Saravanan666/pypi-org huggingface dataset:
Package: cosmos-sdk
Python SDK for the Cosmos Ecosystem(Unfamiliar with Cosmos?Check out the Cosmos Network Docs)This (SDK) in Python is a simple library toolkit for building software that can interact with the supported Cosmos blockchains and provides simple abstractions over core data structures, serialization, key management, and API request generation.Note - This is currently a W.I.P.FeaturesThis SDK follows a module specific approach, and new Cosmos Modules will be added to the SDK on a priority basis.Written in Python with extensive support librariesVersatile support for key management solutions❗ This SDK currently does not support Terra Classic. If you want to communicate with Terra Classic, use terra-sdk==2.xRequirementsCosmos.py SDK requiresPython v3.7+.InstallationNOTE:All code starting with a$is meant to run on your terminal (a bash prompt). All code starting with a>>>is meant to run in a python interpreter, likeipython.Cosmos SDK can be installed (preferably in avirtual environmentfrom PyPI usingpip) as follows:$ pip install -U cosmos_SDKYou might havepip3installed instead ofpip; proceed according to your own setup.Usage Examplesfrom cosmos_sdk.client.lcd import LCDClient
from cosmos_sdk.key.mnemonic import MnemonicKey

mnemonic = <MNEMONIC_PHRASE>

# Terra client
terra_client = LCDClient(chain_id="phoenix-1", url="https://phoenix-lcd.terra.dev")
mk = MnemonicKey(mnemonic,"terra")
terra_wallet = terra_client.wallet(mk)

# Persistence client
persistence_client = LCDClient(chain_id="core-1", url="http://rest.core.persistence.one")
mk = MnemonicKey(mnemonic,"persistence")
persistence_wallet = persistence_client.wallet(mk)DependenciesCosmos SDK usesPoetryto manage dependencies. To get set up with all the required dependencies, run:$ pip install poetry
$ poetry installIf you encounter "version solving failed." error, trypoetry add <package_name>, followed bypoetry lockand thenpoetry installContributingCommunity contribution, whether it's a new feature, correction, bug report, additional documentation, or any other feedback is always welcome. Please read through this section to ensure that your contribution is in the most suitable format for us to effectively process.LicenseThis reporitory was forked fromTerra SDKand is being repurposed from there on.
Package: cosmotech-run-orchestrator
cosmotech-run-orchestratorThis library contains thecsm-orccli developed byCosmo Techfor its Digital
Twin platform.If you are interested in using it, please contactCosmo TechInstallationDocumentation on the installation is available on
ourGitHub PageOnce installed you will have access to thecsm-orcCLI and all its commands.LicenceCopyright - 2023 - 2023 - Cosmo TechYou are authorized to use this Cosmo Tech software following the execution of a specific agreement with Cosmo Tech or with an authorized licensee of Cosmo Tech.Accordingly you should refer to this specific agreement for the purpose of the extent of the use that is permitted. This use shall mainly consist in using, displaying and executing the Cosmo Tech software for your own needs.Except otherwise provided in the above-mentioned specific agreement:you are not allowed to use this Cosmo Tech software for commercial purpose or to redistribute it without prior authorization from Cosmo Tech;you are not allowed to copy, duplicate, modify, adapt and translate this Cosmo Tech software except as authorized under section L122-6-1 of the French Intellectual Property Code or specifically under the specific agreement with Cosmo Tech;you must not remove this notice or any mention related to the copyright of Cosmo Tech;This Cosmo Tech software is provided 'as is', without any express or implied warranty towards the users.Any software generated through the use of this Cosmo Tech software shall be your own property to the extent this software does not contain any part of Cosmo Tech software source code.
Package: cosmotile
CosmotileCreate cosmological lightcones from coeval simulations.This algorithm is taken from the code inhttps://github.com/piyanatk/cosmotile, but
is repackaged and re-tooled.FeaturesFast tiling of finite, periodic cosmic simulations onto arbitrary angular coordinates.Generate different realizations by translation and rotation.InstallationYou can installCosmotileviapipfromPyPI:$pipinstallcosmotileUsagePlease see theCommand-line Referencefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,Cosmotileis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz'sHypermodern Python Cookiecuttertemplate.The algorithm used in this repository is derived from thecosmotilemodule inhttps://github.com/nithyanandan/AstruUtils, which was later modularised inhttps://github.com/piyanatk/cosmotile.AcknowledgmentsIf you findcosmotileuseful in your project, please star this repository and, if
applicable, citehttps://arxiv.org/abs/1708.00036.
Package: cosmpy
CosmPyA python library for interacting with cosmos based blockchain networksWe recently stopped using thedevelopbranch for feature consolidation and renamedmastertomain. Please see theContribution Guidesfor up-to-date instructions.To Installpip3installcosmpyGetting StartedBelow is a simple example for querying an account's balances:fromcosmpy.aerial.clientimportLedgerClient,NetworkConfig# connect to Fetch.ai network using default parametersledger_client=LedgerClient(NetworkConfig.fetchai_mainnet())alice:str='fetch12q5gw9l9d0yyq2th77x6pjsesczpsly8h5089x'balances=ledger_client.query_bank_all_balances(alice)# show all coin balancesforcoininbalances:print(f'{coin.amount}{coin.denom}')DocumentationThe full documentation can be foundhere.ExamplesUnder theexamplesdirectory, you can find examples of basic ledger interactions usingcosmpy, such as transferring tokens, staking, deploying and interacting with a smart contract, and performing atomic swaps.ContributingAll contributions are very welcome! Remember, contribution is not only PRs and code, but any help with docs or helping other developers solve their issues are very appreciated!Read below to learn how you can take part in the CosmPy project.Code of ConductPlease be sure to read and follow ourCode of Conduct. By participating, you are expected to uphold this code.Contribution GuidelinesRead ourcontribution guidelinesto learn about our issue and pull request submission processes, coding rules, and more.Development GuidelinesRead ourdevelopment guidelinesto learn about the development processes and workflows.Issues, Questions and DiscussionsWe useGitHub Issuesfor tracking requests and bugs, andGitHub Discussionsfor general questions and discussion.LicenseThe CosmPy project is licensed underApache License 2.0.
Package: cospar
CoSpar - dynamic inference by integrating state and lineage informationCoSparis a toolkit for dynamic inference from lineage-traced single cells.
The methods are based onWang et al. Nat. Biotech. (2022).Dynamic inference based on single-cell state measurement alone requires serious simplifications. On the other hand, direct dynamic measurement via lineage tracing only captures partial information and its interpretation is challenging. CoSpar integrates both state and lineage information to infer a finite-time transition map of a development/differentiation system. It gains superior robustness and accuracy by exploiting both the local coherence and sparsity of differentiation transitions, i.e., neighboring initial states share similar yet sparse fate outcomes.  Building around theanndataobject, CoSpar provides an integrated analysis framework for datasets with both state and lineage information. When only state information is available, CoSpar also improves upon existing dynamic inference methods by imposing sparsity and coherence. It offers essential toolkits for analyzing lineage data, state information, or their integration.Seehttps://cospar.readthedocs.iofor documentation and tutorials.ReferenceS.-W. Wang*, M. Herriges, K. Hurley, D. Kotton, A. M. Klein*, CoSpar identifies early cell fate biases from single cell transcriptomic and lineage information, Nat. Biotech. (2022). [* corresponding authors]SupportFeel free to submit anissueor send us anemail.
Your help to improve CoSpar is highly appreciated.
Package: cospy
CospyA Simple CLI tool do a lot of things.Table of contentsDescriptionInstallationAPIUsageDescriptionA CLI to do a lot of stuff in a more easier(maybe) way.InstallationpipinstallcospyAPIBelow is the list of currently supported API.cos init- Start a programming project in any language.cos clean- Sort directory.cos tasks- Manage taskscos venv- Manage virtual environmentsUsageAll the docs and usage are listedhere.
Package: cos-python-sdk-v5
介绍腾讯云COSV5Python SDK, 目前可以支持Python2.6与Python2.7以及Python3.x。安装指南使用pip安装pip install -U cos-python-sdk-v5手动安装:python setup.py install使用方法使用python sdk，参照https://github.com/tencentyun/cos-python-sdk-v5/blob/master/demo/demo.pycos最新可用地域，参照https://cloud.tencent.com/document/product/436/6224python sdk 快速入门，参照https://cloud.tencent.com/document/product/436/12269python sdk 接口文档，参照https://cloud.tencent.com/document/product/436/12270
Package: cos-python-sdk-v5-logging
介绍腾讯云COSV5Python SDK, 目前可以支持Python2.6与Python2.7。安装指南使用pip安装pip install -U cos-python-sdk-v5手动安装:python setup.py install使用方法使用python sdk，参照https://github.com/tencentyun/cos-python-sdk-v5/blob/master/qcloud_cos/demo.pycos最新可用地域,参照https://www.qcloud.com/document/product/436/6224# 设置用户属性, 包括secret_id, secret_key, regionsecret_id='xxxxxxxx'# 替换为用户的secret_idsecret_key='xxxxxxx'# 替换为用户的secret_keyregion='ap-beiging-1'# 替换为用户的regiontoken=''# 使用临时秘钥需要传入Token，默认为空,可不填config=CosConfig(Region=region,Secret_id=secret_id,Secret_key=secret_key,Token=token)#获取配置对象client=CosS3Client(config)#获取客户端对象############################################################################# 文件操作                                                                 ############################################################################## 1. 上传单个文件response=client.put_object(Bucket='test01-123456789',Body='TY'*1024*512*file_size,Key=file_name,CacheControl='no-cache',ContentDisposition='download.txt')# 2. 下载单个文件response=client.get_object(Bucket='test01-123456789',Key=file_name,)# 3. 获取文件属性response=client.head_object(Bucket='test01-123456789',Key=file_name)# 4. 删除单个文件response=client.delete_object(Bucket='test01-123456789',Key=file_name)# 5. 创建分片上传response=client.create_multipart_upload(Bucket='test01-123456789',Key='multipartfile.txt',)uploadid=get_id_from_xml(response.text)# 6. 删除分片上传response=client.abort_multipart_upload(Bucket='test01-123456789',Key='multipartfile.txt',UploadId=uploadid)# 7. 再次创建分片上传response=client.create_multipart_upload(Bucket='test01-123456789',Key='multipartfile.txt',)uploadid=response['UploadId']# 8. 上传分片response=client.upload_part(Bucket='test01-123456789',Key='multipartfile.txt',UploadId=uploadid,PartNumber=1,Body='A'*1024*1024*4)etag=response['ETag']# 9. 列出分片response=clieent.list_parts(Bucket='test01-123456789',Key='mutilpartfile.txt',UploadId=uploadid)lst=response['Part']# list_parts最大数量为1000# 10. 完成分片上传response=client.complete_multipart_upload(Bucket='test01-123456789',Key='multipartfile.txt',UploadId=uploadid,MultipartUpload={'Part':lst}# 超过1000个分块，请本地保存分块信息，再complete)############################################################################# Bucket操作                                                                 ############################################################################## 1. 创建Bucketresponse=client.create_bucket(Bucket='test02-123456789',ACL='public-read')# 2. 删除Bucketresponse=client.delete_bucket(Bucket='test02-123456789')# 3. 获取文件列表response=client.list_objects(Bucket='test01-123456789')
Package: cos-python-sdk-v5-private
介绍腾讯云COSV5Python SDK, 目前可以支持Python2.6与Python2.7。安装指南使用pip安装pip install -U cos-python-sdk-v5手动安装:python setup.py install使用方法使用python sdk，参照https://github.com/tencentyun/cos-python-sdk-v5/blob/master/qcloud_cos/test.pycos最新可用地域,参照https://www.qcloud.com/document/product/436/6224# 设置用户属性, 包括appid, secret_id, secret_key, regionappid=100000# 替换为用户的appidsecret_id=u'xxxxxxxx'# 替换为用户的secret_idsecret_key=u'xxxxxxx'# 替换为用户的secret_keyregion="ap-beiging-1"# 替换为用户的regiontoken=''# 使用临时秘钥需要传入Token，默认为空,可不填config=CosConfig(Appid=appid,Region=region,Access_id=secret_id,Access_key=secret_key,Token=token)#获取配置对象client=CosS3Client(config)#获取客户端对象############################################################################# 文件操作                                                                 ############################################################################## 1. 上传单个文件response=client.put_object(Bucket='test01',Body='TY'*1024*512*file_size,Key=file_name,CacheControl='no-cache',ContentDisposition='download.txt')# 2. 下载单个文件response=client.get_object(Bucket='test01',Key=file_name,)# 3. 获取文件属性response=client.head_object(Bucket='test01',Key=file_name)# 4. 删除单个文件response=client.delete_object(Bucket='test01',Key=file_name)# 5. 创建分片上传response=client.create_multipart_upload(Bucket='test01',Key='multipartfile.txt',)uploadid=get_id_from_xml(response.text)# 6. 删除分片上传response=client.abort_multipart_upload(Bucket='test01',Key='multipartfile.txt',UploadId=uploadid)# 7. 再次创建分片上传response=client.create_multipart_upload(Bucket='test01',Key='multipartfile.txt',)uploadid=response['UploadId']# 8. 上传分片response=client.upload_part(Bucket='test01',Key='multipartfile.txt',UploadId=uploadid,PartNumber=1,Body='A'*1024*1024*4)etag=response['ETag']# 9. 列出分片response=clieent.list_parts(Bucket='test01',Key='mutilpartfile.txt',UploadId=uploadid)lst=response['Part']# list_parts最大数量为1000# 10. 完成分片上传response=client.complete_multipart_upload(Bucket='test01',Key='multipartfile.txt',UploadId=uploadid,MultipartUpload={'Part':lst}# 超过1000个分块，请本地保存分块信息，再complete)############################################################################# Bucket操作                                                                 ############################################################################## 1. 创建Bucketresponse=client.create_bucket(Bucket='test02',ACL='public-read')# 2. 删除Bucketresponse=client.delete_bucket(Bucket='test02')# 3. 获取文件列表response=client.list_objects(Bucket='test01')
Package: coss
Change of Spatial Support (CoSS)Python tools for spatial interpolationIntroductionChange of Spatial Support (CoSS) is a package that adds support forspatial interpolation.
Spatial interpolation is often referred in the literature as the process of using points with known values to estimate values of the same variable at other points.
However, spatial vector data (points, lines and polygons) is not limited to just points and several methods exist for making interpolations between these different vector types.Several packages exist that allow different areal interpolation methods to be applied but they are often written in multiple languages and are not standardised - making them inaccessible and comparisons between methods challenging.
This package aims to make "spatial interpolation" more accessible by using a standardised classes.Areal interpolationTheareal_interpolationclass supports several areal interpolation, which includes;Areal weightingDasymetric mappingBinaryPycnophylactic interpolation (fromtobler)Model (regression)General Linear ModelOrdinary Least SquareAreal geobootstrap -Arealgeobootstrapis a novel simulation based method, which pools neighbouring observations based on a distance decay funcion.Further spatial interpolation methods will be supported, including those between different vector types, with raster support.This repository is an alpha release andwillcontain errors - if you spot anything, file an issue or submit a PR.
Package: cossif
CosSIF: Cosine Similarity-based Image FilteringSuppose we are presented with two categories of skin lesions: benign and malignant. Our objective is to sift out benign images that bear resemblance to malignant ones, and similarly, malignant images that bear resemblance to benign ones.fromcossifimportcossifcsf=cossif.CosSIF()# Filter benign imagescsf.calculate_and_filter(target_path=cls_benign,# Path to the folder that contains benign images.secondary_path=cls_malignant,# Path to the folder that contains malignant images.filtered_path=cls_benign_filtered,# Path to the folder that will contain filtered benign images.removed_path=cls_benign_removed,# Path to the folder that will contain removed benign images.record_save_path=save_path,# Save path of the similarity calculation record.record_keep=False,# Chose whether to keep the similarity calculation record or not.file_name='t_benign_x_s_malignant',# Name of the similarity calculation record file.filter_type='dissimilar',# Type of filtering. Can be either "similar" or "dissimilar".filter_range=0.85,# The filtering ratio. A value of 0.85 means that 15% of the images will be removed.)# Filter malignant imagescsf.calculate_and_filter(target_path=cls_malignant,secondary_path=cls_benign,filtered_path=cls_malignant_filtered,removed_path=cls_malignant_removed,record_save_path=save_path,record_keep=False,file_name='t_malignant_x_s_benign',filter_type='similar',filter_range=0.85,)
Package: cosSim
cosSim - See how similar your files areIt is hard to determine how similar two text files are.  Without much complication, cosSim uses simple tokenization and vectorization with which a word similarity can be calculated.This is very usefull in cases where the context is not important, but the spelling has a big impact (as in OCR with pdf files).This Project has been brought to life with the help of theAfZ(Archive of Contemporary History) at theETH Zürich.OverviewThe tool is suited to compare texts that do not depend on context, but rather rely on correct spelling. The output is presented in percent. Some use cases could be:comparing two different OCR outputs to a ground truthcomparing hand written digitalized text with a ground truthchecking if your AI has a correct spelling regarding your ground truthSo if you want to get a similarity in terms of semantics, this is not the right tool for you.The CLI tool uses theNLTK Libraryto tokenize the texts, Numpy to store the vector data and the cosine similarity to compare the vectors.GuideThe following shows how to get and use cosSim.Installation$ pip install cosSimIf you would rather like to customize the code to your needs, grab a stable version under "Releases".UsageThe CLI can be used in two ways. It is able tocompare two files or directories to a ground truth. It can alsocompare one file or directory to a ground truth. The amout of files or directories is specified in thepositional argumentbehind the command:$ cosSim path_to_dir_or_fileor$ cosSim path_to_dir_or_file another_pathThe programm recognises with the--diror--fileflag, which kind of parsing you would like to do. So if you desire to compare two files to the integrated corpus, simply type:$ cosSim path1 path2 --fileBecause the integrated corpus mostly generates an output, that represents language similarty (that is not useful in many cases), cosSim accepts your ground truth under the--baseflag:$ cosSim path1 path2 --file --base path_to_ground_truthRegarding language support right now, cosSim supportsgermanenglishtokenization as well as corpora. If neede, more language support will be added in the future.
You can specify the language by addingdeorento the--langflag. If no language is explicitly stated, the program defaults togerman.Of course you can access a help menu in within the CLI by adding--helpor-hto the end of the line.Common error messagesBecause the program uses the nltk library, there is a possibility that an error occurs, which notes a missing installation. In order to prevent this from happening again, see their dedicateddocumentationregarding these rather small problems.
Package: costa
Costa: populate incomplete performance tablesCosta(Complete andsupplement performancetables) is a Python package
whose purpose is to fill incomplete performance maps using correction curves.It is meant to create variable capacity air-to-air heat pumps
performance maps that can be used by theType 3254in TRNSYS.With Costa, the whole process of extending and formatting
performance maps becomes quite straightforward,
seebasic usage.FeaturesPerformance map manipulation using pandas DataFramesExtend performance maps using custom correction curvesAutomatic normalizationRated values adjustmentsWrite performance maps in the format required by theType 3254Incoming featuresPlot slices of the performance mapUse basic functionalities with a user interfaceInstallationInstall Costa withpipby running$ pip install costaBasic usageImport the package and load the (incomplete) performance map into a DataFrameimportcostahpm=costa.build_heating_permap("heating-performance-map.dat")Specify the entries of the variable you want to extend,
e.g. the frequency# Add entries 0.1, 0.2, ..., 1.0hpm.pm.entries['freq']=np.arange(1,1.1,0.1)Specify the operating mode
(required to use the appropriate corrections)hpm.pm.mode='heating'Fill the missing performance values for the specified frequencieshpm_full=hpm.pm.fill()Note:TheType 3254uses normalized performance maps.
Normalization can be carried out with thenormalizemethod,
or directly through thefillmethod using the rated values
of any two quantities amongstcapacity,powerandCOP.
For example, with a rated capacity of 4.69 W and a rated power
of 1.01 W,rated_values=pd.DataFrame({'capacity':[4.69],'power':[1.01]})hpm_full=hpm.pm.fill(norm=rated_values)Extend the operating frequency range to [0,1]hpm_full.pm.ranges['freq']=[0,1]And finally write the full performance maphpm_full.pm.write("permap-heating.dat")Now the generated filepermap-heating.datshould be compatible
with theType 3254.SupportIf you are having problems, please open an issue in the issue tracker
and submit a minimal working example to highlight what is not working.
Package: costcalc2
costcalc2costcalc2is a Python library for calculating the overall raw materials costs
of a user-defined synthetic route.RequirementsPython >= 3Numpy >= 1.0Pandas >= 1.0Matplotlib >= 3.0Openpyxl >= 3.0Web Application and DocumentationA minimalworking web applicationis
available for running cost calculations without installing this package. See
thepackage documentationfor
information on the package and instructions for creating the required input
tables. There is also anannotated Jupyter
notebookthat demonstrates a common use case and most of the class methods.InstallationThecostcalc2package can be installed using eitherpip$ pip install costcalc2orconda$ conda install -c rnelsonchem costcalc2
Package: costcla
CostSensitiveClassificationcostcla is a Python module for cost-sensitive machine learning (classification)
built on top ofScikit-Learn,SciPyand distributed under the 3-Clause BSD license.In particular, it provides:A set of example-dependent cost-sensitive algorithmsDifferent reald-world example-dependent cost-sensitive datasets.The project is part of the PhD research ofAlejandro Correa Bahnsen.InstallationYou can installcostclawithpip:# pip install costclaDocumentationDocumentation is available athttp://albahnsen.github.com/CostSensitiveClassificationTutorials are available athttp://albahnsen.github.io/CostSensitiveClassification/Tutorials.htmlDevelopmentSource code is available athttps://github.com/albahnsen/CostSensitiveClassification
Package: costeopy-package
Bienvenido al paquete de Costeo para intervenciones pedagógicas en Python creado por el equipo de Analítica de Datos. Este paquete ha sido diseñado para ayudar a los científicos de datos, analistas y programadores a trabajar de manera más eficiente con sus datos.Nuestro equipo ha invertido tiempo y esfuerzo en desarrollar una herramienta de análisis de datos poderosa, fácil de usar y flexible que pueda satisfacer las necesidades de cualquier usuario.Esperamos que encuentres este paquete útil y esperamos recibir tus comentarios y sugerencias para mejorar continuamente nuestro trabajo.