Saravanan666/pypi-org huggingface dataset:
Package: arcos4py
arcos4pyArcos4py is a python package to detect collective Spatio-temporal phenomena.Documentation:https://bgraedel.github.io/arcos4pyGitHub:https://github.com/bgraedel/arcos4pyPyPI:https://pypi.org/project/arcos4py/Free software: MITFeaturesAutomated Recognition of Collective Signalling for python (arcos4py) aims to identify collective spatial events in time-series data.
The software identifies collective protein activation in 2- and 3D cell cultures and can track events over time. Such collective waves have been recently identified in various biological systems and have been demonstrated to play a crucial role in the maintenance of epithelial homeostasis (Gagliardi et al., 2020, Takeuchi et al., 2020, Aikin et al., 2020),
in the acinar morphogenesis (Ender et al., 2020), osteoblast regeneration (De Simone et al., 2021), and the coordination of collective cell migration (Aoki et al., 2017, Hino et al., 2020). Arcos4py is the python equivalent of the R package ARCOS (https://github.com/dmattek/ARCOS).Despite its focus on cell signaling, the framework can also be applied to other spatiotemporally correlated phenomena.Data FormatThe time series should be arranged in a long table format where each row defines the object's location, time, and optionally the measurement value.ARCOS defines an ARCOS object on which several class methods can be used to prepare the data and calculate collective events.
Optionally the objects used in the ARCOS class can be used individually by importing them from arcos.toolsInstallationArcos4py can be installed from PyPI with:pip install arcos4pyNapari PluginArcos4py is also available as a Napari Pluginarcos-gui.arcos-guican simplify parameter finding and visualization.CreditsMaciej Dobrzynskicreated the original ARCOS algorithm.This package was created withCookiecutterand thewaynerv/cookiecutter-pypackageproject template.
Package: arcovid19
This library contains the utilities to access different
Argentina-Related databases of COVID-19 data from the IATE task force.Installation$pipinstallarcovid19AuthorsJuan B Cabral (CIFASIS-UNR, IATE-OAC-UNC).Mauricio Koraj (Liricus SRL.).Vanessa Daza (IATE-OAC-UNC, FaMAF-UNC).Mariano Dominguez (IATE-OAC-UNC, FaMAF-UNC).Cristian Giuppone (IATE-OAC-UNC, FaMAF-UNC).Marcelo Lares (IATE-OAC-UNC, FaMAF-UNC).Nadia Luczywo (LIMI-FCEFyN-UNC, IED-FCE-UNC, FCA-IUA-UNDEF)Dante Paz (IATE-OAC-UNC, FaMAF-UNC).Rodrigo Quiroga (INFIQC-CFQ, FCQ-UNC).Martín de los Ríos (ICTP-SAIFR).Bruno Sanchez (Department of Physics, Duke University).Federico Stasyszyn (IATE-OAC, FaMAF-UNC).DocumentationCheck our documentation and tutorial here:https://arcovid19.readthedocs.io/Raw DataViewerCSVXLSXCitationPlease acknowledge arcovid19 in any research report or publication that
requires citation of any author’s work. Our suggested acknowledgment is:The authors acknowledge the arcovid19 project that contributed to
the research reported here.https://github.com/ivco19/libs/ABOUT THE DATAPlease cite:Luczywo, N. A., Daza, V., Koraj, M., Dominguez, M., Lares, M., Paz,
D. J., Quiroga, R., Rios, M. E. D. L., Sánchez, B. O., Stasyszyn,
F., & Cabral, J. B. (2020). Infecciones de COVID-19 en Argentina.
Unpublished.https://doi.org/10.13140/RG.2.2.22519.78246@misc{https://doi.org/10.13140/rg.2.2.22519.78246,
    doi = {10.13140/RG.2.2.22519.78246},
    url = {http://rgdoi.net/10.13140/RG.2.2.22519.78246},
    author = {
        Luczywo,  Nadia Ayelen and Daza,  Vanessa and Koraj,  Mauricio and
        Dominguez,  Mariano and Lares,  Marcelo and Paz,  Dante Javier and
        Quiroga,  Rodrigo and Rios,  Martín Emilio De Los and
        Sánchez,  Bruno Orlando and Stasyszyn,  Federico and
        Cabral,  Juan Bautista},
    language = {es},
    title = {Infecciones de COVID-19 en Argentina},
    publisher = {Unpublished},
    year = {2020}
}Afiliations:Centro Franco Argentino de Ciencias de la Información y de Sistemas
(CIFASIS-UNR)Instituto de Astronomía Téorico y Experimental
(IATE-OAC-UNC)Facultad de Matemática Física y Computación
(FaMAF-UNC)Laboratorio de Ingeniería y Mantenimiento Industrial
(LIMI-FCEFyN-UNC)Instituto De Estadística Y Demografía - Facultad de Ciencias
Económicas
(IED-FCE-UNC)Department of Physics, Duke UniversityFacultad de Ciencias de la Administación
(FCA-IUA-UNDEF)Instituto de Investigaciones en Físico-Química de Córdoba
(INFIQC-CONICET)Liricus SRLICTP South American Institute for Fundamental Research
(ICTP-SAIFR)
Package: arcproxyutilities
Python package for ArcProxyUtilitiesThis package contains the utilites which can be leveraged by azure cli extensions for the proxy command of provisioned clusters.How to useImport this package in the cli extension and make sure to add this as a dependency in the respective setup.py file by specifying the latest version number. Configure the proxy command to call client_side_proxy_wrapper() of this package with the required parameters.CSP Port number usage guidelinesWhile passing the csp port number in client_side_proxy_wrapper() from cli extension, make sure that you are passing a port number which is not used by other cli extensions. This will enable users to run multiple instances of proxy in future... :changelog:Release History1.0.2
++++++ADAL to MSAL auth support for CLI>=2.30.0.1.0.1
++++++Fetching tenantID from graphclient instead of subscription object.1.0.0
++++++Intial release.
Package: ar-crm-groups
# ar-crm-groups> Your project description goes here> Note:
This branch also uses [ar-crm-super](https://bitbucket.org/arteria/ar-crm-super/src/). It supports having people and companies as members (cura).Versions in legacy should be <=0.0.99 to avoid conflicts with the master branch.History0.0.1 (1970-01-01)First release on PyPI.
Package: arcsecond
[![Upload Python Package to Pypi](https://github.com/arcsecond-io/cli/actions/workflows/pythonpublish.yml/badge.svg)](https://github.com/arcsecond-io/cli/actions/workflows/pythonpublish.yml) [![Run Tests and Linting](https://github.com/arcsecond-io/cli/actions/workflows/pythonpackage.yml/badge.svg)](https://github.com/arcsecond-io/cli/actions/workflows/pythonpackage.yml) [![Deploy Docs](https://github.com/arcsecond-io/cli/actions/workflows/docsdeploy.yml/badge.svg)](https://github.com/arcsecond-io/cli/actions/workflows/docsdeploy.yml) [![Downloads](http://pepy.tech/badge/arcsecond)](http://pepy.tech/project/arcsecond)# Arcsecond CLIThe Command-line interface (CLI) for arcsecond.io. It can be used as Python
module too.[Read the docs](https://docs.arcsecond.io/cli)# DevelopmentTo start developing the arcsecond CLI, fork the project,git cloneit,
then, in the arcsecond-cli folder, do
(assuming [virtualenv](https://virtualenv.pypa.io/en/stable/) is installed):`bash $ cd~/arcsecond-cli$ virtualenv--python=pythonX.Yenv $ source env/bin/activate $ pip install-e. `The last line ensure you can call the “locally installed” version of the
code of that folder. Once done one first time, only thesource
env/bin/activateis needed when you restart a debugging session.
Package: arcs.shibboleth.client
.. contents::.. sectionauthor:: Russell Sim <russell@vpac.org>Introduction============This library provides classes to help with using urllib2 to access a shibboleth protected URL.shib-login-----------This commandline tool was designed to dump cookies from the CookieJar after successfully accessing an SP. These cookies can then be used by other tools like ``wget`` of ``curl`` to access the same shibboleth protected URL.::$ shib-login -i VPAC https://slcs1.arcs.org.au/SLCS/loginUsername [russell]:Password:Successfully authenticated to https://slcs1.arcs.org.au/SLCS/login$ cat ~/.shibboleth/cookies.txt# Yummy shibboleth cookiesslcs1.arcs.org.au       FALSE   /       FALSE   1250598932_shibsession_64656561756c7468747470766a2f2f736c6373312e617263732e6f72672e61752f73686962626f6c657468_186abd265ea87bf980fae2a16b0243e2$ curl -b ~/.shibboleth/cookies.txt https://slcs1.arcs.org.au/SLCS/login<?xml version="1.0" encoding="UTF-8" ?><SLCSLoginResponse>......$ wget --load-cookies ~/.shibboleth/cookies.txt https://slcs1.arcs.org.au/SLCS/login--2009-08-18 14:35:53--  https://slcs1.arcs.org.au/SLCS/loginResolving slcs1.arcs.org.au... 202.158.218.211Connecting to slcs1.arcs.org.au|202.158.218.211|:443... connected.HTTP request sent, awaiting response... 200 OK.......Changelog=========0.2 - Unrelased---------------* added https proxy support via httpsproxy_urllib2 [Russell]* buildout now used for testing [Russell]* removed dependency on xpath, adds 2.6 compatability [Russell]0.1 - 2009-10-27----------------* pushed try count responsibility to credential manager class [Russell]* chain now stops when cookie is set for initial host [Russell]* added shib-login and shib-logout cli tools [Russell]* updated to use adapter like pattern for processing forms [Russell]* Initial release
Package: arc-tasks
arc-tasksabout'arc-tasks' is minimalistic program designed for command-line-interface,
working with asci escape sequences supported terminals, handy with drop-down
terminals, nicely formatted task-objectives right in terminal with
group separation, nice progress/status symbols, terminal-fitting design,
minimal statistics per group, overall statistics, start date, finish date,
archiving multi-group tasks and navigating through archive with UI.requirementspython 3.8+pip 22.2+xterm-256color (terminal emulator or other that supports ansi)  # need to be added string in src/arc/resolve.pyinstallation# PyPi package:
pip install arc-tasks

# add PATH in .bashrc or .zshrc  # your shell configuration file
# and optionally add short alias arc
export PATH="$PATH:$HOME/.local/bin/"
alias arc="arc-tasks"usage/guideusage: arc-tasks [OPT]
    for usage examples type: arc-tasks --usage

    -c, --create        create group & task
    -t, --task          add task to the group
    -g, --group         change name of the group
    -e, --edit          edit task description
    -r, --remove        remove task
    -a, --archive       create archive and archive whole group
    -p, --purge         purge whole group and tasks
    -s, --start         change state of task(s) to 'in-progress'
    -f, --finish        change state of task(s) to 'done'

    --board             show tasks board (default)
    --append            add group to existing archive
    --expand            expand task description
    --show              show archived tasks
    --reset             restart program to no task entries
    --help              show this help message
    --usage             show examples of usageprogram usage examples:

    arc-tasks -c <group_name> <task_desc>
    arc-tasks -t <group_id> <task_desc>
    arc-tasks -g <group_id> <new_group_name>
    arc-tasks -e <group_id> <task_id> <new_task_desc>
    arc-tasks -r <group_id> <task_id>
    arc-tasks -a <group_id> <archive_name>
    arc-tasks -p <group_id>
    arc-tasks -s <group_id> <task_id> <task_id> ...
    arc-tasks -f <group_id> <task_id> <task_id> ...
    arc-tasks --board
    arc-tasks --append <group_id> <archive_name>
    arc-tasks --expand <group_id> <task_id>
    arc-tasks --show
    arc-tasks --reset
    arc-tasks --help
    arc-tasks --usagelicenseGNU General Public License v3
Package: arctic-ai
ArcticAIWelcome to the ArcticAI wiki! 🎉ArcticAI is a computational workflow designed to expedite tissue preparation, histological inspection, and tumor mapping to improve solid tumor removal. The package includes a command line interface and a web application demonstrating 3D specimen grossing recommendations, histological examination, and mapping of histological results back to the surgical site for select cases.IntroductionSuccessful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Intraoperative and postoperative radial sectioning, the most common form of margin assessment, can lead to incomplete excision and increase the risk of recurrence and repeat procedures. Mohs Micrographic Surgery (MMS) is associated with complete removal of basal cell and squamous cell carcinoma through real-time margin assessment of 100% of the peripheral and deep margins. Real-time assessment in many tumor types is constrained by tissue size, complexity, and specimen processing / assessment time during general anesthesia.FeaturesThis computational workflow places the surgeon, histotechnician, and pathologist in the same virtual space to:Reduce the amount of time a histotechnician takes to process tissue and generate pathology reports through 3D modeling techniques and smart grossing recommendations (e.g., reporting of tissue size and where to ink).Improve the efficiency of pathologic analysis through a collection of sophisticated graph neural networks to map tumor and artifacts on whole slide images (WSI) acquired from serial tissue sections.Automatically generate a descriptive and visual pathology report easily interpreted by the surgeon either in real-time or post-operatively.UsageThe package includes a command line interface that can be used to run the ArcticAI workflow. Additional documentation for the command line interface can be accessed via the --help flag. API usage can be found at the following URL (readthedocs):https://jlevy44.github.io/ArcticAI/For more information on how to use the command line interface, please see our wiki:https://github.com/jlevy44/ArcticAI/wikiWeb application demoA web application demonstrating 3D specimen grossing recommendations, histological examination, and mapping of histological results back to the surgical site for select cases can be accessed at the following URL:https://arcticai.demo.levylab.host.dartmouth.edu/.Additional informationWe are still updating tutorials for running 3D gross tissue recommendations and mapping the tumor back to the surgical site (for now, see the web application). We will also soon provide tutorials on the nuclei and follicle detection workflows.Patent pending, original submission from 2020:https://patentimages.storage.googleapis.com/45/11/5a/717cb5cc2eb269/US20220375604A1.pdfThank you for using ArcticAI! 💻🔬🧬
Package: arctic-spa
Arctic Spa Hot Tub libraryThis library talks to Arctic Spa brand hot tubs and reports sensor information.As of this initial library version, this only supports polling the hot tub for
status information, not controlling it.OverviewTo request status information from the hot tub, create anArcticSpaobject with
the device's hostname and callpoll():import asyncio
from arctic_spa import ArcticSpa

spa = ArcticSpa('192.168.123.42')
packets = asyncio.run(spa.poll())packetsis a list of the latest packets received by the network client.poll()will
return once all the requested types have been returned. Currently this only supports
requestingLiveandOnzenLivepacket types, but may return other types if the hot tub
sends them in addition to the requested packets.Seenetwork.pyfor a complete example.Network DiscoveryYou can search the local subnet for a hot tub. To do so, provide the local
address of the calling code and the subnet mask you'd like it to scan:searcher = NetworkSearch(local_addr, subnet_mask)
results = asyncio.run(searcher.search())resultsis a list of all the discovered devices (empty if there are none).Seenetwork.pyfor a complete example.LicenseCopyright (c) 2022 Steve Pomeroysteve@staticfree.infoThis is licensed under the Apache 2.0 software license.DisclaimerThis library is neither endorsed nor sponsored by Arctic Spa. By using this
library, you agree to take full responsibilty for any actions that may occur
from the use of this library.That said, we love our Arctic Spa hot tub and don't want to damage it, so we try
very hard to not do anything risky with it.
Package: arctic-tern
You can be strongly migratory, too!Feature SupportPlain SQL update scriptsTimestamped update scriptsCode-level integration (no CLI needed)Arctic Tern officially supports Python 3.6+InstallationTo install Arctic Tern, simplypip install arctic-ternPublishingpython setup.py sdist bdisttwine upload –repository-urlhttps://test.pypi.org/legacy/dist/*twine upload dist/arctic_tern-0.1.11.tar.gzDocumentationDocumentation is good!  We should get some.How to ContributeThis would also be good.
Package: arctix
arctixOverviewarctixis a Python library that provides simple functions to check in a single line if two
complex/nested objects are equal or not.arctixwas initially designed to work
withPyTorchTensorsandNumPyndarray, but it
is possible to extend it
tosupport other data structures.MotivationDocumentationInstallationContributingAPI stabilityLicenseMotivationLet's imagine you have the following dictionaries that contain both a PyTorchTensorand a
NumPyndarray.
You want to compute a string representation of it.
By default, Python tries to show the values of all the tensor/array.
Thearctixlibrary was developed to easily compute structured string representation of nested
objects.arctixprovides a functionsummarythat can indicate if two complex/nested objects are equal or
not.>>>importnumpy>>>importtorch>>>fromarctiximportsummary>>>print(summary({"torch":torch.ones(2,3),"numpy":numpy.zeros((2,3))}))<class 'dict'> (length=2)(torch): <class 'torch.Tensor'> | shape=torch.Size([2, 3]) | dtype=torch.float32 | device=cpu(numpy): <class 'numpy.ndarray'> | shape=(2, 3) | dtype=float64>>>print(...summary(...{..."torch":[torch.ones(2,3),torch.zeros(6)],..."numpy":numpy.zeros((2,3)),..."other":[42,4.2,"abc"],...},...max_depth=3,...)...)<class 'dict'> (length=3)(torch): <class 'list'> (length=2)(0): <class 'torch.Tensor'> | shape=torch.Size([2, 3]) | dtype=torch.float32 | device=cpu(1): <class 'torch.Tensor'> | shape=torch.Size([6]) | dtype=torch.float32 | device=cpu(numpy): <class 'numpy.ndarray'> | shape=(2, 3) | dtype=float64(other): <class 'list'> (length=3)(0): <class 'int'> 42(1): <class 'float'> 4.2(2): <class 'str'> abcPlease check thequickstart pageto learn more on
how to usearctix.InstallationWe highly recommend installing
avirtual environment.arctixcan be installed from pip using the following command:pipinstallarctixTo make the package as slim as possible, only the minimal packages required to usearctixare
installed.
To include all the packages, you can use the following command:pipinstallarctix[all]Please check theget started pageto see how to
install only some specific packages or other alternatives to install the library.ContributingPlease check the instructions inCONTRIBUTING.md.API stability:warning: Whilearctixis in development stage, no API is guaranteed to be stable from one
release to the next.
In fact, it is very likely that the API will change multiple times before a stable 1.0.0 release.
In practice, this means that upgradingarctixto a new version will possibly break any code that
was using the old version ofarctix.Licensearctixis licensed under BSD 3-Clause "New" or "Revised" license available inLICENSEfile.
Package: arctool
Documentation:http://arctool.readthedocs.ioGitHub:https://github.com/JIC-CSB/arctoolPyPI:https://pypi.python.org/pypi/arctoolFree software: MIT LicenseOverviewThe arctool project project provides tools for archiving (scientific) data.
It aims to help in three areas:Adding structure and meta data to your project and filesVerifying the integrity of the files in your projectCreating archives for long term storageDesign philosophyThe tools in this project should produce outputs that can be understood without
access to these tools. This is important as it is likely that the outputs of
from these tools may outlive these tools.
Package: arcu
ARCUFind representative subpopulations in single cell imaging data.
â€‹IntroductionARCU is a simple algorithm for finding coordinates in single-cell imaging data where measured features are relatively variable. This is a useful task for finding representative images for publication that illustrate difference in cell types. ARCU finds regions in an image where cells are different; in other words, it finds regions where at least one cell is above a threshold and at least one cell is below a threshold for features of interest. Thresholds are given by:mu + u*sig
mu - u*sigwheremu = mean expression for feature across whole population
sig = standard deviation for feature across whole population
u = a scaling coefficentâ€‹â€‹InstallationDependenciesPython >= 3.6, numpy >= 1.22.4, pandas >= 1.3.2
â€‹
You can install the package and necessary dependencies withpipby,pip install arcuâ€‹Example useTo find regions of interest using ARCU, first read in a pandas dataframe formatted such that the first column is numeric labels, the second is x-coordinates, the third is y-coordinates, and columns 4 through n are features of interest. Rows should be interpretable as "cells" profiled from segmented images with single-cell resolution.
â€‹importpandasA=pandas.read_csv('dir/file.csv')â€‹
Then execute ARCU usingimportarcucentroids=arcu.arcu(A,r,c,u)whereInputs:
  A = dataframe of single cell location and feature data
  r = radius, in pixels, of regions in which to search for subpopulations
  c = the minimum number of cells a region of interest can contain to be considered for reporting
  u = the scaling coefficient on standard deviation for a cell to be considered interesting

Returns:
  a dataframe containing the x,y coordinates of groupings that meet feature expression criteria
Package: arcus
arcus-pythonArcus python3.6+ client library for API version 3.1 ofArcus.Install$pipinstallarcusTesting$maketestAuthentication and account infoCurrent version support direct endpoint calls.fromarcus.clientimportClient# Create an Arcus client instanceclient=Client('your-api-key','your-secret-key')# Get account infoaccount_info=client.get('/account')# create billaccount_info=dict(biller_id=40,account_number='501000000007')bill=client.post('/bills',account_info)Pay billsfromarcusimportClientclient=Client('your-api-key','your-secret-key')# create billbill=client.bills.create(40,'501000000007')# pay billtransaction=bill.pay()# refresh transactiontransaction.refresh()# cancel transactioncancellation=transaction.cancel()assertcancellation['code']=='R0'assertcancellation['message']=='Transaction successful'asserttransaction.status=='refunded'Top-upfromarcusimportClientclient=Client('your-api-key','your-secret-key')biller_id=808080phone_number='5599992222'amount=100.0# by default, currency is MXNtopup=client.topups.create(biller_id,phone_number,amount)asserttopup.bill_amount==100.0Billers listYou can list all active billers or filter in a specific fieldfromarcus.clientimportClient# Create an Arcus client instanceclient=Client('your-api-key','your-secret-key')# Get all billers in Mexico which use MXN as currencymx_biller_list=client.biller.list(country='MX',currency='MXN')# Get all billers with an specific biller_typeelectricity_biller_list=client.biller.list(biller_type='Electricity')
Package: arcusapi
Arcus Data Platform Client SDKInstallationInstall with PyPI (pip):pip install arcusapiYou can verify your installation by runningimport arcusin Python:$ python
Python 3.9.16 (main, Feb 28 2023, 06:13:28)
[Clang 12.0.5 (clang-1205.0.22.9)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import arcusRefer to the Arcusdocumentationfor more
information.