Saravanan666/pypi-org huggingface dataset:
Package: sabctools
SABCTools - C implementations of functions for use within SABnzbdThis module implements three main sets of C implementations that are used within SABnzbd:yEnc decoding and encoding using SIMD routinesCRC32 calculationsNon-blocking SSL-socket readingMarking files as sparseOf course, they can also be used in any other application.yEnc decoding and encoding using SIMD routinesyEnc decoding and encoding performed by usingyencodefrom animetosho,which utilizes x86/ARM SIMD optimised routines if such CPU features are available.CRC32 calculationsWe used thecrcutillibrary for very fast CRC calculations.Non-blocking SSL-socket readingWhen Python reads data from a non-blocking SSL socket, it is limited to receiving 16K data at once. This module implements a patched version that can read as much data is available at once.
For more details, see thecpython pull request.Marking files as sparseUses Windows specific system calls to mark files as sparse and set the desired size.
On other platforms the same is achieved by callingtruncate.Utility functionsUsesabctools.bytearray_malloc(size)to get anbytearraythat is uninitialized (not set to0's).
This is much faster than the built-inbytearray(size)because the data inside the newbytearraywill be whatever is present in the memory block.InstallingAs simple as running:pip install sabctools --upgradeWhen you want to compile from sources, you can run in thesabctoolsdirectory:pip install .SIMD detectionTo see which SIMD set was detected on your system, run:python -c "import sabctools; print(sabctools.simd);"OpenSSL detectionTo see if we could link to OpenSSL library on your system, run:python -c "import sabctools; print(sabctools.openssl_linked);"TestingFor testing we usepytest(install viapip install -r tests/requirements.txt) and test can simply be executed by browsing to thesabctoolsdirectory and running:pytestNote that tests can fail ifgitmodified the line endings of data files when checking out the repository!
Package: sabemos
sabemos(Portuguese and Spanish: “we know”)A cloud-native self-hosting-friendly Q&A applicationDeclaration of IntentBuild a web application using the following techniques:Python WSGIOIDC authenticationRDBMS backendContainer buildFeatures:Few application roles: admin, moderator, userQuestions and answers (Q&A) are written and stored in aGitHuborGitLabflavored MarkDown subsetQ&A can be taggedAnswers can be voted for or against by all usersOne answer can be marked as the selected solution by the question posterSearch over Q&A, questions only or answers onlyContributions welcomeIf you would like to contribute, please request project access using the link on theoverview page.
Package: saberio
SetupYou can find the package,here.pip3 install saberioUsageThere is a simple video with some examples,HERE.You can use the help (-h) for more information.saberio-pPlaylistWow--auto-fpath/to/file/songs-wow.txtParametersThere are some parameters you can combine to customize the downloading process.The package can be use with atext fileor asingle song.# text file must be specified via parametersaberio-fpath/to/file/myfile.txt# single song must be specified via parametersaberio-s"Alone - Alan Walker"Note: in the text file you can write the code of the song (found in the URL of bsaber.com)Example: "Alan Walker #69ec" or "#69ec"The package can be runned in 3 mode:auto,listortest. If you don't specify the parameter, mode will be asked later.# download the first matching songsaberio--auto[default]# provide a list with the best matching songssaberio--list# test the auto mode without downloading all the songssaberio--testThe package will ask you for theplaylist name, and it can be provided via parameter. If you don't specify it, it will be asked later.saberio-pMyPlaylist[default:songs]You can specify thepathwhere to download all the song with an optional parameter.saberio-d/path/to/playlist[default:.]
Package: sabertooth
SabertoothAn OpenSubtitles ClientInstall (automatic)$ pip install sabertoothInstall (manual)$ git clone git@github.com:walidsa3d/sabertooth.git
$ cd sabertooth
$ python setup.py installUsage$ sabertooth <query> -n <number_of_results> -l <language>LicenseThe MIT License (MIT)

Copyright (c) 2015 Walid Saad

Permission 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.
Package: sabia-utils
Sabia UtilsThis is a collection of utilities for Sabia.Concat ModuleThis module is used to concatenate files.Concatenate all files from a pathReturns a concatenated dataframe from all files in a path.Can save the concatenated dataframe to a file.fromsabia_utilsimportconcatconcat.concatenate_all_from_path(path='path\\to\\files',output_file='output\\file\\path',# optionalfine_name='file_name'# optional)Concatenate some files from a pathReturns a concatenated dataframe from some files in a path.Can save the concatenated dataframe to a file.fromsabia_utilsimportconcatconcat.concatenate_files(path='path\\to\\files',files=['file1','file2'],output_file='output\\file\\path',# optionalfine_name='file_name'# optional)Copy files from a path to anotherVerify if the files exist in the path before copy.fromsabia_utilsimportgroupgroup.copy_new_files(PATH_IN='path\\to\\files1',PATH_OUT='path\\to\\files2')Group ModuleThis module is used to group files.Process files in both pathsVerify if the files exist in the path before process.fromsabia_utilsimportgroupgroup.process_existent_files(PATH_IN='path\\to\\files1',PATH_OUT='path\\to\\files2')Process all filesapply the function of copy and process files between the paths.fromsabia_utilsimportgroupgroup.process_all_files(PATH_IN='path\\to\\files1',PATH_OUT='path\\to\\files2')Pre_process ModuleThis module is used to pre_process parquet files.Process all parquet filesDefine a class that inherit sabia_utils.pre_process.ProcessingOverride method apply_to_df(self, df, column), defining the pre-processing to be appliedApply this function on a folder containing parquet files to process them.fromsabia_utils.pre_processimportProcessingfromsabia_utilsimportpre_processclassMyProcessor(Processing):defapply_to_df(self,df,column):# Your pre-processing stepspre_process.pre_process_parquets(folder_path='path\\to\\folder',colomun_to_pre_process='column_name_to_be_processed',pre_processed_column='processed_column_name',processor=MyProcessor())
Package: sable
SableSable is a testing tool for SQL, which is committed to providing a simple and user-friendly solution for testing SQL queries.By design, the user would specify his/her test cases in an easy-to-read YAML file, andsablewill run those test cases and generate a report.For example, given a test file under the demo folder named test_find_email.yaml, the user should be able to usesable demo/test_find_email.yamlto run tests and get results printed in the console.Please note that the sable project is still in the planning stage and interfaces may be changed in the future.Test Case DefinationTest SuiteEach YAML document will be considered as a test suite. It should be an associative array that contains at leastversion,suite,casesas keys.The value inversionis referring to the version of test define schema.The value insuiteis the name of this test suite.The value incasesshould be a list of test cases.Test CaseThe test case is also been defined in an associative array. It is mandatory to includeuid,sqlandexp, and other keys are optional:msg,var,env.uidUnique Identifier (uid) is the key that will show in the final result and it helps the user quickly find the test case definition in files.msgMessage (msg) will show to the user when the test cases if failed.sqlSQL query or SQL query template which will be executed for testing.It is a good practice to split the code and the tests. So, sable allows users to define SQL query in a separate file and use!filefollowed by file path to identify the file.For example, given a SQL stored in find_email.sql under the same folder demo with the test definition YAML, the test case can be written as:sql:!filefind_email.sqlvarIf using a template in SQL, this is the place to set the actual value.For example:Given a SQL query template:SELECT${x}+${y}When specifyingvaras the following:var:x:1y:2The SQL be executed will become:SELECT1+2envThe pre-defined environment before executing SQL query. It should be a list of associative arrays. Each member in the list represents a table, which should contain a DDL inmetadataand tabulation-like data inrecords.To give tabulation-like data in YAML, users can use!fwfand!csv.For example:records:!fwf|user_id   first_name  last_name1         Jack        Ma2         Tony        Ma3         Robin       Lirecords:!csv|user_id,email1,jack_ma@alibaba.com2,tony_ma@qq.com6,robin_li@baidu.comexpExpected result (exp) defineswhereto inspect and whatrecordsshould the program expected to see.For example:where:result setrecords:!fwf|mailjack_ma@alibaba.comtony_ma@qq.com
Package: saboteurs
Saboteurs is a Python library to detect bad elements (orweakest links)
from success/failure data. It can also be used to design “test batches” which
will allow to easily identify bad elements.We use it at theEdinburgh Genome Foundryto
identify defectuous genetic parts early:When assembling large fragments of DNA, each with typically 5 to 25 parts, we
observe that some assemblies have far fewer successes (“good clones”) than
some others. We use Saboteurs to identify possible parts which would be
causing the damage. This would generally mean that the sample corresponding
to these parts has been compromised.Before launching a large batch of assemblies which reuse the same few parts,
we use Saboteurs to design a smaller “test batch” of carefully selected
assemblies to detect and identify possible bad parts.InfosPIP installation:pipinstallsaboteursDocs:https://edinburgh-genome-foundry.github.io/saboteurs/Github Page:https://github.com/Edinburgh-Genome-Foundry/saboteursWeb apps:Saboteurs detectionBatch designLicense:MIT, Copyright Edinburgh Genome FoundryMore biology softwareSaboteurs is part of theEGF Codonssynthetic biology software suite for DNA design, manufacturing and validation.
Package: sabreMapper
qiskit_sabre_mapperDescriptionA quantum program mapping and routing algorithm based on the open source code of the SABRE algorithm of Qiskit.InstallationpipinstallsabreMapperUsagefromsabreMapper.sabre_mapperimportSabreMappermapper=SabreMapper()success=mapper.map_schedule(origin_qasm_fn,chip_info_fn,mapped_qasm_fn,qubit_mapping_fn)Interface DescriptionInput parametersorigin_qasm_fn: This is a file path name, which corresponds to the quantum program file (such as Oenqasm 2.0 format file) before mapping  and routing.chip_info_fn: This is a file path name, which corresponds to a configuration file that describes the information of the quantum chip (such as a configuration file in JSON format).mapped_qasm_fn:This is a file path name, and the file corresponding to the path is the quantum program file after mapping and routing.qubit_mapping_fn: This is a file path name, and the file corresponding to the path stores information such as the result of the qubit mapping information.Output parameterssuccess: The parameter type is 'bool'. If this parameter is true, it means that the quantum program has successfully completed the mapping and is dispatched, and if it is false or null, it means that the mapping and routing of quantum program failed.
Package: sabrmetrics
SABRmetricsBackgroundAn open-source library of web-scraping software for popular SABRmetrics websites.Sabermetrics (or originally as SABRmetrics) is the empirical analysis of baseball, especially baseball statistics that measure in-game activity.--Wikipediasabermetrics, the statistical analysis of baseball data. Sabermetrics aims to quantify baseball players’ performances based on objective statistical measurements, especially in opposition to many of the established statistics (such as, for example, runs batted in and pitching wins) that give less accurate approximations of individual efficacy.--BritannicaPrimary FeaturesEasy scraping of various Internet Player ID databasesInstallationFrom PyPI:python -m pip install sabrmetricsDirectly from GitHub:python -m pip install -e git+https://github.com/JacobLee23/SABRmetrics.git#egg=sabrmetricsRequirementsThis project requires Python 3.6+.Note: Some of the packages listed in the project Pipfile underdev-packagesare not compatible with Python 3.6.
This includespytest, so project tests cannot be run using Python 3.6.
However, all primary dependencies (underdefault) are compatible with Python 3.6, so Python 3.6 supports most normal functionality.Dependenciesbeautifulsoup4(Documentation)lxml(Documentation)numpy(Documentation)pandas(Documentation)python-dateutil(Documentation)requests(Documentation)This project usespipenv(Documentation) for virtual environment dependency management.
See thePipfileto see a full list of package dependencies, including development dependencies.TestingThe tests for this project are written usingpytest(Documentation).
To run the project tests, run:pytest sabrmetrics/tests/Thepipenvcommand script shorthand defined in the projectPipfilecan also be used to run the project tests; run:pipenv pytestAnotherpipenvcommand script shorthand is defined in the projectPipfilecan be used to output an HTML test report, usingpytest-html(Documentation); run:pipenv pytest-htmlLicenseThis project is license under theMIT License.DocumentationThe documentation for this project is hosted byRead the Docs:Official Documentation
Package: sabrute
Программа для подбора паролей SAMP MobileРазработчик не несет ответсвенности за неправомерное использование данной программыУстановка и использованиеУстановка Python:WindowsСкачать Python можно с официальногосайта, при установке необходимо поставить галочку Add Python to PATHLinuxaptinstallpython3
aptinstallpython3-pipAndroid (экспериментально)Необходимо установитьTermux, для ввода в него следующей командыpkginstallpythonУстановка sabrute:pip3installsabruteИспользование:sabrute--nickname[nickname]АвторVKTG
Package: sabs
SQUID Advanced Background SubstractionThis python script takes raw data from SQUID magnetometers (for now only the MPMS3 is supported) and subtracts a raw background.FeaturesAlmost complete error propagation thanks to the greatuncertaintiespackageFitting using Orthogonl Distance Regression (ODRPACK 77)Easy per scan inspection of the fit quality.Rudimenrtary support for the rotator option.Dependenciespython > 3.9scipynumpyuncertaintiesmatplotlibSpecial Notes for Windows usersSee theWindows Guide.UsageMeasurementWhen thinking about using SABS there are a few things to consider before you start measuring. First of all you should
know that SABS is only for the DC mesuring mode. In most situations where you can use the VSM mode, you should do so.
For VSM mode the background can be subtracted more easily by just subtracting the resulting data directly.
If your measurement needs DC mode you can use SABS.You will need two separate measurements one for background and one with the sample. You should use the same sequence for both measurements. Make sure to enable RAW file recording. To get the most accurate results make sure to change as little as possible between measurements. The sampleposition is determined by touching down (As far as we know). So make sure the sampleholder has the same length between measurements. If you are using a protective straw. Use the same straw at the same possition.Further take care to output each measurement into its own file. If you are for example sweeping temperature at different fields, use one file for each field.SoftwareTypepython sabs.py -hto show the help message. Use-s [sampledata]and-b [background]to point to the sampledata or
background raw file (*.rw.dat) respectively. Since there is no information about the rotation angle stored in the*.rw.datfile, it is necessary to also read the*.datfile. Make sure the*.datfile is present in the same folder and has the same name before the file extension.Specify your outputfile with-o [outfile]. With-pplotting can be turned on. This is recommended to check the validity of the fit.
There are two modes for subtraction:dirty mode-m dirtyEven when using the same sequence there is no guarantee, that all points in your measurement are recorded.
This leads to a different number of scans between background and sampledata. Usingdirty modethe arrays
of scans are cut to fit in length.nearest mode-m nearest(default)dirty modecan lead to problems, when a scan or multiple scans in the middle of your sequence were not recorded.
Look out for larger errorbars in your variable.nearest modelooks for the nearest scan in background to each scan in sampledata.
Only the distance in your variable is regarded. Which observable was your variable is determined automatically.Examplepythonsabs.py-p\-ssampledata/2021-11-08_Fe5Ge2Te5_SE5749_grease-quartz-brass_MvT_1000Oe.rw.dat\-bsampledata/2021-11-04_Test_background_grease-quartz-brass_MvT_1000Oe.rw.dat\-osampledata/2021-12-17_Fe5Ge2Te5_SE5749_grease-quartz-brass_MvT_1000Oe.sabs.datIn case of problems or if you need help, please write me an E-Mail:b.mehlhorn at ifw-dresden.deand open an issue in the issue tracker.
Package: sabyenc
Mofied the original yenc module by Alessandro Duca for use within SABnzbd.The module was extended to do header parsing and full yEnc decoding from a Python
list of chunks, the way in which data is retrieved from usenet.Currently CRC-checking of decoded data is disabled to allow for increased performance.
It can only be re-enabled by locally altering ‘sabyenc.h’ and setting ‘CRC_CHECK 1’.
Package: sabyenc3
SABYenc 3 - yEnc decoding of usenet data using SIMD routinesModification of the originalyencmodule for use within SABnzbd.
The module was extended to do header parsing and full yEnc decoding from a Python
list of chunks, the way in which data is retrieved from Usenet.
This is particularly beneficial when SSL is enabled, which limits the size of each chunk to 16K. Parsing these chunks in python is much more costly.
Additionally, this module releases Python's GIL during decoding, greatly increasing performance of the overall download process.Further improved by usingyencodefrom animetosho, which utilizes x86/ARM SIMD optimised routines if such CPU features are available.InstallingAs simple as running:pip install sabyenc3 --upgradeWhen you want to compile from sources, you can run in thesabyencdirectory:pip install .SIMD detectionTo see which SIMD set was detected on your system, run:python -c "import sabyenc3; print(sabyenc3.simd);"TestingFor testing we usepytest(install viapip install -r tests/requirements.txt) and test can simply be executed by browsing to thesabyencdirectory and running:pytestNote that tests can fail ifgitmodified the line endings of data files when checking out the repository!
Package: sabyvok
Saby Vok API ClientInstallationpipinstallsabyvokUsagefromsabyvokimportSabyVokClientclient=SabyVokClient("your_client_id","your_client_secret","your_secret_key",)data=client.req(inn='7605016030')print(data[0]['company_name'])# Компания "Тензор", ООО
Package: sacc
SaccSACC (Save All Correlations and Covariances) is a format and reference library for general storage
of summary statistic measurements for the Dark Energy Science Collaboration (DESC) within the Large Synoptic
Survey Telescope (LSST) project.InstallationYou can install sacc with the command:pip install sacc(For local installation you might need to add--user)Or for development versions you can download the repository with git and install from there usingpython setup.py installDocumentation & ExamplesDocumentation can be foundon ReadTheDocs.Theexamples directoryon github contains ipython notebooks showing various ways of constructing and using sacc data files.If you have a problem you've not been able to debug, or a feature request/suggestion the you canopen an issueto discuss it.VersionsThe current release isversion 0.12.You can find a list ofprevious releases here.  The releases above 0.2 are all backwards compatible; 0.1 is a previous version of the format.The master branch includes more recent (non-released) development changes.CitationSacc has been submitted to theAstrophysics Source Code Library; follow the link that will appear there to NASA ADS to export a bibtex citation.The core developers of Sacc are Joe Zuntz (maintainer), David Alonso, and Matt Becker.
Package: sacf
Selective Estimator for the Autocorrelation FunctionS-ACF: A selective estimator for the autocorrelation function of irregularly sampled time series
(credit Lars Kreutzer, c++ implementation by Josh Briegaljtb34@cam.ac.uk)InstallationRequirements:CMAKE (https://cmake.org) > 3.8.C++14From above top level directory runpip install ./sacfin python:SACF follows Astropy LombScargle implementation:fromsacfimportSACFlag_timeseries,correlations=SACF(timeseries,values,errors=None).autocorrelation()with options:sacf.autocorrelation(max_lag=None,lag_resolution=None,selection_function='natural',weight_function='fast',alpha=None)NOTE: If users specifyselection_function="fast",weight_function="fractional_squared"orweight_function="gaussian", a python implementation of the SACF will be invoked which is considerably slower than the default C++ option.TestsFrom root directory run:tox
Package: sachannelupdate
Utility for pushing updates to Spamassassin update channelssachannelupdate is a utility for pushing updates to Spamassassin update
channels. It is used topublishrules to a custom Spamassassin sa-update channel.InstallationInstall from PyPipip install sachannelupdateInstall from Githibgit clone https://github.com/akissa/sachannelupdate.git
cd sachannelupdate
python setup.py installUsagesachannelupdate can be used from within python code or using the
commandline utility updatesachannel.Python usageTODOCommand line usageupdatesachannel-hUsage:updatesachannel[options]Options:-h,--helpshowthishelpmessageandexit-cFILENAME,--config=FILENAMEconfigurationfile-d,--deleteDeletesexistingenvironmentContributingFork it (https://github.com/akissa/sachannelupdate/fork)Create your feature branch (git checkout-bmy-new-feature)Commit your changes (git commit-am'Add some feature')Push to the branch (git push originmy-new-feature)Create new Pull RequestLicenseAll code is licensed under theAGPLv3+
License.
Package: sachi
sachiSachi is a TV Series and Movie TUI renamer made withTextual.Getting StartedRequirements:Python 3.12 andmediainfopip3installsachiOr, with Docker:dockerrun--rm-it\-v/path/to/config:/root/.config/sachi\-v/path/to/media:/media\ghcr.io/nextfire/sachi:latest--helpFeaturesDevelopment is still in early stages, here's what's implemented so far:File selectionTVDB series search (requires an API key)Filename jinja2 templatingIn progress:FileBot context (https://www.filebot.net/naming.html)The global configuration can be edited withsachi config.