Saravanan666/pypi-org huggingface dataset:
Package: dkist-processing-math
dkist-processing-math contains the math functions used throughout the DKIST data processing software stack.DKIST:https://nso.edu/telescopes/dki-solar-telescope/Developmentgitclonegit@bitbucket.org:dkistdc/dkist-processing-visp.gitcddkist-processing-visppre-commitinstallpipinstall-e.[test]pytest-v--covdkist_processing_vispChangelogWhen you makeanychange to this repository itMUSTbe accompanied by a changelog file.
The changelog for this repository uses thetowncrierpackage.
Entries in the changelog for the next release are added as individual files (one per change) to thechangelog/directory.Writing a Changelog EntryA changelog entry accompanying a change should be added to thechangelog/directory.
The name of a file in this directory follows a specific template:<PULL REQUEST NUMBER>.<TYPE>[.<COUNTER>].rstThe fields have the following meanings:<PULL REQUEST NUMBER>: This is the number of the pull request, so people can jump from the changelog entry to the diff on BitBucket.<TYPE>: This is the type of the change and must be one of the values described below.<COUNTER>: This is an optional field, if you make more than one change of the same type you can append a counter to the subsequent changes, i.e.100.bugfix.rstand100.bugfix.1.rstfor two bugfix changes in the same PR.The list of possible types is defined the the towncrier section ofpyproject.toml, the types are:feature: This change is a new code feature.bugfix: This is a change which fixes a bug.doc: A documentation change.removal: A deprecation or removal of public API.misc: Any small change which doesn’t fit anywhere else, such as a change to the package infrastructure.Rendering the Changelog at Release TimeWhen you are about to tag a release first you must runtowncrierto render the changelog.
The steps for this are as follows:Runtowncrier build –version vx.y.zusing the version number you want to tag.Agree to have towncrier remove the fragments.Add and commit your changes.Tag the release.NOTE:If you forget to add a Changelog entry to a tagged release (either manually or automatically withtowncrier)
then the Bitbucket pipeline will fail. To be able to use the same tag you must delete it locally and on the remote branch:# First, actually update the CHANGELOG and commit the updategitcommit# Delete tagsgittag-dvWHATEVER.THE.VERSIONgitpush--deleteoriginvWHATEVER.THE.VERSION# Re-tag with the same versiongittagvWHATEVER.THE.VERSIONgitpush--tagsoriginmain
Package: dkist-processing-pac
Modules to both determine telescope polarization parameters and compute instrument demodulation matricesIt is primarily a library used by the DKIST automated processing pipelinesDeploymentdkist-processing-pac is deployed toPyPIDevelopmentgitclonegit@bitbucket.org:dkistdc/dkist-processing-pac.gitcddkist-processing-pacpre-commitinstallpipinstall-e.[test]pytest-v--covdkist_processing_pacChangelogWhen you makeanychange to this repository itMUSTbe accompanied by a changelog file.
The changelog for this repository uses thetowncrierpackage.
Entries in the changelog for the next release are added as individual files (one per change) to thechangelog/directory.Writing a Changelog EntryA changelog entry accompanying a change should be added to thechangelog/directory.
The name of a file in this directory follows a specific template:<PULL REQUEST NUMBER>.<TYPE>[.<COUNTER>].rstThe fields have the following meanings:<PULL REQUEST NUMBER>: This is the number of the pull request, so people can jump from the changelog entry to the diff on BitBucket.<TYPE>: This is the type of the change and must be one of the values described below.<COUNTER>: This is an optional field, if you make more than one change of the same type you can append a counter to the subsequent changes, i.e.100.bugfix.rstand100.bugfix.1.rstfor two bugfix changes in the same PR.The list of possible types is defined the the towncrier section ofpyproject.toml, the types are:feature: This change is a new code feature.bugfix: This is a change which fixes a bug.doc: A documentation change.removal: A deprecation or removal of public API.misc: Any small change which doesn’t fit anywhere else, such as a change to the package infrastructure.Rendering the Changelog at Release TimeWhen you are about to tag a release first you must runtowncrierto render the changelog.
The steps for this are as follows:Runtowncrier build –version vx.y.zusing the version number you want to tag.Agree to have towncrier remove the fragments.Add and commit your changes.Tag the release.NOTE:If you forget to add a Changelog entry to a tagged release (either manually or automatically withtowncrier)
then the Bitbucket pipeline will fail. To be able to use the same tag you must delete it locally and on the remote branch:# First, actually update the CHANGELOG and commit the updategitcommit# Delete tagsgittag-dvWHATEVER.THE.VERSIONgitpush--deleteoriginvWHATEVER.THE.VERSION# Re-tag with the same versiongittagvWHATEVER.THE.VERSIONgitpush--tagsoriginmain
Package: dkist-processing-test
dkist-processing-testOverviewThe dkist-processing-test library serves as an example implementation of a Tasks and Workflows using thedkist-processing-coreframework anddkist-processing-commonTasks.The recommended project structure is to separate tasks and workflows into separate packages.BuildArtifacts are built throughbitbucket pipelinesThe pipeline can be used in other repos with a modification of the package and artifact locations
to use the names relevant to the target repo.e.g. dkist-processing-test -> dkist-processing-vbi and dkist_processing_test -> dkist_processing_vbiDeploymentDeployment is done withturtlebotand follows
the process detailed indkist-processing-coreEnvironment VariablesOnly those specified bydkist-processing-coreanddkist-processing-commonDevelopmentgitclonegit@bitbucket.org:dkistdc/dkist-processing-test.gitcddkist-processing-testpre-commitinstallpipinstall-e.[test]pytest-v--covdkist_processing_test
Package: dkist-quality
dkist-qualityProvides the ability to create a pdf quality report from structured quality data.Sample Usagefromdkist_quality.reportimportformat_reportdefcreate_quality_report(report_data:dict|list[dict],dataset_id:str)->bytes:"""
    Generate a quality report in pdf format.

    :param report_data: Quality data for the dataset.
    :param dataset_id: The dataset id.

    :return: quality report in pdf format
    """returnformat_report(report_data=report_data,dataset_id=dataset_id)Developer Setuppipinstall-e.[test]pipinstallpre-commitpre-commitinstallLicenseThis project is Copyright (c) NSO / AURA and licensed under
the terms of the BSD 3-Clause license. This package is based upon
theOpenastronomy packaging guidewhich is licensed under the BSD 3-clause licence. See the licenses folder for
more information.
Package: dkist-service-configuration
A configuration for thelogurulogger and base configuration object usingpydanticbase settings.It is important that it be the first import to run so the standard logging basicConfig method has an effect.FeaturesStderr outputIntercepted logging from client librariesDisabled better exceptions for log levels above debug to mitigate secret leakingConfiguration logging with secrets redactedDKIST Mesh Service configuration parsingInstallationpipinstalldkist-service-configurationExamplesconfig.pyfromdkist_service_configurationimportloggerfromdkist_service_configurationimportMeshServiceConfigurationBaselogger.debug('hello world)classNewConfiguration(MeshServiceConfigurationBase):username:str="me"password:str="pass"new_configuration=NewConfiguration()new_configuration.log_configurations()The code above will register the existing loggers, retrieve mesh configuration
and custom configuration from the environment and log it while redacting the
password.
Package: dkist-spectral-lines
OverviewThe dkist-spectral-lines package consolidates the definition of metadata associated with spectral lines observed by the
DKIST instruments.UsageThe spectral lines are all defined as classes with properties capturing their relevant metadata.  Spectral lines can be
retrieved using one of the helper functionsfromdkist_spectral_linesimportget_spectral_linesfromdkist_spectral_linesimportget_closest_spectral_linespectral_lines_in_a_range=get_spectral_lines(wavelength_min=1,wavelength_max=1000)closest_line=get_closest_spectral_line(wavelength=100)Builddkist-spectral-lines is built usingbitbucket-pipelinesDeploymentdkist-spectral-lines is deployed toPyPIEnvironment VariablesnoneDevelopmentgitclonegit@bitbucket.org:dkistdc/dkist-spectral-lines.gitcddkist-spectral-linespre-commitinstallpipinstall-e.[test]pytest-v--covdkist_spectral_linesChangelogWhen you makeanychange to this repository itMUSTbe accompanied by a changelog file.
The changelog for this repository uses thetowncrierpackage.
Entries in the changelog for the next release are added as individual files (one per change) to thechangelog/directory.Writing a Changelog EntryA changelog entry accompanying a change should be added to thechangelog/directory.
The name of a file in this directory follows a specific template:<PULL REQUEST NUMBER>.<TYPE>[.<COUNTER>].rstThe fields have the following meanings:<PULL REQUEST NUMBER>: This is the number of the pull request, so people can jump from the changelog entry to the diff on BitBucket.<TYPE>: This is the type of the change and must be one of the values described below.<COUNTER>: This is an optional field, if you make more than one change of the same type you can append a counter to the subsequent changes, i.e.100.bugfix.rstand100.bugfix.1.rstfor two bugfix changes in the same PR.The list of possible types is defined the the towncrier section ofpyproject.toml, the types are:feature: This change is a new code feature.bugfix: This is a change which fixes a bug.doc: A documentation change.removal: A deprecation or removal of public API.misc: Any small change which doesn’t fit anywhere else, such as a change to the package infrastructure.Rendering the Changelog at Release TimeWhen you are about to tag a release first you must runtowncrierto render the changelog.
The steps for this are as follows:Runtowncrier build –version vx.y.zusing the version number you want to tag.Agree to have towncrier remove the fragments.Add and commit your changes.Tag the release.NOTE:If you forget to add a Changelog entry to a tagged release (either manually or automatically withtowncrier)
then the Bitbucket pipeline will fail. To be able to use the same tag you must delete it locally and on the remote branch:# First, actually update the CHANGELOG and commit the updategitcommit# Delete tagsgittag-dvWHATEVER.THE.VERSIONgitpush--deleteoriginvWHATEVER.THE.VERSION# Re-tag with the same versiongittagvWHATEVER.THE.VERSIONgitpush--tagsoriginmain
Package: dkmri
dkmri.pydkmri.pystands for diffusion kurtosis magnetic resonance imaging in Python. It is a Python package for estimating diffusion and kurtosis tensors from diffusion-weighted magnetic resonance data. The estimation is performed using regularized non-linear optimization informed by a fully-connected feed-forward neural network that is trained to learn the mapping from data to kurtosis metrics. Details can be found in thearXiv preprintandsource code.This software can be used from the command line or in a Python interpreter.The command-line interface does not require any knowledge about Python.Python interface is for people comfortable with basic Python programming.InstallationFirst, make sure you have installedPython.If you just want to use the command-line interface, the recommended way of installingdkmri.pyis to usepipx:pipx install dkmripipx automatically creates an isolated environment in which the dependencies are installed.If you want to use the Python interface, you can usepip(you should installdkmri.pyin an isolated environment usingvenvorcondato avoid dependency issues):pip install dkmriUsage exampleCommand-line interfaceThe command for usingdkmri.pyisdkmri.py data bvals bvecs optional-argumentswheredata,bvals, andbvecsare the paths of the files containing the
diffusion-weighted data, b-values, and b-vectors, andoptional-argumentsis
where to define things such as which parameter maps to save.For example, a command for computing a mean kurtosis map fromdata.nii.gzand
saving it inmk.nii.gzcould bedkmri.py data.nii.gz bvals.txt bvecs.txt -mask mask.nii.gz -mk mk.nii.gzTo see a full description of the arguments, execute the following:dkmri.py -hPython interfaceSee theexample notebook.SupportIf you have questions, found bugs, or need help, please open anissue on Github.CitationIf you find this repository useful in work that leads to a scientific publication, please cite thearXiv preprint.
Package: dkmri-WIN
> рџ’Ў Instead of the code in this repository, it is recommended to use the [non-negativity-constrained](https://doi.org/10.1016/j.neuroimage.2019.116405) diffusion kurtosis imaging available in, for example, [DIPY](https://github.com/dipy/dipy).#dkmri.pydkmri.pystands for diffusion kurtosis magnetic resonance imaging in Python. It is a Python package for estimating diffusion and kurtosis tensors from diffusion-weighted magnetic resonance data. The estimation is performed using regularized non-linear optimization informed by a fully-connected feed-forward neural network that is trained to learn the mapping from data to kurtosis metrics. Details can be found in the [arXiv preprint](https://arxiv.org/abs/2203.07327) and [source code](https://github.com/kerkelae/dkmri/blob/main/dkmri/dkmri.py).This software can be used from the command line or in a Python interpreter.The command-line interface does not require any knowledge about Python.Python interface is for people comfortable with basic Python programming.## InstallationFirst, make sure you have installed [Python](https://www.python.org/downloads/).If you just want to use the command-line interface, the recommended way of installingdkmri.pyis to use [pipx](https://github.com/pypa/pipx/#install-pipx):` pipx install dkmri `pipx automatically creates an isolated environment in which the dependencies are installed.If you want to use the Python interface, you can use [pip](https://pip.pypa.io/en/stable/) (you should installdkmri.pyin an isolated environment using [venv](https://docs.python.org/3/library/venv.html) or [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) to avoid dependency issues):` pip install dkmri `## Usage example### Command-line interfaceThe command for usingdkmri.pyis` dkmri.py data bvals bvecsoptional-arguments`wheredata,bvals, andbvecsare the paths of the files containing the
diffusion-weighted data, b-values, and b-vectors, andoptional-argumentsis
where to define things such as which parameter maps to save.For example, a command for computing a mean kurtosis map fromdata.nii.gzand
saving it inmk.nii.gzcould be` dkmri.py data.nii.gz bvals.txt bvecs.txt-maskmask.nii.gz-mkmk.nii.gz `To see a full description of the arguments, execute the following:` dkmri.py-h`### Python interfaceSee the [example notebook](https://github.com/kerkelae/dkmri/blob/main/docs/example.ipynb).## SupportIf you have questions, found bugs, or need help, please open an
[issue on Github](https://github.com/kerkelae/dkmri/issues).## CitationIf you find this repository useful in work that leads to a scientific publication, please cite the [arXiv preprint](https://arxiv.org/abs/2203.07327).
Package: dkopt
deck-optimizerdeck-optimizeris a Python module to support TCG deck building especially Yu-Gi-Oh!. This module suggest the best combination of cards under given conditions.DependencyUseDockeror seerequirements.txt.UsageMake config yaml file to describe the ideal hand. For example, if you want to draw at least one PSY-Framegear Gamma and keep PSY-Frame Driver in deck, your config file will be written as below.num_hand: 5
num_deck:
  min: 40
  max: 60
condition:
  - name: Gamma
    min: 2
    max: 3
    require: 1
    exact: False
    in_hand: True
  - name: Driver
    min: 1
    max: 1
    require: 1
    exact: True
    in_hand: FalseThen, run the app by following command.dockerpullghcr.io/hasoya/deck-optimizer:v0.0.1
dockerrun-v/absolute/path/to/config/file:/config.yamlghcr.io/hasoya/deck-optimizer:v0.0.1-f/config.yamlYou will get the suggestion of deck composition and the probability of the ideal hand.ThebestdeckTotalNumDeck:40NumGammaindeck:3NumDriverindeck:1Theprobabilityoftheidealhand30.21%
Package: dkp
DKP - DocKer compose PackerPacks existent docker-compose project in executable single encrypted (GPG
AES256) archive (tar + gz). It means, that you don't need to install DKP or
remember procedure details during restore which dramatically reduces stress and
make your life simpler. In the end - it's hard to remember how to restore backup
made by N generations of administrators years ago before you...The pipeline is:flowchart TD
    A[tar volumes] -->B(tar images)
    B --> C[add env files and manifests]
    C --> D[add restore scripts]
    D --> E[compress and encrypt]
    E --> F[wrap to self-extracting file]For backup you need:gpgpython 3.8+ (see tests in workflow)shtargzipdocker with compose pluginFor restore you need:gpgsedtargzipInstallationRecomendedpip install dkpAlternative - just downloaddkp/dkp.pyand make it executable.Backward compatibilityOnce backup created, the version of DKP doesn't matter anymore since archive is
self-complete independent file.New DKP versions may introduce more features as well as different layout of
final archive, but it will not affect previous backups.UsageCreate backupusage: dkp [-h] [--output OUTPUT] [--skip-images] [--passphrase PASSPHRASE] [project]

DocKer compose Packer - backup compose project with all batteries included

positional arguments:
  project               Compose project name. Default is docker-compose-pack

options:
  -h, --help            show this help message and exit
  --output OUTPUT, -o OUTPUT
                        Output file. Default docker-compose-pack.bin
  --skip-images, -S     Do not archive images
  --passphrase PASSPHRASE, -p PASSPHRASE
                        Passphrase to encrypt backup. Can be set via env PASSPHRASERestoreUsage:

./path/to/backup/file [--restore/-r] [-s/--start] [-h/--help] [passphrase]

   passphrase      Key to decrypt archive. Can be set by env PASSPHRASE

  -h, --help       Show this help
  -r, --restore    Automatically restore project after unpacking
  -s, --start      Automatically start project after unpacking. Implicitly enables --restore
Package: dkpro-cassis-tools
Dkpro cassis toolsToolkit for managing uima cas xmi files.Installpipinstalldkpro-cassis-toolsLoad cas from a zip filefromdkpro_cassis_toolsimportload_cas_from_zip_filewithopen('cas.zip','rb')asf:cas=load_cas_from_zip_file(f)Save cas to a zip filefromdkpro_cassis_toolsimportdump_cas_to_zip_filewithopen('cas.zip','rb')asf:dump_cas_to_zip_file(cas,f)Restore cas segmentation by newlinefromdkpro_cassis_toolsimportload_cas_from_zip_filefromdkpro_cassis_toolsimportrestore_cas_segmentation_by_newlinefromdkpro_cassis_toolsimportdump_cas_to_zip_file# Open the caswithopen('cas.zip','rb')asf:cas=load_cas_from_zip_file(f)# Restore segmentationre_segmented_cas=restore_cas_segmentation_by_newline(cas)# Save itwithopen('re_segmented_cas.zip','rb')asf:dump_cas_to_zip_file(cas,f)Combine sentences from one or more casfromdkpro_cassis_toolsimportload_cas_from_zip_filefromdkpro_cassis_toolsimportdump_cas_to_zip_filefromdkpro_cassis_toolsimportcreate_cas_from_sentencesfromdkpro_cassis_toolsimportSENTENCE_NSsentences=[]# Extract some sentences from cas1withopen('cas1.zip','rb')asf:cas1=load_cas_from_zip_file(f)forsentenceincas1.select(SENTENCE_NS):iflen(sentence.get_covered_text())>10:sentences.append((cas1,sentence))# Extract some sentences from cas2withopen('cas2.zip','rb')asf:cas2=load_cas_from_zip_file(f)forsentenceincas2.select(SENTENCE_NS):iflen(sentence.get_covered_text())>10:sentences.append((cas2,sentence))# Create the new casnew_cas=create_cas_from_sentences(sentences)# Save itwithopen('new_cas.zip','rb')asf:dump_cas_to_zip_file(new_cas,f)Tokenize casfromdkpro_cassis_toolsimportload_cas_from_zip_filefromdkpro_cassis_toolsimporttokenize_caswakati=MeCab.Tagger("-Owakati")deftokenize(text:str)->List[str]:returnwakati.parse(text).split()withopen('data/cas_tokenize.zip','rb')asf:cas=load_cas_from_zip_file(f)mecab_tokenized_cas=tokenize_cas(cas,tokenize)
Package: dkr
# dkrExtensible Docker CLI Client## To Use`dkr --help` for use.## To Install```bashpip install dkr```## To ExtendAdd any .py file to `~/.dkr/commands/`. It should have the following functions:* `command()`: returns a `str` or `list` of `str`. The name of the subcommand(s).* `help_summary(str)`: takes a `str` returns a `str`. The returned string is what is displayed for the given command in `--help`.* `import_command(docker_client, args, state)`: Takes the docker-py docker client and a subparser for argparse.ArgumentParser for the command. The state is a simple dictionary that remains persistent between running the dkr command. You can use this to track state, e.g. to remember what the last container was used so you can provide a shortcut.`args.set_defaults(func=somefunc)` should be called. `somefunc` will be invoked when your particular command is run from the CLI. It should accept the same three arguments as import_command.## Create OptionsThe `dkr create` command has the `--option` (`-o`) flag that can be specified multiple times. The format is explained below.This can be used to specify any docker create arg, using the naming conventions used by [docker-py](http://docker-py.readthedocs.io/en/latest/api/#create_container)## Create Options FormatThese arguments get expanded to a dict. For example:```bashdkr create -o key=value image_name```Is expanded to:```python{"key": "value"}```This also handles nested values:```bashdkr create -o nested.key=value image_name```Is expanded to:```python{"nested": {"key": "value"}}```You can also specify raw json values:```bashdkr create -o nested.key:=true image_name```Is expanded to:```python{"nested": {"key": True}}```And:```bashdkr create -o "nested.key:=[true, false, 0]" image_name```Is expanded to:```python{"nested": {"key": [True, False, 0]}}```You can use multiple options:```bashdkr create -o "nested.key:=[true, false, 0]" -o "nested.key2=value" image_name```Is expanded to:```python{"nested": {"key": [True, False, 0],"key2": "value"}}```You can test this yourself by running `dkr_core/cmd_to_json.py` with naked arguments. i.e. no `-o`:```bashpython3 dkr_core/cmd_to_json.py "nested.key:=[true, false, 0]" "nested.key2=value"```
Package: dkron
Command line interface client and python library forDkron.PrerequisitesPytnon 3InstallingTo install use pip:pip install dkronOr clone the repo:git clone https://github.com/Eyjafjallajokull/dkron-python.git
python setup.py installCLI UsageBefore you begin, set environment variableDKRON_HOSTSto point
running dkron instance.export DKRON_HOSTS=http://my-dkron.example.com,http://my-dkron-2.example.com:8080Alternatively, you can instert--hostsargument to every invocation
of dkron-cli.Fetch all jobsdkron-cli get jobsIt works well withjq, to list all job names:dkron-cli get jobs | jq '.[].name'Fetch specific jobdkron-cli get job [job_name]Create or update jobdkron-cli apply job [json_file] ...You can pass multiple files at once.Execute jobdkron-cli run [job_name]Delete jobdkron-cli delete job [job_name]Export all jobsdkron-cli export [backup_dir]Cluster statusdkron-cli get status
dkron-cli get leader
dkron-cli get membersLibrary UsagefromdkronimportDkronhosts=['http://localhost:8080']api=Dkron(hosts)print(api.get_job('my-dkron-job')['error_count'])api.run_job('my-dkron-job')Running testsmake test
make coverage
Package: dk-tasklib
dk-tasklib - pyinvoke task libraryInstalling from PyPIThis is what you want if you just want to use dk-tasklib:pip install dk-tasklibCreating a default tasks.py fileYou can create a defaulttasks.pyfile. From the root of your package:dk-tasklib installYou’ll need to specify--forceto overwrite an existing file.As a source packageThis is what you want if you are developing dk-tasklib or want
to make local changes to the source code.pip install -e <path>See docs/ folder for documentation.