Saravanan666/pypi-org huggingface dataset:
Package: dropteaplatfrom
一、安装（python版本建议3.7以上）pipinstalldubborequests二、升级包pipinstall--upgradedubborequests三、示例获取dubbo服务详情# 导入importdubborequests# 获取dubbo服务详情data=dubborequests.search('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')获取服务下的所有方法# 导入importdubborequests# 获取dubbo服务下的所有方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')# 获取dubbo服务指定的方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService','login')通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口importdubborequestsfromdubborequestsimportConfig# 先配置zookeeper中心地址Config.zookeeper_url_list=['192.168.240.15:2181','192.168.240.15:2182','192.168.240.15:2183']invoke_data={"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# 通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口data=dubborequests.zk_invoke(*invoke_data)Telnet命令测试dubbo接口importdubborequestsinvoke_data={"ip":'xxxx',"port":7777,"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# Telnet命令测试dubbo接口data=dubborequests.telnet_invoke(*invoke_data)
Package: droptopus
DroptopusDroptopus melds drag'n'drop with scripts and folders. It allows you to create visual drop targets for files, links, and text, allowing you to process the dragged object (file|text|url) via script or copy it to a folder.Dependenciespython3.5+pyqt5Installationpip3 install droptopusLaunchnohup python3 -m droptopus &Example scriptsCheck theexamplesdirectory for some useful drop actions.AcknowledgementsDroptopus usesNokia's:QtRiverbank Computing Limited's:SIPRiverbank Computing Limited's:PyQt5Icon8's:awesome iconsThanks!SponsorsLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: drosolf
Installationpip install drosolfIf you need elevated permissions to install (if thepip installline
fails with some sort of permissions error), you can try:sudo -H pip install drosolfExamplesTo get Hallem and Carlson ORN responses, with the baseline added back
in. Returned as a pandas DataFrame with columns of receptor and row
indices of odor. The transpose (i.e.orn_responses.T) will have
odors as the columns.from drosolf import orns
orn_responses = orns.orns()To get simulated projection neuron responses, using the Olsen input gain
control model and the ORN responses.from drosolf import pns
pn_responses = pns.pns()TodoDoOR integrationKC model(s)sympy description of transformations applied to ORN data
Package: drosSRA
drosSRAdrosSRAbuilds a url containing all relevantGEOtracks based on one or more user specified parameters (i.e. sex, tissue, cell type)Free software: MIT licenseDocumentation:https://drosSRA.readthedocs.io.InstallationTo download and install the latest version ofdrosSRA:pip install drosSRAUsageThere are five possible flags that users can use to search for tracks of interest: PubMed ID (-p), sex (-s), developmental stage (-d), tissue (-t), and cell type (-c).
For example, if you are interested in viewing all tracks with testis data:>> drosSRA -t testis
https://www.ncbi.nlm.nih.gov/genome/gdv/browser/?context=genome&acc=GCF_000001215.4&context=GEO&acc=ERX2162339%2CERX2162340%2CERX2162341%2CSRX014984%2CSRX014985%2CSRX1026313%2CSRX1045309%2CSRX1045369%2CSRX105953%2CSRX109278%2CSRX109279%2CSRX135547%2CSRX135548%2CSRX142027%2CSRX142028%2CSRX142029%2CSRX142030%2CSRX142031%2CSRX142032%2CSRX1512980%2CSRX1542553%2CSRX1542554%2CSRX1542555%2CSRX1542556%2CSRX1542557%2CSRX1637725%2CSRX1637726%2CSRX1637727%2CSRX1637728%2CSRX1637729%2CSRX1637730%2CSRX1637731%2CSRX1637732%2CSRX1637733%2CSRX1637734%2CSRX1637735%2CSRX1637736%2CSRX1720957%2CSRX1720958%2CSRX1842650%2CSRX1842775%2CSRX2166012%2CSRX2166014%2CSRX2166016%2CSRX2166017%2CSRX2166019%2CSRX2166020%2CSRX2166021%2CSRX2325622%2CSRX2325623%2CSRX2325625%2CSRX2325626%2CSRX2325628%2CSRX2325629%2CSRX2325630%2CSRX2325631%2CSRX2416970%2CSRX2416971%2CSRX2416972%2CSRX2416973%2CSRX2416974%2CSRX2416975%2CSRX2416976%2CSRX2416977%2CSRX2497546%2CSRX2497547%2CSRX2497548%2CSRX2497549%2CSRX2497550%2CSRX2497551%2CSRX2504297%2CSRX2504298%2CSRX2504299%2CSRX2504300%2CSRX2549197%2CSRX2549198%2CSRX2549199%2CSRX2677260%2CSRX2677261%2CSRX2677262%2CSRX2677263%2CSRX2683539%2CSRX2683540%2CSRX2683541%2CSRX2683542%2CSRX2683543%2CSRX2683545%2CSRX2683546%2CSRX2683547%2CSRX2683548%2CSRX2683550%2CSRX2683551%2CSRX2829107%2CSRX2829108%2CSRX2829109%2CSRX2829110%2CSRX3015350%2CSRX3015366%2CSRX319676%2CSRX319687%2CSRX320021%2CSRX320263%2CSRX321521%2CSRX321778%2CSRX3488043%2CSRX3488044%2CSRX3488045%2CSRX3488046%2CSRX3488047Or, if you are interested in the intersection of two or more parameters,>> drosSRA -d 'larval stage' -t CNS
 https://www.ncbi.nlm.nih.gov/genome/gdv/browser/?context=genome&acc=GCF_000001215.4&context=GEO&acc=ERX2162997%2CERX2162999CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.4.1 (2018-10-24)patch to fix deployment with travis0.4.0 (2018-10-23)Genome browser automatically opens to pita0.3.0 (2018-10-22)Rework of drosSRA.py to handle multiple flags0.2.0 (2018-10-16)First release on PyPI. (0.1.0 got messed up)
Package: droughtMoPro
droughtMoProThis is a set of scripts to generate drought monitoring products. Input datasets can be generated using the packagepysdi.Documentation is in development.Main source of the projectThe project repository is available athttps://github.com/rrealrangel/droughtmopro.InstallationWrite the following line in a terminal:
pip install droughtmoproAuthorRoberto A. Real-Rangel. Institute of Engineering of the National Autonomous University of Mexico (II-UNAM).rrealr@iingen.unam.mx.
Package: droughty
#### droughty. ## adjective, drought·i·er, drought·i·est. ##
dry.Droughty helps keep your workflowah hemdryWhat is droughty?droughty is an analytics engineering toolkit. It takes warehouse
metadata and outputs semantic files.Current tools and supported platforms are:lookml - generates a lkml with views, explores and measures from a
warehouse schemadbt - generates a base schema from specified warehouse schemas.
Includes standard testing routinesdbml - generates an ERD based on the warehouse layer of your
warehouse. Includes pk, fk relationshipscube - generates a cube schema including dimensions, integrations
and meassuresThe purpose of this project is to automate the repetitive, dull elements
of analytics engineering in the modern data stack. It turns out this
also leads to cleaner projects, less human error and increases the
likelihood of the basics getting done...DocumentationInstallation, configuration and usage documentation can be found onReadTheDocsInstallationdroughty is available throughpip:pip install droughty
Package: drove
droveis a modern monitoring tool which support alerting
(with escalation), auto-register nodes, statistics gathering
and much more in a few lines of python code.InstallationThe stable releases of drove will be uploaded toPyPi, so you can install
and upgrade versions usingpip:pip install droveYou can use unstable versions at your risk using pip as well:pip install -e git://github.com/droveio/droveBy the way:droveworks better with python3, but 2.7 is also
supported.UsageYou need to configuredroveto enable the features that you
like to use. In factdroveact as a producer-consumer daemon,
which means that there are readpluginsand writeplugins.Areaderread a metric or an event from somewhere and
report it todrove(in a internal cache).Aconsumerwrite a metric (taken from the internal cache)
in somewhere.Somepluginscan act as reader and writer at the same time.By defaultdrovestart with a very basic readers configured.To start the daemon just type:drove -c myconfig.conf daemonYou can avoid to daemonize with--no-daemonoption in the
command line.Developmentdroveis under heavy development. If you want to contribute,
please us the usual github worflow:Clone the repoHack!Make a pull-requestMerged!If you don’t have programming skills, just open aissuein the
Github project.
Package: drow-onimage
drow_onimageYou can draw on any image using tkinter and PIL. The drawn image is saved in that folder as a ps file, and you can download the drawn image by converting it athttps://convertio.co/ja/ps-jpg/.Packages required to run drow_onimage$ brew install tcl-tk$ pip install opencv-python$ pip install pillowHow to usehttps://github.com/YTsubono/drow_onimage
Package: drowse
# drowsedrowse is a human readable Python slim REST client inspired by [Siesta](https://github.com/scastillo/siesta) and powered by [requests](https://github.com/kennethreitz/requests).## Quickstart`python from drowse import API api =API('https://api.github.com/')repos = api.users.lgaggini.repos.get() `## Features
* slim
* human-readable
* powered by rock-solid requests
* json only
* errors and exceptions leaved to upper layers## Install` git clonehttps://github.com/lgaggini/drowse.gitcd drowse python2 setup.py install `pip package to come.## Status
Beta version 0.2.0, manual tested on a medium tests set.## Documentation
See the [readme](https://github.com/lgaggini/drowse/tree/master/docs/README.md) in the docs folder.
Package: drozer
drozer (formerly Mercury) is the leading security testing framework for Android.drozer allows you to search for security vulnerabilities in apps and devices by assuming the role of an app and interacting with the Dalvik VM, other apps’ IPC endpoints and the underlying OS.drozer provides tools to help you use, share and understand public Android exploits. It helps you to deploy a drozer Agent to a device through exploitation or social engineering. Using weasel (MWR’s advanced exploitation payload) drozer is able to maximise the permissions available to it by installing a full agent, injecting a limited agent into a running process, or connecting a reverse shell to act as a Remote Access Tool (RAT).drozer is open source software, maintained by MWR InfoSecurity, and can be downloaded from:http://mwr.to/drozer
Package: dr-packager
Django-React PackagerReact-Django packager helps you ship a react front end with django as backend.
This package automatically builds your react project and places the static and index file in their appropriate place in your django project.InstallationInstall the package using pip:pipinstalldr_packagerand thats it!MotivationReact is one of the most popular front-end frameworks, and with django being one of the most popular backend choices, there are a lot of websites built with this stack.
You can serve django on a different server than the react project and communicate between them using an API, but for smaller projects (or even bigger ones), Having 2 separate server is not justified. Copying react build files to your server is not fun either (which can be simplified with a CI\CD).This project helps you serve react project with django (that can be deployed on a single server). This package was made out of frustration of building react project and manually copying everything to django project.UsageTo use the packager, you should have both the react folder & django folder in the same parent folder as follows (Absolute path is still not supported in this version):├───root_folder
│├───django_project
│├───react_projectYour django project should contain:An app that serves theindex.htmlA static folder (set by STATIC_ROOT variable in your settings)IMPORTANT: Your django project root folder and base app should have the same name
for example, settings.py should be located atexample.example.settings.pyOpen terminal inroot_folder, and run the following command:dr_package<react_path><django_path><django_front_app_name>Following the above folder example, consider that we have an app namedfrontin our django project:dr_packagereact_projectdjango_projectfrontIMPORTANT: Your django settings files should contain STATIC_ROOT variableCommandsusage:dr_package[-h][-S][--npm][--yarn][-D][-I][--folders-old[FOLDERS_OLD...]]react_pathdjango_pathapp_name

BuildReactProjectandDeploywithDjango!

positionalarguments:react_pathReactfoldername(RELATIVEPATH)django_pathDjangofoldername(RELATIVEPATH)app_nameNameoftheappthatholdsindex.html

optionalarguments:-h,--helpshowthishelpmessageandexit-S,--skip-buildSkipreactbuild(Usewhatisalredyin\build\)--npmusenpmasthepackagemanagerofthereactproject--yarnuseyarnasthepackagemanagerofthereactproject-D,--delete-oldDeleteoldstaticfiles-I,--installRuninstallcommandbeforebuilding(useifyouhaven't installed the required packages)--folders-old [FOLDERS_OLD ...]Name of the folders to delete (Doesn'tworkif--delelete-oldisnotprovided),separatedbyspaceRoadmapSupport for absolute pathDefault setting fileMore customizationCreating a separate django package (to run as management commands) to use with git hooksContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Package: drpangloss
drpanglossthe best of all possible interferometry modelsContributors: Dori Blakely,Benjamin PopeWhat is drpangloss?drpangloss is a package for modelling optical interferometry data in Jax.Installationdrpangloss is hosted on PyPI (though this is currently a placeholder): the easiest way to install this is withpip install drpanglossYou can also build from source. To do so, clone the git repo, enter the directory, and runpip install .We encourage the creation of a virtual enironment to run drpangloss to prevent software conflicts as we keep the software up to date with the lastest version of the core packages.Use & DocumentationDocumentation will be foundhere, though this is currently a placeholder.Collaboration & DevelopmentWe are always looking to collaborate and further develop this software! We have focused on flexibility and ease of development, so if you have a project you want to use drpangloss for, but it currently does not have the required capabilities, don't hesitate toemail meand we can discuss how to implement and merge it! Similarly you can take a look at theCONTRIBUTING.mdfile.NameWhy is it called drpangloss?The leading optical interferometry model fitting code isCANDID. In Voltaire'sCandide, Dr Pangloss' belief that we live in the best of all possible worlds is a satire of Leibniz' theodicy. But wedolive in a world with Jax, so that if we can't optimize the world, at least we can optimize our fits to VLTI data.
Package: drparse
# Date Range ParseCrudely extends [dateparser](https://github.com/scrapinghub/dateparser) to extract out one or two dates from a string.
Times are deliberately ignored, itonlyextracts the dates.### Examplesfrom drparse import parseSingle Date with time rangedates = parse(“Thursday, July 28 at 8 PM - 11 PM”)dates.start => datetime.datetime(2016, 7, 28, 0, 0)Date range without timedates = parse(“Thu, 6 -  Sun, 16 Oct 2016”)dates.start => datetime.datetime(2016, 10, 6, 0, 0)
dates.end => datetime.datetime(2016, 10, 16, 0, 0)Date range with timedates = parse(“Sat May 28, 2016 to Sun May 29, 2016, 10PM till late”)dates.start => datetime.datetime(2016, 5, 28, 0, 0)
dates.end => datetime.datetime(2016, 5, 29, 0, 0)### Further infoHow to work with time/date ranges, changing default values, etchttps://github.com/bear/parsedatetime/issues/162Search text for dateshttps://github.com/scrapinghub/dateparser/issues/82### AlternativesYou should probably just use this one instead!https://github.com/robintw/daterangeparser
Package: drpg
dRPG downloads and keeps your purchases from DriveThruRPG up to dateInstallationThis script runs with Python 3.8 and newer.You can install dRPG from PyPI:pipinstall--userdrpg
drpg--help# or python -m drpg --helpUsageGo toyour account settingsand generate a new application key.Copy the key and run the script:drpg --token <YOUR_DRPG_TOKEN>- or setDRPG_TOKENenv variable and rundrpg.Now just sit, relax and wait. Initial synchronization may take a while so
why don't you grab a cup of tea or whatever your favourite beverage is. On
consecutive runs the script will download only new and changed files which
will be a way faster.CompatibilityBecause of the nature of using an undocumented API, this software may break
without a notice. Version number indicates a year and a month when the software
was proved to be working with a real DriveThruRPG account.File name compatibilityThe DriveThruRPG client does some interesting things with the names of directories.
For example, if you buy a product from publisher "Game Designers' Workshop (GDW)"
the DriveThruRPG client app will download it to a directory with the unwieldy name
"Game Designers__039_ WorkshopGDW".By default,drpggives directories more user friendly name. In the example above,
the directory would be "Game Designers' Workshop (GDW)". However, this causes a
problem if you intend to try to manage the same e-book library using bothdrpgand
the DriveThruRPG client app. When you run the former, you'll get a friendly name,
then when you run the latter it will download all the same files again and put them
in a directory with the unfriendly name.You can use the command line option--compatibility-modeto makedrpguse the
same naming scheme for files and directories as the DriveThruRPG client. We have
also done our best to imitate DriveThruRPG's bugs while in--compatibility-modebut I'm sure there are some we missed.Advanced optionsYou can change where your files will be downloaded by using--library-path path/to/your/directory.By default the script does not compare files by md5 checksum to save time. You
can turn it on by using--use-checksums.You can change a log level by using--log-level=<YOUR_LOG_LEVEL>. Choices are
DEBUG, INFO, WARNING, ERROR, CRITICAL.You can do a "dry run" of the app by specifying--dry-run. This will determine
all the digital content you have purchased, but instead of downloading each file
it will print one line of information to show what filewouldhave been downloaded
if the--dry-runflag wasn't on. Use this if you want to test out the app without
taking the time to download anything.For more information, run the script with--help.Found a bug?Pull requests and bug reports are welcomed! SeeCONTRIBUTING.mdfor more details.
Package: drsa
Deep Recurrent Survival Analysis in PyTorchDocumentationThis project features a PyTorch implementation of theDeep Recurrent Survival Analysismodel that is intended for use on uncensored sequential data in which the event is known to occur at the last time step for each observation
More specifically, this library is made up of two small modules.functions.py, which contains utilities for computing conventional survival analysis quantities, given atorch.Tensorof predicted conditional hazard rates.model.py, which contains theDRSAclass (a subclass oftorch.nn.Module), and is easily extended to handle categorical embeddings, additional layers, or any other arbitrary PyTorch operations.Installation$ pip install drsaUsagefromdrsa.functionsimportevent_time_loss,event_rate_lossfromdrsa.modelimportDRSAimporttorchimporttorch.nnasnnimporttorch.optimasoptim# generating random databatch_size,seq_len,n_features=(64,25,10)defdata_gen(batch_size,seq_len,n_features):samples=[]for_inrange(batch_size):sample=torch.cat([torch.normal(mean=torch.arange(1.,float(seq_len)+1)).unsqueeze(-1)for_inrange(n_features)],dim=-1)samples.append(sample.unsqueeze(0))returntorch.cat(samples,dim=0)data=data_gen(batch_size,seq_len,n_features)# generating random embedding for each sequencen_embeddings=10embedding_idx=torch.mul(torch.ones(batch_size,seq_len,1),torch.randint(low=0,high=n_embeddings,size=(batch_size,1,1)),)# concatenating embeddings and featuresX=torch.cat([embedding_idx,data],dim=-1)# instantiating embedding parametersembedding_size=5embeddings=torch.nn.Embedding(n_embeddings,embedding_size)# instantiating modelmodel=DRSA(n_features=n_features+1,# +1 for the embeddingshidden_dim=2,n_layers=1,embeddings=[embeddings],)# defining training loopdeftraining_loop(X,optimizer,alpha,epochs):forepochinrange(epochs):optimizer.zero_grad()preds=model(X)# weighted average of survival analysis lossesevt_loss=event_time_loss(preds)evr_loss=event_rate_loss(preds)loss=(alpha*evt_loss)+((1-alpha)*evr_loss)# updating parametersloss.backward()optimizer.step()ifepoch%100==0:print(f"epoch:{epoch}- loss:{round(loss.item(),4)}")# running training loopoptimizer=optim.Adam(model.parameters())training_loop(X,optimizer,alpha=0.5,epochs=1001)epoch: 0 - loss: 12.485
epoch: 100 - loss: 10.0184
epoch: 200 - loss: 6.5471
epoch: 300 - loss: 4.6741
epoch: 400 - loss: 3.9786
epoch: 500 - loss: 3.5133
epoch: 600 - loss: 3.1826
epoch: 700 - loss: 2.9421
epoch: 800 - loss: 2.7656
epoch: 900 - loss: 2.6355
epoch: 1000 - loss: 2.5397
Package: drs-bloom-filter
A pure python bloom filter (low storage requirement, probabilistic
set datastructure) is provided.Includes mmap, in-memory and disk-seek backends.The user specifies the desired maximum number of elements and the
desired maximum false positive probability, and the module
calculates the rest.Example use:>>> bf = bloom_filter_mod.Bloom_filter(ideal_num_elements_n=100, error_rate_p=0.01)
>>> for i in range(0, 200, 2):
...     bf.add(i)
...
>>> for i in range(0, 200, 3):
...     print(i in bf)
...
Package: dr-sdk-lotr
The Lord Of The Rings SDKA SDK built upon The One API (https://the-one-api.dev/) written in Python.Installationpipinstalldr-sdk-lotr==0.0.1UsageTo use the SDK, get an Access Token fromhttps://the-one-api.dev/sign-up. Once installed and the Access Token is obtained, you can start using the SDK by following the below:Using the SDKBook Datafromdr_sdk_lotr.booksimportBooksbooks=Books('YOUR_API_KEY')# Lists of all "The Lord of the Rings" Booksbooks.get_books()# Request one specific Lord of the Rings book by IDbooks.get_book_by_id('5cf58077b53e011a64671583')# Request all chapters of one specific bookbooks.get_book_by_id('5cf58077b53e011a64671583')Character Datafromdr_sdk_lotr.chractersimportCharacterscharacters=Characters('YOUR_API_KEY')# Lists of all characters including metadata like name, gender, realm, race and morecharacters.get_all_characters()# Request one specific character by idcharacters.get_character_by_id('5cd99d4bde30eff6ebccfbed')# Request all movie quotes of one specific charactercharacters.get_quotes_by_character_id('5cd99d4bde30eff6ebccfbed')# Get character details by namecharacters.get_character_by_name('Gandalf')Movie Datafromdr_sdk_lotr.moviesimportMoviesmovies=Movies('YOUR_API_KEY')# List of all movies including "The Lord of the Rings" and the "The Hobbit" trilogiesmovies.get_movies()# Request one specific movie by idmovies.get_movie_by_id('5cd95395de30eff6ebccde56')# Request all movie quotes for one specific movie (only working for LOTR Trilogy)movies.get_all_quotes_by_movie('5cd95395de30eff6ebccde5c')Quote Datafromdr_sdk_lotr.quotesimportQuotesquotes=Quotes('YOUR_API_KEY')#  List of all movie quotesquotes.get_all_movie_quotes()# Request one specific movie quotequotes.get_movie_by_id("5cd96e05de30eff6ebccebce")Chapter Datafromdr_sdk_lotr.chaptersimportChapterschapters=Chapters('YOUR_API_KEY')# Lists of all book chapterschapters.get_all_book_chapters()# Request one specific book chapterchapters.get_book_chapter_by_id('6091b6d6d58360f988133bc5')# Get book chapter by namechapters.get_book_chapter_by_name('A Long-expected Party')
Package: drserpent
Description:Dr Serpent 🐍  is an easy to use testing framework for Python focusing on Behaviour Driven Development and Test Driven Development processes.Example:# filedefrun():return1+1# test_filedeftest_number():Expect(run(2)).to_equal(2)How to use:Click Hereto visit the PyPi URL.View DrSerpentdocumentationGetting StartedCompulsory ConventionsMatchers:to_equalto_be_truthyto_be_falseyto_be_noneto_be_greater_thanto_be_less_thanto_includeto_not_includeto_output_to_stdoutto_throw_errorContributors:Alex McCarrollRicky HewittTom BettsHemesh UnkaHow to contribute to this project:SeeCONTRIBUTING.mdLicence:Distributed under the terms of theMITlicense, DrSerpent is free and open source software.