Saravanan666/pypi-org huggingface dataset:
Package: sas2db
sas2dbImportsSASfiles to SQL databases. Supports both*.sas7bdatand XPORT (*.xpt) files.InstallationThe SAS software itself is not required.InstallPython 3andpip.Install the package.pip3installsas2dbUsageSQLite3sas2dbsupportsSQLite3with no additional dependencies or setup, so we'll start with that. To import from SAS to SQLite3:Run the conversion.sas2dbpath/to/src.sas7bdatAsrc.db(matching your input file name) will be created.Run SQL! Example:$ sqlite3 src.db
sqlite> .tables
mydata
sqlite> SELECT COUNT(*) FROM mydata;
200For more options:sas2db-hOther databasesAside from SQLite3,sas2dbsupports other databases like PostgreSQL and MySQL. This support comes fromSQLAlchemyunder the hood, so seetheir list of supported "dialects".To use another database:Ensure that the destination database is installed, running, created, and accessible from wherever you will be doing the import.Install the corresponding driver.On theDialectspage, click your preferred database, then under "DBAPI Support", click one of the options.The first DBAPI option is probably fine, though you may have to try multiple.Runsas2db, passing thedatabase URLto the--dbargument.Example for PostgreSQL:# create the databasecreatedb-Upostgressas_import# install driverpip3installpsycopg2# run the importsas2db--dbpostgresql+psycopg2://postgres@localhost:5432/sas_importpath/to/src.sas7bdatDevelopmentInstallPipenv.Clone/download the repository.From the project directory, runpipenvinstall--dev
pipenvshellRun PostgreSQL. Example in Docker:dockerrun--rm-it-p5432:5432--namepgpostgresCreatesas2dbdatabase in PostgreSQL for testing. Example in Docker:dockerexec-itpgcreatedb-Upostgressas2dbRun tests:python-munittestTo use the script:python3sas2db/run.pypath/to/src.sas7bdatdata.gov has data sets you can test with.More InformationOther tools to look at for importing/exporting data to/from SAS:sas7bdat Python packageUseful in case you can't use Pandas for some reasonPandas (used insas2db) usesa modified version of the sas7bdat packageSAS ODBC driversSAS'sPROC EXPORTsas2bdat R packageInformation about data types:DocumentationSQLite typesSAS data typesSAS formatsSAS to Python type parsingsas7bdatPandas
Package: sas2nb
sas2nbA Jupyter Lab extension to convert *.sas files to *.ipynb splitting on run boundaries.This extension is composed of a Python package namedsas2nbfor the server extension and a NPM package namedsas2nbfor the frontend extension.RequirementsJupyterLab >= 3.0Installpipinstallsas2nbTroubleshootIf you are seeing the frontend extension, but it is not working, check
that the server extension is enabled:jupyterserverextensionlistIf the server extension is installed and enabled, but you are not seeing
the frontend extension, check the frontend extension is installed:jupyterlabextensionlistContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the sas2nb directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseUninstallpipuninstallsas2nb
Package: sas2sqlite3
sas2sqlite3Import sas7bdat files to sqlite3 dbase. Uses thesas7bdatSAS
dataset file parser.Command-line and python library interface with minimal requirements.CLI usageCreate and import data intosome_datasettable insome_sqlite.dbsas2sqlitesome_dataset.sas7bdatsome_sqlite.dbImport data into existingsome_tabletable insome_sqlite.dbsas2sqlitesome_dataset.sas7bdatsome_sqlite.db--tablesome_table--no-create-tableImport data storing dates and datetimes as Julian days, and times asHH:MM:sas2sqlitesome_dataset.sas7bdatsome_sqlite.db\--time"%H:%M"--datejulian--datetimejulianInstallRequires python 3.6+ and pip.pipinstallsas2sqlite3Note: the package has a '3' at the end. The executable does not.Other toolssas2db: Import SAS dataset and XPT files to any database supported
bySQL Alchemy. UsespandasSAS parser under the hood.
Package: sas32kd
A library to simplify communication with TCP/IP Server Module for Sierra Automated Systems audio routers SAS 32KD.Communication protocol description is available here:http://www3.sasaudio.com/downloads/SAS%20Documentation/USI_Protocol_32KD_Rev_13.pdf.Source Codehttps://github.com/dgalus/sas32kdPyPI packagehttps://pypi.org/project/sas32kd/Documentationhttps://sas32kd.rtfd.ioDocumentation can be also found in thedocs/directory as reStructuredText files. Build the docs by runningmakein thedocs/directory, then viewingdocs/_build/html/index.html.Installationpython -m pip install sas32kdAuthordgalus / avc <avcsec@protonmail.com>
Package: sas7bdat
sas7bdat.pyThis module will read sas7bdat files using pure Python (2.6+, 3+). No SAS software
required! The module started out as a port of the R script of the same name
found here:https://github.com/BioStatMatt/sas7bdatbut has since been
completely rewritten.Also included with this library is a simple command line script,sas7bdat_to_csv, which converts sas7bdat files to csv files. It will also
print out header information and meta data using the--headeroption and it
will batch convert files as well. Use the--helpoption for more information.As is, I've successfully tested the script almost three hundred sample files I
found on the internet. For the most part, it works well. We can now read
compressed files!I'm sure there are more issues that I haven't come across yet. Please let me
know if you come across a data file that isn't supported and I'll see if I can
add support for the file.UsageTo install, run:pip install sas7bdatTo create a sas7bdat object, simply pass the constructor a file path. The
object is iterable so you can read the contents like this:#!python
from sas7bdat import SAS7BDAT
with SAS7BDAT('foo.sas7bdat', skip_header=True) as reader:
    for row in reader:
        print rowEach row will be a list of values of typestring,float,datetime.date,datetime.datetime, ordatetime.time. Withoutskip_header, the first row
returned will be the SAS variable names.If you'd like to get a pandas DataFrame, use theto_data_framemethod:#!python
df = reader.to_data_frame()Variable
attributesare available fromreader.columns. The order of these columns will be the same
as the corresponding values in eachrow. EachColumnhas the following
attributes:col_id(int) - the column numbername(bytes)label(bytes)format(str)type(str)length(int)
Package: sas7bdat-converter-cli
sas7bdat Converter CLICLI to convert sas7bdat and xport files into other formatsInstallationInstallation withpipxis recommended.pipxinstallsas7bdat-converter-cliAlternatively sas7bdat Converter CLI can be installed with pip.pipinstallsas7bdat-converter-cliUsageUsage: sas7bdat-converter [OPTIONS] COMMAND [ARGS]...Options:-v, --version                   Show the installed version--install-completion [bash|zsh|fish|powershell|pwsh]Install completion for the specified shell.--show-completion [bash|zsh|fish|powershell|pwsh]Show completion for the specified shell, tocopy it or customize the installation.--help                          Show this message and exit.Commands:dir-to-csv      Convert a directory containing sas7bdat or xpt files to...dir-to-excel    Convert a directory of sas7bdat or xpt files to xlsx...dir-to-json     Convert a directory of sas7bdat or xpt files to json...dir-to-parquet  Convert a directory of sas7bdat or xpt files to parquet...dir-to-xml      Convert a directory of sas7bdat or xpt files to xml files.to-csv          Convert a sas7bdat or xpt file to a csv file.to-excel        Convert a sas7bdat or xpt file to a xlsx file.to-json         Convert a sas7bdat or xpt file to a JSON file.to-parquet      Convert a sas7bdat or xpt file to a parquet file.to-xml          Convert a sas7bdat or xpt file to a xml file.ContributingContributions to this project are welcome. If you are interesting in contributing please see ourcontributing guide
Package: sasa
This is a tool for doing sentiment analysis. The short-term goal of this
project is to share the sentiment analysis research. The long-term goal of
this project is to allow researchers the ability to demonstrate and test
sentiment analysis tools, so that performance can be evaluated and compared.Currently, the models are trained on Twitter data from the 2012 US
Presidential election. In the future we hope to have general models as well as
models for other domains.This code was first released by the collaboration of two labs at the
University of Southern California (USC), the Signal Analysis and
Interpretation Laboratory (SAIL) and Annenberg Innovation Laboratory
(AIL). The sentiment research at SAIL is supported by grants including NSF,
NIH, and DARPA. The sentiment research at AIL is sponsored by the lab
sponsors, especially IBM, an AIL founding sponsor, and Orange, the flagship
brand of France Telecom.This work was made possible by using existing open source projects and
code. NLTK (nltk.org) provides some of the basic classes of the SASA tool,
e.g. frequency distributions and classifiers. Christopher Potts’
twitter-specific tokenizer for sentiment analysis is used for tokenization.
Package: sasa-db
SASA DatabaseI don't think this is usable by anyone lese but it's a dependency tosasa_stackerand I wanted to package it separately. An explanation to the whole project can be foundhere.Usageexl_to_sql.py -h:exl_to_sql.py [-h] [-n SHEET_NUMBER] [-v] [-s] exl db

positional arguments:
  exl                   path to excel-file
  db                    path to sqlite3-db


optional arguments:
  -h, --help            show this help message and exit
  -n SHEET_NUMBER, --sheet-number SHEET_NUMBER
                        which excel-sheet to convert
  -v, --verbose         verbose output
  -s, --skip-existing   skipping rows already contained in the dbWrites the excel fileexlinto the sqlite databasedb. Every row in the Excel sheet represents one simulation run of metasurfaces. The problem is with our current setup they are saved as one big.matfile but thesasa_stackerneeds to access them and their parameters individually. This script assigns each single metasurface an address and saves its parameters in the db separately. Examples for the formating of the excel sheet can be found indata/NN_smats.xlsx.CrawlerThe Crawler class allows access to the db and loads the simulation data. The main functions are:find_smatCrawler.find_smat(name,adress=None)Loads the simulation data toname. If an adress is provided it only loads this single S-matrix.Argumentsname: string, name of the simulation in the databaseadress: list, for example[1,4,5,3]the adress can also be found in the databasefind_smat_by_idCrawler.find_smat_by_id(id)Same as above but takes the simulation idArgumentsid: int, simulation id found in the databaseextract_paramsCrawler.extract_params(id)Queries meta_materials.db for all the parameters to the given ID.Argumentsid: int, simulation id found in the databaseReturnsparam_dict: dict, contains the combined data from the simulations and geometry tables with coresponding names
Package: sasa-phys
SASA "Semi Analytic Stacking Algorithm"Documentation:https://sasa.readthedocs.io/en/latest/index.html#Functionality:create arbitrary meta surface stacks and calculate their scatter matricesapply geometric transformations to single layerssimulate different spacer heightsReferences:[1] J. Sperrhake, M. Decker, M. Falkner, S. Fasold, T. Kaiser, I. Staude, T. Pertsch,
"Analyzing the polarization response of a chiral metasurface stack by semi-analytic modeling",
Optics Express 1246, 2019[2] C. Menzel, J. Sperrhake, T. Pertsch,
"Efficient treatment of stacked metasurfaces for optimizing and enhancing the range of accessible optical functionalities",
Physical Review A 93, 2016[3] J. Sperrhake, T. Kaiser, M. Falkner, S. Fasold, T. Pertsch,
"Interaction of reflection paths of light in metasurfaces stacks"
Package: sasawrapper
sasa_wrapperCeVIO AIの外部連携インターフェイスのラッパーCeVIO AIの起動が必要（していない場合は自動で起動する）python 3.8で動作確認済APIドキュメントhttps://takana-v.github.io/sasawrapper/インストールpipinstallsasawrapper使用例コマンドラインからpython-msasawrapperspeakこんにちは
python-msasawrapperspeak--castさとうささら--volume60--emotion怒り50--emotion哀しみ50色々オプション付きの例ですコマンドの一覧はpython -m sasawrapper -h参照コマンドの詳細はpython -m sasawrapper コマンド名 -h参照モジュールとして利用importsasawrappersasawrapper.speak("こんにちは")
Package: sascar_client
sascar_clientA client to Sascar SOAP web service, using SUDS Python library.Installationpip install sascar_clientUsageUsage:sascar_client [OPTIONS] <user> <password> <database> <dbuser> <dbpassword>Get the routes of all vehicles and save it to a database.
User and password fields refer to your sascar user and password.Options:--get_vehiclesSave vehicles to the database.--hostTEXTSet URL of the database server. Default value is localhost.--portINTEGERSet port of database server. Default value is 5432.--hoursINTEGERSet the number of prior hours you want to query. Default
value is 24--helpShow this message and exit.Requirementsclicksudspsycopg2shapelyLicenseGPLv3
Package: sas-cvpy
CVPy - SAS Viya Computer Vision API for PythonOverviewCVPy is a Python package that makes SAS image analytics more accessible to Python users in a variety of different ways. This high-level Python library provides helpful APIs that assist with the processing, analyzing, and visualization of images. This allows users of the image and biomedimage action set in SAS Viya to have more flexibilty when working in Python.Currently available within CVPy are visualization APIs that allow users to easily be able to move images out of CAS and into common open source tools such as Mayavi and Matplotlib. These APIs streamline the visualization of image data fetched from a CAS table and can assist with further image analysis.PrerequisitesPython version 3 or greater is requiredInstall SAS Scripting Wrapper for Analytics Transfer (SWAT) for Python using pip install swat or conda install -c sas-institute swatAccess to a SAS Viya 3.5 environment with Visual Data Mining and Machine Learning (VDMML) is requiredA user login to your SAS Viya back-end is required. See your system administrator for details if you do not have a SAS Viya account.Install Mayavi for scientific data visualization in PythonCreate a New Python Environment [Optional]Follow the steps below to create a new Python 3.8 environment for your CVPy installation named "cvpy".conda create --name cvpy python=3.8activate cvpyMayavi InstallationMayavi is a heavy, complex package that is required by CVPy. If you do not already have Mayavi installed, follow the steps below to install it.pip install mayaviInstall PyQt, a GUI toolkit needed to run Mayavi.pip install PyQt5CVPy InstallationTo install CVPy, use the following command:pip install sas-cvpyInstall and Run Jupyter NotebookTo install and start Jupyter Notebook, run the following steps:pip install jupyterjupyter notebookContributingWe welcome your contributions! Please readCONTRIBUTING.mdfor details on how to submit contributions to this project.LicenseThis project is licensed under theApache 2.0 License.Additional ResourcesPython-CVPy API DocumentationBiomedimage action setImage action set
Package: sasdata
SasdataA package for loading and handling reduced small angle scattering data.The data loaders provided are usable as a standalone package, or in conjunction with the sasview analysis package.InstallThe easiest way to use sasdata is by usingSasView.You can also install sasdata as a standalone package in python. To create a python environment with the sasdata dependenciesUsing a python virtual environment::$ python -m venv sasdata
$ python -m pip install numpy lxml h5py xmlrunner "pytest<6"Activate the environment::
$ .\sasdata\Scripts\activateUsingminicondaoranaconda::$ conda create -n sasdata -c conda-forge numpy lxml h5py xmlrunner "pytest<6"Activate the environment and install sasdata::$ conda activate sasdata
$ (sasdata) $ pip install sasdataView the latest release on thesasdata pypi page.UsageLoading data sets:(sasdata) $ python
>>> from sasdata.dataloader.loader import Loader
>>> loader_module = Loader()
>>> loaded_data_sets = loader_module.load(path="/path/to/file.ext")The Loader() class is not callable and must be instantiated prior to use.The load() method returns a list of Data1/2D objects as loaded from the specified path.Saving loaded data:>>> loaded_data_set = loaded_data_sets[0]
>>> loader.save(path='/path/to/new/file.ext', data=loaded_data_set, format=None)The save() method accepts three (3) arguments:path: The file name and path to save the data.data: A Data1D or Data2D object.format (optional): The expected file extension for the file. Options include:.xml: for the canSAS XMl format.h5: for the NXcanSAS format.txt: for the multi-column ascii format.csv: for a comma delimited text format.The file extension specified in the save path will be superseded by the format value.
Package: sasdbpy
SasdbpyThis is a simple helper class to read and use data stored in sas7bdat format, without having Sas.This code will help you:explore a folder and identify all sas7bdat present thereinhave them organized in a python dictionary with, for each file:the data as a pandas Dataframea field name mapper from the coded field name to the description from the meta dataa more detailed fields characteristics dataframe with information of Type, Length, FormatTwo search functions on the field names:search_field, to identify fields from the database containing a certain stringsearch_field_description, to reverse search fields from a string contained in their descriptionUsagefrom sasdbpy.sasdbreader import SasDbReader
sdb = SasDbReader("[the folder path with the sas files]")

# get list of tables found
list_tables = list(sdb.res_data.keys())

#get the dataframe for one of the tables
table_name = list_tables[0]
df = sbd.res_data[table_name]['data']

#get field description map
sdb.res_data[table_name]['detail_fields']

# search for a field in the whole database
sdb.search_field('USERID')
# with information on the table where it sits
sdb.search_field('USERID', location=True)

# reverse search
sdb.search_field_description('identity')
Package: sasecli
sasecliCommand-line access to available Prisma SASE CLI resources. (Specifically, Prisma SD-WAN as of now.)SynopsisThis is a placeholder for the soon-to-be released utility.RequirementsActive Prisma SASE AccountPython >=3.6Python modules:cloudgenix -http://docs.python-requests.org/en/master/fuzzywuzzy -https://github.com/seatgeek/fuzzywuzzy/Installationpip install sasecliLicenseMITVersionVersionBuildChanges0.0.1b1Placeholder for future release.For more infoPlease look at Prisma SASE information onhttps://docs.paloaltonetworks.com/sase
Package: saseg-runner
Overviewsaseg_runner get you run egp file from python or command line.Requirmentwindowspython 3.6 laterSAS Enterprise guideInstallpipinstallsaseg_runnerUsageas python libraryfromsaseg_runnerimportrun_egp,SASEGRuntimeErrorrun_egp(egp_path='test_success.egp',eg_version='7.1',profile_name='Your Profile')# opening SAS Enterprise Guide 7.1# -> application instance created# activating profile:[SAS Asia]# -> profile:[SAS Asia] activated# opening test_success.egp# -> egp file opened# running test_success.egp# -> run finished# -> saved to test_success_20191031-1102.egp# getting logs from test_success_20191031-1102.egp# -> log created# successfully finished exectuing test_success.egp# elapsed_time:40[sec]In case some tasks in the egp file fails, 'run_egp' can raise Exception of SASEGRuntimeError!fromsaseg_runnerimportrun_egp,SASEGRuntimeErrorrun_egp(egp_path='test_fail.egp',eg_version='7.1',profile_name='Your Profile')# opening SAS Enterprise Guide 7.1# -> application instance created# activating profile:[SAS Asia]# -> profile:[SAS Asia] activated# opening test_fail.egp# -> egp file opened# running test_fail.egp# -> run finished# -> saved to test_fail_20191031-1056.egp# getting logs from test_fail_20191031-1056.egp# -> log created# [task 2] failed in test_fail.egp# ERROR: CLI error occured: [DataDirect][ODBC lib] Specified driver could not be loaded# Traceback (most recent call last):#   File "saseg_runner/runner.py", line 123, in <module>#     run_egp(SCRIPTDIR_PATH.parent.parent / 'test/test_fail.egp')#   File "saseg_runner/runner.py", line 103, in run_egp#     raise SASEGRuntimeError# __main__.SASEGRuntimeErrorParametersegp_path : Union[str, Path]SAS Enterprise Guide file path.profile_name : strprofile name to useoverwrite: boolcontrols whether to save the egp file after exection. if False, timestamp is added to filename. The default is False.remove_log: boolwhether to remove log files or not. the default is True.verbose: booldefault is Falsefrom command linerun_egp<youregpfilepath>
Package: sashay
SashayIntroduction:information_source: Sashay is an all in one automatic installer for useful Linux based tools.There are over 340 tools available.Installation⬇️pip install -U sashayTool CategoriesInformation GatheringVulnerability ScannerExploitation ToolsWireless TestingForensics ToolsWeb HackingStress TestingSniffing & SpoofingPassword AttacksMaintaining AccessIP-tracking ToolsProgramming LanguagesDDOS AttacksWeb ServersDistrosHow to use👨‍💻Typesashayorsshyfrom anywhere in your terminalPress[TAB]to show  completion, each number represents a specific output::bulb:1to show all available tools. Type the number corresponding number of a tool which you want to install.:bulb:2to show tools categories:bulb:3to  update sashay:bulb:4to know more about sashay:bulb:xto exit SashayYou can also type the numbers manualy intested of pressing[TAB]RequirementsPython3.8+ContributingIf you run into an issue, we would be very happy if you would file a bug on theissue tracker. You can also contribute by adding more tools by creating a pull requestLicensesashay is released under MIT LicenseDonateCheck more toolsScalabli.
Package: sashimi-cli
exactcliInstallpipx install sashimiBut you may use pip as well.Config exampleConfig file is either.envin current directory or override it withSASHIMI_DOTENV=/path/fileenv variable.config example:SASHIMI_PROJECT=http://localhost:8000/ds/sandbox
SASHIMI_TOKEN=envtokenThese defaults will make your commands shorter.Database support# postgresql
pip install psycopg2

# mariadb / mysql
pip install mysqlclientUsageGet info about your project and datasetsexactcli --infoUpload new JSON file to projectexactcli --upload ~/repo/exact/tests/products.json --ds products --keypath productsQuery itexactcli --ds products --expr 'True' --sort price --limit 1
exactcli --ds products --expr 'brand=="Apple" and price<1000' --fields id brand title description priceexactcli --ds products --filter brand=\"Apple\" price__lt=2000
exactcli --ds products --filter 'brand="Apple"' price__lt=2000