Saravanan666/pypi-org huggingface dataset:
Package: smallerize
smallerizeA Python implementation of minimisation for clinical trialsOpen source: Mozilla Public License 2.0Documentation:https://smallerize.readthedocs.io.Source:https://gitlab.com/warsquid/smallerize.FeaturesImplements minimization as described in Pocock + Simon (1975):Sequential
treatment assignment with balancing for prognostic factors in the
controlled clinical trialTested usingpytestto ensure the results match the original
implementation.Pure Python module with no dependencies (pandasis useful when conducting
simulations but is optional)Includes all functions described in the article: range, standard deviation,
variance, etc.Also implements the biased-coin minimization method described in Han et al. (2009):Randomization by minimization for unbalanced treatment allocation, to
allow for unequal allocation ratios.Allows pure random assignment for comparisonSimulation module to allow simulating the effects of different assignment
schemes.ExampleComparing minimization to purely random assignment by simulation:See the examplenotebookfor details of the simulation.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.5.0 (2019-05-08)Improved performance with large numbers of factors/factor levels by
making the count table sparse.0.4.0 (2019-02-02)Switched to Mozilla Public License.0.3.0 (2019-01-02)Make it easier to check the valid total imbalance and
probability methods.0.2.0 (2018-10-22)Add the biased coin minimization method to allow
for unequal treatment allocations.0.1.0 (2018-10-07)First release on PyPI.
Package: smallestenclosingcircle
Smallest enclosing circlepip install smallestenclosingcircleimportsmallestenclosingcirclecenter_x,center_y,radius=smallestenclosingcircle.make_circle(points)# points must be iterable of points e.g.:# numpy array of shape (N, 2)# list of tuples# [(1, 2), (8, 9), (4, 5), (-6, -7), ... ]## Note: if no points are input, returns NoneOriginal source from:https://www.nayuki.io/page/smallest-enclosing-circle
Package: smallexpimv
smallexpimvThis repository provides some Fortran routines and python wrappers to apply the imaginary exponential of a matrix (real tridiagonal or complex) to a vector. More precisely,$$
\beta\mathrm{e}^{\pm\mathrm{i} t X} u \in\mathbb{C}^n,
$$for a matrix $X\in\mathbb{C}^{n\times n}$, a time-step $t\in\mathbb{R}$, a scaling factor $\beta\in\mathbb{R}$, a vector $u\in\mathbb{C}^n$ and the imaginary number $\mathrm{i}^2 = -1 $. Our first routine computes this vector by an adaptive and restarted Taylor approximation. We also provide a second routine for a tridiagonal case, namely$$
\beta\mathrm{e}^{\pm\mathrm{i} t T} e_1 \in\mathbb{C}^n,
$$where $T\in\mathbb{R}^{n\times n}$ refers to a tridiagonal symmetric matrix and $e_1$ denotes the first unit vector, i.e., $e_1 = (1,0,\ldots,0)^\ast \in\mathbb{R}^{n}$. For the tridiagonal case, the action of the matrix exponential is computed using an eigendecomposition of $T$ via the lapack $\texttt{dstevr}$ routine.The matrix exponentials above have some relevance for the Lanczos or Arnoldi approximations to the action of large-dimensional matrix exponentials, e.g., for time-dependent Schrödinger-type problems. In this context, $X$ or $T$ correspond to small dimensional Krylov representations of a large problem.The main code is written in Fortran, and can be used in Python using f2py.To generate the main python module smallexpimv.so runpython3 -m numpy.f2py -c src/F_smallexpimv.F90 -m smallexpimv -lblasoptional, generate f2py header files:python3 -m numpy.f2py -h smexp.pyf -m smallexpimv src/F_smallexpimv.F90For more user-friendly Python code which also handles working memory (most relevant when applying the matrix exponential multiple times)
we also provide the module smallexpimv_pyclass.py. Keep in mind that this module requires still the smallexpimv.so module.makefile: runmake testto run the python and fortran tests, andmake libsto generate smallexpimv.so and smallexpimv_pyclass.pyc. To use the Makefile, first check that (in the makefile) the compiler commands and the f2py filename extension are defined correctly for your system!python3.7 -m build --sdist
python3.7 -m twine upload --repository testpypi dist/*
python3.7 -m pip install --index-urlhttps://test.pypi.org/simple/--no-deps mypackagetjxpython3.7 -m pip install mypackagetjx==0.0.14 --extra-index-url=https://test.pypi.org/simple/from mypackagetjx import onef
Package: smallgraphlib
Small Graph LibInstalling$ git clone https://github.com/wxgeo/smallgraphlib

$ pip install --user smallgraphlibUsageMain classes areGraph,DirectedGraph,WeightedGraphandWeightedDirectedGraph:>>> from smallgraphlib import DirectedGraph
>>> g = DirectedGraph(["A", "B", "C"], ("A", "B"), ("B", "A"), ("B", "C"))
>>> g.is_simple
True
>>> g.is_complete
False
>>> g.is_directed
True
>>> g.adjacency_matrix
[[0, 1, 0], [1, 0, 1], [0, 0, 0]]
>>> g.degree
3
>>> g.order
3
>>> g.is_eulerian
False
>>> g.is_semi_eulerian
TrueSpecial graphs may be generated using factory functions:>>> from smallgraphlib import complete_graph, complete_bipartite_graph
>>> K5 = complete_graph(5)
>>> len(K5.greedy_coloring)
5
>>> K33 = complete_bipartite_graph(3, 3)
>>> K33.degree
6
>>> K33.diameter
2If the graph is not too complex, Tikz code may be generated:>>> g.as_tikz()
...DevelopmentGet last version:$ git clone https://github.com/wxgeo/smallgraphlibInstall Poetry.Poetry is a tool for dependency management and packaging in Python.Installation instructions are here:https://python-poetry.org/docs/#installationInstall developments tools:$ poetry installOptionally, update development tools:$ poetry updateOptionally, install library in editable mode:$ pip install -e smallgraphlibMake changes, add tests.Launch tests:$ toxEverything's OK ? Commit. :)
Package: smallinvoice
===========smallinvoice api wrapper===========This python package is a wrapper for the web service api of http://www.smallinvoice.ch, an elegant online invoicing andproject management service. For more information about the API consult: http://developer.smallinvoice.com/Implemented Services=========The following services are completely wrapped* Invoices* Clients* Offers* Assigns* Catalog* Projects* Times* Costunits* Receipts* Accounts* Client AccountsUsage=========from smallinvoice import Smallinvoicesmallinvoice = Smallinvoice(YOUR_API_TOKEN)smallinvoice.clients.all()
Package: smallneuron
smallneuronNetwork Programming platformRequirementsmqtt bridge:
sudo pip3 install paho-mqttgpio bridge:
sudo pip3 install --upgrade OPi.GPIOTo use gpio h3: install gpio_h3, run
./buildPython PackageInstall toolssudo apt install python3-pip python3-venv
python3 -m pip install build # pip3 install buildBuildpython3 -m build
python3 -m twine upload --repository testpypi dist/*InstallInstalling module
sudo python3 -m pip install --upgrade --index-urlhttps://test.pypi.org/simple/--no-deps smallneuron-pelainux
Package: small-particle-detection
SPADE: Small PArticle DEtectionAn algorithm primarily design to detect objects whose sizes aren’t larger a
few pixels (particles) on fluorescence microscopy images.It is an simplified version of marked point process.RequirementsSPADE has only been tested with Python 3.5 but should work with older versions.
The only strict requirement is the packagenumpy.
In order to use SPADE to its full extent, we recommend installing thescikit-imagepackage and the fullscipyecosystem.UsageIn a python scriptYou can install the package using PIP. The main function,spade2d, is in thespade.detection_2dmodule. It takes an image, a shapes library and a
threshold as input. A typical usage would look like likefromskimage.ioimportimread,imshowfromskimage.filtersimportthreshold_otsufromskimage.colorimportlabel2rgbfromnumpyimportpercentileimportmatplotlib.pyplotaspltfromspade.detection_2dimportspade2dfromspade.shapes.examplesimportpotatoids5x5_smallest4px# Load the example image.image=imread("example_2d.png")# Separate cell image from background, using by Otsu's thresholding method.cell=image>threshold_otsu(image)# Focus on brightest pixels onlypotential_centers=image>percentile(image[cell],99)# Detect particles.particles=spade2d(image=image,shapes_library=potatoids5x5_smallest4px,threshold=20,potential_centers=potential_centers,mask=cell)# Show detected particles as overlay on our original image.imshow(label2rgb(particles,image,bg_label=0))plt.show()See the examples in theexamplefolder of the source dist for more
information. Note thatmatplotlibandskimageare not required for SPADE
to work.From the command lineAlso provided is a command line interface, that can be used as following:python-mspade.cliINPUT_IMAGETHRESHOLD-o/path/to/output_image.pngLaunch this command with-hfor more information on its usage.Authors & AcknowledgmentsSPADE was developed byNicolas Cedilnik,Éric DebreuveandXavier Descombesat theMORPHEME team.If you use SPADE for your scientific work, please include thisbibtex(or
equivalent) entry:@misc{cedilnik2016spade,
  title={SPADE: Small particle detection}author={Cedilnik, Nicolas and Debreuve, Éric and Descombes, Xavier}url={https://pypi.python.org/pypi/small-particle-detection}year={2016}}LicenseSPADE is released under theCeCILL-2.1 licence.
Package: smallparts
SmallpartsA humble collection of convenience modules for use with Python 3This package mainly focuses on text conversion and generation.When coding in Python, I found myself copying the same modules into my
projects over and over again, doing a few changes here and there and
ended up with slightly different modules with the same functionality.
So one day I decided to put an end to that and transfer my frequently used
helper modules to a single source of truth.Here they are, in good hope that they might be useful to others as well.Package documentation:https://blackstream-x.github.io/smallparts/Installable resources:https://pypi.org/project/smallparts/Please note: When creating this project on GitHub, I forked it off
the sample project (https://github.com/pypa/sampleproject)
from the Python Packaging User Guide’s recommendableTutorial on Packaging and Distributing Projects.Apart from the basic structure of the package,
this project shares nothing anymore with its parent.
Nevertheless, GitHub shows a huge number of forks, but these are forks
of the sample project, not of this one.
Package: small-pipe
small_pipesmall_pipe (small_pp for short) is a minimal abstraction layer for piping operations in python.ExamplesYou can alias your functions and then use it in the pipe function like soimportsmall_pipeasppexists=pp.where(lambdax:xisnotNone)pp.pipe([1,2,3,None,4],exists)[1,2,3,4]# returnsFor more complex cases consider importing functions directly as infromsmall_pipeimportpipe,each,where,reducepipe.debug=Truedouble=lambdax:x*2pipe(range(15),each(double),where(lambdax:x%2==0),list,reduce(lambdax,y:x+y,20),double)<mapobjectat0x000002055852B1F0><filterobjectat0x000002055852B040>[0,2,4,6,8,10,12,14,16,18,20,22,24,26,28]230460# returnsDocspipeis literally just a function that loops over *args and calls them on the positional argumenteach,whereandreducewrap higher order functionsmap,filterandfunctools.reducerespectively so that they can be used without the iterable argument
Package: small-probs-wlad111
small_probsThis is the framework for estimation of small probabilities!Getting startedInstall from pipFirst, we need pymc3_ext:pip install git+https://github.com/wlad111/pymc3.gitThen installsmall_probswithpip:pip install git+https://github.com/wlad111/small_probs.gitRunning examplesAfter installation, example notebooks fromsmall_probs/notebooksare available
Package: small-small-hr
small-small-hrsmall-small-hris a really really small and light-weight Django application that helps you (yes you!) to manage certain aspects of HR (human resource) in your small or large business/organization/whatever.Installationpipinstallsmall-small-hrFeaturesAt this time,small-small-hrsupports the following:Employee recordsYou can keep track of all your employee's details.  This is achieved through a customStaffProfilemodel attached to yourUsermodel via aone-to-onerelationship.  Some of the available fields on this model are:first namelast namegenderbirth dayphotonumber of allowed leave days per yearwhether or not overtime is allowed for the employeedata - a JSON field that allows you to store any extra informationEmployee DocumentsYou can keep track of an unlimited number of employee documents (think employment contracts, performance reviews, scans of identification documents, etc).  This is achieved by anStaffDocumentmodel that has aone-to-manyrelationship with theStaffProfilemodel (above).The fields on this model arestaffprofile_idname of documentdescription of documentdcoument fileLeave managementAll employees can log in and make a request for leave.  To achieve this, there exists aLeaveRequestmodel with these fields:staffprofile_idrequest datestart dateend datereason for leavestatus (pending approval/approved/rejected)comments (made by the admin, e.g. reasons for refusal)Once a LeaveRequest object is created, an administrator should review it and approve/reject it.Overtime hours trackingEmployees who are allowed overtime can log in and record overtime hours.  This is done by anOvertimeHourmodel with these fields:staffprofile_iddatestart timeend timereason for overtimestatus (pending approval/approved/rejected)comments (made by the admin, e.g. reasons for refusal)Admins can download overtime hours reports for a particular period.ContributionAll contributions are welcome.To set up the project:Clone this repopip install -r requirements/dev.txtpre-commit installTestingpipinstall-Utox

tox
Package: smallssd
smallSSD: The small robot company's semi-supervised detection datasetsmallSSDis an open source agricultural semi-supervised object detection dataset, containing960images labelled with wheat and weed bounding boxes and100,032unlabelled images.All images were collected by theSmall Robot Company's Tom robot in 8 experimental fields with varying drill rates and fertilizer and herbicide application, and are available onZenodo.This repository returns a dataset, modelled off thetorchvisiondatasets:fromtorch.utils.dataimportDataLoaderfromsmallssd.dataimportLabelledData,UnlabelledDatalabelled_loader=DataLoader(LabelledData())By default, this code expects the labelled data to be in thedatafolder (and will automatically download it fromZenodoif it is not available there).More in-depth examples on how to get started with this data are available in thebenchmarks, where we train torchvision models against the data using both fully-supervised and pseudo labelling approaches.InstallationsmallSSDcan be installed with the following command:pipinstallsmallssdLicensesmallSSDhas aCreative Commons Attribution-NonCommercial 4.0 Internationallicense.
Package: smallstep-py
python-stepPython interface tosmallstep cliapi,
andsmallstep caapi.Currently is basically just a wrapper to the cli, with a bit of output parsing.
The end goal is replicating all the functionality of step-cli within python itself.Current Features:Replication of step cli tool as python objectSome types of parsed outputPlanned Features:Client CA BootstrappingSSH logging inSSH Host setupx509 User/host certsCA initializationCA management (provisioners, policy, etc)LicenseLicensed under GPLv3+, seeLICENSEfor full license text
Copyright by Clayton Rosenthal
Package: smallstep-python
smallstep-pythonsmallstep-python is a Python library at allows you to interface with the Smallstep API.We useopenapi-python-clientto generate a low level Python client that is located insmallstep/api_client/. See thesmallstep/README.mdfor more information on using this generated library. We built a wrapper library on top ofsmallstep/api_clientthat is located inapi.py. This adds a handful of helper features. At this timesmallstep/api.pyonly supports a few API endpoints. Seesmallstep/api.pyfor details.RequirementsPython 3.9Smallstep AccountA very strong desire to allow a cute snake to manage your Smallstep accountInstallationWe publish this package tohttps://pypi.org/project/smallstep-python/and it can be installed via pip.pipinstallsmallstep-pythonDevelopmentConfigurationCreate a.envfile in the project root and add the following lines:# Not needed unless you are using our run anywhere offering# SMALLSTEP_API_HOST="https://gateway.smallstep.com/api"SMALLSTEP_API_TOKEN="your_smallstep_api_token"Adjust them to your needs. Seeconfig.pyfor configuration details. We usePydantic Settingsfor easy settings management. Below is the order of precedence for configuration settings:Arguments passed to the Settings class initialiser.Environment variables, e.g. SMALLSTEP_MY_CONFIG_SETTING as described above.Variables loaded from a dotenv (.env) file.Variables loaded from the secrets directory.The default field values for the Settings model.See the Pydantic Settingsfield value prioritysection for more information.With PoetryInstall Poetry on your system withthis.Run this from the repo directory:poetryinstallEnter the Poetry shell with this:poetryshell
pre-commitinstallWith venvexportVIRTUAL_ENV=${PWD}/.venv
python3-mvenv$VIRTUAL_ENVexportPATH="$VIRTUAL_ENV/bin:$PATH"pipinstallwheel
pipinstall-rrequirements.txt
..venv/bin/activate
pre-commitinstallAdding packagesAdd packages to Poetry and then run pre-commit which will generate a requirements.txt file for you.Development:poetryadd--group=devrich
pre-commitProduction:poetryaddrich
pre-commitLicenseApache License Version 2.0Copyright 2023 Smallstep Labs Inc.
Package: smallteacher
SSOD for AgricultureThis is intended to be a starting point for researching and deploying semi-supervised object detection models for agriculture.This is achieved by exposing aPyTorch Lightningmodule which trains a teacher-student model, given labelled and unlabelled data:fromsmallteacher.modelsimportSemiSupervisedmodel=SemiSupervised(model_base="SSD",num_classes=2,)PyTorch torchvision detection models should be drop in replaceable to this pipeline; we currently support Faster R-CNN, Retinanet, YOLO and SSD models.Given aLabelled Dataset, which returns tuples of images and annotations (as expected by any torchvision detection model), and anUnlabelled Dataset(which returns only unlabelled images), users can construct a DataModule which can be used to train this model:fromsmallteacher.dataimportDataModuledatamodule=DataModule(labelled_train_ds,labelled_val_ds,labelled_test_ds)datamodule.add_unlabelled_data(unlabelled_ds)An example of this code being applied to asemi-supervised datasetis available in thesmallSSDfolder.Installationsmallteachercan be installed with the following command:pipinstallsmallteacherLicensesmallteacherhas aCreative Commons Attribution-NonCommercial 4.0 Internationallicense.
Package: smalltime
smalltimeWhat is it?smalltimeis a quick and dirty library for timing Python function, sections of code and arbitrary programs on the command line.Requirementssmalltimerequires Python >= 3.6.2.Seepyproject.tomlfor dependencies and dev dependencies.InstallationViapoetry:poetryaddsmalltimeViapip:pipinstallsmalltimeBasic UsageIn Python CodeIn-Lineimportsmalltimetimer=smalltime.Timer(name="hello world timer")timer.start()print("Hello, ",end="")print("World!")timer.stop()Via Decoratorimportsmalltimeimportsubprocess@smalltime.timed(name="thing_timer")defthing_you_want_to_time():subprocess.call(["python","-c","\"import this\""])thing_you_want_to_time()From the Command LineN.B.: Assumes installation via Poetry and an activePoetry shell.# Usage: st <program> [args]stsleep10Startingcounter(BNM8rBqP)Counterstopped(BNM8rBqP):10007777130nselapsedCan I use this in my project?Yes, please do! The code is all open source and BSD-3-Clause licensed.
Package: small-timer
TimerSmall timer that can do parallel computingInstallationpip install small-timerDocumentationtimer_point(time: Any, repeat:bool = False)time in secondsThis function start timercheck_timer() -> boolThis function checkWas timer fishedcheck_time() -> listThis function checkHow much time is leftcheck_time()[0] - minutescheck_time()[1] - secondsrestart()This function restart timerUsageImport classTimer:from small_timer import TimerCreate objecttimer:timer = Timer()Create timer and start it:timer.timer_point(10)Check timer if timer finished:if timer.check_timer():
    print("Timer finished!!!")
else:
    print("Timer didn't finish")Example codefrom small_timer import Timer

timer = Timer()
timer.timer_point(10)

while 1:
    if timer.check_timer():
       print("Timer finished!!!")
       break
    else:
       print("Timer didn't finish")
       print(f"{timer.check_time()[0]}:{timer.check_time()[1]}")
       print()
Package: small-tools-python
small_toolsRepository composed by some small Python tools to validade strings, values and others so as to facilitate the coding processLeia-me, isto Ã© importante!SUPORTADA A VERSÃƒO 3 DE PYTHON1.1. Instalado o VirtualenvATENTO AOS REQUIREMENTS:2.1. para instalar atravÃ©s do terminal: "(.venv) pip install -r requirements-dev.txt"DEFINIDO PEP-8 no flake8:3.1. Criar arquivo ".flake8"'''
[flake8]max-line-length=120exclude=.venv'''3.2. Rodar o teste no terminal: ".venv) flake8"ESTABELECIDO O SETUP.PY para o lanchamento da biblioteca prÃ³pria4.1. Importado o cÃ³digo para o 'setup.py' derenzon/tekton-micro4.2. Criado o setup.py no PyCharm, dentro da pasta princial '/small_tools'4.3. IMPORTANTE: Alteradas algumas configuraÃ§Ãµes dentr do SETUP.PY, como nome, e classificaÃ§Ã£o #Para ver classificaÃ§Ã£o:
(Pypi-Classifiers)4.4. IMPORTANTE 2: Alterado o cÃ³digoinitda pyThon Package "libipythonpro":"""__ version __ = '0.1'"""4.5. Para a realizaÃ§Ã£o do teste do setup.py, foi criado um VENV na pasta principal do PycharmProjects.4.5.1. Utilize o comando "<.venv>pip install -e small_tools" que ele jÃ¡ vai localizar o setup.py5. FEITO O LANÃ‡AMENTO DO PRIMEIRO RELEASE NO GITHUB5.1. No terminal, definda uma TAG para a Ãºltima atualizaÃ§Ã£o com " _(.venv) git tag 0.1_ "5.2. Posteriormente, foi indicado a TAG para ser destacada no GITHUB com o cmdo " _(.venv) git pull --tags_ "5.3. Feito teste de intalaÃ§Ã£o com o Package em VENV com o link fornecido pelo GITHUB: " _(.venv) pip install 
@github_link_do_release.zip _"6. FEITA A CRIAÃ‡ÃƒO DO PACKAGE .DIST/libpythonpro-0.2.tar.gz E DISTRIBUIÃ‡ÃƒO NO PYPI:6.1. Primeiramente, criou-se o "MANIFEST.in" para informar que devem ser copiados o README.md e o LICENCE:>   MANIFEST.ininclude README.mdinclude LICENCE6.2. No terminal, escrever o cÃ³digo para a criaÃ§Ã£o do package .DIST:" (.venv) python setup.py sdist "6.3 Ainda no terminal, fazer o upload do arquivo:" (.venv) twine upload dist/* "6.4 ApÃ³s a instalaÃ§Ã£o, colocar as credenciais do PYPI e verificar no site se tudo estÃ¡ funcionandoTOPICOS ABORDADOS:1. GIT
2. VIRTUALENV
3. PIP
4. Atento ao pull request
5. Trabalhando com o ".gitignore"
5.1. Adicionado "rascunho" no ".gitignore"
6. Realizar alguns PULLREQUEST e testÃ¡-los
7. Obter uma reprova no PULLREQUEST7.1. cuidado com o comando no terminal 'git reset --hard HEAD^1', pode causar problemas7.2. Por isso, o jeito mais adequado de se fazer Ã© atravÃ©s do ...8. Realizado treinamento para a configuraÃ§Ã£o de setpu.py para instalaÃ§Ã£o de Packages e Libraries9. Realizado o primeiro release no [ramos-rr/small_tools/releases](https://github.com/ramos-rr/small_tools/releases)
Package: smallurl
Smallyurl
The project is aimed at changing a long URL to a small url.We usesually see some ugly and long urls in emails or
somewhere else likehttps://www.amazon.com/BLACK-DECKER-LCC140-Lithium-Trimmer/dp/B00JGUAP8W/ref=gbps_img_s-3_5402_77b2d6e0?smid=ATVPDKIKX0DER&pf_rd_p=2558495402&pf_rd_s=slot-3&pf_rd_t=701&pf_rd_i=gb_main&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=KZRQ9PST7PV2YR29SHFTIt would be a nightmare if we want to cut such a url. How about changing the long url into a small url which directs to the same direction? That what this little project is doing.Actually this project is using the service provided the websitehttp://tinyurl.com. The website can return a small url if the user input a long url. Our project provides a simple script to get access to the website’s service without opening the browsers. It’s easy and portable.