Saravanan666/pypi-org huggingface dataset:
Package: earlybyte-mvv
Earlybyte mission, vision, and valuesThis tool helps us to remind the mission, vision, and value statement of Earlybyte.InstallationTo run this tool Python 3.7 is required.You might need to upgread your pip environment first before installing earlybyte-mvv.pipinstall--upgradepipThe tool can then be installed using the following command:pipinstallearlybyte-mvvUsageThe tool can be used as follows:# print the mission statement of earlybyteearlybytemission# print the vision statement of earlybyteearlybytevision# print all values of earlybyteearlybytevalues# print a specific value of earlybyte [use 0-4]earlybytevalues2About EarlybyteEarlybyte GmbH is a software company from Winterthur Switzerland. We build IoT, mobile and web applications and solutions that help freeing up time for what really matters.Find us underhttps://earlybyte.ch/
Package: early-stopping
Early StoppingI'm too lazy to read the Tensorflow documentation, so I made this simple early stopper. After each training step, feed the object the testing loss result for that epoch and it will return a boolean that says whether or not to break the training loop.InstallationInstall through pip as shown:pipinstallearly-stoppingExample Usagefromearly_stoppingimportEarlyStoppingearly_stopper=EarlyStopping(depth=5,ignore=20,method='consistency')# Your training loopforepochinrange(EPOCHS):# Train step here# Test step here# Check if we should break the loopifearly_stopper.check(testing_loss):print('BREAKING THE TRAINING LOOP')break
Package: earnapi
EarnAPIAsynchronous EarnApp API wrapper written in Python based onpyEarnapp(synchronous).InstallationpipinstallearnapiExample usageimportasynciofromearnapiimportClientloop=asyncio.get_event_loop()client=Client("your_auth_token_cookie")asyncdefmain():# Get user informationuserdata=awaitclient.get_user_data()print("User data:",userdata)# Get information about earningsearnings=awaitclient.get_earnings()print("Earnings:",earnings)# Get a list of devicesdevices=awaitclient.get_devices()print("Devices:",devices)# Get your transactionstransactions=awaitclient.get_transactions()print("Transactions:",transactions)# Get referralsreferrals=awaitclient.get_referrals()print("Referrals:",referrals)# Device statusesdevice_statuses=awaitclient.get_device_statuses()print("Device statuses:",device_statuses)# IP checkip_allowed=awaitclient.is_ip_allowed("your_ip_address_here")print("IP","is"ifip_allowedelse"is not","allowed to use EarnApp")# Redeem to PayPal, may raise an exceptionredeemed=awaitclient.redeem_to_paypal("your_paypal@email_he.re")print("Redeemed"ifredeemedelse"Could not redeem","your balance to PayPal.")# Add a device (not tested)#await client.add_device("sdk-xxxxx-xxxxxxxxxxxx")# Delete a device (not tested)#await client.delete_device("sdk-xxxxx-xxxxxxxxxxxx")loop.run_until_complete(main())ExceptionsThe following exceptions are defined.ExceptionReasonEarnAppError: The exception all exceptions are based on.AuthenticationError: Raised on authentication failure.DeviceOperationError: Raised when any operation with a device fails.DeviceNotFoundError: Raised when a device to add wasn't found.DeviceAlreadyAddedError: Raised when a device is already added.TooManyRequestsError: Raised when you reach EarnApp's rate limit.IPCheckError: Raised when there's an error checking for the IP.InvalidIPError: Raised when the IP address isn't valid.RedeemError: Raised when an error occurs trying to redeem.
Package: earnest-airflow-plugin
airflow-pluginOperators and Hooks for AirflowAirflow at EarnestWe use heavily theKubernetesPodOperatorfor the following reasons:Keep non-scheduling logic out of the scheduler.Avoid locking logic inside Airflow (containers can run through other schedulers).Avoid accumulating python dependencies in the Airflow image.Align with our engineering culture (functional programming and strongly typed languages lead to more maintainable code, Haskell preferred :) ).As such, you will find most of the Operators we share inherit from theKubernetesPodOperator.Running the tests locallyInstall dev requirementspipinstall-rrequirements_dev.txtRun the unit testspytest-v-m"not kubernetes"Start a local kubernetes cluster throughdocker-for-desktop(Mac/Windows) orminikube(Linux)Bootstrap the cluster (it'll use theairflownamespace)tests/resources/kubernetes/bootstrap.shRun the kubernetes testsAIRFLOW__KUBERNETES__IN_CLUSTER=Falsepytest-v-m"kubernetes"ReleasingCurrently, publishing a new version of the project is a manual process:Install dev requirementspipinstall-rrequirements_dev.txtEdit theversioninsetup.cfgCreate a new git release:# e.g.gittag-av0.0.1There's a simple template inRELEASE_NOTES_TEMPLATE.mdfor a release message.Push the release:gitpushoriginv0.0.1Create a source distribution:pythonsetup.pysdistbdist_wheelUpload to PyPitwineuploaddist/*
Package: earnings
Earnings Python packageOverviewearningsis a lightweight and featureful Python package tailored to retrieve insightful earnings details, allowing users to collect previous and future earnings calendars, filter by US-equity ticker, define a custom portfolio and easily integrate reliable financial data into your applications.DISCLAIMER:Data are providedAS ISby external providers. No warranty on data quality and accuracy. Please do your own researchbefore deploying on production.OverviewGetting startedRoadmapProject architectureLicense & CreditsGetting startedPackage:Install package from official PyPi:pip3installearningsImport package in your project codeexample.py:fromearningsimport*defmain(...)->None:# Insert your logicif__name__=="__main__":main()Execute the sample script:python3example.pySourceClone the repository from GitHub:gitclonehttps://github.com/lcsrodriguez/earnings.gitcdearnings/Build virtual env and install dependenciespython3-mvenvvenvsource./venv/bin/activate
pip3install-rrequirements.txtRoadmapSeeROADMAP.mdfile.Project architecture./
├── AUTHORS
├── CITATION.cff
├── Dockerfile
├── LICENSE
├── Makefile
├── README.md
├── chver.sh
├── docs/
│   ├── README.md
│   └── ROADMAP.md
├── earnings/
│   ├── __init__.py
│   ├── constants.py
│   ├── main.py
│   └── utils.py
├── examples/
│   └── main.py
├── out/
├── portfolios/
│   └── template.pff
├── requirements.txt
└── setup.pyLicense & CreditsLucas RODRIGUEZ(Maintainer & Developer)TheLICENSEfile contains the full license details.If you are using this package for research purposes, you can quote it as shown below:@software{RODRIGUEZ_PyEarnings_2023,author={RODRIGUEZ,Lucas},month=dec,title={{earnings}},url={https://github.com/lcsrodriguez/earnings},version={1.1.0},year={2023}}
Package: earningstrats
Earnings Strategies and Stock DataThis package shows multiple data for stocks nearing earnings like its expected move, historical earnings effect, the Gaussian probability of an option being assigned, etc. This package allows traders to conveniently formulate the best strategies for any stock with earnings every day.Check Today is the aggregate function that contain all the necessary information such as the list of companies with earnings, combined option chain, and historical earnings price effect to help trade stocks with earnings for that day. It pretty-prints its output to HTML files, allowing for a quick and convenient scan.Common StrategiesExpected Move > Earning's Effect EstimateGain from Gamma CrushSelling CSPSelling Straddles and StranglesIron Condor or ButterflyExpected Move < Earning's Effect Estimate even accommodating Gamma RiskBuying Undervalued OptionsBuying SpreadsQuick StartFunctions return pandas DataFrame unless specified elsewiseToday_Dataimportearningstrats.today_dataastd"""Returns tuple of today AMC and tomorrow BMO companies' put option chain and historical earnings price effectChange days_added to shift day by that much. Customize minimum volume sum and stock price[0] - OTM Put option Chain[1] - Historical earnings price effect = Change period to change how far back historical price effect showsOutputs to today_companies.html, today_options.html, today_price_effect.html"""td.check_today(period="5y",day_shift=0,min_vol_sum=3000,min_stock_price=10)Earningsimportearningstrats.earningsasea"""Get a stock's historical earnings price effect. Calculates earnings effect from market close -> next day open and next day closeChange period to change how far back historical price effect shows"""ea.get_stock_earnings_price_effect(symbol,period="5y")"""Get all the stocks with earnings today AMC or tomorrow BMO"""ea.get_companies_with_earnings_today_AMC_or_tomm_BMO(day_shift=0,min_vol_sum=3000,min_stock_price=10)Optionsimportearningstrats.optionsasopd"""Get Expected Move for a stock for the earliest option deadline. Returns tuple[0] - IV Expected Move[1] - ATM Straddle Expected Move[2] - ATM Straddle + 1st Strangle Expected Move[3] - Aggregate Estimate of Stock Expected Move (Used in calculations)"""opd.get_expected_move(symbol)"""Gets the put option chain. Defaults to finding only OTM puts. Set price_ceiling to set cutoffContains the Gaussian probability of option being assigned as well as the expected moves of a stock"""opd.get_put_option_chain(symbol,price_ceiling=-1)# Get current stock price of stockopd.get_current_stock_price(symbol)# Get days left until earliest option expirationopd.get_days_to_expiration(symbol)# Get the earliest expiration option chainopd.get_earliest_deadline_options_chain(symbol)# Get the entire put option chainopd.get_all_put_options(symbol)Option_Setimportearningstrats.option_setasops# Returns a set of stocks with weekly optionsops.get_stocks_with_weekly_options()# Returns a set of stocks with optionsops.get_stocks_with_options()Licenseearningstrats is distributed under the Apache Software License. See the LICENSE file in the release for details
Package: earring
Bravo!  You have received a Medical Diploma in Earring fromthe Orbital Convergence University International Air and Water Embassy of the Tangerine Planet.You are now officially certified to include "earring" in your practice.earringdescriptionThis is an implementation of possibly one of the mostsecure authentication mechanisms, an ED448 elliptic curve.installation[ZSH] pip install earringkey generationimport earring.modules.ED448.private.creator as ED448_private_key_creator
import earring.modules.ED448.public.creator as ED448_public_key_creator

import pathlib
from os.path import dirname, join, normpath
import os

seed = "4986888b11358bf3d541b41eea5daece1c6eff64130a45fc8b9ca48f3e0e02463c99c5aedc8a847686d669b7d547c18fe448fc5111ca88f4e8"
format = "PEM"

private_key_path = normpath (join (pathlib.Path (__file__).parent.resolve (), "ED448_private_key")) + "." + format
public_key_path = normpath (join (pathlib.Path (__file__).parent.resolve (), "ED448_public_key")) + "." + format

private_key = ED448_private_key_creator.create (seed, format, private_key_path)
private_key_instance = private_key ["instance"]
private_key_string = private_key ["string"]

public_key = ED448_public_key_creator.create (
	private_key_path = private_key_path,
	public_key_path = public_key_path,
	public_key_format = format
)
public_key_instance = public_key ["instance"]
public_key_string = public_key ["string"]signatures and verificationsignaturesimport earring.modules.ED448.sign as sign

unsigned_bytes = b"{}"
signed = sign.start (
	private_key_path = "",
	unsigned_bytes = unsigned_bytes
)

signed_bytes = signed.bytesverificationimport earring.modules.ED448.verify as verify

unsigned_bytes = b"{}"
signed_bytes = signed.bytes	
	
#
#	returns a boolean 
#
verification_status = verify.start (
	public_key_path = "",
	
	signed_bytes = signed_bytes,
	unsigned_bytes = unsigned_bytes
)
assert (verification_status == True), verification_status
Package: earsegmentationai
Efficient and Lightweight Ear SegmentationDownload Model 📂⚙️ Requirements ⚙️Python 3.8 to Python3.10 (Virtualenv recommended)Display Server for showing resultsOptional: poetryOptional: Nvidia CUDA for cuda usage🛠️ Installation 🛠️Pip installationpipinstall-rrequirements.txtPoetry installationpoetryshell
poetryinstallOptional (If you have multiple python installation)poetryenvuse$(whichpython3.10)poetryshell
poetryinstallUsageusage: earsegmentationai_cli.py [-h] -m {c,p} [-d [{cpu,cuda}]] [-fp FOLDERPATH] [-id [DEVICEID]]

options:
  -h, --help            show this help message and exit
  -m {c,p}, --mode {c,p}
                        Select camera or picture mode
  -d [{cpu,cuda}], --device [{cpu,cuda}]
                        Run in gpu or cpu mode
  -fp FOLDERPATH, --folderpath FOLDERPATH
                        Folder path for image(s) for image mode only
  -id [DEVICEID], --deviceId [DEVICEID]
                        Camera deviceId /dev/videoX for camera mode onlyWebcam Mode 📷pythonearsegmentationai_cli.py--modec--devicecpu
pythonearsegmentationai_cli.py--modec--devicecuda
pythonearsegmentationai_cli.py--modec--deviceId1--devicecudaImage Mode 🖼️pythonearsegmentationai_cli.py--modep--fp/path/xxx/Youtube Video 📸 ✨
Package: eArsivPortal
🧾 eArsivPortalGİB e-Arşiv Portal e-Fatura OluşturucuBu paketFatih Kadir Akın'ın hazırlamış olduğufatura(js)paketininPythondili ile yazılmış versiyonudur.🚀 KurulumPyPI# YüklemekpipinstalleArsivPortal# Güncellemekpipinstall-UeArsivPortal📝 KullanımLibfromeArsivPortalimporteArsivPortalportal=eArsivPortal(kullanici_kodu="33333301",sifre="1",test_modu=True)portal.bilgilerim()#--------------------------------------------------------------#Bilgilerim(vknTckn='3333333315',unvan='XYZ Yaz',ad='',soyad='',cadde='Sahil yolu',apartmanAdi='Dali çıtır pide',apartmanNo='12',kapiNo='3',kasaba='adana',ilce='GEMLİK',il='Antalya',postaKodu='16000',ulke='Türkiye',telNo='053982456665',faksNo='',ePostaAdresi='info@vekamp.com',webSitesiAdresi='',vergiDairesi='orhangazi vergi dairesi',sicilNo='00000000000000',isMerkezi='',mersisNo='')portal.kisi_getir(vkn_veya_tckn="3333333301")#--------------------------------------------------------------#Kisi(unvan='XYZ Yaz',adi='',soyadi='',vergiDairesi='orhangazi vergi dairesi')portal.fatura_olustur(tarih="07/10/1995",saat="14:28:37",vkn_veya_tckn="11111111111",ad="Ömer Faruk",soyad="Sancak",unvan="",vergi_dairesi="",urun_adi="Python Yazılım Hizmeti",fiyat=100,fatura_notu="— QNB Finansbank —\nTR70 0011 1000 0000 0118 5102 59\nÖmer Faruk Sancak")#--------------------------------------------------------------#FaturaOlustur(ettn='8cb401e3-ca6d-442a-8389-894459372134')faturalar=portal.faturalari_getir(baslangic_tarihi="01/05/2023",bitis_tarihi="28/05/2023")#--------------------------------------------------------------#[Fatura(belgeNumarasi='GIB2023000002672',aliciVknTckn='16045751784',aliciUnvanAdSoyad='Sercan AYDIN',belgeTarihi='29-05-2023',belgeTuru='FATURA',onayDurumu='Silinmiş',ettn='7386c1dc-8a23-4d46-9c8d-de3512b630b4'),Fatura(belgeNumarasi='GIB2023000001918',aliciVknTckn='16045751784',aliciUnvanAdSoyad='Sercan AYDIN',belgeTarihi='29-05-2023',belgeTuru='FATURA',onayDurumu='Onaylanmadı',ettn='2ef98bfa-8787-4429-a1fa-a0514560e7eb'),Fatura(belgeNumarasi='GIB2023000001919',aliciVknTckn='16045751784',aliciUnvanAdSoyad='Sercan AYDIN',belgeTarihi='29-05-2023',belgeTuru='FATURA',onayDurumu='Onaylandı',ettn='8cb401e3-ca6d-442a-8389-c4d87c9eb67c')]html_fatura=portal.fatura_html(ettn=faturalar[0].ettnonay_durumu=faturalar[0].onayDurumu)withopen(f"{faturalar[0].aliciUnvanAdSoyad}.html","w",encoding="utf-8")asdosya:dosya.write(html_fatura)portal.fatura_sil(faturalar=[faturalar[0],faturalar[1]]aciklama="Fatura silindi.")#--------------------------------------------------------------#FaturaSil(mesaj='2 fatura başarıyla silindi.')imza=portal.gib_imza()portal.gib_sms_onay(faturalar=faturalar[3],oid=imza.oid,sifre=input("SMS Doğrulama Kodu: "))#--------------------------------------------------------------#GibSMSOnay(mesaj='SMS şifreniz doğrulandı, işlem başarılı.')portal.cikis_yap()🌐 Telif Hakkı ve LisansCopyright (C) 2023 bykeyiflerolsun❤️️GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007Koşullarına göre lisanslanmıştır..♻️ İletişimBenimle iletişime geçmek isterseniz,Telegram'dan mesaj göndermekten çekinmeyin;@keyiflerolsun💸 Bağış Yap☕️ Kahve Ismarla@KekikAkademiiçin yazılmıştır..
Package: earth2observe
Current release infoNameDownloadsVersionPlatformsearthobserve - Remote Sensing packageearthobserveis a Remote Sensing packageearthobserveMain FeaturesERA Interim DownloadCHIRPS Rainfall data DownloadERA5 from Amason S3 data sourceFuture workGoogle earth engineERA 5Installing earthobserveInstallingearthobservefrom theconda-forgechannel can be achieved by:conda install -c conda-forge earthobserveIt is possible to list all of the versions ofearthobserveavailable on your platform with:conda search earthobserve --channel conda-forgeInstall from Githubto install the last development to time you can install the library from githubpip install git+https://github.com/MAfarrag/earthobservepipto install the last release you can easly use pippip install earthobserve==0.2.2Quick start>>> import earthobserveother code samples=======
History0.1.5 (2022-12-07) : add ECMWF data catalogFirst release on PyPI.0.1.6 (2022-12-26) :Use environment.yaml and requirements.txt instead of pyproject.toml and replace poetry env byconda envlock numpy to 1.23.50.1.7 (2022-12-26)fix pypi package names in the requirements.txt filefix python version in requirement.txt0.2.0 (2023-01-15)bump up numpy and pyramids versionscreate an abstract class for datasource and catalog as a bluebrint for all data sourcestest all classes in CIuse pathlib to deal with paths0.2.1 (2023-01-25)add amazon S3 data source and catalog for the data available in era5 bucket (ERA5 only tested)replace utility functions with the serapeum_utils package.0.2.2 (2023-01-29)add documentationbump up pyramids versions
Package: earth-age-calculator
Earth age calculatorIn collaboration with George Tilton, Patterson developed the uranium–lead dating method into lead–lead dating. By using lead isotopic data from the Canyon Diablo meteorite, he calculated an age for the Earth of 4.55 billion years, which was a figure far more accurate than those that existed at the time, and one that has remained largely unchanged since 1956.The History of Earth's Age Calculation패터슨은조지 틸튼(George Tilton) 과 협력하여 우라늄-납 연대 측정 방법을 납-납 연대 측정 으로 개발했습니다 .그는 Canyon Diablo 운석 의 납 동위원소 데이터를 사용하여 지구의 나이를 45억 5천만년으로 계산했는데 ,이는 당시 존재했던 수치보다 훨씬 더 정확한 수치였으며 1956년 이후 크게 변하지 않은 수치입니다.How to use$python
Python3.9.17(main,Jun162023,00:28:17)[Clang14.0.0(clang-1400.0.29.202)]ondarwin
Type"help","copyright","credits"or"license"formoreinformation.
>>>importearth_age_calculator.earthasour_earth
>>>our_earth.calculate_earth_age(2023)4550000067>>>Test$pytest
$pytest-q
$pytest-qtests/test_earth.pyThank youHelponhowtodistributepipwasreceivedintherepobelow.

./＼＿／ヽ
／_ノヽ_＼|━━i
＼ミ(_人_)彡／￣￣⌒＼/⌒)――――https://github.com/edu-data-mario/nothing-pip
./＿／.|＼＼〇_＼＼ノ.＼_ノ&ThankstotheBodenscientistswhodidtheresearchtodeterminetheageoftheEarth.
Package: earthchem
Earthchem & PyDataThis project wraps the Earthchem web services to provide easy access to geochemical data fromIEDAin ready-to-use format in your favourite PyData environment.Maintainer: Jess Robertson (jesse.robertsonatcsiro.au)ServicemasterdevelopTest statusTest coverageSo why would I want to use this?Say you wanted to know how many Archean-aged samples have been submitted to IEDA by your colleague named Dr S Barnes:>>>importearthchem>>>q=earthchem.Query(author='barnes',geologicalage='archean')>>>q.count()876Nice, let's take a look at the compositions of these>>>df=q.dataframe()>>>df.head()Downloading pages: 100%|██████████| 18/18 [00:29<00:00,  1.66s/it]Hmm looks like Dr Barnes is a bit of a komatiite expert>>>fig=df.mgo.hist()>>>fig.set_xlabel('MgO (wt %)')>>>fig.set_ylabel('Sample count')Maybe we'd like to see this as a ternary plot instead...>>>earthchem.plot.ternaryplot(df,components=['mgo','al2o3','cao'])If spiderplots are more your thing we have you covered too (although we have to make up some data for this one...):>>>reels=ec.geochem.REE(output='string')>>>df=pd.DataFrame({k:vfork,vinzip(reels,np.random.rand(len(reels),2))})>>>ec.plot.spiderplot(df)Great, I'm sold. How do I get it?Provided you have python installed, this library is just apip install earthchemaway.If you don't have Python we recommend taking a look at the marvellousAnaconda distribution- just pick your relevant platform downloadfrom here.
Package: earthcube-utilities
Earthcube UtilitiesUsersEarthcube Utilities:https://pypi.org/project/earthcube-utilities/Manual Installpython3 -m pip install  earthcube-utilitiesDevelopersscriptsWhen installed via pip:query_graph SPARQL_FILE --graphendpoint https://graph.geocodes-dev.earthcube.org/blazegraph/namespace/earthcube/check_sitemap SITEMAP_URL --output FILE --no-check-urlgenerategraphstats --graphendpoint https://graph.geocodes-dev.earthcube.org/blazegraph/namespace/earthcube/ -s3 localhost:9000 --s3bucket gleanerfromconsole scriptslocal development modecdearthcube_utiltiies
pip3install-e.Developementcreate a virutal env and activatesource {envname}/bin/activateuse editable installcdearthcube_utiltiies
pip3install-e.If you edit the pyproject.toml and want to test an added script,cdsummarize
pip3uninstall-eearthcube_utiltiies
pip3install-e.building a test packagetest packagingLocally,  see if a package buildspython3 -m pip install buildinbuild/libyou can see what files are included in packagebuild a wheelto see what is added to a package,python -m build --wheeldistdirectory will contain the package. This is actually a zip file so unzip to see
what got includedPlanning:The planned functionality will be found in the docs folder,Earthcube Utilities Functionality
Package: earthdaily
EarthDaily Python ClientYour gateway to the Earth Data Store STAC Catalog.Who we areProject description·Report Bug·Request FeatureTable of ContentsAbout The ProjectGetting StartedPrerequisitesInstallationUsageSupport developmentLicenseContactCopyrightsAbout The ProjectIn the realm of geospatial data analysis and Earth observation, the EarthDaily Python package emerges as a powerful toolset that seamlessly connects you to the vast and invaluable Stac catalog Earth Data Store. This package is designed with the vision of simplifying and optimizing your workflow, ensuring that you can harness the full potential of Earth observation data with ease and efficiency.Our package is built upon a foundation of best practices, meticulously crafted to elevate your data analysis experience. With EarthDaily, you can effortlessly navigate the complexities of datacube creation, including crucial processes like conversion to reflectance and automatic clipping to your area of interest. Additionally, we've taken care to make EarthDaily fully compatible with Dask, enabling you to scale your data preprocessing tasks with confidence and precision.FeaturesSeedocumentationfor more informationGetting startedPrerequisitesMake sure you have valid EDS credentials. If you need to get trial access, please contact us.This package has been tested on Python 3.10, 3.11 and 3.12.InstallationUsing pippip install earthdailyPlanned : Using conda/mambaAuthenticationAuthentication credentials are accessed from environment variables. As a convenience python-dotenv is supported.
Copy the.env.samplefile and rename to simply.envand update with your credentials. This file is gitignored.
Then add to your script/notebook:fromdotenvimportload_dotenvload_dotenv()# take environment variables from .env.UsageSee the documentation for more information.Support developmentIf you find this package useful, please consider supporting its development.Support developmentIf this project has been useful, that it helped you or your business to save precious time, don't hesitate to give it a star.(back to top)LicenseDistributed under the MIT License.(back to top)ContactFor any additonal information, pleaseemail us.(back to top)Copyrights© EarthDaily | All Rights Reserved.(back to top)