Saravanan666/pypi-org huggingface dataset:
Package: slackuiblocks
slackuiblocks - Simplify Slack UI Block Creation with Python Classesslackuiblocksis a Python library that provides an intuitive and elegant way to create Slack UI blocks using Python classes instead of dealing with complex JSON structures. With this library, you can easily craft interactive and visually appealing messages for your Slack channels or direct messages.FeaturesSimplified Syntax: Define Slack UI blocks using Python classes, making it easier to structure your messages.Interactive Elements: Create buttons, menus, and other interactive components effortlessly.Customization: Customize the appearance and behavior of UI elements using Python attributes.Well-Organized: Maintain a clear and organized codebase by encapsulating UI elements within classes.InstallationYou can installslackuiblocksusing pip:pipinstallslackuiblocksUsage ExampleHere's a simple example of how you can useslackuiblocksto create a message with interactive buttons:fromslack_sdkimportWebClientfromslackuiblocksimportSection,MarkdownText,Datepicker,PlainTextmessage=Blocks(blocks=[Section(text=MarkdownText(text=("*Sally* has requested you set the deadline for the Nano ""launch project")),accessory=Datepicker(action_id="datepicker123",initial_date="1990-04-28",placeholder=PlainText(text="Select a date"),),)])# Get the JSON representation of the blockblock_json=message.dict(exclude_none=True)# Use block_json in your Slack API requestchannel="your channel id"try:client.chat_postMessage(channel=channel,text="message",blocks=block_json.get("blocks"),)exceptExceptionase:print(e)For more complex interactions, you can create menus, input fields, and more using Python classes just like the example above.DocumentationFor detailed usage instructions, examples, and a comprehensive list of available UI elements, refer to theDocumentation.ContributingContributions are welcome! To contribute toslackuiblocks, follow the guidelines outlined inCONTRIBUTING.md.LicenseThis project is licensed under theMIT License.
Package: slackutils
IntroductionNOTE: This is an alpha version. Expect bugs and expect everything to change.slackutilsis a small library for simplifying some tasks with theSlack Web API. It is an extension ofslackclient, and mostly contains methods for looking up users or channels by other parameters than their ID.InstallationRecommended method is to install using pip. Note that we're still in alpha:pip install --upgrade slackutilsUsageAuthenicationYou need a slack token to use this library.slackutilslooks for the token using one of two alternatives:All methods support a named parameterSLACK_API_TOKENwhich is used to authenticate.If no named parameter is found, slackutils will look for the environment variableSLACK_API_TOKEN.If neither of the above variables exist, an error will be raised.ExamplesSimplify looking up users and channels by other parameters than their IDimport slackutils
marketing_channel = slackutils.conversations.get(name='marketing')

from slackutils import get_user
dave = get_user(email='dave@example.com')
Package: slack-utils
Slack UtilsVarious Slack utils (at the moment just one) I’m using in my Slack related projects.Free software: MIT licenseDocumentation:https://slack-utils.readthedocs.io.Featuresverify slack signaturerespond to slack challengeCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-01-02)First release on PyPI.0.1.1 (2020-01-02)Minor improvements (mostly typos)0.2.0 (2020-01-02)Verify Slack challenge0.2.1 (2020-01-02)Docs
Package: slackviews
IntroductionIf you're reading this, is because either you love Slack, or just need to work with its API somehow. Recently I had
to start implementing a bot in Slack which content was more complex than usual. Then, I found that Slack API was quite
easy to use but.... its views, those contents in json can be soooo huge and complex, that I realized I spent most of my
time designing templates instead of coding the logic of the bot.
This is when I started to look around searching for some library that would allow me to code my views in Python easily,
unfortunately, I didn't found any (hey, if it does exist, please let me know, I had no idea about it) so I decided to
write this one.
The lib, includes "almost" all Block types, I just left out those that I was not gonna use for sure, but once you see
the source code you'll realize it can be extended to include those pretty easily.DocumentationCheck included Readme.md or latest doc atSlackviews Readme.md
Package: slack-votebot
The bot for voting on stuff.InstallationThis bot uses extensively features from Python 3.4.$python3-mvenvslack$cdslack$.bin/activate(slack)$pipinstallslack-votebotUsage(slack)$exportSLACK_TOKEN=xoxb-123(slack)$exportSLACK_CHANNEL=general(slack)$exportPYTHONASYNCIODEBUG=True(slack)$exportDEBUG=True(slack)$python-mvotebot
Package: slack-webclient-logger
This package provides logging.Handler implementations that will send message to a
slack channel. Supports updatedslack.WebClientAPI released in 2020.AuthorizationSeehttps:://api.slack.comto for information on setting up app and get
an authorization token. Only a “bot” token is required to use
SlackHandler, however, the unittest is more complete with a “user” token.LimitationsHow often messages can be sent is limited by slack. Seehttps://api.slack.com/docs/rate-limits. By default, this handler only
sends accumulated messages every 60 seconds. That can be adjusted by the
optionalupdateargument toSlackHandler.send_remaining()should be called prior to program exit to send any remaining
messages.Examplefrom nmrbox_slack import SlackHandler
token = 'your token here'
channel = 'your channel name'
handler = SlackHandler(token, channel)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
example_logger = logging.getLogger("Test Logger")
example_logger.propagate = False
example_logger.addHandler(handler)
example_logger.setLevel(logging.INFO)
example_logger.info("Then shalt thou use Python three, no more, no less.")
...
# at end of program
handler.send_remaining()VariantsTwo versions are currently supported.SlackHandlerconnects to slack upon creation and validates the token and channel name.LazySlackHandlerdoes not attempt to connect to slack until the first message is sent.SlackHandler is recommended if it is known messages are to be sent. LazySlackHandler is available
to reduce overhead of processes which typically do not send messages.Additional channelsSending to additional channels may be done more efficiently with theSlackHandler.additional_channel_handler()method.from nmrbox_slack.slacklogger import SlackHandler
token = 'your token here'
channel = 'your channel name'
handler = SlackHandler(token, channel)
second_handler = heandler.additional_channel_handler('second channel name')From mappingfrom_mapping(mapping:Mapping,,lazy:bool)* will return a SlackHandler or LazySlackHandler from
a mapping:{'slack:
    {
    'channel':<channel name>,
    'token file': <file with token in it>
    }
}Release history2.0 New API from slack
2.2.2 Add paginationFunding acknowledgmentThis work supported by National Institutes of Health (NIH) / National
Institute of General Medical Sciences (NIGMS), grant 1P41GM111135.
Package: slack-webhook
slack-webhookslack-webhook is a python client library for slack api Incoming Webhooks on Python 3.6 and above.Installation$ pip install slack-webhookUsagebasicfromslack_webhookimportSlackslack=Slack(url='https://hooks.slack.com/services/T00/B00/XXX')slack.post(text="Hello, world.")advancedfromslack_webhookimportSlackslack=Slack(url='https://hooks.slack.com/services/T00/B00/XXX')slack.post(text="Robert DeSoto added a new task",attachments=[{"fallback":"Plan a vacation","author_name":"Owner: rdesoto","title":"Plan a vacation","text":"I've been working too hard, it's time for a break.","actions":[{"name":"action","type":"button","text":"Complete this task","style":"","value":"complete"},{"name":"tags_list","type":"select","text":"Add a tag...","data_source":"static","options":[{"text":"Launch Blocking","value":"launch-blocking"},{"text":"Enhancement","value":"enhancement"},{"text":"Bug","value":"bug"}]}]}])Getting startedFor help getting started with Incoming Webhooks, view our onlinedocumentation.ContributingFork itCreate your feature branch (git checkout -b my-new-feature)Commit your changes (git commit -am 'Add some feature')Push to the branch (git push origin my-new-feature)Create new Pull Request
Package: slack-webhook-alpa
slack_webhookThis is a simple python API for sending messages into a specific
slack channel using incoming webhook.PrerequisiteIn order to send messages using incoming webhook into slack, an Application
must be configured. This needs to be done from a slack account with
administrator rights.  This is done on a per slack channel basis.Request an incoming webhook from the administrator with the name of an
existing channel for receiving the alerts.An unique URL will be generated by the administrator.Note that there is no authentication mechanism.  Anyone (script) with
knowledge of this URL will be able to generate messages into this channel.Following is a (fake) sample of such an URL:https://hooks.slack.com/services/T02TQT999/B01PZ5XXXXX/GevkYac4s55lE97fXXXXXXXInstallationpip install slack-webhook-alpaUsagefrom slack_webhook import SlackWebhook, SlackWebhookException

url = 'https://hooks.slack.com/services/XXXXXXXXX/XXXXXXXXX/XXXXXXXXXXXXXXX'

slack_alert = SlackWebhook(url)
slack_alert.send('TEST TEST TEST')Content formatted in block kit is also supported.blocks = [
    {
        "type": "header",
        "text": {
            "text": "\ud83d\udd34 This is a header",
            "type": "plain_text",
            "emoji": true
        }
    }
]

slack_alert.send('TEST TEST TEST', blocks=blocks)Referenceshttps://api.slack.com/messaging/webhookshttps://pypi.org/project/blockkit/
Package: slack-wordcloud
Slack WordcloudThis package is intended to take the history of a given Slack Channel, and run it through a Python Wordcloud API, and generate a wordcloud image. Compatibile with Python 3.7+.This package uses theconversions.historySlack endpoint and will require a valid Slack API Token.Installationpip install slack-wordcloudThedocoptandwordcloudpackages are also required. They should be automatically installed simulaneously withslack-wordcloud. However, if it fails you can do the following:pip install docopt wordcloudClassesSlackHistory(token, channel)GenerateWordCloud(options, *args, **kwargs)Command Line Usageslack_wordcloud [-f] --channel <CHANNEL_NAME> [--token=SLACK_TOKEN]-fwill create a png file in your current directory of the output--channelis required. Any valid slack channel.--tokenoptional argument if the token is already in your environment. Otherwise, you will need to pass it in.You will need to use Bot tokens and invite the Bot to the channel whose history you wish to fetch. This will hopefully be smoothed out in the future, but for now it's necessary due to the deprecation of the legacy tokens.
Package: slackybot
SlackybotThis Python package helps you with sending messages to Slack. It offers an object-oriented approach - every message or reply in a thread is an object, so they can be easily managed.Getting startedInstallationpython -m pip install slackybotAdd following token scopes to your Slack bot:channels:joinchannels:readchat:writechat:write.customizechat:write.publicSimple usageImport and initialize the slack objectfromslackybotimportSlackslack=Slack(token='XXX')As a best practice, it is better to pass the token using an environment variable (e.g..envfile)To send a message you have to use the Slack object method. It takes two arguments:channelandtextthey are both strings.slack.send_message(channel='channel-name',text='Sample text')The above method returns theMessageobject.To update a message you may use the Message method.update(). It takes one argument:textas a string.message=slack.send_message(channel='',text='')message.update(text='New text')To send a reply in the message thread simply use.send_reply()method. It takes one argument:textas a string.message=slack.send_message(channel='',text='')message.send_reply(text='Reply message in the thread')The above method returns theReplyobject.Both objectsMessageandReplycan be deleted by using.delete()method on them. Those objects can be listed using:.get_messages()on the Slack object.get_replies()on the Message object
Package: sla-cli
SLA-CLIA Skin Lesion Acquisition (SLA) CLI tool designed to help source data for skin lesion research.IntroductionWhile working on an academic project in the domain of automatic skin lesion detection it became clear that there was no
easy way to track down datasets cited highly in the literatureThis is what motivated the creation ofSDA-CLI.SDA-CLIis targeted toward academic and medical researchers looking to source lesion dataset quickly to accelerate
their research efforts.Features at a GlanceAvailableDataset summaries and label distribution.Console-based dashboards.Full support for downloading datasets + metadata from the ISIC Archive API.Full support for downloading PH2 dataset.Full support for downloading PAD-UFES-20 dataset.Full support for downloading MEDNODE dataset.WIPMatplotlib integration for data distribution visualisation.Dataset downloading (public datasets only).Metadata extraction on applicable datasets.Data background information sources and links.Preprocessing of datasets forbinary classification.Datasets AvailableThe table below shows the dataset currently available to acquire via the tool.DatasetAvailableAltlas of Dermoscopy❌BCN 20000✅BCN 20000 Challenge✅Brisbane ISIC Challenge 2020✅DERMOFIT❌Dermoscopedia (CC BY)✅DermIS⚠️DermQuest⚠️HAM10000✅ISIC 2020 Challenge MSKCC Contribution✅ISIC 2020 Vienna Part 1✅ISIC 2020 Vienna Part 2✅JID Editorial Images 2018✅MClass (Dermoscopy)⚠️MClass (Dermoscopy)⚠️MEDNODE✅ ️MSK-1✅MSK-2✅MSK-3✅MSK-4✅MSK-5✅PAD-UFES-20✅ ️PH2✅ ️SONIC✅Sydney MIA SMDC 2020 ISIC Challenge Contribution✅UDA-1✅UDA-2✅✅ - Fully implemented⚠️- Work in progress.❌ - Private datasetLoading ConfigurationsThere are three ways to load configuration files when using the CLI interface.The first method is using an explicit YAML configuration file like so:sla-cli-f/--config-file<FILE_PATH><COMMAND>...The second method is referencing theSLA_CLI_CONFIG_FILEenvironment variable. Once the variable is set, it will
auto-reference the environment variables value, which should be a path to the configuration file you wish to load
into the tool.The third and final method of loading a configuration with the tool is creating a ".sla_cli_config.yml" file in the
directory you plan to run the tool in. This method is helpful if you wish to check-in your configuration to SCM.CommandsThe following sub sections discuss the how to use the tool.The following conventions are used to describe tool usage.<NAME>--->Requiredargument.[NAME:DEFAULT_VALUE]--->Optionalargumentshowingdefaultvalue.If unsure of how to use a command, use-h/--helpon any command to get context on what commands are available and
what they do.lsThelscommand is to gain quick insight into what data is available.sla-clils[regex:'.*']# Shows a list of dataset names available.sla-clils-vtotals[regex:'.*']# Shows a list of dataset names and the number of images it contains.sla-clils-vall[regex:'.*']# Shows a list of dataset names and a full breakdown of all image label distributionA sample of thesla-cli ls -v alloutput is shown below:Sample output of 'sla-cli ls -v all' command.
Package: slagg
SLAGG - Simplified Load-balancing Algorithm for General GeometriesThis package provides a set of Python-based tools for creating load-balanced
domain decompositions that can be used for numerical simulations.FeaturesAbility to specify Grid, Geometry as STL, and desired Decomp slabsBasic Decomposition can be augmented with refinements to load-balance and reduce memory usageInstallationslagg is available fromPyPI,
so can be easily installed usingpipas follows:pipinstallslaggDocumentationDecomposition Tutorial
Package: slakh-dataset
Slakh PyTorch DatasetUnofficial PyTorch dataset forSlakh.This project is a work in progress, expect breaking changes!RoadmapAutomatic music transcription (AMT) usecase with audio and labelsSpecify dataset split (original,splits_v2,redux)Add new splits (redux_no_pitch_bend, ...) (Should also be filed upstream) (implemented byskip_pitch_bend_tracks)Load audiomix.flac(all the instruments comined)Load individual audio mixes (need to combine audio in a streaming fashion)Specifytrain,validationortestgroupChoose sequence lengthReproducable load sequences (usefull for validation group to get consistent results)Add more instruments (eletric-bass,piano,guitar, ...)Choose between having audio in memory or stream from disk (solved bymax_files_in_memory)Add to pipAudio source separation usecase with different audio mixesList to comeUsageDownload the Slakh dataset (see the officialwebsite). It's about 100GB compressed so expect using some time on this point.Install the Python package with pip:pipinstallslakh-datasetConvert the audio to 16 kHz (seehttps://github.com/ethman/slakh-utils)You can use the dataset (AMT usecase):fromtorch.utils.dataimportDataLoaderfromslakh_datasetimportSlakhAmtDatasetdataset=SlakhAmtDataset(path='path/to/slakh-16khz-folder'split='redux',# 'splits_v2','redux-no-pitch-bend'audio='mix.flac',# 'individual'label_instruments='electric-bass',# or `label_midi_programs`# label_midi_programs=[33, 34, 35, 36, 37],groups=['train'],skip_pitch_bend_tracks=True,sequence_length=327680,max_files_in_memory=200,)batch_size=8loader=DataLoader(dataset,batch_size,shuffle=True,drop_last=True)# train model on dataset...AcknowledgementThis code is based on the dataset inOnset and Framesby Jong Wook Kim which is MIT Lisenced.Slakhhttp://www.slakh.com/
Package: slam3d
slam3dSimultaneous localization and mapping (SLAM) tools in 3DPython WrapperCurrently implementsparticleFilterLoc. Supports Python 3.6+.InstallationInstall theslam3d package from PyPi:python3 -m pip install slam3dOr use the source distribution or wheels in thelatest release.To install the latest unreleased code, download or clonethis source repositoryand run the following to build and install:python3 -m pip install .ExampleimportnumpyasnpfromparticlefilterimportParticleFilterLoc,setSeed# If deterministic tests are needed, call this before anything elsesetSeed(123456789)# Argument is np.uint32pf=ParticleFilterLoc()pf.depositVio(t:np.float64,x:np.float32,y:np.float32,z:np.float32,dist:np.float32)pf.depositRange(bx:np.float32,by:np.float32,bz:np.float32,range:np.float32,stdRange:np.float32)pf.depositRssi(bx:np.float32,by:np.float32,bz:np.float32,rssi:np.int32)pf.getTagLoc()# returns tuple: (status: np.int32, t: np.float64, x: np.float32, y: np.float32, z: np.float32, theta: np.float32)DevelopmentTo install for development, download or clone and run:python3 -m pip install -e .Source found in./particlefilter/cython.CSource found in./particlefilter. A bundled version of MUSLrand_r()is included for Windows builds. Force it on other platforms with-DPF_FORCE_MUSL_RANDR=1. Precompiled shared libraries can be found in thelatest release.Shared librarygcc -fPIC -shared -o particlefilter.so -Iparticlefilter/include particlefilter/src/*.c -lmOr on Windows withcl.exe:cl.exe particlefilter/src/*.c /Iparticlefilter/include /MT /link /DLL /OUT:build/particlefilter.dllDevelopmentCompile tests with:gcc -o build/test -Iparticlefilter/include particlefilter/src/*.c test/test.c -lmOn Windowscl.exealso works (from Development Command Prompt):cl.exe -o build/test -Iparticlefilter/include particlefilter/src/*.c test/test.c -lmCompile and debug tests with:gcc -o build/test -Iparticlefilter/include particlefilter/src/*.c test/test.c -lm -g
gdb ./build/testTestsBothtest/test.candtest/test.pyimplement mostly the same test.test [--nofail] <test folder> <output file> [expected file (required without --nofail)]The currently provided test folder is./test/data.--nofailwill cause the test to always exit with status code 0 and allow omitting testing against an expected file (by not providing the last argument).When running the C test, some expected files are provided in./test/datafor various operating systems and compilers. When running the Python test, you should test against the output of running the C test on your system. The Python test will also for some floating point precision deviation. Since the Python test tests each value individually, it is impervious to CRLF/LF differences.LicenseCopyright (c) 2021, Wireless Sensing and Embedded Systems Lab, Carnegie Mellon University
All rights reserved.This source code is licensed under the BSD-3-Clause license found in the
LICENSE file in the root directory of this source tree.
Package: slam-dataset-sdk
SLAM Dataset SDKDue to increase in dataset targeted for SLAM, major gap is created in usability of the datasets. Major Datasets have there unique structure of storing and retrieving the sensor inputs. This project deals with combining all the dataset parsing and creating a simple and generic dataset interface, to enable fast evaluations and implementation of new SLAM systems. Due to cumbursome setup requirements for each dataset use, most of the time is consumed for just understanding how the dataset is sturctured and ways to decode them to be used for actual experiments. This created a delay and hence many implementations just use datasets which are easy to setup for experiments.
Our efforts are to reduce the gap between dataset and its usablity out of the box.
Additionally, we support basic Odometry pipeline stub, where user is allowed to use there custom deskew, registration functions enabling quick evaluation, without doing any tedious setup.InstallWe released a python-package supported on.To get started, just runpipinstallslam-dataset-sdkIf you also want to install all the(optional)dependencies, like Open3D for running the visualizer:pipinstall"slam-dataset-sdk[all]"Install (developer mode)If you plan to modify the code then you need to setup the dev dependencies, luckilly, the only real
requirements are a modern C++ compiler and thepippackage manager, nothing else!, in Ubuntu-based
sytems this can be done with:sudoaptinstallg++python3-pipAfter that you can clone the code and install the python api:gitclonehttps://github.com/pranayspeed/slam_dataset_sdk.gitcdslam_dataset_sdk
pipinstall--verbose.Install (expert mode)If you want to have more controll over the build, you should then installcmake, ,ninja,tbb,Eigen, andpybind11as extra dependencies in your system, the ubuntu-way of doing this is:sudoaptinstallbuild-essentiallibeigen3-devlibtbb-devpybind11-devninja-buildUsing LibraryCheck slam_dataset_sdk/eval/sdk_test.py
A sample application is developed for example usage of the libraryAuthorsPranay MeshramCreditsMajor code for Lidar dataset pre-processing is used from 'https://github.com/PRBonn/kiss-icp'KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way.