Saravanan666/pypi-org huggingface dataset:
Package: salvage
Salvage distributes sensitive data to multiple people such that it can only be
recovered by several people working together. This is useful for storing
information with both a low risk of losing access to it and a low risk of
accidental disclosure. A classic application is to create a “recovery kit” for a
server or infrastructure, which can be used in the event that conventionally
stored keys and credentials become lost or unavailable.Salvage works by encrypting a file or directory with a random master key and
then applying a simple key-splitting scheme to distribute the key across
multiple shares. You can create a kit for any number of participants with any
threshold required to recover the information. For example, you might create a
kit for five people, any three of whom may combine their shares to recover the
data.Salvage runs under Python 2.7 or Python 3.2 and later. The only external
dependency isgpg, for the cryptography. For maximum utility, it is packaged
as a single flat Python script that can be run with no installation. The
algorithms and file formats are simple and carefully documented to ensure that
recovery is always possible even if this software is unavailable for some
reason.Installation$ pip install salvageThis package will only install thesalvageexecutable. It does not depend on
any Python packages.Quick StartTo create a new salvage kit for five participants with a recovery threshold of
three:% salvage new 5 3 path/to/source/dirThis will create five shares, each containing an encrypted archive and some
metadata. To decrypt and unpack the archive:% salvage recover path/to/share1 path/to/share2 path/to/share3The three paths must be three of the shares generated in the first step. The
master key will be reconstructed and the data will be decrypted and unpacked.Seesalvage-hfor additional options.
Package: salvia-blockchain
salvia-blockchainSalviais a modern cryptocurrency forked fromChiaproject, designed to be efficient, decentralized, and secure. Here are some of the features and benefits:Proof of space and timebased consensus which allows anyone to farm with commodity hardwareVery easy to use full node and farmer GUI and cli (thousands of nodes active on mainnet)Simplified UTXO based transaction model, with small on-chain stateLisp-style Turing-complete functionalprogramming languagefor money related use casesBLS keys and aggregate signatures (only one signature per block)Pooling protocolthat allows farmers to have control of making blocksSupport for light clients with fast, objective syncingA growing community of farmers and developers around the worldPlease check out the Chia Blockchain for detailed information on Salvia project.
Package: salvo
Apache Bench replacement=====
Salvo
=====This is the replacement forhttps://github.com/tarekziade/boomSalvois a simple command line tool to send some load to an HTTP(S)
endpoint.Salvo is a script you can use to quickly smoke-test your
web app deployment. If you need a more complex tool,
I’d suggest looking atMolotovSalvo was specifically written to replace my Apache Bench (ab) usage.
Salvo is based on Molotov, which uses Python 3 asyncio & aiohttp.InstallationSalvo requires Python 3.6+ andMolotov, which gets installed as a
dependency.Just do:$ pip install salvoBasic usageBasic usage example: 100 queries with a maximum concurrency of 10 users:% salvo http://localhost:80 -c 10 -n 100
-------- Server info --------

Server Software: nginx/1.18.0
Host: localhost

-------- Running 100 queries - concurrency 10 --------

[================================================================>.] 99%

-------- Results --------

Successful calls                    1000
Total time                          16.0587 s
Average                             0.0161 s
Fastest                             0.0036 s
Slowest                             0.2524 s
Amplitude                           0.2488 s
Standard deviation                  0.011326
Requests Per Second                 62.27
Requests Per Minute                 3736.29

-------- Status codes --------
Code 200                    1000 times.You can also use–durationif you want to run for a given amount of time.For a full list of features, runsalvo –helpContributeSalvo is very simple and anyone familiar with Python can contribute.If you are interested in this project, you are welcome to join the fun athttps://github.com/tarekziade/salvoMake sure to add yourself to the contributors list if your PR gets merged. And
make sure it’s in alphabetical order!Release History0.2 - 2020-12-02Massive cleanupWorks with the latest Molotov and aiohttpImproved test coverage0.1 - 2020-11-18Initial working releaseOwner and creatorTarek Ziade <tarek@ziade.org>ContributorsYou ? :)
Package: salvus
In-memory credential store with yubikey authorization.Stores a set of ID and VALUE for each KEY. Usually ID is the username
and VALUE is the password. Neither field may contain newlines.Command line interfaceUsage:salvus serve [daemon] [-p PORT] [-e EXPIRY]
salvus auth [-p PORT]
salvus get <KEY> [-a] [-p PORT]
salvus set <KEY> <ID> [-a] [-p PORT]
salvus list [-a] [-p PORT]
salvus kill [-p PORT]
salvus ping [-p PORT]
salvus -hOptions:-h,--helpThis help-pPORTPort to listen to (always on localhost) [default: 59999]-eEXPIRYAuth expiry in seconds, if 0 thenget,setandlistrequires -a [default: 3600]-aAdd auth to each command, so requires yubikey OTPInterpreting the outputAll errors are printed on stderr and the exit code will be non-zero.If exit code is zero, the output will either be blank or in the case
ofgettwo lines, first the ID and second the secret.When yubikey OTP (one time password) is needed, the prompt is output
on stderr, so it can be separated from the desired output.ExamplesStarting the server:salvus serverEnter a recognition phrase used to identify that this is the server
you trust.Touch the yubikey and the server starts on the default port.Setting a credential:salvus set github philipbergenYou will be prompted on stdout to enter the secret, press enter when
you are done.Getting a credential:salvus get githubFirst line in the output on stdout is the ID (username) and the second
is the secret (password).If you have set up zero expiry (auth on each request), then you need
to add-ato each call toget,setorlist. In the
case ofget, you can separate prompts from results easily, since
results are the only thing on stdout:salvus get github -a > userpassThat will outputPlease touch the yubikey:on stderr, and the file
userpass will contain two lines, the first is the username and the
second line is password.Killing the server:salvus killThis obviously requires yubikey OTP.Pinging the server:salvus pingNever needs yubikey OTP. The only relevant output is the exit code,
zero for server running, non-zero (and a reason on stderr) for connect
failed.
Package: sam
SAMSAM (Smart Asset Management) is a Python package fortimeseries analysis,anomaly detectionandforecasting.The documentation is available onReadTheDocs.Author:Royal HaskoningDHVEmail:ruud.kassing@rhdhv.comGetting startedInstallationThe easiest way to install is package is using pip:pip install samThere are different optional dependencies for SAM, if you are unsure usepip install 'sam[all]'other options includeplotting(just use the plotting functionality),data_science(all dependencies needed for a data scientist) anddata_engineering(dependencies for data engineer).Keep in mind that the sam package is updated frequently, and after a while, your local version may be out of date with the online documentation. To be sure, run thepip install -U samcommand to install the latest version.Simple exampleBelow you can find a simple example on how to use one of our timeseries models. For more examples, check ourexample notebooksfromsam.datasetsimportload_rainbow_beachfromsam.modelsimportMLPTimeseriesRegressorfromsam.feature_engineeringimportSimpleFeatureEngineerdata=load_rainbow_beach()X,y=data,data["water_temperature"]# Easily create rolling and time features to be used by the modelsimple_features=SimpleFeatureEngineer(rolling_features=[("wave_height","mean",24),("wave_height","mean",12),],time_features=[("hour_of_day","cyclical"),],keep_original=False,)# Define your model, see the docs for all parametersmodel=MLPTimeseriesRegressor(predict_ahead=(1,2,3),# Multiple predict aheads are possiblequantiles=(0.025,0.975),# Predict quantile bounds for anomaly detectionfeature_engineer=simple_features,epochs=20,)model.fit(X,y)ConfigurationA configuration file can be created as.configand should be located in your working directory. This configuration file only stores api credentials for now, but more options may be added in the future. The configuration file is parsed using thePython3 configparser, and an example configuration is shown below:[regenradar]url=https://rhdhv.lizard.net/api/v3/raster-aggregates/?user=user.namepassword=secret[openweathermap]apikey=secretIssue tracking and Feature RequestsAnyone can create feature requests or bug reports! You can browse and create new issues on GitHub:https://github.com/RoyalHaskoningDHV/sam/issues
Package: sam2lca
sam2lcaLowest Common Ancestorfrom aSAM/BAM/CRAMsequence alignment file.TLDRAnalysis of sequencing reads aligned to a DNA database with NCBI accession numbers, using the NCBI taxonomysam2lcaanalyzemyfile.bamSee all optionssam2lca--help
sam2lcaupdate-db--help
sam2lcalist-db--help
sam2lcaanalyze--helpFor further infos, check out thesam2lca documentationandtutorialInstallationWithConda(recommended)condainstall-cbiocondasam2lcaWithpippipinstallsam2lcaFor development purposes, from the dev branch# clone repositorygitclonegit@github.com:maxibor/sam2lca.git# work on the dev branchgitcheckoutdev# work in the sam2lca conda environmentcondaenvcreate-fenvironment.yml
condaactivatesam2lca# install sam2lca in editable modepipinstall-e.# Run the unit and integration testspytest-s-vv--script-launch-mode=subprocessorpipinstallgit+ssh://git@github.com/maxibor/sam2lca.git@devDocumentationThe documentation of sam2lca, including tutorials, is available here:sam2lca.readthedocs.ioCitesam2lca has been published in JOSS with the following DOI:10.21105/joss.04360@article{Borry2022,
  doi = {10.21105/joss.04360},
  url = {https://doi.org/10.21105/joss.04360},
  year = {2022},
  publisher = {The Open Journal},
  volume = {7},
  number = {74},
  pages = {4360},
  author = {Maxime Borry and Alexander Hübner and Christina Warinner},
  title = {sam2lca: Lowest Common Ancestor for SAM/BAM/CRAM alignment files},
  journal = {Journal of Open Source Software}
}
Package: samABPhfunction
samABPhfunctionPackage used to compute the h-function of steady-state active brownian
particles (ABPs), according to the derivations made by Tannie and I.The true steady-state of ABPs can be taken to have the form P_{ss}= e^{-h}/Z,
where h is what we are talking about when we refer to the h function.We simplify h a great deal, assuming a separable form asso we are really only concerned with calculating w(r_{ij}) from above.
Package: samadb
South Africa Macroeconomic Database APIA python API providing access to a relational database with macroeconomic data for South Africa, obtained from the South African Reserve Bank (SARB) and Statistics South Africa (STATSSA), and updated on a weekly basis via the EconData (https://www.econdata.co.za/) platform and automated scraping of the SARB and STATSSA websites. The database is maintained at the Department of Economics at Stellenbosch University.
Package: sam-alignment-reconstructor
OverviewThis package is design to reconstruct pairwise alignments from SAM files using the CIGAR string and MD:Z tagIt can be used as a standalone command line tool, or as a library when given the three elements above.UsageTo install the package, enable the virtual environment where it’s going to be used and run$ pip install sam-alignment-reconstructor
$ cat file.sam | sam-alignment-reconstructor

MS2007487-600V3:1:2109:08999:01136
TTCGAAATCTCACGCTCTTTACTGAAGACCCAGATAGAGCTTATCCTAAT
|||||||||||||||||||||||||||||||||:||||||||||||||||
TTCGAAATCTCACGCTCTTTACTGAAGACCCAGNTAGAGCTTATCCTAAT

CAGGATACTGTGTGGGAGAGGTTCGAGCAGGTATTTTTAGTGGCCTATG-
|||||||||||||||||:||:|||||||||||||||||||||:||||||
CAGGATACTGTGTGGGANAGNTTCGAGCAGGTATTTTTAGTGNCCTATGC

----GGTTGGTCACTTATGCCCCTGTCTTTAAAGACTACCTCTATGAAGG
    :::|||||||||||||||||||||||||||||||||||||||||||
CCCACTATGGTCACTTATGCCCCTGTCTTTAAAGACTACCTCTATGAAGG

TCTCCGACAGTTTTATGAGGACAACATCATGTATGTGGAGATCAGAGCAC
|||||||||||||||||||||||||||||:|||||||||:||||||||||
TCTCCGACAGTTTTATGAGGACAACATCACGTATGTGGAAATCAGAGCACDevelopingTo prepare the environment for developing the library, create a virtual environment, go to project root and then run:$ pip install -e .[dev]TestingThe recommended way is to test using detox.
This allows for testing in all the supported python versions using virtual environments effortlessly.
To use, install it, then run in the project root:$ pip install detox
$ detoxAlternatively, testing can be done in the same environment as the dev one by installing it’s dependencies, then running pytest:$ pip install -e .[test]
$ python -m pytest -s
Package: samanage-py
samanage-pyDetailed usage documentation is still in progressMy personal package to easily connect to Samanage ServiceDesk Platform. This is still a work in progress and doesn't support all modules provided by Samanage API but will add it in the future.Installationpipintallsamanage-pyProject dependencies:requestsUsagefromsamanageimportSamanageclient=Samanage(<tokenhere>)
Package: sam-anomaly-detector
Time series forecasting and anomaly detection library on top of fbprophet.. code:: pythonimport pandas as pdfrom psycopg2 import connectfrom sam_anomaly_detector import Forecasterdf_data = pd.read_csv('dataset.csv', columns=['ds', 'y'])json_data = df_data.to_json(orient='records')anomalies = Detector().forecast_today(dataset=json_data)print(anomalies)- Input data should be a panda DataFrame having time and aggregated data- Passed columns to forecaster should be 'ds' for 'time' and 'y' for 'aggregated data'- Output is a panda DataFrame of anomalies. Important columns are:- actual: today's actual value- yhat_lower: forecast lower boundary- yhat: : forecastted value- yhat_upper: forecast upper boundary- std: standard diviation from boundaries. negative value means how far it is from 'yhat_lower',positive value means how far it is from 'yhat_upper'
Package: samar
samarA machine learning tool for medical research.Features✅ Straight forward from.xlsx filetocharts and tables.✅ Robust comparison and prediction of multiple methods.✅ Feature importance analysis with RandomForest.✅ Robust comparison in independent verification dataset.Quick startInstall the samar via pip.pipinstallsamarUse samar likeexamples.pyOnce the package is installed, the main ways of use are in examples.py.
Package: samarche
SamarcheAPI checker inspired bythis
postQuickstartSay you’re developping a module with a public API, for people working
with it, this API is supposed to stay the same until the next major
release. What would happen if a commit makes a change it shouldn’t have
?The solution is testing :# A class which is part of your APIclassFoo:bar=Truedef__init__(self,stuff=False):self.stuffdefthing(self):return"doh"# Your tests to make sure the class stays consistentself.assertTrue(hasattr(Foo,'bar'))self.assertTrue(hasattr(Foo,'thing'))self.assertTrue(hasattr(Foo(),'stuff'))Unfortunatly those tests are basically writting all your API a second
time, there must be a better (and less boring) way !Samarche allow you to take a snapshop of your API (called asignature) and then be able to test your evolving codebase against
it.Samarche register only public members (i.e. everything starting by_is skipped) You can create signatures from amodule,module.packageand evenmodule.package:package_attributeExampleFirst build and save the signature of your API:# Do this at each major releaseimportsamarchesignature=samarche.build_signature("my_api")withopen('my_api.signature','wb')asfd:samarche.dump(signature,f)Then put somewhere in your testswithopen('my_api.signature','rb')asfd:original_signature=samarche.load(fd)try:samarche.check_signature('my_api',original_signature)exceptsamarche.ValidationErrorase:print("API has changed :{}".format(e))
Package: samarium
SamariumSamarium is a dynamic interpreted language transpiled to Python.
Samarium, in its most basic form, doesn't use any digits or letters.Here's aHello, World!program written in Samarium:"Hello, World!"!;Documentation on how to program in Samarium can be foundhere.InstallationpippipinstallsamariumAURgitclonehttps://aur.archlinux.org/samarium.git&&cdsamarium&&makepkg-sircor use your favoriteAUR helper.Using SamariumYou can run Samarium programs withsamarium program.sm.samarium-debugmay be used instead, which will first print out the intermediary Python code that the Samarium program is transpiled into, before executing it.ShortLongDescription-c <cmd>--command <cmd>Can be used to execute Samarium code from the stringcmd,directly in the terminal.cmdcan be one or more statementsseparated by semicolons as usual. Note that the last statementofcmdwill be printed if it does not end in a semicolon.-h--helpShows the help message-v--versionPrints Samarium versionThere is also a VSCode syntax highlighting extension for Samarium, which can be foundhere. The source code can be foundhere.CreditsSamarium was inspired by several languages, includingbrainfuck,Rust, andPython.Special thanks to:tetraxilefor helping with design choices and writing the docsMithicSpiritfor making an AUR package for SamariumDarviL82for fixing some issuesEndercheiffor making the documentation look fancy, helping with design choices, and adding partial Python InteroperabilityIf you have any questions, or would like to get in touch, join myDiscord server!
Package: sambaflux
SAMBAfluxDescriptionSAMpling Biomarker Analysis is a python package for running flux sampling on metabolic networks. Once installed, it can
be imported into new python scripts or used with scripts from theSAMBA repository.Visuals[add example plots here]Getting startedRequirementsPython 3.7cobrapyA solver (GLPK, CPLEX, GUROBI)Access to a computer cluster or powerful computer when sampling larger modelsInstallationInstall via pip:pipinstallsambafluxUsage-h, --help            show this help message and exit
  -m MODEL, --model MODEL
                        Metabolic model. Supported file formats: SBML, json, mat.
  -n NSAMPLES, --nsamples NSAMPLES
                        The number of samples
  -p PROCESSORS, --processors PROCESSORS
                        Number of processors
  -t THINNING, --thinning THINNING
                        The thinning factor of the generated sampling chain. A thinning of 10 means samples are returned every 10 steps.
  -o OUTPATH, --outpath OUTPATH
                        Outfile path (without filename)
  -k KO, --ko KO        KO file containing reactions to KO, specify nothing if you want to sample WT
  -s SEPKO, --sepko SEPKO
                        Separator for the KO file
  -r RESULTS, --results RESULTS
                        File containing reactions to output, specify nothing if you want only exchange reactions
  -q, --quiet           Use this flag to silence INFO logging.
  -d, --debug           Use this flag to turn on DEBUG logging.
  --dryrun              Use this flag to run the code without running sampling.
  --log LOG             Log file path + filename. Set to None to output to console.
  -b BIOMASS, --biomass BIOMASS
                        Number between 0 and 1, fraction of biomass to optimize
  --solver {cplex,gurobi,glpk}
                        Solver to use
  --exchangemin EXCHANGEMIN
                        The value used to set the minimum flux through exchange reactions (will be negative). Set to None if you want the default exchange reaction bounds.
  --biomassrxn BIOMASSRXN
                        A .tsv containing a Model column with the name of models and a Biomass column with the name of the corresponding biomass reaction.
  -f, --fva             Use this flag to run FVA as well as sampling.
  --onlyfva             Use this flag to run FVA instead of sampling.Seeherefor set-up and usage with the project's
scripts.AuthorsJuliette CookeAcknowledgmentsLicenseMIT License (see LICENSE file)Project statusCurrently active on this project.
Package: sambal
sambalsambal provides functions touniformly sample a sphereuniformly sample a spherical cap of a sphereUsageRandom vector on sphereGenerate a random vector on a 100-dimensional unit sphere.importnumpyasnpfromsambalimportrandom_on_spheredim=100print(random_on_sphere(dim))The same as above, but with a random number generator seeded to 0.importnumpyasnpfromsambalimportrandom_on_spheredim=100rng=np.random.default_rng(0)print(random_on_sphere(dim,rng))Random vector on spherical capGenerate a 100-dimensional random vector on a spherical cap whose
central axis is[1, 1, 1, ..., 1] / sqrt(100)and whose maximum
planar angle ispi/3.importnumpyasnpfromsambalimportrandom_on_capdim=100axis=np.ones(dim)axis=axis/np.linalg.norm(axis)max_planar_angle=np.pi/3print(random_on_cap(axis,max_planar_angle))The same as above, but with a random number generator seeded to 0.importnumpyasnpfromsambalimportrandom_on_caprng=np.random.default_rng(0)dim=100axis=np.ones(dim)axis=axis/np.linalg.norm(axis)max_planar_angle=np.pi/3print(random_on_cap(axis,max_planar_angle,rng))CitingIf you use this code for your research, please cite the paperAn O(n)
algorithm for generating uniform random vectors in n-dimensional
cones.Licensesambal is free software released under the terms of theGNU General
Public License, either version
3 of the License, or (at your option) any later version.