Saravanan666/pypi-org huggingface dataset:
Package: scabbard
ScabbardAboutScabbard is a Pythonic client for the Sabre Dev Studio REST APIs.  With Scabbard, it
is not necessary to create extensive low-level boilerplate code before you can use each API call.
You can begin to interact with the Sabre Dev Studio APIs with as few as 3 lines of code.A scabbard is a sheath for holding a sword, such as a sabre -https://en.wikipedia.org/wiki/ScabbardFeaturesDefined Scabbard REST endpoints can be reviewed in the SwaggerUI:https://bundgus.github.io/scabbard/SwaggerUI/index.htmlDocumentationSabre Dev Studio API Documentation:https://developer.sabre.com/docs/read/HomeSabre Dev Studio REST API documentation:https://developer.sabre.com/docs/read/rest_apis/https://developer.sabre.com/io-docsScabbard docs:https://bundgus.github.io/scabbard/html/Scabbard SwaggerUI:https://bundgus.github.io/scabbard/SwaggerUI/index.htmlScabbard GitHub Home:https://github.com/bundgus/scabbardScabbard PyPi Home:https://pypi.python.org/pypi/scabbardGetting Started(1)
You can register for a free Sabre Dev Studio account at the following URL:https://developer.sabre.com/apps/mykeys(2)
Install the scabbard library with pip.$pipinstallscabbard(3)
A file called api_connect_parameters.json must exist in the directory
in which python is run, with your Sabre Dev Studio clientID and clientSecret credentials.api_connect_parameters.json{"clientId":"zzzzzzzzzzzzzzzz","clientSecret":"xxxxxxxx","environment":"https://api.test.sabre.com","group":"DEVCENTER","domain":"EXT","formatVersion":"V1"}(4)
Run code to exercise the API.  For example:example_V1ListsSupportedCountriesGet.pyimportscabbardclient=scabbard.get_client()countries=client.Air_Utility.V1ListsSupportedCountriesGet(pointofsalecountry='NZ').result()print('PointOfSale')print(countries.PointOfSale)print('OriginCountries')forcincountries.OriginCountries:print(c.CountryCode,c.CountryName)print('DestinationCountries')forcincountries.DestinationCountries:print(c.CountryCode,c.CountryName)print('Links')forlincountries.Links:print(l.rel)print(l.href)(5)
Run your python example.$pythonexample_V1ListsSupportedCountriesGet.pyPointOfSaleNZOriginCountriesAUAustraliaIDIndonesiaIEIrelandNZNewZealandTHThailandGBUnitedKingdomDestinationCountriesAUAustraliaFRFranceDEGermanyHKHongKongINIndiaIDIndonesiaIEIrelandMYMalaysiaNLNetherlandsNZNewZealandPHPhilippinesSGSingaporeTHThailandGBUnitedKingdomUSUnitedStatesLinksselfhttps://api.test.sabre.com/v1/lists/supported/countries?pointofsalecountry=NZlinkTemplatehttps://api.test.sabre.com/v1/lists/supported/countries?pointofsalecountry=<pointofsalecountry>Installation$pipinstallscabbardLicenseCopyright (c) 2018, Mark Bundgus. All rights reserved.
* not a Sabre provided or supported software library
* all referenced Sabre content and services are the property of SabreScabbard is licensed with aBSD 3-Clause
License.
Package: scabi
ScabiImplement vulnerabilities scanning on top of package management system like apt, pip, composer...InstallYou can installscabieither via pip (PyPI) or from source.
To install using pip:python3-mpipinstallscabiOr manually:git clone https://github.com/remiflavien1/scabi
cd scabi
python3 setup.py installCLIScabi

Usage:
  scabi <pms> <package> [--verbose --detail ] [--oss  --mitre] [-s FILE]
  scabi -h --help --version

Options:
  -v --verbose      Show full output.
  -d --detail       Show CVE details.
  -o --oss          Search vulnerabilities only through OSS.
  -m --mitre        Search vulnerabilities only through MITRE.
  -s --save FILE    Save output to file.
  -h --help         Show this screen.Example of output for the python moduledjango:$scabi-vpipdjangoThe dependencies for <django> are :
... pytz
... sqlparse
... asgiref
... argon2-cffi
... bcrypt

>>>>>>>>>>>>>>> SEARCH IN OSS INDEX <<<<<<<<<<<<<<<
NO VULNERABILITIES FOUND

>>>>>>>>>>>>>>> SEARCH IN MITRE DATABASE <<<<<<<<<<<<<<<

-------------- Package: <bcrypt> --------------

CVE : CVE-2020-5229
CVE DETAIL https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-5229
DESCRIPTION Opencast before 8.1 stores passwords using the rather outdated and cryptographically insecure MD5 hash algorithm. ...

CVE : CVE-2019-13421
CVE DETAIL https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-13421
DESCRIPTION Search Guard versions before 23.1 had an issue that an administrative user is able to retrieve bcrypt password hashes of other users configured in the internal user database.
...
Package: scace
scAce: an adaptive embedding and clustering method for scRNA-seq dataOverviewscAce is consisted of three major steps, a pre-training step based on a variational autoencoder, a cluster initialization step to obtain initial cluster labels, and an adaptive cluster merging step to iteratively
update cluster labels and cell embeddings. In the pre-training step, scAce takes the single-cell gene expression matrix as its input to train a VAE network. For each gene, the VAE learns and outputs three parameters of a ZINB distribution (mean, dispersion, and proportion of zero). In the cluster initialization step, scAce offeres two manners. Withde novoinitialization, Leiden is used to obtain initial cluster labels; with clustering enhancement, initial cluster labels are obtained by applying a cluster splitting approach to a set of existing clustering results. In the adaptive cluster merging step,  given the pre-trained VAE network and the initial cluster labels, the network parameters, cell embeddings, cluster labels and centroids are iteratively updated by alternately performing network update and cluster merging steps. The final results of cell embeddings and cluster labels are output by scAce after the iteration process stops.InstallationPlease installscAcefrom pypi with:pipinstallscaceOr clone this repository and usepipinstall-e.in the root of this repository.Quick startLoad the data to be analyzed:importscanpyasscadata=sc.AnnData(data)Perform data pre-processing:# Basic filteringsc.pp.filter_genes(adata,min_cells=3)sc.pp.filter_cells(adata,min_genes=200)adata.raw=adata.copy()# Total-count normlize, logarithmize and scale the datasc.pp.normalize_per_cell(adata)adata.obs['scale_factor']=adata.obs.n_counts/adata.obs.n_counts.median()sc.pp.log1p(adata)sc.pp.scale(adata)Run the scAce method:fromscaceimportrun_scaceadata=run_scace(adata)The output adata contains cluster labels inadata.obs['scace_cluster']and the cell embeddings inadata.obsm['scace_emb']. The embeddings can be used as input of other downstream analyses.Please refer totutorial.ipynbfor a detailed description of scAce's usage.
Package: scache
Python Simple Cache - scacheInstallation$[sudo]pipinstallscacheHow it worksscache uses .json files in the data subdirectory by default. If they exist, they are overwritten. If they do not, they are created../data/<key>.jsonby defaultGet Started'''Optional parameters:path: (string) Path where to store the .json cache data.defaults to './data/'debug: (boolean) Prints debug information such as whether or not keys were found.defaults to False'''fromscacheimportSCachescache=SCache()scache.set('key','value')Functionsfunction__doc__scache.get(key)Gets cache value.scache.get(key, value)Sets cache value.scache.get(key, default)Get cache value. Return default if not foundscache.rm(key)Remove cache by keyscache.exists(key)returns True if key exists, else returns Falsescache.empty()Clears the entire cache.Examples>>>importscache>>>scache.set("key",{'test':1})>>>scache.get("key")"{'test': 1}">>>scache.exists("key")True>>>scache.rm("key")>>>scache.empty()# clears entire cache
Package: scada-data-analysis
This library helps lower the entry barrier for domain experts
in the wind energy industry to leverage advanced analytics and methodologies
developed in related scientific research.Modules are implemented for different routine and non-routine analysis of time series
data collected from operating wind turbines. Practicing wind engineers and analysts
can build on fundamental modules as part of a larger project development.Library installationClone the github repo to have access to example notebooks and public data.Next, pip install the library# Clone github repo
git clone https://github.com/abbey2017/wind-energy-analytics.git

# Pip install library
pip install scada-data-analysisCurrent modulesIterative power curve filter (returns normal and abnormal datapoints)Expected power estimator (returns expected power based on operational data)Usage of power curve filterimport pandas as pd

from scada_data_analysis.modules.power_curve_preprocessing import PowerCurveFiltering

# Load turbine scada data
df = pd.read_csv('path\to\data')

# Instantiate power curve filtering class
pc_filter = PowerCurveFiltering(turbine_label='Wind_turbine_name', windspeed_label='Ws_avg',
                                power_label='P_avg', data=df, cut_in_speed=3, bin_interval=0.5,
                                z_coeff=2.5, filter_cycle=5, return_fig=True, image_path='..\images')

# Process raw scada data
normal_df, abnormal_df = pc_filter.process()Usage of expected power estimatorimport pandas as pd

from scada_data_analysis.modules.expected_power import ExpectedPower

# Load turbine scada data
df = pd.read_csv('path\to\data')

# Instantiate expected power estimator
power_model = ExpectedPower(turbine_label='Wind_turbine_name', windspeed_label='Ws_avg',
                            power_label='P_avg', method='binning', kind='linear')

# Fit estimator based on training data
power_model = power_model.fit(train_df)

# Estimate expected power based on operation power curve
pred_df = power_model.predict(test_df)
Package: scadi
scadiCommand-line tool for rolling up all includes into the main file of your model so that you can easily share it online.Installationpip3 install scadiUsagescadi inline ./my-model.scadThe above command will create a file called./inline-my-model.scadthat can be shared on sites that have OpenSCAD customizers.LicenseCopyright 2021 Nascent Maker, nascentmaker.com.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.Support meIf you found that this tool saved you some time and you want to give back, please consider using Ko-Fi to buy me a coffee.
Package: scaespy
scAEspy is a user-friendly and standalone tool that embodies six of the most advanced autoencoders (AEs), easily accessible by setting up only two user-defined parameters (i.e., alpha and lambda), and different loss functions, which are fundamental to deal with the different RNA sequencing platforms.Specifically, scAEspy contains the following most advanced AEs: Variational AE, Gaussian-mixture VAE (GMVAE), Maximum Mean Discrepancy (MMD) AE, MMDVAE (a combination of MMDAE and VAE), and two novel Gaussian-mixture AEs that we developed, called GMMMD and GMMMDVAE.
GMMMD is a modification of the MMDVAE where more than one Gaussian distribution is used to model different modes and only the MMD loss function is used as a divergence function.
GMMMDVAE is a combination of MMDVAE and GMVAE where both the MMD function and the Kullback–Leibler (KL) divergence function are used.scAEspy gives easy access to the latent space generated by the selected AE, which can be utilised to perform downstream analysis or to generate synthetic cells.For further information, please visithttps://gitlab.com/cvejic-group/scaespy
Package: scaffolder
Interactive command line tool for rapid prototyping and automatic code generation used for creating full-stack web, mobile, TV and other apps. It can be used to create wire-frames as well by utilizing any existing front-end or back-end framework. It can generate boilerplate skeleton for your project according to strict rules and naming conventions for the files and folders been used. Trough a simple and easy command line oriented interface it creates multiple YAML files and based on it generates all the code and configuration files in a predefined directory tree.
Package: scaffoldfitter
Scaffold FitterScaffold/model fitter using Zinc.Install with the following command:pip install scaffoldfitterLicenseCopyright 2022 University of Auckland

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: scaffoldmaker
Scaffold MakerAnatomical scaffold generator using Zinc.
Install with:pip install scaffoldmakerFor developing scaffolds:git clone git+https://github.com/ABI-Software/scaffoldmaker
cd scaffoldmaker
pip install -e .Requires the following Python libraries to be installed.cmlibs.zinccmlibs.utilsWe recommend installing scaffoldmaker into a virtual python environment.
Seepython virtual environment documentationfor information on virtual environments.LicenseCopyright 2022 University of Auckland

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: scaffy
scaffyis a small tool to create, manage and apply project scaffolds (or
any directory scaffold for that matter). It includes a web service that allows
storing the scaffolds remotely.Installation$pipinstallscaffyUsageContributingPrincipal ruleMake it work, then make it right, then make it fast.Setting up development repo$gitclonegit@github.com:novopl/scaffy.git$cdscaffy$virtualenvenv$source./env/bin/activate$pythonsetup.pydevelop$pipinstall-rops/devrequirements.txtThe project usespeltakfor management. Seepeltakdocs for more information.
Package: scahpy
What is SCAHpy?SCAHpyis an open-source Python package that facilitate the analysis and visualization of the ouputs from atmospheric, oceaninc and hydrological component from the Geophysical Institute of Peru Regional Earth System Model Croco-Oasis-WRF (IGP-RESM-COW)Why is SCAHpy?Atmospheric component of the coupled model generate a large volumes of output data, making the analysis of model data harder. SCAHpy facilitates the manage of this volumes of data, also enables a manage of coordinates and times to local times.How to use SCAHpy?SCAHpy can be used as a standalone package or it can also be run on the HPC-IGP-Cluster, which has the diagnostic simulations of 22 years of runnings centered on Peru Region.Note:SCAHpy has been developed and tested using IGP-RESM-COW model outputs. However, it is designed to work with any WRF outputs. We are open to contributions from users!DocumentationThe official documentation is hosted here:DocumentationInstallationUsing MambaFirst, download and install mamba or miniconda throughMiniforge.The easiest way to install SCAHpy and the above mentioned dependencies is to use the environment.yml from therepository. Open a terminal, then run the following command:mambaenvcreate--fileenvironment.yml-nscahpy_envUsing pipTo install SCAHpy directly. Open a terminal, then run the following command:pipinstallscahpyNote:Checkout the contribution page if you want to get involved and help maintain or develop SCAHpy
Package: scaife-viewer-atlas
Aligned Text and Linguistic Annotation Server (ATLAS)SettingsSettings can be overridden at a project level using via theSV_ATLAS_<name>naming convention.Data modelDATA_DIRDefault:NoneThe path to the directory containing ATLAS dataINGESTION_CONCURRENCYDefault:NoneSets the number of processes available to ProcessPoolExecutors during ingestion.WhenNone, defaults to number of processors as reported by multiprocessing.cpu_count()TREE_PATH_ALPHABETDefault:"0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"Used bydjango-treebeardto calculate the maximum path steps.See thedjango-treebeard docsfor more information.INGESTION_PIPELINEDefault:["scaife_viewer.atlas.importers.versions.import_versions",]A list of callables that are ran by theprepare_atlas_dbmanagement
command to ingest data into ATLAS.DatabaseDB_LABELDefault:"atlas"The label to use for the ATLAS-specific database (required when using theATLASRouterdatabase router)DB_PATHDefault:NoneThe path to the SQLite database referenced byDB_LABEL.SearchSEARCH_TEMPLATE_FIXTURE_PATHDefault:scaife_viewer/atlas/backports/scaife_viewer/search/fixtures/base_template.jsonThe path to the JSON fixture used to create ElasticSearch index mappings for ATLAS.AnnotationsEXPAND_IMAGE_ANNOTATION_REFSDefault:TrueSets the text part relation toalltext parts within a passage reference (descendants within the passage reference).Example:Reference is1-2Text parts1.1, 1.2, ... 2.999are linked to the annotation within ATLASWhenFalse, applies annotationsonlyto the text parts
specified in the passage reference.Example:Reference is1-2Text parts1, 2are linked to the annotation within ATLAS, butnotany children / descendantsGraphQLIN_MEMORY_PASSAGE_CHUNK_MAXDefault:2500Sets the upper limit on the number of text parts used for in-memory passage chunking.When the number of text parts exceeds this limit, ATLAS will fall back to a database-backed
chunking alogrithm.For most smaller passages, the in-memory chunking is faster than using the database.OtherHOOKSETDefault:"scaife_viewer.atlas.hooks.DefaultHookSet"The path to a hookset that can be used to customize ATLAS functionality.GraphQL EndpointURL Name:sv_atlas:graphql_endpointPrimary GraphQL endpoint forscaife-viewer-atlasprojects.When accessedvia a browser, delivers aGraphiQL Playgroundthat can be used
to explore ATLAS GraphQL fields.
Package: scaife-viewer-core
Scaife Viewer :: Core functionalityThis package was extracted fromhttps://github.com/scaife-viewer/scaife-viewerSettingsALLOW_TRAILING_COLONDefault:FalseWhenFalse, to maintain compatability with the MyCapitain resolver,
the trailing colon will be stripped from URNs.REDIRECT_VERSION_LIBRARY_COLLECTION_TO_READERDefault:TrueWhenTrue, will redirect a version / exemplarlibrary_collectionURL to the first passage of the version in the reader`HOOKSETDefault:"scaife_viewer.core.hooks.DefaultHookSet"The path to a hookset that can be used to customize package functionality.hookset.content_manifest_pathDefault: Callable returningpathlib.Pathresolving todata/content-manifests/production.yamlCan be customized via theCONTENT_MANIFEST_PATHenv var.RESOLVER_CACHE_LABELDefault:"cts-resolver"The label to use for the cache backend that holds cached resolver dataUSE_CLOUD_INDEXERDefault:FalseWhenTrue, sets GCE-specific metadata for the search index management
commandCLIFor information about the CLI:scaife--help
Package: scake
scakeA flexible framework for PythonNOTEScake version from v0.3.0+ is not compatible to older version (v0.2.4-). Accepted annotation of Scake has the following format:a:
    b: 10
c: /a/b         # 10

instead of this in the older Scake version:

a:
    b: 10
c: =/a/b         # 10USAGEUpdate laterCONTRIBUTINGStep 1. Fork onmasterbranch.Step 2. Installpre-commiton the local dev environment.pip install pre-commit
pre-commit install
#pre-commit autoupdateStep 3. Write test case(s) for the new feature or the bug.Step 4. Write code to pass the tests.Step 5. Make sure that the new code passes all the pre-commmit conditions.pre-commit run -aStep 6. Create pull request.
Package: scaks
Shanghai CAtalysis Kinetics Software.  A Micro-Kinetic Analysis package for Catalytic Reactions in PythonInstallVia pip:pipinstallscaksFrom sourcegitclonehttps://github.com/PytLab/scaks.gitcdscakspythonsetup.pyinstallTestpythonsetup.pytestorpython-mscaks.tests.test_allDocumentationFor more details, see SCAKS documentation:https://scaks-docs.readthedocs.io/en/latest/
Package: scal
I use this program about once a year to print a one-page school-year
calendar. But it can be used to represent any calendar.It is heavily inspired by the simple yet powerful Robert Krause’scalendar, itself using the complex yet powerful Till Tantau’sTikZLaTeX package.ExamplesOne-page calendar of a school yearEnglish:2023-2024(source).French2023-2024:Zone A(source);Zone B(source);Zone C(source).2024-2025:Zone A(source);Zone B(source);Zone C(source).2025-2026:Zone A(source);Zone B(source);Zone C(source).Weekly planners (How to print?)English:2023-2024(source;imposed).French2023-2024:Zone A(source;imposé);Zone B(source;imposé);Zone C(source;imposé).2024-2025:Zone A(source;imposé);Zone B(source;imposé);Zone C(source;imposé).2025-2026:Zone A(source;imposé);Zone B(source;imposé);Zone C(source;imposé).What’s new?Seechangelog.Download and installSee the end of list for a (quick and dirty) Debian package.Non-Python dependencies.
This program produces LuaLaTeX code, but does not compile it. So, LaTeX is not
needed to run this program. However, to compile the generated code, you will
need a working LaTeX installation, withlualatex, and LuaLaTeX packagesgeometry,babel,tikz,fontspec,
andtranslator(provided by thebeamerpackage).
Those are provided byTeXLiveon GNU/Linux,MiKTeXon Windows, andMacTeXon MacOS.From sources:Download:https://pypi.python.org/pypi/scalInstall (in avirtualenv, if you do not want to mess with your distribution installation system):python3 setup.py installFrom pip:pip install scalQuick and dirty Debian (and Ubuntu?) packageThis requiresstdebto be installed:python3 setup.py --command-packages=stdeb.command bdist_deb
sudo dpkg -i deb_dist/scal-<VERSION>_all.debDocumentationThe compiled documentation is available onreadthedocsTo compile it from source, download and run:cd doc && make htmlDeveloppersA partially supportedautosclscript is available in thebindirectory. It can automatically download holiday dates from the internet, and generate the relevant.sclfile. Seeautoscl –helpfor more information.