Saravanan666/pypi-org huggingface dataset:
Package: dearpygui-map
dearpygui-mapMap widget for Dear PyGuiInstallationRequirementsPython >= 3.10Dear PyGui >= 1.3.1Option 1 - pipYou can install this package from pip, withpip install dearpygui-mapOption 2 - Local install from sourcesClone code repository from your local machine, install from there.Poetryis required.git clone https://github.com/mkouhia/dearpygui-map.git
cd dearpygui-map
poetry build
# take note of the build step output, install package from the dist folder
pip install dist/PRODUCED_PACKAGE.whlUsageThis basic example creates a map widget with defined size, initial center point (latitude, longitude) and initial zoom level. The zoom levels are same as ontiled maps, for example 12 could represent a town-sized view. Larger is more zoomed in.importdearpygui.dearpyguiasdpgimportdearpygui_mapasdpg_mapdpg.create_context()withdpg.window(label="Map demo"):dpg_map.add_map_widget(width=700,height=500,center=(60.1641,24.9402),zoom_level=12)dpg.create_viewport(title="Dear PyGui map widget demo",width=800,height=600)dpg.setup_dearpygui()dpg.show_viewport()dpg.start_dearpygui()dpg.destroy_context()The functionadd_map_widgetplaces a Dear PyGui drawlist with map contents,
return value is the drawlist tag.By default, OpenStreetMap is used as the map tile source. This can be configured withadd_map_widgetargumenttile_source, with similar source definition:OpenStreetMap=TileServer(name="OpenStreetMap",base_url="http://{subdomain}.tile.openstreetmap.org/{z}/{x}/{y}.png",subdomains=["a","b","c"],max_zoom_level=19,license_text="© OpenStreetMap contributors",thread_limit=2,)Technical detailsTiles are downloaded from the supplier on a background thread.
Whenever map is zoomed or moved, more tiles are downloaded.
The tiles are cached to local storage path in user cache directory - for different platforms, cache directories are:Windows:    C:\Users\<username>\AppData\Local\dearpygui_map\Cache
Mac OS X:   ~/Library/Caches/dearpygui_map
Other:      ~/.cache/dearpygui_mapKnown issuesAs for now, dearpygui-map is in its early design phase and contains some items that may not be acceptable in production environments.
These issues will be addressed laterTile download threads are created every timeTileManager.draw_layeris called.Non-visible tiles are not removed from dearpyguiZooming and panning leads to wait times before tiles are ready to be shown.DevelopmentEnvironmentPoetry is required for development (seeinstallation instructions)Create development environment withpoetry installEnter environment withpoetry shellCode quality and testingAll code is to be formatted withblack:black dearpygui_mapand code quality checked withpylint:pylint dearpygui_mapTests should be written inpytest, targeting maximum practical code coverage. Tests are run with:pytestand test coverage checked withpytest --covOptionally, html test coverage reports can be produced withpytest --cov dearpygui_map --cov-report htmlContributionsContributions are welcome. Please also see GitHub issues and milestones.
Package: dearpygui-task-list
Python Task ListA task list application, written in Python3 using the dearpygui libraryInstalling as a binary (PyPI)NOTE: While binary packages should work, the dependency management has not been tested!  Please file an issue report if you encounter problems.Runspam@eggs:~$pip3installdearpygui-task-listNOTE: You may need to substitutepip3forpipon Windows-based systems.  To use a specific version of python from your system:/path/to/your/python -m pipcan be used instead ofpip3Installing as a binary (downloaded)NOTE: While binary packages should work, the dependency management has not been tested!  Please file an issue report if you encounter problems.Runspam@eggs:~$pip3install/path/to/the/downloaded/wheel.whlNOTE: You may need to substitutepip3forpipon Windows-based systems.  To use a specific version of python from your system:/path/to/your/python -m pipcan be used instead ofpip3To start (installation as a binary) (any binary source)Runspam@eggs:~$/path/to/your/python-mtask_list.tasks.json will be searched for and written to the current working directory.Installation from sourceIt is suggested that a virtual environment is used, although this is not required.To install dependencies, runspam@eggs:~$pip3install-r/path/to/the/cloned/repository/requirements.txtNOTE: You may need to substitutepip3forpipon Windows-based systems.  To use a specific version of python from your system:/path/to/your/python -m pipcan be used instead ofpip3To start (installation from source)Runspam@eggs:~$cd/path/to/the/cloned/repository
spam@eggs:~$/path/to/your/pythonsrc/task_list/__main__.pyIn this case, data is stored in .tasks.json in the root of the cloned repository.  .tasks.json will be searched for and written to the current working directory.Building packages from sourceRunspam@eggs:~$cd/path/to/the/cloned/repository
spam@eggs:~$/path/to/your/python-mbuild.Built packages will be output to the dist directory.
Package: dear-ros-node-viewer
https://user-images.githubusercontent.com/105265012/177068238-eaf4fed9-12c0-4c5b-ac7f-9597483c4c3c.mp4Dear RosNodeViewerAboutVisualize ROS2 node diagramSupport the following sources:architecture.yamlgenerated byCARETrosgraph.dotgenerated by rqt_graphrunning ROS graph analysisRequirementsUbuntu 20.04 or 22.04graphviz is requiredoptional: ROS 2 Galactic or Humble if you need runtime ROS graph analysisNot tested in Windows / MacGet Started# Install requirementssudoaptinstallgraphviz# Install Dear RosNodeViewerpipinstalldear-ros-node-viewer# Download sample graphwgethttps://raw.githubusercontent.com/iwatake2222/dear_ros_node_viewer/main/sample/architecture_autoware.yaml# Run Dear RosNodeViewerdear_ros_node_viewerarchitecture_autoware.yamlQuick operation guide:Middle button drag: move graph areaMouse scroll: zoom in/out (zoom function is tentative)How to UseSee WiKiAcknowledgementsDear RosNodeViewer utilizesDear PyGuiDear RosNodeVieweris named in honor of Dear PyGuiDear RosNodeViewer containsRoboto fontlicensed under the Apache License, Version 2.0.
Package: dearwatson
WATSON(VisualVetting andAnalysis ofTransits fromSpace ObservatioNs) is a lightweight software package
that enables a comfortable visual vetting of a transiting signal candidate from Kepler, K2 and TESS missions.Any transiting candidate signal found in a space-based mission could have been potentially generated by
different astrophysical sources or even by instrumental artifacts induced into a target's light curve.
To rule-out these scenarios, the Science Processing Operation Center (SPOC) of the NASA implemented
the Data Validation (DV) Reports, which are showing different metrics to qualify
or discard an analyzed candidate. These scenarios are mainlyTransit shape model fitOdd-even transits checks,Weak secondary eventsCentroids shiftsOptical ghost effectsTransit source offsetsRolling band contamination histogramsWATSON is also implementing similar but more simplified checks for all of those scenarios
(SPOC fits transits models and we just compute the SNR of the possible candidate signal) except the
rolling band contamination. In addition, we included a new check comparing the transit SNRs in the different
available cadences and also all the single-transit plots computed with the official pipeline aperture and a
smaller one. With all of these data, we compute metrics that might alert the scientist about problematic
signals not complying with any of the thresholds.For more information check the docs:https://dearwatson.readthedocs.ioCode exampleLet's say that you found a new candidate in the TOI-175 with a period of 1.04 days (this is a known false positive
from the TESS mission). You could run WATSON easily with the next code chunk:period = 1.0491800670761966
epoch = 1354.7155898963902
duration_mins = 54
depth_ppts = 0.158
sectors = 'all'
rp_rstar = 0.01205
a_rstar = 9 
ra = 124.531756290083
dec = -68.3129998725044
# We need to mask the previous candidates to ensure they don't add noise to the analysis
known_transits_mask = [{'P': 3.6906682312153136, 'T0': 1356.20298505518, 'D': 56}, 
                 {'P': 2.2531273092040185, 'T0': 1354.9020729263193, 'D': 45}, 
                 {'P': 7.450690528437917, 'T0': 1355.2917484673462, 'D': 69}]
your_execution_dir = os.getcwd() 
object_dir = your_execution_dir + '/results'
if not os.path.exists(object_dir):
    os.mkdir(object_dir)
Watson(object_dir).vetting("TIC 307210830", period, epoch, duration_mins, depth_ppts, sectors, rp_rstar,
                                       a_rstar=a_rstar, cadence=120,
                                       cpus=os.cpu_count() // 2,
                                       transits_list=None, ra=ra,
                                       dec=dec, clean=True, transits_mask=known_transits_mask)This same example is done in one of our tutorials. Go and check it out!:TOI-175 tutorial
Package: deasciiify
Usedeasciiifyto translate ASCII text into readable non-ASCII text. It was
created for the purpose of test data generation to detect unicode bugs and to
use as a fake language on a webpage to simulate how foreign languages can break
webpage layouts and whatnot. The output is deterministic to make automated
testing easier.Installation$ pip install deasciiifyUsage>>>fromdeasciiifyimportdeasciiify>>>print(deasciiify('The quick brown fox jumps over the lazy dog.'))Ṱḥẹʠǚîĉʞɓṙōẇṇᶂø×ǰúṁᶈșóʋᶒṝᵵĥĕȴạᵶýḍọḡ.>>># elongate by 50%>>>print(deasciiify('The quick brown fox jumps over the lazy dog.',...elongate=0.5))𝕋ȟẹʠůḭċḱᵬṙồẁñḟḟᶂᵮợẋᶍǰưḿṁᵱṣᵴṣǫṽếṛᵳṭħȇɫäǎąẩžɀẓẑẕᶎᶎẓẓỹÿŷӮḋöᵷ.>>># elongate by 100 characters>>>print(deasciiify('The quick brown fox jumps over the lazy dog.',...elongate=100))Ʈɦẻʠűȋḉƙḱᶄḵḳķʞǩƙḱᶄḵḳƀᶉŏẁἦƒḟḟᶂḟƒởᶍ×ẍẋᶍĵứȗửǔǚṷȕǜûủũṳṹữụṻưṃɱḿṁḿɱḿṁᶈşᵴșŝșȫṽḙɽᵳɾŕŕŗȓṙɼᵳȑᵳɽȓȑᶉᵳᶉƭẖėℓἂậẳẩầạẳἇầǡᶏạᶏƶžźƶȥᶎẑᵶżȥᶎẑᶎγƴẏӲӰýỳŷẏγẏỳᶑốǥ.>>># preserve HTML, entities, and gettext params>>>print(deasciiify(...'The <em>quick</em> <strong style="color:brown;">brown</strong> '...'fox%(verb)sover the &ldquo;lazy&rdquo;%s.'))Ṯȟè<em>ʠúᶖćḵ</em><strongstyle="color:brown;">ḃᵲóẅἢ</strong>ḟọẍ%(verb)sȏʋềŕṱȟḝ&ldquo;ḽӓżӮ&rdquo;%s.
Package: deasy
Deasy PackageDeasypackage provide a single cli command*oo*to intercat with odoo instancesDeasyis a Python package providing an easy way to
pilot and interact withOdooinstances throughCLI.Features supported:manage informations of the Odoo instances,backup and restore databases,security audit,search the xml-ids of records,show data,execute functions,python shell,JSON-RPC protocol (SSL supported),Many thanks toOdooRPCandOdoo.Installation :pipinstalldeasyManage informations of Odoo instances :Create an entry for an Odoo instance :oocreate_sectionYOUR_SECTION_NAMEList sections :oosectionsYou can also use  other commands to manage the informations of instances like `section_update` `delete_section` see `oo –help`Manage databasesBackup a database :oo-lYOUR_SECTION_NAMEbackupdb/to/your/pathRestore a database :oo-lYOUR_SECTION_NAMErestoredb/from/your/path/yourdb.zipYou can also use other commands to manage databses like `listdb` `dropdb` see `oo –help`Manage modulesInstall some modules :oo-lYOUR_SECTION_NAMEinstallsale,fleet,purchaseUpdate some modules :oo-lYOUR_SECTION_NAMEupdatesale,fleet,purchaseYou can also uninstall modules using the argumentuninstallseeoo –helpIntercat with dataShow some data on res.partner :oo-lYOUR_SECTION_NAMEdatares.partnerWith more options :oo-lYOUR_SECTION_NAMEdatares.partner-fname-fcustomer-fsupplier-dnamelikeASUSTEK-oname-l1Seeoo data –helpShow fields of the model res.partner :oo-lYOUR_SECTION_NAMEfieldsres.partnerLaunch python shellShow some data on res.partner :oo-lYOUR_SECTION_NAMEshell>>>Partner=odoo.env['res.partner']>>>partner_ids=Partner.search([])>>>forpartnerinPartner.browse(partner_ids):...printpartner.name...Refer toOdooRPCdocumentationYou can also retrieve data with a simple command :oo-lYOUR_SECTION_NAMEshell>>>partners=browse('res.partner')# or browse('res.partner', [])>>>forpartnerinpartners:...printpartner.name...LicenceThis software is made available under the LGPL v3 license.Bug TrackerPlease, feel free to report bugs or suggestions in theBug Tracker!Credits:Mohamed Cherkaoui <http://mohamedcherkaoui.com>News2.0.0Release date: 03-Jun-2017First stable version
Package: deasync
deasync provides a decorator calleddeasyncfor Python 3.5 (or higher) to make async functions synchronous in places where only synchronous functions are expected, for example inunittest.TestCase:fromasyncioimportsleepfromunittestimportTestCasefromdeasyncimportdeasyncasyncdeffunction42():awaitsleep(0.001)return42classTestFunction42(TestCase):@deasyncasyncdeftest_function42(self):result=awaitfunction42()self.assertEquals(42,result)
Package: death
Deathdeathis a simple python library that permit parse argument for some generic tools like lutin, island, qworktree.![Badge](https://badge.fury.io/py/death.pngInstructionsTBD...git repositoryhttp://github.com/HeeroYui/death/Documentationhttp://HeeroYui.github.io/death/InstallationRequirements:Python >= 2.7andpipJust run:pip install deathInstall pip on debian/ubuntu:sudo apt-get install pipInstall pip on ARCH-linux:sudo pacman -S pipInstall pip on MacOs:sudo easy_install pipLicense (MPL v2.0)Copyright death Edouard DUPINLicensed under the Mozilla Public License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttps://www.mozilla.org/MPL/2.0/Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: deathcomp
How to install deathcompYou may need matplotlib library.$ pip install matplotlib$ pip install deathcompHow to run milspendDeathcomp program allows you to compare the number of deaths from COVID-19 for two specified countries and for a specified period (days).
For example, the following command can be executed to graph the number of deaths due to COVID-19 in Japan and China for 30 days.$ deathcomp Japan China 30
Package: deathdaily
COVID-19 daily death predictiondeathdaily is a Python program using new_deaths.csv for predicting daily deaths due to COVID-19 in the next seven days. The prediction is based on the Xthe degree polynomial curve-fitting.According to PyPI Stats, deathdaily has been downloaded by 10356 users worldwide as of Oct.14 2021.X-axis is the xth day from Jan.22 2020 to the day you have downloaded new_deaths.csv file. Y-axis depicts the number of daily deaths in the country.new_deaths.csvdeathdaily needs a new_deaths.csv file for predicting the daily deaths of the next seven days.The new_deaths.csv file is automatically downloaded by deathdaily from the following site:https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/jhu/new_deaths.csvHow to install deathdaily$ pip install deathdailyHow to run deathdaily using country with days and degree of polynomial curve-fitting"country" shows which country you would like to predict the daily deaths of the next seven days."days" indicates how many days are used for Xth degree polynomial curve-fitting."degree" determined the degree of polynomial curve-fitting.$ deathdaily Japan days degree$ deathdaily Japan 200 7country="Japan", days=200, degree=11$ deathdaily 'United States' 200 11$ deathdaily Israel 200 9$ deathdaily 'United Kingdom' 100 11
Package: deathFriends
Death Friends is a third-person shooter in multiplayer format. The game is a dispute between 6 users, in which the winner is the one who survives.🚧  Death Friends 🚀 Under construction...  🚧FeaturesInstall$ pip3 install deathFriendsDeath Friends works with Python 3.6+.To update it just run $ pip3 install --upgrade deathFriends.PlayJust run $ deathFriends.
Package: deathstar
About Death StarDeath Star is intended for destroying stuff.Importingpip install deathstarin your file:import deathstarInitializingimportdeathstarDeathStar=deathstar.DeathStar()parameters: dialogue (boolean, turns on or off printing dialogue, default ifFalse)Deleting Objectsobj=["a","b","c"]DeathStar.trash_compacter(obj)print(obj)#obj go bye bye this should throw error as obj has been destroyedDeletes stuff!Deleting FilesDeathStar.fire_at("text.txt")open("text.txt")#errorsdeletes files. Cannot delete directories, you're thinking of Starkiller base silly
Package: deatool
安装Deatool首先需要您电脑内装有Python，且版本号低于3.9在CMD命令行输入：pip install deatool由于需要安装相关依赖库，可能需要一段时间，请您耐心等待安装完成使用Deatool可以选用以下两种方式打开Deatool在CMD命令行输入：python -m deatool[推荐]直接在CMD命令行输入：deatoolDeatool功能介绍恭喜您已经成功打开了Deatool，接下来将向您介绍这个Python库的功能：计算CCR模型、SBM模型、DDF模型的效率值计算CCR模型、SBM模型、DDF模型在CRS假设下的超效率值针对以上模型可以选取不同的规模报酬性，分别是：CRS（constant returns to scale），VRS（variable returns to scale）选择CCR模型，会自动输出CCR、BCC模型下的效率值，并判断规模报酬情况针对DDF模型可以选取不同的方向并设置对非期望产出的处置性针对SBM模型可以输出相应指标的改进量（投入冗余与产出不足）可以处理包含非期望产出的模型计算Malmquist CCR模型与Malmquist SBM模型，同时可以选取不同的规模报酬性注意事项请严格按照数据格式进行输入使用超效率模型时，会由于模型不可行的问题导致无法产生最优解，会在表格里进行标注"infeasible"请注意使用DDF模型时必须选取方向本代码禁止商用，作者享有最终解释权如有问题，请联系作者
Package: debacl
DeBaCl is an open-source Python library for DEnsity-BAsed CLustering with level set trees.Level set trees are a statistically-principled way to represent the topology of a probability density function. This representation is particularly useful for several core tasks in statistics:clustering, especially for data with multi-scale clustering behaviordescribing data topologyexploratory data analysisdata visualizationanomaly detectionDeBaCl is a Python implementation of the Level Set Tree method, with an emphasis on computational speed, algorithmic simplicity, and extensibility. The code is available at:https://github.com/CoAxLab/DeBaCland the API documentation is at:https://debacl.readthedocs.org/en/v1.1/
Package: debaterpy
A simple library to keep track of your progress and history in competitive debating.The library is largely meant to interface withDebaterJSONand as such
provides a significant number of functions and classes to analyse and manipulate these records.InstallationBecause the package is available on PyPi simply run the following command to install it:pip install debaterpyUsageInstantiating objects is really simple, assuming you have a valid DebaterJSON string loaded indatait only takes two
lines of code to generate an object from them.>>> import debaterpy
>>> record = debaterpy.Record.from_json(data)In fact, this method should cover the vast majority of use cases for generating records. In case more control over
record creation (e.g. for generating test data or fetching a record from an external source) most classes are standard
python dataclasses and as such offer fine programmatic control.Having aRecordobject in memory it is incredibly simple to do even relatively complex manipulations. For example,
in order to get a speaker’s average speaks in rounds where their team won, do:>>> winning_speeches = debaterpy.get_all_speeches(
    record,
    lambda tournament, round, speech: tournament.format == "BP" and round.result == 3
)
>>> winning_speaks = [speech.speak for speech in winning_speeches]
>>> sum(winning_speaks)/len(winning_speaks)
78.88888888888889DocumentationThe documentation lives in thedocsdirectory as well as onReadTheDocs.
Package: debayer-ohweier
Debayer OhweierSmall toolkit to debayer and stack images.How to capture imagesUse the setting (Bayer) for taking the images.Examplefromdebayer_ohweierimportdebayer,get_gradient_stack,get_argmax,zstack,save_image,save_plotimportmatplotlib.pyplotaspltimportnumpyasnpimportosimportglobpath='sample data'#Path to the tif-files from Microscopeim_paths=sorted(glob.glob(os.path.join(path,'*.tif')))# load and debayer all filesims=[]forpinim_paths:im=debayer(path=p)ims.append(im)dirname,filename=os.path.split(p)save_image(im,os.path.join(dirname,'debayered',filename))print('debayered')# calc the gradient of all images. the gradient is highest for sharb edges in the image, hence, areas in Focusa_grad_stack,a_grad_b_stack=get_gradient_stack(ims)print('gradient')# chose the image with the higest gradient per location to get height informationa_max_b=get_argmax(a_grad_b_stack)a_max=get_argmax(a_grad_stack)save_image(a_max,os.path.join(path,'stack','height.tif'))save_plot(a_max,os.path.join(path,'stack','height.pdf'),pixelsize=800/60,s_unit='$\mu m$')save_plot(a_max,os.path.join(path,'stack','height.svg'),pixelsize=800/60,s_unit='$\mu m$')save_plot(a_max,os.path.join(path,'stack','height.png'),pixelsize=800/60,s_unit='$\mu m$')print('argmax')# Stack debayered images acording to height informationa_neu=zstack(ims,a_max_b,blur=True,blursize=30)save_image(a_neu,os.path.join(path,'stack','stack.tif'))save_image(a_neu,os.path.join(path,'stack','stack.jpg'))print('zstack')Developer: Jan Paschenjan.paschen@ise.fraunhofer.de
Package: debbuild
DebbuildCommand line utility to build Deb package without Debian.DescriptionThis project allows to create archives for Debian (.deb) using only Python technologies. It is inspired by theFpm projectandjdeb projectwhich allows to create different types of archives on any platform.I decided to create the Debbuild project, because it is a better way to create a Debian package on any platform. Previsouly, creating packages for Debian required the creation of adebiandirectory, the creation of several configuration files and tools likedpkgto build the package. With Debbuild it is possible to build the package without Debian by using a python script. This makes the creation of packages easier and the process simpler and more understandable.A good usage scenario is the creation of a package containing the binary version of your project created fromPyInstaller.InstallationInstall Debbuild from pypi.pipinstalldebbuildUsageA simple example how to use Debbuild to quickly create a Debian package.debbuild--namemypackage--version1.0.1--data-src/opt/mypackage=<path-to-dir>SupportIf you need help or experience problem while using Debuild, open a ticket inGitlabor Github.RoadmapMore specialized feature could be added:Service Unit creationSupport more compression type likezstdLicenseThis project is release under the MIT License.