Saravanan666/pypi-org huggingface dataset:
Package: dadbscan
AdaptiveDBSCANThis is a normalized form of DBSCAN algorithm that is based on varying number of neighbour. This algorithm is useful when your data has different density pattern. To get more information about the algorithm, please refer to the paper.installationTo install the package, you can use pip:pip install dadbscanGetting StartedAfter installing the package, you can use it as follows by importing the modules:from dadbscan.density import EQ_Density
from dadbscan.dbscan import EQ_DBSCAN###Phase1.
The first line is being used for creating density map and the second one is for applying the Density-Adaptive DBSCAN algorithm.
Now by defining the N value you having database as a csv file, you can run the density algorithm:initiating the EQ_density class:N = 65
density = EQ_Density(N, database)To test the program, you can download the test file from the github repo and use decl_cat.csv as database.database = 'decl_cat.csv'running calc_density method:heat_matrix = density.calc_density()plotting the density map:density.plot_density(heat_matrix)a feature that can be used is smoothing the density map. This can be done by using the following method:smoothed_heat_matrix = density.cell_smoother(apply_smooth=True)! All the matrixes are saved physically in the folder 'Results'.###Phase2.
Now that you have the density map, you can run the Density-Adaptive DBSCAN algorithm. To do so, you need to define the following parameters:radius = density.radius
density_file_names = f"Results/den_decl_cat__65_smooth.csv"As it can be seen above, radius can be derived from the denisty class.
now it is time to initiate the dbscan class and run the algorithm:clustering = clustering = dbscan(radius, density_file_name)
final = clustering.clustering()
clustering.plot_clusters()
final.to_csv(f"Results/R__{density_file_name}")ReferenceSabermahani, S., Frederiksen, A., 2023, Improved earthquake clustering using a Density-Adaptive DBSCAN algorithm: an example from Iran, Seismological Research LettersLicenseThis project is licensed under the MIT License - see theMIT Licensefile for details.
Package: dader
DADER: Domain Adaptation for Deep Entity ResolutionEntity resolution (ER) is a core problem of data integration. The state-of-the-art (SOTA) results on ER are achieved by deep learning (DL) based methods, trained with a lot of labeled matching/non-matching entity pairs. This may not be a problem when using well-prepared benchmark datasets. Nevertheless, for many real-world ER applications, the situation changes dramatically, with a painful issue to collect large-scale labeled datasets. In this paper, we seek to answer: If we have a well-labeled source ER dataset, can we train a DL-based ER model for target dataset, without any labels or with a few labels? This is known as domain adaptation (DA), which has achieved great successes in computer vision and natural language processing, but is not systematically studied for ER. Our goal is to systematically explore the benefits and limitations of a wide range of DA methods for ER. To this purpose, we develop a DADER (Domain Adaptation for Deep Entity Resolution) framework that significantly advances ER in applying DA. We define a space of design solutions for the three modules of DADER, namely Feature Extractor, Matcher, and Feature Aligner. We conduct so far the most comprehensive experimental study to explore the design space and compare different choices of DA for ER. We provide guidance for selecting appropriate design solutions based on extensive experiments.This repository contains the implementation code of six representative methods of [DADER]: MMD, K-order, GRL, InvGAN, InvGAN+KD, ED.DataSetsThe dataset format is <entity1,entity2,label>. SeeHugging Facefor details.Quick StartStep 1: RequirementsBefore running the code, please make sure your Python version is 3.6.5 and cuda version is 11.1. Then install necessary packages by :pip install daderIf Pytorch is not installed automatically, you can install it using the following command:pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.htmlStep 2: Run Example```python
#!/usr/bin/env python3
from dader import data, model

# load datasets
X_src, y_src = data.load_data(path='source.csv')
X_tgt, X_tgt_val, y_tgt, y_tgt_val = data.load_data(path='target.csv', valid_rate = 0.1)


# load model
aligner = model.Model(method = 'invgankd', architecture = 'Bert')
# train & adapt
aligner.fit(X_src, y_src, X_tgt, X_tgt_val, y_tgt_val, batch_size = 16, ada_max_epoch=20)
# predict                    
y_prd = aligner.predict(X_tgt)
# evaluate
eval_result = aligner.eval(X_tgt, y_prd, y_tgt)

```
Package: dadi
Diffusion Approximation for Demographic Inferencedadi implements methods for demographic history and selection inference from genetic data, based on diffusion approximations to the allele frequency spectrum. One of dadi's main benefits is speed: fitting a two-population model typically takes around 10 minutes, and run time is independent of the number of SNPs in your data set. dadi is also flexible, handling up to three simultaneous populations, with arbitrary timecourses for population size and migration, plus the possibility of admixture and population-specific selection.Originally dadi was initially developed by  Ryan Gutenkunst in the labs of Scott Williamson and Carlos Bustamante (https://bustamantelab.stanford.edu/) in Cornell's Department of Biological Statistics and Computational Biology. Ryan is now faculty in Molecular and Cellular Biology at the University of Arizona, and his group continues to work on dadi and other topics (http://gutengroup.mcb.arizona.edu).If you use dadi in your research, please cite RN Gutenkunst, RD Hernandez, SH Williamson, CD Bustamante "Inferring the joint demographic history of multiple populations from multidimensional SNP data" PLoS Genetics 5:e1000695 (2009).Getting startedSee the manual (https://dadi.readthedocs.io) and the example files in the source distribution. Full API documenation is available (https://dadi.readthedocs.io/en/latest/api/dadi/) and is in the source distribution, under doc/api/dadi/index.html .Also, please sign up for our mailing list, where we help the community with ∂a∂i. Please do search the archives before posting. Many questions come up repeatedly. (http://groups.google.com/group/dadi-user)The easiest way to install dadi is via conda,conda install -c conda-forge dadi.Notebook examples
Package: dadjokeAPI
dadjokeAPI - A simple API for your dad joke needs.Non-asynchronous usage:fromdadjokeAPIimportsearchDadJokesfromdadjokeAPIimportgetDadJoke# Prints a dad joke with a random or specified ID.# Raises JokeNotFoundException if the joke was not found.print(getDadJoke("0189hNRf2g"))# Prints a list of dad jokes with a random or provided term.# Returns a empty list if no jokes were found.print(searchDadJokes("fish"))Asynchronous usage:fromdadjokeAPIimportsearchDadJokesAsyncfromdadjokeAPIimportgetDadJokeAsync# Prints a dad joke with a random or specified ID.# Raises JokeNotFoundException if the joke was not found.# Has an optional aiohttp.ClientSession argument called http# in case you want to provide your session.print(awaitgetDadJokeAsync("0189hNRf2g"))# Prints a list of dad jokes with a random or provided term.# Returns a empty list if no jokes were found.# Has an optional aiohttp.ClientSession argument called http# in case you want to provide your session.print(awaitsearchDadJokesAsync("fish"))
Package: dadjokes
DadjokesicanhazdadjokeAPI wrapper, to programatically cheer your code up.InstallationpipinstalldadjokesUsageExamplefromdadjokesimportDadjokedadjoke=Dadjoke()print(dadjoke.joke)The previous snippet will print a random joke.Dadjokeobjects can be built from an id as well. You can get those calling the.idproperty of aDadjoke. If you are using this in aSlackapp, you might find the.as_slackproperty useful.You can use the iterableDadjokeSearchiterable class to search jokes by term, with an optional limit.cliPlease check available options with:dadjokes-h
Package: dadjokes-cli
dadjokes-cli : Dad Jokes on your TerminalAll the jokes that make you laugh and cringe in equal measure right on your TerminalInstallationpip install dadjokes-cliOnly Dad jokesDad jokes +CowsayHow'd you like your Dad jokes ?GroaningChoose from reddit, github or icanhazdaddadjoke --reddit --cowsay
dadjoke --github --cowsay
dadjoke --icanhazdad --cowsaySourceshttps://github.com/wesbos/dad-jokes/blob/master/readme.mdhttps://www.reddit.com/r/dadjokes/https://icanhazdadjoke.com/Maybe More...
Package: dadjokes-plus-plus
Image of dadjokes++ geting used on terminal startup.Dadjokes++Dad jokes on steroid.Fetch jokes from icanhazdadjoke. Unlike other alternatives, dadjokes++ can download all jokes to your computer for faster and offline retrieval.InstallationMake sure you have python 3 and pip installed, then execute this in your terminal:pipinstalldadjokes-plus-plusUsageFrom terminalFetch a random joke online$dadjokes
Iusedtobeabanker,butIlostinterest.Download all jokes to jokes.txt$dadjokes-d"jokes.txt"Load a random joke from jokes.txt$dadjokes-l"jokes.txt"Asteakpunisararemediumwelldone.Note:if you don't provide -d or -l a path it will automatically be "jokes.txt".From pythonFetch a random joke onlineimportdadjokesprint(dadjoke.joke())Download all jokes to jokes.txtimportdadjokesprint(dadjoke.save_jokes("jokes.txt"))Load a random joke from jokes.txtimportdadjokesprint(dadjoke.joke("jokes.txt"))
Package: dados
dados *Beta*================Random word generatorPlease ★ this repo if you found it useful ★ ★ ★Contributing------------1. Fork it!2. Create your feature branch: ``git checkout -b my-new-feature``3. Commit your changes: ``git commit -m 'Add some feature'``4. Push to the branch: ``git push origin my-new-feature``5. Submit a pull request :DLicense-------`MIT License`_`Jam Risser`_ © 2017Credits--------  `Jam Risser`_ - Author
Package: dadosgovbr
# dadosgovbr[![PyPI version](https://badge.fury.io/py/dadosgovbr.svg)](http://badge.fury.io/py/dadosgovbr)[![Build Status](https://travis-ci.org/georgeyk/dadosgovbr.svg?branch=master)](https://travis-ci.org/georgeyk/dadosgovbr)[![Coverage Status](https://coveralls.io/repos/georgeyk/dadosgovbr/badge.svg?branch=master)](https://coveralls.io/r/georgeyk/dadosgovbr?branch=master)---A library to programatically explore the Brazilian open data program.More details at [dados.gov.br][1]## MotivationBasically, I did this library because their web interface was broken for me, specially the search feature.Also, I feel more confortable exploring the data using a python shell with all the libraries available to handle json,csv and so on.## InstallAvailable in PyPi, just:```shell$ pip install dadosgovbr```## ExamplesThe library just provides an "object-wrapping" for the API results, so we can access every attribute as weinspect any regular python object:```python>>> from dadosgovbr import DadosGovBR>>> api = DadosGovBR()>>> api.get_tag('VT')Tag(vocabulary_id=None, packages=<generator object model_list_factory at 0x7f7dac17af00>, display_name=u'VT',id=u'41a7ab78-3cec-44a3-b2c3-ca15ad55a50f', name=u'VT')```Check the tests directory for more examples, like [this one][2].## NOTEThis is very *work-in-progress* lib.Contributions are super-welcome. =)## Development1. fork the project2. create virtualenv (optional)3. pip install -r requirements-devel.txt4. pre-commit install5. tox[1]: http://dados.gov.br/dataset[2]: example.py## History## 0.1.3* First tag release* Bug fixes## 0.1.2* Improve test coverage* Minor fixes## 0.1.1* Fix import errors## 0.1.0* Initial release
Package: dados-pessoais
# Gustavo NogueiraA instaÃ§Ã£o deste arquivo te auxilia no armazenamento de dados pessoais fornecidos por diversas pessoas.Criador: Gustavo Nogueira
E-mail:gustavonogueiralopes@gmail.comdata de criaÃ§Ã£o: 28/01/2021
Package: dadosSPF
dadosSPFGetting StartedDependenciesVocê precisa do Python 3.7 ou posterior para usar dadosSPF.
Você pode encontrá-lo em [python.org] (https://www.python.org/).
Você precisa do PySpark 2.4.7 para usar dadosSPF.
Você pode encontrá-lo em [spark.apache.org] (https://spark.apache.org/docs/latest/api/python/).You also need pandas, holidays and impyla packages, which is available fromPyPI. If you have pip, just run:
'''
pip install pandas
pip install holidays
pip install impyla
pip install unidecode
pip install tqdm
'''InstallationClone this repo to your local machine using:
'''
git clonehttps://gitlab.com.br/dados-migracao/dadosSPF'''FeaturesPacote de conexção e ferramentas de consulmo de dadosPacote de ferramentas uteis
Package: dadra
Python Library for Data-Driven Reachability AnalysisWhat is it?DaDRA(day-druh) is a Python library for Data-Driven Reachability Analysis. The main goal of the package is to accelerate the process of computing estimates of forward reachable sets for nonlinear dynamical systems. For more information about the library, seethis poster from the NSF funded SUPERB ProgramInstallationTo install the current release of DaDRA:$ pip install --upgrade dadraor$ conda install -c jaredmejia dadraResourcesPyPiAnacondaDocumentationIssue trackingUsageSee these examples from thedocumentation:Lorenz System with disturbance using Scenario ApproachDuffing Oscillator using Christoffel Functions12-state Quadrotor using Scenario ApproachContributingFor contributions, please follow the workflow:Forkthe repo on GitHubClonethe project to your own machineCommitchanges to your own branchPushyour work back up to your forkSubmit aPull requestso that your changes can be reviewedBe sure to fetch and merge from upstream before making a pull request.AcknowledgementSpecial thanks to@alexdevonportfor contributions.LicenseMIT LicenseBibTeX@article{JaredMejia,title={DaDRA},author={Mejia, Jared},journal={GitHub. Note: https://github.com/JaredMejia/dadra},volume={1},year={2021}}
Package: dad-tool
Dummy Address Data (DAD) Python LibraryDummy Address Data (DAD) - Retrieve real addresses from all around the world. (Python Client Library)The DAD Python library is the perfect companion to quickly bootstrap address data in your application. DAD provides real addresses from all over the world with a consistent data structure so you can spend less time looking up addresses and address rules and more time coding.Install# Install toolpip3installdad_tool# Install locallyjustinstallAddress DataAddress objects will look like thesample below. The data type of each field on an address object is astring. A list of addresses is anarrayofjsonobjects.Attempts have been made to verify addresses and ensure that street1, city, state, and zip are present on all records. Some lists may be shorter than others to avoid complexity or because of a lack of accurate data.The following files can be found in thedatadirectory.AustraliaLocationsTagVictoria AreaAU_VTCanadaLocationsTagBC AreaCA_BCChinaLocationsTagBeijing AreaCN_BJHong Kong - Wan Chai AreaCN_HKEuropeLocationsTagGermany - Wesel AreaEU_DESpain - CountrywideEU_ESFrance - Paris AreaEU_FRUnited Kingdom - England AreaEU_UKMexicoLocationsTagMexico - Mexico City AreaMX_MXUnited StatesLocationsTagArizona - Gilbert AreaUS_AZCalifornia - Anaheim AreaUS_CAIdaho - Boise AreaUS_IDKansas - Barton CountyUS_KSNevada - Lincoln AreaUS_NVNew York - Rochester AreaUS_NYOregon - Portland AreaUS_ORTexas - Austin AreaUS_TXUtah - Provo AreaUS_UTWashington - Spokane AreaUS_WAUsageimportdad_tool# Grab a random UT addressaddress=dad_tool.random_address('US_UT')print(address)# Alternatively, grab the entire UT listaddresses=dad_tool.list_addresses('US_UT')print(addresses)# Get the list of all ISO country codesiso_data=dad_tool.list_iso_country_codes()print(iso_data)Sample Address ObjectA sample address object will look like the following:{"street1":"231 N 1200 W","street2":"UNIT 104","city":"OREM","state":"UT","zip":"84057","country":"US"}Sample ISO Country Object{"country":"United States of America","alpha_2_code":"US","alpha_3_code":"USA"}Development# To setup the `DAD` git submodulejustsetup-dad# Get a comprehensive list of development toolsjust--listAttributionAddresses provided byDAD.
Package: daedalus-5g
Blue TeamThe following directory has a series of tools that are used for defending and building out environments for running scenarios in.4G/5G Environment Creation ToolDependenciesLinux with a recent kernelDockerpython3 and pip3uhd-host (sudo apt-get install -y uhd-host)InstallationThe main tool in this directory is calleddaedalusand can be installed with easily with pip:pip3 install daedalus-5gor locally:git clone https://github.com/IQTLabs/Daedalus
cd Daedalus/blue
sudo python3 setup.py installOnce installed, you can simply executedaedalusand you will be prompted for the 4G/5G environments you'd like it to create.** Note **AMD64 CPUs must support SSE4.1, AVX, AVX2, and FMA. If you don't have those, you can alternatively try starting daedalus with -b which will build the images locally and might work.

ARM64 CPUs don't have those requirements, but you will need g++ when pip installing for grpc.
Package: daemonator
Daemonator (0.4.1.1) Formerly Daemon-Python==================Lightweight and no-nonsense POSIX daemon library. Extensible, completewith process forking and`PID <http://en.wikipedia.org/wiki/Process_identifier>`__ management.Inspired by `SanderMarechal <http://www.jejik.com/articles/2007/02/a_simple_unix_linux_daemon_in_python/>`__.License-------MIT/X11 - See`LICENSE <https://github.com/stackd/daemon-py/blob/master/LICENSE>`__Getting Started---------------**Installation**::# git clone https://github.com/flaviocpontes/daemonator.git# cd daemon-py/# python setup.py install1. **Instantiation**::import daemonclass MyDaemon(daemon.Daemon):"""Subclass Daemon-Python."""...def run(self):"""Define what to daemonize by implementing the run() method."""...2. **Implementing control**Finally, we want to be able to control our daemon.::...if __name__ == "__main__":daemon = MyDaemon('/tmp/mydaemon.pid') #Define a pidfile location (typically located in /tmp or /var/run)if len(sys.argv) == 2:if 'start' == sys.argv[1]:daemon.start()elif 'stop' == sys.argv[1]:daemon.stop()elif 'restart' == sys.argv[1]:daemon.restart()elif 'status' == sys.argv[1]:daemon.status()else:sys.stdout.write("Unknown command\n")sys.exit(2)sys.exit(0)else:sys.stdout.write("Usage: %s start|stop|restart|status\n" % sys.argv[0])sys.exit(2)
Package: daemonctl
# Daemon Control (daemonctl)Description:  A python framework and tools to manage small applicationsTechnology stack: Plain pythonStatus:  Running in production [CHANGELOG](CHANGELOG.md).When administrating alot of applications on different servers or developing alot of small services it’s often desireable to keep the inhouse services seperate from system services.## Dependenciespython >= 2.6setprocname: to have cleaner names in ps for the daemons (not required)fast-entry_points## Installationsudo ./setup.py install## Configurationconfigfile in /usr/local/etc/daemonctl.conf (or where DAEMONCTL is installed)Configformat:logpath = logdir # Path to directory where log files will be placedpidpath = piddir # Path to directory where pid files will be placedmodules {module1 {name = modulename # name of daemon in status and other daemonctl commands (should include %(id)s if type=dynamic)type = moduletype # single or dynamicpath = modulepath # Path to where the files are (will do chdir to here before running command)execcmd = commandline # Command to run (will be prefixed with path)listcmd = listcommand # Command that returns id:s for type=dynamic, one id per rowlogpath = logdir # Can override the global logpathpidpath = piddir # Can override the global pidpathrunas = username # Run daemon as this user}}## Usage
|  Usage: daemonctl [options] <command> [daemon]
|       Commands:
|          start        Start daemons
|          stop         Stop daemons (“-f” to force)
|          restart      Restart daemons (stop+start)
|          forcestop    Force daemons to stop (kill -9)
|          status       Get daemon status
|          enable       Enable an application
|          disable      Disable an application
|          hide         Hide daemon from status
|          show         Unhide daemon from status
|          tail         Tail a daemon log
|          less         Less a daemon log
|          csvstatus    Get daemon status in csv format
|
|      Options:
|        -h, –help            show this help message and exit
|        -f, –force
|        -r, –regex           Select daemons using regexp only
|        -g, –glob            Select daemons using globbing only
|        -e, –exact           Select daemons using exact match only
|        -c CONFIG, –config=CONFIG
|        -a, –showall         Show hidden
|        -v, –version         Print version## Known issuesThe code is very messy## Getting helpIf you have questions, concerns, bug reports, etc, please file an issue in this repository’s Issue Tracker.## Getting involvedFeature request with documentation, fixes, new features and general beutification is welcome.## Open source licensing infoCopyright: SVT 2018
GNU General Public License version 3
[LICENSE](LICENSE)except for [fastentrypoints.py](https://github.com/ninjaaron/fast-entry_points) which is Copyright (c) 2016, Aaron Christianson—## Primary MaintainerAndreas Åkerlundhttps://github.com/thezulk
Package: daemondev-config-server
Requirementsrequire .env file with these contentsCONFIG_SERVER=localhost | config.wecourier4u.com
CONFIG_SCHEMA=https | http
CONFIG_USERNAME=xxx
CONFIG_PASSWORD=xxxxxxxxxxxxxxxxxxxxxxxx
ENVIRONMENT=local | production
LOCAL_CONFIG_DIR=/path/to/my/config-files/repo/dir  #Optional
DEBUG=1                                             #Optional(Optional): create .configserver file in root project and set LOCAL_CONFIG_DIR(Optional): export GLOBAL_CONFIG_DIR in your .*rc file#~/.bashrc
export GLOBAL_CONFIG_DIR=/path/to/config-files/dir

#$PROJECT_ROOT/.configserver
LOCAL_CONFIG_DIR=/path/to/config-files/dirAppend this commands to package.json inscriptssection#package.json
...
    "install": "docker run -it --rm --name nest -v $(pwd):/app node:14.19-alpine3.14 sh -c 'cd /app && npm install'",
    "docker": "docker run -it --rm --name nest -p ${PORT:-3000}:${PORT:-3000} -e PORT=${PORT} -v ${GLOBAL_CONFIG_DIR}:/app/config-files -e DEBUG=${DEBUG} -v $(pwd):/app node:14.19-alpine3.14 sh -c 'cd /app && npm run start'",
...