Saravanan666/pypi-org huggingface dataset:
Package: tidecv-light
A GeneralToolbox forIdentifying ObjectDetectionErrors████████╗██╗██████╗ ███████╗
╚══██╔══╝██║██╔══██╗██╔════╝
   ██║   ██║██║  ██║█████╗  
   ██║   ██║██║  ██║██╔══╝  
   ██║   ██║██████╔╝███████╗
   ╚═╝   ╚═╝╚═════╝ ╚══════╝An easy-to-use, general toolbox to compute and evaluate the effect of object detection and instance segmentation on overall performance. This is the code for our paper:TIDE: A General Toolbox for Identifying Object Detection Errors(ArXiv) [ECCV2020 Spotlight].Check out our ECCV 2020 short video for an explanation of what TIDE can do:InstallationTIDE is available as a python package for python 3.6+ astidecv. To install, simply install it with pip:pip3installtidecvThe current version is v1.0.1 (changelog).This repo contains a light version which only depends on numpy.
To achieve this, most reporting features were removed.
It is available on PyPi as well:pip3installtidecv-lightUsageTIDE is meant as a drop-in replacement for theCOCO Evaluation toolkit, and getting started is easy:fromtidecvimportTIDE,datasetstide=TIDE()tide.evaluate(datasets.COCO(),datasets.COCOResult('path/to/your/results/file'),mode=TIDE.BOX)# Use TIDE.MASK for maskstide.summarize()# Summarize the results as tables in the consoletide.plot()# Show a summary figure. Specify a folder and it'll output a png to that folder.This prints evaluation summary tables to the console:-- mask_rcnn_bbox --

bbox AP @ 50: 61.80

                         Main Errors
=============================================================
  Type      Cls      Loc     Both     Dupe      Bkg     Miss
-------------------------------------------------------------
   dAP     3.40     6.65     1.18     0.19     3.96     7.53
=============================================================

        Special Error
=============================
  Type   FalsePos   FalseNeg
-----------------------------
   dAP      16.28      15.57
=============================And a summary plot for your model's errors:Jupyter NotebookCheck out theexample notebookfor more details.DatasetsThe currently supported datasets are COCO, LVIS, Pascal, and Cityscapes. More details and documentation on how to write your own database drivers coming soon!CitationIf you use TIDE in your project, please cite@inproceedings{tide-eccv2020,
  author    = {Daniel Bolya and Sean Foley and James Hays and Judy Hoffman},
  title     = {TIDE: A General Toolbox for Identifying Object Detection Errors},
  booktitle = {ECCV},
  year      = {2020},
}ContactFor questions about our paper or code, make an issue in this github or contactDaniel Bolya. Note that I may not respond to emails, so github issues are your best bet.
Package: tidegravity
tidegravity is a Python library which implements Ivor Longman’s scheme for computing the tidal accelerations due to the
moon and sun, as published by I.M. Longman in the Journal of Geophysical Research, Vol 64, no. 12, 1959
This can be useful for correcting gravimetric survey data, as the gravitational forces due to the tidal effects of the
Sun and Moon can be on the order of several hundred microGals, depending on the surveyors location and the relative
positions of the Sun and Moon to each other, and the surveyor.RequirementsnumpypandasThe numpy and pandas libraries are required for processing tide corrections, and importing trajectory data for correctionThe matplotlib library is an optional requirement and is currently only used in the examples to plot a visual
representation of the data.APIThe following API functions are provided (subject to change in future releases):solve_longman_tide(lat,lon,alt,time)Solve for total gravity correction due to Sun/Moon from numpy array inputssolve_longman_tide_scalar(lat,lon,alt,time)Wrapper around solve_longman_tide, accepts single scalar values for lat/lon/alt and a single DateTime objectsolve_point_corr(lat,lon,alt,t0,n=3600,increment='S')Return tidal correction over a time span defined by t0 with n points at given increment for static (scalar)
position parameterssolve_tide_df(df,lat='lat',lon='lon',alt='alt')Wrapper accepting a pandas DataFrame (df) object as the input, df should have a DatetimeIndex, and lat/lon/alt
columns. Alternate column names can be provided via parameters, which will then be used to extract components from
the input DataFrame.ReferencesI.M. Longman “Forumlas for Computing the Tidal Accelerations Due to the Moon
and the Sun” Journal of Geophysical Research, vol. 64, no. 12, 1959,
pp. 2351-2355P. Schureman “Manual of harmonic analysis and prediction of tides” U.S. Coast and Geodetic Survey, 1958AcknowledgementsThis library is based on the work of John Leeman’s LongmanTide Python implementation.LongmanTideExamplesThere are several example scripts in the examples directory illustrating how to use the longmantide solving functions.Here is a simple demonstration of calculating a correction series for a static latitude/longitude/altitude over a
specified time period, with intervals of 1 second.fromdatetimeimportdatetimefromtidegravityimportsolve_point_corr# Example static data for Denver, January 1, 2018lat=39.7392lon=-104.9903# Note: West should be entered as a negative longitude valuealt=1609.3t0=datetime(2018,1,1,12,0,0)# Calculate corrections for one day (60*60*24 points), with 1 second resolutionresult_df=solve_point_corr(lat,lon,alt,t0,n=60*60*24,increment='S')# Result is a pandas DataFrame, with a DatetimeIndex, and correction# values in the 'total_corr' column i.e.corrections=result_df['total_corr'].values# Plot the corrections using matplotlibfrommatplotlibimportpyplotaspltplt.plot(corrections)plt.ylabel('Tidal Correction [mGals]')plt.show()
Package: tides
TidesThis module acts as a wrapper around the Tidal Data providers such as Admiralty
(UK Tide provider).UsageUsing tides is pretty straightforward. You will need to have your
TideProvider's (currently on only Admiralty is implemented) access token set as
an environment variable namedTIDE_KEY.tides=Tides('admiralty')tides.provider.get_tides(station="Leith")The TideProvdier interface defines the following methods:defget_all_stations():defget_station_by_id():defget_station_by_name():defget_tides():defnext_tide():deftime_to_next_tide():Install$pipinstalltidesContributingSee theContributing Guide
Package: tidetable
Tidetable is a Python module for downloading annual tide prediction
tables from theNOAA Tides &
Currentssite.Install withpip install tidetable.BasicsFirst, get the Station ID for your tide station from theNOAA Tides &
Currents
site.Then, use thegetmethod to download the table. This returns aTideTableobject, which is just alistwith a few additional
metadata parameters.In this example, we use a tide station in Maine:>>>importtidetable>>>table=tidetable.get(8416092)>>>tabletidetable.TideTable(stationid=8416092)>>>table[0]{'pred_cm':149.0,'datetime':datetime.datetime(2014,12,31,3,44),'pred_ft':4.9,'high_low':'H'}TheTideTableobject’s metadata includes some location data,
information about the parent tide station, and error intervals:>>>table.stationname,table.state('Monhegan Island','ME')>>>table.datum'MLLW'>>>table.referencetostationid'8418150'>>>table.timeoffsetlow,table.timeoffsethigh('-9','-13')Time framesBy default, the NOAA returns the tide prediction table for the current
year. To get years in the recent past or future, use theyearkeyword argument. It seems that roughly five years in the past and two
years in the future are available. There isn’t a way to get smaller time
ranges of predictions than a full year.>>>importtidetable>>>table=tidetable.get(8416092,year=2016)>>>tabletidetable.TideTable(stationid=8416092)>>>table[4]['datetime']datetime.datetime(2016,1,1,3,39)(Note that the first few rows returned may be for the previous year.)WritingTheTideTableobject has a write_csv method. It accepts either a
file name or any file-like object.importtidetabletable=tidetable.get(8416092)table.write_csv('tide_table.csv')Time zonesAs you can see,TideTableis a list ofdicts, each of which
has adatetime, a prediction in feet and cm, and a high-or-low flag.
The predictions are relative to thedatum, which in this case is
MLLW, or the mean lower low water level. Note that MLLW is different
from sea level.By default, thedatetimeis in the local time, which could be
standard or daylight savings time. Use thetime_zonekeyword
argument to fetch times in either GMT or the local standard time zone.
TideTable always returns naivedatetimeobjects.Use these constants for specifying the time zone:tidetable.GMT,tidetable.LOCAL_STANDARD_TIME,tidetable.LOCAL_TIME.>>>importtidetable>>>table=tidetable.get(8416092,time_zone=tidetable.GMT)>>>table[0]['datetime']datetime.datetime(2014,12,31,0,15)(Note the difference from the datetime in the first example, which is in
Eastern Standard Time)
Package: tidevice3
tidevice3wrapper for pymobiledevice3 to make it more easy to use.InstallMacpipinstalltidevice3# or install as Isolated environmentbrewinstallpipx
pipxinstalltidevice3Linux# required by pytun-pmd3sudoaptinstallpython3-devgccpipx
pipxinstalltidevice3
pipxensurepathCLI UsageiOS >= 17screenshot,app:psconnect through Ethernet over USB (NCM device) instead of usbmuxd
So tunneld should start first.# start tunneld for iOS>=17# launch process (pmd3 remote start-tunnel) when new usb device connected# root required$sudot3tunneldBasic usage$t3list
...# enable developer mode and mount develoepr image$t3developer# install & uninstall$t3installhttps://....ipa
$t3install./some.ipa
$t3uninstallcom.example# take screenshot$t3screenshotout.png# reboot$t3reboot# file operation$t3fsync<ls|rm|pull|push>[Arguments...]# app$t3app<ps|list|launch|kill|instal|uninstall|foreground># screenrecord$t3screenrecordout.mp4# show help$t3--helpAPI UsageThe API alone is insufficient for all operations; combining it with the pymobiledevice3 library can accomplish more things.fromtidevice3.apiimportlist_devices,connect_service_provider,screenshotfordinlist_devices(usb=True):print("UDID:",d.Identifier)service_provider=connect_service_provider(d.Identifier)pil_im=screenshot(service_provider)pil_im.save("screenshot.png")iOS 17 supportMac (supported)Windows (https://github.com/doronz88/pymobiledevice3/issues/569)Linux (https://github.com/doronz88/pymobiledevice3/issues/566)Mac,Windows,Linux all supported iOS<17WDA其实WDA启动可以不用XCUITest，下面是具体的方法（适用于iOS >= 15)iOS企业重签名方法https://zhuanlan.zhihu.com/p/673521212iOS WDA脱离xcode & tidevice运行自动化https://zhuanlan.zhihu.com/p/673319266DEVELOP & CONTRIBUTEseeDEVELOP.mdLICENSEMIT
Package: tideway
TidewaySimplified Python library for BMC Discovery API Interface that makes use of the Python Requests modulehttps://github.com/psf/requestsand uses the same response handler.>>>importtideway>>>tw=tideway.appliance('appliance-hostname','auth-token')>>>tw.about().url'https://appliance-hostname/api/about'>>>tw.about().status_code200>>>tw.about().text{"api_versions":["1.0","1.1","1.2","1.3","1.4","1.5"],"component":"REST API","product":"BMC Discovery","version":"12.5"}Tideway follows BMC Discovery's well-structured and documented REST API which can be viewed fromhttps://<appliance>/swagger-ui/.Tideway removes the extra layer of manually constructing a URL and parameters for python requests allowing you to query API supported features of Discovery seamlessly and faster than if you were to navigate via the GUI.DocumentationDocumentation can be found athttps://traversys.github.io/Tideway/.ReleasesVersionSummaryKnown IssuesFixed0.1.1- Updated to API v1.2- Addedhelp(),search_bulk()search call retains last parameters foroffset,results_id0.1.2Bug FixesBulk search with larger limit than dataset will fail on missingnext_offset- Fixed issue withoffsetandresults_idvalues- Fixed issue with bulk search parameter lower limit.0.1.3Bug FixesAdded check fornext_offset.0.1.4Search bulk updateDiscovery 12.3 (21.3) enforces strict case for "Bearer" header - api calls will not current work.Now includes headers for non-formatted search.0.1.5Updated to support Discovery 12.3 (API version 1.3)- Missing 'complete' parameter option on graphNode() function.- Fixed issue with Bearer capitalisation.- Search Bulk will now return the full response on failure0.2.0Updated to include Kerberos, Models and Taxonomy endpoints.Added new high level generic endpoint function callsRefactored function names/decorators to match API endpoints as close as possible.Supports Discovery 22.2 (12.5) (API version 1.5) and Outpost API version 1.0Not all new unctions have been tested.Added 'complete' parameter toget_data_nodes_graph()(replacesgraphNode())
Package: tidi
TidiA small dependency injection Python library.Inspired byKent Tong'sdislandFastAPI'sDepends.MotivationI found myself wanting to learn more about how dependency injection can be done
in apythonicway, with type-hinting, so had the itch to develop (yet another)
library for it and share it as open source 🧑‍💻✌️Read more about the motivation inthis Medium article.Primary APIThe top level import oftidiprovides everything needed it's primary intended
use.@tidi.inject- a decorator that will replace certain keyword arguments
with dependencies, based on their type & if they haven't been passed intidi.Injected[DependencyClass]- a type alias, wrapping typing.Annotated,
that indicates that a keyword argument should be injectedtidi.register(dependency_instance)- a function that registers an object to
be available for injection as a dependencytidi.Provider(get_dependency_function)- a wrapper class around a function
that will be called to provide a dependencytidi.UNSET- a sentinel object to indicate that a dependency should be
loaded from the registrytidi.field_factory(DependencyClass)- a helper function for injecting
dependencies into dataclass fieldsExample of useConsider a micro-sized interactive CLI that lets a user choose a handbag then
search through it,# search-handbag.pyimporttidifromhandbagsimportHandbag,HandbagItem,load_handbag@tidi.injectdefdig_through_handbag(item_type:str,handbag:tidi.Injected[Handbag]=tidi.UNSET,)->HandbagItem|None:returnhandbag.get_items_by_type(item_type).first(default=None)definit_handbag():selected_handbag=input("Select a handbag: ")tidi.register(load_handbag(selected_handbag))defrun_search():item_type=input("What are you looking for? ")# ⬇️ registered `Handbag` instance gets injected ✨item=dig_through_handbag(item_type)ifitemisNone:print("Uh oh, can't find it 🤷‍♀️, try again")run_search()else:print(f"We're in luck! Here's your{item.name}😎")if__name__=="__main__":init_handbag()run_search()Running it looks something like this,$ python search-handbag
Select a handbag: BCBGMAXAZRIA
What are you looking for? Nail file
Uh oh, can't find it 🤷‍♀️, try again
What are you looking for? Lip balm
We're in luck! Here's your Blistex 😎We can see Dependency Injection happening hear to achieve Inversion of Control
and obey the Law of Demeter.dig_through_handbagisn't responsible for creating aHandbagand doesn't
require its caller to know about it, rather aHandbagis injected ✨init_handbagcreates theHandbag, but doesn't need to return it. An
example of separating the app initialisation from the main logic.run_searchdoesn't need to know about anything that it doesn't use (in this
case theHandbag), obeying the Law of Demeter.When testing,a mockHandbagcould be passed in as a keyword argument to testdig_through_handbag, andpatchingdig_through_handbagwith a stub could be done to testrun_searchwith no requirement for a mockHandbag.More examplesYou can find some executable examples in thedemo/directory of the repo.Also see theUsage documentationfor
more examples.Interested in contributing?Feel free to create an issue or author a PR 😊For the latter, check out theCONTRIBUTINGguide for a quick
start on development.
Package: tidkvideochange
TIDKVideoChangeCustom modification of VideoText, incorporating pyscenedetect and batching frames.Free software: MIT licenseDocumentation:https://tidkvideochange.readthedocs.io.FeaturesTODOAdditional requirementstensforflow >= 1.12.0 (NOT 2.X)pytorch >= 1.0.0torchvisionffmpeg installed and linked to in PATHHistory0.1.0 (2019-07-15)First release on PyPI.0.1.1 (2019-07-16)Fixed compatibility issuesAdded timestamp field to frames0.1.2 (2019-07-17)Removed tensorflow and pytorch from dependencies
Package: tidml
Installationpip install tidmlUsageSeeexamplesandtestsdirectories.Developmentmake install-dev

make test

make coverageTo-DoMultinomial Naive Bayes classifier exampleEvaluation / Hyperparameter tuningIncremental training (eg. partial_fit)Document, document, document…Wish listCLIFlask/Django/other microserviceDocker/other deploymentH2O.ai integration
Package: tid-titanic-classification-model
Titanic ClassifierBuild and distribute packageOnly Windows.
Go tohttps://packaging.python.org/en/latest/tutorials/packaging-projects/to see instructions in Linux.(First time)py -m pip install --upgrade pip(First time)py -m pip install --upgrade buildpy -m build(First time)py -m pip install --upgrade twineFor testpypi:py -m twine upload --repository testpypi dist/*For Pypi:py -m twine upload dist/*
Package: tidy3d
Tidy3DTidy3D is a software package for solving extremely large electrodynamics problems using the finite-difference time-domain (FDTD) method. It can be controlled through either anopen source python packageor aweb-based graphical user interface.This repository contains the python API to allow you to:Programmatically define FDTD simulations.Submit and manage simulations running on Flexcompute's servers.Download and postprocess the results from the simulations.InstallationSigning up for tidy3dNote that while this front end package is open source, to run simulations on Flexcompute servers requires an account with credits.
You can sign up for an accounthere.
After that, you can install the front end with the instructions below, or visitthis pagein our documentation for more details.Quickstart InstallationTo install the Tidy3D Python API locally, the following instructions should work for most users.pip install --user tidy3d
tidy3d configure --apikey=XXXWhereXXXis your API key, which can be copied from youraccount pagein the web interface.In a hosted jupyter notebook environment (eg google colab), it may be more convenient to install and configure via the following lines at the top of the notebook.!pip install tidy3d
import tidy3d.web as web
web.configure("XXX")Advanced installation instructions for all platforms is available in thedocumentation installation guides.Authentication VerificationTo test the authentication, you may try importing the web interface via.python -c "import tidy3d; tidy3d.web.test()"It should pass without any errors if the API key is set up correctly.To get started, our documentation has a lot ofexamplesfor inspiration.Common Documentation ReferencesAPI ResourceURLInstallation Guidehttps://docs.flexcompute.com/projects/tidy3d/en/latest/install.htmlDocumentationhttps://docs.flexcompute.com/projects/tidy3d/en/latest/index.htmlExample Libraryhttps://docs.flexcompute.com/projects/tidy3d/en/latest/notebooks/docs/index.htmlFAQhttps://docs.flexcompute.com/projects/tidy3d/en/latest/faq/docs/index.htmlRelated Source RepositoriesNameRepositorySource Codehttps://github.com/flexcompute/tidy3dNotebooks Sourcehttps://github.com/flexcompute/tidy3d-notebooksFAQ Source Codehttps://github.com/flexcompute/tidy3d-faqIssues / Feedback / Bug ReportingYour feedback helps us immensely!If you find bugs, file anIssue.
For more general discussions, questions, comments, anything else, open a topic in theDiscussions Tab.LicenseGNU LGPL
Package: tidy-archive-catalogues
The Tidy Archive Catalogues projectThe aim of the Tidy Archive Catalogues project is to enable archivist catalogues to be human- and computer-readable. We want to create a python package which will:enable researchers to analyse and visualise at the level of a catalogue or a subset of the catalogue that they find interesting.make checking for mistakes in archiving (typos, or non-compliance with archiving standards) easy for archivists, who work quickly and make many subtle decisions.Road-mapThe project currently aims to have the following functionality:Enable archivists to search their archives for potential typos/mistakes. Specifically, they will be able to be returned with:
A. Specific places to check in the archive (e.g. specific reference numbers/codes, and columns)
for mistakes, particularly for:
i.  Dates
ii. Named entities (places, people, and businesses)
B. Flag inconsistencies in the archive, according to a chosen set of archiving guidelines, for
example if 'c.', 'c', 'circa' are used to mean approximately, offer one option.
i. According to national archives guidelines
ii. According to Theatre Collection guidelinesEnable digital humanities researchers to digitally visualise/analyse the collection, by creating
machine-readable and human-readable (for labels, etc) versions of the following c:
A. Dates, date ranges, and date uncertainty
B. Named entities (places, people, and businesses)The output of (2.) should give the data in a useful format to interact with existing python/R libraries.For example dates should be in a datetime format, places should be either points in longitude/latitude, or polygonal areas (e.g. in geojson), or easy to convert to. There should be tutorial notebooks, for creating simple visualisations of:
A. Social networks
B. Geographical areas
C. TimelinesBoth (1) and (2) should be tested on large archives (e.g. British Library and The National Collection), and smaller ones (e.g. The Theatre Collection, and the Harry Ransom Centre)ContributorsContributors are recognised usingall-contributorsguidelines.Natalie Thurlby
Julian Warren
Jo Elseworth
Elaine McGirr
Emma Howgill