Saravanan666/pypi-org huggingface dataset:
Package: write-tight
write-tightThis project improves your professional writing with the help of simple rule-based writing patterns. The inspiration of this project comes from two sources:The bookWrite Tight: Say Exactly What You Mean With Precision and Powerby William Brohaugh.The Udemy courseBusiness Writing & Technical Writing Immersionby Paul Siegel (Starweaver Instructor Team).Both sources emphasise that eliminating ‘deadwood’ is critical to improve your writing.“Deadwood is the the unnecessarily difficult, long, or simply unnecessarily phrases or words that clog the arteries of professional writing.”William BrohaughExamples of deadwood writing patterns are:Adverbs that end on ‘ly’“I amveryoutraged by this!” -> What doesveryadd to this sentence?“That movie isreallygood.” -> Words likegreatorawesomeare better alternatives for ‘really good’.“That isbasicallya good idea.” -> Just leave it out.Passive voiceActive voice ensures clarity and achieves greater writing precision by answeringwhoorwhatperforms an action.“The README.md filewas modifiedwith new content.” -> Passive voice, who or what modified the README.md file?“John Doe modified the README.md file with new content.” -> This sentence in active voice is more precise.Ambiguous Openings“The weather was great and the food was lovely.It wasamazing.” -> What was amazing? The weather? the food? Something else?All the patterns and their details are described in thetests.Both sources provide detailed descriptions on how to detect and handle deadwood patterns. This project translates the descriptions to rule-based matching patterns with the help ofspaCy, and specifically thespaCy Matcher.Getting startedCreate and activate a new virtual environment, and installwrite-tightwith pip.pip install write-tightThis project depends on a trained spaCy pipeline calleden_core_web_sm, and you need to download this pipeline manually if you do not already have it.python -m spacy download en_core_web_smPlease refer to thespaCy installation pagein case of any issues.Write-tight provides a command line interface and prints the detected patterns and suggestions on how to improve your text. The only argument you have to provide is the path to your text file.(venv) writetight <path_to_your_text_file>Although this project is developed with Markdown (.md) files in mind, your text file is read as-is, so you are not limited to a certain format.LimitationsOnly the English language is supported.The suggestions are rule-based which make them easy to use and understand. The downside is that these rules do not cover every edge case, and are limited in the complexity they can handle. Furthermore, the rule-based suggestions adhere to a writing style that you might not like. If that is the case I hope the project is structured well enough so that you can easily modify the existing patterns to a writing style of your liking.
Package: writeup-tool
Writeup is a text markup format that translates to HTML5. It is similar to Markdown in spirit, but is not equivalent. Some differences:
- the line-oriented syntax is designed to make correct implementation simple and unambiguous.
- hierarchical sections.
- special handling of license lines at the beginning of a file.
- builtin javascript presentation mode.
- unambiguous character escape syntaxes.
- versioning scheme will allow for format evolution while preserving the ability to build old source files.
Package: writing-style-converter
Writing Style ConverterOverviewThe WSC (writing_style_converter) library provides a utility for converting dictionaries between different naming conventions.fromwriting_style_converterimportWSCall_case_dict={"snake_case":{"value_one":123,"value_two":[{"value_three":123}]},"kebab-case":{"value-one":123,"value-two":[{"value-three":123}]},"camelCase":{"valueOne":123,"valueTwo":[{"valueThree":123}]},"PascalCase":{"ValueOne":123,"ValueTwo":[{"ValueThree":123}]},}# Example with humidities set to True (default)wsc_dict=WSC(all_case_dict)# Access different naming conventionsprint(wsc_instance.value)# Original dictionaryprint(wsc_instance.snake)# Converted to snake_caseprint(wsc_instance.kebab)# Converted to kebab-caseprint(wsc_instance.camel)# Converted to camelCaseprint(wsc_instance.pascal)# Converted to PascalCase# Example with humidities set to Falsewsc_dict_humidities=WSC(all_case_dict,humidities=False)print(wsc_instance_without_humidities.snake)# Converted to snake_case without humiditiesThehumiditiesparameter controls whether the conversion process includes nested dictionaries and lists. When set toTrue (default), the conversion includes nested structures. When set to False, only the top-level dictionary is converted.Installationpipinstallwriting_style_converter
Package: wrk2img
Wrk2ImgYou findwrkandwrk2awesome for latency analysis but reading km of log file doesnt seems to be fun ?Introducingwrk2img, your new tool to go to generate latency graph, as easily as possible.Goalwrk2img has two goal:Direct use in a pipeline to output beautiful latency graphSave multiple output then pipe to wrk2img to combine themInstallationpip install wrk2imgPython3.5is required.How to useDirect usage :wrk :wrk --latency http://localhost:8000 | wrk2img output.pngwrk2 :wrk --latency -R 300 http://localhost:8000 | wrk2img output.pngGenerating latency data at multiple load level using wrk2 and them combining themwrk --latency -R 300 http://localhost:8000 > 300
wrk --latency -R 500 http://localhost:8000 > 500
wrk --latency -R 700 http://localhost:8000 > 700
cat 300 500 700 | wrk2img output.pngFeaturelog scaletransparent backgroundchange background colorcombine graph from wrk and wrk2 in the same output
Package: wrkloadgeneratortoolbox
wrktoolboxA tool to run wrk and wrk2 benchmarks, store their output, and generate reports.FeaturesSupport for YAML and JSON configuration files, to define benchmark suitesParses the output ofwrkandwrk2HTTP benchmarking toolsPossibility to define performance goals, which are evaluated and stored with resultsStrategy to store benchmarks results and whole suite configurationSupport forplugins, loaded dynamically to define new types of stores, performance goals, and reports writersStrategy to produce reports of results, for exampleto XLSX, with wrktoolbox-xlsx- seeWikiDocker images for Ubuntu and AlpineCLIQuick exampleinstallpipinstallwrktoolsverify that the cli works (optional)wrktoolbox--helpprepare a YAML, or JSON file with configuration.
A basic example, with a single type of benchmark, looks like this:# the array of benchmarks contains the configuration of benchmarks to runbenchmarks:-url:https://this-is-an-example.it/api/alivethreads:10# threads countconcurrency:100# concurrent usersduration:30# test duration in seconds# the type of stores to use, to collect benchmark results# it is possible to define custom stores, using plugins;# for example to store results in a database, or send them to an APIstores:-jsonrun a suite of benchmarks using a settings filewrktoolboxrun--settingsbasic.yamlRefer to examples folder for an example of full configuration file, defining plugins for authentication and custom store.
Package: wrk-load-tools
wrktoolboxA tool to run wrk and wrk2 benchmarks, store their output, and generate reports.FeaturesSupport for YAML and JSON configuration files, to define benchmark suitesParses the output ofwrkandwrk2HTTP benchmarking toolsPossibility to define performance goals, which are evaluated and stored with resultsStrategy to store benchmarks results and whole suite configurationSupport forplugins, loaded dynamically to define new types of stores, performance goals, and reports writersStrategy to produce reports of results, for exampleto XLSX, with wrktoolbox-xlsx- seeWikiDocker images for Ubuntu and AlpineCLIQuick exampleinstallpipinstallwrk-load-toolsverify that the cli works (optional)wrktoolbox--helpprepare a YAML, or JSON file with configuration.
A basic example, with a single type of benchmark, looks like this:# the array of benchmarks contains the configuration of benchmarks to runbenchmarks:-test_id:this_is_an_example_test_id#preferred test identifier to be added in reportsurl:https://this-is-an-example.it/api/alivethreads:10# threads countconcurrency:100# concurrent usersduration:30# test duration in seconds# the type of stores to use, to collect benchmark results# it is possible to define custom stores, using plugins;# for example to store results in a database, or send them to an APIstores:-jsonrun a suite of benchmarks using a settings filewrktoolboxrun--settingsbasic.yamlRefer to examples folder for an example of full configuration file, defining plugins for authentication and custom store.
Package: wrktools
wrktoolboxA tool to run wrk and wrk2 benchmarks, store their output, and generate reports.FeaturesSupport for YAML and JSON configuration files, to define benchmark suitesParses the output ofwrkandwrk2HTTP benchmarking toolsPossibility to define performance goals, which are evaluated and stored with resultsStrategy to store benchmarks results and whole suite configurationSupport forplugins, loaded dynamically to define new types of stores, performance goals, and reports writersStrategy to produce reports of results, for exampleto XLSX, with wrktoolbox-xlsx- seeWikiDocker images for Ubuntu and AlpineCLIQuick exampleinstallpipinstallwrktoolsverify that the cli works (optional)wrktoolbox--helpprepare a YAML, or JSON file with configuration.
A basic example, with a single type of benchmark, looks like this:# the array of benchmarks contains the configuration of benchmarks to runbenchmarks:-url:https://this-is-an-example.it/api/alivethreads:10# threads countconcurrency:100# concurrent usersduration:30# test duration in seconds# the type of stores to use, to collect benchmark results# it is possible to define custom stores, using plugins;# for example to store results in a database, or send them to an APIstores:-jsonrun a suite of benchmarks using a settings filewrktoolboxrun--settingsbasic.yamlRefer to examples folder for an example of full configuration file, defining plugins for authentication and custom store.
Package: wrktools-xlsx
wrktoolbox-xlsxXLSX spreadsheet reports forwrktoolbox.pipinstallwrktools-xlsxExample configurationYAML# importers read benchmarks resultsimporters:-type:jsonroot_folder:data/results# reports generation supports plugins, like benchmarks logicplugins:-wrktoolboxxlsx.xlsx# writers write reportswriters:-type:xlsxfile_name:example.xlsxJSON{"importers":[{type:"json",root_folder:"data/results"}],"plugins":["wrktoolboxxlsx.xlsx"],"writers":[{type:"xlsx",file_name:"example.xlsx"}],}Usagewrktoolboxreports--settingsconfiguration.yaml
Package: wrlparser
wrlparserPython library to parse VRML files (.wrl) using an LALR parser with a Context-Free Grammar.Getting startedInstallingpipinstallwrlparserSample programThis program parses the file which name has been given as a command line argument. Two sample models have been provided in thetests/modelsfolder.fromwrlparserimportparseimportsysfile=sys.argv[1]withopen(file)asf:l="".join(f.readlines())f.close()scene=parse(l)DocumentationContext-Free GrammarThis parser is based on the grammar provided by theVRML97 specification. The grammar described in the specification is a Context-Sensitive Grammar which isn't deterministic. The following changes have been made to make it a Context-Free Grammar.Recursive rules have been generalized to allow collections to be empty as this will resolve shift/reduce conflicts. This should only broaden the spectrum of parsable files.elements : element                  elements : element elements
         | element elements    ->            | empty      
         | emptyIDs have been optimized to the raw ID typeField values have been optimized to the raw typesffloatValue :
sfcolorValue :
sfvec2fValue :  ->  sffloatValues : FLOAT sffloatValues
sfvec3fValue :                    | empty
...Intermediate Representation (IR)Once parsed, the file's content is returned to the user as classes. Their structure strictly corresponds to the one described in the specification.For instance, amaterialnode has the following fields :- ambientIntensity : float
  - diffuseColor     : list[3]
  - specularColor    : list[3]
  - emissiveColor    : list[3]
  - transparency     : float
  - shininess        : floatIt should be noted that the specification doesn't require a file to specify every field of a node. They should therefore be check before accessed as follows:m=Material()ifm.ambientIntensity!=None:# Use the valueTestsThis parser has been tester with models from the KiCad catalog.IssuesEven though the parser fully supports VRML's grammar, a very small subset of the intermediate representation has been implemented. This is due to the lack of test models for specific node types.If you every need to parse a file which has an unimplemented feature, feel free to share the file in a ticket so that I can support it.
Package: wroclawtaxonomy
wroclaw_taxonomy packageAbout the methodThis package enables user to create dendrite out of set of points based on Wroclaw taxonomy method.
It uses Euclidean distance calculated from provided variables or geographic coordinates to group objects into possibly similar groups.
In the following steps, each group is merged with the other closest group until the whole dataset is combined into one coherent dendrite.
In the end, dendrite can be separated into several groups/clusters to classify data points based on provided variables.InstallationYou can install this package from PyPI by running this command in terminal:pip install wroclawtaxonomyOr download development version of the package directly from this repository:pip install git+https://github.com/TomekMatuszek/wroclaw_taxonomy.gitExampleBasic workflow:importwroclaw_taxonomyaswtdendrite=wt.Dendrite(src='data/citiesPL_pop.geojson')dendrite.calculate(columns=['lat','lon'],normalize=False)dendrite.export_objects(out_file='dendrite_points.geojson')dendrite.export_dendrite(out_file='dendrite.geojson')plotter=wt.Plotter(dendrite)plotter.plot()Customizing plot:plotter.plot(level=1,lines=True,style={"markersize":20,"cmap":'gist_rainbow',"line_color":'#000000',"object_color":'#0000ff'})Animation showing every stage of the dendrite creation:plotter.animate(out_file='dendrite.gif',frame_duration=1)More examples can be found in fileexamples.ipynb
Package: wrongbutusefulsbi
A brief description of the wrongbutusefulsbi package.Getting StartedInstructions on getting started with wrongbutusefulsbi will be added here.ContributeIssue Tracker:https://github.com/RyanJafefKelly/wrongbutusefulsbi/issuesSource Code:https://github.com/RyanJafefKelly/wrongbutusefulsbiSupportIf you are having issues, please let us know.
We have a mailing list located at:ryan@kiiii.comLicenseThe project is licensed under the GPLv3 license.
Package: wronnay-search-lib
Wronnay Search LibraryA library of classes which can be used to build a search engine.Project inspired by the simple "Wronnay Search Engine" from 2014 (which is deprecated).General InformationLicense: GNU General Public LicenseAuthor: Christoph Daniel MikscheDependenciesThe crawler uses BeautifulSoup for handling the html code and MongoEngine for Data-Management.Installationpip install wronnay-search-libDatabasePlease install MongoDB first:On Debian Servers:apt install mongodbNext steps: create a MongoDB Database and edit the wronnay_search_lib/settings.py file.
Package: wr-schemas
A schema describes:a data structurea mapping of one data structure into anotherA schema consists of a list of fields.A field doesn’t have a type – it is a type itself, in a way. Instead of a type, a field has a bi-directional
mapping. Given two different data structuresxandy, a mapping describes how to calculatex.ffromy.fand how to calculatey.ffromx.f.Fields support following attributes:namemappingdefaultsource_name (source_names)min_len, max_len, auto_trimmin, maxchoicesregexrequired, forbiddennullableAlso:Nested fields are supported.Fields are easy to clone for reuse.Fields and schemas are easy to reverse.Schemas are easy to chain.fromwr_schemasimportField,Schema,MappingsclassFields:user_id=Field('id',mapping=int,min=1)user_username=Field('username',min_len=5,max_len=100,regex=r'^[a-zA-Z0-9_\-\.@]+$')user_password=Field('password',min_len=10,max_len=100,regex=r'^[a-zA-Z0-9]+$')user_dob=Field('date_of_birth',mapping=Mappings.date())CreateUser=Schema(Fields.user_username.clone(required=True),Fields.user_password.clone(default=None),Fields.user_dob.clone(default=None),)payload=CreateUser.load({'username':'marcus.aurelius@rome.gov'})assertpayload.username=='marcus.aurelius@rome.gov'assertpayload.passwordisNoneassertpayload.date_of_birthisNoneprint(CreateUser.dump(payload))Flask:fromwr_schemasimportField,Schema,Mappingsfromwr_schemas.flask_requestimportFlaskRequestSchemaMixinCreateUser=Schema(Field('username',required=True),Field('password',required=True),mixins=[FlaskRequestSchemaMixin],)user=CreateUser.from_request()
Package: wrtc
A Python extension that provides bindings to WebRTC M92Examples•Documentation•PyPIPython WebRTCLet's use the native WebRTC with strict compatibility and fully implemented stuff!This project follows theW3C specificationwith some modifications and additions to make it work better with Python applications, with useful APIs like programmatic audio and video.DISCLAIMERThis project is still under development and isn't ready for any serious use. In the current stage, it's possible to establish connection and work with audio, but many interfaces and methods not implemented yet.You can easily check status of methods and interfaces availabilityhere.Snippetimportasyncioimportwebrtcasyncdefmain():pc=webrtc.RTCPeerConnection()stream=webrtc.get_user_media()fortrackinstream.get_tracks():pc.add_track(track,stream)audio_source=webrtc.RTCAudioSource()track=audio_source.create_track()pc.add_track(track)local_sdp=awaitpc.create_offer()print(local_sdp.sdp)if__name__=='__main__':asyncio.run(main())RequirementsPre-built wheels:Python 3.7 or higherpip 21 or higherAnd compatible platform:LinuxmacOSWindowsarmv7larm64x86_64IntelApple Silicon64bitPython3.7N/AN/A✅✅N/A✅3.8N/AN/A✅✅✅✅3.9N/AN/A✅✅✅✅3.10N/AN/A✅✅✅✅Building from sources (sdist):~15 GB of free disk spaceCMake 3.14 or higherGCC 7.5 or higherglibc 2.18 or higherARM toolchain (ARM only)Full building instruction will be present laterInstallingPre-built wheel:pip3install--prewrtcBuild from sources:pip3install--prewrtc--no-binarywrtcDocumentationThe documentation is live atreadthedocs.io.Getting helpYou can get help in several ways:Report bugs, request new features bycreating an issue.Ask questions bystarting a discussion.ContributingContributions of any sizes are welcome.Special thanks toAuthorsofnode-webrtc.LicenseThepython-webrtcis published under theBSD 3-Clause License.
Package: ws2g
Webserver to goGet startedFirst, take a look at the example app, foundhere.FeaturesEasily display your pagesThis library supports all displaying images, videos, HTML/CSS and JavaScript files. The example website is built using Bootstrap.[!IMPORTANT]All HTML/CSS/JS and other media files shouild be stored incontent/.Code templates<tableid="gbentries-table"border="1"><tr><th>ID</th><th>Username</th><th>Comment</th><th>Posted on</th></tr>{{customcontent}}
  {{for gbentry in gbrepo}}<tr><td>{{gbentry.id}}</td><td>{{gbentry.username}}</td><td>{{gbentry.comment}}</td><td>{{gbentry.posted_on}}</td></tr>{{endfor}}
{{endcustomcontent}}</table>Notice the{{customcontent}}and{{endcustomcontent}}tags? They represent the beginning, respectively the end of the area of code that contains custom content. Inside, there is a loop which iterates over a list of objects and displays some of its attributes on the screen.The resulting HTML content is this:TODO add codeAnd the result on the user's screen is this:TODO add picNo boilerplate for repositories!frombuilt_to_serve_arazi47.repositoryimportRepositoryclassGuestBookRepository(Repository):passThis is all you need to have a fully functioning repository (except the DTO, of course) The following methods are already implemented:save,update,fetch_all,delete_by_id. The data is stored using an SQL database.[!IMPORTANT]
Be aware that you need to create aModeland have its repository namedModelRepository, i.e for a class namedUser, its corresponding repositorymustbeUserRepository.Easily create routesRoutes can be easily created using@route("path/to/file").@route("/")@route("/index.html")classIndex(BaseView):def__init__(self,file_path,status_code=200,content_type="text/html")->None:super().__init__(file_path,status_code,content_type)defbuild_GET_response(self)->str:withopen(self.file_path)asf:returnf.read()The get response is as basic as it gets: it opens and displays the page located atfile_path.
Package: ws2udp
ws2udpA WebSocket to UDP proxy.ws2udpwill forward messages coming from the WebSocket server to UDP sockets, it is especially useful
to send OSC messages from JavaScript applications to SuperCollider or any other software that doesn't support
WebSockets. It registers all websockets connected and will broadcast messages sent to its own UDP port.InstallationYou will need Python>=3.7 and the websockets package.The simplest way to install is using pip:$ pip install ws2udpExample$ ws2udp
WebSocket to UDP proxy

optional arguments:
  -h, --help           show this help message and exit
  --udp-addr UDP_ADDR  Address of the UDP receiver for broadcasting messages (default=localhost)
  --udp-port UDP_PORT  Port of the UDP receiver (default=57142)
  --addr ADDR          WebSocket address to listen (default=0.0.0.0)
  --port PORT          WebSocket port to listen (default=8765)The server expects binary messages following the format:address_length(uint32)address(string)port(uint32)dataaddress_lengthis an integer representing the total length of the addressaddressthe address where to forward the data as a string (example: localhost)portas an integerdatathe original message dataFor example, to send the messagehello worldtolocalhost:57120, one would send:b'\x09\x00\x00\x00localhost\x20\xdf\x00\x00hello, world'The first 4 bytesb'\x09\x00\x00\x00represents 9, then comesb'localhost'and lastlyb'\x20\xdf\x00\x00'for 57120. Whatever comes after this is forwarded.
Package: ws2x-vjsrinivas
LEDStripPython simulation of WS2812 (or probably other WS-type LEDs) LED strips commonly used by NeoPixel API.Examplefrom led import LEDSim
import cv2
import numpy as np

pixel = LEDSim()
pixel.fill((0,0,0))

p = 0
for r in range(1,6):
	for g in range(1,6):
		for b in range(1,6):
			pixel[p] = (r,g,b)
			p += 1

pixel.visualize()
Package: wsa_cli
Web-Standard-Apps Command Line ToolCommand line tool to generate basic folder, file structure for a WSA module.https://github.com/GannettDigital/web-standard-appsRequirementsPython 2.7, Python 3.4Installpipinstallwsa-cli-OR-pythonsetup.pyinstallUsagemkwsaFor helpmkwsa--helpTO DOCreate model.pyCreate views.pyCreate urls.pyCreate default testsCreate tests for wsa-cliCreate default HTML templates for different module typesCreditsEric BowerChange Logv0.0.2, 2014-01-27Removed defaults folder and imported text into main modulev0.0.1, 2015-01-27mkwsa command