Saravanan666/pypi-org huggingface dataset:
Package: landborn
Landborn: Easy Visualization LibraryLandborn is a Python visualization library designed to make it easy to generate common types of plots using matplotlib, pandas, and numpy. It simplifies the process of creating bar plots, scatter plots, and line plots with minimal code.FeaturesBar Plot: Generate vertical or horizontal bar plots.Scatter Plot: Create scatter plots with customizable colors, markers, and size.Line Plot: Plot lines with options for color, size, style, and markers.Joint Plot: Plot a scatter and line plot all in one!NoteThis is a school project and not meant for industry use.InstallationYou can install Landborn directly from PyPI:pipinstalllandbornTestingTo run the tests, inside main dir, run pytest tests/tests.py
Package: landbosse
LandBOSSEWelcome to LandBOSSE!The Land-based Balance-of-System Systems Engineering (LandBOSSE) model is a systems engineering tool that estimates the balance-of-system (BOS) costs associated with installing utility scale wind plants (10, 1.5 MW turbines or larger). It can execute on macOS and Windows. At this time, for both platforms, it is a command line tool that needs to be accessed from the command line.The methods used to develop this model (specifically, LandBOSSE Version 2.1.0) are described in greater detail the following report:Eberle, Annika, Owen Roberts, Alicia Key, Parangat Bhaskar, and Katherine Dykes.
2019. NREL’s Balance-of-System Cost Model for Land-Based Wind. Golden, CO:
National Renewable Energy Laboratory. NREL/TP-6A20-72201.https://www.nrel.gov/docs/fy19osti/72201.pdf.User GuidesFirst, read the technical report to understand the big picture of LandBOSSE. In the technical report, you will find process diagrams, equations and the modules that implement them. Then, come back to this documentation and read the user guide.In brief, LandBOSSE takes.xlsxspreadsheets, reads input data from tabs on the spreadsheets, and writes the results to an output.xlsxfile. There are three sections in the user guide to demonstrate how to perform these steps.The user guide comes in three parts:Software installation,Input data configuration, andOutput data analysis.Software InstallationThere are two options depending on whether you are a developer or an end user and what operating system you are running.Windows end-user: If you run the Microsoft Windows operating system and aren't setting up as a developer who is going to be modifying the core library, these instructions are for you.Find out how to configure Windows for end users.macOS end userandmacOS developer: If you run the macOS operating system, either as an end-user or as a developer, these instructions are for you. Both developers and end-users will need most of the steps.Find out how to configure macOS for end users and developers.Operation after the installationReview the installation instructions on how to activate a virtual environment, if you haven't already.Then, read theOperation and Folder Structurefor details on running the command that executes LandBOSSE from the command line.
Package: landfall
Landfall: Easy to use functions for plotting geographic data on static mapsWhat is it?Landfallis a Python package with easy to use functions for plotting geographic data on a static map.Where to get itThe source code is currently hosted on GitHub at:https://github.com/eddiethedean/landfall# PyPIpipinstalllandfallDependenciespy-staticmaps - A python module to create static map images (PNG, SVG) with markers, geodesic lines, etc.distinctipy - A lightweight package for generating visually distinct colours.Exampleimportlandfalllats=[27.88,27.92,27.94]lons=[-82.49,-82.49,-82.46]landfall.plot_points(lats,lons)
Package: landinggear
landinggearwas born out of the complaints of some friends who travel a lot
and like to write software while on aeroplanes.usageUselandinggear path/to/wheelhouseto populate your wheelhouse from your
pip cache. Then usepip install--no-index-fpath/to/wheelhouse mypackageto install packages.TODOFigure out how to write some more useful tests.
Package: landing-zone
Landing ZoneLanding Zone is a proof of concept tool that checks if specific user inputs can lead to execution of known-vulnerable
functions.Example use cases:you are an exploit researcher trying to determine if a list of inputs can lead to a vulnerable functionyou are a vulnerability management practioner who wants to varify that your security controls can prevent specific
inputs from reaching known-vulnerable functions in an application.This tool was created to satisfy classwork for Dakota State University's CSC-842 Security Tool Development.Demo video:herePrerequisitesYou must have the following software installed prior to using this tool:Python 3 interpreterPip3GNU DebuggerUsageInstallationpipinstalllanding-zone

miley--helpScan a hash (MD5, SHA-1, or SHA-256)miley--hashf617abd6a9ccb98e34a6e32184004d5a08ea11d198fca9fed88b04b9dfc96de2Scan a directory for malwaremiley--path/path/to/directory/Scan a remote container imagemiley--imgalpine:latestFuture WorkIf I were to maintain this program going forward, I would:speed up the programsupport additional malware databases, such as Virus Totaladd a GUIWhy is it named Miley?Miley is my beloved family dog, who passed away in 2023 due to old age.Miley was always good at detecting and alerting to potentially dangerous situations, despite being blind.I wrote this tool with her memory at the forefront of my mind.
Package: landlab-rest
A RESTful interface to landlab graphs.QuickstartUsecondato install the necessary requirements andlandlab_rest,$ conda install --file=requirements.txt -c conda-forge
$ pip install .Start the server,$ start-sketchbookLook at the line containingServing onto see what host and port the
server is running on. Alternatively, you can use the–hostand–portoptions to specify a specific host and port (–helpfor help).Now you should be able to send requests to the server. For instance,
to get aRasterModelGrid,$ curl https://0.0.0.0:8080/graphs/rasterFor a list of supported graphs$ curl https://0.0.0.0:8080/graphs/You can pass parameters like,$ curl 'https://0.0.0.0:8080/graphs/raster?shape=4,5&spacing=2.,1.'DockerTo build a new docker image that will be a landlab-rest server,docker build . -t landlab-restAfter building, run the server,docker run -it -p 80:80 landlab-restOnce running, you can then send requests to the server. For example,$ curl https://0.0.0.0/graphs/raster
Package: landlock
🔒🐍 Landlock for PythonHarden your Python code by with rule-based file system access restrictions.ExampleLet's write a simple HTTP server that serves files in the local directory.fromhttp.serverimportHTTPServer,SimpleHTTPRequestHandlerserver=HTTPServer(("",8000),SimpleHTTPRequestHandler)server.serve_forever()But if there's a symlink in the local directory, the program can "escape".$ln-s/etcoops
$python3test.py&[1]...
$curllocalhost:8000
...
$curllocalhost:8000/oops/passwd
uhoh
$kill$![1]+Terminatedpython3test.pyNow let's harden our server with Landlock!fromhttp.serverimportHTTPServer,SimpleHTTPRequestHandlerfromlandlockimportRulesetserver=HTTPServer(("",8000),SimpleHTTPRequestHandler)# the ruleset by default disallows all filesystem accessrs=Ruleset()# explicitly allow access to the local directory hierarchyrs.allow(".")# turn on protectionsrs.apply()server.serve_forever()And now we get a permission denied error if we try and access files outside the current directory,
even via a symlink:$python3test.py&[1]...
$curllocalhost:8000
...
$curllocalhost:8000/oops/127.0.0.1--[DD/MMM/YYYYHH:MM:SS]code404,messageNopermissiontolistdirectory
...
$kill$![1]+Terminatedpython3test.pySuccess! Instead of dumping the password file, we instead get a permission error!Landlock is great for hardening applications against both accidental programming mistakes,
and attacks.
It won't prevent an exploited application from all malicious behavior,
but it can stop it reading with the filesystem and interacting with device files.Developer InformationTestingTests are run usingpytest.
Each test is run in a separate subprocess usingpytest-forkedso Landlock rules don't conflict.
Package: landmark-detection
Full-Body-LandMarks-DetectionGithubExplainedIn this repo usedMediapipesolutions in sections:Face MeshHandsPoseTogether, this will extract all coordinates points for any part of the body. In any class of them, there are two functions:Find PointsDrawingFind_Pointsfunction has some of sub functions those are body parts after that returns a list of class points such as: Face pointsDivide Find_Points function in classFaceinto:Left EyeRight EyeNoseMouthHeadDivide Find_Points function in classHandsinto:FingersDivide Find_Points function in classBodyinto:ChestArmsLegsDrawingfunction returns an image with labeled points.OutputIn the face parts, It returns a 2d list the first for all face points and the second for point coordinatesotherwiseit returns a 3d list  the first for all class points, the second for the beginning and end point in the part, and the third for point coordinatesUsage#ImportsimportlandmarkasLMimportcv2ascvcap=cv.VideoCapture(0)# 0 as a default webcamface_detector=LM.Face()# Make instance from classwhile1:_,img=cap.read()imgRGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)# Any function of them need two parameters (image, imageRGB)# color, thickness and circle_radius are default parametersface_detector.Drawing(img,imgRGB,color=(0,110,100),thickness=3,circle_radius=3)face_detector.Find_Points(img,imgRGB)# To detect any part of the body you should call (Find_points) function# Any function of Them does not need parametersprint(face_detector.Left_Eye())cv.imshow("img",img)cv.waitKey(1)
Package: landmarker
Landmarker is aPyTorch-based toolkit for (anatomical) landmark detection in images. It is designed to be easy to use and to provide a flexible framework for state-of-the-art landmark detection algorithms for small and large datasets. Landmarker was developed for landmark detection in medical images. However, it can be used for any type of landmark detection problem.🛠️ Installationcommandpippip install landmarker🚀 Getting StartedTechnical documentation is available atdocumentation.Examples and tutorials are available atexamples✨ FeaturesModular: Landmarker is designed to be modular. It is easy to add new models, datasets, and loss functions.Flexible: Landmarker provides a flexible framework for landmark detection. It is easy to customize the training and evaluation process.Easy to use: Landmarker is easy to use. It provides a simple API for training and evaluation.State-of-the-art: Landmarker provides state-of-the-art landmark detection models and loss functions.📈 Future WorkExtension to 3D landmark detection.Extension to landmark detection in videos.Add uncertainty estimation....👪 ContributingWe welcome contributions to Landmarker. Please read thecontributing guidelinesfor more information.📖 CitationIf you use Landmarker in your research, please cite the following paper:SCIENTIFIC PAPER UNDER REVIEW📝 LicenseLandmark is licensed under the IMEClicense.👤Jef Jonkers
Package: lan-download
lan_download 介绍顾名思义 lan_download 可以下载蓝奏云文件， 并支持批量下载和提取动态直链功能！lan_download 还在开发阶段， 未来将保持更新， 推出蓝奏云文件夹内文件批量下载/带密文件解密下载等功能警告！本程序仅供学习使用，请勿用于非法途径，本人一概不负责！注意！本程序的下载请求也会被蓝奏云服务器所记录，文件下载次数计数会增加，请酌情使用本库！下载示例单文件下载示例：import lan_download
lan_download.start_download(url='https://lzt666.lanzoui.com/iUp9hsahs0d')

> result :
>>> https://vip.d0.baidupan.com/file/?AGZSb+uBmUwgXGsBgBpVnkANVE1WikHZVIxUysBYVxp 
                                                    (所有示例直链均已失效且经过省略处理)
>>> 清华学员笔记数学-化学-生物(2).pdf  文件保存完成

>>>多文件下载(批量)示例：import lan_download
lan_download.start_download(url=['https://lzt666.lanzoui.com/iUp9hsahs0d', 
                                 'https://lzt666.lanzoui.com/iLHbtonnwxa',
                                 'https://lzt666.lanzoui.com/irGVKe2mgih'])

> result :
>>> https://vip.d0.baidupan.com/file/?A2UHOVlY13wZdeltnBGhQYwViVg4GaVVtVWkDNwdpWTYAYFMzUTMGZwU3VytSMVV5U2wEOwczAWtWYQIyAGcJbVQ3A3NTdVRwXWFbMwQxUD0FNlZ+BjBVMVV7AzMHZlkrADdTNlFjBjEFMlc0UmBVPlM3BGIHMwE7VmMCNwA1CTVUNwMxU2BUOV1vW2MEZVBpBWFWNQYzVWtVYgM0B2VZPAB6U3VRYwY0BSJXeFIkVW9TIwRqB2UBYFZmAjAAZAlhVDcDZFM8VCZdKFtoBGxQagVhVmwGMVU/VWcDNgdmWTQAY1M8UTcGZAUqVyNScVVsUz0EdAc8AWxWYQIxAGIJYlQwA2FTNVQ4XW1bJwR0UH8FcFZsBjFVP1VmAzsHYlk1AGxTPVExBmUFIld4Uj5VelNsBDIHMAFoVnkCNwBkCXpUNgNgUytUNF1o
>>> 清华学员笔记数学-化学-生物(2).pdf  文件保存完成
>>> https://vip.d0.baidupan.com/file/?UzVUag4/BGLQVARgVHMGOgB2AmBUYwI0U2FXOFpjATMGdw0gAycFbgVhAGQHNABrBy0CbQE3VX1SZwJuUH5TMlRmDmcEaAJjBWYEYVRgBj4ANgJuVG8CMVNtVz1abQExBjQNZgNnBTAFMABsB2IAaAdkAj4BZVUwUmACaFBhU31UeQ46BCACcgUmBCNUMAYuAGwCOVRrAjRTZ1c4Wm4BNgZgDWkDcQUnBToAOQdjADwHPwJsATVVYVJhAmhQYVNkVDoOYgRhAnoFfQR2VDMGMAByAmBUZgIjUyBXe1osATwGYA1mA2YFagVkAGEHMQBoBzoCbwElVSdSPQIuUGxTYlQyDmYEfgJhBWAEflRjBmkAKwJpVGI=
>>> EasiNote.zip  文件保存完成
>>> https://vip.d0.baidupan.com/file/?BGJSbFpcsAypNkBMzB2EBZwN6B2NRZgZzBjEAaFR5BTUEMQBoVzMEYFJjWjEDMgRiAmRQMVQ3AD4PPgQ4VzADZlM3B2JRZ1NrBjMLMAdjAWEDMQcxUWIGZQYxAGtUYgUsBHgANVcmBCJScFp0AzIEIAI9UGBUaABrDzgEaFdgAzNTZAdiUXZTJwY4CzsHNgExA2gHYlFmBm8GNgBpVGYFNQQ7AG1XZAQqUitaIQMxBD4CI1A5VGUAfA9/BCtXIgM5U2QHblFpU2AGYwtkB2sBYgNjB2JRdgYpBmoAL1RrBTMEMwBpV3gEMVI2WikDYQRnAnpQMFRh
>>> zy.zip  文件保存完成
>>> 已完成批量下载

>>>如果您不希望 lan_download 打印信息， 可以提供show_info参数来取消所有消息的打印， 该参数默认为True示例如下：import lan_download
lan_download.start_download(url='https://lzt666.lanzoui.com/iUp9hsahs0d', 
                            show_info=False)

> result :
>>>可见，lan_download 此时不会打印任何信息。下载配置lan_download 的下载配置变量名称为__save_param类型为字典，修改示例如下import lan_download

lan_download.__save_param = {'save_dir': 'your_path'}
lan_download.start_download(url='xxxxx')lan_download 历代更新信息0.1.29 无新增功能, 加强并恢复解析、下载功能 (2022/07/17)0.1.2 无新增功能，加强下载功能的应变能力并恢复下载功能0.1.1 无新增功能，紧急恢复下载功能0.1.0  初代，仅支持修改文件保存路径
Package: landroidcc
Landroid Cloud ClientPython library and command line tool to communicate with mowers like:WR141E / Landroid M500WR142E / Landroid M700WR143E / Landroid M1000InstallationInstallation using pippip install landroidccCommandline usageAfter installation the command 'landroidcc' is available and can be used for example
to get the status and start the mower:landroidcc username password --status --startThe output will look like:2019-05-12 22:25:32 __init__ _api_authentificate INFO     Successfully logged in
2019-05-12 22:25:33 __init__ on_connect INFO     Successfully connected to the cloud
landroid info
#############
Name:   Schaf
Serial: xxxxxxxxxxxxxxxxxxxx
Type:   WR141E

landroid status
###############
LastUpdate: 22:25:34 12/05/2019
State:      Home
Error:      No error
Battery:    100%/9.2C/19.63vAPI UsageFor using the landroid client directly from another Python using the Landroid class. The
status returned has the type: LandroidStatusfromlandroidccimportLandroidlandroid=Landroid()landroid.connect("user","pass")status=landroid.get_status()print("Battery:{}%".format(status.get_battery().percent))landroid.start()# Start mowing
Package: landsat2geojson
DescriptionThe landsat2geojson package is written in python and uses theEarthExplorerandOverpassportal interface to search, download, and process scenes from Landsat
Collections via the command line.Requirementslandsat2geojson uses thelandsatxplorepackage to connect and download
the landsat images, the credentials can be obtained inEarth Explorer Page--usernameand--passwordcan be provided as command-line options or as environment variables:exportLANDSATXPLORE_USERNAME=<your_username>exportLANDSATXPLORE_PASSWORD=<your_password>Installationrepositorythe installation from the repositorygitclonehttps://github.com/yunica/landsat2geojson.gitcdlandsat2geojson
pipinstall.dockerbuildexportLANDSATXPLORE_USERNAME=<your_username>exportLANDSATXPLORE_PASSWORD=<your_password>

docker-composebuildrundockerrun--rm-v${PWD}:/mnt\-eLANDSATXPLORE_USERNAME=$LANDSATXPLORE_USERNAME\-eLANDSATXPLORE_PASSWORD=$LANDSATXPLORE_PASSWORD\landsat2geojson:v1\landsat2geojson--geojson_file=test.geojson--data_folder=data/--geojson_output=data/salida123.geojsonordockerrun--rm-v${PWD}:/mntlandsat2geojson:v1\landsat2geojson-u=<LANDSATXPLORE_USERNAME>-p=<LANDSATXPLORE_PASSWORD>--geojson_file=test.geojson--data_folder=data/--geojson_output=data/salida123.geojsonUsagehelpyou can review the parameters menu with the param--helplandsat2geojson--helpUsage:landsat2geojson[OPTIONS]Options:-u,--usernameTEXTEarthExplorerusername.-p,--passwordTEXTEarthExplorerpassword.--geojson_fileTEXTPathfilefromgeojsoninput[required]--data_folderTEXTPathfromdownloaddata--lansat_index[WATER]Landsarnormalizedindex--geojson_outputTEXTPathfilefromgeojsonoutput[required]--helpShowthismessageandexit.examplelandsat2geojson--geojson_file=area.geojson--data_folder=data/--geojson_output=data/output_data.geojson
Package: landsatbuoycalib
Calculates and compares the radiance of a thermal LANSAT scene to the "ground truth"radiance as measured by a NOAA buoy. Based on work by Frank Padula and Monica Cook.If you want to use this code, you should have a basic knowledge of python and/or basic coding.No warranty. Use it on armstrong or related servers for best results. Developed on Fedora x64 by Nathan Dileas.Copyright RIT 2015-2016OVERVIEW:This code essentially has two funtions: calculating the radiance from the landsat imageprovided, and calculating the corresponding ground truth radiance from outside data,atmospheric (NARR or MERRA-2), NOAA buoy data, and MODTRAN. Use the file controller.py asa convinient command line interface or the underying class itself, in bin/BuoyCalib.py.Sources:http://scholarworks.rit.edu/theses/2961/ - Padula 08 Thesishttp://scholarworks.rit.edu/theses/8513/ - Cook 14 ThesisNARR:This is the primary atmospheric data source for the project. Height, Temperature,Humidity as a funtion of Pressure. NCEP Reanalysis data provided by the NOAA/OAR/ESRLPSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/Website: http://www.esrl.noaa.gov/psd/data/gridded/data.narr.htmlFTP: ftp://ftp.cdc.noaa.gov/Datasets/NARR/pressure/MERRA-2:This is the secondary atmospheric data source for the project. Height, Temperature,Humidity as a funtion of Pressure. It was instituted as a result of the NARR datasetnot being up to date. Until late 2016, the NARR archive only reaches to late 2014.Website: http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/FTP: ftp://goldsmr5.sci.gsfc.nasa.gov/data/s4pa/MERRA2/M2I3NPASM.5.12.4/NOAA:This is the only source of water temperature information for the project.Website: http://www.ndbc.noaa.gov/Data: http://www.ndbc.noaa.gov/data/stations/station_table.txthttp://www.ndbc.noaa.gov/data/stdmet/http://www.ndbc.noaa.gov/data/historical/stdmet/USAGE:./buoy-calib, in this directory. Use the -h option for more informationon flags and options that are available../buoy-calib [options] <Landsat_ID>Examples:./buoy-calib -i LC80130332013145LGN00 : Output an image with the locationof the buoy and narr points drawn on it./buoy-calib -m LE70160382012268EDC00 : Use MERRA data instaed of NARR./buoy-calib -b 44009 LT50410372011048PAC01 : use a specific NOAA buoy./buoy-calib -imvr LC80130332013145LGN00 : Output an image, verbose, reprocesss, with MERRA data./buoy-calib LE70160382012268EDC00 LC80130332013145LGN00 : process more than one sceneThe Landsat ID can be any valid ID from landsat 5 or 8 with a level 1 product available.If there is an error, check earth explorer first. It is frequently down for maitenance.In future, there may be a tool included with this package to check if the ID is valid beforetrying to download. Stay tuned.TOOLS:tools/to_csv.py: used to compile results quickly and easily.tools/generate_atmo_figure.py : generate a figure using information from a already processed scene.test/functional/run_all_scenes.bash: run a batch of scenes. Move it to this directory before use.
Package: landsat-cogeo-mosaic
landsat-cogeo-mosaicTools to create MosaicJSONs for Landsat imagery.OverviewAMosaicJSONis a file that defines how to combine multiple
(satellite) imagery assets across time and space into a web mercator tile. This
repository is designed to be used to create such files, so that they can be used
for on-the-fly satellite tile generation, such as withlandsat-mosaic-tiler.Installpipinstalllandsat-cogeo-mosaicFor CLI commands and some modules, you need additional dependencies:pipinstall'landsat-cogeo-mosaic[cli]'Create MosaicsIn order to create the mosaic, you need the metadata on where and when Landsat
took images, and also the cloud cover of each image. There are a couple ways to
get this data. One is to ping theSat
APIhosted by Development Seed. This
allows you to find the metadata from specific regions and times, and is easier
when you're creating a mosaic for a specific region.Alternatively, for large mosaics it's easier to do bulk processing. In order to
not overload the Sat API, I download a file from AWS with metadata from all
images.Changelog[0.2.1] - 2020-09-21Reproject to web mercator for intersection computations when creating index file[0.2.0] - 2020-09-13Update for rio-tiler v2Look in commit history for more detailed changes here[0.1.1] - 2020-05-19Initial release on PyPI
Package: landsat-util
Landsat-util is a command line utility that makes it easy to search, download, and process Landsat imagery.DocsFor full documentation visit:https://pythonhosted.org/landsat-util/To run the documentation locally:$ pip install -r requirements/dev.txt
$ cd docs
$ make htmlRecently Added FeaturesImproved pansharpeningUse BQA bands for cloud/snow coverage and use in color correctionAdd support for different NDVI color maps (three included)Add support for image clipping using the new–clipflagChange LogSeeCHANGES.txt.