Saravanan666/pypi-org huggingface dataset:
Package: picaf
picafpicaf(Pick-up a file) is a tool to generate a clickable map of files.Show the window that allows you to click filenames, from text containing file names.A slightly similar tool ispython -m http.server, which serves a web page including links to files in local directories.For installation instructions, please refer to thepicaf website.Example of Usepicafwas originally designed for use withdendro_text, which finds the similarity of text files and creates a dendrogram.In this example, make the filenames clickable in the output ofdendro_textso that you can investigate the content of each file with a text editor.
Package: pical
picalis a python icalendar library.Supported features:parsing, building an ics file (icalendar version 2.0/rfc5545,rfc2445)querying for component with time-range (caldav/rfc4791)values will be accessible as python native typesUnsupported features:leap second supportfloating values with precision higher thandoubleSimple usage:from datetime import datetime
import pical
cals = pical.parse(open("tests/google_calendar_ex1.ics"))
cal = cals[0]
# subcomponents are stored in children
for c in cal.children:
       if c.name == "VEVENT":
               print c["DTSTART"]Time-range query example:from datetime import datetime
import pical
cals = pical.parse(open("tests/google_calendar_ex1.ics"))
expcal = cals[0].time_range(expand=[datetime(2010,1,1,tzinfo=pical.utc), datetime.now(pical.utc)])
for line in expcal.clone(in_utc=True).serialize():
       print line
Package: picalib
PiCaPiCa is lightweight and minimalist package for autodiff.This library is still under development, so it is recommended that it is only used on an experimental basis!PiCa provides:Representing mathematical functions using computational graphs.Evaluation of functionsObtain the derivatives of the variables of the functionContribution:The PiCa library is still under development, so you may not find some nodes you want to work with. More features will be added to the library continuously, and anyone is free to make a pull request. To make a pull request, it is important to point out the changes you have made and why you have made them.InstallationpipinstallpicalibDocumentation:DocsCode:Github
Package: picamera
This package provides a pure Python interface to theRaspberry Picameramodule for Python 2.7 (or above) or Python 3.2 (or above).LinksThe code is licensed under theBSD licenseThesource codecan be obtained from GitHub, which also hosts thebug
trackerThedocumentation(which includes installation, quick-start examples, and
lots of code recipes) can be read on ReadTheDocsPackages can be downloaded fromPyPI, but reading the installation
instructions is more likely to be useful
Package: picamerab
This package provides a pure Python interface to theRaspberry Picameramodule for Python 2.7 (or above) or Python 3.2 (or above).LinksThe code is licensed under theBSD licenseThesource codecan be obtained from GitHub, which also hosts thebug
trackerThedocumentation(which includes installation, quick-start examples, and
lots of code recipes) can be read on ReadTheDocsPackages can be downloaded fromPyPI, but reading the installation
instructions is more likely to be useful
Package: picamerax
PiCamera + ExtrasThis package provides a pure Python interface to theRaspberry Picameramodule for Python 3.2 or above, with extra features and fixes.Lens-shading table support (from rwb27/master)Read-write analog gain (from rwb27/master)Greyworld AWB (from chrisruk/greyworld)Bayer array support for HQ camera (from AlecVercruysse/master)Dropped support for Python 2Why?The upstream waveform80/picamera library has reached a point of stability meaning some more cutting-edge
features will be unsuitable for inclusion, or face delays.
This fork will include new features and fixes more rapidly, allowing the latest camera revisions, and
more experimental software features, to be easily used much earlier than they would otherwise.Installationpip install picameraxLinksThe code is licensed under theBSD licenseThesource codecan be obtained from GitHub, which also hosts thebug
trackerThedocumentation(which includes installation, quick-start examples, and
lots of code recipes) can be read on ReadTheDocsPackages can be downloaded fromPyPI, but reading the installation
instructions is more likely to be usefulDeveloper notesWhy calendar versioning?We wantpicameraxto stay more or less in sync with upstreampicamera. However, this fork
adds significant new features that would constitute new version numbers under semantic versioning.
If we were to adopt semantic versioning, we would rapidly fall completely out of sync with the
upstream version numbers.By using calendar versioning, and clearly tracking new additions to the library, we
can ensure the upstream base version is obvious (by looking at its release date in relation to
our current calendar version), without worrying about what new features count as justifying
new semantic versions.This can change however if everyone else deems it a bad idea.
Package: picam-iot
picam_iotpicam_iot provides simple interfacing with the picam V2 and intergrates it with Python computer vision or image processing libraries.Free software: MIT licenseDocumentation:https://picam-iot.readthedocs.io.FeaturesBasic image and video capture and recordingFace detection captured or existing imagesMotion detection from videosLocal storage management for images and videosDropbox storage management for images and videosUsageRequirementsRaspberry pi camera libraryOpenCVDropbox Core API account and access tokenEnough storage on the raspberry pi, 3GB recommended minimumGet Startedinstall dependencies:$ pip3 install -requirements_dev.txtbasic usage:>>> from picam_iot import *
>>> img = image() # image management class
>>> vid = video() # video management class
>>> dpx = dropbox_storage(access_token = "<access_token from dropbox>")CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-08-29)First release on PyPI.
Package: picamip
PicamipPython simple Raspberry-Pi camera module web interfaceThis software runs on a RPi, it starts a webserver accessible via it's
IP address. The web interface has a preview screen, buttons to take
picture, download and delete images.InstallingInstall with pippip install picamipRunningThe software can be started with$ picamipThen go to the interface athttp://<Raspberry IP address>:8000Additional options may be passed to the program:picamip--help
usage:picamip[-h][-pPICTURE_DIR][-fFILES_PREFIX][-tFLASK_TEMPLATE][-sFLASK_STATIC][-oFLASK_OVERLOAD][-dDEFAULT_ROUTE][-v][host][port]picamip:PythonsimpleRaspberry-Picameramodulewebinterface

positionalarguments:hostServerhostportServerport

optionalarguments:-h,--helpshowthishelpmessageandexit-pPICTURE_DIR,--picture-dirPICTURE_DIRPicturesstoragedirectory-fFILES_PREFIX,--files-prefixFILES_PREFIXDirectorytostorethepictures.Default:~/Pictures-tFLASK_TEMPLATE,--flask-templateFLASK_TEMPLATEFlaskadditionaljinja2templatesdirectory,overwritesdefaults.-sFLASK_STATIC,--flask-staticFLASK_STATICFlaskadditionalstaticfilesdirectory,overwritesdefaults.-oFLASK_OVERLOAD,--flask-overloadFLASK_OVERLOADFlaskappfunctionsoverload.-dDEFAULT_ROUTE,--default-routeDEFAULT_ROUTEDefaultrootroute.Eg:index.html-v,--versionshowprogram'sversionnumberandexitCustomizingIt's possible to customize the frontend by specifying another static
and template directories with:--flask-staticand--flask-template.Endpoints may be customized by declaring callback functions into a
python script and using--flask-overload. Overload functions must
start withoverloadand they receive an instance offlask.Flask(app) and an instance ofpicamip.StreamPiCamera(camera).EndpointsDefault endpoints are:/- GET: root route/files- GET: Gets the current storage indexes and filenames/stream- GET: Camera preview (mjpeg)/picture- GET: Gets an image of given indexQuery params: index (int) - picture index, download (bool)- Downloads the image/picture- POST: Takes a picture from the cameraQuery params: download (bool)- Downloads the image/downloadAll- GET: Downloads all the images as a zip file/deleteAll- DELETE: Deletes all images/delete- DELETE: Deletes an image of given indexQuery params: index (int) - picture index/shutdown- POST: Shuts down the Raspberry PiLicensePython simple Raspberry-Pi camera module web interface
Copyright (C) 2021 Luiz Eduardo Amaralluizamaral306@gmail.comThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program.  If not, seehttp://www.gnu.org/licenses/.
Package: picammodapipkg
picammodapipkgPython Boilerplate contains all the boilerplate you need to create a Python package.Free software: MIT licenseDocumentation:https://picammodapipkg.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-09-09)First release on PyPI.
Package: picamraw
picamrawLibrary for extracting raw bayer data from a Raspberry Pi JPEG+RAW file.Installable fromPyPI; usable without camera hardware present.Usage exampleExtract raw bayer arrayfrompicamrawimportPiRawBayer,PiCameraVersionraw_bayer=PiRawBayer(filepath='path/to/image.jpeg',# A JPEG+RAW file, e.g. an image captured using raspistill with the "--raw" flagcamera_version=PiCameraVersion.V2,sensor_mode=0)raw_bayer.bayer_array# A 16-bit 2D numpy array of the bayer dataraw_bayer.bayer_order# A `BayerOrder` enum that describes the arrangement of the R,G,G,B pixels in the bayer_arrayraw_bayer.to_rgb()# A 16-bit 3D numpy array of bayer data collapsed into RGB channels (see docstring for details).raw_bayer.to_3d()# A 16-bit 3D numpy array of bayer data split into RGB channels (see docstring for details).TestingThis package is tested usingtox.
To run tests, simplypip install toxand then runtox.Note: this code has only been tested against an image captured with camera version V2 and sensor_mode 0.AttributionThis library was forked from thePiCamerapackage and heavily modified.
Package: picard
MusicBrainz PicardMusicBrainz Picardis a cross-platform (Linux, macOS, Windows) audio tagging application. It is the officialMusicBrainztagger.Picard supports the majority of audio file formats, is capable of using audio fingerprints (AcoustIDs), performing CD lookups anddisc IDsubmissions, and it has excellent Unicode support. Additionally, there are several plugins available that extend Picard's features.When tagging files, Picard uses an album-oriented approach. This approach allows it to utilize the MusicBrainz data as effectively as possible and correctly tag your music. For more information,see the illustrated quick start guide to taggingand thedocumentation.FeaturesMultiple formats:Picard supports all popular music formats,
including MP3, FLAC, OGG, M4A, WMA, WAV, and more.AcoustID:Picard usesAcoustIDaudio fingerprints, allowing files to be identified by the actual
music, even if they have no metadata.Comprehensive database:Picard uses the open and
community-maintainedMusicBrainz databaseto provide accurate information about millions of music releases.CD lookups:Picard can lookup entire music CDs with a click.Plugin support:If you need a particular feature, you can choose
from a selection ofavailable pluginsorwrite your own.Scripting:A flexible and powerful, yet easy to learn, scripting language
allows you to exactly specify how your music files will be named and how the
tags will look like.Cover Art:Picard can find and download the correct cover art for your albums.Open Source:Picard is licensed under theGNU General Public License 2.0or later, and is hosted on GitHub where it is actively
developed.InstallationBinary downloads are available on thePicard download page.INSTALL.md has instructions on building this codebase.Support and issue reportingPlease report all bugs and feature requests in theMusicBrainz issue tracker. If you need support in using Picard please read thedocumentationfirst and have a look at theMusicBrainz community forums.TriviaPicard is named afterCaptain Jean-Luc Picardfrom the TV seriesStar Trek: The Next Generation.
Package: picard.py
“Make it so.”–Captain Jean-Luc PicardThe idea ofAnsiblewith the execution ofMake.With Picard, you define a set of targets, each with a recipe that leaves it in
a desired state, e.g. a compiled executable or a running service. Targets may
depend on each other, e.g. “this executable depends on that source file” or
“this service depends on that host”, in a directed acyclic graph. Like Make,
Picard executes the recipes for targets in dependency order.Like Ansible, Picard comes with many sophisticated recipes out-of-the-box
that behave likersync: they find the differences between a target’s present
state and its goal state, and execute just the changes necessary to transition
from the first to the second.Make is limited to considering targets on the local filesystem, while Ansible
can consider more general targets and states, e.g. the existence and
configuration of remote machines. Ansible’s input is a rigid declarative
template (based onJinja), while Make’s input is an executable script that
builds the abstract definitions of the targets and gets to leverage functions
and variables. Picard tries to combine the best of both worlds in pure Python.HelpPlease see the documentation onRead the Docs.If you have any questions, please askmein theissues, byemail, overTwitter, or however you want to reach me. I’ll be happy to help you, because
it will help me make this documentation better for the next reader.
Package: picasa-downloader
A script that automatically downloads all images from a picasa web album.###Installation
For a stable install run` pip installpicasa-downloader`If you wish to run the latest copy out of git` python setup.py install `###Operation
Run it by using:` picasa user_id[--resume=album_name][--force][--username=google_username][--password=google_password]`user_id can either be the user’s numeric Google ID or their Gmail address (with‘@gmail.com’ at the end)The optional resume flag allows you to skip all albums until you reach the album specified.Force will overwrite existing image files with re-downloaded copies.Username and password allows the user to login and download private albums–if both are not provided, the script will simply ignore logging in
Package: picasa_fetcher
This script allows you to download images from Picasa and optionally delete
them.Usage:$ picasa_fetcher --username=<username> --password=<password> images/This will download all images for the given user into the images/ folder (which
you must create).This will leave images on the server. To delete images older than N days, do:$ picasa_fetcher --username=<username> --password=<password> --delete 1 images/By default, the script will stop as soon as it finds an image with a timestamp
it has seen before (it fetches images in reverse date order). To keep going,
potentially overriding local files, use the--forceparameter.To limit the number of downloads, use the--limitparameter.To get more usage information:$ picasa_fetcher --helpChangelogAdded the–albumoption. To get the album id, open an album in the web GUI
and get the URL of the RSS feed. The album id is in the URL after/albumid/.
Package: picasso-viz
PicassoA CNN model visualizerSee theMedium postfor an introduction to Picasso.Free software: Eclipse Public LicenseDocumentation:https://picasso.readthedocs.io.If you use Picasso in your research,please cite our paper:@misc{picasso2017,
      Author = {Ryan Henderson and Rasmus Rothe},
      Title = {Picasso: A Neural Network Visualizer},
      Year = {2017},
      Eprint = {arXiv:1705.05627},
      Url = {https://arxiv.org/abs/1705.05627}
     }QuickstartPicasso usesPython 3.5+so use a virtual environment if necessary (e.g.virtualenv env--python=python3) andactivate it!Install with pip or from source.With pip:pip install picasso-vizFrom the repository:git clone git@github.com:merantix/picasso.git
cd picasso
pip install -e .Note: you’ll need the Tensorflow backend for Keras for these examples to work.  Make sure your~/.keras/keras.jsonfile looks like:{
    "backend": "tensorflow",
    "image_dim_ordering": "tf",
    "floatx": "float32",
    "epsilon": 1e-07
}Start the Flask serverexport FLASK_APP=picasso
flask runPoint your browser to127.0.0.1:5000and you should see the landing page!  When you’re done,Ctrl+Cin the terminal to kill your Flask server.Building the docsThe documentation is much more extensive than this README, and includes instructions on getting the Keras VGG16 and Tensorflow NMIST models working, as well as guides on building your own visualizations and using custom models. This assumes you’ve cloned the repository. First install the required packages:pip install -e .[docs]Then build them:cd docs/
make htmlThen you can open_build/html/index.htmlin your browser of choice.NotesModels generated on Keras using the Theano backend should in principle be supported.  The only difference is the array ordering of convolutions.  I haven’t tried this yet though, so an extra config parameter may be needed.CreditsEliasandFilippofor early code contributions and finding bugs and issues.John,Josh,Rasmus, andStefanfor their careful code review and feedback.The favicon is a modification of thisphotographof the painting “Les Demoiselles d’Avignon”, 1907 by Pablo Picasso. Photograph by Max Braun.This package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.2.0 (2017-06-07)Add RESTful APIRework configuration loadersMajor refactor0.1.2 (2017-06-07)Fix Keras loading issuesCheck tensorflow installation before installing0.1.1 (2017-05-16)First release on PyPI.
Package: picat-kernel
An Picat Kernel for JupyterPrerequisitesPicatJupyter NotebookmetakernelLinux or MacOSInstalation via pippip install jupyter
pip install metakernel
pip install picat-kernelAdd--userto install in your private environment.Usejupyter notebook
# In the notebook interface, select Picat from the 'New' menuorjupyter qtconsole --kernel picatorjupyter console --kernel picatRestrictionsIt doesn't work on Windows because restrictions ofpexpectandsubprocesslibrariesIt isn't possible to have interation using;or debugger.
Package: picbed-oauth2-github
这是基于picbed的一个小的扩展模块，
用来接入 GitHub OAuth2 登录。安装正式版本$ pip install -U picbed-oauth2-github开发版本$ pip install -U git+https://github.com/staugur/picbed-oauth2-github.git@master开始使用此扩展请在部署picbed图床后使用，需要
其管理员进行添加扩展、设置钩子等操作。添加：请在站点管理-钩子扩展中点击安装第三方包，可在弹窗列表中选择
picbed-oauth2-github，点击行尾图标，或者在弹窗底部按照正式/开发版本填写安装。安装完成后，在站点管理-钩子扩展中点击添加第三方钩子，
输入名称：oauth2gh，确认后提交即可加载这个模块。配置：在站点管理-网站设置底部的钩子配置区域配置GitHub OAuth ID和
GitHub OAuth Secret！使用：1、在GitHub中注册一个OAuth AppAuthorization callback URL是picbed地址，比如http://demo.picbed.pro其他参数根据实际填写。提交后生成的Client ID和Client Secret是需要配置到picbed中的。2、在picbed站点管理-网站设置底部钩子配置区域中选择第三方认证为
oauth2gh即可。启用后，在登录页面会显示使用GitHub登录，跳转到授权页，授权后跳回picbed。PS：登录状态是依靠session用户名有gh-前缀
Package: picbed-smtp
这是基于picbed的一个小的模块，用以扩展邮件发送服务。安装正式版本$ pip install -U picbed-smtp开发版本$ pip install -U git+https://github.com/staugur/picbed-smtp.git@master开始使用此扩展请在部署picbed图床后使用，需要
其管理员进行添加扩展、设置钩子等操作。添加：请在站点管理-钩子扩展中添加第三方钩子，输入模块名称：picbed_smtp，
确认后提交即可加载这个模块（请先手动或在线安装好此模块）。配置：在站点管理-网站设置底部邮件服务设置中配置邮箱相关信息。使用：发送邮件时，程序会调用邮件发送功能，目前有一内置钩子sendmail实现了功能。如启用此picbed-smtp钩子扩展，那么，程序在发送时会按序调用（不一定谁优先），
某个发送失败才会进入下一个，任一发送成功则停止，全部发送失败则本次邮件发送
宣告失败。所以，如果不想用内置邮件发送钩子，可以在站点管理-钩子扩展中禁用内置
的sendmail，仅启用本扩展。
Package: picbed-video
这是基于picbed的一个小的扩展模块，用来上传视频。安装正式版本$ pip install -U picbed-video开发版本$ pip install -U git+https://github.com/staugur/picbed-video.git@master开始使用此扩展请在部署picbed图床后使用，需要
其管理员进行添加扩展、设置钩子等操作。添加：请在站点管理-钩子扩展中添加第三方钩子，输入模块名：upvideo，
确认后提交即可加载这个模块（请在服务器先手动安装或Web上安装第三方包）。使用说明：针对已登录用户，不允许匿名，上传到默认存储中。登录后，右侧菜单下拉有我的视频，允许上传mp4|ogg|ogv|webm|3gp|mov格式，
大小不超过系统限制，支持删除。ps：上传、删除效果同图片上传、删除，即如果钩子支持则会物理删除。ps：上传按用户/年/月/日/原名保存，同一天同文件会覆盖。
Package: piccard
PiccardIntroductionpiccardis a Python package which provides an alternative framework to traditional harmonization techniques for combining spatial data with inconsistent geographic units across multiple years. It uses a network representation containing nodes and edges to retain all information available in the data. Nodes are used to represent all the geographic areas (e.g., census tracts, dissemination areas) for each year. An edge connects two nodes when the geographic area corresponding to the tail node has at least a 5% area overlap with the geographic area corresponding to the head node in the previous available year.ResearchThe method behind this package can be found in the following research paper:Dias, F., & Silver, D. (2018). Visualizing demographic evolution using geographically inconsistent census data. California Digital Library (CDL).https://doi.org/10.31235/osf.io/a3gtdInstallationThe latest released version is available at thePython Package Index (PyPI)pip install piccardImporting the packagefrom piccard import piccard as pcUseful Functionspiccard.preprocessing(ct_data, year, id)Return a cleaned GeoDataFrame of the input data with a new column showing the area of each census tract.piccard.create_network(census_dfs, years, id, threshold=0.05)Creates a network representation of the temporal connections present incensus_dfsoveryearswhen each yearly geographic area has at mostthresholdpercentage of overlap with its corresponding area(s) in the next year.piccard.create_network_table(census_dfs, years, id, threshold=0.05)Return the final network table with all the temporal connections present incensus_dfsoveryearswhen each yearly geographic area has at mostthresholdpercentage of overlap with its corresponding area(s) in the next year.piccard.draw_subnetwork(network_table, G, num_cts=4)Draws a subgraph of the network representation wherenum_ctsis the number of census tracts in the first census year which are followed through all census years.piccard.plot_num_cts(network_table, years, id)Plots the number of census tracts across all given census years.Note: Further explanation of the parameters and example code for all the above functions can be found in thedocumentation.DependenciesGeoPandas - Allows spatial operations in Python, making it easier to work with geospatial dataMatplotlib - a comprehensive library for creating visualizationsNetworkX - Adds support for analyzing networks represented by nodes and edgesNumPy - Adds support for large, multi-dimensional arrays and matrices, with functions to operate on these arrayspandas - Offers data structures and operations for manipulating numerical tablesAuthorsMaliha Lodi, Fernando Calderon Figueroa, Daniel Silver