Saravanan666/pypi-org huggingface dataset:
Package: insupdel4stac
INSUPDEL4STACA Python package to provide seamless functionality for INSerting, UPDating, and DELeting STAC-Metadata toward either pgSTAC or STAC-API.InstallationInstall this package in a dedicated python environment viapython-mvenvvenvsourcevenv/bin/activate
pipinstallinsupdel4stacTo use this in a development setup, clone thesource codefrom
gitlab, start the development server and make your changes::gitclonehttps://codebase.helmholtz.cloud/cat4kit/ds2stac/insupdel4staccdinsupdel4stac
python-mvenvvenvsourcevenv/bin/activate
makedev-installMore detailed installation instructions my be found in thedocs.Technical noteThis package has been generated from the templatehttps://codebase.helmholtz.cloud/hcdc/software-templates/python-package-template.git.See the template repository for instructions on how to update the skeleton for
this package.License informationCopyright © 2023 Karlsruher Institut für TechnologieCode files in this repository are licensed under the
EUPL-1.2, if not stated otherwise
in the file.Documentation files in this repository are licensed under CC-BY-4.0, if not stated otherwise in the file.Supplementary and configuration files in this repository are licensed
under CC0-1.0, if not stated otherwise
in the file.Please check the header of the individual files for more detailed
information.License managementLicense management is handled withreuse.
If you have any questions on this, please have a look into thecontributing guideor contact the maintainers ofinsupdel4stac.
Package: insupportable
Some stupid badges:I am really annoyed, more and more, especially in old project to figure out what code is a war a workaround
for old version of python, or another library.This library should provide a simple way to warn you as early as possible when you can remove some legacy code,
that deals with unsupported library version.By default come pre-configured with Python 2/3 convenience function, but works
with other libraries and more fine grained version numbering.# warn you you have dead code if you# drop Python2 supportfrominsupportableimportsupportifsupport('PY2'):print("You are on python 2")else:print("You are on python 3")# warn you you have dead code if you# drop Python2 supportfrominsupportableimportsupportifsupport('PY3'):print("You are on python 3")else:print("You are on python 2")Set it up to drop Python2 support.Quick and dirty way, modify global config, which is not recommended as it may
affect other libraries that use this too, but super usefull.support.config(PY2=False)ifsupport(PY2):print("You are on python 2")else:print("You are on python 3")warn the following:mymodule/myfile.py:3: UserWarning: You are not supporting PY2 anymore
  if support(PY2):
mymodule/myfile.py:3: UserWarning: PY2 is the last supported feature of this group, you can simplifiy this logic.
  if support(PY2):More involve way, create a config context that have effect only locally:frominsupportableimportSsupport=S(PY2=False).support....Advance configuration/custom features:Example:support.config(config=({'WindowsPhone':True,'Android':False,'iOS':False},))ifsupport('WindowsPhone'):print('Click on start menu')else:print("Probably Android - but you don't support it anymore")mymodule/myfile.py:1: UserWarning: WindowsPhone is the last supported feature of this group, you can simplifiy this logic.
  if support('WindowsPhone'):TODO:Write predicates and document like:ifworkaround('tornado==2.2'):# do something specialThe predicate would decide wether or not to yield depending on the version oftornado,
and warn if min tornado is  higher than 2.2Deprecation decorator:@deprecated_since('2.3.1',will_remove='3.0.0')defmy_api('something'):"""deprecated fucntion that should
    warn user when function is **called**

    when module version is >= 3.0, the decorator should warn **developper** at **compile** time
    that code has to be removed.
    """This case is more interesting thansupport()as there is 2 pass, the decoration of the function itsef,
and the execution of the function.will_removeshould infer next major I guess.
Should we add option to deprecate after/at a date for some case like API.@deprecate_after(date='2015/10/15'):defmarty_from_the_future(self):"""
    Docs has some invalid ssl certificates ?
    """Free software: MIT licenseDocumentation:https://insupportable.readthedocs.org.
Package: insuranceqa_data
保险行业语料库Welcome该语料库包含从网站Insurance
Library收集的问题和答案。据我们所知，这是保险领域首个开放的QA语料库：该语料库的内容由现实世界的用户提出，高质量的答案由具有深度领域知识的专业人士提供。
所以这是一个具有真正价值的语料，而不是玩具。在上述论文中，语料库用于答复选择任务。
另一方面，这种语料库的其他用法也是可能的。
例如，通过阅读理解答案，观察学习等自主学习，使系统能够最终拿出自己的看不见的问题的答案。欢迎任何进一步增加此数据集的想法。阅读详细文档声明声明1 :insuranceqa-corpus-zh本数据集使用翻译insuranceQA而生成，代码发布证书Chunsong Public License, version 1.0<https://docs.cskefu.com/licenses/v1.html>。数据仅限于研究用途，如果在发布的任何媒体、期刊、杂志或博客等内容时，必须注明引用和地址。InsuranceQA Corpus, Hai Liang Wang, https://github.com/chatopera/insuranceqa-corpus-zh, 07 27, 2017任何基于insuranceqa-corpus衍生的数据也需要开放并需要声明和“声明1”和“声明2”一致的内容。声明2 :insuranceQA此数据集仅作为研究目的提供。如果您使用这些数据发表任何内容，请引用我们的论文：Applying
Deep Learning to Answer Selection: A Study and An Open
Task。Minwei Feng, Bing Xiang,
Michael R. Glass, Lidan Wang, Bowen Zhou @ 2015
Package: insurancespell
Insurance Spell CheckInsurance Spell Check using Conditional Random Field (CRF)Install Library!pip install insurancespellHow to useInput:เงินเวรคืนของก็มาทันimportinsurancespellcheckprint(insurancespellcheck.check("เงินเวรคืนก็มาทัน"))Output:เงิน<คำผิด>เวรคืน</คำผิด><คำผิด>ก็มาทัน</คำผิด>The words in <คำผิด>(*)</คำผิด> are wrongword in Insurance .if you want to use auto_correct for change to correctword, useauto_correct=Truein code.importinsurancespellcheckprint(insurancespellcheck.check("เงินเวรคืนก็มาทัน",auto_correct=True))Output:เงินเวนคืนของกรมธรรม์if you want to use correct_word for get specific correct word, usecorrect_word=Truein code.importinsurancespellcheckprint(insurancespellcheck.check("เงินเวรคืนก็มาทัน",correct_word=True))Output:['เวนคืน', 'กรมธรรม์']LicenseCopyright 2023 Thiraphat Chorakhe
Package: insutance
hey-insutanceinsutance에 대한 정보를 알려주는 패키지.Getting StartedBuilt WithPython >= 3.8Installation$pipinstallinsutanceUsage해당 명령어를 입력해 질문 리스트들을 알아보고, 궁금한 내용의 옵션을 입력해보세요:)$heyinsutance--help# example$heyinsutance--name
Package: insynsregistret
IntroductionInsynsregistretis a Swedish financial registry maintained by theSwedish Finansinspektionen(FI). It contains
information regarding insider trading on the Stockholm Stock Exchange (OMX) which is the largest financial market in Sweden.All insider trading is reported to FI, which publishes the data to this public database. This python library makes it easier to automate data extraction from Insynsregistret.Please note that this library currently is under development. See the tests for usage instructions.UsageFor most users, the recommended method to install is via pip:$ pip install insynsregistretDevelopment# Sets up environment and installs dependencies
$ make env

# Activate the environment
$ . env/bin/activate

# Shows the list of commands available
$ make help

  env         create a development environment using virtualenv
  deps        install dependencies
  clean       remove unwanted stuff
  lint        check style with flake8
  test        run all your tests using nose
  production  run test suite and do a release
  release     package and upload a release
  sdist       packageLicenseInsynsregistret is BSD licensed, so feel free to use it as you like.
Package: int
Int is a simple command line utility for converting integers between bases. It accepts an integer in its [b]inary, [o]ctal, [d]ecimal, or he[x]adecimal form, then prints it out in all four bases:$ int 64
hex: 40
dec: 64
oct: 100
bin: 01000000You can specify the base of the input using a single letter prefix.
(Defaults to decimal if omitted.) Int also accepts multiple arguments:$ int b1001 o777 d256 x1EALeading zeros are ignored so integer literals in the form0x123are
also accepted.
Package: int3
int3Synopsisint3is a Python library and command-line tool for generating and encoding binary payloads.by writing them word-by-word to the stack. Its initial release is focused on Windows 32-bit and 64-bit shellcoding.Installationint3is available on PyPI. Install it with your favorite Python packaging manager. For example:pipinstallint3UsageFormattingTODOEncodingTODOFactoringTODODevelopmentEnvironment SetupSystem DependenciesThe following command will install requisite system dependencies:sudoapt-getinstallcmakePlease note that this step must precede the Python dependency installation step.Python DependenciesThis project usesPoetryto manage its Python dependencies. Followthe Poetry installation instructionsfor its setup.You can then install this project's Python dependencies with:poetryinstallReleasesAssumingPoetry credentials are properly setup, publishing to PyPI should be simple:poetrypublishLicenseint3is intended for educational purposes and events such as CTFs only. It should never be used to target machines and/or networks without explicit prior consent. This code is released under theMIT license, as per theLICENSE.txtfile.ReferencesMany helpful resources were used in the development of this tool, including:Phrack - Writing ia32 alphanumeric shellcodesMazeGen's x86 referenceNASM
Package: intake
Intake: Take 2A general python package for describing, loading and processing dataTaking the pain out of data access and distributionIntake is an open-source package to:describe your data declarativelygather data sets into catalogssearch catalogs and services to find the right data you needload, transform and output data in many formatswork with third party remote storage and compute platformsDocumentation is available atRead the Docs.Please report issues athttps://github.com/intake/intake/issuesInstallRecommended method using conda:condainstall-cconda-forgeintakeYou can also install usingpip, in which case you have a choice as to how many of the optional
dependencies you install, with the simplest having least requirementspipinstallintakeNote that you may well need specific drivers and other plugins, which usually have additional
dependencies of their own.DevelopmentCreate development Python environment with the required dependencies, ideally withconda.
The requirements can be found in the yml files in thescripts/ci/directory of this repo.e.g.conda env create -f scripts/ci/environment-py311.ymland thenconda activate test_envInstall intake usingpip install -e .Usepytestto run tests.Create a fork on github to be able to submit PRs.We respect, but do not enforce, pep8 standards; all new code should be covered by tests.
Package: intake-accumulo
# Intake Accumulo Plugin[![Build Status](https://travis-ci.org/ContinuumIO/intake-accumulo.svg?branch=master)](https://travis-ci.org/ContinuumIO/intake-accumulo)
[![Documentation Status](https://readthedocs.org/projects/intake-accumulo/badge/?version=latest)](http://intake-accumulo.readthedocs.io/en/latest/?badge=latest)This plugin is for the [Intake data loader library](http://intake.readthedocs.io/en/latest/).### Installation` conda install-cintakeintake-accumulo`### CaveatsThis plugin has only been verified with Accumulo 1.5.2 and Thrift 0.11. Support
for other configurations is unverified at this time.
Package: intake-avro
# Intake-avro[![Build Status](https://travis-ci.org/ContinuumIO/intake-avro.svg?branch=master)](https://travis-ci.org/ContinuumIO/intake-avro)
[![Documentation Status](https://readthedocs.org/projects/intake-avro/badge/?version=latest)](http://intake-avro.readthedocs.io/en/latest/?badge=latest)The [Apache Avro](https://avro.apache.org/) format is a popular method for information
exchange, particularly for message and reord distribution in high-throughput systems.In [Intake](https://github.com/ContinuumIO/intake/), there are two plugins provided for reading avro data:avro_tableis appropriate for data which have a schema simple enough to be
represented as columns with simple types, i.e.,flatschema. The output of
this plugin are dataframes, and the reading is optimized for speed.avro_sequenceis more generic and can handle all possible avro schema, but
it produces generic python sequences of dictionaries, and is consequently
much slower than the table reader.### InstallationThe conda install instructions are:` conda install-cconda-forgefastavro uavro conda install-cintake intake_avro `### ExamplesSee the notebook in the examples/ directory.
Package: intake-civis
intake-civisThis is anintakedata source for data warehoused in theCivisplatform.Requirementscivis-python
intakeInstallationintake-civisis published on PyPI.
You can install it by running the following in your terminal:pipinstallintake-civisUsageYou can specify Civis schemas and tables using a YAML intake catalog:sources:# An entry representing a catalog for an entire schema.postgres:driver:"civis_schema"args:database:"CityofLosAngeles-Postgres"schema:"transporatation"# An entry representing a single tablebike_trips:driver:"civis"args:database:"CityofLosAngeles-Postgres"table:"bike_trips"schema:"transportation"As a convenience, there is also a top-level function which creates a catalog from the
entire Redshift or PostgreSQL databases.You can create it withimportintake_civisredshift_cat=intake_civis.open_redshift_catalog()postgres_cat=intake_civis.open_postgres_catalog()You can then use these catalogs to drill down to different schemas and tables, e.g.:bike_trips=postgres_cat.transportation.bike_trips.read()For more examples, see thisdemo notebook.Geospatial supportBoth Redshift and Postgres support geospatial values.
We can tell the source to read in a table/query as a GeoDataFrame
by passing in a string or list of strings in thegeometryargument.
You can also pass in a GeoPandas-compatiblecrsargument to set the
coordinate reference system for the GeoDataFrame.
When more than one column is provided, the primary
geometry column for the GeoDataFrame is assumed to be the first in the list.TheCivisSchemaobject attempts to automatically determine the geometry columns
and coordinate reference systems from the database table metadata.Ibis supportSometimes a table might be too large to load the entire thing into memory.
In that case, it is useful to query a subset of the table.
Ibis is a tool that has a pandas-like API for generating SQL queries.
Civis table catalog entries have ato_ibis()function which provides a lazy ibis expression.
This can then be used to query a smaller amount of data:# Get a lazy ibis objectbike_trips=postgres_cat.transportation.bike_trips.to_ibis()# Subset itbike_trips_subset=bike_trips[bike_trips.start_datetime>"2019-04-01"]# Execute the query to get an in-memory dataframe:df=bike_trips_subset.execute()Important limitation:Due to network restrictions on the Civis databases,
you can only use this feature while in the platform. It will be unable to establish
a connection from your local machine.
Package: intake-coops
intake-coopsIntake interface to NOAA CO-OPS dataUses thenoaa_coopspackage to read in NOAA CO-OPS data.Currently limited to currents only with limited selections. Returns anxarrayDataset, but there areintakeSources for bothDataFrameandxarray.Project based on thecookiecutter science project template.InstallationClone from github (HTTPS or SSH)>>> git clone https://github.com/axiom-data-science/intake-coops.gitInstall environment file>>> conda env create -f environment.ymlActivate new environment>>> conda activate intake-coopsInstall package locally in package directory>>> pip install -e .Example UsageIf you input tointake.open_coops_cat()the keyword argumentprocess_adcp=True, the ADCP Dataset will contain velocity on u and v components, along- and across-channel components, and along- and across-channel subtidal signal (processed with pl33 tidal filter, also included).import intake

stations = ["COI0302", "COI0512"]
cat = intake.open_coops_cat(stations)

# sources in catalog
print(list(cat))

# look at a source
print(cat["COI0302"])

# read in data to a Dataset
ds = cat["COI0302"].read()DevelopmentTo also develop this package, install additional packages with:$condainstall--filerequirements-dev.txtTo then check code before committing and pushing it to github, locally run$pre-commitrun--all-files
Package: intake-dal
Welcome to Intake DAL (data access layer) pluginThisIntakeplugin helps
abstract a dataset over disparate storage systems (eg: bulk, streaming, serving, …).
It also provides an easy way to specialize ahierarchical catalogto a default DAL storage system.Sample Catalog source entry:user_events:driver:dalargs:default:'local'storage:local:'csv://{{CATALOG_DIR}}/data/user_events.csv'serving:'in-memory-kv://foo'batch:'parquet://{{CATALOG_DIR}}/data/user_events.parquet'Example code using sample catalog:# Specialize the catalog dal default storge mode datasources# to be "serving".cat=DalCatalog(path,storage_mode="serving")# reads from the serving storage system# using the in-memory-kv Intake plugindf=cat.user_events.read()
Package: intake-dataframe-catalog
A simple intake plugin for a searchable table of intake sources and associated metadata.DocumentationPackageCI/CDDevelopmentLicenseOverviewintake-dataframe-catalog is a simple intake plugin for a searchable table of intake sources.
The table is represented in memory as a pandas DataFrame and can be serialized and shared as
a CSV file. Each row in the dataframe catalog corresponds to an intake source and the columns
contain metadata associated with each source that a user may want to peruse and/or search.
The original use-case for intake-dataframe-catalog was to provide a user-friendly catalog of
a large numberintake-esmdatastores.
intake-dataframe-catalog enables users to peruse and search on core metadata from each
intake-esm datastore to find the datastores that are most relevant to their work (e.g.
“which datastores contain model X and variable Y?”). Once a users has found the datastores(s)
that interest them, they can load those datastores and access the data they reference.Why?Intake already provides the ability tonest sources in a catalogand search across them. However, data discoverability is limited in the case of very large numbers
of nested sources, and the search functionality does not readily provide the ability to execute
complex searches on nested source metadata. intake-dataframe-catalog aims to provide a very
simple catalog of intake sources that emphasises source search and discoverability.
Package: intake-dremio
Intake-DremioDremio Plugin for Intake based on pyarrow FlightUser InstallationTo install the intake-dremio plugin, execute the following commandconda install -c conda-forge intake-dremioor:pip install intake-dremioExampleAn Intake catalog referencing a Dremio dataset consists of theuripointing to the Dremio instance along with a username and password and a SQL expression (sql_expr), e.g.:sources:dremio_vds:driver:dremioargs:uri:grpc+tcp://{{ env(DREMIO_USER) }}:{{ env(DREMIO_PASSWORD) }}@x.x.x.x:32010sql_expr:SELECT * FROM TABLE ORDER BY "timestamp" ASC
Package: intake-duckdb
Intake-DuckDBDuckDB Plugin for IntakeInstallationFrom PyPIpipinstallintake-duckdbOr conda-forgecondainstall-cconda-forgeintake-duckdbUsageLoad an entire table into a dataframesource=intake.open_duckdb("path/to/dbfile","tablename")df=source.read()Or a custom SQL invalid DuckDB query syntaxsource=intake.open_duckdb("path/to/dbfile","SELECT col1, col2 FROM tablename")df=source.read()Can also iterate over table chunkssource_chunked=intake.open_duckdb("path/to/dbfile","tablename",chunks=10)source_chunked.discover()forchunkinsource_chunked.read_chunked():# do something...DuckDB catalog: create an Intake catalog from a DuckDB backendcat=intake.open_duckdb_cat("path/to/dbfile")# list the sources in 'cat'list(cat)df=cat["tablename"].read()df_chunks=[chunkforchunkincat["tablename"](chunks=10).read_chunked()]Run DuckDB queries on other Intake sources (that produce pandas DataFrames) within the same catalog# cat.yamlsources:csv_source:args:urlpath:https://data.csvdescription:Remote CSV sourcedriver:csvduck_source:args:targets:-csv_sourcesql_expr:SELECT col FROM csv_source LIMIT 10description:Source referencing other sources in catalogdriver:duckdb_transformcat=intake.open_catalog("cat.yaml")duck_source=cat.duck_source.read()
Package: intake-erddap
forked fromhttps://github.com/jmunroe/intake-erddap.Intake-ERDDAPCopyright 2022 Axiom Data ScienceSee LICENSECopyright 2022 James MunroeFor changes prior to 2022-10-19, all contributions are Copyright James Munroe, see PREV-LICENSE.Check out our Read The Docs page for additional documentationIntake is a lightweight set of tools for loading and sharing data in data
science projects. Intake ERDDAP provides a set of integrations for ERDDAP.Quickly identify all datasets from an ERDDAP service in a geographic region,
or containing certain variables.Produce a pandas DataFrame for a given dataset or query.Get an xarray Dataset for the Gridded datasets.The Key features are:Pandas DataFrames for any TableDAP dataset.xarray Datasets for any GridDAP datasets.Query by any or all:bounding boxtimeCFstandard_namevariable namePlaintext Search termSave catalogs locally for future use.User InstallationIn the very near future, we will be offering the project on conda. Currently the
project is available on PyPI, so it can be installed usingpippip install intake-erddapDeveloper InstallationPrerequisitesThe following are prerequisites for a developer environment for this project:conda(optional but highly recommended)mambaHint:conda install -c conda-forge mambaNote: ifmambaisn't installed, replace all instances ofmambain the following instructions withconda.Create the project environment with:mamba env update -f environment.ymlInstall the development environment dependencies:mamba env update -f dev-environment.ymlActivate the new virtual environment:conda activate intake-erddapInstall the project to the virtual environment:pip install -e .ExamplesTo create an intake catalog for all of the ERDDAP's TableDAP offerings use:importintakecatalog=intake.open_erddap_cat(server="https://erddap.sensors.ioos.us/erddap")The catalog objects behave like a dictionary with the keys representing the
dataset's unique identifier within ERDDAP, and the values being theTableDAPSourceobjects. To access a source object:source=catalog["datasetid"]From the source object, a pandas DataFrame can be retrieved:df=source.read()Consider a case where you need to find all wind data near Florida:importintakefromdatetimeimportdatetimebbox=(-87.84,24.05,-77.11,31.27)catalog=intake.open_erddap_cat(server="https://erddap.sensors.ioos.us/erddap",bbox=bbox,start_time=datetime(2022,1,1),end_time=datetime(2023,1,1),standard_names=["wind_speed","wind_from_direction"],)df=next(catalog.values()).read()time (UTC)wind_speed (m.s-1)wind_from_direction (degrees)02022-12-14T19:40:00Z7.0140.012022-12-14T19:20:00Z7.0120.022022-12-14T19:10:00ZNaNNaN32022-12-14T19:00:00Z9.0130.042022-12-14T18:50:00Z9.0130.0............482962022-01-01T00:40:00Z4.0120.0482972022-01-01T00:30:00Z3.0130.0482982022-01-01T00:20:00Z4.0120.0482992022-01-01T00:10:00Z4.0130.0483002022-01-01T00:00:00Z4.0130.0