpipinstallpoetry
$>poetryinstallYAML SpecificationTasks to perform are specified in a custom YAML syntax, arranged into two
distinct object types:!Jobthat describes a single task to perform;!JobGroupthat describes a set of tasks to perform, also supporting layers
of nested groups;!JobArraysimilar to a!JobGroup, but repeats the set of described tasks
a specified number of times.A simple specification may look like this:!JobGroupid:topjobs:# Nested layer-!JobGroupid:innerjobs:-!Jobid:say_hicommand:echoargs:["hi"]# Arrayed job - waits for 'say_hi' to complete-!JobArrayid:countingon_pass:-say_hirepeats:4jobs:-!Jobid:echo_countcommand:echoargs:["$GATOR_ARRAY_INDEX"]# Directly attached to root - waits for 'counting' to complete-!Jobid:say_byeon_pass:-countingcommand:echoargs:["bye"]Executing a Job SpecificationTo run a given job specification, use the Gator CLI:$>python3-mgatorexamples/job.yaml[17:58:50]StartingGator🐊[INFO]Launchingtask:echoheythereyou[INFO]Monitoringtask[INFO]heythereyou[INFO]Taskcompletedwithreturncode0[INFO]Recorded0warningsand0errorsHubTo run the hub:$>poehubTODOGet hub workingPass artefacts between jobs and form artefact based dependenciesArbitrary metrics gathering - replace warning and error counts with a generalised mechanism that supports aggregation while summarising min, max, mean, sum, and count of metrics recordedRandom number seedingHooksTool based log parsersCustom runners - currently everything is shell, perhaps support other things?Non-environment variable based parameters
Package: gator-models
gator-modelsMongoDB models for Gator APIPackage managerGator-models uses thepoetrypackage manager to manage its dependencies. To install the dependencies, run the following command:poetry installSee thepoetrydocumentation for more information and
installation instructions.ToolsLinting the codebaseFor detecting code quality and style issues, runflake8For checking compliance with Python docstring conventions, runpydocstyleNOTE: these tools will not fix any issues, but they can help you identify potential problems.Formatting the codebaseFor automatically formatting the codebase, runautopep8 --in-place --recursive .For more information on this command, see theautopep8documentation.For automatically sorting imports, runisort .
Package: gators
gatorsDocumentation|InstallationGators is an in-house machine learning library developed by the Simility Data Team. While data pre-processing and machine learning models are developed in Python, the pre-processing aspect is replaced by faster compiled programming languages in the production environment. This change of programming language is an added complexity to the model deployment process but is required to cope with the large number of queries per second that can be observed.The goal of Gators is to be able to manage both model building and model serving using only Python, a language that data scientists are generally familiar with. Gators is built on top of Pandas, Koalas, NumPy and Cython. Pandas and Koalas are used for the offline model building, while NumPy and Cython are used to speed-up the model predictions in real-time. Gators was originally built for fraud modelling but can be generalized to other modelling domains.Gators helps to streamline the model building and productionization processes. The model building part is done using the Pandas library for datasets held in memory, or Koalas for big data. On the model serving side, the pre-processing is carried out directly with Python, using NumPy and Cython. As a result, the speed-up using both NumPy and Cython for pre-processing is around x100 compared to standard Python code. Moreover, the per-sample response time becomes similar to other compiled languages (microsecond scale).In summary, Gators is a package to handle model building with big data and fast real-time pre-processing, even for a large number of QPS, using only Python.
Package: gatree
GATreeFree software:MIT licenseDocumentation:http://gatree.readthedocs.ioPython: 3.9.x, 3.10.xOperating systems: Windows, Ubuntu, macOSAbout 📋GATree is a Python library designed for implementing generative decision trees using a genetic algorithm approach. The library provides functionalities for selection, mutation, and crossover operations within the decision tree structure, allowing users to evolve and optimize decision trees for various classification tasks. 🌲🧬The library's core objective is to empower users in creating and fine-tuning decision trees through an evolutionary process, opening avenues for innovative approaches to classification problems. GATree enables the dynamic growth and adaptation of decision trees, offering a flexible and powerful tool for machine learning enthusiasts and practitioners. 🚀🌿InstallationpipTo install GATree using pip, run the following command:pipinstallgatreeLicenseThis package is distributed under the MIT License. This license can be found online athttp://www.opensource.org/licenses/MIT.DisclaimerThis framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!
Package: gats-torch
GATSImplementation of GATS from the paper: "GATS: Gather-Attend-Scatter" in pytorch and zeta.Installpip install gatsUsageCitation@misc{zolna2024gats,title={GATS: Gather-Attend-Scatter},author={Konrad Zolna and Serkan Cabi and Yutian Chen and Eric Lau and Claudio Fantacci and Jurgis Pasukonis and Jost Tobias Springenberg and Sergio Gomez Colmenarejo},year={2024},eprint={2401.08525},archivePrefix={arXiv},primaryClass={cs.AI}}LicenseMIT
Package: gattfuzz
# GattFuzz
GattFuzz 是一款针对BLE Gatt的模糊测试工具。### 环境
Linux### Requirements
Bluepy、Scapy### 安装与使用
#### 方法一` pip install-rrequirements.txt python main.py `#### 方法二` python setup.py install gattfuzz `#### 方法三` pip install gattfuzz gattfuzz `#### 使用` gattfuzz-h`支持两个参数，-f 和 -m，-f为可选。 -m为目标的mac地址。-f 就是你抓取到的pcap包，会根据包提取去进行变异fuzz。如果不指定，则直接遍历目标设备gatt，并进行fuzzing 测试。### 功能描述#### 支持两种方式：基于抓到的数据包进行pyload变异，具体变异规则待述；没有数据包的情况下支持随机数Fuzz。#### 预期结果：当检测到目标设备结束广播、断连会push error，可进一步人工核实目标设备状态，重放pyload### 可能遇到的一些问题目前支持手机hci log抓的pcap包（需要自己转格式）和抓取的空中包两种pcap包，其他的包解析出可能欢迎提issue
Package: gattlib-py
gattlib-pyis a wrapper forgattliblibrary.DevelopmentSetPYTHONPATH:export PYTHONPATH=$PWD/gattlib-py:$PYTHONPATHBuild native librarymkdir -p build && cd build
cmake ..
makeSetexport LD_LIBRARY_PATH=$PWD/dbusfor Python module to find native library.
Package: gatum-api-py
Python library for Gatum APIThe library for interacting withGatum GET APIRequires Python 3.10 or later.Getting StartedTo use the library, first install it from PyPI usingpip:pip install gatum-api-pyUsage for Gatum GET APIOnce you have installed the library, get an API token from Gatum WEB application.Send SMSfromgatum_api_pyimportGatumAPI# base_url parameter must be set according to your vendor:client=GatumAPI(base_url='https://api.gatum.io',token='your_token')# .send_sms() returns a requests library object with server response dataresponse=client.send_sms(phone='380957225759',# required, destination phone number in MSISDN formatsender_id='Verify',# required, numeric SID length must be 3-15 symbols,# alphanumeric SID length must be <= 11 symbolstext='Your OTP is 4542',# requiredsms_type='sms',# optional, allowed types: 'sms', 'hlr', 'mnp', by default: 'sms'lifetime='86400',# optional, how many seconds this SMS will live, by default: '86400'begin_date='2023-03-19',# optional, the date when SMS should be sent,# format: 'YYYY-MM-DD', by default: the current datebegin_time='19:16:00',# optional, the time when SMS should be sent in GMT+0 in selected beginDate,# format: 'HH:MM:SS', by default: the current timedelivery=False)# optional, whether to return the DLR, 'TRUE' or 'FALSE', by default: 'TRUE'print(response.json())Receive DLRTo receive DLR, you need to go to theAPI>GETsection in your WEB cabinet and in theDLR sending to webhookfield, specify the webhook URL where Gatum should send them.For example, you specified the URL of your webhook ashttps://yourdomain.com/apidlr, in this case, to send DLR Gatum will make a GET request to the URLhttps://yourdomain.com/apidlr?id_state=598801503&state=DELIVRD&time=2023-07-23+21%3A41%3A20To confirm that you received the DLR, you need to return the value of theid_stateparameter in response to this request.An example of a simple listener written using the Python Flask framework:fromflaskimportFlask,requestfromurllib.parseimporturlparse,parse_qs,unquoteapp=Flask(__name__)@app.route('/apidlr',methods=['GET'])defreceive_dlr():url=request.urlparsed_url=urlparse(url)query_string=unquote(parsed_url.query)query_params=parse_qs(query_string)ifparsed_url.queryelse{}params={}forkey,valuesinquery_params.items():params[key]=values[0]iflen(values)==1elsevaluesprint(params)returnparams['id_state'],200if__name__=="__main__":app.run(debug=False,port=3000)The output:{'id_state': '672887248', 'state': 'DELIVRD', 'time': '2023-11-28 13:23:13'}
Package: gauchian
Gauchian: WGS-based GBA variant callerGauchian is a targeted variant caller for the GBA gene based on a whole-genome sequencing (WGS) BAM file. Gauchian uses a novel method to solve the problems caused by the high sequence similarity with the pseudogene paralog GBAP1 and is able to detect variants accurately in the Exons 9-11 homology region, such as large deletions or duplications between GBA and GBAP1, and GBAP1-like variants in GBA, including p.A495P, p.L483P, p.D448H, c.1263del, RecNciI, RecTL and c.1263del+RecTL. In addition to these challenging variants, Gauchian also calls known pathogenic or likely pathogenic GBA variants classified in ClinVar. Gauchian has been tested on Illumina WGS data with standard sequencing depth (>=30X). Gauchian does not work on targeted sequencing data. Please refer to ourpreprintfor more details about the method.InstallationThis Python package is supported for Linux and macOS. It has been tested on CentOS 7.9.2009.The Python dependencies can be found inrequirements.txt. Installation takes a few seconds.gitclonehttps://github.com/Illumina/GauchiancdGauchian
python3setup.pyinstallRunning the programgauchian--manifestMANIFEST_FILE\--genome[19/37/38]\--prefixOUTPUT_FILE_PREFIX\--outDirOUTPUT_DIRECTORY\--threadsNUMBER_THREADSThe manifest is a text file in which each line should list the absolute path to an input WGS BAM/CRAM file. Full WGS BAM/CRAM files are recommended. If you would like to use a subsetted bamlet, please subset using region files in gauchian/data/GBA_region_*.bed.For CRAM input, it’s suggested to provide the path to the reference fasta file with--referencein the command.Interpreting the outputThe program produces a .tsv file in the directory specified by --outDir.
The fields are explained below:Fields in tsvExplanationSampleSample nameis_biallelic(GBAP1-like_variant_exon9-11)Whether the sample is called as biallelic for GBAP1-like variants in exon9-11is_carrier(GBAP1-like_variant_exon9-11)Whether the sample is called as a carrier for GBAP1-like variants in exon9-11CN(GBA+GBAP1)Total copy number of GBA+GBAP1deletion_breakpoint_in_GBAWhether the deletion breakpoint is in GBA gene if a deletion existsGBAP1-like_variant_exon9-11GBAP1-like variants called in exon9-11, two alleles separated by /other_unphased_variantsOther variants called (non-GBAP1-like variants or variants outside of exon9-11)A .json file is also produced that contains more information about each sample.Fields in jsonExplanationCoverage_MADMedian absolute deviation of depth, measure of sample qualityMedian_depthSample median depthdeletion_CNCN of the unique region between GBA and GBAP1. This value plus 2 is the total CNdeletion_CN_rawRaw normalized depth of the unique region between GBA and GBAP1variant_raw_countSupporting reads for each variantsnp_callGBA copy number call at GBA/GBAP1 differentiating sitessnp_rawRaw GBA copy number at GBA/GBAP1 differentiating siteshaplotypesSummary of haplotypes assembled across GBA/GBAP1 differentiating sites in Exon9-11
Package: gaudi
Build StatusVersionA helper library for Clean Architectures in PythonFull documentation available athttp://gaudilib.readthedocs.io/en/latest/About GaudiGaudi (pronounced /ˈɡaʊdi/) is a library that provides some helper
structures to build projects based on a clean architecture in Python.
The very nature of a clean architecture is the opposite of a framework,
so Gaudi provides the minimum amount of classes and methods to avoid
boring repetition in your code.Gaudi is opinionated, as some of the structures provided enforce minimum
conventions, like requests being dictionary-like objects and responses
having a category and a content. It is also extensible, however, as you
are free to change the behaviour of the components at any time.As you are following the clean architecture model you are free to build
the internal protocols and objects like you prefer. Using Gaudi saves
you some typing and enforces a minimum of conventions in your project.
You are free to use just some of the components of Gaudi without
breaking the clean architecture model.OriginIn 2016 I wroteClean architectures in Python: a step-by-step
example,
a detailed analysis of a clean architecture written in Python from
scratch following a pure TDD methodology. Since then I created many
successful projects following this model, but I quickly realised that
there was a core of code that I copied from project to project (thesharedmodule in the original article). So I decided to try to clean
it up and to publish it as a library.The name is an homage toAntoni
Gaudía genius that
gave the world some of the most beautiful architectural works ever
conceived by men.DevelopmentGaudi is an helper library for clean architectures, so it provides the
very minimum amount of code to avoid repetitions among projects. This
means that the library shouldn’t grow too much in the future. There will
be bug fixes and maybe some new helpers if there are good use cases (no
pun intended) for them. I’m however ready to be surprised, so it might
be that there are many other aspects of the clean architecture that can
be automated while keeping the nature of the whole methodology: clean
separation between layers.Feel free to submit issues or pull requests or to get in touch if you
have ideas about Gaudi. Maybe you can see what I can’t! And thanks for
using Gaudi and the Clean Architecture model!InstallationGaudi is available for Python 3 through pip. Just create a virtual
environment and runpipinstallgaudiContributingSee the CONTRIBUTING file for detailed information. Please remember that
this project is actively developed in thedevelopbranch, so be sure
to work there if you try to implement new feature of fix bugs.History1.0.1 (2019-06-26)Minor review of README and Python packaging1.0.0 (2018-08-01)First release on PyPI.
Package: gaugan
gaugan.pyA Python package providing an interface to NVIDIA's gauGAN project (http://nvidia-research-mingyuliu.com/gaugan/), which turns crude drawings into realistic images using AI.Installationpip install gauganUsageprocessImage(image = imageBytes, [style = 1], [url = 'http://12.34.56.78:90/'])Returns a bytes object of the processed image in JPG format.ParametersimageA bytes object of a PNG image containing the drawing.style(optional)Valid values are 'random' or an integer. Default is 'random'.url(optional)An URL of a gauGAN server. Useful for bulk processing, as by default it gets a new URL every timeprocessImage()is called. It is necessary because the URLs are changed periodically. You can get an URL from thegetUrl()function.ExamplesProcess image and display it using PILimport gaugan, PIL, io
from PIL import Image

with open('input.png', "rb") as fh:
    image = processImage(fh.read())

PIL.Image.open(io.BytesIO(image)).show()Process image and save it to diskimport gaugan

with open('input.png', "rb") as fh:
    image = gaugan.processImage(fh.read())

with open('output.jpg', "wb") as fh:
    fh.write(image)
Package: gauge-api-steps
Gauge API StepsA Python module, that provides re-usable steps for testing APIs with theGaugeframework.DescriptionThis is an extensible and flexible test-automation library forGauge. It enables users with and without programming knowledge to create end-to-end test scenarios inMarkdownsyntax. Developers can still easily extend their test scenarios with custom code. Python'surllibis used to make requests against APIs. XML and JSON are supported and API responses can be validated with XPath and JSONPath.Gauge Step OverviewFind the documentation on all Gauge steps of this project in the overview:Gauge Step OverviewQuick StartThis is a library for the Gauge framework, so Gauge+Python must be installed first.Install Python >= 3.10 on your platform and make it available in the $PATHInstallGaugeandcreate a test project with PythonIt is useful to understand the basic workings of Gauge first. Thedocumentationis excellent.Installthis moduleFind out the path to this module after installation:echo$(python-msite--user-site)/gauge_api_stepsAdd that path to the propertySTEP_IMPL_DIRinside the test project fileenv/default/python.properties. Paths to multiple modules are comma separated.Example on a Mac:STEP_IMPL_DIR = /Users/<user>/Library/Python/3.10/lib/python/site-packages/gauge_api_steps, step_implReload Visual Studio CodeWrite a new scenario inspecs/example.spec. VSC offersauto-completionInstallationThis module can be installed from source:cdpath/to/gauge-api-steps
pipinstall--user.Or the latest package can be downloaded and installed fromPyPi:pipinstallgauge-api-steps--user--upgradeDevelopmentWhen coding on this project, unit tests can be executed like this:python-munittestdiscover-v-stests/-p'test_*.py'Contributions are welcome.Placeholders and ExpressionsStep parameters allow the use of placeholders, that can be defined in the environment properties files. Some steps also allow to set a placeholder value manually. Property keys act as placeholders, they are defined like "${key}" and they will be replaced by its value if such a property key/value pair exists in anyenv/*/*.propertiesfile or within the execution scope.
Some steps include parameters, that allowexpressions, that will be evaluated.
Examples of allowed expressions include:=1,>1,<1,>=1,<=1.Internal PlaceholdersFollowing placeholders are used internally to store data over multiple steps:_opener_response_csrf_header_request_csrf_header_csrf_value_body_response_headersIt is possible to access and manipulate them with certain steps.ConfigurationThe Configuration follows theGauge configurationapproach.
Some behaviour can be determined with properties.Configuration OverviewMaintainersMaintainers
Package: gauge-cli
PIP Installer for getgauge-cli toolThissetup.pyfile is used for installed getgauge-cli tool.
the version number is manually bumped for now.Python package can be installed through multiple waysusing setup.pyNavigate to the current directorygauge\build\pip\Runpython build.py --installon command prompt (it will install the latest released version)To install a specific version setGAUGE_VERSIONenvironment variable.using pippre-requisite: pip should be installed and available on machinerun the commandpip install getgauge-cliCheck to ensure getgauge-cli is installedOnce the package has been setup. please exit the current terminal and relaunch terminal againrun the commandgauge, you should be able to see similar outputUsage:
  gauge <command> [flags] [args]

Examples:
  gauge run specs/
  gauge run --parallel specs/

Commands:
  config      Change global configurations
  daemon      Run as a daemon
  docs        Generate documentation using specified plugin
  format      Formats the specified spec files
  help        Help about any command
  init        Initialize project structure in the current directory
  install     Download and install plugin(s)
  list        List specifications, scenarios or tags for a gauge project
  man         Generate man pages
  refactor    Refactor steps
  run         Run specs
  telemetry   Configure options for sending anonymous usage stats
  uninstall   Uninstalls a plugin
  update      Updates a plugin
  validate    Check for validation and parse errors
  version     Print Gauge and plugin versions

Flags:
  -d, --dir string         Set the working directory for the current command, accepts a path relative to current directory (default ".")
  -h, --help               Help for gauge
  -l, --log-level string   Set level of logging to debug, info, warning, error or critical (default "info")
  -m, --machine-readable   Prints output in JSON format
  -v, --version            Print Gauge and plugin versions

Use "gauge [command] --help" for more information about a command.
Complete manual is available at https://manpage.gauge.org/.Using setup.pyInstall required dep by runningpython/python3 -m pip install requirements.txt.Check through and obtain a valid tag/build number fromreleasesRun the commandpython build.py --install(set GAUGE_VERSION env to install specific version)This would install the version as specified along with latest release of Gauge-CLI VersionRun the commandpython build.py --distto generate the PyPi distrubutable (set GAUGE_VERSION env to install specific version)Uninstalling Gauge CLIGauge CLI uninstall should be done manually for now.
Run the following command on your prompt$ pip uninstall gauge-cli
Package: gaugesrv
gaugesrvA simple gauge web service/visualization using gauge.js.Currently under development.  The way to get this demo installed and
running may be done using the following commands:$ git clone https://github.com/metatoaster/gaugesrv.git
$ cd gaugesrv
$ pip install -e .[dev,webpack,scss]
$ calmjs npm --install -i -D gaugesrv[dev,webpack,scss]
$ python setup.py build
$ gaugesrv-demoConnect a web browser tohttp://localhost:8000to view the demo once
everything is successfully installed.For running the tests for of both Python and JavaScript code shipped
with this project, run the following (provided that thedevextras
were installed, which the above instructions will do so.:$ python -m unittest gaugesrv.tests.make_suite
$ calmjs karma webpack -w gaugesrvRequireJS may be used, however the default app is not set up to include
the RequireJS boilerplates needed for the system to work, but if therjsextras is also specified in the relevant install commands, the
dependencies will be pulled and thecalmjs rjscommand may be used
to generate the artifacts.0.0.0 (Unreleased)Initial release.
Package: gaunn
This contains Feedforward and Recurrent neural networksFeaturesFeedforward and Recurrent neural netsContains logistic, tanh, ReLU, softmax neuronsStandard loss functions (squared error, cross entropy, sparsity constraints),
and support for custom loss functionsDifferent methods to initialize weightsCustomizable connectivity between layersInputs can be generated dynamically by some other system (like a simulation)Quick startInstall the package via:pipinstallgaunnRequirementspython 2.7 or 3.5numpy 1.9.2matplotlib 1.3.1
Package: gau-python
gau_pythonThis library gets urls from AlienVault's Open Threat Exchange, the Wayback Machine, and Common Crawl. Inspired byCorbin Leo'sgauInstallationpip install gau-pythonhttps://pypi.org/project/gau-python/ProvidersThis library gets urls from AlienVault's Open Threat Exchange, the Wayback Machine, and Common Crawl.UsageTo get all urls use:get_all_urls(domain)