Saravanan666/pypi-org huggingface dataset:
Package: dcos-quobyte
DCOS Quobyte Subcommand==========================Basic DCOS subcommandSetup-----#. Make sure you meet requirements for installing packages_#. Clone git repo for the dcos quobyte cli::git clone git@github.com:quobyte/dcos-quobyte.git#. Change directory to the repo directory::cd dcos-quoybyte#. Make sure that you have virtualenv installed. If not type::sudo pip install virtualenv#. Create a virtualenv for the project::make envConfigure Environment and Run-----------------------------#. TODO: Talk about how to configure the root dcos cli#. :code:`source` the setup file to add the :code:`dcos-quobyte` command line interface to your:code:`PATH`::source env/bin/activate#. Get started by calling the DCOS Quobyte CLI's help::dcos quobyte helpRunning Tests:--------------Setup#####Tox, our test runner, tests against both Python 2.7 and Python 3.4 environments.If you're using OS X, be sure to use the officially distributed Python 3.4 installer_ since theHomebrew version is missing a necessary library.Running#######Tox will run unit and integration tests in both Python environments using a temporarily createdvirtualenv.You should ensure :code:`DCOS_CONFIG` is set and that the config file points to the Marathoninstance you want to use for integration tests.There are two ways to run tests, you can either use the virtualenv created by :code:`make env`above::make testOr, assuming you have tox installed (via :code:`sudo pip install tox`)::toxOther Useful Commands######################. List all of the supported test environments::tox --listenvs#. Run a specific set of tests::tox -e <testenv>.. _packages: https://packaging.python.org/en/latest/installing.html#installing-requirements
Package: dcov
增量覆盖率工具 DCOV介绍dcov 是一个通过 git 统计不同 branch 之间差异，并生成报告的工具， 它主要有以下功能:统计两个 branch 之间的差异，生成差异报告# dcov --compare-branch HEAD^ 
Diff changes between HEAD^ and HEAD
changed files: 11, changed lines: 185

dcov/diff_cover_tool.py (1 lines): 231
dcov/git_diff.py (1 lines): 102
dcov/report_generator.py (12 lines): 23-25, 27-28, 35, 103-105, 111, 178, 188
dcov/templates/console_coverage_report.txt (6 lines): 1-2, 4, 13, 19, 22
dcov/templates/console_quality_report.txt (5 lines): 1, 5, 17, 23, 26
dcov/templates/html_coverage_report.html (1 lines): 9
dcov/templates/markdown_coverage_report.md (7 lines): 1-3, 8, 10, 12-13
dcov/violationsreporters/violations_reporter.py (38 lines): 5, 28, 30, 32-33, 37, 68, 74-75, 77, 192-193, 218-223, 225-226, 228, 230, 232-245, 248, 251
pyproject.toml (2 lines): 3-4
README.md (73 lines): 4-76
tests/data/cobe.xml (39 lines): 1-39根据标准的cobertura或则clover格式的单元测试覆盖率报告，生成增量覆盖率, 下面这个例子是我用phpunit单元测试覆盖率报告 cobe.xml 生成增量覆盖率报告，可以看到工具会生成每个文件对应的覆盖率情况，以及没有覆盖的代码所在的行号# dcov --compare-branch HEAD^ --coverage_xml tests/data/cobe.xml
--------------------------
Diff Coverage Report
Diff: HEAD^ HEAD, staged and unstaged changes
--------------------------
dcov/diff_cover_tool.py (0.0%): Missing lines 231
dcov/git_diff.py (0.0%): Missing lines 102
dcov/report_generator.py (0.0%): Missing lines 23-25,27-28,35,103-105,111,178,188
dcov/templates/console_coverage_report.txt (0.0%): Missing lines 1-2,4,13,19,22
dcov/templates/console_quality_report.txt (0.0%): Missing lines 1,5,17,23,26
dcov/templates/html_coverage_report.html (0.0%): Missing lines 9
dcov/templates/markdown_coverage_report.md (0.0%): Missing lines 1-3,8,10,12-13
dcov/violationsreporters/violations_reporter.py (2.6%): Missing lines 5,28,30,32-33,37,68,74,77,192-193,218-223,225-226,228,230,232-245,248,251
pyproject.toml (0.0%): Missing lines 3-4
README.md (0.0%): Missing lines 4-76
tests/data/cobe.xml (0.0%): Missing lines 1-39
--------------------------
Total:   185 lines
Missing: 184 lines
Coverage: 0%
--------------------------前提本工具需要系统安装git工具安装直接通过pip安装pip install dcov
dcov --version通过dcov --help查看常用的功能# 黑白名单, 如我不想把 app/ 和 scripts/ 目录下的文件加入覆盖率计算
dcov --compare-branch @~15 --exclude */app/* */scripts/*

# 忽略空白差异
dcov --compare-branch @~15 --ignore-whitespace

# 生成覆盖率报告,支持 html json markdown 格式, 以下是生成 html 格式报告
dcov --compare-branch @~15 --coverage_xml coverage_unit_test.xml --exclude */app/* */scripts/* --html-report this_is_our_html_report.html贡献代码开发需要使用到poetry工具pip install poetry在 fork 本仓库dcov, 在您本人仓库开发后再提交PR合并回本仓库git clone https://github.com/xxxxxxx/dcov.git
cd dcov
poetry run dcov --version感谢本工具原作者为Bachmann1234dcov 在原工具基础上加入一些新特性，以及修改了原来的覆盖率计算方法。
Package: dcp
dcpDeep copyis a command line utility for coping files and folders via python bypassing system call.FeaturesCopies file to file, file to folder, folder to folder.Records files count, size, time and speed.Performs smart copying without equal files overwrite.Support dry runs (without real copying).Supports overwrite prompting for each file.UsageOutput fromdcp --help:Usage:dcp.py[OPTIONS]SOURCEDESTINATIONCopyfileordirectoryrecursivelyfromsourcelocationtodestinationlocation.

Arguments:SOURCEPathtodirectoryorfiletocopyfrom.[required]DESTINATIONPathtodirectoryorfiletocopyto.Incaseofcopyingfiletodirectoryitissavedintothatdirectory.[required]Options:-b,--bufferINTEGERIn-memorybuffermaximumsizeforreadingandwritingfiles.Bydefaultsystembuffersizeisused.[default:<systemvalueisprinted>]-o,--overwriteWhethertooverwriteexistingfiles.Bydefaultuserispromptedabouteachfile.-d,--dry-runDonotactuallyperformcopyoperation.[default:False]-v,--versionPrintversionandexit.[default:False]-q,--quietDonotprintanythingtostdout,onlytostderr.[default:False]--install-completionInstallcompletionforthecurrentshell.--show-completionShowcompletionforthecurrentshell,tocopyitorcustomizetheinstallation.--helpShowthismessageandexit.Why another tool?Because I sometimes getDestination Path Too Longerror when try to copy files from unix disks. Actually this is Explorer's issue that can not be fixed since 2016.Issue tracker atgithub.Built with nice CLI frameworkTyper.
Package: dcpAlive
dcpAliveRuns DCP-o-Matic with Alive Progress BarsRequiresRequires DCP-o-Matic CLI and FFMPEG to be installed.UsagefromdcpAliveimportdcpAlivedcpAlive('input.mkv')Parametersinput- the input video fileoutput- the output directory, defaults to'.'name- the name of the film. Defaults toFalse. WhenFalse, uses the input filename.type- the type of content. Defaults to'FTR'
Package: dcqc
py-dcqcPython package for performing quality control (QC) for data coordination (DC)This Python package provides a framework for performing quality control (QC) on data files. Quality control can range from low-level integrity checks (e.g.MD5 checksum, file extension) to high-level checks such as conformance to a format specification and consistency with associated metadata.Early versions of this package were developed to be used by its sibling, thenf-dcqcNextflow workflow. You can see examples of how to leverage py-dcqc there. Note that the initial command-line interface (CLI) was developed with nf-dcqc in mind, so smaller steps were favored to enable parallelism in Nextflow. Future iterations of this package will include user-friendly, high-level CLI commands.PyScaffoldThis project has been set up using PyScaffold 4.3. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.putup --name dcqc --markdown --github-actions --pre-commit --license Apache-2.0 py-dcqc
Package: dc-qiskit-algorithms
qiskitis an open-source compilation framework capable of targeting various
types of hardware and a high-performance quantum computer simulator with emulation capabilities, and various
compiler plug-ins.This library sports some useful algorithms for quantum computers using qiskit as a basis.FeaturesMulti Qubit Quantum Fourier TransformDraper adderUniform RotationsState PreparationInstallationThis library requires Python version 3.5 and above, as well as qiskit.
Installation of this plugin, as well as all dependencies, can be done using pip:$python-mpipinstalldc_qiskit_algorithmsTo test that the algorithms are working correctly you can run$maketestGetting startedYou can use the state preparation as followsfromdc_qiskit_algorithms.MÃ¶ttÃ¶nenStatePrepimportstate_prep_mÃ¶ttÃ¶nenvector=[-0.1,0.2,-0.3,0.4,-0.5,0.6,-0.7,0.8]vector=numpy.asarray(vector)vector=(1/numpy.linalg.norm(vector))*vectorqubits=int(numpy.log2(len(vector)))reg=QuantumRegister(qubits,"reg")c=ClassicalRegister(qubits,"c")qc=QuantumCircuit(reg,c,name='state prep')state_prep_mÃ¶ttÃ¶nen(qc,vector,reg)After this, the quantum circuit is prepared in the given state. All complex vectors are possible!Please refer to thedocumentation of the dc qiskit algorithm Plugin.ContributingWe welcome contributions - simply fork the repository of this plugin, and then make apull requestcontaining your contribution.
All contributers to this plugin will be listed as authors on the releases.We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects or applications built on PennyLane.AuthorsCarsten BlankSupportSource Code:https://github.com/carstenblank/qiskit-algorithmsIssue Tracker:https://github.com/carstenblank/qiskit_algorithms/issuesIf you are having issues, please let us know by posting the issue on our Github issue tracker.LicenseThe data cybernetics qiskit algorithms plugin isfreeandopen source, released under
theApache License, Version 2.0.
Package: dc-quantum-scheduling
Scheduling software for qiskit -- ClientAttention: this is a work-in-progess implementation.What this means: it basically works. But:edge cases are not coveredeven broad cases aren't entirely covered!!in code documentation is not thereno usage or API documentation existsCI/CD is not setupSo: use at your own risk.ContributingWe welcome contributions - simply fork the repository of this plugin, and then make a pull request containing your contribution. All contributers to this plugin will be listed as authors on the releases.We also encourage bug reports, suggestions for new features and enhancements!AuthorsCarsten BlankSupportSource Code:https://github.com/carstenblank/dc-quantum-schedulingIssue Tracker:https://github.com/carstenblank/dc-quantum-scheduling/issuesIf you are having issues, please let us know by posting the issue on our Github issue tracker.LicenseThe dc-quantum-scheduling is free and open source, released under the Apache License, Version 2.0.
Package: dcraft
dcraftData management library based on data lake concept especially for data science and machine leaning.This helps your daily job's data management by raw, trusted and refined layer concept from data lake. The data is versioned and saved for each layer on specified storages and tables.ConceptFor daily individual work and for team work, we need to manage and organize our datasets to keep clean workflow. This library is to help that based on the data lake's layer concept.For each layer, you can save the data and metadata to several places such as local file system, GCP and MongoDB which you choose.Covered Data typepd.DataFrameDictList of DictCovered FormatcsvparquetjsonCovered Storage and TableYou can save the metadata and data on several places. The list below is the present coverage.MetadataLocal File SystemBigQueryMongoDBDataLocal File SystemGoogle Cloud StorageMinIOInstallationpip install dcraftTo use GCP resources.pip install dcraft[gcp]ExampleCreate layer's data. There arecreate_trustedandcreate_refinedtoo.fromdcraftimportcreate_rawimportpandasaspddata=pd.DataFrame({"a":[1,2],"b":[None,4]})raw_layer_data=create_raw(data,"fake-project","Shuhei Kishi","This is fake project",{"version":"0.0.1"})You can choose where the data and metadata should be saved. On this example, it saves both on local.importosfromdcraftimportLocalDataRepository,LocalMetadataRepositoryCURRENT_DIR=os.getcwd()DATA_DIR_PATH=os.path.join(CURRENT_DIR,"data")METADATA_DIR_PATH=os.path.join(CURRENT_DIR,"metadata")data_repository=LocalDataRepository(DATA_DIR_PATH)metadata_repository=LocalMetadataRepository(DATA_DIR_PATH)raw_layer_data.save("parquet",data_repository,metadata_repository)The data was saved to raw layer and information were saved as metadata.You can read the saved data from metadata's id. The format is kept.fromdcraftimportread_layer_dataloaded_raw_layer_data=read_layer_data(<id-from-metadata>,data_repository,metadata_repository)If you want to save the metadata and data on different places such as BigQuery and Google Cloud Storage, you can use differentRepositoryclass.fromdcraftimportBqMetadataRepository,GcsDataRepositoryGCP_PROJECT="your-project-id"GCS_BUCKET="your-bucket-name"data_repository=GcsDataRepository(GCP_PROJECT,GCS_BUCKET)metadata_repository=BqMetadataRepository(GCP_PROJECT,"test_dataset","test_table")raw_layer_data.save("csv",data_repository,metadata_repository)
Package: dcre
dCREComputation and decomposition of Cloud Radiative Effects (CRE)[!WARNING]
This package is still under development. Things will break.Installationpip install dcreUsageimportdcre# Add reference and perturbed sourcessrc1=dcre.cre_decomposition.Simulation(CWP,CF,NCCN,albedo,albd_clear,SOLIN)src2=dcre.cre_decomposition.Simulation(CWP,CF,NCCN,albedo,albd_clear,SOLIN)# Configure decompositiondecompositer=dcre.cre_decomposition.CRE_Decompositer(src1,src2)# Decomposedecompositer.decompose(cre_scaling=False)# Write results to diskds=decompositer.to_dataset()ds.to_netcdf("decomposition.nc")
Package: dcron
## Distributed Cronlike Scheduler [![Build Status](https://travis-ci.org/witlox/dcron.svg?branch=master)](https://travis-ci.org/witlox/dcron)The aim of dcron is to offer [cron](https://en.wikipedia.org/wiki/Cron) like behaviour spanning multiple machines.
The system offers a web interface to manage your jobs, and reports the health of the cluster.
Everything is self contained, so you only need to start the system to have a working setup.
We do however recommend that you run the system behind a reverse proxy, since there is no authentication mechanism.
Please check the [docs](https://dcron.readthedocs.io) regarding installation, configuration and options.## Detailsdcron requires Python 3.7+ in order to work.all nodes running dcron need to have the same software installed you want dcron to rundcron runs tasks at-most-once (according to schedule)## Installationpip install dcron
Package: dcron-cli
## dcron-cli [![Build Status](https://travis-ci.org/witlox/dcron.svg?branch=master)](https://travis-ci.org/witlox/dcron)The aim of [dcron](https://github.com/witlox/dcron) is to offer [cron](https://en.wikipedia.org/wiki/Cron) like behaviour spanning multiple machines.
This is the command line interface for interacting with a dcron cluster.
Please check the [docs](https://dcron-cli.readthedocs.io) regarding configuration and options.## Detailsdcron-cli requires Python 3.4+ in order to work.configuration is stored in ~/.dcron/sites.json by default## Installationpip installdcron-cli
Package: dcrpm
dcrpmdcrpm ("detect and correct rpm") is a tool to detect and correct common issues around RPM database corruption. It attempts a query against your RPM database and runs db4'sdb_recoverif it's hung or otherwise seems broken. It then kills any jobs which had the RPM db open previously since they will be stuck in infinite loops within libdb and can't recover cleanly.UsageRundcrpmwith no option to detect and correct any outstanding issues with RPM on your host. Additional options can be used to customize logging or select specific remediations. dcrpm is meant to be run from cron regularly to keep things happy and healthy.Requirementsdcrpm requires Python 2.7 and above and the package psutil. It also requireslsofto be in$PATH. It should work on any Linux distribution with RPM and on Mac OS X.To usesetup.pyyou need setuptools >= 40.9.0 (seesetup.cfg-only projects).
Substitutelegacy_setup.pyif you have an older setuptools (e.g. when building on EL 8).Installing dcrpmdcrpm is packaged in Fedora as of Fedora 32 and in EPEL as of EPEL 8. It can be installed with:dnf install dcrpmThis will also install any necessary dependencies at the same time.Building and installing dcrpm from sourceThe easiest way to manually install dcrpm is get the source and install it using setup.py:python setup.py installThis will fetch psutil from PyPI for you. dcrpm also assumes that the system will have RPM and Yum or DNF installed.Building and installing for developmentIf you want to develop, the easiest way to get dcrpm is by using pip:pip install -r requirements-test.txt # get extra packages
python setup.py installWhen developing it's important to make sure the tests continue to pass, and to ensure new features have the appropriate test coverage. You can run the test suite with:pytestPreparing a new releaseFirst, install some extra packages:pip install -r requirements-release.txtThen create the source distribution and the wheel:Old stylepython setup.py sdist bdist_wheel --universalWith the build moduleThis has the advantage of performing the build in an isolated virtual environment. However, it does not build a universal (py2/py3) wheel anymore.python -m buildThen verify them:twine check dist/*AMakefileis provided to simplify thismake dist
make dist USE_BUILD=1  # use the build module, soon to be default
make dist USE_LEGACY=1 # use legacy_setup.py
make check-releaseContributeSee the CONTRIBUTING file for how to help out.Licensedcrpm is GPLv2-licensed.
Package: dcrx-kv
dcrx-kvDCRX-KV is a RESTful key/value service that utilizes a combination of an in-memory filesystem and permanent storage to facilitate easy upload and download of files. DCRX-KV permanent storage is accomplished via integrations with:AWS S3Azure Blob StorageGoogle Cloud Storageand/or writing to disk.DCRX-KV also includes basic JWT authorization and user management (no-RBAC), and is intended to be deployed as an internal-facing service. dcrx also provides OpenAPI documentation to make getting started intuitive, and features integrations with SQLite, Postgres, and MySQL for storing and managing users.Setup and InstallationDCRX-KV is available both as a Docker image and as a PyPi package. For local use, we recommend using PyPi, Python 3.10+, and a virtual environment. To install, run:python-mvenv~/.dcrx&&\source~/.dcrx/bin/activate&&\pipinstalldcrx-kvTo get the Docker image, run:docker pull adalundhe/dcrx-kv:latestGetting StartedTBA
Package: dcs-interactive
DCS InteractiveInteractive Simulations and Visualizations for the Lecture inDigital
Communication Systemsat
the Otto-von-Guericke-University Magdeburg.InstallationPrerequisites:Python 3.10+ must be installed on the systemFor Windows systems, ensure that Python is available in the PATH variable.For installation, execute in a terminal (the example shows a Bash terminal
on Linux):# 1. Create a virtual environmentpython-mvenvdcs_venv# 2. Activate the virtual environmentsource./dcs_venv/bin/activate# 3. Installationpipinstalldcs-interactive# 4. Deactivate the virtual environmentdeactivateRun the Application# 1. Activate the virtual environmentsource./dcs_venv/bin/activate# 2. Run itdcs-interactive# 3. Deactivate the virtual environmentdeactivateTroubleshootingApplication does not startPerhaps, an incorrect value has been set in an input field. Unfortunately,
the application does not validate the values before saving, but before
loading it from the permanent storage. This leads to an error. This bug
is going to be fixed in future versions.Solution:Edit the permanent configuration storage or delete it. The
storage can be found:on Linux systems:$HOME/.config/dcs_interactive/config.tomlon Windows:C:\Users\<username>\AppData\Roaming\Philipp Le\dcs_interactive\config.tomlScreenshotContentsThe currently available simulations and visualizations are listed belows.
The chapters correspond to the Lecture Notes of the CourseDigital
Communication Systems.Chapter 2 - Time-Continuous Signals and SystemsPhasorFourier SeriesChapter 3 - Stochastic and Deterministic ProcessesErgodic ProcessCross CorrelationPower Spectral DistributionChapter 4 - Sampling and Time-Discrete Signals and SystemsSamplingWindowingQuantization NoiseChapter 5 - Modulation and MixingModulationIQ MixingQAM ModulationChapter 6 - Digital Signal ProcessingDigital FilterDigital MixerDown SamplingUp SamplingChapter 7 - Spread Spectrum and Multiple AccessSpreading
Package: dcsm
Python packagedcsmPackage for managing mounts and users on the DCSM serverInstallationAs developerBuild requirementsPython (3.9 minimum)makePoetryInstructionsgit clone https://gitlab.com/dcsm/server_deployment.git
cd server_deployment/mount_api
make installThis will install thedcsmpackage as well as all the development dependencies (e.g.for testing)
Package: dcs-mizmerge
OverviewA command line utility for combining two DCS World missions together.FeaturesCurrently this tool does one thing:  It moves all the client flights from one mission into another.This can be useful for mission devs who add lots of client flights to their missions.  You can work on the static groups/air defences or whatever, then bring in all the client flights afterward.Installation and UsageInstallationpip install the pypi packagepip install dcs-mizmergeUntil the pypi wheel for pydcs is updated to 0.13.0, you may need to manually install it using this commandpip install -e git+https://github.com/pydcs/dcs@fac1bd084f22150acfde3bff220f8e69487048d1#egg=pydcsUsagedcs-mizmerge "C:\PathToMissionWithClientFlightsHere.miz" "C:\PathToMissionYouWantToMergeClientFlightsIntoHere.miz"By default, the merged mission file will be saved at the path of the second argument, with_mergedappended to the end of the filename.  You can change the output file location with the--outputoption.dcs-mizmerge "C:\PathToMissionWithClientFlightsHere.miz" "C:\PathToMissionYouWantToMergeClientFlightsIntoHere.miz" --output "C:\SomeNewPathHere"ThanksOnly possible due to the work of rp- and the other devs at!pydcs.
Mod support was copied from!DCS Liberation, thanks to everyone there.
Package: dcs-ms-word-ieee-patch
Microsoft Word IEEE Citation PatcherPost-processing script that patches IEEE citation format inMicrosoft Worddocuments in thedocx format.Word does only allow to list citations one by one and has no integrated logic to group or sort them.
Especially in scientific documents it is often the case that multiple citations are added to the same paragraphs and it is likely that they are not in order.
Sorting and grouping citation references is a tedious task, so this script is here to help.An original text like thisas shown by Brown [5], [4]; as mentioned earlier [9], [4], [5], [2], [7], [6]; Smith [4] and Brown and Jones [5]; Wood et al. [7]is transformed to thisas shown by Brown [4], [5]; as mentioned earlier [2], [4]-[7], [9]; Smith [4] and Brown and Jones [5]; Wood et al. [7]or with an optional compression option to thisas shown by Brown [4,5]; as mentioned earlier [2,4-7,9]; Smith [4] and Brown and Jones [5]; Wood et al. [7]InstallationPython >= 3.8 is required for this tool to work.
The package is hosted onPyPI.To install with pip, use the following command:$pipinstalldcs-ms-word-ieee-patchThis installs two CLI scripts,ieee-patchandxml-pretty-print.UsageRun the script with the path to the$ieee-patch/path/to/file.docx# on unix$ieee-patchC:\Users\foobar\Desktop\file.docx# on windowsThe script by default creates a file with the filename suffix.patchedin the same folder as the original file and patches the content within this file which means the original file is left untouched.In case the replacement should be done in-place, e.g., when space limitations apply, use the--overwriteCLI flag.Please use this flag only if really necessary, since the original content cannot be restored after is has been overwritten!Legal noticeThis project is not affiliated, associated, authorized, endorsed by, or in any way officially connected with the Microsoft Corporation, or any of its subsidiaries or its affiliates.