Saravanan666/pypi-org huggingface dataset:
Package: simsusy
SimSUSY: simple SUSY spectrum calculatorsA framework to call respective “calculators”.A simple example is:simsusyrunmssm.tree_calculatorinput.slhawhereinput.slhais a proper input file andcalculator is specified by handas the command-line argument.Remarks / Policiescalculators are stored assimsusy/MODEL/NAME_calculator.pyand called asMODEL.NAME_calculator.SIMSUSYblock in input files must be “eaten” by the calculator and absent in the output file.SIMSUSY0–99 are reserved for SimSUSY program.SIMSUSY100–999 are for each calculator. However,these values should not alter physical output(e.g., “loop order” cannot be specified there!). Instead, prepare another calculator to have different physical output.LicenseThis code set (“software”) is licensed to you underthe Apache License, version 2.0.
SeeLICENSEfile andNOTICEfile for further information.
Package: simtext
simtextsimtext可以计算两文档间四大文本相似性指标，分别为：Sim_Cosine    cosine相似性Sim_Jaccard   Jaccard相似性Sim_MinEdit  最小编辑距离Sim_Simple  微软Word中的track changes具体算法介绍可翻看Cohen, Lauren, Christopher Malloy&Quoc Nguyen(2018) 第60页安装pip install simtext使用中文文本相似性fromsimtextimportsimilaritytext1='在宏观经济背景下，为继续优化贷款结构，重点发展可以抵抗经济周期不良的贷款'text2='在宏观经济背景下，为继续优化贷款结构，重点发展可三年专业化、集约化、综合金融+物联网金融四大金融特色的基础上'sim=similarity()res=sim.compute(text1,text2)print(res)Run{'Sim_Cosine': 0.46475800154489, 
'Sim_Jaccard': 0.3333333333333333, 
'Sim_MinEdit': 29, 
'Sim_Simple': 0.9889595182335229}英文文本相似性fromsimtextimportsimilarityA='We expect demand to increase.'B='We expect worldwide demand to increase.'C='We expect weakness in sales'sim=similarity()AB=sim.compute(A,B)AC=sim.compute(A,C)print(AB)print(AC)Run{'Sim_Cosine': 0.9128709291752769, 
'Sim_Jaccard': 0.8333333333333334, 
'Sim_MinEdit': 2, 
'Sim_Simple': 0.9545454545454546}

{'Sim_Cosine': 0.39999999999999997, 
'Sim_Jaccard': 0.25, 
'Sim_MinEdit': 4, 
'Sim_Simple': 0.9315789473684211}参考文献Cohen, Lauren, Christopher Malloy, and Quoc Nguyen.Lazy prices. No. w25084. National Bureau of Economic Research, 2018.如果如果您是经管人文社科专业背景，编程小白，面临海量文本数据采集和处理分析艰巨任务，个人建议学习《python网络爬虫与文本数据分析》视频课。作为文科生，一样也是从两眼一抹黑开始，这门课程是用五年时间凝缩出来的。自认为讲的很通俗易懂o(￣︶￣)o，python入门网络爬虫数据读取文本分析入门机器学习与文本分析文本分析在经管研究中的应用感兴趣的童鞋不妨 戳一下《python网络爬虫与文本数据分析》进来看看~更多B站:大邓和他的python公众号：大邓和他的python知乎专栏：数据科学家
Package: simtool
SimToolFunctions for creating and running Simulation Tools on theHUBzeroplatform lead bynanoHUBFree software: MIT licenseDocumentation:https://simtool.readthedocs.io.FeaturesEasily declare and validate inputs and outputs of a simulation using Python and Jupyter notebooks. The entire simulation code can run inside a notebook or the notebook can be a wrapper that invokes complex external codes.Usespapermillto run parameterized notebooks, saving a new copy for each run.Results saved in a datastore (filesystem or web service based).  The datastore can be used for machine learning and statistical analysis.  Additionally, it functions as a cache.Can be containerized for remote execution.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-08-09)First release on PyPI.0.2.0 (2020-03-04)Second release on PyPI.0.2.1 (2020-03-08)Third release on PyPI.0.2.2 (2020-03-08)PyPI release with updated documentation.0.2.3 (2020-09-21)Improved search function to locate simTool notebook0.3.1 (2021-06-14)Improved input data validationAdded web service based cacheing support0.3.2 (2021-09-22)Fixed for use outside of HUBzero environment0.3.3 (2021-10-04)Improved automatic documentation generationFixed Image value setting0.3.4 (2022-05-10)Verify location of installed or published sim2L notebookDifferentiate between missing path and wrong path type (file/directory)Stricter enforcement of param attributesRetain directory structure when saving results.Close open files to avoid file descriptor leakageAdded magic functions needed to run notebooks in parallel (MPI)0.3.5 (2022-05-16)Trap missing simToolSaveErrorOccurred and simToolAllOutputsSaved in result notebookReturn list of input files whether inputs is specified as dictionary or YAMLAdd run preparation method used for web service application. Previously used external shell script0.3.6 (2022-08-11)Extend utility functions for getting information about input files to use either parameter or simple dictionary description.Prepare for newer papermill versions0.4.1 (2022-12-13)Added new parameter classes Tag and File.Use checksum and file size to compute sim2L run squidId.Improve eapermill efficiency by reducing implicit I/O done during execution.Added support for sim2L metrics collection - record hit and miss by squidid.Added getParamsFromDictionary() and content() methods to standardize processing on INPUTS in sim2L notebooks.
Package: sim-tools
sim-tools: tools to support the simulation process in python.sim-tools is being developed to support simulation education and applied simulation research.  It is MIT licensed and freely available to practitioners, students and researchers via PyPi.  There is a longer term plan to make sim-tools available via conda-forge.Vision for sim-toolsDeliver high quality reliable code for simulation education and practice with full documentation.Provide a simple to use pythonic interface.To improve the quality of simulation education and encourage the use of best practice.Features:Implementation of classic optimisation via Simulation procedures such as KN, KN++, OBCA and OBCA-mDistributions module that includes classes that encapsulate a random number stream, seed, and distribution parameters.Implementation of Thinning to sample from Non-stationary poisson processes in a discrete-event simulationThree simple ways to explore sim-toolspip install sim-toolsClick on the launch-binder at the top of this readme. This will open example Jupyter notebooks in the cloud via Binder.Oneline documentation:https://tommonks.github.io/sim-toolsCitationIf you use sim0tools for research, a practical report, education or any reason please include the following citation.Monks, Thomas. (2021). sim-tools: tools to support the forecasting process in python. Zenodo.http://doi.org/10.5281/zenodo.4553642@software{sim_tools,
  author       ={Thomas Monks},
  title        ={sim-tools: fundamental tools to support the simulation process in python},
  year         ={2021},
  publisher    ={Zenodo},
  doi          ={10.5281/zenodo.4553642},
  url          ={http://doi.org/10.5281/zenodo.4553642}}Online TutorialsOptimisation Via SimulationContributing to sim-toolsPlease fork Dev, make your modifications, run the unit tests and submit a pull request for review.Development environment:conda env create -f binder/environment.ymlconda activate sim_toolsAll contributions are welcome!
Package: simtorch
simtorchA Pytorch library to measure the similarity between two neural network representations. The library currently supports the following (dis)similarity measures:Centered Kernel Alignment (CKA) -Kornblith, et al, ICML 2019Deconfounded CKA -Cui, et al, NeurIPS 2022Procrustes [WIP]CCA [WIP]DesignThe package consists of two components -SimilarityModel- which is a thin wrapper ontorch.nn.Module()which adds forwards hooks to store the layer-wise activations (aka representations) in a dictionary.BaseSimilarity- which sets the interface for classes that compute similarity between network representationsInstallationThe package is indexed by pypipip install simtorchUsageThe torch model objects need to be wrapped withSimilarityModel. A list of names of the layers we wish to compute the representations is passed as an attribute to this class.model1 = torchvision.models.densenet121()
model2 = torchvision.models.resnet101()

sim_model1 = SimilarityModel(
    model1,
    model_name="DenseNet 121",
    layers_to_include=["conv", "classifier",]
)

sim_model2 = SimilarityModel(
    model2,
    model_name="ResNet 101",
    layers_to_include=["conv", "fc",]
)An instance of a similarity metric can then be initialized with theseSimilarityModels. Thecompute()method can be used to obtain a similarity matrix $S$ for these two models where $S[i, j]$ is the similarity metric for the $i^{th}$ layer of the first model and the $j^{th}$ layer of the second model.sim_cka = CKA(sim_model1, sim_model2, device="cuda")
cka_matrix = sim_cka.compute(torch_dataloader)The similarity matrix can be visualized using thesim_cka.plot_similarity()method to obtain the CKA similarity plotCitationsIf you use Deconfounded Centered Kernel Alignment (dCKA) for your research, please cite:@article{cui2022deconfounded,
  title={Deconfounded Representation Similarity for Comparison of Neural Networks},
  author={Cui, Tianyu and Kumar, Yogesh and Marttinen, Pekka and Kaski, Samuel},
  journal={Neural Information Processing Systems (NeurIPS)},
  year={2022}
}CreditsThis has been built by using the following awesome repos as reference:anatome, maintained by@moskomulePytorch-Model-Compare, maintained by@AntixKcentered-kernel-alignment, maintained by@Kennethborup
Package: simu
simuA simulation data generatorUsageRandomConstantsDEFAULT_MAX=1<<12# the default maximumDEFAULT_MIN=-DEFAULT_MAX# the default minimumDEFAULT_SIZE=10# the default length of listBooleansimu.random.boolean()return a random boolean valuesimu.random.booleans(size: int = DEFAULT_SIZE)return a list of random boolean valuesparameter list:parameterdescriptiondefaultoptionalsizethe length of list10:heavy_check_mark:Natural numbersimu.random.natural(min: int = 0, max: int = DEFAULT_MAX)return a random natural numberparameter list:parameterdescriptiondefaultoptionalminlower limit0:heavy_check_mark:maxupper limit4096:heavy_check_mark:simu.random.naturals(min: int = 0, max: int = DEFAULT_MAX, size: int = DEFAULT_SIZE)return a list of natural numbersparameter list:parameterdescriptiondefaultoptionalminlower limit0:heavy_check_mark:maxupper limit4096:heavy_check_mark:sizethe length of list10:heavy_check_mark:Integersimu.random.integer(min: int = DEFAULT_MIN, max: int = DEFAULT_MAX)return a random integerparameter list:parameterdescriptiondefaultoptionalminlower limit-4096:heavy_check_mark:maxupper limit4096:heavy_check_mark:simu.random.integers(min: int = DEFAULT_MIN, max: int = DEFAULT_MAX, size: int = DEFAULT_SIZE)return a list of random integersparameter list:parameterdescriptiondefaultoptionalminlower limit-4096:heavy_check_mark:maxupper limit4096:heavy_check_mark:sizethe length of list10:heavy_check_mark:Floating numbersimu.random.floating(min: float = DEFAULT_MIN, max: float = DEFAULT_MAX, ndigits: int = 2)return a random floating numberparameter list:parameterdescriptiondefaultoptionalminlower limit-4096:heavy_check_mark:maxupper limit4096:heavy_check_mark:ndigitsrounded tondigitsprecision after the decimal point2:heavy_check_mark:simu.random.floatings(min: float = DEFAULT_MIN, max: float = DEFAULT_MAX, ndigits: int = 2, size: int = DEFAULT_SIZE)return a list of random floating numbersparameter list:parameterdescriptiondefaultoptionalminlower limit-4096:heavy_check_mark:maxupper limit4096:heavy_check_mark:ndigitsrounded tondigitsprecision after the decimal point2:heavy_check_mark:sizethe length of list10:heavy_check_mark:Charactersimu.random.string(pool: str = string.ascii_letters, min: int = 3, max: int = 10)return a random character from the character poolparameter list:parameterdescriptiondefaultoptionalpoolcharacter poolstring.ascii_letters:heavy_check_mark:Stringsimu.random.string(pool: str = string.ascii_letters, min: int = 3, max: int = 10)return a string which consists of the characters from the character poolparameter list:parameterdescriptiondefaultoptionalpoolcharacter poolstring.ascii_letters:heavy_check_mark:minthe minimum length of string3:heavy_check_mark:maxthe maximum length of string10:heavy_check_mark:
Package: simublocks
simublocksSimublocks is a python program, made with Tkinter, for simulating dynamic systems.This project is still under construction. Please feel free to contribute.InstallationInstall dependenciessudo apt install python3 python3-pip python3-tkDownload and run simublockspip3 install simublocks
simublocksIf a new version is available, upgrade simublocks withpip3 install simublocks --upgradeScreenshotsLicenseMIT LicenseCopyright (c) 2020 Anderson Vitor Bento
Package: simulagora
SummaryA python library to use Simulagora programatically either directly from the
command line or via a Python program.PrerequisitesYou will need a proper installation ofcwclientlib, which itself requires
Python 2.7 andPython requests.An account onSimulagorais of course also needed.Using Simulagora clientLog in onSimulagoraand consult the token that was generated for you at thededicated URL;Create acwclientlibrcfile in your user’s.configdirectory (only
readable by yourself), like:[simulagora]
url = https://www.simulagora.com
token-id = the token id
secret = the token itselfExamplesExamples directly from the command line. Get a list of the studies you can
access:$simulagorastudies[{'eid':4173,'name':u'Study 1'},{'eid':4277,'name':u'Study 2'},{'eid':4310,'name':u'Study 3'}]$simulagoraexecutables[{'eid':2454,'name':u'Attente (secondes)'},{'eid':2470,'name':u'paraview'},{'eid':4672,'name':u'bash_command #0'},{'eid':4883,'name':u'lmgc90_donut'}]A more complete example using Python. Create a “Code Aster piston test” study
and an eponym folder, upload the data in it, and run a Code Aster computation
with this data as an input on a “m1.large” server, equiped with the last
Simulagora machine image (which currently has Code Aster 11.5):fromsimulagoraimportSimulagorafromtimeimportsleepclient=Simulagora.for_instance('simulagora')# create the folder, upload the files and get their identifiersfolder=client.create_folder('Code Aster piston test')file_eids=client.upload_files(folder,'piston.comm','piston.mmed','piston.export')# get the "bash command" executable which will run the "as_run" commandexecutable=client.find_one('Executable',name='bash command')params={'command':'as_run piston.export'}# get the server type, create the study and the run, then start itserver_type=client.find_one('CloudServerType',name='m3.xlarge')study=client.create_study('Code Aster piston test')run=client.create_run(study,executable,server_type,file_eids,params)client.start_run(run)# check its state every 5 seconds until its crashed or completedstate=Nonewhilestatenotin('wfs_run_crashed','wfs_run_completed'):state=client.state(run)sleep(5)print"Run "+state.rsplit('_',1)[-1]
Package: simulai
It is a library that facilitates the use of Artificial Intelligence (AI) techniques for the optimization of variables derived from the Simulation of Flexible Manufacturing Systems (FMS). The objective is to improve the decision-making process in organizations, making it more efficient and safe.Development 🔨Initially the work will focus on the integration of Tecnomatix Plant Simulation software with diverse methods of optimization, which will be submitted to a process of improvement throughout the Doctorate stage in development.In the future, it is expected to achieve a library of greater accessibility, allowing the application in various simulation software, as required by the user.OS Windows, Tecnomatix Plant Simulation version 15.0 and pywin32 library are currently required for its operation.Authors 👥Perez Colo Ivo-@ivoperezcoloPirozzo Bernardo-@bermpirozzoSaavedra Sueldo Carolina-@carosaav
Package: simulaqron
SimulaQron - simple quantum network simulator (4.0.0)The purpose of this simulator of quantum network nodes is to allow you to develop new applications for
a future quantum internet, while we do not yet have real quantum network nodes available for testing.Since version 4.0, SimulaQron is compatible withNetQASM.
See its documentation for how to use SimulaQron as a backend for running NetQASM applications.Installation:pip3 install simulaqronDocumentation and examples are explained in the html documentationhttps://softwarequtech.github.io/SimulaQron/html/index.htmlFor upcoming and previous changes see the fileCHANGELOG.mdMore info athttp://www.simulaqron.org
Package: simularium-metrics-calculator
Simularium Metrics CalculatorCalculate plot metrics from spatial agent dataInstallationStable Release:pip install simularium-metrics-calculatorDevelopment Head:pip install git+https://github.com/simularium/metrics-calculator.gitTo install in editable mode with all dev dependencies:just installQuickstartfromsimulariumioimportFileConverter,InputFileDatafromsimularium_metrics_calculatorimport(PLOT_TYPE,SCATTER_PLOT_MODE,MetricsService,PlotInfo,)# create main classmetrics_service=MetricsService()# check the metrics that are available to plotmetrics=metrics_service.available_metrics()# load simularium trajectory data using simulariumiotraj_data=FileConverter(input_file=InputFileData(file_path=("simularium_metrics_calculator/tests/data/""aster_pull3D_couples_actin_solid_3_frames_small.json")))._data# configure some plotsplot1=PlotInfo(# Number of agents vs time scatterplotplot_type=PLOT_TYPE.SCATTER,metric_info_x=metrics[0]["uid"],metric_info_y=metrics[2]["uid"],scatter_plot_mode=SCATTER_PLOT_MODE.LINES,)plot2=PlotInfo(# Nearest neighbor distance histogramplot_type=PLOT_TYPE.HISTOGRAM,metric_info_x=metrics[3]["uid"],)# calculate the plot dataresult=manager.plot_data(traj_data,plots=[plot1,plot2,],)DocumentationFor full package documentation please visitsimularium.github.io/metrics-calculator.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.Apache Software License 2.0
Package: simulate365py
Simulate365Pyis a web-based library designed to access and calculate DWSIM flowsheets from the browser.Website:https://simulate365.com/Documentation:https://simulate365.com/docs/simulate365py/Support:https://support.capital-gain.eu/InstallationInstall package by runningpip install simulate365pyQuick StartIn order to use this library with a DWSIM flowsheet, you need access to the Simulate 365 Dashboard.Sign up viahttps://simulate365.com/registration/and activate your accountAccess the Simulate 365 Dashboard onhttps://dashboard.simulate365.com/Upload your DWSIM flowsheet and follow the documentation onhttps://simulate365.com/docs/simulate365py/Run the flowsheet calculation in your Python scriptExampleimport time
from simulate365py import DispatcherClient, NewJobFromFilterPostModel, JobProcessingStatus

client = DispatcherClient("XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX")                    
filter_unique_id = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"

post_model = NewJobFromFilterPostModel(
    filter_unique_id,
    {
        "N_stages": 25, # Number of stages
        "RD": 3 # Reflux ratio
    }
)

job_id = client.submit_job_from_filter(post_model)
print("Submitted job with ID: " + str(job_id))

while True: # Prints the status of the submitted job
    result = client.get_job_results(job_id)    

    if (result.processing_status != JobProcessingStatus.NotCalculated):
        if (result.processing_status == JobProcessingStatus.Failed):
            print("Job calculation failed. Error message is: \r\n" +  result.error)            
        else:
            print("Received result for job #" + str(job_id))
            for p in result.output_parameters:
                print(p.alias + " = " + str(p.value) + " " + p.unit)
        break
Package: simulatedistribution
{\rtf1\ansi\ansicpg1252\cocoartf1561\cocoasubrtf200{\fonttbl\f0\fswiss\fcharset0 Helvetica;}{\colortbl;\red255\green255\blue255;}{\*\expandedcolortbl;;}\paperw11900\paperh16840\margl1440\margr1440\vieww10800\viewh8400\viewkind0\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0\f0\fs24 \cf0 Installation Instructions\\pip is the recommended installer\\$ pip install simulatedistribution\\There are no additional dependancies.\\Run module by\import simulatedistribution\simulatedistribution.simulate(size, distribution, parameter1, parameter2)\\Recognised distributions are normal, poisson, and binomial\}
Package: simulateur-ironcar
Ce simulateur permet de générer un dataset préqualifié d'images de
route tel que la verrait la caméra d'uneironcarassociées à la commande de direction pour l'ironcar.Ironcarest un championnat de courses de voitures
autonomes en modèle réduit !2 exemples de sortie de simulateur_ironcarDernière version du code source de ce programmegitclonehttps://github.com/ironcarocto/simulateur_ironcar.gitUtiliser le simulateurInstaller le simulateur avec pippipinstallsimulateur_ironcarCréer un dossier pour un profil de génération de routemkdirmydirectorycdmydirectoryInitialiser un profile de génération de route depuis ce dossiersimulateur_ironcarinitVous devez obtenir le dossier suivant.
├── configuration.json
├── grounds
│   ├── IMG_1417.JPG
│   └── IMG_5993.JPG
└── photosle dossiergroundscontient les terrains sur lesquels vous voulez générer des routesConfigurez votre profile de génération de routeplacez de nouveaux grounds dans le dossiergroundconfigurez les valeurs souhaités dans le fichierconfiguration.jsonContribuer au projetcréer un environnement virtuelvirtualenv venv -p python3installer les dépendances de développement.venv/bin/activate;pipinstall-e.[dev]exécuter les tests.venv/bin/activate;pytestLicenseMIT LicenseCopyright (c) 2019 Constant BridonPermission 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.
Package: simulation
What is this?This is a collection of Python modules containing functions to handel simulations.InstallationThis package can be installed using pip (recommended way):pip3 install simulationor by downloading and running:python3 setup.py installWeb sitesThe source code of this package is hosted on:https://github.com/jor-/simulationA Python wheel is available on:https://pypi.python.org/pypi/simulationAuthorJoscha Reimerjor@informatik.uni-kiel.deCopyrightCopyright (C) 2011-2016  Joscha Reimerjor@informatik.uni-kiel.deThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.