Saravanan666/pypi-org huggingface dataset:
Package: ctlearn
CTLearn is a package under active development to run deep learning models to analyze data from all major current and future arrays of imaging atmospheric Cherenkov telescopes (IACTs). CTLearn can load DL1 data fromCTA(Cherenkov Telescope Array),FACT,H.E.S.S.,MAGIC, andVERITAStelescopes processed byctapipeorDL1DataHandler.Code, feature requests, bug reports, pull requests:https://github.com/ctlearn-project/ctlearnDocumentation:https://ctlearn.readthedocs.ioLicense: BSD-3Installation for usersDownload and installAnaconda, or, for a minimal installation,Miniconda.The following command will set up a conda virtual environment, add the
necessary package channels, and install CTLearn specified version and its dependencies:CTLEARN_VER=0.7.0wgethttps://raw.githubusercontent.com/ctlearn-project/ctlearn/v$CTLEARN_VER/environment.ymlcondaenvcreate-n[ENVIRONMENT_NAME]-fenvironment.ymlcondaactivate[ENVIRONMENT_NAME]pipinstallctlearn==$CTLEARN_VERctlearn-hThis should automatically install all dependencies (NOTE: this may take some time, as by default MKL is included as a dependency of NumPy and it is very large).See the documentation for further information likeinstallation instructions for developers,package usage, anddependenciesamong other topics.Citing this softwarePlease cite the corresponding version using the DOIs below if this software package is used to produce results for any publication:0.6.0 :0.5.2 :0.5.1 :0.5.0 :0.4.0 :0.4.0-legacy :0.3.1 :TeamAri BrillBryan KimTjark MienerDaniel NietoCollaboratorsQi FengRuben Lopez-CotoAlumniJaime SevillaHéctor RuedaJuan Redondo PizarroLuca RomanatoSahil YadavSergio García Heredia
Package: ctlib
ctlib, for Continuous Trading, is a small project intended as a small test case.Its goal is to integrate several technologies and to apply them in a single supercomputing application:Python scriptingExtension modules and SwigMPIOpenMPGrid applications with twistedMultiplatform development.The application computes the value of some derivatives (very few) using a client server paraigm with Twisted.  You need both the server and the client to value derivatives.
Package: ct-lifecycle
User Lifecycle Managementlifecycleis a piece of software intended to help implement theSingle Source of Truthprinciple across disparate applications and services.  It is intended to be used alongside a single-sign-on authentication system such as SAML or OIDC, and provide lifecycle management for the users in these applications.Why?Some software we use doesn't have good user lifecycle management.  While we can use SSO to log into
these pieces of software, we end up with ghost users, manual user and role management, and worse of all, users
with non-sso authentication methods.How?The application is intended to be run in an automated scheduled fashion.  A docker container will be provided where a config volume can be mounted to configure your applications.LayoutA typical workflow forlifecyclewill have onesource, such as LDAP or a CSV file, and multipletargets, such as Google Apps or SuiteCRM.  The entire user and group list will be extracted from thesource, and used to set up thetargetin a matching way.  This can include adding and removing users, changing contact details and disabling user accounts.Ideally if a target has an access-level system thats vaguely sensible, we can manage a user's access too - atargetwill manageroleswithin its application, and make sure users are in the correctrolesas specified insourceUser StoriesUser CreationAlice starts at company, and has an LDAP account created for them by Bob.When lifecycle is next run, it pulls Alice's account details from LDAP, and uses them to create a matching user for them in SuiteCRM.Alice is able to log into SuiteCRM immediately via SAML, and has the correct permissions and access.User DisablementCharlie decides to move on from company, and on their final day their LDAP account is disabled.When lifecycle is next run, it notices Charlie's LDAP account is disabled, and disables their SuiteCRM access.Charlie is no longer able to authenticate to SuiteCRM, and they no longer show up as a valid user for other SuiteCRM users.User Access ChangeDavid moves from the engineering department to the sales department, and is added to the sales groupWhen lifecycle is next run, it will modify David's roles to give them access to sales specific functionality within SuiteCRM.David is able to log into SuiteCRM and has correct access.
Package: ctlimit
A Glib and systemd based daemon to limit the daily computer usage time of our children.DocumentationPlease read the source or create the html-documentation using the following command:python setup.py build_sphinxChangelogxxxx-xx-xx Version 0.0.5:Documentation updates.2016-06-08 Version 0.0.4:Fix an incompatibility with the latest released version of pydbus.2016-06-08 Version 0.0.3:Add a DBus interface to ctlimit. It is now possible to get the
current usage time of a user and to set/increase the limit of the current day.
Code cleanups and a bit more documentation.2016-05-07 Version 0.0.2:Separate the sd-daemon logic and the time-logic
The new module sddaemon contains a base class for new style daemons.2016-04-24 Version 0.0.1:A first working version.
Package: ctlog
CTLog: SAT-Based Combinatorial Testing in PythonFull documentation availableonline.CTLogis a complete framework for Combinatorial Testing of Constrained Systems Under Test developed by theLogic & Optimization Group (LOG)at the University of Lleida.
It uses Satisfiability (SAT) technology through theOptiLogPython framework, also developed and mantained by the same research group.CTLog implements all the algorithms that we developed in our research, such asMaxSAT MCACandmaxsat-itsand all the family of theBOT-itsalgorithms.
It also implements several state-of-the-art algorithms for generating Mixed Covering Arrays with Constraints, such asIPOGorCALOT.Additionally, we provide theSUT-Ggenerator, which can be used to generate new Constrained Systems Under Tests where all these MCAC algorithms can be applied.InstallationCTLog currently supports Python versions 3.6, 3.7, 3.8, 3.9 and 3.10 for Linux.
Wheels are provided through PyPI, you can install CTLog directly with pip:pip3 install ctlogCTLog also requires a software license, which must be placed in your${HOME}directory under the name.ctlog.lic.
You can download a valid license for academic useshere, which is valid untilSeptember 27, 2023.Before using CTLog please read ourlicensecarefully.
If you need CTLog for industrial scenarios please contact us.If you use CTLog in your research please cite our work:@article{DBLP:journals/corr/abs-2105-12552,
    title    = {Incomplete {MaxSAT} approaches for combinatorial testing},
    volume   = {28},
    issn     = {1572-9397},
    url      = {https://doi.org/10.1007/s10732-022-09495-3},
    doi      = {10.1007/s10732-022-09495-3},
    language = {en},
    number   = {4},
    urldate  = {2022-09-02},
    journal  = {Journal of Heuristics},
    author   = {Ansótegui, Carlos and Manyà, Felip and Ojeda, Jesus and Salvia, Josep M. and Torres, Eduard},
    month    = aug,
    year     = {2022},
    keywords = {Combinatorial testing, Constraint programming, Maximum satisfiability},
    pages    = {377--431}
}
Package: ctlogging
CtloggingMiddleware for loading or generating correlation IDs for each run. Correlation IDs can be added to your logs, making it simple to retrieve all logs generated from a single run.It also help in logging message in mysql databaseWhen the process starts a correlation ID is set in the contextvar and retrieve using the filter during logging and inject into log record.1. How to installUsing pippip install ctloggingUsing poetrypoetry add ctlogging2. How to useThere are main three step requires -Initalize the logger at starting point of applicationfrom ctlogging.config import set_logger_from_yaml, set_logger

logger = set_logger_from_yaml(logconfig_yaml) # using file
logger = set_logger(config) # using dictget logger at module/file level and start logging using logger built-in methodfrom ctlogging.config import get_logger

logger = get_logger(__name__)
logger.info(message...)
logger.debug(message...)for correlation_id, set it at entry level of pipelinefrom uuid import uuid4
from ctlogging.context import correlation_id

def pipeline():
    uid = uuid4().hex
    correlation_id.set(uid) # uid is string
    "do task....."3. Configurationusing config.yaml fileversion: 1
formatters:
  simple:
    format: '%(asctime)s - %(correlation_id)s - %(levelname)s - %(name)s - %(message)s'
filters:
  correlation_id:
    (): ctlogging.CorrelationId
handlers:
  console:
    class: logging.StreamHandler
    level: DEBUG
    formatter: simple
    filters: [correlation_id]
    stream: ext://sys.stdout
  file:
    class : logging.handlers.RotatingFileHandler
    formatter: simple
    filename: extraction.log
    maxBytes: 3000000
    backupCount: 3
    filters: [correlation_id]
  db:
    class : ctlogging.MysqlHandler
    level: DEBUG
    host: localhost
    database: ares
    user: root
    password: root
    port: 3306
loggers:
  root:
    level: DEBUG
    handlers: [console, db]
    propagate: true
root_logger_name: root3. for DevelopementSteps: -git clone the repoinstall poetry fromhttps://python-poetry.org/docs/master/#installing-with-the-official-installergotoctloggingdirectorypoetry install
Package: ct-loss
Contrastive Token loss function for PyTorchThis repo is the clean (PyTorch) implementation of the contrastive token loss proposed in our paper:A Simple Contrastive Learning Objective for Alleviating Neural Text Degeneration.For reproducing our results, please checkthis repo.Installpip install ct-lossUsageYou can use our CT objective whenpretrainingorfinetuningyour augoregressive language models.
With CT, the resulting language models will have significantly lessrepetitivegenerations, even with deterministic decoding such as greedy and beam search.
It only takes several lines of code to use CT loss, around where you calculate PyTorch'sCrossEntropyLoss.
Here is an example:importtorch# Suppose we already have the model output logits and labels (sequences of token indices).# For example when the batch size is 10, sequence length is 50 and vocabulary size is 1000:logits=torch.rand(10,50,1000)labels=torch.randint(0,999,(10,50))# This is how you normally use cross-entropy for a language model:fromtorch.nnimportCrossEntropyLossce_criterion=CrossEntropyLoss()ce_loss=ce_criterion(logits.view(-1,1000),labels.view(-1))# This is how you can use our contrastive token loss:fromct.ct_lossimportContrastiveTokenLossct_criterion=ContrastiveTokenLoss(pad_id=999)# we need pad tokens for masking out tokens in a sequence that should not be used as negative tokensct_loss=ct_criterion(logits,labels)# In our paper [1], we use CE and CT togetherloss=ce_loss+ct_lossprint(ce_loss,ct_loss)>>>tensor(6.9536)tensor(1.5848)Cite our paper
Package: ctmatching
Welcome to ctmatching DocumentationTheTreatment and control groupexperiment is widely used in comparative experiments. In order to verify that whether a method is significantly working for certain problem, usually we select group of object having same problem, and having similar circumstance. Then we divide them into control and treatment, and perform the method to treatment group.BUT! Sometimes there’s not enough people available that willing to accept the treatment. Usuallytreatment group is greatly less than control group. Then we have to select control group with similar circumstance from public.stratified propensity score matching algorithm(PSM) is a method for selecting control group.ctmatchingis a python implementation of it.中文介绍 (Chinese Introduction)Propensity score matching (PSM) 倾向评分匹配PSM主要用于生物统计中。在生物统计中, 很多实验都要设置对照组/控制组（control group）和实验组（treatment group）。在一个精心设计的实验中, 对照组和实验组通常在除了实验所导致的结果部分, 都要保证其他情况尽量一致, 这样实验结果才有说服力。例如我们研究X药品对A疾病的治疗效果, 如果我们选择100个病人, 他们的身体状况都比较相似, 给其中50个人吃药, 另外50个人不吃药。如果过了一个月对照组中的病人有30%身体恶化, 10%的状态不变, 10%的稍许好转。而实验组中的病人有30%都有好转, 那么我们大体上可以说明这个药是有效的。但是, 在实际的情况下我们通常做不到这一点。因为我们很难找到大量的状态相似的实验对象, 往往我们只能对部分对象做实验, 而去在其他大量没有做实验的对象中, 根据数理统计选择对照组。同样是X药品和A疾病的例子: 我们在测试A药对B病的治疗效果, 于是我们对50名病病人做实验, 给他们吃了药。接着就需要到社会上找与这50名病人情况类似, 但没有吃药的病人。为了研究X药的有效性, 对于每一个病人我们需要以某种方式在大量人群中, 找到与他们在其他条件相似的对照组。而这种选取对照组的方法, 就叫PSM。Quick LinksGitHub HomepageOnline DocumentationPyPI downloadInstallIssue submit and feature requestAPI reference and source codeInstallctmatchingrequiresnumpy >= 1.8.1,scipy >= 0.14.1,pandas >= 0.14.1,scikit-learn>= 0.15.2.ctmatchingis released on PyPI, so all you need is:$pipinstallctmatchingTo upgrade to latest version:$pipinstall--upgradectmatching
Package: ctmc
ctmcTable of ContentsInstallationUsageCommandsSupportContributingInstallationThectmcgit repois available asPyPi packagepip install ctmcUsageCheck theexamplesfolder for notebooks.CommandsCheck syntax:flake8 --ignore=F401Run Unit Tests:python -W ignore -m unittest discoverRemove.pycfiles:find . -type f -name "*.pyc" | xargs rmRemove__pycache__folders:find . -type d -name "__pycache__" | xargs rm -rfUpload to PyPi with twine:python setup.py sdist && twine upload -r pypi dist/*DebuggingNotebooks to profile python code are in theprofilefolderSupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow. Create a branch, add commits, andopen a pull request.
Package: ctmc2
ctmc2Continous Time Markov Chain (with automatic error correction)Table of ContentsInstallationUsageCommandsSupportContributingInstallationThectmc2git repois available asPyPi packagepip install ctmc2UsageCheck theexamplesfolder for notebooks.CommandsCheck syntax:flake8 --ignore=F401Remove.pycfiles:find . -type f -name "*.pyc" | xargs rmRemove__pycache__folders:find . -type d -name "__pycache__" | xargs rm -rfUpload to PyPi with twine:python setup.py sdist && twine upload -r pypi dist/*DebuggingNotebooks to profile python code are in theprofilefolderSupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow. Create a branch, add commits, andopen a pull request.
Package: ctmd5
change the MD5有时候在使用某网盘的时候总会遇到文件无法上传或者是上传后无法分享的情况，所以抽了个时间写了个改变文件 MD5 值的小工具，目前只能算是基本能用吧，后续再继续完善。安装pip3installctmd5使用$ctmd5--help
Usage:ctmd5[OPTIONS]FILEACLItoolusedtochangetheMD5offiles

Options:-s,--showshowthemd5value.(Whenthefileislarge,itwilltakealongtime.)--versionShowtheversionandexit.--helpShowthismessageandexit.示例运行$ctmd5-sThe.Big.Bang.Theory.mp4
beforemd5:23aefe6872325590956548dec285849f
Thefile[The.Big.Bang.Theory.mp4]md5valuehasbeenchanged.
aftermd5:447e1bb20de228dcdead50db56415b7cLICENSEThe MIT License@shkey
Package: ctm-python-client
Control-M Python ClientControl-M Python Client is a python library to programmatically design, schedule and run your Control-M workflows. The design of the Control-M Python Client is oriented towards data scientists and developers who prefer a more programmatic approach to workflow orchestration.Getting StartedThe best way to get familiar is to check theDocumentation. Start with theTutorialsto get familiar with the syntax.You do not need to have an in-depth knowledge of Control-M, but some familiarity with Control-M will help you understand the different job types and syntax offered by Control-M Python Client.InstallationControl-M Python client requiresPythonversion 3.7 or higher.We recommend to install Control-M Python Client in avirtual environment.For Linux:python -m venv venv
source venv/bin/activateFor Windows:python -m venv venv
venv\Scripts\activateInstalling via pipYou will need pip and git installed on your system before you run this command.pip install ctm-python-clientInstalling from sourcegit clone https://github.com/controlm/ctm-python-client.git
pip install ctm-python-clientContributing and SupportSeeContributingLinksControl-M DocumentationHelix Control-M DocumentationAutomation API DocumentationAutomation API with Helix Control-M DocumentationProvisioning an agent with Automation APIApplication IntegratorLicenseSeeLicense
Package: ctnas
CT-NASThis module contains an API access to a Neural Architecture Search dataset on a search space called "computational themes".
Such themes are small directed acyclic graphs (DAGs) with a vertex collapse condition.
This condition makes the left and the right graph in the picture being equivalent / isomorphic.The dataset contains computations of feed-forward neural networks with different hidden structural priors based on these themes.
By this, you can search for connections between graph theoretic properties of the search space and computational properties of the neural network models.InstallationViapoetry(recommendedfor projects) using PyPi:poetry add ctnasDirectly withpipfrom PyPi:pipinstallctnasViacondain yourenvironment.yml(recommended for reproducible experiments):name:exp01channels:-defaultsdependencies:-pip>=20-pip:-ctnasFrom public GitHub:pipinstall--upgradegit+ssh://git@github.com:innvariant/ctnas.gitUsage examplesfromctnas.apiimportCTNASApiapi=CTNASApi()print(api.get_datasets())# Should give you:# ['spheres-b8c16fd7', 'mnist', 'spheres-23aeba4d', 'spheres-bee36cd9',#  'spheres-b758e9f4', 'spheres-0a19afe4', 'cifar10', 'spheres-6598864b']fromctnas.apiimportCTNASApiapi=CTNASApi()print(api.get_graph_properties().head())Gives you s.th. like:test_dev.py .                             graph_uuid  num_nodes  ...  degree_var  undir_ecc_var
0  6e302aa7-6208-42a9-b1e0-08ce6d9d83ba          6  ...    1.222222       0.222222
1  ecd9c934-90ae-460c-855f-90c0b24a4150          6  ...    0.666667       0.000000
2  d111e38f-3ed1-454f-9d0e-8ded0428c9d9          6  ...    1.000000       0.222222
3  d23cac47-047c-4ec6-aaa4-e393b2ebeccd          5  ...    0.640000       0.240000
4  c56bb6f8-a9ec-44db-8c17-37b166fb5b06          6  ...    0.888889       0.222222[5 rows x 19 columns]importnetworkxasnximportmatplotlib.pyplotaspltfromctnas.apiimportCTNASApiapi=CTNASApi()graph=api.get_graph("0a1ded7d-677a-41f7-9361-c7079c8a34a7")nx.draw(graph)plt.show()Cite our work@misc{stier2022ctnas,title={CT-NAS: Analysis of Hidden Structural Priors for Neural Architecture Search},author={Julian Stier and Michael Granitzer},year={2022}}MinIO Policy{"Version":"2012-10-17","Statement":[{"Action":["s3:GetObject"],"Effect":"Allow","Resource":["arn:aws:s3:::homes/stier/ctnas/*"]}]}{"Version":"2012-10-17","Statement":[{"Action":["s3:GetObject"],"Effect":"Allow","Principal":{"AWS":["*"]},"Resource":["arn:aws:s3:::homes/stier/ctnas/*"],"Sid":""}]}
Package: ctns
CTNSContact Tracing Network Simulator, a tool to simulate digital contact networks beetween people in a population where a disease is spreading.
The simulation is highly customizable and will return (or dump) a time series of networks. We suggest to usehttps://gitlab.com/migliouni/ctns_visualizerto analize the results.
If you prefer to interact with this library througt a web app, you can take a look tohttps://gitlab.com/migliouni/ctns_simulator, which provides a web interface to run simulation and some summary plots.Getting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes.InstallingThe easiest way to install this library is simply run:pip install ctnson you terminal.UsageTo run a simulation, you first need to import this library in your python code:from ctns.contact_network_simulator import run_simulationand then simply call the function run_simulation.If you would like to launch the tool directly from Terminal/CMD, you can just typectnsfrom Linux terminal orctns.batfrom Windows CMD/Power Shell.Remember to specify a path to a file for the network dump.You can alternatively clone the repo, navigate to the ctns/ctns folder and runpython3 contact_network_simulator.pyNetwork structureThe networks are igraph networks that will have nodes (representing people) and weighed edges (representing contacts).
Each node will have the following relevant attributes:sex -> either man or womanage -> a number representing the age slice of the node (e.g., 40 represent a node with age between [40,50))family_id -> id of the family of the nodesociability -> a value that can be either low, medium or high and represent how social (average number of edge) a node ispre_existing_conditions -> number of existing pathologies of the nodeagent_status -> the status of the node in the simulation. It can be either S(susceptible), E(Exposed), I(Infective), R(Recovered) or D(Dead)infected -> if the node is currently infected or notquarantine -> 0 if the node is not in quarantine, an int representing how many days of quarantine remains otherwisetest_result -> -1 if no test has been made on the node, 0 if the test result is negative, 1 if the test result is positivesymptoms -> a list of symptoms shown by the nodePlease note that you will find other attributes used in the simulation process.Each edge will have a weight, representing the duration of the contact between two people and a category, that can be either family, frequent, occasional or random.
Package: cto-ai
sdk-pythonThis is the Python version of The Ops Platform SDK. In
conjunction with our Docker base images, it allows the development of
automations with rich user interfaces, that run in both the local
terminal and in Slack. Other than the Ops Platform itself, this SDK
depends on:Python 3.6+Therequestslibrary, version 2+The Ops PlatformThis SDK is designed to work in conjunction with The Ops
Platform. Please seehttps://cto.ai/platformfor more details.To build and publish Ops to The Ops Platform, you will need to use
The Ops CLI. Find installation instructions and reference
documentation athttps://cto.ai/docs.
Package: ctodd-python-lib-csv
Christopher H. Todd's Python Lib for CSV FilesThe ctodd-python-lib-csv project is responsible for interacting with .csv files and .csv formatted data. This includes converting to and from .csv format, reading .csv files, writing .csv files, and other general CSV related tasks.Table of ContentsDependenciesLibrariesExample ScriptsNotesTODODependenciesPython Packagessimplejson>=3.16.0Librariescsv_general_helpers.pyCSV General Helpers. This library is used to interact with .csv files not specificlly related to reading or writing them.Functions:def convert_csv_file_to_json_file(csv_filename, json_filename=None, seperator=","):
    """
    Purpose:
        Convert .csv File to .json
    Args:
        csv_filename (String): .csv file to convert to .json
        json_filename (String): filename for the resulting .json
        seperator (String): String seperator of fields in the .csv
    Return:
        json_filename (String): filename for the resulting .json
    """csv_reading_helpers.pyCSV Reading Helpers. This library is used to aid in reading with .csv filesFunctions:N/Acsv_writing_helpers.pyCSV Writing Helpers. This library is used to aid in writing with .csv filesFunctions:N/AExample ScriptsExample executable Python scripts/modules for testing and interacting with the library. These show example use-cases for the libraries and can be used as templates for developing with the libraries or to use as one-off development efforts.N/ANotesRelies on f-string notation, which is limited to Python3.6.  A refactor to remove these could allow for development with Python3.0.x through 3.5.xTODOUnittest framework in place, but lacking tests