Saravanan666/pypi-org huggingface dataset:
Package: divbrowse
Website:https://divbrowse.ipk-gatersleben.deDocumentation:https://divbrowse.readthedocs.ioPaper:https://doi.org/10.1093/gigascience/giad025Table of contents:About DivBrowseTry out DivBrowseScreenshotsUsage workflow conceptArchitectureAbout DivBrowseDivBrowse is a web application for interactive exploration and analysis of very large SNP matrices.It offers a novel approach for interactive visualization and analysis of genomic diversity data and optionally also gene annotation data. The use of standard file formats for data input supports interoperability and seamless deployment of application instances based on established bioinformatics pipelines. The possible integration into 3rd-party web applications supports interoperability and reusability.The integrated ad-hoc calculation of variant summary statistics and principal component analysis enables the user to perform interactive analysis of population structure for single genetic features like genes, exons and promoter regions. Data interoperability is achieved by the possibility to export genomic diversity data for genomic regions of interest in standardized VCF files.Try out DivBrowseIf you want to test DivBrowse please visit the demo instances listed here:https://divbrowse.ipk-gatersleben.de/#demo-instancesScreenshotsUsage workflow conceptArchitecture
Package: dive-bin
pybinThis project was inspired by howMaturin packages rust binaries.  The key observation is that in the wheel format, thedistribution-1.0.data/scripts/ directory is copied to bin, which means we can leverage this to seamlessly copy binaries into a user's PATH.Combined with Python's platform-specific wheels, this allows us to use pip as a "cross-platform package manager" for distributing single-binary CLI applications.Wheel Building ProcessThe core of the logic lies in thebuildlib.pymodule.A mapping from download URL (often github releases) to pypi platform tag is required.For each platform:Download the release & extract the binary.Set file permissions and place into scripts directory within wheel archive.Create the METADATA and WHEEL files within wheel archive.Place wheel intodist/folder.Once all wheels are constructed, the distribution is uploaded to PyPI.CICD is configured to automatically recognize newPyPIreleases by looking for a diff on the PYPI_VERSION.  When this happens, a build-and-release cycle is performed for that release version.Catalogpip install dive-bin:pybin version,upstream sourcepip install hadolint-bin:pybin version,upstream sourcepip install just-bin:pybin version,upstream sourcepip install lazydocker-bin:pybin version,upstream sourcepip install scc-bin:pybin version,upstream sourcepip install usql-bin:pybin version,upstream source
Package: dive-jupyterlite-xeus-python
dive-jupyterlite-xeus-pythonThexeus-pythonPython kernel for JupyterLite running in the browser.RequirementsJupyterLite >= 0.1.0b12InstallTo install the extension, execute:pipinstalldive-jupyterlite-xeus-pythonThen build your JupyterLite site:jupyterlitebuildPre-installed packagesxeus-python allows you to pre-install packages in the Python runtime. You can pre-install packages by passing theXeusPythonEnv.packagesCLI option tojupyter lite build.
This will automatically install any labextension that it founds, for example installing ipyleaflet will make ipyleaflet work without the need to manually install the jupyter-leaflet labextension.For example, say you want to installNumPy,Matplotlibandipyleaflet, it can be done with the following command:jupyterlitebuild--XeusPythonEnv.packages=numpy,matplotlib,ipyleafletThe same can be achieved through ajupyterlite_config.jsonfile:{"XeusPythonEnv":{"packages":["numpy","matplotlib","ipyleaflet"]}}ContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the dive_jupyterlite_xeus_python directory# Install package in development modepython-mpipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).Development uninstallpipuninstalldive_jupyterlite_xeus_pythonIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink nameddive-jupyterlite-xeus-pythonwithin that folder.Packaging the extensionSeeRELEASE
Package: divemarkdownit
divemarkdownitA JupyterLab extension to includemarkdown-itand its extensions for markdown rendering.Currently supported markdown-it extensions include:markdown-it-docutilsmarkdown-it-diagramsmarkdown-it-task-checkboxmarkdown-it-footnotemarkdown-it-anchorRequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstalldivemarkdownitContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environmentgitclonehttps://github.com/dive4dec/divemarkdownit.git# Change directory to the divemarkdownit directorycddivemarkdownit# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstalljupyterlab_markdownitWhen uninstalling you will also need to remove the symlink created byjupyter labextension develop . --overwritecommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink nameddivemarkdownitin that folder.Packaging the extensionSeeRELEASEAcknowledgementCredit must be given to@agoose77/jupyterlab-markup. Actually this repo is more of a replica ofjupyterlab-markup, rearranged with my own flavor.
Package: divemathjax3
jupyterlab-mathjax3A JupyterLab extension for rendering math withMathJax 3.The default LaTeX renderer in JupyterLab usesMathJax 2.
This extension substitutes the MathJax 2 renderer with the MathJax 3 renderer.RequirementsJupyterLab >= 3.0Installpipinstalljupyterlab-mathjax3ContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the jupyterlab-mathjax3 directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseUninstallpipuninstalljupyterlab-mathjax3
Package: divent
DiventThe discord scheduled event calendar generatorSimple website that guides you to invite a bot to read and format scheduled events to a subscribable calendar.Installing / Getting started1) Create the botGo to theDiscord Developer Portaland create a new application.Enable theBuild-A-Botoption in theBotpanel.Click onReset Tokenand keep it in a safe place, you will need it.Click onReset Secretin theOAuth2panel, copy bothClient IDandClient Secretand keep it in a safe place, you will need it.Configure the rest of your app and bot as you like (name, icon, username, etc.)2) With DockerInstallDockerRundockerrun-p5000\-eDISCORD_TOKEN=your_bot_token\-eOAUTH2_CLIENT_ID=your_client_id\-eOAUTH2_CLIENT_SECRET=your_client_secret\xefir/divent2) Without DockerInstallPython 3InstallPipRunpip install diventRunDISCORD_TOKEN=your_bot_token\OAUTH2_CLIENT_ID=your_client_id\OAUTH2_CLIENT_SECRET=your_client_secret\divent3) Open your browserThe app is accessible athttp://localhost:5000LinksProject homepageSource repositoryIssue trackerMy other projectsThe WTFPL licenceDocker hubPypiDonations
Package: diveplane-amalgam-api
Amalgam python apiThis module loads an amalgam library, and exposes an api for communicating to it.SetupThe default location for the amalgam library files is~/.diveplane/lib/dev/amlg/ie~/.diveplane/lib/dev/amlg/amalgam.sofor linux and _~/.diveplane/lib/dev/amlg/Amalgam.dll for windows.If download is set to True (and no library_path is set), then the wrapper will attempt to retrieve the correct library file from the appropriate repository.  Pass in the download_apikey parameter to the Amalgam constructor or set theDP_CONFIG_DIVEPLANE_AMLG_APIKEYenvironment variable to set the apikey to access the repository.returnAmalgam(version='10.0.3',download=True,debug=True)Setting version to 'latest' can be used to automatically update to the latest on every use.Build StatusVersioningVersioning will occur automatically.  Patch versions are the default, change the PR name suffix to MAJOR/MINOR to change the incremented version level.  Use the PR pipeline build to validate the resulting version.CHANGELOGThe CHANGELOG.md will update automatically from PR/Merge-commit information.
Package: diveplane-reactor-api
Diveplane Reactor™Diveplane is an Understandable AI platform which is rooted in instance-based
machine learning and harnesses a fast spatial query system and information
theory for performance, accuracy, and auditability. With Diveplane, your data is
the model. As such, your data is stored in memory and predictions about a new
data point are made given its relationship to the original data. This enables
powerful decision making based on historical observations, augmented by rich
explanations to understand why those decisions were made. As an explainable AI
toolkit, Diveplane's interpretability features provide users opportunities to
evaluate which data contribute to predictions, identify similar data to the
predictions, and determine if new data and predictions are surprising compared
to the original data. Additional Diveplane capabilities include targetless
learning, allowing users to characterize and predict any set of variables,
real-time editing for online learning, data imputation, and discriminative and
generative modeling. By combining the features of Diveplane, users can analyze
their data in way not typically achievable using other state-of-the-art AI
techniques.For questions or support, please emailsupport@diveplane.com.
Package: diver
DIVERDiveris theDatasetInspector,Visualiser andEncoderlibrary, automating and codifying common data science project steps as standardised and reusable methods.Seeexample-notebooks/house-price-demo.ipynbfor a full walkthrough.dataset_inspectorA set of functions which help perform checks for common dataset issues which can impact machine learning model performance.dataset_conditionerAscikit-learn-formatted module which can perform various data-type encodings in a single go, and save the associated attributes from a train-set encoding to reuse on a test-set encoding:The.fit_transformmethod learns various encodings (feature means and variances; categorical feature elements - yellow in the flow chart below) and then performs the various encodings on the feature train setThe.transformmethod applies train-set encodings to a test setdataset_visualiserFunctions for visualising aspects of the datasetCorrelation analysisDisplay the correlation matrix for the topncorrelating features (nspecified by the user) against the dependent variable (at the bottom row of the matrix)Future Workcategorical_excess_cardinality_flagger_and_reducerOption for instances where there are no categorical featuresmissing_value_conditionerChoose between either {use means from train set (default), calculate means for test set}Missing values for categorical featuresImplement missing value imputation:https://measuringu.com/handle-missing-data/GOOD READING:https://towardsdatascience.com/6-different-ways-to-compensate-for-missing-values-data-imputation-with-examples-6022d9ca0779ordinal_encoderCreate a function to do thistimestamp_encoderis_public_holiday : boolUpdate above diagramRemove warningsMake robust to non-consecutive indices in input dfUnit test all functionsExtreme valuesPCA option?Label balanced class checker (for classification problems)https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/Distribution and correlation analysisDisplay correlation matrix for topncorrelates alongside target at the bottomDisplay pairplot for topncorrelates alongside target at the bottomOr instead oftop ncorrelates, instead threshold ofcumulative varianceOption to DROOP lower correlates (lower than threshold) if desiredUseful readinghttps://machinelearningmastery.com/process-for-working-through-machine-learning-problems/https://towardsdatascience.com/smarter-ways-to-encode-categorical-data-for-machine-learning-part-1-of-3-6dca2f71b159https://medium.com/apprentice-journal/pca-application-in-machine-learning-4827c07a61db
Package: diverge-flow
DivERGe implements various ERG examplesDivERGe provides a versatile framework to set up (one,) two and three
dimensional functional renormalization group (FRG/ERG) calculations under the
static vertex approximation.It implements three backends, the grid FRG, truncated unity FRG (TUFRG) and
orbital space n-patch FRG.For maximum performance, the code is written in C/C++ with extensions in CUDA
(GPUs). It makes minimal use of other dependencies, only FFTW and LAPACK are
required. MPI may be used if desired. DivERGe can be interfaced from C/C++ or
python, with an existing python FFI wrapper. This wrapper is published in pypi,
such that you can runpip install diverge-flowon a 64bit linux machine and directly use divERGe. For different architectures,
compilation is additionally required (and putting the correctlibdivERGe.soin yourLD_LIBRARY_PATH). You can verify the .so file
in use by callingdiverge.info()from python. For any other language, you
must write all the FFI wrappers yourself.Documentationhttps://frg.pages.rwth-aachen.de/diverge/Download CPU releaseGeneric linux (amd64) builds (GLIBC>=2.17, this should be given almost anywhere
to date) can be downloadedhere. We
recommend building from source for an optimized version on the HPC
infrastructure to your availability.TestingWe use a slightly modified version ofCatch2for testing. To check divERGe's
health from python, runimport diverge
diverge.init(None, None)
diverge.run_tests()
diverge.finalize()CitationPlease citearXiv.2311.07667when
using divERGe.LicensedivERGe is published under theGPLv3. The releases include
non-free parts (CUDA) and
differently licensed software (OpenBLAS,FFTW) in binary form.AuthorsJonas B. HauckandLennart Klebl, 2023.
Package: divergence
DivergenceDivergence is a Python package to compute statistical measures of entropy and divergence from probability distributions and samples.The following functionality is provided:(Information) Entropy [1], [2]Cross Entropy: [3]Relative Entropy or Kullback-Leibler (KL-) Divergence [4], [5]Jensen-Shannon Divergence [6]Joint Entropy [7]Conditional Entropy [8]Mutual Information [9]The units in which these entropy and divergence measures are calculated can be specified by the user.
This is achieved by setting the argumentbase, to2.0,10.0, ornp.e.In a Bayesian context, relative entropy can be used as a measure of the information gained by moving
from a prior distributionqto a posterior distributionp.Installationpip install divergenceExamplesSee the Jupyter notebookDivergence.References:[1]https://en.wikipedia.org/wiki/Entropy_(information_theory)[2] Shannon, Claude Elwood (July 1948). "A Mathematical Theory of Communication". Bell System Technical Journal. 27 (3): 379–423[3]https://en.wikipedia.org/wiki/Cross_entropy[4]https://en.wikipedia.org/wiki/Kullback–Leibler_divergence[5] Kullback, S.; Leibler, R.A. (1951). "On information and sufficiency". Annals of Mathematical Statistics. 22 (1): 79–86[6]https://en.wikipedia.org/wiki/Jensen–Shannon_divergence[7]https://en.wikipedia.org/wiki/Joint_entropy[8]https://en.wikipedia.org/wiki/Conditional_entropy[9]https://en.wikipedia.org/wiki/Mutual_information
Package: diversify
DiversifyThe ProjectDiversify is a playlist generator based on theSpofity WEB APIand theSpotipy modulethat aims to use concepts of AI to suggest playlists based on musical preferences between multiple people.GoalsThe goal is to use AI algorithms to generate a spotify playlist based on a user's preference and
a friend of his choice. Currently the script will use a genetic algorthm to generate the playlists
but this may improve in the future.How to installFirst you need to get your spotify API key and save it to the .env file.Go tospotify application web page,login with your spotify account and create a new appput whatever name you'd like on the project info and say no to commercial integrationclick on edit settings and whitelisthttps://edujtm.github.io/diversify/redirectget your client ID and client secret (by clickingshow client secret)put them on aconfig.inifile and move it to$HOME/.config/diversify/runpip install diversifyrundiversify --helpto see if everything went ok.How to run$ diversify login
$ diversify playlist PLAYLIST NAMEHow to contributeThis project usespoetryfor dependency managementClone this repo:git clone https://github.com/edujtm/diversify.gitcd diversifyandpoetry installThen runpoetry shell
Package: diversipy
Descriptiondiversipy is a collection of algorithms dealing with three different but related
topics. The first topic is super-uniform sampling of the unit hypercube.
‘Super-uniform’ in this context means that the obtained point sample should be
more uniform than a random uniform sample, which is a desirable property in many
applications. One such application is the design of computer experiments, where
typically space-filling experimental designs are used. After creation, the
samples can be transformed from the unit hypercube to arbitrary cuboids.The task of subset selection is defined as follows: suppose you have a set of
points in R^n and want to select a sample of them distributed as uniformly as
possible. This may be necessary because the original set is too large to be
processed entirely. The selection problem is related to clustering, with the
difference that when using clustering, you usually want to retain the
structure of the original point set.Once one has created (or obtained from somewhere) a point set, one may want to
assess its properties. Therefore, diversipy contains several functions to
measure diversity and a few related concepts. Several different indicators are
offered because they have different advantages and disadvantages (in terms
of run time and what they measure).Example>>> from diversipy import *
>>> design = transform_spread_out(lhd_matrix(50, 2)) # create latin hypercube design
>>> subset = psa_select(design, 10) # select subset, for whatever reason
>>> unanchored_L2_discrepancy(subset) # calculate discrepancyNote that points are stored row-wise, in accordance with numpy convention.DocumentationThe documentation is located athttps://www.simonwessing.de/diversipy/doc/
Package: diversity
🎨diversitydiversityis a package that checks for and scores repeated structures and patterns in the output of language models.InstallationpipinstalldiversityCommand-linepythonexamples/summarization.py<DATASETCSV>LibraryThis library supports various scoring methods for evaluating the homogeneity and diversity of outputs.fromdiversityimportcompression_ratio,homogenization_score,ngram_diversity_scoredata_example=["I enjoy walking with my cute dog for the rest of the day, but this time it was hard for me to figure out what to do with it. When I finally looked at this for a few moments, I immediately thought.","I enjoy walking with my cute dog. The only time I felt like walking was when I was working, so it was awesome for me. I didn't want to walk for days. I am really curious how she can walk with me","I enjoy walking with my cute dog (Chama-I-I-I-I-I), and I really enjoy running. I play in a little game I play with my brother in which I take pictures of our houses."]cr=compression_ratio(data_example,'gzip')hs=homogenization_score(data_example,'rougel')# hs = homogenization_score(data_example, 'bertscore')self_bleu=homogenization_score(data_example,'bleu')nds=ngram_diversity_score(data_example,4)print(cr,hs,nds)1.6410.2223.315You can also visualize various ngram patterns using this library:n=6# get the token-level patternspatterns_token=token_patterns(outputs,n)# get the POS patternsjoined_pos,tuples=get_pos(outputs)ngrams_pos=token_patterns(joined_pos,n)# for the top n-gram patterns, cycle through and get the matching texttext_matches={}forpattern,_inngrams_pos:text_matches['pattern']=pos_patterns(tuples,pattern)
Package: divert
DivertDivert the execution flow of your code.
This is useful for complex code with lots of method nesting and unpredictable return values.Plus, no sub-dependencies.Example usageFor example, if we have an inner function (or multiple), we can immediately divert to a flow edge of our choice.fromdivert.flowimportcustom_flow_edge,divertdefinner_function(number:int)->int:"""Function that doubles the value or signals that the flow should change."""ifnumber==4:# Stop condition for sake of exampledivert()# If we want to specify the return value here we can instead use `payload_to_edge(VALUE)`returnnumber*2@custom_flow_edge(default_return=42)defouter_function()->int:"""Larger function that calls the inner function multiple times."""first_response=inner_function(2)second_response=inner_function(4)third_response=inner_function(8)returnfirst_response+second_response+third_responseFor further motivation for using the library, seethe documentation.
For example usage, seethe examples.