Saravanan666/pypi-org huggingface dataset:
Package: pyope
This is an implementation of Boldyreva symmetricorder-preserving encryptionscheme (Boldyreva’s paper).Supported Python versions: 2.7 and 3.4+Disclaimer 1This is an experimental implementation, which should be thoroughly reviewed and evaluated before using in production and/or sensitive applications.Disclaimer 2The Boldyreva scheme is not a standardized algorithm, so there are no test vectors and fixed plaintext-ciphertext
mapping for a given key. It means that, generally speaking, a plaintext encrypted with the same key by two different versions of the package might not be equal to each other.Quick examplesQuick startfrompyope.opeimportOPErandom_key=OPE.generate_key()cipher=OPE(random_key)assertcipher.encrypt(1000)<cipher.encrypt(2000)<cipher.encrypt(3000)assertcipher.decrypt(cipher.encrypt(1337))==1337You can specify input and output ranges. Otherwise, default input (0..2^15-1) and output (0..2^31-1) ranges are used.frompyope.opeimportOPE,ValueRangecipher=OPE(b'long key'*2,in_range=ValueRange(-100,100),out_range=ValueRange(0,9999))assert0<cipher.encrypt(10)<cipher.encrypt(42)<9999About order-preserving encryptionOrder-preserving encryption (OPE) allows to compare ciphertext values in order to learn the corresponding relation
between the underlying plaintexts. By definition, order-preserving encryption methods areless securethan
conventional encryption algorithms for the same data sizes, because the former leak ordering information of the plaintext
values.How can OPE be useful? For example, some systems may need OPE to perform a certain set of queries (such as range SQL
queries) over encrypted data. These systems includeCryptDBandMonomito name a few.SecurityAs mentioned above, security guarantees for Boldyreva’s schema are weaker than those of deterministic encryption schemes,
but security can still be improved if the encryption keys are long enough. It is advised to use randomly generated keys at
least 256 bits long.Running testsPyTest is used as a test framework. Run all tests:$ py.test tests/TODOMore testsOptimize performanceSecurity guarantees?History0.1.0 (2017-01-01)Use ‘cryptography’ instead of ‘pycrypto’
Package: pyopen
pyopen is a command-line tool for quickly inspecting files using python. It drops you into an interactive IPython session with the parsed files available as local variables.Currently supports csv/tsv files (uses pandas and also supports gzip / bz2 compression), excel, json, yaml, hdf5, and pickle files.Installationpip install pyopenTo run the tests:nosetestsExample$ pyopen test/data/data1.csv

f1 test/data/data1.csv 4.9 kB pandas_csv

Python 2.7.10 (default, May 26 2015, 13:01:57)
Type "copyright", "credits" or "license" for more information.

IPython 4.0.0 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.
1 files loaded:

f1 test/data/data1.csv 4.9 kB pandas_csv
    pandas.DataFrame: 99 rows x 8 cols.
    Columns: patient genome contig interbase_start interbase_end ref alt validation

Variables defined:
    f1 : the parsed file
    loaded : object with attributes for each parsed file (abbreviated filenames, tab completes)
    loaded_filenames : dict from filename to parsed file
    loaded_absolute_filenames : dict from absolute filename to parsed file
    loaded_list : list of the parsed files

To print this message again later, type 'info()'. To quit type 'quit()'.The variable ‘f1’ is a pandas DataFrame with the contents of the csv file:In [1]: f1.head()
Out[1]:
  patient  genome contig  interbase_start  interbase_end ref alt validation
0     PT1  GRCh37   chr1         10719783       10719784   T   G   untested
1     PT1  GRCh37   chr1         11137693       11137694   G   A   verified
2     PT1  GRCh37   chr1         11826130       11826131   A   T   verified
3     PT1  GRCh37   chr1         17664605       17664606   C   G   verified
4     PT1  GRCh37   chr1         26670492       26670493   G   T   verifiedYou can also pass URLs to pyopen:$ pyopen https://github.com/timodonnell/pyopen/raw/master/test/data/nasa_19950801.tsv.bz2
...
In [1]: f1.mean()

Out[1]:
time         8.072857e+08
response     2.107552e+02
bytes        1.556397e+04
referer               NaN
useragent             NaN
dtype: float64
Out[1]: (30969, 9)
Package: py_openai_assistant
OpenAI Assistant API Wrapper: Enhance Your Workflow with EaseWelcome to this GitHub repository, where we offer an efficient and user-friendly wrapper for OpenAI's Assistant API.Our journey began when we encountered numerous production challenges that extended beyond the current capabilities of the Assistant API. The most pressing issue we faced was file management. This wrapper simplifies complex tasks such as uploading files, verifying uploads, deciding when to re-upload, and deleting files, among others. It's designed to streamline your workflow and handle file interactions seamlessly.Another key feature of this wrapper is the streamlined management of Assistants. We understand the need for a hassle-free process to create, update, delete, and list Assistants. This tool was born out of a necessity to organize and maintain a clear record of Assistants, eliminating the clutter of unused and duplicate entries.Our goal is to enhance your experience with the OpenAI Assistant API, making it more intuitive and less time-consuming. Dive into our repository and discover how you can elevate your productivity today!
Package: pyopenair
A simple package to convert geo data fromWKTtoOpenAir format.This project has been developped by LPO Auvergne-Rhône-Alpes to addOpenAirexport feature inGeoTrek-adminto sensitivity module for aerial areas.Documentationhttps://pyopenair.readthedocs.io/en/latest/Installationpip install pyopenairLicenceGNU GPLv3Team@lpofredc(LPO Auvergne-Rhône-Alpes), main developper@BPascal-91for advices, tests and recommendations
Package: pyOpenAnnotate
Automated Annotation ToolAutomate your image annotation pipeline usingpyOpenAnnotate. It is built harnessing the power of OpenCV. Perfect for annotating single class datasets. Check out accompanying blog post to understand howpyOpenAnnotatehas been designed.Automated Image Annotation Tool Using OpenCV.Example Use CasesHow To Use pyOpenAnnotate?Annotating images using pyOpenAnnotate is pretty simple. Use the commandannotatefollowed by the following flags as per the requirement.1. Annotate Imagesannotate --img <images_directory_path>2. Annotate Videoannotate --vid <path_to_video_file>3. Global Flags-T : View mask window.
--resume <existing-annotations-dir>: Continue from where you left off.
--skip <int(Frames)> : Frames to skip while processing a video file.4. Mouse ControlsClick and Drag: Draw bounding boxes.
Double Click: Remove existing annotation.Display AnnotationsVisualize your annotations using theshowlblscommand.showlbls --img <single_image_or_a_directory> --ann <single_annotation_text_file_or_a_directory>Keyboard NavigationN or D : Save and go to next image
B or A : Save and go back
C : Toggle clear screen (during annotation)
T : Toggle mask window (during annotation)
Q : Quit
Package: pyOpenAnnotate-xlabd
Automated Annotation ToolAutomate your image annotation pipeline usingpyOpenAnnotate. It is built harnessing the power of OpenCV. Perfect for annotating single class datasets. Check out accompanying blog post to understand howpyOpenAnnotatehas been designed.Automated Image Annotation Tool Using OpenCV.Example Use CasesHow To Use pyOpenAnnotate?0. Installationpip install pyOpenAnnotateAnnotating images using pyOpenAnnotate is pretty simple. Use the commandannotatefollowed by the following flags as per the requirement.1. Annotate Imagesannotate --img <images_directory_path>2. Annotate Videoannotate --vid <path_to_video_file>3. Global Flags-T : View mask window.
--resume <existing-annotations-dir>: Continue from where you left off.
--skip <int(Frames)> : Frames to skip while processing a video file.4. Mouse ControlsClick and Drag: Draw bounding boxes.
Double Click: Remove existing annotation.Display AnnotationsVisualize your annotations using theshowlblscommand.showlbls --img <single_image_or_a_directory> --ann <single_annotation_text_file_or_a_directory>Keyboard NavigationN or D : Save and go to next image
B or A : Save and go back
C : Toggle clear screen (during annotation)
T : Toggle mask window (during annotation)
Q : Quit
Package: py-openapi-apollo-client
py_openapi_apollo_clientpython-apollo客户端封装支持的python版本python3.x功能点获取apollo中指定namespaceName下的配置数据修改apollo中指定namespaceName下的配置数据说明支持鉴权token的传入安装pipinstallpy-openapi-apollo-client二次开发继承PrivateApolloClient类后，增加自己的方法即可官方接口文档Apollo开放平台使用frompy_openapi_apollo_client.apollo_clientimportPrivateApolloClientclient=PrivateApolloClient(app_id='test',portal_address='http://test.com',authorization='xxx')print(client.get_namespace_items_key(key='test.switch'))
Package: pyopencc
What is pyOpenCC?pyOpenCC is a Python wrapper forOpen Chinese ConverterInstallationYou need to install opencc-dev first, To install OpenCC:Debianapt-get install libopencc-dev -yFreeBSDcd /usr/ports/chinese/opencc
make install cleanHow to use it?Following is a simple example# -*- coding: utf8 -*-
import opencc
cc = opencc.OpenCC('zht2zhs.ini')
print cc.convert(u'Open Chinese Convert（OpenCC）「開放中文轉換」，是一個致力於中文簡繁轉換的項目，提供高質量詞庫和函數庫(libopencc)。')And the output should beOpen Chinese Convert（OpenCC）「开放中文转换」，是一个致力于中文简繁转换的项目，提供高质量词库和函数库(libopencc)。There are four convertion in opencc.zht2zhs.ini - Traditional Chinese to Simplified Chinesezhs2zht.ini - Simplified Chinese to Traditional Chinesemix2zht.ini - Mixed to Traditional Chinesemix2zhs.ini - Mixed to Simplified Chinese
Package: pyopencdms
OpenCDMS Python library:pyopencdmsOverviewA Climate Data Management System (CDMS) is an integrated computer-based system that facilitates the effective archival, management, analysis, delivery and utilization of a wide range of integrated climate data (WMO 2014).pyopencdmsaims to build a common Python API supporting multiple Climate Data Management Systems (CDMS) that use different underlying database engines.The image below shows theCliDE,Climsoft,MCH,MIDASand other CDMSs being accessed through a single common API.In addition we will add support for theWIGOS Meta Data Representation (WMDR)and collaborate with experts to create a new "CDMS Data Model Representation" that will support addional capabilities that are beyond the scope of WMDR.Theopencdms-test-datarepository will be used as a source of test data for development to ensure interoperability between systems works as intended.Dependenciespyopencdmsofficially supports Python 3.7.1 and above, 3.8, and 3.9 (in line with thePandas package)pyopencdmsuses SQLAlchemy (2.0-style) to connect to multiple database technologies including PostgreSQL, MySQL/MariaDB, Oracle and SQLite.It is expected that SQLAlchemy objects, Panda'sDataFramesand JSON will be key data types for exchanging data.CHANGELOG0.1.0 (YY-MM-DD)First release on PyPI.
Package: pyopencell
:heart: Inspired byPyOTRS:heart:PyOpenCell is a Python wrapper for accessingOpen Cell(Version 6) using the
REST API.You can see all the API informationhereFeaturesAccess an OpenCell instance to:find a Customer by IDInstallationDependenciesCreate a virtualenv before installing dependencies.If you are usingvirtualenvwrapper(https://virtualenvwrapper.readthedocs.io/en/latest/).Create virtualenv with python 3.7.7$ which python
/usr/bin/python3.7
$ mkvirtualenv --python=/usr/bin/python pyopencellIf you are usingpyenvwithpyenv-virtualenvplugin:$ pyenv virtualenv pyopencellInstall requirements:$ pip install -r requirements.txtConfiguration EnvironmentYou need define the OpcenCell API credentials as environment variables. You need define:OPENCELL_BASEURL=<YOUR OC HOST>/opencell
OPENCELL_USER=<YOUR OC USER>
OPENCELL_PASSWORD=<YOUR OC PASSWORD>If this envvars are not defined, a exception will be raised with the name of the envvar not defined.Python UsageRun test suite$ toxRelease processUpdate CHANGELOG.md following this steps:Add any entries missing from merged merge requests.Duplicate the[Unreleased]header.Replace the secondUnreleasedwith a version number followed by the current date. Copy the exact format from previous releases.Then, you can release and publish the package to PyPi:Update theVERSIONvar insetup.pymatching the version you specified in the CHANGELOG.Open a merge request with these changes for the team to approveMerge it, add a git tag on that merge commit and push it.Once the pipeline has successfully passed, go approve thepublishstep.License
Package: pyopencl
PyOpenCL lets you access GPUs and other massively parallel compute
devices from Python. It tries to offer computing goodness in the
spirit of its sister projectPyCUDA:Object cleanup tied to lifetime of objects. This idiom, often
calledRAIIin C++, makes it much easier to write correct, leak- and
crash-free code.Completeness. PyOpenCL puts the full power of OpenCL’s API at
your disposal, if you wish.  Every obscureget_info()query and
all CL calls are accessible.Automatic Error Checking. All CL errors are automatically
translated into Python exceptions.Speed. PyOpenCL’s base layer is written in C++, so all the niceties
above are virtually free.Helpful and completeDocumentationas well as aWiki.Liberal license. PyOpenCL is open-source under theMIT licenseand free for commercial, academic, and private use.Broad support. PyOpenCL was tested and works with Apple’s, AMD’s, and Nvidia’s
CL implementations.Simple 4-stepinstall instructionsusing Conda on Linux and macOS (that also install a working OpenCL implementation!)
can be found in thedocumentation.What you’ll need if you donotwant to use the convenient instructions above and
instead build from source:gcc/g++ new enough to be compatible with pybind11
(see theirFAQ)numpy, andan OpenCL implementation. (See thishowtofor how to get one.)LinksDocumentation(read how things work)Conda Forge(download binary packages for Linux, macOS, Windows)Python package index(download releases)C. Gohlke’s Windows binaries(download Windows binaries)Github(get latest source code, file bugs)
Package: pyopencl-extension
This package extends PyOpenCl by providing an object-oriented kernel programming framework and debugging capabilities.What makes pyopencl-extension special?Build on top ofPyOpenClwhich can increase performance significantly.Usage of this framework forces consistent code when programming for GPU.Allows debugging of OpenCl-Programs through kernel emulation in Python using a visual debugger (tested with Pycharm).OpenCl emulation allows to find out-of-bounds array indexing more easily.Integrated profiling features give quick overview over performance bottlenecks.The project is in an early development stage and actively maintained.
For any feature requests/feedback/etc. you can get in touch viaGithubor by E-Mail (philipp.mohr@tuhh.de).InstallationInstall this library withpip install pyopencl-extension.When automatic installation of PyOpenCl fails (happens when using Windows):Download appropriate .whl binary fromPyOpenCl binaries for WindowsMake sure to haveproperOpenCl driver for your graphics card installed.Runpip install "pyopencl-X-cpX-cpX-X.whl"in terminal, where X must be replaced with corresponding version.UsageOne very simple example is given below.frompyopencl_extensionimportKernel,Global,Scalar,Types,zerosimportnumpyasnpary=zeros((10,),Types.short)knl=Kernel('some_operation',{'ary':Global(ary),# notice that ary is set as default argument'number':Scalar(Types.short)},"""
                ary[get_global_id(0)] = get_global_id(0) + number;
             """,global_size=ary.shape).compile(emulate=True)knl(number=3)assertnp.allclose(ary.get(),np.arange(10)+3)By setting the argument ‘emulate=True’ the kernel will be compiled in emulation mode. This mode creates a
file ‘some_operation.py’, which can be inspected using a visual debugger:More advanced examples can by found in thetests hosted on  Github.
Package: pyopencv
PyOpenCV brings Willow Garage’s Open Source Computer Vision Library
(OpenCV) verion 2.x to Python. The package takes a completely new and
different approach in wrapping OpenCV from traditional swig-based and
ctypes-based approaches. It is intended to be a successor of
ctypes-opencv and to provide Python bindings for OpenCV 2.x.
Ctypes-based approaches like ctypes-opencv, while being very flexible at
wrapping functions and structures, are weak at wrapping OpenCV’s C++
interface. On the other hand, swig-based approaches flatten C++ classes
and create countless memory management issues. In PyOpenCV, we use
Boost.Python, a C++ library which enables seamless interoperability
between C++ and Python. PyOpenCV will offer a better solution than both
ctypes-based and swig-based wrappers. Its main features include:A Python interface similar to the new C++ interface of OpenCV 2.x,
including features that are available in the existing C interface
but not yet in the C++ interface.Access to C++ data structures in Python.Elimination of memory management issues. The user never has to
worry about memory management.Ability to convert between OpenCV’s Mat and arrays used in
wxWidgets, PyGTK, and PIL.OpenCV extensions: classes DifferentialImage, IntegralImage, and
IntegralHistogram.To the best of our knowledge, PyOpenCV is the largest wrapper among
existing Python wrappers for OpenCV. It exposes to Python 200+ classes
and 500+ free functions of OpenCV 2.x, including those instantiated from
templates.In addition, we use NumPy to provide fast indexing and slicing
functionality to OpenCV’s dense data types like Vec-like, Point-like,
Rect-like, Size-like, Scalar, Mat, and MatND, and to offer the user an
option to work with their multi-dimensional arrays in NumPy. It is
well-known that NumPy is one of the best packages (if not the best) for
dealing with multi-dimensional arrays in Python. OpenCV 2.x provides a
new C++ generic programming approach for matrix manipulation (i.e.
MatExpr). It is a good attempt in C++. However, in Python, a package
like NumPy is without a doubt a better solution. By incorporating NumPy
into PyOpenCV to replace OpenCV 2.x’s MatExpr approach, we seek to bring
OpenCV and NumPy closer together, and offer a package that inherits the
best of both world: fast computer vision functionality (OpenCV) and fast
multi-dimensional array computation (NumPy).
Package: pyopendart
pyOPENDART - OPEN DART Python API (for Humans)전자공시시스템 API 를 편리하게 사용하기 위해 저수준 HTTP API부터 데이터프레임을 리턴하는 고수준 API 등 각종 편리한 API와 유틸리티들을 제공합니다.Disclaimer본 소프트웨어는 금융감독원의 전자공시시스템 OPEN API 를 추가적으로 가공하고 부가기능을 제공하는 소프트웨어로써 MIT 라이선스에 따라 저자 또는 저작권자는 소프트웨어와 소프트웨어와 연관되어 발생하는 문제에 대해 책임을 지지 않습니다.OPEN DART API에 관한 정보는 opendart.fss.or.kr 를 참조하시기 바랍니다.InstallationpipinstallpyopendartWhat is DART?전자공시시스템(DART ; Data Analysis, Retrieval and Transfer System)은 상장법인 등이 공시서류를 인터넷으로 제출 하고, 투자자 등 이용자는 제출 즉시 인터넷을 통해 조회할 수 있도록 하는 종합적 기업공시 시스템입니다.bydart.fss.or.kr - DART소개What is OPEN DART?DART에 공시되고있는 공시보고서 원문 등을 오픈API를 통해 활용할 수 있습니다. 활용을 원하시는 누구든지(개인, 기업, 기관 등) 이용하실 수 있습니다.byopendart.fss.or.kr - 오픈API 소개FeaturesOPEN API 데이터프레임, 딕셔너리 클라이언트dart의 축약된 필드명을 자세한 한글, 영어 필드명으로 변환날짜, 숫자등에 대해 데이터 타입 변환공시원문, 재무재표 등 원본파일 다운로드 클라이언트로우레벨 OPEN API HTTP 클라이언트커넥션 풀, 타임아웃등 네트워크 옵션 조정 기능 제공xml, json, zip 리소스 접근 메서드 제공편리하고 타입 정의된 클라이언트 인터페이스들요청 인자 중 공시유형등의 필드에 대한 Enum 제공예외 클래스 제공개발가이드에 나와있는 출력설명란의 출력과 설명의 매핑 제공 (비고 등)Usagehttps://pyopendart.seonghyeon.dev/에서 자세한 문서를 확인할 수 있습니다.LicenseThis project is licensed under the terms of the MIT license.
Package: pyopendata
Overviewpyopendatais a Python utility to offer an unified API to read world various data sources,
and outputpandas.DataFrame. Which covers:CKAN websites (data.gov,data.go.jp, etc)EurostatOECDWorldBankDocumentationhttp://pyopendata.readthedocs.org/InstallationpipinstallpyopendataBasic UsageThis section explains how to retrieve data from website which uses CKAN API.You can createDataStoreinstance to access CKAN website by passing CKAN URL toDataStoreclass.In this example, we’re going to retrieve the ‘California Unemployment Statistics’ data from data.gov. The target URL is:https://catalog.data.gov/dataset/california-unemployment-statistics/resource/ffd05307-4528-4d15-a370-c16222119227We can read above URL as:CKAN API URL:https://catalog.data.gov/Package ID: california-unemployment-statisticsResource ID: ffd05307-4528-4d15-a370-c16222119227>>>importpyopendataaspyod>>>store=pyod.DataStore('http://catalog.data.gov/')>>>storeCKANStore(http://catalog.data.gov)DataStore.serchperforms search by keyword. Results will be the list of packages. You can select a target package by slicing.>>>packages=store.search('Unemployment Statistics')>>>packages[annual-survey-of-school-system-finances(1resource),current-population-survey(1resource),federal-aid-to-states(1resource),current-population-survey-labor-force-statistics(2resources),dataferrett(1resource),mass-layoff-statistics(1resource),unemployment-rate(3resources),consolidated-federal-funds-report(1resource),annual-survey-of-state-and-local-government-finances(1resource),local-area-unemployment-statistics(2resources)]>>>packages[0]annual-survey-of-school-system-finances(1resource)Otherwise, specify the package name to be retrieved.>>>package=store.get('california-unemployment-statistics')>>>packagecalifornia-unemployment-statistics(4resources)A package has resources (files) which contains actual data. You usegetmethod to retrieve the resource.>>>resource=package.get('ffd05307-4528-4d15-a370-c16222119227')>>>resourceResourceID:ffd05307-4528-4d15-a370-c16222119227ResourceName:CommaSeparatedValuesFileResourceURL:https://data.lacity.org/api/views/5zrb-xqhf/rows.csv?accessType=DOWNLOADFormat:CSV,Size:NoneOnce you get the resource, usereadmethod to read data as pandasDataFrame.ImportantThe target file must be the correct format which can be parsed bypandasIO functions.>>>df=resource.read()>>>df.head()YearPeriodAreaUnemploymentRateLaborForce\02013JanCalifornia10.4%1855650012013JanLosAngelesCounty10.9%489150022013JanLosAngelesCity12%191560032013FebCalifornia9.699999999999999%1864830042013FebLosAngelesCounty10.3%4924000EmploymentUnemploymentAdjustedPreliminary0166319001924600NotAdjNotPrelim14357800533800NotAdjNotPrelim21684800230800NotAdjNotPrelim3168359001812400NotAdjNotPrelim44418000506000NotAdjNotPrelimOr you can get raw data by specifyingraw=True.>>>raw=resource.read(raw=True)>>>raw[:100]'Year,Period,Area,Unemployment Rate,Labor Force,Employment,Unemployment,Adjusted,Preliminary\n2013,Jan'
Package: pyopendds
PyOpenDDSPyOpenDDS is a framework for usingOpenDDSfrom Python. It has the
goal of providing the standard full DDS API in OpenDDS in a Pythonic form.This project is still a work in progress though. It currently only supports
what is necessary fortests/basic_testand little else. See the GitHub issues
for the current status of the limitations.RequirementsTested on Linux, macOS, and WindowsCPython >= 3.7This uses the C API of CPython, so other Python implementations like PyPy
are not offically supported.If building Python from source, make sure to run the configure script with--enable-shared. PyOpenDDS doesn't currently support building with the
statically-linkable Python library.OpenDDS >= 3.16CMake >= 3.12A compiler that supports C++14 or later.Building PyOpenDDS and Running the Basic TestOnce$DDS_ROOT/setenv.shhas been sourced or the equivalent, run the commands
below in this directory.# Build and Install PyOpenDDSpipinstall.# Build Basic Testcdtests/basic_test
mkdirbuildcdbuild
cmake..
make# Build and Install Basic Test Python Type Supportitl2py-obasic_outputbasic_idlopendds_generated/basic.itl# If using OpenDDS 3.19 or before, then just specify basic.itlcdbasic_output
pipinstall.# Run Basic Testcd../..
bashrun_test.sh