Saravanan666/pypi-org huggingface dataset:
Package: dc-api
dcinside-python3-apiDeadly simple non official async dcinside api for python3# 프로그래밍 갤러리 글 무한 크롤링importasyncioimportdc_apiasyncdefrun():asyncwithdc_api.API()asapi:asyncforindexinapi.board(board_id="programming"):print(index.title)# => 땔감 벗어나는법.tipdoc=awaitindex.document()print(doc.contents)# => 자바를 한다forimgindoc.images:img.download('./img')# => ./img.gifasyncforcomminindex.comments():print(com.contents)# => ㅇㅇ(1.224) 지랄 ㄴasyncio.run(run())importasyncioimportdc_apiasyncdefrun():api=dc_api.API()# 댓글 작성awaitapi.write_comment(board_id="programming",doc_id=149123,name="ㅇㅇ",password="1234",contents="ㅇㅈ")# 글 작성doc_id=awaitapi.write_document(board_id="programming",title="java vs python",contents="닥치고 자바",name="ㅇㅇ",password="1234")# 글 삭제awaitapi.remove_document(board_id="programming",doc_id=doc_id,password="1234")# 마이너갤 글 작성doc_id=awaitapi.write_document(board_id="aoegame",title="java vs python",contents="닥치고 자바",name="ㅇㅇ",password="1234",is_minor=True)awaitapi.close()asyncio.run(run())Dependencypython(>3.6) aiohttp, lxmlFeaturesBoard crawlingFetch document bodyFetch commentsFetch document imagesWrite/Modify/Delete documentWrite commentDelete commentLogin/LogoutUpvote/DownvoteUsagePlace dc_api.py in your working directoryor install via pippip3 install --user dc_apiimportdc_apiapi=dc_api.API()asyncforindexinapi.board(board_id="programming",num=-1,start_page=1,document_id_upper_limit=None,document_id_lower_limit=None):index.id# => 835027index.board_id# => programmingindex.title# => "땔감 벗어나는법.tip"index.author# => "ㅇㅇ(10.20)"index.time# => datetime("2020-01-01 01:41:00.000000")index.comment_count# => 3index.voteup_count# => 0index.view_count# => 14doc=awaitindex.document()doc.id# => 835027doc.board_id# => "programming"doc.title# => "땔감 벗어나는법.tip"doc.author# => "ㅇㅇ(10.20)"doc.author_id# => None (고닉일 경우 고닉 아이디 반환)doc.time# => datetime("2020-01-01 01:41:00.000000")doc.comment_count# => 3doc.voteup_count# => 0doc.logined_voteup_count# => 0doc.votedown_count# => 0doc.view_count# => 14.iddoc.contents# => "자바를 한다"doc.html# => "<p> 자바를 한다 </p>"forimageindoc.images:image.src# => "https://..."image.document_id# => 835027image.board_id# => "programming"awaitimage.load()# => raw image binaryawaitimage.download(path)# => download image to local path(automatically add ext)asyncforcominindex.comments():com.id# => 123123com.is_reply# => Falsecom.time# => "1:55"com.author# => "ㅇㅇ(192.23)"com.author_id# => None (고닉일 경우 아이디 반환)com.contents# => "개솔 ㄴㄴ"com.dccon# => None (디시콘일경우 디시콘 주소 반환)com.voice# => None (보이스리플일경우 보이스리플 주소 반환)doc=awaitapi.document(board_id="programming",document_id=835027)asyncforcomminapi.comments(board_id="programming",document_id=835027):commdoc_id=awaitapi.write_document(board_id="programming",name="점진적자살",password="1234",title="제목",contents="내용",is_minor=False)doc_id=awaitapi.modify_document(board_id="programming",document_id=document_id,name="얄파고",pw="1234",title="수정된 제목",contents="수정된 내용",is_minor=False)com_id=awaitapi.write_comment(board_id="programming",document_id=doc_id,name="점진적자살",password="1234",contents="설리")awaitapi.remove_document(board_id="programming",document_id=document_id,password="1234")
Package: dcar
D-CarLow level, pure Python D-Bus implementation for message bus clients.For more information see thedocumentation.History2020-08-08 (0.3.0)Add property bus_type to class BusAdd module remoteBugfix: UnknownMethod and InvalidArgs errors for incoming method calls are now handled2020-08-02 (0.2.0)Add parameter timeout to Bus.block()Add field is_signal to class MessageInfoThe handler functions for incoming method calls and signals are not executed
in the same thread as the recv-loop any more but in a separate thread sequentially2019-06-03 (0.1.0)First public release
Package: dcard_coworker
dcard_coworker==============Dcard crawler using asyncio(coroutine)Feature-------| Get article list and content using coroutineDependencies------------* Python 3.3 and :mod:`asyncio` or Python 3.4+* aiohttpInstallation------------::python setup.py installor::pip install dcard_coworkerExample-------::import asyncioimport aiohttpimport dcard_coworker@asyncio.coroutinedef get_funny_articles():session = aiohttp.ClientSession()forum_name = 'funny'page_index = 1result = yield from dcard_coworker.get_articles_of_page(session, forum_name, page_index)print(result)def main():loop = asyncio.get_event_loop()loop.run_until_complete(asyncio.wait([get_funny_articles()]))if __name__ == '__main__':main()Todo----* Add testings* Add more examplesAuthors and License-------------------The ``dcard_coworker`` package is written by Chien-Wei Huang. It’s MIT licensed and freely available.Feel free to improve this package and send a pull request to GitHub.
Package: dcase-util
DCASE UtilitiesA collection of utilities for Detection and Classification of Acoustic
Scenes and EventsThis collection of utilities is for Detection and Classification of
Acoustic Scenes and Events (DCASE). These utilities were originally
created for the DCASE challenge baseline systems
(2016&2017) and
are bundled into a standalone library to allow their re-usage in other
research projects.The main goal of the utilities is to streamline the research code, make
it more readable, and easier to maintain. Most of the implemented
utilities are related to audio datasets: handling meta data and various
forms of other structured data, and provide standardized usage API to
audio datasets from various sources.DocumentationSeehttps://dcase-repo.github.io/dcase_util/for detailed instruction,
manuals and tutorials.Installation instructionsThe latest stable release is available on PyPI, and you can install with
pip:pip install dcase_utilLicenseCode released under theMIT
license.
Package: dcaspt2-input-generator
dcaspt2_input_generatorDescriptionThis is a GUI tool for supporting the generation of input files for DIRAC_CASPT2 calculation.PrerequisitesPython 3.7 or laterpipInstallpipinstall-Udcaspt2_input_generatorUsagedcaspt2_input_generatororpython-mdcaspt2_input_generatorFor more information, please see thewiki.LICENSEApache LICENSE 2.0AuthorKohei Noda
Package: dcat-ap-de-validator
Validierung von Metadaten pro PortalWir können eine Portal URL (DKAN oder CKAN Portale) übergeben und dann bekommen wir eine
JSON Datei mit der Validierung aller vorhandenen Datensätze.InstallationZur Installation des Packages mit pipx (pip geht auch):pipxinstalldcat-ap-de-validatorVerfügare Kommandos:portalfür PortalvalidierungParameterurlder erste Parameter ist die URL des Portals oder einzelne Metadaten-URL-pPortal Typ:ckanoderdkanAufrufWenn als Package installiert:dcat_ap_de_validatorportalhttps://opendata.heilbronn.de/-pdkanDirekter aufruf:python-mdcat_ap_de_validatorportalhttps://opendata.heilbronn.de/-pdkanRückgabeDie Ausgabe ist ein json File im Root Ordern, der den Namen des Portales beinhaltet.FormatIn dem JSON File ist auf der Root Ebene folgendes verfügbar:{"portal":"Name of the Portal title""valid_datasets":1,"errors":2,"warnings":5,"infos":2,"results":[{"package":"idofthepackage","url":"http://portal.gov/idofpakcage","result":{"valid":true,"warning":1,"error":0,"info":0}}]}
Package: dc-auth
dc-auth is a dedicated Django app to connect the Data Central authentication
backend (LDAP) to multiple dc sites (api, cms etc) via CAS.
It supports session-based authentication (tested) and JWT authentication
(untested).For users outside of Data Central, you’ll need to send us details about your
application in order to be able to connect, please contact us athttps://jira.aao.org.au/servicedesk/customer/portal/3.Quick startAdd “dc_auth” + the following apps to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    'rest_framework',
    'rest_framework.authtoken',
    'rest_auth',
    ...
    'dc_auth',
]Add django_settings_export.settings_export to your project’s
context_processors:'OPTIONS': {
    'context_processors': [
        'django.template.context_processors.debug',
        'django.template.context_processors.request',
        'django.contrib.auth.context_processors.auth',
        'django.contrib.messages.context_processors.messages',
        'django_settings_export.settings_export',                   # <----
    ],
},Include the dc_auth URLconf in your project urls.py like this:path('auth/', include('dc_auth.urls')),Migrate to create the Profile table. Runpython manage.py migrate.Start the development server and visithttp://127.0.0.1:8000/auth/loginto log
into your account.TestsFrom the top-level directory run:$ toxdcauth comes with some helpers that tools building on dcauth can use,
specifically pytest fixtures for affiliation (affiliation) and test passwords
(secure_password), as well as factories and fixtures for theUserandProfileclasses.Installing for internal applicationsTo install into another Data Central project, adddc_authto your requirements
file (lock to a specific version), and ensure that pip is pointed to the DC
devpi instance.
Package: dc-avro
Dataclasses Avro Schema CLICommand line interface fromdataclasses-avroschemato work withavscresourcesRequirementspython 3.8+Documentationhttps://marcosschroh.github.io/dc-avro/UsageYou can validate oneavro schemaeither from alocal fileorurl:Assuming that we have a local fileschema.avscthat contains anavro schema, we can check whether it is validdc-avrovalidate-schema--pathschema.avsc

Validschema!!👍{'type':'record','name':'UserAdvance','fields':[{'name':'name','type':'string'},{'name':'age','type':'long'},{'name':'pets','type':{'type':'array','items':'string','name':'pet'}},{'name':'accounts','type':{'type':'map','values':'long','name':'account'}},{'name':'favorite_colors','type':{'type':'enum','name':'FavoriteColor','symbols':['BLUE','YELLOW','GREEN']}},{'name':'has_car','type':'boolean','default':False},{'name':'country','type':'string','default':'Argentina'},{'name':'address','type':['null','string'],'default':None},{'name':'md5','type':{'type':'fixed','name':'md5','size':16}}]}You can validate several.avscfiles withlintcommanddc-avrolinttests/schemas/example.avsctests/schemas/example_v2.avsc

👍Totalvalidschemas:2tests/schemas/example.avsc
tests/schemas/example_v2.avscTo see all the commands executedc-avro --helpUsage in pre-commitAdd the following lines to your.pre-commit-config.yamlfile to enable avro schemas linting-repo:https://github.com/marcosschroh/dc-avro.gitrev:0.7.0hooks:-id:lint-avscadditional_dependencies:[typing_extensions]FeaturesValidateschemaLintschemasGeneratemodelsfromschemasData deserialization withschemaData serialization withschemaView diff betweenschemasGenerate fake data fromschemaDevelopmentInstall requirements:poetry installCode linting:./scripts/formatRun tests:./scripts/test
Package: dcbase
Base REST APIVersion 1.13.5DigitalCubeWIKIInstallationpip3 install dcbase
pip3 install -r https://raw.githubusercontent.com/digital-cube/BASE/master/requirements.txtInitialize projectbasemanager init project_nameStart projectcd project_name
./project_nameInitialize databasecd project_name
basemanager db_init db_username db_password
Package: dcbench
A benchmark of data-centric tasks from across the machine learning lifecycle.Getting Started|What is dcbench?|Docs|Contributing|Website|About⚡️ QuickstartpipinstalldcbenchOptional: some parts of Meerkat rely on optional dependencies. If you know which optional dependencies you'd like to install, you can do so using something likepip install dcbench[dev]instead. See setup.py for a full list of optional dependencies.Installing from dev:pip install "dcbench[dev] @ git+https://github.com/data-centric-ai/dcbench@main"Using a Jupyter notebook or some other interactive environment, you can import the library
and explore the data-centric problems in the benchmark:importdcbenchdcbench.tasksTo learn more, follow thewalkthroughin the docs.💡 What is dcbench?This benchmark evaluates the steps in your machine learning workflow beyond model training and tuning. This includes feature cleaning, slice discovery, and coreset selection. We call these “data-centric” tasks because they're focused on exploring and manipulating data – not training models.dcbenchsupports a growing list of them:Minimal Data SelectionSlice DiscoveryMinimal Feature Cleaningdcbenchincludes tasks that look very different from one another: the inputs and
outputs of the slice discovery task are not the same as those of the
minimal data cleaning task. However, we think it important that
researchers and practitioners be able to run evaluations on data-centric
tasks across the ML lifecycle without having to learn a bunch of
different APIs or rewrite evaluation scripts.So,dcbenchis designed to be a common home for these diverse, but
related, tasks. Indcbenchall of these tasks are structured in a
similar manner and they are supported by a common Python API that makes
it easy to download data, run evaluations, and compare methods.✉️ Aboutdcbenchis being developed alongside the data-centric-ai benchmark. Reach out to Bojan Karlaš (karlasb [at] inf [dot] ethz [dot] ch) and Sabri Eyuboglu (eyuboglu [at] stanford [dot] edu if you would like to get involved or contribute!)
Package: dcc
DCCTools for interactive and programmatic access to theLIGO Scientific
CollaborationDocument Control Center(DCC).FeaturesInteractive command line interfaceRetrieval and modification of DCC record metadataExtraction of attachment descriptions and URLsLocal archival of downloaded contentURL scraping and automatic, recursive archival of related documents and filesDocumentationFor help installing and usingdcc, please visit theonline
documentation.Installationdcchas the following system requirements:Python 3.8 or laterkinitandklistprovided as part of KerberosMost users will find it easiest to use one of thereference software
environmentsprovided by
IGWN. Theigwn-py38environment, for example, provides the required dependencies. If
you choose to manage system dependencies yourself, ensure you have the relevant Kerberos
packages above (provided on Linux by e.g.krb5-useron Debian derivatives orkrb5-workstationon Red Hat derivatives).Once you have the required system dependencies, you can installdccusing your
favourite Python package manager, e.g. using pip:$pipinstalldccCreditsSean Leavey<sean.leavey@ligo.org>Jameson Graef Rollins<jameson.rollins@ligo.org>Christopher WipfDuncan Macleod
Package: dcc2
dcc2dcc2 is a tool for compiling core set data forh1susing predicted orthologs from OMA, both OMA-browser and OMA-standalone. Outputs of this tool are 3 (optional 4) folders required for a HaMStR run, including (1) core_orthologs (comprises of OMA orthologous group - OG, or OMA pairs - OP. Each OG/OP has its own directory, where a multiple fasta file and a corresponding profile HMM can be found), (2) genome_dir (contains gene sets of taxa, from which the orthologs are originated), (3) blast_dir (holds the blast databases of those gene sets within genome_dir), and an optional (4) weight_dir (contains feature architecure annotations of all gene sets).Table of ContentsHow to installInstall the dcc2 packageSetup dcc2UsageBugsContributorsContactHow to installdcc2is distributed as a python package calleddcc2. It is compatible withPython ≥ v3.7.Install the dcc2 packageYou can installdcc2usingpip:python3 -m pip install dcc2or, in case you do not have admin rights, and don't use package systems like Anaconda to manage environments you need to use the--useroption:python3 -m pip install --user dcc2and then add the following line to the end of your~/.bashrcor~/.bash_profilefile, restart the current terminal to apply the change (or typesource ~/.bashrc):export PATH=$HOME/.local/bin:$PATHSetup dcc2After installingdcc2, you need to run the prepare script to download and parse required OMA browser data.You can do it by running this commanddcc2.prepare -o /output/path/for/oma/dataUsageFor parsing OMA orthologs by using an OMA group ID:dcc2.parseOmaById -g 1 -n HUMAN,THEAM,DESM0 -o /output/path/ -j jobName --cpus 8Or using list of OMA taxa:dcc2.parseOmaBySpec -n HUMAN,ECOLI,YEAST -o /output/path/ -j jobName --annoFas --cpus 8If only 2 OMA taxa are given, you can choose to use OMA pairs instead of OMA groups:dcc2.parseOmaBySpec -n HUMAN,ECOLI -t pair -o /output/path/ -j jobName --annoFAS --cpus 8For parsing an output from OMA-standalone, dcc2 requires:the output orthoXML file from OMA,a taxon mapping file in tab-delimited format containing 3 columns<NCBI taxon ID>   <Original taxon name>   <Abbr. taxon name>, whereoriginal taxon nameis the name that is written in the orthoXML input file andabbr. taxon nameis its abbreviation species code (for example: HOMSA forHomo sapiens)protein set of included taxa. This can be either given as a folder, or automatically downloaded from OMA databasedcc2.parseOrthoxml -i input.orthoxml -m mapping_file.txt -g /path/to/gene/set -o /output/path/ -j jobName --annoFas --cpus 8BugsAny bug reports or comments, suggestions are highly appreciated. Pleaseopen an issue on GitHubor be in touch via email.ContributorsVinh TranHannah MülbaierContactFor further support or bug reports please contact:tran@bio.uni-frankfurt.de
Package: dcca
Time-Lagged Detrended Cross Correlation AnalysisThis package allows to implement Detrended Cross Correlation Analysis [1] and its Time-Lagged version. Implementations are based on [1] and [2].The code allows to easily calculate theDetrended Cross-Correlation Coefficient, which is a variation of the more famous Pearson's correlation coefficient, meant for cases in which the time series is non stationary. Indeed, in many domains, stationarity of a time series is not guaranteed and running traditional correlation coefficients can give skewed or untrue results.UsageThe package allows to calculate the detrended version with both no time lag, or with a given time lag.fromdccaimportdetrended_correlationfromnumpy.randomimportrandx:ndarray=rand(100)y:ndarray=rand(100)print(detrended_correlation(x=x,y=y,time_scale=3,time_lag=0))-0.04848682827863634Thetime_lagcoefficient is meant to confront the time series when one is moved with respect to the other. At the moment, only the time seriesycan be moved using this. To run the full cross-correlation analysis, one may want to loop over different time lags:fromdccaimportdetrended_correlationfromnumpy.randomimportrandx:ndarray=rand(100)y:ndarray=rand(100)time_lags:list[int]=range(0,10)dccas=[detrended_correlation(x=x,y=y,time_scale=3,time_lag=0)fortime_lagintime_lags]In the example provided, the coefficient is always going to be the same, since the arrays are randomly sampled.Thetime_scalecoefficient specifies the box size of the local detrending. In short, the Detrended Cross-Correlation Coefficient, in order to account for non-stationarity in the two series, calculates the correlation over smaller parts of the series (boxes), whose size is indeed given by the aforementioned parameter. For more detail, please look at the referenced material.The package also allows to calculate the standard Pearson's correlation coefficient, even with different time lags. While the method can be easily implemented, I though it might be useful to provide it for confronttions.fromdcca.cross_correlationimportcross_correlationfromnumpy.randomimportrandx:ndarray=rand(100)y:ndarray=rand(100)print(cross_correlation(x=x,y=y,time_lag=0))InstallationAt the moment, the packe is only installable viapip. For following releases, I will try to port it intocondaas well.pipinstalldccaReferences[1] Podobnik, Boris, and H. Eugene Stanley. "Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series." Physical review letters 100.8 (2008): 084102.[2] Shen, Chenhua. "Analysis of detrended time-lagged cross-correlation between two nonstationary time series." Physics Letters A 379.7 (2015): 680-687.ContactsFor any information, contact me, Leonardo Alchieri, atleonardo@alchieri.eu. This package was developed as part of my PhD at USI (Università della Svizzera italiana), Switzerland.
Package: dccd
This is the starting point of a python package[*]to automaticallydownloaddata andupdatedatabase withcrypto-currencydata (bitcoin, ether, litecoin, etc.) from different API exchanges (allow only Binance, Bitmex, Bitfinex, GDAX, Kraken and Poloniex for the moment).Presentation:Thedccdpackage allow you two main methods to download data. The first one is recommended to download data at high frequency (minutelyortick by tick), and the second one is recommended to download data at a lower frequency (hourlyordaily):Continuous Downloaderdccd.continuous_dl:Download and update continuously data (orderbook, trades tick by tick, ohlc, etc) and save it in a database.Currently only support Bitfinex and Bitmex exchanges.Historical Downloaderdccd.histo_dl:Download historical data (ohlc, trades, etc.) and save it.Currently only support Binance, GDax, Kraken and Poloniex exchanges.Historical Downloader:The‘dccd.histo_dl’module contains a main class and four classes to download and update data for each exchange, and a module‘date_time’to manage specific time format needed for each API.The four classes to download historical data areFromBinance,FromGDax,FromKrakenandFromPoloniex. All have the same methods and almost the same parameters:__init__(path, crypto, span, fiat(optional), form(optional))initialisation withpathis the path where save the data (string),cryptois a crypto currency (string) andspanis the interval time between each observation in seconds (integer) or can be a string as ‘hourly’, ‘daily’, etc. (see details on the doc string). The optional parameters arefiatthe second currency (default is ‘USD’ and ‘USDT’ for poloniex and binance) andformthe format to save the data (default is ‘xlsx’).import_data(start, end)download data withstartandendthe timestamp (integer) or the date and time (string as ‘yyyy-mm-dd hh:mm:ss’), respectively of the first observation and the last observation (default are special parametersstart=’last’allow the last data saved andend=’now’allow the last observation available). Exclusion: Kraken don’t allow theendparameter and provide only the thousand last observations.save(form(optional), by(optional))save the data withformthe format of the saved data (default is ‘xlsx’) andbyis the “size” of each saved file (default is ‘Y’ as an entire year). Exclusion: This optional parameters are in progress, let the default parameter for the moment, other are not allow.get_data()returns the data frame without any parameter.Method chaining is available.Installation:Install the library from pip:$ pip install dccdInstall the library from source:$ git clonehttps://github.com/ArthurBernard/Download_Crypto_Currencies_Data$ cd Download_Crypto_Currencies_Data$ python setup.py install –userRequirements:numpy>=1.14.1pandas>=0.22.0requests>=2.18.4xlrd>=1.1.0xlsxwriter>=1.0.2websockets>=7.0.0scipy>=1.2.0SQLAlchemy>=1.3.0Supported exchanges:Binance.Bitfinex.Bitmex.GDAX.Kraken.Poloniex.[*]Package not achieved, always in progress. This is my first one package, all advice is welcome.
Package: dc-cli
Dataclass Command-line InterfaceInstallationFrompip:pipinstalldc-cliUsagefromdataclassesimportdataclass,fieldsimportdataclass_cli@dataclass_cli.add@dataclassclassModel:num_layers:intlearning_rate:floattraining:boolmodel=Model()print(model)Output:❯pythontest.py--help
usage:test.py[-h]--model_num_layersMODEL_NUM_LAYERS--model_learning_rateMODEL_LEARNING_RATE(--model_training|--no-model_training)optionalarguments:-h,--helpshowthishelpmessageandexitmodel:--model_num_layersMODEL_NUM_LAYERS--model_learning_rateMODEL_LEARNING_RATE--model_training--no-model_training
❯pythontest.py--model_num_layers10--model_learning_rate0.1--no-model_training
Model(num_layers=10,learning_rate=0.1,training=False)LicenseMIT