Saravanan666/pypi-org huggingface dataset:
Package: sciformlib
SciformlibSciformlib(Science Formula Library) is a library created to make science formulations and calculations easier. This library comprises many scientific formulations already written which is easy to use and deploy.Installationpip install sciformlibHow to use it?from sciformlib import *Using this command you can import all the script files in the moduleScript Usagesum_of_pos_int_pow(base_value,start_point,end_point)It is used to find the sum of same base integers with increasing power
eg: if base value = 2 , start_point = 3 , end_point = 7
it evaluates as 2³+2⁴+2⁵+2⁶+2⁷ (Geometric Progression)Mechanics - Instantaneous Velocitymechanics_instant_velocity(pos,time)pos = distance , and time = timeMechanics - Average Velocitymechanics_average_velocity(initial_pos,final_pos,initial_time,final_time)Displacement - Formula 1mechanics_displacement_f1(initial_velocity,acceleration,timeDisplacement - Formula 2mechanics_displacement_f2(initial_velocity,final_velocity,time)Final Velocity - Formula 1mechanics_velocity_f_f1(initial_velocity,acceleration,time)Final Velocity - Formula 2mechanics_velocity_f_f2(initial_velocity,acceleration,position)This function gives value of "v not v²"Accelerationmechanics_acce_f1(initial_velocity,final_velocity,time)Instantaneous Accelerationmechanics_instant_acce(velocity,time)Average Accelerationmechanics_average_acce(final_velocity,initial_velocity,final_time,initial_time)LicenseMIT LISCENSE© 2021 Iamrealbhuvi .This repository is licensed under the MIT license. See LICENSE for details.
Package: scigym
Status:Development (expect bug fixes, minor updates and new
environments)SciGymSciGym is a curated library for reinforcement learning environments in science.This is thescigymopen-source library which gives you access to a standardized set of science environments.
Visit our webpage atscigym.ai. This website serves as a open-source database for science environments: A port where science and reinforcement learning meet.See What's New section belowBasicsThis project is in line with the policies of theOpenAI gym:There are two basic concepts in reinforcement learning: the environment
(namely, the outside world) and the agent (namely, the algorithm you are
writing). The agent sendsactionsto the environment, and
the environment replies withobservationsandrewards(that is, a score).The coregyminterface isEnv, which is the unified
environment interface. There is no interface for agents; that part is
left to you. The following are theEnvmethods you should know:reset(self): Reset the environment's state. Returnsobservation.step(self, action): Step the environment by one timestep. Returnsobservation,reward,done,info.render(self, mode='human', close=False): Render one frame of the environment. The default mode will do something human friendly, such as pop up a window. Passing thecloseflag signals the renderer to close any such windows.InstallationThere are two main options for the installation ofscigym:(a) minimal install (recommended)This method allows you to install the package with no environment specific dependencies, and later add the dependencies for specific environments as you need them.You can perform a minimal install ofscigymwith:pipinstallscigymorgitclonehttps://github.com/hendrikpn/scigym.gitcdscigym
pipinstall-e.To later add the dependencies for a particularenvironment_name, run the following command:pipinstallscigym[environment_name]or from the folder containingsetup.pypipinstall-e.[environment_name](b) full installThis method allows you to install the package, along with all dependencies required for all environments. Be careful, scigym is growing, and this method may install a large number of packages. To view all packages that will be installed during a full install, see therequirements.txtfile in the root directory. If you wish to perform a full installation you can run:pipinstallscigym['all']orgitclonehttps://github.com/hendrikpn/scigym.gitcdscigym
pipinstall-e.['all']Available EnvironmentsAt this point we have the following environments available for you to play with:teleportationentangled-ionsWhat's New2021-06-16 Added theToric Gameenvironment2021-06-09 Addedentangled-ionsenvironment.2021-06-08 This isscigymversion 0.0.3! Now compatible with gym version 0.18.02019-10-10scigym.aiis online!2019-08-30 This isscigymversion 0.0.2!2019-08-30scigymis now available as a package onPyPI.2019-08-06 AddedTravis-CI.2019-08-06: Addedteleportationenvironment.2019-07-21: Added standardized unit testing for all scigym environments.2019-03-04: Addedsurfacecodeenvironment.2019-02-09: Initial commit. Hello world :)
Package: scihub
SciHub |Python| |Build Status| |Pypi|======``scihub`` is an unofficial API for sci-hub.cc. scihub.py can downloadpapers from ``sci-hub``.If you believe in open access to scientific papers, please donate toSci-Hub.Setup-----::pip install scihubUsage-----fetch~~~~~.. code:: pythonfrom scihub import SciHubsh = SciHub()# fetch specific article (don't download to disk)# this will return a dictionary in the form# {'pdf': PDF_DATA,#  'url': SOURCE_URL# }result = sh.fetch('http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1648853')with open('output.pdf', 'wb+') as fd:fd.write(result['pdf'])License-------MIT.. |Python| image:: https://img.shields.io/badge/Python-3%2B-blue.svg:target: https://www.python.org.. |Build Status| image:: https://travis-ci.org/alejandrogallo/python-scihub.svg?branch=master:target: https://travis-ci.org/alejandrogallo/python-scihub.. |Pypi| image:: https://badge.fury.io/py/scihub.svg:target: https://badge.fury.io/py/scihub
Package: sci-hub
PDF Downloader with SCI-HUBAdvantagescould detect url automaticlycould download in batchescould run in command line modeInstallationpip3installsci-hub# orpython3-mpipinstallsci-hubCommand Line Examples# checking available urlsscihub-c# searching pmid(s)scihub-s1,2,3# searching doiscihub-s10.1038/s41524-017-0032-0# searching with a filescihub-spmid-list.txt# searching with a specific urlscihub-s1-uhttps://sci-hub.ren# name the output file by searching stringscihub-s123-ns# specify the output directoryscihub-s1,2,3-Oout# overwrite or not when file exists, default will askscihub-s1,2,3-owN
scihub-s1,2,3-owYAcknowledgementURL Detecting From:https://lovescihub.wordpress.com/
Package: scihub2pdf
Descriptionscihub2pdf is a module ofbibcureDownloads pdfs via a DOI number, article title or a bibtex file, using
the database of libgen, Sci-Hub and Arxiv.Install$ sudo python /usr/bin/pip install scihub2pdfIf you want to download files from scihub you will need to get PhantomJSOSX$ npm install -g phantomjsLinux Using npm$ sudo apt-get install npm
$ sudo npm install -g phantomjsFeatures and how to useGiven a bibtex file$ scihub2pdf -i input.bibGiven a DOI number…$ scihub2pdf 10.1038/s41524-017-0032-0Given a title…$ scihub2pdf --title An useful paperArxiv…$ scihub2pdf arxiv:0901.2686
$ scihub2pdf --title arxiv:Periodic table for topological insulatorsLocation folder as argument$ scihub2pdf -i input.bib -l somefoler/Use libgen instead sci-hub$ scihub2pdf -i input.bib --uselibgenSci-hub:StableAnnoying CAPTCHAFastLibgenUnstalbeNo CAPTCHASlowDownload from list of itemsGiven a text file like10.1038/s41524-017-0032-0
10.1063/1.3149495
.....download all pdf’s$ scihub2pdf -i dois.txt --txtGiven a text file likeSome Title 1
Some Title 2
.....download all pdf’s$ scihub2pdf -i titles.txt --txt --titleGiven a text file likearXiv:1708.06891
arXiv:1708.06071
arXiv:1708.05948
.....download all pdf’s$ scihub2pdf -i arxiv_ids.txt --txt
Package: scihub_dmunozg
Sci-Hub libForked fromScihub Downloaderwhich is a fork ofscihub.py.UsageFirst, import the module and create an instance of theSciHubobject. At this point, you can optionally specify which Sci-Hub mirror will be employed.fromscihubimportSciHubsh=SciHub(base_url="https://sci-hub.ru")Thefetchmethod will obtain a URL for direct downloading of an specified reference:sh.fetch("https://doi.org/10.1111/gwmr.12285")> 'http://dacemirror.sci-hub.ru/journal-article/0b1ec328b87368b809e0913c8591b9bc/miller2018.pdf?download=true'Thedownloadmethod will automatically download the PDF from the reference into a specified location:sh.download(reference="https://doi.org/10.1111/gwmr.12285",output_dir="./",pdf_filename="paper.pdf"# This is optional)CLI toolThe package features a tool to download PDF files from the command line.scihub"https://doi.org/10.1111/gwmr.12285"It can be called with the arguments--output/-oto specify where to save the PDF file, and with--sci-hub-urlto specify the Sci-Hub mirror.scihub"https://doi.org/10.1111/gwmr.12285"-ohere.pdf--sci-hub-url"https://sci-hub.ru"LicenseMIT
Package: sci-igm
igmIGM (Intelligence Guidance Manager for AI).The ultimate purpose of AI is to serve science (as ai4science does), so let's call itsci-igm.InstallationYou can simply install it withpipcommand line from the official PyPI site.pipinstallsci-igmOr install from latest source code as follows:gitclonehttps://github.com/igm4ai/igm.gitcdigm
pipinstall.--userQuick Start for IGMHere is a simple example to create a hello world project:igmnewgit+https://github.com/igm4ai/template-simplehelloword# create helloworld projectcdhelloword
igmrun# run the helloworld projectWhat Happened?After you enter theigm new <template> <proj_dir>command to your terminal, igm operate as the following stages:Initialization Stage- Check the template, if remote url or repository detected, download it to local storage.Project Creation StageLoad Step- Load the template's meta information.Inquire Step- Ask the user to provide some necessary.Build Step- Build the project based on the template, the project will be placed at<proj_dir>.Project Use Stage(Optional)Prerequisite Installation- runigm run installcommand to install the dependencies.Code Run- runigm runcommand to run the main project code.What Scripts Are Provided?- runigm run -hto see the list of provided scripts.(Optional) Other custom scripts - you can use the other scripts provided by template, or custom the extra scripts
inigmeta.py.How to Create A New Project TemplateThe detailed documentation is still preparing, but you can take a look at the following examples:template-simple, a helloworld template exampletemplate-linear-regression, a more advanced example of linear
regression problem,with visualization exampletemplate-resnet18, template for resnet18, including resource download
and usage of tensorboardIGM-di, example of usage of DI-engine, including custom complex generating of
training codeTest Template, a test template for unittest of igm tools,
more advanced usage can be found here.For information on template syntax, see the following:Jinja2, the template render framework we used in IGM.potc, transform any object to readable python object, will be useful when render
python source code. It is integrated into IGM with a filter namedpotc.ContributingWe appreciate all contributions to improveigm, both logic and system designs. Please refer to CONTRIBUTING.md
for more guides.Licenseigmreleased under the Apache 2.0 license.
Package: scikeras
Scikit-Learn Wrapper for KerasScikit-Learn compatible wrappers for Keras Models.Why SciKerasSciKeras is derived from and API compatible withtf.keras.wrappers.scikit_learn. The original TensorFlow (TF) wrappers are not actively maintained,
andwill be removedin a future release.An overview of the advantages and differences as compared to the TF wrappers can be found in ourmigrationguide.InstallationThis package is available on PyPi:# Normal tensorflowpipinstallscikeras[tensorflow]# or tensorflow-cpupipinstallscikeras[tensorflow-cpu]SciKeras packages TensorFlow as an optional dependency because there are
several flavors of TensorFlow available (tensorflow,tensorflow-cpu, etc.).
Depending ononeof them in particular disallows the usage of the other, which is why
they need to be optional.pip install scikeras[tensorflow]is basically equivalent topip install scikeras tensorflowand is offered just for convenience. You can also install just SciKeras withpip install scikeras, but you will need a version of tensorflow installed at
runtime or SciKeras will throw an error when you try to import it.The current version of SciKeras depends onscikit-learn>=1.0.0andTensorFlow>=2.7.0.Migrating fromtf.keras.wrappers.scikit_learnPlease see themigrationsection of our documentation.DocumentationDocumentation is available athttps://www.adriangb.com/scikeras/.ContributingSeeCONTRIBUTING.md
Package: scikick
Scikick - Notebook-Centric Analysis WorkflowsOverviewCollections of computational notebooks often lose coherence during complex and branching investigations. Scikick is a command line utility for managing ensembles of computational notebooks developed throughout a project by providing simple commands for workflow configuration, report generation, and state management.Getting StartedRead the introductionfor further details and installation requirements.See thetutorialfor a simple example usage of Scikick.View a data analysis report generated by Scikick.Use theissue boardto provide feedback on Scikick (feature requests, bugs, and further software-related comments).
Package: scikit2pmml
sklearn2pmml==========sklearn2pmml is simple exporter for sklearn models (for supported models see bellow) into PMML text format which addressthe problems mentioned bellow.Storing predictive models using binary format (e.g. Pickle) may be dangerous from several perspectives - naming few:* **binary compatibility**:you update the libraries and may not be able to open the model serialized with older version* **dangerous code**: when you would use model made by someone else* **interpretability**: model cannot be easily opened and reviewed by human* etc.In addition the PMML is able to persist scaling of the raw input features which helps gradient descent to run smoothlythrough optimization space.Installation------------To install sklearn2pmml, simply:.. code-block:: bash$ pip install sklearn2pmmlExample-------Example on Iris data - for more examples see the examples folder... code-block:: pythonfrom scikit2pmml import scikit2pmmlfrom sklearn.datasets import load_irisimport numpy as npfrom sklearn.ensemble import RandomForestClassifieriris = load_iris()X = iris.data.astype(np.float32)y = iris.target.astype(np.int32)model = RandomForestClassifier(max_depth=2, n_estimators=10, random_state=0)model.fit(X, y)params = {'pmml_version': '4.2','feature_names': iris.feature_names,'target_values': iris.target_names,'target_name': 'specie','copyright': 'Václav Čadek','description': 'Simple Iris RF model.','model_name': 'Iris Model'}scikit2pmml(estimator=model, file='iris.pmml', **params)Params explained----------------- **estimator**: Sklearn model to be exported as PMML (for supported models - see bellow).- **transformer**: if provided (and it's supported - see bellow) then scaling is applied to data fields.- **file**: name of the file where the PMML will be exported.- **feature_names**: when provided and have same shape as input layer, then features will have custom names, otherwise generic names (x\ :sub:`0`\,..., x\ :sub:`n-1`\) will be used.- **target_values**: when provided and have same shape as output layer, then target values will have custom names, otherwise generic names (y\ :sub:`0`\,..., y\ :sub:`n-1`\) will be used.- **target_name**: when provided then target variable will have custom name, otherwise generic name **class** will be used.- **copyright**: who is the author of the model.- **description**: optional parameter that sets *description* within PMML document.- **model_name**: optional parameter that sets *model_name* within PMML document.What is supported?------------------- Linear Model* sklearn.linear_model.LinearRegression* sklearn.linear_model.LogisticRegression- Tree* sklearn.tree.DecisionTree* sklearn.tree.ExtraTreeClassifier- Ensemble* sklearn.ensemble.RandomForestClassifier* sklearn.ensemble.ExtraTreesClassifier- Scalers* sklearn.preprocessing.StandardScaler* sklearn.preprocessing.MinMaxScalerLicense-------This software is licensed under MIT licence.- https://opensource.org/licenses/MIT
Package: scikit-aero
scikit-aeroName:scikit-aeroWebsite:https://github.com/Pybonacci/scikit-aeroAuthor:Juan Luis Cano <juanlu001@gmail.com>Version:0.1scikit-aero is a Python package for various aeronautical engineering
calculations. It is based on several existing Python packages on the field,
but intends to provide pythonic syntax, use of SI units and full NumPy arrays
support among other things. scikit-aero is licensed under the BSD license.It was started by Juan Luis Cano in 2012 and it is currently developed and
maintained by him. The source code and issue tracker are both hosted on
GitHubhttps://github.com/Pybonacci/scikit-aeroNotice: This package is under heavy development and the API might change
at any time until a 1.0 version is reached. It is stable but not feaure
complete yet, and it might contain bugs.FeaturesPythonic interface.Use of SI units.Full support of NumPy arrays.Support for both Python 2 and 3.Fully tested and documented.Standard atmosphere properties up to 11 kilometers (troposphere).Gas dynamics calculations.FutureFull COESA model.Airspeed conversions.Coordinate systems.Most of the PDAS.UsageAtmosphere properties:>>> from skaero.atmosphere import coesa
>>> h, T, p, rho = coesa.table(1000)  # Altitude by default, 1 kmInverse computations allowed with density and pressure, which are monotonic:>>> h, T, p, rho = coesa.table(p=101325)  # Pressure of 1 atmGas dynamics calculations:>>> from skaero.gasdynamics import isentropic, shocks
>>> fl = isentropic.IsentropicFlow(gamma=1.4)
>>> p = 101325 * fl.p_p0(M=0.8)  # Static pressure given total pressure of 1 atm
>>> ns = shocks.NormalShock(M_1=2.5, gamma=1.4)
>>> M_2 = ns.M_2  # Mach number behind a normal shock waveDependenciesThis package depends on Python, NumPy and SciPy and is usually tested on
Linux with the following versions:Python 2.7, NumPy 1.6, SciPy 0.11Python 3.3, NumPy 1.7.0b2, SciPy 0.11.0but there is no reason it shouldn’t work on Windows or Mac OS X. If you are
willing to provide testing on this platforms, pleasecontact meand if you find any bugs file them
on theissue tracker.InstallThis package uses distutils. To install, execute as usual:$ python setup.py installIt is recommended that younever ever use sudowith distutils, pip,
setuptools and friends in Linux because you might seriously break your
system [1][2][3][4]. I recommend usingvirtualenv,per user directoriesorlocal installations.Testingscikit-aero recommends py.test for running the test suite. Running from the
top directory:$ py.testBug reportingI am pretty sure I never introduce bugs in my code, but if you want to prove
me wrong please refer to theissue trackeron GitHub.CitingIf you use scikit-aero on your project, pleasedrop me a line.Licensescikit-aero is released under a 2-clause BSD license, hence allowing commercial use
of the library. Please refer to the COPYING file.See alsoAeroCalc, package written by Kevin Horton which inspired scikit-aero.MATLAB Aerospace Toolbox,PDAS, the Public Domain Aeronautical Software.
Package: scikit-allel
A Python package for exploratory analysis of large scale genetic variation data.Documentation:http://scikit-allel.readthedocs.orgSource code:https://github.com/cggh/scikit-allelDownload:https://pypi.python.org/pypi/scikit-allelDevelopment statusPlease note that scikit-allel is no longer under active development
and is now in “maintenance-only” mode. The intended successor
project issgkit(the Statistical
Genetics Toolkit) which should support all the functionality of sckit-allel,
but provide better scaling, performance and maintainability.
Sgkit is not yet at feature parity with scikit-allel and is therefore
not ready for all applications, but we welcomecontributions!
Package: scikit-base
Welcome to skbaseA framework factory for scikit-learn-like and sktime-like parametric objectsskbaseprovides base classes for creating scikit-learn-like parametric objects,
along with tools to make it easier to build your own packages that follow these design patterns.:rocket: Version 0.7.2 is now available. Check out ourrelease notes.OverviewCI/CDCodeDownloadsDocumentation and TutorialsTo learn more about the package check out:ourdocumentationourintroductory tutorial(jupyter notebooks and video presentation):hourglass_flowing_sand: Install skbaseFor trouble shooting or more information, see ourdetailed installation instructions.Operating system: macOS X · Linux · Windows 8.1 or higherPython version: Python 3.8, 3.9, 3.10, 3.11 and 3.12Package managers:pippipskbase releases are available as source packages and binary wheels via PyPI
and can be installed using pip. Checkout the full list of pre-compiledwheels on PyPi.To install the core package use:pipinstallscikit-baseor, if you want to install with the maximum set of dependencies, use:pipinstallscikit-base[all_extras]
Package: scikit-beam
MAILING LISTscikit-beamDocumentationExamplesscikit-beam-examples repositoryPowder calibration (still needs tilt correction)1-time correlationdirJupyter notebookDifferential Phase ContrastdirJupyter notebookFast conversion to reciprocal spaceX-Ray Speckle Visibility SpectroscopyBasic Plotting of X-Ray Fluorescence Elemental LinesQuick startinstall with condaconda install scikit-beam -c nsls2forgeinstall development version with setuptoolsgit clone git@github.com:scikit-beam/scikit-beam.git
cd scikit-beam
python setup.py installset up for developmentgit clone git@github.com:scikit-beam/scikit-beam.git
cd scikit-beam
python setup.py develop
pip install pytest coverage setuptoolsmake sure all the tests pass!python run_tests.pyand you can check the code coverage withcoverage run run_tests.py
coverage report -m
Package: scikit-blocks
# scikit-blocks## project structure` │ ├── notebooks/          <- Jupyter notebooks. Naming convention is a short`-`delimited │                          description, a number (for ordering), and the creator's initials, │                          e.g.`initial-data-exploration-01-hg`.├── tests/              <- Unit tests. ├── skblocks/           <- Python module with source code of this project. ├── Makefile            <- Makefile with commands like `make environment` └── README.md           <- Thetop-levelREADME for developers using this project. `## installationInstallscikit-blocksin the virtual environment via:`bash $ pip install--editable. `
Package: scikit-brain
BrainPyis a lightweight framework based on the latest Just-In-Time (JIT)
compilers (especiallyNumba).
The goal ofBrainPyis to provide a unified simulation and analysis framework
for neuronal dynamics with the feature of high flexibility and efficiency.
BrainPy is flexible because it endows the users with the fully data/logic flow control.
BrainPy is efficient because it supports JIT acceleration on CPUs and GPUs.
Package: scikit-cache
Scikit CachePickle-based caching library. Supports file-system caching only.Installationpip install scikit_cacheOr to develop package you may install dev dependencies:pip install -e ".[dev]" && pip uninstall -y scikit_cacheHow to disable logsOption 1: Disable all logs in cache controllerfrom scikit_cache import CacheController

cache = CacheController(..., logger=None)Option 2: Disable specific logsTo disable specific logs you need to add one of these lines before executing code with cache:import logging

# Disable basic logs like "cache enabled" or "cache disabled"
logging.getLogger('scikit_cache.controller').setLevel(logging.ERROR)

# Disable logs from "@cache.decorator" only
logging.getLogger('scikit_cache.decorator').setLevel(logging.ERROR)

# Disable logs for estimators created by "make_cached_estimator"
logging.getLogger('scikit_cache.estimator').setLevel(logging.ERROR)

# Disable hashing errors
logging.getLogger('scikit_cache.hashing').setLevel(logging.ERROR)