Saravanan666/pypi-org huggingface dataset:
Package: physics-gabri432
physics-gabri432A library containing several physics formulas and constants for making various calculations.Project structureroot.gitignorelicense (MIT)pyproject.tomlREADME.mdsetup.pytests (folder)src/physics (folder)init.pyBody.pyclassical.pyconstants.pymathem.pythermodynamics.pyelectromagnetism.pygravity.pyfluids.pyrelativity.pyDescriptionBody.py, a class to ease the use of some formulas;classical.py, collection of formulas from Cinematics and Dynamics fields;constants.py, collection of constants from all fields;electromagnetism.py, collection of formulas from Electromagnetism field;fluids.py, collection of formulas from Fluids field;gravity.py, collection of formulas from Gravitation field;mathem.py, collection of utility math formulas;relativity.py, collection of formulas from Relativity field;thermodynamics.py, collection of formulas from Thermodynamics field;How to install itpip install physics-gabri432How to use the libraryUse functionsimportphysicsfromphysicsimportclassical#importing the classical moduleprint(classical.force(3,4))#using a function from the classical moduleResult>>> (12, 'N') // Respectevely, the result and the measurement unitOr create a custom object and use its methodsfromphysics.BodyimportBody#importing the Body classearth=Body('Earth',5.97e24,0)#Initializing a variable 'earth' with its name, mass and speed.print(earth)#Showing the string representation of the class.print(earth.grav_field(6.371e6))#using a function from the classical moduleResult>>> Name: Earth         // The string representation of a Body object
    Mass: 5.97e+24 kg
    Speed: 0 m/s.
>>> (9.810360234523877, 'm/s^2') // Respectevely, the result and the measurement unit
Package: physicsml
What is PhysicsML?PhysicsML is a package for all physics based/related models. It provides a standardised interface for handling, building,
and training 3d models!InstallationWith pippipinstallphysicsmlOptional DependenciesSome functionalities inphysicsmlrequire additional dependencies, which you can install as needed. For examplepipinstallphysicsml[rdkit]DocumentationTo learn more about how to usephysicsml, see thedocumentation.Contribution guidelinesAnyone is welcome to submit PRs for new functionality or fixes! Just create a pull request and we will review and get back to
you.LicenseMIT LicenseAcknowledgementsThephysicsmlpackage has been developed by researchers at ExscientiaAdam BaskervilleAayush GuptaWard HaddadinKavindri RanasingheBen Suutari
Package: physicsrl
Physics-rlPhysics-rl allows users to easily train tensorflow agents using reinforcement learning in environments simulated using FENiCSx.This library provides the following functionalities:Tools to handle a mix of distributed computation and single-node computation (Coordinator and Coordinated classes and@paralleldecorator).Ready to use simulation of two physical environments governed by the heat diffusion equation (HeatEnvironment class) and by a monodomain Mitchell-Schaeffer model (MonodomainMitchellSchaeffer class).A class that handles data collection and training using user-provided agents and environments with reasonable opinionated defaults (Dojo class).Coordination systemAny class that needs to run distributed FENiCSx computation should inherit from Coordinated and the methods that need to be run on all processes should be marked with the@paralleldecorator. The result is a class that can be used in the leader process as if it was running in a single process (thus allowing compatibility with tf-agent's ecosystem), while still running on all processes the necessary computations.All processes should have a Coordinator instance; in most cases there should be exactly one per process. All instances inheriting from Coordinated must be registered in the Coordinator, the corresponding instances of the same class in different processes are registered in a common namespace (the namespace can be manually set, but in most cases it is inferred automatically). This allows a function call in an instance in the leader process to be replicated on the correct instances in the follower processes.The typical usage of the library is as followscoordinator=Coordinator()env=System(coordinator)# System inherits from Coordinated and PyEnvironmentwithcoordinator:ifcoordinator.is_leader():env=tf_py_environment.TFPyEnvironment(env)q_net=QNetwork(env.observation_spec(),env.action_spec())dojo=Dojo(q_net,env)dojo.train(100)Check out the fileexample_dojo_fenics.pyfor a complete example of how the library can be used.SetupUninstall MPI if you have already installed it (in some cases it conflicts with conda's MPI installation)Create environmentconda create --name physicsrl python==3.10
conda activate physicsrlinstall dolfinx following theofficial instructionsconda install -c conda-forge fenics-dolfinx mpich pyvistainstall the other dependenciespip install -r requirements.txtCompatibilityPython 3.9 or more recent is required.
Package: physik
This is a Python library that allows you to do the following calculations:
acceleration, force, frequency, velocity, wavelenght and angular_velocity.Remember to use the International System of Units for the variables.
If you have any question, send me a DM on my Instagram: @miguel_barroso6Will be uploaded daily updates that will add other features and new calculus.Created by: Miguel BarrosoChange Log0.0.1 (05/09/2020)First Release
Package: physio
physioSimple python toolbox to analyse physio signals (Respiration, ECG, and RSA)Please jump to documentation nowhttps://physio.readthedocs.ioManuscriptThis work has been published at eNeuro :https://www.eneuro.org/content/10/10/ENEURO.0197-23.2023Featuresrespiration cycle detectionrespiration cycle features (amplitude, duration, volumes ...)simple preprocess on signal : filter using scipy and smoothingecg peak detectionecg/hrv metrics (time domain and freq domain)rsa : new approach to get cycle-per-cycle metricscyclic deformation machinery : a simple strecher of any signal to cycle templatesimple reader of micromed and brainvision using neo"automagic" parameters for differents species## Installation from PyPipipinstallphysio## Installation from sourcegitclonehttps://github.com/samuelgarcia/physio.gitcdphysio
pipinstall-e.Update:cdphysio
gitpulloriginmainAuthorsSamuel Garcia with the help of Valentin Ghibaudo and Jules GrangetThis toolbox is used in the CMO team from the CRNL.
Package: physio-cassette
Physio Cassette: Storage structures for signals, metadata and event annotationsManaging signals in physiology and associated metadata can be a pain.You want abstraction, but not much from any underlying Numpy array holding them.You want annotations in signals, but not obscure proprietary representations.Physio Cassette is just that: Numpy arrays and dictionaries with flair. Physio Cassette provides also automatic caching operations using pickle and matlab storageBasic data structuresSignal: a numpy array with associated sampling frequency, timestamps and minor metadata. Zero-cost abstraction, the data can be accessed directlyEventRecord: a class for time annotated events based ontracesTimeSeries with support for binary, trains, and multilevel eventsSignals can be iterated using EventRecord events as anchor points and viceversa Events can be converted to a sampled SignalContainersDataHolder: your box of cables based on Python dictionary. Parent class of SignalFrame and EventFrame.SignalFrame: A container for Signal data structures.EventFrame: A container for EventRecord structures, with support to merge operations (e.g. events annotated across multiple channels).Supported InputsPhysio-cassette aims to support seamlessly different file and data formats. All parsers can be customized without additional code to support more exotic / less interoperable data representations.XML format is currently based on NSRR interpretation of data annotations.Some functionalities will be added in the future. Other format specific features (e.g. physical/digital ranges in EDF and WFDB) are absent on purpose.StructureNumpy arraysCSV/columnar filesMatlab filesEDF filesPhysionet WFDBXMLSignalYESYES(use SignalFrame)(use SignalFrame)SignalFrameYESYES                (1 file/signal)YESYESEventRecordYESYESYESYESEventFrame(use EventRecords)YESYESYESCachingTo cache an operation simply do:fromphysio_cassetteimportautocachedefyour_function(x:int)->bool:# Some long operation you want to cachereturnTrueresult=autocache(your_function,'~/path_to_cache_folder','desired_cache_file')(1)InstallationTo install PhysioCassette run:$pipinstallphysio_cassetteDependenciesNumpyScipy (Matlab IO)tracesopenpyxl-dictreader(excel files IO)xlrd(old excel format)pyedflibpymatreaderdateutilwfdbxmltodictContributingLooking for people more experienced in writing unit tests and overall beta-testers to help with the reliability of the libraryIf you feel generous and this library helped your project:
Package: physiofit
PhysioFitWhat is PhysioFit?PhysioFit is a scientific tool designed to quantify cell growth parameters and uptake & production fluxesFluxes are estimated using mathematical models by fitting time-course measurements of the concentration of
cells and extracellular substrates and products. PhysioFit is shipped with some common growth models, and
additional tailor-made models can be implemented by users.It is one of the routine tools that we use at theMetaSys teamandMetaToul platformin functional studies of metabolic systems.The code is open-source, and available under a GPLv3 license. Additional information can be found in the followingpublication.Detailed documentation can be found online at Read the Docs
(https://physiofit.readthedocs.io/).Key featurescalculation of growth rate and extracellular (uptake and production) fluxes,a set of steady-state and dynamic modelsare shipped with PhysioFit,tailor-made modelscan be constructed by users,Monte-Carlo sensitivity analysis toestimate the precision on the calculated fluxes,evaluation of the goodness of fit and visual inspection of the fitted curves,shipped as alibrarywith both agraphicaland acommand lineinterface,open-source, free and easy to installeverywhere where Python 3 and pip run,biologist-friendly.Quick-startPhysioFit requires Python 3.9 or higher and run on all platforms.
Please checkthe documentationfor complete
installation and usage instructions.Usepiptoinstall PhysioFit from PyPi:$pipinstallphysiofitThen, start the graphical interface with:$physiofitPhysioFit is also available directly from command-line and as a Python library.Bug and feature requestsIf you have an idea on how we could improve PhysioFit please submit a newissuetoour GitHub issue tracker.Developers guideContributionsContributions are very welcome! :heart:Please work on your own fork,
followPEP8style guide,
and make sure you pass all the tests before a pull request.Local install with pipIn development mode, do apip install -e /path/to/PhysioFitto install
locally the development version.Build the documentation locallyBuild the HTML documentation with:$cddoc
$makehtmlThe PDF documentation can be built locally by replacinghtmlbylatexpdfin the command above. You will need a recent latex installation.How to citePhysioFit: a software to quantify cell growth parameters and extracellular fluxes.
Le Grégam L., Guitton Y., Bellvert F., Jourdan F., Portais J.C., Millard P.
bioRxiv preprint,doi: 10.1101/2023.10.12.561695AuthorsLoïc Le Grégam, Pierre MillardContact:email:legregam@insa-toulouse.fr,millard@insa-toulouse.fr
Package: physiokit
A Python toolkit to process raw ambulatory bio-signals.🚧 PhysioKit is under active developmentDocumentation:https://ambiqai.github.io/physiokitSource Code:https://github.com/AmbiqAI/physiokitKey Features:Handles a variety of physiological signals including ECG, PPG, RSP, and IMU.Geared towards real-time, noisy wearable sensor data.Provide advanced signal processing and feature extraction methods.Create synthetic signals for testing and benchmarking.RequirementsPython 3.11+InstallationInstalling PhysioKit can be done usingPoetryorpip.pip install physiokitpoetry add physiokitExampleIn this example, we will generate a synthetic ECG signal, clean it, and compute heart rate and HRV metrics.importphysiokitaspksample_rate=1000# Hzheart_rate=64# BPMsignal_length=8*sample_rate# 8 seconds# Generate synthetic ECG signalecg,segs,fids=pk.ecg.synthesize(duration=10,sample_rate=sample_rate,heart_rate=heart_rate,leads=1)# Clean ECG signalecg_clean=pk.ecg.clean(ecg,sample_rate=sample_rate)# Compute heart ratehr_bpm,_=pk.ecg.compute_heart_rate(ecg_clean,sample_rate=sample_rate)# Extract R-peaks and RR-intervalspeaks=pk.ecg.find_peaks(ecg_clean,sample_rate=sample_rate)rri=pk.ecg.compute_rr_intervals(peaks)mask=pk.ecg.filter_rr_intervals(rri,sample_rate=sample_rate)# Re-compute heart ratehr_bpm=60/(np.nanmean(rri[mask==0])/sample_rate)# Compute HRV metricshrv_td=pk.hrv.compute_hrv_time(rri[mask==0],sample_rate=sample_rate)bands=[(0.04,0.15),(0.15,0.4),(0.4,0.5)]hrv_fd=pk.hrv.compute_hrv_frequency(peaks[mask==0],rri[mask==0],bands=bands,sample_rate=sample_rate)LicenseThis project is licensed under the terms of BSD 3-Clause.
Package: physion
Vision Physiology SoftwareCode for experimental setups and analysis pipelines to study cellular and network physiology in visual cortical circuitsThe software is organized into several modules to perform the acquisition, the preprocessing, the visualization and the analysis of multimodal recordings (seeDocumentation below).Software screenshotInstallSimply:pip install physionFor an installation on an acquisition setupconda env create -n acquisition -f acquisition_environment.ymlModules and documentationThe different modules of the software are documented in the following links:Visual stimulation-- relying onPsychoPyPerforming multimodal recordingsIntrinsic ImagingElectrophysiologyCalcium imaging-- pipeline based onSuite2PPupil trackingBehavioral monitoringAssembling pipelineHardware controlVisualization-- relying on the excellentPyQtGraphAnalysisTroubleshooting / IssuesUse the dedicatedIssuesinterface of Github.
Package: physiossl
PhysioSSL: A Python Toolbox for Physiological Time-series Representation LearningIntroductionInstallationInstall from PyPi:pipinstallphysiosslor install via the GitHub link:pipinstallgit+https://github.com/larryshaw0079/PhysioSSLGetting StartedImplemented AlgorithmsAlgoTitleYearRefTCL[1]RP[2]TS[2]CPCMocoSimCLRDPCDPCMTripletLossDCCTNCTSTCCCoSleep2021Supported DatasetsSleep Stage ClassificationEmotion RecognitionHuman Activity RecognitionCiting@misc{qfxiao2021physiossl,
  author ={Qinfeng Xiao},
  title ={PhysioSSL: A Python Toolbox for Physiological Time-series Representation Learning},
  howpublished ={\url{https://github.com/larryshaw0079/PhysioSSL}},
  year ={2021}}Reference[1] Hyvarinen, Aapo and Morioka, Hiroshi,. (2016). Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA. Advances in Neural Information Processing Systems.[2] Banville, H., Chehab, O., Hyvarinen, A., Engemann, D., & Gramfort, A. (2020). Uncovering the structure of clinical EEG signals with self-supervised learning. Journal of neural engineering, 10.1088/1741-2552/abca18. Advance online publication. https://doi.org/10.1088/1741-2552/abca18
Package: physoce
physocePython tools for physical oceanography. Includes functions for plotting, time series analysis, statistics and surface wave dynamics.Documentation can be found athttps://physoce.github.io/physoce-pyTo install using pip, run the following the terminal/command prompt:pip install git+https://github.com/physoce/physoce-pyFeedback and ideas for future work are welcome. Please feel free to submit an issue.
Package: physrisk
PhysriskPhysical climate risk calculation engine.About physriskAnOS-Climateproject, physrisk is a library for assessing the physical effects of climate change and thereby the potential benefit of measures to improve resilience.An introduction and methodology is availablehere.Physrisk is primarily designed to run 'bottom-up' calculations that model the impact of climate hazards on large numbers of individual assets (including natural) and operations. These calculations can be used to assess financial risks or socio-economic impacts. To do this physrisk collects:hazard indicators andmodels of vulnerability of assets/operations to hazards.Hazard indicators are on-boarded from public resources or inferred from climate projections, e.g. from CMIP or CORDEX data sets. Indicators are created from code in thehazard repoto make calculations as transparent as possible.Physrisk is also designed to be a hosted, e.g. to provide on-demand calculations.physrisk-apiandphysrisk-uiprovide an example API and user interface. Adevelopment version of the UIis hosted by OS-Climate.Using the libraryThe library can be run locally, although access to the hazard indicator data is needed. The library is installed via:pip install physrisk-libHazard indicator data is freely available. Members of the project are able to access OS-Climate S3 buckets. Credentials are availablehere. Information about the project is available via thecommunity-hub. Non-members are able to download or copy hazard indicator data.Hazard indicator data can be downloaded or copied from the 'os-climate-public-data' bucket. A list of the keys to copy is available fromhttps://os-climate-public-data.s3.amazonaws.com/hazard/keys.txtAn inventory of the hazard data is maintainedhere(this is used by the physrisk library itself). TheUI hazard vieweris a convenient way to browse data sets.Access to hazard event data requires setting of environment variables specifying the S3 Bucket, for example:OSC_S3_BUCKET=physrisk-hazard-indicators
OSC_S3_ACCESS_KEY=**********
OSC_S3_SECRET_KEY=**********For use in a Jupyter environment, it is recommended to put the environment variables in a credentials.env file and do, for example:from dotenv import load_dotenv
load_dotenv(dotenv_path=dotenv_path, override=True)
Package: physrisk-lib
PhysriskPhysical climate risk calculation engine.About physriskAnOS-Climateproject, physrisk is a library for assessing the physical effects of climate change and thereby the potential benefit of measures to improve resilience.An introduction and methodology is availablehere.Physrisk is primarily designed to run 'bottom-up' calculations that model the impact of climate hazards on large numbers of individual assets (including natural) and operations. These calculations can be used to assess financial risks or socio-economic impacts. To do this physrisk collects:hazard indicators andmodels of vulnerability of assets/operations to hazards.Hazard indicators are on-boarded from public resources or inferred from climate projections, e.g. from CMIP or CORDEX data sets. Indicators are created from code in thehazard repoto make calculations as transparent as possible.Physrisk is also designed to be a hosted, e.g. to provide on-demand calculations.physrisk-apiandphysrisk-uiprovide an example API and user interface. Adevelopment version of the UIis hosted by OS-Climate.Using the libraryThe library can be run locally, although access to the hazard indicator data is needed. The library is installed via:pip install physrisk-libHazard indicator data is freely available. Members of the project are able to access OS-Climate S3 buckets. Credentials are availablehere. Information about the project is available via thecommunity-hub. Non-members are able to download or copy hazard indicator data.Hazard indicator data can be downloaded or copied from the 'os-climate-public-data' bucket. A list of the keys to copy is available fromhttps://os-climate-public-data.s3.amazonaws.com/hazard/keys.txtAn inventory of the hazard data is maintainedhere(this is used by the physrisk library itself). TheUI hazard vieweris a convenient way to browse data sets.Access to hazard event data requires setting of environment variables specifying the S3 Bucket, for example:OSC_S3_BUCKET=physrisk-hazard-indicators
OSC_S3_ACCESS_KEY=**********
OSC_S3_SECRET_KEY=**********For use in a Jupyter environment, it is recommended to put the environment variables in a credentials.env file and do, for example:from dotenv import load_dotenv
load_dotenv(dotenv_path=dotenv_path, override=True)
Package: physt
P(i/y)thon h(i/y)stograms. Inspired (and based on) numpy.histogram, but designed for humans(TM) on steroids(TM).The goal is to unify different concepts of histograms as occurring in numpy, pandas, matplotlib, ROOT, etc.
and to create one representation that is easily manipulated with from the data point of view and at the same
time provides nice integration into IPython notebook and various plotting options.In short, whatever you want to do with histograms, physt aims to be on your side.P.S. I am looking for anyone interested in using / developing physt. You can contribute by reporting errors,
implementing missing features and suggest new one.
Package: phytoClassifier
phytoClassifierPython Classifier for plant secondary metabolites. PhytoClassifier is a database-based tool for classifying plant compounds.Installing phytoClassifierpipinstallphytoClassifier==1.0.0# orpy-mpipinstallphytoClassifier==1.0.0Example UsageimportphytoClassifieraspc# get class by SMILEScls=pc.getClassBySmiles("CC(C)CCCC(C)C1CCC2C1(CCCC2=CC=C3CC(CC(C3=C)O)O)C")# get class by PubChem Idcls1=pc.getClassByCid("131")# get class by InChICodecls2=pc.getClassByInchi("InChI=1S/C8H12NO6P/c1-5-8(11)7(3-10)6(2-9-5)4-15-16(12,13)14/h2,10-11H,3-4H2,1H3,(H2,12,13,14)")# get class by InChIKeycls3=pc.getClassByInChiKey("HXVZGASCDAGAPS-UHFFFAOYSA-N")# get class by Name !!Fuzzy search, deprecated! returns multiple entries.cls4=pc.getClassByName("Anthrapurpurin")print(cls,cls1,cls2,cls3,cls4,sep="\n")Python Dependenciessqlite3pkg_resources
Package: phytoolkit
phytoolkitInstallation kit for material simulation tools. Currently, it supports installation of:VASP 5.4.4In progress:SiestaQuantum ESPRESSOHow to use this toolPrerequisitesIt is only tested with Ubuntu 18.04 and 20.04 and Python 3. Rest of the platforms may or may not work.Steps to installpython3-mpipinstallphytoolkitAccessing documentationphytoolkit--helpIf the above command doesn't work, it means that you have installed the Python packages locally. Add
your localbinfolder to path. Usually~/.local/bin. You can do this by adding the following to
the end of your.bashrcfile.exportPATH=$PATH:~/.local/binOnce you have done this, you have to restart the terminal application or run the following command.source~/.bashrcNoticeVASP source is licensed content. It is not shipped with this tool. You need to obtain it from the authorized parties. See:https://www.vasp.at/faqs/
Package: phytorch
φtorch: spark up your physicsutilities for computational physics based on PyTorchphytorch(φtorch) is a package for doing physics usingPyTorchas the backend for computations, which allows
parallelisation on GPUs and automatic differentiation.Check out the docsto see what's on offer!Installationφtorch consists of Python modules and a PyTorch extension written in C++/CUDA.
Therefore, installation has two steps, but should otherwise be fully automatic.First, clone (or download) the source code, thencdinto the root directory
and runpipinstall-e.This will automatically install the few dependencies and the pure-Python
code (in developer mode, which means that it won't be installed in thesite-packagesbut will run out of the current directory). If you are not
interested in special functions and differentiable cosmographic distances,
you’re good to go!NoteIn that case, you can also now install the pure-Python components from
PyPI usingpipinstallphytorchThen, to compile the extensions,cdphytorch-extensions
pythonsetup.pybuild_extWarningBuilding the extensions currently requires you to have a CUDA compiler,
which, I realise, sucks. I’m working on making a cpu-only version possible.Finally, the extensions, which have now been built in a folder likephytorch-extensions/build/lib*, need to be linked tophytorch/extensions:cd../phytorch/extensions
ln-s../../phytorch-extensions/build/lib*/*.This program is free software and comes with no warranty except that which can
be reasonably expected of computer logic. It is, however, licensed under the
GNU General Public Licence v3.0, which disallows closed source re-distribution
and any such foul nonsense.