Saravanan666/pypi-org huggingface dataset:
Package: ltp-kona
LTP 4LTP（Language Technology Platform） 提供了一系列中文自然语言处理工具，用户可以使用这些工具对于中文文本进行分词、词性标注、句法分析等等工作。If you use any source codes included in this toolkit in your work, please kindly cite the following paper. The bibtex are listed below:@article{che2020n,
  title={N-LTP: A Open-source Neural Chinese Language Technology Platform with Pretrained Models},
  author={Che, Wanxiang and Feng, Yunlong and Qin, Libo and Liu, Ting},
  journal={arXiv preprint arXiv:2009.11616},
  year={2020}
}快速使用fromltpimportLTPltp=LTP()# 默认加载 Small 模型seg,hidden=ltp.seg(["他叫汤姆去拿外衣。"])pos=ltp.pos(hidden)ner=ltp.ner(hidden)srl=ltp.srl(hidden)dep=ltp.dep(hidden)sdp=ltp.sdp(hidden)详细说明模型模型下载模型大小Base(v2)531MBSmall(v2)170MBTiny(v2)34MBSmall(v1)170MBTiny(v1)34MB备注: Tiny模型使用electra前三层进行初始化, 4.0.3.post1 版本包含对v1模型的fix，新版本请使用v2模型V2 指标模型分词词性命名实体语义角色依存句法语义依存速度(句/S)LTP 4.0 (Base)98.798.595.480.689.575.2LTP 4.0 (Small)98.498.294.378.488.374.712.58LTP 4.0 (Tiny)96.897.191.670.983.870.129.53备注: 本版本SDP采用CCL2020语义依存分析语料，其他语料同V1V1 指标模型分词词性命名实体语义角色依存句法语义依存速度(句/S)模型大小LTP 3.X97.898.394.177.92(Gold Pi)81.178.92.751940MLTP 4.0 (Small)98.498.294.377.2(端到端)88.079.912.58171MLTP 4.0 (Tiny)96.897.291.668.1(端到端)82.675.529.5334M测试环境如下：Python 3.7LTP 4.0 Batch Size = 1Centos 3.10.0-1062.9.1.el7.x86_64Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz备注: 速度数据在人民日报命名实体测试数据上获得，速度计算方式均为所有任务顺序执行的结果。另外，语义角色标注与语义依存新旧版采用的语料不相同，因此无法直接比较（新版语义依存使用Semeval 2016语料，语义角色标注使用CPB3.0语料）。模型算法分词: Electra Small1+ Linear词性: Electra Small + Linear命名实体: Electra Small + Relative Transformer2+ Linear依存句法: Electra Small + BiAffine + Eisner3语义依存: Electra Small + BiAffine语义角色: Electra Small + BiAffine + CRF构建 Wheel 包pythonsetup.pysdist
python-mtwineuploaddist/*作者信息冯云龙 <<ylfeng@ir.hit.edu.cn>>开源协议语言技术平台面向国内外大学、中科院各研究所以及个人研究者免费开放源代码，但如上述机构和个人将该平台用于商业目的（如企业合作项目等）则需要付费。除上述机构以外的企事业单位，如申请使用该平台，需付费。凡涉及付费问题，请发邮件到car@ir.hit.edu.cn洽商。如果您在 LTP 基础上发表论文或取得科研成果，请您在发表论文和申报成果时声明“使用了哈工大社会计算与信息检索研究中心研制的语言技术平台（LTP）”. 同时，发信给car@ir.hit.edu.cn，说明发表论文或申报成果的题目、出处等。脚注1::Chinese-ELECTRA2::TENER: Adapting Transformer Encoder for Named Entity Recognition3::A PyTorch implementation of "Deep Biaffine Attention for Neural Dependency Parsing"
Package: ltplugins
LTPlugins - Lite plugins for simple Python 3 projectsInstallationpipinstallltpluginsUsage examplesImagine there is a directorypluginswith two scripts:ltp_example.pyandltp_test.pyFor simplicity they contain a singlemainfunction:defmain(*args,**kwargs):print(args,kwargs)LTPlugins could dynamically load them and unload during the runtime of the main script:importltpluginsp=ltplugins.LTPlugins(prefix='ltp_',path='plugins')p.load('example')p.load('test')p.list(state='a',status=True)p.name['example'].main('123',a=456)p.run('test','main','789',a=123)p.unload('test')p.reload('example')ChangelogDateVersionRemarks2020.04.29v1.0.0Initial release2020.04.29v1.0.1setup.py fix for README.md2020.05.01v1.1.0List modules: all, loaded and unloaded2020.05.01v1.1.1Readme update2020.05.07v1.1.2Default status='a' in LTPlugins.list()2020.05.07v1.1.3LTPlugins.list() now returns module names2020.05.07v1.1.4LTPlugins.list(state='a', status=False)2020.05.07v1.1.4.1fnmatch module reference removed2020.05.07v1.1.5Dummy release to make PyPI happyOn any issues, questions and suggestions contact me: Mikhail Zakharovzmey20000@yahoo.com
Package: ltr
rankYA fast Learning to Rank library based on RankLib written in Cython.Explore the docs »Report Bug·Request FeatureTable of ContentsAbout The ProjectGetting StartedRoadmapContributingLicenseContactAbout The ProjectrankY is a Learning to Rank library written in Cython and based on the famousRankLiblibrary. The main goal of this project is to provide a simple, fast and memory safe wich implements a wide variety of LTR models.Getting StartedpipinstallltrRoadmapThe project is in the early stages of development. Thus, feel free to contribute and help ltr++ to grow up!See theopen issuesfor a list of proposed features (and known issues).OBS: To propose new features or report bugs, check out the correct templates.ContributingContributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make aregreatly appreciated.Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull RequestLicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactMarcos Pontes -mfprezende@gmail.comProject Link:https://github.com/matchup-ir/ranky
Package: ltr559
LTR559 Proximity/Presence/Light SensorSuitable for detecting proximity of an object at close range the LTR-559 is great for approach detection and ambient light compensation. The range is useful to around 5cm, and this is the type of sensor you might find in a smartphone to determine if you're holding it against your head.InstallingStable library from PyPi:Just runpython3 -m pip install ltr559Latest/development library from GitHub:git clone https://github.com/pimoroni/ltr559-pythoncd ltr559-python./install.sh --unstable1.0.0Repackage to latest boilerplateRequire i2cdevice>=1.0.0 (now using smbus2 instead of smbus)0.1.1Fix set_proximity_rate_ms, set_light_repeat_rate_ms and set_interrupt_mode (thanks @mkende)Improve library comments & docstrings0.1.0Breaking API change to class for CircuitPython compatibilityPort to new i2cdevice set/get API0.0.4Removed rogue print() of reset status0.0.3Fix PyPi readme formatting0.0.2Added default light-sensor optionsFixed bugs & linted0.0.1Initial Release
Package: ltsa
# ltsaThe Local Tangent Space Alignment framework in Python.* ltsa [homepage](http://gitlab.com/charles1992/ltsa/)<!--- * [![licence](https://img.shields.io/badge/licence-BSD-blue.svg)](http://opensource.org/licenses/BSD-3-Clause)--><!---## Citation@Misc{ltsa2017,author =   {{ltsa}},title =    {{ltsa}: A Local Tangent Space Alignment framework in python},howpublished = {\url{http://gitlab.com/charles1992/ltsa}},year = {since 2017}}-->## InstallingYou can install the latest release with:$ pip install ltsaIf you'd like to install from source, or want to contribute to the project (i.e. by sending pull requests via github)then:$ git clone https://gitlab.com/charles1992/ltsa.git$ cd ltsa$ git checkout devel$ python setup.py build_ext --inplace## Running unit tests (development):We use nosetests, this can be installed using pip with:$ pip install noseRun nosetests from the root directory of the repository:$ nosetests -v ltsa/testingor using setuptools$ python setup.py test<!---### Commit new patch to develA usual workflow should look like this:$ git fetch origin$ git checkout -b <pull-origin>-devel origin/<pull-origin>-devel$ git merge devel**Make changes for tests to cover corner cases (if statements, None arguments etc.)**Then we are ready to make the last changes for the changelog and versioning:$ git commit -am "fix: Fixed tests for <pull-origin>"$ bumpversion patch # [optional]$ gitchangelog > CHANGELOG.md$ git commit -m "chg: pkg: CHANGELOG update" CHANGELOG.mdNow we can merge the pull request into devel:$ git checkout devel$ git merge --no-ff <pull-origin>-devel$ git push origin develThis will update the devel branch of GPy.-->
Package: ltscaddstore
Windows LTSC Microsoft Store 最新版下载安装脚本Windows LTSC 2021Windows LTSC 2019测试通过1.安装Python镜像下载官方下载安装时需勾选Add Python 3.10 to PATH🔽下载软件包pip install ltscaddstore -i https://mirror.sjtu.edu.cn/pypi/web/simple/2.运行脚本# 写入文件
echo "import ltscaddstore" > ltscaddstore.py
echo "ltscaddstore.download()" >> ltscaddstore.py
# 运行
python ltscaddstore.py软件来源所有软件来自https://store.rg-adguard.net/Url(Link)https://www.microsoft.com/store/productId/9WZDNCRFJBMPRP关于转载版权属于Microsoft,禁止商用⚠️不允许转载后的内容需要”回复“、”登录“、”关注公众号“、”积分购买“以及其他任何不能直接下载的形式展现
Package: ltsearthquake-indonesia
latest-IndonesiaEarthquakeThis package will get the latest Earthquake from BMKG Badan Meteorologi, Klimatologi, dan GeofisikaHOW IT WORKSThis packages will scrape fromBMKGto get latet quake happenend in Indonesia.My-First Python codeIni adl project pertama sy dengan pythonTahap 1 buat readme.mdTahap 2 copast project dr sebelahTahap 3 Mengisikan setup.pyHOW TO USEif __name__ == '__main__':
    print('Aplikasi  utama')
    result = gempa_terkini.ekstraksi_data()
    gempa_terkini.tampilkan_data(result)#AUTHOR
Adjie pratamaWith SSH!#Tahap1 : Membuat README.md
#Tahap2 : Copast Project dr sebelah
#Tahap3 : Mengisikan setup.py
Package: ltspice
LTSpice data parsing library for pythonInstallation$pipinstallltspiceSupported Filesencoding : UTF8 / UTF16-LEformat : Binary / Asciiextenstion : .raw / .fftUsageimportltspicefilepath='Your ltspice output file (.raw)'l=ltspice.Ltspice(filepath)l.parse()# Data loading sequence. It may take few minutes for huge file.time=l.get_time()V1=l.get_data('V(N1)')Examples01 - RC CircuitLTSpice file (.asc)Python code (.py)importltspiceimportmatplotlib.pyplotaspltimportnumpyasnpimportosl=ltspice.Ltspice(os.path.dirname(__file__)+'\\rc.raw')# Make sure that the .raw file is located in the correct pathl.parse()time=l.get_time()V_source=l.get_data('V(source)')V_cap=l.get_data('V(cap)')plt.plot(time,V_source)plt.plot(time,V_cap)plt.show()Output result02 - Multi point simulationLTSpice file (.asc)Python code (.py)importltspiceimportmatplotlib.pyplotaspltimportnumpyasnpimportosl=ltspice.Ltspice(os.path.dirname(__file__)+'\\rectifier.raw')# Make sure that the .raw file is located in the correct pathl.parse()time=l.get_time()V_source=l.get_data('V(source)')V_cap_max=[]plt.plot(time,V_source)foriinrange(l.case_count):# Iteration in simulation casestime=l.get_time(i)# Case number starts from zero# Each case has different time point numbersV_cap=l.get_data('V(cap,pgnd)',i)V_cap_max.append(max(V_cap))plt.plot(time,V_cap)print(V_cap_max)plt.xlim((0,1e-3))plt.ylim((-15,15))plt.grid()plt.show()Output result$[8.299080580472946,7.855469107627869,7.391375303268433,6.944645524024963,6.529755532741547]If you want to find more usage examples, please check examples folder.
Package: ltsv
Parser implementation in PythonLTSV:Labeled Tab-separated Valueshttp://ltsv.org/Installationpip install ltsvUsageparsingcsv like interface:>>> import sys
>>> if sys.version_info[0] == 3:
...     from io import StringIO
... else:
...     from cStringIO import StringIO
...
>>> import ltsv
>>> reader = ltsv.reader(StringIO("ip:127.0.0.1\thost:localhost"))
>>> next(reader)
[['ip', '127.0.0.1'], ['host', 'localhost']]writingdic = {u'ip': u'127.0.0.1', u'host': u'localhost'}
string = ltsv.writer(dic)
print(string)
# ip:127.0.0.1    host:localhost
Package: lts_workflows
lts-workflowsHelper functions, utilities and Docker images for SciLifeLab long-term
support (lts) workflows.FeaturesThis repository serves as the main entry point for lts workflows. It
does not provide any workflow code /per se/, but rather the following:pytestwrappers for setting up testsbaseDockerimage for creating workflow imagesgeneraldocsthat apply to all lts workflowsFor more information, see thedocs.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.2.5 (2018-04-05)Change to use Ubuntu as base Docker image and removed LaTeX packagesRemoved Nextflow environment0.2.4 (2017-11-14)Hotfix: remove versioneer from setup_requirements (issue #24)0.2.3 (2017-11-14)Add setup requirements to install tagged lts-workflows version in docker image0.2.2 (2017-11-13)Add options to snakemake_run (issue #23)0.2.1 (2017-09-26)Minor changes to conda/meta.yamlUpdate docsUpdate development requirementsAdd snakemake utilitiesAdd inconsolata fonts to dockerCRLF to LF and Dockerfile organizationMake conda builds with conda build-all (issue #22)Fix pytest mark for slow tests (issue #19)Add pytest entry point (issue #18)Add configfile option to pytest (issue #16)Redirect subprocess stderr to stdout and use stdout variable (issue #17)0.2.0 (2017-03-21)Add docker base image and make it smallish (issue #3)Update docs0.1.1 (2017-03-01)Convert threads argument to string (issue #7)Add population layouts to helper function (issue #4)0.1.0 (2017-02-12)First release on conda.
Package: lttb
Numpy implementation of Steinarsson’sLargest-Triangle-Three-Bucketsalgorithm
for downsampling time series–like data
while retaining the overall shape and variability in the dataLTTB is well suited to filtering time series data for visual representation,
since it reduces the number ofvisually redundantdata points,
resulting in smaller file sizes and faster rendering of plots.Note that it is not a technique for statistical aggregation,
cf. regression models or non-parametric curve fitting / smoothing.This implementation is based on the original JavaScript code athttps://github.com/sveinn-steinarsson/flot-downsampleand Sveinn Steinarsson’s 2013 MSc thesisDownsampling Time Series for Visual Representation.Licence: MITUsageInstall thelttbpackage into your (virtual) environment:$ pip install lttbThe functionlttb.downsample()can then be used in your Python code:importnumpyasnpimportlttb# Generate an example data set of 100 random points:#  - column 0 represents time values (strictly increasing)#  - column 1 represents the metric of interest: CPU usage, stock price, etc.data=np.array([range(100),np.random.random(100)]).T# Downsample it to 20 points:small_data=lttb.downsample(data,n_out=20)assertsmall_data.shape==(20,2)A test data set is provided in the source repo intests/timeseries.csv.
It was downloaded fromhttp://flot.base.is/and converted from JSON to CSV.This is what it looks like, downsampled to 100 points:Input validationBy default,downsample()checks that the input data satisfies the following constraints:it is a two-dimensional array of two columns;the values in the first column are strictly increasing; andthere are no missing (NaN) values in the data.These checks can be skipped (e.g. if you know that your data will always meet these conditions),
or additional checks can be added (e.g. that the time values must be evenly spaced),
by passing in a different list of validation functions, e.g.:# No input validation:small_data=lttb.downsample(data,n_out=20,validators=[])# Stricter check on x values:fromlttb.validatorsimport*small_data=lttb.downsample(data,n_out=20,validators=[has_two_columns,x_is_regular])History0.3.1 / 2020-10-14All modules and functions now have docstrings.[dev] The library is now also tested against Python 3.9.
No changes to the code were required.[dev] The project is now hosted on SourceHut;
the links on the PyPI page have been updated.[dev] CI testing has been migrated from Travis to builds.sr.ht.0.3.0 / 2020-09-15Validation of input data is now configurable.New default:downsample()raisesValueErrorif input data contains NaN values.
This can be disabled by removingcontains_no_nans()from the list of validators.[dev] Imports are now sorted with isort.0.2.2 / 2020-01-08setup.pywas fixed so that this package can be installed in Python 2 again.0.2.1 / 2019-11-25[dev] Versions are now managed withsetuptools_scmrather thanbumpversion.[dev] The code is formatted with Black.0.2.0 / 2018-02-11Performance improvementsReleased on PyPI (on 2019-11-06)0.1.0 / 2017-03-18Initial implementationContributorsJA Viljoen – original Numpy implementationGuillaume Bethouart – performance improvementsJens Krüger – fix for py27
Package: lttbc
lttbc: Largest-Triangle-Three-Buckets (Python using a c implementation)This is a low-level implementation of theLargest-Triangle-Three-Buckets(LTTB) downsampling algorithm written in Python.The code has been translated from the work of Sveinn Steinarsson (https://github.com/sveinn-steinarsson/flot-downsample/).Known features and requirements:The algorithm requires monotonically increasing x data (finite)The algorithm requires finite y data (otherwise problems might occur)x and y data have to be of same length (of course)The algorithm returns arrays ofdtypedoubleHow to use on the fieldThe modulelttbcdiffers in the standard input from other largest triangle three buckets implementations.
Thedownsamplefunction takes an input forxandyin addition to thethreshold:import lttbc
import numpy as np

ARRAY_SIZE = 10000
THRESHOLD = 1000

x = np.arange(ARRAY_SIZE, dtype=np.int32)
y = np.random.randint(1000, size=ARRAY_SIZE, dtype=np.uint64)

nx, ny = lttbc.downsample(x, y, THRESHOLD)

assert len(nx) == THRESHOLD
assert len(ny) == THRESHOLD
assert nx.dtype == np.double
assert ny.dtype == np.double

# List data or a mixture is accepted as well ...
x = list(range(ARRAY_SIZE))
y = [np.random.uniform(0, 1000) for _ in range(ARRAY_SIZE)]

assert isinstance(x, list)
assert isinstance(y, list)

nx, ny = lttbc.downsample(x, y, THRESHOLD)

assert len(nx) == THRESHOLD
assert len(ny) == THRESHOLD
assert nx.dtype == np.double
assert ny.dtype == np.double
Package: ltvpy
ltvpyA small implementation of theLiteVectorsserializer in Python.NumPyis used for numeric arrays.Usageencoded=ltvpy.dumpb({'Name':'data','values':[1,2,3]})decoded=ltvpy.loadb(encoded)Notes:Deterministic serialized field order relies on the new default behavior for dictionaries in Python 3.7+ which maintains a fields by insertion order. If you need struct fields serialized in a specific order, use Python 3.7 or higher.TestingThis package runs the LiteVector test vectors and other unit tests with the standard Python unittest framework, which can be run from the project root:python3 -m unittest
Package: ltyp
ltypPackages that useltypDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nltyp--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallltypnbdev# activate conda environment$condaactivateltyp# make sure the ltyp package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the ltyp package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/ltyp.gitor fromconda$condainstall-cdsm-72ltypor frompypi$pipinstallltypDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.A = AliasC = ClassVarQ = OptionalE = (Over)-EngineeredZ = CustomR = RichP = PlotD = DataB = BioM = Machine Learning
Package: luabins-py
luabins_pyA simple python library for encoding and decodingluabinsformatted serialized data.APIdef decode_luabins(stream: BytesIO) -> List[Any]Given a file-like object (here BytesIO, but a raw file pointer should work as well), this will return the list of values decoded from the file.def encode_luabins(values: List[Any]) -> bytesGiven a set of values, it will serialize them to bytes in the luabins format.Supported types are:StringIntFloatDictListNoneBoolLimitationsIn lua, arrays and maps have the same underlying representation, whereas in python, they're differentiated more. This means that you can serialize a list, but it will deserialize to a dict, where the keys are the original indexes, and there is some potential for weirdness there.I believe this library supports the full scope of the lua data model, so this mostly crops up if you try to do some weird python stuff.
Package: luadata
luadataThis is a Python package that can serializePythonlist & dictionary toLuatable, or unserializeLuatable toPythonlist & dictionary.InstallBinary installers for the latest released version are available at thePypi.python -m pip install --upgrade luadataUsagewriteSerializepythonvariable toluadata string, and save to specific path.importluadataluadata.write(path,data,encoding="utf-8",indent="\t",prefix="return ")readUnserializeluadata string topythonvariable from file.importluadatadata=luadata.read(path,encoding="utf-8")serializeSerializepythonvariable toluadata string.importluadataluadata.serialize(var,encoding="utf-8",indent="\t",indent_level=0)unserializeUnserializeluadata string topythonvariable.importluadataluadata.unserialize(luadata_str,encoding="utf-8",multival=False)LicenseBSD
Package: lua-fastutils
lua-fastutilsCollection of simple utils.Installpipinstalllua-fastutilsLUA modulesfastutils.httputilsget_request_dataheader_lines_to_headers_mappingurl_path_matchfastutils.strutilscapitalizecamelis_in_arraystartswithendswithis_match_patternstrimltrimrtrimtrim_stringsremove_empty_string_from_tablesplit2splitis_emptyfastutils.tableutilsconcatfastutils.typingutilsis_stringis_numberis_booleanis_nilis_functionis_tableis_listis_maptostringInstalled Command Utilsmanage-lua-fastutilsUsageUsage:manage-lua-fastutils[OPTIONS]COMMAND[ARGS]...

Options:--helpShowthismessageandexit.

Commands:installCreatealuapackageandtheninstallit.packCreatealuapackage.Releasesv0.1.8-1 2021/03/08httputils.get_request_data add cookies support.v0.1.5-1 2020/11/05Fix luaproject.get_version problem.v0.1.4-1 2020/11/05Add typingutils.Add httputils.Add tableutils.v0.1.0-1 2020/08/27First release.
Package: lua-imports
lua_importsImport Lua modules directly from within Python.Basic UsageOncelua_imports.lua_importerhas been registered, write animportstatement in your Python code, referring to your Lua module.Examplefoo.luareturn{say_hello=function(person)print("Hello, "..person)end}bar.pyimportfoofoo.say_hello("World")Registrationlua_importermay be registered within Python code:fromlua_importsimportlua_importerlua_importer.register()(Note that this must comebeforeany Lua imports)Alternatively, to registerlua_importerenvironment-wide, create alua-imports.pthfile in your environment'ssite-packagesfolder with the following contents:import lua_imports; lua_imports.lua_importer.register()CaveatsThis module wrapsLupa, and so comes with all the same caveats about Lua vs. Python data types.
Package: luana-santos-csv-converter
File ConverterCSV to JSON and JSON to CSV converter.IntroductionLearning how to deploy a lib on PyPi at PUC using Poetry.What this project can doRead acsvfile or afolderand convert them toJSONand vice versa.
This project is a program running on terminal, preferably install with pipx:pipxinstallluana_santos_csv_converterTo use, just type in:csv_converter--helpOr:json_converter--help
Package: luanon
$${\textbf{Luanon} \space \textbf{Python} \space \textbf{Library}}$$$${An \space intellectual \space product \space of \space \color{lightyellow}Lua \space \color{lightgreen}Non \space \color{lightblue}Team}$$💜🩷💛ツ💚💙🩵Ghi chúNhóm hỗ trợ Telegram:LuaNonCommunityPhiên bản hiện tại: 1.0.1-dev (là chưa có gì hết😁)MãGiải thíchDSKhông hoạt động, đang sửa lạiTNKhông hoạt động, không có thời gian sửa lạiCOHoạt động bình thườngCây dự ánCái trạng thái xem ở 👆.ModulePythonTrạng tháiMô tảcloudscraper≥ 3.12DSBypass cloudflare bằng thư viện requests và jsdom.hcaptcha≥ 3.12TNBypass hcaptcha bằng thư viện requests.image≥ 3.12TNBộ xử lý hình ảnh để train AI cho hcaptchajs_runtime≥ 3.12COChạy Javascript trong Python.CloudScraperChủ:@luanon404Cu li:@ToDuyMột số link tham khảo:Giải thích mã lỗiMột số ví dụ:fromluanon.cloudscraperimportCloudflareScrapertry:scraper=CloudflareScraper(user_agent="Mozilla/5.0",cf_max_retries=1,cf_debug=True)resp=scraper.get("https://nowsecure.nl/",proxies={"http":"http://ip:port","https":"http://ip:port"},verify=False)print(f"Status code:{resp.status_code}")print(f"Source:{resp.text}")exceptExceptionasex:print(f"Lỗi:{repr(ex)}")HCaptchaChả biết ghi gì nữa.ImageChả biết ghi gì nữa.JS_RuntimeMột số ví dụ:fromluanon.js_runtimeimportJSRuntimetry:withJSRuntime()asjs_runtime:result,error,log=js_runtime.eval("console.log('Hello, world!'); 1 + 1;")iferror:raiseException(f"Lỗi:{error}")else:print(f"Kết quả:{result}, log:{log}")exceptExceptionasex:print(f"Lỗi:{repr(ex)}")fromluanon.js_runtimeimportJSRuntimetry:js_runtime=JSRuntime()result,error,log=js_runtime.eval("console.log('Hello, world!'); 1 + 1;")iferror:raiseException(f"Lỗi:{error}")else:print(f"Kết quả:{result}, log:{log}")js_runtime.close()exceptExceptionasex:print(f"Lỗi:{repr(ex)}")$${\textbf{©2023 LuaNonTeam. All Rights Reserved.}}$$