Saravanan666/pypi-org huggingface dataset:
Package: cockroachdb-cloud-client
cockroachdb-cloud-clientA client library for accessing the CockroachDB Cloud API.Read theCockroachDB Cloud OpenAPI Specon our docs page.UsageFirst, create a client, then, call your endpoint and use your models:fromcockroachdb_cloud_clientimportAuthenticatedClientfromcockroachdb_cloud_client.modelsimportListClustersResponsefromcockroachdb_cloud_client.api.cockroach_cloudimportcockroach_cloud_list_clustersfromcockroachdb_cloud_client.typesimportResponseimportoscc_key=os.environ['CC_KEY']client=AuthenticatedClient(base_url="https://cockroachlabs.cloud",token=cc_key,headers={"cc-version":"2022-09-20"},)resp:Response[ListClustersResponse]=cockroach_cloud_list_clusters.sync_detailed(client=client)forxinresp.parsed.clusters:print(x.name)# Output:# cute-otter# gummy-rabbit# half-weasel# itchy-donkey# redear-thrushThings to knowEvery path/method combo becomes a Python module with four functions:sync: Blocking request that returns parsed data (if successful) orNonesync_detailed: Blocking request that always returns aRequest, optionally withparsedset if the request was successful.asyncio: Likesyncbut async instead of blockingasyncio_detailed: Likesync_detailedbut async instead of blockingAll path/query params, and bodies become method arguments.If your endpoint had any tags on it, the first tag will be used as a module name for the function (my_tag above)Any endpoint which did not have a tag will be incockroachdb_cloud_client.api.default
Package: cocktaildb
CocktailDBTheCocktailDB API client forPython.DownloadPyPIpip install cocktaildbExamplefromcocktaildbimportsearchforcocktailinsearch("beer"):print(cocktail.strDrink)DocumentationpdocLicenseCocktailDB is released under theApache License 2.0.Copyright 2022 XXIV

 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.
Package: cocktaildbpy
cocktaildbpyA small python library to interact with cocktaildb api (https://www.thecocktaildb.com/api.php)Installationpip install cocktaildbpyExample UsagefromcocktaildbpyimportApifromcocktaildbpy.constantsimportIngredientdefmain():# "1" is the development/testing api keyclient=Api(api_key="1")drinks=client.search_by_ingredient(Ingredient.GIN)drink=client.lookup_cocktail_by_id(drinks[0].id)print(drink)drink=client.get_cocktail_by_name("old fashioned")print(drink)if__name__=="__main__":main()
Package: cocktail-info
cocktail_infoThis project is called cocktail_info. The functions are built to get the information of cocktails users want to know. Below are the explanations for what each function can do:Function get_id(): This function can help users to get the id of the cocktail they input so that they could use for further search.Function get_cocktail(): This function can help users to get the details of the cocktails they input, such as ID, Name, Category, whether they contain alcohol, the ways to make them, etc.Function get_one(): This function can help users to get the information of the exact one cocktail they want through the unique id.Function get_ingredient(): This function can help users to get the names and measures of the ingredients in the cocktail after the users input the name of cocktails.Function get_pics(): This function can help users to get the pictures of the cocktails along with the IDs and names they input.Function decsription(): This function can give users the introduction of cocktails users input.Function is_in(): This function allows users to check whether the ingredient they input is in the ingredients list.Installation$pipinstallcocktail_infoUsagefromcocktail_infoimportcocktail_infoGet the id of the cocktailcocktail_info.get_id('gin')Get the information of the cocktailscocktail_info.get_cocktail('gin')Get the information of the exact cocktail you want to knowcocktail_info.get_one('gin')Get the ingredients of the cocktailscocktail_info.get_ingredient('gin')Get the pictures of the cocktailscocktail_info.get_pics('gin')Get the introduction of the cocktailcocktail_info.description('gin')Tell whether one ingredient is in the cocktailcocktail_info.is_in('gin')ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensecocktail_infowas created by Kun Yao. It is licensed under the terms of the MIT license.Creditscocktail_infowas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: cocktail-shaker
Cocktail Shaker: Drug Expansion and Enumeration for Peptides!cocktail-shaker is ahigh-performance drug enumeration and expansion
library. cocktail-shaker leverages the computational power ofRDKitto create and enumerate large volumes of drug compounds.AnnouncementsRelease!Version 1.0.0-beta, August 26, 2019RDkit UGM 2019: talk at September 25th at the University of
Hamburg, Germany.Using Cocktail Shakercocktail-shaker is a young library under heavy development at this time.
It targets two categories of users:Users familiar with RDKit, or those willing to learn RDKit, who want to
create fast sets of data for high throughput screening or machine
learning.Open-Science Scientists without any knowledge of RDKitwho are
seeking a a high-level wrapper to create chemical files for their
software.If you're in the first category, then you can already start using RDKit.
cocktail-shaker offers a Pythonic, easy-to-use library and you can start
channeling molecules in the expansion library. Instead of validating the
sanity of the data, cocktail-shaker takes care of that for you. With
each molecule being generated it will head into a 1D and/or 2D
validation check (3D not supported yet).If you're in the second category, we're starting to build experimental
high-level python code to take care a lot of the underpinnings of RDKit.InstallationCocktail-shaker runs on Python 3.5+ and within a conda environment due to the RDKit dependency. A conda installation is coming soon (as soon as I figure it out).For the time being, here is the following:AnacondaRDKitVersion: 2019.09.1Cocktail shaker is distributed through PyPi and can be installed via:your_env/bin/python -m pip install cocktail-shakerDevelopment InstallationAs cocktail-shaker is under heavy development at this time, we highly
recommend developers to use the development version on Github (master
branch). You need to clone the repository and install cocktail-shaker withpython setup.py install.As a one-liner, assuming git is installed:git clone https://github.com/Sulstice/Cocktail-Shaker.gitThis will automatically install the latest version of cocktail-shaker.Structure of cocktail-shakerCurrently, the main subpackages are:cocktail_shaker: Contains a lot of the high level functionality; request
handling, file parsing/writing, enumeration, and expansion.docs: An access point for the readthedocs implementation.cocktail_shaker/datasources: This is where the system stores its data on
predfined functional groups and/or shapes (coming soon).tests: Tests that are for the file handling, requests, and
testing molecule pattern recognition.The API of all public interfaces are subject to change in the future,
althoughdatasourcesarerelativelystable at this point.Genesiscocktail-shaker began when one developer/scientist wanted an open source
drug library.Lead DeveloperSuliman sharifArtworkElena ChowTechnical DocumentationRose GierthQA TesterMarvin CorroNow cocktail-shaker looks to build on the expertise of these
developers/scientists and the broader open-science community to build an
effective drug library.External linksDocumentation
Package: cocktail-sort
Практическая работа №6 - Модульные программыЭрлан Назарбеков,КИ22-16/1Б,Вариант 24$ python example.py -v— примеры$ python generate_random.py— функция для генерации случайного списка$ python sort_acdending.py— функция для сортировки перемешиванием (по возрастанию)$ python sort_decsending.py— функция для сортировки перемешиванием (по убыванию)example:fromop_packageimportcocktail_sort_increasingprint(cocktail_sort_increasing([5,6,8,3,2,1]))fromop_packageimportcocktail_sort_decreasingprint(cocktail_sort_decreasing([5,6,8,3,2,1]))[1, 2, 3, 5, 6, 8][8, 6, 5 ,3 ,2, 1]
Package: cocktail-wrapper
Usage of search function:importcocktaildbasyncdeftequila_info():returnawaitcocktaildb.search(query='tequila')Parameters include query, id, key, category, dict: bool, first_letter_only: bool, ingredient: bool, random: bool.
All of which default to either None or False.Lists of categories, ingredients etc. that may appear:importcocktaildb# This is in literally a list, not in the form of a dict or stringasyncdeflist_of_categories():returnawaitcocktaildb.categories()There are more functions such as glasses() and ingredients().This is a simple api wrapper that only supports free endpoints (for now atleast).
Package: coclico
coclicoPython tools for Coastal Climate Core ServicesInstallation$pipinstallcoclicoUsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensecoclicowas created by Floris Calkoen. It is licensed under the terms of the MIT license.Creditscoclicowas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: coclust
Coclustprovides both a Python package which implements several diagonal
and non-diagonal co-clustering algorithms, and a ready to use script to
perform co-clustering.Co-clustering (also known as biclustering), is an important extension of
cluster analysis since it allows to simultaneously groups objects and features
in a matrix, resulting in both row and column clusters.The script enables the user to process a dataset with
co-clustering algorithms without writing Python code.The Python package provides an API for Python developers.
This API allows to use the algorithms in a pipeline with scikit-learn library
for example.coclustis distributed under the 3-Clause BSD license.See the availabledocumentationfor details and usage samples.Changelog0.2.0 - February, 2017Improved documentationRestructuringEvaluation, visualization and loading utilitiesEasier installation of optional dependencies0.1.3 - April, 2016New visualization methods in the utils module.New demos.Better PEP 8 conformanceImproved documentation.0.1.1 - March 07, 2016First release.CodeYou can check the latest sources with the command:git clone https://github.com/franrole/cclust_package.git
Package: coco-agent
A set of tools and utilities for extracting and shipping raw data to ConnectedCompany ("CC").Pre-requisitespython 3.6+ (python3 --versionto check)CC connector ID - this is a string provided by CC, strucured likecustomer-id>/<source-type>/<source-id>optionally, a credentials file, to push data extracts and / or logs to CCInstallationCreate a new directory for this tool, with a Python virtual environment (venv), then activate the venv:mkdir coco-agent
cd coco-agent
python3 -m venv venv
source venv/bin/activateInstall the latest version of the tool the virtual environment:pip install -U coco-agentExtract metadata from a Git repositoryTo extract metadata from a cloned repo accessible via the file system:coco-agent extract git-repo --connector-id=<connector id> repo-dirwhereconnector idis the connector ID mentioned above, provided by CCrepo-diris the directory of the Git repositoryBy default, output is written to theoutdirectory.For additional options, including specifying date ranges, see./coco-agent extract git-repo --helpAdditional data sourcesEach will have its own connector-id. Simply re-run theextractcommand, pointing at each additional source as desired.Upload dataOnce desired data has been extracted, it can be securely uploaded to CC from the output directory:coco-agent upload data --credentials-file=<credentials file path> <connector id> <directory>wherecredentials file pathis the location of the upload credentials JSON file, provided by CCconnector idis, as before, the connector ID for the data sourcedirectoryis the directory where data was previously extracted (./outby default)Supported optionsInvokingcoco-agentwithout arguments will display supported commands and options.coco-agent versionwill display the current version.In the same way, description and options for each sub-command can be seen by passing the--helpargument - e.g.coco-agent extract git-repo --help.
Package: cocoai
Coco - Your CLI AssistantInstant answers to your CLI questions in the CLI of your choice.InstallationTo install Coco CLI, make sure you have Python 3.x installed. Then, use pip to install it:pipinstallcocoaiUsageAsk anythingAsk Coco anything, and it will fetch the right CLI command for you:coco"How do I list files in a directory?"You can even skip the quotes:cocoHowdoIlistfilesinadirectory?Or simply:coco-aResetting Your TokenTo set or reset your OpenAI Token, use the following command:coco-t<YourOpenAIToken>
Package: cocoapods-graph
cocoapods-graphThis tool generates a dependencies graph report, printing on console or saving on a json or creating a html with a interactive disc to navigate through it.Example for Wordpress iOS app dependencieslinkInstalling[sudo]pipinstallcocoapods-graphHow to usecocoapods-graph-fPodfile.lock--htmlThanksAll html rendering is done with:d3.jsDependency Wheel
Package: coco-assistant
COCO-AssistantHelper for dealing with MS-COCO annotations.OverviewThe MS COCO annotation format along with the pycocotools library is quite
popular among the computer vision community. Yet I for one found it difficult to
play around with the annotations. Deleting a specific category, combining
multiple mini datasets to generate a larger dataset, viewing distribution of
classes in the annotation file are things I would like to do without writing a
separate script for each scenario.The COCO Assistant is designed (or being designed) to assist with this problem.Please note that currently, the Assistant can only help out with object
detection datasets. Any contributions and/or suggestions are welcome.Package featuresCOCO-Assistant currently supports the following features:Merge datasets.Remove specfiic category from dataset.Generate annotations statistics - distribution of object areas and category distribution.Annotation visualiser for viewing the entire dataset.
Package: cocoa-system
COCOACOrrelation COefficient-Aware Data AugmentationTable of ContentsPrerequisitesPython version and packagesDatabase schemasInstallationUsagePrerequisitesPython version and packagesThis packages requires python version >= 3.6. Additionally, the following packages have to be installed:pandas
numpyDatabase schemasTo run the system, the following set of tables need to be created in the database (e.g. Postgres):main_tokenized: Inverted index, tokenized -> table, col, rowdistinct_tokens: Likemain_tokenized, but maps only distinct tokenized -> table_col_id to compute
overlap fasterorder_index: Index for each column. The index is created offline and stored in the db.max_column: Maps table-> number of columns for quicker accessSchemas:CREATETABLEmain_tokenized(tokenizedTEXT,tableidINTNOTNULL,rowidINTNOTNULL,table_col_idTEXTNOTNULL,);CREATETABLEdistinct_tokens(tokenizedTEXT,table_col_idTEXTNOTNULL);CREATETABLEorder_index(table_col_idTEXTNOTNULLis_numericBOOLEAN,min_indexINTNOTNULL,order_listTEXT,binary_listTEXT,);CREATETABLEmax_column(tableidINTNOTNULL,max_col_idINTNOTNULL,PRIMARYKEY(tableid));To filldistinct_tokensandmax_column, run the following queries:INSERTINTOdistinct_tokensSELECTDISTINCTtokenized,table_col_idFROMmain_tokenized;INSERTINTOmax_columnSELECTtableid,MAX(colid)FROMmain_tokenizedGROUPBYtableid;The order index can be created by callingCOCOA.create_index(values)where values is a column (list of values). The index then has to be stored in theorder_indextable in the db for
every column in the dataset.InstallationRun the following command to install COCOA to your python environment:pip install cocoaUsageimportpandasaspdimportpsycopg2fromcocoa-systemimportDataAugmentationCONN_INFO={'host':'127.0.0.1','dbname':'db','user':'postgres','password':'password',}DB_TABLES={'dt':'distinct_tokens','mt':'main_tokenized_overlap','mc':'max_column','oi':'order_index',}DATASET_PATH='datasets/query.csv'dataset=pd.read_csv(DATASET_PATH)conn=psycopg2.connect(**CONN_INFO)cocoa=DataAugmentation.COCOAHandler(conn,DB_TABLES)result=cocoa.enrich(dataset,k_c,k_t,'my_query_column','my_target_column')
Package: coco-cli
COCOCoco is a tool for collecting commands in an easy and organized way.InstallationCoco is available inPyPiand can be installed via pip:pipinstall--usercoco-cliFeaturesCustom prompts :star2:List existing prompts :notebook:Add or remove prompts :cactus:Easy to remember commands likelsfor listing orrmfor removing :bulb:UsageUsage:coco[OPTIONS]COMMAND[ARGS]...

Options:--helpShowthismessageandexit.

Commands:addAddapromptlsListallpromptsnewGenerateadefaultconfigfilermRemoveapromptrunRunapromptGenerate config filesTo generate a new config file and simultaneously add the prompt you can simply use the commandcoconew[PATH][NAME]Where[PATH]specifies the destination of the config file and[NAME]the name of the prompt that coco saves.For examplecoco new ~/.config/coco/maven.json mvnwill generate a file namedmaven.jsonin~/.config/coco, which can be executed withcoco run mvn.Note that the name of the config file as well as the file extension donotmatter.
However since coco expects valid json it is a nice convention to use the.jsonextension.Add promptIf you've already created a valid config file with your desired commands you can add it viacocoadd[PATH][NAME]Where[PATH]specifies the location of the config file and[NAME]the name of the prompt that coco saves.SeeGenerate Config Filefor a more detailed example.Run a promptIn order to view an added prompt simply entercocorun[NAME]If the prompt requires parameters you can either append them at the end or let coco ask the user for input:And a possible config file entry could look like this{"prompt":"Select command to run:","choices":{"Show package details":"pip show {package}",...}}List available promptsTo list available prompts usecocolsRemove a promptRemoving an existing prompt is as easy as using:cocorm[NAME][Name]of course specifies the name of the prompt that you wish to remove.
To list the names of all available prompts usecoco ls.If you wish to also delete the associated config file from your system add the--delete-configflag:cocorm--delete-config[NAME]Config FilesAn example config file can be viewedhereAs you can probably guess the file needs to be valid json, however the file extension doesnotmatter.If you want to specify arguments for your commands you can do so by wrapping them in curly braces:{"prompt":"Pacman commands:","choices":{"install":"sudo pacman -S {package}"}}TODOSupport for piped operationsUsage of the same argument in multiple places likepip search {package} | grep {package}Add new command to easily edit the config files
Package: cocodrilo
CocodriloA large semiaquatic reptileDocumentation:https://altaf-ali.github.io/cocodriloGitHub:https://github.com/altaf-ali/cocodriloPyPI:https://pypi.org/project/cocodrilo/Free software: MITFeaturesTODOCreditsThis package was created withCookiecutterand thealtaf-ali/cookiecutter-pypackageproject template.
Package: cocof
CocofCocof, short for consistent config file, is a python module that allows the
modification of different key-value config files on the comand line.Most importantly: For YAML and TOML itIt will keep line breaks and comments
the same.So the file will look more or less like the original (depending on
the operations on it of course).Currently supported formats are TOML, YAML, JSON and PLIST.Install$pipinstallcocofUsage$cocof--help
Usage:cocof[OPTIONS]FILEPATHJSONPATCHCocofrunstheprovided'jsonpatch'modificationsontheconfigurationfilegivenwiththe'filepath'argument.Usethe'--format'optiontotellthefileformat.Ifnotgivencocofwilltrytoguessthefileformatbasedonthefileextension.Use'-'asfilepathforstdin,inwhichcasetheoutputgoestostdoutandyoumustprovidetheformatofthedataviathe'--format'option.

Options:-f,--format[toml|yaml|json|plist]Theformatofthefile.Obligatoryiffilepathis'-'(stdin).--helpShowthismessageandexit.Cocof takes a file path and aJSON patchstring as arguments.
It then modifies the datastructure given by the file's content accordingly and
writies it back to the same file (in-place editing).
You can also tellcocofto read fromstdin, in which case it will output
it's result tostdout. TOML, YAML and JSON expect their content to beutf-8encoded. PLIST expects either anutf-8encoded xml content or binary content.Examples# example.tomltitle="Example"[owner]name="Tom Preston-Werner"dob=1979-05-27T07:32:00-08:00# Inline comment[database]server="192.168.1.1"ports=[8001,8001,8002]Using{"op": "add", "path": "/subtitle", "value": "Sub"}as modification
yields:$cocof./example.toml'[{"op": "add", "path": "/subtitle", "value": "Sub"}]'`$cat./example.toml# example.tomltitle="Example"subtitle="Sub"[owner]name="Tom Preston-Werner"dob=1979-05-27T07:32:00-08:00# Inline comment[database]server="192.168.1.1"ports=[8001,8001,8002]