Saravanan666/pypi-org huggingface dataset:
Package: poi-clustering
PoI Clustering with Stay Points/Stay Region DetectionPython implementation of PoI (Point-of-Interest) clustering algorithm based on:Yang Ye, Yu Zheng, Yukun Chen, Jianhua Feng, and Xing Xie. 2009. Mining Individual Life Pattern Based on Location History. In Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware (MDM '09).http://dx.doi.org/10.1109/MDM.2009.11Raul Montoliu, Jan Blom, and Daniel Gatica-Perez. 2013. Discovering Places of Interest in Everyday Life from Smartphone Data. Multimedia Tools And Applications, 62, 1, 179-207.http://dx.doi.org/10.1007/s11042-011-0982-zVincent W. Zheng, Yu Zheng, Xing Xie, and Qiang Yang. 2010. Collaborative Location and Activity Recommendations with GPS History Data. In Proceedings of the 19th International Conference on World Wide Web (WWW '10).https://doi.org/10.1145/1772690.1772795.To find PoI, those studies propose astay point, that is a micro cluster of temporal-spatial trajectories, and astay region, that is a macro cluster of stay points.Simple Description of AlgorithmFind stay points regarding temporal and spatial distance between two trajectories.Build grids that embodying stay points.Cluster neighboring grids and give labels.Installationpip install poi-clusteringHow to UseThis implementation follows scikit-learn's grammar;fitandpredict. For more details, please see docstrings in codes.Example
Package: poi-kml
poi_kmlThis module defines classes and functions for converting a CSV file of points
of interest with addresses to a KML file for importing into mapping software.InstallationTo install poi_kml, use pip (or similar):pip install poi-kmlDocumentationFirst generate a test csv filefake_file=poi_kml.make_fake_poi_csv('sample_poi.csv',n=20)Example for generating a kml filedb=poi_kml.POIdb(fake_file)db.set_home('1200 E California Blvd, Pasadena, CA 91125')db.filter(miles=25)db.to_kml(filename='poi_near')print(db.sort(by='Name'))
Package: poimage
👉项目官网：https://www.python-office.com/👈👉本开源项目的交流群👈📚简介poimage 是python自动化办公之图片操作的第三方库。来自于开源项目：python-office，📘官网：https://www.python-office.com/。📦安装🍊pip 自动下载&更新pip install -i https://mirrors.aliyun.com/pypi/simple/ poimage -U📝功能已有功能的说明如下：序号方法名功能视频文档演示代码1add_watermark👩给图片 加 水印👩播放待编辑打开2down4img下载图片播放查看打开3del_watermark图片去水印待录制查看打开4txt2wordcloud根据文档内容，生成词云图片播放待编辑打开5compress_image压缩图片待录制待编辑打开6image2gif图片转Gif待录制待编辑打开7img2Cartoon生成动漫头像待录制待编辑待开发8split4img朋友圈切图待录制查看待开发🏗️添砖加瓦本项目欢迎任何人参与开发，如果是小白，可以看一下开发教程👉如何参与开源项目？0基础入门：怎么打开GitHub？什么是issue？什么是PR？🐞提供bug反馈或建议提交问题反馈时，请务必填写和该项目本身有关的问题，不进行有关python学习，甚至是个人练习的知识答疑和讨论。Github issuegitee issue🧬贡献代码的步骤在Gitee或者Github上fork项目到自己的repo把fork过去的项目也就是你的项目clone到你的本地修改代码commit后push到自己的库登录Gitee或Github在你首页可以看到一个 pull request 按钮，点击它，填写一些说明信息，然后提交到master分支即可。等待程序员晚枫合并，＋微信说一声更好👉CoderWanFeng🪙支持项目o基础的朋友，想快速入门自动化办公，可以学习下面这2套原创课程，👇。📌联系作者读者福利几个没有套路的福利，每天都可以领一次~
Package: poinspection
Python oject inspection for ipythonipython magic function %pi, print instanceWritten and maintained by Luis Montiel <luismmontielg@gmail.com>%pi, print instanceIn [2]: obj_or_dict = {‘akey’: ‘value’, ‘another’: ‘value2’}
In [3]: %pi obj_or_dict
dict
akey           = ‘value’
another        = ‘value2’You can pass a pattern and a separator after the object:In [3]: %pi obj_or_dict anot
dict
another        = ‘value2’In [4]: %pi obj_or_dict * ‘| ‘
dict
akey= ‘value’| another= ‘value2’Installationpip install poinspectionUsage%load_ext poinspectionTo autoload when IPython starts:Create ipython profile config file, or skip if already presentipython profile createAnd you will have a default ipython_config.py in your IPython directory under profile_defaultAdd/modify this ipython config filec.InteractiveShellApp.extensions = [‘poinspection’]InformationThe IPython magic commands work for versions of IPython with the
decorators IPython.core.magic.magics_class and
IPython.core.magic.line_magic.LicensePublic domain. You can do whatever you want with this.
Package: point-annotator
point-annotatorThis package provides functionalities to annotate data with labels based on the
Mann-Whitney U test and Hypergeometric test. Currently, we provide examples of
the fast annotation of gene expression data with the cell types based on marker
genes.InstallationThe package is available at PIP and is installed withpip install point-annotatorBuild the documentationFor building the documentation one needs to have sphinx python package
installed.cd docs
make hmtlAfter build the documentation is available in the _build directory.
Package: pointa-to-pointb
DescriptionThis package helps to calculate spatial and euclidean distance between two points of interest, say Point A and Point B.How to UseFor calculating Geo spatial distance between two points in space.Inputs: Latitude and Longitude pairs for Point A and Point B.Output: distance in meters between the two sets of coordinatesfrom pointa_to_pointb import geo_distance

longA = 52.370216
latA = 4.895168
longB = 52.520008
latB = 13.404954

geometrical_distance = geo_distance.haversine(longA, latA, longB, latB)
print(geometrical_distance)For calculating Geo spatial distance between two bounding boxes.Inputs: Bounding box coordinates (top left and bottom right) for for Point A and Point B pairs .Output: nanhattan distance inbetween the two sets of coordinates representing two bounding boxesfrom pointa_to_pointb import bbox_distance

bbox1 = (20, 21, 25, 18)
bbox2 = (55, 55, 100, 10)

manh_distance = bbox_distance.manhanttan_distance(bbox1, bbox2)
print(manh_distance)

gap_distance = bbox_distance.gap_distance(bbox1, bbox2)
print(gap_distance)
Package: pointbreak
PointbreakPointbreak lets you write python programs that debug or analysis other programs. It works like a debugger but because it's a Python library you can extend and interact with it very easily.Pointbreak is designed to make debugging and analysing complex things like JITs simpler without having to added lots of debugging code to the target.General AdvantagesBreakpoints are any Python callableSimple APICan be used from a Python REPL like IPythonCan be easily embedded in other things e.g. a Flask appEasy to add support for custom symbols and JIT generated codeSimpler and less restrictive than GDB Python APIDisadvantageEarly developementMissing many standard debugger featuresOther issues seehttps://github.com/tonysimpson/pointbreak/issuesExample print BOO! when function is calledIf you compilegcc ghost.c -o ghostand run./ghostnothing much happens
But if we runpython boo_ghost.pyit printsBoo!
Boo!
Boo!
Boo!
Boo!
Boo!
Boo!
Boo!
Boo!because our python function is getting called each time there_is_a_ghost is called.
There are more examples in the examples directory.ghost.c/* I'm a silly program that does nothing much on its own. *//* Compile me with "gcc ghost.c -o ghost".                */voidthere_is_a_ghost(void){return;}intmain(intargc,char*argv[]){inti;for(i=0;i<10;i++){there_is_a_ghost();}return0;}boo_ghost.pyfrom__future__importprint_functionimportpointbreakdefprint_boo(debugger):print("Boo!")returnTruedb=pointbreak.create_debugger('ghost')db.add_breakpoint('there_is_a_ghost',print_boo)db.continue_to_last_event()
Package: pointcloud2
PointCloud2PointCloud2 lib for non ROS environment.Usage>>>importnumpyasnp>>>frompointcloud2importcreate_cloud,read_points,PointField>>>fields=[...PointField('x',0,PointField.FLOAT32,1),...PointField('y',4,PointField.FLOAT32,1),...PointField('z',8,PointField.FLOAT32,1),...PointField('rgb',12,PointField.UINT32,1),...]>>>points=np.array([...[1,0,0,255],...[0,1,0,255],...[0,0,1,255],...],dtype=np.float32)>>>cloud=create_cloud(header=None,fields=fields,points=points)>>>cloud.height,cloud.width,cloud.point_step,cloud.row_step,len(cloud.data)(1,3,16,48,48)>>>read_points(cloud)array([(1.,0.,0.,255),(0.,1.,0.,255),(0.,0.,1.,255)],dtype=[('x','<f4'),('y','<f4'),('z','<f4'),('rgb','<u4')])
Package: pointcloud-extraction
@geospatial-pipelines/data_preparation:This package provides the tools and utils for the first stage of the operation after fetching data: allowing users to do operations of the current raw dataset of pointcloud (.las, .laz, .pcd) for operations like:
- Cropping (2D/3D cropping)
- conversion of the coordinates
- combination of point clouds
- conversion across various formats.installation and setup:Install from the pip directory:pip install pointcloud_extraction_toolkit.Or via the source by cloning the wholegeospatial-pipelineand then installing the file via thepip install -r requirements.txt.Various packages:package_fileremarkscameras.utilsconsist of methods to use the API across colmap / neuralangelo for photogrammetrypdal.pipeline_generationscripts to generate the pipeline for PDAL to do necessary transformationscroppingScript to crop the given portion that you want to fetch.threed_pointcloudscript that integrates theopen3Dfor 3D data cropping at microlevelAPI's :There are colab tutorials intest/folder that explain the various usecase, but now try to fetch theImport the cameras package for the photogrammetry pipeline processing to fetch the pointcloudImportant: You need to setup colmap before this package in order to work.frompointcloud_extraction_toolkit.cameras.utilsimportColmapDataParsingimportoscolmap_progressing=ColmapDataParsing(filepath="demo.mp4",output_dir="./demo_output")colmap_progressing.convert_photo_to_video(downsampling_rate=5)## also fetch the image metadata for the algorithm / reviewer in order to showcase the details.## now fetching the image metadata from the given details in order to later on do the required transformation on the specific frame <> pose basis if needed.files='./output/imgs'data_info={}forfilenameinos.listdir(files):full_path=os.path.join(files,filename)imagemetadata=colmap_progressing.get_image_metadata(full_path)data_info[filename].append(imagemetadata)## and finally the colmap transformationawaitcolmap_processing.colmap_transformation()## in the results you seem to see some of the outputs are not compatible with the alignment then run the following method to fix and rerun colmap_transformation().## analyze_colmap_images(self,camera_bin_path, transform_file, camera_depth, coordinates_adjust = ["0", "0", "0", "1"] )colmap_processing.analyze_colmap_images(camera_bin_path=files+"stereo/camera.bin",transform_file=files+"transforms.json",camera_depth="",coordinates_adjust=[])Tutorial for 3D data processing directly:
Package: pointcloud-extraction-toolkit
@geospatial-pipelines/data_preparation:This package provides the tools and utils for the first stage of the operation after fetching data: allowing users to do operations of the current raw dataset of pointcloud (.las, .laz, .pcd) for operations like:
- Cropping (2D/3D cropping)
- conversion of the coordinates
- combination of point clouds
- conversion across various formats.installation and setup:Install from the pip directory:pip install -r requirements.txtOr via the source by cloning the wholegeospatial-pipelineand then installing the file via thepip install -r requirements.txt.Various packages:| package_file | usecases |
Package: pointcyto
pointcytoCytometry data as point cloud representation for use withpytorchandpytorch_geometric.A longer description of your project goes here…Package setupThis project has been set up using PyScaffold 4.5. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.pipinstallpyscaffoldputuppointcytocdpointcyto# Create pointcyto within gitlab, without READMEgitbranch-mmastermaingitremoteaddorigingit@git.uni-regensburg.de:ggrlab/pointcyto.gitgitpush--set-upstreamorigin--allgitpush--set-upstreamorigin--tagscondacreate-y-nconda_pointcytopython=3.10condaactivateconda_pointcyto# Select conda_nbnode as default python interpreter in VsCode
#   select a single python file, then on the bottom right, the current python interpreter name
#   pops up. Click on it and select the "conda_nbnode" interpreter.
# Make sure that the correct pip is used:
#   Something like: /home/gugl/.conda_envs/conda_pointcyto/bin/pipwhichpippipinstalltoxtox--help# Have a clean git, then add "gitlab-CI" with pyscaffoldputup--update.--gitlabputup--update.--github# Add pre-commit hookspipinstallpre-commitpre-commitautoupdate# Documentation
# To use gitlab-pages, you need to place the created htmls in the "public/" directory
# as artifact within the gitlab-ci.yml file.
# Right now, it is created within: .gitlab-ci.yml//docs
Package: pointdns
python-pointdns===========pointhq.com API client.This module provides easy access to point zone & record management. For information about the services offered on Point see [the website](http://pointhq.com)This module is based on a fork of [Richard Gray's](https://github.com/vortura/pointhq) pointhq module which is based on [Mike Yumatov's](https://github.com/yumike/pointhq) pointhq modulewith the following additional modifications:- Added support for Python3.x- Retrieve zones by groups- Record type validation**Authentication**To access your Point account, you'll need to define your username & apitoken. The username is your email address and the apitoken is the API token which, can be found in My Account tab.**Installation**Install ``pointdns`` with pip:$ pip install pointdnsor directly from GitHub$ pip install git+https://github.com/cognetoapps/python-pointdns.gitIt will also install `requests` library.## Usage example* Create new `pointdns.Point` object:```pythonfrom pointdns import Pointpoint = Point(username='john@example.com', apitoken='secret-key')```\* Play with zones::```pythonzones = point.zones.retrieve()new_zone = point.zones.create(name='example.com')``````pythonzone = point.zones(1).retrieve()point.zones(1).update(group='Clients')zones_group_clients = point.zones.retrieve(group='Clients')point.zones(1).delete()```* Play with zone records::```pythonzone_records = point.zones(1).records.retrieve()new_record = point.zones(1).records.create(name='example.com.', data='123.45.67.89', record_type='A')``````pythonzone_record = point.zones(1).records(1).retrievepoint.zones(1).records(1).update(data='234.56.78.90')point.zones(1).records(1).delete()```## ContributingFeel free to fork, send pull requests or report bugs and issues on github.
Package: pointer-brakes
pointer-brakesPointer Brakes is a library for simulating mouse pointer motion.  The pointer will behave like it is a little car with brakes.  If you push it, it moves.  If you let go, it keeps moving but slowly comes to a stop as it applies the brakes.Table of ContentsInstallationUsageLicenseInstallationpip install pointer-brakesUsagea_brakes=1sim_instance=PointerMotionSim(a_brakes)sim_instance.tick(time.monotonic(),(-52,-5))sim_instance.tick(time.monotonic(),(21,-92))change_in_position=sim_instance.delta_positionFor more information check out thedocumentation.Licensepointer-brakesis distributed under the terms of theMITlicense.
Package: pointers-clone.py
pointers.pyBringing the hell of pointers to PythonWhy would you ever need thisDocumentationSourcePyPIExamplesfrompointersimport_text:str="hello world"ptr=_&text# creates a new pointer objectptr<<="world hello"print(text)# world hellofrompointersimportc_mallocasmalloc,c_freeasfree,strcpy,printfptr=malloc(3)strcpy(ptr,"hi")printf("%s\n",ptr)free(ptr)FeaturesFully type safePythonic pointer APIBindings for the entire C standard librarySegfaultsWhy does this exist?The main purpose of pointers.py is to simply break the rules of Python, but has some other use cases:Can help C/C++ developers get adjusted to PythonProvides a nice learning environment for programmers learning how pointers workMakes it very easy to manipulate memory in PythonWhynot?InstallationLinux/macOSpython3 -m pip install -U pointers.pyWindowspy -3 -m pip install -U pointers.py
Package: pointersect
Pointersect: Neural Rendering with Cloud-Ray Intersection[Website]
[Paper]
[Docs]
[Examples]
[Videos]Pointersect is aplug-and-playmethod for rendering point clouds.
It is differentiable and does not require per-scene optimization.UsageGiven a point cloud and a ray, pointersect returns:the intersection point between the ray and the underlying surface represented by the point cloud;surface normal at the intersection point; andcolor/material interpolation weights of neighboring points.You can use point clouds containing only xyz---neither color nor vertex normal is needed.ExamplesWe use the surface normal estimated by pointersect to relight a point cloud.Even though pointersect is designed to render clean point clouds, here is an example where we use a pretrained pointersect model
to render a lidar-scanned point cloud without any optimization.Edit and render without re-optimization.We use pointersect with path tracing to render the global illumination of a scene.How to useYou can use pointersect by installing the pypi package:pipinstallpointersector# in the repo rootpipinstall.We provide API, command line tool, and training script for using pointersect.
Seethe documentationfor instructions.We also provide a fewexamplesif you want to jump in directly :)CitationIf you use this software package, please cite our paper:@inproceedings{chang2023pointersect,
  author={Jen-Hao Rick Chang and Wei-Yu Chen and Anurag Ranjan and Kwang Moo Yi and Oncel Tuzel},
  title={Pointersect: Neural Rendering with Cloud-Ray Intersection},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year = {2023}
}
Package: pointers.py
pointers.pyBringing the hell of pointers to PythonWhy would you ever need thisDocumentationSourcePyPIExamplesfrompointersimport_text:str="hello world"ptr=_&text# creates a new pointer objectptr<<="world hello"print(text)# world hellofrompointersimportc_mallocasmalloc,c_freeasfree,strcpy,printfptr=malloc(3)strcpy(ptr,"hi")printf("%s\n",ptr)free(ptr)FeaturesFully type safePythonic pointer APIBindings for the entire C standard librarySegfaultsWhy does this exist?The main purpose of pointers.py is to simply break the rules of Python, but has some other use cases:Can help C/C++ developers get adjusted to PythonProvides a nice learning environment for programmers learning how pointers workMakes it very easy to manipulate memory in PythonWhynot?InstallationLinux/macOSpython3 -m pip install -U pointers.pyWindowspy -3 -m pip install -U pointers.py
Package: pointevector-zip
OverviewProcess a ZIP file in a single-pass stream without loading the whole archive into memory.Usageimport pointevector.zip

parser = pointevector.zip.StreamParser()
for chunk in chunks():
    for header, file_data in parser.feed(chunk):
        pass # Do something with the file
    
    # (Optional) Stop when no more files are in the archive
    if parser.end_of_files:
        break
Package: pointextract
pointextractTopological transforms by point sampling. Includes:Unwrapping circular and annular regions of images into linear or 2D arraysDesigned to unwrap 2D cross section images of 3D X-ray computed tomography scans.
The transformation enables the surface of a circular object to be aligned for downsteam analysis and a topological transform.InstallationYou can install the package with:pipinstallpointextractBefore using this package, ensure you have Python installed on your system. This package requires the following dependencies:numpyskimageExampleSimple example:importpointextractfromskimage.ioimportimreadimg_arr=imread('example.png')ellipse=ellipse_detect(img_arr,sigma=2)img_unwrap=unwrap_image(img_arr,ellipse,radial_distance=50,num_points=400)QuestionsThis package is still in early development. Please feel free to contact us directly with any questions or bugs. We are currently working to provide an open-source repository where issues and feature requests can be submitted.AcknowledgementsThis material is based upon research in the Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3-COE).[Case Western Reserve University, SDLElab]1
Package: pointfree
pointfree is a small module that makes certain functional programming
constructs more convenient to use in Python.Specifically, it provides:A decorator to enableautomaticpartial application of functions and
methods.Notations for function composition through operator overloading.Helper functions to make composing generators more elegant.The objective is to support thepointfree programming stylein a
lightweight and easy to use manner – and in particular, to serve as a nice
syntax for the kind of generator pipelines described in David Beazley’s
PyCon 2008 presentation,“Generator Tricks for Systems Programmers”.Full documentation is available on the web at:http://pointfree.readthedocs.org/en/latest/
Package: pointgroup
PointGroupSmall utility to determine the point symmetry group of molecular geometriesFeaturesGet point symmetry group labelPure python implementationRequisitesnumpySimple examplefrompointgroupimportPointGrouppg=PointGroup(positions=[[0.000000,0.000000,0.000000],[0.000000,0.000000,1.561000],[0.000000,1.561000,0.000000],[0.000000,0.000000,-1.561000],[0.000000,-1.561000,0.000000],[1.561000,0.000000,0.000000],[-1.561000,0.000000,0.000000]],symbols=['S','F','F','F','F','F','F'])print('Point group: ',pg.get_point_group())AcknowledgmentsThis utility adapts a code originally written by Efrem BernuzContact infoAbel Carrerasabelcarreras83@gmail.comDonostia International Physics Center (DIPC)Donostia-San Sebastian (Spain)
Package: pointhq
pointhq.com API client.InstallationInstallpointhqwith pip:$ pip install pointhqIt will also installhttplib2library.If you use Python 2.5,simplejsonis required:$ pip install simplejsonUsage exampleCreate newpointhq.Pointobject:import pointhq
point = Point(username='john@example.com', apitoken='secret-key')Play with zones:zones = point.zones.retrieve()
new_zone = point.zones.create(name='example.com')

zone = point.zones(1).retrieve()
point.zones(1).update(group='Clients')
point.zones(1).delete()Play with zone records:zone_records = point.zones(1).records.retrieve()
new_record = point.zones(1).records.create(name='example.com.', data='123.45.67.89', record_type='A')

zone_record = point.zones(1).records(1).retrieve
point.zones(1).records(1).update(data='234.56.78.90')
point.zones(1).records(1).delete()ContributingFeel free to fork, send pull requests or report bugs and issues on github.
Package: pointillism
Image manipulation for various "pointillism" effects, built from scratch in Python. Image handling courtesy of Pillow.Under active development. Please let me know if you have feedback.Web app versionCurrently deployed atpointillizer.com, check it out to try out the effect or see examples.Examples and usageSee examples and detailed usage inexample notebookSee other example images ingoogle albumInstallationInstall withpip install pointillismModulespointillism.image- core image methods for pointillizingpointillism.pipeline- methods for making image manipulation pipelines and gifspointillism.batch- batch processing of images or gifs in series (parallel coming soon)pointillism.movies- coming soon!Basic usageMaking a pointillized image with default presets# Import 
import pointillism as pt

# Initialize
point = pt.image(image_location)

# Render
# optional setting can be 'balanced', 'fine', 'ultrafine', 'coarse', or 'uniform'
point.make() 

# Save
# or point.display() if using IPython
point.save_out(image_location)
Package: pointlessutils
Pointless UtilsA collection of utilities that are (probably) pointless.DecoratorsThere are currently 6 pointless decorators available to use:@none- Causes the function to always returnNone@true- Causes the function to always returnTrue@false- Causes the function to always returnFalse@maybe- Causes the function to return a random choice ofTrueorFalse@never- The function will never return and will block indefinitely@always- The function is now a generator that indefinitely yieldsTrue@alwaysv(value)- The function is now a generator that indefinitely yields the given valueThese decorators can be used on either normal or async functions. For example:@maybedefex_1():pass@maybeasyncdefex_2():pass