Saravanan666/pypi-org huggingface dataset:
Package: outgoing
GitHub|PyPI|Documentation|Issues|Changelogoutgoingprovides a common interface to multiple different e-mail sending
methods (SMTP, sendmail, mbox, etc.).  Just construct a sender from a
configuration file or object, pass it anEmailMessageinstance, and let the
magical internet daemons take care of the rest.outgoingitself provides support for only basic sending methods; additional
methods are provided by extension packages.Seethe documentationfor more
information.Installationoutgoingrequires Python 3.7 or higher.  Just usepipfor Python 3 (You have pip, right?) to installoutgoingand its dependencies:python3 -m pip install outgoingExamplesA sample configuration file:[outgoing]method="smtp"host="mx.example.com"ssl="starttls"username="myname"password={file="~/secrets/smtp-password"}Sending an e-mail based on a configuration file:fromemail.messageimportEmailMessageimportoutgoing# Construct an EmailMessage object the standard Python way:msg=EmailMessage()msg["Subject"]="Meet me"msg["To"]="my.beloved@love.love"msg["From"]="me@here.qq"msg.set_content("Oh my beloved!\n""\n""Wilt thou dine with me on the morrow?\n""\n""We're having hot pockets.\n""\n""Love, Me\n")# Construct a sender object based on the default config file (assuming it's# populated)withoutgoing.from_config_file()assender:# Now send that letter!sender.send(msg)As an alternative to using a configuration file, you can specify an explicit
configuration by passing the configuration structure to theoutgoing.from_dict()method, like so:fromemail.messageimportEmailMessageimportoutgoing# Construct an EmailMessage object using the eletter library# <https://github.com/jwodder/eletter>:fromeletterimportcomposemsg1=compose(subject="No.",to=["me@here.qq"],from_="my.beloved@love.love",text=("Hot pockets?  Thou disgusteth me.\n""\n""Pineapple pizza or RIOT.\n"),)msg2=compose(subject="I'd like to place an order.",to=["pete@za.aa"],from_="my.beloved@love.love",text="I need the usual.  Twelve Hawaiian Abominations to go, please.\n",)SENDING_CONFIG={"method":"smtp","host":"smtp.love.love","username":"my.beloved","password":{"env":"SMTP_PASSWORD"},"ssl":"starttls",}withoutgoing.from_dict(SENDING_CONFIG)assender:sender.send(msg1)sender.send(msg2)
Package: outgoing-mailgun
GitHub|PyPI|Issues|Changelogoutgoing-mailgunis an extension foroutgoingthat adds the ability to
send e-mails viaMailgun.  Simply installoutgoing-mailgunalongsideoutgoing, and you’ll be able to specify “mailgun” as a sending method in
youroutgoingconfiguration.Installationoutgoing-mailgunrequires Python 3.7 or higher.  Just usepipfor Python 3 (You have pip, right?) to installoutgoing-mailgunand its dependencies (includingoutgoing):python3 -m pip install outgoing-mailgunConfigurationWhen using “mailgun” as the sending method in anoutgoingconfiguration,
the following configuration fields are recognized:base-urlHTTP URL (optional)The base URL to use for Mailgun API requests.  This should be either"https://api.mailgun.net"(the default) for domains in Mailgun’s US
region or"https://api.eu.mailgun.net"for domains in Mailgun’s EU
region.  Trailing slashes on the URL are optional.domainstring (required)The domain name you registered with Mailgun for sending e-mailapi-keypassword (required)A Mailgun API key for your domain; seetheoutgoingdocumentation on passwordsfor ways to write this field.When using thekeyringpassword scheme or another scheme that takes
optional host/service and username fields, if the service and/or username
is not supplied in the password specifier, then the service defaults to the
domain name of thebase-urlfield, and the username defaults to the
value of thedomainfield.tagslist of strings (optional)A set of tags to apply to sent e-mailsdeliverytimedatetime (optional)Desired time of delivery for sent e-mails; if no timezone offset is given,
it is assumed to be in the local system timezonedkimboolean (optional)Enable/disable DKIM signatures on sent e-mailstestmodeboolean (optional)Whether to send intest modetrackingboolean (optional)Whether to enablemessage trackingtracking-clicksboolean or"htmlonly"(optional)Whether to enable clicks tracking in e-mailstracking-opensboolean (optional)Whether to enable opens tracking in e-mailsheaderstable with string values (optional)A collection of custom MIME headers to append to sent e-mailsvariablestable with string values (optional)A collection ofMailgun variablesto attach to sent e-mailsExample Configuration[outgoing]method="mailgun"domain="mydomain.nil"api-key={file="~/secrets/mailgun.key"}dkim=truetags=["sent-with-outgoing","my-campaign"]tracking-clicks="htmlonly"headers={Reply-To="me@mydomain.nil"}variables={sender="outgoing",foo="bar"}Sender-Specific BehaviorTheMailgunSenderclass provided by this extension is areentrantandreusablecontext manager, and itssend()method can be called outside of
a context.  In addition, on success, thesend()method returns the message
ID of the newly-sent e-mail (without enclosing angle brackets).
Package: outgraph
outgraphoutgraphis a simple outlier detection algorithm for graph datasets. Given a list of graphs, it usesMahalanobis distancedetect which graphs are outliers based on either their topology or node attributes.Note:outgraphonly works for datasets where each graph has an equal number of nodes.InstallationYou can installoutgraphwithpip:$pipinstalloutgraphHow it WorksUnlike most approaches to graph outlier detection,outgraphdoes not use machine learning. Instead, each graph is converted into a vector representation using one of three available methods:Averaging the node feature/attribute vectorsFlattening the adjacency matrixA concatenation of 1 and 2Then, theMahalanobis distancebetween each vector and the distribution of vectors is calculated. Lastly, aChi-Squared distributionis used to model the distribution of distances and identify the distances outside a cutoff threshold (e.g. p < 0.05).This approach is based offthis article.UsageEach graph in your dataset needs to be an instance ofoutgraph.Graph. This object has two parameters,node_attrsandadjacency_matrix–– both numpy arrays where the indices correspond to nodes. Example:importnumpyasnpfromoutgraphimportGraphnode_attrs=np.array([[-1],[0],[1]])adj_matrix=np.array([[1,1,0],[1,1,1],[0,1,1]])graph=Graph(node_attrs,adj_matrix)Once you have a list ofGraphobjects, simply submit them intooutgraph.detect_outliers:fromoutgraphimportGraph,detect_outliersgraphs=[Graph(),...]outliers,indices=detect_outliers(graphs,method=1,p_value=0.05)Notice themethodandp_valueparameters. Themethodparameter is an integer between 1 and 3 that corresponds to one of the three graph vectorization methods described in the ![How it Works](##How it Works) section.p_valueis the outlier cutoff threshold.
Package: outjack
outjackJACK and PortAudio integration for PythonInstallThese are generic installation instructions.To use, permanentlyThe quickest way to get started is to install the current release from PyPI:pip3 install --user outjackTo use, temporarilyIf you prefer to keep .local clean, install to a virtualenv:python3 -m venv venvname
venvname/bin/pip install outjack
. venvname/bin/activateTo developFirst clone the repo using HTTP or SSH:git clone https://github.com/combatopera/outjack.git
git clone git@github.com:combatopera/outjack.gitNow use pyven's pipify to create a setup.py, which pip can then use to install the project editably:python3 -m venv pyvenvenv
pyvenvenv/bin/pip install pyven
pyvenvenv/bin/pipify outjack

python3 -m venv venvname
venvname/bin/pip install -e outjack
. venvname/bin/activate
Package: outlawg
outlawg=======================Description-----------Not your grandmother's logging tool.Simple logging, designed for non-application output.No timestamping, no component listing with focus on header customization.[![Build Status](https://travis-ci.org/rpappalax/outlawg.svg?branch=dev)](https://travis-ci.org/rpappalax/outlawg)  [![Build Status](https://badge.fury.io/py/outlawg.svg)](https://badge.fury.io/py/outlawg)Install-------```bash$ pip install outlawg```or```bash$ git clone https://github.com/rpappalax/outlawg.git$ cd outlawg$ python setup.py develop```Example-----[https://github.com/rpappalax/outlawg/blob/master/Examples/output.txt](Example output)example code:```pythonfrom outlawg import Outlawgout = Outlawg()out.header('YOUR HEADER')print('some stuff here')out.subheader('MY_SUB_HEADER')print('some stuff here')out.header('YOUR HEADER', 'XL')print('some stuff here')out.header('YOUR HEADER', 'L')print('some stuff here')out.header('YOUR HEADER', 'M')print('some stuff here')out.header('YOUR HEADER', 'S')print('some stuff here')out.header('YOUR HEADER', 'XL', '~')print('some stuff here')out.header('YOUR HEADER')out.subheader('YOUR SUB-HEADER')print('some stuff here\n\n\n')out.banner('YOUR BANNER')print('some stuff here\n\n\n')```
Package: outleap
outleapA Python library using asyncio to control a Second Life viewer over the LEAP protocol.Seehttps://bitbucket.org/lindenlab/leap/src/main/for more details on LEAP.Installingpip install outleap, orpip install -e .to install from source.If you want to use the LEAP REPL or UI inspector, dopip install outleap[tools], orpip install -e .[tools].UsageLook in the "examples" directory.You can run a LEAP script withyour_viewer --leap some_script.pyif you have the executable bit set.importasyncioimportsysfromoutleapimportLEAPClient,LLViewerControlAPIasyncdefamain():# Create a client speaking LEAP over stdin/stdout and connect itasyncwithawaitLEAPClient.create_stdio_client()asclient:# Use our typed wrapper around the LLViewerControl LEAP APIviewer_control_api=LLViewerControlAPI(client)# Ask for a config value and print it in the viewer logsprint(awaitviewer_control_api.get("Global","StatsPilotFile"),file=sys.stderr)asyncio.run(amain())If you just want to play around with the LEAP APIs:$outleap-repl# ... in another terminal ...$viewer--leapoutleap-agentwill give you an interactive REPL with a LEAPclientobject, with all the
API wrappers already imported:>>> floater_api = LLFloaterRegAPI(client)
>>> floater_api.show_instance(name="preferences")
>>> window_api = LLWindowAPI(client)
>>> prefs_path = UIPath.for_floater("Preferences")
>>> pprint.pp(await window_api.get_info(prefs_path))
{'available': True,
 'class': '19LLFloaterPreference',
 'enabled': 1,
 'enabled_chain': 1,
 'path': '/main_view/menu_stack/world_panel/Floater View/Preferences',
 'rect': {'bottom': 234, 'left': 593, 'right': 1255, 'top': 762},
 'value': None,
 'visible': 1,
 'visible_chain': 1}Similarly, there's an interactive UI tree inspector available throughoutleap-inspector.
It can be launched throughviewer --leap outleap-inspector.What viewers does LEAP even work in?Due to the fact that LEAP has only historically been used internally for testing, or for
integration with the official viewer's updater, many viewers have disabled LEAP
both intentionally or accidentally.The code in the upstream viewer also appears to refuse to launch LEAP scripts if the updater
isn't present, which I don't entirely understand. I can't compile it to check.Does it work in Firestorm?No, the code to launch LEAP scripts iscommented out.
If you do your own build with those lines uncommented it'll work fine.Does it work in Alchemy?Probably not, and definitely not on Linux.
Alchemy does the same SL updater presence checks as upstream before attempting to launch LEAP scripts, which
I imagine wouldn't succeed. Haven't tried.Does it work in LL's official viewer?Yeah, probably.Does it work in<other viewer>?No, probably not.CreditsThe project scaffolding is based on code fromhttps://github.com/MatthieuDartiailh/bytecode
Package: outlier
Usagedefremove_outlier(df,col,partitionby=None,kernel='both',n_std=2,eps=1e-5):""":param col: label column:param partitionby: remove outliers in each partition:param kernel: kernel options: ["std", "quartile", "both"]:type df: pandas.DataFrame:type col: str:type partitionby: list:type kernel: str:type n_std: int:type eps: float:return: pandas.DataFrame"""
Package: outlier-101703382
Library for removing outliers from pandas dataframePROJECT 2, UCS633 - Data Analysis and Visualization
Paras Arora  
COE18
Roll number: 101703382Takes two inputs - filename of input csv, intended filename of output csv.Output is the number of rows removed from the input dataset.It also shows new dataset in case of IQROutput is the number of rows removed from the input dataset in case of z-scoreInstallationpip install outlier_101703382Recommended - test in a virtual environment.Use via command lineoutliers_cli in.csv out.csv
outliers_cli in.csv out.csv 1.5First argument after outcli is the input csv filename from which the dataset is extracted. The second argument is for storing the final dataset after processing.Use in .py scriptfrom outlier_101703382 import remove_outliers_iqr
remove_outliers('input.csv', 'output.csv')from outlier_101703382 import remove_outliers
remove_outliers('input.csv', 'output.csv',threshold)
Package: outlier-101703396
outlier_removerA outlier removal tool, removes outlier row-wise using z-score or InterQuartile Range methodPyPI Project linkhereUsageUse below commands to install and usepip install outlier-remover-csvpython3>>>from outlier import outlier>>>t = outlier.outlier(input_filename,output_filename,[method])method is optional. It can be "z_score" or "iqr", by default it is "iqr"e.g 1. t = outlier.outlier("mydata.csv","out.csv","z_score")e.g 2. t = outlier.outlier("mydata.csv","out.csv")
Package: outlier-101883071
This package helps to remove the outliers from the dataset.
It is implemented using IQR(interquartile range method)
This packages will work on command line interfaceUse command to runpython <input.csv> <output.csv>input.csv is your input csv fileoutput.csv file will store the data without rows having outliers.You can install this package using following command
pip install outlier_101883071Author-Yogesh.
Batch:Coe18.
Roll no:101883071.
Course:Data Analytics and Visualization.
Package: outlier-asharma-3027
Outlier Removal Using InterQuartile RangeProject 2 : UCS633Submitted By:Abhishek Sharma - 101703027InterQuartile Range (IQR) DescriptionAny set of data can be described by its five-number summary. These five numbers, which give you the information you need to find patterns and outliers, consist of:The minimum or lowest value of the dataset.The first quartile Q1, which represents a quarter of the way through the list of all data.The median of the data set, which represents the midpoint of the whole list of data.The third quartile Q3, which represents three-quarters of the way through the list of all data.The maximum or highest value of the data set.These five numbers tell a person more about their data than looking at the numbers all at once could, or at least make this much easier.Calculation of IQRIQR = Q3 – Q1MIN = Q1 - (1.5IQR)MAX = Q3 + (1.5IQR)Sample datasetMarksStudents3Student157Student265Student398Student443Student544Student654Student799Student81Student9Output Dataset after RemovalMarksStudents57Student265Student398Student443Student544Student654Student7It is clearly visible that the rows containing Student1, Student8 and Student9 have been removed due to them being Outliers.LicenseMIT
Package: outlier-ashita-khurana
This Python package is created to remove outlier rows from a dataset. To use thi dataset you need to create a pandas dataframe(you can use pd.read_csv() to do it), and to to use this package your dataset should have only numerical values and if you have any strings then you can use one hot encoding or simple label encoder to do that, but make sure before using it that it is all done. Now this function takes one argument that is the dataframe. To use it you have to write:from outliers-ashita_khurana import outlier df = outlier.rem_out(data)and your df variable will now contain the new data which will not contain outlier rows and this function will also print the number of rows deleted!
Package: outlierchat
⚔️ OUTLIERInstructionAre you mad or bothered by wannaChat with your friends but your boss standing behind you?Tell some secret about boss or company you serve but your computer and network is under survellience?Make some import decisions with nobody finding out?Try this ⚔️ outlier chat room !⚔️ Outlier, known as the famous movie, standing for the people who act out of laws, is a simple tool for you to communicate with your friends. You could build a server really simply and quickly with it. And invite your friends to setup clients on it. It's designed to use your own "Symmetic Encryption Algorithm" and setup a secret key to make your communication hardly to decryped.SetupInstall with pippipinstalloutlierchatQuick TourAfter setup. You can start a server just start the module outlier aspython-moutlier-s[-iip_addr][-pport]Then you and your friends can easily connect to the server bypython-moutlier-nname-iip_addr[-pport]After connected, it's easy to regard it as an online game system. The client firstly is
at the server hall where one can type$infoto check what rooms there exist and
how many people are in it. Then type$room [room name]to choose one room or build a new one to enter it and you can easily send and receive messages with your friends.NoteYou should check you network configuration carefully and thus some knowledge about internet technology shoule be used, such asNAT. The ipv4 address and the port of the server should be correctly configured to find the server.FeatureWe currently use a rolling dynamic asymmetric encrpytion algorithm to keep the channel untracked. The server rolls a pair of public key and private key with RSA algorithm to transmit secret key for a symmetic encryption channel, then the encryped secret key can be safe once the key pair rolls. Only if the network traffic was completely recorded, the encrypting key pais of secret would be lost forever. This means once the channel is disconnected, NO ONE could recover the secret key. However, this design keeps no RSA certificated public key, so anyone could mock a fake one, thus, it's very vulnerable to a fake server or MIIT attack.DisclaimersThis program is only for temporary secret communication channel and definitely CANNOT escape from decryption by professional security team especially when the source code is open. Thus before you
use this tool, you should know that we DO NOT take ANY responsiblility for any behaviours you take and the consequences that would come.Development logSeeDEVLOG.mdCopyright and License InformationCopyright Phoenix Wang, Micca shi, 2023-2024Distributed under the term ofApache 2.0 license
Package: outlier-detection
Outlier DetectionDetect outliers from pandas dataframe using various statistical toolsINSTALLATION AND USAGE!pipinstalloutlier-detectionfromoutlier_detectionimportdetect_outliers_using_iqrimportpandasaspd# Pandas Dataframedata=pd.read_csv('titanic.csv')# Detect outliers using IQR Method# On overall datadetect_outliers_using_iqr(df,'Fare')# Based on factors datadetect_outliers_using_iqr(data,'Fare',is_factor=True,factor='Sex')Github Repository:https://github.com/bilalProgTech/outlier-detection.gitTutorial Data Credit:https://www.kaggle.com/c/titanic
Package: outlier-detector
Outlier Detector toolkitThis project features a set of tools for outlier detection, marking or filtering away samples
as they come to your Python analysis code.Most of the tools rely on double tailed Dixon's Q-test (https://en.wikipedia.org/wiki/Dixon%27s_Q_test).Installationpipinstalloutlier-detectorTL;DRI have asample, and a know datadistribution: is the sample an outlier?sample=-14.5distribution=[0.1,1.1,4.78,2.0,7.2,5.3]fromoutlier_detector.functionsimportis_outlierprint(is_outlier(distribution,sample))I have adistributionand I iterate over it: is the n-thsamplean outlier?distribution=[0.1,1.1,4.78,2.0,7.2,5.3,8.1,-14.1,5.4]fromoutlier_detector.detectorsimportOutlierDetectorod=OutlierDetector(buffer_samples=5)forsampleindistribution:print(od.is_outlier(sample))I have a generating object from which Ipopsamples; and I want only valid samples:
    how can I reject outliers?distribution=[0.1,1.1,4.78,2.0,7.2,5.3,8.1,-14.1,5.4]fromoutlier_detector.filtersimportfilter_outlierclassMyGen:def__init__(self):self.cursor=-1@filter_outlier()defpop(self):self.cursor+=1returndistribution[self.cursor]g=MyGen()whileTrue:try:r=g.pop()print(r)exceptIndexError:print('No more data')breakI have a generating object from which Ipopsamples; and I want to iterate only on valid samples:
    how can I reject outliers and get an iterator?distribution=[0.1,1.1,4.78,2.0,7.2,5.3,8.1,-14.1,5.4]fromoutlier_detector.filtersimportOutlierFilterclassMyGen:def__init__(self):self.cursor=-1defpop(self):self.cursor+=1returndistribution[self.cursor]g=MyGen()of=OutlierFilter()try:forsampleinof.filter(g.pop):print(sample)exceptIndexError:print('No more data')DocumentationThe toolkit is organized so you can exploit one of the following pattern in the easiest way possible:functionsfor static analysis,detectorsfor objects with internal buffers, andfiltersfor decorators.For documentation seedoc file
Package: outlier-detector-z
outlier-detector-zOutlier detector using Z-score. application accepts only list or tuple.
By default threshold set as 3 std. Any element z-score more than threshold will be considered as outlierrun pip commandSample codefrom outlier_detector import outlier_detectoroutlier = outlier_detector.detect_outlier([1,2,3,4,5,6,7,8,9,10,20,12,14,200])print(outlier)
Package: outlier-hub
OutlierHuba curated hub for outlier and anomaly datasets built on top ofdatastack.Currentyly supported datasets:ATISARRHYTHMIAKDDREUTERSTRECMNISTFashion-MNISTEMNISTNewsgroupsFurthermore, OutlierHub supports the following toy datasets:Half MoonsNested CirclesNoisy x -> x^3 regressionUniform NoiseGaussian ClusterCircular SegementXOR SquaresInstallpipinstalloutlier-hubFor the latest version, clone or download the repository andcdinto OutlierHub's root folder and runpipinstallsrc/UsageEach dataset has a factory that can be instantiated with aStorageConnectorproviding the IO operations.
After factory instantiation, the factory is able to create a dataset iterator when calling its member methodget_dataset_iterator(config={...})Copyright 2020 Fraunhofer IAISFor license please see:https://github.com/fraunhofer-iais/outlierhub/blob/master/LICENSE
Package: outlier-naveen
Package for removing outliers from a datasetNaveen Budhwal  
COE-16
Roll number: 101703362DescriptionPackage for removing outliers from a given dataset using the IQR method.Installationpip install outlier_naveenCommand Line Argumentsoutlier_naveen <input_filename>.csv <output_filename>.csvFirst argument is the input csv filename containing the dataset.
The second argument is the name of the output csv file that will store the modified dataset.Use via py scriptfrom outlier_naveen import remove_outliers
remove_outliers('<input_filename>.csv', '<output_filename>.csv')
Package: outlierpack-NG
OUTLIER REMOVALPROJECT 2, UCS633 - Data Analysis and Visualization
Nikhil Gupta  
COE17
Roll number: 101703371Output is the number of rows removed from the input dataset. The remaining rows of the dataset are streamed to a new csv file whose name is required as an input.The no of rows removed: 5Note the outlier removal is performed usingIQRmethod.Installationpip install outlierpack_NGNote the name has an underscore not a hyphen. If installation gives error or package is not found after installing, install as sudo.Recommended - test it out in a virtual environment.To use via command lineOutcli myData.csv newData.csvFirst argument after outcli is the input csv filename from which the dataset is extracted. The second argument is for storing the final dataset after processing.To use in .py scriptfrom outlib.models import r_outliers
r_outliers('myData.csv', 'newData.csv')Can email me for any issues or suggestions