Saravanan666/pypi-org huggingface dataset:
Package: odk-mailer
ODK MailerA simple CLI to send Mails for ODK.
Built with Typer and packaged with Poetry.RequirementsPython 3PIPcronSetupInstall the cli via pippipinstallodk-mailerConfiguration exampleConfig location:~/.env:SMTP_HOST=smtp.freesmtpservers.com
    SMTP_PORT=25
    SMTP_USER=
    SMTP_PASS=
    CRON_FREQUENCY=15
    NOTIFY_TO=foo@bar.com,faz@baz.com
    NOTIFY_FROM=odk-mailer@freesmtpservers.com
    ODK_HOST=https://your.odk-central.host.tldUsageCreate and run a new mail job# Create a new mail job with a unique IDodk-mailerjobscreate# Run the job by IDodk-mailerjobsrun<job-id># Remove a jobodk-mailerjobsdelete<job-id>List mail jobs and evaluate them# Show all jobsodk-mailerjobslist# Run all jobs that are ready to be sentodk-mailerjobsevaluateInitiate crontab to periodically evaluate mailjobs and run them directly afterodk-mailercroninitDevelopmentBuilding a Package - Guide:https://typer.tiangolo.com/tutorial/package/#create-your-appRequirementsPython 3.10Poetry 1.7# clone the repo and cd intopoetryshellpoetryinstall# Run it withpoetryrunodk-mailerPush to PyPipoetry build
    poetry publish
    # requires credentials to be setup: poetry config pypi-token.pypi <my-token>
Package: odl
Operator Discretization Library (ODL) is a Python library for fast prototyping focusing on (but not restricted to) inverse problems. ODL is being developed atKTH Royal Institute of Technology, Stockholm, andCentrum Wiskunde & Informatica (CWI), Amsterdam.The main intent of ODL is to enable mathematicians and applied scientists to use different numerical methods on real-world problems without having to implement all necessary parts from the bottom up.
This is reached by anOperatorstructure which encapsulates all application-specific parts, and a high-level formulation of solvers which usually expect an operator, data and additional parameters.
The main advantages of this approach is thatDifferent problems can be solved with the same method (e.g. TV regularization) by simply switching operator and data.The same problem can be solved with different methods by simply calling into different solvers.Solvers and application-specific code need to be written only once, in one place, and can be tested individually.Adding new applications or solution methods becomes a much easier task.FeaturesEfficient and well-tested data containers based on Numpy (default) or CUDA (optional)Objects to represent mathematical notions like vector spaces and operators, including properties as expected from mathematics (inner product, norm, operator composition, …)Convenience functionality for operators like arithmetic, composition, operator matrices etc., which satisfy the known mathematical rules.Out-of-the-box support for frequently used operators like scaling, partial derivative, gradient, Fourier transform etc.A versatile and pluggable library of optimization routines for smooth and non-smooth problems, such as CGLS, BFGS, Chambolle-Pock and Douglas-Rachford splitting.Support for tomographic imaging with a unified geometry representation and bindings to external libraries for efficient computation of projections and back-projections.Standardized tests to validate implementations against expected behavior of the corresponding mathematical object, e.g. if a user-defined norm satisfiesnorm(x + y) <= norm(x) + norm(y)for a number of input vectorsxandy.
Package: odls.client
TheODLSclient scans local filesystems, looks for changes, and reports
them to anODLSserver. The states of files/directories scanned are
stored in a local SQLite database.Theodls.clientpackage mainly provides a scriptindexerthat
will be available in somebin/directory after install (the
concrete location will depend on the type of install).Theindexerscript is configured by commandline options (tryindexer--help) and configuration files. All this is explained in
more detail in the package documentation (see below).This Python implementation of theODLSclient is the successor of the
C++ implementation used in the years before.Whileodls.clientitself is pure Python, some extensions (notably
the SQLite support) require C extensions.WarningThis package is in a really early state!!! Some options
do simply not work yet and Windows support is yet
completely untested.DocumentationThe full package documentation can be found at:http://packages.python.org/odls.clientPrerequisitesodls.clientis currently tested on Linux only.you need Python >= 2.4you needpysqlitewhih in turn might require the SQLite libraries
and header files.for full install (including tests etc.) you also need the Python
header files and a working C-compiler likegcc.Installing the LibraryUseeasy_installto install the library.Or download the sources and in the root dir of the extracted package
do:$ python setup.py installYou might need superuser permissions to do that.Installing for DevelopmentAfter downloading and extracting the sources, in the root dir of the
downloaded file tree do:$ python bootstrap/bootstrap.pywhich will configure the package for your system. Then, run:$ ./bin/buildoutwhich will generate all scripts needed for development in the localbin/directory.Running TestsAfterwards you can run the tests by doing:$ ./bin/testCreating Docsodls.clientcomes with some documentation in thedocs/folder. It can be turned intoSphinxbased HTML by running:$ ./bin/make-docsThe docs then can be found indocs/build/html.Creating Coverage ReportsWe try to keepodls.clientat a 100%-test-covered level. You can
do the coverage report by issuing on the command line:$ ./bin/coverage-detect
$ ./bin/coveragereportThis will create HTML docs of the test coverage of each module. You
can find the generated HTML pages in thecoveragereportsubdir.CHANGES0.1 (2010-12-21)Initial implementation.
Package: odltools
odltoolsA tool to troubleshoot the NetVirt OpenDaylight OpenStack integration.The tool can fetch mdsal model dumps, openvswitch flow dumps
and extract dumps from CSIT output.xml files.It also provides a set of BIST (built-in self-tests) that provide a
starting point for debugging an ODL deployment.odltool’s documentation can be found atRead The Docs.Requirementspython 2.7, 3.3, 3.4, 3.5, 3.6requestsInstallationpip install odltoolsUsageusage: python -m odltools [-h] [-v] [-V]
                          {bist,csit,karaf,model,monitor,analyze,show} ...

OpenDaylight Troubleshooting Tools

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         verbosity (-v, -vv)
  -V, --version         show program's version number and exit

subcommands:
  Command Tool

  {bist,csit,karaf,model,monitor,analyze,show}Contributeodltoolsis an open source project that welcomes any and all contributions
from the community. odltools is hosted onGitHub.Feel free to contribute to:code,documentation,bug reports,contribution reviews.Please see theContributor Guidelinesfor information about contributing.LicensingSee theLICENSEfile
Package: odm2api
ODM2 Python API====A Python-based application programmer's interface for the [Observations Data Model 2 (ODM2)](http://odm2.org).[List of current and planned functions included in the API](https://github.com/ODM2/ODM2PythonAPI/blob/master/doc/APIFunctionList.md)## InstallationThe easiest and most reliable way to install the ODM2 Python API (`odm2api`) is using the [Conda package management system](http://conda.pydata.org/docs/) via either [Anaconda](https://www.continuum.io/downloads) or [Miniconda](http://conda.pydata.org/miniconda.html). To start using conda (if it's not your system default), add conda to the PATH; on MacOSX and Linux, it's something like `export PATH=$HOME/miniconda/bin:$PATH`, but the exact path may vary.To activate a conda environment, say, "myenv":```bashactivate myenv  # On Windowssource activate myenv  # On MacOSX or Linux```**Note:** `odm2api` currently is only tested on Python 2.7. Some changes have been made to support Python 3.x, but they haven't been tested thoroughly.### Latest release, from ODM2 anaconda.org channelThe [latest `odm2api` release](https://github.com/ODM2/ODM2PythonAPI/releases) is available on the [ODM2 anaconda.org channel](https://anaconda.org/odm2/odm2api) for all major OS paltforms (linux, OSX, win32/win64). To install it on an existing conda environment:```conda install -c odm2 odm2api```All dependencies are installed, including Pandas and its dependencies (numpy, etc).To create a new environment "myenv" with the `odm2api` package:```conda create -n myenv -c odm2 python=2.7 odm2api```### Installing the development version from the `master` branch on github**Note from 4/26/2016:** These instructions may be slightly outdated. Follow these directions for installing the bleeding edge github master branch, mainly for development and testing purposes.To create a new environment "myenv" with `odm2api`, first download the conda environment file [condaenvironment_1.yml](https://raw.githubusercontent.com/ODM2/ODM2PythonAPI/master/condaenvironment_1.yml). Go to the directory where `condaenvironment_1.yml` was downloaded. Then, on a terminal shell:```bashconda env create -n myenv --file py2_conda_environment.yml```Activate the new environment, then install `odm2api` into the environment:```bashactivate myenv  # On Windowssource activate myenv  # On MacOSX or Linuxpip install --process-dependency-links git+https://github.com/ODM2/ODM2PythonAPI.git```## CreditsThis work was supported by National Science Foundation Grants [EAR-1224638](http://www.nsf.gov/awardsearch/showAward?AWD_ID=1224638) and [ACI-1339834](http://www.nsf.gov/awardsearch/showAward?AWD_ID=1339834). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Package: odm2datamodels
ODM2DataModelsWhat is this?odm2datamodels is a Python package that provides a set of object-relational mapping (ORM) data models for theObservations Data Model Version 2.1. This data models are built of theSQLAlchemyand provide a convenient way of interfacing with an ODM2.1 database.Core FeaturesThe primary is theODM2DataModelsclass, which once instantiated, provides access to the set of ORM ODM2.1 data models and an instance of an ODM2Engine which provide utility function to perform basic Create, Read, Update, Delete operations as well are read execution of custom SQLQueries constructed using a SQLAlchemySelect objectorQuery ObjectHow to install?Presently the build files are only available on ourgithub repositoryThough we are aiming to release to thePython Package Index (PyPI)andCondain the near future.Testing and Database Dialect SupportTesting MethodPresently very limited testing has been conducted and has primarily been through an implementation of a REST API with limited coverage of selected data models. Further expanding and automating testing is an area for future updates.Database Dialect SupportThese data models have only been validated for a PostgreSQL database running a deployment of the ODM2.1 schema.
Package: od-metrics
A metrics package for Object Detection.Supported MetricsSupported metrics includemAP(Mean Average Precision),mAR(Mean Average Recall)
and Intersection over UnionIoU.Why OD-Metrics?User-friendly: simple to set and simple to use;Highly Customizable: every parameters that occur in the definition ofmAPandmARcan be set by user to custom values;Compatibility withCOCOAPI: each
calculated metric is tested to coincide with COCOAPI metrics.InstallationInstall from PyPIpip install od-metricsInstall from Githubpip install git+https://github.com/EMalagoli92/OD-MetricsDocumentationFor help, usage and API reference, please refer toDocumentationSimple Examplefromod_metricsimportODMetrics# Ground truthsy_true=[{# image 1"boxes":[[25,16,38,56],[129,123,41,62]],"labels":[0,1]},{# image 2"boxes":[[123,11,43,55],[38,132,59,45]],"labels":[0,0]}]# Predictionsy_pred=[{# image 1"boxes":[[25,27,37,54],[119,111,40,67],[124,9,49,67]],"labels":[0,1,1],"scores":[.88,.70,.80]},{# image 2"boxes":[[64,111,64,58],[26,140,60,47],[19,18,43,35]],"labels":[0,1,0],"scores":[.71,.54,.74]}]metrics=ODMetrics()output=metrics.compute(y_true,y_pred)print(output)"""{'mAP@[.5 | all | 100]': 0.2574257425742574,'mAP@[.5:.95 | all | 100]': 0.10297029702970294,'mAP@[.5:.95 | large | 100]': -1.0,'mAP@[.5:.95 | medium | 100]': 0.10297029702970294,'mAP@[.5:.95 | small | 100]': -1.0,'mAP@[.75 | all | 100]': 0.0,'mAR@[.5 | all | 100]': 0.25,'mAR@[.5:.95 | all | 100]': 0.1,'mAR@[.5:.95 | all | 10]': 0.1,'mAR@[.5:.95 | all | 1]': 0.1,'mAR@[.5:.95 | large | 100]': -1.0,'mAR@[.5:.95 | medium | 100]': 0.1,'mAR@[.5:.95 | small | 100]': -1.0,'mAR@[.75 | all | 100]': 0.0,'classes': [0, 1],'n_images': 2}"""AknowledgmentTorchMetricsCOCO APIObject-Detection-MetricsLicenseThis work is made available under theMIT License
Package: odmfclient
API client for ODMFThis package allows programatic access toODMFdatabases with Python.
It is based onrequests.The code is found onGithubunder a free license.InstallationFrom PyPI (current version):usingpippip install odmfclientIn an IPython console (eg in Spyder) you can use the same command with%pip install odmfclientTo install the newest version from github do:pip installhttps://github.com/jlu-ilr-hydro/odmfclient/archive/main.zipFor other branches thenmainuse the above command as.../archive/<branch>.zipUsageDirect access to ODMFfromodmfclientimportloginwithlogin('https://path/to/odmf','user','password')asapi:print(api)# Get all datasets at site #1 with valuetype 1datasets=api.dataset.list(site=1,valuetype=1)# Get values for the first dataset found as pandas.DataFramedf=api.dataset.values_parquet(dsid=datasets[0])Access ODMF behind an reverse proxy requiring authentificationAt JLU, we are protecting our ODMF instances behind a reverse proxy
(Apache) requiring basic authentification for access from outside the
universities network using shared credentials. Of course, any other authentification at your reverse proxy with
a requests method is possible. To access such an instance, where the shared credentials for basic authentification isbasic-userandbasic-password:fromodmfclientimportloginfromrequests.authimportHTTPBasicAuthbasic_auth=HTTPBasicAuth('basic-user','basic-password')withlogin('https://path/to/odmf','user','password',session_auth=basic_auth)asapi:print(api)# Get all datasets at site #1 with valuetype 1datasets=api.dataset.list(site=1,valuetype=1)# Get values for the first dataset found as pandas.DataFramedf=api.dataset.values_parquet(dsid=datasets[0])
Package: odmltables
An interface to convert odML structures to and from table-like representations, such as spreadsheets.odMLtables provides a set of functions to simplify the setup, maintenance and usage of a metadata
management structure usingodML.
In addition to thePythonAPI, odMLtables provides its main functionality also
via a graphical user interface.Code StatusDependenciesodMLtables is based onodML. A complete list of dependencies is available in the odMLtables
documentation atRead the Docs.Release VersionsOfficial release versions are available at thePython Package Indexand can be installed usingpip:$ pip install odmltablesThe graphical user interface can be installed using:$ pip install odmltables[gui]Latest versionTo install the latest version of odMLtables you first need to download the odMLtables source files and install it in a second step.DownloadThe latest version of odMLtables is available onGitHub. You can either use git and download
odMLtables directly under Linux using:$ cd /home/usr/toolbox/
$ git clone https://github.com/INM-6/python-odmltables.gitor alternatively download odMLtables as ZIP file and unzip it to a folder.DocumentationThe odMLtables documentation is available onRead the Docs. It is based onSphinxand also
locally be built in multiple formats. E.g., to access  the   documentation in html format
navigate to the documentation folder within odMLtables and  compile  the html documentation:$ cd /home/usr/toolbox/python-odmltables/doc
$ make htmlAll output format available can be listed using:$ make -nInstallationInstallation guidelines are available in the official odMLtables documentationRead the Docs.BugsIf you observe a bug in odMLtables please add a bug report at theGitHub issue tracker
Package: odmltools
odmltoolsodmltools is a package collecting convenience scripts for the odML python-project.
The Python odML library is available onGitHub.The open metadata Markup Language is a file based format (XML, JSON, YAML) for storing
metadata in an organised human- and machine-readable way. odML is an initiative to define
and establish an open, flexible, and easy-to-use format to transport metadata.Convenience scriptsCurrently the package contains a convenience script to import DataCite XML files to odML:The script is automatically installed and available via the command line. It provides
detailed usage descriptions by adding thehelpflag to the command.odmlimportdatacite -hBuilding from sourceTo download the odmltools library please either use git and clone
the repository from GitHub:$ git clone https://github.com/G-Node/odmltools.gitIf you don't want to use git download the ZIP file also provided on
GitHub to your computer (e.g. as above on your home directory under a "toolbox"
folder).To install the Python-odML library, enter the corresponding directory and run:$ cd odmltools
  $ python setup.py installDocumentationMore information about the project including related projects as well as tutorials and
examples can be found at ourodML project page.Bugs & QuestionsShould you find a behaviour that is likely a bug or feel there is something missing,
just create an issue over at the GitHubodmltools issue tracker.
Package: odmpy
odmpy is a python package for creating valid ASCII OPM, OMM, and OEM
files.Currently, only the orbital parameter message (OPM) module has been
implemented.Installation$pipinstallodmpyExampleimportodmpy.opmasopmfromdatetimeimportdatetimeheader=opm.Header(originator='NASA')metadata=opm.Metadata(object_name='International Space Station',object_id='1998-067A',center_name='Earth',ref_frame=opm.RefFrame.EME2000,time_system=opm.TimeSystem.UTC)sv=opm.DataBlockStateVector(epoch=datetime(2014,11,7,15,30,23),x=6794,y=0,z=0,x_dot=0,y_dot=7.6,z_dot=0)data=opm.Data(state_vector=sv)iss=opm.Opm(header,metadata,data)withopen('iss.opm','w')asf:iss.write(f)Go to Package Documentation
Package: odm-report-shot-coverage
OpenDroneMap - A shot coverage reportFrom anOpenDroneMapreconstruction, our goal is to see which part of the orthophoto is
covered by each shot.We propose a web app with the orthophoto and camera positions displayed. Clicking on photo displays the covered area.How to run it?How to installYou need Python >= 3.8pip install odm-report-shot-coverageProcessingodm-report-shot-coverage PATH_TO_ODM_PROJECTAnd follow the instructions to open the local web page. (Execution time is ~15 seconds for 60 images on a macbook pro)How does it work?From an OpenDroneMap reconstruction (odm by default), the reports needs access to the files stored in the project
directory:cameras.json
images/*
odm_report/shots.geojson
odm_report/stats.json
odm_orthophoto/odm_orthophoto.tif
odm_orthophoto/odm_orthophoto_corners.txt
odm_texturing_25d/odm_textured_model_geo.objThe reporting toolodm-report-shot-coverage.pycreates a directoryodm_report/shot_coveragewith a web
page (index.html) containing the interactive report.Browsing through the resultsOpen theodm_report/shot_coverage/index.htmlfile and mouse over the shot position (blue dots) to see the image. Click
on one or more shot to display the ground boundaries.LimitationsNo ray tracingThe shot boundaries are estimated based on the shot position and rotation, and the 2.5d model, but without ray tracing.
Therefore, The extent of the shot boundaries is projected behind a higher structure.Our purpose was at first to tackle rather flat area, shot from above. Therefore, this limitation is not a big deal in
such situations.Perspective projectionTo map x,y,z points from the 2.5d model onto camera pixels, we use theperspectivemodel, as I have not found the
information for other projections (such as theBrown, used by the GoPro).Code ArchitectureTwo main components:Python to reconstruct the shot boundaries, resize original images and set up the web app directoryA JavaScript + D3.js for the front endPython ProcessingThe code is insrc/and the entry pointodm_report_shot_coverage/scripts/report.py.Beside copying (and resizing) original images, setting up the web app, the main purpose is to recompute the shot boundaries:parse the 2.5d model from theodm_texturing_25dwavefront object file (only vertices are used)parse camera specs fromcameras.jsonExtract thenativecoordinates system fromstats.jsonGet shot position + rotation fromshot.geojson; shot positions are shifted from native to the 25d model/ortho photoConvert and get the ortho photo boundariesFor each vertex, see if they appear in the camera image (with the limitation of the perspective projective + absence of ray tracing described above)For each shot, compute the boundaries around the subset of vertices within each frameJavaScriptBase on the web app asset + files computed by the Python processing, the code uses some d3.jsCI/CDBeing hosted on Github, we use actions for the CI/CD:testing the Python code (pytest)linting and security checkdeployment on Azure static web appAuthorAlexandre Masselot (alexandre.masselot@gmail.com), with the help of the vibrant ODM community.LicenseMIT
Package: odm-sfm
RTK校准GPS 使用说明1、运行环境（链接中含安装方法）Ubuntu>= 18.04Python3>= 3.6.9Docker>= 20.10.16OpenSfM2、安装脚本程序pip3install-Uodm-sfm--user更多信息可参见：https://pypi.org/project/odm-sfm/3、运行脚本程序Bash方式：odm_sfm_lla
--rtk=./RTK# RTK图像的目录，尚不支持子目录搜索--gps=./GPS# RPS图像的目录，尚不支持子目录搜索--dst=./out# 工作目录，存放输出的结果--log=log.txt# 将屏幕输出保存到{dst}/log.txt--cpu=3# 最大并行计算/并发数；范围[1,cpu内核数]--type=sift# 特征提取的类型；默认SIFT--min=4000# 每张图像特征点数的下限；默认4000--quality=0.5# 每张图像的特征粒度，越大特征越多约细致；默认0.5，范围(0,1]更多参数项目，详见：odm_sfm_lla --help可能--log功能失效，可手动重定向，即在上述命令后添加>> log.txt 2>&1。例如：odm_sfm_lla --gps=GPS --rtk=RTK --dst=out >> out/log.txt 2>&1首次运行程序时，docker会自动下载odm的镜像：需要联网，耗时较长。程序运行过程中需要sudo权限，会提示输入sudo密码。若想永不超时：输入sudo visudo，在Defaults env_reset后添加, timestamp_timeout=-1Python方式：fromodm_sfmimport*ODM_SfM.SfM_CFG['processes']=3# 等价于Bash方式中：--cpu=3ODM_SfM.SfM_CFG['feature_type']='SIFT'# 等价于Bash方式中：--type=siftODM_SfM.SfM_CFG['feature_process_size']=0.3# 等价于Bash方式中：--quality=0.3ODM_SfM.SfM_CFG['feature_min_frames']=5000# 等价于Bash方式中：--min=5000ODM_img_lla2(GPS='./GPS',RTK='./RTK',dst='./out')# GPS图像、RTK图像与结果存放的目录4、运行结果：下述符号说明：{dst}、{rtk}、{gps}分别是上述--dst、--rtk、--gps中的目录名。GCP列表的路径：{dst}/sfm-GCP-{rtk}-{gps}/gcp_list.txt或者：{dst}/odm-GCP-{rtk}-{gps}/opensfm/gcp_list.txt文件首行是编码格式，之后每行的格式是：<geo_x> <geo_y> <geo_z> <im_x> <im_y> <image_name>其中，<geo_x> = longitude, <geo_y> = latitude, <geo_z> = altitude，<im_x> <im_y>是GCP在图像中的像素坐标，<image_name>是图像的文件名。POS文件的路径：{dst}/odm-GCP-{rtk}-{gps}/opensfm/image_geocoords.tsv文件每行的格式是：<image_name> <geo_x> <geo_y> <geo_z>。其中，<image_name>是图像的文件名，<geo_x> = longitude, <geo_y> = latitude, <geo_z> = altitude。POS的差异比对：{dst}/odm-GCP-{rtk}-{gps}/opensfm/image_geocoords_dif.txt文件每行的格式是：<image_name>  <xyz_dif>。其中，<image_name>是图像的文件名， = [longitude, latitude, altitude]，xyz_dif = 在局部空间坐标系下，原始经纬坐标与的差值（单位是米）。
Package: odnoklassniki-urls-validator
odnoklassniki_urls_validatorLibrary for validatingOdnoklasnsikiurls and brings them to one standart.How method works?Add prefix "https://"Change all hostnames to "ok.ru"Examplesfromodnoklassniki_urls_validatorimportvalidate_urlvalidated_url=validate_url(url='www.ok.ru/group/50582132228315/')# >> https://ok.ru/group/50582132228315validated_url=validate_url(url='http://ok.ru/group/50582132228315')# >> https://ok.ru/group/50582132228315Installingpip3 install odnoklassniki_urls_validator