Saravanan666/pypi-org huggingface dataset:
Package: tray-runner
Tray RunnerTray Runner is a simple application that runs in the system tray and executes periodically the commands configured. This is util when you want to run command line scripts without having a terminal open ow worry to remember to execute them.The application has been tested on Linux (Ubuntu) and Windows 10, but should run on any modern operating system, as the UI relies on QT.Checkmore screenshots here.InstallationPython 3.10 or greater is required.It is recommended to usepipxso you can install Tray Runner and its dependencies without affecting other applications installed withpip:pipxinstalltray-runnerCheck and upgrade with:pipxupgradetray-runnerIn a near future, single file binaries will be provided.Once installed, you can run the application running the command:tray-runner-guiCheck the options runningtray-runner-gui --help. The first time the program is executed, a shortcut in the applications menu and in the auto start directory will be created. Also, you will be asked to configure the application:Fedora/RHEL basedsudodnfinstall-ygnome-shell-extension-appindicator
gnome-extensionsenableappindicatorsupport@rgcjonas.gmail.comNote: the indicator icon will be shown, but the notifications will remain in the notifications list.RunningFrom the CLI:tray-runner-cli--helpFrom the GUI:tray-runner-gui--helpTODOTranslations (raw Python and QT)One-file executables (and portables) for Linux and WindowsLog viewerDevelopmentTranslationsUpdate the template:poetryrunpybabelextract-otray_runner/locale/messages.potGenerate a new language:poetryrunpybabelinit-lde_DE-itray_runner/locale/messages.pot-dtray_runner/localeUpdate the languages with the new translations found:poetryrunpybabelupdate-itray_runner/locale/messages.pot-dtray_runner/localeCompile the translations:poetryrunpybabelcompile-dtray_runner/localeCode qualityRunning directly the commands:poetryrunpylinttray_runner
poetryrunblacktray_runner
poetryrunmypytray_runner
poetryrunisorttray_runnerUsingpre-commit:gitadd--intent-to-add.
poetryrunpre-commitrun--all-filesCreditsIcons:Fugue IconsFont AwesomeIkonateMore screenshotsList of commands:General configuration:Commands common configuration:Command configuration:Command overrides:Command environment variables:Command statistics:
Package: traytable
traytableA python package for tabulating crystallization results across many traysDocumentation built via Read the Docstraytableprovides methods forstoring all information about a crystallization screen in a dictionary of dictionariesextracting and tabulating all data about "hits" into apandasdataframe.The goal oftraytableis for all crystallization data to be inputted once and only once, and then conveniently looked up and reused whenever needed.You can find a jupyter notebook with a brief demonstration of package functionalityhere.Installationpipinstalltraytable
Package: traze-client
Traze Client PythonA Traze client based on Python.TrazeTraze is a multi client online tronlike game with MQTT protocol. It aims to provide a playground
for various game clients, AI game bots, and showcase of a resonable secure MQTT application, and
it’s fun! You can write your own game client and participate in the game using the protocol
documented on the homepage.LinksHomepage -traze.iteratec.deSources -traze-client-pythonIssues -traze-client-python/issuesExample Bots -traze-bots-python
Package: trazer
TrazerA lightweight trace analysis framework (trace analyzer) for execution and network traces,
focusing on event chain analysis.Getting StartedPrerequisitesPython >= 3.8InstallationpipinstalltrazerUsageCreate Trace and Add Events>>>fromtrazerimportTrace,TraceEventDurationBegin,TraceEventDurationEnd>>>trace=Trace()>>>trace.add_event(TraceEventDurationBegin('my_event',1.0,pid=0,tid=0))# my_event begins at 1.0 s>>>trace.add_event(TraceEventDurationEnd('my_event',2.0,pid=0,tid=0))# my_event ends at 2.0 sExport Trace to Chrome Tracing JSON>>>fromioimportStringIO>>>s=StringIO()>>>trace.to_tef_json(file_like=s)# Exported timestamps are in milliseconds by default>>>print(s.getvalue()){"traceEvents":[{"name":"my_event","ts":1000.0,"pid":0,"tid":0,"args":{},"ph":"B"},{"name":"my_event","ts":2000.0,"pid":0,"tid":0,"args":{},"ph":"E"}],"displayTimeUnit":"ms"}The exported.jsonfile can be opened in the trace visualization toolschrome://tracingin Chrome
andPerfetto.Match Event ChainsDifferent related events in a trace can be merged and represented as an event chain at a higher hierarchical level.An event chain is described using an event pattern, where the following symbols have special interpretation:+following an event name: the event begins.-following an event name: the event ends.*: arbitrary events, excluding repetitions.>>>fromtrazerimportTrace,TraceEventDurationBegin,TraceEventDurationEnd>>>trace=Trace()# Add an event sequence: event1 begins, event2 begins, event2 ends, event1 ends>>>trace.add_events([...TraceEventDurationBegin('event1',1),...TraceEventDurationBegin('event2',2),...TraceEventDurationEnd('event2',3),...TraceEventDurationEnd('event1',4)...])>>>print(trace)[1ms]:event1(B)[2ms]:event2(B)[3ms]:event2(E)[4ms]:event1(E)>>>fromtrazerimportTraceAnalyzer>>>trace_analyzer=TraceAnalyzer(trace)# We want to find the event chains matching the sequence:# event1 begins -> event2 begins -> event2 ends -> event1 ends>>>trace_analyzer.match('event1+event2+event2-event1-','event_chain')[[1-4ms]:event_chain(4events)]# Or use an alternative pattern employing wildcards.# We want to find the event chains that begins with event1 and ends with event1.>>>trace_analyzer=TraceAnalyzer(trace)>>>trace_analyzer.match('event1+*-event1-','event_chain')[[1-4ms]:event_chain(4events)]Export Trace with Event ChainsThe event chains can be visualized together with the original trace in the same view.
For visualization inchrome://tracingorPerfetto, a dedicated process ID needs to be assigned to
the event chains, such that they will be displayed separately from the original trace.trace_analyzer.to_tef_json(5555)# Process ID = 5555ContributingInstall development dependenciespipinstall-rrequirements-dev.txtInstall runtime dependenciespipinstall-rrequirements.txtSetup pre-commit hook (formatting with black and linting with flake8)pre-commitinstallMake sure all tests are passed by runningpytest
Package: trazhash
TrazHashDescriptionTrazHash is a Python library designed to hash data strings, called a System-Dependent Hash, or a SDHASH, or file contents using a combination of your system's specific values and cryptographic hash functions. By incorporating system-specific values such as the system name, node name, release, version, machine, processor, and hostname into the hashing process, the library produces hashes that are tied to the particular system on which the hashing was performed. This can be useful for generating unique identifiers, checksums, or verifying the integrity and origin of data.The library supports multiple levels of hashing (e.g., hash inside another hash) and allows users to specify the desired cryptographic hash functions (e.g., SHA256, SHA512) for each level.TrazHash is easy to use, highly configurable, and can be integrated into various Python projects including security applications, data verification tools, and more.FeaturesSystem-specific Hashing: Combines data strings or file contents with system-specific values before hashing, ensuring that hashes are unique to the system.Multi-level Hashing: Supports hashing data inside another hash, which can be used for additional security and integrity assurance.Configurable Hash Algorithms: Allows users to specify which cryptographic hash functions to use at each level.File Hashing: Conveniently hash the contents of a file.Data Verification: Includes a function for verifying if the hash of a given data string matches a provided hash.Custom Exceptions: Provides custom exceptions for error handling, particularly when reading files.Use CasesSecurity Applications: Produce system-tied hashes for secure authentication or encryption processes.Data Integrity Checks: Verify the integrity and origin of data by comparing hashes.Unique Identifier Generation: Generate unique identifiers for data that are also tied to the system.Checksum Generation: Create checksums for files which are unique to the system for verification purposes.Example Use CaseTrazHash can be particularly useful in scenarios where you want to ensure that certain data or files have not been altered or tampered with, and also verify that they originated from a specific system. For instance, in a network of devices where each device generates data files, you can use TrazHash to generate a checksum for each file which includes system-specific values. When collecting data from the devices, you can verify not only that the file has not been altered (by comparing checksums) but also confirm which device the data originated from.DisclaimerPlease note that while the library provides cryptographic hash functions, it should not be solely relied upon for security-critical applications without additional safeguards and considerations of the security context in which it is used.
Package: trboxcmp
trboxcmpThis is a library that interfaces with the MotoTRBO XCMP protocol. This allows control of a radio via remote.!! NOTE !!This is beta software. Not everything will work and it will be buggy. I am by no means a real developer, I just try stuff until it works.TODOsTodos:Add brightness control and parsingAdd channel, display text, etc parsingAdd keypad button opcodesBasic LayoutMost of the basic data services can be set up as such:from trboxcmp import xcmp
keys = [
    0x00000000,
    0x00000000,
    0x00000000,
    0x00000000
]
delta = 0x00000000
listener = xcmp.XCMP(keys, delta)
listener.connect()
listener.sendChUp()The true keys and delta are property of Motorola Solutions and will not be included with this library.Documentationlistener.close()Closes the connection to the radio.listener.setChannel(chan)Sets the radio to channel number  in the current zone.listener.setZone(zone)Sets the radio to zone number .listener.chUp()Increments the current channel.listener.chDown()Decrements the current channel.listener.enterRSSI()Quick macro to enter the "hidden" RSSI menu on the radio (left, left, left, right, right, right).listener.pressButton(button)Presses  on the radio.  can be any of those found in ButtonCodes:
class ButtonCodes():
STATUS_PRESSED = b'\x01'
STATUS_RELEASED = b'\x00'
LEFT = b'\x80'
RIGHT = b'\x82'
UP = b'\x87'
DOWN = b'\x88'
MENU = b'\x8b'
BACK = b'\x81'
OK = b'\x55'
P1 = b'\xa0'
P2 = b'\xa1'
P3 = b'\xa2'
P4 = b'\xa3'
Package: trc-data-reader
Python package for reading track row column (trc) motion capture data.
TRC data reader captures the motion capture data and puts it into a dict like object.TRC data reader can read motion capture data from file or from a multi-line string.For a valid trc file or multi-line data string the following keys will be present (among others):FileNameDataFormatMarkersDataRateNumFramesNumMarkersUsagefrom trc import TRCData

mocap_data = TRCData()
mocap_data.load('/path/to/file/')

num_frames = mocap_data['NumFrames']
Package: trch-file-recoverer
File RecovererThis is a tool you can use to recover files from partition images. It is a very simple tool, but it can be useful in some cases where you need to recover files from a partition image.Pre-requisitesYou must install The Sleuth Kit (TSK) to use this tool. To install it on Ubuntu, run the following command:sudoapt-getinstallsleuthkitTo install on other Linux distributions, please refer to theTSK website. To install on a Mac, you can useHomebrew:brewinstallsleuthkitInstallationTo install this tool, you can use the following command:pipinstalltrch-file-recovererUsageTo use this tool, you must first create a partition image. You can do this using theddcommand. For example, to create an image of the first partition on a disk, you can run the following command:ddif=/dev/sda1of=sda1.imgOnce you have created the image, you can run the tool as follows:recover-filessda1.img
Package: trclip
All information will be updated after INISTA 2022trclipA Turkish CLIPFree software: MIT licenseDocumentation:https://trclip.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2022-04-02)First release on PyPI.
Package: trdb2py
trdb2py这是tradingdb2的python库，初衷是希望有一个更方便的研究工具，能在jupyter里使用。大量的运算，都是通过tradingdb2发送给tradingnode2节点进行分布式运算，而tradingdb2负责负载均衡 和 结果缓存，这样就算jupyter有什么问题，也不至于要重新运算。图形化选择的是plotly。注意：jupyter运行效率有些低，如果不在研究阶段，建议直接python运行脚本更合适。如果进行非常大数据量的运算，在jupyter里很容易内核挂掉，这种脚本需要自己处理好内存管理，直接python运行脚本更合适。提供了vscode可用的devcontainer，建议用这个配置开发，避免污染本地环境。安装直接用pip安装即可。pip install trdb2py更新日志v0.3需要搭配TradingDB2v0.6 以上版本使用。v0.2加入更多本地分析功能，不会把所有计算都放云端。v0.1实现了基本功能。
Package: trddatetime
trddatetime (Trade Datetime)============================================================Overview.============================================================TimeZone 설정값에 따라 그 나라의 거래소 거래일시를 사용할 수 있다.한국거래소: STOCK_TZ={+9, UTC+9}
미국거래소: STOCK_TZ={-4, ET}
영국거래소: STOCK_TZ={0, CET}============================================================사용법.========================================================================================================================주의사항 !!========================================================================================================================Configuration============================================================
Package: trddt
TradeDatetime한국거래소 기준 Datetime Functions 패키지사용법패키지 설치 후 최초 1회 다음을 파이썬 인터프리터 또는 쥬피터에서 실행해야 합니다.!주의 : 데이터 수집에 시간이 꽤 걸리니 자주 실행하면 안됩니다.import trddt
trddt.build_HolidaysText()그러면 trddt 패키지가 설치된 경로에 Holidays.txt 파일이 생성됩니다.편의상 기본적으로 다음 연도(Next Year)까지 수집됩니다.주식시장 특성상 00시 00분 부터 05시 59분까지는 이전일(어제)로 취급합니다.예를들어, 오늘이 2023년 8월 11일 새벽 02시 00분 이라고 가정하면trddt.date(day=None) # 날짜를 특정하지 않으면, 현재시각을 기준으로 자동계산됨
>> datetime.datetime(2023, 8, 10, 0, 0, tzinfo=datetime.timezone(datetime.timedelta(seconds=32400), '대한민국 표준시'))
Package: treacle
treacle is an AGI (Asterisk Gateway Interface) script which handles
public holidays and opening hours in different offices with different
timezones and different public holidays.This allows conditional call routing in Asterisk, and also allows one
system to route calls for different time zones and different offices.This means if you have callers in Adelaide and callers in Melbourne, you
can give each a different number, and their calls will be routed to
voicemail after hours in their timezone, and not available on public
holidays in their timezone.Complete documentation is available within the Sphinx documentation in thedocfolder of this distribution.Treacle on PyPI (releases)GitHub repositoryOn-line DocumentationlicensingCopyright 2013 - 2014Caramel.This program is free software: you can redistribute it and/or modify it
under the terms of the GNU Lesser 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 Lesser
General Public License for more details.You should have received a copy of the GNU Lesser General Public License
along with this program. If not, seehttp://www.gnu.org/licenses/.
Package: treads
TREADS: Tool for Recovery Estimation And Downtime Simulation of buildingsIntroductiontreadsis a Python package to evaluate earthquake induced downtime and model recovery of buildings. This tool implements the framework presented in:Molina Hutt, C., Vahanvaty, T. and Kourehpaz, P. (2021). “An analytical framework to assess earthquake induced downtime and model recovery of buildings.”Earthquake Spectra, Accepted.This tool is fully compatible with SimCenter’s tool for loss assessment, i.e., pelicun (https://github.com/NHERI-SimCenter/pelicun)Requirementstreadsruns under Python 3.6+. The following packages are required for it to work properly:numpypandasossysmore_itertoolsjsonYou can install these usingpip.Installationtreadsis available at the Python Package Index (PyPI). You can simply install it usingpipas follows:pip install treadsBasic DemoimportDT_calculation# refer to "Example" folderinput_parameters='input_parameters.json'RCtable_input='Repair_Class_Table.csv'IF_delays_input='IF_delays_input.csv'DMG_input='DMG.csv'# pelicun outputDL_summary_input='DL_summary.csv'# pelicun outputDV_rec_time_input='DV_rec_time.csv'# pelicun outputoutput_path='**insert output directory here**'DT_calculation.run_treads(input_parameters,RCtable_input,IF_delays_input,DMG_input,DL_summary_input,DV_rec_time_input,output_path)Outputstreadsestimates earthquake-induced downtime to achieve Functional Recovery (FR), Re-Occupancy (RO), and Shelter-in-Place (SiP) post-earthquake recovery states for residential buildings. The following output files will be generated once you runtreads:RC_component.csv:Component repair class matrix.DT_summary.csv:10th percentile, 90th percentile, median, and mean downtime estimates.RS_stats.csv:Probability of a building not achieving different recovery states immediately after an earthquake.DT_stepfunc_xx.csv:Governing recovery trajectories to each recovery state (xx= FR, RO, SiP).DT_path_xx.xlsx:Recovery trajectories to each recovery state for each repair path (xx= FR, RO, SiP).RT_stepfunc_xx.xlsx:Repair time stepping functions for each repair sequence when each recovery state is achieved (xx= FR, RO, SiP).RT_RSeq_xx.csv:Repair time per story for each repair sequence when each recovery state is achieved (xx= FR, RO, SiP).IF_delays.csv:Impeding factor delays.TutorialsYouTube tutorials coming soon.ContactPouria Kourehpaz, University of British Columbia, Vancouver, BC, Canada. email:pouria.kourehpaz@ubc.ca
Package: treadscan
treadscanThis package provides utilities for extracting tire tread (pattern) from images, or even from video of passing vehicles.Three main classes aretreadscan.Detector- picks out stopped vehicles from continoous footage (like at an intersection, under a traffic light).treadscan.Segmentor- finds tires in an image, represented by an ellipse (using basic image processing methods) or keypoints (using a Keypoint-RCNN model).treadscan.TireModel- creates a tire model from keypoints (or an ellipse), then "unwraps" the tire tread.There is a pre-trained Keypoint-RCNN model for detecting tire keypoints available in this repository, or you can train your own. Before training a model, you need annotated training data. To annotate images, you can use themodel_annotation.py, which conveniently saves the keypoints and bounding boxes as a JSON in the same format as used in the training notebook. This script is included inthis folder.This GUI utilises the OpenCV window, using mouse position and keyboard input to place the points. So to place a point, user can hover over a location and press a specific key, or hold the key while dragging with the mouse.
The points which have to be placed manually are:T- top of vehicle's rimB- bottom of vehicle's rimR- third point on the rim (best to always use the same location)S- sidewall height (recommended location is above theTpoint)W- inner side of tireOther controls are:SPACEBAR- show a preview of tire treadBACKSPACE- hide tread previewN- submit keypoints, keep annotating (you might wish to annotate ALL tires in image if there are multiple)ENTER- to export keypoints to JSON fileESCAPE- quit annotation without exportingThe 5 keypoints are used to construct a tire model, defined by outer and inner ellipses.Example usageimportcv2importtreadscan# Grayscale picture of backgroundbackground_sample=cv2.imread('background.png',cv2.IMREAD_GRAYSCALE)# BackgroundSubtractorSimple can be substituted with any cv2 background subtractor, for example cv2.BackgroundSubtractorKNNbackground_subtractor=treadscan.BackgroundSubtractorSimple(background_sample)# Pre-recorded video, could also be a live stream from the cameraframe_extractor=treadscan.FrameExtractor('recording.mp4',treadscan.InputType.VIDEO)# Detects stopped vehicles from footagedetector=treadscan.Detector(backsub=background_subtractor,frame_extractor=frame_extractor)# Keypoint-RCNN finds five keypoints per tire in imagesegmentor=treadscan.SegmentorRCNN('RCNN_model/saved_model.pth')i=1# For each stopped vehicle from footage:forimageindetector.detect():# Save detected vehiclecv2.imwrite(f'image{i:04d}.jpg',image)# Find all tires in imagelist_of_keypoints=segmentor.find_keypoints(image)# For each tire in image:forjinrange(len(list_of_keypoints)):keypoints=list_of_keypoints[j]# Construct tire modeltire_model=treadscan.TireModel(image.shape)tire_model.from_keypoints(*keypoints)# Unwrap tire tread from the modeltread=tire_model.unwrap(image)# Tread postprocessingtread=treadscan.remove_gradient(tread)tread=treadscan.clahe(tread)# Save unwrapped treadcv2.imwrite(f'image{i:04d}_tread{j+1:02d}.jpg',tread)i+=1
Package: treadSRCNN
TreadSRCNNThis package providesSRCNNmodel implemented in PyTorch. This model is only intended for use ongrayscale images, and was created as an extension totreadscan.Treadscanis a Python package containing computer vision tools for extracting tire treads. Sometimes, the scanned treads are in lower quality, because a vehicle hasn't stopped in the correct position, or the camera was out of focus. Applying upsampling to these images might help mitigate these issues.Example of occurence of lower resolution tire treads (vehicles stopped far away, in the other lane):Quick summary of this project is contained inthis paperin the root of the repository. It was made as semestral work for Computational Intelligence Methods course at FIT CTU.Example usageimportcv2fromtreadSRCNNimportSRCNNlow_resolution_image=cv2.imread('low_resolution_image.png',cv2.IMREAD_GRAYSCALE)# Pretrained models can be found in https://github.com/bohundan/treadscan-SRCNN/tree/main/pretrained_modelssrcnn=SRCNN('pretrained_weights.pth')# Factor determines the upscaling factor# The higher the batch_size, the more memory is consumed during upsampling# (my 6GiB VRAM GPU can do around 500 batch_size comfortably)upsampled_image=srcnn.upsample(low_resolution_image,factor=4,batch_size=100)Upsampling previewThere is some tradeoff between sharper image and added noise.
Package: treams
treamsThe packagetreamsprovides a framework to simplify computations of the
electromagnetic scattering of waves at finite and at periodic arrangements of particles
based on the T-matrix method.InstallationInstallation using pipTo install the package with pip, usepipinstalltreamsIf you're using the system wide installed version of python, you might consider the--useroption.DocumentationThe documentation can be found athttps://tfp-photonics.github.io/treams.PublicationsWhen using this code please cite:D. Beutel, I. Fernandez-Corbaton, and C. Rockstuhl, treams - A T-matrix scattering code for nanophotonic computations, arXiv (preprint), 2309.03182 (2023).Other relevant publications areD. Beutel, I. Fernandez-Corbaton, and C. Rockstuhl, Unified Lattice Sums Accommodating Multiple Sublattices for Solutions of the Helmholtz Equation in Two and Three Dimensions, Phys. Rev. A 107, 013508 (2023).D. Beutel, P. Scott, M. Wegener, C. Rockstuhl, and I. Fernandez-Corbaton, Enhancing the Optical Rotation of Chiral Molecules Using Helicity Preserving All-Dielectric Metasurfaces, Appl. Phys. Lett. 118, 221108 (2021).D. Beutel, A. Groner, C. Rockstuhl, C. Rockstuhl, and I. Fernandez-Corbaton, Efficient Simulation of Biperiodic, Layered Structures Based on the T-Matrix Method, J. Opt. Soc. Am. B, JOSAB 38, 1782 (2021).FeaturesT-matrix calculations using a spherical or cylindrical wave basis setCalculations in helicity and parity (TE/TM) basisScattering from clusters of particlesScattering from particles and clusters arranged in 3d-, 2d-, and 1d-latticesCalculation of light propagation in stratified mediaBand calculation in crystal structures