Saravanan666/pypi-org huggingface dataset:
Package: bigcummies
PyPi:https://pypi.org/project/bigcummies/วิธีติดตั้งเปิด CMD / Terminalpipinstallbigcummiesวิธีใช้งานแพ็คเพจนี้เปิด IDLE ขึ้นมาแล้วพิมพ์…if__name__=='__main__':my=Profile('Cum')my.company='pusspuss'my.hobby=['Youtuber','Reading','Sleeping']print(my.name)my.show_email()my.show_myart()my.show_hobby()พัฒนาโดย: ลุงวิศวกร สอนคำนวณ FB:https://www.facebook.com/UncleEngineerYouTube:https://www.youtube.com/UncleEngineer
Package: big-data-normality
Empirical Normality TestA normality test based on empirical rule for big dataInstallationpip install big-data-normalityGet startedHow to get normality test for dataset with this lib:# Library importfrombig_data_normality.empirical_normality_testimportEmpiricalNormalityTest# Graph outputfrombig_data_normality.empirical_normality_testimportEmpiricalNormalityTestGraph# This line of code will allow shorter importsfrombig_data_normalityimportEmpiricalNormalityTest# Graph outputfrombig_data_normalityimportEmpiricalNormalityTestGraph# Instantiate a EmpiricalNormalityTest object"""Firstly, import the excel file, then run the libraryP.S. Excel file must include name of each column. Like that;name1	name2	name3	name4 ...73,36	72,64	68,45	66,52 ...78,97	67,04	60,85	70,96 ......     ...     ...     ...   ..."""importosimportpandasaspdpath=r'C:\data'os.chdir(path)df=pd.read_excel("my_dataset.xlsx")# Test outputprint(EmpiricalNormalityTest(df))# Graph outputprint(EmpiricalNormalityTestGraph(df))
Package: big-data-visual
方便进行大数据处理、呈现的工具类封装包https://pypi.org/project/big-data-visual/安装：` pip installlbig-data-visual`bdvisual.plot.radordef draw(title,propname, propvalue1, propvalue2, outfilename, propvalueShow = 0, gridShow = 0, gridLabelShow = 0)bdvisual.plot.outdirdef get_output_dir()
Package: bigdict
bigdictimplements a persisted, out-of-memory dict for Python.The usage API strives to be as close as possible to the built-indict, with a small number of extras.The “engine”, or “back-end”, is currentlylmdb. However,lmdbis not the point; the point is rather a “persisted, out-of-memory dict”. Criteria used in choosing the engine:It must beembedded, as opposed to client/server.It must be a mature project with a reliable Python binding.In addition,It must use more files for storage as the data size grows, hence keeping file sizes modest,
as opposed to a single file growing in size.If the chosen backend does not meet this requirement, it’s the responsibility ofbigdictto implement that.Installation:$ pip install bigdict
Package: bigdoorkit
BigDoor ClientThis is a Python client library for theBigDoor API.Basic UsageThebigdoorkitclient requires your keys as provided by BigDoor:>>> from bigdoorkit import Client
>>> client = Client(app_secret, app_key)
>>> currency_types = client.get('currency_type')
>>> from pprint import pprint
>>> pprint(currency_types)
[[{u'can_be_cross_publisher': 0,
   u'can_be_purchased': 1,
   u'can_be_rewarded': 0,
   u'created_timestamp': 1263933875,
   u'description': None,
   u'has_dollar_exchange_rate_integrity': 1,
   u'id': 1,
   u'modified_timestamp': 1263933875,
   u'read_only': 0,
   u'resource_name': u'currency_type',
   u'title': u'Purchase'},
  {u'can_be_cross_publisher': 0,
   u'can_be_purchased': 0,
   u'can_be_rewarded': 1,
   u'created_timestamp': 1263933875,
   u'description': None,
   u'has_dollar_exchange_rate_integrity': 0,
   u'id': 2,
   u'modified_timestamp': 1263933875,
   u'read_only': 0,
   u'resource_name': u'currency_type',
   u'title': u'Reward'},
  {u'can_be_cross_publisher': 0,
   u'can_be_purchased': 1,
   u'can_be_rewarded': 1,
   u'created_timestamp': 1263933875,
   u'description': u'',
   u'has_dollar_exchange_rate_integrity': 0,
   u'id': 3,
   u'modified_timestamp': 1264002256,
   u'read_only': 0,
   u'resource_name': u'currency_type',
   u'title': u'Hybrid'},
  {u'can_be_cross_publisher': 1,
   u'can_be_purchased': 0,
   u'can_be_rewarded': 0,
   u'created_timestamp': 1263933875,
   u'description': None,
   u'has_dollar_exchange_rate_integrity': 1,
   u'id': 4,
   u'modified_timestamp': 1263933875,
   u'read_only': 0,
   u'resource_name': u'currency_type',
   u'title': u'\xdcber'}],
 {}]Known IssuesTheresourcemodule is under heavy development and should not be used for now.Very little formal documentation.Unit tests are sparse.News0.1.3Release date: 29-Jul-2010Some progress on the resource moduleFixed deprecation warning from RESTkitRequests no longer modify params/payload dicts in place, using a copy to build the request.  This prevents the original dicts from being filled with request specific data (auto-generated token, time, etc).0.1.2Release date: 23-Jun-2010DELETE methods now automatically add thedelete_tokenparameter.DELETE methods no longer try to decode the JSON response as the BigDoor API returns204 NO CONTENT.0.1.1Release date: 18-Jun-2010Improved docstrings.Removed hard coded logging to client_log.txt.0.1Release date: 10-Jun-2010Initial release.
Package: bigearthnet-common
BigEarthNet CommonA collection of common tools to interact with the BigEarthNet dataset.This library provides a collection of high-level tools to better work with theBigEarthNetdataset.bigearthnet_commontries to:Collect the most relevantconstantsinto a single place to reduce the time spent looking for these, like:The 19 or 43 class nomenclature stringsURLBand statistics (mean/variance) as integer and floatChannel namesetc.Provide common metadata related functionsSafe JSON parser for S1/S2Get the original splitGet a list of snowy/cloudy patchesConvert theoldlabels to thenewlabel nomenclatureGenerate multi-hot encoded label viewsEasily filter patches and generate subsets as CSV filesAllow to quickly test code on BigEarthNet data without requiring to download the entire archvieInstallationThe package is available via PyPI and can be installed with:pip install bigearthnet_commonThe package hasPython-onlydependencies and should cause no issues in more complex Conda environments with various binaries.Review constantsTo quickly search for BigEarthNet constants of interest, call:ben_constants_promptorpython -m bigearthnet_common.constantsSets generatorTo generate sets/subsets from the data and to store them as a CSV file, use:ben_build_csv_sets --helpThis command-line tool lets the user easily create subsets from common constraints.
To generate a CSV file that contains all Sentinel-2 patches from Serbia only during the Summer and Spring months, call the function as:ben_build_csv_sets <FILE_PATH> S2 --seasons Winter --seasons Summer --countries Serbia --remove-unrecommended-dl-patches:::{note}By default, this tool will always remove theunrecommendedpatches, i.e., patches that contain seasonal snow, shadows, clouds, or that have no labels in the 19-class nomenclature:::Describe patchThe library provides a tool to quickly visualize meta-data information about each patch:ben_describe_patch<S1/S2patchname>
Package: bigearthnet-encoder
BigEarthNet EncoderA flexible BigEarthNet encoder that allows one to quickly convert BigEarthNet to a DL-optimization data format.The goal of the BigEarthNet Encoder library is to quickly transform the original BigEarthNet archive into a deep-learning optimized format.
The long-term goal is to support multiple output formats.To simplify the process of working with BigEarthNet, each patch is first converted to aBigEarthNet-Patch-Interface.
This interface will guarantee that the data is complete and valid before moving on to creating the desired format.
The patch data is internally stored as a NumPy array to keep the data in a framework-agnostic format.The library should provide all the necessary functionality via a CLI to allow for quick conversion without requiring an in-depth understanding of the library.As of now, the only supported target format is theLMDBarchive format.
Package: bigearthnet-gdf-builder
BigEarthNet GDF BuilderA package to generate and extend BigEarthNet GeoDataFrames.This library provides a collection of functions to generate and extend GeoDataFrames for theBigEarthNetdataset.bigearthnet_gdf_buildertries to accomplish two goals:Easily generategeopandasGeoDataFrame's by passing a BigEarthNet archive directory.Allow for easy top-level statistical analysis of the data in a familiarpandas-styleProvide functions to enrich GeoDataFrames with often required BigEarthNet metadata (like the season or country of the patch)Simplify the building procedure by providing a command-line interface with reproducible resultsOne of the primary purposes of the dataset is to allow deep learning researchers and practitioners to train their models on therecommendedBigEarthNet satellite data.
In that regard, there is a general recommendation to drop patches that are covered by seasonal snow or clouds/shadows.
Also, the novel 19-class nomenclature should be preferred over the original 43-class nomenclature.
As a result of these recommendations, some patches have to beexcludedfrom the original raw BigEarthNet dataset that is provided atBigEarthNet.To simplify the procedure of pre-converting the JSON metadata files, the library provides a single command that will generate a recommended GeoDataFrame with extra metadata (country/season data of each patch) while dropping all patches that are not recommended for deep learning research.
Functions for both archives, BEN-S1 and BEN-S2, are provided.
Package: bigearthnet-patch-interface
BigEarthNet Patch InterfaceA simple interface class that includes all the relevant information about BigEarthNet patches.A common issue when using a BigEarthNet archive is that the code to load a patch isSlowThe necessary libraries to load the data have complex dependencies and cause issues with popular deep-learning librariesThe issue is often caused by a binary mismatch between the underlyingnumpyversionsHard to understand how to access the optimized dataA popular approach is to use the key-value storageLMDB.
The patch names are set as keys, and the value issomehowencoded.
Decoding the values is a common source of bugs when different deep-learning libraries are used.The goal of this repository is to alleviate this issue.
The actual image data will be encoded asnumpyarrays to support the most popular deep-learning libraries.
Usually, these arrays can be loaded without copying the underlying data.The provided patch interface will define a Python class containing the relevant bands, encoded as annp.ndarray, and may include some metadata.
The interface class allows for fast introspection, validation, and data loading.It is easier to convert this intermediate data format to a more deep-learning optimized format or use the class itself for embedding in a key-value storage format like LMDB.
Please refer to the official {{BenEncoder}} documentation for details on how togeneratevarious output formats.In general, the encoding pipeline is as follows:Convert the BigEarthNet patches into numpy arraysUse these arrays to initialize the interface classProvide any additional metadata information to the interfaceSerialize/convert the data to the desired output formatThe decoder should be as simple as possible with minimal dependencies.
Ideally, the correct usage of the decoder should also be depicted in the corresponding {{BenEncoder}} section.:::{note}
This interface is tightly coupled with the {{BenEncoder}} libary!The main reason for providing this interface as astandalonepackage is to minimize the required dependencies of a user that wants to load/use already encoded patches.
Creating the interface from therawdata requires heavy libraries that often cause binary conflicts.
The interface class can be installed as a standalone library and only requires the (easier to install)decoderlibraries.:::
Package: biget
Bilibili Tools内容列表背景已完成安装使用说明维护者背景本项目主要是通过爬虫获取Bilibili各种信息，包括个人信息，视频下载，弹幕下载等。具体有多少功能主要取决于我自己有什么需求。但是会尽可能多
的实现各种功能。这些功能并不会以web应用或者GUI等形式出现，更像是以一个可调用的库的形式出现，这样可以很方便的给我自己去构建很多应用。当
然我自己感觉这个项目算是自己对于Python的一个实践吧。学了很多，但是平常写的机会挺少的，好些技巧都已经忘记了，借着这次机会也是重新学习和
巩固（如果不是这次疫情还抽不出这么多时间来写）。最后本项目仅用于学习交流，请勿用于任何商业用途。已完成目前所有的代码都是在Windows下完成的，并没有在Linux下进行测试，但是应该没有太大问题。视频下载(在2020年2月23号B站开始使用BV号而不是AV号，昨天稍微修把项目修改了一下)视频信息获取(包括点赞数，弹幕等)安装首先要确保你的电脑安装了ffmpeg并且设置了环境变量，如果没有，那么请前往它的官网下载。在确保你安装了ffmpeg的情况
下，请在命令行输入如下的命令以此来安装运行该项目所必须的依赖。pip3 install biget使用说明首先导入该模块from biget import Video然后给Video类的构造函数传递参数来构建video对象，传入的参数就是视频的bv号v = Video('BV1fE411F7AF')在这个情况下v对象有如下的属性：bv：视频的bv号码bv_url：视频地址title：视频标题date：视频发布日期page_list：B站的视频可能一个视频有多个p也就是多集，这里是bv号下所有的视频的信息page_num：集数也就是p的数量data：关于视频的信息，在没有调用access方法之前改返回值为None否则返回值为下面的形式{
    'bvid': 视频bv号码,
    'aid': 视频对应的av号码,
    'page_num': 同一个bv下p的数量,
    'title': 视频标题,
    'date': 发布日期,
    'owner': up主,
    'owner_id': up主id,
    'view': 观看量,
    'danmaku': 弹幕数量,
    'reply': 评论数,
    'favorite': 收藏数量,
    'coin': 投币数量,
    'share': 转发数量,
    'now_rank': 当前排名,
    'his_rank': 全站排名,
    'like': 点赞数,
    'page': 每一个p的信息,
    'tags': 视频的标签信息,
}bullet_comments: 视频的弹幕内容，在默认情况下为None，在调用了get_bullet方法之后才会有数据。执行之后该对象的bullet_comments会是
一个二维list对象，该对象存的每一个元素是一个bs4.element.Tag对象，要想获取里面的文本信息可以使用.text方法。对象方法(下面假设类对象是video)：download(pages, path='.', cover=True, keep=True, bilingual=True, insert=False)pages：要下载该bv号下管理的哪几集视频，需要传入一个可迭代对象，如果想下载全集可以传入range(video.page_num)。如果你想选择跳跃
的集数，那么请记住，输入的集数要比真实的小1。例如你想要获取第2集，第5集，那么你可以传入[1, 4]。path：下载的路径，默认情况下会在当前如路径下载（下载的视频会在给定路径下创建文件夹，以视频名字命名）cover：当cover为True的时候会如果文件夹里有同名文件会直接覆盖keep：是否保存m4s文件，因为b站直接爬取保存的是m4s，将m4s做处理之后才是我们的最终文件bilingual：为True时，视频在合并字幕的时候会合并两个CC字幕，当然这个的前提是该视频有两个CC字幕（当CC字幕数量大于2的时候就会取前
两个CC字幕），否则只合并一个insert：是否将CC字幕作为内嵌字幕，为True是则是作为内嵌字幕，（如果为True可能会需要比较长的时间处理），该参数为True只有在
bilingual为false的情况下才有效access()get_bullet(pages)pages：因为bv下的每一个视频都有自己的弹幕，所以你可以选择要获取的集数，同download需要传入一个可迭代对象，如果想下载全集则传入
range(video.page_num)，如果你想选择跳跃的集数，那么请记住，输入的集数要比真实的小1。例如你想要获取第2集，第5集，那么你可以传入
[1, 4]。维护者维护者邮箱@ujay-zheng897013045@qq.com
Package: bigeye-airflow1
Bigeye Airflow Operators for Airflow Versions 1.10.15OperatorsCreate Metric Operator (bigeye_airflow.oerators.create_metric_operator)The CreateMetricOperator creates metrics from a list of metric configurations provided to the operator.
This operator will fill in reasonable defaults like setting thresholds.  It authenticates through an Airflow connection
ID and offers the option to run the metrics after those metrics have been created.  Please review the link below to
understand the structure of the configurations.Create or Update Metric SwaggerParametersconnection_id: str - The Airfow connection ID used to store the required Bigeye credential.warehouse_id: int - The Bigeye source/warehouse id to which the metric configurations will be deployed.configuration: List[dict] - A list of metric configurations conforming to the following schema.schema_name: str
table_name: str
column_name: str
metric_template_id: uuid.UUID
metric_name: str
notifications: List[str]
thresholds: List[dict]
filters: List[str]
group_by: List[str]
user_defined_metric_name: str
metric_type: SimpleMetricCategory
default_check_frequency_hours: int
update_schedule: str
delay_at_update: str
timezone: str
should_backfill: bool
lookback_type: str
lookback_days: int
window_size: str
_window_size_secondsrun_after_upsert: bool - If true it will run the metrics after creation.  Defaults to False.Run Metrics OperatorThe RunMetricsOperator will run metrics in Bigeye based on the following:All metrics for a given table, by providing warehouse ID, schema name and table name.Any and all metrics, given a list of metric IDs.Currently, if a list of metric IDs is provided these will be run instead of metrics provided for
warehouse_id, schema_name, table_name.Parametersconnection_id: str - The Airfow connection ID used to store the required Bigeye credential.warehouse_id: int - The Bigeye source/warehouse id for which metrics will be run.schema_name: str - The schema name for which metrics will be run.table_name: str - The table name for which metrics will be run.metric_ids: List[int] - The metric ids to run.
Package: bigeye-sdk
Bigeye SDKBigeye SDK is a collection of protobuf generated code, functions, and models used to interact programmatically
with the Bigeye API.  Bigeye currently supports a Python SDK.  The main entry point is the DatawatchClient
abstraction and, in this core package, a basic auth client has been implemented.  The abstract base class
includes core functionality (methods to interact with the API) and each implementation should enable a
different authorization methods.Installpipinstallbigeye_sdkBasic AuthBasic authorization credentials can be stored as Json either on disk or in a secrets/credentials manager.  This
format will be marshalled into an instance ofBasicAuthRequestLibApiConf.{"base_url":"https://app.bigeye.com","user":"","password":""}
Package: bigfastapi
BigFastAPIBigFastAPI is an extension ofFastAPIthat adds a bunch of things that are commonly used in APIs.BigFastAPI Documentation:https://bigfa.st/,FastAPI Documentation:https://fastapi.tiangolo.comFeaturesImplementedAuthentication (login, logout)UsersOrganizationsCommentsBlogFAQCountriesPagesContactFilesNotificationsImageWalletPDF ConverterCreditsProductsIn ProgressTransactional Email sending + templatesPlansBank AccountsSubscriptionsSettingsPlannedCurrency RatesMarketing EmailsResearch bank format providers to build onAnalyticsActivityLogHow to use BigFastAPICreate a new python projectCreate a main.py file and set it up as described in the fastapi documentationinstall the latest version of bigfastapi by runningpip install bigfastapiIn your main.py, import FastAPI, CORSMiddleware and the create_database function (from bigfastapi.db.database.py).
You can look into the bigfastapi main.py to see everything you should importcreate a .env file and provide all the required environment variables.
check check .env.sample file for a sample of what the env should containMake sure you have a main that calls uvicorn in the bottom of your fileYou now have access to all the functions in bigfastapi. You can include any of them by importing (from bigfastapi.countries import app as countries) and then including a router app.include_router(countries, tags=["Countries"])Run the main.py filepython main.pyto start up your server.learn more about how to use bigfastapi athttps://bigfa.st/How to contribute to BigFastAPIFork and clone the bigfastapi repository.Create a virtual environment withpython3 -m venv envorpython -m venv envActivate the virtual environment using.\env\Scripts\Activate.ps1(windows-powershell)
or.\env\Scripts\activate.bat(windows-command prompt) orsource /path/to/venv/bin/activate(linux/mac)runpip install -r requirements.txtCreate a .env file by copying the .env.sample fileRunpython main.py. Check the code to understand how to use the librarycommands for building bigfastapi into a library:python setup.py sdist bdist_wheelYou can install your local version of the library into another project run:pip install <path to local bigfastapi>\dist\<name of whl file>update on pypiusingtwine upload --repository-url https://upload.pypi.org/legacy/ dist/*(first install twine usingpip install twine)DocumentationWhen you run the sample code, visithttp://127.0.0.1:7001/docsto view the documentation for all endpointsLicenseThis project is licensed under the terms of the MIT license.
Package: bigfeta
BigFetaGlobal linear-least squares solution of transforms for alignment of features.Level of supportWe are planning on occasional updating this tool with no fixed schedule. Community involvement is encouraged through both issues and pull requests.Documentationhttps://bigfeta.readthedocs.io/en/latest/index.htmlAcknowledgement of Government SponsorshipSupported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior / Interior Business Center (DoI/IBC) contract number D16PC00004. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/IBC, or the U.S. Government.
Package: big-fish
Big-FISHBig-FISHis a python package for the analysis of smFISH images. It includes various methods toanalyze microscopy images, suchspot detectionandsegmentation of cells and nuclei. The package allows the user represent the extract properties of a cell as coordinates (see figure below). The ultimate goal is to simplifylarge scale statistical analysisand quantification.Cell image (smFISH channel) and its coordinates representationInstallationDependenciesBig-FISH requires Python 3.6 or newer. Additionally, it has the following dependencies:numpy (>= 1.16.0)scipy (>= 1.4.1)scikit-learn (>= 0.24.0)scikit-image (>= 0.14.2)matplotlib (>= 3.0.2)pandas (>= 0.24.0)mrc (>= 0.1.5)For segmentation purpose, two additional dependencies can be requested:tensorflow (== 2.3.0)tensorflow-addons (== 0.12.1)Virtual environmentTo avoid dependency conflicts, we recommend the the use of a dedicatedvirtualorcondaenvironment.  In a terminal run the command:condacreate-nbigfish_envpython=3.6sourceactivatebigfish_envWe recommend two options to then install Big-FISH in your virtual environment.Download the package from PyPiUse the package managerpipto install Big-FISH. In a terminal run the command:pipinstallbig-fishClone package from GithubClone the project'sGithub repositoryand install it manually with the following commands:gitclonegit@github.com:fish-quant/big-fish.gitcdbig-fish
pipinstall.UsageBig-FISH provides a toolbox for the full analysis pipeline of smFISH images. A completedocumentationis available online.This package is part of theFISH-Quantframework and several examples are also available asJupyter notebooks.SupportIf you have any question relative to the repository, please open anissue. You can also contactArthur ImbertorFlorian Mueller.Roadmap (suggestion)Version 1.0.0:Complete code coverage.Unpin deep learning dependenciesAdd a pretrained pattern recognition modelDevelopmentSource codeYou can access the latest sources with the commands:gitclonegit@github.com:fish-quant/big-fish.gitcdbig-fish
gitcheckoutdevelopContributingPull requestsare welcome. For major changes, please open anissuefirst to discuss what you would like to change.TestingPlease make sure to update tests as appropriate if you open a pull request. You can install exacts dependencies and specific version ofpytestby running the following command:pipinstall-rrequirements_dev.txtTo perform unit tests, run :pytestbigfishCitationIf you exploit this package for your work, please cite:Arthur Imbert, Wei Ouyang, Adham Safieddine, Emeline Coleno, Christophe Zimmer, Edouard Bertrand, Thomas Walter, Florian Mueller. FISH-quant v2: a scalable and modular analysis tool for smFISH image analysis. bioRxiv (2021)https://doi.org/10.1101/2021.07.20.453024
Package: big-fiubrother-core-application
big-fiubrother-core-applicationbig-fiubrother-core-applicationmodule is a basic frame to be used in all the applications from the Big Fiubrother project.
This module concentrates common tasks that are needed in all the microservices, for example:Parsing argumentsLoading configurationStarting (distributed) loggingHandle Linux signalsUsagefrombig_fiubrother_core_applicationimportrunwithrun('application_name')asapplication:whilenotapplication.is_stopped():work()