Saravanan666/pypi-org huggingface dataset:
Package: mesconduit
42Q- MES - Conduit Integration Setup!Author  - Melvin Paul Miki
Version - 1.1.2#Importing the Module:> import mesconduit#Creating a session:> session = mesconduit.conduit(url,username,password,device_id,clinet_id)

    Attributes Descriptions

            #  url        => Enter the url to do the conduit
            #  Username   => Enter the username of the user
            #  password   => Enter the password of the user
            #  station_id => Enter the device id to be processed
            #  client_id  => Enter the device id to be processedLogin Valdation:
> session.mes_login()==> True  - Authorised User
    ==> False - Unathorised UserStage Pasing the unit> session.mes_PassUnit("123456")

    ==> STATUS :: Respective Message from the MES

    Attributes Descriptions

            #  Unit Serial Number   => "123456"Mutli Units Pasing> session.mes_MultiPass(["1234","5678",.,.,.,.,n])

    ==> [STATUS :: Respective Message from the MES,STATUS :: Respective Message from the MES,.,.,.,.,.,n]

    Attributes Descriptions

            #  Unit Serial Numbers   => ["123456","5678"]Applying MEO the Serial Number> session.mes_ApplyMeo("123456","Screw 1 - Passed")

    ==> STATUS :: Respective Message from the MES

    Attributes Descriptions

            #  Unit Serial Number   => "123456"
            #  Added MEO Message    => "Screw = 1"Adding non tracked component> session.mes_AddNonTrackedComponent("123456","Strip","345363"):

    ==> STATUS :: Respective Message from the MES

    Attributes Descriptions

            #  Unit Serial Number   => "123456"
            #  Component Key        => "Strip" 
            #  Component Value      => "345363"Data Collector
Execute in Command Prompt
>python -c "import  mesconduit; mesconduit.start_app()"Ex:
    $Enter the conduit URL
        >www.conduit.com
    $Enter the operator Username
        >1234567
    $Enter the operator Password
        >1234567
    $Enter the Station Id
        >123
    $Enter the Client Id
        >abcb123
    $Serial Number?
        >12334BC123
    $STATUS :: Respective Message from the MES
Package: mescrappy
mescrappy - Python + SeleniumYoutubescrapperYoutube Srapping With Python (Selenium)Table of ContentsAbout The ProjectBuilt WithGetting StartedInstallationUsageRoadmapContributingLicenseContactAcknowledgementsAbout The ProjectBuilt Withpython3seleniumGetting StartedInstallationTo use this package first install it:packagepip install mescrappyimport srapper to your projectfrom mescrappy.srappy import scrapperscrapper.youtube_video_data_scrapper(url='', driver='')UsageYou can use this package to scrap youtube video's:titleview countdescriptionlikedislikepost dateownerRoadmapSee theopen issuesfor a list of proposed features (and known issues).LicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactMerdan Chariyarov -@merlinemris96-merdanchariyarov@gmail.comProject Link:https://github.com/MerlinEmris/youtube_srapping_with_pythonAcknowledgementshttps://towardsdatascience.com/how-to-package-your-python-code-df5a7739ab2e
Package: mesh2scattering
Mesh2scattering is based onMesh2HRTFand is an open-source project aiming an easy-to-use software package for the numerical calculation of scattering pattern and scattering and diffusion coefficients of any surface. In a nutshell, Mesh2scattering consists of three parts:input: prepares geometrical data and acoustic parameters for the simulation,numcalc: based on the input frominput, it calculates the corresponding sound fieldoutput: processes the output from NumCalc to scattering pattern.process: processes the output to scattering and/or diffusion coefficients.utils: helping functions.Please notice that this project does not support HRTF post processing, useMesh2HRTFinstead.Getting StartedCheck out the examples folder for a tour of the most important mesh2scattering
functionality andread the docsfor the complete documentation.InstallationUse pip to install mesh2scattering$pipinstallmesh2scattering$pipinstallgit+https://github.com/pyfar/imkar.git@scattering-freefield(Requires Python 3.8 or higher)Note that NumCalc need to be build on Linux and MacOS. For Windows it can be downloaded.for Linux:Install the C++ build essentials by running$sudoapt-getinstallbuild-essentialGo into the NumCalc directory by running$cdpath/to/your/Mesh2scattering/mesh2scattering/numcalc/srcCompile NumCalc by running make. It is now located in the foldermesh2scattering/numcalc/bin$makeCopy NumCalc to a folder in your program path: in the same directory run$sudocpNumCalc/usr/local/bin/Now NumCalc can be used by running NumCalc (don’t do this yet).for MacOS:Install the C++ build essentials by installingxcodeGo into thenumcalc/srcdirectory by running$cdpath/to/your/Mesh2scattering/mesh2scattering/numcalc/srcCompile NumCalc by runningmake. It is now located in the foldermesh2scattering/numcalc/bin$makeNow NumCalc can be used by runningpath/to/mesh2scattering/numcalc/bin/NumCalc(don’t do this yet)$path/to/mesh2scattering/numcalc/bin/NumCalcfor Windows:download the executable from the release.ContributingRefer to thecontribution guidelinesfor more information.
Package: mesh2sdf
Mesh2SDFConverts an input mesh to a signed distance field. It can work with arbitrary
meshes, evennon-watertightmeshes from ShapeNet.Mesh2SDF is used in our paperDual Octree Graph Networks (SIGGRAPH 2022)to generate the training data.InstallationInstall via the following command:pipinstallmesh2sdfAlternatively, install from source via the following commands.gitclonehttps://github.com/wang-ps/mesh2sdf.git
pipinstall./mesh2sdfExampleAfter installingmesh2sdf, run the following command to process an input mesh
from ShapeNet:pythonexample/test.pyHow does it work?Given an input mesh, we first compute theunsigneddistance field with the
fast sweeping algorithm implemented byChristopher Batty (SDFGen).
Note that the unsigned distance field can always be reliably and accurately
computed even though the input mesh is non-watertight.Then we extract the level sets with a small valuedwith the marching cube
algorithm. The extracted level sets are represented with triangle meshes and
are guaranteed to be manifold.There exist multiple connected components in the extracted meshes, and we only
keep the mesh with the largest bounding box.Compute the signed distance field again with the kept triangle mesh as the
final output. In this way, the signed distance field (SDF) is computed for a
non-watertight input mesh.
Package: mesh2vec
Mesh2VecQuickstartmesh2vecTurn CAE mesh data into aggregated element feature vectors for MLLatest Documentation🚀 IntroductionMesh2vec is a tool that facilitates the import of Computer-Aided Engineering (CAE) mesh data fromLS-DYNA.
It utilizes various quality metrics of elements and their surrounding neighborhood to aggregate feature vectors for each element. These feature vectors are of equal length and can be effectively utilized as inputs for machine learning methods. This represents a simpler and more efficient alternative to traditional mesh and graph-based approaches for automatic mesh quality analysis.⏱️ QuickstartInstallationCreate and activate avirtual environment.Use the following command to install mesh2vec into your environment:pipinstallmesh2vecPlease make sure you have an environment variable ANSA_EXECUTABLE set pointing to your ANSA executable to use ANSA depended features like shell and feature import.You may temporarily need to set an environment variable SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True.Load MeshfrompathlibimportPathfrommesh2vec.mesh2vec_caeimportMesh2VecCaem2v=Mesh2VecCae.from_ansa_shell(4,Path("data/hat/Hatprofile.k"),json_mesh_file=Path("data/hat/cached_hat_key.json"))Add element featuresm2v.add_features_from_ansa(["aspect","warpage"],Path("data/hat/Hatprofile.k"),json_mesh_file=Path("data/hat/cached_hat_key.json"))Aggregateimportnumpyasnpm2v.aggregate("aspect",[0,2,3],np.nanmean)Extract Feature Vectorsm2v.to_dataframe()Optional: Visualize a single aggregated feature on meshm2v.get_visualization_plotly("aspect-nanmean-2")
Package: meshcat-shapes
meshcat-shapesUseful shapes to decorate MeshCat scenes.Installationpip install meshcat-shapesShapesmeshcat_shapes.framemeshcat_shapes.pointmeshcat_shapes.textareaPRs are welcome: open one if you have implemented a shape that can be useful to others.Usageimportmeshcatimportmeshcat_shapesvis=meshcat.Visualizer().open()meshcat_shapes.frame(vis["my_frame"],axis_length=0.2,axis_thickness=0.01,opacity=0.8,origin_radius=0.02,)meshcat_shapes.point(vis["red_point"],opacity=0.3,radius=0.05,color=0xFF0000,)meshcat_shapes.textarea(vis["text"],"super easy")
Package: meshcut
A python library to compute the planar cross-section of a 3D mesh.Given a 3D mesh and a cut plane, this computes a (set of) polyline(s) that
results from cutting the mesh with the plane.Requires python and numpy. Mayavi is used for visualisation in the examples.UsageRun the examples withpython3 examples/0_cross_section.pyGivenIn [17]: verts
Out[17]:
array([[ 1.6315   , -0.1505   ,  0.       ],
       [ 1.6376   , -0.1516   ,  0.       ],
       [ 1.6493   , -0.1518   ,  0.       ],
       ...,
       [ 1.62088  , -0.0263264, -0.0716966],
       [ 1.63381  , -0.0500889,  0.0726852],
       [ 1.63381  , -0.0500889, -0.0726852]])

In [20]: faces
Out[20]:
array([[1530, 1337, 1338],
       [4549, 4725, 4548],
       [2940, 2950, 3787],
       ...,
       [3107, 3438, 3439],
       [2263, 2281, 2262],
       [3116, 3119, 4171]])

In [21]: meshcut.cross_section(verts, faces, plane_orig=(1.2, -0.125, 0),
   ....:                       plane_normal=(1, 0, 0))
Out[21]:
[array([[  1.20000000e+00,  -1.28957692e-01,  -1.02180769e-01],
        [  1.20000000e+00,  -1.37182869e-01,  -8.06541833e-02],
        [  1.20000000e+00,  -1.36863816e-01,  -6.95125000e-02],ThanksThanks to @superzanti for the STL use case and the sphere model.AuthorsJulien Rebetez
Package: meshemy
Meshemy: Python toolbelt for manipulating meshConsolidation package for manipulating mesh. Comes with cookbook models from each packageInstallationpipinstallmeshemy[full]Use it in your poetry packagepoetryaddmeshemy-EfullUsageYou need to pick at least one extra for this package to be useful. Install all modules by installingfull.CookbookCurrently, Blender, Open3D, and Trimesh is supported.BlenderTheblenderextra must be installed.frommeshemy.cookbook.blenderimportBlenderCookbookblender_cook=BlenderCookbook.from_file("path_to_mesh.<any_format>")blender_cook.planar_decimate(degree_tol=5.0)You can convert to any other cookbook.to_o3d(), for instance.Open3DTheopen3dextra must be installed.frommeshemy.cookbook.open3dimportOpen3dCookbooko3d_cook=Open3dCookbook.from_file("path_to_mesh.<any_format>")o3d_cook.smoothen(5)o3d_cook.repair()Open3DThetrimeshextra must be installed.frommeshemy.cookbook.trimeshimportTrimeshCookbooktrimesh_cook=TrimeshCookbook.from_file("path_to_mesh.<any_format>")trimesh_cook.contains(vertices)MoreFor more information, look at the source code, it is relatively easy to read. Start in thecookbooksubmodule.
Package: mesher
Mesher is a novel multi-objective unstructured mesh generation software that allows mesh generation to be generated from an arbitrary number of hydrologically important features while maintaining a variable spatial resolution.Triangle quality is guaranteed as well as a smooth graduation from small to large triangles. Including these additional features resulted in a better representation of spatial heterogeneity versus classic topography-only mesh generation.
The paper describing mesher can befound hereKey pointsA novel multi-objective unstructured mesh generation software, MesherHeterogeneity in topography is resolved as well as hydrologically important surface and sub-surface attributesSpatial heterogeneity is better preserved compared to existing mesh generators
Package: mesh-generator
This is a collection of Python subroutines and examples that illustrate how to build a
1D Mesh with custom mesh spacing. The meshes created in the package can be used for variety of applications, including
physical simulation.Installationpip install mesh-generatorDependenciespython >= 3.5.0 
numpy >= 1.15.2 
matplotlib >= 3.0.0 
scipy >= 1.1.0
h5py >= 2.8.0 
tk >= 8.6.0 
pyhdf >= 0.9.10UsageThis an example for creating a 1D theta mesh. Read the comments inside the python scripts for more details.Step 1: Input mesh requirements. Make sure to specify:Mesh:Setlower_bndandupper_bndlimits of mesh.Setperiodic.SetDEFAULT_BG_REGION_RATIO- Ratio in areas without ds constraint. (Optional)SetDEFAULT_FG_REGION_RATIO- Ratio in areas with ds constraint. (Optional)Mesh segment:Sets0ands1for segment domain limits.Setdsto the resolution you want.Setvar_ds_ratioas segment maximum cell to cell mesh expansion ratio. (Optional)frommesh_generatorimportMeshfrommesh_generatorimportMeshSegmentfrommesh_generator.bin.call_psi_mesh_toolimportcreate_psi_meshimportnumpy# ratio in regions you do not care about. (Default is 1.06)MeshSegment.DEFAULT_BG_REGION_RATIO=1.06# ratio in regions you do care about. (Default is 1.03)MeshSegment.DEFAULT_FG_REGION_RATIO=1.03# mesh boundaries and if periodic.mesh=Mesh(lower_bnd=0.00,upper_bnd=numpy.pi,periodic=False)# Mesh segment requirements:# s0 - segment begin, s1- segment end, ds- mesh spacing# (Optional) var_ds_ratio- the maximum ratio between each point in the mesh segment.mesh.insert_mesh_segment(MeshSegment(s0=1.10,s1=1.40,ds=0.01,var_ds_ratio=1.05))mesh.insert_mesh_segment(MeshSegment(s0=1.30,s1=1.90,ds=0.02))mesh.insert_mesh_segment(MeshSegment(s0=0.40,s1=2.80,ds=0.04,var_ds_ratio=1.02))Step 2: Get final mesh and write results.input_mesh=mesh.json_dict()adjusted_mesh=mesh.resolve_mesh_segments().json_dict()legacy_mesh=mesh.build_legacy_mesh().json_dict()create_psi_mesh(adjusted_mesh,legacy_mesh,mesh_type="t",dir_name=os.getcwd(),output_file_name="tmp_mesh_t.dat",mesh_res_file_name="mesh_res_t.dat",save_plot=True,show_plot=True,save_plot_path=os.getcwd(),plot_file_name="t_mesh_spacing.png",input_mesh=input_mesh)Mesh Generator User Interfacefrommesh_generatorimportMeshGeneratorUIMeshGeneratorUI()LicenseApacheAuthorsPredictive Science Inc.Opal Issan (oissan@predsci.com)Cooper Downs (cdowns@predsci.com)
Package: meshgraph
IntroThis project is born to easily conduct static analysis on 3D mesh geometries. Meshgraph allows you to convert 3D objects
into graphs and thus perform algorithms on them.How to use MeshgraphInstallInstall themeshgraphpackage using:python3  -m pip install meshgraphConvert .obj to GraphLoad a .obj file and convert it to a graph using:frommeshgraphimportObjLoadergraph=ObjLoader.load_graph_from_file(path_to_obj_file)or use a local string:graph=ObjLoader.load_graph_from_string(obj_string)NodesNodes can be accessed using:frommeshgraph.NodeimportNode#...graph_nodes=graph.nodes#Accessing by index:node0=graph.nodes[0]#Or using the accessor function:node1=graph.get_node(1)Access a node's position using:print(node0.pos)Get a distance between two nodesnode0andnode1using:distance=node0.distance(node1)Access the neighbors of a node using:node0.neighborsCopyrightCopyright(c) 2022 Sebastiano Campisi -ianovir.com.
Read the LICENSE file for more details.
Package: meshify
# Meshify Python APIA python library for interacting with the Meshify API## Requirements-   [python3](https://www.python.org/downloads/)## Installation1.  Install the python package```Shellpip3 install meshify```## AuthenticationIn order to retrieve data from Meshify, you must have a username and password to access the Meshify API. The username and password should be stored in environment variables MESHIFY_USERNAME nad MESHIFY_PASSWORD. If not stored, you will be prompted to enter username and password each time the script is run. You must also store or set MESHIFY_BASE_URL.## Usage### HelpDescribes the usage of the function.```Shellmeshify --helpUsage: meshify [OPTIONS] COMMAND [ARGS]...Command Line Interface.Options:--help  Show this message and exit.Commands:create_modbusmap       Create modbusMap.p from channel csv file.get_channel_csv        Query the meshify API and create a CSV of the...post_channel_csv       Post values from a CSV to Meshify Channel...print_channel_options  Print channel options for use with the csv...```### Getting Channel CSV fileGenerates a CSV file of the existing channels for a devicetype. This function will check to see if there is a modbusMap.p file in order to generate the full configuration of the channels (for M1 Data Collection).```Shellmeshify get_channel_csv --helpUsage: meshify get_channel_csv [OPTIONS] DEVICE_TYPE_NAMEQuery the meshify API and create a CSV of the current channels.Options:-o, --output-file TEXT     Where to put the CSV of channels.-m, --modbusmap-file TEXT  The location of the modbusMap.p file--help                     Show this message and exit.```### Post Channel CSVThis function will examine a CSV file and POST all the channels to Meshify.```Shellmeshify post_channel_csv --helpUsage: meshify post_channel_csv [OPTIONS] DEVICE_TYPE_NAME CSV_FILEPost values from a CSV to Meshify Channel API.Options:--help  Show this message and exit.```### Print Channel OptionsHelper function to print all posible options to enumerations within the CSV file.```Shellmeshify print_channel_options --helpUsage: meshify print_channel_options [OPTIONS]Print channel options for use with the csv files.Options:--help  Show this message and exit.```## Contributors-   [Patrick McDonagh](https://github.com/patrickjmcd) - Owner
Package: meshiphi
Meshiφ (MeshiPhi)Introducing Meshiφ, a versatile software package designed for comprehensive earth modeling and navigation planning. Meshiφ works by discretizing the Earth's surface into a non-uniform grid, allocating higher resolution in regions of geographic diversity, and conserving lower resolution in more uniform regions. The software also incorporates data-driven vehicle models, with the ability to calculate speed limits and fuel needs for specific vessels within each grid cell. These mesh objects can be output in standard formats, such as GeoJSON and GeoTIFF, enabling data-visualisation via GIS software such as ArcGIS.InstallationMeshiφ can be installed via pip or by cloning the repository from GitHub.Pip:pip install meshiphiGithub: (for local development)git clone https://github.com/Antarctica/MeshiPhi
cd MeshiPhi
pip install -e .The Meshiφ package has an optional dependency on GDAL, which is required to produce outputs in GeoJSON or GeoTIFF formats. More information on setting up GDAL can be found in the manual pages linked above. Once these requirements are met then the software can be installed by:NOTE: The installation process may vary slightly dependent on OS. Please consult the documentation for further installation guidance.DocumentationSphinx is used to generate documentation for this project. The dependencies can be installed through pip:pip install sphinx sphinx_markdown_builder sphinx_rtd_theme rinohtypeWhen updating the docs, run the following command within the MeshiPhi directory to recompile.sphinx-build -b html ./docs/source ./docs/htmlSometimes the cache needs to be cleared for internal links to update. If facing this problem, run this from the MeshiPhi directory.rm -r docs/build/.doctrees/Required Data sourcesMeshiφ has been built to work with a variety of open-source atmospheric and oceanographic data sources.
A list of supported data sources and their associated data-loaders is given in the
'Data Loaders' section of the manualDevelopersSamuel Hall, Harrison Abbot, Ayat Fekry, George Coombs, Jonathan Smith, Maria Fox, and James ByrneCollaborationWe are currently assessing the best practice for collaboration on the codebase, until then please contactpolarroute@bas.ac.ukfor further info.LicenseThis software is licensed under a MIT license, but request users cite our publication.Jonathan D. Smith, Samuel Hall, George Coombs, James Byrne, Michael A. S. Thorne,  J. Alexander Brearley, Derek Long, Michael Meredith, Maria Fox,  (2022), Autonomous Passage Planning for a Polar Vessel, arXiv,https://arxiv.org/abs/2209.02389For more information please see the attachedLICENSEfile.
Package: meshkernel
MeshKernelPyMeshKernelPyis a library for creating and editing meshes.
It supports 1D and 2D unstructured meshes.
The underlying C++ libraryMeshKernelcan be foundhere.InstallationThe library can be installed fromPyPIby executingpipinstallmeshkernelUnder Windows, If you encounter any issues importing the pip wheels, you may need to install theVisual C++ Redistributable for Visual Studio 2019.ExamplesCreating a triangular mesh inside a polygonIn this example a mesh is created by discretizing the polygon perimeter with the desired edge length.Mesh orthogonalizationFinite volume staggered flow solvers require the mesh to be as orthogonal as possible.
MeshKernel provides an algorithm to adapt the mesh and achieve a good balance between mesh orthogonality and smoothness.Mesh refinementA mesh can be refined in areas based on samples or polygon selections.ContributingIn order to installMeshKernelPylocally, please execute the following line inside your virtual environmentpipinstall-e".[tests, lint, docs]"Then add a compiledMeshKernelApi.dllinto yoursrcfolder.Also make sure that your editor is configured to format the code withblackandisort.
When modifyingJupyternotebooks, thejupyterlab-code-formattercan be used.Building and installing the wheelPlatform-specific buildA setup script is provided for building the wheel. The script is known to work under Windows, Linux and macOS.To install the dependencies, usepython-mpipinstall--upgradepippython-mpipinstallwheelnumpymatplotlibpytestThe environment variableBACK_END_BRANCHmust be set prior to building the wheel. It specifies whichMeshKernelbranch should be built during the generation of the wheel. If one is on themainbranch of MeshKernelPy,BACK_END_BRANCHmust be either set tomaster. If one is an a release branch,BACK_END_BRANCHshould be set torelease. The version of the MeshKernel release branch is hardcoded inmeshkernel/version.py.While in the project's root directory, to build the wheel usepythonsetup.pybuild_extpythonsetup.pysdistbdist_wheelTo install use:
The wheel is installedpython-mpipinstall<meshkernel_wheel_name>where<meshkernel_wheel_name>is the name of the generated wheel.To test, simply runpytest.Manylinux Docker imageTo deploy Linux wheels to PyPI, we provide a Docker image that is based on manylinux2014_x86_64.
This image includes cmake and boost, which are necessary for compiling the native MeshKernel library (written in C++).
To build the Docker image, use:dockerbuild--progress=plain./scripts-tbuild_linux_libraryOnce the Docker image has been built, build the manylinux wheel using the following command:dockerrun-eBACK_END_BRANCH=<meshkernel_back_end_branch_name>-v$(pwd):/root--rmbuild_linux_librarywhere<meshkernel_back_end_branch_name>is eithermasterorrelease, as described inPlatform-specific build.The deployable manylinux wheels will be located in dist/wheelhouse.LicenseMeshKernelPyuses the MIT license.
However, the wheels on PyPI bundle the LGPL licensedMeshKernel.
Please make sure that this fits your needs before depending on it.
Package: meshless
|  Version	| Download | Travis CI | Test Coverage || :-------:	| :--- 	   | :---      | :---          ||   Master	|          | [![Linux Status](https://img.shields.io/travis/compmech/meshless/master.svg)](https://travis-ci.org/compmech/meshless) | [![Coverage Status](https://coveralls.io/repos/github/compmech/meshless/badge.svg)](https://coveralls.io/github/compmech/meshless) |Meshless Methods for Computational Mechanics========Repository created during studies with meshless methods.- ES-PIMLicense-------Distrubuted in the 2-Clause BSD license (https://raw.github.com/compmech/meshless/master/LICENSE).Contact: castrosaullo@gmail.com