Saravanan666/pypi-org huggingface dataset:
Package: autoskip
spotify-skipperA python program to automatically skip unwanted songs.InstallationPython PackageWorks on Windows and Linux, but song score is unavailable on Windows.You can install thepypipackage usingpip3 install autoskip.Make sure wherever it is installed to is on your path. You can check withpip3 show autoskip.Usageautoskip# Normal usageautoskip-h# See helpUsing any flags will not start the CLI, to make scripting easier. Use --run to prevent this.ShortLongDescription-h--helpShows CLI help and exits-s--skipSkips current track-wls--whitelist-songToggle whitelist for current song-wla--whitelist-artistToggle whitelist for current artist-bls--blacklist-songToggle blacklist for current song-bla--blacklist-artistToggle blacklist for current artist-n--notifyToggle notifications-r--runRun the CLI regardless of flags
Package: auto-sklearn
auto-sklearnauto-sklearnis an automated machine learning toolkit and a drop-in replacement for ascikit-learnestimator.Find the documentationhere. Quick links:Installation GuideReleasesManualExamplesAPIauto-sklearn in one imageauto-sklearn in four lines of codeimportautosklearn.classificationcls=autosklearn.classification.AutoSklearnClassifier()cls.fit(X_train,y_train)predictions=cls.predict(X_test)Relevant publicationsIf you use auto-sklearn in scientific publications, we would appreciate citations.Efficient and Robust Automated Machine LearningMatthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum and Frank HutterAdvances in Neural Information Processing Systems 28 (2015)Linkto publication.@inproceedings{feurer-neurips15a,
    title     = {Efficient and Robust Automated Machine Learning},
    author    = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina and Springenberg, Jost and Blum, Manuel and Hutter, Frank},
    booktitle = {Advances in Neural Information Processing Systems 28 (2015)},
    pages     = {2962--2970},
    year      = {2015}
}Auto-Sklearn 2.0: The Next GenerationMatthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer and Frank Hutter*
arXiv:2007.04074 [cs.LG], 2020Linkto publication.@article{feurer-arxiv20a,
    title     = {Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning},
    author    = {Feurer, Matthias and Eggensperger, Katharina and Falkner, Stefan and Lindauer, Marius and Hutter, Frank},
    booktitle = {arXiv:2007.04074 [cs.LG]},
    year      = {2020}
}Also, have a look at the blog onautoml.orgwhere we regularly release blogposts.
Package: auto-sktime
auto-sktimeAutomatic creation of time series forecasts, regression and classification.InstallationFor trouble shooting and detailed installation instructions, see the documentation.Operating system: Linux
Python version: Python 3.8, 3.9, and 3.10 (only 64 bit)
Package managers: pippipauto-sktime is available in pip. You can see all available wheelshere.pipinstallauto-sktimeor, with maximum dependencies,pipinstallauto-sktime[all_extras]Remaining Useful Life Predictions (AutoRUL)This section describes how to reproduce the results in theAutoRULpaper. First, checkout the exact code that was used
to create the results. Therefore, you can use the tagv0.1.0gitcheckouttags/v0.1.0-bautorulNext, switch to thescriptsdirectory and usepythonremaining_useful_lifetime.py<BENCHMARK>to run a single benchmark data set. To view the available benchmarks and all configuration parameters runpythonremaining_useful_lifetime.py--helpReproducing resultsYou can use the following commands to recreate the reported baseline results in the experiments of the paper.pythonremaining_useful_lifetime.py<BENCHMARK>--runcount_limit1--timeout3600--multi_fidelityFalse--includebaseline_lstm
pythonremaining_useful_lifetime.py<BENCHMARK>--runcount_limit1--timeout3600--multi_fidelityFalse--includebaseline_cnn
pythonremaining_useful_lifetime.py<BENCHMARK>--runcount_limit1--timeout3600--multi_fidelityFalse--includebaseline_transformer
pythonremaining_useful_lifetime.py<BENCHMARK>--runcount_limit1--timeout7200--multi_fidelityFalse--includebaseline_rf
pythonremaining_useful_lifetime.py<BENCHMARK>--runcount_limit200--timeout7200--multi_fidelityFalse--ensemble_size1--includebaseline_svmwith<BENCHMARK>being one of{cmapss,cmapss_1,cmapss_2,cmapss_3,cmapss_4,femto_bearing,filtration,phm08,phme20}.
For theAutoRULevaluation only the benchmark is provided and all remaining default configurations are used.pythonremaining_useful_lifetime.py<BENCHMARK>To reproduce the results from AutoCoevoRUL, checkout therepositoryfrom
Github and use theautocoevorul.pyfile to either export the data sets or import the results.NoteThis project has been set up using PyScaffold 4.2.1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.BuildingTo create a new release ofauto-sktimeyou will have to installbuildandtwinepipinstallbuildtwine
python-mbuild
Package: autoslack
AutoSlack is a simple module designed to automate some tasks forhttp://slack.com.
Currently it includes an invite method for automatically inviting persons
to a Slack community.Installationpip install autoslack(you could also simply add autoslack to your requirements.txt, setup.py or buildout file)UsageTo use this you will need to generate a token by visitinghttps://api.slack.com/web#authentication, the token is passed to the autoslack.invite() method.The invite() method returns feedback from the Slack API in json formatimport autoslack
output = autoslack.invite(group="SLACKCOMMUNITYNAME",
                token="XXXXXXX",
                firstname="XXXXXXXX",
                lastname="XXXXXXX",
                emailaddress="XXXXXXX",
                channels=['XXXXX','XXXXX']
                )
print(output)NoteThechannelsargument is optional.A script like the one above would give output similar to this{u'ok': True}
Package: autoslot
autoslotAutomatic “__slots__”.DemofromautoslotimportSlotsclassCompact(Slots):def__init__(self,a,b):self.x=aself.y=bThis producesexactlythe same class as if you had done:classCompact:__slots__={'x','y'}def__init__(self,a,b):self.x=aself.y=bSimply: the code inside__init__()is scanned to find all assignments
to attributes onself, and these are added as__slots__.The benefit of usingautoslot.Slotsover a manual slots declaration is
that you can modify the
code inside the__init__()method to add more attributes, and those
changes willautomaticallybe reflected in the__slots__definition.You can also have the best of both worlds: slots for fields you expect,as well asa__dict__for those you don’t:fromautoslotimportSlotsPlusDictclassSemiCompact(SlotsPlusDict):def__init__(self,a,b):self.x=aself.y=binst=SemiCompact(1,2)inst.z=123# <-- This won't fail!Attributesxandywill be stored in slots, while all other
dynamically-assigned attributes will go into the usual__dict__instance
inside the class.  If most of your class’s attributes appear in the__init__()method (these will become slots), then the space bloat caused by dictionary
hash-table expansion will be contained to only the dynamically-assigned
attributes.How does it work?See for yourself! The code is tiny.In words: the metaclass finds the__init__()method, if present, and
accesses its bytecode. It looks for all assignments to attributes ofself, and considers those to be desired__slots__entries. Then the
metaclass injects__slots__into the namespace of the class definition
and thereafter allows class creation to proceed as normal.WeakrefWhen__slots__are used, weak references (e.g. using theweakrefstandard library module) won’t work. If you need weak references, just
set it up on a new__slots__class variable as you would normally
do without usingautoslot:fromautoslotimportSlotsclassCompact(Slots):__slots__=['__weakref__']def__init__(self,a,b):self.x=aself.y=bEverything else will still work, and instances ofCompactwill now
also play nicely with theweakrefmodule.
Package: autosmith
autosmithMake tools quicklyInstallYou must have docker installed and running.pipinstallautosmithUsageThe package will inspect a python function, its imports, argument types,
and start a container that serves it on an endpoint.fromautosmith.smithimportsmithimportrequestsdefhello():"""Return hello"""return'hello'withsmith(hello)asenv:r=requests.get(env.url+'/hello')print(r.text)# 'hello'By default, the container will be removed when returnedToolEnv(envbelow) is
garbage collected. You can keep the container viaenv.save().
You can then load viaenv.load().defdouble(x:int):"""Double an integer"""returnx*2env=smith(double)url=env.url+'/double?x=2'r=requests.get(url)print(r.text)# 4env.name='myenv'env.save()delenvr=requests.get(url)print(r.text)# 4env=ToolEnv.load('myenv')# now the container will be removed since we didn't savedelenvTheToolEnvobject is meant to collect functions
and slowly build up an environment. Note that the functions you
define need not be executable in your python environment.defnparange(n:int):"""Return a numpy array"""importnumpyasnp# can only output standard python typesreturnnp.arange(n).astype(int).tolist()defdouble(x:int):"""Double an integer"""returnx*2env=smith(nparange)env=smith(double,env=env)r=requests.get(env.url+'/nparange?n=5')print(r.text)# [0, 1, 2, 3, 4]env.close()# another way to close the container
Package: auto-smooth
Auto SmoothApply data smoothing/filtering to a time series by automatically selecting parameters.Currently available smoothing/filtering techniques in the package:Savitzky–Golay filterQuickstartfromauto_smoothimportauto_savgol# apply savgol filterdata_filtered=auto_savgol(data)>>>wl_best=7,po_best=2Savitzky-Golay FilteringSavitzky–Golay (Abraham Savitzky and Marcel J. E. Golay) filter is a type of low-pass filter used for smoothing noisy data.^1It is based on local least-squares fitting.^2auto_savgolmethod applies a Savitzky–Golay filter using thescipysavgol_filter()method.fromauto_smoothimportauto_savgol# apply savgol filterdata_filtered=auto_savgol(data)# pass window-length and polynomial-order argumentsdata_filtered=auto_savgol(data,wl_min=10,wl_max=30,po_min=2,po_max=10)ReferencesSmoothing and Differentiation of Data by Simplified Least Squares ProceduresScipy savgol filterSavitzky–GolayConvolutionWhat Is a Savitzky-Golay Filter?
Package: autosphere-excel
AutoSphere-excel libraryThis AutoSphere Openpyxl library that provides keywords to Open, Read, Write and Save XLSX files. This library uses Openpyxl python module to read and write datas in xlsx files.Pre-requisities:
- Python - 2.7 (Not tested with Python 3.0)
- AutoSphere - 3.0.2 (Not tested with other versions)
- OpenpyxlInstallation:pip install AutoSphere-excelExample:| Library    |    excelrary    |
| Open Excel |   |
Package: autosphinx
Python package to create documentation easily with SphinxRequirementspipinstall-rrequirements.txt
pipinstallautosphinxGetting StartedfromautosphinximportAutoSphinxsphinx=AutoSphinx(lib_root="/media/jcadic/PythonProjects/project",version="1.0a29",export_dir="/home/jcadic/PythonDocs/project",logo_img="/home/jcadic/PythonImages/logo_project.png")sphinx.run()ContributionsEmail me atj.cadic@9dw-lab.comfor any questions.
Package: autosql
autosql一键生成SQL脚本支持的数据库Oracle; MySQL; PostgreSQL支持的操作建表；建索引；删除表安装pip install autosql使用# test.py
    from autosql import autosql

    obj = autosql.get("path\to\excel\", file_name, dbtype, ifdrop, transform)
    obj.create_table()
    obj.create_index()
    obj.drop_table()autosql.get()参数：path: excel文件路径file_name: excel文件名dbtype: 数据库ifdrop: 是否生成drop语句transform: 是否进行格式转换EXCEL格式规范sheets目录结构：
 
 中文名 | 表名 | 是否生成脚本 | 日期分区
 测试表 | Test | 1           | 0索引示例：
 表名 | 索引列   | 是否主键 | 是否唯一索引 | 是否有效
 Test | isValid | 0       | 0           | 1Test(表名)示例:
 字段     | 含义  | 类型 | 非空 | 默认值 | 键          | 扩展
 id       | 标识 | int  | 是   |        | primary key | auto_increment
 initdate | 时间 | Date | 是   | now()  |             |函数表中的函数、数据类型、扩展等都默认为mysql格式，或者你可以自行填写，然后将格式转换设置为False，前提是你确保填写正确transform = False
Package: auto-sql
auto_sql==Work in progress==auto_sql is very early in development,  more features to comeDescriptionauto_sql is a memory aware csv to sqlite converter capable of converting multi-gigabyte tabular files to sqlite
databases on low memory machines. auto_sql focuses on speed by enabling multi-processing on multi-core machines.Installation$pip install auto_sqlUsage Casefromauto_sqlimportAutoSqltab_obj=AutoSql(file='file.csv',db_name='database',sep='\t',out_dir=".")if__name__=="__main__":tab_obj.run()Currently auto_sql only supports csv's with headersDependenciesPython 3.4, 3.5, or 3.6pandas==0.21.1psutil==5.4.7
Package: autosrt
autosrt is a utility for automatic speech recognition and subtitle generation.It takes a video or an audio file as input, performs voice activity detection to find speech regions,  makes parallel requests to Google Web Speech API  to generate transcriptions for those regions,  (optionally) translates them to a different language, and finally saves the resulting subtitles file to disk.   It supports a variety of input and output languages and can currently produce subtitles in SRT, VTT, JSON, and RAW format.
Package: auto-ss
Auto acquiress-linkfree shadowsocks accountsIf you want to use auto-ss, you need to registr ass-link account. Free shadowsocks accounts will be invalid ina period time, If accounts are invalid, you need to runauto-ssagainVersionsauto-ss works with Python2.7+Installationpip / easy_install$pipinstallauto-ssor$easy_installauto-ssGithub$pipinstallgit+https://github.com/steven-hl/ss-link-auto.gitNoteIf you run auto-ss in modespeedorrun, you need installshadowsocksfirstly.Usage$auto-ss-husage:auto-ss[-h][--modeMODE][--timeoutTIMEOUT]emailpasswordAutoacquiress-linkfreeshadowsocksaccounts.positionalarguments:emailss-linkemailpasswordss-linkpasswordoptionalarguments:-h,--helpshowthishelpmessageandexit--modeMODErunmode,deault`avaiable`.ifmodeis`speed`,willtestnetworkconnection--timeoutTIMEOUTconnectiontimeoutinseconds,default10DemoRun in avaiable mode➜~auto-ssxxx@xxx.comxxx############### 2015-08-27 23:19:30 Shadowsocks Accounts ###############server_addressserver_portserver_passwordencrypt_method========================================================================76.164.197.155998082057967aes-256-cfb72.46.135.119998082057967aes-256-cfb173.252.220.201998082057967aes-256-cfb104.250.144.10998082057967aes-256-cfb104.250.143.243998082057967aes-256-cfb104.250.146.37998082057967aes-256-cfb104.250.146.212998082057967aes-256-cfb104.250.147.22998082057967aes-256-cfbRun in speed modeIt will test shadowsocks conection speed by viewhttps://www.google.com.hk➜~auto-ssxxx@xxx.comxxxx--modespeed############### 2015-08-27 23:29:56 Shadowsocks Accounts ###############server_addressserver_portserver_passwordencrypt_method========================================================================76.164.197.155998082057967aes-256-cfb72.46.135.119998082057967aes-256-cfb173.252.220.201998082057967aes-256-cfb104.250.146.212998082057967aes-256-cfb104.250.144.10998082057967aes-256-cfb104.250.143.243998082057967aes-256-cfb104.250.146.37998082057967aes-256-cfb104.250.147.22998082057967aes-256-cfb########################### Connection Test ############################76.164.197.155speedis:6kb/s72.46.135.119speedis:8kb/s173.252.220.201speedis:3kb/s104.250.146.212speedis:9kb/s104.250.144.10speedis:7kb/s104.250.143.243speedis:7kb/s104.250.146.37speedis:4kb/s104.250.147.22speedis:8kb/s
Package: autostack
autostack is a command-line debugging tool for Python projects that automatically displays Stack Overflow answers for thrown errors.What is the first thing you do when a confusing error message is displayed in your terminal window? You search for an answer on Stack Overflow, of course! With autostack, you no longer have to search for answers on Stack Overflow, they are found for you. Gone are the days of scouring the internet for hours to find an answer to your development questions! autostack is here to automate the debugging process and in turn, expedite Python project development.Table of ContentsInstallationUsageDemoContributingLicenseShow your supportAuthorsInstallation1. Clone the repo and use the install script.Clone the repo.gitclonehttps://github.com/autostack-team/autostack.gitNavigate to the project directory, and run the install bash script.cd/path/to/project/
chmod+xinstall.sh
./install.sh2. Or just use pip to install.pip3installautostackUsageIn one terminal window, execute "autostack capture" which will capture all errors in the terminal. You can run this command in as many terminal windows as you'd like.autostackcaptureIn another terminal window, execute "autostack display" to display Stack Overflow posts for all captured errors.autostackdisplayTo stop running autostack, use the exit command in the terminals that executed "autostack capture". This automatically stops the terminal window displaying Stack Overflow posts for captured errors.exitDemoCheckout the demo below!ContributingFor information on how to get started contributing to autostack, see thecontributing guidlines.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.Show your supportGive a ⭐️ if autostack has helped you!AuthorsElijah SawyersBenjamin SandersCaleb Werth
Package: autostarter
AutostarterA Python module for managing startup scripts across different operating systems, requiring no additional dependencies.InstallationTo install the module, usepip:pipinstallautostarterUsageYou can use the "add" method to include a startup script, and the "remove" method to remove a startup script.importautostarterimportsys# Add a python scriptscript_location='/path/to/script.py'autostarter.add(script_location,identifier='your-app-name',interpreter=sys.executable)# Remove a startup scriptautostarter.remove('your-app-name')API Referenceadd(script_location, **kwargs) -> strAdds a startup script with the specified parameters.Parameters:script_location (str): The location of the script to be added as a startup script.identifier (str, optional): An identifier for the startup script. If not provided, a random UUID will be generated.system_wide (bool, optional): Make program open at start for all users. Requires root/admin privileges.arguments (str, optional): CLI Arguments to provide to script.interpreter (str, optional): Program to run the script with. See usage for how to run a Python scriptReturns:The identifier for the added startup scriptremove(identifier, **kwargs) -> Union[bool, str]Removes the startup script with the specified identifier.Parameters:identifier (str): The identifier of the startup script to be removed.Returns:True if the startup script was successfully removed, False otherwise.Supported Operating SystemsAutostarter supports the following operating systems:Windows, tested on Windows 11MacOS, tested on 13.1Linux, tested on Ubuntu 22.04ContributingIf you want to contribute to Autostarter, please fork the repository and make your changes in a separate branch. Then, submit a pull request with a detailed description of your changes.LicenseAutostarter is licensed under the MIT License.
SeeLICENSEfor more information.
Package: auto-struct
auto_structPython 3.8 module using annotations and dataclasses for easily writing parsers for binary dataParse packed binary data using declarative formsEasily validate parsed dataEasily nest structsNo Dependencies :)Usage exampleSeethisContributionContributors are welcome!TODO:Method documentation (WIP)Documentation (WIP)More examplesTestsIntermediate types/Binding two types - (E.g. in memory type vs how should it be used)API Should probably be somedef twin(self)?Dynamic sized structs (e.g.struct a{ int len; char data[] };)
Package: autosub
Autosub is a utility for automatic speech recognition and subtitle generation. It takes a video or an audio file as input, performs voice activity detection to find speech regions, makes parallel requests to Google Web Speech API to generate transcriptions for those regions, (optionally) translates them to a different language, and finally saves the resulting subtitles to disk. It supports a variety of input and output languages (to see which, run the utility with –list-src-languages and –list-dst-languages as arguments respectively) and can currently produce subtitles in either the SRT format or simple JSON.
Package: autosub1
Copyright (c) 2015 Anastasis GermanidisPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.Description: autosub1 is a utility for automatic speech recognition and subtitle generation. It takes a video or an audio file as input, performs voice activity detection to find speech regions, makes parallel requests to Google Web Speech API to generate transcriptions for those regions, (optionally) translates them to a different language, and finally saves the resulting subtitles to disk. It supports a variety of input and output languages (to see which, run the utility with –list-src-languages and –list-dst-languages as arguments respectively) and can currently produce subtitles in either the SRT format or simple JSON.
Platform: UNKNOWN
Package: autosub3
Copyright (c) 2015 Anastasis GermanidisPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.Description: This project upgrades original autosub and adds support for windows.Autosub is a utility for automatic speech recognition and subtitle generation. It takes a video or an audio file as input, performs voice activity detection to find speech regions, makes parallel requests to Google Web Speech API to generate transcriptions for those regions, (optionally) translates them to a different language, and finally saves the resulting subtitles to disk. It supports a variety of input and output languages (to see which, run the utility with –list-src-languages and –list-dst-languages as arguments respectively) and can currently produce subtitles in either the SRT format or simple JSON.
Platform: UNKNOWN