Saravanan666/pypi-org huggingface dataset:
Package: atlas-consortia-commons
commonsThis repository contains the code supporting several restful microservices used by the consortia.Getting StartedThe Atlas Consortia Commons library is available through PyPi via the command:pipinstallatlas-consortia-commonsThe atlas-consortia-commons requirements can be foundhereContentsThe code includes:UBKG: For querying Unified Biomedical Knowledge Graph (UBKG) application interfaceREST: For making standardised rest responsesCoding ConventionsPlease use:snake_caseto name methods and variables.PascalCasefor class names.Do follow any additional code formatting and styles as seen in the project
Package: atlas-core
atlasatlasis a totallyopen-source,self-hosted,interoperableassistant written in Python 3. It uses the MQTT protocol to communicate with third party skills.Ever wanted to build your own Alexa, Siri or Google Assistant and host it yourself? That's whyatlashas been created!It manages dialog states with the help of thetransitionslibrary and parses natural language withsnips. If snips doesn't fit your needs, you can subclass theInterpreterclass and make your own 😉Have a look at the📚 documentation folder.InstallationPIPpip install atlas-coreorpip install atlas-core[snips]to already include the snips backend.DockerBuild the image withdocker build . -t atlasRundocker run --rm -it -v <a folder containing an atlas.yml file>:/atlas -p 5000:5000 atlasSourcegit clonethis repository and runpython setup.py install.NLU backendsOnce installed,atlaswill not have any dependency with a NLU backend (unless you usepip install atlas-core[snips]) . So you may want to install it yourself. Once done, don't forget to use the appropriated interpreter in theatlas.ymlconfiguration file.For example, if you usesnips-nlu, you must install it withpip install snips-nluand useinterpreter: 
  type: 'atlas.interpreters.snips_interpreter.SnipsInterpreter'in the atlas configuration file.Quickstart⚠️ As of now, the PWA exposed at<server.url>lacks offline support.Starts with the prebuiltsampleor make your own by reading below.Those quick steps use Snips as the NLU backend.Once installed, you must configure it using aconfiguration file.Drop your training files (appropriate for your backend) into the<loader.training_path>, you may use a tool such astracyorchatitoto generates it.Drop your configuration files into the<loader.env_path>.Drop some skills scripts inside<executor.path>, each one should be in its own subfolder with anatlasfile containing the command to run to executes your skillStart a MQTT broker, such asmosquittoStart atlas withatlas -c <your_configuration_file_path>.ymlGo to the<server.url>in your favorite browser to access the exposed PWA! (you may use another channel if you prefer). By default it uses theen-USlanguage, if you want to change it, just add a query parameters such ashttp://localhost:5000?lang=fr-FRThat's it!I encourage you to read thearchitecture overviewif you want to know how it works alltogether.DeployingIf you want the exposed PWA to work on your mobile device, you will have to use a valid certificate since this is required by web browsers for the Web Speech API to use your mic.Please note that the Web Speech API used by the PWA only works in Chrome as far as I know.ContributingContributions are welcome!You're a developer or just want to know where this project is heading to? Have a look at theextended TODOfile.You want to supportatlasby giving some money? Head over to Liberapay with this tiny button, thanks ❤️!CreditsPhoto Sphere by Denis Klyuchnikov from the Noun Project(Logo)Banner icons made by Freepik, Good Ware and Skyclick fromflaticonsnips-nlu(base interpreter)transitions(dialog management)
Package: atlasdcat
atlasdcatA Python library for mappingApache AtlasGlossary terms to DCAT metadata and vice versa.Specificationthe Norwegian Application Profileofthe DCAT standard.NoticeThis library is part of the DCAT for Apache Atlas lifecycle andnot a complete solution. The complete lifecycle contains the following actions:Create glossaryAdd dataset descriptions by using atlasdcat mapper (this library)Assign dataset description glossary terms to entities (actual datasets)Fetch glossary terms as DCAT catalog in RDF format (this library)UsageInstall%pipinstallatlasdcatGetting startedSetup mapper# Example...fromatlasdcatimportAtlasDcatMapper,AtlasGlossaryClientfrompyapacheatlas.authimportBasicAuthenticationatlas_auth=BasicAuthentication(username="dummy",password="dummy")atlas_client=AtlasGlossaryClient(endpoint_url="http://atlas",authentication=atlas_auth)mapper=AtlasDcatMapper(glossary_client=atlas_client,glossary_id="myglossary",catalog_uri="https://domain/catalog",catalog_language="http://publications.europa.eu/resource/authority/language/NOB",catalog_title="Catalog",catalog_publisher="https://domain/publisher",dataset_uri_template="http://domain/datasets/{guid}",distribution_uri_template="http://domain/distributions/{guid}",language="nb",)Map glossary terms to DCAT Catalog RDF resourcetry:mapper.fetch_glossary()catalog=mapper.map_glossary_terms_to_dataset_catalog()print(catalog.to_rdf())exceptExceptionase:print(f"An exception occurred:{e}")Map DCAT Catalog RDF resource to glossary termscatalog=Catalog()catalog.identifier="http://catalog-uri"catalog.title={"nb":"mytitle"}catalog.publisher="http://publisher"catalog.language=["nb"]catalog.license=""dataset=Dataset()dataset.title={"nb":"Dataset"}dataset.description={"nb":"Dataset description"}catalog.datasets=[dataset]try:mapper.fetch_glossary()mapper.map_dataset_catalog_to_glossary_terms(catalog)mapper.save_glossary_terms()exceptExceptionase:print(f"An exception occurred:{e}")For an example of usage of this library in a simple server, seeexample.DevelopmentRequirementspyenv(recommended)python3poetrynox%pipinstallpoetry==1.1.13
%pipinstallnox==2022.1.7
%pipinjectnoxnox-poetry==1.0.0Install developer tools%gitclonehttps://github.com/Informasjonsforvaltning/atlasdcat.git
%cdatlasdcat
%pyenvinstall3.8.12
%pyenvinstall3.9.10
%pyenvinstall3.10.
%pyenvlocal3.8.123.9.103.10.
%poetryinstallRun all sessions%noxRun all tests with coverage reporting%nox-rstestsDebuggingYou can enter intoPdbby passing--pdbto pytest:nox-rstests----pdbYou can set breakpoints directly in code by using the functionbreakpoint().
Package: atlas-direction-vectors
OverviewThis project contains the commands which create direction vectors for several brain regions including the cerebellum, the isocortex, and the thalamus of the AIBS P56 mouse brain.Direction vectors are 3D unit vectors associated to voxels of a brain region.
They represent the directions of the fiber tracts and their streamlines are assumed to cross transversely layers in laminar brain regions.Direction vectors are used in placement-algorithm to set cells orientations.Direction vectors are also used to compute placement hints and split layer 2/3 of the AIBS mouse isocortex.After installation, you can display the available command lines with the followingbashcommand:atlas-direction-vectors--helpInstallationpipinstallatlas-direction-vectorsExamplesDirection VectorsGet the required input files:mkdir-pdata/ccfv3# hierarchy file:curl-odata/1.jsonhttp://api.brain-map.org/api/v2/structure_graph_download/1.json# CCFv3 annotation volume:curl-odata/ccfv3/annotation_25.nrrdhttp://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/annotation/ccf_2017/annotation_25.nrrdRun the code (note, depending on the resolution of the volume, this may require significant amounts of available RAM):atlas-direction-vectorsdirection-vectors\isocortex\--hierarchy-pathdata/1.json\--annotation-pathdata/ccfv3/annotation_25.nrrd\--algorithmshading-blur-gradient\--output-pathdata/ccfv3/direction_vectors.nrrdFor the placeholder orientations, one can compute the vectors for a particular
region with:atlas-direction-vectorsdirection-vectors\from-center\--regionSSp\--hierarchy-pathdata/1.json\--annotation-pathdata/ccfv3/annotation_25.nrrd\--output-pathplaceholder-direction_vectors.nrrdInstructions for developersRun the following commands before submitting your code for review:cdatlas-direction-vectorsisort-l100--profileblackatlas_direction_vectorstestssetup.pyblack-l100atlas_direction_vectorstestssetup.pyThese formatting operations will help you pass the linting checktestenv:lintdefined intox.ini.CitationWhen you use this software, we kindly ask you to cite the following DOI:AcknowledgementsThe development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.For license and authors, see LICENSE.txt and AUTHORS.txt respectively.Copyright © 2022-2024 Blue Brain Project/EPFL
Package: atlas-fit
Atlas-FitPython tools to fit solar spectra with known atlantes forwavelength calibrationcontinuum correctionstray-light analysiseffective FWHM computationchi^2 error computation of the atlas fitThis package provides two tools:bin/prepare:This script is used to generate a list of lines from the atlas and data
to use for finding a wavelength solution (dispersion).
If you have a list of lines already you can still use this to test and
fine-tune your configuration and to inspect the fitting and analysis
results with interactive plots.bin/amend_spectroflat:This tool allows to amend the calibration results from thespectroflatlibrary. The fitted dispersion can be applied
to theOffsetMap(to include wavelength calibration) and
the continuum correction can be included with the soft-flat.Both tools can (and should) be run with the same configuration.
Run thepreparefirst to find the right configuration values
and use theamend_spectroflatscript to apply the results.ConfigurationLines to be used for dispersion fitting can be selected interactively:After thepreparescript ran successful on the selected lines, it will
create anatlas_fit_lines.yamlconfiguration file to be used withamend_spectroflatscript. Find an example underexamples/line_selection.ymllabel:"Testconfig"input:path:examples/test.fits.gzroi:"[25,10:]"wl_start:410.6wl_end:413.4fwhm:0.00112stray_light:7line_window:12atlas:key:'hhdc'line_window:50Find a fully commented example underexamples/config.ymlUsageRun either script with<script-name> path/to/config.yml [path/to/linefile.yml]prepareThis script allows you to inspect the fit of your data
against the selected atlas and lines in detail.
Use this to fine-tune the configuration and generate plots.amend_spectroflatThis script creates an improved version of the calibration
data created withspectroflat. It will add the dispersion
correction (and error information) to theOffsetMapand
the continuum correction to the soft flat.Using the amended calibration data results in a fully corrected
dataset suitable for demodulation and spectral restoration.SupportDeveloped and maintained byhoelken@mps.mpg.de
Package: atlasify
The Python packageatlasifyapplies the ATLAS style to matplotlib plots. This includesUse free bundled Helvetiva clone as fallback font (not Helvetica since it’s not widely available),Adding ticks on all edges,Making ticks to inward,Adding the*ATLAS*badge with optional labels (e.g. Internal),Adding a description below the badge, andMoving the*ATLAS*badge outside the axes area.QuickstartThe package will use Helvetica. The
package ships with GPL-licensed Nimbus Sans L as a fallback.Theatlasifypackage can be installed using pip.pip install atlasify
# or
pip install https://gitlab.cern.ch/fsauerbu/atlasify/-/archive/master/atlasify-master.tar.gzIndices and tablesGit repositoryPyPI repositoryDocumentation
Package: atlas-metadata-validator
Expression Atlas metadata validatorThis is a Python module to parse a set of MAGE-TAB files and check for compatibility with the Expression Atlas and Single Cell Expression Atlas analysis pipelines. The main validation script automatically detects the experiment type from the MAGE-TAB and runs the respective tests. Currently general checks (for bulk and single-cell experiment) as well as Single Cell Expression Atlas specific checks are supported. The controlled vocabulary for the checks are stored in themetadata-validation-configrepository.RequirementsPython 3.6, requests (tested with version 2.20.1), and GitPython (tested with version 3.1.7)InstallInstall with pip:pip install atlas-metadata-validatorSet environment variables to fetch the validation configuration for controlled vocabulary terms:exportVALIDATION_CONFIG_REPO='https://github.com/ebi-gene-expression-group/metadata-validation-config'exportVALIDATION_CONFIG_FILE='atlas_validation_config.json'(if not specified the above values are used as default)Set local path where to store the validation config files, default is working dir when calling the scriptexportVALIDATION_CONFIG_LOCAL_PATH='/path/to/your/local/copy/'Single-cell MAGE-TAB validatorA MAGE-TAB pre-validation module for running checks that guarantee the experiment can be processed forSingle Cell Expression Atlas. The checks are mainly covering the pre-validation byhttps://github.com/ebi-gene-expression-group/scxa-control-workflow/blob/master/bin/sdrfToNfConf.Rin order to guarantee correct processing. It reads metadata directly from the MAGE-TAB and generates a log file in the directory of the IDF file.The checks can be invoked using the atlas_validation script with an IDF file path as input:atlas_validation.py path/to/test.idf.txtOptionsThe SDRF file is expected in the same directory as the IDF file. If this is not the case, the location of the SDRF and other data files can be specified with-d PATH_TO_DATAoption.The script guesses the experiment type (sequencing, microarray or single-cell) from the MAGE-TAB. If this was unsuccessful the experiment type can be set by specifying the respective argument-seq,-maor-sc.The URI checks may take long time. Hence there is an option to skip these checks with-x.Verbose logging can be activated with-v.Special validation rules for HCA-imported experiments can be invoked with-hcaoption. The validator will otherwise guess if the experiment is an HCA import based on the HCAD accession code in the ExpressionAtlasAccession field.
Package: atlas-placement-hints
OverviewThis project contains tools to compute placement hints.
Placement hints are used by the placement algorithm to place 3D cells in the context of circuit building.After installation, you can display the available command lines with the followingbashcommand:atlas-placement-hints--helpInstallationThis python project depends onUltraliserfor the algorithms which follow a mesh-based approach.OnceUltraliseris installed, run the followingbashcommands:pipinstallatlas-placement-hintsExamplesTo generate the placement hints datasets for the isocortex, one can do the following:Note that the required datasets underdata/*can be generated by following the example inatlas-splitter.Also note, depending on the resolution of the volume, this may require significant amounts of available RAM).atlas-placement-hintsisocortex\--hierarchy-pathdata/ccfv3/split_hierarcy.json\--annotation-pathdata/ccfv3/split_annotations.nrrd\--direction-vectors-pathdata/ccfv3/direction_vectors.nrrd\--algorithmvoxel-based\--output-dirdata/Instructions for developersRun the following commands before submitting your code for review:cdatlas-placement-hintsisort-l100--profileblackatlas_placement_hintstestssetup.pyblack-l100atlas_placement_hintstestssetup.pyThese formatting operations will help you pass the linting checktestenv:lintdefined intox.ini.CitationWhen you use this software, we kindly ask you to cite the following DOI:AcknowledgementsThe development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.For license and authors, see LICENSE.txt and AUTHORS.txt respectively.Copyright © 2022 Blue Brain Project/EPFL
Package: atlasplots
ATLAS PlotsATLAS Plotsprovides a simple interface to produce plots inROOTfollowing the standardATLASstyle guidelines. It usesmatplotlib-like syntax and idioms while still giving access to the underlying ROOT objects.Behold, the simplicity of ATLAS Plots:importatlasplotsasapltaplt.set_atlas_style()fig,ax=aplt.subplots(1,1)ax.set_xlabel("X [GeV]")ax.set_ylabel("Events")ax.set_xlim(0,100)ax.set_ylim(0,10)fig.savefig("figure.png")Compare with the equivalent PyROOT code:importROOTimportatlasplotsasapltaplt.set_atlas_style()canv=ROOT.TCanvas("canv","",800,600)frame=ROOT.TH1F("frame","",1,0,1)frame.GetXaxis().SetTitle("X [GeV]")frame.GetYaxis().SetTitle("Events")frame.GetXaxis().SetLimits(0,100)frame.GetYaxis().SetLimits(0,10)frame.Draw("AXIS")canv.SaveAs("figure.png")Installing$pipinstallatlasplotsYou can also install in editable mode if you need a quick-and-dirty way to make changes to the source code:$gitclone<project-url>$cdatlas-plots$pipinstall[--user]-e.ExamplesFor those who learn by exampleSeveral examples of more complex plots, such as histogram fits, ratio and fit-residual panels, Data/MC plots, etc., are available under theexamples/directory.DocumentationTheATLAS Plotsdocumentation is available athttps://atlas-plots.readthedocs.io.
Package: atlas-provider-django
atlas-provider-djangoLoadDjangomodels into anAtlasproject.Use-casesDeclarative migrations- use a Terraform-likeatlas schema apply --env djangoto apply your Django schema to the database.Automatic migration planning- useatlas migrate diff --env djangoto automatically plan a migration from the current database version to the Django schema.InstallationInstall Atlas for macOS or Linux by running:curl-sSfhttps://atlasgo.sh|shSeeatlasgo.iofor more installation options.Install the provider by running:pipinstallatlas-provider-djangoConfigurationAdd the provider to your Django project'sINSTALLED_APPSinsettings.py:INSTALLED_APPS=[...,'atlas_provider_django',...]In your project directory, create a new file namedatlas.hclwith the following contents:data"external_schema""django"{program=["python","manage.py","atlas-provider-django","--dialect", "mysql"// mariadb | postgresql | sqlite | mssql// if you want to only load a subset of your app models, you can specify the apps by adding// "--apps", "app1", "app2", "app3"]}env"django"{src=data.external_schema.django.urldev="docker://mysql/8/dev"migration{dir="file://migrations"}format{migrate{diff="{{ sql . \"  \" }}"}}}UsageApplyYou can use theatlas schema applycommand to plan and apply a migration of your database to your current Django schema.
This works by inspecting the target database and comparing it to the Django Apps models and creating a migration plan.
Atlas will prompt you to confirm the migration plan before applying it to the database.atlasschemaapply--envdjango-u"mysql://root:password@localhost:3306/mydb"Where the-uflag accepts theURLto the
target database.DiffAtlas supports aversioned migrationsworkflow, where each change to the database is versioned and recorded in a migration file. You can use theatlas migrate diffcommand to automatically generate a migration file that will migrate the database
from its latest revision to the current Django schema.atlasmigratediff--envdjangoSupported DatabasesThe provider supports the following databases:MySQLMariaDBPostgreSQLSQLiteMicrosoft SQL ServerIssuesPlease report any issues or feature requests in theariga/atlasrepository.LicenseThis project is licensed under theApache License 2.0.
Package: atlas-provider-sqlalchemy
atlas-provider-sqlalchemyLoadSQLAlchemymodels into anAtlasproject.Use-casesDeclarative migrations- use a Terraform-likeatlas schema apply --env sqlalchemyto apply your SQLAlchemy schema to the database.Automatic migration planning- useatlas migrate diff --env sqlalchemyto automatically plan a migration from the current database version to the SQLAlchemy schema.InstallationInstall Atlas from macOS or Linux by running:curl-sSfhttps://atlasgo.sh|shSeeatlasgo.iofor more installation options.Install the provider by running:# The Provider works by importing your SQLAlchemy models and extracting the schema from them.# Therefore, you will need to run the provider from within your project's Python environment.pipinstallatlas-provider-sqlalchemyStandaloneIf all of your SQLAlchemy models exist in a single package,
you can use the provider directly to load your SQLAlchemy schema into Atlas.In your project directory, create a new file namedatlas.hclwith the following contents:data"external_schema""sqlalchemy"{program=["atlas-provider-sqlalchemy","--path", "./path/to/models","--dialect", "mysql"// mariadb | postgresql | sqlite | mssql]}env"sqlalchemy"{src=data.external_schema.sqlalchemy.urldev="docker://mysql/8/dev"migration{dir="file://migrations"}format{migrate{diff="{{ sql . \"  \" }}"}}}As Python ScriptIf you want to use the provider as a python script, you can use the provider as follows:Create a new file namedload_models.pywith the following contents:# import all modelsfrommodelsimportUser,Taskfromatlas_provider_sqlalchemy.ddlimportprint_ddlprint_ddl("mysql",[User,Task])Next, in your project directory, create a new file namedatlas.hclwith the following contents:data"external_schema""sqlalchemy"{program=["python","load_models.py"]}env"sqlalchemy"{src=data.external_schema.sqlalchemy.urldev="docker://mysql/8/dev"migration{dir="file://migrations"}format{migrate{diff="{{ sql . \"  \" }}"}}}UsageOnce you have the provider installed, you can use it to apply your SQLAlchemy schema to the database:ApplyYou can use theatlas schema applycommand to plan and apply a migration of your database to your current SQLAlchemy schema.
This works by inspecting the target database and comparing it to the SQLAlchemy schema and creating a migration plan.
Atlas will prompt you to confirm the migration plan before applying it to the database.atlasschemaapply--envsqlalchemy-u"mysql://root:password@localhost:3306/mydb"Where the-uflag accepts theURLto the
target database.DiffAtlas supports aversion migrationworkflow, where each change to the database is versioned and recorded in a migration file. You can use theatlas migrate diffcommand to automatically generate a migration file that will migrate the database
from its latest revision to the current Sequelize schema.atlasmigratediff--envsqlalchemySupported DatabasesThe provider supports the following databases:MySQLMariaDBPostgreSQLSQLiteMicrosoft SQL ServerCreditThe code in this repository is based onnoamtamir/atlas-provider-sqlalchemy.IssuesPlease report any issues or feature requests in theariga/atlasrepository.LicenseThis project is licensed under theApache License 2.0.