Saravanan666/pypi-org huggingface dataset:
Package: skill-ovos-fallback-chatgpt
ChatGPT Fallback SkillWhen in doubt, ask ChatGPT, powered byOpenAI Solver.You need to configure akey, get it athttps://platform.openai.com/api-keysAboutCapabilities:Remembers what user said earlier in the conversationTrained to decline inappropriate requestsLimitations:May occasionally generate incorrect informationMay occasionally produce harmful instructions or biased contentLimited knowledge of world and events after 2021ConfigurationUnder skill settings you can tweak some parameters for chatGPT.key- your api_key to access OpenAIpersona- can be used to create a "persona", give a personality to chatGPTmodel- LLM model to use, eggpt-3.5-turbo, see all optionshereThe default persona ishelpful, creative, clever, and very friendly.mkdir-p~/.config/mycroft/skills/skill-ovos-fallback-chatgpt.openvoiceos
cat<<EOF>~/.config/mycroft/skills/skill-ovos-fallback-chatgpt.openvoiceos/settings.json{"key": "sk-XXXYYYZZZAAABBB123","model": "gpt-3.5-turbo","persona": "You are a helpful voice assistant with a friendly tone and fun sense of humor","__mycroft_skill_firstrun": false}EOFExamples"Explain quantum computing in simple terms""Got any creative ideas for a 10 year old’s birthday?"
Package: skill-ovos-fallback-dialogpt
DialoGPT Fallback SkillWhen in doubt, ask DialoGPT, powered byDialoGPT PersonaOffline alternative to ChatGPT skill  (GPT 2 based)AboutWhile this can not compete withChatGPT Personait is an offline alternative, you can use any model from huggingface to have different personasFind modelshereConfigurationUnder skill settings you can tweak some parameters for DialoGPTmodel- model to use, default"microsoft/DialoGPT-medium"Examples"What is best in life?""Do you like dogs""Does God exist?"
Package: skill-plotter
The Skill Plotter: OverviewThe Skill Plotter is a Python Command Line Interface (CLI) tool to create skill plots for your CV or other documents.
It can be used to easily and interactively create the plots.
Instead of using some designer software like Illustrator or just the office package we are going to use matplotlib and python.
Because, well, we can.InstallationThe skill plotter package is available as a CLI command and can be installed via pip:pipinstallskill-plotterUsageJust use the CLI command to run the plotter, the help shows you all possible things to do:skill-plotter--help# Add a skillskill-plotteraddPython9# or use it interactivelyskill-plotterinteractive-add# Remove a skillskill-plotterremovePython# or use it interactivelyskill-plotterinteractive-remove# Do the plotskill-plotterIf you want to have a complete walkthrough, you can have a look at theusage documentation.DevelopmentMinimal Requirements- Python 3.9
- matplotlib 3.8ExamplesHere you can see some examples of generated skill plots:
Package: skillshare_dl
skillshare-dlA cli tool for downloading Skillshare lesson videos.InstallInstall withpipx.pipxinstallskillshare-dlUsageLog into your Skillshare account.You'll need an active Skillshare account to download lesson videos. As of June 2023,
Skillshare offers a 1-month free trial to new users.Go to the course homepage that you want to download lesson videos for.For example,here is the homepagefor Kurzgesagt's Motion Graphics course.Get your access token.Open your browser's Developer Tools and navigate to the browser storage tab. Find where
cookies for "https://www.skillshare.com" are stored and find the cookie "access_token".Download the lesson videos.Use the url of the course homepage and your access token with theskillshare-dlCLI
tool.skillshare-dl\"[your-course-url]"\"[your-access-token]"A complete example of this can look like:skillshare-dl\"https://www.skillshare.com/en/classes/Motion-Graphics-with-Kurzgesagt-%E2%80%93-Part-1/631970755/projects?via=Selected-SearchSuggestion"\"eyJhbGciOi98y1yCho98y112Cl8Ga21dac3usImtpZCI6IldC..."If you're getting 401 errors, refresh the page and use the new access token that was
generated.
Package: skill-tree-parser
skill_tree_parser技能树解解释器是技能森林的一部分，属于技能树服务构建部分。说明CSDN 技能树结构（章节、元信息、习题）解释器，支持技能树结构和内容的分析、解释。what's new0.0.5修正错误的程序流程0.0.4支持在习题中内嵌 markdown 源码0.0.5捕获习题编码错误捕获 json 文件编码错误0.0.6为keywords_must和keywords_forbid的读取操作添加默认值0.0.7为 config.json 添加默认的keywords_must和keywords_forbid空列表0.0.8补充对 $ 符号的宽容处理0.0.9添加 init 方法0.0.10修复一个在 windows 环境下的安装错误0.0.11增加 group 字段0.0.12增加 FAQ修正requirements.txt包拼写错误0.0.13增加 subtree 字段0.1.0增加新的习题类型0.1.1bug 修复0.1.2清理输出信息
Package: skiml-cluster
skiml-clusterTable of ContentsQuickstartInstallationLicenseQuickstartFor RIVM users, the best approach is to runskiml-clusteron a computing node of the HPC, instead of on the home node.Step by step:Run apollo-mapping on the samples you want to cluster, or find the apollo-mapping results of these samples.Copy allSNPVCFs (<APOLLO-MAPPING-OUTPUT>/variants/snps/*.vcf) for the relevant samples to a single directory.Run skiml-cluster as follows on the SNP VCF directory, which will submit a job to the HPC:bsub -M 10G skiml-cluster run --input <INPUT FOLDER> --output <OUTPUT FOLDER>It is also possible to runskiml-clusterlocally (without submitting to the HPC):skiml-cluster run --input <INPUT FOLDER> --output <OUTPUT FOLDER>However, please do this with care as some steps might require a lot of resources if you're running a lot of samples.Installationpip install --user skiml-clusterTo updateskiml-clusterto the most recent version, run:pip install --user --upgrade skiml-clusterTroubleshootingQ: I get this error:ValueError in file /home/boas/mambaforge/lib/python3.10/site-packages/skiml_cluster/workflow/Snakefile, line 16:
No VCF files found in input directory.A:skiml-clusterlooks for files ending in.vcfin the input folder. If zero files can be found with thisexactextension, the pipeline will not run. Please make sure there are valid VCF files (preferably fromapollo-mapping) in the input folder with the extension.vcf.Q: I get this error:ValueError in file /home/boas/mambaforge/lib/python3.10/site-packages/skiml_cluster/workflow/Snakefile, line 11:
Input directory does not exist.A: The input directory cannot be found. Please make sure there are no typos in theskiml-clustercommand.Methodologyskiml-clusterdoes a couple of things, which is managed by Snakemake.SNP VCF files are compressed and indexed (required for other analyses).All indexed VCF files are merged into a multi-sample VCF.A UPGMA tree is generated from the multi-sample VCF usingvcfkit.A Fasta pseudo-alignment is generated from the multi-sample VCF usingvcfkit.SNP distances are calculated from the pseudo-alignment usingsnp-dists.A command line interface of the Snakemake pipeline has been generated usingsnk.Licenseskiml-clusteris distributed under the terms of theMITlicense.
Package: skimpy-ext
Skimpy ExtendedA light weight tool for creating summary statistics from dataframes.This is a replica of the original Skimpy package, but for polars!skimpyis a light weight tool that provides
summary statistics about variables in data frames within the console or your interactive Python window.
Think of it as a super-charged version ofdf.describe().You can find the documentation here.MVP TODOsInfo tablesSupport for numerical dataSupport for categorical dataSupport for timeseries dataSupport for boolean dataRemove zero-variance columnsIdentifies rare categories and groupsFind outliers based on Inter Quartile RangeDetects possibly mixed data types columnsDetects high cardinality featuresDetects high correlated featuresDetects duplicated rows (may be an option to pass when call the Skimpy class)Indicate skewed ditributionsDetects imbalanced classesDetects feature leakage
Package: skim-unicode-table
skim-unicode-tableInteractive fuzzy-searchable Unicode table usinglotabout's skim.Can be used to quickly search for a character and copy it to the clipboard.Demo videohttps://user-images.githubusercontent.com/3827982/127855578-b052bf0b-f4e5-4686-b171-5e5fea784326.movInstallationInstall via pip:(NOTE:If prebuilt wheels are not available for your operating system,
installation will take a long time and use a lot of space because it has to
compile a fork of skim from scratch.)pipinstallskim-unicode-tableThis will put two scripts into pip's preferred binary directory:skim-unicode-tableandskim-unicode-table-xsel. The former just displays
the table and prints the selected character and its various names separated by
two spaces when the user pressesEnter, then exits. The latter also copies
the selected character into the clipboard usingxsel(check if this is
installed!), which might not work on all platforms. If it doesn't work, you can
try to build your own script for copying the character into the clipboard usingskim-unicode-table.Optional dependenciesatanunq's viu: Required for enlarged character previews like in the demo
video above to work. Make sure it can be found fromPATH.Launching in new terminal windowThe most common setup will probably be to bindskim-unicode-table-xselto
a hotkey that launches it in a new terminal window. How to do this depends on
the specific terminal emulator used (there is no unified CLI, unfortunately).Ingnome-terminal's case, it would be (absolute path in case pip's preferred
binary installation folder isn't inPATHfor the application that handles
hotkeys, commonly the window manager):gnome-terminal--/path/to/skim-unicode-table-xselDevelopment notesWhy use a forked skim?fuzzy-matcher(used internally by skim) had default scoring that didn't seem
very suitable for this use case, as it kept returning something unexpected as
the best match, so I had totweak it a bitand fork skim so that it uses
this modified version.
Package: skinfer
Simple tool to infer and/or merge JSON schemasFree software: BSD licenseDocumentation:https://skinfer.readthedocs.org.FeaturesGenerating schema inJSON Schema draft 4formatInferring schema from multiple samplesMerging schemas - nice for generating schema in Map-Reduce fashion
or updating an old schema with new dataExample of usingskinferto generate a schema from a list of samples:$ cat samples.jsonl
{"name": "Claudio", "age": 29}
{"name": "Roberto", "surname": "Gomez", "age": 72}
$ skinfer --jsonlines samples.jsonl
{
    "$schema": "http://json-schema.org/draft-04/schema",
    "required": [
        "age",
        "name"
    ],
    "type": "object",
    "properties": {
        "age": {
            "type": "number"
        },
        "surname": {
            "type": "string"
        },
        "name": {
            "type": "string"
        }
    }
}Install with:$ pip install skinferOr, if you don’t havepip, you can still install it with:$ easy_install skinferHistory0.2.0 (2015-08-10)Renamed entry-point script schema_inferer -> skinferAdded json_validatorDropped Python 2.6 supportAdded more tests0.1.2 (2015-08-04)Bugfix: removed buggy -o argumentAutomated PyPI release via Travis0.1.1 (2015-05-01)Support more complex string-type schemasAttempt to infer JSON lines format instead of just failingAPI cleanup: no need for long imports anymoreUpdated documentation, added docstringsFixed merging schema for arrays with tuple vs list validationFixed compatibility issues with Python 2.6Improved test coverage, added end-to-end tests0.1.0 (2015-03-03)First release on PyPI.
Package: skingrabber
SkinGrabberA Python API wrapper for Minecraft skins and UUID'sCurrent Version =0.1.5DocumentationDocumentation is available here:https://skingrabber.readthedocs.io/InstallationTo install skingrabber, do:pip install skingrabberorpip3 install skingrabberorpython -m pip install skingrabberExamplesSome examples are available in theexamples folderFor more detailed explanations and functions, visitthe docs
Package: skinny-orm
Skinny ORM"Skinny ORM" - a lightweight Python package that simplifies data storage, manipulation, and retrieval from a SQLite (maybe others later) database using Python dataclasses.It's not really a "Relational Mapper". It's just a simple way to persist data.Installation:pipinstallskinny_ormorpoetryaddskinny_ormExample:Create your model:fromdataclassesimportdataclassfromdatetimeimportdatetime@dataclassclassUser:id:intname:strage:intbirth:datetimepercentage:floatCreate a connection et an "orm" objectimportsqlite3fromskinny_orm.ormimportOrmconnection=sqlite3.connect('database.db')orm=Orm(connection)And Voila (no need to create tables. if they don't exist, it will create them automatically)users=[User(id=1,name='Naruto',age=15,birth=datetime.now(),percentage=9.99),User(id=2,name='Sasuke',age=15,birth=datetime.now(),percentage=9.89),User(id=3,name='Sakura',age=15,birth=datetime.now(),percentage=9.79),]# Bulk insertions (if the table "User" does not exist, it will create it)orm.bulk_insert(users)# Selections (always end with .first() or .all() )naruto:User=orm.select(User).where(User.name=='Naruto').first()the_boys:list[User]=orm.select(User).where((User.name=='Naruto')|(User.name=='Sasuke')).all()# Update data by setting specific fieldsorm.update(User).set(User.age==30).where(User.id==1)# Or you can simply update the object with all the fieldsnaruto.age=30orm.update(naruto).using(User.id)# Bulk update objectsusers_20_year_later=[User(id=1,name='Naruto',age=35,birth=datetime.now(),percentage=9.99),User(id=2,name='Sasuke',age=35,birth=datetime.now(),percentage=9.89),User(id=3,name='Sakura',age=35,birth=datetime.now(),percentage=9.79),]orm.bulk_update(users_20_year_later).using(User.id)
Package: skinport
skinportAn unofficial Python wrapper around theSkinportAPI.Installationskinportis available on PyPI:$pipinstallskinportTo install from source use:$pythonsetup.pyinstallUsageimportskinportskinport.CLIENT_ID="<skinport_client_id>"skinport.CLIENT_SECRET="<skinport_client_id>"skins=skinport.SkinPort()print(skins.items())DocumentationDocumentation for the skinport library is availablehere.To learn more about the Skinport API, check out theofficial documentation.Skinport API KeyYou will need an API key to Skinport to access the API. To obtain a key, follow
these steps:Register for and verify anaccount.Log intoyour account.Scroll down to the Skinport API section.Click onGenerate API keyand follow the instructions.LicenseCopyright 2022 Martin Simon

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.Buy me a coffee?If you feel like buying me a coffee (or a beer?), donations are welcome:BTC : bc1qq04jnuqqavpccfptmddqjkg7cuspy3new4sxq9
DOGE: DRBkryyau5CMxpBzVmrBAjK6dVdMZSBsuS
ETH : 0x2238A11856428b72E80D70Be8666729497059d95
LTC : MQwXsBrArLRHQzwQZAjJPNrxGS1uNDDKX6
Package: skinport.py
skinport.pyAn API wrapper for the Skinport API written in Python.InstallingPython 3.10 or higher is requiredTo install the library, you can just run the following command:# Linux/macOS$python3-mpipinstall-Uskinport.py# Windows>py-3-mpipinstall-Uskinport.pyTo install the development version, do the following:$gitclonehttps://github.com/PaxxPatriot/skinport.py
$cdskinport.py
$python3-mpipinstall-U.Quick Exampleimportasyncioimportskinportasyncdefmain():client=skinport.Client()# Get a list of all listed CS:GO items on skinport.comitems=awaitclient.get_items()print(items)if__name__=="__main__":asyncio.run(main())Advanced ExampleWith this library an automated script was created which forwards cheap listings to a Discord webhook. You can find it atPaxxPatriot/cheapbot.
Package: skinpy
SkinpyA Python library for Minecraft skins.Load skins from a file, or start from scratchIndex with 3D coordinates to get/set skin pixel colorOperate at the skin level, the body part level, or even just one faceGenerate skin images for use in-gameRender isometric ("angled/tilted view", like above) images of your skinTODO: Add support for second layerQuickstartCreating/Loading/Saving a skinfromskinpyimportSkin# make a new skinnew_skin=Skin.new()new_skin.to_image().save("blank.png")# or load a skin from diskloaded_skin=Skin.from_path("my_skin.png")loaded_skin.to_image().save("copy.png")Rendering Isometric ImagesYou can render isometric images with the CLI tool:python-mskinpyrendersteve.png-orender.png# see help with `python -m skinpy render --help`Or, here'e the API interface:fromskinpyimportSkin,Perspectiveskin=Skin.from_path("steve.png")# create a perspective from which to view the renderperspective=Perspective(x="left",y="front",z="up",scaling_factor=5,# bigger numbers mean bigger image)# save the renderskin.to_isometric_image(perspective).save("render.png")Outputted file:Pixel IndexingfromskinpyimportSkinskin=Skin.from_path("steve.png")magenta=(211,54,130,255)# RGBA# get/set using entire skin's 3D coordinatescolor=skin.get_color(4,2,0,"front")# somewhere on steve's right footprint(f"Skin pixel at (4, 2, 0, 'front') was{color}")skin.set_color(4,2,0,"front",magenta)# get/set on just a head. coordinates become relative to just that partcolor=skin.head.get_color(0,1,2,"left")print(f"Head pixel at (0, 1, 2, 'left') was{color}")skin.head.set_color(0,1,2,"left",magenta)# or finally, just on one face. faces only have two dimensions# NOTE: Face does not necessarily mean just a character's face! It just# refers to the side of a cubiod, which all body parts are in Minecraftcolor=skin.left_arm.up.get_color(5,5)print(f"Left arm up pixel at (5, 5) was{color}")skin.head.set_color(0,1,2,"left",magenta)skin.to_image().save("some_magenta.png")Here's an animated visualization of equivalent ways to access a certain pixel:(This image was made withexamples/index.py.)Pixel EnumerationfromskinpyimportSkinskin=Skin.from_path("steve.png")for(x,y,z),body_part_id,face_id,colorinskin.enumerate_color():print(f"{x=},{y=},{z=},{body_part_id=},{face_id=},{color=}")for(x,y,z),face_id,colorinskin.torso.enumerate_color():print(f"{x=},{y=},{z=},{face_id=},{color=}")for(x,y),face_id,colorinskin.torso.back.enumerate_color():print(f"{x=},{y=},{color=}")Coordinate systemSkinpy uses a coordinate system with the origin at the left-down-front of the
skinfrom the perspective of an observer looking at the skin.FaceIdIn some methods, aFaceIdtype is asked for or provided. These are string literals:updownleftrightfrontbackBodyPartIdSimilarly, body parts are string literals underBodyPartId:headtorsoright_armleft_armright_legleft_legBody parts that are "left" or "right" follow the same perspective as before: from the observer's point of view.ExamplesYou can find the skins/renders, and the code to produced them, inexamples.
Package: skinseg
Unsup-SegmentationThis repository contains code that was a part of the project "SEGMENTATION AND CLASSIFICATION OF SKIN LESION IMAGES USING DEEP LEARNING BASED TECHNIQUES", published as part of my final year project. This was an experiment to create an unsupervised segmentation pipeline that can create segmentation masks of any given skin lesion image from the ISIC datasets. Although this was predominantly tested on the 2016 and 2017 datasets, this can be extended to latter datasets as well. These are the results tested on the 2016 dataset.The complete pipeline will require you to run the "mask_generator.py" file first, which should save the unfiltered mask images into an 'UNFILTERED/' directory inside your 'MASKS/' directory. After that, you can run either of "color_clean.py" or "texture.py" (first one being a simpler cleaning approach while the second one being a slightly more complicated but accurate approach), this will create the final masks for you. At any point of time you can run the "metrics.py" file to check the performance of the model. You can run all of these algorithms sequentially or in parellel. Graph execution won't be an issue as they read each image and process it in real time. For example, if you have been runningmask_generatorfor a few minutes and have a few images in theUNFILTERED/directory, you can runtexturewhich will start working on the unfiltered images in real-time.tqdmwill consider the existing number of files and start accordingly. Simiarly, you can runmetricsat any point to check the performance on the masks generated so far.You can download the original ISIC 2016 dataset fromhttps://challenge.isic-archive.com/data/. Or you can try this on your own dataset. To runmetrics, you'll need to have reference masks against which the generated masks will be compared.You can play with "permute_channels.py" to get an idea of how the different channels interact when combined."mask_overlay.py" will help you draw the mask outlines on the original images so that you can have a better idea of how the algorithm is performing.Feel free to play around with the code. Also make sure to modify the directory locations as required.
Package: sk-in-torch
sk_in_torchSome torch models in sklearn format.Goal:Installation of pytorch is everywhere Why not use torch to compute some of the traditional models as well? Also, it is possible to make torch conform to sklearn's grammar, making it almost plug and play. Here are some of the results & I show you that sklearn and packages in sklearn style (like linear-tree) can work with these functions seamlessly.Quickstart:an early version of call is athttps://www.kaggle.com/code/hli111111/sklearn-in-pytorch-with-gpunote that you do not need to rewrite the classes if you import this.
Package: skip32
skip32Wraps the original c implementation of skip32 by Greg Rose, QUALCOMM Australia with a thin layer of python.Example Usage`python >>> import random >>> import skip32 >>> key =''.join(chr(random.randint(0,255)) for _ in xrange(10)) >>> encrypted = skip32.encrypt(key, 12345) >>> encrypted 3798503945L >>> skip32.decrypt(key, encoded) 12345L `
Package: skipdict
A skip dict is a Python dictionary which is permanently sorted by
value. This package provides a fast, idiomatic implementation written
in C with an extensive test suite.The data structure uses askip listinternally.An example use is a leaderboard where the skip dict provides
logarithmic access to the score and ranking of each user, as well as
efficient iteration in either direction from any node.Compatible with Python 2.7+ and Python 3.3+.UsageThe skip dict works just like a normal dictionary except it maps only
to floating point values:from skipdict import SkipDict

skipdict = SkipDict(maxlevel=4)

skipdict['foo'] = 1.0
skipdict['bar'] = 2.0TheSkipDictoptionally takes a dictionary or a sequence of(key, value)pairs:skipdict = SkipDict({'foo': 1.0, 'bar': 2.0})
skipdict = SkipDict(('foo', 1.0), ('bar', 1.0), ('bar', 1.0))Note that duplicates are automatically aggregated to a sum. To
illustrate this, we can count the occurrences of letters in a text:skipdict = SkipDict(
    (char, 1) for char in
    "Everything popular is wrong. - Oscar Wilde"
)

# The most frequent letter is a space.
skipdict.keys()[-1] == " "Theskipdictis sorted by value which means that iteration and
standard mapping protocol methods such askeys(),values()anditems()return items in sorted order.Each of these methods have been extended with optional range argumentsminandmaxwhich limit iteration based on value. In addition,
the iterator objects support the item and slice protocols:>>> skipdict.keys(min=2.0)[0]
'bar'
>>> skipdict.keys(max=2.0)[1:]
['bar']Note that the methods always return an iterator. Uselistto
expand to a sequence:>>> iterator = skipdict.keys()
>>> list(iterator)
['bar']Theindex(value)method returns the first key that has exactly the
required value. If the value is not found then aKeyErrorexception is raised.>>> skipdict.index(2.0)
'bar'AlternativesFrancesco Romani wrotepyskiplistwhich also provides an
implementation of the skip list datastructure for CPython written
in C.Paul Colomiets wrotesortedsetswhich is an implementation
in pure Python. The randomized test cases were adapted from this
package.LicenseCopyright (c) 204 Malthe Borch <mborch@gmail.com>This software is provided “as-is” under the BSD License.Changes1.0 (2014-09-26)Initial public release.