Saravanan666/pypi-org huggingface dataset:
Package: pyforce-rl
🧐 AboutA simple and modular reinforcement learning library based on PyTorch.🏁 Getting Startedpip install pyforce-rl🎈 Usagefrompyforce.envimportwrap_openai_gymfrompyforce.nnimportdefault_network_componentsfrompyforce.agentsimportPPOAgentimportgymimporttorchdevice="cuda:0"iftorch.cuda.is_available()else"cpu"env=wrap_openai_gym(gym.make("LunarLanderContinuous-v2"))observation_processor,hidden_layers,action_mapper=default_network_components(env)agent=PPOAgent(observation_processor,hidden_layers,action_mapper,save_path="./evals/ppo_example",value_lr=5e-4,policy_lr=5e-4).to(device)agent.train(env,episodes=1000,train_freq=2048,eval_freq=50,render=True,batch_size=128,gamma=.99,tau=.95,clip=.2,n_steps=32,entropy_coef=.01)🚀 Implement custom RL Agentsfrompyforce.agents.baseimportBaseAgentfromtorchimportnnclassMyAgent(BaseAgent):def__init__(self,observationprocessor,hiddenlayers,actionmapper,save_path=None):super().__init__(save_path)self.policy_net=nn.Sequential(observationprocessor,hiddenlayers,actionmapper)self.value_net=...defforward(self,state):returnself.policy_net(state)defget_action(self,state,eval,args):#return action + possible additional information to be stored in the memoryreturnself(state).sample(),{}defafter_step(self,done,eval,args):ifnoteval:ifself.env_steps%args["train_freq"]==0andlen(self.memory)>0:#do trainingifdoneandeval:#do evaluation⛏️ Built UsingPyTorch- ML FrameworkOpenAI Gym- Environment APINumPy- Numerical calculations outside PyTorch
Package: pyforchange
Python For ChangeWelcome to the community!Python For Change is aopen sourceproject.Get started
nowView on
GithubOur softwareCovid PlotPython For Change FormatCellInstallation (last stable version)Install pyforchangepip install pyforchangeImport pyforchange in your python fileimport pyforchangeEnjoy!ContributingLearn more about how to help others contribute, check out theCONTRIBUTING
GUIDELINESfor
repository contributors.CreditsEmmanuel Norambuena-initial work-EanorambuenaCommunity-contributingAndrés Villanueva-this web page is based on the README made
by him-VillanuevandJust the Docs-this site uses their documentation theme for
Jekyll-View in
GithubLicenseThis proyect is under theMIT
License.Made withPython- Programming languageWikiYou can find much more about how to use this project in ourWiki📖Support or ContactHaving trouble with Python For Change Software? Check out ourdocumentation.⌨️ with ❤️ byEanorambuena😊
Package: pyforecast
PyForecastWork In ProgressThis project is currently under development. During sleepless nights, with two screaming kids :)Please be patient. I would be grateful for any feedback & suggestions.IntroductionPython binding forForecast App(Harvest) APIForecast is the fast and simple way to schedule your team across projects.As perForecast FAQthere is not apublic APIfor the app. This project uses the exposed backend API (looks like Forecast guys are OK with that)Free software: Apache Software License 2.0Documentation:https://pyforecast.readthedocs.io. (do not go there yet, it is also WIP. Basic usage is described in this README)FeaturesImplementedClientProjectPersonAssignmentsMilestonesRolesUser ConnectionsPlaceholdersWho am ITO DOAll PUT/POST requests (inserting/modifying data)DocumentationUsageCreate an Authorization token in Forecast App:https://id.getharvest.com/developersCreate an instance of theforecast.Apiusing the Account ID and Authorization token:>>> import forecast
>>> api = forecast.Api(account_id='account_id', authorization_token='authorization_token')Example usage:>>> for project in api.get_projects():
>>>    print(project.name, project.id)
Demo Project 101234
Killer App 106555

>>> person = api.get_person(42)
>>> print(person.first_name, person.last_name, person.email)
Pavel Pribyl pribyl.pavel@gmail.comCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.4 (2018-05-31)Add caching for the remote API requests0.1.3 (2018-05-30)Support for exporting model data to JSON0.1.2 (2018-05-30)Stabilize the release numberingMinor bugfixes, typos0.1.1 (2018-05-29)Complete the API wrappersGive up on Python 2 support … for now?0.1.0 (2018-05-26)First release on PyPI.
Package: pyforecho
Why does this exist?EchoMobileprovide a great communications platform, but not much in terms of programmatic access. There isan APIbut that needs a lot of raw requests to get to working (it's a pain to say the least), so I decided to build this!!Pre-requisitesAnEchoMobileaccount, enterprise or otherwisePython 3.6 or higherSee how to get the user credentialshereInstallationpip install pyforechoUsagefrompyforechoimportEchoMobileif__name__=="__main__":echo=EchoMobile(acc_id=12345,eid=6789,e_passw="pass")echo.test_connection()client=echo.clients.lookup(phone="254718953619")fora_clientinecho.clients.get_all(since=1604188800,group_name="MYGROUP"):print(a_client)The more detailed documentation -https://pyforecho.readthedocs.io/en/latest/TODOsurveys.list_surveys() doesn't filter by project_name or project_id/api/cms/project creates a new project instead of listing existing onesChangelog0.0.6 7th Dec 2020Addedinbox.list_messages()surveys.list_surveys()surveys.get_survey_responses()0.0.5 7th Dec 2020Fixedapi.clients.create() to include the client's name in the payload0.0.4 7th Dec 2020FixedException Handling. Instead of throwing exceptions, bools are returned instead0.0.3 7th Dec 2020AddedSurvey triggeringUtility to check if client is available for surveyUpdating client custom fieldCustom Exceptions0.0.2 7th Dec 2020AddedSending bulk messages0.0.1 23rd Nov 2020ReleasedBare bones version with only the ability to create, lookup and get all contacts
Package: pyforestscan
PyForestScan: Airborne Point Cloud Analysis for Forest StructureThis library is currently experimental and is under heavy development.OverviewPyForestScan is a Python library optimized for the analysis and visualization of forest structure through airborne 3D point cloud data. Engineered to work seamlessly with point clouds derived from drones and other UAV (Unmanned Aerial Vehicle) devices, the library is a go-to solution for researchers, ecologists, and forestry professionals. It specializes in deriving key forest metrics such as Height Above Ground (HAG), Leaf Area Index (LAI), Canopy Cover, and Leaf Area Density (LAD).Core Focus Areas🌳 Forest MetricsGain unprecedented insights into forest structure with metrics like HAG, LAI, Canopy Cover, and LAD, specially tuned for airborne data.🚁 Airborne Data CompatibilityDesigned for compatibility with LiDAR and Structure from Motion (SfM) data acquired through drones and UAVs, ensuring high-resolution and accurate analysis.🌱 Intuitive VisualizationUnlock actionable insights with sophisticated 2D and 3D visualizations that are tailored to represent airborne point cloud data effectively.FeaturesSpecialized Forest Metrics: Calculate and visualize crucial airborne forest metrics such as LAI and LAD.Height Above Ground Filtering: Customize your data filtering based on the height above ground.2D and 3D Visualization: Get interactive and focus on forest-centric metrics in both 2D and 3D visualizations.Extensibility: Adapt PyForestScan to your specific needs with its built-in support for custom filters and visualization techniques.InstallationTo jumpstart your forest analysis with airborne data, run:pipinstallpyforestscanDependenciesNumPyMatplotlibMayaviQuick StartDeriving Forest Metrics from Airborne Datafrompyforestscan.pipelineimportcalculate_lailai=calculate_lai(arrays)2D Visualization of Forest Metricsfrompyforestscan.visualizationimportplot_laiplot_lai(lai,extent=[0,100,0,100])3D Visualization of Airborne Canopy Structurefrompyforestscan.visualizationimportplot_3dplot_3d([array1,array2],z_dim='HeightAboveGround')DocumentationExplore ourcomplete documentationto fully leverage the capabilities of PyForestScan.ContributingContribute to this airborne-focused forest analysis toolkit! Check ourContributing Guidelinesfor more details.LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details.For further information, dive into ourdocumentationor open an issue.
Package: pyforfluids
PyForFluidsPyForFluids (Python-Fortran-Fluids) is a Python package focused in the
calculation of Fluid properties based on Ecuations of State (EoS). It provides
a simple interface to work from Python but also exploits the high performance
Fortran code for the more heavy calculations.It’s designed with modularity in mind, in a way that new thermodyinamic models
are easy to add, they even can be written either in Python or Fortran.Multifluid equations:GERG-2008Paper linkAvailable propertiesReduced Temperature and DensityIdeal Helmholtz Energy (Ao)Residual Helmholtz Energy (Ar)Compresibility Factor (Z)Isochoric Heat (Cv)Isobaric Heat (Cp)Speed of sound (w)Isothermal throttling coefficent (δ)Pressure derivatives:TemperatureDensityVolumePressure (P)Entropy (S)Gibbs Free Energy (G)Enthalpy (H)Joule-Thompson coefficentIsoentropic exponentVirial Terms:BCMotivationWhile nowadays there are a lot of tools for calculation of thermodynamic
properties of fluids, most of them either are hard to mantain and don't have an
integrated testing system or are embeded to other softwares (as spredsheat
software) limiting the things that can be done to that enviroment.PyForFluids aims to be a tool:With high performance, since most of it's calculations are done in FortranEasy to scale due to it's modular design using the power of Python objects.Continuosly tested (at everypush)to spot any problems as soon as possible.InstalationFor installingPyForFluidsyou just need to:pipinstallpyforfluidsMake sure to check the requirements first!RequirementsBe sure to installnumpyand a fortran compiler previously, since both are
needed for the compilation ofFortrancode.NumPypipinstallnumpyFortran CompilerLinuxDebian-based(Debian, Ubuntu, Mint,...)sudoaptinstallgfortranArch-based(Arch, Manjaro, Garuda, ...)sudopacman-SgfortranWindowsWe recommended using the Windows Subsystem for LinuxWSLIf WSL ain't being used, the native Windows wheels will be download instead,
so no need to worry!MacOSbrewinstallgfortranAuthorsFederico E. Benelli (federico.benelli@mi.unc.edu.ar); M. Candelaria
Arpajou
Package: pyforked-daapd
pyforked-daapd Python Libraryversion number: 0.1.14
author: Justin WongOverviewA simple library to interface with an Owntone server.Installation / UsageTo install use pip:$ pip install pyforked-daapdOr clone the repo:$ git clone https://github.com/uvjustin/pyforked-daapd.git
$ python setup.py installContributingTBDExampleTBD
Package: pyforkurento
A python client for Kurento Media StreamerCONTENTSSetupSample UsageChangelogSETUPInstall Kurentosudodockerrun--namekms-d-p8888:8888kurento/kurento-media-serverInstall pyforkurentopipinstallpyforkurentopyforkurento runs as an application server. You'll need to install relevant packages for the web or mobile clientFor Node, Angular etc:npminstallkurento-utilsTODO: For vanilla Javascript:[Optional] To use GStreamer filters:sudoapt-getinstalllibgstreamer1.0-0gstreamer1.0-plugins-basegstreamer1.0-plugins-goodgstreamer1.0-plugins-badgstreamer1.0-plugins-uglygstreamer1.0-libavgstreamer1.0-docgstreamer1.0-toolsgstreamer1.0-xgstreamer1.0-alsagstreamer1.0-glgstreamer1.0-gtk3gstreamer1.0-qt5gstreamer1.0-pulseaudio[Optional] To develop custom filters:sudoapt-getupdate&&sudoapt-getinstall--yeskurento-media-server-devIf you get the error:Unable to locate package kurento-media-server-devtry:Paste this link (https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Kurento/kurento-media-server/tree/master/scaffold) to your browserExtract the downloaded folder to a location of choicecdinto that folder then test using:shkurento-module-scaffold.shTestKMSFilter../custom_kurento_moduleopencv_filterThis SDK is not yet 'prod-ready'and was inspired bythis abandoned projectSAMPLE USAGEBeforeyou start usingpyforkurentoand have a pretty good understanding of KMS, go throughthis refresherfirstRead the complete documentation here:https://pyforkurento.readthedocs.io/en/latest/CHANGELOGThis changelog loosely follows semantic versioning0.1.7 5th Mar 2020ImprovedStreaming speeds by reducing player endpoint latency0.1.6 18th Feb 2020Changedcreate_endpoint()toadd_endpoint()0.1.5 14th Feb 2020ImprovedDocumentation significantly, and hosted on readthedocs0.1.4 13th Feb 2020ImprovedAddition of event listeners to media elements0.1.3 12th Feb 2020ImprovedCreation of media elements in a media pipelineFixedClass inheritance structureAddedImageOverlayFilter0.0.2 5th Feb 2020FixedClass method parameters error0.0.2 5th Feb 2020FixedImport error0.0.1 5th Feb 2020Released a very basic version of the project under the name 'pyforkurento' with only the capability to:Create:Media PipelinesPlayer EndpointsWebRTC EndpointsSubscribe to ICE candidates events on a WebRTC Endpoint
Package: pyFormal
pyFormalpyFormalis a tool for creating formal figures of grids of images in Python. This tool allows users to take a folder of images and transform them into a neatly formatted grid with desired parameters such as label font size, border width, blur intensity, and more.PrerequisitesBefore you can usepyFormal, you need to install a LaTeX distribution:For macOS users: InstallMacTeX.brewinstall--caskmactex-no-guiFor Windows users: InstallMiKTeX.For Linux users: Most distributions have TeX Live in their repositories. For example, on Debian/Ubuntu:sudoapt-getinstalltexlive-fullInstallationOnce the prerequisites are installed, you can installpyFormalvia pip:pipinstallpyFormalUsageTo usepyFormal, navigate to the directory containing your images and run the following command:pyFormal--label_font_size<FONT_SIZE>--use_clipboard_for_folder_path<TRUE/FALSE>--save_folder_path<FOLDER_PATH>--border_width_ratio<BORDER_RATIO>--blur_intensity<BLUR_INTENSITY>--manual_image_folder_path<MANUAL_PATH>--border-color<BORDER_COLOR>--resize-mode<RESIZE_MODE>Options:--label_font_size, -l: Font size for the image labels (default: 18).--use_clipboard_for_folder_path, -c: Use the clipboard for the folder path (default: True).--save_folder_path, -f: Folder path to save the images (default: './pyFormal-figures').--border_width_ratio, -b: How much to make the border width of the output image (default: 0.01).--blur_intensity, -i: How intense to make the border width blur (default: 10).--manual_image_folder_path, -m: Manually enter the image folder path. To use this, use_clipboard_for_folder_path must be set to False (default: None).--border-color, -bc: Color of the border (default: 'white').--resize-mode, -r: Mode for resizing the images. Options are "crop" and "pad" (default: 'crop').Example 1: Using Clipboard PathIf you've copied the path to your folder (e.g.,C:\Users\Username\Documents\my_images) to your clipboard, you can utilize the clipboard to fetch the path directly and run the command simply with all of the default settings like so:pyFormalBy default,pyFormalwill try to use the folder path from the clipboard if the--use_clipboard_for_folder_pathoption is not set toFalse. In this example, the application will fetch the path from your clipboard, create a grid of images, and save the output in thepyFormal-figuresfolder using the default settings. Furthermore, once you close the figure preview window, it will copy the image data to your clipboard so it can be pasted easily into whatever document you are working on.Example 2: Specifying the Image Folder in the Command LineAssuming you have a folder namedmy_imagesand you want to save the grid to a folder namedoutput, you would run:pyFormal-cFalse-m./my_imagesThis will create a grid of images from themy_imagesfolder and save the output in thepyFormal-figuresfolder using the default settings.ContributingIf you find any issues or have suggestions, please open an issue!
Package: pyformanceanalytics
pyformanceanalyticsA python wrapper around the EconometricsPerformanceAnalytics R package.Dependencies :globe_with_meridians:Python 3.11.6:rpy2 3.5.15pandas 2.1.4Pillow 10.2.0numpy 1.26.3R 4.3.2:PerformanceAnalytics 2.0.4ggplot2 3.4.4gridExtra 2.3quantreg 5.97RobStatTM 1.0.8Raison D'être :thought_balloon:PerformanceAnalytics is an outstanding Econometrics library written in R that has been battle tested by many quantitative finance firms.
We have a need to use this alongside some machine learning we are doing in python, so to bridge the gap we created this wrapper library.
The library very closely follows the conventions laid out in PerformanceAnalytics, for example to determine information ratio you can simply use the following code:frompyformanceanalyticsimportInformationRatioinf_rat_flt=InformationRatio(df)The functions and modules are designed to be as close to the PerformanceAnalytics package as possible for easy porting.Installation :inbox_tray:This is a python package hosted on pypi, so to install simply run the following command:pip install pyformanceanalyticsNote that upon running this package for the first time, you may notice a slight delay as it downloads the relevant R packages.Usage example :eyes:To get familiar with the individual functions and charts check out the documents in thepyformanceanalytics README. This library ports over 100 functions, 20 charts and 20 tables.This supports both tables, functions and charts. An example of generating a chart:importpandasaspdfrompyformanceanalytics.chartsimportPerformanceSummarydf=pd.read_csv("pyformanceanalytics/managers.csv",index_col=0)df.index=pd.to_datetime(df.index)PerformanceSummary(df).show()This outputs aPILimage, which automatically shows on colab instances.You can feed in multiple portfolios to get your results in aDataFrame, else they may be reported as single floats.License :memo:The project is available under theGPL2 License.
Package: pyformat
pyformatformats Python code to follow a consistent style.FeaturesFormats code to follow the PEP 8 style guide (usingautopep8).Removes unused imports (usingautoflake).Formats docstrings to follow PEP 257 (usingdocformatter).Makes strings all use the same type of quote where possible (usingunify).InstallationFrom pip:$ pip install --upgrade pyformatExampleAfter running:$ pyformat --in-place example.pyThis code:deflaunch_rocket():"""Launch
the
rocket. Go colonize space."""deffactorial(x):'''

    Return x factorial.

    This uses math.factorial.

    '''importmathimportreimportosreturnmath.factorial(x);defprint_factorial(x):"""Print x factorial"""print(factorial(x))defmain():"""Main
    function"""print_factorial(5)iffactorial(10):launch_rocket()Gets formatted into this:deflaunch_rocket():"""Launch the rocket.

    Go colonize space.

    """deffactorial(x):"""Return x factorial.

    This uses math.factorial.

    """importmathreturnmath.factorial(x)defprint_factorial(x):"""Print x factorial."""print(factorial(x))defmain():"""Main function."""print_factorial(5)iffactorial(10):launch_rocket()
Package: pyformatters-afp_quality
RequirementsPython 3.8+Flit to put Python packages and modules on PyPIPydantic for the data parts.Installationpip install flit
pip install pymultirole-pluginsPublish the Python Package to PyPIIncrement the version of your package in the__init__.pyfile:"""An amazing package!"""

__version__ = 'x.y.z'Publishflit publish
Package: pyformatters-bel_table
RequirementsPython 3.8+Flit to put Python packages and modules on PyPIPydantic for the data parts.Installationpip install flit
pip install pyconverters-pluginsPublish the Python Package to PyPIIncrement the version of your package in the__init__.pyfile:"""An amazing package!"""

__version__ = 'x.y.z'Publishflit publish
Package: pyformatters-consolidate
pyformatters_consolidateConsolidate annotations coming from different annotatorsInstallationYou can simplypip install pyformatters_consolidate.DevelopingPre-requesitesYou will need to installflit(for building the package) andtox(for orchestrating testing and documentation building):python3 -m pip install flit toxClone the repository:git clone https://github.com/oterrier/pyformatters_consolidateRunning the test suiteYou can run the full test suite against all supported versions of Python (3.8) with:toxBuilding the documentationYou can build the HTML documentation with:tox -e docsThe built documentation is available at `docs/_build/index.html.
Package: pyformatters-summarizer
pyformatters_summarizerFormatter based on Facebook's SummarizerInstallationYou can simplypip install pyformatters_summarizer.DevelopingPre-requesitesYou will need to installflit(for building the package) andtox(for orchestrating testing and documentation building):python3 -m pip install flit toxClone the repository:git clone https://github.com/oterrier/pyformatters_summarizerRunning the test suiteYou can run the full test suite against all supported versions of Python (3.8) with:toxBuilding the documentationYou can build the HTML documentation with:tox -e docsThe built documentation is available at `docs/_build/index.html.
Package: pyformatters-tabular
RequirementsPython 3.8+Flit to put Python packages and modules on PyPIPydantic for the data parts.Installationpip install flit
pip install pyconverters-pluginsPublish the Python Package to PyPIIncrement the version of your package in the__init__.pyfile:"""An amazing package!"""

__version__ = 'x.y.z'Publishflit publish
Package: pyformatters-textranksummarizer
pyformatters_textranksummarizerFormatter/processor based on TextRankInstallationYou can simplypip install pyformatters_textranksummarizer.DevelopingPre-requesitesYou will need to installflit(for building the package) andtox(for orchestrating testing and documentation building):python3 -m pip install flit toxClone the repository:git clone https://github.com/oterrier/pyformatters_textranksummarizerRunning the test suiteYou can run the full test suite against all supported versions of Python (3.8) with:toxBuilding the documentationYou can build the HTML documentation with:tox -e docsThe built documentation is available at `docs/_build/index.html.
Package: pyformatters-xml-rf
RequirementsPython 3.8+Flit to put Python packages and modules on PyPIPydantic for the data parts.Installationpip install flit
pip install pyconverters-pluginsPublish the Python Package to PyPIIncrement the version of your package in the__init__.pyfile:"""An amazing package!"""

__version__ = 'x.y.z'Publishflit publish
Package: pyformatting
pyformattingPyformattingis a collection of useful formatting features.>>>frompyformattingimportoptional_format,defaultformatter>>>optional_format('{:.3f}{other:.5f}{}',.12345)'0.123{other:.5f}{}'>>>optional_format('{0[0]}{1!a}{2}{0[1]!r}','cool')"c{1!a}{2}'o'">>>default_format=defaultformatter(str)>>>default_format('{nothing}{data}{quotes!r}',data={1:2})"{1: 2}''"Installing PyformattingPyformatting is available on PyPI:python -m pip install -U pyformattingSupported VersionsPyformatting supports Python 3.1+.Development StatusAlpha