Saravanan666/pypi-org huggingface dataset:
Package: sceptre-request-resolver
sceptre-request-resolverA Sceptre resolver to make requests from REST API endpoints.MotivationThere are some pretty useful REST API endpoints on the internet.  The endpoints
can return lots of different types of data, typically in JSON format.
This simple resolver can retrieve that data and pass it to Sceptre parameters
or scepter_user_data parameters.InstallationTo install directly from PyPIpipinstallsceptre-request-resolverTo install from this git repopipinstallgit+https://github.com/Sceptre/sceptre-request-resolver.gitUsage/Examplesparameters|sceptre_user_data:<name>:!request<API ENDPOINT>parameters|sceptre_user_data:<name>:!requesturl:<API ENDPOINT>auth:<authentication type>user:<auth user name>password:<auth password>Note:This resolver always returns a string.Supported authentication types:basicExampleSimple request:parameters:wisdom:!request'https://ron-swanson-quotes.herokuapp.com/v2/quotes'Request with basic authentication:parameters:wisdom:!requesturl:https://private.endpoint.com/auth:basicuser:MyUsernamepassword:MyUserPwd
Package: sceptre-resolver-template
READMEAdd your resolver readme here. Remember to include the following:Tell people how to install it (e.g. pip install ...).Be clear about the purpose of the resolver, its capabilities and limitations.Tell people how to use it.Give examples of the resolver in use.Read our wiki to learn how to use this repo:https://github.com/Sceptre/sceptre-resolver-template/wikiIf you have any questions or encounter an issueplease open an issue
Package: sceptre-s3-packager
sceptre-s3-packagerHook and resolver forSceptrev2to
package code dependencies and uploading it to an S3 bucket for usage in a
template.Inspired byhttps://github.com/cloudreach/sceptre-zip-code-s3Getting startedOnly Python 3 is actively being supported.Install using pip:pipinstallsceptre-s3-packagerUse the hook and resolver in your template config:template_path:...hooks:before_create:-!sceptre_s3_upload./directory-to-zip-to-s3before_update:-!sceptre_s3_upload./directory-to-zip-to-s3sceptre_user_data:Code:S3Bucket:my-s3-bucketS3Key:!sceptre_s3_key./directory-to-zip-to-s3Use the data in the template, e.g. by using Jinja2 template with something
like:MyLambda:Type:AWS::Lambda::FunctionProperties:Code:S3Bucket:{{sceptre_user_data.Code.S3Bucket}}S3Key:{{sceptre_user_data.Code.S3Key}}UsageHook:!sceptre_s3_upload <directory>(readsS3Bucketfromsceptre_user_data.Code.S3Bucket)!sceptre_s3_upload <directory>^^<s3-bucket>Resolver:!sceptre_s3_key <directory>(returns a path where the packaged
directory is uploaded, e.g.sceptre/68063a99bb6d95401d688d28f19ee412)DetailsThe hook will zip the contents of the directory and upload it assceptre/MD5HASHto the S3-bucket, withMD5HASHbeing md5 hash of the zip
file content.When zipping all files will be given a fixed modification time, so that only
the contents of the files are used to determine the upload file and cause
invalidation on changes.
Package: sceptre-secretsmanager-resolver
READMEAdd your resolver readme here. Remember to include the following:Tell people how to install it (e.g. pip install ...).Be clear about the purpose of the resolver, its capabilities and limitations.Tell people how to use it.Give examples of the resolver in use.Read our wiki to learn how to use this repo:https://github.com/Sceptre/project/wiki/Sceptre-Resolver-TemplateIf you have any questions or encounter an issueplease open an issue
Package: sceptre-ssm-resolver
OverviewThe purpose of this resolver is to retrieve values from the AWS SSM.Installpipinstallsceptre-ssm-resolverAvailable ResolversssmFetches the value stored in AWS SSM Parameter Store.Note:Sceptre must be run with a user or role that has access to the parameter storeSyntax:parameter|sceptre_user_data:<name>:!ssm/prefix/paramparameter|sceptre_user_data:<name>:!ssmname:/prefix/paramregion:us-east-1profile:OtherAccountparameter|sceptre_user_data:<name>:!ssm{"name":"/prefix/param","region":"us-east-1","profile":"OtherAccount"}Parametersname - SSM key name, mandatoryregion - SSM key region, optional, stack region by defaultprofile - SSM key's account profile , optional, stack profile by defaultExample:Add a secure string to the SSM parameter storeawsssmput-parameter--name/dev/DbPassword--value"mysecret"\--key-idalias/dev/kmskey--type"SecureString"Retrieve and decrypt SSM parameter from the same account that the
stack is being deployed to:parameters:database_password:!ssm/dev/DbPasswordRetrieve and decrypt SSM parameter from another AWS account:parameters:database_password:!ssmname:/dev/DbPasswordprofile:OtherAccount
Package: sceptre-stack-termination-protection-hook
OverviewThe purpose of this hook is to enable and disablestack termination protection.A common use case is execute this hook with your CI/CD
system. In that case you may need to execute this on every
change.  That's where thesceptre-date-resolvermay help. It will allow you to force AWS cloudformation to execute the
template on on every commit.Available HooksStackTerminationEnables and disables cloudformation stack termination.Syntax:parameter|sceptre_user_data:<name>:!stack_termination_protection<setting>Valid setting:enableddisabledExample:Enable stack termination protection after creating stack
and disable stack termination protection before stack deletion:hooks:
  after_create:
    - !stack_termination_protection 'enabled'
  before_delete:
    - !stack_termination_protection 'disabled'
Package: sc-excel-column-calculator
A simple Python projectThis is a simple python project.InstallationIt is possible to install the tool withpip:pip install sc-excel-column-calculatorConfigurationConfiguration files reading in this order, the first is the top most priority:production.xml in current directory,production.xml in <project_name> directory under User directory,production.xml in <project_name> directory under /var/opt/sc/ directory,default.xml in <project_name> directory under /var/opt/sc/ directory.The default configuration file looks like this:dev:
  # whether this program is running is development mode
  dev_mode: FalseLicenseThe script is released under the MIT License.  The MIT License is registered
with and approved by the Open Source Initiative[1].[1]https://opensource.org/licenses/MIT
Package: sc-excel-splitter
A simple Python project sample structureThis project provides a sample structure for creating python project.InstallationIt is possible to install the tool withpip:pip install sc-excel-splitterConfigurationConfiguration files reading in this order, the first is the top most priority:production.xml in current directory,production.xml in <project_name> directory under User directory,production.xml in <project_name> directory under /var/opt/sc/ directory,default.xml in <project_name> directory under /var/opt/sc/ directory.The default configuration file looks like this:dev:
  # whether this program is running is development mode
  dev_mode: False

excel:
  # 源文件路径
  source_file_path: "/path/to/source_excel_file.xlsx"
  # 导出文件的保存路径
  target_directory: "/path/to/target/"
  # 网点名称列表
  branch_list:
    - "A支行"
    - "B支行"
  # 需要按列拆分的Sheet以及拆分所在列
  sheet_config:
    # 需要拆分的Sheet名称，用双引号包围
    "Sheet1":
      # 机构列所在列的索引（从0开始计数，第一列为0，依此类推）
      branch_column: 3
      # 表头所在行的索引（从0开始计数，第一列为0，依此类推）
      header: 2
    # 需要拆分的Sheet名称，用双引号包围
    "Sheet2":
      # 机构列所在列的索引（从0开始计数，第一列为0，依此类推）
      branch_column: 17
      # 表头所在行的索引（从0开始计数，第一列为0，依此类推）
      header: 0
    # 其他需要导出的Sheet名称列表
    other_sheets:
      # Sheet名称
      - "Sheet3"
      # Sheet名称
      - "Sheet4"LicenseThe script is released under the MIT License.  The MIT License is registered
with and approved by the Open Source Initiative[1].[1]https://opensource.org/licenses/MIT
Package: scex-py
scex_pyPython wrapper for SCEX - Loan calculation packagePre-commitRun the below command to install the pre-commit hooks:$poetryrunpre-commitinstallTo execute the hooks without making any commit:$poetryrunpre-commitrun--all-filesUsage>>> from scex_py.scex_client import ScexClient

>>> client = ScexClient("host", "api_key")

>>> client.process_equal_payment_loan({
        "LoanDate": "2020-02-02",           # Year-month-date
        "PmtDate": "2020-02-02",            # Year-month-date
        "IntStartDate": "2020-02-02",       # Year-month-date
        "Proceeds": 20000,
        "Term": 5,
        "IntRate": 10,
        "TotalDown": 2000
    })
Package: scf
# scf cli## 什么是 scfSCF CLI 是腾讯云无服务器云函数 SCF（Serverless Cloud Function）产品的命令行工具。通过 scf 命令行工具，您可以方便的实现函数打包、部署、本地调试，也可以方便的生成云函数的项目并基于 demo 项目进一步的开发。scf cli通过一个函数模板配置文件，完成函数及相关周边资源的描述，并基于配置文件实现本地代码及配置部署到云端的过程。目前 scf cli以开源形式发布，您可以在本项目中查看命令行源代码及更多帮助文档，并可以通过项目 issue 反馈问题。## 功能特性通过 scf 命令行工具，你可以：快速初始化云函数项目在本地开发及测试你的云函数代码使用模拟的 COS、CMQ、Ckafka、API 网关等触发器事件来触发函数运行验证 TCSAM 模板配置文件打包、上传函数代码，创建函数及更新函数配置获取函数列表，删除指定函数SCF CLI 是腾讯云无服务器云函数 SCF（Serverless Cloud Function）产品的命令行工具。通过 scf 命令行工具，您可以方便的实现函数打包、部署、本地调试，也可以方便的生成云函数的项目并基于 demo 项目进一步的开发。## 快速入门
您可以前往腾讯云官网查看[SCF 快速入门](https://cloud.tencent.com/document/product/583/33446)## 详细使用指导[安装与配置](https://cloud.tencent.com/document/product/583/33449)[初始化示例项目](https://cloud.tencent.com/document/product/583/33450)[打包部署](https://cloud.tencent.com/document/product/583/33451)[日志查看](https://cloud.tencent.com/document/product/583/36352)[本地调试(native invoke)](https://cloud.tencent.com/document/product/583/35402)[本地调试(local invoke)](https://cloud.tencent.com/document/product/583/35401)[测试模板](https://cloud.tencent.com/document/product/583/33453)[模板文件](https://cloud.tencent.com/document/product/583/33454)[TCSAM说明](https://cloud.tencent.com/document/product/583/36198)[更新日志](https://cloud.tencent.com/document/product/583/36908)[常见问题 FAQ](https://cloud.tencent.com/document/product/583/33456)
Package: sc-file
Library and Utility for converting encryptedstalcraftgame files, such as models and textures into well-known formats.You can use compiled cli utility fromReleasespage.⚠️ DisclaimerDo not use game assets folder directly.You can get banned for any changes in game client.Copy files you need to another folder and work there.📁 FormatsTypeSource formatOutput formatModel.mcsa.objTexture.mic.pngTexture.ol.dds💻 CLI UtilityUsageYou can drag and drop one or multiple files toscfile.exe.From bash:scfile[OPTIONS][FILES]...ArgumentsFILES:List of file paths to be converted. Multiple files should be separated byspaces. Accepts both full and relative paths.Does not accept directory.Options-O,--output:One path to output file or directory. Can be specified multiple times for different output files or directories. If not specified, file will be saved in the same directory with a new suffix. You can specify multipleFILESand a single--outputdirectory.ExamplesConvert a single file:scfilefile.mcsaWill be saved in the same directory with a new suffix.Convert a single file with a specified path or name:scfilefile.mcsa--outputpath/to/file.objConvert multiple files to a specified directory:scfilefile1.mcsafile2.mcsa--outputpath/to/folderConvert multiple files with explicitly specified output files:scfilefile1.mcsafile2.mcsa-O1.obj-O2.objIf the count ofFILESand-Ois different, as many files as possible will be converted.Convert all.mcsafiles in the current directory:scfile*.mcsaIn this case,-Oaccepts only a directory. Subfolders are not included.Convert all.mcsafiles with subfolders to a specified directory:scfile**/*.mcsa-Opath/to/folderIn this case,-Oaccepts only a directory. With-Ospecified, the folder structure is not duplicated.📚 LibraryInstallPippipinstallsc-file-UManualgitclonegit@github.com:onejeuu/sc-file.gitcdsc-filepoetryinstallUsageSimplefromscfileimportconvert# Output path is optional.# Defaults to source path with new suffix.convert.mcsa_to_obj("path/to/file.mcsa","path/to/file.obj")convert.mic_to_png("path/to/file.mic","path/to/file.png")convert.ol_to_dds("path/to/file.ol","path/to/file.dds")AdvancedfromscfileimportMcsaFilewithMcsaFile("path/to/file.mcsa")asmcsa:obj:bytes=mcsa.to_obj()withopen("path/to/file.obj","wb")asfp:fp.write(obj)Buildpoetryinstallpoetryrunbuild
Package: scflowpy
scFlowPy is a helper package to bridge scFlow with the Python ecosystem.
A typical use case is to allow tertiary scRNAseq analyses on scFlow outputs
using Python tools (e.g. Tensorflow, PyTorch, Scanpy,and others).The core function currently is provided byread_sce(), allowing users to read any SingleCellExperiment object
output by scFlow (or by the nf-core/scflow pipeline) into the Annotated data
(AnnData) format.Free software: GNU General Public License v3Documentation:https://scflowpy.readthedocs.io.CreditWritten by Dr Combiz Khozoie for The Department of Brain Sciences, Imperial College London.
Package: scfw2d
StablerCharacterStablerCharacter is a Game engine which is mainly used for creating Advance Text Adventure gameStablerCharacter will bring the New Exprerience of playing a Text adventure game.PlanStablerCharacter is planned to have a Complete Rendering engine by the end of 2021.StablerCharacter is based onArcadelibrary. (2D) and soon will be based onUrsina engineas well. (3D)
Package: scg
scgSend Your Custom Gmail with Python第一步開啟 Google 低安全性https://myaccount.google.com/lesssecureappsInstallpip3installscg第二步在config.py輸入你的帳號密碼第三步在send_mail.py修改你要使用的 csv 檔案第四步在helper.py修改你所要寄信的內容，如果需要使用 html 可以在 HackMD 打完內容後，使用下載成純HTML。把內容複製貼上get_mail_content函數中。第五步python3send_mail.py
Package: scgen
scGenIntroductionscGen is a generative model to predict single-cell perturbation response across cell types, studies and species(Nature Methods, 2019). scGen is implemented using thescvi-tools framework.Getting StartedWhat you can do with scGen:Train on a dataset with multiple cell types and conditions and predict the perturbation effect on the cell type
which you only have in one condition. This scenario can be extended to multiple species where you want to predict
the effect of a specific species using another or all the species.Train on a dataset where you have two conditions (e.g. control and perturbed) and predict on second dataset
with similar genes.Remove batch effect on labeled data. In this scenario you need to provide cell_type and batch labels to
the method. Note thatbatch_removaldoes not require all cell types to be present in all datasets (batches). If
you have dataset specific cell type it will preserved as before.We assume there exist two conditions in you dataset (e.g. control and perturbed). You can train the model and with
your data and predict the perturbation for the cell type/species of interest.We recommend to use normalized data for the training. A simple example for normalization pipeline using scanpy:importscanpyasscadata=sc.read(data)sc.pp.normalize_total(adata)sc.pp.log1p(adata)We further recommend to use highly variable genes (HVG). For the most examples in the paper we used top ~7000
HVG. However, this is optional and highly depend on your application and computational power.InstallationInstallation with pipTo install the latest version scGen via pip:pipinstallscgenor install the development version via pip:pipinstallgit+https://github.com/theislab/scgen.gitExamplesSee examples at ourdocumentation site.Reproducing paper resultsIn order to reproduce paper results visithere.ReferencesLotfollahi, Mohammad and Wolf, F. Alexander and Theis, Fabian J."scGen predicts single-cell perturbation responses."Nature Methods, 2019.pdf
Package: sc-generate-hash
A simple utilities for generating hash values from filesThis project provides a simple utilities for generating hash values from files.InstallationIt is possible to install the tool withpip:pip install sc-generate-hashConfigurationFirst, make sure /var/opt/sc directory exists, if not create this directory and make sure current user has the right
to create files in this directory.You can copydefault.ymlto /var/opt/sc/.sc-generate-hash/production.yml to initialize the production configuration.The default configuration file looks like this:dev:
  # whether this program is running is development mode
  dev_mode: False
# directories to be scanned for jars
scan_libs:
  - /tmp/libs
  - /tmp/libs2Dependenciessc-utilities>= 0.0.2sc-config>= 0.0.3LicenseThe script is released under the MIT License.  The MIT License is registered
with and approved by the Open Source Initiative[1].[1]https://opensource.org/licenses/MIT
Package: sc-gitlab-msg-consumer
A simple message consumer for Gitlab WebHook messagesThis project provides A simple message consumer for Gitlab WebHook messages that were previously sent to a rocketmq serverInstallationIt is possible to install the tool withpip:pip install sc-gitlab-msg-consumerConfigurationFirst, make sure /var/opt/sc directory exists, if not create this directory and make sure current user has the right
to create files in this directory.You can copydefault.ymlto /var/opt/sc/.sc-gitlab-msg-consumer/production.yml to initialize the production configuration.The default configuration file looks like this:dev:
  # whether this program is running is development mode
  dev_mode: False

# rocketmq configurations
rocketmq:
  # name server ip
  name_server_ip: "localhost"
  # name server port
  name_server_port: 9876
  # group id
  group_id: "GITLAB_WEBHOOK_MSG_CONSUMER"
  # message topic
  msg_topic: "GITLAB_WEBHOOK"RunningAfter installation and configuration, you can simply run this program using:sh sc-gitlab-msg-consumerLogs will be saved at /tmp/logs/sc-sys.logDependenciessc-utilities>= 0.0.2sc-config>= 0.0.3rocketmq-client-python>= 2.0.0LicenseThe script is released under the MIT License.  The MIT License is registered
with and approved by the Open Source Initiative[1].[1]https://opensource.org/licenses/MIT
Package: sc-gitlab-webhook
A simple Gitlab WebHookThis project provides a Gitlab web hook to receive Gitlab events and process these events.InstallationIt is possible to install the tool withpip:pip install sc-gitlab-webhookConfigurationFirst, make sure /var/opt/sc directory exists, if not create this directory and make sure current user has the right
to create files in this directory.You can copydefault.ymlto /var/opt/sc/.sc-gitlab-webhook/production.yml to initialize the production configuration.The default configuration file looks like this:dev:
  # whether this program is running is development mode
  dev_mode: False

# flask server info
server:
  # flask server IP
  ip: "localhost"
  # flask server port
  port: 8080

# rocketmq configurations
rocketmq:
  # name server ip
  name_server_ip: "localhost"
  # name server port
  name_server_port: 9876
  # group id
  group_id: "GITLAB_WEBHOOK_MSG"
  # message topic
  msg_topic: "GITLAB_WEBHOOK"
  # message keys
  msg_keys: "GITLAB"
  # message tags
  msg_tags: "GITLAB"Dependenciessc-utilities>= 0.0.2sc-config>= 0.0.3flask>= 1.1.2rocketmq-client-python>= 2.0.0LicenseThe script is released under the MIT License.  The MIT License is registered
with and approved by the Open Source Initiative[1].[1]https://opensource.org/licenses/MIT
Package: scgiwsgi
scgiwsgi=============A WSGI server based on scgi_server(http://python.ca/scgi/).Example------------```pythonfrom scgiwsgi import WSGIServerfrom yourapp import applicationWSGIServer(application).run(port=4000, max_children=5)```Installation----------------------```pip install scgiwsgi```Nginx.conf Example-------------------```location / {include scgi_params;scgi_pass localhost:4000;}```
Package: scglue
GLUE (Graph-Linked Unified Embedding)Graph-linked unified embedding for single-cell multi-omics data integrationFor more details, please check out ourpublication.Directory structure.
├── scglue                  # Main Python package
├── data                    # Data files
├── evaluation              # Method evaluation pipelines
├── experiments             # Experiments and case studies
├── tests                   # Unit tests for the Python package
├── docs                    # Documentation files
├── custom                  # Customized third-party packages
├── packrat                 # Reproducible R environment via packrat
├── env.yaml                # Reproducible Python environment via conda
├── pyproject.toml          # Python package metadata
├── LICENSE
└── README.mdInstallationThescgluepackage can be installed via conda using one of the following commands:condainstall-cconda-forge-cbiocondascglue# CPU onlycondainstall-cconda-forge-cbiocondascgluepytorch-gpu# With GPU supportOr, it can also be installed via pip:pipinstallscglueInstalling within aconda environmentis recommended.UsagePlease checkout the documentations and tutorials atscglue.readthedocs.io.DevelopmentInstall scglue in editable form via flit (first install flit via conda or pip
if not installed already):flitinstall-sRun unit tests:pytest--cov="scglue"--cov-report="term-missing"tests[--cpu-only]Build documentation:sphinx-build-bgettextdocsdocs/_build/gettext
sphinx-intlupdate-pdocs/_build/gettext-lzh_CN-ddocs/locale
sphinx-build-bhtml-Dlanguage=endocsdocs/_build/html/en# English versionsphinx-build-bhtml-Dlanguage=zh_CNdocsdocs/_build/html/zh_CN# Chinese versionReproduce resultsCheckout the repository to v0.2.0:gitcheckouttags/v0.2.0Create a local conda environment using theenv.yamlfile,
and then install scglue:condaenvcreate-pconda-fenv.yaml&&condaactivate./conda
flitinstall-sSet up a project-specific R environment:packrat::restore()# Packrat should be automatically installed if not available.install.packages("data/download/Saunders-2018/DropSeq.util_2.0.tar.gz",repos=NULL)install.packages("custom/Seurat_4.0.2.tar.gz",lib="packrat/custom",repos=NULL)R 4.0.2 was used during the project, but any version above 4.0.0 should be compatible.Follow instructions indatato prepare the necessary data.Follow instructions inevaluationfor method evaluation.Follow instructions inexperimentsfor case studies.
Package: scgpm-seqresults-dnanexus
seqcenter_dnanexusUtilities for working with the SCGPM Sequencing Center application logic on DNAnexusAPI documentation is onRead the Docs.Provides high level methods and scripts for working with sequencing results that are stored in DNAnexus projects. This repository is geared towards sequencing result projects that the Stanford Genome Sequencing Center creates in DNAnexus, since there are many project properties that are unique to their workflow which are utilized and queried here.The heart of this API rests in theDxSeqResults()class in thednanexus_utils.pymodule. Given a DNAnexus project of interest, a user can use high level methods around that project to do things such as:Download QC reports and JSON stats for one or more barcoded samples,Download FASTQ files or fetch them as DNAnexus DXFile objects,Retrieve the properties that are set on specific FASTQ files,accept project transfers in DNAnexus,and more.Thescriptsare many, and include tools such as:Clean up projects to save space,List projects and their properties for projects billed to a specific org,Download fastqs of interest,Add properties to a project,Accept project transfersThe first point above has been heavily used to save space and costs. The script is calledscgpm_clean_raw_data.pyand works by removing unneccessary extras in the raw_data folder of a project.  It works by running an app on DNAnexus by the same name and cleans out all projects that have been created within the last N days.
Package: scgraph-data
scgraph_dataLarge geograph data for thescgraphpackage. This package includes larger geographs that would be too large to include in the main scgraph package. These geographs include larger and more complete networks like the world highway and world railway networks.Please note that these geographs are simplified and are not 100% accurate. They are also not directional geographs and should not be used for navigation or any other safety critical applications.SetupMake sure you have Python 3.6.x (or higher) installed on your system. You can download ithere.Installationpip install scgraph_dataIncluded GeoGraphsworld_highways_geograph:What: World highway network from OpenStreetMapUse:from scgraph_data.world_highways import world_highways_geographAttribution:OpenStreetMapsLength Measurement: KilometersWorld Highways Pictureworld_railways_geograph:What: World railway network from OpenStreetMapUse:from scgraph_data.world_railways import world_railways_geographAttribution:OpenStreetMapsLength Measurement: KilometersWorld Railways Picture