Saravanan666/pypi-org huggingface dataset:
Package: smallvectors
smallvectorsis a lightweight library that implements linear algebra operations
in low dimensions. These objects were create to be used in a game engine, but
may be useful elsewhere.The library has an opt-in process for optimizing code with C-extensions written
in Cython. The optimized version of the library is (or will be) one of the
fastest Python packages for low dimensional linear algebra operations.Small vectors supports:Vector, point and direction typesArbitrary matrices and some specialized ones (square matrices, unitary
matrices, rotational matrices, etc).Affine transformsQuaternions
Package: small-web-dataset
small-web-datasetThe Small Web Dataset is a command line tool used to generate a dataset
by aggregating of all the data from theKagi Small Web
index.What is the Small Web? The Small Web is the web of independent websites
that are not part of the big tech platforms. Here are some more
reference about the concept
[1][2][3][4][5].There are different purpose for this tool and the dataset it creates:help analyzing the Kagi Small Web index, to detect and eventually
remove the sites that doesn’t comply with the policy of the indexcreate a dataset of all the sites that compose the index. This
dataset is a very specialized subset of websites that are created
and maintained by independent people, mostly old school bloggers.
This dataset can be used for different specialized ML training, for
example to train a classifier to detect the Small Web sites from the
Big Web sites, etc.InstallTo install the command line tool, you simply have to:gitclonehttps://github.com/fgiasson/small-web-dataset.gitcdsmall-web-dataset

makebuild
makeinstall-local-buildThis will clone the repository, build the command line tool and install
it in your local Python environment.ConfigureYou have to make those environment variables available in your
environment:VariableDescriptionFEEDS_PATHThe path where you want to save all the feeds on your local file systemDB_PATHThe path where you want to save the SQLite dataset on your local file systemHow to useYou can make sure that the command line tool is installed by running,
and that the latest version is available by running:small-web-datasetversionYou can get the help documentation by running:small-web-dataset--helpYou can check what are the current configuration options for the tool in
the current environment by running:small-web-datasetconfigTo create the dataset, you simply have to run the following command:small-web-datasetsync-feedsThis command will do three things:it will download all the RSS and Atom feeds from the Kagi Small Web
index in theFEEDS_PATHfolderit will read all the local feeds files and import them in a local
SQLite database in theDB_PATHfolderit will infer the core language of a feed from the language used to
write the articles in the feed, and it will add this information in
the databaseOptionally, if you already have a local cache of the feeds and you only
want to update/recreate the database, you simply have to specify theDDMMYYYYfolder of the feeds you want to process:small-web-datasetsync-feeds18092023
Package: smallworld
Generate and analyze small-world networks according to the revised
Watts-Strogatz model where the randomization atβ= 1 is truly equal to the Erdős-Rényi network model.In the Watts-Strogatz model each node rewires itsk/2
rightmost edges with probalityβ. This means that each node has halways
minimum degreek/2. Also, atβ= 1, each edge has been rewired.
Hence the probability of it existing is smaller thank/(N-1), contrary to the ER model.In the adjusted model, each pair of nodes is connected with a certain
connection probability. If the lattice distance between the potentially
connected nodes is d(i,j) <=k/2 then they are connected with
short-range probabilityp_S = k / (k + β(N-1-k)), otherwise they’re
connected with long-range probabilityp_L = β * p_S.Installpip install smallworldBeware:smallworldonly works with Python 3!ExampleIn the following example you can see how to generate and draw according
to the model described above.fromsmallworld.drawimportdraw_networkfromsmallworldimportget_smallworld_graphimportmatplotlib.pyplotaspl# define network parametersN=21k_over_2=2betas=[0,0.025,1.0]labels=[r'$\beta=0$',r'$\beta=0.025$',r'$\beta=1$']focal_node=0fig,ax=pl.subplots(1,3,figsize=(9,3))# scan beta valuesforib,betainenumerate(betas):# generate small-world graphs and drawG=get_smallworld_graph(N,k_over_2,beta)draw_network(G,k_over_2,focal_node=focal_node,ax=ax[ib])ax[ib].set_title(labels[ib],fontsize=11)# showpl.subplots_adjust(wspace=0.3)pl.show()visualization example
Package: small-world-propensity
Small World PropensityThis python package was adapted from the MATLAB package as first presented inSmall-World Propensity and Weighted Brain Networks(2016) by Sarah Feldt Muldoon, Eric W. Bridgeford & Danielle S. Bassett. Their original MATLAB implementation can be foundhere.UseThe small-world propensity package can be installed using pippython -m pip install small-world-propensitysmall_world_propensitycan be called in two ways: either with a single adjacency matrix, or with a list of adjacency matrices and a boolean list denoting whether each matrix is binary or not. In either case,small_world_propensitywill return apandasdataframe similar to the following:Generation of regular and random matricesUsing the structural network of the cat cortex obtained from tract-tracing studies between 52 brain regions, we can visualize the process behind the calculation of $\phi$. The matrix is loaded usingcat = sio.loadmat('data/cat.mat')['CIJctx']We can then ensure symmetry by callingsymm_cat = swp.make_symmetric(cat)In order to get the regular version of the cat matrix, we first find the effective average radius:r = swp.get_avg_rad_eff(symm_cat)
cat_reg = swp.regular_matrix_generator(symm_cat, r)Finally we produce the randomized cat matrix:cat_rand = swp.randomize_matrix(cat_symm)The graphs visualized in a circular layout look as follows:Comparison of $\phi$ in real networksWe can take the networks used inMuldoon et aland plot $\phi$, $\Delta_L$, $\Delta_C$, and $\delta$. Note that these networks are not the exact same as the ones used inMuldoon et al, and due to differences in how Numpy performs permutations, and the use of NetworkX and iGraph libraries, the results are not identical, but still match closely.The adjacency matrices:And the results:To cite this work, please use:@software{small-world-propensity,author={{Daniels, R. K.}},title={small-world-propensity},year=2023,publisher={Zenodo},version={v0.0.8},doi={10.5281/zenodo.10299681},url={https://github.com/rkdan/small-world-propensity}}Please also cite the authors of the original MATLAB implementation:@article{Muldoon2016,author="Muldoon, Sarah Feldt and Bridgeford, Eric W. and Bassett, Danielle S.",title="{Small-World Propensity and Weighted Brain Networks}",doi="10.1038/srep22057",journal="Scientific Reports",volume="6",number="1",pages="P07027",year="2016"}
Package: smally
SmallyHow to CompressJPEGPNGGIFHow to InstallHow to UseAPIsShowcaseSmally is a simple tool to compress JPEG, PNG and GIF files losslessly,
by invoking the famousjpegtran,optipngandgifsicletools,
in batch mode, in-place and keep mtime unchanged. It is written
in Python, but heavily rely on shell.How to CompressJPEGUsingjpegtranto remove all metadata, create a baseline version
and a progressive version.To compare among the original file, baseline and progressive
files, choose the smallest one in size.Whenever possible, choose progressive version.PNGCallingoptipngto compress PNG, in the most crazy-o7 -zm1-9level.GIFCallinggifsicleto compress GIF, by using-O3 --color 256.How to Install# install tools on Fedora$sudodnfinstalllibjpeg-turbo-utilsoptipnggifsicle# install tools on Ubuntu$sudoaptinstalllibjpeg-turbo-progsoptipnggifsicle# install smally$pipinstallsmallyHow to Use# inline help$python-msmally-h# to compress a single file, if option is not presented,# smally will use file command to get file type info.$python-msmally[-j|-p|-g]<pathname># to compress a directory$python-msmally-r-P<N><pathname># to compress all png file in a directory$python-msmally-r-P<N>-p<pathname>-r, recursively, it's a command line convention and normally you should
use it when deal with a directory.-P<N>, parallel process number, if it is missing, the logical cpu
count number will be used.APIs4 APIs provided by smally:# importfromsmallyimport(jpegtran,optipng,gifsicle,is_jpeg_progressive)# signaturedefjpegtran(pathname:str)->tuple[int,int]:...defoptipng(pathname:str)->tuple[int,int]:...defgifsicle(pathname:str)->tuple[int,int]:...defis_jpeg_progressive(pathname:str)->bool:...The first int in returned tuple is the byte number saved. It could be zero,
which means no save. The second int is the original file size.Showcase$python-msmally-rsmally/tpic-P4# parallel process number:  4smally/tpic/102.jpg-24157-16.38%[p]smally/tpic/302.gif-333056-19.67%
smally/tpic/testpic/302.gif-333056-19.67%
smally/tpic/testpic/102.jpg-24157-16.38%[p]smally/tpic/201.png-548-26.37%
smally/tpic/testpic/201.png-548-26.37%
smally/tpic/testpic/testpic/102.jpg-24157-16.38%[p]smally/tpic/testpic/testpic/302.gif-333056-19.67%
smally/tpic/testpic/testpic/201.png-548-26.37%
Package: sma-manager
SMA Manager📝 CONTRIBUTIONSBefore doing any contribution readCONTRIBUTING.📧 CONTACTEmail:amng835@gmail.comGeneral Discord:https://discord.gg/dFD5HHaDeveloper Discord:https://discord.gg/rxNNHYN9EQ📥 INSTALLINGLatest PyPI stable releasepipinstallsma-manager⚙ HOW TO USEimportsma_manager_apisma_manager_api.SMA(<PARAMS>)ORfromsma_manager_apiimportSMASMA(<PARAMS>)🤝 PARAMETERSname : str, requiredSensor name.Used only for Home Assistant Integration, a random value can be used if not implementing as a Home Assistant Componentip : str, requiredIP of SMA Managerport : str, requiredPort of SMA Managerrefresh_time : int, optionalOption to disable/enable the default progress bar (Default: 1)
Package: smap-effect-prediction
SMAP Effect PredictionSMAP effect-prediction was created to predict the effects of naturally occuring sequence variants or CRISPR-cas induced mutations in a given reference gene sequence on the encoded protein. It takes as input a genotyping mastertable generated bySMAP haplotype-windowthat lists the relative frequency of observed short haplotypes per locus per sample.InstallationThis software is part of the SMAP framework. Please check theSMAP documentationfor how to install it as part of SMAP.From PyPipython3-mvenv.venvsource.venv/bin/activate
pipinstall--upgradepip
pipinstallsmap-effect-predictionFrom GitEither download a release tarball, extract it and change your current working directory to the extracted folder, or clone the git repository:gitclonegit@gitlab.com:dschaumont/smap-effect-prediction.gitcdsmap-effect-prediction
python3-mvenv.venvsource.venv/bin/activate
pipinstall--upgradepip
pipinstall.DocumentationBuilding from a tarball or the git respositoryFirst, install the documentation build dependencies, then build the documentation in thedocsdirectory:pipinstall.[docs]cddocs
sphinx-build-Mhtml.buildCitationIf you use SMAP, please cite "Schaumont et al., (2022). Stack Mapping Anchor Points (SMAP): a versatile suite of tools for read-backed haplotyping.https://doi.org/10.1101/2022.03.10.483555". Source code is available online athttps://gitlab.com/truttink/smap/.ContributionsThis package was created using the help of the following people:Arne Verstichele (INARI)Dries Schaumont and Tom Ruttink (ILVO)Kevin Debray (VIB-ILVO)
Package: smap-haplotype-window
SMAP-Haplotype-WindowSMAP-Haplotype-Window is a plugin for SMAP that allows for the analysis of sequencing data from CRISPR edited loci.InstallationInstallation should be performed by installingSMAP, and afterwards install this plugin.python3 -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install ngs-smap
pip install smap-haplotype-window
Package: smapi
SteamMarketAPISteamMarketAPiis api that provides you to use the Steam Community Market for CS:GO game simplify, through the
fetching and parsing lots info from Steam Community Market and CSGO Float APIsCommon FeaturesSimple to useAsynchronousFastAlertIf you want to use this api heavily you should to host CSGO Float API yourselfCSGO Float Api GitHubCode SampleFor more samples look toexamplesfromsmapiimportSteamMarketHandlersmapi_object=SteamMarketHandler('AK-47 | Vulcan (Field-Tested)',quantity=30)forlotinsmapi_object.get_as_dataclass:print(lot)Installingpip3installsmapi
Package: smap-io
SMAP (Soil Moisture Active Passive) data readers.Works great in combination withpytesmo.CitationIf you use the software in a publication then please cite it using the Zenodo DOI.
Be aware that this badge links to the latest package version.Please select your specific version athttps://doi.org/10.5281/zenodo.596391to get the DOI of that version.
You should normally always use the DOI for the specific version of your record in citations.
This is to ensure that other researchers can access the exact research artefact you used for reproducibility.You can find additional information regarding DOI versioning athttp://help.zenodo.org/#versioningInstallationSetup of a complete environment withcondacan be performed using the following
commands:$condacreate-q-nsmap_io-cconda-forgenumpyh5pypyprojnetcdf4pyresamplepandas$sourceactivatesmap_io$pipinstallsmap_ioYou can also install all needed (conda and pip) dependencies at once using the
following commands after cloning this repository. This is recommended for
developers of the package.$gitclonehttps://github.com/TUW-GEO/smap_io.git--recursive$cdsmap_io$condacreate-nsmap_iopython=3.6# or any supported python version$sourceactivatesmap_io$condaupdate-fenvironment.yml$pythonsetup.pydevelopSupported ProductsSPL3SMP: SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil MoistureAdditional products will we added when need arises, feel free to open an issue to
add a new data product or even better a pull request.ContributeWe are happy if you want to contribute. Please raise an issue explaining what
is missing or if you find a bug. We will also gladly accept pull requests
against our master branch for new features or bug fixes.GuidelinesIf you want to contribute please follow these steps:Fork the smap_io repository to your accountmake a new feature branch from the smap_io master branchAdd your featureplease include tests for your contributions in one of the test directories
We use py.test so a simple function called test_my_feature is enoughsubmit a pull request to our master branch
Package: smaplib
ThesmaplibPython PackageThesmaplibpackage provides some useful similarity mapping related modeling functions.CommandsUpdate pre-commit checks to their latest versionpre-commit autoupdateRun all pre-commit checkspre-commit run --all-filesConfigurationMain configuration files for the project:Black:.blackFlake8:.flake8Pre-Commit:.pre-commit-config.yamlPackage requirements:requirements.txtPackage requirements for the development environment:requirements-dev.txtGit ignore list:.gitignore
Package: smappi
Support python>=2.7, python>=3.0More information onhttps://smappi.org/InstallationThis client is delivered without any dependencies!pipinstallsmappiExamplesUse the helper:>>>fromsmappiimportsmappi>>>smappi('adw0rd/example').greeting(name='friend')"Hello, friend!"Or use the full version:>>>importsmappi>>>smappi.Request('adw0rd/example','json').calculator(digits=[42,42,42])126>>>smappi.Request('adw0rd/example','json').calculator(digits='42,42,42')126For local development and debug specify host as path:>>>fromsmappiimportsmappi>>>smappi('localhost:8000').greeting(name='friend')"Hello, friend!"
Package: sma-query-sw
SMA Sunny Boy Inverter Query ToolTHE 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.The SMA Sunny Boy Inverter Query tool uses the SpeedWire™ protocol to query models of SunnyBoy inverter that support the protocol. The protocol was primarily decoded from analysis and replay of commands extracted from network dumps.Some critical information regarding login was found in this posthttps://community.openhab.org/t/example-on-how-to-access-data-of-a-sunny-boy-sma-solar-inverter/50963on the openHAP forum. Thank you to jan_r and all contributors to that thread!This library supports a stand-alone MQTT mode, however, it is primarily intended to be used with Home Assistant.
Package: smarkov
Simple, lightweight and easy to read implementation of Markov chains and
HMMs.This is a toy project, don’t expect any exciting speeds or robustness.Happy hacking!Installingpip3 install git+git://github.com/greenify/smarkov.gitHackinggit clone https://github.com/greenify/smarkov
cd smarkov
python3 setup.py developTrain with a corpusfrom smarkov import Markov
chain = Markov(["AGACAGACGAC"])Attributescorpus: given corpus (a corpus_entry needs to be a tuple or array)order: maximal order to look back for a given state (default 1)
tokenize: function how to split an element of the corpus (e.g
sentences into words)Generate text from a chainprint("".join(chain.generate_text()))Generate_text()generates exactly one element from the Markov chain.
In other words: It goes in the Markov chain the universal start state to
universal end state.More ExamplesSeeexamplesComingDocumentation how to use it with HMM.LicenseMIT
Package: sma-rsi-indicators
'A package that allows you to calculate sma and rsi indicators from a given csv file and outputs the indicators to a given csv file.'Example Usage:importsma_rsi_indicatorscsv_path="orcl.csv"# Insert the path to your csv file herelist_of_data=sma_rsi_indicators.convert_to_dict(csv_path)sma_rsi_indicators.calculate_sma(list_of_data,"orcl-sma.csv")sma_rsi_indicators.calculate_rsi(list_of_data,"orcl-rsi.csv")Your csv file should contain columns called "Date" and "Close" indicating the dates and the closing values of those dates for this module to work.
Package: smars-library
SMARSScrewless Modular Assemblable Robotic SystemSMARSfan.comVisitwww.smarsfan.comfor more information about this project. The site includes build instructions, videos, an interview with the designer and much more.About this libraryThis library will enable you to get the quad robot walking and detecting its environment.Link to thingiverse original model:https://www.thingiverse.com/thing:2662828Link to Quad versionhttps://www.thingiverse.com/thing:2755973Version 2.0 ReleasedVersion 2.0 has now been released, including:fixes for the walking cyclenew Morse code feature - usetap_message()to tap out messagechanging the name withname = "name"also taps out the new nameAdded a newconfigfunction to return the current configurationAdded a new identify feature, for identifying each limb - useidentify(channel)to make each limb wiggle one at a timeadded a new channel property so that PCA9685 channels can be set at runtimeOverview and BackgroundFrom the Creator - Kevin ThomasThis robot is really easy and cheap to 3d print, build and program. It can be assembled without screws or
soldering and it's modular so it can be adapted for different purposes. I'm Swiss, so I don't know American
scholastic system but I would use SMARS in last year of middle school, high school or universities/ colleges.
There is more "open electronics" compared to a Lego NXT or similar, so students need a few knowledges about
security, electrical laws and so on. It can be use to improve software development skills, CAD skills or
electronics skills, students can design their own modules and customize their SMARS.Programming SMARSYou can use most common small hardware platforms as the brains inside SMARS, the Arduino platform (and compatible) was used in the original design as its cheap, commonly available, and the tools for programming it are easy to use.For the quad version of the robot a Raspberry Pi Zero was used. The language commonly used on the Pi (and where it gets it name from) is Python. A library module has been written for both Arduino and Python to help you get started.Getting the Arduino IDEVisithttp://www.arduino.ccto download the latest Arduino Integrated Development Environment (IDE).Learn PythonTo quickly get up to speed with Python, checkout the SMARS Learning Platform -https://www.smarsfan.com/learn/python_101
Package: smarsy
Python REST API wrapper for smarsy.ua websiteThis package provides REST API endpoints to work with smarsy.ua database.Run from sourcesIn order to run this app from sources you need following initial setup:Create new environment variableSMARSY_PYTHON_INTERPRETER_PATHwith the path of the Python environment (venv). In windows you use something likemyvenv\Scripts\python.exeCreatecfg/login.jsonfile and addusername,passwordandlanguagesettings
Package: smarsy-dkultasev
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Package: smart7-orm
Smart7_ORM项目依赖包名版本tortoise-orm≥ 0.17.7aerich≥ 0.5.8配置数据库0x01 项目根目录配置settings.py# mysql settingdatabase={"db":'MySQL',"host":"localhost","username":"hubert","password":"hubert","db_name":"test_table",}# PostgreSQL settingdatabase={"db":'PostgreSQL',"host":"localhost","username":"hubert","password":"hubert","db_name":"test_table",}# sqlite settingdatabase={"db":'SQLite',"db_name":"test_table.db",}0x02 自动获取模型所在包fromsmart7_orm.utilsimportdiscover_modelsmodel_list=discover_models()0x03 数据库设置fromsmart7_orm.coreimportmake_sqlfromsmart7_orm.utilsimportdiscover_modelsTORTOISE_ORM={"connections":{"default":make_sql().get_url()},"apps":{"models":{"models":["aerich.models",*discover_models()],"default_connection":"default",},},}迁移数据库0x01 初始化迁移配置aerichinit0x02 初始化数据库aerichinit-db0x03 更新模型并进行迁移aerichmigrate0x04 升级到最新版本aerichupgrade0x05 降级到指定版本aerichdowngrade0x06 显示历史aerichhistory0x06 显示要迁移的头aerichheads更多请查看aerichgithub
Package: smartagro
SmartAgroSmart, Iot-enabled greenhouse monitoring and control APIFree software: GNU General Public License v3Documentation:https://smartagro.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-09-03)First pre-release on PyPI.0.1.1 (2020-10-12)Second pre-release on PyPI. Added modules0.2.0 (2020-10-26)First production release on PyPI. Fully Functional
Package: smart-algo-trading
Algo_TradingSimple and smart Algo Trading module to build robust and scalable application
Help Coders and Traders to build own strategies and earnInfo:Version: 1.0.0Module Details:IDModule NameModule DescriptionSample COde1.Sample Code:fromind_equity_derivatives.nse_dataimportNSEIndianseindia=NSEIndia()out1=nseindia.get_nse_company_symbols()print(out1)How to contribute:FollowDeveloper GuideMIT LicenseCopyright (c) 2023 Indranil PalPermission 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.