Saravanan666/pypi-org huggingface dataset:
Package: simses
SimSESSimSES (Simulation of stationary energy storage systems) is an open source modeling framework for simulating stationary energy storage systems.
Further information can be found in the accompanying research article:https://doi.org/10.1016/j.est.2021.103743. If you are using SimSES, or plan to do so, please cite this work.Setup and installation1. Create a virtual environmentCreate a virtual environment, for example with eithervenvorconda, or directly through your IDE likePyCharmandVS Code.2. Install dependenciesInstallsimsesand all other required python packages in your virtual environment. This can be done with a single command:pip install -e .3. Exemplary simulationsVisitthis pageto read more about some exemplary simulations and setting up a simulation with pre-configured parameters.AcknowlegdementsThe tool, originally developed in MATLAB, was initiated by Maik Naumann and Nam Truong, transferred to Python by Daniel Kucevic and Marc Möller and now continuously improved at the Chair of Electrical Energy Storage of the Technical University of Munich.
Package: simsimix
SIMSIMISimsimiAPI in Python.DescriptionWorld first popular Chatbot for daily conversation (Service launched in 2002). A unique daily conversation with diversity, fun and vitality. Service provided with 130 million sets of daily small talks in 81 languages compiled through more than 20 million panels. Service in 81 languages. More than 350 million cumulative users worldwide. (Based on June-2018), Records of more than 200 million times of responses made per day.REQUIREMENTSPYTHON >=3.9.X+REQUESTSInstallationpip3installsimsimixSimiTalkImport ModulefromsimsimiximportsimiTalkUse SimSimi talk functionfromsimsimiximportsimiTalkreq=simiTalk("hi","en")print(req)Example for SimSimi talkimportjsonfromsimsimiximportsimiTalkmsgAsk="hi"lang="en"req=simiTalk(msgAsk,lang)data=json.loads(req)print(data.["ans"])Response{"status":"true","time":"06:55:47 PM","type":"text","lc":"en","ans":"Hello back","author":"𝙷𝙰𝚇𝙾𝚁 𝚇𝙽𝚇","owner":"https://t.me/haxor_xnx","channel":"https://t.me/Toxinum"}Realtime Example for SimSimi talkimportjsonfromsimsimiximportsimiTalkmsg="hi"lang="en"req=simiTalk(msg,lang)data=json.loads(req)print(data.["ans"])ResponseHellothereSimiTeachImport ModulefromsimsimiximportsimiTeachUse SimSimi teach functionfromsimsimiximportsimiTeachreq=simiTeach("hi","hello, "en", "YOUR_SECRET_KEY")print(req)Response{"status":"true","time":"06:55:47 PM","type":"text","lc":"en","ask":"hello world","ans":"Hello back","author":"𝙷𝙰𝚇𝙾𝚁 𝚇𝙽𝚇","owner":"https://t.me/haxor_xnx","channel":"https://t.me/Toxinum"}Realtime Example for SimSimi teachimportjsonfromsimsimiximportsimiTeachmsgAsk="hi"msgAns="hello"lang="en"secretKey=req=simiTalk(msgAsk,msgAns,lang,secretKey)data=json.loads(req)print(data.["ask"])print(data.["ans"])Responsehi
helloLicenseMIT LicenseCopyright (c) 2023 Mohammad AlaminPermission 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.Build withSimsimiofficial api.Contact With Developer
Package: simsi-transfer
SIMSI-TransferTransferring identifications using MS2 spectrum clustering with MaxQuant search results.Hamood, F., Bayer, F. P., Wilhelm, M., Kuster, B., & The, M. (2022).SIMSI-Transfer: Software-assisted reduction of missing values in phosphoproteomic and proteomic isobaric labeling data using tandem mass spectrum clustering.Molecular & Cellular Proteomics, 100238.Test datasetFor testing SIMSI-Transfer after installation, we recommend downloading the TMT11 MS2 raw files from this publication:
Thompson, A., Wölmer, N., Koncarevic, S., Selzer, S. et al.,TMTpro: Design, Synthesis, and Initial Evaluation of a Proline-Based Isobaric 16-Plex Tandem Mass Tag Reagent Set.Analytical Chemistry 2019, 91, 15941–15950. doi:10.1021/acs.analchem.9b04474PRIDE link:https://www.ebi.ac.uk/pride/archive/projects/PXD014750Raw files for TMT-MS2:19070-001.raw19070-002.raw19070-003.raw19070-006.raw19070-007.raw19070-008.rawThe MaxQuant results needed as input to SIMSI-Transfer can be downloaded from Zenodo:10.5281/zenodo.6365902For reference, the original SIMSI-Transfer results (v0.1.0) for this dataset can also be downloaded from Zenodo:10.5281/zenodo.6365638Running SIMSI-Transfer using the GUIOn Windows, you can download theSIMSI-Transfer_GUI_windows.zipfrom the latest release, unzip it and openSIMSI-Transfer.exeto start the GUI (no installation necessary).Alternatively, on all platforms, first install SIMSI-Transfer as explained below. Then installPyQt5(pip install PyQt5) and run:pythongui.pyRunning SIMSI-Transfer from the command lineFirst install SIMSI-Transferas explained below, then run SIMSI-Transfer:python-msimsi_transfer--mq_txt_folder</path/to/txt/folder>--raw_folder</path/to/raw/folder>--output_folder</path/to/output/folder>Alternative command for MS3 acquisition, using the TMT correction factor file exported from MaxQuant:python-msimsi_transfer--mq_txt_folder</path/to/txt/folder>--raw_folder</path/to/raw/folder>--output_folder</path/to/output/folder>--tmt_ms_levelms3--tmt_requantify--tmt_reporter_correction_file</path/to/correction/factor/file.txt>Alternative command using the meta input file for MS3 acquisition, with filtered decoys:python-msimsi_transfer--meta_input_file</path/to/meta/file>--output_folder</path/to/output/folder>--tmt_ms_levelms3--tmt_requantify--filter_decoysA list of all possible arguments is displayed using the help argument:python-msimsi_transfer--helpInstallationSIMSI-Transfer is available on PyPI and can be installed withpip:pipinstallsimsi-transferAlternatively, you can install SIMSI-Transfer after cloning from this repository:gitclonehttps://github.com/kusterlab/SIMSI-Transfer.git
pipinstall.
Package: simsity
simsitySimsity is a Super Simple Similarities Service[tm].It's all about building a neighborhood. Literally!This repository contains simple tools to help in similarity retrieval scenarios
by making a convenient wrapper aroundhnswlib.
Typical usecases include early stage bulk labelling and duplication discovery.InstallYou can install simsity via pip.python -m pip install simsityThe goal of simsity is to be minimal, to make rapid prototyping very easy and to be "just enough" for medium sized datasets. You will mainly interact with these two functions.fromsimsityimportcreate_index,load_indexAs their names imply, you can use these to create an index or to load one from disk.Quickstartfromsimsityimportcreate_index,load_index# Let's fetch some demo datafromsimsity.datasetsimportfetch_recipesdf_recipes=fetch_recipes()recipes=df_recipes["text"]# Let's use embetter for embeddingsfromembetter.textimportSentenceEncoderencoder=SentenceEncoder()# Populate the ANN vector index and use it.index=create_index(recipes,encoder)texts,dists=index.query("pork")# You can also query using vectorsv_pork=encoder.transform(["pork"])[0]texts,dists=index.query_vector(v_pork)You can also provide a path and then you'll be able to store/load everything.# Make an index with a pathindex=create_index(recipes,encoder,path="demo")# Load an index from a pathreloaded_index=load_index(path="demo",encoder=encoder)texts,dists=reloaded_index.query("pork")That's it! Happy hacking!
Package: simsm
🚌 simsm 🚌Veja os horários doSIM - SMno seu terminal.InstalaçãoBasta executar o seguinte comando no terminal:pipinstallsimsmUtilizaçãoO programa deve conter três argumentos: linha, saída, e dia. Exemplo:Linha universidade, saindo da UFSM no dia de hojesimsmuniversidadeufsmhojeVocê também pode filtrar por faixa velha/nova:simsmuniversidadeufsmhoje-fnLinha bombeiros, saindo do bairro no sábadosimsmbombeirosbairrosabadoDocsusage:simsm[-h][-fv][-fn][-re]linhasaidadia

positionalarguments:linhaLinhadoônibus.Exemplo:universidade,bombeiros,circularsaidaDeondeoônibussairá.Exemplo:bairro,centro,ufsmdiaHoje:hojeDiasúteis:utilSábado:sabadoDomingoseferiados:domingo

optionalarguments:-h,--helpshowthishelpmessageandexit-fv,-faixa-velhaParalistarsomenteônibusquepassampelafaixavelha.-fn,-faixa-novaParalistarsomenteônibusquepassampelafaixanova.-re,-requestUtilizaçãodabibliotecarequestsaoinvésdecURL.)ContribuaCaso queira adicionar uma nova função, encontrou um bug ou sugestão, faça um pull request ou abra uma issue. :)To do:Identificar feriadosAdicionar todas as linhas do site SIM - SM (No momento tem apenas as linhas que vão até o campus)LicençaMIT © José Victor Viriato
Package: sim-sm
🚌 sim-sm 🚌Aplicação em CLI para ver os horários de ônibus emSIM - SM.InstalaçãoBasta executar o seguinte comando no terminal:pipinstallsim-smUtilizaçãoO programa deve conter três argumentos: linha, saída, e dia. Exemplo:Linha universidade, saindo da UFSM no dia de hojesim-smuniversidadeufsmhojeVocê também pode filtrar por faixa velha/nova:sim-smuniversidadeufsmhoje-fnLinha bombeiros, saindo do bairro no sábadosim-smbombeirosbairrosabadoDocsusage:sim-sm.py[-h][-fv][-fn][-re]linhasaidadia

positionalarguments:linhaLinhadoônibus.Exemplo:universidade,bombeiros,circularsaidaDeondeoônibussairá.Exemplo:bairro,centro,ufsmdiaHoje:hojeDiasúteis:utilSábado:sabadoDomingoseferiados:domingo

optionalarguments:-h,--helpshowthishelpmessageandexit-fv,-faixa-velhaParalistarsomenteônibusquepassampelafaixavelha.-fn,-faixa-novaParalistarsomenteônibusquepassampelafaixanova.-re,-requestUtilizaçãodabibliotecarequestsaoinvésdecURL.)DependênciasO programa usa as seguintes bibliotecas:terminaltablescoloramaContribuaCaso queira adicionar uma nova função, encontrou um bug ou sugestão, faça um pull request ou abra uma issue. :)To do:Identificar feriadosAdicionar todas as linhas do site SIM - SM (No momento tem apenas as linhas que vão até o campus)LicençaMIT © José Victor Viriato
Package: simsnn
SimSNNA spiking neural network simulator developed and maintained by Radboud University staff and students.RequirementsTo run the program, make sure you have installed the dependencies listed in environment.yml.
We recommend creating a conda environment for every project. You can do this with the following command:conda env create --file environment.ymlUsageOn Google ColabClick on the badge to open the notebook in google colab. Then run every cell to see the example output.Locally using jupyter notebooksClone this repository or download the zipped version. Unzip and open a terminal in the project directory. From there you can install the conda requirements, as mentioned before. You can also run the notebook by runningjupyter lab ./example.ipynbLocally from the command lineTo run the example network, located in the examples directory, open up a terminal in the project directory and run the following command:python -m simsnnYou should be presented with a read out from the connected measuring tools like this:(seehomepageif the image doesn't render correctly.)
Package: sim-solps
sim-solpssim-solps is a python package which provides synthetic-instrument modelling for SOLPS-ITER.FeaturesTheSolpsInterfaceclass, which provides a convenient python interface to access the
results of SOLPS-ITER simulations stored inbalance.ncfiles.Instrument modelling for Thomson scattering systems as theThomsonScatteringclass.Installationsim-solps is available fromPyPI,
so can be easily installed usingpipas follows:pipinstallsim-solpsDocumentationThe package documentation is available atsim-solps.readthedocs.io.
Package: simsopt
simsoptsimsoptis a framework for optimizingstellarators.
The high-level routines ofsimsoptare in python, with calls to C++
or fortran where needed for performance. Several types of components
are included:Interfaces to physics codes, e.g. for MHD equilibrium.Tools for defining objective functions and parameter spaces for
optimization.Geometric objects that are important for stellarators - surfaces and
curves - with several available parameterizations.Efficient implementations of the Biot-Savart law and other magnetic
field representations, including derivatives.Tools for parallelized finite-difference gradient calculations.The design ofsimsoptis guided by several principles:Thorough unit testing, regression testing, and continuous
integration.Extensibility: It should be possible to add new codes and terms to
the objective function without editing modules that already work,
i.e. theopen-closed principle.
This is because any edits to working code can potentially introduce bugs.Modularity: Physics modules that are not needed for your
optimization problem do not need to be installed. For instance, to
optimize SPEC equilibria, the VMEC module need not be installed.Flexibility: The components used to define an objective function can
be re-used for applications other than standard optimization. For
instance, asimsoptobjective function is a standard python
function that can be plotted, passed to optimization packages
outside ofsimsopt, etc.simsoptis fully open-source, and anyone is welcome to use it, make
suggestions, and contribute.Several methods are available for installingsimsopt. One
recommended approach is to use pip:pip install simsoptFor detailed installation instructions on some specific systems, seethe wiki.
Also, a Docker container is available withsimsoptand its components pre-installed, which
can be started usingdocker run -it --rm hiddensymmetries/simsoptMoreinstallation
options,instructions for the Docker
container, and
other information can be found in themain simsopt documentation
here.Some of the physics modules with compiled code reside in separate
repositories. These separate modules includeVMEC, for MHD
equilibrium.SPEC, for MHD
equilibrium.booz_xform, for
Boozer coordinates.If you usesimsoptin your research, kindly cite the code usingthis reference:[1] M Landreman, B Medasani, F Wechsung, A Giuliani, R Jorge, and C Zhu,
"SIMSOPT: A flexible framework for stellarator optimization",J. Open Source Software6, 3525 (2021).See alsothe simsopt publications page.We gratefully acknowledge funding from theSimons Foundation's Hidden
symmetries and fusion energy
project.
Package: simspec
Install / Upgradepip install –no-cache-dir –upgrade simspecInstalling modulesBefore you start using you need to install Instrument/Telscope module(s) as per you requirement.Download pre-built SimSpec module fromhttp://astro.subhashbose.com/simspec/and install as,simspec -installmodule <MODULE_PACKAGE>Installing SimSpec modules require same level of file system permission as it was used for installing sismpecFor Usage Helpsimspec –help
Package: simsplitchem
SimplifySplittingChemicalsSimplify your train:test Splitting for Chemicals in Machine LearningOnline DocumentationClick here to read the docuemtntationTODO Listadd directory strucuretests and methods signaturesgithub actionsliterature search for splitting methods to implementDirectory StructureDirectory structureSimplifySplittingChemicals
â”‚   README.md
|
â””â”€â”€â”€simsplitchem
â”‚   â”‚   __init__.py
â”‚   â”‚   simsplitchem.py
â”‚   â”‚
â”‚   â””â”€â”€â”€samplers
â”‚       â”‚   __init__.py
|       |  abstract_sampler.py
|       |  random.py
|       |  max_min.py
|       |  kennard_stone.py
â”‚   â””â”€â”€â”€utils
â”‚       â”‚   __init__.py
â”‚       â”‚   AIMSIM_wrappers.py
â”‚   
â””â”€â”€â”€test
â””â”€â”€â”€docs
â””â”€â”€â”€examples
Package: simspy
This package is specifically designed for solvingsingle-machine scheduling problems. It comprises a total of 8 functions, including 7 heuristic functions and 1 metaheuristic function.Heuristic MethodEarliest Due Date (EDD) RuleShortest Processing Time (SPT) RuleLargest Penalty per Unit Length (LPUL) RuleShortest Processing Time and LPUL RuleShortest Weighted Processing Time (SWPT) RuleLargest Weight (WT) and LPUL RuleCritical Ratio (CR) RuleMetaheuristic MethodGenetic Algorithm (GA)Data InputThis package requires 3 essential data inputs:Processing Time,Due Date, andPenalty.FunctionDescriptionfc_eddSort the job sequences using the Earliest Due Date Rule by arranging the jobs in ascending order based on their due dates.fc_sptSort the job sequences using the Shortest Processing Time Rule by arranging the jobs in ascending order based on their processing times.fc_lpulSort the job sequences using the Largest Penalty per Unit Length Rule by arranging the jobs in descending order based on their U value, which is obtained by dividing the penalty by the processing time.fc_spt_lpulSort the job sequences using the Shortest Processing Time and LPUL Rule by arranging the jobs in ascending order based on their processing times. In case of a tie, use the LPUL Rule.fc_swptSort the job sequences using the Shortest Weighted Processing Time Rule by arranging the jobs in ascending order based on their S value, which is obtained by dividing the processing time by the penalty.fc_wt_lpulSort jobs using the Largest Weight and LPUL Rule by arranging the jobs in ascending order based on their penalty. In case of a tie, use the LPUL Rule.fc_crSort the job sequences using the Critical Ratio Rule by arranging the jobs in ascending order based on their CR value, which is obtained by subtracting T (the sum of processing times for all the jobs that have been scheduled) from the due date and dividing by the processing time.fc_gaSort the job sequences using the Genetic Algorithm by arranging the jobs in a way that minimizes the total penalty.How to installTo install the package, execute the following command:pipinstallsimspyTo import the package, execute the following command:importsimspyClick on the "Run" button. The package will display a window where you can select the method and specify the number of jobs, as well as an empty box to input the data.How to useInput parameters of the package.After importing the package, a window will appear for selecting the method and entering the required number of job schedules.Upon clicking the "OK" button, a window will appear for entering job data for each schedule. After entering the data, click the "Sort Job" button to proceed.Output results of the package.The result will be displayed in the terminal window, as shown in the image below.
Package: sims-reia
Sims2 Reia File LibraryThis is a parser for Sims 2.reiafiles in Python.Basic UsageRead a.reiafile:withopen("N001.reia","rb")asf:reia_file=sims_reia.read_from_file(input)print(f"resolution={reia_file.width}x{reia_file.height}, fps={reia_file.frames_per_second}")fori,frameinenuemrate(reia_file.frames):frame.image.save(f"frame{i}.png")Testingpoetry run pytestFormattingpoetry run black .
Package: simStatus
SimStatusA simple interface to get updates from simulation programs.Installationusing pip:pip install simStatusUsageImport it and create your simStatus record:import simStatus
simstatus = simStatus.simStatus('simulation title',qrcode = True, link = True)it will generate a qrCode and link, which can be used to get updates from your simulation program.In your code, at any time, you can send a new update.
Example:simstatus.sendStatus(progress=50,status='run 4 of 8')Note 1: it is recommended to keep the reported progress between 0 and 100 (%).
Note 2: you can leavestatusblank.
Package: simstock
SimstockSimstock is a python package for taking geographical and contextual data, processing it into a form compatible with EnergyPlus, and running thermal simulations. This provides an intermediate layer between various data types and EnergyPlus, allowing urban building energy models (UBEMs) to be run straightforwardly from QGIS etc. The software performs the following setps:Geometrical pre-processing. This ensures the input data is compatible with EnergyPlus.The creation of thermal zone object data for EnergyPlus.Running the simulation and handling the results.Full docs are available atReadtheDocs.InstallationNOTE:Simstock requires Python 3.8 or above, as well as an EnergyPlus installation.After ensuring you have EnergyPlus installed, and python >= v3.8, simply runpipinstallsimstockin the command line.UsageSimstock is structured around two objects: theSimstockDataframeand theIDFmanager. TheSimstockDataframeis an extension of aPandas Dataframe. It allows data to be read in from a variety of formats. It also performs geometric simplification on the data to ensure it conforms to EnergyPlus input standards. TheSimstockDataframealso contains the EnergyPlus settings, allowing easy manipulation of materials etc. Once these settings have been set, and any geometrical simplification perfomed, theIDFmanagerthen creates the necessary thermal zones from theSimstockDataframe. TheIDFmanagercan also be used to run an EnergyPlus simulation.Below is an example of a typical Simstock work flow.# Import the simstock packageimportsimstockassim# Let's say we have some test data stored in a file called test.csv.# We can read it in as a SimstockDataframe:sdf=sim.read_csv("test.csv")# We now perform geometrical pre-processing:sdf.preprocessing()# Now create an new instance of an IDFmanager object that takes the# processed SimstockDataframe as an argument:simulation=sim.IDFmanager(sdf)# Create the thermal zones necessary for EnergyPlussimulation.create_model_idf()# Run the energy plus simulationsimulation.run()
Package: simstring-fast
simstringA Python implementation of theSimString, a simple and efficient algorithm for approximate string matching.Docs arehereFeaturesWith this library, you can extract strings/texts which has certain similarity from large amount of strings/texts. It will help you when you develop applications related to language processing.This library supports variety of similarity functions such as Cossine similarity, Jaccard similarity, and supports Word N-gram and Character N-gram as features. You can also implement your own feature extractor easily.SimString has the following features:Fast algorithm for approximate string retrieval.100% exact retrieval. Although some algorithms allow misses (false positives) for faster query response, SimString is guaranteed to achieve 100% correct retrieval with fast query response.Unicode support.Extensibility. You can implement your own feature extractor easily.no japanese supportPlease see this paper for more details.Installpip install simstring-fastUsagefromsimstring.feature_extractor.character_ngramimportCharacterNgramFeatureExtractorfromsimstring.measure.cosineimportCosineMeasurefromsimstring.database.dictimportDictDatabasefromsimstring.searcherimportSearcherdb=DictDatabase(CharacterNgramFeatureExtractor(2))db.add('foo')db.add('bar')db.add('fooo')searcher=Searcher(db,CosineMeasure())results=searcher.search('foo',0.8)print(results)# => ['foo', 'fooo']If you want to use other feature, measure, and database, simply replace these classes. You can replace these classes easily by your own classes if you want.fromsimstring.feature_extractor.word_ngramimportWordNgramFeatureExtractorfromsimstring.measure.jaccardimportJaccardMeasurefromsimstring.database.dictimportDictDatabasefromsimstring.searcherimportSearcherdb=DictDatabase(WordNgramFeatureExtractor(2))db.add('You are so cool.')searcher=Searcher(db,JaccardMeasure())results=searcher.search('You are cool.',0.8)print(results)Supported String Similarity MeasuresCosineDiceJaccardOverlapLeft OverlapSupported database backendsdictionarydiskcache (sqlite)redis (in development #37)