Saravanan666/pypi-org huggingface dataset:
Package: compassionai-common
CompassionAI common repositoryCommon utilities and dataset preparation tools in use by various CompassionAI projects.InstallationThere are two modes for this library - inference and research. We provide instructions for Linux.Inference should work on MacOS and Windowsmutatis mutandis.Wevery stronglyrecommend doing researchonlyon Linux. We will not provide any support to people trying to perform research tasks without installing Linux.Virtual environmentWe strongly recommend using a virtual environment for all your Python package installations, including anything from CompassionAI. To facilitate this, we provide a simple Conda environment YAML file. We recommend first installing miniconda, seehttps://docs.conda.io/en/main/miniconda.html. We then recommend installing Mamba, seehttps://github.com/mamba-org/mamba.bashMiniconda3-latest-Linux-x86_64.sh
condainstallmamba-cconda-forge
mambaenvcreate-fenv-minimal.yml-nmy-env
condaactivatemy-envInferenceJust install with pip:pipinstallcompassionai-commonResearchBegin by installing for inference. Then install the CompassionAI data registry repo and set two environment variables:$CAI_TEMP_PATH$CAI_DATA_BASE_PATHWe strongly recommend setting them with conda in your virtual environment:condaactivatemy-env
condaenvconfigvarssetCAI_TEMP_PATH=#directory on a mountpoint with plenty of space, does not need to be fastcondaenvconfigvarssetCAI_DATA_BASE_PATH=#absolute path to the CompassionAI data registryOur code uses these environment variables to load datasets from the registry, output processed datasets and store training results.You probably also want to install CUDA and PyTorch (>=1.12) with CUDA support - follow the instructions herehttps://pytorch.org/get-started/locally/. You don't need torchvision or torchaudio but it is safe to install them if you like. You can reinstall CUDA-enabled PyTorch withpipin your conda environment after installing everything as above.For fine-tuning, you will need a powerful NVidia GPU. A GTX 1080 might work. We recommend at least an RTX 3080 Ti in a home setup, or a V100 if using a cloud. We have not tested non-NVidia GPUs.For pre-training, you will need a TPU on GCP. We do not recommend fine-tuning on GPUs. We do not expect it to work on anything less than a DGX-2 or a p3dn.24xlarge instance.UsageInferenceThis is a supporting library for our main inference repos, such as Lotsawa. You shouldn't need to use it directly.ResearchThis library contains components that are common to the various tasks performed by the other libraries, such as Manas and Garland.Implements data loader objects such as KangyurLoader, TengyurLoader and TibetanDict.Implements common PyTorch dataset objects, such as TokenTagDataset.Provides utility functions for models and data, such as Hydra-Huggingface adapters, PyTorch callbacks, model downloaders and configuration providers.
Package: compassionai-garland
CompassionAI project Garland - neural machine translation from classical TibetanMachine translation from classical literary Tibetan. Current focus is on:Custom neural machine translation models that build on Hugging Face Transformers.Using short sentence translation models published by the big research labs, such as FAIR, as backbone models for translating long texts.Eventually these techniques may generalize to other low resource languages.InstallationThere are two modes for this library - inference and research. We provide instructions for Linux.Inference should work on MacOS and Windowsmutatis mutandis.Wevery stronglyrecommend doing researchonlyon Linux. We will not provide any support to people trying to perform research tasks without installing Linux.Virtual environmentWe strongly recommend using a virtual environment for all your Python package installations, including anything from CompassionAI. To facilitate this, we provide a simple Conda environment YAML file in the CompassionAI/common repo. We recommend first installing miniconda, seehttps://docs.conda.io/en/main/miniconda.html. We then recommend installing Mamba, seehttps://github.com/mamba-org/mamba.bashMiniconda3-latest-Linux-x86_64.sh
condainstallmamba-cconda-forgecdcompassionai/common
mambaenvcreate-fenv-minimal.yml-nmy-env
condaactivatemy-envInferenceJust install with pip:pipinstallcompassionai-garlandResearchBegin by installing for inference. Then install the CompassionAI data registry repo and set two environment variables:$CAI_TEMP_PATH$CAI_DATA_BASE_PATHWe strongly recommend setting them with conda in your virtual environment:condaactivatemy-env
condaenvconfigvarssetCAI_TEMP_PATH=#directory on a mountpoint with plenty of space, does not need to be fastcondaenvconfigvarssetCAI_DATA_BASE_PATH=#absolute path to the CompassionAI data registryOur code uses these environment variables to load datasets from the registry, output processed datasets and store training results.UsageInferenceThis is a supporting library for our main inference repos, such as Lotsawa. You shouldn't need to use it directly.ResearchThis library implements neural machine translation models from classical Tibetan to English, with experiments for other target languages as well.Dataset preparation code, especially seecai_garland/data/parallel_dataset_prep.py.Implementation of modified tokenizers and neural model architectures that builds on Hugging Face Transformers.Training drivers to fine-tune models on tasks relevant to translation, such as translation itself or text segmentation.Utility code for the above, including simple libraries of preprocessors and segmenters, as well as a translation utility class that implements the core loops of our contextual translation algorithms.
Package: compassionai-manas
CompassionAI project Manas - classical Tibetan language understanding modelsMonolingual classical literary Tibetan modeling. The current focus is on pretrained transformer models for:Monolingual tasks that are useful for teaching to read Tibetan, especially word segmentation, part-of-speech tagging and named entity recognition.Use as an encoder for the machine translation model.InstallationThere are two modes for this library - inference and research. We provide instructions for Linux.Inference should work on MacOS and Windowsmutatis mutandis.Wevery stronglyrecommend doing researchonlyon Linux. We will not provide any support to people trying to perform research tasks without installing Linux.Virtual environmentWe strongly recommend using a virtual environment for all your Python package installations, including anything from CompassionAI. To facilitate this, we provide a simple Conda environment YAML file in the CompassionAI/common repo. We recommend first installing miniconda, seehttps://docs.conda.io/en/main/miniconda.html. We then recommend installing Mamba, seehttps://github.com/mamba-org/mamba.bashMiniconda3-latest-Linux-x86_64.sh
condainstallmamba-cconda-forgecdcompassionai/common
mambaenvcreate-fenv-minimal.yml-nmy-env
condaactivatemy-envInferenceJust install with pip:pipinstallcompassionai-manasResearchBegin by installing for inference. Then install the CompassionAI data registry repo and set two environment variables:$CAI_TEMP_PATH$CAI_DATA_BASE_PATHWe strongly recommend setting them with conda in your virtual environment:condaactivatemy-env
condaenvconfigvarssetCAI_TEMP_PATH=#directory on a mountpoint with plenty of space, does not need to be fastcondaenvconfigvarssetCAI_DATA_BASE_PATH=#absolute path to the CompassionAI data registryOur code uses these environment variables to load datasets from the registry, output processed datasets and store training results.UsageInferenceThis is a supporting library for our main inference repos, such as Lotsawa. You shouldn't need to use it directly.ResearchThis library implements language understanding for classical Tibetan.Tokenization.Pre-training code.Fine-tuning on language understanding tasks, such as word segmentation and part-of-speech tagging.
Package: compas-skeleton
compas_skeletonTopologically generating mesh from a set of lines and allowing interactive editing.install requirementAnaconda/conda and COMPAS >= 0.15.5 is required.Set up a dedicatedcondaenvironment.On Windows.condacreate-nyourenvpython=3.7On Mac, also install Python as a framework (addpython.app).condacreate-nyourenvpython=3.7python.appActivate the environmentcondaactivateyourenvInstall COMPAS.navigate to the root of thecompasrepo, and dopipinstall-e.more about installing compas:COMPASInstall Skeleton.navigate to the root of thecompas_skeletonrepo, and dopipinstall-e.To verify, open an interactive Python prompt and import the installed packages.>>>importcompas>>>importcompas_skeleton>>>exit()Install packages for Rhino.From the root ofcompas_skeleton, dopython-mcompas_rhino.install-pcompascompas_skeleton
Package: compas-slicer
compas_slicerSlicing package for FDM 3D Printing with COMPAS.Main featuresPlanar slicing (default method, and method based on Cgal library)Curved slicing (interpolation of boundaries, UV slicing, scalar field slicing)Generation of fabrication-related informationExport print data to Json and gcode formatsGetting startedStep 1: InstallationThe recommended way to installcompas_sliceris withconda.
For example, create an environment namedmy-project(or replace with your own environment name) and install as follows:conda config --add channels conda-forge
conda create -n my-project compas_slicerStep 2: Optional installation stepsCOMPAS ViewersFollow the instructions to installcompas_view2(https://github.com/compas-dev/compas_view2).COMPAS CGAL (for planar slicing)conda install -n my-project compas_cgalLIBIGL python bindings (for curved slicing)conda install -c conda-forge iglGrasshopperThe Grasshopper components are automatically installed with thecompas_rhinoinstallation, e.g.:conda activate my-project
python -m compas_rhino.install -v 6.0Step 3. Test if the library worksActivate your environment and run the following command:conda activate my-project
python -m compas_slicerEnjoy!TroubleshootingSee here:https://compas.dev/compas_slicer/installation.html#troubleshooting-1ContributionsBefore contributing code:Install development dependencies:pipinstall-rrequirements-dev.txtMake sure all tests pass:invoketestMake sure you pass flake8 tests. (hint: This is the most annoying part of the process)invokelintAdd an example on the examples folder that uses the new functionality. Run the example and make sure it works smoothly.Create a pull request for the master branch, where you explain in detail what you fixed. When you create a pull request, there is an automatic action that runs the tests for your code again on the server.
Make sure the pull request passes the automatic tests as well. If not, then inspect the result, find what went wrong, fix it, and push again the result on your branch. The action will run again automatically on the open pull request.AuthorsIoanna Mitropoulou <mitropoulou@arch.ethz.ch>@ioannaMitropoulouJoris Burger <burger@arch.ethz.ch>@joburgerAndrei Jipa <jipa@arch.ethz.ch>@stratocaster
Package: compass-school
Computational Modelling of Primary School Segregation (COMPASS)This Python implementation tries to model school choice and resulting school segregation based on the work of Schelling (1971) and Stoica & Flache (2014).UsageInstall and RunIt's required to firstly installGDALon your computer.To install the package, runpip install compass-school.To run a demo with web-based GUI, runpython run.pyfrom the root directory of the project. This will start a local server to automatically open your web browser and show the program as a webpage. If not seeing the webpage open, manually inputhttp://localhost:5004/in the link bar.Update DocumentationInstall pdoc3 if you haven't already done so. Browse to the compassproject folder in your terminal and runpdoc3 --html --force --output-dir docs compass. The documentation should be updated now.OverviewThe repository consists of:run.py:a script that runs the model interactively with a visualisation (browser)testrun.py:a test script (work in progress)household.py:the household classstudent.py:the student classneighbourhood.py:the neighbourhood classschool.pythe school classallocator.py:allocates the students to their school of choiceagents_base.py:overarching agent used for inheritancemodel.py:initialises the entire system and all of its componentsparameters.py:contains all the parameter values for the simulationscheduler.py:takes care of the activation, sequence and placement of all agentsvisualisation.py:browser based visualisationutils.py:containing all measurementsfunctions.py:containing some math functions to be used by the classesSimulationsInformation on how to run the code here.Testing and developmentSetup a virtualenv with the required dependencies.$python-mvenvenv
$.env/bin/activate
$pipinstall-rrequirements.txtInstall the package locally (in developement, or editing mode):$pipinstall-e.Then run the tests with:$pytestProfilingSome profiling result can be found in thisnotebook. Also, some scaling graphs can be found in thisnotebook.
Package: compass-stylesheets
This repository is automatically updated fromhttps://github.com/Compass/compassCompassCompass is a Stylesheet Authoring Environment that makes your website design simpler to implement and easier to maintain.http://compass-style.orgInstallationTo get going with Compass python module you can install it fromPyPi package:pipinstallcompass-stylesheetsDocumentationCompass documentation pages are available athttp://compass-style.org/Python packageAfter installation you can usepkg_resourcemodule to access assets:importpkg_resourcesas_string=pkg_resources.resource_string("compass_stylesheets","stylesheets/_compass.scss")full_path_to_file=pkg_resources.resource_filename("compass_stylesheets","stylesheets/_compass.scss")file_like=pkg_resources.resource_stream("compass_stylesheets","stylesheets/_compass.scss")Package consists ofscssfiles.
Package: compas-timber
compas_timbercompas_timberis a user-friendly open-source software toolkit to streamline the design of timber frame structures. Despite its advances in digitalization compared to other building techniques, timber construction is often perceived as a challenging field, involving intricate processes in design, planning, coordination, and fabrication. We aim to increase the use of timber in architecture by lowering the threshold of creating versatile and resource-aware designs.InstallationIt is recomended you installcompas_timberinside a virtual environment.pipinstallcompas_timberTo installcompas_timberto Rhino runpython-mcompas_rhino.installFirst StepsDocumentationCOMPAS TIMBER Grasshopper TutorialCOMPAS TIMBER API ReferenceQuestions and feedbackWe encourage the use of theCOMPAS framework forumfor questions and discussions.Issue trackerIf you found an issue or have a suggestion for a dandy new feature, please file a new issue in ourissue tracker.ContributingWe love contributions!Check theContributor's Guidefor more details.Creditscompas_timberis currently developed by Gramazio Kohler Research. See thelist of authorsfor a complete overview.
Package: compas-tna
compas_tnaCOMPAS package for Thrust Network Analaysis.DocumentationTo get started withcompas_tnahave a look atthe documentation.Questions and FeedbackFor questions and feedback, have a look at theCOMPAS Forum.IssuesIf you run into problems, please file an issue on theissue tracker. If we don't know it is broken, we can't fix it...ContributingGuidelines for developers are under construction. However, we always accept contributions in the form of Pull Requests.Licensecompas_tnais licensed under the MIT License. SeeLICENSE, for more information.
Package: compas-tno
COMPAS TNOCOMPAS TNOis a Python package to find admissible thrust networks in masonry vaulted structures built in theCOMPASframework.Based onRicardo Maia Avelino's doctoral thesis at ETH Zurich, this Package enables finding multi-objective particular internal stress solutions in masonry vaults, as the ones presented below.InstallationThe recommended an editable install ofCOMPAS TNOwithAnaconda/conda. Here we create an environment calledtnoand install it:conda create -n tno -c conda-forge python COMPAS triangle compas_view2
conda activate tno
git clone https://github.com/BlockResearchGroup/compas_tno.git
cd compas_tno
pip install -e .First Steps: Read the docsA walkthrough the package is available in the documentation:https://blockresearchgroup.github.io/compas_tno/Issue trackerIf you find a bug, please help us solve it byfiling a report.CitingIf you useCOMPAS TNOfor your research, cite one of ourpapers.LicenseCOMPAS TNOisreleased under the MIT license.
Package: compas-triangle
COMPAS TriangleCOMPAS-firendly wrappers for the Triangle library.Getting Startedcompas_trianglecan be installed from PyPI.pipinstallcompas_triangleor from local source using pippipinstallpath/to/compas_triangleor directly from the github repo.pipinstallgit+https://github.com/blockresearchgroup/compas_triangle.git#egg=compas_triangleLicensecompas_triangleuses the Cython wrapper for Jonathan Richard Shewchuk's Triangle library.
The Cython wrapper is available here:https://github.com/drufat/triangleUse of the Triangle library is restricted to personal or academic purposes.
The license of the library is included in this repo:LICENSE.TriangleExamplesFour examples are available:examples/delaunay1.pyexamples/delaunay2.pyexamples/delaunay3.pyexamples/delaunay4_rhino.pyNote that the Rhino example usescompas.rpcto provide a proxy for the package.
Package: compas-view2
COMPAS Viewers 2Second generation viewers for the COMPAS framework.FeaturesFull support for all COMPAS objects (primitives, shapes, network, mesh, volmesh)Pick select and Box selectShaded, Ghosted, Wireframe, Specular visualization modesMesh faces with individual colorsDynamic visualization with simple decoratorsText annotationsCustomizable UI and UI ControllerTransformations in object space...InstallationSee theGetting Startedinstructions in the docs.TutorialsThistutorial pagecontains basic lead-through, configuration setting, and software concepts.ExamplesExamples can be found in ourexample page.LicenseThe code in this repo is licensed under theMIT License.Known IssuesPlease check theIssue Trackerof the repo for known issues and their solutions.FutureThis project targets tocompas. The next generation of it with compas_2 compatibility can be found in a separate repohere.
Package: compas-viewer
COMPAS VIEWERStandalone viewer for COMPAS 2.0More information is available in the docs:https://compas.dev/compas_viewer/FeaturesPerformative and easy-to-use new architecture based oncompas_v1'scompas_view2.Complete configurable settings and flexible customization.Full support for all COMPAS objects (primitives, shapes, graph, mesh, volmesh) based oncompas.scenearchitecture.Up-to-datePySide6andPyOpenGLcompatibility.Action-based user interaction: drag, select, zoom, pan, rotate, etc.Data-driven visualization with simple decorators: facecolor, edgecolor, vertexcolor, linewidth, pointsize, etc.Text tags and grids.Customizable UI and UI ControllerTransformations in object space...InstallationSee theGetting Startedinstructions in the docs.TutorialsThistutorial pagecontains basic lead-through, configuration setting, and software concepts.ExamplesExamples can be found in ourexample page.ContributingContributing pageprovides the guidelines for contributing to this project. We very much welcome any contributions to this project (Bug Fix, Action class contribution, etc.)LicenseThe code in this repo is licensed under theMIT License.Known IssuesPlease check theIssue Trackerof the repo for known issues and their solutions.
Package: compas-wood
COMPAS_WOOD - Project for Generating Timber JointsAuthor:Petras VestartasInterface compas_woodInstallationhttps://petrasvestartas.github.io/compas_wood/latest/installation.htmlIf you install this library on a new computer that did not have Anaconda Prompt or VScode follow these stepsInstall Anaconda Prompt:https://www.anaconda.com/Type in the anaconda prompt: "conda config --add channels conda-forge"Add this environment path. On windows search type "Edit the system environmental variables", click "Environmental Variables..", under second tab click "path", then edit and add the following path: "C:\ProgramData\Anaconda3\Scripts". This process can be avoided if during installation of Anaconda Prompt you ticked the option to the Anaconda Prompt to the Environmental Variables.Open Anaconda Prompt terminal and update conda:
conda activate base
conda update --allIf you never used VSCodeInstall VSCode:https://code.visualstudio.com/DownloadInstall VSCode Extensions: PythonLaunch VScode, type CTRL+SHIFT+P and >Python: Create TerminalVideohttps://user-images.githubusercontent.com/18013985/147587822-cfb43c96-4a81-4b89-987f-8d201ec2e5ba.mp4ToDorecenter geometry before processingctypes floating number conversion, currently precision is lostctypes user toleranceExamples"tests/compas_wood_1_test.py" - test if the library is loading"tests/compas_wood_2_read_xml.py" - get data-set a set of polylines and display in viewer"tests/compas_wood_3_joints.py" - get connection-areas between timber plates"tests/compas_wood_4_rtree.py" - get neighbors of an element"tests/compas_wood_5_get_connection_zones.py" - generate timber joints"tests/compas_wood_6_closed_mesh_from_polylines.py" - create a solid mesh from polylines with holes"tests/compas_wood_7_mooc_homework.py" - create a special connection of the Vidy projectTestsRhino Implementation
Package: compath-hgnc
This package support the mapping across different protein/gene identifiers.CitationGray KA, Yates B, Seal RL, Wright MW, Bruford EA. genenames.org: the HGNC resources in 2015. Nucleic Acids Res. 2015
Jan;43(Database issue):D1079-85. doi: 10.1093/nar/gku1071.http://www.ncbi.nlm.nih.gov/pubmed/25361968Installationcompath_hgnccan be installed easily fromPyPIwith the
following code in your favorite terminal:$python3-mpipinstallcompath_hgncor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/compath/compath_hgnc.git@masterSetupHGNC can be downloaded and populated from either the Python REPL or the automatically installed command line
utility.Python REPL>>>importcompath_hgnc>>>compath_hgnc_manager=compath_hgnc.Manager()>>>compath_hgnc_manager.populate()Command Line Utilitycompath_hgncpopulateAcknowledgementsThis package heavily relies on Andrej Konotopez’s packagePyHGNCand theBio2BEL HGNC.