Saravanan666/pypi-org huggingface dataset:
Package: dryad-pqueue
inherit from Maestro and overridecreate_generatorand/orhandle_itemsecrets:DATABASE_URLSUPABASE_API_KEYoptions:MODELSUPABASE_URLADMIN_URLflags:FREEEXITEXIT_ON_LOADPOWEROFFTWITTER: post tweets, requires comma-seperated TWITTER_CREDS and TwitterAPI to be installed
Package: drydock
A Docker cluster construction utility.DryDock takes a simple (YAML) specification file then can construct and
configure a cluster ofDockercontainers. DryDock will automatically
setup a reverse proxy, exposure of ports, and even persistent storage to
allow for easy future upgrading by simply rebuilding the DryDock
specification!FeaturesSimpleYAMLconfiguration.AutomaticDockercluster provisioning/configurationNginxreverse proxy configuration with HTTPS/SSL supportEasy setup for persistent volumes.Share your DryDock specifications with the world.Container supervisor utilizing theDockerAPI.TODOProvide a better update path, specific update commands, custom pre-post.Container monitoring, ensure a container is working else reconstruct it.Better support for stateless-ness and volumesBetter unittest coverage.Ability to provide a custom docker images repository for master containers.An external facing DNS server or config generation.Control over a master container cluster, Drydock as a Service?.Web interface for control, logging and monitoring. Far future.DocumentationThe full documentation is athttp://dry-dock.readthedocs.org.
Package: dryjq
Drastically Reduced YAML / JSON QueryLightweight package providing a subset ofyqorjqfunctionality:get a single value from a YAML or JSON filechange a single value in a YAML or JSON fileRequirementsPyYAML(Version 5.4.1 or newer)Installationpip install dryjqInstallation in a virtual environment is strongly recommended.UsagePlease see the documentation athttps://blackstream-x.gitlab.io/python-dryjqfor detailed usage information.The documentation is generated from the MarkDown files
in this repository’sdocs/directory.Output ofdryjq --help(orpython3 -m dryjq --help):usage: dryjq [-h] [--version] [-i] [-d | -v | -q] [-o OUTPUT_FORMAT]
             [--indent {2,4,8}] [--sort-keys] [--separator SEPARATOR]
             [--subscript-indicators SUBSCRIPT_INDICATORS]
             [query] [input_file]

Drastically Reduced YAML / JSON Query

positional arguments:
  query                 the query (simplest form of yq/jq syntax, default is
                        the separator character alone).
  input_file            the input file name (by default, data will be read
                        from standard input)

options:
  -h, --help            show this help message and exit
  --version             print version and exit
  -i, --inplace         modify the input file in place instead of writing the
                        result to standard output

Logging options:
  control log level (default is WARNING)

  -d, --debug           output all messages (log level DEBUG)
  -v, --verbose         be more verbose (log level INFO)
  -q, --quiet           be more quiet (log level ERROR)

Output options:
  control output formatting

  -o OUTPUT_FORMAT, --output-format OUTPUT_FORMAT
                        output format (choice of 'JSON': set output format to
                        JSON, 'YAML': set output fromat to YAML, 'input': keep
                        input format, 'toggle': change JSON to YAML or vice
                        versa; default: 'input')
  --indent {2,4,8}      indentation depth of blocks, in spaces (default: 2)
  --sort-keys           sort mapping keys (by default, mapping keys are left
                        in input order)

Query syntax options:
  control the query syntax

  --separator SEPARATOR
                        the separator character (default: '.')
  --subscript-indicators SUBSCRIPT_INDICATORS
                        the subscript indicator character(s) (default: '[]')Issues, feature requestsPlease open an issuehereif you found a bug or have a feature suggestion.
Package: drymail
drymailMakes sending emails easy andDRY— For Python 3.Drymailis a minimalist wrapper over Python’s existingsmtplibandemaillibraries, designed to be friendly but unrestrictive. Here’s how you might send a simple email with an attachment usingdrymail.fromdrymailimportSMTPMailer,Messageclient=SMTPMailer(host='smtp.email.com',user='johndoe',password='password',tls=True)message=Message(subject='Congrats on the new job!',sender=('John Doe','john@email.com'),receivers=[('Jane Doe','jane@message.com'),'jane.doe@mail.io'],text='When is the party? ;)')withopen('congrats.pdf','rb')aspdf_file:message.attach(filename='congrats.pdf',data=pdf_file.read(),mimetype='application/pdf')client.send(message)FeaturesSupports creating email with HTML content, plaintext content, or both!Supports mentioning contacts in the“John Doe" <john@email.com>format.Support standard headers likeCC,BCC,Reply-ToandAuthors.Supports injecting custom headers.Supports adding attachments.And most importantly — the library being minimalist, it doesn’t restrict you in any way like some of the most fancier email frameworks do.InstallationInstalldrymailby running —$pip3installdrymailDocumentationDocumentation is available athttps://drymail.readthedocs.io/AgendaTest suite.Restructure theMessageclass to handle dynamicprepare.ContributeAll kinds of contribution are welcome.Issue Tracker —https://github.com/drymail/issuesSource Code —https://github.com/drymailLicenseThis project is licensed under the MIT license.
Package: dry-monads
dry-monadsMonads forpythonmade simple and safe.FeaturesProvides primitives to write declarative business logicFully typed with annotations and checked withmypy,
allowing you to write type-safe code as wellNo operator overloading or other unpythonic stuff that makes your eyes bleedInstallationpipinstalldry-monadsWhat's inside?We have several the most iconic monads inside:Result, Failure, and Success(also known asEither,Left, andRight)Maybe, Some, and NothingWe also care about code readability and developer experience,
so we have included some useful features to make your life easier:Do notationHelper functionsExamplefromdry_monads.do_notationimportdo_notationfromdry_monads.eitherimportResult,Success,FailureclassCreateAccountAndUser(object):"""Creates new Account-User pair."""@do_notationdef__call__(self,username:str,email:str)->Result['User',str]:"""Can return a Success(user) or Failure(str_reason)."""user_schema=self._validate_user(username,email).unwrap()account=self._create_account(user_schema).unwrap()returnself._create_user(account)# Protected methods# ...We arecovering what's going on in this examplein the docs.InspirationsThis module is heavily based on:dry-rb/dry-monadsØpymonad
Package: drypatrick
Open source home automation that puts local control and privacy first. Powered by a worldwide community of tinkerers and DIY enthusiasts. Perfect to run on a Raspberry Pi or a local server.Check outhome-assistant.iofora
demo,installation instructions,tutorialsanddocumentation.Featured integrationsThe system is built using a modular approach so support for other devices or actions can be implemented easily. See also thesection on architectureand thesection on creating your own
components.If you run into issues while using Home Assistant or during development
of a component, check theHome Assistant help sectionof our website for further help and information.
Package: dry-pipe
A Python DSL for pipelinesDon't Repeat Yourself (D.R.Y) while writing pipelines, and stay away from leaky abstractions, use DryPipe !What is a pipeline ?A pipeline could be described as"a bunch of programs feeding data to one another".Programs within a pipeline tend to:run for a long timeneed large amounts of resources (cpu, memory, disk space, etc), requiring clusters to run (Slurm, Torque,etc)be written in different languaged, have different CLI interfaces, file formats, etc.long CODE->RUN->DEBUG->CODE cyclesThe length of the development cycle makes pipelines difficult to debug.https://drypipe.readthedocs.io/
Package: drypy
The module helps you to implementdryrunmode in your
Python projects with an easy switch ‘on/off’ feature.Authors:Daniele Zanotelli (dazano@gmail.com)Copyright:2017-2020License:MITPython version:3.xDocumentationThe package is supplied withSphinxcompilable documentation
under the docs directory.You can read the latest docs visiting theproject docsonline.InstallationVia pip:$ pip install drypyor download the project from the github and compile the package$ git clone https://github.com/dzanotelli/drypy.git
$ cd drypy
$ make whland you’ll find your brand-new whl under the dist/ subdirectory.Basic usageApply theshamdecorator to your function and set dryrun as on.from drypy import dryrun
from drypy.patterns import sham

>>> @sham
>>> def foo(bar):
        pass
>>> dryrun(True)
>>> foo(42)Will log the following output:[DRYRUN] call to 'foo(42)'using the python standard logging facility, thus it’s up to you
to correctly configure it (check the docs for a working example).To use custom substitutes with specific beahviours please search in the docs
for thesheriff-deputypattern.
Package: dryrun
Dryrun-PythonDryrun-Python makes it very easy to run a python program without actual i/o operations.
It can help you to fully focus on your business-logic without worrying about the i/o operation.Key featuresIt supports async functions.You can specify a return value.Or, you can use mock function which is called instead of original function.How to installpip install dryrunfromdryrunimportdryrunHow to useJust import dryrun and use it as a decorator.@dryrun()asyncdefdelete_comment(comment_id:str)->bool:response=awaitapi_client.delete_comment(comment_id)returnresponse.successIf dryrun is enabled, the real function won't be called.And If you want to specify a return value, you can usereturn_valueargument.@dryrun(return_value=True)asyncdefdelete_comment(comment_id:str)->bool:response=awaitapi_client.delete_comment(comment_id)returnresponse.successBut it's not enough for some cases. Then, you can use mock function which is called instead of the real function.Just like it!@dryrun()asyncdefcreate_comment(payload:dict)->dict:response=awaitapi_client.create_comment(payload)returnresponse.paylaod@create_comment.mockdefcreate_comment_mock(payload:dict)->dict:return{"id":uuid.uuid4(),"content":payload["content"],}
Package: dryscrape
# OverviewAuthor:Niklas Baumstarkdryscrape is a lightweight web scraping library for Python. It uses a
headless Webkit instance to evaluate Javascript on the visited pages. This
enables painless scraping of plain web pages as well as Javascript-heavy
“Web 2.0” applications like
Facebook.It is built on the shoulders of
[capybara-webkit](https://github.com/thoughtbot/capybara-webkit)’s[webkit-server](https://github.com/niklasb/webkit-server). A big thanks goes
to thoughtbot, inc. for building this excellent piece of software!# Changelog1.0: Added Python 3 support, small performance fixes, header names are now
properly normalized. Also added the functiondryscrape.start_xvfb()to
easily start Xvfb.0.9.1: Changed semantics of theheadersfunction in
a backwards-incompatible way: It now returns a list of (key, value)
pairs instead of a dictionary.# Supported PlatformsThe library has been confirmed to work on the following platforms:Mac OS X 10.9 Mavericks and 10.10 YosemiteUbuntu LinuxArch LinuxOther unixoid systems should work just fine.Windows is not officially supported, although dryscrape should work
with [cygwin](https://www.cygwin.com/).# Installation, Usage, API DocsDocumentation can be found at
[dryscrape’s ReadTheDocs page](http://readthedocs.org/docs/dryscrape/).Quick installation instruction:# pip install dryscrape# Contact, Bugs, ContributionsIf you have any problems with this software, don’t hesitate to open an
issue on [Github](https://github.com/niklasb/dryscrape) or open a pull
request or write a mail toniklas baumstark at Gmail.
Package: dry-scraper
dry_scraperdry_scraperis a framework for retrieving and parsing hockey data into useful forms.In its current form, it supports only NHL data sources, but its object-oriented structure is intended to enable
extensibility to other leagues with comparable data sources.It's simple to get started. Here's how you can retrieve a pandas dataframe of the team stats for a given game:from dry_scraper.nhl.nhl_api_sources import NhlGameLiveFeedApiSource
   team_stats = NhlGameLiveFeedApiSource(season=2022, gamePk=20720)
                  .fetch_content()
                  .parse_to_pyd()
                  .yield_team_stats_df()
Package: drytools
DRYtoolsversion number: 0.1.3author: Dan EliasThis library provides tools to make Python code shorter, less repetitive and
more readable.Featuresfunction annotations to coerce and/or validate parameters, anddecorators and mixins to add groups of Python standard methods to classesExampleThis example provides implementation with and without drytools for a class
representing a person’s name and age.  The required behaviour is:The constructor should accept a name and an ageage should be coerced to an integerif name is not of type str, raise a TypeErrorif age is negative or exceeds 200, raise a ValueErrorThe full set of comparison methods (ie: __lt__, __eq__ etc) should be
implemented reflecting an ordering by age (with alphabetical ordering
by name for people with the same age)Without drytoolsfromfunctoolsimporttotal_ordering@total_orderingclassperson:def__init__(self,name,age):ifnotisinstance(name,str):raiseTypeError(name)age=int(age)if(age<0)or(age>200):raiseValueError(age)self.name=nameself.age=agedef_comp(self):return(self.age,self.name)def__eq__(self,other):returnself._comp()==other._comp()def__gt__(self,other):returnself._comp()>other._comp()With drytoolsfromoperatorimportge,lefromdrytoolsimportargs2attrs,check,compose_annotations,ordered_by@ordered_by('age','name')classperson:@compose_annotations@args2attrsdef__init__(self,name:check(isinstance,str,raises=TypeError),age:(int,check(ge,0),check(le,200))):passGetting started$pipinstalldrytoolsDocumentationhttps://drytools.readthedocs.io/en/latest/ContributingIssue Tracker:https://github.com/dan-elias/drytools/issuesSource Code:https://github.com/dan-elias/drytools.gitContinuous integrationhttps://travis-ci.org/dan-elias/drytoolsLicenseThe project is licensed under theGPL-3license.
Package: dryxDropboxCL
dryxDropboxCLExtra helper tools for the dropbox command-line.UsagedryxDropboxCLbumpdryxDropboxCLresync-s<pathToSettingsFile>-h,--helpshowthishelpmessage-v,--versionshowversion-s,--settingsthesettingsfileDocumentationDocumentation for dryxDropboxCL is hosted byRead the Docs(laststable versionandlatest version).InstallationThe easiest way to install dryxDropboxCL us to usepip:pipinstalldryxDropboxCLOr you can clone thegithub repoand install from a local version of the code:gitclonegit@github.com:thespacedoctor/dryxDropboxCL.gitcddryxDropboxCLpythonsetup.pyinstallTo upgrade to the latest version of dryxDropboxCL use the command:pipinstalldryxDropboxCL--upgradeDevelopmentIf you want to tinker with the code, then install in development mode.
This means you can modify the code from your cloned repo:gitclonegit@github.com:thespacedoctor/dryxDropboxCL.gitcddryxDropboxCLpythonsetup.pydevelopPull requestsare welcomed!IssuesPlease report any issueshere.LicenseCopyright (c) 2016 David YoungPermission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the
“Software”), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: dr-zoidberg
zoidbergAre you ready to operate, Doctor? - I’d love to, but first I have to perform surgery.Free software: MIT licenseDocumentation:https://zoidberg.readthedocs.io.FeaturesZoidberg is a small lobster crawler that useScrapyto get surgeon reviews from an Internet message board.
Sometimes it is hard to find real reviews of surgeons in Google.For example (my case), I have Femoroacetabular Impingement in the hip and the only medical solution is surgery.
I thought Margalet was the best doctor to perform this operation but when I searched with Zoidberg, I realized that
López Carro was a much better choice.By typing:python zoidberg.py -c es -d margalet -a traumatologia -i femoroacetabular -p test.csv -o csvyou get all the Doctor Margalet reviews for the femoroacetabular impingement in the test.csv file.At this moment it only implements Femoroacetabular Impingement in Spain, but hopefully with some collaboration,
Zoidberg will extend its functionality.InstallJust:pip install dr-zoidbergor clone this repository.UsageTo use Zoidberg from CLI just type zoidberg and follow the instructions.To use Zoidberg in a project:from zoidberg.zoidberg_main import Zoidberg

z = Zoidberg(country='es', doctor='margalet', area="traumatologia", illness="femoroacetabular", path='test.csv', output='csv')
z.conf()
z.run()TODODONE - CLI: Change argparse to click.pocoo.org.TODO - Get a list of countries available.DONE - Get a list of areas available for a country.DONE - Get a list of illnesses available for an area.TODO - Add keywords of illness for each search.TODO - Search a doctor for every area or illness.TODO - Comprehensive testsCreditsGino Palazzoginopalazzo@gmail.comThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.Zoidgber image from Wikipedia:https://en.wikipedia.org/w/index.php?curid=18173215History0.1.1 (2018-03-06)First release on PyPI.0.1.3 (2018-03-14)Minor changes.
Package: ds1054z
ds1054zThis package allows you to connect to your Rigol DS1054Z oscilloscope
via Ethernet. It comes with a versatile command line tool.Off course, you can also use it to control the scope with your own
script by usingthe
APIthis
package has to offer.While this software is named after the entry level oscilloscope DS1054Z,
I believe there is no reason it wouldn’t work with just any scope of the
DS1000Z and MSO1000Z series by Rigol:DS1054ZDS1074ZDS1104ZDS1074Z-SDS1104Z-SMSO1074ZMSO1104ZMSO1074Z-SMSO1104Z-SFeaturesDiscovering your scope via mDNS / DNS-SDSaving Screenshots (incl. adjustable dimming of on-screen controls)Running / stopping the scopeAcquiring waveforms… more to come!InstallationThe installation is dead simple:pip install ds1054z[savescreen,discovery]ds1054z depends onpython-vxi11which
should automatically get installed along with itself.For more information on the installation, please consult theinstallation
sectionof thepackage
documentation.Recommended FirmwareOlder versions of the oscilloscope’s firmware have many issues. The
VXI-11 connection could be unstable or commands might return unexpected
data. Specifically, I’m recommending version 00.04.04.SP3 (alias
00.04.04.03.02) published on 2017-02-08.UsageCommand Line ToolThis package installs a versatile command line (CLI) tool calledds1054z. You can use it to save the screen of your scope, for
example:ds1054zsave-screen--overlay0.6192.168.0.23You can find out more ways to use the CLI tool withds1054z--helpor by readingthe
manual.DevelopersIf you’re into Python programming, usethe DS1054Z
classin
your own code:fromds1054zimportDS1054Zscope=DS1054Z('192.168.0.23')print("Connected to: ",scope.idn)print("Currently displayed channels: ",str(scope.displayed_channels))AuthorPhilipp Klausphilipp.l.klaus@web.deResourcesThis Python package was inspired byDS1054Z_screen_capture.The device discovery built into this software is largely based onthis
codebyMerseyViking / GeoSpark.There is a Qt4 based GUI interface for the scope calledDSRemote.
Package: ds18b20pi
* no kernel patches needed* any GPIOs allowed* any number of GPIOs allowedMake sure you have:*build-essential package*python-dev or python3-dev package*Raspberry Pi device*sudo access for the scriptUsage:import timeimport ds18b20pids18b20pi.bus.convert(pin)time.sleep(0.75)temperatureDict = ds18b20pi.bus.search(pin)The Package is fresh and installation could be buggy,sorry for any inconvenience.
Package: ds2
A Book and a LibraryThis repository hosts both the book,A First Course on Data Structures in Pythonand the python packageds2that is extracted from that book.The book covers fundamentals of data structures in Python and the library contains fully-tested implementations of the data structures in the book.The book is a literate program.
The code is woven into the text.
As such, there are few inline comments.
The comments are the book.
Moreover, the code files are automatically generated from the markdown files that define the book.A First Course on Data Structures in Pythonby Don SheehyOverviewThis book is designed to cover a lot of ground quickly, without taking shortcuts.What does that mean?It means that concepts are motivated and start with simple examples.
The ideas follow the problems.
The abstractions follow the concrete instances.What does it not mean?The book is not meant to be comprehensive, covering all of data structures,
nor is it a complete introduction to all the details of python.
Introducing the minimum necessary knowledge to make interesting programs and learn useful concepts is not taking shortcuts, it's just being directed.There are many books that will teach idiomatic python programming, and many others that will teach problem solving, data structures, or algorithms.
There are many books for learning design patterns, testing, and many of the other important practices of software engineering.
The aim of this book is cover many of these topics as part of an integrated course.Towards that aim, the organization is bothsimpleandcomplex.
Thesimplepart is that the overall sequencing of the main topics is motivated by the data structuring problems, as is evident from the chapter titles.
Thecomplexpart is that many other concepts including problem solving strategies, more advanced python, object-oriented design principles, and testing methodologies are introduced bit by bit throughout the text in a logical, incremental way.As a result, the book is not meant to be a reference.
It is meant to be worked through from beginning to end.
Many topics dear to my heart were left out to make it possible to work through the whole book in a single semester course.The full book as a pdf can be foundhere.
Package: ds4finance
ds4financeA collection of data science tools for finance.InstallationInstall the package from PyPI using pip:pipinstallds4financeUsageTo use the package, import it using the alias dsf:import ds4finance as dsfExample: Computing Standard Deviationimportpandasaspdimportds4financeasdsf# Load or create a DataFrame containing financial datadata=pd.read_csv("your_data.csv")# Compute the annualized standard deviation for the datastd_dev=dsf.compute_std_dev(data)LicenseThis project is licensed under the MIT License. See theLICENSE.txtfile for more information.ContributingWe welcome contributions to the project. If you'd like to contribute, please follow these steps:Fork the repository.Create a new branch for your feature or bugfix.Make your changes, and ensure that the code follows PEP 8 guidelines.Submit a pull request to the main repository.SupportIf you encounter any issues or have questions, pleaseopen an issueon the GitHub repository.AuthorsLuis Silva -luis_paulo_silva@hotmail.com